Singh Sohana P, Groeneveld Johan C, Willows-Munro Sandi
Oceanographic Research Institute Durban South Africa.
School of Life Sciences University of KwaZulu-Natal Pietermaritzburg South Africa.
Ecol Evol. 2020 Nov 18;10(24):14394-14410. doi: 10.1002/ece3.7043. eCollection 2020 Dec.
We incorporated genetic structure and life history phase in species distribution models (SDMs) constructed for a widespread spiny lobster, to reveal local adaptations specific to individual subspecies and predict future range shifts under the RCP 8.5 climate change scenario.
Indo-West Pacific.
MaxEnt was used to construct present-day SDMs for the spiny lobster and individually for the three genetically distinct subspecies of which it comprises. SDMs incorporated both sea surface and benthic (seafloor) climate layers to recreate discrete influences of these habitats during the drifting larval and benthic juvenile and adult life history phases. Principle component analysis (PCA) was used to infer environmental variables to which individual subspecies were adapted. SDM projections of present-day habitat suitability were compared with predictions for the year 2,100, under the RCP 8.5 climate change scenario.
In the PCA, salinity best explained habitat suitability, compared with current velocity in and sea surface temperature in . Drifting and benthic life history phases were adapted to different combinations of sea surface and benthic environmental variables considered. Highly suitable habitats for benthic phases were spatially enveloped within more extensive sea surface habitats suitable for drifting larvae. SDMs predicted that present-day highly suitable habitats for will decrease by the year 2,100.
Incorporating genetic structure in SDMs showed that individual spiny lobster subspecies had unique adaptations, which could not be resolved in species-level models. The use of sea surface and benthic climate layers revealed the relative importance of environmental variables during drifting and benthic life history phases. SDMs that included genetic structure and life history were more informative in predictive models of climate change effects.
我们将遗传结构和生活史阶段纳入为一种分布广泛的多刺龙虾构建的物种分布模型(SDMs)中,以揭示各个亚种特有的局部适应性,并预测在代表性浓度路径8.5气候变化情景下未来的分布范围变化。
印度-西太平洋。
使用最大熵模型(MaxEnt)为多刺龙虾构建当前的物种分布模型,并分别为其包含的三个遗传上不同的亚种构建模型。物种分布模型纳入了海面和底栖(海底)气候层,以重现这些栖息地在浮游幼体以及底栖幼体和成体生活史阶段的不同影响。主成分分析(PCA)用于推断各个亚种所适应的环境变量。将当前栖息地适宜性的物种分布模型预测结果与在代表性浓度路径8.5气候变化情景下对2100年的预测结果进行比较。
在主成分分析中,与海流速度和海面温度相比,盐度最能解释栖息地适宜性。浮游和底栖生活史阶段适应于所考虑的海面和底栖环境变量的不同组合。底栖阶段的高度适宜栖息地在空间上被包含在更广泛的适合浮游幼体的海面栖息地范围内。物种分布模型预测,到2100年,当前对该龙虾高度适宜的栖息地将减少。
在物种分布模型中纳入遗传结构表明,各个多刺龙虾亚种具有独特的适应性,这在物种水平模型中无法得到解决。使用海面和底栖气候层揭示了环境变量在浮游和底栖生活史阶段的相对重要性。包含遗传结构和生活史的物种分布模型在气候变化影响的预测模型中提供了更多信息。