IPM~2: toward better understanding and forecasting of population dynamics

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作   者：

FLORIANE PLARD;  DANIELTUREK;  MARTIN U. GRüEBLER;  MICHAEL SCHAUB; 

作者机构：

CH-6204 Sempach;  Switzerland;  Department of Mathematics and Statistics;  Williams College;  Massachusetts 01267 USA;  Williamstown;  Swiss Ornithological Institute;  18 Hoxsey Street; 

关键词：

barn swallow;  integral projection model;  environmental variation;  individual plasticity;  individual response;  integrated population model; 

期刊名称：

Ecological Monographs: Official Publication of the Ecological Society of America

i s s n：

0012-9615

年卷期：

2019 年 89 卷 3 期

页   码：

e01364-1-e01364-18

页   码：

摘   要：

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 (IPM~2) that is a combination of an integrated population model (IPM_(pop)) and an integral projection model (IPM_(ind)). IPM~2 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 IPM~2 produces adequate predictions, because IPM_(ind) does not link predictions between the population level and observed data and because IPM_(pop) does not include the individual trait. Second, we apply IPM~2 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, IPM~2 will improve our ability to test ecological and evolutionary hypotheses and improve the accuracy of population forecasting to aid management programs.