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Data for: Using distribution models to identify range shifts of four Acroneuria Pictet, 1841 (Plecoptera: Perlidae) species in the Midwest USA

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NIAID Data Ecosystem2026-05-01 收录
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http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.gqnk98st4
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Regional faunal assessments of stoneflies in the United States Midwest (herein defined as Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio, and Wisconsin) indicate increasing imperilment resulting from human disturbance and climate change. Large-bodied Perlid stoneflies with multivoltine life cycles are among the most at risk for regional extirpation, with losses reported in several midwestern states. Species distribution modeling was undertaken to describe distribution shifts for four widespread riverine species: Acroneuria abnormis (Newman, 1838), A. frisoni Stark & Brown, 1991, A. internata (Walker, 1852) and A. lycorias (Newman, 1839). The distribution modeling algorithm MaxEnt was selected to predict both the historical (i.e., pre-1960) and contemporaneous distributions for each species using separate occurrence datasets. These models permit the identification of suitable habitat loss through range contractions associated with human disturbance. Predictions of suitable habitat losses were recorded for multiple species but were greatest for A. abnormis and A. internata. These models serve to guide future collection efforts and to further describe patterns of regional biodiversity loss. The data presented within this dataset contain the occurrence data used for modeling with distinction between temporal periods modeled. Methods These collection data are representative of specimens stored in regional institutions that were viewed and compiled by R.E.D. and colleagues. This curated dataset includes specimen collection data for four Acroneuria species from 20 regional institutions. Collection data were associated with a WGS84 longitude and latitude from the locality description available. These data are distinguished between pre-1960 and post-1960 temporal periods for range dynamics modeling.
创建时间:
2023-06-12
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