Habitat patches providing south–north connectivity are under-protected in a fragmented landscape

As species’ ranges shift to track climate change, conservationists increasingly recognize the need to consider connectivity when designating protected areas (PAs). In fragmented landscapes, some habitat patches are more important than others in maintaining connectivity, and methods are needed for their identification. Here, using the Condatis methodology, we model range expansion through an adaptation of circuit theory. Specifically, we map ‘flow’ through 16 conservation priority habitat networks in England, quantifying how patches contribute to functional South–North connectivity. We also explore how much additional connectivity could be protected via a connectivity-led protection procedure. We find high-flow patches are often left out of existing PAs; across 12 of 16 habitat networks, connectivity protection falls short of area protection by 13.6% on average. We conclude that the legacy of past protection decisions has left habitat-specialist species vulnerable to climate change. This situation may be mirrored in many countries which have similar habitat protection principles. Addressing this requires specific planning tools that can account for the directions species may shift. Our connectivity-led reserve selection procedure efficiently identifies additional PAs that prioritize connectivity, protecting a median of 40.9% more connectivity in these landscapes with just a 10% increase in area.

The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

By shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom–up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

Improving insect conservation across heterogeneous landscapes using species–habitat networks

Background One of the biggest challenges in conservation is to manage multiple habitats for the effective conservation of multiple species, especially when the focal species are mobile and use multiple resources across heterogeneous protected areas. The application of ecological network tools and the analysis of the resulting species–habitat networks can help to describe such complex spatial associations and improve the conservation of species at the landscape scale. Methods To exemplify the application of species–habitat networks, we present a case study on butterflies inhabiting multiple grassland types across a heterogeneous protected area in North-East Italy. We sampled adult butterflies in 44 sites, each belonging to one of the five major habitat types in the protected area, that is, disturbed grasslands, continuous grasslands, evolved grasslands, hay meadows and wet meadows. First, we applied traditional diversity analyses to explore butterfly species richness and evenness. Second, we built and analyzed both the unipartite network, linking habitat patches via shared species, and the bipartite network, linking species to individual habitat patches. Aims (i) To describe the emerging properties (connectance, modularity, nestedness, and robustness) of the species–habitat network at the scale of the whole protected area, and (ii) to identify the key habitats patches for butterfly conservation across the protected area, that is, those supporting the highest number of species and those with unique species assemblages (e.g., hosting specialist species). Results The species–habitat network appeared to have a weak modular structure, meaning that the main habitat types tended to host different species assemblages. However, the habitats also shared a large proportion of species that were able to visit multiple habitats and use resources across the whole study area. Even butterfly species typically considered as habitat specialists were actually observed across multiple habitat patches, suggesting that protecting them only within their focal habitat might be ineffective. Our species–habitat network approach helped identifying both central habitat patches that were able to support the highest number of species, and habitat patches that supported rare specialist species.