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Understanding the drivers underlying disease dynamics is still a major challenge in disease ecology, especially in the case of long-term disease persistence. Even though there is a strong consensus that density-dependent factors play an important role for the spread of diseases, the main drivers are still discussed and, more importantly, might differ between invasion and persistence periods. Here, we analysed long-term outbreak data of classical swine fever, an important disease in both wild boar and livestock, prevalent in the wild boar population from 1993 to 2000 in Mecklenburg-Vorpommern, Germany. We report outbreak characteristics and results from generalized linear mixed models to reveal what factors affected infection risk on both the landscape and the individual level. Spatiotemporal outbreak dynamics showed an initial wave-like spread with high incidence during the invasion period followed by a drop of incidence and an increase in seroprevalence during the persistence period. Velocity of spread increased with time during the first year of outbreak and decreased linearly afterwards, being on average 7.6 km per quarter. Landscape- and individual-level analyses of infection risk indicate contrasting seasonal patterns. During the persistence period, infection risk on the landscape level was highest during autumn and winter seasons, probably related to spatial behaviour such as increased long-distance movements and contacts induced by rutting and escaping movements. In contrast, individual-level infection risk peaked in spring, probably related to the concurrent birth season leading to higher densities, and was significantly higher in piglets than in reproductive animals. Our findings highlight that it is important to investigate both individual- and landscape-level patterns of infection risk to understand long-term persistence of wildlife diseases and to guide respective management actions. Furthermore, we highlight that exploring different temporal aggregation of the data helps to reveal important seasonal patterns, which might be masked otherwise.
Climate change and land use management practices are major drivers of biodiversity in terrestrial ecosystems. To understand and predict resulting changes in community structures, individual-based and spatially explicit population models are a useful tool but require detailed data sets for each species. More generic approaches are thus needed. Here we present a trait-based functional type approach to model savanna birds. The aim of our model is to explore the response of different bird functional types to modifications in habitat structure. The functional types are characterized by different traits, in particular body mass, which is related to life-history traits (reproduction and mortality) and spatial scales (home range area and dispersal ability), as well as the use of vegetation structures for foraging and nesting, which is related to habitat quality and suitability. We tested the performance of the functional types in artificial landscapes varying in shrub:grass ratio and clumping intensity of shrub patches. We found that an increase in shrub encroachment and a decrease in habitat quality caused by land use mismanagement and climate change endangered all simulated bird functional types. The strength of this effect was related to the preferred habitat. Furthermore, larger-bodied insectivores and omnivores were more prone to extinction due to shrub encroachment compared to small-bodied species. Insectivorous and omnivorous birds were more sensitive to clumping intensity of shrubs whereas herbivorous and carnivorous birds were most affected by a decreasing amount of grass cover. From an applied point of view, our findings emphasize that policies such as woody plant removal and a reduction in livestock stocking rates to prevent shrub encroachment should prioritize the enlargement of existing grassland patches. Overall, our results show that the combination of an individual-based modelling approach with carefully defined functional types can provide a powerful tool for exploring biodiversity responses to environmental changes. Furthermore, the increasing accumulation of worldwide data sets on species’ core and soft traits (surrogates to determine core traits indirectly) on one side and the refinement of conceptual frameworks for animal functional types on the other side will further improve functional type approaches which consider the sensitivities of multiple species to climate change, habitat loss, and fragmentation.
Ecosystems respond in various ways to disturbances. Quantifying ecological stability therefore requires inspecting multiple stability properties, such as resistance, recovery, persistence and invariability. Correlations among these properties can reduce the dimensionality of stability, simplifying the study of environmental effects on ecosystems. A key question is how the kind of disturbance affects these correlations. We here investigated the effect of three disturbance types (random, species-specific, local) applied at four intensity levels, on the dimensionality of stability at the population and community level. We used previously parameterized models that represent five natural communities, varying in species richness and the number of trophic levels. We found that disturbance type but not intensity affected the dimensionality of stability and only at the population level. The dimensionality of stability also varied greatly among species and communities. Therefore, studying stability cannot be simplified to using a single metric and multi-dimensional assessments are still to be recommended.