Abstract: Lemmings are a key component of tundra food webs and changes in their dynamics can affect the whole ecosystem. We present a comprehensive overview of lemming monitoring and research activities, and assess recent trends in lemming abundance across the circumpolar Arctic. Since 2000, lemmings have been monitored at 49 sites of which 38 are still active. The sites were not evenly distributed with notably Russia and high Arctic Canada underrepresented. Abundance was monitored at all sites, but methods and levels of precision varied greatly. Other important attributes such as health, genetic diversity and potential drivers of population change, were often not monitored. There was no evidence that lemming populations were decreasing in general, although a negative trend was detected for low arctic populations sympatric with voles. To keep the pace of arctic change, we recommend maintaining long-term programmes while harmonizing methods, improving spatial coverage and integrating an ecosystem perspective.

Abstract: Burrow-nesting seabirds constitute an important part of seabird diversity, yet accurate estimates of their abundance are largely lacking, limiting our understanding of their population dynamics and conservation status. We conducted a survey to estimate the number of South Georgia diving petrel (Pelecanoides georgicus) burrows during the 2013–14 breeding season on Ile de la Possession, Crozet archipelago, southern Indian Ocean. We used distance sampling and acoustic playback in order to estimate burrow densities in a priori-selected favourable nesting areas. A total of 855 burrows were detected. The mean altitude of burrows was 601.8 ± 69.4 m. The mean burrow detection distance was 1.77 ± 1.63 m. The burrow density was estimated at 15.649 burrows ha-1 (95% confidence interval (CI): 10.245–23.903) and the slope-corrected total favourable area was 2365.53 ha, which yielded an estimate 37 018 burrows (95% CI: 24 235–56 544). The playback response rate was 15.8 ± 1.3%, and 40.8 ± 1.7% of burrows were occupied or showed signs of occupation. Occupancy rates were low compared to those measured by systematic burrow inspection in other studies. Assuming that laying occurred in 80–93% of the estimated number of burrows, as estimated by previous studies, gives an estimate of 29 614 (95% CI: 19 388–45 235) to 34 426 (95% CI: 22 538–52 585) breeding pairs.

Abstract: This paper presents the results of a number of aircraft- and boat-based surveys for seabird breeding colonies in East and North Greenland carried out in the period 2003 to 2018 and gives the first comprehensive overview of the distribution and size of the seabird breeding colonies in this remote and mainly uninhabited region. Seventeen seabird species breed in approximately 800 sites distributed very unevenly along the coasts, with high concentrations at the polynyas and long stretches with very few breeding seabirds. Climate changes are in full progress in East and North Greenland, especially affecting the sea ice regime, and seabirds are expected to respond to these changes in different ways. For example, since the 1980s, Common Eiders (Somateria mollissima) have extended their breeding range more than two latitudinal degrees towards the north, now reaching the northernmost land on Earth. Lesser Black-backed Gulls (Larus fuscus) and Great Cormorants (Phalacrocorax carbo) have immigrated, and Sabine’s Gulls (Xema sabini) have increased and extended their range. Besides presenting survey results, this report may also serve as a baseline for future studies of the abundance of breeding seabirds in East and North Greenland.

Abstract: Machine learning is becoming increasingly important in scientific and technological progress, due to its ability to create models that describe complex data and generalize well. The wealth of publicly-available seismic data nowadays requires automated, fast, and reliable tools to carry out a multitude of tasks, such as the detection of small, local earthquakes in areas characterized by sparsity of receivers. A similar application of machine learning, however, should be built on a large amount of labeled seismograms, which is neither immediate to obtain nor to compile. In this study we present a large dataset of seismograms recorded along the vertical, north, and east components of 1487 broad-band or very broad-band receivers distributed worldwide; this includes 629,095 3-component seismograms generated by 304,878 local earthquakes and labeled as EQ, and 615,847 ones labeled as noise (AN). Application of machine learning to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings, even if applied in regions not represented in the training set. Achieving an accuracy of 96.7, 95.3, and 93.2% on training, validation, and test set, respectively, we prove that the large variety of geological and tectonic settings covered by our data supports the generalization capabilities of the algorithm, and makes it applicable to real-time detection of local events. We make the database publicly available, intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in signal processing.