title: Testing Three Species Distribution Modelling Strategies to Define Fish Assemblage Reference Conditions for Stream Bioassessment and Related Applications.
abstract: Species distribution models are widely used for stream bioassessment, estimating changes in habitat suitability and identifying conservation priorities. We tested the accuracy of three modelling strategies (single species ensemble, multi-species response and community classification models) to predict fish assemblages at reference stream segments in coastal subtropical Australia. We aimed to evaluate each modelling strategy for consistency of predictor variable selection; determine which strategy is most suitable for stream bioassessment using fish indicators; and appraise which strategies best match other stream management applications. Five models, one single species ensemble, two multi-species response and two community classification models, were calibrated using fish species presence-absence data from 103 reference sites. Models were evaluated for generality and transferability through space and time using four external reference site datasets. Elevation and catchment slope were consistently identified as key correlates of fish assemblage composition among models. The community classification models had high omission error rates and contributed fewer taxa to the 'expected' component of the taxonomic completeness (O/E50) index than the other strategies. This potentially decreases the model sensitivity for site impact assessment. The ensemble model accurately and precisely modelled O/E50 for the training data, but produced biased predictions for the external datasets. The multi-species response models afforded relatively high accuracy and precision coupled with low bias across external datasets and had lower taxa omission rates than the community classification models. They inherently included rare, but predictable species while excluding species that were poorly modelled among all strategies. We suggest that the multi-species response modelling strategy is most suited to bioassessment using freshwater fish assemblages in our study area. At the species level, the ensemble model exhibited high sensitivity without reductions in specificity, relative to the other models. We suggest that this strategy is well suited to other non-bioassessment stream management applications, e.g., identifying priority areas for species conservation.
authors.affiliation: Australian Rivers Institute, Griffith University, Nathan, Australia.
authors.forename: Peter M
authors.initials: PM
authors.lastname: Rose
authors.affiliation: Australian Rivers Institute, Griffith University, Nathan, Australia.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Kennard
authors.affiliation: Department of Science, Information Technology and Innovation, Ecosciences Precinct, Brisbane, Australia.
authors.forename: David B
authors.initials: DB
authors.lastname: Moffatt
authors.affiliation: Australian Rivers Institute, Griffith University, Nathan, Australia.
authors.forename: Fran
authors.initials: F
authors.lastname: Sheldon
authors.affiliation: NSW Department of Primary Industries, Grafton Fisheries Centre, Grafton, Australia.
authors.forename: Gavin L
authors.initials: GL
authors.lastname: Butler
mesh_terms: D000818:Animals; D019540:Area Under Curve; D001315:Australia; D019992:Databases as Topic; D017753:Ecosystem; D005399:Fishes / Q000502:physiology*; D040362:Geographic Information Systems; D005843:Geography; D008962:Models, Theoretical; D045483:Rivers; D013045:Species Specificity