Predicting hedgehog mortality risks on British roads using habitat suitability modelling.

Road vehicle collisions are likely to be an important contributory factor in the decline of the European hedgehog (Erinaceus europaeus) in Britain. Here, a collaborative roadkill dataset collected from multiple projects across Britain was used to assess when, where and why hedgehog roadkill are more likely to occur. Seasonal trends were assessed using a Generalized Additive Model. There were few casualties in winter-the hibernation season for hedgehogs-with a gradual increase from February that reached a peak in July before declining thereafter. A sequential multi-level Habitat Suitability Modelling (HSM) framework was then used to identify areas showing a high probability of hedgehog roadkill occurrence throughout the entire British road network (∼400,000 km) based on multi-scale environmental determinants. The HSM predicted that grassland and urban habitat coverage were important in predicting the probability of roadkill at a national scale. Probabilities peaked at approximately 50% urban cover at a one km scale and increased linearly with grassland cover (improved and rough grassland). Areas predicted to experience high probabilities of hedgehog roadkill occurrence were therefore in urban and suburban environments, that is, where a mix of urban and grassland habitats occur. These areas covered 9% of the total British road network. In combination with information on the frequency with which particular locations have hedgehog road casualties, the framework can help to identify priority areas for mitigation measures.

A spatial framework for targeting urban planning for pollinators and people with local stakeholders: A route to healthy, blossoming communities?

Pollinators such as bees and hoverflies are essential components of an urban ecosystem, supporting and contributing to the biodiversity, functioning, resilience and visual amenity of green infrastructure. Their urban habitats also deliver health and well-being benefits to society, by providing important opportunities for accessing nature nearby to the homes of a growing majority of people living in towns and cities. However, many pollinator species are in decline, and the loss, degradation and fragmentation of natural habitats are some of the key drivers of this change. Urban planners and other practitioners need evidence to carefully prioritise where they focus their resources to provide and maintain a high quality, multifunctional green infrastructure network that supports pollinators and people. We provide a modelling framework to inform green infrastructure planning as a nature based solution with social and ecological benefits. We show how habitat suitability models (HSM) incorporating remote sensed vegetation data can provide important information on the influence of urban landcover composition and spatial configuration on species distributions across cities. Using Edinburgh, Scotland, as a case study city, we demonstrate this approach for bumble bees and hoverflies, providing high resolution predictive maps that identify pollinator habitat hotspots and pinch points across the city. By combining this spatial HSM output with health deprivation data, we highlight 'win-win' opportunity areas in most need of improved green infrastructure to support pollinator habitat quality and connectivity, as well as societal health and well-being. In addition, in collaboration with municipal planners, local stakeholders, and partners from a local greenspace learning alliance, we identified opportunities for citizen engagement activities to encourage interest in wildlife gardening as part of a 'pollinator pledge'. We conclude that this quantitative, spatially explicit and transferable approach provides a useful decision-making tool for targeting nature-based solutions to improve biodiversity and increase environmental stewardship, with the aim of providing a more attractive city to live, work and invest in.

Using historical woodland creation to construct a long-term, large-scale natural experiment: the WrEN project.

Natural experiments have been proposed as a way of complementing manipulative experiments to improve ecological understanding and guide management. There is a pressing need for evidence from such studies to inform a shift to landscape-scale conservation, including the design of ecological networks. Although this shift has been widely embraced by conservation communities worldwide, the empirical evidence is limited and equivocal, and may be limiting effective conservation. We present principles for well-designed natural experiments to inform landscape-scale conservation and outline how they are being applied in the WrEN project, which is studying the effects of 160 years of woodland creation on biodiversity in UK landscapes. We describe the study areas and outline the systematic process used to select suitable historical woodland creation sites based on key site- and landscape-scale variables - including size, age, and proximity to other woodland. We present the results of an analysis to explore variation in these variables across sites to test their suitability as a basis for a natural experiment. Our results confirm that this landscape satisfies the principles we have identified and provides an ideal study system for a long-term, large-scale natural experiment to explore how woodland biodiversity is affected by different site and landscape attributes. The WrEN sites are now being surveyed for a wide selection of species that are likely to respond differently to site- and landscape-scale attributes and at different spatial and temporal scales. The results from WrEN will help develop detailed recommendations to guide landscape-scale conservation, including the design of ecological networks. We also believe that the approach presented demonstrates the wider utility of well-designed natural experiments to improve our understanding of ecological systems and inform policy and practice.

Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts.

Conservation increasingly operates at the landscape scale. For this to be effective, we need landscape scale information on species distributions and the environmental factors that underpin them. Species records are becoming increasingly available via data centres and online portals, but they are often patchy and biased. We demonstrate how such data can yield useful habitat suitability models, using bat roost records as an example. We analysed the effects of environmental variables at eight spatial scales (500 m - 6 km) on roost selection by eight bat species (Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula, Myotis mystacinus, M. brandtii, M. nattereri, M. daubentonii, and Plecotus auritus) using the presence-only modelling software MaxEnt. Modelling was carried out on a selection of 418 data centre roost records from the Lake District National Park, UK. Target group pseudoabsences were selected to reduce the impact of sampling bias. Multi-scale models, combining variables measured at their best performing spatial scales, were used to predict roosting habitat suitability, yielding models with useful predictive abilities. Small areas of deciduous woodland consistently increased roosting habitat suitability, but other habitat associations varied between species and scales. Pipistrellus were positively related to built environments at small scales, and depended on large-scale woodland availability. The other, more specialist, species were highly sensitive to human-altered landscapes, avoiding even small rural towns. The strength of many relationships at large scales suggests that bats are sensitive to habitat modifications far from the roost itself. The fine resolution, large extent maps will aid targeted decision-making by conservationists and planners. We have made available an ArcGIS toolbox that automates the production of multi-scale variables, to facilitate the application of our methods to other taxa and locations. Habitat suitability modelling has the potential to become a standard tool for supporting landscape-scale decision-making as relevant data and open source, user-friendly, and peer-reviewed software become widely available.

Defining landscape resistance values in least-cost connectivity models for the invasive grey squirrel: a comparison of approaches using expert-opinion and habitat suitability modelling.

Least-cost models are widely used to study the functional connectivity of habitat within a varied landscape matrix. A critical step in the process is identifying resistance values for each land cover based upon the facilitating or impeding impact on species movement. Ideally resistance values would be parameterised with empirical data, but due to a shortage of such information, expert-opinion is often used. However, the use of expert-opinion is seen as subjective, human-centric and unreliable. This study derived resistance values from grey squirrel habitat suitability models (HSM) in order to compare the utility and validity of this approach with more traditional, expert-led methods. Models were built and tested with MaxEnt, using squirrel presence records and a categorical land cover map for Cumbria, UK. Predictions on the likelihood of squirrel occurrence within each land cover type were inverted, providing resistance values which were used to parameterise a least-cost model. The resulting habitat networks were measured and compared to those derived from a least-cost model built with previously collated information from experts. The expert-derived and HSM-inferred least-cost networks differ in precision. The HSM-informed networks were smaller and more fragmented because of the higher resistance values attributed to most habitats. These results are discussed in relation to the applicability of both approaches for conservation and management objectives, providing guidance to researchers and practitioners attempting to apply and interpret a least-cost approach to mapping ecological networks.