Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density‐independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter‐annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete‐time Gompertz model, and assessed the performance of four models using information criteria values: density‐independent models with (1) and without (2) environmental covariates; and density‐dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one‐third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within‐sample performance by the most complex model (density‐dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density‐dependent model without covariates performed nearly as well predicting out‐of‐sample colony densities. The hindcast results indicated that the complex model over‐fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape‐scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long‐term data and revealed how a known limitation of information criteria (over‐fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.

中文翻译：

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生态预测揭示了常见模型选择方法的局限性：预测海狸群体密度的变化。


在过去的二十年里，人们多次呼吁通过对生态模型和预测的直接实证评估，使生态学成为一门更具预测性的科学。虽然使用信息标准进行模型选择的广泛使用推动生态学更加重视预测，但很少有人尝试验证信息标准正确识别具有最高预测准确性的最简约模型的能力。在这里，我们使用生态预测框架来测试信息标准准确预测密度依赖性和密度无关因素（饲料供应、收获、天气、狼[ Canis lupus ]密度）对年际波动的相对贡献的能力。海狸（加拿大蓖麻）群体密度。我们使用离散时间 Gompertz 模型对菌落密度的变化进行建模，并使用信息标准值评估四个模型的性能：具有 (1) 和不具有 (2) 环境协变量的密度无关模型；以及具有（3）和不具有（4）环境协变量的密度依赖模型。然后，我们通过保留每个总体的最后三分之一的观测值来评估每个模型的预测准确性，并比较观测到的密度与预测的密度。信息标准和我们的预测准确性指标都提供了海狸群体密度年度动态中补偿密度依赖性的有力证据。 然而，尽管使用信息标准确定的最复杂的模型（具有协变量的密度相关）具有很强的样本内性能，但菌落密度的后报表明，没有协变量的更简单的密度相关模型在预测外样本时表现几乎一样好。样本集落密度。事后结果表明，复杂的模型过度拟合了我们的数据，这表明通过信息标准识别为重要预测变量的参数对于预测景观规模海狸群体动态仅具有边际价值。我们的研究证明了用长期数据评估生态模型和预测的重要性，并揭示了信息标准的已知局限性（复杂模型的过度拟合）如何影响我们对生态动态的解释。虽然纳入影响动物种群动态因素的知识可以改善种群预测，但我们建议比较预测绩效指标同样可以提高我们对驱动种群动态因素的了解。