Addressing potential local adaptation in species distribution models: implications for conservation under climate change
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Abstract
Species distribution models (SDMs) have been criticized for involving assumptions that ignore or categorize many ecologically relevant factors such as dispersal ability and biotic interactions. Another potential source of model error is the assumption that species are ecologically uniform in their climatic tolerances across their range. Typically, SDMs to treat a species as a single entity, although populations of many species differ due to local adaptation or other genetic differentiation. Not taking local adaptation into account, may lead to incorrect range prediction and therefore misplaced conservation efforts. A constraint is that we often do not know the degree to which populations are locally adapted, however. Lacking experimental evidence, we still can evaluate niche differentiation within a species' range to promote better conservation decisions. We explore possible conservation implications of making type I or type II errors in this context. For each of two species, we construct three separate MaxEnt models, one considering the species as a single population and two of disjunct populations. PCA analyses and response curves indicate different climate characteristics in the current environments of the populations. Model projections into future climates indicate minimal overlap between areas predicted to be climatically suitable by the whole species versus population-based models. We present a workflow for addressing uncertainty surrounding local adaptation in SDM application and illustrate the value of conducting population-based models to compare with whole-species models. These comparisons might result in more cautious management actions when alternative range outcomes are considered.
Publication type | Article |
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Publication Subtype | Journal Article |
Title | Addressing potential local adaptation in species distribution models: implications for conservation under climate change |
Series title | Ecological Applications |
DOI | 10.1890/15-0926 |
Volume | 26 |
Issue | 4 |
Year Published | 2016 |
Language | English |
Publisher | Ecological Society of America |
Contributing office(s) | Great Lakes Science Center |
Description | 16 p. |
First page | 1154 |
Last page | 1169 |
Online Only (Y/N) | N |
Additional Online Files (Y/N) | N |
Google Analytic Metrics | Metrics page |