Stacking Species Distribution Models and Adjusting Bias by Linking Them to Macroecological Models. Calabrese, J. M., Certain, G., Kraan, C., & Dormann, C. F. 23(1):99–112.
Stacking Species Distribution Models and Adjusting Bias by Linking Them to Macroecological Models [link]Paper  doi  abstract   bibtex   
[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.
@article{calabreseStackingSpeciesDistribution2014,
  title = {Stacking Species Distribution Models and Adjusting Bias by Linking Them to Macroecological Models},
  author = {Calabrese, Justin M. and Certain, Grégoire and Kraan, Casper and Dormann, Carsten F.},
  date = {2014-01},
  journaltitle = {Global Ecology and Biogeography},
  volume = {23},
  pages = {99--112},
  issn = {1466-822X},
  doi = {10.1111/geb.12102},
  url = {http://mfkp.org/INRMM/article/12818271},
  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.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-12818271,~to-add-doi-URL,bias-correction,ecology,integration-techniques,modelling,species-richness},
  number = {1}
}
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