Data from: Ecological resistance surfaces predict fine scale genetic differentiation in a terrestrial woodland salamander

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 using a
non-linear 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 behavior 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（Landscape genetics）自创立以来取得了长足进展，但抗性表面（resistance surfaces）的参数化与优化仍面临诸多显著挑战。尽管空间数据的可获取性与分辨率持续提升，但鲜有研究整合实证数据，将生态过程直接表征为遗传抗性表面（genetic resistance surfaces）。本研究旨在明确影响西部滑螈（Plethodon albagula）基因流的景观与生态因子。本研究依托野外调查数据，通过冠层覆盖度、地形湿润度、地形位置、太阳辐射量以及距沟壑距离的组合方式，构建出表征螈类种群丰度与失水速率的抗性表面。这类具备生态明确性的复合抗性表面，可直接表征研究对象的生态过程与生理限制特征。本研究采用广义线性混合效应模型（generalized linear mixed effects models），通过非线性优化算法最小化模型赤池信息准则（Akaike Information Criterion, AIC），完成抗性表面的优化。研究结果清晰表明，表征成年螈类夏季失水速率的抗性表面模型得到了有力支持。当失水速率处于中等水平时，抗性值最低；而当失水速率预计偏高或偏低时，抗性值则相对更高。该模式可能源于螈类在亚最优生境中的补偿性移动行为，同时也反映了螈类自身的生理限制特征及其对极端环境条件的敏感性。本研究表明，相较于生态隐式的景观抗性表面，具备生态明确性的过程复合表征能够提供全新的研究视角，且可更好地解释遗传分化现象。此外，本研究还强调了一项关键结论：生境适宜性或种群丰度的空间估算结果，或许无法作为基因流描述的合适替代指标——因为预测得到的种群丰度并不能有效预测遗传分化。