jats:titleAbstract</jats:title>jats:sec jats:titleContext</jats:title> jats:pLand-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.</jats:p> </jats:sec>jats:sec jats:titleObjectives</jats:title> jats:pWe 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.</jats:p> </jats:sec>jats:sec jats:titleMethods</jats:title> jats:pWe 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.</jats:p> </jats:sec>jats:sec jats:titleResults</jats:title> jats:pThe 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.</jats:p> </jats:sec>jats:sec jats:titleConclusions</jats:title> jats:pWe 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.</jats:p> </jats:sec>