The Future of Wild-Caught Fisheries: Expanding the Scope of Management

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Water Quality Trading and Offset Initiatives in the U.S.: A Comprehensive Survey

This document summarizes water quality trading and offset initiatives in the United States, including state-wide policies and recent proposals. The following format was used to present information on each program. We attempted to have each program summary reviewed by at least one contact person for program accuracy. In the cases where this review occurred, we added the statement "Reviewed by.." at the end of the case summary

Network metrics can guide nearly-optimal management of invasive species at large scales

Invasive species harm biodiversity and ecosystem services, with global economic costs of invasions exceeding $40 billion annually. Widespread invasions are a particular challenge because they involve large spatial scales with many interacting components. In these contexts, typical optimization-based approaches to management may fail due to computational or data constraints. Here we evaluate an alternative solution that leverages network science, representing the invasion as occurring across a network of connected sites and using network metrics to prioritize sites for intervention. Such heuristic network-guided methods require less data and are less computationally intensive than optimization methods, yet network-guided approaches have not been bench-marked against optimal solutions for real-world invasive species management problems. We provide the first comparison of the performance of network-guided management relative to optimal solutions for invasive species, examining the placement of watercraft inspection stations for preventing spread of invasive zebra mussels through recreational boat movement within 58 Minnesota counties in the United States. To additionally test the promise of network-based approaches in limited data contexts, we evaluate their performance when using only partial data on network structure and invaded status. Metric-based approaches can achieve a median of 100% of optimal performance with full data. Even with partial data, 80% of optimal performance is achievable. Finally, we show that performance of metric-guided management improves for counties with denser and larger networks, suggesting this approach is viable for large-scale invasions. Together, our results suggest network metrics are a promising approach to guiding management actions for large-scale invasions.Comment: 29 pages, 8 figures, 3 table

Defining the economic scope for ecosystem-based fishery management

Ecosystem-based fisheries management provides a framework for incorporating ecological linkages between fisheries into policymaking. However, relatively little attention has been given to economic linkages between fisheries: If fishers consider multiple fisheries when deciding where, when, and how much to fish, there is potential for management decisions in one fishery to generate spillover impacts in other fisheries. We evaluate changes in participation and economic connectivity of fisheries following the implementation of Alaska�s catch-share programs. Catch shares are increasingly used worldwide and typically implemented and evaluated on a single-fishery basis. We provide evidence that changes beyond the catch-share fishery have occurred, suggesting that spillovers should be considered when designing and evaluating catch-share policies.The emergence of ecosystem-based fisheries management (EBFM) has broadened the policy scope of fisheries management by accounting for the biological and ecological connectivity of fisheries. Less attention, however, has been given to the economic connectivity of fisheries. If fishers consider multiple fisheries when deciding where, when, and how much to fish, then management changes in one fishery can generate spillover impacts in other fisheries. Catch-share programs are a popular fisheries management framework that may be particularly prone to generating spillovers given that they typically change fishers� incentives and their subsequent actions. We use data from Alaska fisheries to examine spillovers from each of the main catch-share programs in Alaska. We evaluate changes in participation�a traditional indicator in fisheries economics�in both the catch-share and non�catch-share fisheries. Using network analysis, we also investigate whether catch-share programs change the economic connectivity of fisheries, which can have implications for the socioeconomic resilience and robustness of the ecosystem, and empirically identify the set of fisheries impacted by each Alaska catch-share program. We find that cross-fishery participation spillovers and changes in economic connectivity coincide with some, but not all, catch-share programs. Our findings suggest that economic connectivity and the potential for cross-fishery spillovers deserve serious consideration, especially when designing and evaluating EBFM policies

Limited carbon and biodiversity co-benefits for tropical forest mammals and birds

The conservation of tropical forest carbon stocks offers the opportunity to curb climate change by reducing greenhouse gas emissions from deforestation and simultaneously conserve biodiversity. However, there has been considerable debate about the extent to which carbon stock conservation will provide benefits to biodiversity in part because whether forests that contain high carbon density in their aboveground biomass also contain high animal diversity is unknown. Here, we empirically examined medium to large bodied ground-dwelling mammal and bird (hereafter "wildlife") diversity and carbon stock levels within the tropics using camera trap and vegetation data from a pantropical network of sites. Specifically, we tested whether tropical forests that stored more carbon contained higher wildlife species richness, taxonomic diversity, and trait diversity. We found that carbon stocks were not a significant predictor for any of these three measures of diversity, which suggests that benefits for wildlife diversity will not be maximized unless wildlife diversity is explicitly taken into account; prioritizing carbon stocks alone will not necessarily meet biodiversity conservation goals. We recommend conservation planning that considers both objectives because there is the potential for more wildlife diversity and carbon stock conservation to be achieved for the same total budget if both objectives are pursued in tandem rather than independently. Tropical forests with low elevation variability and low tree density supported significantly higher wildlife diversity. These tropical forest characteristics may provide more affordable proxies of wildlife diversity for future multi-objective conservation planning when fine scale data on wildlife are lacking

Protected area, easement, and rental contract data reveal five communities of land protection in the United States

Land protection efforts represent large societal investments and are critical to biodiversity conservation. Land protection involves a complex mosaic of areas managed by multiple organizations, using a variety of mechanisms to achieve different levels of protection. We develop an approach to synthesize, describe, and map this land protection diversity over large spatial scales. We use cluster analysis to find distinct "communities" of land protection based on the organizations involved, the strictness of land protection, and the protection mechanisms used. We also associate identified land protection communities with socioenvironmental variables. Applying these methods to describe land protection communities in counties across the coterminous United States (US), we recognize five different land protection communities. Two land protection communities occur in areas with low human population size at higher elevations and include a large amount of protected land primarily under federal management. These two community types are differentiated from one another by the particular federal agencies involved, the relative contributions of smaller actors, and the amount of protection by designations vs. conservation easements or covenants. Three remaining land protection communities have less overall protection. Land in one community is primarily protected by federally managed rental contracts and government managed easements; another is managed by a diversity of non-federal actors through fee-ownership and easements; and the third stands out for having the lowest amount of formally recorded protection overall. High elevation and poor quality soils are over-represented in US protected lands. Rental contracts help fill in gaps in counties with high productivity soil while the US Fish and Wildlife Service fills in gaps in low-elevation counties. Counties with large numbers of threatened species have more and stricter protection, particularly by regional entities like water management districts. The ability to synthesize and map land protection communities can help conservation planners tailor interventions to local contexts, position local agencies to approach collaborations more strategically, and suggest new hypotheses for researchers regarding interactions among different protection mechanisms.Description of each column This is a brief summary of columns. The README file contains column by column descriptions. A county_fips_code: B county_name: The name of the county as of 2016 (including change of Shannon County to Oglala Lakota). Many counties of the same name are found in different states. C state_name: The name of the state in which the county resides. D-DO [116 columns]: Hectares of each protected area type in a county, where protected area type is a unique combination of strictness (GAP status), agency, and mechanism. (see Table 1 and Appendix S1: Table 1 for more details) DQ-DU [5 columns]: cluster membership scores given a 5 cluster partition based on fuzzy clustering (see article)DP ha_total_area_of_county: size of the county in hectares DV dominant_cluster: The cluster for which a county has the highest cluster membership score. DW-EF [10 columns]: socioenvironmental variables by county (see Appendix S1: Table S2 for descriptions and sources) Funding provided by: U.S. Department of AgricultureCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000199Award Number: 2017-67023-26270The following is from the Methods section of the related journal article. Please see the article and its accompanying appendix to read more about these data. We considered the spatial configuration of land protection in the coterminous US. Specifically, we used all 3108 US counties as our units of analysis (median county area in coterminous US = 1670 km2). While other choices of spatial unit would have been possible, counties provide meaningful spatial units for many smaller conservation actors, a convenient reporting unit for relevant socioenvironmental data, and a large enough area to encompass a range of conservation actors. Protected area data Protected area data were obtained from the PAD-US 2.0 (US Geological Survey Gap Analysis Project 2018). Data for lands managed by the Bureau of Indian Affairs were collected from PAD-US 1.4 (US Geological Survey Gap Analysis Project 2016) because those data are absent from PAD-US 2.0. Data for rental contract lands managed by the USDA Farm Service Agency (FSA) under the Conservation Reserve Program in 2016 were collected from the USDA Conservation Reserve Program Statistics (US Department of Agriculture Farm Services Agency 2018). All easement data were contained in PAD-US 2.0, which gathered its data from the National Conservation Easement Database in February 2018. We note that this database has been updated since February 2018 to include data missing from our analysis. Just between April 2019 and September 2020, the hectares listed under easement in the National Conservation Easement Database grew by 32%, or 3.2 million ha. We consider protected areas along a continuum, from those managed strictly for biodiversity outcomes (GAP 1 and 2) on one end to those for which conservation is not a primary objective (GAP 4) or for which protection is temporary on the other (not given GAP status). GAP 3 lands are multi-use lands with mixed conservation and social objectives. Only lands under GAP 1, 2, and 3 status would be considered "protected areas" under the IUCN definition (Dudley et al. 2013). GAP 4 protection and protection under temporary rental contracts, however, often also support conservation objectives and might be classified as "other effective area-based conservation measures" (IUCN-WCPA 2019). With a few adjustments, we used the categories defined by PAD-US 2.0 to describe land protection by strictness of protection, managing agency, and protection mechanism (Table 1). Land protection agencies were placed in groups according to PAD-US "Agency Type" categories: federal (FED), state, regional districts, city and county governments (hereafter local governments), non-governmental organizations (NGO), Native American tribes (hereafter tribes), private entities, and unknown agencies. When an agency was the fee-owner, easement holder, and/or designating agency for more than 10 million ha of land, we specified the individual agency by name, including the US Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM), the US Forest Service (USFS), the Bureau of Indian Affairs (BIA), the US Department of Agriculture Farm Services Agency (FSA), and the National Park Service (NPS). These were all federal agencies. We combined the PAD-US "joint" agency category (< 0.5% of protected lands) with the unknown agency category (0.13% of protected lands). We added a "no reported protection" category to describe the area of a county not covered by any recorded protection (i.e., not recorded by PAD-US or FSA). These lands are likely to be managed by nonfederal actors, especially private entities, local governments, and small NGOs, for whom records are less complete. We retained spatial overlaps in protected area data. For example, a portion of the Beaverbrook Watershed in Clear Creek County, Colorado is fee-owned by the US Forest Service (GAP 3), is designated as a watershed protection area by the county (GAP 3) and is protected by an easement held by an NGO (GAP 2). Tiering of conservation activity, with multiple actors involved in protection on the same land, is common in other countries as well (Eigenbrod et al. 2010, Scullion et al. 2014) and provides additional information about the conservation community in an area. Retaining overlapping protection types means that the sum of the area covered by different protected area types in a county can add up to more than the total area of the county in our dataset. We identified 116 protected area types for our analysis, where a protected area type is a unique combination of strictness, managing agency, and protection mechanism (see Appendix S1: Table S1). There are only 116 types because several potential combinations of these aspects do not occur in the data. Several other combinations exist but are so uncommon (present in < 0.13% of counties) that they were also excluded and removed from county totals. Similarly rich complexes of land protection activities have been documented elsewhere (Shwartz et al. 2017, Donald et al. 2019). Although some protected areas in our dataset could have been "slivers", or artifacts of geoprocessing errors, most are likely to represent real protected areas (Baldwin and Fouch 2018). One benefit of the update from PAD-US 1.4 to 2.0 is a reduction in the number of slivers (https://www.sciencebase.gov/ ). To control for variations in county area, we focus on the relative abundance of each protected area type. In community ecology, relative abundance is a measure of how common or rare a species (or, in our case, protection type) is relative to other types in a given location (Hubbell 2001). Socioenvironmental variables Socioenvironmental variables were also summarized by county and included average elevation, average soil productivity, state population density in 1900, county population density in 2010, number of IUCN-listed threatened species whose range overlaps with a county, median household income in 2018, and the percentage of the adult population over age 25 with a bachelor's degree (five year average from 2013-2017). Spatial variables included county size, longitude, and latitude. Details about how we put together our dataset are in Appendix S1: Table S2. A description of correlations among socioenvironmental predictors can be found in Appendix S1: Fig. S1

Defining the economic scope for ecosystem-based fishery management.

The emergence of ecosystem-based fisheries management (EBFM) has broadened the policy scope of fisheries management by accounting for the biological and ecological connectivity of fisheries. Less attention, however, has been given to the economic connectivity of fisheries. If fishers consider multiple fisheries when deciding where, when, and how much to fish, then management changes in one fishery can generate spillover impacts in other fisheries. Catch-share programs are a popular fisheries management framework that may be particularly prone to generating spillovers given that they typically change fishers' incentives and their subsequent actions. We use data from Alaska fisheries to examine spillovers from each of the main catch-share programs in Alaska. We evaluate changes in participation-a traditional indicator in fisheries economics-in both the catch-share and non-catch-share fisheries. Using network analysis, we also investigate whether catch-share programs change the economic connectivity of fisheries, which can have implications for the socioeconomic resilience and robustness of the ecosystem, and empirically identify the set of fisheries impacted by each Alaska catch-share program. We find that cross-fishery participation spillovers and changes in economic connectivity coincide with some, but not all, catch-share programs. Our findings suggest that economic connectivity and the potential for cross-fishery spillovers deserve serious consideration, especially when designing and evaluating EBFM policies