The potential for species distribution models to distinguish source populations from sinks.

IF 3.5 1区 环境科学与生态学 Q1 ECOLOGY Journal of Animal Ecology Pub Date : 2024-10-21 DOI:10.1111/1365-2656.14201
Bilgecan Şen, Christian Che-Castaldo, H Reşit Akçakaya
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Abstract

While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source populations from sinks. Filling this knowledge gap is critical for conservation science, as applications of SDMs for management purposes ultimately depend on these typically unobserved population or metapopulation dynamics. Using linear regression, we associated previously published population level estimates of intrinsic growth and abundance derived from a Bayesian analysis of mark-recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then used the area under the ROC curve (AUC) to measure how well SDMs can distinguish populations categorized as sources and sinks. We built SDMs using two different approaches, boosted regression trees (BRT) and generalized linear models (GLM), and compared their source/sink predictive performance. Each SDM was built with presence points obtained from eBird (a web-available database) and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species. We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs, however, estimated higher suitability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks. Habitat suitability have the potential to be a useful measure to indicate a population's ability to sustain itself as a source population; however more research on a diverse set of taxa is essential to fully explore this potential. This interpretation of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.

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物种分布模型区分源种群和汇种群的潜力。
虽然物种分布模型(SDM)经常被用来预测物种的出现,为保护管理提供信息,但评估栖息地适宜性是否能可靠地预测内在增长率或区分源种群与汇种群的证据却很有限。填补这一知识空白对保护科学至关重要,因为用于管理目的的 SDM 最终取决于这些通常无法观察到的种群或元种群动态。通过线性回归,我们将以前发表的、对美国毗连地区 17 种鸟类的标记重捕数据进行贝叶斯分析后得出的种群水平内在增长和丰度估计值,与根据这些相同物种的机会数据拟合的 SDM 栖息地适宜性估计值联系起来。然后,我们使用 ROC 曲线下的面积(AUC)来衡量 SDMs 区分源和汇的能力。我们使用两种不同的方法,即增强回归树(BRT)和广义线性模型(GLM)建立了 SDM,并比较了它们的源/汇预测性能。每个 SDM 都是利用从 eBird(网络数据库)中获取的存在点和 10 个环境变量建立的,这些环境变量之前已被选定用于模拟这些物种的固有增长率和丰度。我们的研究表明,利用机会主义数据建立的 SDM 对物种种群分布的预测效果一般较差;BRT 和 GLM 对内在增长率和种群丰度的空间变化的解释能力都很低(两种 SDM 方法对 17 个物种的 R2 中位数都接近 0.1)。然而,与汇相比,SDM 对源种群适宜性的估计更高。在既有源种群又有汇种群的 13 个物种中,有 7 个物种的 BRT 和 GLM 在区分源与汇时的 AUC 值均大于 0.7。栖息地适宜性有可能成为一种有用的衡量标准,以表明一个种群作为源种群的自我维持能力;然而,要充分发掘这一潜力,必须对不同的分类群进行更多的研究。这种对栖息地适宜性的解释对保护实践特别有用,而确定 SDM 在何时以及如何与种群数量不匹配的明确案例,则可为推进生态理论提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Animal Ecology
Journal of Animal Ecology 环境科学-动物学
CiteScore
9.10
自引率
4.20%
发文量
188
审稿时长
3 months
期刊介绍: Journal of Animal Ecology publishes the best original research on all aspects of animal ecology, ranging from the molecular to the ecosystem level. These may be field, laboratory and theoretical studies utilising terrestrial, freshwater or marine systems.
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