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Antimicrobial resistance (AMR) is a serious global health threat with extended-spectrum beta-lactamase (ESBL)-producing Enterobacterales as the most critical ones. Studies on AMR in wild birds imply a possible dissemination function and indicate their potential role as sentinel animals. This study aimed to gain a deeper insight into the AMR burden of wild waterfowl by sampling semi-wild mallard ducks used as sentinels and to identify if AMR bacteria could be recommended to be added to the pathogens of public health risks to be screened for. In total, 376 cloacal and pooled fecal samples were collected from the sentinel plant over a period of two years. Samples were screened for ESBL-carrying E. coli and isolates found further analyzed using antimicrobial susceptibility testing and whole-genome sequencing. Over the sampling period, 4.26% (16/376) of the samples were positive for ESBL-producing E. coli. BlaCTX-M-1 and blaCTX-M-32 were the most abundant CTX-M types. Although none of the top global sequence types (ST) could be detected, poultry-derived ST115 and non-poultry-related STs were found and could be followed over time. The current study revealed low cases of ESBL-producing E. coli in semi-wild mallard ducks, which proves the suitability of sentinel surveillance for AMR detection in water-associated wildlife.
Antimicrobial resistance (AMR) is a serious global health threat and extended-spectrum beta-lactamase (ESBL)-producing Enterobacterales are a major contributor. This study aimed to gain a deeper insight into the AMR burden of wild animals. In total, 1595 fecal samples were collected by two systematic searches in Mecklenburg-Western Pomerania, north-east Germany. Samples were screened for ESBL-carrying Escherichia (E.) coli and isolates found were further analyzed using antimicrobial susceptibility testing and whole-genome sequencing. We found an estimated prevalence of 1.2% ESBL-producing E. coli in wild boar and 1.1% in wild ruminants. CTX-M-1 was the most abundant CTX-M type. We also examined fecal samples from wild boar and wild ruminants using shotgun metagenomics to gain insight into the resistome in wild animals. The latter revealed significantly lower normalized counts for AMR genes in wildlife samples compared to farm animals. The AMR gene levels were lower in wild ruminants than in wild boar. In conclusion, our study revealed a low prevalence of ESBL-producing E. coli and a low overall AMR gene burden in wild boar and wild ruminants, probably due to the secluded location of the search area.
Simple Summary
Monitoring animal behavior provides an indicator of their health and welfare. For this purpose, video surveillance is an important method to get an unbiased insight into behavior, as animals often show different behavior in the presence of humans. However, manual analysis of video data is costly and time-consuming. For this reason, we present a method for automated analysis using computer vision—a method for teaching the computer to see like a human. In this study, we use computer vision to detect red foxes and their body posture (lying, sitting, or standing). With this data we are able to monitor the animals, determine their activity, and identify their behavior.
Abstract
The behavior of animals is related to their health and welfare status. The latter plays a particular role in animal experiments, where continuous monitoring is essential for animal welfare. In this study, we focus on red foxes in an experimental setting and study their behavior. Although animal behavior is a complex concept, it can be described as a combination of body posture and activity. To measure body posture and activity, video monitoring can be used as a non-invasive and cost-efficient tool. While it is possible to analyze the video data resulting from the experiment manually, this method is time consuming and costly. We therefore use computer vision to detect and track the animals over several days. The detector is based on a neural network architecture. It is trained to detect red foxes and their body postures, i.e., ‘lying’, ‘sitting’, and ‘standing’. The trained algorithm has a mean average precision of 99.91%. The combination of activity and posture results in nearly continuous monitoring of animal behavior. Furthermore, the detector is suitable for real-time evaluation. In conclusion, evaluating the behavior of foxes in an experimental setting using computer vision is a powerful tool for cost-efficient real-time monitoring.