Şen Bilgecan, Che-Castaldo Christian, Akçakaya H Reşit
Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, USA.
Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, Maryland, USA.
J Anim Ecol. 2024 Dec;93(12):1924-1934. doi: 10.1111/1365-2656.14201. Epub 2024 Oct 21.
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 R 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.
虽然物种分布模型（SDM）经常被用于预测物种出现情况，以辅助保护管理决策，但评估栖息地适宜性能否可靠地预测内在增长率或区分源种群和汇种群的证据有限。填补这一知识空白对保护科学至关重要，因为将SDM应用于管理目的最终依赖于这些通常未被观测到的种群或集合种群动态。我们运用线性回归，将先前发表的、通过对美国本土17种鸟类的标记重捕数据进行贝叶斯分析得出的内在增长率和丰度的种群水平估计值，与在此处根据这些相同物种的机会性数据拟合的SDM栖息地适宜性估计值相关联。然后，我们使用ROC曲线下面积（AUC）来衡量SDM区分源种群和汇种群的能力。我们采用两种不同方法构建SDM，即增强回归树（BRT）和广义线性模型（GLM），并比较它们在源/汇预测方面的表现。每个SDM均使用从eBird（一个可在线获取的数据库）获得的出现点以及先前选定的10个环境变量构建，这些变量用于模拟这些物种的内在增长率和丰度。我们发现，一般而言，基于机会性数据构建的SDM对物种种群统计学特征的预测能力较差；BRT和GLM对内在增长率和种群丰度的空间变化解释都很少（两种SDM方法在17个物种上的中位数R均接近0.1）。然而，与汇种群相比，SDM对源种群的适宜性估计更高。在13个同时拥有源种群和汇种群的物种中，当区分源种群和汇种群时，BRT和GLM对7个物种的AUC值均大于0.7。栖息地适宜性有可能成为一项有用的指标，用以表明一个种群作为源种群维持自身的能力；然而，对更多不同类群进行研究对于充分发掘这一潜力至关重要。这种对栖息地适宜性的解读对于保护实践可能特别有用，识别SDM何时以及如何无法与种群统计学特征相匹配的具体案例，对于推进生态理论具有参考价值。
