OOS 32-4 - IPMĀ²: Combining integral projection and integrated population models

Thursday, August 10, 2017: 9:00 AM
Portland Blrm 254, Oregon Convention Center
Floriane Plard, Swiss Ornithological Institute, Daniel Turek, Mathematics and Statistics, Williams College, Williamstown, MA and Michael Schaub, Swiss Ornithological Institute, Sempach, Switzerland

Models of population dynamics often predict demographic rates and population size in relation to environmental variations. Indeed, these variations often directly shape demographic rates. However, population models rarely include the diversity of individual responses facing these environmental pressures. But, when resources become scarce, the performances of low-quality individuals are often the first ones to be impacted. Here, we combined the advantaged of two widely used model of population dynamics: integrated population model (IPMpop) and integral projection model (IPMind) into IPM². This model allowed using individual data to estimate population dynamics while keeping the estimates at population level close to reality. To compare the performance of the three models, we simulated sample populations under 3 scenarios with no, an homogeneous, and an heterogeneous environmental influence on individuals survival and reproduction.


We showed that in the three scenarios, the predictions of IPM² were more accurate than the ones made by IPMind and IPMpop. Moreover, when the response of large individuals to environmental variations was higher than the response of small individuals, only IPM² was able to predict population dynamics correctly according to heterogeneous individual performances. We applied these models to 12 populations of barn swallows living in Switzerland. We showed that IPM² was able to gather the predictions of both IPMpop and IPMind to predict correctly the influence of individual laying date and on spring precipitation on demographic rate. In this way we could predict the dynamics of the index of the Swiss barn swallow population from independent datasets. The model we developed allowed thus to better understand the individual mechanisms that shape population dynamics to be able to make better predictions about population in the future and in the context of climate change.