The loss of suitable spawning habitats is a global threat to amphibians. Thus, establishing man-made ponds may serve as a successful conservation practice to counteract this threat. In this experiment, we used a citizen science (CS) approach to create mini-ponds in private gardens and asked participants to monitor amphibian activity. We provided 302 polyethylene mini-ponds (120 L × 90 W × 40 D cm) to citizen scientists across the range of the target species, the European green toad (Bufotes viridis), in Austria. In the first year of the experiment (2024), B. viridis and 12 other amphibian taxa were recorded at 38% of the monitored sites. Signs of amphibian reproduction were detected in 10% of the mini-ponds. Bayesian occupancy models showed detection probabilities consistent with other studies. Over time, we expect more amphibians to discover the mini-ponds and more individuals to use them for reproduction. Our experiment demonstrates that the creation of relatively small ponds is a promising method for supporting and monitoring amphibian populations.

Machine learning approaches for pattern recognition are increasingly popular. However, the underlying algorithms are often not open source, may require substantial data for model training, and are not geared toward specific tasks. We used open-source software to build a green toad breeding call detection algorithm that will aid in field data analysis. We provide instructions on how to reproduce our approach for other animal sounds and research questions. Our approach using 34 green toad call sequences and 166 audio files without green toad sounds had an accuracy of 0.99 when split into training (70%) and testing (30%) datasets. The final algorithm was applied to amphibian sounds newly collected by citizen scientists. Our function used three categories: “Green toad(s) detected”, “No green toad(s) detected”, and “Double check”. Ninety percent of files containing green toad calls were classified as “Green toad(s) detected”, and the remaining 10% as “Double check”. Eighty-nine percent of files not containing green toad calls were classified as “No green toad(s) detected”, and the remaining 11% as “Double check”. Hence, none of the files were classified in the wrong category. We conclude that it is feasible for researchers to build their own efficient pattern recognition algorithm.

Pesticides pose risks to human health and the environment. Monitoring schemes often exclude small sites with fluctuating water levels, inhabited by many species, including amphibians. We sampled for pesticides and their metabolites in 68 amphibian spawning waters in Austria. We analyzed the number of pesticides and concentrations in relation to the surrounding land use. We tested for 165 pesticides and metabolites and found 54; 46% were herbicides. The median total pesticide concentration per site was 0.097 µg/L (range: 0–20.419 µg/L). Pesticides found were generally of lower toxicity. We calculated the pesticide toxicity for aquatic invertebrates, fish, birds, and mammals, as proxies for amphibians and the overall ecosystems. Arable land was associated with pesticide number, concentration and toxicity according to the Boruta model selection. However, due to highly variable concentrations (and therefore also variable toxicity) only pesticide number showed significances in a generalized linear model. In this model arable land was positively associated with the number of pesticides detected, while the proportion of grassland, scrubs, artificial surfaces and tree cover had negative effects according to the linear model. Our results highlight (i) a widespread contamination of small temporary water bodies, (ii) the presence of multiple pesticide residues (cocktails), commonly more harmful than single pesticides, at amphibian spawning waters. These results provide a basis for future studies investigating the effects of common pesticide combinations in controlled settings using realistic scenarios and enable better assessment of the potential risks to wildlife.