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Seasonal variability of the daily activities of cattle. Asterisks indicate statistical significance: * = p< 0.05; ** = p< 0.01; *** = p< 0.001. The temporal domain was also important to describe the distribution of animals and dung. Large variability existed both intraday and between days. In this study, the interactions among ambient temperature, distance to water and canopy cover explained the dynamics of animal distribution and their relationship with feces deposition. Contrary to what was observed in previous studies [ 27, 53], relative humidity did not significantly influence dung dispersal in our trial, probably because of its limited range when aggregated weekly. Data processing was important to extract value from the information provided by commercial devices and open data sources. The consideration of the temporal and spatial scales also had a large impact on the results of the study. Further research is needed to include diverse production systems and management strategies in model calibration data sets, which could make models more general and applicable under different conditions.

In this study, regression models have been used to describe the relationship between cattle presence characterized through GPS collars and dung distribution. Previous studies on this topic have also used regression analysis [ 2, 20, 61], although more complex procedures, such as generalized additive models or machine learning algorithms, would be expected to better predict the distribution of dung. However, since the goal of this study was not to develop final models but to understand the importance of integrating several data sources in predicting the distribution of dung, regression models were preferred because of their interpretability. Regarding factors influencing the temporal variability of animal behavior, data on weather and vegetation dynamics were collected from open data sources. Dehesas, which occupy more than four million hectares in the Iberian Peninsula, are a classic example of agrosilvopastoral systems. Dehesas are derived from the Mediterranean forest ecosystem, consisting of grassland featuring herbaceous species used for grazing cattle and sheep and tree species belonging to the genus Quercus (oak), such as the holm oak ( Quercus ilex L.), although other tree species may also be present. Oaks are protected and pruned to produce acorns, which Iberian pigs feed on during the period called montanera. The understory is usually cleared every seven to ten years to prevent shrub invasion of the grassland. Despite their importance, the economic, ecological and social sustainability of dehesas is seriously threatened [ 4]. Dehesas are complex production systems, including both spatial and temporal domains, so their adequate management demands advanced and specific skills. However, the current trend is the reduction of system complexity rather than its management [ 3], for example, by eliminating crops or shrubs or by reducing pasture diversity, which can severely affect the long-term provision of ecosystem services. Several authors have proposed the continuous monitoring of dehesas as a key step to ensure their sustainability [ 5]. The coefficients of determination were similar in all cases, ranging from 0.5 to 0.6. Nonetheless, slightly better results were obtained when the number of segments intersecting each plot was used as the independent variable. In Figure 12a–c, the hotspots accumulating most of the feces can be easily identified. The total count of dung pats in hotspots was 175 and 442 for the whole period, i.e., 2.2 and 5.6 droppings per m 2 for 19 weeks. As hotspot data had a large leverage effect on the relationship between feces counting and animal presence, Figure 12d–f shows the correlation between both variables, excluding hotspots. The values of coefficients of determination were not greatly affected when hotspot data were excluded, although the relationship between the number of droppings and the number of segments was, in fact, improved and showed the highest linearity. Fifth instars of Triatoma infestans with established Trypanosoma cruzi infections were dissected after different periods of starvation to determine the population density and the percentage of different developmental stages of T. cruzi in the small intestine and rectum of the bugs. After a short starvation period of 20 days, the population density in the small intestine was 20% (about 60,000) of the rectal population. The population in the small intestine was strongly reduced after an additional ten days of starvation, and no flagellates could be found there 60, 90 and 120 days after the last feeding. In the rectum, this reduction went down to 1% of the initial population, but a total elimination never occurred. Usually the remaining population contained more live than dead flagellates. Starvation also resulted in an increase in the rectum in the number and percentage of drop-like forms, intermediates between sphero- and epi- or trypomastigotes, from 1% initially to about 10% after 90 days of starvation. The percentage of spheromastigotes increased from 2% at 20 days after the last feeding to about 20% after an additional 40 and 70 days. Therefore, the spheromastigotes of T. cruzi seem to be induced by stress conditions.Regarding herd behavior, our results coincide with [ 58, 59], who showed that the distance of individuals to herd centroid was affected by the time of the year. We also observed that this distance depended on the type of activity carried out by animals, with a maximum average distance during grazing. Auto parts at wholesale prices is what you will find at AutohausAZ. Have you been searching the web for auto parts for your vehicle? You have come to the right place! Here at Autohaus, we strive to offer low prices on quality replacement parts and providing first rate customer service.

On the other hand, as cattle performed different activities on pasture, we assigned a distinctive behavior to each GPS fix or trajectory segment. Following the methodology described in previous studies [ 12, 49], the average speed between two consecutive locations was used as an indicator of the type of behavior. On average, those authors found that cattle spend 57.5% of their time resting (including rumination), 40% grazing and 2.5% walking. Thus, percentiles 57.5 and 97.5 for the data distribution of the average speed between consecutive fixes were used as thresholds to classify cattle locations as corresponding to resting (low speed), grazing (medium speed) or walking (high speed). Relationships between feces counting and animal presence estimated as location fix counting ( a, d), trajectory segment counting ( b, e) or time spent inside plot ( c, f), including ( a– c) and excluding ( d– f) hotspots.

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