Accurately predicting species ranges is a primary goal of ecology. Demographic distribution models (DDMs), which correlate underlying vital rates (e.g. survival and reproduction) with environmental conditions, can potentially predict species ranges through time and space. However, tests of DDM accuracy across wide ranges of species' life histories are surprisingly lacking. Using simulations of 1.5 million hypothetical species' range dynamics, we evaluated when DDMs accurately predicted future ranges, to provide clear guidelines for the use of this emerging approach. We limited our study to deterministic demographic models ignoring density dependence, since these models are the most commonly used in the literature. We found that density‐independent DDMs overpredicted extinction if populations were near carrying capacity in the locations where demographic data were available. However, DDMs accurately predicted species ranges if demographic data were limited to sites with mean initial abundance less than one half of carrying capacity. Additionally, the DDMs required demographic data from at least 25 sites, over a short time‐interval (< 10 time‐steps), as populations initially below carrying capacity can saturate in long‐term studies. For species with demographic data from many low density sites, DDMs predicted occurrence more accurately than correlative species distribution models (SDMs) in locations where the species eventually persisted, but not where the species went extinct. These results were insensitive to differences in simulated dispersal, levels of environmental stochasticity, the effects of the environmental variables and the functional forms of density dependence. Our findings suggest that deterministic, density‐independent DDMs are appropriate for applications where locating all possible sites the species might occur in is prioritized over reducing false presence predictions in absent sites. This makes DDMs a promising tool for mapping invasion risk. However, demographic data are often collected at sites where a species is abundant. Density‐independent DDMs are inappropriate in this case.

中文翻译：

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评估密度独立的人口统计学模型预测物种范围的准确性

准确预测物种范围是生态学的主要目标。将潜在生命率（例如生存和繁殖）与环境条件相关联的人口分布模型（DDM）可以潜在地预测物种在时间和空间上的分布。但是，令人惊讶地缺乏跨物种生命史的DDM准确性测试。通过对150万种假设物种的范围动态进行仿真，我们评估了DDM何时准确预测了未来范围，从而为使用这种新兴方法提供了明确的指导原则。我们将研究限于忽略密度依赖性的确定性人口统计学模型，因为这些模型是文献中最常用的模型。我们发现，如果人口统计数据可得的地区的人口接近承载能力，则密度无关的DDM会高估灭绝的危险。但是，如果人口统计数据仅限于平均初始丰度小于承载能力一半的地点，则DDM可以准确预测物种范围。此外，DDM需要在短时间间隔（<10个时间步长）内至少来自25个站点的人口统计数据，因为长期低于最初承受能力的人群可能会饱和。对于具有来自许多低密度站点的人口统计学数据的物种，与相关物种分布模型（SDM）相比，DDM可以更准确地预测发生在物种最终持续存在的位置而不是物种灭绝的位置。这些结果对模拟扩散的差异不敏感，环境随机性的水平，环境变量的影响以及密度依赖性的函数形式。我们的发现表明，确定性，密度无关的DDM适用于那些优先考虑减少该物种可能出现的所有可能发生地点的应用程序，而不是减少在不存在该地点的错误存在预测的情况。这使DDM成为映射入侵风险的有前途的工具。但是，人口统计数据通常是在物种丰富的地点收集的。在这种情况下，与密度无关的DDM是不合适的。与密度无关的DDM适用于那些优先考虑定位物种可能发生的所有可能站点而不是减少在不存在站点中的错误存在预测的应用程序。这使DDM成为映射入侵风险的有前途的工具。但是，人口统计数据通常是在物种丰富的地点收集的。在这种情况下，与密度无关的DDM是不合适的。与密度无关的DDM适用于那些优先考虑定位物种可能发生的所有可能站点而不是减少在不存在站点中的错误存在预测的应用程序。这使DDM成为映射入侵风险的有前途的工具。但是，人口统计数据通常是在物种丰富的地点收集的。在这种情况下，与密度无关的DDM是不合适的。