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Marine Ecology Progress Series

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MEPS 508:233-246 (2014)  -  DOI:

Comparison and review of models describing sea turtle nesting abundance

Andrea U. Whiting1,*, Milani Chaloupka2, Nicolas Pilcher3, Paul Basintal4, Colin J. Limpus

1School of Environmental and Life Sciences, Charles Darwin University, Northern Territory 0909, Australia
2Ecological Modelling Services P/L, PO Box 6150, University of Queensland, St Lucia, Queensland 4067, Australia
3Marine Research Foundation, 136 Lorong Pokok Seraya 2, Taman Khidmat, Kota Kinabalu, Sabah 88450, Malaysia
4Sabah Parks, Lot 45&46, Level 1-5, Block H Signature Office, KK Times Square Coastal Highway, Kota Kinabalu, Sabah 88100, Malaysia
5Department of Environment and Heritage Protection, PO Box 2454, Brisbane, Queensland 4001, Australia
*Corresponding author:

ABSTRACT: Count data are often used to assess relative population size and population trends with sufficient power and confidence for wildlife population studies, including those for nesting sea turtles. Although access to sea turtles while nesting is relatively simple compared to many other migratory marine animals, optimal surveys tagging every individual through the nesting season are often not feasible due to time, financial and other logistic constraints. Partial survey counts can then be used to estimate population abundance. Several models have previously been published describing the seasonal shape in abundance for nesting turtles, but none have compared different model fits using a numerical approach and all have limited general application as they describe only 1 location or 1 species. We compared 22 non-parametric and parametric modelling approaches for 9 populations of sea turtles comprising 3 different species: green sea turtles Chelonia mydas, loggerhead sea turtles Caretta caretta and leatherback sea turtles Dermochelys coriacea. Although models showed marked differences in the shape of their fit, all models provided reasonable estimates of annual nesting abundance, with mean errors less than 8% for 50% data coverage and mostly 8 to 10% for 20% random coverage. Of the 3 models that produced significantly lower mean absolute error, we recommend using generalized additive models to estimate annual abundance due to their ease of fitting, flexibility across populations and seasonal shapes and their good predictive ability.

KEY WORDS: Population study · Partial count · Generalized additive model · GAM · Green turtle · Chelonia mydas · Loggerhead · Caretta caretta · Leatherback · Dermochelys coriacea

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Cite this article as: Whiting AU, Chaloupka M, Pilcher N, Basintal P, Limpus CJ (2014) Comparison and review of models describing sea turtle nesting abundance. Mar Ecol Prog Ser 508:233-246.

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