@article {5017,
	title = {Remote sensing for species distribution models: An illustration from a sentinel taxon of the world{\textquoteright}s driest ecosystem},
	journal = {Ecology},
	volume = {106},
	year = {2025},
	month = {02/2025},
	pages = {e70035},
	abstract = {<p>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 spp.) 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.</p>},
	keywords = {LTER-MCM, Antarctica, McMurdo Dry Valleys, microbial mats, remote sensing, species distribution modeling, species occurrence},
	issn = {0012-9658},
	doi = {10.1002/ecy.v106.210.1002/ecy.70035},
	url = {https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecy.70035},
	author = {Thapa-Magar, Khum B. and Eric R. Sokol and Michael N. Gooseff and Salvatore, Mark R. and John E. Barrett and Joseph S. Levy and Knightly, J. Paul and Power, Sarah N.}
}
@article {4449,
	title = {Remote characterization of photosynthetic communities in the Fryxell basin of Taylor Valley, Antarctica},
	journal = {Antarctic Science},
	year = {2020},
	month = {03/2020},
	abstract = {<p>We investigate the spatial distribution, spectral properties and temporal variability of primary producers (e.g. communities of microbial mats and mosses) throughout the Fryxell basin of Taylor Valley, Antarctica, using high-resolution multispectral remote-sensing data. Our results suggest that photosynthetic communities can be readily detected throughout the Fryxell basin based on their unique near-infrared spectral signatures. Observed intra- and inter-annual variability in spectral signatures are consistent with short-term variations in mat distribution, hydration and photosynthetic activity. Spectral unmixing is also implemented in order to estimate mat abundance, with the most densely vegetated regions observed from orbit correlating spatially with some of the most productive regions of the Fryxell basin. Our work establishes remote sensing as a valuable tool in the study of these ecological communities in the McMurdo Dry Valleys and demonstrates how future scientific investigations and the management of specially protected areas could benefit from these tools and techniques.</p>},
	keywords = {LTER-MCM, ecology, hydrology, McMurdo Dry Valleys, microbiology, remote sensing, spectroscopy},
	doi = {10.1017/S0954102020000176},
	url = {https://www.cambridge.org/core/journals/antarctic-science/article/remote-characterization-of-photosynthetic-communities-in-the-fryxell-basin-of-taylor-valley-antarctica/8576F6BB1BCFDCA8409F5EA96CA00C6F},
	author = {Salvatore, Mark R. and Borges, Schuyler R. and John E. Barrett and Eric R. Sokol and Lee F. Stanish and Power, Sarah N. and Morin, Paul}
}
