Logistic regression as a tool for defining habitat requirements of two common gammarids
Logistic regression as a tool for defining habitat requirements of two common gammarids
复制标题
逻辑回归作为定义两种常见伽马科动物栖息地要求的工具
DOI：
10.1046/j.1365-2427.1998.00304.x
复制
发表时间：
1998
影响因子：
2.7
通讯作者：
J.J.P. Gardeniers
中科院分区：
文献类型：
--
作者：
E. Peeters;J.J.P. Gardeniers
1.逻辑回归预测一个物种作为环境变量的函数出现的概率。该技术应用于描述两种常见的钩虾物种，钩虾和钩虾分布的大型数据集。在荷兰的溪流中，以评估其在确定栖息地要求方面的有用性。
2.提出了一种方法，推导出最佳生境范围的环境变量的逻辑回归方程。计算出的最佳栖息地范围，这是有关的最大可能性存在于该领域，同意与栖息地的要求和生态容忍度的文献。
3.单逻辑回归提供了很好的描述的最佳栖息地的要求和多逻辑回归洞察到每个环境变量的相对重要性。正是这种结合使得逻辑回归成为构建生境适宜性指数的有价值的工具。
4.在此基础上，流速、pH值、凯氏氮、总磷、铵态氮、电导率、宽度和深度是预测格纹藻发生概率的最重要的环境变量。fossarum，而流速、凯氏氮、pH和深度是预测G.蚤。
1. Logistic regression predicts the probability of occurrence of a species as a function of environmental variables. This technique was applied to a large data set describing the distribution of two common gammarid species, Gammarus fossarum and G. pulex, in streams in the Netherlands, to evaluate its usefulness in defining habitat requirements.
2. A method is presented that derives optimum habitat ranges for environmental variables from logistic regression equations. The calculated optimum habitat ranges, which are related to the maximum likelihood of presence in the field, agreed with habitat requirements and ecological tolerances in the literature.
3. Single logistic regressions provide good descriptions of the optimum habitat requirements and multiple logistic regressions give insight into the relative importance of each environmental variable. It is the combination that makes logistic regression a valuable tool for constructing habitat suitability indices.
4. Current velocity, pH, Kjeldahl nitrogen, total phosphorus, ammonium nitrogen, conductivity, width and depth are, in this sequence, the most important environmental variables in predicting the probability of occurrence of G. fossarum, whereas current velocity, Kjeldahl nitrogen, pH and depth are the most important variables for the prediction of the probability of occurrence of G. pulex.