The effects of habitat heterogeneity, as measured by satellite image texture, on tropical forest bird distributions
Issued Date
2023-05-01
Resource Type
ISSN
00063207
Scopus ID
2-s2.0-85150888770
Journal Title
Biological Conservation
Volume
281
Rights Holder(s)
SCOPUS
Bibliographic Citation
Biological Conservation Vol.281 (2023)
Suggested Citation
Suttidate N., Pidgeon A.M., Hobi M.L., Round P.D., Dubinin M., Radeloff V.C. The effects of habitat heterogeneity, as measured by satellite image texture, on tropical forest bird distributions. Biological Conservation Vol.281 (2023). doi:10.1016/j.biocon.2023.110002 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/81510
Title
The effects of habitat heterogeneity, as measured by satellite image texture, on tropical forest bird distributions
Other Contributor(s)
Abstract
Global biodiversity loss is most pronounced in the tropics. Monitoring of broad-scale patterns of habitat is essential for biodiversity conservation. Image texture measures derived from satellite data are proxies for habitat heterogeneity, but have not been tested in tropical forests. Our goal was to evaluate image texture to predict tropical forest bird distributions across Thailand for different guilds. We calculated a suite of texture measures from cumulative productivity (1-km fPAR-MODIS data) for Thailand's forests, and assessed how well texture measures predicted distributions of 86 tropical forest bird species in relation to body size, and nesting guild. Finally, we compared the predictive performance of combining (a) satellite image texture measures, (b) habitat composition, and (c) habitat fragmentation. We found that texture measures predicted occurrences of tropical forest birds well (AUC = 0.801 ± 0.063). Second-order homogeneity was the most predictive texture measure. Our models based on texture were significantly better for birds with larger body size (p < 0.05), but did not differ among nesting guilds (p > 0.05). Models that combined texture with habitat composition measures (AUC = 0.928 ± 0.038) outperformed models that combined fragmentation with habitat composition measures (AUC = 0.905 ± 0.047) (p < 0.05). The incorporation of texture, composition, and fragmentation variables significantly improved model accuracy over texture-only models (AUC = 0.801 ± 0.063 to AUC = 0.938 ± 0.034; p < 0.05). We suggest that texture measures are a valuable tool to predict bird distributions at broad scales in tropical forests.