The extent to which populations in nature are regulated by density-dependent processes is unresolved. While experiments increasingly find evidence of strong density-dependence, unmanipulated population time series yield much more ambiguous evidence of regulation, especially when accounting for effects of observation error. Here, we re-examine the evidence for density-dependence in time series of population sizes in nature, by conducting an aggregate analysis of the populations in the Global Population Dynamics Database (GPDD). First, following the conventional approach, we fit a density-dependent and a density-independent variant of the Gompertz state-space model to each time series. Then, we conduct an aggregate analysis of the entire database by considering two random-effects density-dependent models that leverage information across data sets. When individual time series are tested independently, we find very little evidence for density-dependence. However, in the aggregate, we find very strong evidence for density-dependence, even though, paradoxically, estimated strengths of density-dependence for individual time series tend to be weaker than when each individual time series is analyzed independently. Furthermore, a hierarchical model that accounts for taxonomic variation in the strength of density-dependence reveals that density-dependence is consistently stronger in insects and fish than in birds and mammals. Our findings resolve apparent inconsistencies between observational and experimental studies of density-dependence by revealing that the observational record does indeed contain strong support for the hypothesis that density-dependence is widespread in nature.

中文翻译：

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分层建模加强了人口动态观测时间序列中密度依赖性的证据

自然界中的种群在多大程度上受密度依赖过程的调节尚未解决。虽然实验越来越多地发现了强密度依赖性的证据，但未经操纵的人口时间序列产生了更加模糊的监管证据，尤其是在考虑观察误差的影响时。在这里，我们通过对全球人口动态数据库 (GPDD) 中的人口进行汇总分析，重新检查了自然界中人口规模时间序列中密度依赖性的证据。首先，按照传统方法，我们将 Gompertz 状态空间模型的密度相关和密度无关变体拟合到每个时间序列。然后，我们通过考虑两个利用跨数据集信息的随机效应密度相关模型对整个数据库进行汇总分析。当单独测试单个时间序列时，我们发现密度依赖性的证据很少。然而，总的来说，我们发现了密度依赖的非常有力的证据，尽管矛盾的是，单个时间序列的密度依赖的估计强度往往比独立分析每个时间序列时要弱。此外，解释密度依赖性强度的分类学变异的分层模型表明，昆虫和鱼类的密度依赖性始终强于鸟类和哺乳动物。我们的发现通过揭示观察记录确实包含对密度依赖性在自然界中普遍存在的假设的有力支持，解决了密度依赖性的观察性研究和实验性研究之间的明显不一致。