<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thapa‐Magar, Khum B.</style></author><author><style face="normal" font="default" size="100%">Eric R. Sokol</style></author><author><style face="normal" font="default" size="100%">Michael N. Gooseff</style></author><author><style face="normal" font="default" size="100%">Salvatore, Mark R.</style></author><author><style face="normal" font="default" size="100%">John E. Barrett</style></author><author><style face="normal" font="default" size="100%">Joseph S. Levy</style></author><author><style face="normal" font="default" size="100%">Knightly, J. Paul</style></author><author><style face="normal" font="default" size="100%">Power, Sarah N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Remote sensing for species distribution models: An illustration from a sentinel taxon of the world's driest ecosystem</style></title><secondary-title><style face="normal" font="default" size="100%">Ecology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Antarctica</style></keyword><keyword><style  face="normal" font="default" size="100%">McMurdo Dry Valleys</style></keyword><keyword><style  face="normal" font="default" size="100%">microbial mats</style></keyword><keyword><style  face="normal" font="default" size="100%">remote sensing</style></keyword><keyword><style  face="normal" font="default" size="100%">species distribution modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">species occurrence</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2025</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecy.70035</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">106</style></volume><pages><style face="normal" font="default" size="100%">e70035</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Power, Sarah N.</style></author><author><style face="normal" font="default" size="100%">Salvatore, Mark R.</style></author><author><style face="normal" font="default" size="100%">Eric R. Sokol</style></author><author><style face="normal" font="default" size="100%">Lee F. Stanish</style></author><author><style face="normal" font="default" size="100%">Borges, Schuyler R.</style></author><author><style face="normal" font="default" size="100%">Byron Adams</style></author><author><style face="normal" font="default" size="100%">John E. Barrett</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Remotely characterizing photosynthetic biocrust in snowpack-fed microhabitats of Taylor Valley, Antarctica</style></title><secondary-title><style face="normal" font="default" size="100%">Science of Remote Sensing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Antarctica</style></keyword><keyword><style  face="normal" font="default" size="100%">biocrust</style></keyword><keyword><style  face="normal" font="default" size="100%">carbon</style></keyword><keyword><style  face="normal" font="default" size="100%">reflectance spectroscopy</style></keyword><keyword><style  face="normal" font="default" size="100%">snow</style></keyword><keyword><style  face="normal" font="default" size="100%">soil ecology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2024</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S266601722400004X</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">100120</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Microbial communities are the primary drivers of carbon cycling in the McMurdo Dry Valleys of Antarctica. Dense microbial mats, consisting mainly of photosynthetic cyanobacteria, occupy aquatic areas associated with streams and lakes. Other microbial communities also occur at lower densities as patchy surface biological soil crusts (hereafter, biocrusts) across the terrestrial landscape. Multispectral satellite data have been used to model microbial mat abundance in high-density areas like stream and lake margins, but no previous studies have investigated the lower detection limits of biocrusts. Here, we describe remote sensing and field-based survey and sampling approaches to study the detectability and distribution of biocrusts in the McMurdo Dry Valleys. Using a combination of multi- and hyperspectral tools and spectral linear unmixing, we modeled the abundances of biocrust in eastern Taylor Valley. Our spectral approaches can detect low masses of biocrust material in laboratory microcosms down to biocrust concentrations of 1% by mass. These techniques also distinguish the spectra of biocrust from both surface rock and mineral signatures from orbit. We found that biocrusts are present throughout the soils of eastern Taylor Valley and are associated with diverse underlying soil communities. The densest biocrust communities identified in this study had total organic carbon 5x greater than the content of typical arid soils. The most productive biocrusts were located downslope of melting snowpacks in unique soil ecosystems that are distinct from the surrounding arid landscape. There are similarities between the snowpack and stream sediment communities (high diversity of soil invertebrates) as well as their ecosystem properties (e.g., persistence of liquid water, high transfer of available nutrients, lower salinity from flushing) compared to the typical arid terrestrial ecosystem of the dry valleys. Our approach extends the capability of orbital remote sensing of photosynthetic communities out of the aquatic margins and into the drier soils which comprise most of this landscape. This interdisciplinary work is critical for measuring and monitoring terrestrial carbon stocks and predicting future ecosystem dynamics in this currently water-limited but increasingly dynamic Antarctic landscape, which is particularly climate-sensitive and difficult to access.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Power, Sarah N.</style></author><author><style face="normal" font="default" size="100%">Salvatore, Mark R.</style></author><author><style face="normal" font="default" size="100%">Eric R. Sokol</style></author><author><style face="normal" font="default" size="100%">Lee F. Stanish</style></author><author><style face="normal" font="default" size="100%">John E. Barrett</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimating microbial mat biomass in the McMurdo Dry Valleys, Antarctica using satellite imagery and ground surveys</style></title><secondary-title><style face="normal" font="default" size="100%">Polar Biology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Antarctica</style></keyword><keyword><style  face="normal" font="default" size="100%">microbial mat</style></keyword><keyword><style  face="normal" font="default" size="100%">multispectral imagery</style></keyword><keyword><style  face="normal" font="default" size="100%">NDVI</style></keyword><keyword><style  face="normal" font="default" size="100%">Nostocales</style></keyword><keyword><style  face="normal" font="default" size="100%">remote sensing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007%2Fs00300-020-02742-y</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cyanobacterial mat communities are the main drivers of primary productivity in the McMurdo Dry Valleys, Antarctica. These microbial communities form laminar mats on desert pavement surfaces adjacent to glacial meltwater streams, ponds, and lakes. The low-density nature of these communities and their patchy distribution make assessments of distribution, biomass, and productivity challenging. We used satellite imagery coupled with in situ surveying, imaging, and sampling to systematically estimate microbial mat biomass in selected wetland regions in Taylor Valley, Antarctica. On January 19th, 2018, the WorldView-2 multispectral satellite acquired an image of our study areas, where we surveyed and sampled seven 100 m&lt;sup&gt;2&lt;/sup&gt; plots of microbial mats for percent ground cover, ash-free dry mass (AFDM), and pigment content (chlorophyll-a, carotenoids, and scytonemin). Multispectral analyses revealed spectral signatures consistent with photosynthetic activity (relatively strong reflection at near-infrared wavelengths and relatively strong absorption at visible wavelengths), with average normalized difference vegetation index (NDVI) values of 0.09 to 0.28. Strong correlations of microbial mat ground cover (R&lt;sup&gt;2&lt;/sup&gt;&amp;thinsp;=&amp;thinsp;0.84), biomass (R&lt;sup&gt;2&lt;/sup&gt;&amp;thinsp;=&amp;thinsp;0.74), chlorophyll-a content (R&lt;sup&gt;2&lt;/sup&gt;&amp;thinsp;=&amp;thinsp;0.65), and scytonemin content (R&lt;sup&gt;2&lt;/sup&gt;&amp;thinsp;=&amp;thinsp;0.98) with logit transformed NDVI values demonstrate that satellite imagery can detect both the presence of microbial mats and their key biological properties. Using the NDVI&amp;mdash;biomass correlation we developed, we estimate carbon (C) stocks of 21,715 kg (14.7 g C m&lt;sup&gt;&amp;minus;2&lt;/sup&gt;) in the Canada Glacier Antarctic Specially Protected Area, with an upper and lower limit of 74,871 and 6312 kg of C, respectively.&lt;/p&gt;</style></abstract></record></records></xml>