Liu Changdong, Liu Junchao, Jiao Yan, Tang Yanli, Reid Kevin B
Department of Fisheries, Ocean University of China, Qingdao, Shandong, China.
Department of Fish and Wildlife Conservation, Virginia Polytechnic Institute & State University, Blacksburg, VA, USA.
PeerJ. 2019 Jul 25;7:e7350. doi: 10.7717/peerj.7350. eCollection 2019.
Global regression models under an implicit assumption of spatial stationarity were commonly applied to estimate the environmental effects on aquatic species distribution. However, the relationships between species distribution and environmental variables may change among spatial locations, especially at large spatial scales with complicated habitat. Local regression models are appropriate supplementary tools to explore species-environment relationships at finer scales.
We applied geographically weighted regression (GWR) models on Yellow Perch in Lake Erie to estimate spatially-varying environmental effects on the presence probabilities of this species. Outputs from GWR were compared with those from generalized additive models (GAMs) in exploring the Yellow Perch distribution. Local regression coefficients from the GWR were mapped to visualize spatially-varying species-environment relationships. -means cluster analyses based on the -values of GWR local regression coefficients were used to characterize the distinct zones of ecological relationships.
Geographically weighted regression resulted in a significant improvement over the GAM in goodness-of-fit and accuracy of model prediction. Results from the GWR revealed the magnitude and direction of environmental effects on Yellow Perch distribution changed among spatial locations. Consistent species-environment relationships were found in the west and east basins for adults. The different kinds of species-environment relationships found in the central management unit (MU) implied the variation of relationships at a scale finer than the MU.
This study draws attention to the importance of accounting for spatial nonstationarity in exploring species-environment relationships. The GWR results can provide support for identification of unique stocks and potential refinement of the current jurisdictional MU structure toward more ecologically relevant MUs for the sustainable management of Yellow Perch in Lake Erie.
在空间平稳性的隐含假设下，全局回归模型通常用于估计环境对水生物种分布的影响。然而，物种分布与环境变量之间的关系可能会在不同空间位置发生变化，尤其是在具有复杂栖息地的大空间尺度上。局部回归模型是在更精细尺度上探索物种与环境关系的合适补充工具。
我们对伊利湖中的黄鲈应用地理加权回归（GWR）模型，以估计空间变化的环境对该物种出现概率的影响。在探索黄鲈分布时，将GWR的输出结果与广义相加模型（GAM）的结果进行比较。将GWR的局部回归系数进行映射，以可视化空间变化的物种与环境关系。基于GWR局部回归系数的p值进行均值聚类分析，以表征生态关系的不同区域。
地理加权回归在拟合优度和模型预测准确性方面比GAM有显著改进。GWR的结果表明，环境对黄鲈分布的影响程度和方向在不同空间位置有所变化。在西部和东部流域，成年黄鲈的物种与环境关系一致。在中央管理单元（MU）中发现的不同类型的物种与环境关系，意味着在比MU更精细的尺度上关系存在变化。
本研究提请注意在探索物种与环境关系时考虑空间非平稳性的重要性。GWR的结果可为识别独特种群以及对当前管辖的MU结构进行潜在优化提供支持，使其朝着更具生态相关性的MU发展，以实现伊利湖黄鲈的可持续管理。
