@article{cooper_creating_nodate,
	title = {Creating individual accessible area hypotheses improves stacked species distribution model performance},
	issn = {1466-8238},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/geb.12678/abstract},
	doi = {10.1111/geb.12678},
	abstract = {Aim

Stacked species distribution models (SDMs) are an important step towards estimating species richness, but frequently overpredict this metric and therefore erroneously predict which species comprise a given community. We test the idea that developing hypotheses about accessible area a priori can greatly improve model performance. By integrating dispersal ability via accessible area into SDM creation, we address an often-overlooked facet of ecological niche modelling.


Innovation

By limiting the training and transference areas to theoretically accessible areas, we are creating more accurate SDMs on the basis of a taxon's explorable environments. This limitation of space and environment is a more accurate reflection of a taxon's true dispersal properties and more accurately reflects the geographical and environmental space to which a taxon is exposed. Here, we compare the predictive performance of stacked SDMs derived from spatially constrained and unconstrained training areas.


Main conclusions

Restricting a species’ training and transference areas to a theoretically accessible area greatly improves model performance. Stacked SDMs drawn from spatially restricted training areas predicted species richness and community composition more accurately than non-restricted stacked SDMs. These accessible area-based restrictions mimic true dispersal barriers to species and limit training areas to the suite of environments to those which a species is exposed to in nature. Furthermore, these restrictions serve to ‘clip’ predictions in geographical space, thus removing overpredictions in adjacent geographical regions where the species is known to be absent.},
	language = {en},
	urldate = {2017-12-06TZ},
	journal = {Global Ecology and Biogeography},
	author = {Cooper, Jacob C. and Soberón, Jorge},
	keywords = {accessible area, dispersal, ecological niche models, hummingbirds, macroecological models, presence–absence matrix, stacked niche models},
	pages = {n/a--n/a}
}

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We test the idea that developing hypotheses about accessible area a priori can greatly improve model performance. By integrating dispersal ability via accessible area into SDM creation, we address an often-overlooked facet of ecological niche modelling. Innovation By limiting the training and transference areas to theoretically accessible areas, we are creating more accurate SDMs on the basis of a taxon's explorable environments. This limitation of space and environment is a more accurate reflection of a taxon's true dispersal properties and more accurately reflects the geographical and environmental space to which a taxon is exposed. Here, we compare the predictive performance of stacked SDMs derived from spatially constrained and unconstrained training areas. Main conclusions Restricting a species’ training and transference areas to a theoretically accessible area greatly improves model performance. Stacked SDMs drawn from spatially restricted training areas predicted species richness and community composition more accurately than non-restricted stacked SDMs. These accessible area-based restrictions mimic true dispersal barriers to species and limit training areas to the suite of environments to those which a species is exposed to in nature. Furthermore, these restrictions serve to ‘clip’ predictions in geographical space, thus removing overpredictions in adjacent geographical regions where the species is known to be absent.","language":"en","urldate":"2017-12-06TZ","journal":"Global Ecology and Biogeography","author":[{"propositions":[],"lastnames":["Cooper"],"firstnames":["Jacob","C."],"suffixes":[]},{"propositions":[],"lastnames":["Soberón"],"firstnames":["Jorge"],"suffixes":[]}],"keywords":"accessible area, dispersal, ecological niche models, hummingbirds, macroecological models, presence–absence matrix, stacked niche models","pages":"n/a–n/a","bibtex":"@article{cooper_creating_nodate,\n\ttitle = {Creating individual accessible area hypotheses improves stacked species distribution model performance},\n\tissn = {1466-8238},\n\turl = {http://onlinelibrary.wiley.com/doi/10.1111/geb.12678/abstract},\n\tdoi = {10.1111/geb.12678},\n\tabstract = {Aim\n\nStacked species distribution models (SDMs) are an important step towards estimating species richness, but frequently overpredict this metric and therefore erroneously predict which species comprise a given community. We test the idea that developing hypotheses about accessible area a priori can greatly improve model performance. By integrating dispersal ability via accessible area into SDM creation, we address an often-overlooked facet of ecological niche modelling.\n\n\nInnovation\n\nBy limiting the training and transference areas to theoretically accessible areas, we are creating more accurate SDMs on the basis of a taxon's explorable environments. This limitation of space and environment is a more accurate reflection of a taxon's true dispersal properties and more accurately reflects the geographical and environmental space to which a taxon is exposed. Here, we compare the predictive performance of stacked SDMs derived from spatially constrained and unconstrained training areas.\n\n\nMain conclusions\n\nRestricting a species’ training and transference areas to a theoretically accessible area greatly improves model performance. Stacked SDMs drawn from spatially restricted training areas predicted species richness and community composition more accurately than non-restricted stacked SDMs. These accessible area-based restrictions mimic true dispersal barriers to species and limit training areas to the suite of environments to those which a species is exposed to in nature. Furthermore, these restrictions serve to ‘clip’ predictions in geographical space, thus removing overpredictions in adjacent geographical regions where the species is known to be absent.},\n\tlanguage = {en},\n\turldate = {2017-12-06TZ},\n\tjournal = {Global Ecology and Biogeography},\n\tauthor = {Cooper, Jacob C. and Soberón, Jorge},\n\tkeywords = {accessible area, dispersal, ecological niche models, hummingbirds, macroecological models, presence–absence matrix, stacked niche models},\n\tpages = {n/a--n/a}\n}\n\n","author_short":["Cooper, J. C.","Soberón, J."],"key":"cooper_creating_nodate","id":"cooper_creating_nodate","bibbaseid":"cooper-sobern-creatingindividualaccessibleareahypothesesimprovesstackedspeciesdistributionmodelperformance","role":"author","urls":{"Paper":"http://onlinelibrary.wiley.com/doi/10.1111/geb.12678/abstract"},"keyword":["accessible area","dispersal","ecological niche models","hummingbirds","macroecological models","presence–absence matrix","stacked niche models"],"downloads":0,"html":""},"search_terms":["creating","individual","accessible","area","hypotheses","improves","stacked","species","distribution","model","performance","cooper","soberón"],"keywords":["accessible area","dispersal","ecological niche models","hummingbirds","macroecological models","presence–absence matrix","stacked niche models"],"authorIDs":[],"dataSources":["pJXBJ7FpDvMhH6hs5"]}