Disruptive selection arises when extreme phenotypes have a fitness advantage compared to more intermediate phenotypes. Theory and evidence suggest that intraspecific resource competition is a key driver of disruptive selection. However, while competition can be indirect (exploitative) or direct (interference), the role of interference competition in disruptive selection has not been tested, and most models of disruptive selection assume exploitative competition. We experimentally investigated whether the type of competition affects the outcome of competitive interactions using a system where disruptive selection is common: Mexican spadefoot toads (Spea multiplicata). Spea tadpoles develop into alternative resource-use phenotypes: carnivores, which consume fairy shrimp and other tadpoles, and omnivores, which feed on algae and detritus. Tadpoles intermediate in phenotype have low fitness when competition is intense, as they are outcompeted by the specialized tadpoles. Our experiments revealed that the presence of carnivores significantly decreased foraging behavior in intermediate tadpoles, and that intermediate tadpoles had significantly lower growth rates in interference competition treatments with carnivores but not with omnivores. Interference competition may therefore be important in driving disruptive selection. As carnivore tadpoles are also cannibalistic, the ‘fear’ effect may have a greater impact on intermediate tadpoles than exploitative competition alone, similarly to non-consumptive effects in predator-prey or intraguild relationships.

Background: Taeniasis and cysticercosis are human infections caused by the pork tapeworm, Taenia solium. This study is a baseline for the Community-based intervention. We determined the prevalence of human cysticercosis and associated risk factors following deworming program conducted throughout the country, Mbulu District being among the districts in Northern Tanzania.Methods: Human cysticercosis was determined by enzyme-linked Immunosorbent assay (Ag- ELISA). Household interviews and observations were conducted to identify risk factors for cysticercosis transmissions among households and communities. Results: Three hundred individuals participated in the study. The age ranged from five to 89 years, with a median of 19 years. The prevalence of human cysticercosis was 23(7.67%). The prevalence was high 6(11.76%) among individuals aged 26 to 35 years and ±45 years. There was no statistically significant difference in the prevalence by age group, sex, or occupation. Among the 300 participants, 82(27.3%) had received anthelmintic during the previous year; among these, 5(21.7%) were infected. The likelihood of infection was low among anthelmintic users by 28% [0.72 (0.26-2.01)], but the protection was not significant. The communities differed in risk factors on the availability of clean and safe water supply; 52.7% (46/86) of households visited had no pit latrine. The cysticercosis prevalence showed a significant difference by communities. Conclusion: The prevalence of human cysticercosis was high and associated with higher age groups. The prevalence was low among those who had taken anthelmintic and was associated with lower age groups. The school current school deworming program has a positive effect for school children meanwhile the elderly are at higher risk because the intervention did not target them. It is recommended to scale up anthelmintic intervention to higher age groups.

The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely land management of sustainability indicators such as ground cover and grassland biomass, given the uncertainty of future climate and drought conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while maximising pasture biomass productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 hectare) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months) with each compared with control treatments with lighter stocking rates for longer periods (2,000 DSE). Pastures were composed of wallaby grass (Austrodanthonia species), kangaroo grass (Themeda triandra), Phalaris (Phalaris aquatica, and cocksfoot (Dactylis glomerata) were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We then invoked a machine learning model using Sentinel-2 imagery to quantify TSDM, standing green biomass and standing dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM and green biomass. However, regenerative treatments significantly impacted litter fall and trampled material, with the high intensity grazing treatments causing more dry matter trampling, increasing litter, enhancing decomposition rates and surface organic matter. Pasture digestibility was greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were significantly greater for the treatment with 15-month spell periods. Estimates of TSDM using machine learning with Sentinel-2 imagery underestimated TSDM in treatment plots but explained spatiotemporal variability associated within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3-6 months) are most conducive to increasing pasture production under high rainfall conditions, and we speculate that high intensity grazing is likely to positively impact on soil organic carbon through increased litterfall and trampling. Our study paves the way forward for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling management of pastures from afar.