Biodiversity and Conservation

, Volume 21, Issue 12, pp 3269–3276

Safety in numbers? Supplanting data quality with fanciful models in wildlife monitoring and conservation

Authors

    • Department of Evolutionary EcologyNational Museum of Natural History (CSIC)
  • Fabrizio Sergio
    • Department of Conservation BiologyEstación Biológica de Doñana (CSIC)
  • José A. Sanchéz-Zapata
    • Department of Applied BiologyUniversity Miguel Hernández
  • Juan M. Pérez-García
    • Department of Applied BiologyUniversity Miguel Hernández
  • Francisco Botella
    • Department of Applied BiologyUniversity Miguel Hernández
  • Félix Martínez
    • Sociedad para la Conservación de los Vertebrados
  • Iñigo Zuberogoitia
    • Estudios Medioambientales Icarus S.L.
  • Oscar Frías
    • Department of Evolutionary EcologyNational Museum of Natural History (CSIC)
  • Federico Roviralta
    • Sociedad para la Conservación de los Vertebrados
  • José E. Martínez
    • Departamento de Ecología e HidrologíaUniversidad de Murcia
  • Fernando Hiraldo
    • Department of Conservation BiologyEstación Biológica de Doñana (CSIC)
Brief Communication

DOI: 10.1007/s10531-012-0344-9

Cite this article as:
Blanco, G., Sergio, F., Sanchéz-Zapata, J.A. et al. Biodivers Conserv (2012) 21: 3269. doi:10.1007/s10531-012-0344-9

Abstract

Ecologists and conservation biologists seem increasingly attracted to sophisticated modelling approaches, sometimes at the expense of attention to data quality and appropriateness of fieldwork design. This dissociation may lead to a loss of perspective promoting biological unrealities as conclusions, which may be used in conservation applications. We illustrate this concern by focusing on recent attempts to estimate population size of breeding birds at large scales without any explicit testing of the reliability of the predictions through comparison with direct counts. Disconnection of analysts from “nature” can lead to cases of biological unrealities such as that used here to illustrate such trends. To counter this risk, we encourage investment in well-rounded scientists or more collaborative, multi-disciplinary teams capable of integrating sophisticated analyses with in-depth knowledge of the natural history of their study subjects.

Keywords

Biological unrealitiesData qualitySophisticated modellingPopulation size estimates

Copyright information

© Springer Science+Business Media B.V. 2012