The dog and cat population on Maio Island, Cape Verde: characterisation and prediction based on household survey and remotely sensed imagery

  • Ana Carolina Lopes Antunes | aclan@vet.dtu.dk National Veterinary Institute, Technical University of Denmark, Frederiksberg, Denmark.
  • Els Ducheyne Avia-GIS, Zoersel, Belgium; Ministry of Rural Development, Maio, Cabo Verde.
  • Ward Bryssinckx Avia-GIS, Zoersel, Belgium; Ministry of Rural Development, Maio, Cabo Verde.
  • Sara Vieira Ministry of Rural Development, Maio, Cabo Verde.
  • Manuel Malta Veterinarians Without Borders, Lisbon, Portugal.
  • Yolanda Vaz Faculty of Veterinary Medicine, University of Lisbon, Portugal.
  • Telmo Nunes Faculty of Veterinary Medicine, University of Lisbon, Portugal.
  • Koen Mintiens Avia-GIS, Zoersel, Belgium; Ministry of Rural Development, Maio, Cabo Verde.

Abstract

The objective was to estimate and characterise the dog and cat population on Maio Island, Cape Verde. Remotely sensed imagery was used to document the number of houses across the island and a household survey was carried out in six administrative areas recording the location of each animal using a global positioning system instrument. Linear statistical models were applied to predict the dog and cat populations based on the number of houses found and according to various levels of data aggregation. In the surveyed localities, a total of 457 dogs and 306 cats were found. The majority of animals had owners and only a few had free access to outdoor activities. The estimated population size was 531 dogs [95% confidence interval (CI): 453-609] and 354 cats (95% CI: 275-431). Stray animals were not a concern on the island in contrast to the rest of the country

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Published
2015-11-04
Section
Original Articles
Keywords:
Dog, Cat, Household survey, Remote sensing, Maio Island
Statistics
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How to Cite
Lopes Antunes, A. C., Ducheyne, E., Bryssinckx, W., Vieira, S., Malta, M., Vaz, Y., Nunes, T., & Mintiens, K. (2015). The dog and cat population on Maio Island, Cape Verde: characterisation and prediction based on household survey and remotely sensed imagery. Geospatial Health, 10(2). https://doi.org/10.4081/gh.2015.386