Ecosystem Status Report for the Northeast Large Marine Ecosystem
8. Human Dimensions
Central to EBM is an understanding of coupled socio-ecological systems (human and natural environment) which reflects the interface and reciprocal interactions that link human (e.g., economic, social, cultural) and natural (e.g., oceanographic, atmospheric, geological, biological) sub-systems. Coastal communities of the NES LME (and around the U.S.) depend on the ocean for meeting economic, social, and cultural needs. Fishing (commercial, recreational, and subsistence), coastal tourism and recreation, shipping, and spiritual or cultural practices centered on marine locations or species are but a few examples. In turn, human activities shape the marine environment, generating a feedback mechanism between the coupled systems. The following overview highlights some indicators of these dependencies, and new avenues by which our scientific understanding of the underlying processes are being bolstered.
It also provides an initial understanding of the potential tradeoffs that must be made under both EBM and MSP, as we analyze the nation’s use of the marine environment and understand: 1) how marine resources are utilized; and 2) potential user conflicts inherent in access to these resources. As technology allows new development in and uses of ocean waters, traditional uses of marine resources (e.g., boating, fishing, shipping, spiritual practices) must be considered in the planning process for evolving new activities such as renewable energy in the form of wind farms or tidal generators. MSP is utilized by ocean resource managers, in conjunction with EBM, to better determine how resources may be sustainably used and/or protected.
Approximately half of the population of the conterminous United States resides within coastal counties (Crosset et al. 2004) with population and per capita income levels along the Northeast coast increasing sharply since 1969 (Figure 8.1). These trends highlight increasing user competition for and pressure on coastal and estuarine ecosystems, and changes to human community dynamics. Increased human population density in coastal areas is related to factors such as nitrogen loading and destruction of habitat (including wetland nursery areas) as coastal development expands. This development, often fueled by rising incomes, can also lead to coastal gentrification (see Colburn and Jepson 2012 and the Community Vulnerability section on this page). All of this underscores the need for the analytical tools and information to help manage the myriad competing interests represented within a growing regional economy.
Since 1990, total U.S. revenues from federally permitted commercial fishing vessels in the NES LME waters have fluctuated around an average of $1.66 billion, ending with a 2012 total revenue of just over $1.46 billion (Figure 8.2). In 2012 these revenues were dominated by dredge gear, consisting primarily of Atlantic sea scallop, Atlantic surf clam, and ocean quahog landings, and pot and trap gears (87% of which are explained by lobster landings). These results continue to highlight the shifting economic dependence on both lower trophic levels and a less diversified species mix (see 2011 Ecosystem Status Report).
Marine related employment in the Northeast has mirrored the general downward employment trend in the economy, with the boating-related sector (boat dealers and builders, marinas, tourism, and recreation, including party/charter boat fishing) accounting for the majority of the marine contraction (Figure 8.3). The seafood sector (commercial fisheries, dealers, processors, and markets) has remained steady at around 41,000 jobs since 2008, and shipping (ship building, shipping, harbor operations, and other shipping-related services) employment is near pre-recession numbers.
Coastal communities are currently experiencing impacts of multiple stressors: economic, social, and ecological. Factors affecting vulnerability include levels of access to resources and power (political, cultural, economic, and social) and of susceptibility to harm or loss. Existing levels of social vulnerability affect the level of impact that a community experiences from stressors. Therefore, identification and monitoring of socially vulnerable communities in the coastal zone is a critical aspect of EBM. Similarly, levels of dependence on and use of ocean-related resources and conditions create greater or lesser likelihood of specific kinds of impacts. Further, coastal gentrification trends may be an indication of community vulnerability to development that can transform the coastal zone and increase coastal community vulnerability to the impacts of disruptive events (Jepson and Colburn 2013), such as extreme weather conditions.
The NMFS Community Social Vulnerability Indicators (CSVIs; Jepson and Colburn 2013) are statistical measures of the vulnerability of communities to events such as regulatory changes to fisheries, wind farms, and other ocean-based businesses, as well as to natural hazards, disasters, and climate change. The CSVIs currently serve as indicators of social vulnerability, gentrification pressure vulnerability, and commercial and recreational fishing dependence (with dependence being a function of both reliance and engagement, explained in more detail below). These indicators are based on the indices shown in Table 8.1.
The CSVIs are constructed annually, using demographic data from the U.S. Census’ American Community Survey (ACS) five-year rolling estimates, NOAA Fisheries annual commercial fisheries data and annual Marine Recreational Information Program (MRIP) data, as well as a small number of publically available but non-government online databases. Ongoing data collection will allow the CSVIs to be continually updated to show long term trends. The baseline ACS data cover the years 2005 to 2009 and will be compared to 2010 to 2014 estimates once those are available. The 2006 to 2010 ACS data were used to construct the social and gentrification pressure vulnerability indicators found in Figure 8.4 and Figure 8.5. NOAA Fisheries and MRIP data from 2010 were used to construct the recreational and commercial fishing reliance and engagement indicators found in Figure 8.6, Figure 8.7, Figure 8.8, and Figure 8.9. Combined, the reliance and engagement indicators show overall dependence. Recreational fishing reliance (Figure 8.6) is based on per capita shore, private vessel and for-hire fishing activity, while recreational fishing engagement (Figure 8.7) is based on the absolute value of the same measures. Commercial fishing reliance (Figure 8.8) is based on per capita value landed, number of commercial fishing permits and dealers and the % of the population identified in the U.S. Census as employed in agriculture, forestry and fishing, while commercial fishing engagement (Figure 8.9) is the absolute value of a similar set of measures (see Table 8.1 for details). As an example, both Stonington, ME and New Bedford, MA are highly involved in commercial fishing. However, due to Stonington’s comparatively small population size it is considered more reliant on commercial fishing than New Bedford.
Communities in the Northeastern U.S. are ranked as high, moderate, or low relative to the respective indicator. Figure 8.4 shows a high concentration of socially vulnerable communities in the Mid-Atlantic, while Figure 8.5 shows a high to moderate concentration of communities that may be vulnerable to gentrification pressure in Massachusetts, New York, and New Jersey. Community dependence on recreational and commercial fishing is mixed, with notably more communities in the Mid-Atlantic engaged in than reliant on recreational fishing (Figures 8.6 to Figure 8.7). This is in contrast to northern New England were there are notably more communities reliant on than engaged in commercial fishing (Figures 8.8 to Figure 8.9).
Analysis of fishing patterns has demonstrated clear “discrete clusters” of communities at sea in the Northeast based on common ports of origin and common gears used (St. Martin and Hall-Arber 2008). Moreover, these groups of fishermen can reinforce clustering behavior through greater tendencies to share information and cooperate, which can create community-type groups and restrict resource access (Acheson and Gardner 2004; St. Martin 2001: 132). Long-held distinctions between inshore and offshore fishing (e.g. Miller and Van Maanen 1979; Pollnac and Poggie 1988) also continue to show salience in the Northeast. A comparison between vessels dependent on one statistical area for the majority of their fishing with vessels that are highly mobile demonstrates another dimension to inshore versus offshore in the Northeast (Figure 8.10). Fixed area fishermen (who may use mobile or fixed gear) tended to be inshore and close to their homeports, but were composed of both small and larger boats; mobile fishermen fished offshore as well as closer to shore but landed a considerably higher volume of fish than fixed area fishermen (Olson 2011). Such variations demonstrate the importance of particular fishing grounds to different kinds of fishing (see also Holland and Sutinen 1999: 259). Many of these participants feel this diversity—in vessels, gear, species, and grounds—is essential to resilience in their occupation and in fishing communities (Charles et al 2009; Boyd and Charles 2006).
The terms Local Ecological Knowledge (LEK), Traditional Ecological Knowledge (TEK), and several variants are becoming more prominent in fisheries management circles (re. Sepez 2005). Though each has a slightly different focus, all refer generally to knowledge acquired by resource users in the course of pursuing their livelihoods and/or their subsistence practices. This type of knowledge is different from scientific knowledge in that it is more niche-based, concentrated in particular areas. However, it is like science in its meticulous observations of flora and fauna and their interactions, habitat and general environment, and seasonal and temporal changes. Participatory and collaborative research of fishermen with scientists can allow for both avenues of research and research design that benefit from these complementary knowledge sets (Lam 2014; Johannes and Neis 2007; Wiber et al. 2004).
When seeking to integrate Western scientific and local ecological knowledge, a number of factors must be kept in mind. It is important to choose local experts wisely, for example by choosing fishermen recognized as experts by their peers (Davis and Wagner 2003; García-Quijano 2007), to use appropriate data collection methods (Huntington 2000; García-Quijano 2007), and to remember to involve local experts in all steps of the scientific process – true collaboration, or participation, as opposed to simple cooperation (Brook and McLachlan 2005; Yochum et al. 2011).
In the Northeast Region, work on LEK of Atlantic cod (Gadus morhua), for instance, has shown that detailed sub-stocks, often based in niche spawning grounds (Figure 8.11), were once more prevalent than they are today (Ames 1998, 2003, 2004, 2007, 2010; Ames et al. 2000; re. Neis, Schneider et al. 1999, Neis, Felt et al. 1999, working on cod in Atlantic Canada). In contrast to the rich spawning ground structure of cod and haddock throughout the Gulf of Maine evident in Figure 8.11, over the last four decades we have seen a progressive diminution in abundance in the eastern Gulf, particularly of cod (see Movies 6.1 and 6.2 in Fish Communities section of this report), suggesting the possibility that the importance of the eastern spawning grounds has declined sharply. Neis (1998) showed that Canadian fishermen have complex taxonomies for cod that, though localized, can together provide a broader overview of a region and its stock structure through careful organization of interview locations (re. García-Quijano 2007, 2009, on sub-tropical assemblages in Puerto Rico), providing niche-level data that larger scientific studies can miss.