Department of Natural Resources, Isfahan University of Technology, 84156-83111, Isfahan, Iran.
CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade Do Porto, Instituto de Ciências Agrárias de Vairão R. Padre Armando Quintas, 4485-661, Vairão, Portugal.
Sci Rep. 2021 Jan 12;11(1):597. doi: 10.1038/s41598-020-79783-0.
Species Distribution Models (SDMs) can be used to estimate potential geographic ranges and derive indices to assess species conservation status. However, habitat-specialist species require fine-scale range estimates that reflect resource dependency. Furthermore, local adaptation of intraspecific lineages to distinct environmental conditions across ranges have frequently been neglected in SDMs. Here, we propose a multi-stage SDM approach to estimate the distributional range and potential area of occupancy (pAOO) of Neurergus kaiseri, a spring-dwelling amphibian with two climatically-divergent evolutionary lineages. We integrate both broad-scale climatic variables and fine-resolution environmental data to predict the species distribution while examining the performance of lineage-level versus species-level modelling on the estimated pAOO. Predictions of habitat suitability at the landscape scale differed considerably between evolutionary level models. At the landscape scale, spatial predictions derived from lineage-level models showed low overlap and recognised a larger amount of suitable habitats than species-level model. The variable dependency of lineages was different at the landscape scale, but similar at the local scale. Our results highlight the importance of considering fine-scale resolution approaches, as well as intraspecific genetic structure of taxa to estimate pAOO. The flexible procedure presented here can be used as a guideline for estimating pAOO of other similar species.
物种分布模型（SDMs）可用于估计潜在的地理分布范围，并得出评估物种保护状况的指标。然而，生境专家物种需要精细的范围估计，以反映资源依赖性。此外，种内谱系对不同范围内独特环境条件的局部适应在 SDM 中经常被忽视。在这里，我们提出了一种多阶段 SDM 方法来估计 Neurergus kaiseri 的分布范围和潜在的占有面积（pAOO），Neurergus kaiseri 是一种具有两种气候分化进化谱系的春季栖息两栖动物。我们整合了广泛的气候变量和精细分辨率的环境数据来预测物种分布，同时检查谱系水平和物种水平建模对估计的 pAOO 的性能。在进化水平模型之间，对生境适宜性的景观尺度预测差异很大。在景观尺度上，谱系水平模型得出的空间预测重叠度较低，识别出的适宜栖息地数量多于物种水平模型。在景观尺度上，谱系的变量依赖性不同，但在局部尺度上相似。我们的结果强调了考虑精细分辨率方法以及估计 pAOO 的分类群种内遗传结构的重要性。这里提出的灵活程序可以用作估计其他类似物种的 pAOO 的指南。
