BIOGEOGRAPHIC AFFINITIES OF THE NORTH 
ATLANTIC DEEP-WATER SCLERACTINIA 
Stephen D. Cairns and Ralph E. Chapman 
National Museum of Natural History, Smithsonian Institution 
Washington, D.C., USA 
SUMMARY 
We investigated thebiogeographic properties and relationships of Scleractinia 
in the North Atlantic. The North Atlantic was divided into 3 8 geographic regions 
and each of the 134 North Atlantic species known from deeper than 200 m 
was scored for each region, resulting in a data matrix of 5092 cells. The species 
composition of each region was then analyzed by calculating a matrix of Bray- 
Curtis distance coefficients and performing a cluster analysis (UPGMA) and 
ordination {Non-Metric Multi-Dimensional Scaling) to determine biogeo- 
graphic afEnities and relationships of these localities. Three major clusters 
(superclusters) were distinguished: western, eastern and North Atlantic. The 
westem Atlantic supercluster consists of the tropical and warm temperate west- 
em Atlantic regions and is characterized by 58% endemic species and a rel- 
atively low percentage of deep-water species. It consists of three subordinate 
clusters, which represent insular, continental and warm temperate areas. The 
eastern Atlantic supercluster extends from the Faroe Islands to the Gulf of 
Guinea, and is characterized by 38% endemic species and a disproportion- 
ately high number of amphi-Atlantic and cosmopolitan species, as well as a 
higher than average number of deep-living species. It also consists of three 
subordinate clusters: a Lusitanian warm temperate group, tropical localities, 
and a third cluster consisting of the Cape Verde and Canary Islands. The North 
Atlantic supercluster is characterized not by endemic species, but rather by 
a high number of cosmopolitan and amphi-Atlantic species, as well as a dis- 
proportionately high number of deep-living, unattached, and flabellid species. 
Two subordinate clusters correspond to the cold temperate northeast and 
northwest Atlantic. 
INTRODUCTION 
There is a deep-rooted desire among people to organize data into systems of 
classification (e.g., clusters). This probably originates in the need to organ- 
ize multi-dimensional data into smaller packets that can be visualized and manip- 
ulated by researchers, but also probably reflects a degree of real clustering in 
natural systems. Within natural history, if species are the objects of classifi- 
cation, then cladistics or phenetics may provide the algorithms used, but if 
zoogeography is the subject under consideration, then geographic regions are 
usually the units of classification and various clustering and ordination algo- 
rithms are the tools typically used to synthesize these data. According to Clarke 
and Warwick (1994) there are literally hundreds of clustering methods, indeed 
30 DEEP-SEA CORALS 
whole volumes (e.g., Hartigan, 1975) dedicated just to clustering algorithms. 
Cormack (1971) warned that the "availability of... classification techniques 
has led to the waste of more valuable scientific time than any other 'statisti- 
cal' innovation." Nonetheless, a judicious use of clustering and ordination tech- 
niques can lead to thought-provoking and valuable Insights about the distribution 
of organisms and it is becoming increasingly common for authors of bio- 
geographic analyses to include a clustering dendrogram and/or an ordination 
to explore their data set. Several examples using cnidarian taxa are presented 
below, in increasing order of sophistication, each with a slightly different method 
and purpose for the analyses, but all using the Bray-Curtis or Jaccard simi- 
larity coefficients and various clustering (typically UPGMA; Sneath and 
Sokal, 1973) and ordination algorithms (usually non-metric multi-dimen- 
sional scaling, MDS; Clarke and Warwick, 1994), 
For instance, Veron (1995) used clustering techniques in a simple way to 
document patterns of geographic affinity of shallow-water Scleractinia from 
adjacent geographic regions. Similarly, Wallace (1999) used clustering meth- 
ods to demonstrate geographic afifmities among acroporid Scleractinia, but 
also analyzed the composition of her clusters based on the overall distribu- 
tion of the component species. Ketchum and Reyes-Bonilla ( 1997) and Reyes- 
Bonilla and L?pez-P?rez (1998), based on shallow-water Scleractinia, clustered 
the regions of the eastern Pacific followed by an analysis of these patterns 
for evidence of the presumed direction of colonization, and to test for the valid- 
ity of Zoogeographie barriers. In an elegant pair of papers based on shallow- 
water corals, Sheppard (1997, 1999) used clustering and ordination (MDS) 
to find the geographic affinities of various localities in the Indian Ocean and 
then used these results to assess the role of the Chagos Archipelago as a pos- 
sible stepping stone for dispersal. Clustering and ordination can also be used 
for studying finer-scale ecological processes, such as assessing the relation- 
ship between a circumscribed ecological zone of occurrence versus various 
physical factors, such as light and substrate, as was done by S?nchez et al. 
(1998) and S?nchez (1999) for shallow-water Caribbean Octocorallia on 
selected reefs. 
The current study seeks to use clustering and ordination analyses to 
address many of the parameters described above. First, we seek to establish 
how many species of Scleractinia occur in the North Atlantic below 200 m, 
where they occiu", and at what depths. Using these data, we then try to docu- 
ment the relationships among the faunas of various regions, in both gross and 
fine detail, based on the shared presence of these taxa. We attempt to char- 
acterize these groupings based on physical characteristics of the environment 
and the morphological characteristics of the species, in an effort to understand 
why these various localities cluster in the way they do. In so doing we com- 
pare otir clusters to the traditional shallow-water Zoogeographie provinces. 
We also compare the results of traditional clustering with the results of the 
ordination (MDS) in an effort to better understand the clusters. Finally, we 
ask if those transitional localities that do not fit nicely within any single clus- 
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