Resource selection is often studied by ecologists interested in the environmental drivers of animal space use and movement. These studies commonly produce spatial predictions, which are of considerable utility to resource managers making habitat and population management decisions. It is thus paramount that predictions from resource selection studies are accurate. We evaluated model building and fitting strategies for optimizing resource selection function predictions in a use-availability framework. We did so by simulating low- and high-intensity spatial sampling data that respectively predicted study area and movement-based resource selection. We compared one of the most commonly used forms of statistical regularization, Akaike's Information Criterion (AIC), with the lesser used least absolute shrinkage and selection operator (LASSO). LASSO predictions were less variable and more accurate than AIC and were often best when considering additive and interacting variables. We explicitly demonstrate the predictive equivalence using the logistic and Poisson likelihoods and how it is lost when the available sample is too small. Regardless of modeling approach, interpreting the sign of coefficients as a measure of selection can be misleading when optimizing for prediction.

中文翻译：

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改进动物资源选择的空间预测以指导保护决策

对动物空间使用和运动的环境驱动因素感兴趣的生态学家经常研究资源选择。这些研究通常会产生空间预测，这对于做出栖息地和人口管理决策的资源管理者非常有用。因此，资源选择研究的预测准确至关重要。我们评估了在可用性框架中优化资源选择功能预测的模型构建和拟合策略。我们通过模拟分别预测研究区域和基于运动的资源选择的低强度和高强度空间采样数据来做到这一点。我们将最常用的统计正则化形式之一、赤池信息准则 (AIC) 与较少使用的最少绝对收缩和选择算子 (LASSO) 进行了比较。LASSO 预测比 AIC 变量更小、更准确，并且在考虑加性和相互作用变量时通常是最好的。我们使用逻辑和泊松似然以及当可用样本太小时它是如何丢失的，明确地证明了预测等价性。不管建模方法如何，在优化预测时，将系数的符号解释为选择的度量可能会产生误导。