Informing indicator species selection by combining species associations and environmental responses

Abstract

Biodiversity is changing at an unprecedented pace, underscoring the need for effective and scalable ecological monitoring. Yet selecting which species to monitor remains a major challenge. Indicator species can offer cost‐effective proxies for broader biodiversity trends, but there is a critical gap in robust, data‐driven approaches to identify them across complex and rapidly changing ecosystems shaped by climate, land management, and their interactions. We provide a model‐based approach to select potential indicator species to monitor boreal understory communities, which are crucial part of forest biodiversity but often not as continuously monitored as trees. We focus on vascular plants due to their associations with multiple taxa. With joint species distribution models, we investigate species–species and species–environment relationships across Finland, utilizing nationwide understory vegetation data covering nearly 3000 study sites across different soil types and bioclimatic zones together with key environmental factors representing climate, habitat, and forest management. Species' associations and environmental responses differed between mineral soils and peatlands and across bioclimatic zones. We ranked species based on their association with other species and environmental responsiveness and observed that some species responded strongly to both, whereas many species were more related to one aspect than the other. Furthermore, we show that considering the effect of forest conditions together with the climate variables is important as they represent interdependent effects on biodiversity. Our study provides a robust framework for selecting indicator species that could be used in a wide range of biodiversity monitoring purposes and ecosystems, such as national forest monitoring programs. Combining information on species co‐occurrences and environmental responses allows identifying species that are informative of both local community composition and broader environmental change supporting more informed and efficient indicator selection.