Assessing the accuracy of density‐independent demographic models for predicting species ranges. Issue 3 (2nd December 2020)

• Record Type: Journal Article
• Authors: Holden, Matthew H.; Yen, Jian D. L.; Briscoe, Natalie J.; Lahoz‐Monfort, José J.; Salguero‐Gómez, Roberto; Vesk, Peter A.; Guillera‐Arroita, Gurutzeta
• Is Part Of: Ecography. Volume 44:Issue 3(2021)
• Page Start: 345
• Page End: 357
• Publication Date: 2020-12-02
• Subjects: demographic distribution model -- invasion risk map -- matrix population model -- range dynamics -- range shifts -- species distribution model; Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.05250
• Languages: English
• ISSNs: 0906-7590

Abstract

Abstract : Accurately predicting species ranges is a primary goal of ecology. Demographic distribution models (DDMs), which correlate underlying vital rates (e.g. survival and reproduction) with environmental conditions, can potentially predict species ranges through time and space. However, tests of DDM accuracy across wide ranges of species' life histories are surprisingly lacking. Using simulations of 1.5 million hypothetical species' range dynamics, we evaluated when DDMs accurately predicted future ranges, to provide clear guidelines for the use of this emerging approach. We limited our study to deterministic demographic models ignoring density dependence, since these models are the most commonly used in the literature. We found that density‐independent DDMs overpredicted extinction if populations were near carrying capacity in the locations where demographic data were available. However, DDMs accurately predicted species ranges if demographic data were limited to sites with mean initial abundance less than one half of carrying capacity. Additionally, the DDMs required demographic data from at least 25 sites, over a short time‐interval (< 10 time‐steps), as populations initially below carrying capacity can saturate in long‐term studies. For species with demographic data from many low density sites, DDMs predicted occurrence more accurately than correlative species distribution models (SDMs) in locations where the species eventually persisted, but not where the species went extinct. These results were insensitive to differences in simulated dispersal, levels of environmental stochasticity, the effects of the environmental variables and the functional forms of density dependence. Our findings suggest that deterministic, density‐independent DDMs are appropriate for applications where locating all possible sites the species might occur in is prioritized over reducing false presence predictions in absent sites. This makes DDMs a promising tool for mapping invasion risk. However, demographic data are often collected at sites where a species is abundant. Density‐independent DDMs are inappropriate in this case.