The invasion of wetlands by Phragmites australis is a conservation concern across North America. We used the invasion of Chesapeake Bay wetlands by P. australis as a model system to examine the effects of regional and local stressors on plant invasions. We summarized digital maps of the distributions of P. australis and of potential stressors (especially human land use and shoreline armoring) at two spatial scales: for 72 subestuaries of the bay and their local watersheds and for thousands of 500 m shoreline segments. We developed statistical models that use the stressor variables to predict P. australis prevalence (% of shoreline occupied) in subestuaries and its presence or absence in 500 m segments of shoreline. The prevalence of agriculture was the strongest and most consistent predictor of P. australis presence and abundance in Chesapeake Bay, because P. australis can exploit the resulting elevated nutrient levels to enhance its establishment, growth, and seed production. Phragmites australis was also positively associated with riprapped shoreline, probably because it creates disturbances that provide colonization opportunities. The P. australis invasion was less severe in areas with greater forested land cover and natural shorelines. Surprisingly, invasion was low in highly developed watersheds and highest along shorelines with intermediate levels of residential land use, possibly indicating that highly disturbed systems are
Accurate fishing effort information is fundamental to the successful management of fisheries resources. Automated, independent, and reliable methods for quantifying fishing effort are needed. The use of vessel speed from Global Positioning System (GPS) data to identify fishing activity has worked well for trawl fisheries but has been less successful in stationary fisheries. Therefore, five trips on four vessels from a vertical hook‐and‐line reef fish fishery were used to examine the efficacy of GPS (speed and time) and electronic video monitoring (EVM) sensor (drum and video) data to corroborate an observer's account of effort using binary logistic regression classification (logit) models as well as a simple speed and time filter (filter). One minute was the minimum data collection interval examined that documented 100% of fishing events. As no fishing occurred at night, opportunistically defined as the 7 h between 2200 and 0500 hours, these records were excluded from analyses. During the day, vessels spent on average 45.2% of the time fishing. Classification success of the approaches examined ranged from 82.4% to 89.5%. Models that included both GPS and EVM sensor data outperformed the filter and GPS‐only models. In general, the filter and most model results can be used as a proxy for observer effort data, at least for the trips examined here. The GPS‐based speed + time logit model was chosen as the preferred approach because of its discriminatory power compared with the filter and the existing widespread use and lower costs of GPS data collection relative to EVM systems and sensors. The speed + time logit model outlined here may have broad utility in this and similar vertical‐line fisheries, including the offshore marine recreational fishing sector.
Received September 9, 2015; accepted December 15, 2015
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