EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 29 / No 2 (April-June 2016) Aquat. Living Resour., 29 2 (2016) 205 References
Free Access
Issue
Aquat. Living Resour.
Volume 29, Number 2, April-June 2016
Article Number 205
Number of page(s) 12
DOI https://doi.org/10.1051/alr/2016018
Published online 16 June 2016
  • Alagaraja K., 1984, Simple methods for estimation of parameters for assessing exploited fish stocks. Indian J. Fish. 31, 177–208. [Google Scholar]
  • Alós J., Palmer M., Balle S., Grau A.M., Morales-Nin B., 2010, Individual growth pattern and variability in Serranus scriba: a Bayesian analysis. ICES J. Mar. Sci. 67, 502–512. [CrossRef] [Google Scholar]
  • Apostolaki P., Hillary R., 2009, Harvest control rules in the context of fishery-independent management of fish stocks. Aquat. Living Resour. 22, 217–224. [CrossRef] [EDP Sciences] [Google Scholar]
  • Ault J.S., Smith S.G., Bohnsack J.A., Luo J., Zurcher N., McClellan D.B., Ziegler T.A., Hallac D.E., Patterson M., Feeley M.W., Ruttenberg B.I., Hunt J., Kimball D., Causey B., 2013, Assessing coral reef fish population and community changes in response to marine reserves in the Dry Tortugas, Florida, USA. Fish. Res. 144, 28–37. [CrossRef] [Google Scholar]
  • Babcock E.A., MacCall A.D., 2011, How useful is the ratio of fish density outside versus inside no-take marine reserves as a metric for fishery management control rules? Can. J. Fish. Aquat. Sci. 68, 343–359. [CrossRef] [Google Scholar]
  • Bunnell D.B., Miller T.J., 2005, An individual-based modeling approach to spawning-potential per-recruit models: an application to blue crab (Callinectes sapidus) in Chesapeake Bay. Can. J. Fish. Aquat. Sci. 62, 2560–2572. [CrossRef] [Google Scholar]
  • Butterworth D.S., Punt A.E., 1999, Experiences in the evaluation and implementation of management procedures. ICES J. Mar. Sci. J. Cons. 56, 985–998. [CrossRef] [Google Scholar]
  • Butterworth D.S., Bentley N., Oliveira J.A.A.D., Donovan G.P., Kell L.T., Parma A.M., Punt A.E., Sainsbury K.J., Smith A.D.M., Stokes T.K., 2010, Purported flaws in management strategy evaluation: basic problems or misinterpretations? ICES J. Mar. Sci. 67, 567–574. [Google Scholar]
  • Carruthers T.R., Kell L.T., Butterworth D.D.S., Maunder M.N., Geromont H.F., Walters C., McAllister M.K., Hillary R., Levontin P., Kitakado T., Davies C.R., 2015, Performance review of simple management procedures. ICES J. Mar. Sci. J. Cons. fsv212. [Google Scholar]
  • Chapman M.R., Kramer D.L., 1999, Gradients in coral reef fish density and size across the Barbados Marine Reserve boundary: effects of reserve protection and habitat characteristics. Mar. Ecol. Prog. Ser. 181, 81–96. [CrossRef] [Google Scholar]
  • Chapman M.R., Kramer D.L., 2000, Movements of fishes within and among fringing coral reefs in Barbados. Environ. Biol. Fishes 57, 11–24. [CrossRef] [Google Scholar]
  • Cochran W.G., 1977, Sampling Techniques, 3rd edn. John Wiley & Sons, New York. [Google Scholar]
  • Codling E.A., 2008, Individual-based movement behaviour in a simple marine reserve – fishery system: why predictive models should be handled with care. Hydrobiologia 606, 55–61. [CrossRef] [Google Scholar]
  • Cook R.M., 2013, A fish stock assessment model using survey data when estimates of catch are unreliable. Fish. Res. 143, 1–11. [CrossRef] [Google Scholar]
  • Cope J.M., Punt A.E., 2009, Length-based reference points for data-limited situations: applications and restrictions. Mar. Coast. Fish. 1, 169–186. [CrossRef] [Google Scholar]
  • DeAngelis D.L., Godbout L., Shuter B.J., 1991, An individual-based approach to predicting density-dependent dynamics in smallmouth bass populations. Ecol. Model. 57, 91–115. [CrossRef] [Google Scholar]
  • De Oliveira J.A.A., Butterworth D.S., 2004, Developing and refining a joint management procedure for the multispecies South African pelagic fishery. ICES J. Mar. Sci. J. Cons. 61, 1432–1442. [CrossRef] [Google Scholar]
  • Dowling N.A., Dichmont C.M., Haddon M., Smith D.C., Smith A.D.M., Sainsbury K., 2015, Empirical harvest strategies for data-poor fisheries: A review of the literature. Fish. Res. 171, 141–153. [CrossRef] [Google Scholar]
  • Farmer N., Ault J., 2011, Grouper and snapper movements and habitat use in Dry Tortugas, Florida. Mar. Ecol. Prog. Ser. 433, 169–184. [CrossRef] [Google Scholar]
  • Fournier D.A., Skaug H.J., Ancheta J., Ianelli J., Magnusson A., Maunder M.N., Nielsen A., Sibert J., 2012, AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim. Methods Softw. 27, 233–249. [CrossRef] [Google Scholar]
  • Gerber L.R., Botsford L.W., Hastings A., Possingham H.P., Gaines S.D., Palumbi S.R., Andelman S., 2003, Population models for marine reserve design: a retrospective and prospective synthesis. Ecol. Appl. 13, 47–64. [CrossRef] [Google Scholar]
  • Gerber L.R., Wielgus J., Sala E., 2007, A decision framework for the adaptive management of an exploited species with implications for marine reserves. Conserv. Biol. 21, 1594–1602. [CrossRef] [PubMed] [Google Scholar]
  • Geromont H.F., Butterworth D.S., 2015a, Complex assessments or simple management procedures for efficient fisheries management: a comparative study. ICES J. Mar. Sci. 72, 262–274. [CrossRef] [Google Scholar]
  • Geromont H.F., Butterworth D.S., 2015b, Generic management procedures for data-poor fisheries: forecasting with few data. ICES J. Mar. Sci. 72, 251–261. [CrossRef] [Google Scholar]
  • Grimm V., Berger U., Bastiansen F., Eliassen S., Ginot V., Giske J., Goss-Custard J., Grand T., Heinz S.K., Huse G., Huth A., Jepsen J.U., Jørgensen C., Mooij W.M., Müller B., Pe’er G., Piou C., Railsback S.F., Robbins A.M., Robbins M.M., Rossmanith E., Rüger N., Strand E., Souissi S., Stillman R.A., Vabø R., Visser U., DeAngelis D.L., 2006, A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198, 115–126. [Google Scholar]
  • Guénette S., Lauck T., Clark C., 1998, Marine reserves: from Beverton and Holt to the present. Rev. Fish Biol. Fish. 8, 251–272. [Google Scholar]
  • Harford W.J., 2014, Integrated monitoring and stock assessment of spatially heterogeneous reef fisheries (Dissertation). Miami, Florida, USA, University of Miami. [Google Scholar]
  • Harford W.J., Smith S.G., Ault J.S., Babcock E.A., 2015, Cross-shelf habitat occupancy probabilities for juvenile groupers in the Florida Keys coral reef ecosystem. Mar. Coast. Fish. 8, 147–159. [CrossRef] [Google Scholar]
  • Hart D.R., 2001, Individual-based yield-per-recruit analysis, with an application to the Atlantic sea scallop, Placopecten magellanicus. Can. J. Fish. Aquat. Sci. 58, 2351–2358. [CrossRef] [Google Scholar]
  • Hatton I.A., McCann K.S., Umbanhowar J., Rasmussen J.B., 2006, A dynamical approach to evaluate risk in resource management. Ecol. Appl. 16, 1238–1248. [CrossRef] [PubMed] [Google Scholar]
  • Hertz D.B., Thomas H., 1983, Risk analysis and its applications. Wiley, New York. [Google Scholar]
  • Hilborn R., 1979, Comparison of fisheries control systems that utilize catch and effort data. J. Fish. Res. Board Can. 36, 1477–1489. [CrossRef] [Google Scholar]
  • Hilborn R., Walters C., 1992, Quantitative fisheries stock assessment: choice, dynamics, and uncertainty. N.Y., Chapman and Hall, New York. [Google Scholar]
  • Hilborn R., Parma A., Maunder M., 2002, Exploitation rate reference points for west coast rockfish: are they robust and are there better alternatives? North Am. J. Fish. Manag. 22, 365–375. [CrossRef] [Google Scholar]
  • Hilborn R., Micheli F., De Leo G.A., 2006, Integrating marine protected areas with catch regulation. Can. J. Fish. Aquat. Sci. 63, 642–649. [CrossRef] [Google Scholar]
  • Houk P., van Woesik R., 2013, Progress and perspectives on question-driven coral-reef monitoring. Bioscience 63, 297–303. [CrossRef] [Google Scholar]
  • Huse G., 2001, Modelling habitat choice in fish using adapted random walk. Sarsia 86, 477–483. [CrossRef] [Google Scholar]
  • Huse G., Giske J., 1998, Ecology in Mare Pentium: an individual-based spatio-temporal model for fish with adapted behaviour. Fish. Res. 37, 163–178. [CrossRef] [Google Scholar]
  • Jennings S., 2001, Patterns and prediction of population recovery in marine reserves. Rev. Fish Biol. Fish. 10, 209–231. [CrossRef] [Google Scholar]
  • Keitt T.H., 2000, Spectral representation of neutral landscapes. Landsc. Ecol. 15, 479–494. [CrossRef] [Google Scholar]
  • Kellner J.B., Tetreault I., Gaines S.D., Nisbet R.M., 2007, Fishing the line near marine reserves in single and multispecies fisheries. Ecol. Appl. 17, 1039–1054. [CrossRef] [PubMed] [Google Scholar]
  • Larkin P.A., 1977, An epitaph for the concept of maximum sustained yield. Trans. Am. Fish. Soc. 106, 1–11. [CrossRef] [Google Scholar]
  • Little L.R., Wayte S.E., Tuck G.N., Smith A.D.M., Klaer N., Haddon M., Punt A.E., Thomson R., Day J., Fuller M., 2011, Development and evaluation of a cpue-based harvest control rule for the southern and eastern scalefish and shark fishery of Australia. ICES J. Mar. Sci. J. Cons. 68, 1699–1705. [CrossRef] [Google Scholar]
  • Li X., He H.S., Wang X., Bu R., Hu Y., Chang Y., 2004, Evaluating the effectiveness of neutral landscape models to represent a real landscape. Landsc. Urban Plan. 69, 137–148. [CrossRef] [Google Scholar]
  • Lorenzen K., 1996, The relationship between body weight and natural mortality in juvenile and adult fish: a comparison of natural ecosystems and aquaculture. J. Fish. Biol. 49, 627–647. [CrossRef] [Google Scholar]
  • Luke S., Cioffi-Revilla C., Panait L., Sullivan K., Balan G., 2005, MASON: a multi-agent simulation environment. Simulation 82, 517–527. [CrossRef] [Google Scholar]
  • Magnusson A., Hilborn R., 2007, What makes fisheries data informative? Fish. Fish. 8, 337–358. [Google Scholar]
  • Manly B.F., McDonald L., Thomas D.L., McDonald T.L., Erickson W.P., 2002, Resource Selection by Animals: Statistical Design and Analysis for Field Studies, 2nd edition. Springer. [Google Scholar]
  • Martell S., Froese R., 2012, A simple method for estimating MSY from catch and resilience. Fish Fish. DOI: 10.1111/j.1467–2979.2012.00485.x. [Google Scholar]
  • McDonald A.D., Little L.R., Gray R., Fulton E., Sainsbury K.J., Lyne V.D., 2008, An agent-based modelling approach to evaluation of multiple-use management strategies for coastal marine ecosystems. Math. Comput. Simul. 78, 401–411. [CrossRef] [Google Scholar]
  • McGilliard C.R., Hilborn R., MacCall A., Punt A.E., Field J.C., 2011, Can information from marine protected areas be used to inform control-rule-based management of small-scale, data-poor stocks? ICES J. Mar. Sci. J. Cons. 68, 201–211. [CrossRef] [Google Scholar]
  • Meester G.A., Ault J.S., Smith S.G., Mehrotra A., 2001, An integrated simulation modeling and operations research approach to spatial management decision making. Sarsia 86, 543–558. [CrossRef] [Google Scholar]
  • Meester G.A., Mehrotra A., Ault J.S., Baker E.K., 2004, Designing marine reserves for fishery management. Manag. Sci. 50, 1031–1043. [CrossRef] [Google Scholar]
  • MER, 2015, A Management Strategy Evaluation of a Multi-Indicator Adaptive Management Framework for Data-Poor Fisheries. MER Consultants, submitted to The Nature Conservancy May 5, 2015. [Google Scholar]
  • Mesnil B., Cotter J., Fryer R.J., Needle C.L., Trenkel V.M., 2009, A review of fishery-independent assessment models, and initial evaluation based on simulated data. Aquat. Living Resour. 22, 207–216. [CrossRef] [EDP Sciences] [Google Scholar]
  • Miethe T., Pitchford J., Dytham C., 2009, An individual-based model for reviewing marine reserves in the light of fisheries-induced evolution in mobility and size at maturation. J. Northwest Atl. Fish. Sci. 41, 151–162. [CrossRef] [Google Scholar]
  • Mohn R.K., 1980, Bias and error propagation in logistic production models. Can. J. Fish. Aquat. Sci. 37, 1276–1283. [CrossRef] [Google Scholar]
  • Olsen A.R., Sedransk J., Edwards D., Gotway C.A., Liggett W., Rathbun S., Reckhow K.H., Yyoung L.J., 1999, Statistical Issues for Monitoring Ecological and Natural Resources in the United States. Environ. Monit. Assess. 54, 1–45. [CrossRef] [Google Scholar]
  • Peitgen H.-O., Saupe D. (Eds.), 1988, The Science of Fractal Images. Springer-Verlag, New York. [Google Scholar]
  • Pelletier D., Mahévas S., 2005, Spatially explicit fisheries simulation models for policy evaluation. Fish Fish. 6, 307–349. [CrossRef] [Google Scholar]
  • Peterman R.M., 1990, Statistical power analysis can improve fisheries research and management. Can. J. Fish. Aquat. Sci. 47, 2–15. [CrossRef] [Google Scholar]
  • Pomarede M., Hillary R., Ibaibarriaga L., Bogaards J., Apostolaki P., 2010, Evaluating the performance of survey-based operational management procedures. Aquat. Living Resour. 23, 77–94. [CrossRef] [EDP Sciences] [Google Scholar]
  • Porch C.E., Eklund A.-M., Scott G.P., 2006, A catch-free stock assessment model with application to goliath grouper (Epinephelus itajara) off southern Florida. Fish. Bull. 104, 89–101. [Google Scholar]
  • Prince J.D., Dowling N.A., Davies C.R., Campbell R.A., Kolody D.S., 2011, A simple cost-effective and scale-less empirical approach to harvest strategies. ICES J. Mar. Sci. 68, 947–960. [CrossRef] [Google Scholar]
  • Punt A.E., Butterworth D.S., de Moor C.L., De Oliveira J.A.A., Haddon M., 2014, Management strategy evaluation: best practices. Fish Fish. n/a–n/a. [Google Scholar]
  • Railsback S.F., Lamberson R.H., Harvey B.C., Duffy W.E., 1999, Movement rules for individual-based models of stream fish. Ecol. Model. 123, 73–89. [CrossRef] [Google Scholar]
  • Ralston S., O’Farrell M.R., 2008, Spatial variation in fishing intensity and its effects on yield. Can. J. Fish. Aquat. Sci. 65, 588–599. [CrossRef] [Google Scholar]
  • R Development Core Team, 2012, R: A Language and Environment for Statistical Computing, Vienna, Austria http://www.R-project.org. Vienna, Austria. [Google Scholar]
  • Richards B.L., Williams I.D., Nadon M.O., Zgliczynski B.J., 2011, A Towed-Diver Survey Method for Mesoscale Fishery-Independent Assessment. Bull. Mar. Sci. 87, 55–74. [CrossRef] [Google Scholar]
  • Rose K.A., Rutherford E.S., McDermot D.S., Forney J.L., Mills E.L., 1999, Individual-based model of yellow perch and walleye populations in Oneida Lake. Ecol. Monogr. 69, 127–154. [CrossRef] [Google Scholar]
  • Russo T., Mariani S., Baldi P., Parisi A., Magnifico G., 2009, Progress in modelling herring populations: an individual-based model of growth. ICES J. Mar. Sci. 66, 1718–1725. [CrossRef] [Google Scholar]
  • Sainsbury K.J., 1991, Application of an experimental approach to management of a tropical multispecies fishery with highly uncertain dynamics. ICES J. Mar. Sci. Symp. 193, 301–320. [Google Scholar]
  • Sainsbury K., Punt A.E., Smith A.D.M., 2000, Design of operational management strategies for achieving fishery ecosystem objectives. ICES J. Mar. Sci. 57, 731–741. [Google Scholar]
  • Sale P.F., Cowan R.K., Danilowicz B.S., Jones G.P., Kritzer J.P., Lindeman K.C., Planes S., Polunin N.V.C., Russ G.R., Sadovy Y.J., Steneck R.S., 2005, Critical science gaps impede use of no-take fishery reserves. Trends Ecol. Evol. 20, 74–80. [Google Scholar]
  • Saul S., Die D., Brooks E.N., Burns K., 2012, An individual-based model of ontogenetic migration in reef fish using a biased random walk. Trans. Am. Fish. Soc. 141, 1439–1452. [CrossRef] [Google Scholar]
  • Schaefer 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, 27–56. [Google Scholar]
  • Schlather M., Menck P., Singleton R., Pfaff B., R Core Team, 2013, RandomFields: Simulation and Analysis of Random Fields. R package version 2.0.66. http://CRAN.R-project.org/package=RandomFields. [Google Scholar]
  • SEDAR, 2010, Stock assessment report: Gulf of Mexico and South Atlantic Black Grouper. South Atlantic Fishery Management Council. SEDAR 19. [Google Scholar]
  • Sluka R., Chiappone M., Sealey K.M.S., 2001, Influence of habitat on grouper abundance in the Florida Keys, USA. J. Fish Biol. 58, 682–700. [CrossRef] [Google Scholar]
  • Smith S.G., Ault J.S., Bohnsack J.A., Harper D.E., Luo J., McClellan D.B., 2011, Multispecies survey design for assessing reef-fish stocks, spatially-explicit management performance, and ecosystem condition. Fish. Res. 109, 25–41. [CrossRef] [Google Scholar]
  • Stein M.L., 2002, Fast and exact simulation of fractional Brownian surfaces. J. Comput. Graph. Stat. 11, 587. [Google Scholar]
  • Thompson S.K., 2012, Sampling, Wiley series in probability and statistics, 3rd edition. Wiley, Hoboken, N.J. [Google Scholar]
  • Thompson S.K., Seber G.A.F., 1996, Adaptive Sampling. John Wiley & Sons, Inc, New York. [Google Scholar]
  • Thorson J.T., Stewart I.J., Punt A.E., 2012, Development and application of an agent-based model to evaluate methods for estimating relative abundance indices for shoaling fish such as Pacific rockfish (Sebastes spp.). Ices J. Mar. Sci. 69, 635–647. [CrossRef] [Google Scholar]
  • Train K.E., 2002, Discrete Choice Methods with Simulation. 2nd edition, Cambridge University Press. [Google Scholar]
  • Tyler J.A., Rose K.A., 1994, Individual variability and spatial heterogeneity in fish population models. Rev. Fish Biol. Fish. 4, 91–123. [CrossRef] [Google Scholar]
  • van Winkle W., Jager H.I., Railsback S.F., Holcomb B.D., Studley T.K., Baldrige J.E., 1998, Individual-based model of sympatric populations of brown and rainbow trout for instream flow assessment: model description and calibration. Ecol. Model. 110, 175–207. [CrossRef] [Google Scholar]
  • von Bertalanffy L., 1938, A quantitative theory of organic growth (Inquiries on growth laws II). Hum. Biol 10, 181–213. [Google Scholar]
  • Walters C., 1986, Adaptive Management of Renewable Resources. MacMillan Publishing Company, New York. [Google Scholar]
  • Walters C.J., Martell S.J.D., 2004, Fisheries Ecology and Management. Princeton University Press, USA. [Google Scholar]
  • Walters C., Ludwig D., 1994, Calculation of Bayes posterior probability distributions for key population parameters. Can. J. Fish. Aquat. Sci. 51, 713–722. [Google Scholar]
  • With K.A., King A.W., 1997, The use and misuse of neutral landscape models in ecology. Oikos 79, 219–229. [CrossRef] [Google Scholar]
  • Zhang Z., 2013, Evaluation of logistic surplus production model through simulations. Fish. Res. 140, 36–45. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.