Jaap van Schaik

Automated monitoring technologies can increase the efficiency of ecological data collection and support data‐driven conservation. Camera traps coupled with infrared light barriers can be used to monitor temperate‐zone bat assemblages at underground hibernacula, where thousands of individuals of multiple species can aggregate in winter. However, the broad‐scale adoption of such photo‐monitoring techniques is limited by the time‐consuming bottleneck of manual image processing. Here, we present BatNet , an open‐source, deep learning‐based tool for automated identification of 13 European bat species from camera trap images. BatNet includes a user‐friendly graphical interface, where it can be retrained to identify new bat species or to create site‐specific models to improve detection accuracy at new sites. Model accuracy was evaluated on images from both trained and untrained sites, and in an ecological context, where community‐ and species‐level metrics (species diversity, relative abundance, and species‐level activity patterns) were compared between human experts and BatNet . At trained sites, model performance was high across all species (F1‐score: 0.98–1). At untrained sites, overall classification accuracy remained high (96.7–98.2%), when camera placement was comparable to the training images (<3 m from the entrance; <45° angle relative to the opening). For atypical camera placements (>3 m or >45° angle), retraining the detector model with 500 site‐specific annotations achieved an accuracy of over 95% at all sites. In the ecological case study, all investigated metrics were nearly identical between human experts and BatNet . Finally, we exemplify the ability to retrain BatNet to identify a new bat species, achieving an F1‐score of 0.99 while maintaining high classification accuracy for all original species. BatNet can be implemented directly to scale up the deployment of camera traps in Europe and enhance bat population monitoring. Moreover, the pretrained model can serve as a baseline for transfer learning to automatize the image‐based identification of bat species worldwide.

Extra‐organismal DNA (eoDNA) from material left behind by organisms (noninvasive DNA, e.g., feces, hair) or from environmental samples (eDNA, e.g., water, soil) is a valuable source of genetic information. However, the relatively low quality and quantity of eoDNA, which can be further degraded by environmental factors, results in reduced amplification and sequencing success. This is often compensated for through cost‐ and time‐intensive replications of genotyping/sequencing procedures. Therefore, system‐ and site‐specific quantifications of environmental degradation are needed to maximize sampling efficiency (e.g., fewer replicates, shorter sampling durations), and to improve species detection and abundance estimates. Using 10 environmentally diverse bat roosts as a case study, we developed a robust modeling pipeline to quantify the environmental factors degrading eoDNA, predict eoDNA quality, and estimate sampling‐site‐specific ideal exposure duration. Maximum humidity was the strongest eoDNA‐degrading factor, followed by exposure duration and then maximum temperature. We also found a positive effect when hottest days occurred later. The strength of this effect fell between the strength of the effects of exposure duration and maximum temperature. With those predictors and information on sampling period (before or after offspring were born), we reliably predicted mean eoDNA quality per sampling visit at new sites with a mean squared error of 0.0349. Site‐specific simulations revealed that reducing exposure duration to 2–8 days could substantially improve eoDNA quality for future sampling. Our pipeline identified high humidity and temperature as strong drivers of eoDNA degradation even in the absence of rain and direct sunlight. Furthermore, we outline the pipeline's utility for other systems and study goals, such as estimating sample age, improving eDNA‐based species detection, and increasing the accuracy of abundance estimates.