phenology (version 4.2.4)

phenology-package: Tools to Manage a Parametric Function that Describes Phenology

Description

Functions used to fit and test the phenology of species based on counts. The lastest version can always been installed using: install.packages("http://www.ese.u-psud.fr/epc/conservation/CRAN/phenology.tar.gz", repos=NULL, type="source") Please note that only the most significant changes are reported in the NEWS. To do: * I must finish to adapt fitRMU for all methods of optimx. * Auto-scaling for optim. * I must adapt TCF (total clutch frequency) fit from OCF-ECF (observed clutch frequency-estimated cluth frequency) table based on: Briane, J.-P., Rivalan, P., Girondot, M., 2007. The inverse problem applied to the Observed Clutch Frequency of Leatherbacks from Yalimapo beach, French Guiana. Chelonian Conservation and Biology 6, 63-69. Until now it is an Excel spreadsheet. * Fit tag-loss rate based on: Rivalan, P., Godfrey, M.H., Prévot-Julliard, A.-C., Girondot, M., 2005. Maximum likelihood estimates of tag loss in leatherback sea turtles. Journal of Wildlife Management 69, 540-548. Until now it is a RealBasic software.

Details

Fit a parametric function that describes phenology

ll{ Package: phenology Type: Package Version: 4.2.4 build 336 Date: 2015-06-29 License: GPL (>= 2) LazyLoad: yes }

References

Girondot, M. 2010. Estimating density of animals during migratory waves: application to marine turtles at nesting site. Endangered Species Research, 12, 85-105.

Girondot M. and Rizzo A. 2015. Bayesian framework to integrate traditional ecological knowledge into ecological modeling: A case study. Journal of Ethnobiology, 35, 339-355.

Girondot, M. 2010. Editorial: The zero counts. Marine Turtle Newsletter, 129, 5-6.

See Also

Girondot, M., Rivalan, P., Wongsopawiro, R., Briane, J.-P., Hulin, V., Caut, S., Guirlet, E. & Godfrey, M. H. 2006. Phenology of marine turtle nesting revealed by a statistical model of the nesting season. BMC Ecology, 6, 11.

Delcroix, E., Bédel, S., Santelli, G., Girondot, M., 2013. Monitoring design for quantification of marine turtle nesting with limited human effort: a test case in the Guadeloupe Archipelago. Oryx 48, 95-105.

Weber, S.B., Weber, N., Ellick, J., Avery, A., Frauenstein, R., Godley, B.J., Sim, J., Williams, N., Broderick, A.C., 2014. Recovery of the South Atlantic’s largest green turtle nesting population. Biodiversity and Conservation 23, 3005-3018.

Examples

library(phenology)
# Get the lastest version at:
# install.packages("http://www.ese.u-psud.fr/epc/conservation/CRAN/phenology.tar.gz",
     repos=NULL, type="source")
# Read a file with data
data(Gratiot)
# Generate a formatted list nammed data_Gratiot
data_Gratiot<-add_phenology(Gratiot, name="Complete",
		reference=as.Date("2001-01-01"), format="%d/%m/%Y")
# Generate initial points for the optimisation
parg<-par_init(data_Gratiot, parametersfixed=NULL)
# Run the optimisation
result_Gratiot<-fit_phenology(data=data_Gratiot,
		parametersfit=parg, parametersfixed=NULL, trace=1)
data(result_Gratiot)
# Plot the phenology and get some stats
output<-plot(result_Gratiot)