<?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%">Novis, Phil M.</style></author><author><style face="normal" font="default" size="100%">Monks, Adrian</style></author><author><style face="normal" font="default" size="100%">Hunt, John E.</style></author><author><style face="normal" font="default" size="100%">Adams, Byron J.</style></author><author><style face="normal" font="default" size="100%">Dhami, Manpreet K.</style></author><author><style face="normal" font="default" size="100%">Kim, Ji Hee</style></author><author><style face="normal" font="default" size="100%">Mitchell, Caroline</style></author><author><style face="normal" font="default" size="100%">Morgan, Fraser</style></author><author><style face="normal" font="default" size="100%">Ian Hawes</style></author><author><style face="normal" font="default" size="100%">Aislabie, J</style></author><author><style face="normal" font="default" size="100%">P. Broady</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inference from eDNA-based field distributions vs laboratory analysis of isolated strains: Physiological performance of non-marine Antarctic biota</style></title><secondary-title><style face="normal" font="default" size="100%">Polar Biology</style></secondary-title><short-title><style face="normal" font="default" size="100%">Polar Biol</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Antarctic</style></keyword><keyword><style  face="normal" font="default" size="100%">distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">eDNA</style></keyword><keyword><style  face="normal" font="default" size="100%">inference</style></keyword><keyword><style  face="normal" font="default" size="100%">microbial biodiversity</style></keyword><keyword><style  face="normal" font="default" size="100%">physiology</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%">03/2025</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007/s00300-025-03356-y</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">36</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Environmental DNA (eDNA) is frequently used to infer distributions of microorganisms in Antarctica. Their distributions relative to environmental variables are, in turn, sometimes used to infer their physiological range (and a relationship between the two is generally assumed for conservation purposes). We sought to determine whether ecological inferences based on distributions accurately reflect tolerances of the organisms concerned, using 249 legacy non-marine samples from a latitudinal gradient between 72 and 86&amp;deg;S, Antarctica. A cyanobacterium, a heterotrophic bacterium, two eukaryotic algae, two fungi, and a moss were isolated into culture, and their field distributions inferred using eDNA analysis of the samples above. Tolerances of each organism with respect to environmental predictors were then inferred from the eDNA distribution and metadata using Generalised Additive Models. We then measured growth of the cultured isolates in response to a set of these predictors. Laboratory responses were then compared to inferences from the eDNA/metadata. Predictions from eDNA/metadata agreed with the results of physiological laboratory experiments for strains that were detected at high taxonomic resolution in the field samples. However, errors were never completely eliminated, and direct contradictions occurred when strains were represented at lower taxonomic resolution in the field data. We found that accurate ecological inference from eDNA studies would be best achieved via maximising both taxonomic resolution (through marker choice/read length) and ecological signal (through careful sampling design and rigorous metadata collection).&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</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%">Patriarche, Jeffrey D.</style></author><author><style face="normal" font="default" size="100%">John C. Priscu</style></author><author><style face="normal" font="default" size="100%">Cristina D. Takacs-Vesbach</style></author><author><style face="normal" font="default" size="100%">Winslow, Luke A.</style></author><author><style face="normal" font="default" size="100%">Myers, Krista F.</style></author><author><style face="normal" font="default" size="100%">Heather N. Buelow</style></author><author><style face="normal" font="default" size="100%">Rachael M. Morgan-Kiss</style></author><author><style face="normal" font="default" size="100%">Peter T. Doran</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Year‐round and long‐term phytoplankton dynamics in Lake Bonney, a permanently ice‐covered Antarctic lake</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Geophysical Research: Biogeosciences</style></secondary-title><short-title><style face="normal" font="default" size="100%">J Geophys Res Biogeosci</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">algae</style></keyword><keyword><style  face="normal" font="default" size="100%">Antarctic</style></keyword><keyword><style  face="normal" font="default" size="100%">fluorometry</style></keyword><keyword><style  face="normal" font="default" size="100%">ice</style></keyword><keyword><style  face="normal" font="default" size="100%">lakes</style></keyword><keyword><style  face="normal" font="default" size="100%">light</style></keyword><keyword><style  face="normal" font="default" size="100%">profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">winter</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JG005925</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">126</style></volume><pages><style face="normal" font="default" size="100%">e2020JG005925</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Lake Bonney (McMurdo Dry Valleys, east Antarctica) represents a year‐round refugium for life adapted to permanent extreme conditions. Despite intensive research since the 1960s, due to the logistical constraints posed by 4‐months of 24‐h darkness, knowledge of how the resident photosynthetic microorganisms respond to the polar winter is limited. In addition, the lake level has risen by more than 3 m since 2004: impacts of rapid lake level rise on phytoplankton community structure is also poorly understood. From 2004 to 2015 an in situ submersible spectrofluorometer (bbe FluoroProbe) was deployed in Lake Bonney during the austral summer to quantify the vertical structure of four functional algal groups (green algae, mixed algae, and cryptophytes, cyanobacteria). During the 2013&amp;ndash;2014 field season the Fluoroprobe was mounted on autonomous cable‐crawling profilers deployed in both the east and west lobes of Lake Bonney, obtaining the first daily phytoplankton profiles through the polar night. Our findings showed that phytoplankton communities were differentially impacted by physical and chemical factors over long‐term versus seasonal time scales. Following a summer of rapid lake level rise (2010&amp;ndash;2011), an increase in depth integrated chlorophyll a (chl‐a) occurred in Lake Bonney caused by stimulation of photoautotrophic green algae. Conversely, peaks in chl‐a during the polar night were associated with an increase in mixotrophic haptophytes and cryptophytes. Collectively our data reveal that phytoplankton groups possessing variable trophic abilities are differentially competitive during seasonal and long‐term time scales owing to periods of higher nutrients (photoautotrophs) versus light/energy limitation (mixotrophs).&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record></records></xml>