Transience effect in capture-recapture studies: The importance of its biological meaning


Autoři: Meritxell Genovart aff001;  Roger Pradel aff003
Působiště autorů: CEAB (CSIC), Theoretical and Computational Ecology, Blanes, Catalonia, Spain aff001;  IMEDEA (CSIC-UIB), Esporles, Spain aff002;  CEFE, CNRS, University Montpellier, University Paul Valéry Montpellier 3, EPHE, IRD, Montpellier, France aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222241

Souhrn

Capture–recapture (CR) models are an essential tool for estimating demographic parameters in most animal and some plant populations. To avoid drawing incorrect conclusions in any statistical inference, a crucial prerequisite is to assess the goodness of fit of a general model to the data. In CR models, a frequent cause of lack of fit, is the so-called transience effect, which is due to a lower expectation of re-observation of individuals marked for the first time as compared to other individuals. The transience effect may result either from different biological causes or from the sampling procedure. A transience effect is usually treated by distinguishing at least two age-classes in the survival probability, but other approaches may be more suitable. Here we develop a conceptual and analytical framework for including a transience effect in capture-recapture models. We show the implementation of two additional parametrizations that incorporate a transience effect. With these parametrizations, we can directly estimate the “transience probability” defined as the probability that a newly caught individual disappear from the population beyond what is expected based on the behavior of the previously caught individuals in the same sample. Additionally, these parametrizations allow testing biological hypotheses concerning drivers affecting this probability. Results from our case study show differences between parametrizations, with the parametrization most currently used giving different estimates, especially when including covariates. We advocate for a unifying framework for treating a transience effect, that helps clarifying the ideas and terminology, and where the biological reasons should be the rule for choosing the appropriate analytical procedure. This framework will also open new and powerful ways for the detection and exploration of ecological processes such as the costs of the first reproduction or the deleterious effects of some types of marking.

Klíčová slova:

Research and analysis methods – Mathematical and statistical techniques – Statistical methods – Statistical inference – Decision analysis – Decision trees – Physical sciences – Mathematics – Statistics – Statistical data – Computer and information sciences – Software engineering – Software tools – Computer software – Engineering and technology – Management engineering – Biology and life sciences – Organisms – Eukaryota – Animals – Vertebrates – Amniotes – Birds – Evolutionary biology – Evolutionary theory – Earth sciences – Atmospheric science – Climatology – El Niño-Southern Oscillation – Marine and aquatic sciences – Oceanography


Zdroje

1. Lebreton J-D, Burnham KP, Clobert J, Anderson DR. Modeling Survival and Testing Biological Hypotheses Using Marked Animals: A Unified Approach with Case Studies. Ecological Monographs. 1992;62: 67–118. doi: 10.2307/2937171

2. Kéry M. Extinction rate estimates for plant populations in revisitation studies: importance of detectability. Conservation biology. 2004;18: 570–574.

3. D’Agostino RB, Stephens MA. Goodness-of-fit techniques. Marcel Dekker; 1986.

4. Cooch EG, White Gary. Goodness of fit testing at Program MARK: a gentle introduction. Colorado State University, Fort Collins, Colorado, USA; 2008.

5. Pollock KH. Capture-recapture models: A review of current methods, assumptions and experimental design. 1980; 32.

6. Pollock KH, Hines JE, Nichols JD. Goodness-of-Fit Tests for Open Capture-Recapture Models. Biometrics. 1985;41: 399–410. doi: 10.2307/2530865

7. Choquet R, Reboulet A-M, Lebreton J-D, Gimenez O, Pradel R. U_Care2.2: User’s Manual http://ftp.cefe.cnrs.fr/biom/Soft-CR/. CEFE-CNRS; 2005.

8. Gimenez O, Lebreton J-D, Choquet R, Pradel R. R2ucare: An r package to perform goodness-of-fit tests for capture–recapture models. Methods in Ecology and Evolution. 2018;9: 1749–1754. doi: 10.1111/2041-210X.13014

9. Pradel R, Hines JE, Lebreton J-D, Nichols JD. Capture-recapture survival models taking account of transients. Biometrics. 1997;53: 60–72.

10. Oro D. Doak D. Estimating costs of first reproduction and consequences for local population dynamics in a long-lived species. Under review.

11. Genovart M, Pradel R, Oro D. Exploiting uncertain ecological fieldwork data with multi-event capture-recapture modelling: an example with bird sex assignment. Journal of Animal Ecology. 2012;81: 970–977. doi: 10.1111/j.1365-2656.2012.01991.x 22548508

12. Weinbach A, Cayuela H, Grolet O, Besnard A, Joly P. Resilience to climate variation in a spatially structured amphibian population. Scientific Reports. 2018;8. doi: 10.1038/s41598-017-18329-3

13. Cayuela H, Akani GC, Hema EM, Eniang EA, Amadi N, Ajong SN, et al. Population dynamics and age-dependent mortality processes in tropical reptiles. In press. Biological Journal of the Linnean Society.

14. Brown WS, Kéry M, Hines JE. Survival of Timber Rattlesnakes (Crotalus Horridus) Estimated by Capture–recapture Models in Relation to Age, Sex, Color Morph, Time, and Birthplace. Copeia. 2007;2007: 656–672.

15. Nott MP, DeSante DF. Demographic monitoring and the identification of transients in mark-recapture models. Predicting Species Occurrences: Issues of Scale and Accuracy Island Press, NY. 2002; 727–736.

16. Schmidt BR, Schaub M, Steinfartz S. Apparent survival of the salamander Salamandra salamandra is low because of high migratory activity. Frontiers in Zoology. 2007;4: 19. doi: 10.1186/1742-9994-4-19 17803829

17. Perret N, Pradel R, Miaud C, Grolet O, Joly P. Transience, dispersal and survival rates in newt patchy populations. Journal of Animal Ecology. 2003;72: 567–575. doi: 10.1046/j.1365-2656.2003.00726.x 30893969

18. Schaub M, Liechti F, Jenni L. Departure of migrating European robins, Erithacus rubecula, from a stopover site in relation to wind and rain. Animal Behaviour. 2004;67: 229–237. doi: 10.1016/j.anbehav.2003.03.011

19. Sanz-Aguilar A, Tavecchia G, Mínguez E, Massa B, Lo Valvo F, Ballesteros G, et al. Recapture processes and biological inference in monitoring burrow-nesting seabirds. Journal of Ornithology. 2010;151: 133–146.

20. Pradel R, Cooch E, Cooke F. Transient animals in a resident population of snow geese: Local emigration or heterogeneity? Journal of Applied Statistics. 1995;22: 695–710. doi: 10.1080/02664769524559

21. Roff DA. The evolution of Life Histories. Theory and Analysis. New York: Chapman & Hall; 1992.

22. Stearns SC. The Evolution of Life Histories. OUP Oxford; 1992.

23. Tavecchia G, Pradel R, Boy V, Johnson AR, Cézilly F. Sex- and Age-Related Variation in Survival and Cost of First Reproduction in Greater Flamingos. Ecology. 2001;82: 165–174. doi: 10.2307/2680094

24. Pradel R. Multievent: an extension of multistate capture-recapture models to uncertain states. Biometrics. 2005;61: 442–447. doi: 10.1111/j.1541-0420.2005.00318.x 16011690

25. Gimenez O, Rossi V, Choquet R, Dehais C, Doris B, Varella H, et al. State-space modelling of data on marked individuals. Ecological Modelling. 2007;206: 431–438. doi: 10.1016/j.ecolmodel.2007.03.040

26. Royle JA. Modeling individual effects in the Cormack-Jolly-Seber model: a state-space formulation. Biometrics. 2008;64: 364–370. doi: 10.1111/j.1541-0420.2007.00891.x 17725811

27. Pradel R, Sanz-Aguilar A. Modeling Trap-Awareness and Related Phenomena in Capture-Recapture Studies. PLOS ONE. 2012;7: e32666. doi: 10.1371/journal.pone.0032666 22396787

28. Genovart M, Sanz-Aguilar A, Fernández-Chacón A, Igual JM, Pradel R, Forero MG, et al. Contrasting effects of climatic variability on the demography of a trans-equatorial migratory seabird. Roulin A, editor. Journal of Animal Ecology. 2013;82: 121–130. doi: 10.1111/j.1365-2656.2012.02015.x 22823099

29. Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach [Internet]. Springer Science & Business Media; 2002. Available: http://books.google.com/books?hl=ca&lr=&id=fT1Iu-h6E-oC&oi=fnd&pg=PR7&dq=burham+and+anderson&ots=tets9-Yzp8&sig=JOb772LHos1HfG93O1NI-MFi0C0

30. Igual JM, Forero MG, Tavecchia G, González-Solis J, Martínez-Abraín A, Hobson KA, et al. Short-term effects of data-loggers on Cory’s shearwater (Calonectris diomedea). Marine Biology. 2005;146: 619–624. doi: 10.1007/s00227-004-1461-0


Článek vyšel v časopise

PLOS One


2019 Číslo 9
Nejčtenější tento týden