Abstract:
:Monitoring long-term changes in aquatic biodiversity requires the effective use of historical data that were collected with different methods and varying levels of effort. Aggregating data into different spatial scales can control for such differences and provide a robust framework for monitoring distribution trends. We used a quantitative, multi-scale assessment to evaluate the potential drivers of distribution change for 60 fish species at three spatial scales, using 503 unique sampling events conducted between 1931 and 2019 in a stream biodiversity hotspot (French Creek, Pennsylvania, U.S.A). Trends delineated at multiple scales demonstrated that only one cyprinid species consistently declined through time. In contrast, several species, particularly centrarchids (bass and sunfish), appeared to increase with time. However, evidence for species' increases varied among the different spatial scales, and our observations suggest that differences in effort and detection across time periods may contribute to patterns of species increases. There was agreement among scales that agricultural land use, non-native brown trout (Salmo trutta), and anthropogenic barriers did not explain patterns in biodiversity change from the distribution trends in this study. The lack of species declines is likely due to the limited levels of historical impacts in the watershed compared with other locations in the region that experienced more acute pollution bottlenecks. Species increases were most prevalent for sportfish and baitfish species, suggesting that distribution increases were human mediated. Similar multi-scale assessments should provide more robust insight into patterns of biodiversity loss and distribution changes by maximizing the use of historical data.
journal_name
Environ Managejournal_title
Environmental managementauthors
Kirk MA,Wissinger SAdoi
10.1007/s00267-020-01298-1subject
Has Abstractpub_date
2020-07-01 00:00:00pages
136-148issue
1eissn
0364-152Xissn
1432-1009pii
10.1007/s00267-020-01298-1journal_volume
66pub_type
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