How predation and landscape fragmentation affect vole population dynamics

Background: Microtine species in Fennoscandia display a distinct north-south gradient from regular cycles to stable populations. The gradient has often been attributed to changes in the interactions between microtines and their predators. Although the spatial structure of the environment is known to influence predator-prey dynamics of a wide range of species, it has scarcely been considered in relation to the Fennoscandian gradient. Furthermore, the length of microtine breeding season also displays a north-south gradient. However, little consideration has been given to its role in shaping or generating population cycles. Because these factors covary along the gradient it is difficult to distinguish their effects experimentally in the field. The distinction is here attempted using realistic agent-based modelling. Methodology/Principal Findings: By using a spatially explicit computer simulation model based on behavioural and ecological data from the field vole (Microtus agrestis), we generated a number of repeated time series of vole densities whose mean population size and amplitude were measured. Subsequently, these time series were subjected to statistical autoregressive modelling, to investigate the effects on vole population dynamics of making predators more specialised, of altering the breeding season, and increasing the level of habitat fragmentation. We found that fragmentation as well as the presence of specialist predators are necessary for the occurrence of population cycles. Habitat fragmentation and predator assembly jointly determined cycle length and amplitude. Length of vole breeding season had little impact on the oscillations. Significance: There is good agreement between our results and the experimental work from Fennoscandia, but our results allow distinction of causation that is hard to unravel in field experiments. We hope our results will help understand the reasons for cycle gradients observed in other areas. Our results clearly demonstrate the importance of landscape fragmentation for population cycling and we recommend that the degree of fragmentation be more fully considered in future analyses of vole dynamics

Landscape structure mediates the effects of a stressor on field vole populations

Spatio-temporal landscape heterogeneity has rarely been considered in population-level impact assessments. Here we test whether landscape heterogeneity is important by examining the case of a pesticide applied seasonally to orchards which may affect non-target vole populations, using a validated ecologically realistic and spatially explicit agent-based model. Voles thrive in unmanaged grasslands and untreated orchards but are particularly exposed to applied pesticide treatments during dispersal between optimal habitats. We therefore hypothesised that vole populations do better (1) in landscapes containing more grassland and (2) where areas of grassland are closer to orchards, but (3) do worse if larger areas of orchards are treated with pesticide. To test these hyposeses we made appropriate manipulations to a model landscape occupied by field voles. Pesticide application reduced model population sizes in all three experiments, but populations subsequently wholly or partly recovered. Population depressions were, as predicted, lower in landscapes containing more unmanaged grassland, in landscapes with reduced distance between grassland and orchards, and in landscapes with fewer treated orchards. Population recovery followed a similar pattern except for an unexpected improvement in recovery when the area of treated orchards was increased. Outside the period of pesticide application, orchards increase landscape connectivity and facilitate vole dispersal and so speed population recovery. Overall our results show that accurate prediction of population impact cannot be achieved without taking account of landscape structure. The specifics of landscape structure and habitat connectivity are likely always important in mediating the effects of stressors

Post-Hoc Pattern-Oriented Testing and Tuning of an Existing Large Model:Lessons from the Field Vole

Pattern-oriented modeling (POM) is a general strategy for modeling complex systems. In POM, multiple patterns observed at different scales and hierarchical levels are used to optimize model structure, to test and select sub-models of key processes, and for calibration. So far, POM has been used for developing new models and for models of low to moderate complexity. It remains unclear, though, whether the basic idea of POM to utilize multiple patterns, could also be used to test and possibly develop existing and established models of high complexity. Here, we use POM to test, calibrate, and further develop an existing agent-based model of the field vole (Microtus agrestis), which was developed and tested within the ALMaSS framework. This framework is complex because it includes a high-resolution representation of the landscape and its dynamics, of the individual’s behavior, and of the interaction between landscape and individual behavior. Results of fitting to the range of patterns chosen were generally very good, but the procedure required to achieve this was long and complicated. To obtain good correspondence between model and the real world it was often necessary to model the real world environment closely. We therefore conclude that post-hoc POM is a useful and viable way to test a highly complex simulation model, but also warn against the dangers of over-fitting to real world patterns that lack details in their explanatory driving factors. To overcome some of these obstacles we suggest the adoption of open-science and open

Age structure for males and females based on Myllymaki (1977) and the best fit model simulations, and final fit resulting from the POM exercise (accepted fit).

<p>A) Actual male age structure; C) Best fit model male age structure; E) Accepted fit model male age structure; B) Actual female age structure; D) Best fit model female age structure; F) Accepted fit model female age structure.</p

Three simplified landscapes used for testing the model’s ability to produce vole population cycling.

<p>To test for the emergence of cycles, predator characteristics were varied in conjunction with these landscape structures.</p

Three examples of 50 years of simulation using the parameterized model on three different landscapes (see Fig. 3) A) 1 patch; B) three patches; C) 16 patches.

<p>Three examples of 50 years of simulation using the parameterized model on three different landscapes (see Fig. 3) A) 1 patch; B) three patches; C) 16 patches.</p

GIS map of the island comprising the Ahtiala study area from which the real world data was obtained to test model vole sex ratios and population age-structure.

<p>GIS map of the island comprising the Ahtiala study area from which the real world data was obtained to test model vole sex ratios and population age-structure.</p

Graphs of sensitivity analysis for the 15 parameters tested.

<p>Fits to density, sex ratios and age structure are shown as proportion deviation from target pattern. X-axis denotes the parameter values used in each case, and the vertical line the actual parameter value chosen following POM testing (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045872#pone-0045872-t001" target="_blank">Table 1</a>). Overall measure of fit (black line) is the mean deviance and is capped at 1.0. All graphs are scaled to ±1.0 for proportion deviance from real world patterns (left y-axis), and 0–1.0 for measure of fit (zero being perfect fit) (right y-axis).</p