Species-habitat networks: a tool to improve landscape management for conservation.
Land-use change is reshaping terrestrial ecosystems world-wide and is recognized as a key driver of biodiversity loss with negative consequences on ecosystem functioning. Understanding how species use resources across landscapes is essential for the design of effective management strategies. Despite recent advances in network ecology, there is still a gap between theory and applied ecological science, and we lack the information to manage entire landscapes to maximize biodiversity conservation and ecosystem service delivery. While several pioneering approaches have tried to link ecological networks and conservation science, applied ecologists still struggle to incorporate these models into research due to their inherent complexity. We propose the application of bipartite networks principles to create species-habitat networks. This approach explicitly links multiple species and habitat resources, provides tools to estimate the importance of particular species or specific habitat in a given landscape, and quantifies emerging properties of entire habitat networks. Most existing metrics used to study properties of bipartite ecological networks can easily be adapted to investigate species-habitat relationships. The tool use is relatively simple and does not require advanced computational expertise. Synthesis and applications. One of the biggest challenges in applied ecology is managing multiple habitats for the effective conservation of multiple species. One key advantage of this proposed approach is that the scale of the derived ecological information could match the scale of landscape management interventions. The versatility, visualization power and ease of interpretation of these networks will enable application of the species-habitat network concept to a wide array of real-world problems, such as multispecies conservation, habitat restoration, ecosystem services management or invasion ecology. In particular, species-habitat networks could be applied to identify optimal landscape compositions and configurations to design effective interventions at the landscape scale. This approach also enables the detection of emerging network properties that could also be used to test the effects of large-scale drivers of global change upon ecosystem structure and stability.