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Evaluation of Predicted Fish Distribution Models for Rare Fish Species in South Dakota
Author(s) -
Hayer CariAnn,
Wall Steven S.,
Berry Charles R.
Publication year - 2008
Publication title -
north american journal of fisheries management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 72
eISSN - 1548-8675
pISSN - 0275-5947
DOI - 10.1577/m07-086.1
Subject(s) - weighting , fish <actinopterygii> , species distribution , statistics , habitat , sampling (signal processing) , rare species , kappa , sampling bias , distribution (mathematics) , ecology , fishery , environmental science , sample size determination , mathematics , biology , computer science , medicine , mathematical analysis , geometry , radiology , filter (signal processing) , computer vision
Predictive fish distribution models have utility in planning conservation measures for rare fish species. However, species rarity creates sampling and modeling difficulties that require an understanding of model accuracy. We evaluated existing distribution models for 10 rare fishes based on 2,026 community fish samples and associated riverine habitat. Our fieldwork provided an independent fish species inventory for 143 sample sites. This inventory was used to quantify species detectability for use as a weighting factor with which to correct false‐negative modeling errors. Presence/absence data were compared with predictions to evaluate model accuracy as determined by Cohen's kappa and correct classification rates. Detection probabilities were generally small but ranged from 0 to 0.68. The models predicted species occurrence with relatively high success (average kappa = 45.6% and average correct classification rate = 74.1%). The habitat variables for predicting species occurrence varied among species; however, stream size and streamflow were the most influential. All distribution models had adequate predictive abilities and improved our understanding of fish distributions and the factors determining those distributions. Model accuracy statistics can provide managers with a measure of confidence when they are directing conservation activities.