Controlled comparison of species- and community-level models across novel climates and communities. Maguire, K. C., Nieto-Lugilde, D., Blois, J. L., Fitzpatrick, M. C., Williams, J. W., Ferrier, S., & Lorenz, D. J. Proc. R. Soc. B, 283(1826):20152817, March, 2016.
Controlled comparison of species- and community-level models across novel climates and communities [link]Paper  doi  abstract   bibtex   
Species distribution models (SDMs) assume species exist in isolation and do not influence one another's distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa.
@article{maguire_controlled_2016,
	title = {Controlled comparison of species- and community-level models across novel climates and communities},
	volume = {283},
	copyright = {© 2016 The Author(s). http://royalsocietypublishing.org/licencePublished by the Royal Society. All rights reserved.},
	issn = {0962-8452, 1471-2954},
	url = {http://rspb.royalsocietypublishing.org/content/283/1826/20152817},
	doi = {10.1098/rspb.2015.2817},
	abstract = {Species distribution models (SDMs) assume species exist in isolation and do not influence one another's distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa.},
	language = {en},
	number = {1826},
	urldate = {2016-03-11TZ},
	journal = {Proc. R. Soc. B},
	author = {Maguire, Kaitlin C. and Nieto-Lugilde, Diego and Blois, Jessica L. and Fitzpatrick, Matthew C. and Williams, John W. and Ferrier, Simon and Lorenz, David J.},
	month = mar,
	year = {2016},
	pages = {20152817}
}
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