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Testing CPUE-derived spatial occupancy as an indicator forstock abundance: application to deep-sea stocks

Published online by Cambridge University Press:  27 September 2013

Verena M. Trenkel ,
Jonathan A. Beecham ,
Julia L. Blanchard ,
Charles T. T. Edwards  and
Pascal Lorance
Verena M. Trenkel*
Affiliation:
Ifremer, rue de l’île d’Yeu, BP 21105, 44311 Nantes Cedex 3, France
Jonathan A. Beecham
Affiliation:
CEFAS Lowestoft Laboratory, Pakefield Road, Lowestoft, NR33 0HT, UK
Julia L. Blanchard
Affiliation:
Division of Biology, Imperial College London, Silwood Park, Ascot, SL5 7PY, UK
Charles T. T. Edwards
Affiliation:
Division of Biology, Imperial College London, Silwood Park, Ascot, SL5 7PY, UK
Pascal Lorance
Affiliation:
Ifremer, rue de l’île d’Yeu, BP 21105, 44311 Nantes Cedex 3, France
*
a Corresponding author:[email protected]
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Abstract

The status of an exploited population is ideally determined by monitoring changes inabundance and distributional range and pattern over time. Area of occupancy is a measureof the current distribution. Unfortunately, for many populations, scientific abundance anddistribution information is not readily available. To evaluate the reliability ofcommercial fishing data for deriving occupancy indicators that could serve as proxies forstock abundance, we investigated four questions: 1) Occupancy changes with stock biomass,but is this change strong enough to make occupancy a sensitive indicator of populationbiomass? 2) Fishing boats follow fish, but when does such activity alter the positivemacroecological relationship between occupancy and abundance? 3) When does the activity ofpursuing fish adversely affect occupancy estimates derived from catch and effort data? 4)How does uncertainty in fishing effort data affect occupancy estimates? Spatialsimulations mimicking the dynamics of four deep-water fish species showed thatbiomass-occupancy relationships can be weak. Fishers following fish can modify the spatialdistribution of target species, even reversing the sign of the biomass-occupancyrelationship in certain cases, and can affect the reliability of occupancy indicators,which can also be impaired by error in effort data. Using commercial catch and effort dataand abundance indices for deep-sea fish populations to the west of the British Isles itwas found that only for roundnose grenadier might occupancy provide insights into biomasschanges. In conclusion, care should be taken when using occupancy for evaluating rangechanges in cases where fishing might have modified spatial distributions, uncertaincommercial data are used or when the abundance-occupancy relationship is too flat.

Keywords

Catch per unit effortSpatial patternsMacroecologyFisheries managementMarine Strategy Framework DirectiveMSFD
Type
Research Article
Information
Aquatic Living Resources , Volume 26 , Issue 4 , October 2013 , pp. 319 - 332
Copyright
© EDP Sciences, IFREMER, IRD 2013

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References

Augustin, N.H., Trenkel, V.M., Wood, S.N., Lorance, P., 2013, Space-time modelling of blue ling for fisheries stock management. Environmetrics 24 109–119. CrossRefGoogle Scholar
Blanchard, J.L., Maxwell, D.L., Jennings, S., 2008, Power of monitoring surveys to detect abundance trends in depleted populations: the effects of density-dependent habitat use, patchiness, and climate change. ICES J. Mar. Sci. 65, 111–120. CrossRefGoogle Scholar
Blanchard, J.L., Mills, C., Jennings, S., Fox, C.J., Rackham, B.D., Eastwood, P.D., O’Brien, C.M., 2005, Distribution-abundance relationships for North Sea Atlantic cod (Gadus morhua): observation versus theory. Can. J. Fish. Aquat. Sci. 62, 2001–2009.CrossRefGoogle Scholar
Brown, J.H., Maurer, B.A., 1989, Macroecology: the division of food and space among species on continents. Science 243, 1145–1150. CrossRefGoogle ScholarPubMed
Diggle, P.J., Menezes, R., 2010, Geostatistical inference under preferential sampling. Appl. Stat. 59, 191–232. Google Scholar
EC, 2008, Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environment policy (Marine Strategy Framework Directive. Off. J. Eur. Union 25.6.2008 L164, 19-40.
EC, 2010, Commission Decision of 1 September 2010 on criteria and methodological standards on good environmental status of marine waters. Off. J. Eur. Union 2.9.2010 L 232, 14–24.
Ehrich, S., 1983, On the occurrence of some fish species at the slopes of the Rockall Trough. Arch. Fisch. 33, 105–150. Google Scholar
Fisher, J.A.D., Frank, K.T., 2004, Abundance-distribution relationships and conservation of exploited marine fishes. Mar. Ecol. Prog. Ser. 279, 201–213. CrossRefGoogle Scholar
Fretwell, S.D., Lucas, H.L., 1969, On territorial behaviour and other factors influencing habitat distribution in birds. Acta Biotheor. 19, 16–37. CrossRefGoogle Scholar
Frisk, M.G., Duplisea, D.E., Trenkel, V.M., 2011, Exploring the abundance-occupancy relationships for the Georges Bank finfish and shellfish community from 1963-2006. Ecol. Appl. 21, 227–240. CrossRefGoogle Scholar
Gaston, K.J., Blackburn, T.M., Greenwood, J.J.D., Gregory, R.D., Quinn, R.M., Lawton, J.H., 2000, Abundance-occupancy relationships. J. Appl. Anim. Ecol. 37, 39–59. CrossRefGoogle Scholar
Gaston, K.J., Fuller, R.A., 2009, The sizes of species’ geographic ranges. J. Appl. Ecol. 46, 1–9. CrossRefGoogle Scholar
Gelfand, A.E., Sahu, S.K., Holland, D.M., 2012, On the effect of preferential sampling in spatial prediction. Environmentrics 23, 565–578. CrossRefGoogle ScholarPubMed
Gillis, D.M., Peterman, R.M., 1998, Implications of interference among fishing vessels and the ideal free distribution to the interpretation of CPUE. Can. J. Fish. Aquat. Sci. 55, 37–46. CrossRefGoogle Scholar
Gillis, D.M., Peterman, R.M., Tyler, A.V., 1993, Movement dynamics in a fishery: application of the ideal free distribution to spatial allocation of effort. Can. J. Fish. Aquat. Sci. 50, 323–333. CrossRefGoogle Scholar
Gillis, D.M., van der Lee, A., 2012, Advancing application of the ideal free distribution to spatial models of fishing effort: the isodar approach. Can. J. Fish. Aquat. Sci. 69, 1610–1620. CrossRefGoogle Scholar
Harley, S.J., Myers, R.A., Dunn, A., 2001, Is catch-per-unit-effort proportional to abundance? Can. J. Fish. Aquat. Sci. 58, 1760–1772. CrossRefGoogle Scholar
Hilborn, R., 2007, Defining success in fisheries and conflicts in objectives. Mar. Policy 31, 153–158. CrossRefGoogle Scholar
Hilborn R., Walters C.J., 1992, Quantitative fisheries stock assessment: choice, dynamics and uncertainty. New York, Chapman and Hall.
Hutton, T., Mardle, S., Pascoe, S., Clark, R.A., 2004, Modelling fishing location choice within mixed fisheries: English North Sea beam trawlers in 2000 and 2001. ICES J. Mar. Sci. 61, 1443–1452. CrossRefGoogle Scholar
Large, P.A., Agnew, D.J., Pérez, J.Á.Á., Froján, C.B., Cloete, R., Damalas, D., Dransfeld, L., Edwards, C.T.T., Feist, S., Figueiredo, I., González, F., Herrera, J.G., Kenny, A., Jakobsdóttir, K., Longshaw, M., Lorance, P., Marchal, P., Mytilineou, C., Planque, B., Politou, C.-Y., 2013, Strengths and weaknesses of the management and monitoring of deep-water stocks, fisheries and ecosystems in various areas of the world - A roadmap toward sustainable deep-water fisheries in the Northeast Atlantic? Rev. Fish. Sci. 21, 157–180. CrossRefGoogle Scholar
Large, P.A., Diez, G., Drewery, J., Laurans, M., Pilling, G.M., Reid, D.G., Reinert, J., South, A.B., Vinnichenko, V.I., 2010, Spatial and temporal distribution of spawning aggregations of blue ling (Molva dypterygia) west and northwest of the British Isles. ICES J. Mar. Sci. 76, 494–501. CrossRefGoogle Scholar
Lorance, P., Agnarsson, S., Damalas, D., des Clers, S., Figueiredo, I., Gil, J., Trenkel, V.M., 2011, Using qualitative and quantitative stakeholder knowledge: examples from European deep-water fisheries. ICES J. Mar. Sci. 68, 1815–1824. CrossRefGoogle Scholar
Lorance, P., Dupouy, H., 2001, CPUE abundance indices of the main target species of the French deep-water fishery in ICES Sub-areas V-VII. Fish. Res. 51, 137–149. CrossRefGoogle Scholar
Lorance P., Large P.A., Bergstad O.A., Gordon J.D.M., 2008, Grenadiers of the NE Atlantic - distribution, biology, fisheries and their impacts, and developments in stock assessment and management. In: Orlov A., Iwamoto T. (Eds.), Grenadiers of the world oceans: biology, stock assessment and fisheries. Bethesda, MS. Am. Fish. Soc. Symp. 63, 365–397.
Lorance, P., Pawlowski, L., Trenkel, V.M., 2010, Standardizing blue ling landings per unit effort from industry haul-by-haul data using generalized additive models. ICES J. Mar. Sci. 67, 1650–1658 CrossRefGoogle Scholar
Lorance, P., Trenkel, V.M., 2006, Variability in natural behaviour, and observed reactions to an ROV, by mid-slope fish species. J. Mar. Exp. Biol. Ecol. 332, 106–119. CrossRefGoogle Scholar
MacCall A.D., 1990, Dynamic geography of marine fish populations. Seattle, WA, University of Washington Press.
Mauchline, J., Gordon, J.D.M., 1983, Diets of the sharks and chimaeroids of the Rockall Trough, northeastern Atlantic Ocean. Mar. Biol. 75, 269–278. CrossRefGoogle Scholar
Mauchline, J., Gordon, J.D.M., 1984, Diets and bathymetric distributions of the macrourid fish of the Rockall Trough, northeastern Atlantic Ocean. Mar. Biol. 81, 107–121. CrossRefGoogle Scholar
Murawski, S.A., Wigley, S.E., Fogarty, M.J., Rago, P.J., Mountain, D.G., 2005, Effort distribution and catch patterns adjacent to temperate MPAs. ICES J. Mar. Sci. 62, 1150–1167. Google Scholar
Murray, L.G., Hinz, H., Kaiser, M.J., 2011, Functional response of fishers in the Isle of Man scallop fishery. Mar. Ecol. Prog. Ser. 430, 157–169. CrossRefGoogle Scholar
Neves, A., Vieira, A.R., Farias, I., Figueiredo, I., Sequeira, V., Gordo, L.S., 2009, Reproductive strategies in black scabbardfish (Aphanopus carbo Lowe, 1839) from the NE Atlantic. Scient. Mar. 73, 19–31. CrossRefGoogle Scholar
Paloheimo, J.E., Dickie, L.M., 1964, Abundance and fishing success. Rapp. P.-V. Réun. Cons. Int. Explor. Mer 155, 152–163. Google Scholar
Schaeffer, M.B., 1954, Some aspects of the dynamics of populations important to the management of the commercial marine fisheries. Inter-Am. Trop. Tuna Comm. Bull. 1, 23–56. Google Scholar
Shackell, N.L., Frank, K.T., Brickman, D.W., 2005, Range contraction may not always predict core areas: an example from marine fish. Ecol. Appl. 15, 1440–1449. CrossRefGoogle Scholar
Swain, D.P., Sinclair, A.F., 1994, Fish distribution and catchability: What is the appropriate measure of distribution? Can. J. Fish. Aquat. Sci. 51, 1046–1054. CrossRefGoogle Scholar
Wilberg, M.J., Thorson, J.T., Linton, B.C., Berkson, J., 2010, Incorporating time-varying catchability into population dynamics stock assessment models. Rev. Fish. Sci. 18, 7–24. CrossRefGoogle Scholar