Grantee:  Dr. Kevin St. Martin, Rutgers University

Country:  U.S.A.

Contact:  Dr. Kevin St. Martin (kstmarti at sign rci.rutgers.edu)

Project Summary:
Our interest to build a tool that links port communities to resource areas emerges from our work
in fisheries in the Northeast and, particularly, the difficulties of directly integrating human
communities into the marine environment and its management. These difficulties are largely a
result of the lack of data specifying those areas at sea that are utilized by communities of
resource users even as the marine environment is increasingly understood in spatial terms.
Indeed, the recent explosion of new spatial analytical techniques and layers of spatially encoded
marine environmental data (to which M-EBM is closely aligned) is distinctly focused upon
mapping the biophysical rather than the social landscape. This is despite the fact that the latter is
essential to the successful implementation of marine protected areas, integrating local
environmental knowledge, participatory resource management, and M-EBM generally.
Our proposed M-EBM tool will facilitate the creation of maps depicting the resource areas upon
which coastal communities depend and it will work to enhance impact analyses of area-based
management initiatives. While our work is primarily in fisheries and the proposed tool will be
designed to map fishing community domains, we believe that it and the maps it produces will be
an essential resource for fisheries and marine scientists, fisheries managers, marine protected
area initiatives, and coastal communities themselves.

The tool will facilitate two basic functions. The first function will link port communities (as
defined by the end user) to resource areas. The second function will link specific resource areas
(defined by the end user) to particular port communities. For both general functions, the tool will
use vessel logbook data that contains the geographic locations of vessel trips as well as the
“home” or “principle” port of each vessel. These essential variables are the foundational
elements of the procedure that this tool encodes and what allows port communities to be linked to resource areas. Once linked, a host of other vessel and trip attributes can be queried to
describe the nature of the community and its use of the resource area to which it is linked.
Logbook data typically exists as a series of single records for each fishing trip (or gear
deployment) taken by a fishing vessel. While useful for querying the destinations and patterns of
individual vessels (assuming individual records are entered correctly), in its disaggregated form
such data is of limited use for ecosystems-based analysis or management. In addition, as
disaggregated, logbook data runs into a number of problems relative to issues of confidentiality.
Once aggregated, however, logbook data can be used to represent not the destinations of
individuals but the spatial patterns of peer groups of vessels or communities of fishermen. It is
this aggregation and the subsequent community-level analysis that it engenders that the proposed
tool facilitates. The latter is increasingly essential given, at least in the US, mandates to assess
impacts upon “fishing communities.” In addition, the general trend toward more area-based
approaches to both marine science and management suggests the need for more and better
information about just which communities work within and know about which resource areas.
The tool presumes the existence of a single database table where each record represents a fishing
vessel trip (logbook data). The minimum number of attributes for each trip will be trip location
(in lat/long), vessel identifier, trip length, crew size, and vessel “home” or “principle” port.
These are needed to link port communities to at sea locations. Additional attributes that are
recommended will include: trip date, catch, and vessel attributes such as length, horse power,
gear type, gear quantities, etc.

Since our primary concern is to develop a tool that will link port communities to resource areas,
the tool will guide users to map resource areas in terms of the labor time expended by peer
groups of fishermen (as a function of trip length and crew size). Users will be able to define the
“communities” for which labor time will be calculated by choosing parameters such as port, port
groupings, gear type, or other attributes or their combination. For example, a typical selection
might result in a map of those areas frequented by small-scale trawlers from the neighboring
ports of New Bedford and Fairhaven, MA. Another selection might result in a map of those areas
frequented by small-scale vessels with dredge gear from all ports taken together. In both cases,
maps would depict community “presence” as a function of labor time – time spent by a
community or peer group of fishers in a given location. We use the labor time calculation (we
call it “fisherman days”) rather than value or quantity caught because it better represents the
importance and inhabitation of a resource area to a community of resource users.