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Antimicrobial resistance is an increasing global problem and complicates successful treatments of bacterial infections in animals and humans. We conducted a longitudinal study in Mecklenburg-Western Pomerania to compare the occurrence of ESBL-producing Escherichia (E.) coli in three conventional and four organic pig farms. ESBL-positive E. coli, especially of the CTX-M type, were found in all fattening farms, confirming that antimicrobial resistance is widespread in pig fattening and affects both conventional and organic farms. The percentage of ESBL-positive pens was significantly higher on conventional (55.2%) than on organic farms (44.8%) with similar proportions of ESBL-positive pens on conventional farms (54.3–61.9%) and a wide variation (7.7–84.2%) on organic farms. Metadata suggest that the farms of origin, from which weaner pigs were purchased, had a major influence on the occurrence of ESBL-producing E. coli in the fattening farms. Resistance screening showed that the proportion of pens with multidrug-resistant E. coli was similar on conventional (28.6%) and organic (31.5%) farms. The study shows that ESBL-positive E. coli play a major role in pig production and that urgent action is needed to prevent their spread.
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.