Spatiotemporal dynamics drive synergism of land use and climatic extreme events in insect meta-populations.

Ecosystems are increasingly threatened by co-occurring stressors associated with anthropogenic global change. Spatial stressor patterns range from local to regional to global, and temporal stressor patterns from discrete to continuous. To date, most multiple stressor studies covered short periods and focused on local effects and interactions. However, it remains largely unknown how stressors with different spatiotemporal patterns interact in their effects over longer periods. In particular, at higher spatial scales, biotic dynamics in ecological networks complicate the understanding of stressor interactions. We used a spatially explicit meta-population model for a generic freshwater insect, parameterized based on traits of the European damselfly Coenagrion mercuriale, to simulate scenarios of discrete climatic extreme events and continuous land use-related stress. Climatic extreme events were modeled as recurring mortality in all patches, whereas land use permanently influenced meta-populations via patch qualities and network connectivity. We found that the risk of discrete climatic extreme events to meta-populations depended strongly on the proportion of land use types, with effects ranging from negligible to extinction. Land use-related stress limited recovery in meta-populations from effects of climatic extreme events, resulting in synergistic stressor interactions. Moreover, the spatial configuration of land use type influenced the combined stressor effects with clustered configurations resulting in lower effects compared to a random configuration. Finally, we found that combined stressor effects can vary with the time point at which they were determined, indicating that inconclusive results in multiple stressor research can partly be due to differences in the time of determination. We conclude that conservation should focus on regional landscape management to mitigate risks on meta-populations from future, intensified extreme climate events. Reducing land use effects, thus improving patch quality and network connectivity, can compensate for effects of additional discrete stressors and, in turn, synergistic interactions.

Maximising the clustering coefficient of networks and the effects on habitat network robustness.

The robustness of networks against node failure and the response of networks to node removal has been studied extensively for networks such as transportation networks, power grids, and food webs. In many cases, a network's clustering coefficient was identified as a good indicator for network robustness. In ecology, habitat networks constitute a powerful tool to represent metapopulations or -communities, where nodes represent habitat patches and links indicate how these are connected. Current climate and land-use changes result in decline of habitat area and its connectivity and are thus the main drivers for the ongoing biodiversity loss. Conservation efforts are therefore needed to improve the connectivity and mitigate effects of habitat loss. Habitat loss can easily be modelled with the help of habitat networks and the question arises how to modify networks to obtain higher robustness. Here, we develop tools to identify which links should be added to a network to increase the robustness. We introduce two different heuristics, Greedy and Lazy Greedy, to maximize the clustering coefficient if multiple links can be added. We test these approaches and compare the results to the optimal solution for different generic networks including a variety of standard networks as well as spatially explicit landscape based habitat networks. In a last step, we simulate the robustness of habitat networks before and after adding multiple links and investigate the increase in robustness depending on both the number of added links and the heuristic used. We found that using our heuristics to add links to sparse networks such as habitat networks has a greater impact on the clustering coefficient compared to randomly adding links. The Greedy algorithm delivered optimal results in almost all cases when adding two links to the network. Furthermore, the robustness of networks increased with the number of additional links added using the Greedy or Lazy Greedy algorithm.

Optimisation Model of Dispersal Simulations on a Dendritic Habitat Network.

To predict and mitigate biodiversity loss, a better understanding of species distribution and reliable dispersal models are required. A promising approach in dispersal simulation is the method of spatially explicit graph-based analysis. While graph theory is strongly connected to the field of optimisation in a variety of disciplines, the potential of optimisation has not yet been exploited in dispersal models. We introduce an optimisation model built on a graph-based dispersal simulation of an aquatic invertebrate species with a terrestrial life stage. The model simulates a directed dispersal process and investigates the fastest route to colonise predefined vacant habitat patches. The optimisation model run-time is in general an order of magnitude faster than the underlying simulation and provides the minimum time until the considered habitat patches are colonised under the given landscape structure. These results can then be used to estimate how fast newly formed habitat patches can be reached and colonised. Our model can in principle be adapted to other simulation models and can thus be seen as a pioneer of a new set of models that may support landscape conservation and restoration.