No abstract

Policies and regulations designed to address nutrient pollution in coastal waters are often complicated by delays in environmental and social systems. Social and political inertia may delay the implementation of cleanup projects, and even after the best nutrient pollution management practices are developed and implemented, long groundwater travel times may delay the impact of inland or upstream interventions. These delays and the varying costs of nutrient removal alternatives used to meet water quality goals combine to create a complex dynamic decision problem with tradeoffs about when, where, and how to intervene. We use multi‐objective optimization to quantify the tradeoffs between costs and minimizing the time to meet in‐bay nutrient reduction goals represented as a Total Maximum Daily Load (TMDL). We calculate the impact of using in‐bay (in situ) nutrient removal through shellfish aquaculture relative to waiting for traditional source control to be implemented. We apply these methods to the Three Bays Watershed in Cape Cod, Massachusetts. In gross benefit terms, not accounting for any social costs, this equates to an average value of 37¢ (2035 TMDL target date) and 11¢ (2060 TMDL target date) per animal harvested over the plan implementation period. Our results encourage the consideration of alternative and in situ approaches to tackle coastal pollution while traditional source control is implemented and its effects realized over time.

Linking human behavior to environmental quality is critical for effective natural resource management. While it is commonly assumed that environmental conditions partially explain variation in visitation to coastal recreation areas across space and time, scarce and inconsistent visitation observations challenge our ability to reveal these connections. With the ubiquity of mobile phone usage, novel sources of digitally derived data are increasingly available at a massive scale. Applications of mobile phone locational data have been effective in research on urban-centric human mobility and transportation, but little work has been conducted on understanding behavioral patterns surrounding dynamic natural resources. We present an application of cell phone locational data to estimate the effects of beach closures, based on measured bacteria threshold exceedances, on visitation to coastal access points. Our results indicate that beach closures on Cape Cod, MA, USA have a significant negative effect on visitation at those beaches with closures, while closures at a sample of coastal access points elsewhere in New England have no detected impact on visitation. Our findings represent geographic mobility patterns for over 7 million unique coastal visits and suggest that closures resulted in approximately 1,800 (0.026%) displaced visits for Cape Cod during the summer season of 2017. We demonstrate the potential for human-mobility data derived from mobile phones to reveal the scale of use and behavior in response to changes in dynamic natural resources. Potential future applications of passively collected geocoded data to human-environmental systems are vast.

Social network analysis (SNA) tools and concepts are essential for addressing many environmental management and sustainability issues. One method to gather SNA data is to scrape them from environmental organizations’ websites. Web-based research can provide important opportunities to understand environmental governance and policy networks while potentially reducing costs and time when compared to traditional survey and interview methods. A key parameter is ‘search depth,’ i.e., how many connected pages within a website to search for information. Existing research uses a variety of depths and no best practices exist, undermining research quality and case study comparability. We therefore analyze how search depth affects SNA data collection among environmental organizations, if results vary when organizations have different objectives, and how search depth affects social network structure. We find that scraping to a depth of three captures the majority of relevant network data regardless of an organization’s focus. Stakeholder identification (i.e., who is in the network) may require less scraping, but this might under-represent network structure (i.e., who is connected). We also discuss how scraping web-pages of local programs of larger organizations may lead to uncertain results and how our work can combine with mixed methods approaches.