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In situ observed data are commonly used as species occurrence response variables in species distribution models. However, the use of remotely observed data from high‐resolution multispectral remote‐sensing images as a source of presence/absence data for species distribution models remains under‐developed. Here, we describe an ensemble species distribution model of black microbial mats "Nostoc" using presence/absence points derived from the unmixing of 4‐m resolution WorldView‐2 and WorldView‐3 images in the Lake Fryxell basin region of Taylor Valley, Antarctica. Environmental and topographical characteristics such as soil moisture, snow, elevation, slope, and aspect were used as predictor variables in our models. We demonstrate that we can build and run ensemble species distribution models using both dependent and independent variables derived from remote‐sensing data to generate spatially explicit habitat suitability maps. Snow and soil moisture were found to be the most important variables accounting for about 80% of the variation in the distribution of black mats throughout the Fryxell basin. This study highlights the potential contribution of high‐resolution remote‐sensing to species distribution modeling and informs new studies incorporating remotely derived species occurrences in species distribution models, especially in remote areas where access to in situ data is often limited.
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Deep learning models map rapid plant species changes from citizen science and remote sensing data
Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model—Deepbiosphere—not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data,Deepbiospherecan detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.
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- Award ID(s):
- 2419923
- PAR ID:
- 10600047
- Publisher / Repository:
- PNAS
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 121
- Issue:
- 37
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2318296121
