Guest editors J.T.A. Verhoeven (Utrecht University) M.J. Wassen (Utrecht University) D.F. Whigham (Smithsonian Environmental Research Center/Utrecht University) H. Middelkoop (Utrecht University) G. van der Lee (WL Delft Hydraulics) The Integration of Scales in Landscape Ecology Foto: Saxifraga, Joep Dirkx 47 Scale issues are fundamentally important in all aspects of landscape ecology. The spatial scale of observation and analysis in landscape ecology concerns clusters of ecosystems and ranges from local to global. The last four decades have seen an enormous research effort at the ecosystem level, to understand biogeochemical cycles and food webs, explain the dynamics in biotic communities and populations, and find the mechanisms behind the general decline in biodiversity worldwide. More recently, there has been an increasing emphasis on studying ecological processes at the global scale. Understanding the dynamics of system earth over long time periods is necessary to be able to predict and finally influence the ecological effects of regional disturbances such as eutrophication of the coastal zone and more global disturbances such as climate change. The landscape scale is of great importance for regional planning and for developing policies to influence global change. Understanding the processes responsible for our environmental quality not only requires a good knowledge of ecosystem functioning, but also a clear insight in the interactions among ecosystems in the landscape through wa- ter and matter flows, or migration of organisms or diaspores. In order to analyse ecological processes at a global scale, we need to be able to use reliable data sets compiled for whole catchments or regions. Integration of data collected at different scales is a prerequisite in these studies. In landscape ecology the importance of issues of scale has been rec- ognized at an early stage. Problems and solutions regarding integration of scales have been explicitly addressed in many studies, and different approaches have been attempted. This special issue of Landschap deals with this subject and contains six contributions which were presented during the seminar ?The Integration of Scales in Landscape Ecology? organized by the WLO and by Utrecht University on 13 June 2002. These contributions deal with the importance of identifying scale issues in landscape ecological studies and with methodologies to bring data collected at different scales to one common scale level for analysis by applying upscaling or downscaling approaches. The contribution by Burrough and Pfeffer explains techniques for downscaling by using cheap, high-resolution data from digital elevation models to enhance the spatial resolution of mapped vegetation patterns in the Austrian alps. They reason that successful downscaling is only possible through the use of ancillary fine detail (e.g. high resolution remote sensing or digital elevation models), and process-based and empirical modelling (e.g. logistic regression or neural networks) based on substantial data sets. Wassen and Verhoeven focus on up-scaling approaches. They emphasize that predictability depends on the relation be- tween the spatial and the temporal scale of study. Three examples of scale dependent processes illustrate the impor- tance of identifying the scale at which processes operate to avoid erroneous conclusions. They advocate that land- scape studies should at least provide an explicit framework revealing differences in scale, since the questions asked have to be translated into spatial scenarios and subsequently into input maps. Arheimer?s contribution shows how several modelling techniques were combined in an approach of upscaling of data collected at the site scale to the scale of a region or a whole country. She uses the example of nitrogen leaching and transport from small subcatchments and describes how models of local nitrogen transport were linked to a set of nest- ed models describing hydrological processes at different scales to finally estimate the total contribution of Sweden to the nitrogen loading of the Baltic Sea. The Integration of Scales in Landscape Ecology Editorial Landschap 20(2)48 In a forum article, De Wit proposes an alternative approach for analysing and solving environmental problems at the river basin scale. Rather than the use of existing model studies and data sets which were developed for smaller scales and scaling these up to the river catchment scale, he advocates the explicit identification of the interaction framework at the scale of the policy questions, and the collection of new data and process information at this scale level if these are not already available. He illustrates his much simpler approach with examples for the rivers Rhine and Elbe. The contribution of Whigham et al. deals with an attempt to evaluate the condition of wetlands in terms of functioning and biodiversity through the analysis of existing spatial geographical data in a GIS, rather than using the classical way to assess individual wetlands in a field-based approach. This new approach greatly enhances the opportunities to meet the increasing need for evaluating the condition of wetlands at the catchment scale. Whigham et al. used a statis- tical approach to compare both methodologies for a large region in the Chesapeake Bay area in the USA. Finally, Mander et al. investigate the scale issues involved in territorial ecological networks. In their contribution, they demonstrate hierarchical aspects of such networks and analyse the opportunities and limitations for downscaling and upscaling of their functions. They discuss a number of principles which are helpful in understanding scale issues in ecological networks, i.e. connectivity, multifunctionality, continuity, and plenipotentiality. As guest editors for this special issue, we hope that these contributions will give the readership of Landschap an overview of current discussions on the very important issue of scale in landscape ecology. We want to thank the authors for their excellent work and for their respect for the time schedule we had for this special issue. J O S V E R H O E V E N , M A R T I N WA S S E N , D E N N I S W H I G H A M , H A N S M I D D E L K O O P, G U D A VA N D E R L E E 49 The concerted action of scientists to integrate scales in landscape ecology, may offer us an opportunity to determine the landscape scale more clearly and concisely define the term landscape. The definitions that we find in the literature all include wording that describe a landscape as a spatial unit that has the characteristics of an ecosystem. This raises the question why an ecosystem with spatially defined boundaries is not always a landscape. Why are continents, oceans or even the total biosphere not landscapes? This suggests that a landscape has a size below a certain maximum scale. Starting from the other end of the scale spectrum, we may conclude that a landscape must have a size that is above a critical minimum. A heathland pond or a calcareous outcrop have all of the characteristics of ecosystems with clear boundaries, but they would not be called landscapes. A symposium on upscaling and downscaling could perhaps help us identify the upper and the lower scale limits for the term landscape. Systems ecology alone is not sufficient to offer the answer. That discipline goes up and down the to- tal ladder of scales, without splitting off certain groups based on size. This means that landscapes cannot be spatially defined to only consist of the elements of an ecosystem: the physical and chemical components, the flora, the fauna and their interactions. The only component that is not yet included in the conventional definition of the ecosystem is man. This logic leads to the understanding that landscapes are spatially characterized by the action of man and the boundaries of landscapes are created by spatial differences in human actions. To find the specific scale of landscape on the total ladder of scales, should then take the home range of human populations as a starting point. Etymology helps us at this point. Land is an ancient word for home; ?scape? comes from the Dutch word ?schap? which derives from ?scheppen? which means ?to create?. Land-scape is: the home country that man created for himself. As a consequence, two different disciplines can be recognized: landscape ecology and ecology of landscapes. In the first sense, landscape means the study of pattern and processes in ecosystems with no clear definition of space, thus offering a variety of scales. In the second sense, landscape means the study of the structure and the functioning of ecosystems that have man as a characteristic species. The boundaries of that system are found where populations of the human species created clear spatial structures that end up at the limit of their home range. New methodological steps are needed in the study of landscapes in the context of the field of landscape ecology and they should be based on an integration of a number of fields of study including physical geography and biology, through cooperation with the human sciences of anthropology, historic geography, social geography and economy. These human home range sciences may help us to find the landscape scale that is effective in evaluating the effects of humans across the world and developing effective management strategies to meet the needs of nature and human so- cieties. Upscaling and downscaling may help us to place the ecology of landscapes in the total scientific field of land- scape ecology. J A C Q U E S D E S M I D T (President WLO) How to find the landscape scale? Introduction Foto: Saxifraga, Jan van der Straaten Downscaling in environmental research 51 When studying landscapes, and the biological, chemical, physical and anthropological processes operating in them, we frequently must deal simultaneously with the very small and the very large. For example, hydrogen ion concentra- tion determines the base status (pH) of clay minerals, which are the result of rock weathering in past and present climates. The lithologic variation of clay minerals over the landscape depends on processes of erosion, transport and sedimentation that operate over many scales. In turn, these factors affect the storage and supply of nutrients to plant roots, thereby influencing the types, structures and pat- terns of vegetation which determine both the aesthetic and ecological qualities of the land at large (Figure 1). It is no wonder that landscape ecologists have much to discuss concerning the best way to approach their complex study object (Klijn, 2002). The signals that excite them depend very much on the tuning of their antennae to the patterns and processes they consider to be of most importance. Because it is impossible to measure everything at all levels of resolution (in the limit, 100% sampling of soil or land- form would destroy the object of interest!), landscape ecolo- gists are forced to extend the information inherent in their samples and observations to other scales. Upscaling is the process of extending knowledge from small observation units (known in geostatistics as the support ? see Burrough & McDonnell, 1998; Goovaerts, 1997) to units having larger areas; the reverse process of predicting local at- tributes from studies covering large areas is known as downscaling (e.g. Bierkens et al., 2000; Canon & Whit- field, 2002; Sailor & Li, 1999). Many aspects of landscape ecology involve upscaling from data about objects smaller than people to objects that are very much larger than people. Upscaling fre- quently requires interpolation or the use of numerical models to extend the knowledge obtained at point or local observations to the landscape at large. In other situations, which are becoming more frequent thanks to large amounts of data in digital geographical information sys- tems (GIS), we may have more information about the landscape over large areas and need means to extend or combine these data to make statements about local con- ditions. As already indicated, this is known as down- scaling. The aim of this paper is to explain and illustrate how statistical methods of downscaling can enhance the value of expensive-to-measure data having a coarse (and possibly incomplete) spatial coverage through combina- tion with cheap, readily available data having a finer spa- tial resolution. Reasons for downscaling Downscaling is the process of reconstructing fine detail from a general picture. This is a common issue in many Global Change studies, when General Circulation Models (GCMs) are used to predict climate-induced responses of local or regional hydrological conditions (Sailor & Li, 1999). Alternative means are necessary to predict local cli- matic changes at higher levels of spatial and temporal res- olution (e.g. Cannon & Whitfield, 2002). Although most pioneering research on downscaling comes from the Global Change community, the same P E T E R B U R R O U G H & K A R I N P F E F F E R Prof. dr. P. A. Burrough and Dr. K. Pfeffer, Utrecht Centre for Environment and Landscape Dynamics (UCEL), Faculty of Geographical Science, Utrecht University, Heidelberglaan 2, Postbox 80115, 3508 TC Utrecht. Opportunities and constraints of downscaling in environmental research Downscaling Alpine vegetation Detrended correspon- dence analysis Universal kriging K-means Spatial data concerning many aspects of landscape are collected at many levels of resolution, but if combined in numerical models or statistical classifications, they must be brought to a common spatial scale. This can be achieved by upscaling (fine to coarse) or downscaling (coarse to fine). This article explains how downscaling procedures using cheap, high-resolution data from digital elevation models enhance the spatial resolution of mapped vegetation patterns in the Austrian alps. Landschap 20(2)52 principles apply in landscape ecology when one attempts to predict aspects of the short-range spatial variation of vegetation within larger areas for which only generalised maps or sample surveys are available. For example, in landscape ecological studies it is not uncommon to want to predict the ecological condition of a small vegetation plot from generalised information over a whole region. This may be necessary for many reasons. Commonly oc- curring situations are: ? the sources of data have fixed levels of resolution that are too coarse for the application (e.g. attempting to in- fer details of individual patches of vegetation from re- motely sensed imagery having 1 x 1 km pixels), ? numerical models of environmental processes often re- quire data to be brought to a common level of spatial resolution, ? it is difficult to sample an area uniformly because of varying ease of access, ? data are sparse or incomplete. There is much interest in downscaling the coarse resolu- tion digital data obtained by remote sensing or climate models so that they may be linked to regional or local data when required. In recent years there has also been progress in bringing together international digital data sets that can be stored, displayed, analysed and combined in Geographical Information Systems ? GIS ? (Burrough & McDonnell, 1998; Burrough & Masser, 1998; Longley et al., 2001). Drawing on developments in the United States, Europe and international organisations, Global Spatial Data Initiatives (GSDI) have lead to the establishment of digital data sets of elevation, climate, vegetation, hydro- logical basins, etc. that have commensurate levels of spa- tial (but not temporal) resolution (Figure 2). Many of these data sources are linked to standard cartographic map scales that imply a smooth transition in resolution from one level to another. One of the most important recent developments in GIS technology has been the improved availability of high res- olution digital elevation models (DEM). Today, it is quite possible to obtain DEMs of large areas of land with a spa- tial resolution that is finer than 5 x 5m. To give the reader Figure 1. A schematic overview of the range of spatial scales encountered in studies of the physical landscape (adapted from Burrough 1996) Figure 2. Shared global data may improve under- standing of spatial pro- cesses affecting the pla- net, but only at the world scale. This figure and more details from:http://www.iscgm. org/html4/index.html Downscaling in environmental research 53 to these detailed data, we have a means to downscale them to the fine level of detail provided by the DEM. Es- sentially, the global data will be modified by local varia- tions in correlated secondary attributes to provide the more detailed downscaled picture. This can be achieved by using statistical methods and interpolation (Bierkens et al., 2000; Sailor & Li, 1999). The principles of downscaling Figure 4, (modified from Bierkens et al., 2000), illustrates the geostatistical principles of downscaling. The term support is used to indicate the size of the basic spatial unit for which data for a given attribute z are available. The horizontal axis gives the size of the support si while the vertical axis gives the value of the regionalised variable zi for the whole of that support. The size of support s2 is the smallest spatial unit for the generalised data; within this basic unit the value of z is taken to be uniform be- an idea of the level of surface detail that is possible today, Figure 3 illustrates this for a part of the floodplain of the river Maas in a southern province(Zuid Limburg) in the Netherlands. From this figure we see that not only can el- evation differences be computed directly over short dis- tances, but also many ecologically relevant derivatives such as local slopes, aspect and direct received solar radi- ation and local drainage situations (Burrough & McDon- nell, 1998). As we know that many ecological processes in the land- scape are moderated by differences in elevation, slope or incident solar radiation (Burrough et al., 2001) a GIS can be used to calculate the derivatives of a DEM at any re- quired level of spatial resolution, thereby providing a rich source of information on the possible short and long range spatial variation of ecological conditions. If the generalised, or expensive-to-measure attributes of vege- tation types or landscape or regional climate can be linked Figure 3 A comparison of elevation data (mm above local reference) obtained from Laser altimetry of part of the Maas flood- plain , (courtesy Dutch Meetkundige Dienst) and interpolation by kriging. Left: 5 x 5m resolution, right: surface interpolated from 155 surface measure- ments to a grid of 20 x 20m. Clearly, the high resolution surface (left) gives much more informa- tion over surface structu- res and ecological diffe- rences than the low reso- lution surface (right). A B elev155.est 5484 5167 4850 4532 4215 3898 3580 3263 2946 1 0 1 2 kilometers Landschap 20(2)54 cause there is no more information. In other words, when the support is large (s2) there is no information about the spatial variation of z within the dimensions of s2 ? only a mean value is known. The size of the smaller support s1 represents the desired level of spatial resolution. By downscaling we are at- tempting to create information about the more detailed variation of z. In this case the resolution of s1 is eight times better than support s2. It is easy to generalise data from a fine to a coarse support. For a given cell there are many ways to compute the up- scaled value of z2 from the 8 data of z1, the most obvious being the mean, or the mode, the median, the most com- monly occurring value and so on. Downscaling ? i.e. com- puting the values of the z1 data from the z2 is much more difficult. Because the same value of this s2 mean can be obtained from a very large variety of, and operations on, the 8 values of the s1 data: the variation of z(s1) shown is but one possible combination from an infinite set of possi- bilities based on the support s1. This phenomenon, called equifinality, means that determining unique s1 values from the s2 value is impossible without extra information, so, given that we have information on z at the level of s2, how can one predict z at the level of s1? There are two main approaches to downscaling that use various forms of regression: ? Have local, but sufficient amounts of empirical data on z at the level of s1 , ? Use large amounts of cheap, proxy data to predict z at the level of s1 . Local, but sufficient amounts of empirical data on z at the level of s1 Given sufficient amounts of data on z at the level of s1, in principle we can use methods of spatial autocorrelation and interpolation (geostatistics) to estimate the spatial covariance of z for any required level of resolution (Bur- rough & McDonnell, 1998; Goovaerts, 1997; Heuvelink & Pebesma, 1999). Alternatively, through methods of condi- tional simulation, we may create models of the statistical nature of the spatial variation of z at the level of s1. These models of spatial autocorrelation may be extended to areas for which we have none or very few data at the level of s1 (e.g. Lagacherie et al., 1995). Use proxy data to predict z at the level of s1 Proxies are attributes that are easier to measure than those about which information is desired, but which are thought to have a strong correlation with them. A well known example is the oxygen isotope ratio in ice cores, which is thought to provide a strong indication of climate change. As noted before, detailed digital elevation models may provide useful proxies for ecological variations in a landscape. Their value may be enhanced if they can be Figure 4. The principles of downscaling. Given data with the spatial resolution of s2, recon- struct the variation of the attribute z for spatial resolution s1 Downscaling in environmental research 55 A case study: downscaling Alpine vegetation data by a factor of 10 using a digital elevation model, detrended correspondence analysis, universal krig- ing and k-means clustering Although it will be clear from the foregoing that there are many ways to achieve a downscaling of environmental data from the generalised to the particular level, we will attempt to elucidate the process further using a case study taken from recent practice (see Pfeffer, 2003, Pfeffer et al., 2003). The example chosen concerns the need to car- ry out rapid mapping of vegetation in difficult to reach, high altitude areas of the Austrian alps that are much used for skiing so that the impact of the sport has a minimal ef- fect on the natural alpine vegetation. Local planning for optimising the location of ski runs in mountain areas re- quires detailed spatial information on site factors such as vegetation, which is commonly lacking in rugged terrain. The direct sampling of vegetation in high altitude alpine areas is only possible for a limited period of the year and access is difficult so systematic mapping is expensive and rarely carried out. In high altitude alpine areas the collec- tion of data from 10 x 10m quadrats on a 100m grid would be regarded as ?detailed?, though it is clear from recent re- search that important vegetation differences may occur over much shorter distances in the alpine environment (Guisan et al., 1998; 1999; Guisan & Zimmermann, 2000; Hoersch et al., 2002). In contrast to the difficulties of visiting many sample sites, the diversity of alpine flora almost guarantees the recording of large numbers of different plants, leading to a richness of information about plant communities, but little about their spatial patterns. Therefore we may have relatively much information about the composition of dif- ferent plant communities, and relatively little about their spatial distribution. In these circumstances it makes combined with information on the probabilities of partic- ular relations that are known to occur. There are many other computational tools to convert spa- tial data from one level to another. Besides the methods of spatial autocorrelation already mentioned, these include process models (e.g. hydrological models, crop yield models, etc.), and empirical models based on logistic re- gression (e.g. Barendregt et al., 1993), multivariate classi- fication (Burrough et al., 2001; Pfeffer, 2003), neural net- works (Cannon & Whitfield, 2002) and similar approaches. Van Horssen et al. (1999) combined geographical infor- mation systems, geostatistical interpolation (kriging) and logistic regression modelling to predict plant species in wetland ecosystems in the Netherlands. Bierkens et al. (2000) and Burrough & McDonnell (1998) provide more details of these and other methods. In a flat landscape, the values of the attributes of interest or their proxies are usually directly linked to the support in question. In mountainous and hilly landscapes, the data collected for any given instance of the support sj may also depend on other factors. Note that with certain kinds of proxy data (e.g. derivatives from digital elevation mod- els and reflected electromagnetic radiation detected by re- mote sensors), the attributes of an instance of a given support may vary depending on the geometrical orienta- tion of the sampling grid (Demargne, 2001). Neverthe- less, we ignore this complicating issue here. Landschap 20(2)56 sense to use the quadrat samples to develop an optimal (i.e. the best local) classification for the vegetation data and to use a cheap proxy for spatial mapping (c.f. van Horssen et al., 1999). As noted above, current GIS technology makes it easy to create detailed digital elevation models from large scale (1:25000) digitised contour maps or aerial photographs. These topographic attributes and their spatial derivatives (slope, slope curvature, direct received radiation and wet- ness indices) are realistic ecological proxies for the supply of energy, moisture and nutrients that may influence plant growth and vegetation types (Burrough et al., 2001; Hoersch et al., 2002): they can easily be computed from a gridded digital elevation model (DEM) at any desired resolution. As explained in the following sections, the high resolu- tion, cheap data were combined with the vegetation class- es to map the short-range spatial variations of vegetation in the terrain. Study area The study area is located in the ?tztal, a north-south val- ley in the Tyrol, on the upper western slopes of the village of S?lden, which is a popular ski area in the Austrian Alps. It covers an area of approximately 3.6 km2, and has an el- evation range from the timberline, at about 1900m, up to 2650m. Figure 5a shows a general view of the upper part of the study area, while Figure 5b shows short-range vege- tation across narrow (20-50m) valley heads in the lower, east-facing part. Full details of the study area are given in Pfeffer (2003). The procedure was as follows: Vegetation sampling During the summer of the year 2000, plant species occur- rence was recorded at 223 quadrats, each 10m x 10 m, lo- cated on a reference grid of 100m x 100m (Figure 6a). In each quadrat all species were recorded according to ordi- nal abundance: 1 indicates the presence of a plant species, 2 means frequent occurrence and 3 means that a certain plant species was dominant. In total 147 species were identified, neglecting some grass species and all fungi and ferns. Fifteen quadrats were rejected because they fell on tracks or other disturbed ground leaving 208 for anal- ysis. The vegetation data show that the study area contains many common species, known to be typical for alpine grassland and alpine heaths (Reisigl & Keller, 1987). Al- though each species has its own preferences, some are broadly tolerant making it difficult to identify an unam- biguous correlation of species preferences and ecologi- cal attributes. Certain key species were recorded which were characteristic for sites with specific conditions like a certain elevation range, exposure or moisture content. Al- though these key species are important for mapping veg- etation types, they frequently occurred in narrow valleys with different conditions that were too small to be re- solved by the 100 x 100m sampling grid. Therefore we sought a way to downscale these vegetation data so that the vegetation types occurring in the smaller components of the landscape could be predicted. The first step in downscaling was to reduce the 208 x 147 vegetation site/species data matrix to manageable propor- tions. We used detrended correspondence analysis (DCA - Canoco 4.02: Ter Braak & Smilauer, 1998), which returned four axes with a cumulative explained variance that was only 20% of the total of the complete data set (Pfeffer et al., 2003). This result suggests that much of the area is indeed poorly differentiated (i.e. it is covered by a broad range of similar species with a wide range of tolerance) and that rare species, if any, occur in the less frequently sampled parts of the landscape. Downscaling in environmental research 57 Figure 5. a: (Top) view of Hoch S?lden to the north; b: (bottom) west-facing low lying gullies with large variation of vegetation over distances of 20-50m Landschap 20(2)58 Creating high resolution proxies for mapping vege- tation We used a digital elevation model with cell sizes of 10m x 10m, (source: Bundesamt f?r Eich- und Vermessungswe- sen, Austria), which was the level of spatial resolution re- quired for the downscaled vegetation map. The ecological proxies for vegetation namely altitude, slope, planform curvature, profile curvature, potential received annual so- lar radiation, distance to ridges, and mean wetness index were derived from the digital elevation model using PCRaster (PCRaster, 2002; Van Dam, 2001; Wesseling et al., 1996). All results were stored in raster maps having a grid cell size of 10 m. The downscaling procedure has four steps: 1 Compute the regressions between the dependent vege- tation scores (DCA axes) and the independent proxies (elevation, slope, incident radiation, etc.) for the 208 quadrats. 2 Examine the residuals from these trends for spatial cor- relation using semivariogram analysis. 3 For each DCA axis, use the regressions and the semi- variograms to create four DCA score maps at the reso- lution of the DEM. 4 Create 7 vegetation classes using a k-means classifica- tion of the original 208 DCA scores; use the k-means to allocate all points on the 10 x 10m grid to a vegetation class at the fine level of resolution desired. Figure 6. View from the west: a) Sampling network for 100 x 100m survey of vegetation (left): b) final vegetation classes map- ped to 10m resolution by downscaling (right). Step 1 yielded the results given in Table 1, which confirm the assumed links between topographic proxies and vegeta- tion scores, and provide the regression models (see Pfeffer, 2003). Step 2 resulted in four spherical semivariogram models being fitted to the residuals from regression (Table 2). Pa- rameter c0 indicates the level of non-spatial noise, c1 gives the level of spatially correlated variation, and a gives the range in metres over which that variation acts. The re- lations of c1 to c0 show the strong spatial dependence in all four sets of residuals, particularly for the first and third DCA axes. Step 3 involved using the regression models and the semi- variograms of residuals to interpolate each DCA score by universal kriging (Burrough & McDonnell, 1998; Goovaerts, 1997) to all cells on the 10 x 10m grid for the whole of the study area. This yielded 4 maps, one for each DCA axis. In step 4 k-means clustering first created 7 vegetation classes based on the DCA scores from the 208 sampled quadrats. The k-means clustering algorithm (Hastie et al., 2001; MacQueen, 1967) is an iterative descent clustering technique designed to distribute multivariate data among k clusters, where k is typically less than 10 groups. For quantitative variables using a Euclidean distance metric, the total cluster variance is minimized with respect to the cluster means by assigning each observation to the closest mean. The means are recalculated and the observations are reallocated to the nearest clusters; this procedure is it- Downscaling in environmental research 59 ? the extension of knowledge from general levels to local detailed areas, ? methods can be automated, ? enables quick and reproducible coverage of large areas if properties are similar, ? downscaling makes good use of the available ancillary data and proxies, whether in mechanistic models or empirical functions. The constraints include: ? an almost total lack of unique solutions, ? information that has been lost cannot be created from nothing ? if a particular vegetation type has not been sampled then there is no information to link to fine scale proxies, ? many predictions will be based on stochastic relations that may be poorly understood, ? any single means of downscaling may not apply over all levels of the phenomena hierarchy (atoms to oceans), ? non-linearity and feedback loops may obfuscate the re- lations between emergent properties and details, or complexity and simple interactions, erated until cluster memberships are stable. Once the clustering had been carried out, all 10m x 10m grid cells were allocated to a class based on their interpo- lated DCA scores. The final map was displayed draped over the DEM for clarity (Figure 6b). Discussion and conclusions The exercise reported in this paper demonstrates that even with noisy data and many plant species tolerant of a wide range of conditions, it was possible to downscale in- formation from a relatively coarse vegetation survey to a much finer spatial resolution. This was thanks to the ex- tra information obtained from geostatistical interpolation aided by simple proxies derived from a high resolution DEM. Field checking, particularly in the narrow valleys to the east of the study area, showed that in these limited ar- eas the mapped vegetation, which was based on a very sparse sample of less than 10 quadrats, corresponded with the impression of the vegetation obtained in the field. The consistency analysis indicated that it was es- sential to include all kinds of vegetation type in the initial sample, especially if the vegetation type represented was not common. We conclude that although downscaling has many limita- tions, the availability of cheap, spatially well-correlated proxies supported by regression and spatial autocovari- ance studies (i.e. universal kriging) may make it possible to create useful and detailed maps of vegetation types from sparse, expensive data. Downscaling: opportunities and constraints As the case study shows, downscaling is not simple and requires considerable understanding of the methods of data processing being undertaken. There are both oppor- tunities and constraints, however. The opportunities in- clude: Table 2. Parameters of spherical model semivario- grams fitted to the residu- als of each DCA axis Table 1. Main dependent variables contributing to each vegetation axis Dependent variable Independent variables (proxies) Multiple R2 DCA1 Elevation, slope, incident solar radiation 0.7466 DCA2 Mean wetness index, elevation, slope 0.1095 DCA3 Incident solar radiation 0.2733 DCA4 Profile curvature 0.0221 Dependent variable/Parameter c0 c1 a DCA1 0.11 1.33 10823 DCA2 0.42 0.80 612 DCA3 0.48 3.49 12779 DCA4 0.58 1.23 2522 Landschap 20(2)60 ? the quality of the regression models used in downscal- ing may be quite sensitive to relatively small variations in the size and composition of the data set. For exam- ple, omitting only a few sample sites from critical nar- row valley sites resulted in a much poorer performance when downscaling the vegetation patterns of the case study area. References Barendregt, A., M.J. Wassen & J.T. De Smidt, 1993. Hydroecological modelling in a polder landscape: a tool for wetland management. In: Landscape Ecology of a stressed environment, C.C. Vos & P. Opdam (eds), Chapman & Hall, London, pp 79-99. Bierkens, M.F.P., Finke, P.A. & P. de Willigen, 2000. Upscaling & Downscaling methods for Environmental Research. Developments in Plant & Soil Sciences Volume 88, Kluwer Academic Publishers, Dordrecht, 190pp. Burrough, P A., 1996. Opportunities and limitations of GIS-based modeling of solute transport at the regional scale. Chapter 2 in D.L. Corwin & K. Loague (eds), Special SSSA Publication No. 48. Application of GIS to the Modeling of Non-Point Source Pollutants in the Vadose Zone. pp 19-38. Burrough, P.A. & R.A. McDonnell, 1998. Principles of Geographical Information Systems. Oxford University Press, Oxford. Burrough, P.A. & I. Masser, 1998. European Geographic Information Structures. Taylor & Francis, London. Burrough, P.A., P.F.M. Van Gaans & R.A. MacMillan, 2000. High-res- olution landform classification using fuzzy k-means. Fuzzy Sets & Systems, 113, 37-52. Burrough, P.A., J.P. Wilson, P.F.M. Van Gaans, & A.J. Hansen, 2001. Fuzzy k-means classification of topo-climatic data as an aid to forest mapping in the Greater Yellowstone Area, USA. Landscape Ecology 16, 523-546. Cannon, A.J. & P.H. Whitfield, 2002. Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural net- works. Journal of Hydrology 259, 136-151. Demargne, J., 2001. Qualit? des Mod?ls Num?rique de Terrain pour l?Hydrologie. Doctor?s thesis, Universit? de Marne-la-Vallee, 29 June 2001. Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press, New York. Abstract Sizes of discernible spatial units in landscapes (called the support in geostatistics) range from very small (<10-6 m2) for soil particles and bacteria to very large (>109 m2) for geological formations and climatic zones. Many environmental models require data at common levels of spatial resolution but it is clearly impossible to measure everything at either one, or all scales. There- fore, people attempt to link data collected at different scales either by predicting the attributes of large areas from sets of local, high resolution data (upscaling), or by inferring the attributes of small areas from generalised data on large areas (downscaling). Downscaling at- tempts to reconstruct the fine picture from regional pat- terns, but this may be achieved in an infinite number of ways. Successful downscaling is only possible through the use of ancillary fine detail (e.g. high resolution remote sens- ing or digital elevation models), and process-based and empirical modelling (e.g. logistic regression or neural networks) based on substantial data sets of useful prox- ies or mechanistic, physically-based models. In this pa- per, downscaling is illustrated by an example from the Austrian alps in which detailed digital elevation mod- els, universal kriging and multivariate clustering were used to improve the spatial resolution of high altitude, sparsely sampled vegetation patterns. Downscaling in environmental research 61 MacQueen, J.B., 1967. Some methods for classification and analysis of multivariate observations, In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1, 281-297. University of California. PressBerkely, CA. PCRaster, 2002. PCRaster Software. See http://www.geog.uu.nl/pcras- ter.nl. Pfeffer, K., 2003. Integrating spatio-temporal environmental models for planning ski runs. PhD Thesis, Faculty of Geographical Sciences, Utrecht University. Pfeffer, K., E.J. Pebesma & P.A. Burrough, 2003. Mapping alpine veg- etation using vegetation observations and topographic attributes. Landscape Ecology. (in press) Sailor, D.J. & X. Li, 1999. A semi-empirical downscaling approach for predicting regional temperature impacts associated with climatic change. Journal of Climate 12, 103-114. SPSS, 2002. SPSS for Windows. Release 10.0.5. http://www.spss.com Ter Braak, C.J.F. & P. Smilauer, 1998. Canoco 4, Canoco Reference Manual and User?s Guide to Canoco for Windows. Centre for Biometry, Wageningen, the Netherlands. Van Dam, O., 2001. Forest filled with gaps: Effects of gap size on water and nutrient cycling in a tropical rain forest ? A study in Guyana, Ph.D. Thesis, Faculty of Geographical Sciences, Utrecht. 208 pp. Wesseling, C.G., D. Karssenberg, W.P.A. Van Deursen, & P.A. Burrough, 1996. Integrating dynamic environmental model in GIS: the development of a Dynamic Modelling Language. Transactions in GIS 1, 40-48.7 Guisan, A., J.P. Theurillat, & F. Kienast, 1998. Predicting the poten- tial distribution of plant species in an alpine environment. Journal of Vegetation Science 9, 65-74. Guisan, A., S.B. Weiss, & A.D. Weiss, 1999. GLM versus CCA spatial modelling of plant species distribution. Plant Ecology 143, 107-122. Guisan, A. & N.E. Zimmermann, 2000. Predictive habitat distribution models in ecology. Ecological Modelling 153, 147-186. Heuvelink, G.B.M.. & E.J Pebesma, 1999. Spatial Aggregation and soil process modelling. Geoderma 89, 47-65. Hastie, T., R. Tibshirani & J. Friedman, 2001. The Elements of Statistical Learning, Springer Series in Statistics, Springer New York. Hoersch, B., G. Braun, & U. Schmidt, 2002. Relation between land- form and vegetation in alpine regions of Wallis, Switzerland. A multi- scale remote sensing and GIS approach. Computers, Environment & Urban Systems 26, 113-139. Horssen, P.W. van, P.P. Scot & A. Barendregt, 1999. A GIS-based plant prediction model for wetland ecosystems. Landscape Ecology 14, 253-265. Klijn, J. 2002. Kort & klijn. Landschap, 2002/1, 57. Lagacherie, J.P. Legros & P.A. Burrough, 1995. A soil survey proce- dure using the knowledge of soil pattern established on a previously mapped area. Geoderma 65, 283-301. Longley, P.A., M.F. Goodchild, D.J. Maguire, & D.W. Rhind, 2001. Geographic Information Systems and Science. J. Wiley, Chichester. Foto: Saxifraga, Ben Delbaere Interpolation and extrapolation 63 Although much progress has been made in understand- ing landscape processes, a thorough understanding of in- teractions between processes in and between landscape compartments and ecosystems is still largely lacking (Heymans et al., 2002, Rietkerk et al., 2002). This is partly due to discrepancies between the scales at which various processes operate, but more importantly, to discrepancies in scale regarding the questions asked, the models used and the data sources available (Gosselink & Lee, 1989). The scale of an investigation may have profound effects on the patterns one finds. Dynamic, statistical and spa- tial modelling are each used to integrate process infor- mation across scales. Such attempts have two directions. First, detailed studies carried out at finer scales can be in- tegrated through dynamic models that can be used to study coarser scale processes. Typically, landscape mod- els combine information on ecological processes with spatial information available through GIS (Arheimer & Brandt, 2000, Van den Bergh et al., 2001, Pieterse et al., 2002). A second approach to landscape analysis involves downscaling from studies that start at larger scales (e.g., entire river catchments) and work toward understanding relationships between geomorphology, geohydrology and land use patterns at smaller scales (see Burrough & Pfef- fer, Whigham et al., Mander et al.; this issue). In this paper we analyse some scale issues in landscape science and we especially focus on up-scaling. After in- troducing some relevant definitions we address pre- dictability in relation to space-time scaling. Next, we pre- sent three examples from the literature of scale-depen- dent processes each operating at a very different spatial and temporal scale. These examples are chosen to demon- strate that there are constraints in up-scaling approaches and they in fact show us that the problem of scale depen- dency is scale-independent. After discussing the implica- tions of the scale of processes for data analysis and mod- elling we present two modelling studies: an empirical sta- tistical model and a mechanistic model. In developing these models for up-scaling or aggregation we had to overcome several scale issues. Both approaches had their specific scale related constraints and possibilities, which may serve as general lessons. Finally, we formulate rules for application to avoid scaling errors. Definitions Generally speaking the scale of an object or process is its spatial or temporal dimension. In scaling studies the ability to detect patterns in space or time is a function of both the extent and the grain of an investigation (O?Neill et al., 1986). Extent is defined generally as the overall area M A R T I N WA S S E N & J O S V E R H O E V E N Prof. Dr. M.J. Wassen, Environmental Sciences, Faculty of Geography, Utrecht University, P.O.Box 80.115, 3508 TC Utrecht, The Netherlands. m.wassen@geog.uu.nl Prof. Dr. J.T.A. Verhoeven, Landscape Ecology, Geobiology, Faculty of Biology, Utrecht University, P.O.Box 800.84, 3508 TB Utrecht, The Netherlands. j.t.a.verhoeven@bio.uu.nl Up-scaling, interpolation and extrapolation of biogeochemical and ecological processes Models Predictability Space Time Scale discrepanties As regional and global scales become more important to ecologists, methods must be developed for the appli- cation of fine-scale knowledge to predict coarser-scale ecosystem properties. Scaling-techniques for aggregation, up-scaling, interpolation and extrapolation all have their specific constraints and possibilities. In this paper we address scale issues in ecological and landscape ecological research with special emphasis on up-scaling. We conclude that in ecological modelling, limitations in data and their applicability for predictive modelling are more the rule than the exception, since collecting data on fine-grain patterns that are relevant at larger scales is generally costly and time consuming. Nevertheless, ecologically sound models can be obtained at the intermediate landscape scale (c. 100-10000 km2) if they are based on a clear understanding of the scale at which relevant processes operate and serve as a template in choosing the appropriate scale in observation and modelling. Landschap 20(2)64 encompassed by a study or the duration of the study. Grain or support is the size of the individual units of observation (Wiens, 1989) and is usually the largest area or time in- terval for which the property of interest is considered ho- mogeneous (Bierkens et al., 2000). Coverage is the ratio of the sum of areas or time intervals for all support units and the extent (Bierkens et al., 2000). Thus, in a spatial exam- ple coverage refers to the part of the research area that is covered by samples, and in a temporal example it implies the sum of time intervals of observations divided by the total study time. Loosely speaking, up-scaling means transferring information from a smaller scale to a larger scale. More specifically up-scaling or aggregation is defined as increasing the support of the research area or the re- search time. Changing the extent of the research area or research time usually involves going from a smaller to a larger extent. Increasing the extent is called extrapolation. Interpolation involves increasing the coverage of the re- search area or research time, which is in fact the reverse of sampling (Bierkens et al., 2000). Note that MacArthur & Levins (1964) considered grain in a different way as we defined above. They defined grain as a function of how animals exploit resource patchiness in environments. The observational window of a con- sumer is then referred to as the grain at which a consumer perceives its habitat (O?Neill et al., 1988, Milne, 1992, Ritchie, 1998). Differences in the scale of patchiness of the resource and the grain of observation by the consumer will affect the intensity of exploitation by the consumer. The size of the habitat that is covered by the consumer when searching for resource is then called the extent. Predictability and space-time scaling Our ability to predict ecological phenomena depends on the relationships between spatial and temporal scales of variation. Although there are no standard functions that define the appropriate units for space-time comparisons in ecology, with increased spatial scale, the time scale of important processes may also increase. This is because the relevant processes may operate at slower rates, their effects may involve time lags and their indirect effects may become increasingly important (Delcourt et al., 1983, Clark, 1985). Thus, as the spatial scale of a system in- creases, so also may its temporal scale, although these space-time scalings differ for different systems. Studies over a long time and at a fine spatial scale have low pre- dictive capacity at larger scales; they are simply too site- specific. Short-term studies conducted at broad spatial scales generally have a high apparent predictability but may be less capable of characterizing small-scale pro- cesses. This is pseudo-predictability since the natural dy- namics of the system operate at much longer time scales than the period of study. It is as if we were to take two snapshots of a forest a few moments apart and use the first to predict the second (Wiens, 1989). The first photo- graph is a perfect predictor for the second, but it does not teach us anything about the relevant processes in a for- est. Investigations that are designed to include a close cor- respondence between the time and space scales probably have the highest predictive power. In Fig. 1 we present a space-time diagram of ecological, hydrological and atmo- spheric processes illustrating the spatial and temporal scales that must be considered. Processes situated within the elliptic space are hypothesized to have a high pre- dictability, whereas soil processes and peat growth are ex- amples of processes with low predictability. Prediction of the activity of micro-decomposers or meteorological pro- cesses such as a thunderstorm event or the development of a cold front have a high apparent predictability over a wide range of scales. In Figure 2 we depict the relationship between recovery time of events and scale (Dobson et al., 1997). Remarkably, Interpolation and extrapolation 65 modelling and the difficulties that they present in relating ecological information to policy decisions should be kept in mind when reading the three examples presented be- low. The examples illustrate that it is essential to identify the scale at which processes operate in order to design ap- propriate sampling schemes and perform sound analy- ses of data. Example 1: Denitrification in flood- plains Denitrification is the process in which micro-organisms use oxygen obtained from nitrate for their respiration. The process results in the conversion of nitrate to gaseous forms of nitrogen (primarily N2 and N2O) that are lost to the atmosphere. Since denitrification decreases NO3 con- centrations and produces N2O, the concentrations of NO3 and N2O in groundwater should be inversely related. The absence of this relationship found in field samplings (Weller et al. 1994) suggests that the N2O pool is con- trolled by processes in addition to denitrification. N2O can be produced by nitrification and can both be pro- duced and consumed by denitrification. In addition, dis- solved N2O can be carried through the soil in groundwa- ter or lost to the atmosphere. So, instead of measuring concentrations of two variables related to the process, it makes more sense to measure the rate of N2O emission. This can be measured in closed chambers, in which according to these authors a groundwater system needs a longer time to recover after groundwater exploitation than it takes for a part of the land surface to recover after an atomic bomb explosion. An important implication from Figures 1 and 2 is that the questions asked by policy makers rarely are directed to the dynamics of the system and to the means (both financial- ly and in time) that are given to those studying these pro- cesses. Often, ecologists have been urged by resource managers to answer questions and make and test predic- tions on relatively short time scales (some years), regard- less of the spatial scale of the investigation. Politicians are frequently only interested in time horizons related to their careers, and since most of them are not in powerful posi- tions before their mid forties, fifteen years ahead is about the maximum time span still enabling them to harvest within their active career. Thus, policy is often based on relatively short-term studies regardless the extent of the area and the rate at which the important processes occur. Especially, predicting the effects of human interference in processes such as peat growth, groundwater flow, groundwater composition and global climate processes require long term monitoring data. In comparison, short- term studies conducted at broad spatial scales have a high apparent predictability, since the natural dynamics of the system are so much longer than the period of study. The difficulties in matching relevant scales in ecological Figure 1. Predictability in relation to the space-time scaling of processes. (Left) Figure 2. Recovery in relation to spatial scale. (Right) Landschap 20(2)66 gasses emitted from the soil are measured. However closed chambers can only be used for short periods be- cause temperature increase and gas buildup can change gas emission rates (Ryden & Rolston, 1983). Weller et al. (1994) used more than thirty chambers of 1x1 meter in a floodplain and did not find any obvious spatial pattern of N2O emission rates nor any match with the pattern of N2O or NO3 in groundwater. Apart from N2O emission rates being quite spatially variable, repeated measure- ments also showed big differences. Gas emission can also be measured using larger flow-through chambers. Larg- er chambers (20x1m) are more difficult to set up, but the constant flow of air minimizes temperature change and gas buildup over longer periods resulting in more useful data for monitoring emissions for days at a time (Jury et al., 1982). Weller et al. (1994) installed two flow-through chambers in a floodplain, one on a low-lying, frequently waterlogged soil and one on a drier site. They observed a clear seasonal cycle with N2O emission rates increasing from December to May and decreasing from September to December, paralleling seasonal temperature changes. They also observed diurnal variations in N2O emission rates that correlated with temperature in the surface soil. The expected higher emissions in the low-lying flood- plain site (having low redox status) were not observed, rather the reverse. Langeveld & Leffelaar (2002) modeled underground processes to explain N2O profiles in the soil. Their model simulates several biological and physi- cal processes. O2 and CO2 profiles were satisfactorily sim- ulated indicating that the respiration rates used in their model were realistic. The N2O profiles were less well sim- ulated. They concluded that their assumption of homo- geneity within soil layers was probably incorrect. We conclude that it is hard to make realistic inferences about denitrification based on measurements that have high spatial and temporal variability. This is because it is a complex process operating on a fine scale in an environ- ment where spatial heterogeneity of the factors influencing the process is large. This makes denitrification a difficult process to scale-up, to extrapolate and to model. Therefore generally valid estimates of NO3 removal from groundwater by denitrification are lacking. An approach that might work for processes like denitrification is the search for so- called hot spots and hot moments, where the process is operating at a high rate (McClain et al., 2003). These spots and moments probably cause the bulk of the nitrate re- moval in landscapes. They occur because at some points in space and time, an environmental factor that had limited the process is optimised. Denitrification requires low redox, pH>4, nitrate availability, carbon availability and a tem- perature higher than a critical minimum. Searching the conditions creating high rates in spatial data bases may help to identify such hot spots and moments. Example 2: Biodiversity in ponds Chase & Leibold (2002) tested Grime?s (1979) hypothesis that local-scale species diversity first increases with slight increases of productivity, but then declines to low diversi- ty when productivity is high. This so-called hump-shaped curve of species richness in response to productivity is supported by a wide variety of data and predictions of eco- logical models. This pattern is often seen in empirical studies at relatively small spatial scales (Waide et al., 1999, Mittelbach et al., 2001, Leibold, 1999, Dodson et al., 2000). However, at regional spatial scales, species diversity often monotonically increases with increasing productivity in- stead of being hump-shaped (Curry & Paquin, 1987, Mit- telbach et al., 2001). Because studies performed at differ- ent spatial scales often consider different ecosystems and employ different methodology, it remains unclear if these relationships are scale-dependent or whether a single re- lationship holds across scales. Interpolation and extrapolation 67 must possess mechanisms for surviving and averaging environmental variation over temporal scales less than their lifetimes and spatial scales less than their home- ranges. Whales come to the surface regularly to breath. When they dive again, their tail, the so-called fluke, is raised into the air. It is their habit to defecate at this par- ticular moment, visible by a brown patch in the water. So the defecation rate is easy to observe and is defined as the proportion of fluke-ups at which the whale defecates. Whitehead (1996) followed groups of Sperm whales in the Pacific and used temporal and spatial variation in defecation rates, which is a variation in feeding success, for assessing variation in octopus distribution in the deep ocean and the response of whales to this variation on a temporal and spatial scale. Mean defecation rates (per fluke-up), varied among years. When defecation rate is high (a high feeding success), the whales travel only short distances. If the variation in defecation rate is compared with the mean defecation rate, it appears that for time in- tervals of one day the coefficient of variation is somewhat less than the mean. For time intervals between 10 and 100 days variance is low and for intervals of years the variance is high compared to the mean. Apparently, temporal variability in the deep ocean is dom- inated by features with wavelengths of years. If we look at differences in variance with distance, we see that the vari- ance over distances of about 100 kilometers is the same as that over periods of few days: somewhat less than the mean. However, over several hundred kilometres the vari- ance in feeding success is larger, and similar to that over time periods of several years. Over larger distances it is about the same as the mean. What can we learn from this study in which a proxy (defe- cation rate of Sperm whales) is used to estimate variabili- ty in octopus distribution and density in the deep ocean? Temporal variability in the deep ocean is governed by low- Chase & Leibold (2002) chose thirty ponds nested within ten watersheds. Each watershed had three ponds that were similar in productivity and total area. Local species richness within ponds was defined as the number of species in a pond, regional species richness as the total number of species observed in the three ponds within each watershed. At the local scale, both producer and an- imal species richness had a statistically significant hump- shaped relationship with primary productivity. In con- trast, at the regional scale (among watersheds), species diversity linearly increased with productivity. An explana- tion might be that the differences in species composition among localities within regions increase with productivi- ty. To test this hypothesis the authors calculated species dissimilarity of each watershed by quantifying the species compositional differences among the three ponds within a watershed. Species dissimilarity indeed increased with productivity; ponds within watersheds of low productivi- ty shared the majority of their species, whereas ponds within watersheds of high productivity shared few. Without going into the mechanisms causing these differ- ences we may conclude that spatial scale dictates the pro- ductivity-diversity relationship. Species diversity, when viewed at different spatial scales, can respond in funda- mentally different ways to the same environmental factor (productivity in the case of the ponds). Thus, straightfor- ward up-scaling from local to regional scale is not appro- priate in biodiversity studies. Example 3: Variability in the feeding success of Sperm whales Sperm whales (Physeter macrocephalus) feed on octopuses in the deep ocean at depths of 200-1000 meter. Large ani- mals with a low reproductive rate and low mortality like the Sperm whale cannot react to environmental variation through changes in reproduction or mortality, thus they Landschap 20(2)68 frequency, inter-annual features, just as was observed in studies focusing on variability at the surface (Steele 1985). These features are found in the Pacific in the California Current, the Humboldt Current (Peru) and the Equatorial Undercurrent influenced by El-Nino effects. Spatial co- herence of such phenomena is limited to scales of a few hundred kilometres. The Sperm whales anticipate this by using migration over ranges of 300-1000 kilometers as their principal strategy for surviving in an unpredictable habitat. Migration thus allows Sperm whales to survive in an environment with unforeseen periods of food short- age. In other words, migration allows them to maintain high biomass and low reproductive rates in an environ- ment, which at any location contains long unpredictable periods of food shortage. Implications of the scale of processes for data analysis and modelling The three examples of processes operating at very differ- ent spatial and temporal scales illustrate that scale does matter and that it is essential to identify the scale at which processes are operating. More specifically, one needs to identify the spatial scale at which the main factors operate or are distributed: the resources or variables influencing Figure 3. Performance of the empirical statistical species response model VLITORS. For 38 species the models discriminated satisfactorily between areas but poorly within areas (shown is Rumex hydrolapathum). For 37 species the models discri- minated satisfactorily between areas and within areas (shown is Filipendula ulmaria). For 10 species the models discriminated poorly between areas (not shown). Dots indicate the predicted probabilities; the background color of the grid cells indicate the observed presence of the species (blue absent, green present) (after De Becker et al., 2001). Interpolation and extrapolation 69 are averaged before calculation of the average attribute val- ue or if the average attribute value is obtained from aver- aging the separate calculated attribute values. If the rela- tionship were non-linear such a procedure would result in an aggregation error (Rastetter et al., 1992). Such an aggre- gation error will increase as the concavity of the non-linear function increases. To avoid such an error, when dealing with non-linear models, one has to calculate the attribute values first (apply the model at all grains, i.e., locations where input variables are known) and next average the function values (Bierkens et al., 2000). Examples of such non-linear up-scaling functions are up-scaling from indi- vidual-leaf photosynthesis to full-canopy photosynthesis, up-scaling from small scale variation of the phreatic sur- face to regional models, or up-scaling of measured daily precipitation to average precipitation for a decade. Scale problems in empirical statistical versus mechanistic modelling in land- scape ecology Ecological models generally link abiotic information (like water availability and quality) to organisms. Mechanistic ecological models, containing causal relationships de- rived from experimental studies, are available for relative- ly simple and thoroughly studied ecosystems (e.g., Van Liere and Gulati, 1992, Janse et al., 1992). Mechanistic model development is both time-consuming and expen- sive. For the restoration of regional landscapes like wa- tersheds and river valleys, generally applicable models valid for a range of ecosystems are required. These ecosys- tems and their interrelations are so complex that deter- ministic knowledge fully covering all processes is often not available and laborious experimental studies are not feasible. The two examples presented below serve as case studies illustrating the constraints related to scale issues in both types of modelling approaches. What we can learn them (for example temperature, the availability of water or mineral nutrients, the distribution of plant cover or prey) and the organisms consuming a certain resource (for example denitrifying micro-organisms, herbivores or predators). It is also important to identify the spatial scale at which the interaction between resource and influencing variable or consumer takes place, e.g., N-sources in the soil and redox conditions; NO3 and denitrifying micro-or- ganisms; plant growth and herbivores; predator and prey. Van der Koppel et al. (in press) provide a simple frame- work that explains how differences in the spatial scale at which consumers and their resources function affect food chain theory. Such a framework is useful to identify criti- cal scale aspects and to assess the risks of anthropogenic changes for trophic interactions by interfering with their functional scales. Both the denitrification example and the Sperm whale ex- ample also illustrated that the temporal scale at which processes are influenced can vary a lot. Denitrification is affected by temperature and redox-conditions that vary during the day and also among seasons and years. The mi- gration of Sperm whales varied among years. The study of biodiversity in ponds supported the notion that consider- able insight can be gained by increasing the scale, both spatially and temporally, in which species diversity is viewed. Straightforward up-scaling from pond studies to catchments seems inappropriate in this case, since it would lead to erroneous conclusions for biodiversity in catchments, because of the non-linearity between the lo- cal scale and the catchment scale. In the process of up-scaling among fine-scale components (such as biodiversity in local ponds) to predict coarser- scale properties of the aggregate (biodiversity in catch- ments), one has to be aware whether or not the relation- ship between variables and attributes is linear. If the mod- el is linear it does not matter if the values of the variables Landschap 20(2)70 from these examples is that the general principle that dis- crepancies between the scale of observation, dominant processes, and model calculations should be avoided is frustrated in practice by limitations in data. Both modelling studies focus on river valleys: one empirical statistical ap- proach focused on the response of plant species on changes in site factors (De Becker et al., 2001, Bio et al., 2002) and one a mechanistic approach focused on geochemical flows (Van der Peijl, 1997, Van der Peijl & Verhoeven, 1999, 2000). Empirical model for plant species This case is an example of spatial ecological predictive modelling, within the limitations imposed by data avail- ability and model purpose given by environmental policy makers. Policy makers, e.g., water and nature managers, wanted a generally applicable model for Flemish river val- leys although data only were available for four specific val- leys. The data, collected from 1993-1997 in four nutrient- poor Flemish lowland river valleys, consisted of presence and absence records for groundwater-dependent plant species and abiotic site conditions describing manage- ment, soil, groundwater level and several groundwater chemistry parameters. Biotic data, management and soil were mapped in grids of adjacent regular square cells (20 x 20 m). Data on groundwater tables and water chemistry were collected at a limited number of point locations within each grid; hence, at a much smaller sampling scale (or support) and with extensive un-sampled surface in be- tween. This example thus deals with a number of specific scaling constraints: limited extent of the study versus the need for a wider geographical applicability of the model; differences in support between variables; spatial autocor- relation. The differences in support were relatively easy to over- come. The variables sampled with less support were spa- tially interpolated and up-scaled (to grid-cell size) to match the other data. This was done by block-kriging fol- lowing a semi-variogram model, since this gave a much better result than standard block-kriging (De Becker et al., 2001). Next, spatial auto-correlation in vegetation field records and model residuals was assessed through em- pirical semi-variograms; the residual semi-variograms in- dicated spatial structure not accounted for by the model?s explanatory variables (cf. Albert & Mc Shane, 1995). Mul- tiple logistic regression modelling was performed using two modelling frameworks. Generalized Linear Models - GLM- (Nelder & Wedderburn, 1972, McCullagh & Nelder, 1989) have been successfully applied in numerous eco- logical studies (e.g., Austin et al., 1984, Margules et al., 1987, Zimmermann & Kienast, 1999). Generalized Addi- tive Models - GAM - (Hastie & Tibshirani, 1990, Yee & Mitchell, 1991) have been applied in more recent studies (e.g., De Swart et al., 1994, Huntley et al., 1995, Austin & Meyers, 1996, Bio et al., 1998). Both enable ecologists to model species response to a wide range of environmental data using a link function (i.e., logit) between response and predictor variables. Generalized Additive Models form an extension of GLM. While GLM fit functions linear in their parameters, allowing for linear and polynomial response shapes, GAM are more flexible permitting both linear and complex additive response shapes, as well as a combination of the two within the same model (Hastie & Tibshirani, 1990). More than half of the species were modeled more accurately by GAM with data driven smooth response shapes instead of second-order poly- nomials. Model evaluation and comparison was based on cross-validation and model discrimination (Bio et al., 2002). A factor coding for the four sampled valleys was most of the times very significant when added to the final regression model. This points at regional differences (be- tween the valleys) in species distribution that are not ex- Interpolation and extrapolation 71 to the final user, just as model applicability and credibility. The models presented are, for instance, valid for nutrient poor river valleys only, as model input data do not include nutrient rich situations. So far, the predictive power of these models could not be examined on other regions. Validation against data collected elsewhere - i.e., an extra- polation in space - is a next step to be taken to see how far the applicability of these empirical models reaches (Bio et al., 2002). Mechanistic model for biogeochemical flows in wetland ecosystems An example of a model describing carbon, nitrogen and phosphorus dynamics at the ecosystem level is the one de- veloped by Van der Peijl & Verhoeven (1999) for river marginal wetlands. This model was developed in the framework of a European project on Functional Assess- ment in European Wetland Ecosystems (Maltby et al., 1996) to analyse nutrient-related processes and their im- portance for ecosystem functions. In this case the con- straints are: choices to be made in spatial and in tempo- ral extent of the study in relation to the needed general ap- plicability of the model and limited extrapolation possi- bilities. The model is a dynamic simulation model in STELLA and has three layers, one for each element under investiga- tion, i.e., carbon, nitrogen and phosphorus (Figure 4). plained by the models. There may be differences in species response to the explanatory variables due to val- ley-specific pseudo-correlations with non-modeled vari- ables. Overall, the regression models seemed ecologically sound and predicted species distribution in Flemish river val- leys adequately, despite discrepancies between data qual- ity and model assumptions. Figure 3 shows two examples illustrating model performance. The model of Rumex hy- drolapathum only predicted well between areas and not within. The model for Filipendula ulmaria predicted ob- served distribution well both within and between areas. This study demonstrated that predictive modelling using standard statistical regression procedures can be reason- ably successful with GLM or GAM in the presence of data with the following characteristics: non-homogeneous ag- gregated data; data that are spatially auto-correlated; part- ly interpolated and partly measured explanatory variables; explanatory variables and response variables collected at different scales; and correlated explanatory variables. However model application and inference should be hand- led with care, as assumptions of independent, error-free explanatory variables and independent errors are clearly not met. We observe that, in practice, models have to suit model purpose as well as possible even if data do not ful- ly support model assumptions. Shortcomings, if not re- movable, should be assessed and, at least, communicated Figure 4. Conceptual dia- gram of a site-model con- sisting of two unit- models. Each unit-model consists of a nitrogen sub-model, a carbon sub- model and a phosphorus sub-model. Within these sub-models there is internal cycling. Landscape geochemical flows are shown between the unit-models (after Van der Peijl & Verhoeven, 2000). Landschap 20(2)72 Each layer has a basically similar set-up with a number of plant and soil compartments with mass flows between them. Carbon fixation, nutrient uptake, grazing by large herbivores, decomposition, mineralization and denitrifi- cation are important processes described in the model. One of the main features of the model is a factor associ- ated with soil redox potential, water table and soil oxygen content, which influences most process rates. The most important connections between the three model layers are the control of carbon fixation by nitrogen and phos- phorus availability, and the control of mineralization by the litter C:N and C:P ratios. The purpose of the model was to investigate the nature of the interactions between the C, N and P cycles, to assess what consequences these interactions have for water quality flowing through the wetland, for carbon seques- tration and for greenhouse gas emissions. Further, at- tempts were made to quantitatively assess nutrient-relat- ed functions in river marginal wetlands and to simulate the effects of management and other human influences in (or outside) the wetland on these functions. After the initial calibration and validation of the model with data collected in river marginal wetlands in England (Van Oorschot et al., 1997), the model was used to test the nutrient transfers between two connected ecosystems, i.e., a wet, groundwater-fed slope and a floodplain along the river Torridge, SW England (Van der Peijl & Verho- even, 2000). The hydro-geomorphic unit (HGMU) con- cept was used for defining a separate, complete unit-mod- el for each of the two HGMU units within the wetland (Figure 4). These unit-models were connected by defining the flows of nitrogen and phosphorus between them. These flows, also called landscape geochemical flows, usually consist of flows of water containing N and P. The two units at the study site, Kismeldon Meadows, slope and floodplain, were separated by a ditch, which caught most of the run off and shallow groundwater flows from the slope. Only an estimated 1% of the N and P that left the slope unit in the water outflow reached the floodplain unit; the rest was caught in the ditch, which prevented the geochemical flows from taking their natural course. To examine the influence of this ditch, the model was run for the same site, but without the ditch. This is comparable to a situation of a restored site, where run-off and shallow groundwater containing nutrients can freely flow from the slope to the floodplain. The computer simulation experiment reconnecting the slope and floodplain showed that this (1) increased the nutrient input into the floodplain, causing a higher biomass production, and (2) increased the wetness of the floodplain, causing slower decomposition, which togeth- er (3) led to a faster soil organic matter accumulation in the floodplain. Nutrient inflows became relatively more important compared to atmospheric deposition, espe- cially for phosphorus. By connecting the slope and the floodplain, 20 % more nitrogen and 18% less phosphorus flowed into the river. This model has a great level of detail with respect to the various biogeochemical processes involved and requires the availability of field data such as C, N and P stores in plants, soil organic matter, and other soil pools. It also re- quires many environmental parameters, such as climatic data, soil characteristics, water level fluctuations, etc. It has been shown to be effective in describing C-N-P inter- actions in wetland ecosystems, and has been sufficiently robust to implement a two-unit model in a landscape with two hydrologically connected wetland ecosystems (Van der Peijl & Verhoeven, 2000). Further spatial expansion of the model would be possible, although there is not much opportunity for modelling small-scale hydrological patterns in multi-unit (or grid-based) approaches. Interpolation and extrapolation 73 ent conditions, the use of spatial autocorrelation as mod- el term or residual information has serious drawbacks. On the one hand, neighborhood or other spatial depen- dence information is not directly available, and the as- sumption that levels of spatial dependence for new sites or conditions are similar to those found at the modeled sites may not be valid. On the other hand, a spatial depen- dence term in the model will act as an indirect variable accounting for?and, possibly, masking part of?the ef- fect of several direct, ecologically relevant variables. Veg- etation records and records of abiotic site conditions tend to be auto-correlated too, and an explanatory variable defining the neighborhood of a site in terms of a species? occurrence will combine biotic (e.g., species? dispersal ability or inter-species competition) and abiotic (favor- able or non-favorable site conditions) information. This will render robust but less informative and, possibly, less generalizable models. Only part of the spatial autocorre- lation in the response variable is likely to be explained by the explanatory variables in the regression model. Assess- ment of the residual spatial variance can aid model evalu- ation, and highlight shortcomings in explanatory vari- ables or model structure (e.g. Robertson & Freckman, 1995, Begg & Reid, 1997, Gotway & Stroup, 1997, K?hl & Gertner, 1997, Bio et al. 2003). The main problem with empirical statistical species mod- els is that there is little cause-effect knowledge incorpo- rated. Of course, the choice of certain site conditions as potential predictor variables is based on knowledge of how these conditions affect species, but for the rest the model is merely statistic. The potential danger of pseudo- predictions is larger when less predictor variables are in- cluded, when the model is spatially extrapolated and es- pecially when the short time scale of a study is not bal- anced to its large spatial scale. Van der Rijt et al. (1996) developed a model for predicting vegetation zonation in Discussion Empirical ecological models are often based on available data that were not explicitly collected for that purpose or on limited data sets especially collected for the purpose of model development (see De La Ville et al., 1997, Ertsen et al., 1998, Bio, 2000). Therefore, quantity and quality of data is of utmost importance. An ideal data set for eco- logical modelling contains a sufficient number of sam- ples that are representative of and well distributed in the modeled geographical and environmental ranges, and that satisfy model assumptions. Unfortunately, such ide- al data sets are rarely found, and the urgent need for swift restoration measures presses modelers to do with less than ideal data (see Olde Venterink & Wassen, 1997). Classical statistical inference is based on the assumption of independent observations collected at randomly cho- sen locations (De Gruijter & Ter Braak, 1990). However, records of spatial dependence in ecological data are nu- merous (e.g., Rossi et al., 1992; Tilman, 1994, Fielding & Bell, 1997), as neighboring samples tend to be more sim- ilar than samples further apart. Using standard statistics, the presence of spatial autocorrelation in data and in model residuals may render error estimates and associat- ed significance tests unreliable. It may also affect model choice, as variable selection is generally based on ex- plained and residual variance. Nonetheless, these data are generally treated as independent, random samples and modeled using classical statistical procedures (e.g., Nicholls, 1989, Hill, 1991, Buckland & Elston, 1993). Recently, methods have been developed for the modelling of spatial dependence, or auto-correlation, in regression using, for instance, neighborhood information (Sokal & Oden, 1978a, b, Smith, 1994, Wu & Huffer, 1997). Geo- statistical modelling of residual spatial dependence is an alternative approach under development (Pebesma et al., 2000). However, for prediction at other sites or in differ- Landschap 20(2)74 dependence of flooding in outer dike areas. They coupled several maps in a GIS and incorporated vegetation re- sponse regression models (based on a geographically small area) to these spatial data. The model was used for evaluation of the effects of different sluice management schemes on outer dike vegetation zonation in a wider area. There is nothing wrong with such predictions as long as flooding frequency and duration are the causal factors for vegetation zonation in all areas where the model is applied (Wassen et al., 2003). The fact that we have to be cautious with extrapolation in time with this category of models is ironic, since this is what these mod- els were developed for: extrapolations into the future. High-detail (in terms of many processes incorporated) dynamic simulation models such as the one developed by Van der Peijl & Verhoeven (1999, 2000) have the advantage of integrating a strong knowledge base on biogeochemi- cal interactions in order to analyze or predict the effects of major environmental drivers such as water level fluctua- tions and nutrient inputs in run-off on overall ecosystem performance, such as the water quality improvement function in wetlands. The drawback of the approach is that large data sets of site conditions are needed to im- plement the model. These would normally only be avail- able if the site would have been intensively studied. An- other limitation of the model is the coarse grain of study - it assumes homogeneous site conditions within certain hydrogeomorphic units. Such units subdivide the land- scape in a discrete way, comparable with the ?ecotope? concept. Coarse-scale spatial variation in terms of multi- unit wetland landscapes can be tackled by running the model in every unit separately and using extra algorithms to describe the hydrological connections between the units. The model would be easier to apply if it would be simplified and implemented in a raster-GIS. There have been some first attempts to do this, and much simpler dy- namic models simulating C-N-P interactions have been generated, which still kept their original level of pre- dictability. If coarse-scale data for other units are unavail- able, a statistical description of the fine-scale compo- nents across the extent of the coarser scale should be ac- quired. The fine-scale attributes can then be ranked by their contribution to the aggregation error. In such a way the important sources of error can be detected (Rastetter et al., 1992). To detect scale-dependent processes and pat- terns, one depends on observation sets or model calcula- tions of fine grain and large extent. Collecting data of fine grain and large extent is costly and time consuming. Therefore, an a priori choice of a certain scale of observa- tion and/or modelling is often unavoidable. Clear under- standing about the scale at which relevant processes op- erate is essential when choosing the appropriate scale of observation and modelling. A general guideline in choos- ing an appropriate scale of study is that discrepancies be- tween the scale of observation, dominant processes, and model calculations should be avoided (Rietkerk et al., 2002). Since in most environmental studies such discrep- ancies are a given and thus cannot be avoided, they should be explicitly acknowledged. Although we have identified a whole range of pitfalls and possible sources of error involved in attempts to scale up patterns and processes from small-scaled site studies, we can identify several promising approaches, which can be further developed. A first approach is the use of statisti- cal regression of spatial data, with attention for spatial au- tocorrelation including assessment of spatial variance. It is important that statistical correlations found with these models are validated with knowledge on cause-effect re- lations. If such knowledge does not exist for the specific relations found, these should be interpreted with care and should ideally still be studied in a causal-analytical way. A second approach is the implementation of simplified Interpolation and extrapolation 75 these systems. Van den Bergh et al. (2001), Pieterse et al. (2002) and Gielczewski (2003) provide good examples of attempts of such integrated models. Although these mod- els also suffer from scale discrepancies, they at least pro- vide an explicit framework revealing them, since the ques- tions asked have to be translated into spatial scenarios and subsequently into input maps whereas the models provide output maps and for all of these steps the spatial and temporal scale is clear. Acknowledgement We thank Dennis Whigham and an anonymous referee for their helpful suggestions. mechanistic models of biogeochemical and population ecological processes in a raster-GIS, with simultaneous modelling of the spatial relationships between raster cells in a hydrological model. The mechanistic model should be parameterised and calibrated with data from studies in one or two spatial cells in the study area. Only a limited number of sensitive parameters for the model have to be measured in all the raster cells. We advocate a combination of approaches, empirical models for species response and mechanistic modelling of biogeochemical processes, in order to gain insight into regional landscapes and to allow for some form of pre- diction of environmental and management effects on Abstract Inquiries into the issue of scale become increasingly im- portant in the field of landscape ecology and natural re- source modelling and analysis. Scales of observation and modelling are often pre-set based on the a priori descrip- tion of the system of study. In this paper we focus on up- scaling approaches. We emphasize that predictability depends on the relation between the spatial and the tem- poral scale of study. Three examples of scale dependent processes illustrate the importance of identifying the scale at which processes operate to avoid erroneous con- clusions. Two modelling studies show a number of scale related bottlenecks in data, interpolation, extrapolation and modelling. In statistical modelling of spatial data spatial dependence should be examined, truly indepen- dent validation data sets should be available and spatial extrapolation should be done with care. In mechanistic modelling of processes spatial up-scaling requires in- formation on landscape heterogeneity and how this in- fluences the modelled processes. Although a general guideline in choosing an appropriate scale of study is that discrepancies between the scale of observation, dominant processes and model calculations should be avoided, in most landscape ecological studies such dis- crepancies are a given. They should be explicitly ac- knowledged and the information in this paper may help in recognizing them and dealing with them. References Albert P.S. & L.M. McShane. 1995. A generalized estimating equations approach for spatially correlated binary data: applications to the anal- ysis of neuro-imaging data. Biometrics 51: 627-638. Arheimer, B. & M. Brandt. 2000. 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In developing countries, resource limitations will limit most manage- ment and restoration efforts to small-scale community based initiatives that require a clear understanding of landscape interactions followed by identification of key indicators of resources and processes that can be monito- red to indicate where and when problems occur. Education and community involvement, however, will likely be the key to successful application of landscape principles, no matter the scale of the study and the cost of management or restoration. We can study landscapes and ecosystems forever but our efforts will be minimal unless we are able to communicate our knowledge to individuals, communities, and governments. The task is enormous. A recent commentary in Frontiers in Ecology and the Environment indicated that The Netherlands is the only developed count- ry in the world with a national policy of man- aging its environment for purposes of resto- ring the ecological health and integrity of its lands. The remainder of the world awaits the results of our efforts. D E N N I S F. W H I G H A M Prof. dr. D.F. Whigham occupies the WLO chair on landscape ecology at the Section of Landscape Ecology, Department of Geobiology, Utrecht University. He is also attached to the Smithsonian Environmental Research Center, Edgewater, MD (USA). those in the Chesapeake Bay or Florida. Plans to restore only part of the Mississippi system, wetlands along the coast of Louisiana, is esti- mated to cost $20 billion or more and the plan is based on a weak understanding of ecological functioning at the landscape and ecosystem scales. Even in relatively pristine areas, a lack of knowledge about how landscapes and ecosys- tems function hinders planning. In the Kenai Peninsula of Alaska, for example, the Anchor River catchment supports valuable salmonid populations. Development in the catchment has had little impact on salmon to date but small-scale disturbances are beginning to increase in frequency. What scale of distur- bances will result in significant impacts on salmon in the Anchor River catchment? We currently can not answer that question becau- se we know little about linkages between ecosystems in the watershed. Subsequently, it is difficult to convince the public that the level of current and projected future activities may have negative impacts on salmon, an extremely valuable local economic resource. What do these examples mean for scientists involved in the field of landscape ecology? First, it means that we need to work harder and faster to develop the field. The level of resource degradation is already high and the rate at which landscapes are being degraded is certainly increasing. The examples suggest that efforts to understand how landscapes function needs to move forward at several scales. In developed countries, an increased understanding of landscape dynamics is requi- red to develop ecologically useful manage- ment and restoration plans. Management plans in these instances are likely to be com- plex and restoration efforts costly. In develo- ped and developing countries where landscapes are currently in relatively pristine condition, Oracle Flexibility in landscape ecology Much work remains if we are to successfully apply emerging principles from the field of landscape ecology to meeting the needs of human societies around the world. Humans, for example, are having an enormous impact on marine and estuarine ecosystems and most problems result from a lack of understanding of how marine ecosystems function combined with a political unwillingness to apply ecolo- gical principles to management. The continu- ed decline in the ecological health of global ecosystems will eventually result in ecological and human disasters and few possibilities of meaningful restoration. We have already reached the point where the costs of large-scale restoration efforts are beyond the means of many countries. Three examples from the US indicate the scale of the problem. Hundreds of millions of dollars have been spent to restore the Chesapeake Bay, the largest estuary in the U.S., but efforts have only resulted in slowing the rate of decline of most natural resources and the Bay still faces an uncertain future. Several billions of dollars are currently being alloca- ted to restore the Florida Everglades; yet most ecologists believe that the restoration plans, developed mostly from an engineering per- spective, have little chance of success becau- se they do not adequately integrate principles of ecosystem and landscape ecology. The Mississippi is the largest river in the U.S. and recent analyses have shown that the long- term effects of human activities in the catch- ment and in estuarine and marine areas have resulted in environmental problems in the Gulf of Mexico that are much greater than Foto: Saxifraga, Ben Delbaere Nitrogen contribution 81 The nutrient loads to the Baltic Sea have increased suc- cessively during the 20th century (Larsson et al., 1985) and have resulted in an ongoing degradation of the environ- ment (Cloern, 2001). These negative effects have taken such proportions that the riparian countries were forced to take remedy actions. One obvious strategy is to reduce the nutrient load from land to sea, and most countries have reduced their point sources by 50% for phosphorus (P). However, this goal has not been achieved for the largest point sources, which are situated in Poland and Russia (L??ne et al., 2002). Nitrogen (N) reduction from point sources, as well as the overall reduction of load from diffuse sources, has in most countries been less success- ful. Recent estimates based on official statistics indicate that load from agriculture constitutes approximately 60% of the anthropogenic N load and more than 25% of the anthropogenic P load to the Baltic Sea (L??ne et al., 2002). The largest reduction achieved for arable leaching is mainly related to the economic breakdown of the agricul- tural sector in the transition countries. So far it has been difficult to monitor the effects, which is mainly due to large storage of nutrients in the soil and water systems (St?lnacke et al., 2002). The nations around the Baltic Sea regularly report their national load to the Helsinki Com- mission (HELCOM), and for the latest pollution load compilation it was also obliged to specify the contribution from various sources. Water management in Sweden is going through dramatic changes at present, related to the adoption of the EU Wa- ter Framework Directive, a new Environmental Code and revised Environmental Quality Objectives. New policies including catchment-based management plans have been suggested, which also demand catchment-based knowl- edge of nutrient transport processes and appropriate tools for landscape planning. Although Sweden has ef- fectively reduced the nutrient load from treatment plants and industries during the past decades, the problem of eutrophication is not yet solved due to nutrient leaching from diffuse sources, such as arable land, rural house- holds, and traffic. These sources are difficult to monitor and models must be applied to quantify their load, and to quantify possible load reductions, which have been or will be achieved in management programs (Figure 1). A catchment model for the national scale (HBV-N) has therefore been developed to be used both for internation- al reporting and for scenario estimates for more efficient control strategies. This paper provides an example of an interdisciplinary methodology that focuses on water qual- ity and management issues at different scales (Figure 2). It includes upscaling of leaching models from the site scale to whole river basins in order to enable estimation of the N loading from the entire country with relatively high spatial and temporal resolution. The paper mainly de- scribes how the transfers between scales have been han- dled and gives some model results from the application of the model concept for the whole country of Sweden (about 450 000 km2). B E R I T A R H E I M E R Dr. B. Arheimer. Swedish Meteorological and Hydro- logical Institute (SMHI), SE-601 76 Norrk?ping, Sweden. berit.arheimer@smhi.se Handling scales when estimating Swedish nitrogen contribution from various sources to the Baltic Sea Nitrogen Hydrology Modelling Catchment Scales At the national and international policy level, there is an increasing demand for overall estimations of the con- tribution of the runoff from large regions or whole countries to the nutrient loadings of river basins and coastal areas. This article decribes a methodology involving scaling up data on nitrogen leaching and transport from the site scale to the scale of river basins and, eventually to the scale of Sweden as a whole. The upscaling meth- ods are based on the linkage of leaching and transport models at the site scale with a nested model system involving regional hydrological models and source apportionment of N loadings towards the Baltic sea. Landschap 20(2)82 Figure 1. Several reasons why dynamic and predic- tive models are useful tools in environmental assessment, management planning, in the imple- mentation process of measures, and to follow- up environmental goals (exemplified with the structure of the catch- ment model HBV). Figure 2. Various scales of catchment modelling with HBV-N in Sweden, using different databases for different management issues. Nitrogen contribution 83 tire calculation period results in one aggregated concen- tration (i.e., not affected by temporal variations) for each combination of region, soil and crop. For each subbasin, an average root-zone concentration is then calculated based on land-use information of crop and soil distribu- tion. This average leaching concentration is assigned to the water discharge from the root zone in the HBV-N catchment model (Figure 3). Up-scaling of water balance and discharge The water balance at the catchment-scale is estimated by using the conceptual rainfall-runoff model HBV (Bergstr?m, 1995; Lindstr?m et al., 1997), which makes daily calculations in semi-lumped subbasins that are cou- pled along the river network. The HBV model consists of routines for snow melt and accumulation, soil moisture, runoff response and routing through lakes and streams. The runoff generation routine is the response function, which transforms excess water from the soil moisture zone to runoff. It also includes the effect of direct precip- Method The catchment model HBV-N (Figure 3.) has been applied for the national scale within a nested model system, called TRK (Table 1), which calculates flow-normalised annual average of nutrient gross load, N retention and net trans- port, and source apportionment of the N load reaching the sea (Brandt and Ejhed, 2003). The TRK system con- sists of several submodels with different levels of process descriptions that are linked together (Bergstrand et al., 2002). Dynamic and detailed models are included for arable leaching, water balance, and N removal. Daily sim- ulations are made for a 20-year time-period. The results are subsequently aggregated over the entire 20-year period to cancel out short-term weather-induced variations. Landscape information, leaching rates and emissions are combined through GIS. N transport is simulated through the hydrological model, which accounts for transport and decay within subbasins, and routing through the river sys- tem, e.g., when passing lakes, towards the sea. During decay N removal may occur. Up-scaling of root-zone leaching Leaching concentrations from arable land is calculated with the physically based SOILN model (Johnsson et al., 1987) for different field categories. General model input parameters are assumed to represent the average for a whole agricultural region, using the SOILNDB concept (Johnsson et al., 2002). Sweden is then divided into 22 agricultural regions, based on climate and agricultural character. For each region separate calculations are made for 9 soil types, 13 crops, and 2 fertilisation strategies. A crop sequence generator is applied to obtain the average leaching concentration for all acceptable combinations in the crop rotation. Time-series of 20-30 years (calculated with a daily time-step) are used to consider weather-in- duced variability. Accumulation of the loads over the en- Table 1. Definition of spatial and temporal scales in the national model application within TRK, which is a coopera- tion between Swedish Environmental Protection Agency (NV), Swedish University of Agricultural Sciences (SLU), and Swedish Meteorological and Hydrological Institute (SMHI). Dimension Extent* Support* Coverage* Spatial Sweden 1000 subbasins 100% (450 000 km2) (200-700 km2) Temporal Normalised annual average Daily time-series 100% (15-20 years) * Terminology according to Bierkens et al., 2000. Figure 3. Schematic structure of the dynamic catchment model HBV-N. Landschap 20(2)84 itation and evaporation on a part, which represents lakes, rivers and other wet areas. The function consists of one upper, non-linear, and one lower, linear, reservoir. These are the origin of the quick (superficial channels) and slow (base-flow) runoff components of the hydrograph. Driving model variables are daily precipitation and tem- perature. These are achieved from optimal interpolation (i.e., kriging) of climate observations considering topog- raphy, wind speed and direction in a national grid of 4x4 km (Johansson, 2000; 2002). In the model, subbasins can be disaggregated into elevation zones (for temperature corrections) and land-cover types. One of the most important parts of the HBV model is the soil moisture routine, which is based on the oversimpli- fied bucket approach, but with the very important addi- tional condition that the water holding capacity of the soil in the subbasin has a statistical distribution (Bergstr?m & Graham, 1998). This leads to a contributing area concept as concerns runoff generation. Only those parts that have reached field capacity will contribute to runoff in the event of rain or snowmelt. It is very important to note that this approach thus implicitly accounts for the subbasin varia- bilities in both soil water holding properties and input in the form of rain or snowmelt, without explicit separation of the two. The parameter values of the model thus reflect the physical properties of the ground as well as their statistical distribution, and they also reflect the random character of the input. It is similar to the cumulative distribution func- tion used for soil moisture saturation in the ARNO rain- fall-runoff model (Todini, 1995), an approach that has also found its way into climate modelling (D?menil and Todini, 1992) where sub-grid variability is a critical issue. The application of Sweden includes about 1000 sub- basins, ranging in size between 200 and 700 km2. The model is calibrated regionally against measured time-se- ries of water discharge. Up-scaling of land cover, emissions and atmospheric deposition For each subbasin land cover is aggregated into the class- es: arable field-type (13 crops on 9 soils in 22 regions; i.e., 2574 types), forest type (3 types), clear-cut forest (addi- tional leaching according to atmospheric deposition rate), urban, and lakes (3 types according to position in the catchment). Emissions are classified as industrial point sources, municipal treatment plants, and rural households. The first two are based on empirical data, while the latter is based on population statistics and co- efficients considering average treatment level in the re- gion. The emissions are aggregated into one value for each type and subbasin. Atmospheric deposition is cal- culated for each lake surface by using seasonal results from the MATCH model (Langner et al., 1995) and aggre- gated for each lake type (20x20 km; up- or downscaling depending on lake size). Up-scaling of nitrogen removal processes The HBV model calculates average storage (and resi- dence-time) of water and N between root-zone and stream, in rivers and in lakes for each subbasin. In the N- routine (Arheimer and Brandt, 1998), leaching concentra- tions are assigned to the water percolating from the un- saturated zone of the soil to the groundwater reservoir. Different concentrations are used for different land-cov- ers, and the load from rural households is added sepa- rately. Removal processes in groundwater are considered before the water and N enter the stream, where addition- al loads from industry and treatment-plants may be added, as well as river discharge from upstream sub- basins. Removal processes may occur during transport in the river and in lakes, and atmospheric deposition is added to lake surfaces (for other land covers it is included in the soil leaching). The equations used to account for Nitrogen contribution 85 ods 1983-1986 and 1998-1999. Thus, the temporal dy- namics in the model was validated by split-sample test of independent daily time-series. Up-scaling of results to national level Source apportionment for different coast segments or for the entire nation is achieved by adding sources for differ- ent categories in all subbasins. This is done separately for gross and net loads to illustrate the influence of removal processes. Net load is the remaining part of the gross load, which eventually reaches the sea after the cumula- tive N removal in groundwater, rivers and lakes down- stream a specific source and subbasin (Wittgren and Arheimer, 1996). Results and Discussion Model results The model produces time-series that give the daily varia- tion in water flow, N concentrations and N transport. The time-series show rather good agreement with measured values (Figure 4), both regarding levels and dynamics. In general, it is easier to achieve good correspondence at large river outlets than for individual subbasins. The river flow is regulated by the waterpower industry in most Swedish rivers, which highly influences the dynamics of discharge, especially in the northern part of the country. The diagram at the right upper corner in Figure 4 shows that the model manages to reproduce the general hydro- graph, but not the intensive fluctuation in water release for energy production. The results are spatially distributed as results are achieved from each subbasin included in the modelling. Mapping of the results from the TRK application gives the spatial distribution for the whole country. Figure 5 show the spa- tial distribution of annual water discharge, as well as the difference between a dry and a wet year. This information daily removal are conceptual and mainly based on empir- ical relations between load, temperature and concentra- tion dynamics. The N removal is spatially lumped on a subbasin level into the three categories groundwater, rivers and lakes. Model calibration and validation The catchment model includes a number of free parame- ters, which must be calibrated against time-series of dai- ly observations. The parameter values (coefficients) are tuned to minimise the relative volume error and to max- imise the explained variance. About 10 parameters are cal- ibrated for the calculation of water discharge, and 5 to simulate N removal. Calibration is done simultaneously for several observation sites in a region to get robust pa- rameter values, which are then transferred to all sub- basins in that region. For N, the calibration procedure is made step-wise, starting with parameters for groundwa- ter, then rivers and finally lakes (Pettersson et al., 2001). Both calibration and validation is done on a daily basis at the subbasin outlet. In the TRK application covering Sweden, water flow was calibrated against measured daily discharge at the outlet of 230 subbasins, and independent time-series from an- other 130 subbasins were used for model validation. For N concentrations, time-series from 300 subbasin were used for calibration, while 200 subbasins were used for inde- pendent validation. This procedure resulted in a spatial validation of water flow, N-concentrations and transport in the river, according to the proxy-basin concept (Abbott and Refsgaard, 1996). Monthly grab samples were normally available for N con- centrations in rivers, but most time-series only covered part of the period studied. If possible, both water dis- charge and N concentration were calibrated on a daily ba- sis for the period 1987-1997, and validated for the peri- Landschap 20(2)86 is important in environmental studies when comparing the nutrient export from one time with another, so that proper flow normalisation is considered to avoid weather impact on the judgement of anthropogenic impact. Simi- lar maps as in Figure 5 will be produced for each year back to 1961, and the modelled time-series are prolonged ev- ery year so that the database is up-dated continuously. The spatial variation in gross N load follows to some extent the pattern of water discharge (cf. Figure 5 and Figure 6) with higher load in the western part of the country. How- ever, the pattern of N soil leaching also reflects the re- gions in Sweden with most intensive agriculture. For in- stance, the most southern part of Sweden does not have very high water discharge, but releases the highest N load (Figure 6B). When comparing gross load and net load it can be concluded that in general about 40-50% of the to- tal N load in southern Sweden is removed during trans- port from the sources towards the sea. However, this downstream reduction in load is not equally distributed but depends very much on the lake distribution of the re- gion and the character of the catchment area and river net- work downstream the sources. Some areas with intensive agriculture and some major inland point sources do not contribute very much on the N load to the sea (cf. Figure 6B and Figure 6C), while the south-western part has low N retention capacity and still contributes a lot to the total load. When comparing the contribution from various sources (Figure 6A) it can be concluded that the load from arable land is by far the largest source, although the N re- tention is also high on this load. Figure 4. Model perfor- mance of simulated time- series compared to observed values (bars). The figure shows exam- ples of independent vali- dation sites, i.e., these time-series were not included in the model cal- ibration procedure. Nitrogen contribution 87 the river course mainly does spatial scaling in HBV-N. The hydrological model accounts for transport and decay within subbasins, and routing through the river system, e.g., when passing lakes, towards the sea. Removal of N may occur during the transport from the sources to the re- cipient, especially during residence in various water stor- ages, which is considered in the model. The model con- cept is the same when applied on small river basins and the entire Baltic basin, but the model parameters must be recalibrate when changing the subbasin size. The param- eter values of the model reflect the physical properties of the ground, statistical distribution, as well as the random character of the input. The values of the parameters in dif- ferent basins will therefore be identical as long as the bas- inwide distribution functions are the same. The model will then be independent of, or at least only mildly sensi- tive to scale (Bergstr?m and Graham, 1998). This means that to some extent the handling of scales is taken care of within the basic hydrological model concept. Neverthe- less, the parameter values consider variability of the envi- ronmental conditions and are thus scale dependent. Once the division into subbasins has been made when set- ting up the HBV-N model, there is no further spatial reso- lution and both sources and flow paths are lumped. For analyses on a more detailed scale, new subbasin division Handling scales Temporal scaling is done when the results are presented as aggregated values. These are based on time-series of 20-30 years with a daily time-step to consider weather-in- duced variability. An average value for the entire period is considered as normal, i.e., it is assumed not to be affect- ed by specific short-term variations between days, sea- sons or years. All dynamic modelling of hydrology should be done for at least 20 years if averages are to be consid- ered representative for Swedish conditions. Previous studies show that ten years time-series is not enough to avoid natural hydrometeoroloical variations (Andersson and Arheimer, 2001). Aggregated values are requested to separate human impact from natural variations. However, during this up-scaling procedure information is lost that may be of critical con- cern for environmental management. Extreme values of water quality may have severe impact on biology although they appear rarely. Thus, in some situations the extreme sit- uations or seasonal concentrations are of more importance than average conditions. For instance, the daily situation may be of great concern in order to make forecasts on algae concentration close to beaches in the summer time. Statistical soil moisture distribution and water recharge, along with adding, delaying and subtracting loads along Figure 5. Swedish annual water discharge 1985- 2000, according to HBV modelling (modified from Grahn et al., 2002). Landschap 20(2)88 Figure 6. Annual nitrogen transport from land to sea for the southern half of Sweden, based on catch- ment modelling with HBV- N: A.) the contribution from various sources (i.e., source apportionment); B.) gross load from diffuse and point sources, respec- tively; C.) net load after nitrogen removal in the fresh-water system between sources and the river outlet (modified from Arheimer and Brandt, 1998) A. Source appointment Nitrogen contribution 89 be validated at the highest resolution for which results are presented. ? Integrated catchment models are useful tools in eu- trophication management for estimation of nitrogen sources and sinks in the landscape. The coupling of rain- fall-runoff models (e.g., HBV) with detailed, field-scale models (e.g., SOIL-N) and GIS may estimate nitrogen load over a range of scales. Acknowledgements The following persons contributed to the TRK application; I) From the Swedish Meteorological and Hydrological In- stitute: Berit Arheimer, Marie Bergstrand, Maja Brandt, Gun Grahn, Anders Gyllander, Barbro Johansson, Lotta Pers, Anna Pettersson, Peter Svensson. II) From the Swedish University of Agricultural Sciences: Hel?ne Ejhed, Hans-Bj?rn Eriksson, Holger Johnsson, Bert Karlsson, Stefan L?fgren, Kristina M?rtensson, Jakob Nisell, Kjell Olsson, Barbro Ul?n. III) From the Swedish Environmental Protection Agency: Anders Widell. IV) From IVL-Swedish Environmental Institute: Olle Westling. must be made and the model must be recalibrated against observed values at the new spatial level. The resolution must thus be adapted to the environmental issue in ques- tion. As shown in Figure 2, the HBV-N model has been applied at various scales depending on modelling pur- pose. However, restrictions in site specific information, e.g. precipitation or observation sites for calibration, nor- mally makes very detailed modelling less reliable. It is not advised to apply the HBV-N model for subbasins less than 1 km2 if the regular national Swedish databases are used as input data. Conclusions ? Handling of scales in the HBV-N model is mainly done through up-scaling procedures combined with the basic hy- drological model concept. The model is rather insensitive to scale, but parameter values that consider spatial variability of environmental conditions may be scale dependent. ? Temporal and spatial resolution should be adjusted to the purpose with the modelling, as information gets lost at up-scaling. However, it is important that the model can Abstract There is a request in Sweden of useful tools for more effi- cient international reporting of nutrient load, and also for eutrophication management and control planning. An in- tegrated catchment model (HBV-N) has therefore been developed. The model has been applied for the national scale (450 000 km2) within a nested model system, called TRK, in which several models with different levels of pro- cess descriptions are linked together. Dynamic and de- tailed models are included for arable leaching, water bal- ance, and N removal. Landscape information, leaching rates and emissions are combined through GIS. The HBV- N model calculates nutrient load, N retention and source contribution to the sea with a relatively high spatial and temporal resolution. The transfer between scales is main- ly handled through up-scaling procedures, combined with the basic HBV hydrological model concept. The model is rather scale insensitive, but temporal and spatial resolution should be adjusted to the purpose of the mod- elling, input data available and possibilities for calibration and validation. The model is validated against monitored time-series of water discharge and nitrogen concentra- tions. The results show that integrated catchment mod- els are useful tools in eutrophication management for es- timating nitrogen sources and sinks in the landscape. Landschap 20(2)90 References Abbott, M. B., and Refsgaard, J. C. (Eds.), 1996. Distributed hydro- logical modelling, Kluwer Academic Publishers, Amsterdam, 320 pp. Andersson, L. and Arheimer, B., 2001. Consequences of changed wet- ness on riverine nitrogen ? human impact on retention vs. natural cli- matic variability. Regional Environmental Change 2:93-105. Arheimer, B. and Brandt, M., 1998. Modelling nitrogen transport and retention in the catchments of Southern Sweden. Ambio 27(6):471- 480. Brandt, M. and Ejhed, H., 2003. TRK-Transport, Retention, K?llf?rdelning. Belastning p? havet. Swedish Environmental Protection Agency, Report No. 5247. (in Swedish) Bergstr?m, S., 1995. The HBV model. In Singh, V. P. (ed.) Computer Models of Watershed Hydrology, Water Resources Publications, Littleton, Colorado, pp. 443-476. Bergstr?m, S. and Graham, P., 1998. On the scale problem in hydro- logical modelling. Journal of Hydrology 211:253-265. Bergstrand, M., Brandt, M., Arheimer, B., Grahn, G., Gyllander, A., Pers, C., Svensson, P., Ejhed, H., Johnsson, H., Olsson, K., M?rtensson, K., L?fgren, S., and Westling, O., 2002. TRK-nutrient load in Sweden ? an operational system for catchment modelling of nutrient transport, retention and source apportionment. In. Killingtveit, ?. (Ed.) Proceedings of Nordic Hydrological Conference, Nordic Hydrological Programme (NHP) Report 47(1): 211-220. Bierkens, M. F. P., Finke, P. A. and Willigen, P. De. (Eds.), 2000. Upscaling and downscaling methods for environmental research. Kluwer Academic Publishers, London. 190 p. Cloern, J., 2001. Our evolving conceptual model of the coastal eutrophication problem. Mar. Ecol. Prog. Ser. 29, 280-329. D?menil, L. and Todini, E., 1992. A rainfall-runoff scheme for use in the Hamburg climate model. Advances in theoretical hydrology. J. P. o?Kane, (Editor), Elsevier, 129-157. Grahn, G., Gyllander, A., Johansson, B., and Svensson, P., 2002. Runoff map of Sweden ? a method for continous production. In. Killingtveit, ?. (Ed.) Proceedings of Nordic Hydrological Conference, Nordic Hydrological Programme (NHP) Report 47(2): 491-496. Johansson, B., 2000. Areal precipitation and temperature in the Swedish mountains. An evaluation from an hydrological perspective. Nordic Hydrology, 31:207-228. Johansson, B., 2002. Estimation of areal precipitation for hydrologi- cal modelling in Sweden. Earth Science center, G?teborg University, Report Doctoral thesis No. A76 Johnsson, H., Bergstr?m, L., Jansson, P.-E. and Paustian, K., 1987. Simulated nitrogen dynamics and losses in a layered agricultural soil. Agric. Ecosystems Environ. 18, 333-356. Johnsson, H., Larsson, M.H., M?rtensson, K. and Hoffmann, M., 2002. SOILNDB: A decision support tool for assessing nitrogen leach- ing losses from arable land. Environmental Modelling and Software, 17:505-517. Langner , J., Persson, C. and Robertson, L., 1995. Concentration and deposition of acidifying air pollutants over Sweden: Estimates for 1991 based on MATCH model and observation. Water Air Soil Poll. 85:2021- 2026. Larsson, U., Elmgren, R. and Wulff, F., 1985. Eutrophication and the Baltic Sea ? Causes and consequences. Ambio 14:9-14. Lindstr?m, G., Johansson, B., Persson, M., Gardelin, M., and Bergstr?m, S., 1997. Development and test of the distributed HBV-96 hydrological model, J. Hydrol., Vol. 201, pp. 272-288. L??ne, A., Pitk?nen, H., Arheimer, B., Behrendt, H., Jarosinski, W., Lucane, S., Pachel, K., R?ike, A., Shekhovtsov, A., Svendsen, L.M., and Valatka, S., 2002. Evaluation of the implementation of the 1988 Ministerial Declaration regarding nutrient load reductions in the Baltic Sea catchment area. The Finnish Environment Institute (FEI), The Finnish Environment Report No. 524. Helsinki. pp 195. Pettersson, A., Arheimer, B. and Johansson, B., 2001. Nitrogen con- centrations simulated with HBV-N: new response function and calibra- tion strategy. Nordic Hydrology 32(3):227-248. St?lnacke, P., Vandsemb, S.M., Vassiljev, A., Grimvall, A. and Jolankai, G., 2002. Changes in nutrient levels in some Eastern European rivers in response to large-scale changes in agriculture. Proceedings of 6th International Conference on Diffuse Pollution, International Water Association (IWA), Amsterdam, pp 267-274. Swedish EPA, 1997. Nitrogen from land to sea. Main report. Swedish Environmental Protection Agency, Report 4801, Nordstedts tryckeri AB, Stockholm. Todini, E., 1995. The ARNO rainfall-runoff model. J. Hydrol., 175:339- 382. Wittgren, H. B. and Arheimer, B., 1996. Source apportionment of riverine nitrogen transport based on catchment modelling. Water Science and Technology 33(4-5):109-115. Nutrient fluxes 91 For most applied environmental research the scale (extent and resolution) of the available data (information scale) differs from the scale at which most of the underlying processes typically occur (model scale) and the scale at which the outcome of the research is used (policy scale). Therefore, upscaling and downscaling methods (Bierkens et al., 2000) are often an essential part of environmental research (e.g. Feddes, 1995; Addiscott, 1998). However, the use of scale transfer functions does often not improve the transparency of the linkage between question (policy scale) and answer (policy scale). The aim of this paper is to illustrate that before using scale transfer functions to transpose available models and data into the policy scale one may search for data and model concepts that match the policy scale. This is demonstrated with examples de- rived from the analysis of Nitrogen (N) and Phosphorus (P) fluxes in the Rhine and Elbe river basins (Figure 1). The search for an appropriate model to analyse nutrient fluxes at the river basin scale involves consideration of the spatial and temporal extent and resolution needed to ans- M A R C E L D E W I T Dr. M.J.M. de Wit, RIZA, P.O Box 9072, 6800 ED Arnhem, The Netherlands. m.dwit@riza.rws.minvenw.nl Nutrient fluxes at the river basin scale Forum The impact of nutrient pollution can be observed in many rivers and coastal seas all over Europe (Stanners & Bourdeau, 1995). This has led to international directives that aim at a reduction of nutrient levels in European rivers and coastal seas (EEC, 2000). Large scale studies of nutrient fluxes are needed to predict and evaluate the effects of the proposed measures. Figure 1 The Rhine and Elbe river basins cover an area of approximately 300,000 km2 of which about 45 percent is used for agricultural produc- tion. The two river basins have a total population of around 70 million people and they overlap with the borders of 11 different countries. Together these basins cover a wide range of landscape, climatic, and socio-economic zones. The Nitrogen and Phosphorus fluxes in these rivers have increased with time by human activities. This has caused considerable chan- ges in fresh and marine ecosystems and has nega- tively affected the quality of water for human con- sumption and other uses (Stanners & Bourdeau, 1995) Landschap 20(2)92 wer the questions that are relevant for nutrient policy at the river basin scale, and the availability of data to cover the extent of the study at the required resolution. Further- more, it needs to be determined which factors are the main controls of nutrient fluxes at the river basin scale and how these factors should be represented in the mod- el given the quality and resolution of the available data. A matter of scale In applied environmental studies research questions are often scale specific. The scale of the research largely de- Figure 2. Processes that determine the flux of nitrogen in the soil (Burt et al., 1993) termines which method is appropriate to use. Therefore, it is necessary to explicitly define the scale of the research before choosing the methodology. One should consider both the spatial and temporal extent and the spatial and temporal resolution needed to answer the question. In general the larger the extent, the less detailed is the reso- lution, that is considered for the analysis. At a regional scale nutrient fluxes are not analysed to learn about mi- croscopic processes in the soil, but rather to describe long term and regional patterns. Also, the resolution of the available data generally decreases with increasing size of the study area. For the analysis of nitrogen leaching at the scale of a farm one might use data from field experiments, whereas for a regional analysis of nitrogen pollution one has to work with soil maps and regional administrative data. Finally, different factors dominate at different levels of scale (see for example Figure 2). Temperature might be one of the main variables to describe the variation in N concentrations within a year, but it is of much less impor- tance for the description of the variation between different years. The framework presented in Table 1 summarises the fore- going discussion and was used to develop a modelling strategy for the analysis of nutrient fluxes from pollution sources to the river outlets at the river basin scale. The nu- trient study described in this paper aims at answering two questions: I) what is the contribution of the different sources (agriculture, industry etc.) and regions to the nu- Research aim Extent of research Resolution Available data Dominating factors understanding processes point detailed laboratory experiments denitrification,adsorption protecting the trophic small region: decade hectare: month stream flow data, agricultural practices, status of a small lake field measurements flow velocity global/climate change world: century country: year administrative data, climate data population density, economy Table 1. Examples of the analysis of nutrient fluxes at different levels of scale Nutrient fluxes 93 More detail about the data used for the nutrient study is given in De Wit (1999a). The quality and resolution of the available data should be seen as a precondition for the type of model to be devel- oped and not as an excuse afterwards for why an advanced and intricate model does not perform well. There is no point in using a model for which the appropriate data are not available. Moreover, the model should consider those factors that dominate at the scale of the analysis. The question now is: which factors are dominating for nutri- ent fluxes at the river basin scale and how should these factors be represented in the model given the quality and resolution of the available data? The balance between data availability and model complexity The search for an appropriate model at the river basin scale was done by comparing the results of four different models that represent increasing complexity (De Wit & Pebesma, 2001). In the first model only one variable is used, in the second model two variables are used, in the third model three variables are used and in the fourth model a large number of variables are included. The five year average N and P loads measured at 34 different mon- trient load in the river?, and II) what will be the effect of source control measures (e.g. reduction of fertiliser use or the improvement of wastewater treatment plants) on the nutrient load in the river? For large areas such as the river basins analysed in this study (105 km2) these questions need to be evaluated over long time periods (decades rather than years). From a (European) policy point of view a spatial resolution of 103-104 km2 (upstream basins of major tributaries) and a temporal resolution of five years are a reasonable resolution to analyse the past (since 1970) and future (up to 2020) changes in nutrient sources and nutrient loads in the Rhine and Elbe river networks. The next step is to explore what data are available at the scale (extent and resolution) of the research question. It appeared that for the analysis of nutrient fluxes in the Rhine and Elbe basins a lot of data were available that cov- er the entire river basins and have the required (or even more detailed) resolution. An overview of the data avail- able for the analysis of nutrient emissions and nutrient transport (from pollution sources to river outlets) are giv- en in Table 2. Water quality and water quantity data were available for 70 stations spread over the Rhine and Elbe river networks (see Figure 3). The area upstream of these monitoring stations varies between 103-105 km2. These data were available to calibrate and validate the models. Figure 3 Location of monitoring stations used in this study Landschap 20(2)94 itoring stations in the river Rhine network (1970-1995) were used to calibrate the models. The model parameters were tuned in such a way (trial and error) that the differ- ence between measured and modelled five year average river load was minimised. The five year average N and P loads measured at 36 different monitoring stations in the river Elbe network (1980-1995) were used to validate the models. A comparison of the predictive capability of the four models can be used to determine the utility of in- creasing model complexity. The errors in the data that were used to run and validate the models were quantified and it was analysed to what extent the model validation er- rors could be attributed to data errors, and to what extent to shortcomings of the model. For more details the read- er is referred to De Wit & Pebesma (2001). In the first model it is assumed that the five year average river load at a certain monitoring station in the river net- work and for a certain time period (e.g. 1970-1975 or 1990-1995) is proportional to the size of the upstream basin. This model serves as a starting point. It lacks any description of the upstream basin. It represents the level of knowledge that was available before the analysis of pol- lution sources and transport conditions in the Rhine and Elbe basins. The second model is based on the assumption that the riv- er load is proportional to nutrient emissions in the up- stream basin or in other words: ?the larger the nutrient in- put the larger the nutrient output?. A distinction is made between direct nutrient emissions to the surface water (e.g. discharge of wastewater) and nutrient surplus at the soil surface (input from fertilisers, manure, and atmos- pheric deposition minus output from yield). Both were mapped for the entire Rhine and Elbe basins at a resolu- tion of 1 km2 for all five year periods from 1970 to1995 (see De Wit, 1999a) and have been used as input for mod- els two, three and four. The ratio of transport of nutrients through the soil/groundwater system and the ratio of transport through the river network are constant in this model for all regions and time periods. This second mod- el represents the level of knowledge available after the in- ventory of pollution sources and before the analysis of transport conditions. The third model (De Wit, 1999b) describes the ratio of transport of nutrients through the river network as a func- tion of the area specific runoff. The ratio of transport of Table 2. Data available for the analysis of nutrient fluxes at the river basin scale Data available for the analysis of nutrient emissions Data Resolution Period Source Population numbers Regions 1990-1995 National Statistical Agencies Connection rate sewage systems Regions 1990-1995 ,, Connection rate WWTP a Regions 1990-1995 ,, Information WWTP a WWTP a 1990-1995 ,, Industrial emissions Regions 1990-1995 ,, Livestock numbers Regions 1970-1995 ,, Agricultural land use Regions 1990-1995 ,, Crop yields Regions 1990-1995 EUROSTAT b Crop yields Country 1970-1995 FAO Fertiliser use Country 1970-1995 FAO Land Cover 1 km2 1990-1995 Corine, USGS c Data available for the analysis of nutrient transport in soil, groundwater, and river network Data Resolution Period Source Average annual precipitation 9 km2 long term PIK d Average annual temperature 9 km2 long term PIK d Soil type 1:1 M - ESB e Lithology 1:1 M - Derived from soil map, IAH f Elevation 1 km2 - USGS c Slope (relief) 1 km2 - Derived from elevation map River network (LDDg) 1 km2 - Derived from elevation map a Wastewater treatment plant e European Soil Bureau b European Statistical Office f International Association of Hydrogeologists c United States Geological Survey g Local drain direction map d Potsdam Institute for Climate Impact Research Nutrient fluxes 95 count for the delay of nutrient transport in the soil and the groundwater. A drain direction map is used to route the nutrients through the river network. In each river segment (1 km) a certain fraction of the nutrient load is lost, de- pending on the flow regime in the specific cell. From a comparison of the models, it was concluded (De Wit & Pebesma, 2001) that although the addition of more process description is interesting from a theoretical point of view, it does not necessarily improve the predictive ca- pability. Although the analysis is based on an extensive pollution sources-river load database (see Table 2) it ap- peared that the information content of this database was only sufficient to support a model of a limited complexity. However, this model (model three) successfully described most of the observed spatial and temporal variation in nu- trient fluxes at the river basin scale. Moving from model one to model two to model three appeared to be improve- ments. The step from model three to model four did not yield better simulations of nutrient fluxes (see Figure 4). nutrients through the soil/groundwater system is de- scribed as a function of lithology. Here, a different pa- rameter value is used for regions with consolidated and regions with unconsolidated rocks. This model describes the river nutrient load as a function of nutrient emissions in the upstream basin, where the fraction of the nutrients that reaches the outlet of the river is positively related to runoff, and the ratio of transport through the soil/groundwater system is larger for regions with con- solidated rocks than for regions with unconsolidated rocks. The fourth model is a conceptual model that is described in detail in De Wit (2001). It is linked to a GIS environ- ment. The fraction of the nutrient surplus at the soil sur- face that leaches, erodes, volatises or is stored in the soil/groundwater system is related to the total runoff, groundwater recharge, groundwater travel times (see De Wit et al., 2000), slope, soil type, and aquifer type at each specific location (km2). Dynamic functions are used to ac- Figure 4 Measured and modelled area specific nitrogen load. The figure shows that the model per- formance increases when moving from model 1 to model 3. The shift to model 4 does not improve the model outcome. Similar results were obtai- ned for phosphorus. For more details about the performance of the four models, the reader is referred to De Wit & Pebesma (2001). Landschap 20(2)96 The balance between the quality of the available data and the complexity of the model had been reached. Discussion A challenging aspect of this study is its spatial and tem- poral extent; river basins of the order 105 km2 and a time period of interest of 50 years. The pathways, and fate of nutrients in soil, groundwater, and river network are a complex function of biological, chemical, and physical processes. Nonetheless it appeared to be possible to sim- ulate most of the observed spatial and temporal variation of nutrient fluxes in the Rhine and Elbe basins. This good result can be attributed to the following points: ? A consideration of the resolution needed to answer the research question resulted in the choice to model at a tem- poral resolution of five-year periods. This resolution is de- tailed enough to monitor the effects of large scale policy and very much simplified the analysis since short-term vari- ation in nutrient fluxes were not considered. In the same way the use of a less detailed spatial resolution may pre- vent the researcher from being drawn in small scale varia- tion that are not relevant at the scale of an entire river basin. ? The search for data at the river basin scale was more successful than expected. Due to advances in technique there is a growing amount of digital spatial data available for environmental research. Data derived from satellite images, supranational mapping programs (e.g. Corine, European soil map), uniform administrative data (e.g. Eu- rostat), and long term monitoring programs (e.g. water quality monitoring) continuously offer new opportunities for the modelling of environmental issues (Burrough & Masser, 1998). ? The relatively good performance of model three in the analysis of nutrient fluxes shows that most of the spatial and temporal variation in nutrient loads in the river Rhine and Elbe can be explained by an inventory of nutrient emissions and a description of the transport of nutrients as a function of two variables; precipitation surplus and lithology. Apparently these two variables are large scale ?surrogate? variables that reflect the most important pro- cesses that determine the pathways and fate of nutrients from pollution sources to river outlets. An alternative to the method presented in this paper would have been to use existing process-based models for water and nutrient fluxes in soil, groundwater, and rivers and combine these models (using scale transfer func- tions) to derive a tool that can be used for the entire river basin. It would be interesting to compare the results of such a methodology with the results of the river basin models (three and four) presented in this paper. Such a comparison is however, beyond the scope of this study. The message of this paper is that before using scale trans- fer functions to transpose available models and data into the policy scale one may search for data and models that match the policy scale. This appeared to be a successful approach for the analysis of long-term nutrient fluxes at Foto: Saxifraga, Jules Philippona Nutrient fluxes 97 Acknowledgements Most of the work presented in this paper has been per- formed at the department of Physical Geography of Utrecht University with support of RIVM. Three anonymous re- viewers are acknowledged for their valuable comments. the river basin scale and may also be a useful strategy for some other environmental studies. Still, for many other studies (e.g. the influence of global warming on regional flooding) the need for scale transfer functions will prob- ably appear to be unavoidable. Abstract The impact of nutrient pollution can be observed in rivers and coastal seas all over Europe. Much is known about the biological, chemical, and physical processes that determine the pathways and fate of nutrients in soil, groundwater, and surface water. However, there is a large gap between the scale at which these processes typ- ically occur and the understanding of nutrient fluxes at the scale of entire river basins. This paper shows how the scale issue was considered for the analysis of long-term nutrient fluxes in the Rhine and Elbe river basins. Al- though this analysis is based on an extensive pollution sources-river load database it appeared that the infor- mation content of this database was only sufficient to support a model of a limited complexity. Nevertheless, this model successfully described most of the observed spatial and temporal variation in nutrient fluxes in the Rhine and Elbe river basins. References Addiscot, T.M., 1998. Modelling concepts and their relation to the scale of the problem. Nutrient cycling in Agroecosystems 50: 239-245. Bierkens, M.F.P., P.A. Finke & P. de Willigen, 2000. Upscaling and downscaling methods for environmental research. Developments in Plant and Soil Sciences, volume 88. Kluwer, Dordrecht. Burrough, P.A. & I.Masser, 1998. European Geographic Information Infrastructures: opportunities and pitfalls. Taylor & Francis, London. Burt, T.P., A.L. Heathwaite & S.T. Trudgill, 1993. Nitrate: processes, patterns and management. Wiley, Chichester. De Wit, M.J.M., 1999a. Nutrient fluxes in the Rhine and Elbe basins. Netherlands Geographical Studies (NGS): 259. PhD Thesis Utrecht University. De Wit, M.J.M., 1999b. Modelling nutrient fluxes from source to river load: a macroscopic analysis applied to the Rhine and Elbe basins. Hydrobiologia 410: 123-130. De Wit, M.J.M., C. Meinardi, F. Wendland & R. Kunkel, 2000. Modelling water fluxes for the analysis of diffuse pollution at the river basin scale. Hydrological Processes 14: 1707-1723. De Wit, M.J.M., 2001. Nutrient fluxes at the river basin scale. Part I: The PolFlow model. Hydrological Processes 15: 743-759 De Wit, M.J.M. & E.J. Pebesma, 2001. Nutrient fluxes at the river basin scale. Part II: The balance between data availability and model complexity. Hydrological Processes 15: 761-775 EEC, 2000. Council Directive 2000/86/EEC of 23 October 2000 estab- lishing a framework for Community action in the field of water policy. European Commission, Bruxelles. Feddes R.A., 1995. Space and time variability and interdependencies in hydrological processes. International Hydrological Series. University Press, Cambridge. Stanners, D. & P. Bourdeau, 1995. Europe?s environment. The Dobris Assessment. European Environmental Agency. EEA, Copenhagen Foto: Joep Dirkx Ecological condition of wetlands 99 The movement of water through landscapes is most ef- fectively managed at the level of individual catchments (hereafter referred to as watersheds), and wetlands are important components of watersheds because of their ability to retain, store, and transform nutrients, toxics, water, and sediments that originate from both diffuse and point sources (Whigham et al., 1988; Johnston et al., 1990; Dorioz & Ferhi, 1994; Weller et al., 1996; Greiner & Hershner, 1998; Kuusemets & Mander, 1999; Crumpton, 2001; Reed & Carpenter, 2002). Effective watershed man- agement thus requires knowledge about the abundance, location, and ecological condition of wetlands within the watershed. Most assessments of wetland condition occur at the level of individual wetlands (Bartoldus, 1999), and few ap- proaches are available to assess the condition of wetlands at the scale of an entire watershed. Wetlands have been considered as elements of watersheds for purposes of risk assessment (Lemly, 1997; Detenbeck et al., 2000; Cormier et al., 2000; Leibowitz et al., 2000.), but this approach does not result in any characterization of wetland ecological condition. Geographic analysis of digital maps has been used to determine the importance of wetlands in reducing nutrient runoff from watersheds (e.g., Weller et al., 1996) and to identify the location of significant wetlands in wa- tersheds (Cedfeld et al., 2000; Crumpton, 2001). While Weller and colleagues were successful in demonstrating the importance of riparian wetlands in reducing phos- phorus in surface water, Cedfeld and colleagues had lim- ited success in identifying potentially important wetlands in a watershed because of difficulties in correlating re- sults of the geographic analysis with results from field- based assessments. If wetland management and restoration are to be success- ful at the watershed scale, we need analytical methods to evaluate wetland condition, identify important wetlands in watersheds, and determine where wetland restoration efforts should be concentrated (O?Neill et al., 1997). In this paper, we describe an approach that we used to eval- uate the ecological condition of two types of wetlands in- dividually and at the scale of an entire watershed. We de- scribe two of the primary goals of the study. The first is to evaluate the condition of wetlands within the watershed by using a field-based assessment approach in combina- tion with a probability-based method for selecting a spa- tially representative sample. The second goal is to deter- mine if geographic analysis of mapped data can be used separately or in combination with the field-based assess- ment approach to characterize the condition of individual wetlands or the populations of wetlands in a watershed. In this paper we focus on issues related to selection of assessment sites, the range of assessment scores for both wetland classes at the scale of the entire watershed, and the suitability of using geographic data to conduct site assessments. D E N N I S W H I G H A M , D O N A L D W E L L E R , A M Y D E L L E R J A C O B S , T H O M A S J O R D A N & M A R Y K E N T U L A Prof. dr. D. F. Whigham, Smithsonian Environmental Research Center, Box 28, Edgewater, MD 21037, USA. Dr. D. E. Weller, Smithsonian Environmental Research Center, Box 28, Edgewater, MD 21037, USA. A. Deller Jacobs, The Nature Conservancy of Delaware, 100 West 10th Street, Suite 1107, Wilmington, DE 19801, USA ; Present Address: Delaware Department of Natural Resources and Environmental Control, Division of Water Resources, 820 Silver Lake Blvd, Suite 220, Dover, DE 19904, USA. Dr. T. E. Jordan, Smithsonian Environmental Research Center, Box 28, Edgewater, MD 21037, USA. Dr. M. E. Kentula, U.S. Environmental Protection Agency, National Health and Environmental Effects Laboratory?Western Ecology Division, 200 SW 35th Street Corvallis, OR 97330, USA. Assessing the ecological condition of wetlands at the catchment scale Hydrogeomorphic Upscaling Wetland assessment Catchment Chesapeake bay Rapid assessment methods for evaluating the functioning and biodiversity status of wetlands are mostly carried out at the scale of individual wetlands. There is an increasing need for evaluating the condition of wetlands at the watershed scale. We used statistical procedures to determine the relationships between data compiled in field-based assessments of individual wetlands and spatial data from remote sensing or other mapping efforts. The goal was to determine if available geographic data could be used to assess individual wetlands or the over- all condition of wetlands in the watershed without having to do site-specific assessments based on field sam- pling. Landschap 20(2)100 Nanticoke River watershed and its wet- lands The Nanticoke River drains approximately 283,000 ha of three counties in Maryland and two counties in Delaware (Figure 1). Agriculture occurs on more than 40% of the watershed and less than 2% has been characterized as ur- ban and suburban development (The Nature Conservan- cy, 1994). Forests cover approximately 45% of the water- shed but many are intensively managed and harvested (Bohlen & Friday, 1997). Agriculture and forest manage- ment have been supported by extensive drainage and most nontidal wetland losses in the watershed have been the re- sult of drainage by channelization (Tiner, 1985). Water quality problems are common within the watershed and are mostly related to surface and subsurface runoff from intensive agriculture (e.g., Phillips et al., 1993; Jordan et al., 1997). About 27% of the watershed contains both tidal and non-tidal wetlands (Tiner, 1985; The Nature Conser- vancy, 1994; Tiner & Burke, 1995). Non-tidal wetlands, the focus of this project, account for almost 85% of all wetland area and are mostly associated with streams (riverine wetlands), poorly drained depressions (depres- sional wetlands), and poorly drained sites that are rela- tively flat (flats wetlands). The Nanticoke watershed is of interest to conservation or- ganizations such as The Nature Convervancy because of the presence of almost 200 plant species and 70 animal species that have been listed as rare, threatened or en- dangered by the states of Maryland and Delaware (The Nature Conservancy, 1994). Project Design The project design integrated three components (Figure 2). First, the hydrogeomorphic (HGM) method for wetland assessment was used to assess the ecological conditions of individual wetlands. Second, the selection of sites for conducting HGM assessments was accomplished by apply- ing methods developed by the U.S. Environmental Protec- tion Agency Environmental Monitoring and Assessment Program (EMAP). Third, GIS procedures were used for two purposes. Selected spatial data were used to assist in the HGM assessments of individual wetlands and a sepa- rate effort focused on the potential use of spatial data to assess wetland condition from mapped information. The hydrogeomorphic (HGM) method (Brinson et al., 1995; Smith et al., 1995; Brinson & Rheinhardt, 1996; Whigham et al., 1999) is one of more than 40 approaches that have been developed in the U.S. to assess wetland conditions Figure 1. Map of the Chesapeake Bay region showing location of Nanticoke River watershed (shaded area). Ecological condition of wetlands 101 (Bartoldus, 1999). In brief, the method produces Func- tional Capacity Index (FCI) scores for specific wetland functions. FCI scores range from 0.0 to 1.0 and they are calculated from equations that combine scores for in- dividual variables. Individual variable scores also range from 0.0 to 1.0 and they are quantified by evaluating data collected at the assessment site. Variable scores are de- termined based on reference sites; the higher the score the more similar a variable is to a site with minimal dis- turbance. Once models are developed, the HGM proce- dure is intended to be a fairly rapid assessment, requiring 0.5 to 1.0 day of data collection. Details of the HGM pro- cedures can be found in the references cited above and a list of HGM publications found on a web site maintained by the U.S. Army Corps of Engineers (http://www.wes. army.mil/el/wetlands/wlpubs.html). HGM models specific to the Nanticoke watershed were developed in two phases. The Developmental Phase took ap- proximately one year to complete. First, an interdisci- plinary team of biologists, soil scientists, and wetland ecologists identified the dominant wetland classes and selected potential variables (Table 1) for use in the HGM models (Table 2). The selection of variables was based on existing knowledge about wetlands in the study area and information available from efforts to develop HGM mod- els for similar classes of wetlands (e.g., Brinson et al., 1995; Whigham et al., 1999; Rheinhardt et al., 2002). The interdisciplinary team then selected a series of Reference Wetlands (Figure 3) to represent the full range of altered and unaltered conditions. These wetlands were sampled using protocols based on the experiences of the interdis- ciplinary team and procedures published by other groups who had developed HGM models. For riverine wetlands, sampling procedures relied on methods developed by Whigham and colleagues (Whigham et al., 1999) for river- ine wetlands in the same region. For flat wetlands, sam- Figure 2. Box and arrow diagram showing the organizational structure of the project. The three elements of the project described in this paper included the development and application of field-based hydrogeo- morphic (HGM) assessments, the use of mapped geographic data (GIS), and the sample design provided by the Environmental Monitoring and Assessment Program (EMAP). Landschap 20(2)102 pling procedures were based mostly on methods devel- oped by Rheinhardt et al. (2002) for similar wetlands along the Atlantic and Gulf of Mexico Coastal Plains. After Reference Wetland sampling was completed, the Principal Investigators as well as local, regional, and na- tional experts in hydrology, soil sciences, ecology, and bi- ology evaluated the data at workshops. The primary ob- jective of the workshops was to select and scale variables for use in field assessments of wetlands in the second phase of the project, the Assessment Phase. Variables listed in bold in Table 1 are the variables that were selected for use in calculating FCI scores for the HGM models listed in Table 2. Table 2 shows how variables were combined to calculate Functional Capacity Scores (FCI) for five HGM models for the riverine wetland class and four HGM mod- els for the flats wetland class. The HGM models are chosen to represent broad cate- gories of ecological processes in wetland ecosystems. The hydrology function is found in all HGM models because of the importance of hydrologic conditions in wetlands. The variables that are used to evaluate the hydrology func- Table 1. Variables consi- dered for inclusion in HGM models for riverine and flats classes in the Nanticoke River water- shed. Variables that were chosen for use in the models shown in Table 2 are shown in bold. Flats Class Riverine Class VANIMAL Number of vertebrate species VCANOPY Percent tree canopy cover VCANOPY Percent tree canopy cover VCWD Density of coarse woody debris VDISTURB Evidence of vegetation disturbance VDITCH Presence of ditches on floodplain VDRAIN Percent of assessment area affected by drainage VFARBUFFER Condition of buffer within 20-100 m VFILL Presence of anthropogenic derived sediment VFLOODPLAIN Floodplain condition VHERB Species of herbs present VHERB Species of herbs present VANTHRO Number of anthropogenic features VINVASIVE Presence of invasive species VLANDUSE Land-use of adjacent upland habitats VLANDUSE Land-use within 1 km of wetland VLITTER Percent litter cover VMICRO Presence of microtopographic features VLITTDEPTH Litter depth VNEARBUFFER Condition of vegetation buffer within 0-20 m VLOG Density of downed logs VROOT Root abundance VMICRO Presence of microtopographic features VSAPLING Sapling species composition VRUBUS Presence of Rubus sp. VSEEDLING Seedling density VSAPLING Sapling density VSHRUB Shrub density VSHRUB Shrub density VSTRATA Number of vegetation strata VSNAG Density of standing of standing dead trees VSTREAMIN Stream condition inside assessment area VSTRATA Number of vegetation strata VSTREAMOUT Stream condition outside assessment area VTREE Tree species composition VTBA Basal area of trees VTBA Basal area of trees VTDEN Tree density VTDEN Tree density VTREE Tree species composition VTREESEED Number of tree seedling species VTREESEED Number of tree seedling species VVINE Number of vine species VVINE Number of vine species Figure 3. Location of Reference Wetlands within Nanticoke River watershed for riverine and flats sub- classes. Ecological condition of wetlands 103 sen using protocols developed by the U.S. Environmental Protection Agency?s Environmental Monitoring and As- sessment Program (EMAP). One of the PIs (DEW) provid- ed EMAP staff with the most recent digital wetland maps for the Nanticoke River watershed. A Generalized Ran- dom Tessellation Stratified (GRTS) design (Stevens and Olsen 1999, 2000) was used to draw the sample from the maps and generate potential sample sites identified by lat- itude and longitude. The basic concept of GRTS design is to construct a random spatial stratification using equal- sized tessellation cells, and then to select a point at random within each cell. A spatial address is constructed using the pattern of subdivision so that the result is a spatially well- distributed sample. The final set of assessment sites is well-dispersed over the accessible portion of the popula- tion (Stevens and Olsen, in review, 2002) and each point will have a known probability of being selected. Potential sites were chosen for inclusion in the set of as- sessment sites only when it had been determined that they were actually wetlands of the targeted class (flat or river- tion typically are chosen to represent physical features (e.g., stream condition, the presence of absence of human alterations to the stream, the presence of drainage fea- tures in the wetland) that would result in alterations of the site water balance. The biogeochemical function is repre- sentative of nutrient cycling processes that occur in wet- lands. Because it is not possible to measure rates of nu- trient cycling in short-term wetland assessments, the bio- geochemistry models incorporates structural features of the wetland system that are important elements of nutri- ent cycling (e.g., the presence of mature vegetation that includes both living and dead biomass). The plant com- munity and habitat functions are representative of the biodiversity and structural features of wetlands. The mod- els typically include variables that quantify features of the vegetation including biomass and species composition. The habitat model usually represents features of the veg- etation that provide habitat for animals. The landscape function is usually chosen to represent the condition of the landscape adjacent to the assessment site. This mod- el is important because the characteristics of the adjacent landscape determine the degree to which the assessment site may be impacted by human activities. As indicated, variables were scaled from 0.0 to 1.0 and HGM models were mathematically organized to calculate FCI scores, that ranged from 0.0 to 1.0. A score of 1.0 means that the function at a site is in a condition equiva- lent to a reference standard site (i.e., the least altered functionality). As the FCI score declines, the condition of the wetland function degrades until the function is absent at a score of 0.0. Brinson et al. (1995), Smith et al. (1995), Whigham et al. (1999) and Rheinhardt et al. (2002) provide more detailed description of procedures used to scale HGM variables and develop HGM models to calculate FCI scores. During the Assessment Phase of the project, sites were cho- Table 2. HGM models used to calculate functional capacity index (FCI) sco- res for riverine and flats wetland classes. Variables are listed and described in Table 1. HGM function Equation used to calculate FCI score Flats subclass Hydrology 0.25*VFILL + 0 .75*VDRAIN Biogeochemistry ((VMICRO + (VSNAG + VTBA + VTDEN)/3)/2) * Hydrology FCI Habitat (VDISTUR + ((VTBA + VTDEN)/2) + VSHRUB + VSNAG)/4 Plant Community ((VTREE + VHERB)/2) * VRUBUS Riverine subclass Hydrology SQRT((VSTREAMIN + (2 * VFLOODPLAIN))/3) * VSTREAMOUT) Biogeochemistry (VTBA + Hydrology FCI)/2 Habitat (((((VTBA + VTDEN)/2) + VSHRUB + VDISTURB)/3) + VSTREAMIN)/2 Plant Community (.75 * ((VTREE + VSAPLING)/2)) + (.25 * ((VVINE + VINVASIVE)/2)) Landscape (.5 * VNEARBUFFER) + (.25 * VFARBUFFER) +(.25 * VSTREAMOUT) Landschap 20(2)104 ine) and permission for access had been obtained. Figure 4 shows the distribution of assessment sites for both classes of wetlands. The first 17 flats and 15 riverine sites that met our criteria and to which we were allowed access were used as sites for testing the final protocols and models. Following the field testing, final versions of the data sheets and variable scaling procedures were prepared for use in the Assessment Phase. Field-assessments were conducted by teams under the su- pervision of one of the authors (ADJ). The field teams re- ceived training from two of the authors (DFW, ADJ) and they followed formal quality assurance and quality control procedures (The Nature Conservancy, 2000; Whigham et al., 2000). Assessment teams consisted of individuals hired for the project and volunteers, mostly provided through contacts with The Nature Conservancy. Data compiled during the assessment phase of the project were scanned from the field datasheets to create comput- er files using procedures developed by EMAP under the supervision of one of the authors (MEK). Electronic data files were checked with field data sheets and corrected. Comparison of assessment data with remotely sensed spatial data One of our objectives was to determine if it would be pos- sible to use remotely acquired spatial data to produce site assessments with an acceptable degree of accuracy. We evaluated a variety of mapped spatial data (Table 3) for their potential to predict wetland conditions as assessed by HGM field-based assessments. In this paper, we focus on preliminary results using land cover data (Table 3) and metrics of stream disturbance status (natural, channel- ized, or artificial ditch; Tiner et al., 2000, 2001). For each wetland, land cover proportions and lengths of excavated and natural stream channels were determined for radial distances of 100, 500 and 1000 meters from the sampling point provided by EMAP. Step-wise multiple regression analysis was used to determine the relationship between the independent variables and the measured HGM vari- ables (Table 1) and FCI scores (Table 2) for riverine and flats subclasses. Results Selection of assessment sites Digital wetland maps were used to evaluate up to 1050 po- tential assessment sites from a list of 1,992 random points provided by EMAP. Based on an interpretation of digital maps of the 1050 potential sites, we selected a subset of 455 sites to which we sought access. Sites were examined Figure 4. Location of assessment sites in the Nanticoke River watershed for riverine and flats sub- classes. Ecological condition of wetlands 105 or scheduling a meeting. We received no response from 38% of the contacts and 17% of the contacts denied ac- cess. We gained permission to sample 201 sites. Once contact had been made with landowners, we obtained ac- cess to all of the publicly owned sites and 67% of the pri- vately owned sites. Contacting landowners, follow-up contacts with landowners, and examination of the sites to determine if they would be included in the study took ap- proximately 168 person-days (1,200 hours). For compari- son, two other major components of the study took less time. Site selection and forming and training field crews took 97 person-days (776 hours). Sampling assessment sites required 145 person days (1160 hours). Assessment sites for both wetland classes were distributed across the entire watershed (Figure 4) but there was a bias toward public sites in the riverine subclass (D. Stevens, personal communication). The bias was most likely the result of a lower level of accessibility to privately owned riverine sites. EMAP staff will be conducting further tests to determine if adjustments need to be made in the final interpretation of the assessment data. Range of variability of FCI scores A goal of any HGM protocol is to select variables that quantitatively express the range of natural variation in the order provided by EMAP. The coding associated with existing digital wetland maps could not be used to determine the hydrogeomorphic classification of indi- vidual wetlands. Subsequently, each potential wetland as- sessment site identified by EMAP had to be visited to eval- uate the following criteria, which all had to be met in or- der for a site to be selected: ? Point was in the respective testing or assessment group specified by EMAP ? Point was in the Nanticoke River watershed ? Point was a wetland ? Point was in a non-tidal wetland ? Point was in a wetland in the flats or riverine HGM sub- class ? Point was not in a farmed wetland ? Landowner permission had been granted to conduct the assessment One of the most time consuming aspects of this part of the project was the process of obtaining permission from private landowners to visit potential assessment sites. First, landowners were identified through the use of pub- lic ownership documents. We then examined the lists of owners and identified individuals who would be willing to attempt to communicate with the landowner by calling Table 3. Spatial data sets with sources or contacts.Data set Source Orthophotography for Maryland http://www.dnr.state.md.us/MSGIC/techtool/samples/metadata/doqq.htm Orthophotography for Delaware http://bluehen.ags.udel.edu/spatlab/doqs/_doq.html EPA EMAP land cover U.S. EPA., 1994 NLCD land cover Vogelman et al., 2001SSURAGO NRCS county soils data http://www.ftw.nrcs.usda.gov/ssur_data.html ftp://ftp.ftw.nrcs.usda.gov/pub/ssurgo/online98/data/ http://bluehen.ags.udel.edu/spatlab/soils/ EPA Reach File 3 stream maps http://www.epa.gov/r02earth/gis/atlas/rf3_t.htm US Census TIGER road files http://www.census.gov/geo/www/tiger/index.html Stream maps classified by disturbance Tiner et al., 2000; Ralph Tiner (unpublished data) Landschap 20(2)106 across the set of reference sites (Brinson & Rheinhardt, 1996; Wakeley & Smith, 2001). In this project, approxi- mately half of the variables that were initially chosen were eventually used in the HGM models (Tables 1 and 2). FCI scores shown in Figure 5 are typical of scores for all of the models in both hydrogeomorphic subclasses. FCI scores varied from 1.0 (reference standard conditions with no detectable impacts) to 0.1 (function present but at a very low level). These results suggest that the majority of the wetlands in the two classes have been degraded from reference standard conditions. Only a small percentage of un-impacted wetlands remain (e.g., sites with FCI scores > 0.90 for all functions), suggesting that there is a high potential for restoration of wetland functions within the watershed. Further analysis of the FCI scores and variable scores will be conducted to determine which variables were most responsible for lower FCI scores at impacted sites and which wetland features need to be considered in the development of restoration goals. In addition, we will be conducting further analyses to eval- uate how wetland condition varies spatially throughout the watershed. Locations of streams (Marshyhope Creek, Deep Creek, Broad Creek) that drain three subwatersheds are shown on Figure 3. Table 4 shows mean FCI scores for the five riverine functions for Marshyhope Creek, Deep Creek and Broad Creek subwatersheds. Mean FCI scores were significantly lower for four of the functions (hydrol- ogy, biogeochemistry, habitat, and landscape) in the Deep Creek subwatershed (Table 4). Spatial information of this type can potentially be used to identify problem areas with- in the watershed as well as targeting areas within the wa- tershed for restoration. Analysis of spatial information will also allow us to further evaluate the adequacy of the site selection process. The ratio of public to privately owned assessment sites was lower in the Deep Creek sub- watershed, potentially resulting in a bias toward lower quality private sites with lower FCI scores. Suitability of using geographic data to assess individual wetland sites Use of the mapped digital data to predict HGM functions produced variable results. For the flats subclass, there were significant stepwise multiple regressions for each of the HGM functions (data not shown) and the regressions Table 4. Mean FCI scores for five HGM functions for the riverine subclass for the three large subwater- sheds in the Nanticoke River system. The num- ber of riverine assessment sites in each subwater- sheds were: Marshyhope = 24, Deep Creek = 10, and Broad Creek = 13. For each function, means that differ for the subwater- sheds have different superscripts. Subwatershed Hydrology Biogeochemistry PlantCommunity Habitat Landscape Marshyhope Creek .701a .772a .947a .859a .788a Deep Creek .236b .495b .807a .431b .584b Broad Creek .683a .759a .809a .727a .770a Figure 5. Distribution of FCI scores for the hydrolo- gy function for the river- ine subclass sampled in the Nanticoke River water- shed. Sites are aligned so that FCI scores vary from high (left) to low (right). The hydrology model for the subclass is provided in Table 2. Ecological condition of wetlands 107 two phases are equally important to overall success of a project. The Development Phase is essential if site-specific as- sessments are to be conducted in the second phase. The selection and sampling of reference sites and the selec- tion and scaling of variables are essential elements of any field-based HGM assessment. The necessity of selecting reference sites that represent the range of condition for a given wetland class has been described by Brinson and Rheinhardt (1996). Data from reference sites are essential in the selection of HGM variables that can be used to quan- tify differences between assessment sites. Both selection and sampling of sites during the Development Phase require adequate training of field teams (Whigham et al. 1999), im- plementation of procedures to assure accuracy of data gathering and reporting, and development of standard methods for collecting field data (Wakeley & Smith, 2001). The reader can refer to several HGM guidebooks to learn more about the procedures that have been suggested for selecting HGM variables and for selecting and sampling reference wetland sites using HGM procedures (Adamus & Field, 2001; Hauer et al., 2002; Rheinhardt et al., 2002). The Development Phase is time consuming and costly; thus it is often cited as one reason why the HGM approach to wet- land assessment has not been used more widely. While it is unfortunate that there are no faster ways to complete the explained between 17 and 44% of the variability. Multiple regressions were more successful in predicting FCI scores for the riverine class than the flats class (Table 5). All of the multiple regressions in Table 5 were significant at p < 0.0001 and they accounted for between 31% and 70% of the variation in the FCI scores. One variable (length of ex- cavated stream channel within 100 or 500 meters of the site where the assessment was conducted) had a negative relationship to the FCI scores for all models. This result clearly suggests that channelization results in effective drainage of sites and has a negative impact on wetland function as measured by HGM scores. Land-use cate- gories were also important. Increasing amounts of devel- oped land and crop land near the assessment site had a negative influence on FCI scores and the greater the amount of forested land near the site, the higher the FCI score. These results suggest that individual wetlands have important linkages to adjacent land uses and that degra- dation of areas adjacent to wetlands results in negative impacts of ecological functions in the wetlands. Discussion As described earlier, the project was divided into a Develop- ment Phase and an Assessment Phase, with each phase taking approximately one year to complete. We believe that the Table 5. Stepwise multip- le regression results for riverine HGM functions (dependent variables) and landscape cover data (independent variables). All models shown in the Table were significant at p < 0.0001. The sign (+/-) in front indicates whether the variable is positively or negatively related to the HGM function. Variable names are: ex100 Length of excavated stream channel (ditches and channelized) in 100 m circle around the sample point. ex500 Length of excavated stream channel (ditches and channelized) in 500 m circle around the sample point. ex1000 Length of excavated stream channel (ditches and channelized) in 1000 m circle around the sample point. nat1000 Length of natural stream channel in 1000 m circle around sample point. DEV100 Proportion of total developed land (low + high intensity development in 100 m circle around the sample point). DEV1000 Proportion of total developed land (low + high intensity development in 1000 m circle around the sample point). FOREST100 Total amount of forest within 100 m of the sample point. FOREST1000 Total amount of forest within 1000 m of the sample point. FORDEC100 Total amount of deciduous forest within 100 m of the sample point. FOREVER1000 Total amount of evergreen forest within 1000 m of the sample point. CROP100 Total amount of crop within 100 m of the sample point. HGM Function No. of Variables Variables R2 Biogeochemistry 3 -ex100 + nat1000 ? DEV100 0.51 Habitat 2 -ex100 + nat1000 0.42 Hydrology 5 -ex100 + nat1000 +FOREST100 +FOREST1000 ?FORDEC100 0.70 Landscape 6 -ex100 ?ex1000 +nat1000 ?CROP100 ?DEV1000 +FOREVER1000 0.70 Plant Community 2 -ex500 ?DEV100 0.31 Landschap 20(2)108 Development Phase, the results are worth the effort because field-assessments can be done in less than one day when field-tested protocols have been developed. In addition, once the procedures have been developed and verified, methods can be applied in many locations. Thus, the prod- uct of the investment in the Development Phase has applica- tions beyond the initial assessment and the potential for continued use in a monitoring and assessment program that supports decision making. While we have not reached any final conclusions regard- ing the ecological condition of wetlands in the watershed, the approach that we have used clearly suggests that there is a wide range of conditions in the watershed and that most wetlands in the watershed have been degraded at some level. Preliminary data further suggest that wetland condition differs among wetlands in different subwater- sheds of the Nanticoke basin. Finally, the use of spatial geographic data can be important in assessing wetland condition at the scale of entire watersheds for several rea- sons. First, spatial data can be effectively used to identify and conduct preliminary interpretations of potential as- sessment sites. Second, spatial data at appropriate levels of resolution can provide input variables to HGM models. Third, mapped spatial data has the potential to be used as a surrogate for field-based assessments when properly calibrated with field assessments. This study will provide useful information for designing future watershed-based assessments that employ a combination of field-based sampling and assessment based on spatial data. Ecological condition of wetlands 109 an Institution in the form of fellowships to a Post-doctor- al fellow (Vladimir Samarkin) and student internships through the Work-Learn Program. The authors acknowl- edge the assistance of the following individuals who helped with various parts of the project: Chris Bason, Stephanie Behles, Jeff Lin, Mary Pittek, Marcia Snyder, Arthur Spingarn, Don Stevens, Richard Sumner, Ryan Szuch, Christine Whitcraft, Mike Yarkcusko and many dedicated volunteers who assisted with field data collection. Acknowledgments The research was funded in part by the U.S. Environmen- tal Protection Agency under cooperative agreement 82681701. It has been reviewed by the National Health and Environmental Effects Research Laboratory?s Western Ecology Division and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. Additional funding was provided by the Smithsoni- Abstract Ecological processes in wetlands result in important soci- etal values, whether one is considering an individual wet- land or all of the wetlands within a catchment (watershed). In addition to providing habitats for numerous species, wetlands typically intercept surface and groundwater and improve water quality by removing nutrients, contami- nants, and sediments. A variety of approaches have been developed to assess the ecological condition of individual wetlands, but less progress has been made in developing approaches to evaluating the ecological condition of wet- lands at the scale of entire watersheds. In this paper we de- scribe an approach to assessing the ecological condition of two classes of wetlands in the Nanticoke River watershed, a subwatershed in the Chesapeake Bay drainage of North America. We used the hydrogeomorphic (HGM) approach to assess the ecological condition of wetlands along non- tidal streams (riverine class) and wetlands associated with poorly drained soils on interfluves (flats class). Sampling protocols developed by the U.S. Environmental Protection Agency?s Environmental Assessment and Monitoring Pro- gram were used to select a spatially unbiased sample of sites for field-based assessments. Statistical procedures were used to determine the relationships between data compiled in the field-based assessments and spatial data from remote sensing or other mapping efforts. We want- ed to determine if available geographic data could be used to assess individual wetlands or the overall condition of wetlands in the watershed without having to do site-spe- cific assessments based on field sampling. The HGM ap- proach to wetlands assessment appears to be a useful methodology when it is applied in combination with a spa- tially unbiased method for selecting sampling sites. There were significant relationships between results of HGM as- sessments and mapped geographic data, but the strengths of the relationships were variable, demonstrating potential limitations to the use of mapped geographic data to as- sess wetlands condition in relatively flat landscapes such as those present in the Nanticoke River watershed. Future improvements in the resolution of GIS data, however, should result in better correlations between GIS-based as- sessments and field-based assessments of wetlands. Landschap 20(2)110 References Adamus, P. R. & D. Field, 2001. 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It has been declared that landscape represents a crucial organi- zational level and special scale, at which both the effects of global change, as well as site-based biodiversity trends, are apparent, hence, at which appropriate responses will need to be implemented (Hobbs, 1997). The meaningful way in which humans interpret this nature at a landscape scale, and as a modelling instrument in spatial or physical planning, can be called an ecological network (Cook & van Lier, 1994). Most specific initiatives to develop eco- logical networks meet and suit the specific circumstances evident in the particular geographic and, even more im- portantly, hierarchical context. The widely used European-level approach considers ter- ritorial ecological networks as coherent assemblages of areas representing natural and semi-natural landscape el- ements that need to be conserved, managed or, where ap- propriate, enriched or restored in order to ensure the favourable conservation status of ecosystems, habitats, species and landscapes of regional importance across their traditional range (Bennett, 1998). In addition to this approach, there are a wide range of names worldwide given to such ?patch and corridor? spatial concepts: greenways in the USA, Australia and New Zealand (Ahern, 1995; Hobbs, 1997; Viles and Rosier, 2001), ecological infrastructure, ecological framework (van Bu- uren and Kerkstra, 1993), extensive open space systems, multiple use nodules, wildlife corridors, landscape restora- tion network (Ahern, 1995), habitat networks, territorial systems of ecological stability, framework of landscape sta- bility (Jongman, 1995). In Estonia, a concept of ?the net- work of ecologically compensating areas? (Mander et al., 1988) has been developed since the early 1980s. This net- work can be observed as a landscape?s subsystem ? an eco- logical infrastructure ? that counterbalances the impact of the anthropogenic infrastructure in the landscape. In com- parison with the traditional biodiversity-targeted approach, this concept also considers the material and energy cycling, socio-economic and socio-cultural aspects. The network of ecologically compensating areas is, like all territorial ecological networks, a multilevel hierarchi- cal system. Their hierarchy emerges from both the spa- tial range and functions. Although ecological networks are already widely used practice in landscape/territorial planning and nature conservation (Cook and Van Lier, 1994; Ahern, 1995; Jongman, 1995; Bouwma et al., 2002), there are few works available on the hierarchical analysis of territorial ecological networks (Cook, 2002; Villeumier & Prelaz-Droux, 2002). The main objectives of this study are: (1) to demonstrate the hierarchical character of territorial ecological net- works, (2) to recognize common elements and function- ? L O M A N D E R , M A R T K ? LV I K & R O B E R T J O N G M A N Prof. ?. Mander, Institute of Geography, University of Tartu, Vanemuise 46 51014, Tartu, Estonia, mander@ut.ee Dr. M. K?lvik, Environmental Protection Institute, Estonian Agricultural University, POB 222, Tartu, 50002, Estonia, mkulvik@envinst.ee Dr. R.H.G. Jongman, Alterra, Green World Research, PO-box 47 6700 AA, Wageningen, The Netherlands, r.h.g.jongman@alterra.wag-ur.nl Scaling in territorial ecological networks Landscape planning Nitrogen budget Riparian buffer zones Spatial scale Territorial ecological networks Territorial ecological networks are coherent assemblages of areas representing natural and semi-natural landscape elements that need to be conserved, managed or, where appropriate, enriched or restored in order to ensure the favourable conservation status of ecosystems, habitats, species and landscapes of regional importance across their traditional range (Bennett, 1998). In this study we demonstrate the hierarchical character of territorial eco- logical networks, recognize common elements and functional differences between hierarchical levels, and ana- lyze the downscaling and upscaling of the functions of ecological networks. Emerging from the examples of eco- logical networks at different hierarchical levels, we highlighted following common principles: connectivity, multi- functionality, continuity, and plenipotentiality. Figure 1. Schematic example of an ecological network (from Bouwma et al., 2002; with permission of ECNC and I. Bouwma). Landschap 20(2)114 al differences between hierarchical levels of territorial ecological networks; (3) to analyze the downscaling and upscaling of the functions of ecological networks and their spatial distribution. Considering hierarchy in the application of the ecologi- cal network model in practice helps to reflect the com- plexity of pattern and processes at the landscape level. One of the ways to downscale the functions of an ecolog- ical network is to use a strategy based on suitability crite- ria. This approach helps to reveal, evaluate and exploit the impact of protected and sparsely populated areas on the environment in the broader sense. Likewise, it has been used to identify and measure the suitability of potential sites for ecological network development in residential ar- eas (Miller et al., 1998). As an example, a GIS-based habi- tat suitability analysis for the designing of national-level ecological networks in Estonia is presented in this paper. For the upscaling approach from the micro-scale ecolog- ical network to the meso- and macro-scale level, a nutri- ent fluxes modeling attempt in riparian buffer zones will be presented. The use of point models step-by-step with- in elementary watersheds helps to describe the changing gradient of nutrient fluxes along the water filtration path and allows the creation of bridges between the different hierarchical levels of ecological networks. Roots of the concept Development of the idea of territorial ecological networks may be largely based on the central place theory elaborat- ed by J.H. von Th?nen (1826, 1990), W. Christaller (1933, 1966) and A. L?sch (1954). Enhanced by the Von-Th?nen- Christaller-L?sch theory of central places and their hier- archy, Rodoman (1974) used the idea of influence pattern and spatial hierarchy to advance the concept of polarized landscapes. According to this approach, two main poles ? centres of human activities (e.g., cities) on the one hand, and centres of pristine (undisturbed) nature (e.g., large forest and swamp areas) on the other hand ? create the hi- erarchical gradient fields of interactions. Thus, it allows the use of the Von Th?nen-Christaller-L?sch model for reverse situations, not proceeding from the development of economic but ecological benefit. In this case ecological benefit means first of all less disturbance by human activi- ties (K?lvik et al., 2003). Structural components as indicators of functional hierarchy A network of ecologically compensating areas is a func- tionally hierarchical system with the following compo- nents: (A) core areas, (B) corridors; functional linkages between the ecosystems or resource habitat of a species enabling the dispersal and migration of species and re- sulting in a favourable effect on genetic exchange (indi- viduals, seeds, genes) as well as on other interactions be- tween ecosystems; corridors may be continuous (linear; Scaling in territorial ecological networks 115 Local movements, within the home range of a species for foraging, hiding from enemies and optimizing living con- ditions, are normally not included in the analyses and im- plementation of ecological network. However, this kind of movement is most important at lower spatial scales of ecological networks. Spatial hierarchy Most specific initiatives to develop ecological networks ? either theoretically or in practice ? consider the specific circumstances evident in the particular hierarchical con- text. The most practicable is the approach that proceeds from the traditional scaling of maps in cartography: 1:500; 1:1000; 1:5000; 1:10,000; 1:50,000; 1:100,000, 1:500,000 etc. Mander et al. (1995) intuitively defines the network components at four levels: (a) mega-scale: large natural core areas (>10,000 km2) and their buffer zones, sometimes connected with corridors; (b) macro-scale: large natural core areas (>1000 km2) surrounded by buffer Saunders et al., 1991), interrupted (stepping-stones; Brooker et al., 1999) and/or landscape corridors (scenic and valuable cultural landscapes between core areas), (C) buffer zones of core areas and corridors, which support and protect the network from adverse external influences, and (D) nature development and/or restoration areas that support resources, habitats and species (Bennett, 1998; Bouwma et al., 2002; Figure 1). Corridors which provide connectivity between the core ar- eas can be considered as key elements of ecological net- works. According to Ahern (1995), ecological corridors and greenways are a linked or spatially-integrated network of lands that are owned or managed for public uses in- cluding biodiversity, scenic quality, recreation and tradi- tional agriculture. The viability of certain processes in landscapes is dependent on connectivity (the movement of wildlife species and populations, the flow of water, the flux of nutrients, and human movement). Without connectivi- ty, these processes and functions may not otherwise occur. However, connectivity must be understood in terms of the process or function that it is intended to support. Movement, which assumes connectivity, is itself the prod- uct of evolutionary pressures contributing in many ways to the survival and the reproduction of the animal. Ani- mals move through their home range, but may also move long distances from where they were born and their kin remain. Three kinds of movements can be distinguished (Caughley & Sinclair, 1994): Local movements- these are movements within a home range and are on smaller scales; ? Dispersal- movement from the place of birth to the site of reproduction, often away from its family group and usually without return to place of birth; ? Migration- movement back and forth on a regular ba- sis, usually seasonally, e.g. from summer range to win- ter range to summer range. Figure 2. Hierarchy levels of ecological networks and according representa- tive figures of this paper. The degree of detail and the exploredness are increasing and generaliza- tion is decreasing towards lower (detail) levels. Landschap 20(2)116 zones and connected with wide corridors or stepping- stone elements (width >10 km); (c) meso-scale: small core areas (10-1000 km2) and connecting corridors be- tween these areas (e.g., natural river valleys, semi-natural recreation areas for local settlements; width 0.1-10 km); (d) micro-scale: small protected habitats, woodlots, wet- lands, grassland patches, ponds (<10 km2) and connect- ing corridors (stream banks, road verges, hedgerows, field verges, ditches; width <0.1 km; Figure 2). The hierarchical scaling is similar to the classification of core areas based upon insights regarding the minimum required area to sustain viable populations of species (e.g., of European importance). According to this system, very large areas (critical size: >5 km2; guarantees the long-term survival of all populations), large areas (critical size: 1-5 km2; when isolated this area may suffer some loss of species; connection or area enlargement is re- quired), and areas with a sub-optimal size (70-100% of species can maintain viable populations, the most de- manding species can only be maintained or restored by enlargement and/or connections with comparable habi- tats by corridors); Bouwma et al., 2002). Mega-scale ecological networks can be considered at the global level. The Human Footprint Map can serve as a ba- sis for determining global ecological networks (Figure 3; Sanderson et al., 2002). The macro-scale of ecological net- works is represented by regional-level activities like the Pan-European Ecological Network (PEEN) or national- level projects. In the Czech Republic, Slovak Republic and the Netherlands, territorial ecological networks are im- plemented and legislatively supported. In Estonia, Lithua- nia and Poland, networks are designed and some aspects accepted by law. In Hungary, Latvia, Switzerland and Ire- land, network design is under development, and local or landscape-level ecological networks have been estab- lished in some parts of the territory of several European Figure 3. The map of the Human Footprint as a basis for the ecological network system at the global scale (Sanderson et al., 2002). Summarized factors of anthropogenic pressure have been used, such as the Human Influence Index, which is the quantitative basis for the map. Adopted from www.ciesin.columbia.edu/ wild_areas/. The full list of biomes is available at www.wcs.org/humanfoot- print. Figure 4. Habitat map of the Pan-European Ecological Network (PEEN) for Central and Eastern Europe as a basis for the PEEN indicative map. Adopted from Bouwma et al. 2002. Figure 5. Suitability for the ecological network in Estonia (adopted from Remm et al., 2003) as an example of an ecological network at the meso- regional (national) level. Dark grey patches indi- cate protected areas (rel- ative suitability value >1.0), whereas grey areas have a suitability value of 0.5-1.0, and are mostly local core areas, various buffer zones and corri- dors; towns are shown in black. Scaling in territorial ecological networks 117 system of administrative levels, the range of planning ar- eas, as well as the levels and size of core areas and con- necting corridors. Experiences gained from the develop- ment of the concept of the ecological network in Estonia are presented as an example for the national-level ap- proach. The challenge of the ecological-network ap- proach is to integrate ecological principles, biodiversity, and landscape conservation requirements into spatial planning procedures and other land use practices. Functions of territorial ecological net- works Ecological networks are viable because they provide mul- countries such as Germany, Belgium, UK, Italy, Spain, Portugal, Russia, and the Ukraine (Bouwma et al., 2002). Landscape-level ecological networks are designed or im- plemented on a wide range of spatial scales, from macro- and meso- to micro-scale projects. The most significant research on both species migration and dispersal, as well as on energy and material fluxes has been carried out at this level (see Forman, 1995; Farina, 2000). Likewise, the most detailed analysis and implementation schemes have been established at micro-scale (Figure 2). Spatial hierarchy is closely associated with the planning levels of ecological networks. Table 1 presents a possible Range of Administrative levels Hierarchical level Diameter of Width of Planning levels Spatial scale (Fig. 32; planning area of core area core areas corridors in Estonia Mander et al., 1995) 1?1.5*105 km Earth?s geographical space 1 ? 1.5*104 km Geopolitical areas 1 ? 1.5*104 km Group of large countries, cultural , Global I >1000 km >300 km MEGA ldistricts,large groups of countries 3 ? 5*103 km Large country Global II 500 ? 1000 km 200 ? 300 km MEGA 1 ? 1.5*103 km Group of small countries, large Regional-large 300 ? 500 km 100 ? 200 km MACRO group of states or provinces 300 ? 500 km Small country, small group of Regional-small 100 ? 200 km 30 ? 50 km National MACRO provinces or states 100 ? 150 km Districts, small group of counties, National-large 30 ? 50 km 10 ? 20 km National MESO group system of settlement groups District 30 ? 50 km County, large group of parishes National-small 10 ? 20 km 3 ? 5 km District MESO 10 ? 15 km Small group of parishes, District (county)- 3 ? 5 km 1 ? 2 km District large town largebig Comprehensive MESO 3 ? 5 km Parish, town, a part of large District (county)- 1 ? 2 km 300 ? 500 m Comprehensive MESO town, large group of villages small 1 ? 2 km Part of town, settlement, Local I 300 ? 500 m 100 ? 200 m Detailed MICRO countryside of protected area, group of villages 300 ? 500 m Larger group of buildings, quarter, Local II 100 ? 200 m 30 ? 60 m Detailed MICRO village, field complexmassive 100 ? 200 m Countryside, the group of Detailed I 30 ? 50 m 10 ? 20 m Detailed MICRO buildings with it?s surrounding land, field, sectionpartition of forest 30 ? 50 m Homes and house with it?s closer Detailed II 10 ? 20 m 3 ? 6 m MICRO surroundings 10 ? 20 m Apartment, a part of a house MICRO 3 ? 5 m Space occupied by moving person, room 1 ? 2 m Personal space of one person Table 1. Hierarchical levels of planning the ecological network. Landschap 20(2)118 tiple functions within a specific and often limited spatial area, and these functions can be planned, designed and managed to exist compatibly or synergistically (Jongman, 1995). According to a broader concept, ecological networks (net- works of ecologically compensating areas) preserve the following main ecological and socio-economical func- tions in landscapes (Mander et al., 1988): I. Biodiversity. Refuges for species (incl. genetic variability). Migration and dispersal tracts for biota. II. Material and energy flows. Material accumulation, recycling and regeneration of resources. Barrier, filter and buffer for nutrient fluxes. Dispersal of human-induced energy. III. Socio-economic development and cultural heritage. Supporting framework (e.g., recreation area) for settle- ments. Compensation and balancing of inevitable outputs of human society (e.g., supporting traditional rural develop- ment). The relative importance of the ecological functions of the system of ecologically compensating areas depends on the spatial scale (Table 2). This varies, however, across both space and time. Based on the experience of land- scape evaluation for regional and landscape planning in the countries of Central and Eastern Europe (Bastian & Schreiber, 1999), one can assume that the biodiversity support (refuge function) is more important at the macro- scale level than at the medium or micro-level. Larger nat- ural areas with heterogeneous structure can support more species than medium- or small-size core areas (Caughley & Sinclair, 1994). On the other hand, as migration corri- dors and dispersal tracts, the medium-level corridors play a key role in connecting core areas of different scales. Ac- cordingly, in the Human Footprint Map (Figure 3), for in- stance, areas of high value on the Human Influence In- dex (e.g., large areas in North America and densely popu- lated Europe) still have remarkable high biodiversity with a list of species comparable to the period before signifi- cant anthropogenic pressure began. This is largely sup- ported by the connectedness of natural core areas of dif- ferent size. Material accumulation, the regeneration of re- sources, the filtering and buffering effects of material and energy fluxes need more space, and therefore their im- portance is greater on higher hierarchical levels (Table 2). On the other hand, the highest relative importance of all functions can be found at the meso-scale level, which in- tegrates the national, landscape and some detail scale ap- proaches (Table 2, Figure 2). This is one of the explana- tions ? next to cost and complexity ? of the relatively high number of studies and implementation experiences of ecological networks at the landscape level. Global Human Footprint and Last of the Wild: ecological networks at a global level The map of the Human Footprint, worked out by Columbia University, USA, is a global driver of conserva- tion crises on the planet and may be considered as a base for ecological networks at the global level (Figure 3). Anal- ysis of the Human Footprint Map indicates that 83% of the land?s surface is influenced by one or more of the follow- ing factors: human population density greater than one person per square kilometer, location within 15 km of a road or major river, occupied by urban or agricultural land uses, within 2 km of a settlement or railway, and/or pro- ducing enough light to be regularly visible to a satellite at night. About 98% of the areas where it is possible to grow rice, wheat or maize (according to FAO estimates) are sim- ilarly influenced. Summarized factors have been used as Scaling in territorial ecological networks 119 range of ecosystems, habitats, species and their genetic diversity, and landscapes of European importance are conserved; habitats are large enough to place species in a favourable conservation status; there are sufficient op- portunities for dispersal and migration. The development programme for the PEEN will design the physical network of core areas, corridors, restoration areas and buffer zones. The programme includes the following actions: a) the elaboration of the criteria on the basis of which the network of core areas, corridors, restoration areas and buffer zones will be identified, taking the biogeographi- cal zones of Europe into account; b) the selection of the ecosystems, habitat types, species and landscapes of Eu- ropean importance; c) the identification of the specific sites and corridors by way of which the respective ecosys- tems, habitats, species and their genetic diversity, and landscapes of European importance will be conserved and, where appropriate, enhanced or restored; d) the preparation of guidelines that will ensure that actions tak- en to create the network are as consistent and effective as possible. A coherent European Ecological Network of Special Areas of Conservation (SAC) is being set up un- der the title Natura 2000 by each of the EU Member States (as defined in the Habitats Directive (92/43/EEC Article 3). This network, composed of sites hosting the natural habitat types and species listed in Annexes I and II of the Habitats Directive, will enable the natural habitat types and the species? habitats concerned, to be maintained or, where appropriate, restored at a favourable conservation status in their natural range. However, the SAC concept considers only protected or designated areas, while the Human Influence Index that is the quantitative base of the Human Footprint Map (Sanderson et al., 2002). However, human influence is not an inevitably negative impact ? for instance, the hierarchical concept of ecological networks (ecological infrastructure) shows remarkable solutions that allow people and wildlife to co-exist. Nature is often resilient if given half a chance. Hopefully, human beings will be in the position to offer or withhold that chance. The map of the Last of the Wild, which represents the largest least influenced areas in all of the biomes of the world and in all of the world?s regions (Sanderson et al., 2002) is a kind of inversion of the Human Footprint map. They represent a practical starting point for long-term conservation: places where the full range of nature may still exist with a minimum of conflict with existing human structures. If we wish to conserve wildlife and wild places and have a rich and beautiful environment for ourselves, we need to find ways to diminish the negative impacts of human influence, while enhancing the positive impacts. PEEN as an example of ecological net- works at the regional level One of the most important channels for the implementa- tion of the Pan-European Biological and Landscapes Di- versity Strategy (PEBLDS), approved by the 3rd Conference of Ministers of the Environment of 55 European countries entitled ?An Environment for Europe?, held in Sofia on 25 October 1995, is the establishment of the PEEN. The par- ticipating states have agreed that the network should be established by 2005. The PEEN will contribute to achiev- ing the main goals of the PEBLDS by ensuring that a full Functions Macro-scale Meso-scale Micro-scale Biodiversity Refuges for species (incl. genetic variability) high medium low Migration and dispersal tracts for biota low high medium Material and energy flows Material accumulation, recycling and regeneration of resources high medium low Barrier, filter and buffer of nutrient fluxes low medium high Dispersal of human-induced energy high medium low Socio-economical development and cultural heritage Supporting framework (e.g., recreation area) for settlements low high medium Compensation and balancing of inevitable outputs of human medium high low society (e.g., supporting traditional rural development) Table 2. Relative impor- tance of the effects of ecological and socio-eco- nomic function classes of system of ecologically compensating areas at dif- ferent scales. Landschap 20(2)120 PEEN concept also covers large undisturbed areas and their connecting corridors outside protected or designat- ed areas. In addition, many other functions of ecological networks, such as control of energy and material fluxes, are considered by the PEEN concept. One of the first activities of the PEEN development pro- gramme is the Indicative Map of the PEEN for Central and Eastern Europe, which is mainly based on the habitat classification and suitability analysis (Figure 4; Bouwma et al., 2002). Suitability of habitats for ecological net- work at national level We consider an ecological network design to consist of three principal layers: (1) general topographical features like coastlines, the water network, major roads, and place names for locating the network portrayed, (2) habitat- based field of suitability for the ecological network, cal- culated from network values of landscape features using a predefined algorithm, (3) the ecological network as an ad- ministrative decision. The second layer serves as a tool supporting decision-making, while the third layer con- sists of the traditional components of an ecological net- work, such as core areas, corridors, buffer zones, and na- ture development/restoration areas (Remm et al., 2003). In order to create a habitat map, which served as a basis for the ecological networks suitability map, several mod- ifications were made to the Estonian CORINE land cover map (Meiner, 1999; Remm et al., 2003). All habitats, linear structures and designated areas were ranked according to their expert-assessed values (from 0 to 10) based on their naturalness, rarity and potential influence on biodi- versity and landscapes. Each square on the grid (1 x 1 km) is supposed to have a certain suitability for the establish- ment of an ecological network (PS). The suitability of a square kilometre is determined mainly by the square?s habitat structure but also by the location of the grid square relative to main migration routes of species and by management and legislation. The direction and mag- nitude of the influence of these factors on the PS is called the ecological network value (ENV; Remm et al., 2003). We assign ENVs to the habitat classes as non-negative real numbers (e.g., 0 ? presence of the factor excludes the square from the ecological network, 1 ? neutral influence, 2 ? twice as good as the average, the factor doubles the suitability estimation of a square 10 ? the factor improves by ten times the suitability of a square). A multiplicative (logarithmic) scale is suggested because it allows the use of zero value to designate absolutely unsuitable condi- tions. The overall suitability [PS] of a square kilometre unit is calculated as a log product of the suitability values of all categories. The ENV of a habitat class is given as an expert decision considering the importance of certain habitats for wildlife diversity in Estonia, and the distribution of en- dangered taxons in habitats according to the Red Data Book of Estonia (Remm et al., 2003): The mean PS-value of a square kilometre is 0.897, and the median 1.006; the minimum value is 3.648 and the maximum 3.75. The most common network suitability is between 1.0 and 1.5. As a rule, the ecological network suitability of protected areas is higher than that of non-protected areas. The mean natural-PS value of square kilometers that con- tain more than 80% protected area is 1.34, and the mean natural-PS of those square kilometers that do not include protected area is 0.819. The relative amount of protected area correlates positively with natural suitability for the ecological network. Nearly one half (47.4%) of ecologi- cally highly valuable areas (PS >1.0) are under nature pro- tection in Estonia. On the other hand, this means that more than one half is not protected administratively (Figure 5). Scaling in territorial ecological networks 121 populations on different equilibrium levels (Hanski et al., 1995). Connectedness refers to the structural links be- tween elements of the spatial structure of a landscape and can be described from mappable elements (Bouwma et al., 2002). The importance of metapopulation principles, partly derived from the island biogeography theory (MacArthur & Wilson, 1967; re-published in 2001; Op- dam, 1991), is the acknowledgement that the survival of species involves more than solely maintaining nature re- serves; ecological linkages are needed and must be in- cluded in spatial plans. Likewise, corridors between core areas and buffers around sensitive areas can provide im- portant control of energy/material fluxes. Riparian buffer zones as ecological net- work at micro-level Riparian buffer zones are often considered to be multi- functional elements of rural landscapes that serve as ex- amples of ecological networks at the most detailed level. In agricultural areas of Estonia, the preferable land-use al- ternative is perennial grassland (buffer zone) in combina- Habitat mosaic of the cultural landscape: Ecological network at landscape level Landscape level is the most integrative among all the spa- tial scales of ecological networks. On the one hand, there are a great many definitions and, respectively, concepts of landscape, which makes the planning aspects very com- prehensive and multifunctional. In landscape ecology, most commonly a mosaic of habitats is understood as a landscape (Forman, 1995; Farina, 2000). Due to long- term human impact and land use dynamics, European landscapes have been significantly altered. Valuable habi- tats in coastal and alpine areas, especially various grass- lands and forests, but also wetland ecosystems in Europe as a whole have decreased dramatically in area. In large territories of high-level economic development, most nat- ural ecosystems have been destroyed and pushed to the margins by dominant land uses such as agriculture, in- dustrial forestry and urban development. In Europe as a whole, both homogenisation and fragmentation are the main driving factors of landscape change. As a result of fragmentation, mainly relatively small and often isolated natural areas have survived. In this mosaic, and some larger and less disturbed (semi)natural ecosystems (eco- logically compensating areas) and hedgerows and ripari- an zones connecting them create an ecological network (infrastructure) in the cultural landscape (Figure 6), sup- porting the multifunctional character of the landscape. Also, marginalisation, now dominating in Eastern, Cen- tral and Northern Europe as a main driving force of land- scape change, initiates the dramatic loss of valuable sem- inatural ecosystems (Mander & Jongman, 1998). Some of the main functional aspects of these landscapes are con- nectivity and connectedness (Baudry & Merriam, 1988). The former measures the species? migration and dispersal processes by which sub-populations of organisms are in- terconnected into a functional demographic unit: meta- Figure 6. River valley with small-grain land- scape pattern within intensively-used large- grain agricultural fields as a multifunctional land- scape corridor. Hedgerows and other ecologically compensating areas in the traditional agricultural landscape of the river val- ley serve as examples of the ecological network at the micro-scale. Landschap 20(2)122 tion with a forest or bush buffer strip directly on river banks or lake shores (Mander et al., 1997). In some coun- tries the complex structure of buffer zones is officially rec- ommended or legislatively stated. For instance, in the U.S., the recommended complex buffer zone consists of three parts which are perpendicular to the stream bank or lake shore (sequentially from agricultural field to water body): a grass strip, a young (managed) forest strip and an old (unmanaged) forest strip (Lowrance et al., 1984). Ri- parian buffer zones have the following essential func- tions: (1) filtering of polluted overland and subsurface flow from intensively managed adjacent agricultural Scaling in territorial ecological networks 123 moval also depends on input fluxes and nitrogen pools in the systems. Therefore a comprehensive budget analysis is needed to model and control the N flows in riparian ecosystems. In Figure 7, the nitrogen budget in a riparian grey alder stand is presented as an example of such mod- eling (Mander et al., 2003). Discussion and conclusions Emerging from the examples of ecological networks at different hierarchical levels, the following common prin- ciples can be highlighted. First, the most important and specific principle of ecological networks is connectivity. Together with connectedness, these are the main func- tional aspects in the landscape that are of importance for the dispersal and persistence of populations, and the sup- porting/controlling of the flow of water, the flux of nutri- ents, and human movement. According to Baudry and Merriam (1988) connectivity is a parameter of landscape function, which measures the processes by which sub- fields; (2) protecting the banks of water bodies against erosion; (3) filtering polluted air, especially from local sources (e.g., large farm complexes, agrochemically treat- ed fields); (4) avoiding intensive growth of aquatic macro- phytes by canopy shading; (5) improving the microcli- mate in adjacent fields; (6) creating new habitats in land/inland water ecotones; and (7) creating greater con- nectivity in landscapes due to migration corridors and stepping-stones (Mander et al., 1997). According to the hierarchy level of ecological networks, the relevance of buffer functions differs significantly. For instance, the impact of the shading effect is extremely lo- cal. Likewise, water and bank protection functions are very important on the micro-scale (local level of one or a small group of fields) and have no significant relevance on a regional, i.e. macro-scale. On the other hand, bio- logical functions like creation of connectivity in land- scapes due to migration corridors and stepping-stones is more relevant on higher hierarchical levels (Mander, 2001). Filtering of polluted overland and subsurface flow is the key function of buffer zones (Peterjohn & Correll, 1984; Pinay & D?camps, 1988; Jordan et al., 1992; Vought et al., 1994). For instance, three biological processes can re- move nitrogen: (1) uptake and storage in vegetation; (2) microbial immobilization and storage in the soil as or- ganic nitrogen; and (3) microbial conversion to gaseous forms of nitrogen (denitrification: see Pinay et al., 1993; Weller et al., 1994; nitrification: see Watts & Seitzinger, 2000; Wolf & Russow, 2001). Various biophysical condi- tions control the intensity of these processes, and there- fore the variability of that intensity is very high. For in- stance, gaseous emissions and plant uptake can vary from <1 to 1600 and from <10 to 350 kg N ha-1 yr-1, respective- ly (Mander et al., 1997). Thus different processes can play a leading role in nitrogen removal. The efficiency of re- Figure 7. Nitrogen budget of a 15-year riparian grey alder stand (kg ha-1 yr-1) as an example of the buffering function of eco- logical network elements (corridors and buffers) at the micro-scale level. Adopted from Mander et al., 2003. Landschap 20(2)124 populations of organisms are interconnected into a func- tional demographic unit. Connectedness refers to the structural links between elements of the spatial structure of a landscape, which can be described from mappable el- ements. Sometimes biological connectivity (e.g. func- tional patterns) and landscape connectedness (e.g., phys- ical connection of similar landscape elements) match, as in the movements of small forest mammals along wood- ed fencerows from one woodlot to another (Henein and Merriam 1990). Sometimes they do not match, as in the case of ballooning spiders (Asselin and Baudry 1989). Structural elements differ from functional parameters. For some species connectivity is measured in the distance between sites, whereas for other species the structure of the landscape and connectedness through hedgerows represents the presence of corridors and barriers. Area re- duction will cause a reduction of the populations that can survive, and in this way an increased risk of extinction. It also will increase the need for species to disperse between sites through a more or less hostile landscape. Second, the principle of multifunctionality states that eco- logical networks always bear several functions, which are coherent to landscape functions at the relevant hierarchi- cal level (see Bastian & Schreiber, 1999). Therefore the planning of networks following only one principle (dis- persal and migration of species) may mislead the plan- ning purposes. Third, the principle of continuity means that the function- ing of a network at a certain hierarchical level is only guar- anteed if the full spectrum of a networks? hierarchy is per- formed. In practical terms this means that ecological networks should be maintained or if necessary created at all levels. We assume that the network at lower hierarchical levels supports the biodiversity and material cycle control at the adjacent higher levels. For example, it is very complicated to support endangered species at higher scales of large ar- eas (e.g. large and homogeneous forest plantations) if the ecological infrastructure is absent at the lower levels (e.g. meso- and micro-level habitats). Considering that princi- ple, the hierarchical levels between adjacent levels in the hierarchy may integrate functions and characteristics pre- vailing at neighbouring levels. Therefore, for instance, ecological and socio-economic functions have the highest relative importance in meso-scale networks (Table 2). Fourth, according to the principle of plenipotentiality (con- sidering causal relationships between levels of hierarchy, such as causal constraints and determinations of lower- level phenomena by high-level phenomena and vice ver- sa), there are no specific scale-limited functions of eco- logical networks. The relative importance of various func- tions varies depending on the hierarchical level, and plan- ning strategies should therefore follow these variations. For instance, at the global (mega-scale) level, the leading functions of the networks are to control the global bal- ance of CO2 and other greenhouse gases. At the micro- level, local biodiversity support and the control of nutrient fluxes are dominant. At the global level one art of the solution of biodiversity lies in conserving the Last of the Wild -- those few places that are relatively less influenced by human beings in all ecosystems around the globe, and give the opportunity for their connectedness (Sanderson et al., 2002). It allows bet- ter stewarding of natural processes across the gradient of human influence through conservation science and ac- tion. The most important part of the solution for human beings, as individuals and through institutions and gov- ernments, however, is to moderate their influence in re- turn for a healthier relationship with the natural world. On the other hand, at the micro-level, small-scale varia- tions of land-use patches and their ecotones may com- pensate the excess nutrients. Scaling in territorial ecological networks 125 hierarchy. Furthermore, the functions depend on and are complementary to the simultaneous existence of ecolog- ical networks at several levels. Therefore, in land-use planning and conservation practice on different hierar- chy levels, different and coordinated management prin- ciples and strategies are required. Abstract This paper draws attention to and discusses the hierar- chical nature of territorial ecological networks, and in this context their structural and functional aspects are debated. The focus of the article is on implementation and is illustrated with a number of examples, including the Pan-European Ecological Network as an example of ecological networks at the regional level and the riparian buffer zones as an ecological network at the micro-lev- el. The upscaling and downscaling of ecological net- works? functions and spatial distribution are discussed. The paper suggests that the functions of ecological net- works (biodiversity support, energy and material fluxes? regulations, cultural and socio-economic functions) and their shares depend on the level of those networks in the The concept of territorial ecological networks can be con- sidered a new paradigm in nature conservation and ecosystem management. The functions of ecological net- works (biodiversity support, energy and the regulation of material fluxes, cultural and socio-economic functions) and their proportions are coherent within the hierarchy of networks. Therefore different management principles and strategy are required on different hierarchical levels. Further activities in the research, design and implemen- tation of territorial ecological networks should concen- trate on the development of coherent planning and man- agement schemes at higher hierarchical level up to the global scale. In addition, the upscaling of ecological net- works? functions and their spatial distribution is one of the priorities in the further development of this new con- cept of nature conservation. Acknowledgements This study was supported by Estonian Science Foundation Grants Nos. 692, 2471, 5261, and 5247 and Target Fund- ing Projects Nos. 0180549s98 and 0182534s03 of the Min- istry of Education and Science, Estonia. We are particu- larly grateful to all people and organisations who have kindly allowed us to use material from printed sources. We also thank Ms. Helen Alum?e from the Institute of Ge- ography, University of Tartu, Estonia for her valuable comments, and Mr. Alexander Harding for proofreading the final text. Landschap 20(2)126 Hanski, I, J. P?yry, T. Pakkala & M. Kuussaari, 1995. Multiple equi- libria in metapopulation dynamics. Nature 377: 616-621. Henein, K. and G. Merriam, 1990. The element of connectivity where corridor is variable. Landscape Ecology 4: 157-170. 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Redford, A.V. Wannebo & G. Woolmer, 2002. The Human Footprint and the Last of the Wild. BioScience 52(10): 891-904. Landschap 20(2)128 samenleving niet gekozen. Gelukkig maar, want wij zouden als eersten ontslagen wor- den bij een mislukt resultaat. Wij moeten dus aan de besluitnemers onze kennis mee- delen in de vorm die zij begrijpen. Daarvoor heeft onze cultuur het verhaal. Het verhaal is daarvan zelfs de bakermat. Voordat het schrift was uitgevonden werd kennis duizenden jaren lang verzameld en doorgegeven in de vorm van het verhaal. Hoe sterk die is voor de overdracht van inzicht en kennis blijkt uit de fascinatie van de intussen opgeschreven overlevering. Tal van generaties hebben voor ons uitge- dokterd, hoe iets zo te vertellen dat onze menselijke geest complexe zaken kan begrijpen. Nu de wetenschap zo ver in de ingewikkeld- heid van onze omgeving is doorgedrongen dat alleen de daarin gestudeerden het bevatten, moeten wij de draad van het verhaal weer oppakken. Daarmee emanci- peert de wetenschapper zich tot burger. De burger is dank zij ons hoge opleidings- niveau mondig en vraagt daar om. Naast ons specialisme zullen wij ons ook moeten bekwamen in onze oudste cultuurvaardig- heid: het maken van Het Verhaal. J A C Q U E S D E S M I D T, voorzitter WLO Het Verhaal Het verhaal kan ons, landschapsecologen, helpen bij een methodisch probleem. Wij zijn goed in het analyseren en begrijpen van complexe systemen. Maar hoe breng ik mijn inzicht over aan de tot bestuurder gekozen burger en aan burgers die over de inrichting van hun eigen omgeving willen meedenken? De bestuurder kan zich nog laten bijstaan door zijn deskundige. Die vergelijkt voor hem de effecten van verschillende oplos- singen voor een gewenste verandering in een gebied, bijvoorbeeld voor een nieuwe waterwinning, stadswijk, industriegebied of voor meer veiligheid tegen hoogwater. De deskundige vertaalt de uitkomst van de vergelijking in een +, een 0, of een - en zet de scores in een tabel. De bestuurder vertrouwt zijn deskundige en verwijst naar hem als een kritische vraag wordt gesteld door een vertegenwoordiger van het volk. Vaak loopt die discussie stroef door het spreken in termen van goed of slecht, met de stellende vorm ?het is? in plaats van de subjectieve vorm ?ik vind?. Een voorkeur is immers per definitie subjectief. Hoe moet het dan met de kennis die nodig is om te weten wat de effecten zijn van je voor- keur? Kan je van iedere stemgerechtigde in schap, raad, staten of kamer verlangen dat hij of zij over die kennis beschikt? Op die vraag is in ons staatsbestel geen ander antwoord mogelijk dan: JA. Moeten de ver- tegenwoordigers dan allen gediplomeerd zijn in een hele rij disciplines, als ze wil- len meestemmen? Nee, dat gaat niet, maar wat dan wel? Het goede antwoord is volgens mij: Het Verhaal. Met het verhaal bedoel ik, dat daar alle relevante kennis in zit die nodig is om de essentie te begrijpen van het complexe systeem dat landschap heet. Wat relevant is en wat de essentie is, kan de landschap- secoloog uitleggen door zijn kennis van het systeem. Zijn inzicht wordt gevoed door zowel zijn kennis van het ecologisch functioneren van het gebied, als door zijn inzicht in de invloed van het plan op die toestand. Dat hij dat weet is mooi, maar niet genoeg voor de besluitvorming. Het zou genoeg zijn, als wij ecocratische besluiten namen. Maar daarvoor heeft onze Vereniging