Endangered Atlantic Sturgeon in the New York Wind Energy Area: implications of future development in an offshore wind energy site

Abstract

Imminent development of offshore wind farms on the outer continental shelf of the United States has led to significant concerns for marine wildlife. The scarcity of empirical data regarding fish species that may utilize development sites, further compounded by the novelty of the technology and inherent difficulty of conducting offshore research, make identification and assessment of potential stressors to species of concern problematic. However, there is broad potential to mitigate putatively negative impacts to seasonal migrants during the exploration and construction phases. The goal of this study was to establish baseline information on endangered Atlantic Sturgeon in the New York Wind Energy Area (NY WEA), a future offshore development site. Passive acoustic transceivers equipped with acoustic release mechanisms were used to monitor the movements of tagged fish in the NY WEA from November 2016 through February 2018 and resulted in detections of 181 unique individuals throughout the site. Detections were highly seasonal and peaked from November through January. Conversely, fish were relatively uncommon or entirely absent during the summer months (July–September). Generalized additive models indicated that predictable transitions between coastal and offshore habitat were associated with long-term environmental cues and localized estuarine conditions, specifically the interaction between photoperiod and river temperature. These insights into the ecology of marine-resident Atlantic Sturgeon are crucial for both defining monitoring parameters and guiding threat assessments in offshore waters and represent an important initial step towards quantitatively evaluating Atlantic Sturgeon at a scale relevant to future development.

The Role of Fishery‐Independent Bottom Trawl Surveys in Providing Regional and Temporal Context to Offshore Wind Farm Monitoring Studies

“…Other ways in which samples collected during bottom trawl surveys can be leveraged to examine mechanisms of impact or reduce ecological costs include coupling trawl surveys with environmental DNA sampling (Stoeckle et al 2021;Rourke et al 2022) to facilitate a change to less-extractive monitoring methods in the future. Additionally, biochemical assessments of fish lipid stores, protein content, or stable isotope analysis can provide real-time indicators of condition (Davidson and Marshall 2010;McPherson et al 2011;Mavraki et al 2020), and tagging studies using acoustic telemetry can provide information on fish presence, persistence, residency, and movements over extended time periods (Ingram et al 2019;Zemeckis et al 2019). Education and outreach efforts are needed to ensure that results generated by less-traditional monitoring methods are trusted and accepted by fisheries interest groups and resource managers.…”

Toward a decade of ocean science for sustainable development through acoustic animal tracking

“…Using AT, we can gather pre‐development baseline spatiotemporal animal movement data. In fact, Ingram et al (2019) suggested that AT should be a prerequisite to evaluate the impact of an offshore wind energy development to mitigate its potential negative impacts on the endangered Atlantic sturgeon, Acipenser oxyrinchus . Once constructed, human‐made infrastructures can also provide physical habitat for fish aggregation, influencing local biodiversity and ecosystem functioning (Halouani et al, 2020).…”

Telemetry reveals migratory drivers and disparate space use across seasons and age‐groups in American horseshoe crabs

“…For the explanatory variables, individuals were considered to be random effects, whereas temperature, photoperiod, age-group (factor), sex (factor), and prosomal width were considered as fixed effects. An interaction term for photoperiod and temperature was also included in the model to determine whether migration timing was affected non-additively by these two external factors, as is the case with other seasonal migration patterns (Ingram et al 2019). Model selection was carried out by using the dredge function in the MuMin R package to compare the fits of all potential model combinations (Barton and Barton 2015).…”