Advancing single species abundance models by leveraging multi-species data to reveal lake-specific patterns for fisheries predictions

Predicting species abundance is critical for understanding ecological dynamics and guiding conservation and management strategies. Traditional species abundance models (SAMs) rely on environmental variables and the presence or absence of key species, but often overlook community context and unmeasured environmental variation. Community composition can serve as a proxy for both unobserved environmental variables and biotic interactions influencing focal species. Here, we tested whether incorporating community composition via latent variables improves abundance predictions of sport fishing using a large-scale dataset. We assessed how latent variables selection and lake characteristics influences model accuracy across species. Our results show that low-abundance species were better predicted by models based solely on environment, while high-abundance species benefited from latent variables. Lake contribution to accuracy were correlated among species with similar occurrence, but unrelated to environmental characteristics. Model performance varied by species, with no consistent association with trophic level, occurrence, or abundance. These findings underscore the need to tailor models to species-specific contexts and integrating community composition into abundance modelling.