# Atlantic bluefin tuna in the Gulf of Maine, II: precision of sampling designs in estimating seasonal abundance accounting for tuna behaviour

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## Abstract

A primary challenge of animal surveys is to understand how to reliably sample populations exhibiting strong spatial heterogeneity. Building upon recent findings from survey, tracking and tagging data, we investigate spatial sampling of a seasonally resident population of Atlantic bluefin tuna in the Gulf of Maine, Northwestern Atlantic Ocean. We incorporate empirical estimates to parameterize a stochastic population model and simulate measurement designs to examine survey efficiency and precision under variation in tuna behaviour. We compare results for random, systematic, stratified, adaptive and spotter-search survey designs, with spotter-search comprising irregular transects that target surfacing schools and known aggregation locations (i.e., areas of expected high population density) based on a priori knowledge. Results obtained show how survey precision is expected to vary on average with sampling effort, in agreement with general sampling theory and provide uncertainty ranges based on simulated variance in tuna behaviour. Simulation results indicate that spotter-search provides the highest level of precision, however, measurable bias in observer-school encounter rate contributes substantial uncertainty. Considering survey bias, precision, efficiency and anticipated operational costs, we propose that an adaptive-stratified sampling alone or a combination of adaptive-stratification and spotter-search (a mixed-layer design whereby a priori information on the location and size of school aggregations is provided by sequential spotter-search sampling) may provide the best approach for reducing uncertainty in seasonal abundance estimates.

### Keywords

Abundance Behaviour Gulf of Maine Survey Tuna## Notes

### Acknowledgements

This work was funded by the Office of Naval Research Grant No. 0014-99-1-1-1035 to M. Lutcavage and S. Kraus, the National Marine Fisheries Service (Grant NA 06 FM 0460, to M. Lutcavage), a research fellowship from the University of British Columbia (UBC), Vancouver, Canada awarded to N. K. Newlands, and a NSERC discovery grant to Prof. Tony J. Pitcher. The preparation of earlier drafts of this research manuscript was funded by a grant from NSERC Canada awarded to Prof. L. Edelstein-Keshet (Dept. of Mathematics, UBC). We thank the Atlantic Tuna Spotter Association and the East Coast Tuna Association for their partnership in the aerial surveys, and Lee Dantzler (NESDIS) for his support. In addition, we gratefully acknowledge the contributions of Richard Brill (NMFS CMER Program at VIMS), Jennifer Goldstein (UMass Boston), Brad Chase and Greg Skomal (Mass. Div. of Marine Fisheries) for bluefin data used in our analysis. We gratefully acknowledge outside reviewers of earlier versions of this manuscript.

### References

- Angulo J, Bueso M, Alonso F (2000) A study on sampling design for optimal prediction of space-time stochastic processes. Stoch Env Res Risk Assess 14:412–427Google Scholar
- Apostolaki P, Babcock EA, McAllister MK (2003) A scenario based framework for the stock assessment of north Atlantic bluefin tuna taking into account trans-Atlantic movement, stock mixing and multiple fleets. ICCAT (Int. Commission for the Conservation of Atlantic Tunas) Coll Vol Sci Pap 55(3):1055–1079Google Scholar
- Arreguin-Sanchez F (1996) Catchability: a key parameter for fish stock assessment. Rev Fish Biol Fish 6:221–242Google Scholar
- Avery T, Berlin G (1992) Fundamentals of remote sensing and airphoto interpretation. Prentice Hall, New York, NYGoogle Scholar
- Barange M, Hampton I (1997) Spatial structure of co-occurring anchovy and sardine populations from acoustic data: implications for survey design. Fish Oceanogr 6(2):94–108CrossRefGoogle Scholar
- Bertrand A, Bard FX, Josse E (2002a) Tuna food habits related to micronekton distribution in French Polynesia. Mar Biol 140:1023–1037CrossRefGoogle Scholar
- Bertrand A, Josse E, Bach P, Gros P, Dagorn L (2002b) Hydrological and trophic characteristics of tuna habitat: consequences on tuna distribution and longline catchability. Can J Fish Aquat Sci 59:1002–1013CrossRefGoogle Scholar
- Bestley S, Hobday A (2002) Development of an individual-based spatially-explicit movement model for juvenile southern bluefin tuna: school-size sub-model. CSIRO Mar Res Rep RMWS/02/5Google Scholar
- Bourque R (1997) The North American fish spotter. AirSpace (December), 72–79Google Scholar
- Bravington M (2002) Further considerations on the analysis and design of aerial surveys for juvenile SBT in the Great Australian Bight. CSIRO Mar Res Rep RMS/02/11Google Scholar
- Brewer K (1979) A class of robust survey sampling designs for large-scale surveys. J Am Stat Assoc 74:911–915CrossRefGoogle Scholar
- Brill R, Lutcavage M (2001) Understanding environmental influences on movements and depth distributions of tunas and billfishes can significantly improve population assessments. In: Sedberry G (ed) Island in the stream: oceanography and fisheries of the Charleston Bump, vol 25. American Fisheries Society, Besthesda MY, pp 179–198Google Scholar
- Brill R, Lutcavage M, Metzger G, Bushnell P, Arendt M, Lucy J, Watson C, Foley D (2002) Horizontal and vertical movement of juvenile bluefin tuna (
*Thunnus thynnus*) in relation to oceanographic conditions of the western North Atlantic, determined with ultrasonic telemetry. USA Fish Bull 100:155–167Google Scholar - Buckland S, Goudie I, Borchers D (2000) Wildlife population assessment: past developments and future directions. Biometrics 56:1–12PubMedCrossRefGoogle Scholar
- Buckland S, Anderson D, Burnham K, Laake J, Borchers D, Thomas L (2001) Introduction to distance sampling: estimating abundance of biological populations. Oxford University Press, OxfordGoogle Scholar
- Chao M (1982) A general purpose unequal probability sampling plan. Biometrika 69(3):653–656CrossRefGoogle Scholar
- Clark C, Mangel M (1979) Aggregation and fishery dynamics: a theoretical study of schooling and purse seine tuna fisheries. USA Fish Bull 77(2):317–337Google Scholar
- Cowling A, Hobday A, Gunn J (2003) Development of a fishery independent index of abundance for juvenile southern bluefin tuna and improved fishery independent estimates of southern bluefin tuna recruitment through integration of environmental, archival tag and aerial survey data. CSIRO Div. Mar. Fish. Hobart, Tasmania, Australia, Ref. No. 313413Google Scholar
- Craig B, Newton M, Garrott R, Reynolds J, Wilcox J (1997) Analysis of aerial survey data on Florida Manatee using Markov chain Monte-Carlo. Biometrics 53:524–541PubMedCrossRefGoogle Scholar
- Christman MC (1997) Efficiency of some sampling designs for spatially clustered populations Environmetrics 8:145–166CrossRefGoogle Scholar
- Churnside J, Wilson J, Oliver C (1998) Evaluation of the capability of the experimental oceanographic fisheries Lidar (FLOE) for tuna detection in the Eastern Tropical Pacific. NOAA Tech. Memorandum, ERL ETL-287, 71 ppGoogle Scholar
- Dalenius T (1962) Recent advances in sampling survey theory and methods. Ann Math Stat 33(2):325–349Google Scholar
- Efron B, Tibshirani R (1986) Bootstrap methods for standard errors, confidence intervals and other measures of statistical accuracy. Stat Sci 1(1):54–77Google Scholar
- Fiedler P (1978) The precision of simulated transect surveys of Northern Anchovy,
*Engraulis Mordax*, school groups. USA Fish Bull 76(3):679–685Google Scholar - Fromentin J-M (2003) Preliminary results of aerial surveys of bluefin tuna in the western Mediterranean Sea. ICCAT Coll Vol Sci Pap 55(3):1019–1027Google Scholar
- Gerrodette T (1987) A power analysis for detecting trends. Ecology 68(5):1364–1372CrossRefGoogle Scholar
- Godö OR (1998) What can technology offer the future fisheries scientist – possibilities for obtaining better estimates of stock abundance by direct observations. J Northw Atl Fish Sci 23:105–131CrossRefGoogle Scholar
- Günderson D (1993) Surveys for fisheries resources. John Wiley and Sons, New York, New YorkGoogle Scholar
- Gurtman S (1999) An analysis of the use of spotter aircraft in the commercial Atlantic bluefin tuna fishery. M.Sc. Thesis, Duke University NCGoogle Scholar
- Hanselman DH, Quinn TJ, Lunsford C, Heifetz J, Clausen D (2003) Applications in adaptive cluster sampling of Gulf of Alaska rockfish. USA Fish Bull 101:501–513Google Scholar
- Hansen M, Hurwitz W (1943) On the theory of sampling from finite populations. Ann Math Stat 14:333–362Google Scholar
- Hara I (1985) Moving direction of Japanese sardine school on the basis of aerial surveys. Bull Jpn Soc Mar Sci 51(12):1939–1945Google Scholar
- Hartley H, Rao J (1962) Sampling with unequal probabilities and without replacement. Ann Math Stat 33:350–374Google Scholar
- Hastings A (2004) Transients: the key to long-term ecological understanding? Trends Ecol Evol 91(1):39–45CrossRefGoogle Scholar
- Hedley S, Buckland S, Borchers D (1999) Spatial modeling from line-transect data. J Cetacean Res Manag 1(3):255–264Google Scholar
- Helser TE, Hayes DB (1995) Providing quantitative management advice from stock abundance indices based on research surveys. USA Fish Bull 9:290–298Google Scholar
- Humston R, Alt J, Lutcavage M, Olson D (2000) Schooling and migration of large pelagic fishes relative to environmental cues. Fish Oceanogr 9:136–146CrossRefGoogle Scholar
- Johnson DH (1989) An empirical Bayes approach to analyzing recurring animal surveys. Ecology 70(4):945–952CrossRefGoogle Scholar
- Jolly GM, Hampton I (1987) Some problems in the statistical design and analysis of acoustic surveys to assess fish biomass. Rapp Reun Cons Int l’Explor Mer 189:415–421Google Scholar
- Jolly GM, Hampton I (1990) A stratified random transect design for acoustic surveys of fish stocks. Can J Fish Aquat Sci 47:1282–1291Google Scholar
- Josse E, Bach P, Dagorn L (1998) Simultaneous observations of tuna movements and their prey by sonic tracking and acoustic surveys. Hydrobiologia 371/372:61–69CrossRefGoogle Scholar
- Klaer NL, Cowling A, Polacheck T (2002) Commercial aerial spotting for Southern bluefin tuna in the Great Australian Bight by fishing season 1982–2000. CSIRO Div. Mar. Fish. Hobart, Tasmania Australia R99/1498 No. 300667Google Scholar
- Krebs CJ (1999) Ecological methodology. Pearson Benjamin Cummings, New York, NYGoogle Scholar
- Little R (1983) Estimating a finite population mean from unequal probability samples. J Am Stat Assoc 78(383):596–604CrossRefGoogle Scholar
- Lo NC-H, Jacobson LD, Squire JL (1992) Indices of relative abundance from fish spotter data based on delta-lognormal models. Can J Fish Aquat Sci 49:2515–2526Google Scholar
- Lutcavage M, Kraus S (1995) The feasibility of direct photographic assessment of giant bluefin tuna in New England waters. USA Fish Bull 93:495–503Google Scholar
- Lutcavage M, Kraus S (1997) Aerial survey of giant bluefin tuna,
*Thunnus thynnus*, in the Great Bahama Bank, Straits of Florida, 1995. USA Fish Bull 95:300–310Google Scholar - Lutcavage M, Newlands NK (1999) A strategic framework for fishery-independent assessment of bluefin tuna. ICCAT Coll Vol Sci Pap 49(2):400–402Google Scholar
- Lutcavage M, Goldstein J, Kraus S (1996a) Aerial assessment of bluefin tuna in New England waters, 1995. ICCAT Coll Vol Sci Pap 46(2):332–347Google Scholar
- Lutcavage M, Goldstein J, Kraus S (1996b) Aerial assessment of bluefin tuna in New England waters, 1994. Final Report to the National Marine Fisheries Service NOAA No. NA37FL0285, New England Aquarium, 15 February 1996Google Scholar
- Lutcavage M, Golstein J, Kraus S (1997) Distribution, relative abundance and behaviour of giant bluefin tuna in New England waters, 1996. ICCAT Coll Vol Sci Pap 46(2):332–347Google Scholar
- Maclennan D, Mackenzie I (1988) Precision of acoustic fish stock estimates. Can J Fish Aquat Sci 45:605–616CrossRefGoogle Scholar
- Mangel M, Smith PE (1990) Presence–absence sampling in fisheries management. Can J Fish Aquat Sci 47:1875–1887CrossRefGoogle Scholar
- Mangel M (2000) Irreducible uncertainties, sustainable fisheries and marine reserves. Evol Ecol Res 2:547–557Google Scholar
- Manley B (1998) Randomization, bootstrap and Monte-Carlo methods in biology. Chapman and Hall, LondonGoogle Scholar
- Maunder MN (2003) Paradigm shifts in fisheries stock assessment: from integrated analysis to Bayesian analysis and back again. Nat Res Model 16(4):465–475CrossRefGoogle Scholar
- McAllister M (1995) Using decision analysis to choose a design for surveying fisheries resources. Ph.D. Thesis, University of Washington, Seattle, WAGoogle Scholar
- McAllister M, Pikitch E (1997) A Bayesian approach to choosing a design for surveying fishery resources. Application to the eastern Bering sea trawl fishery. Can J Fish Aquat Sci 54(2):301–311CrossRefGoogle Scholar
- Mendelssohn R (1988) Some problems in estimating population sizes from catch-at-age data. USA Fish Bull 86(4):617–630Google Scholar
- Newlands NK (2002) Shoaling dynamics and abundance estimation: Atlantic bluefin tuna (
*Thunnus thynnus*). Ph.D. Thesis, University of British Columbia, Vancouver, B.C. Natl. Libr. Can., Can. Theses Microfilm No. 2003-2129971 (ISBN 0612750590), 601 ppGoogle Scholar - Newlands NK, Lutcavage ME, Pitcher TJ (2006) Atlantic bluefin tuna in the Gulf of Maine, I: estimation of seasonal abundance accounting for movement, school and school-aggregation behaviour. DOI 10.1007/210641-006-9069-5, Online First: http://dx.doi.org/10.1007/s10641-006-9069-5Google Scholar
- Palka D (2000) Abundance of the Gulf of Maine/Bay of Fundy Harbor Porpoise based on shipboard and aerial surveys during 1999. NMFS Northeast Fisheries Science Center Reference Document 00-07Google Scholar
- Palka D, Hammond PS (2001) Accounting for responsive movement in line transect estimates of abundance. Can J Fish Aquat Sci 58:777–787CrossRefGoogle Scholar
- Patterson K, Cook R, Darby C, Gavaris S, Kell L, Lewy P, Mesnil B, Punt A, Restrepo V, Skagan D, Stefansson G (2001) Estimating uncertainty in fish stock assessment and forecasting. Fish Fish 2:125–157Google Scholar
- Pennington M (1983) Efficient estimators of abundance, for fish and plankton surveys. Biometrics 39:281–286CrossRefGoogle Scholar
- Pennington M (1996) Estimating the mean and variance from highly skewed marine data. USA Fish Bull 94:498–505Google Scholar
- Pennington M, Strömme T (1998) Surveys as a research tool for managing dynamic stocks. Fish Res 37:97–106CrossRefGoogle Scholar
- Polacheck T, Pikitch E, Lo N (1997) Evaluation and recommendations for the use of aerial surveys in the assessment of Atlantic Bluefin Tuna. ICCAT Working Document SCRS/97/73, 41 ppGoogle Scholar
- Pollock K, Kendall W (1987) Visibility bias in aerial surveys: a review of estimation procedures. J Wildl Manag 51(2):502–510CrossRefGoogle Scholar
- Quang PX, Lanctot R (1991) A line transect model for aerial surveys. Biometrics 47:1089–1102CrossRefGoogle Scholar
- Ramsey F, Wildman V, Engbring J (1987) Covariate adjustments to effective area in variable-area wildlife surveys. Biometrics 43:1–11CrossRefGoogle Scholar
- Schick RS, Goldstein J, Lutcavage ME (2004) Bluefin tuna (
*Thunnus thynnus*) distribution in relation to sea-surface temperature fronts in the Gulf of Maine (1994–96). Fish Oceangr 13(4):225–238CrossRefGoogle Scholar - Schwarz CJ, Seber GAF (1999) Estimating animal abundance. Review III. Stat Sci 14:427–456CrossRefGoogle Scholar
- Seber G (1986) A review of estimating animal abundance. Biometrics 42:267–292PubMedCrossRefGoogle Scholar
- Simmonds EJ, Fryer RJ (1996) Which are better, random or systematic acoustic surveys? A simulation using North Sea herring as an example. ICES J Mar Sci 53:39–50CrossRefGoogle Scholar
- Squire J Jr (1961) Aerial fish spotting in the United States commercial fisheries. Comm Fish Rev 23(12):1–7Google Scholar
- Squire J Jr (1972) Apparent abundance of some pelagic marine fishes off the southern and central California Coast as surveyed by an airborne monitoring program. USA Fish Bull 70(3):1005–1019Google Scholar
- Squire J Jr (1978) Northern anchovy school shapes as related to problems in school size estimation. USA Fish Bull 76(2):443–448Google Scholar
- Steinhorst RK, Samuel MD (1989) Sightability adjustment methods for aerial surveys of wildlife populations. Biometrics 45:415–425CrossRefGoogle Scholar
- Taylor J (1982) An introduction to error analysis: the study of uncertainties in physical measurements, 2nd edn. Oxford University Press, OxfordGoogle Scholar
- Thompson S (1991a) Adaptive cluster sampling, designs with primary and secondary units. Biometrics 47:1103–1115CrossRefGoogle Scholar
- Thompson S (1991b) Stratified adaptive cluster sampling. Biometrika 78(2):389–397CrossRefGoogle Scholar
- Thompson S, Ramsey F, Seber G (1992) An adaptive procedure for sampling animal populations. Biometrics 48:1195–1199CrossRefGoogle Scholar
- Turnock B, Quinn TJ (1991) The effect of responsive movement on abundance estimation using line transect sampling. Biometrics 47:701–715CrossRefGoogle Scholar
- Vasconcellos M, Pitcher TJ (1998) Harvest control for schooling fish stocks under cyclic oceanographic regimes: a case for precaution and gathering auxiliary information. In: Quinn TJ, Funk F, Heifetz J, Ianelli JN, Powers JE, Schweigert JF, Sullivan PJ, Zhang C-I (eds) Fishery stock assessment models. Alaska Sea Grant, Fairbanks AK, pp 853–872Google Scholar