Milanesi Pietro, Holderegger R, Caniglia R, Fabbri E, Galaverni M, Randi E
Laboratorio di Genetica, Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA), Via Ca` Fornacetta 9, 40064, Ozzano dell'Emilia, Bologna, Italy.
WSL Swiss Federal Research Institute, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.
Oecologia. 2017 Jan;183(1):67-79. doi: 10.1007/s00442-016-3751-x. Epub 2016 Oct 11.
Landscape genetics aims to investigate functional connectivity among wild populations by evaluating the impact of landscape features on gene flow. Genetic distances among populations or individuals are generally better explained by least-cost path (LCP) distances derived from resistance surfaces than by simple Euclidean distances. Resistance surfaces reflect the cost for an organism to move through particular landscape elements. However, determining the effects of landscape types on movements is challenging. Because of a general lack of empirical data on movements, resistance surfaces mostly rely on expert knowledge. Habitat-suitability models potentially provide a more objective method to estimate resistance surfaces than expert opinions, but they have rarely been applied in landscape genetics so far. We compared LCP distances based on expert knowledge with LCP distances derived from habitat-suitability models to evaluate their performance in landscape genetics. We related all LCP distances to genetic distances in linear mixed effect models on an empirical data set of wolves (Canis lupus) from Italy. All LCP distances showed highly significant (P ≤ 0.0001) standardized β coefficients and R values, but LCPs from habitat-suitability models generally showed higher values than those resulting from expert knowledge. Moreover, all LCP distances better explained genetic distances than Euclidean distances, irrespective of the approaches used. Considering our results, we encourage researchers in landscape genetics to use resistance surfaces based on habitat suitability which performed better than expert-based LCPs in explaining patterns of gene flow and functional connectivity.
景观遗传学旨在通过评估景观特征对基因流的影响来研究野生种群之间的功能连通性。与简单的欧几里得距离相比，种群或个体之间的遗传距离通常用从抗性表面得出的成本最低路径（LCP）距离能得到更好的解释。抗性表面反映了生物体穿过特定景观要素的成本。然而，确定景观类型对移动的影响具有挑战性。由于普遍缺乏关于移动的实证数据，抗性表面大多依赖专家知识。栖息地适宜性模型可能比专家意见提供一种更客观的方法来估计抗性表面，但到目前为止它们很少应用于景观遗传学。我们将基于专家知识的LCP距离与从栖息地适宜性模型得出的LCP距离进行比较，以评估它们在景观遗传学中的表现。我们在来自意大利的狼（Canis lupus）的实证数据集的线性混合效应模型中将所有LCP距离与遗传距离相关联。所有LCP距离均显示出高度显著（P≤0.0001）的标准化β系数和R值，但来自栖息地适宜性模型的LCP通常比专家知识得出的LCP显示出更高的值。此外，无论使用何种方法，所有LCP距离都比欧几里得距离能更好地解释遗传距离。考虑到我们的结果，我们鼓励景观遗传学领域的研究人员使用基于栖息地适宜性的抗性表面，其在解释基因流模式和功能连通性方面比基于专家的LCP表现更好。
