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Environmental conditions are dynamic, and plants respond to those dynamics on multiple time scales. Disequilibrium occurs when a response occurs more slowly than the driving environmental changes. We review evidence regarding disequilibrium in plant distributions, including their responses to paleoclimate changes, recent climate change and new species introductions. There is strong evidence that plant species distributions are often in some disequilibrium with their environmental conditions.This disequilibrium poses a challenge when projecting future species distributions using species distribution models (SDMs). Classically, SDMs assume that the set of species occurrences is an unbiased sample of the suitable environmental conditions. However, a species in disequilibrium with the environment may have higher‐than‐expected occurrence probabilities (e.g. due to extinction debts) or lower‐than‐expected occurrence probabilities (e.g. due to dispersal limitation) in different areas. If unaccounted for, this will lead to biased estimates of the environmental suitability.We review methods for avoiding such biases in SDMs, ranging from simple thinning of the occurrence dataset to complex dynamic and process‐based models. Such models require large data inputs, natural history knowledge and technical expertise, so implementing them can be challenging. Despite this, we advocate for their increased use, since process‐based models provide the best potential to account for biases in model training data and to then represent the dynamics of species occupancy as ranges shift.Synthesis. Occurrence records for a species are often in disequilibrium with climate. SDMs trained on such data will produce biased estimates of a species' niche unless this disequilibrium is addressed in the modelling. A range of tools, spanning a wide gradient of complexity and realism, can resolve this bias.
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Improving area of occupancy estimates for parapatric species using distribution models and support vector machines
Abstract As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN’s extent of occurrence (EOO) is the general region including the species’ range, while area of occupancy (AOO) is the subset of EOO occupied by the species. Data‐poor species with incomplete sampling present particular difficulties, but species distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) and do not explicitly account for biotic interactions that can impose range constraints. We sought to improve range estimates for data‐poor, parapatric species by masking out areas under inferred competitive exclusion. We did so for two South American spiny pocket mice:Heteromys australis(Least Concern) andHeteromys teleus(Vulnerable due to especially poor sampling), whose ranges appear restricted by competition. For both species, we estimated EOO using SDMs and AOO with four approaches: occupied grid cells, abiotic SDM prediction, and this prediction masked by approximations of the areas occupied by each species’ congener. We made the masks using support vector machines (SVMs) fit with two data types: occurrence coordinates alone; and coordinates along with SDM predictions of suitability. Given the uncertainty in calculating AOO for low‐data species, we made estimates for the lower and upper bounds for AOO, but only make recommendations forH. teleusas its full known range was considered. The SVM approaches (especially the second one) had lower classification error and made more ecologically realistic delineations of the contact zone. ForH. teleus, the lower AOO bound (a strongly biased underestimate) corresponded to Endangered (occupied grid cells), while the upper bounds (other approaches) led to Near Threatened. As we currently lack data to determine the species’ true occupancy within the post‐processed SDM prediction, we recommend that an updated listing forH. teleusinclude these bounds for AOO. This study advances methods for estimating the upper bound of AOO and highlights the need for better ways to produce unbiased estimates of lower bounds. More generally, the SVM approaches for post‐processing SDM predictions hold promise for improving range estimates for other uses in biogeography and conservation.
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- Award ID(s):
- 1661510
- PAR ID:
- 10375277
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecological Applications
- Volume:
- 31
- Issue:
- 1
- ISSN:
- 1051-0761
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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