A family of process-based models to simulate landscape use by multiple taxa

Context: Land-use change is a key driver of biodiversity loss. Models that accurately predict how biodiversity might be affected by land-use changes are urgently needed, to help avoid further negative impacts and inform landscape-scale restoration projects. To be effective, such models must balance model realism with computational tractability and must represent the different habitat and connectivity requirements of multiple species. Objectives: We explored the extent to which process-based modelling might fulfil this role, examining feasibility for different taxa and potential for informing real-world decision-making. Methods: We developed a family of process-based models (*4pop) that simulate landscape use by birds, bats, reptiles and amphibians, derived from the well-established poll4pop model (designed to simulate bee populations). Given landcover data, the models predict spatially-explicit relative abundance by simulating optimal home-range foraging, reproduction, dispersal of offspring and mortality. The models were co-developed by researchers, conservation NGOs and volunteer surveyors, parameterised using literature data and expert opinion, and validated against observational datasets collected across Great Britain. Results: The models were able to simulate habitat specialists, generalists, and species requiring access to multiple habitats for different types of resources (e.g. breeding vs foraging). We identified model refinements required for some taxa and considerations for modelling further species/groups. Conclusions: We suggest process-based models that integrate multiple forms of knowledge can assist biodiversity-inclusive decision-making by predicting habitat use throughout the year, expanding the range of species that can be modelled, and enabling decision-makers to better account for landscape context and habitat configuration effects on population persistence

A sequential multi-level framework to improve habitat suitability modelling

Context: habitat suitability models (HSM) can improve our understanding of a species’ ecology and are valuable tools for informing landscape-scale decisions. We can increase HSM predictive accuracy and derive more realistic conclusions by taking a multi-scale approach. However, this process is often statistically complex and computationally intensive.Objectives: we provide an easily implemented, flexible framework for sequential multi-level, multi-scale HSM and compare it to two other commonly-applied approaches: single-level, multi-scale HSM and their post-hoc combinations.Methods: our framework implements scale optimisation and model tuning at each level in turn, from the highest (population range) to the lowest (e.g. foraging habitat) level, whilst incorporating output habitat suitability indices from a higher level as a predictor. We used MaxEnt and a species of conservation concern in Britain, the lesser horseshoe bat (Rhinolophus hipposideros), to demonstrate and compare multi-scale approaches.Results: integrating models across levels, either by applying our framework, or by multiplying single-level model predictions, improved predictive performance over single-level models. Moreover, differences in the importance and direction of the species-environment associations highlight the potential for false inferences from single-level models or their post-hoc combinations. The single-level summer range model incorrectly identified a positive influence of heathland cover, whereas sequential multi-level models made biological sense and underlined this species’ requirement for extensive broadleaf woodland cover, hedgerows and access to buildings for roosting in rural areas.Conclusions: we conclude that multi-level HSM appear superior to single-level, multi-scale approaches; models should be sequentially integrated across levels if information on species-environment relationships is of importance

A family of process-based models to simulate landscape use by multiple taxa

Acknowledgements: We would like to thank all the volunteer surveyors who contributed data to the BTO/RSPB/JNCC Breeding Bird Survey and those who contributed data to the Field Survey within the BCT’s National Bat Monitoring Programme, without whom validation of the bird and bat models would not have been possible. We would also like to thank all the volunteer surveyors of Surrey Amphibian and Reptile Group, who kindly contributed their common lizard sightings and enabled validation of the reptile model. Finally, we would like to thank the regional ARGs and all the Toad Patrol volunteers, supported by Froglife, who took the time to fill in the toad habitat use questionnaire, so enabling validation of the amphibian model. EG acknowledges funding from a Research Programme Fellowship provided by the Natural Environment Research Council through UK Research and Innovation’s Landscape Decisions Programme (NE/V007831/1).Context: Land-use change is a key driver of biodiversity loss. Models that accurately predict how biodiversity might be affected by land-use changes are urgently needed, to help avoid further negative impacts and inform landscape-scale restoration projects. To be effective, such models must balance model realism with computational tractability and must represent the different habitat and connectivity requirements of multiple species. Objectives: We explored the extent to which process-based modelling might fulfil this role, examining feasibility for different taxa and potential for informing real-world decision-making. Methods: We developed a family of process-based models (*4pop) that simulate landscape use by birds, bats, reptiles and amphibians, derived from the well-established poll4pop model (designed to simulate bee populations). Given landcover data, the models predict spatially-explicit relative abundance by simulating optimal home-range foraging, reproduction, dispersal of offspring and mortality. The models were co-developed by researchers, conservation NGOs and volunteer surveyors, parameterised using literature data and expert opinion, and validated against observational datasets collected across Great Britain. Results: The models were able to simulate habitat specialists, generalists, and species requiring access to multiple habitats for different types of resources (e.g. breeding vs foraging). We identified model refinements required for some taxa and considerations for modelling further species/groups. Conclusions: We suggest process-based models that integrate multiple forms of knowledge can assist biodiversity-inclusive decision-making by predicting habitat use throughout the year, expanding the range of species that can be modelled, and enabling decision-makers to better account for landscape context and habitat configuration effects on population persistence