The identification of trends in ecosystem indicators has become a core component of ecosystem approaches to resource management, although oftentimes assumptions of statistical models are not properly accounted for in the reporting process. To explore the limitations of trend analysis of short times series, we applied three common methods of trend detection, including a generalized least squares model selection approach, the Mann–Kendall test, and Mann–Kendall test with trend-free pre-whitening to simulated time series of varying trend and autocorrelation strengths. Our results suggest that the ability to detect trends in time series is hampered by the influence of autocorrelated residuals in short series lengths. While it is known that tests designed to account for autocorrelation will approach nominal rejection rates as series lengths increase, the results of this study indicate biased rejection rates in the presence of even weak autocorrelation for series lengths often encountered in indicators developed for ecosystem-level reporting (N = 10, 20, 30). This work has broad implications for ecosystem-level reporting, where indicator time series are often limited in length, maintain a variety of error structures, and are typically assessed using a single statistical method applied uniformly across all time series.
Integrated ecosystem assessments (IEAs) compile and use indicators, risk assessments, and other analyses to address regional policy needs at varying spatial scales. Although approaches to implementing IEAs are context-specific, challenges in data acquisition, management, processing, analysis, and communication are universal. By embracing open science, in which scientific data, methods, and products are made publicly accessible, along with the ever-expanding tools facilitating open science, IEA practitioners will be better equipped to address these challenges. Here, we provide a snapshot of the state of open science practices in IEAs ongoing across the United States. We show that open science has improved the flexibility, reproducibility, and efficiency of the scientific workflows within the IEA framework. Although the initial time investment necessary for developing open science workflows may appear daunting, we show that the subsequent returns provided by the efficient and transparent development of IEA products are worth the effort. By improving the implementation of IEAs, open science tools and principals have the potential to further Ecosystem Based Management (EBM) worldwide.
Blue crabs (Callinectes sapidus) are highly mobile, ecologically-important mesopredators that support multimillion-dollar fisheries along the western Atlantic Ocean. Understanding how blue crabs respond to coastal landscape change is integral to conservation and management, but such insights have been limited to a narrow range of habitats and spatial scales. We examined how local-scale to landscape-scale habitat characteristics and bathymetric features (channels and oceanic inlets) affect the relative abundance (catch per unit effort, CPUE) of adult blue crabs across a > 33 km2 seagrass landscape in coastal Virginia, USA. We found that crab CPUE was 1.7 × higher in sparse (versus dense) seagrass, 2.4 × higher at sites farther from (versus nearer to) salt marshes, and unaffected by proximity to oyster reefs. The probability that a trapped crab was female was 5.1 × higher in sparse seagrass and 8 × higher near deep channels. The probability of a female crab being gravid was 2.8 × higher near seagrass meadow edges and 3.3 × higher near deep channels. Moreover, the likelihood of a gravid female having mature eggs was 16 × greater in sparse seagrass and 32 × greater near oceanic inlets. Overall, we discovered that adult blue crab CPUE is influenced by seagrass, salt marsh, and bathymetric features on scales from meters to kilometers, and that habitat associations depend on sex and reproductive stage. Hence, accelerating changes to coastal geomorphology and vegetation will likely alter the abundance and distribution of adult blue crabs, challenging marine spatial planning and ecosystem-based fisheries management.
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