Conflicting evidence exists supporting linear and nonlinear density‐dependent population growth when species have slow life histories. The Ricker (linear) and θ‐logistic (nonlinear) models are commonly used to analyze survey data for these species, but no evaluation has examined whether these hypotheses can be differentiated with field data. We conducted a simulation exploring effects from shape of density dependence and variation in vital rates on the fit of these models. When vital rates had moderate to high variation, the models had similar fit. The θ‐logistic model differed from the Ricker model and was biologically realistic (θ > 1) when variation in vital rates was low and the growth response was nonlinear. Furthermore, the θ‐logistic model has issues with model convergence when using vague priors and when variation in vital rates as high. These results indicate that the Ricker model is appropriate for population survey data of species with slow life histories.

中文翻译：

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比较Ricker和θ-logistic模型以估算麋鹿种群增长

当物种的生活史缓慢时，存在相互矛盾的证据支持线性和非线性的密度依赖性种群增长。Ricker（线性）模型和θ- logistic（非线性）模型通常用于分析这些物种的调查数据，但尚无评估方法可以检验这些假设是否可以与现场数据区分开。我们进行了模拟，探索了密度依赖性形状和生命率变化对这些模型的拟合的影响。当生命率从中到高变化时，模型具有相似的拟合度。 当生命率变化低且生长反应呈非线性时，θ- logistic模型与Ricker模型不同，并且具有生物学上的现实意义（θ > 1）。此外，θ当使用模糊先验且生命率变化较大时，后勤模型存在模型收敛问题。这些结果表明，Ricker模型适用于具有慢寿命历史的物种的种群调查数据。