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A generalised random encounter model for estimating animal density with remote sensor data.
Lucas, Tim C D; Moorcroft, Elizabeth A; Freeman, Robin; Rowcliffe, J Marcus; Jones, Kate E.
Afiliação
  • Lucas TC; CoMPLEX University College London Physics Building, Gower Street London WC1E 6BT UK; Centre for Biodiversity and Environment Research Department of Genetics, Evolution and Environment University College London Gower Street London WC1E 6BT UK; Department of Statistical Science University College Lond
  • Moorcroft EA; CoMPLEX University College London Physics Building, Gower Street London WC1E 6BT UK; Department of Computer Science University College London Gower Street London WC1E 6BT UK; Institute of Zoology Zoological Society of London Regents Park London NW1 4RY UK.
  • Freeman R; Institute of Zoology Zoological Society of London Regents Park London NW1 4RY UK.
  • Rowcliffe JM; Institute of Zoology Zoological Society of London Regents Park London NW1 4RY UK.
  • Jones KE; Centre for Biodiversity and Environment Research Department of Genetics, Evolution and Environment University College London Gower Street London WC1E 6BT UK; Institute of Zoology Zoological Society of London Regents Park London NW1 4RY UK.
Methods Ecol Evol ; 6(5): 500-509, 2015 05.
Article em En | MEDLINE | ID: mdl-27547297
Wildlife monitoring technology is advancing rapidly and the use of remote sensors such as camera traps and acoustic detectors is becoming common in both the terrestrial and marine environments. Current methods to estimate abundance or density require individual recognition of animals or knowing the distance of the animal from the sensor, which is often difficult. A method without these requirements, the random encounter model (REM), has been successfully applied to estimate animal densities from count data generated from camera traps. However, count data from acoustic detectors do not fit the assumptions of the REM due to the directionality of animal signals.We developed a generalised REM (gREM), to estimate absolute animal density from count data from both camera traps and acoustic detectors. We derived the gREM for different combinations of sensor detection widths and animal signal widths (a measure of directionality). We tested the accuracy and precision of this model using simulations of different combinations of sensor detection widths and animal signal widths, number of captures and models of animal movement.We find that the gREM produces accurate estimates of absolute animal density for all combinations of sensor detection widths and animal signal widths. However, larger sensor detection and animal signal widths were found to be more precise. While the model is accurate for all capture efforts tested, the precision of the estimate increases with the number of captures. We found no effect of different animal movement models on the accuracy and precision of the gREM.We conclude that the gREM provides an effective method to estimate absolute animal densities from remote sensor count data over a range of sensor and animal signal widths. The gREM is applicable for count data obtained in both marine and terrestrial environments, visually or acoustically (e.g. big cats, sharks, birds, echolocating bats and cetaceans). As sensors such as camera traps and acoustic detectors become more ubiquitous, the gREM will be increasingly useful for monitoring unmarked animal populations across broad spatial, temporal and taxonomic scales.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials Idioma: En Revista: Methods Ecol Evol Ano de publicação: 2015 Tipo de documento: Article País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials Idioma: En Revista: Methods Ecol Evol Ano de publicação: 2015 Tipo de documento: Article País de publicação: Estados Unidos