Evaluating resource selection functions
Evaluating resource selection functions
复制标题
DOI：
10.1016/s0304-3800(02)00200-4
复制
发表时间：
2002-11-30
影响因子：
3.1
通讯作者：
Schmiegelow, FKA
中科院分区：
文献类型：
--
作者：
Boyce, MS;Vernier, PR;Schmiegelow, FKA
资源选择函数（RSF）是产生与资源单元的使用概率成比例的值的任何模型。RSF模型通常使用广义线性模型（GLM）拟合，尽管可以使用各种统计模型。信息标准，如赤池信息标准（AIC）或贝叶斯信息标准（BIC）是从一组生物学上合理的候选者中选择模型的有用工具。统计推断程序，如似然比检验，可用于评估模型是否偏离随机零模型。但是对于RSF模型的大多数应用，有用性是通过模型预测生物在景观中的位置来评估的。使用存在/不存在（使用/未使用）数据构建的RSF模型的预测可以使用为逻辑回归开发的程序进行评估，例如混淆矩阵，Kappa统计量和受试者操作特征（ROC）曲线。然而，RSF模型估计的存在/可用的数据创建独特的问题，评估模型的预测。对于存在/可用的模型，我们提出了一种形式的k折交叉验证评估预测的成功。这涉及到计算RSF等级和保留子样本数据的区域调整频率之间的相关性。类似的方法可以应用于评估样本外数据的预测成功率。由于目标生物的生态和行为变化，并不是所有的RSF模型都适用于不同时间或不同地点。(C)2002 Elsevier Science B. V.保留所有权利。
A resource selection function (RSF) is any model that yields values proportional to the probability of use of a resource unit. RSF models often are fitted using generalized linear models (GLMs) although a variety of statistical models might be used. Information criteria such as the Akaike Information Criteria (AIC) or Bayesian Information Criteria (BIC) are tools that can be useful for selecting a model from a set of biologically plausible candidates. Statistical inference procedures, such as the likelihood-ratio test, can be used to assess whether models deviate from random null models. But for most applications of RSF models, usefulness is evaluated by how well the model predicts the location of organisms on a landscape. Predictions from RSF models constructed using presence/absence (used/ unused) data can be evaluated using procedures developed for logistic regression, such as confusion matrices, Kappa statistics, and Receiver Operating Characteristic (ROC) curves. However, RSF models estimated from presence/ available data create unique problems for evaluating model predictions. For presence/available models we propose a form of k-fold cross validation for evaluating prediction success. This involves calculating the correlation between RSF ranks and area-adjusted frequencies for a withheld sub-sample of data. A similar approach can be applied to evaluate predictive success for out-of-sample data. Not all RSF models are robust for application in different times or different places due to ecological and behavioral variation of the target organisms. (C) 2002 Elsevier Science B.V. All rights reserved.