Development and evaluation of species distribution models for fourteen native central US fish species

被引:30
作者
Bouska, Kristen L. [1 ]
Whitledge, Gregory W. [2 ,3 ]
Lant, Christopher [4 ]
机构
[1] Univ Missouri, Dept Fisheries & Wildlife Sci, Columbia, MO 65211 USA
[2] So Illinois Univ, Ctr Fisheries Aquaculture & Aquat Sci, Carbondale, IL 62901 USA
[3] So Illinois Univ, Dept Zool, Carbondale, IL 62901 USA
[4] Utah State Univ, Dept Environm & Soc, Quinney Coll Nat Resources, Logan, UT 84321 USA
基金
美国国家科学基金会;
关键词
Ensemble model; Fish distributions; Model performance; Warm water fishes; Range projections; FRESH-WATER FISH; ADAPTIVE REGRESSION SPLINES; CLIMATE-CHANGE; BIOTIC INTERACTIONS; STREAM FISHES; LAND-USE; PREDICTIVE PERFORMANCE; ECOLOGICAL THEORY; SMALLMOUTH BASS; SAMPLE-SIZE;
D O I
10.1007/s10750-014-2134-8
中图分类号
Q17 [水生生物学];
学科分类号
071004 ;
摘要
Environmental change has and will continue to adversely influence aquatic communities. Efforts to model impacts of environmental change on fisheries have largely focused on cold water, commercial, and recreationally valued species, even though warm water, non-game species have important roles in ecosystem services and processes. We developed species distribution models for fourteen warm water fish species native to the central United States and evaluated environmental drivers and predictive performance. We used an ensemble model approach produced by combining forecasts of five single-model techniques. Response plots and variable importance calculations were used to evaluate the influence of individual variables. The predictive performance of the ensemble models was assessed using area under the curve of the receiver operating characteristic plot (AUC). AUCvalues indicate ensemble models performed better than single-model types, suggesting ensemble models are more reliable and applicable for management purposes than single models. Most models were influenced by a mix of climate, land use, and geophysical variables; however, climate variables were the dominant environmental drivers across models. Given the high sensitivity of models to climate and land use, we expect future climate and land use changes to influence distributions.
引用
收藏
页码:159 / 176
页数:18
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