Distribution models for koalas in South Australia using citizen science‐collected data

Ecology and Evolution

Ana M. M. Sequeira³  Philip E. J. Roetman²  Christopher B. Daniels²  Andrew K. Baker¹ 

[1] CSIRO Land and Water, Glen Osmond, South Australia, Australia;Barbara Hardy Institute, University of South Australia, Adelaide, South Australia, Australia;The Environment Institute and School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, South Australia, Australia

关键词: Citizen science;    climate;    ecological niche models;    pseudo‐absence;    species distribution models;    temperature limitation;    vegetation;   

DOI  :  10.1002/ece3.1094

来源: Wiley

【 摘 要 】

Abstract

The koala (Phascolarctos cinereus) occurs in the eucalypt forests of eastern and southern Australia and is currently threatened by habitat fragmentation, climate change, sexually transmitted diseases, and low genetic variability throughout most of its range. Using data collected during the Great Koala Count (a 1-day citizen science project in the state of South Australia), we developed generalized linear mixed-effects models to predict habitat suitability across South Australia accounting for potential errors associated with the dataset. We derived spatial environmental predictors for vegetation (based on dominant species of Eucalyptus or other vegetation), topographic water features, rain, elevation, and temperature range. We also included predictors accounting for human disturbance based on transport infrastructure (sealed and unsealed roads). We generated random pseudo-absences to account for the high prevalence bias typical of citizen-collected data. We accounted for biased sampling effort along sealed and unsealed roads by including an offset for distance to transport infrastructures. The model with the highest statistical support (wAIC_c ~ 1) included all variables except rain, which was highly correlated with elevation. The same model also explained the highest deviance (61.6%), resulted in high R²(m) (76.4) and R²(c) (81.0), and had a good performance according to Cohen's κ (0.46). Cross-validation error was low (~ 0.1). Temperature range, elevation, and rain were the best predictors of koala occurrence. Our models predict high habitat suitability in Kangaroo Island, along the Mount Lofty Ranges, and at the tips of the Eyre, Yorke and Fleurieu Peninsulas. In the highest-density region (5576 km²) of the Adelaide–Mount Lofty Ranges, a density–suitability relationship predicts a population of 113,704 (95% confidence interval: 27,685–199,723; average density = 5.0–35.8 km⁻²). We demonstrate the power of citizen science data for predicting species' distributions provided that the statistical approaches applied account for some uncertainties and potential biases. A future improvement to citizen science surveys to provide better data on search effort is that smartphone apps could be activated at the start of the search. The results of our models provide preliminary ranges of habitat suitability and population size for a species for which previous data have been difficult or impossible to gather otherwise.

【 授权许可】

CC BY   
© 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.