• CS app SRNA collects metadata on selected mammals and birds across Slovenia. • Separate interfaces for hunters and other nature observers. • First year: 1633 observations from 742 reports; biases, verification issues present. • The app includes a wildlife identification quiz and species identification guides. • Biology-related education facilitates identification of mustelids and canids in quiz. Wildlife monitoring is crucial for successful conservation and management of wildlife populations. To support modern wildlife monitoring approaches in Slovenia, we developed SRNA, a nationwide citizen science application that collects additional metadata on several terrestrial vertebrate species. The app is designed for two groups of citizen scientists, hunters and other nature observers. To foster learning, it features a photo-based wildlife identification quiz and species identification guides. This pilot study evaluated app use and data quality with two aims: (1) to assess users’ ability to identify wildlife species from photos, focusing on taxa expected to be challenging to identify, and to test whether identification accuracy varies across mammal groups; (2) to analyse wildlife observation data submitted through SRNA. Users were successful in identifying several distinctive species but had some difficulty distinguishing between certain mustelids, and between golden jackal and grey wolf, indicating priorities for targeted educational campaigns. Biology-related educational background facilitated identification of mustelids and, to a lesser extent, canids. In its first year, SRNA collected 1633 observations via 742 reports, predominantly of European roe deer. Hunters and nature observers contributed complementary data: differences were observed not only in the number of reported species and amount of data they provided, but also in reporting patterns across biogeographic regions, reporting rates, and the average delay time between encounters and submissions. This study demonstrates that SRNA provides promising complementary data for wildlife monitoring, but also highlights data quality limitations (biases and verification issues) that must be taken into account when using SRNA data.
Velkavrh et al. (Sun,) studied this question.