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.

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.