Data from: Advancing single species abundance models by leveraging multi-species data to reveal lakespecific 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.