European Journal of Wildlife Research

, Volume 59, Issue 2, pp 237–243

Overestimates of maternity and population growth rates in multi-annual breeders

  • Guillaume Chapron
  • Robert Wielgus
  • Amaury Lambert
Original Paper

DOI: 10.1007/s10344-012-0671-x

Cite this article as:
Chapron, G., Wielgus, R. & Lambert, A. Eur J Wildl Res (2013) 59: 237. doi:10.1007/s10344-012-0671-x

Abstract

There has been limited attention to estimating maternity rate because it appears to be relatively simple. However, when used for multi-annual breeder species, such as the largest carnivores, the most common estimators introduce an upward bias by excluding unproductive females. Using a simulated dataset based on published data, we compare the accuracy of maternity estimates derived from standard methods against estimates derived from an alternative method. We show that standard methods overestimate maternity rates in the presence of unsuccessful pregnancies. Importantly, population growth rates derived from a matrix model parameterized with the biased estimates may indicate increasing populations although the populations are stable or even declining. We recommend the abandonment of the biased standard methods and to instead use the unbiased alternative method for population projections and assessments of population viability.

Keywords

Maternity rate Bias Grizzly bear Ursus arctos Growth rate 

Supplementary material

10344_2012_671_MOESM1_ESM.pdf (382 kb)
ESM 1(DOCX 382 kb)

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Guillaume Chapron
    • 1
    • 2
  • Robert Wielgus
    • 3
    • 4
  • Amaury Lambert
    • 5
  1. 1.Grimsö Wildlife Research Station, Department of EcologySwedish University of Agricultural SciencesRiddarhyttanSweden
  2. 2.Conservation Biology Division, Institute of Ecology and EvolutionUniversity of BernBernSwitzerland
  3. 3.Département Ecologie et Gestion de la BiodiversitéMuseum National d’Histoire NaturelleParis Cedex 05France
  4. 4.Large Carnivore Conservation Laboratory, Department of Natural Resource SciencesWashington State UniversityPullmanUSA
  5. 5.Laboratoire de Probabilités et Modèles AléatoiresUPMC Université ParisParis Cedex 05France

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