Efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management

摘要: In many of the nearshore areas where development is most likely to occur, essential fish habitat data are incomplete and there little information on species occurrence that can be used inform management decisions. This research investigated use multivariate remotely sensed geomorphic landscape develop accurate predictive models subarctic, estuarine-associated fishes. The random forest algorithm was predict 26 captured in 49 estuaries Southeast Alaska. Model prediction accuracy ranged from 100 42% for presence 87 15% absence. goodness fit were assessed by comparing number occurrences predicted model against observed presences absences an independent set. Sixty percent able with 70% or better. 521 unsampled Alaskan provide a regional map distributions.