Mechanical-Statistical Modeling in Ecology: From Outbreak Detections to Pest Dynamics

Abstract

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.

Estimating the Delay between Host Infection and Disease (Incubation Period) and Assessing Its Significance to the Epidemiology of Plant Diseases

“…rates of infection), that are commonly hidden, from disease data sets. Nowadays, the study of infectious diseases often involves the use of mechanistic-statistical frameworks that incorporate a theoretical-mechanistic population model and a statistical model of the observation process [30], [54]. Recent advances in stochastic integration methods allow epidemiologists to estimate the parameters of stochastic continuous-time models from censored, discrete and incomplete observations of symptomatic individuals among the susceptible population, using, for instance, Bayesian Markov chain Monte Carlo inference methods with data-augmentation and reversible-jump [30], [55], [56].…”

Fine‐scale environmental variation in species distribution modelling: regression dilution, latent variables and neighbourly advice

“…where light varies below forest canopies (Lichstein et al 2010), and the methods have broader applications in ecology (e.g. Clark et al 2003;Soubeyrand, Neuvonen, & Penttinen 2008).…”

Vulnerability to pine sawfly damage decreases with site fertility but the opposite is true with Scleroderris canker damage; results from Finnish ICP Forests and NFI data

“…in areas with cold winters (Virtanen et al 1996;Soubeyrand et al 2010). This pattern was supported by the significantly negative coefficients for the covariate describing the probability of cold winters in the analyses with ICP Forests Level 1 and NFI 10 data, but this coefficient was positive with NFI 8 and NFI 9 data.…”

“…and Diprion pini L.) are a common problem in boreal pine forests of Northern Europe (Hanski 1987;Juutinen 1967;Kangas 1963;Larsson and Tenow 1984). The outbreaks of pine sawflies typically occur at irregular intervals (Juutinen 1967;Soubeyrand et al 2010), and they tend to be preceded by drought periods Tenow 1983, 1984). Pine sawfly outbreaks are also generally considered to be more common in drier rather than in more fertile forests (Juutinen 1967;Larsson and Tenow 1984;Kouki et al 1998;Meshkova 2006;Virtanen et al 1996).…”