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Modelling insect demography from capture–recapture data: comparison between the constrained linear models and the Jolly–Seber analytical method

Published online by Cambridge University Press:  02 April 2012

Nicolas Schtickzelle ,
Michel Baguette  and
Éric Le Boulengé
Nicolas Schtickzelle*
Affiliation:
Université catholique de Louvain, Biodiversity Research Centre, 4 Place Croix du Sud, B-1348 Louvain-la-Neuve, Belgium
Michel Baguette
Affiliation:
Université catholique de Louvain, Biodiversity Research Centre, 4 Place Croix du Sud, B-1348 Louvain-la-Neuve, Belgium
Éric Le Boulengé
Affiliation:
Université catholique de Louvain, Biodiversity Research Centre, 4 Place Croix du Sud, B-1348 Louvain-la-Neuve, Belgium
*
1Corresponding author (e-mail: [email protected]).
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Abstract

Entomologists traditionally use the Jolly–Seber analytical method (JSAM) to estimate demographic parameters from capture–mark–recapture data, although more powerful approaches like the constrained linear models (CLM) have been developed and are commonly and successfully applied to vertebrates. Demographic parameters (i.e., survival, capture, and recruitment rates, population size, and sex ratio) of a patchy population of the Bog Fritillary butterfly, Proclossiana eunomia (Esp.) (Lepidoptera: Nymphalidae), were estimated using CLM on the basis of daily captures of imagoes during 11 yearly generations (1992–2002). Comparing these results with JSAM results obtained on the same data lead us to stress that CLM are far more powerful tools which allow for optimal exploitation of capture–mark–recapture data. This method allows the identification of the variation patterns of demographic parameters and to link them to life-history traits; furthermore it gives more precise estimates of these crucial input parameters for the modelling of population trends and population viability analysis.

Résumé

Les entomologistes utilisent traditionnellement la méthode analytique de Jolly–Seber (JSAM) pour estimer les paramètres démographiques à partir de données de capture–marquage–recapture alors que des approches plus puissantes comme les modèles linéaires sous contraintes (CLM) ont été développées et sont couramment appliquées avec succès aux vertébrés. Les paramètres démographiques (survie, piégeabilité, recrutement, taille de population et sex ratio) d'une population subdivisée du nacré de la bistorte, Proclossiana eunomia (Esp.) (Lepidoptera: Nymphalidae), ont été estimés par CLM sur base de captures journalières des imagos durant 11 générations annuelles (1992–2002). La comparaison de ces résultats avec ceux obtenus sur les même données par JSAM souligne que CLM représente un outil nettement plus puissant permettant une exploitation optimale des données de capture–marquage–recapture. En effet, cette méthode permet d'identifier les patrons de variation des paramètres démographiques et de les relier à des traits d'histoire de vie; de plus, elle donne des estimations plus précises de ces paramètres cruciaux pour la modélisation des trajectoires de population et l'analyse de viabilité de population.

Type
Articles
Information
The Canadian Entomologist , Volume 135 , Issue 2 , April 2003 , pp. 313 - 323
Copyright
Copyright © Entomological Society of Canada 2003

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References

Arnason, A.N., Schwarz, C.J., Boyer, G. 1998. POPAN-5. A data maintenance and analysis system for mark–recapture data. Scientific Report, Department of Computer Science, University of Manitoba, WinnipegGoogle Scholar
Baguette, M., Nève, G. 1994. Adult movements between populations in the specialist butterfly Proclossiana eunomia (Lepidoptera, Nymphalidae). Ecological Entomology 19: 1–5CrossRefGoogle Scholar
Baguette, M., Vansteenwegen, C., Convié, I., Nève, G. 1998. Sex-biased density-dependent migration in a metapopulation of the butterfly Proclossiana eunomia. Acta Oecologica 19: 17–24.CrossRefGoogle Scholar
Begon, M. 1983. Abuses of mathematical techniques in ecology: applications of Jolly's capture–recapture method. Oikos 40: 155–8Google Scholar
Beissinger, S.R., McCullough, D.R. 2002. Population viability analysis. Chicago: The University of Chicago PressGoogle Scholar
Bergman, K.O. 2001. Population dynamics and the importance of habitat management for conservation of the butterfly Lopinga achine. Journal of Applied Ecology 38: 1303–13CrossRefGoogle Scholar
Burnham, K.P., Anderson, D.R. 1998. Model selection and inference: a practical information – theoretic approach. New York: Springer-VerlagCrossRefGoogle Scholar
Burnham, K.P., Anderson, D.R., White, G.C. 1987. Design and analysis methods for fish survival experiments based on release–recapture. American Fisheries Society Monograph 5Google Scholar
Clobert, J., Lebreton, J.D. 1985. Dépendance de facteurs de milieu dans les estimations de taux de survie par capture–recapture. Biometrics 41: 1031–7Google Scholar
Coffman, C.J., Nichols, J.D., Pollock, K.H. 2001. Population dynamics of Microtus pennsylvanicus in corridor-linked patches. Oikos 93: 3–21CrossRefGoogle Scholar
Cooch, E., White, G.C. 2002. Program MARK – analysis of data from marked individuals. A gentle introduction. 2nd edition. Available from http://www.cnr.colostate.edu/~gwhite/mark/mark.htm (accessed 13 August 2001)Google Scholar
Cormack, R.M. 1964. Estimates of survival from the sighting of marked animals. Biometrika 51: 429–38CrossRefGoogle Scholar
Goffart, P.h., Baguette, M., de Bast, B. 1992. La situation des Lépidoptères rhopalocères en Wallonie ou que sont nos papillons devenus? Bulletin et Annales de la Societe Royale Belge d'Entomologie 128: 355–92Google Scholar
Greenwood, J.J.D. 1996. Basic techniques. pp 11–110in Sutherland, W.J. (Ed), Ecological census techniques. Cambridge: Cambridge University PressGoogle Scholar
Gutierrez, D., Thomas, C.D., Leon-Cortes, J.L. 1999. Dispersal, distribution, patch network and metapopulation dynamics of the dingy skipper butterfly (Erynnis tages). Oecologia 121: 506–17Google Scholar
Hestbeck, J.B., Nichols, J.D., Malecki, R.A. 1991. Estimates of movement and site fidelity using mark–resight data of wintering Canada geese. Ecology 72: 523–33Google Scholar
Jolly, G.M. 1965. Explicit estimates from capture–recapture data with both death and immigration-stochastic model. Biometrika 52: 225–47CrossRefGoogle ScholarPubMed
Krebs, C.J. 2000. Ecological methodology. 2nd edition. New York: LongmanGoogle Scholar
Lebreton, J.D., Burnham, K.P., Clobert, J., Anderson, D.R. 1992. Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecological Monographs 62: 67–118CrossRefGoogle Scholar
Leisnham, P.T., Jamieson, I.G. 2002. Metapopulation dynamics of a flightless alpine insect Hemideina maori in a naturally fragmented habitat. Ecological Entomology 27: 574–80CrossRefGoogle Scholar
Nève, G., Barascud, B., Hughes, R., Aubert, J., Descimon, H., Lebrun, P., Baguette, M. 1996. Dispersal, colonization power and metapopulation structure in the vulnerable butterfly Proclossiana eunomia (Lepidoptera: Nymphalidae). Journal of Applied Ecology 33: 14–22CrossRefGoogle Scholar
Nichols, J.D., Hines, J.E., Pollock, K.H., Hinz, R.L., Link, W.A. 1994. Estimating breeding proportions and testing hypotheses about costs of reproduction with capture–recapture data. Ecology 75: 2052–65Google Scholar
Petit, S., Moilanen, A., Hanski, I., Baguette, M. 2001. Metapopulation dynamics of the bog fritillary butterfly: movements between habitat patches. Oikos 92: 491–500Google Scholar
Pollock, K.H. 2000. Capture–recapture models. Journal of the American Statistical Association 95: 293–6CrossRefGoogle Scholar
Pollock, K.H., Nichols, J.D., Brownie, C., Hines, J.E. 1990. Statistical inference for capture–recapture experiments. Wildlife Monographs 107Google Scholar
Schtickzelle, N. 2003. Metapopulation dynamics and viability of the bog fritillary butterfly Proclossiana eunomia. PhD thesis, Biodiversity Research Centre, Université catholique de Louvain, Louvain-la-NeuveGoogle Scholar
Schtickzelle, N., Le Boulengé, E., Baguette, M. 2002. Metapopulation dynamics of the bog fritillary butterfly: demographic processes in a patchy population. Oikos 97: 349–60Google Scholar
Schwarz, C.J. 2001. The Jolly–Seber model: more than just abundance. Journal of Agricultural, Biological, and Environmental Statistics 6: 195–205CrossRefGoogle Scholar
Schwarz, C.J., Arnason, A.N. 1996. A general methodology for the analysis of capture–recapture experiments in open populations. Biometrics 52: 860–73CrossRefGoogle Scholar
Schwarz, C.J., Seber, G.A.F. 1999. Estimating animal abundance: review III. Statistical Science 14: 427–56Google Scholar
Schwarz, C.J., Bailey, R.E., Irvine, J.R., Dalziel, F.C. 1993. Estimating salmon spawning escapement using capture–recapture methods. Canadian Journal of Fisheries and Aquatic Sciences 50: 1181–97Google Scholar
Seber, G.A.F. 1965. A note on the multiple recapture census. Biometrika 52: 249–59Google Scholar
Southwood, T.R.E. 1978. Ecological methods. 2nd edition. London: Chapman and HallGoogle Scholar
Spendelow, J.A., Nichols, J.D., Nisbet, I.C.T., Hays, H., Cormons, G.D., Burger, J., Safina, C., Hines, J.E., Gochfeld, M. 1995. Estimating annual survival and movement rates of adults within a metapopulation of roseate terns. Ecology 76: 2415–28Google Scholar
Wiklund, C., Fagerström, T. 1977. Why do males emerge before females? A hypothesis to explain the incidence of protandry in butterflies. Oecologia 31: 153–8Google Scholar