dc.contributor.author |
Calabrese, Justin M. |
en |
dc.contributor.author |
Certain, Grégoire |
en |
dc.contributor.author |
Kraan, Casper |
en |
dc.contributor.author |
Dormann, Carsten F. |
en |
dc.date.accessioned |
2014-02-24T20:25:27Z |
|
dc.date.available |
2014-02-24T20:25:27Z |
|
dc.date.issued |
2014 |
|
dc.identifier.citation |
Calabrese, Justin M., Certain, Grégoire, Kraan, Casper, and Dormann, Carsten F. 2014. "<a href="https://repository.si.edu/handle/10088/21830">Stacking species distribution models and adjusting bias by linking them to macroecological models</a>." <em>Global Ecology and Biogeography</em>. 23 (1):99–112. <a href="https://doi.org/10.1111/geb.12102">https://doi.org/10.1111/geb.12102</a> |
en |
dc.identifier.issn |
1466-822X |
|
dc.identifier.uri |
http://hdl.handle.net/10088/21830 |
|
dc.description.abstract |
Aim Species distribution models (SDMs) are common tools in biogeography and conservation ecology. It has been repeatedly claimed that aggregated (stacked) SDMs (S-SDMs) will overestimate species richness. One recently suggested solution to this problem is to use macroecological models of species richness to constrain S-SDMs. Here, we examine current practice in the development of S-SDMs to identify methodological problems, provide tools to overcome these issues, and quantify the performance of correctly stacked S-SDMs alongside macroecological models. Locations Barents Sea, Europe and Dutch Wadden Sea. Methods We present formal mathematical arguments demonstrating how S-SDMs should and should not be stacked. We then compare the performance of macroecological models and correctly stacked S-SDMs on the same data to determine if the former can be used to constrain the latter. Next, we develop a maximum-likelihood approach to adjusting S-SDMs and discuss how it could potentially be used in combination with macroecological models. Finally, we use this tool to quantify how S-SDMs deviate from observed richness in four very different case studies. Results We demonstrate that stacking methods based on thresholding site-level occurrence probabilities will almost always be biased, and that these biases will tend toward systematic overprediction of richness. Next, we show that correctly stacked S-SDMs perform very similarly to macroecological models in that they both have a tendency to overpredict richness in species-poor sites and underpredict it in species-rich sites. Main conclusions Our results suggest that the perception that S-SDMs consistently overpredict richness is driven largely by incorrect stacking methods. With these biases removed, S-SDMs perform similarly to macroecological models, suggesting that combining the two model classes will not offer much improvement. However, if situations where coupling S-SDMs and macroecological models would be beneficial are subsequently identified, the tools we develop would facilitate such a synthesis. |
en |
dc.relation.ispartof |
Global Ecology and Biogeography |
en |
dc.title |
Stacking species distribution models and adjusting bias by linking them to macroecological models |
en |
dc.type |
Journal Article |
en |
dc.identifier.srbnumber |
116679 |
|
dc.identifier.doi |
10.1111/geb.12102 |
|
rft.jtitle |
Global Ecology and Biogeography |
|
rft.volume |
23 |
|
rft.issue |
1 |
|
rft.spage |
99 |
|
rft.epage |
112 |
|
dc.description.SIUnit |
NZP |
en |
dc.description.SIUnit |
Peer-reviewed |
en |
dc.citation.spage |
99 |
|
dc.citation.epage |
112 |
|