Species bias and spillover effects in scientific research on Carnivora in China

A Fine-Grained Image Classification Approach for Dog Feces Using MC-SCMNet under Complex Backgrounds

In a natural environment, factors such as weathering and sun exposure will degrade the characteristics of dog feces; disturbances such as decaying wood and dirt are likely to make false detections; the recognition distinctions between different kinds of feces are slight. To address these issues, this paper proposes a fine-grained image classification approach for dog feces using MC-SCMNet under complex backgrounds. First, a multi-scale attention down-sampling module (MADM) is proposed. It carefully retrieves tiny feces feature information. Second, a coordinate location attention mechanism (CLAM) is proposed. It inhibits the entry of disturbance information into the network's feature layer. Then, an SCM-Block containing MADM and CLAM is proposed. We utilized the block to construct a new backbone network to increase the efficiency of fecal feature fusion in dogs. Throughout the network, we decrease the number of parameters using depthwise separable convolution (DSC). In conclusion, MC-SCMNet outperforms all other models in terms of accuracy. On our self-built DFML dataset, it achieves an average identification accuracy of 88.27% and an F1 value of 88.91%. The results of the experiments demonstrate that it is more appropriate for dog fecal identification and maintains stable results even in complex backgrounds, which may be applied to dog gastrointestinal health checks.

Assessing the Diet of a Predator Using a DNA Metabarcoding Approach

The diet of top predators is vital information needed to determine their ecological function and for their conservation management. However, the elusive habit and low population density of many predators constrains determination of their diets. While the morphological identification of scat contents is the traditional method, DNA metabarcoding has lately proven a more efficient and accurate method of identifying prey taxa. We applied DNA metabarcoding to analyzing the diet of the Eurasian otter (Lutra lutra), a top predator in freshwater ecosystems, using 12S and 16S rRNA mitochondrial primers target vertebrate prey. Diet did not vary among different data removal thresholds of 0.1, 1, 3, and 5%, comprising fishes (>90%), amphibians and birds (>2%), and occasionally mammals (<2%). Both 12S and 16S primers revealed similar otter diets, indicating that a single set of primers with a higher threshold is cost-effective for detecting the main prey taxa. Using 12S primers and a 5% threshold, we found no seasonal variation of otter diet in the Tangjiahe National Nature Reserve. A different prey community was found outside the reserve, which resulted in different prey composition for otters. However, prey taxon richness was not different between otters in- and outside the reserve. Otters preferred Schizothorax spp., the largest-sized fish species in the reserve, whereas they mainly preyed on Triplophysa bleekeri, a small-sized fish species, outside the reserve. Otters’ flexible feeding strategy reflect their high adaptability. However, greater human disturbance outside the reserve may present significant challenges to otters by altering prey communities and reducing prey profitability. Combining fecal DNA metabarcoding and local fish survey will provide opportunities for more detailed studies on the impact of different levels of human disturbances on prey communities and otters.