Model selection in occupancy models: Inference versus prediction.
Model selection in occupancy models: Inference versus prediction.
复制标题
占用模型中的模型选择：推理与预测。
DOI：
10.1002/ecy.3942
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发表时间：
2023
期刊：
影响因子：
4.8
通讯作者：
Stewart PS
中科院分区：
文献类型：
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作者：
Stewart PS
占用模型是生态学家研究物种发生模式和驱动因素的重要工具，但它们的使用通常涉及在具有不同占用和检测协变量集的模型中进行选择。信息论方法采用了赤池信息准则（AIC）等信息准则，可以说是生态学中最流行的模型选择方法，并且经常用于选择占用模型。然而，信息论方法有选择模型的风险，这些模型会产生不准确的参数估计值，这是由于一种称为碰撞机偏差的现象，这是一种在向模型添加解释变量时可能出现的混淆。使用模拟，我们研究了碰撞机偏差（使用称为M-偏差的说明性示例）在占用模型的占用和检测过程中的后果，并探索了使用AIC和常见替代方案（施瓦茨）对模型选择的影响准则（或贝叶斯信息准则，BIC）。我们发现，当占用过程中存在M偏倚时，AIC和BIC选择的模型不准确地估计了焦点占用协变量的影响，同时比候选集中的其他模型更准确地预测了研究中心水平的占用概率。相比之下，检测过程中的M偏倚不影响焦点估计值;所有模型都做出了准确的推断，而AIC/BIC最佳模型的研究中心水平预测略准确。我们的研究结果表明，信息标准可以用来选择入住协变量，如果模型的唯一目的是预测，但必须更加谨慎地对待，如果目的是了解环境变量如何影响入住率。相比之下，检测协变量通常可以使用信息标准来选择，而不管模型的目的如何。这些发现说明了区分生态建模中的参数推断和预测任务的重要性。此外，我们的研究结果强调了对使用信息标准来比较观察性研究中不同生物学假设的关注。
Occupancy models are a vital tool for ecologists studying the patterns and drivers of species occurrence, but their use often involves selecting among models with different sets of occupancy and detection covariates. The information‐theoretic approach, which employs information criteria such as Akaike's information criterion (AIC) is arguably the most popular approach for model selection in ecology and is often used for selecting occupancy models. However, the information‐theoretic approach risks selecting models that produce inaccurate parameter estimates due to a phenomenon called collider bias, a type of confounding that can arise when adding explanatory variables to a model. Using simulations, we investigated the consequences of collider bias (using an illustrative example called M‐bias) in the occupancy and detection processes of an occupancy model, and explored the implications for model selection using AIC and a common alternative, the Schwarz criterion (or Bayesian information criterion, BIC). We found that when M‐bias was present in the occupancy process, AIC and BIC selected models that inaccurately estimated the effect of the focal occupancy covariate, while simultaneously producing more accurate predictions of the site‐level occupancy probability than other models in the candidate set. In contrast, M‐bias in the detection process did not impact the focal estimate; all models made accurate inferences, while the site‐level predictions of the AIC/BIC‐best model were slightly more accurate. Our results show that information criteria can be used to select occupancy covariates if the sole purpose of the model is prediction, but must be treated with more caution if the purpose is to understand how environmental variables affect occupancy. By contrast, detection covariates can usually be selected using information criteria regardless of the model's purpose. These findings illustrate the importance of distinguishing between the tasks of parameter inference and prediction in ecological modeling. Furthermore, our results underline concerns about the use of information criteria to compare different biological hypotheses in observational studies.