Key research themes
1. How does species data type and sampling design impact the suitability and interpretation of species distribution model outputs?
This research theme investigates the critical linkages between the types of occurrence data used in species distribution models (SDMs) (presence-only, presence-absence, occupancy-detection) and how sampling processes (imperfect detection, spatial bias) affect the meaning and applicability of SDM outputs. Understanding this relationship is crucial to ensure SDMs are fit for intended scientific or conservation purposes, avoiding misinterpretation and improving management and inference outcomes.
2. How does spatial and environmental scale, including grain size and environmental heterogeneity, influence species distribution model accuracy and outputs?
This theme explores how the spatial resolution (grain size) of environmental predictors, species niche breadth (specialist vs. generalist), and landscape heterogeneity affect SDM predictive performance and the estimation of species presence or abundance patterns. Clarifying these scale-dependent effects is essential for optimizing SDM construction, selecting appropriate predictor data resolutions, and interpreting model outputs for conservation planning and ecological inference.
3. What role do biotic interactions and species functional traits play in enhancing species distribution model predictions and ecological relevance?
This research area focuses on incorporating interspecific interactions (competition, resource use) and functional/biological traits (growth rate, habitat specialization) into SDMs, moving beyond abiotic-only predictors. Including biotic factors can improve prediction accuracy, reduce commission errors, and better reflect realized niches, thereby enhancing the usefulness of SDMs for conservation and ecological understanding.