Mechanical-statistical modeling in ecology: from outbreak detections to pest dynamics.
Knowledge about large-scale and long-term dynamics of (natural) populations is required to assess the efficiency of control strategies, the potential for long-term persistence, and the adaptability to global changes such as habitat fragmentation and global warming. For most natural populations, such as pest populations, large-scale and long-term surveys cannot be carried out at a high resolution. For instance, for population dynamics characterized by irregular abundance explosions, i.e., outbreaks, it is common to report detected outbreaks rather than measuring the population density at every location and time event. Here, we propose a mechanical-statistical model for analyzing such outbreak occurrence data and making inference about population dynamics. This spatio-temporal model contains the main mechanisms of the dynamics and describes the observation process. This construction enables us to account for the discrepancy between the phenomenon scale and the sampling scale. We propose the Bayesian method to estimate model parameters, pest densities and hidden factors, i.e., variables involved in the dynamics but not observed. The model was specified and used to learn about the dynamics of the European pine sawfly (Neodiprion sertifer Geoffr., an insect causing major defoliation of pines in northern Europe) based on Finnish sawfly data covering the years 1961-1990. In this application, a dynamical Beverton-Holt model including a hidden regime variable was incorporated into the model to deal with large variations in the population densities. Our results gave support to the idea that pine sawfly dynamics should be studied as metapopulations with alternative equilibria. The results confirmed the importance of extreme minimum winter temperatures for the occurrence of European pine sawfly outbreaks. The strong positive connection between the ratio of lake area over total area and outbreaks was quantified for the first time.
Soubeyrand
S
S
INRA, UR546 Biostatistics and Spatial Processes, 84914, Avignon, France. samuel.soubeyrand@avignon.inra.fr
Neuvonen
S
S
Penttinen
A
A
D000818:Animals; D001499:Bayes Theorem; D003080:Cold Temperature; D017753:Ecosystem; D005387:Finland; D005843:Geography; D006927:Hymenoptera; D015233:Models, Statistical; D011156:Population Density; D011158:Population Growth; D012016:Reference Values; D018401:Sample Size; D012621:Seasons; D013997:Time Factors