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IPM2: toward better understanding and forecasting of population dynamics
Ecological Monographs ( IF 6.1 ) Pub Date : 2019-03-28 , DOI: 10.1002/ecm.1364
Floriane Plard 1 , Daniel Turek 2 , Martin U. Grüebler 1 , Michael Schaub 1
Affiliation  

Dynamic population models typically aim to predict demography and the resulting population dynamics in relation to environmental variation. However, they rarely include the diversity of individual responses to environmental changes, thus hampering our understanding of demographic mechanisms. We develop an integrated integral projection model (IPM2) that is a combination of an integrated population model (IPMpop) and an integral projection model (IPMind). IPM2 includes interactions between environmental and individual effects on demographic rates and can forecast both population size and individual trait distributions. First, we study the performance of this model using eight simulated scenarios with variable reproductive selective pressures on an individual trait. When the individual trait interacts with the environmental variable and the selective pressure on the individual trait is nonlinear, only IPM2 produces adequate predictions, because IPMind does not link predictions between the population level and observed data and because IPMpop does not include the individual trait. Second, we apply IPM2 to a population of barn swallows. The model accurately predicts trends of the barn swallow population while also providing mechanistic insights. High precipitation negatively influenced population dynamics through delaying laying dates, which lowered reproductive and survival rates. To predict the future of populations, we need to understand their individual drivers and thus include individual responses to their environment while following the entire population. As a consequence, IPM2 will improve our ability to test ecological and evolutionary hypotheses and improve the accuracy of population forecasting to aid management programs.

中文翻译:

IPM2:更好地了解和预测人口动态

动态人口模型通常旨在预测人口统计学以及与环境变化有关的人口动态。但是,它们很少包括个人对环境变化的反应的多样性,因此妨碍了我们对人口统计学机制的理解。我们开发了集成的集成投影模型(IPM 2),该模型是集成的人口模型(IPM pop)和集成的投影模型(IPM ind)的组合。IPM 2包括环境和个人对人口比率的影响之间的相互作用,可以预测人口规模和个人特征分布。首先,我们使用八个模拟场景研究该模型的性能,这些场景对单个性状具有可变的生殖选择压力。当个体性状与环境变量相互作用并且对个体性状的选择性压力是非线性的时,只有IPM 2会产生足够的预测,因为IPM ind并未将总体水平与观测数据之间的预测联系在一起,并且因为IPM pop不包括个体特征。其次,我们应用IPM 2到一群燕子。该模型可以准确地预测燕子种群的趋势,同时还可以提供机械方面的见解。高降水量通过推迟产卵时间对种群动态产生负面影响,从而降低了生殖和成活率。为了预测人口的未来,我们需要了解他们的个体动因,从而在关注整个人口的同时包括对他们的环境的个体反应。因此,IPM 2将提高我们检验生态学和进化假说的能力,并提高人口预测准确性以援助管理计划。
更新日期:2019-03-28
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