Modelling landscape‐scale habitat use using GIS and remote sensing: a case study with great bustards

Modelling landscape‐scale habitat use using GIS and remote sensing: a case study with great... 1 Many species are adversely affected by human activities at large spatial scales and their conservation requires detailed information on distributions. Intensive ground surveys cannot keep pace with the rate of land‐use change over large areas and new methods are needed for regional‐scale mapping. 2 We present predictive models for great bustards in central Spain based on readily available advanced very high resolution radiometer (AVHRR) satellite imagery combined with mapped features in the form of geographic information system (GIS) data layers. As AVHRR imagery is coarse‐grained, we used a 12‐month time series to improve the definition of habitat types. The GIS data comprised measures of proximity to features likely to cause disturbance and a digital terrain model to allow for preference for certain topographies. 3 We used logistic regression to model the above data, including an autologistic term to account for spatial autocorrelation. The results from models were combined using Bayesian integration, and model performance was assessed using receiver operating characteristics plots. 4 Sites occupied by bustards had significantly lower densities of roads, buildings, railways and rivers than randomly selected survey points. Bustards also occurred within a narrower range of elevations and at locations with significantly less variable terrain. 5 Logistic regression analysis showed that roads, buildings, rivers and terrain all contributed significantly to the difference between occupied and random sites. The Bayesian integrated probability model showed an excellent agreement with the original census data and predicted suitable areas not presently occupied. 6 The great bustard's distribution is highly fragmented and vacant habitat patches may occur for a variety of reasons, including the species’ very strong fidelity to traditional sites through conspecific attraction. This may limit recolonization of previously occupied sites. 7 We conclude that AVHRR satellite imagery and GIS data sets have potential to map distributions at large spatial scales and could be applied to other species. While models based on imagery alone can provide accurate predictions of bustard habitats at some spatial scales, terrain and human influence are also significant predictors and are needed for finer scale modelling. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Ecology Wiley

Modelling landscape‐scale habitat use using GIS and remote sensing: a case study with great bustards

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Publisher
Wiley
Copyright
Copyright © 2001 Wiley Subscription Services
ISSN
0021-8901
eISSN
1365-2664
DOI
10.1046/j.1365-2664.2001.00604.x
Publisher site
See Article on Publisher Site

Abstract

1 Many species are adversely affected by human activities at large spatial scales and their conservation requires detailed information on distributions. Intensive ground surveys cannot keep pace with the rate of land‐use change over large areas and new methods are needed for regional‐scale mapping. 2 We present predictive models for great bustards in central Spain based on readily available advanced very high resolution radiometer (AVHRR) satellite imagery combined with mapped features in the form of geographic information system (GIS) data layers. As AVHRR imagery is coarse‐grained, we used a 12‐month time series to improve the definition of habitat types. The GIS data comprised measures of proximity to features likely to cause disturbance and a digital terrain model to allow for preference for certain topographies. 3 We used logistic regression to model the above data, including an autologistic term to account for spatial autocorrelation. The results from models were combined using Bayesian integration, and model performance was assessed using receiver operating characteristics plots. 4 Sites occupied by bustards had significantly lower densities of roads, buildings, railways and rivers than randomly selected survey points. Bustards also occurred within a narrower range of elevations and at locations with significantly less variable terrain. 5 Logistic regression analysis showed that roads, buildings, rivers and terrain all contributed significantly to the difference between occupied and random sites. The Bayesian integrated probability model showed an excellent agreement with the original census data and predicted suitable areas not presently occupied. 6 The great bustard's distribution is highly fragmented and vacant habitat patches may occur for a variety of reasons, including the species’ very strong fidelity to traditional sites through conspecific attraction. This may limit recolonization of previously occupied sites. 7 We conclude that AVHRR satellite imagery and GIS data sets have potential to map distributions at large spatial scales and could be applied to other species. While models based on imagery alone can provide accurate predictions of bustard habitats at some spatial scales, terrain and human influence are also significant predictors and are needed for finer scale modelling.

Journal

Journal of Applied EcologyWiley

Published: Jan 1, 2001

Keywords: ; ; ; ; ;

References

  • Parámetros demográficos, selección de hábitat y distribución de la avutarda en tres regiones españolas
    Alonso, J.C.; Alonso, J.A.
  • Spatial Processes: Models and Applications
    Cliff, A.D.; Ord, J.K.
  • A review of methods for the assessment of prediction errors in conservation presence/absence models
    Fielding, A.H.; Bell, J.F.
  • World status of the great bustard (Otis tarda) with special attention to the Iberian peninsula populations
    Hidalgo de Trucios, S.J.
  • Bustards, Hemipodes and Sandgrouse. Birds of Dry Places
    Johnsgard, P.A.
  • Habitat preferences of great bustard Otis tarda flocks in the arable steppes of central Spain: are potentially suitable areas unoccupied?
    Lane, S.J.; Alonso, J.C.; Martín, C.A.
  • Large‐Scale Ecology and Conservation Biology
    Lawton, J.H.; Nee, S.; Letcher, A.J.; Harvey, P.H.
  • Relationships among grizzly bears, roads and habitat in the Swan Mountains, Montana
    Mace, R.D.; Walker, J.S.; Manley, T.L.; Lyon, L.J.; Zuuring, H.
  • Viabilidad de la poblacion Navarra de avutardas
    Onrubia, A.; Saenz de Buruaga, M.; Osborne, P.; Baglione, V.; Purroy, F.J.; Lucio, A.J.; Campos, M.A.

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