A comparison of remotely sensed environmental predictors for avian distributions

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  • Laurel M Hopkins
    Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, Newport, Oregon
  • Tyler A Hallman
    Swiss Ornithological Institute
  • John Kilbride
    Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, Newport, Oregon
  • W Douglas Robinson
    Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, Newport, Oregon
  • Rebecca A Hutchinson
    Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, Newport, Oregon
Context

With greater accessibility and processing power from online platforms, summaries of remotely sensed data are increasingly used in species distribution models (SDMs). Comparisons of the predictive power of these environmental variables could inform SDMs moving forward.
Objectives

We evaluated the performance of freely available Landsat data as predictor sets for SDMs. Our objectives were to (1) compare the performance of single season SDMs built on mean values of raw spectral bands, Tasseled Cap transformations, and eight different indices, including NDVI, (2) evaluate the performance gain with the addition of standard deviation, textural metrics, and additional seasons, and (3) compare the performance of SDMs built on these continuous spectral predictor sets to SDMs built on classified land cover data (e.g., percent forest cover).
Methods

We used statewide point counts to build multi-scale SDMs for 13 avian species across Oregon, USA. We compared the performance of SDMs built on each predictor set based on our objectives.
Results

Of the Landsat-derived predictor sets, SDMs built on raw spectral bands had the highest overall performance with nearly equivalent performance in Tasseled-Cap models. While performance gains from standard deviations, textural metrics, and additional seasons were minimal in raw-band and Tasseled-Cap models, gains were appreciable in single-index models. Classified land cover models performed equivalently to raw band models.
Conclusions

When predictive performance is paramount, means of raw Landsat bands are strong predictors for avian SDMs. When parsimonious variables are essential, SDMs of single indices (e.g., NDVI) greatly benefit from additional information, such as standard deviation.
Iaith wreiddiolSaesneg
Tudalennau (o-i)997-1016
CyfnodolynLandscape Ecology
Cyfrol37
Rhif y cyfnodolyn4
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 16 Chwef 2022
Cyhoeddwyd yn allanolIe
Gweld graff cysylltiadau