Illinois Natural History Survey, Prairie Research Institute University of Illinois 1816 S Oak Street Champaign, IL 61820, USA.
Mol Ecol. 2014 May;23(10):2402-13. doi: 10.1111/mec.12747. Epub 2014 May 5.
Landscape genetics has seen tremendous advances since its introduction, but parameterization and optimization of resistance surfaces still poses significant challenges. Despite increased availability and resolution of spatial data, few studies have integrated empirical data to directly represent ecological processes as genetic resistance surfaces. In our study, we determine the landscape and ecological factors affecting gene flow in the western slimy salamander (Plethodon albagula). We used field data to derive resistance surfaces representing salamander abundance and rate of water loss through combinations of canopy cover, topographic wetness, topographic position, solar exposure and distance from ravine. These ecologically explicit composite surfaces directly represent an ecological process or physiological limitation of our organism. Using generalized linear mixed-effects models, we optimized resistance surfaces using a nonlinear optimization algorithm to minimize model AIC. We found clear support for the resistance surface representing the rate of water loss experienced by adult salamanders in the summer. Resistance was lowest at intermediate levels of water loss and higher when the rate of water loss was predicted to be low or high. This pattern may arise from the compensatory movement behaviour of salamanders through suboptimal habitat, but also reflects the physiological limitations of salamanders and their sensitivity to extreme environmental conditions. Our study demonstrates that composite representations of ecologically explicit processes can provide novel insight and can better explain genetic differentiation than ecologically implicit landscape resistance surfaces. Additionally, our study underscores the fact that spatial estimates of habitat suitability or abundance may not serve as adequate proxies for describing gene flow, as predicted abundance was a poor predictor of genetic differentiation.
景观遗传学自问世以来取得了巨大的进展，但阻力表面的参数化和优化仍然存在重大挑战。尽管空间数据的可用性和分辨率有所提高，但很少有研究将经验数据整合到遗传阻力表面中，以直接代表生态过程。在我们的研究中，我们确定了影响西部粘滑螈（Plethodon albagula）基因流动的景观和生态因素。我们使用野外数据来推导阻力表面，这些表面代表了通过冠层覆盖、地形湿度、地形位置、太阳暴露和与沟壑距离的组合来表示蝾螈丰度和失水率。这些生态明确的复合表面直接代表了我们生物体的生态过程或生理限制。使用广义线性混合效应模型，我们使用非线性优化算法来优化阻力表面，以最小化模型 AIC。我们清楚地支持代表夏季成年蝾螈失水率的阻力表面。当预测失水率低或高时，阻力最低，当预测失水率高时，阻力最高。这种模式可能源于蝾螈通过次优栖息地的补偿性运动行为，但也反映了蝾螈的生理限制及其对极端环境条件的敏感性。我们的研究表明，生态明确过程的复合表示可以提供新的见解，并能比生态隐含的景观阻力表面更好地解释遗传分化。此外，我们的研究强调了这样一个事实，即栖息地适宜性或丰度的空间估计可能不能充分描述基因流动，因为预测的丰度是遗传分化的一个很差的预测因子。
