An empirical analysis of the economic value of ocean space associated with commercial fishing |
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| Affiliation: | 1. Marine Policy Center, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, United States;2. John H. Prescott Marine Laboratory, New England Aquarium, Central Wharf, Boston, MA 02110, United States;1. Biology Department & CESAM, University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal;2. NIBIO – Norwegian Institute of Bioeconomy Research, Ås, Norway;3. DelPar Environment Consulting, Rotegatan 4, Linköping 58752, Sweden;4. Potsdam Institute for Climate Impact Research, Telegrafenberg A62, P.O. Box 60 12 03, 14412 Potsdam, Germany;5. Department of Coastal Engineering and Dynamics, Institute of Hydro-Engineering of Polish Academy of Sciences, Koscierska 7, 80-328 Gdansk, Poland;1. Bio-Resource and Environmental Center, Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Songdo-dong, Incheon 22012, South Korea;2. Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Songdo-dong, Incheon, 22012, South Korea;1. Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education & Jiangsu Province, Jiangsu University, Zhenjiang, Jiangsu 212013, China;2. Liuan Academy Institute of Agricultural Science, Liuan, Anhui 237000, China;3. College of Forest Resources and Environment, Nanjing Forestry University, Nanjing, Jiangsu 210037, China;1. Russian Museum of Biodiversity Hotspots, Federal Center for Integrated Arctic Research, Russian Academy of Sciences, Severnaya Dvina Emb. 23, 163000 Arkhangelsk, Russian Federation;2. Northern Arctic Federal University, 163002 Arkhangelsk, Russian Federation |
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| Abstract: | Understanding the economic value of ocean space is critical for implementing marine spatial planning (MSP). Empirical data from 1999 to 2008 are compiled on the economic values arising from commercial fishing in the Gulf of Maine and adjacent areas. The data are analyzed to characterize factors affecting the spatial and temporal distribution of measures of economic productivity and fishing effort. The analysis consisted of four components: (1) estimation of net revenue at the 10-min square level by season and gear; (2) assessment of variability for catch revenue and catch per unit effort; (3) mapping net revenue and variability in the study area; and (4) estimation of interactions among catch, effort, season, and gear type. The results indicated that, at each location, average fishing efforts exhibited a positive response to increases in expected revenues and a negative response to variability in revenues. Most of the variability in catch revenue can be explained by changes in fishing effort, implying that the spatial patterns of fishery resources are relatively stable at the 10-min square level. An important conclusion is that a spatial scale of at least the 10-min square is appropriate for undertaking MSP involving allocations of commercial fisheries. |
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