Abstract:This study presents a 1:25,000 geomorphological map of the northern sector of Ulu Peninsula, James Ross Island, Antarctic Peninsula. The map covers an area of c. 250 km 2 , and documents the landforms and surficial sediments of one of the largest ice-free areas in Antarctica, based on remote sensing and field-based mapping. The large-scale landscape features are determined by the underlying Cretaceous sedimentary and Neogene volcanic geology, which has been sculpted by overlying ice masses during glacial perio… Show more
“…Furthermore, given the already “advanced” age of the young lakes (i.e. decades to a few centuries), coupled with the propensity of aeolian material to disperse in this region (Ambrožová et al 2020 , Kavan et al 2020 ) and the relatively small spatial scale under consideration (ca 250 km 2 ; Jennings et al 2021 ), we predict that dispersal should not be limiting to the microbial communities on the Ulu Peninsula. While dispersal can of course be influential for microbial communities in Antarctica at large spatial scales (Sokol et al 2013 , Sakaeva et al 2016 , Schulte et al 2022 ), preliminary analyses linking microbial diversity and community dissimilarity to spatial variables have thus far found no patterns indicating dispersal limitation on the Ulu Peninsula (Kavan, unpublished).…”
Section: Discussionmentioning
confidence: 82%
“…The lower-levelled coastal regions of the Ulu Peninsula were already deglaciated at the beginning of the Holocene (Nývlt et al 2014 ), and currently, the deglaciated area occupies ca 250 km 2 of the Ulu Peninsula (Jennings et al 2021 ). The Ulu Peninsula itself is underlain by permafrost, with an active layer thickness around 60 cm at the peak of the austral summer (Kaplan Pastíriková et al 2023 ).…”
Glacier recession is creating new waterbodies in proglacial forelands worldwide, including Antarctica. Yet, it is unknown how microbial communities of recently formed ‘young’ waterbodies (originating decades to a few centuries ago) compare with established ‘old’ counterparts (millennia ago). Here, we compared benthic microbial communities of different lake types on James Ross Island, Antarctic Peninsula, using 16S rDNA metabarcoding and light microscopy to explore bacterial and diatom communities, respectively. We found that the older lakes host significantly more diverse bacterial and diatom communities compared to the young ones. To identify potential mechanisms for these differences, linear models and dbRDA analyses suggested combinations of water temperature, pH, and conductivity to be the most important factors for diversity and community structuring, while differences in geomorphological and hydrological stability, though more difficult to quantify, are likely also influential. These results, along with an indicator species analysis, suggest that physical and chemical constraints associated with individual lakes histories are likely more influential to the assembly of the benthic microbial communities than lake age alone. Collectively, these results improve our understanding of microbial community drivers in Antarctic freshwaters, and help predict how the microbial landscape may shift with future habitat creation within a changing environment.
“…Furthermore, given the already “advanced” age of the young lakes (i.e. decades to a few centuries), coupled with the propensity of aeolian material to disperse in this region (Ambrožová et al 2020 , Kavan et al 2020 ) and the relatively small spatial scale under consideration (ca 250 km 2 ; Jennings et al 2021 ), we predict that dispersal should not be limiting to the microbial communities on the Ulu Peninsula. While dispersal can of course be influential for microbial communities in Antarctica at large spatial scales (Sokol et al 2013 , Sakaeva et al 2016 , Schulte et al 2022 ), preliminary analyses linking microbial diversity and community dissimilarity to spatial variables have thus far found no patterns indicating dispersal limitation on the Ulu Peninsula (Kavan, unpublished).…”
Section: Discussionmentioning
confidence: 82%
“…The lower-levelled coastal regions of the Ulu Peninsula were already deglaciated at the beginning of the Holocene (Nývlt et al 2014 ), and currently, the deglaciated area occupies ca 250 km 2 of the Ulu Peninsula (Jennings et al 2021 ). The Ulu Peninsula itself is underlain by permafrost, with an active layer thickness around 60 cm at the peak of the austral summer (Kaplan Pastíriková et al 2023 ).…”
Glacier recession is creating new waterbodies in proglacial forelands worldwide, including Antarctica. Yet, it is unknown how microbial communities of recently formed ‘young’ waterbodies (originating decades to a few centuries ago) compare with established ‘old’ counterparts (millennia ago). Here, we compared benthic microbial communities of different lake types on James Ross Island, Antarctic Peninsula, using 16S rDNA metabarcoding and light microscopy to explore bacterial and diatom communities, respectively. We found that the older lakes host significantly more diverse bacterial and diatom communities compared to the young ones. To identify potential mechanisms for these differences, linear models and dbRDA analyses suggested combinations of water temperature, pH, and conductivity to be the most important factors for diversity and community structuring, while differences in geomorphological and hydrological stability, though more difficult to quantify, are likely also influential. These results, along with an indicator species analysis, suggest that physical and chemical constraints associated with individual lakes histories are likely more influential to the assembly of the benthic microbial communities than lake age alone. Collectively, these results improve our understanding of microbial community drivers in Antarctic freshwaters, and help predict how the microbial landscape may shift with future habitat creation within a changing environment.
“…and has a relatively steep surface with a mean slope of 17° (Davies and others, 2013). By contrast, gentle slopes and braidplains prevail in the proglacial zone (Jennings and others, 2021). Fig.…”
The retreat rates of Triangular Glacier since 1979 and its mass changes during the period 2014/15–2019/20 indicate the sensitive response of small ice masses on the eastern side of the Antarctic Peninsula to air temperature evolution. This cirque glacier in the northern part of James Ross Island receded rapidly during the period of regional warming in the late 20th century, losing 30.8% of its surface area between 1979 and 2006 (−1.7% a−1). The retreat rate then dropped to −0.3% a−1 following the regional cooling trend, but started to accelerate again (−0.8 to −2.3% a−1) with increasing air temperature since the summer 2014/15. Since the glaciological year 2015/16, Triangular Glacier has experienced enhanced snow melt, wind scour and permanent mass loss with annual mass balance ranging from −0.08 ± 0.35 to −0.56 ± 0.25 m w.e. The largest mass loss was observed in the glaciological year 2019/20, which included the warmest summer of the observation period. The cumulative mass balance of −1.66 ± 0.83 m w.e. over the years 2014/15–2019/20 is consistent with the termination of the positive mass-balance period that occurred in the north-eastern Antarctic Peninsula from 2009/10 to 2014/15.
“…During the last 10 years, a considerable number of geomorphological studies have been carried out in Antarctica, encompassing geomorphological mappings developed from visual interpretation of remote‐sensing products (Delpupo et al, 2017; Francelino et al, 2011; Jennings et al, 2021; López‐Martínez et al, 2012; Michel et al, 2014; Rodrigues et al, 2019; Schaefer et al, 2015). Despite good efficiency in landform discrimination, this method is highly subjective and hinders reproducibility, depending directly on the skill and experience of the analyst (Veronesi & Hurni, 2014).…”
Section: Introductionmentioning
confidence: 99%
“…The interaction between the semi‐arid climate and geology, added to the recent glaciological history, induces the formation of an unstable landscape, predominantly paraglacial (Davies et al, 2013). The complexity of the morphogenetic processes, typical of young landscapes, leads to a wide variety of erosional and accumulative landforms, with different structures, genesis and operational functions, and whose distribution and spatial interplays are still little known (Jennings et al, 2021; Siqueira et al, 2021). The main objective in this study was to evaluate the predictive performance of six supervised ML algorithms for the classification of landforms in Vega Island, Weddell Sea.…”
The detailed geomorphology of ice‐free landscapes of Antarctica is key to understanding how their highly fragile environments respond to climate change, at different temporal and spatial scales. Despite the recent advances in geomorphological studies of ice‐free areas, machine learning applications to produce landform maps are still scarce on the Antarctic continent. In this study, we evaluated the predictive performance of different supervised machine learning algorithms to produce digital geomorphological maps in Vega Island—Antarctic Peninsula region. We tested six different models: average artificial neural networks, C5.0 decision tree, random forest, support vector machine, supervised self‐organizing map and weighted k‐nearest neighbours. We used an initial set of 54 geomorphometric and spectral predictors, from which redundant variables with Pearson correlation coefficient >|0.95| were removed, and only the most important predictors for each model were selected using recursive feature elimination. For training, we ran each model 100 times and predictions were assessed by the kappa and global accuracy values. The best predictors were the Red Edge 6 and SWIR 11 bands, roughness concentration index, elevation and drainage density. The decision trees C5.0 and random forest had the best performance, with average validation kappa of 0.85 ± 0.03 and 0.84 ± 0.03, respectively, evidencing excellent prediction. Despite the similar performance, random forest showed greater uncertainty degree and accuracy when classifying complex landforms, attesting to its great robustness. From sensitivity and specificity values, we observed that the glaciers and talus showed higher accuracy, whereas cryoplanated platforms and scree slopes had the worst classification. The presented methodology optimized the classification by selecting the most important predictors, assessing accuracy and evaluating uncertainty. The results indicated that machine learning methods have great potential to produce geomorphological mappings in the Antarctic ice‐free areas, as a promising tool to provide detailed information on remote, harsh polar environments.
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