Applying integrated population models to guide conservation planning: Western Capercaillie in Scotland

Halting and reversing species declines is key to reaching global biodiversity targets, yet ineffective interventions continue due to a lack of rigorous, evidence-based evaluation. Multifactorial drivers and data limitations often hinder conservation planning. Predictive modelling approaches that integrate diverse data sources with biological hypotheses can bridge this gap by clarifying decline drivers and evaluating management options under uncertainty.We developed a Bayesian integrated population model (IPM) applicable to lekking species, combining incomplete data from different life stages and seasons while accounting for observation error. IPMs are widely recognised for improving and reducing bias compared to single-data-stream analyses, yet their application in conservation planning remains limited. We applied the model to the threatened Western Capercaillie population in Scotland which has been declining since at least the 1980s despite decades of concerted conservation efforts, a typical case requiring urgent and evidence-based management.By fitting the model to 30 years of population data, we investigated associations between demographic processes and their potential drivers, including weather variables, fence collision mortality and a proxy of predation pressure. Data integration improved population estimate precision by 17-50% relative to standalone national survey estimates, confirming that the decline that started in the 1980s continued from 1990-2023. Breeding success was related to the pattern of April warming, negatively affected by pre-breeding precipitation and positively by vole abundance, the latter consistent with the alternative prey hypothesis.We use model predictions to evaluate the joint effectiveness of proposed management actions aimed at improving vital rates, including fence management and diversionary feeding. We project extinction risk and potential population trajectories under management scenarios to inform evidence-based recommendations for conservation. This modelling approach forms a key component of the Capercaillie Emergency Plan 2025-2030 and could provide a platform for future adaptive management, enabling iterative evaluation of actions as new data become available. Beyond this case study, our integrated approach offers a transferable framework for managing multifactorial declines in other threatened species, supporting biodiversity targets under uncertainty.