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Abstract Haemonchus contortus is one of the most detrimental gastrointestinal nematode parasites for small ruminants, especially in tropics and subtropics. Gastrointestinal nematode and microbiota share the same microhabitat; thus they interact with each other and their host. Metagenomics tools provide a promising way to examine the alterations in the gastric microbial composition induces by gastrointestinal parasites. In this study, we used metagenomics tools to characterize the impact of H. contortus infection on the caprine abomasal microbiota at early and late stage of infection and compared it with non-infected control. Our results showed that H. contortus infection caused a significant increase in abomasal pH at early (7 days post-infection) and late stage of infection (56 days post-infection). The analysis of alpha and beta diversity showed that the microbiota diversity both in number and in proportion was significantly affected at early and late stage of infection. All microbiota classes are impacted by H. contortus infection but Clostridia and Bacteroidia are more concerned. In infected animals, the genera Prevotella decreased at 7 and 56 days post-infection. Here we showed that the abomasal microbiota was significantly affected early after H. contortus infection, and these changes persist at late stage of the infection.

The recent advances in sensor technologies and data analysis could improve our capacity to acquire long-term and individual dataset on animal behavior. In livestock management, this is particularly interesting when behavioral data could be linked to production performances, physiological or genetical information, with the objective of improving animal health and welfare management. In this study, we proposed a framework, based on computer vision and deep learning, to automatically estimate animal location within pasture and discuss the relationship with the risk of gastrointestinal nematode (GIN) infection. We illustrated our framework for the monitoring of goats allowed to graze an experimental plot, where feces containing GIN infective larvae were previously dropped in delimited areas. Four animals were monitored, during two grazing weeks on the same pasture (week 1 from April 12 to 19, 2021 and week 2, from June 28 to July 5, 2021). Using the monitoring framework, different components of animal behavior were analyzed, and the relationship with the risk of GIN infection was explored. First, in average, 87.95% of the goats were detected, the detected individuals were identified with an average sensitivity of 94.9%, and an average precision of 94.8%. Second, the monitoring of the ability of the animal to avoid infected feces on pasture showed an important temporal and individual variability. Interestingly, the avoidance behavior of 3 animals increased during the second grazing week (Wilcoxon rank sum, p-value < 0.05), and the level of increase was correlated with the level of infection during week 1 (Pearson's correlation coefficient = 0.9). The relationship between the time spent on GIN-infested areas and the level of infection was also studied, but no clear relationship was found. In conclusion, due to the low number of studied animals, biological results should be interpreted with caution; nevertheless, the framework provided here is a new relevant tool to explore the relationship between ruminant behavior and GIN parasitism in experimental studies.