多元统计
生态系统
气候变化
海洋生态系统
环境科学
生态学
环境资源管理
空间生态学
多元分析
时间尺度
地理
海洋学
自然地理学
气候学
计算机科学
生物
地质学
机器学习
作者
Tyler Rose Abruzzo,Michael G. Frisk,Liam Butler,Matthew Sclafani,Paul Nunnenkamp,Rachel Sysak,Robert M. Cerrato
摘要
ABSTRACT Extensive temporal and spatial monitoring data provide an opportunity to identify the drivers of ecosystem change and to understand spatial relationships useful to conservation and management. Such data can potentially overcome the considerable intrinsic variability present in sampling and justify the cost of sustained monitoring. In this study, the temporal and spatial structure and trends in the mobile invertebrate and fish assemblage of the Peconic Estuary were identified. Data were obtained primarily from a small mesh trawl survey conducted by the New York State Department of Environmental Conservation from 1987–2020 at 76 locations distributed throughout the system, supplemented by chlorophyll data and regional climate indices. A set of multivariate statistical tools, including K‐means cluster analysis, redundancy analysis, and multiscale ordination, were applied to the data set in a complementary way. Distinctly different drivers for temporal and spatial patterns were found. Abrupt community shifts on a decadal time scale occurred, including a regime shift in 1999–2000, and were driven by changes in regional climate factors as indexed by the unlagged and lagged Atlantic Multidecadal Oscillation and North Atlantic Oscillation. Spatially distinct habitats and assemblages were identified, separating eastern, inshore, and offshore regions of the system. These were differentiated by local conditions in bottom salinity, water depth and depth gradient, DO percent saturation, and water transparency. Each of these regions responded to the climate drivers in a similar way. Notably, annual bottom temperature and chlorophyll a were never found to be effective in explaining community variation. Overall, the results of this study suggest that, given the time lags in response, climate‐induced changes in the system can be anticipated by continued monitoring and that conservation and management actions can be applied system‐wide and not restricted to specific areas.
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