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A tale of two wildfires; testing detection and prediction of invasive species distributions using models fit with topographic and spectral indices

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Abstract

Context

Developing species distribution models (SDMs) to detect invasive species cover and evaluate habitat suitability are high priorities for land managers.

Objectives

We tested SDMs fit with different variable combinations to provide guidelines for future invasive species model development based on transferability between landscapes.

Methods

Generalized linear model, boosted regression trees, multivariate adaptive regression splines, and Random Forests were fit with location data for high cheatgrass (Bromus tectorum) cover in situ for two post-burn sites independently using topographic indices, spectral indices derived from multiple dates of Landsat 8 satellite imagery, or both. Models developed for one site were applied to the other, using independent cheatgrass cover data from the respective ex situ site to test model transferability.

Results

Fitted models were statistically robust and comparable when fit with at least 200 cover plots in situ and transferred to the ex situ site. Only the Random Forests models were robust when fit with a small number of cover plots in situ.

Conclusions

Our study indicated spectral indices can be used in SDMs to estimate species cover across landscapes (e.g., both within the same Landsat scene and in an adjacent Landsat scene). Important considerations for transferability include the model employed, quantity of cover data used to train/test the models, and phenology of the species coupled with the timing of imagery. The results also suggest that when cover data are limited, SDMs fit with topographic indices are sufficient for evaluating cheatgrass habitat suitability in new post-disturbance landscapes; however, spectral indices can provide a more robust estimate for detection based on local phenology.

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Acknowledgements

We would like to thank many individuals from the U.S. Forest Service, Wyoming Game and Fish, and Colorado State University who helped with field data collection for this study. Special thanks to Katherine Haynes and Ryan Amundson for their continued insights and interest in this project. We thank Thomas Stohlgren for providing comments and edits on an early version of this manuscript. We would like to thank the U.S. Geological Survey’s Invasive Species Program for funding for this research. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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Authors and Affiliations

  1. Natural Resource Ecology Laboratory, Colorado State University, 1499 Campus Delivery, Fort Collins, CO, 80523, USA

    Amanda M. West, Paul H. Evangelista & Darin Schulte

  2. Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO, USA

    Paul H. Evangelista & Catherine S. Jarnevich

  3. U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Ave Bldg. C, Fort Collins, CO, 80526, USA

    Catherine S. Jarnevich

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  1. Amanda M. West
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  2. Paul H. Evangelista
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  4. Darin Schulte
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Correspondence to Amanda M. West.

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West, A.M., Evangelista, P.H., Jarnevich, C.S. et al. A tale of two wildfires; testing detection and prediction of invasive species distributions using models fit with topographic and spectral indices. Landscape Ecol 33, 969–984 (2018). https://doi.org/10.1007/s10980-018-0644-x

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