Ontogenetic shifts in the nesting behaviour of female crocodiles
Body size and age are crucial factors influencing reproductive capacity and success. As females grow, their reproductive investment and success often increase due to improved overall physiological condition and experience gained through successive reproductive events. While much of this work has been conducted on birds and mammals, surprisingly little is known on how body size affects nesting decisions in other long-lived vertebrates. We monitored the movements and nesting behaviour of 57 wild female estuarine crocodiles Crocodylus porosus over a 10-year period (and across consecutive nesting seasons) using externally mounted satellite tags, implanted acoustic transmitters and a network of submerged acoustic receivers. Applying Hidden Markov models to the telemetry-derived location data revealed that female nesting behaviours could be split into three distinct states: (i) ranging movements within home ranges and at nesting sites; (ii) migrations to and from nesting sites; (iii) and nesting/nest guarding. We found that during migration events, larger females migrated further and remained away from dry season territories for longer periods than smaller individuals. Furthermore, not only were migratory movements stimulated by increases in rainfall, larger females migrated to nest sites at lower rainfall thresholds than smaller females. We provide some of the first evidence of body size influencing nesting decisions in an ectothermic vertebrate, with shifts likely resulting from an increased willingness to invest in nest protection among larger and more experienced females.
KeywordsEstuarine crocodile Hidden Markov modelling Nest-site selection Parental investment Telemetry
This study was supported by the Australian Research Council linkage scheme with Australia Zoo and CSIRO as industry partners. We thank Australia Zoo staff for their aid in the capture and tagging process and Gordon C. Grigg for his reviews of the manuscript. All procedures were carried out with approval from The University of Queensland Animal Ethics Committee (SIB/302/08/ARC, SBS/204/11/ARC/AUST ZOO (NF), SBS/215/14/AUST ZOO/ARC) and Queensland Environment Protection Agency Permits (WISP00993703, WISP05268508, WISP13189313).
Author contribution statement
CB, RD, HC and CF conceived the ideas and designed methodology; All authors collected the data; CB and RD led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
Funder: Australian Research Council-Linkage Grant, grant number: LP140100222.
- Bartón K (2018) MuMIn: multi-model inference. R package version 1.42.1. https://CRAN.R-project.org/package=MuMIn
- Burnham KP, Anderson DR (2002) model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New YorkGoogle Scholar
- Hipfner JM, Gaston AJ (2002) Growth of nestling thick-billed murres (Uria lomvia) in relation to parental experience and hatching date. Am Ornithol Soc 119:827–832Google Scholar
- Lazaridis E (2014) Lunar: lunar phase & distance, seasons and other environmental factors (version 0.1-04). https://statistics.lazaridis.eu (April 2018)
- Pinherio J, Bates D, DebRoy S, Sarkar D, R Core Team (2016) Package ‘nlme’: linear and nonlinear mixed effects models., 3.1-118 edn. R packageGoogle Scholar
- QDEH (1995) Conservation and Management of Crocodylus porosus in Queensland 1995–1997. Queensland Department of Environment and Heritage, BrisbaneGoogle Scholar
- R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- Shine R (1989) Parental care in reptiles. In: Gans C, Huey RB (eds) Biology of the reptilia, vol 16. Ecology B: defense and life history. Brata Books, Ann Arbor, MI, pp 275–331Google Scholar
- Thorbjarnarson JB (1996) Reproductive characteristics of the order Crocodylia. Herpetologica 52:8–24Google Scholar
- Webb G, Manolis C (1989) Australian crocodiles a natural history. Reed New Holland, SydneyGoogle Scholar
- Zhao Q, Xu M, Fränti P (2008) Knee point detection on bayesian information criterion tools with artificial intelligence, 2008. In: ICTAI’08. 20th ieee international conference on, vol. 2. IEEE, pp 431–438Google Scholar
- Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009) GLM and GAM for count data. Mixed effects models and extensions in ecology with R. Springer New York, New York, pp 209–243Google Scholar