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Can social media be used to inform the distribution of the marbled polecat, Vormela peregusna?
The marbled polecat (Vormela peregusna) is a small mustelid that occurs from the Balkans to Mongolia and is listed as vulnerable under the International Union for Conservation of Nature's (IUCN) Red List. There are currently no efficient methods to monitor populations at a broad scale and most records come from opportunistic sightings. However, the elusive nature and unique pelage of the species often results in a lot of interest when sighted, with observations regularly being shared on social media platforms. Such records from social media can provide an extensive source of freely available information that could be used to inform the species’ distribution. In this study, we systematically collected marbled polecat records from five social media platforms by using a manual and automated search targeting the western range of the species. We identified 131 unique marbled polecat sightings originating mostly from Facebook, Instagram and Twitter. The records confirmed the species’ presence in 92 50-km grid cells within the study area and outperformed other sources, such as GBIF and scientific literature searches. The combination of all three datasets resulted in 133 presence points, which was sufficient enough to perform further habitat suitability modelling and reliable alpha hull range estimates. The social media search was well suited to clarify broad distribution patterns of marbled polecat, but did not detect the species in areas where its presence was most uncertain. The results of the modelling work, however, can be used to target further dedicated survey work for the species. The framework used in this study can be applied to provide more detailed information on distribution and occurrence patterns for other rare or under-studied species
Comparison of the predicted bat detections (calls and passes) for two different acoustic systems using monitoring data collected from Jersey, UK.
<p>Acoustic systems used were SonoBat (version 3.1.7p) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005995#pcbi.1005995.ref043" target="_blank">43</a>] using analysis in [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005995#pcbi.1005995.ref049" target="_blank">49</a>], and BatDetect CNN<sub>FAST</sub> using a probability threshold of 0.90. Detections are shown within each box plot, where the black line represents the mean across all transect sampling events from 2011–2015, boxes represent the middle 50% of the data, whiskers represent variability outside the upper and lower quartiles, with outliers plotted as individual points. See text for definition of a bat pass.</p
Average precision and recall results for bat search-phase call detection algorithms across three different test sets iBats Romania and Bulgaria; iBats UK; and Norfolk Bat Survey.
<p>Average precision and recall results for bat search-phase call detection algorithms across three different test sets iBats Romania and Bulgaria; iBats UK; and Norfolk Bat Survey.</p
Detection pipeline for search-phase bat echolocation calls.
<p>(a) Raw audio files are converted into a spectrogram using a Fast Fourier Transform (b). Files are de-noised (c), and a sliding window Convolutional Neural Network (CNN) classifier (d, yellow box) produces a probability for each time step. Individual call detection probabilities using non-maximum suppression are produced (e, green boxes), and the time in file of each prediction along with the classifier probability are exported as text files.</p
Spatial distribution of the BatDetect CNNs training and testing datasets.
<p>(a) Location of training data for all experiments and one test dataset in Romania and Bulgaria (2006–2011) from time-expanded (TE) data recorded along road transects by the Indicator Bats Programme (iBats) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005995#pcbi.1005995.ref007" target="_blank">7</a>], where red and black points represent training and test data, respectively. (b) Locations of additional test datasets from TE data recorded as part of iBats car transects in the UK (2005–2011), and from real-time recordings from static recorders from the Norfolk Bat Survey from 2015 (inset). Points represent the start location of each snapshot recording for each iBats transect or locations of static detectors for the Norfolk Bat Survey.</p