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Hydrological modeling of large river basins and flood plains continues to be challenged by the low availability and quality of observed data for modeling input and model calibration. Global datasets are often used to bridge this gap, but are often difficult and time consuming to acquire, particularly in low resource regions of the world. Numerous calls have been made to standardize and share data to increase local basin modeling capacities and reduce redundancy in efforts, but barriers still exist. We discuss the challenges of hydrological modeling in data-scarce regions and describe a freely available online tool site developed to enable users to extract input data for any basin of any size. The site will allow users to visualize, map, interpolate, and reformat the data as needed for the intended application. We used our hydrological model of the Upper Zambezi basin and the Chobe-Zambezi floodplains to illustrate the use of this online toolset. Increasing access and dissemination of hydrological modeling data is a critical need, particularly among users where data requirements and access continue to impede locally driven management of hydrological systems. 

Introduction Campylobacter spp. infections are responsible for significant diarrheal disease burden across the globe, with prevalence thought to be increasing. Although wild avian species have been studied as reservoirs of Campylobacter spp., our understanding of the role of wild mammalian species in disease transmission and persistence is limited. Host factors influencing infection dynamics in wild mammals have been neglected, particularly life traits, and the role of these factors in zoonotic spillover risk is largely unknown. Methods Here, we conducted a systematic literature review, identifying mammalian species that had been tested for Campylobacter spp. infections (molecular and culture based). We used logistic regression to evaluate the relationship between the detection of Campylobacter spp. in feces and host life traits (urban association, trophic level, and sociality). Results Our analysis suggest that C. jejuni transmission is associated with urban living and trophic level. The probability of carriage was highest in urban-associated species ( p = 0.02793) and the most informative model included trophic level. In contrast, C. coli carriage appears to be strongly influenced by sociality ( p = 0.0113) with trophic level still being important. Detection of Campylobacter organisms at the genus level, however, was only associated with trophic level ( p = 0.0156), highlighting the importance of this trait in exposure dynamics across host and Campylobacter pathogen systems. Discussion While many challenges remain in the detection and characterization of Camploybacter spp., these results suggest that host life traits may have important influence on pathogen exposure and transmission dynamics, providing a useful starting point for more directed surveillance approaches. 

Current methods for identifying and predicting infectious disease dynamics in wildlife populations are limited. Pathogen transmission dynamics can be complex, influenced by behavioural interactions between and among hosts, pathogens and their environments. These behaviours may also be influenced directly by observers, with observational research methods being limited to habituated species. Banded mongooseMungos mungoare social, medium size carnivores infected with the novel tuberculosis pathogenMycobacterium mungi. This pathogen is principally transmitted during normal olfactory communication behaviours. Banded mongoose behavioural responses to humans change over the landscape, limiting the use of direct observational approaches in areas where mongoose are threatened and flee.

The accelerometers in bio‐logging devices have been used previously to identify distinct behaviours in wildlife species, providing a tool to quantifying specific behaviours in ecological studies. We deployed Axy‐5X model accelerometers (TechnoSmArt) on captive mongoose to determine whether accelerometers could be used to identify key mongoose behavioural activities previously associated withM. mungitransmission.

After two collaring periods, we determined that three distinct behavioural activities could be identified in the accelerometer data: bipedal vertical vigilance, running and scent marking activity; behaviours that have been shown to vary across land type in the banded mongoose.

Results from this work advance current data analytics and provide modifications to data analysis works flows, updating and expanding upon current methodologies. We also provide preliminary evidence of successful mathematical classification of the target behaviours, supporting the future use of these devices. Methods applied here may allow model estimates ofM. mungitransmission in free‐ranging mongoose to be refined with possible application to other systems where direct observation approaches have limited application.

 

Childhood diarrheal disease causes significant morbidity and mortality in low and middle-income countries, yet our ability to accurately predict diarrhea incidence remains limited. El Niño-Southern Oscillation (ENSO) has been shown to affect diarrhea dynamics in South America and Asia. However, understanding of its effects in sub-Saharan Africa, where the burden of under-5 diarrhea is high, remains inadequate. Here we investigate the connections between ENSO, local environmental conditions, and childhood diarrheal disease in Chobe District, Botswana. Our results demonstrate that La Niña conditions are associated with cooler temperatures, increased rainfall, and higher flooding in the Chobe region during the rainy season. In turn, La Niña conditions lagged 0–5 months are associated with higher than average incidence of under-5 diarrhea in the early rainy season. These findings demonstrate the potential use of ENSO as a long-lead prediction tool for childhood diarrhea in southern Africa.

 

Infectious disease emergence has increased significantly over the last 30 years, with mass mortality events (MMEs) associated with epizootics becoming increasingly common. Factors influencing these events have been widely studied in terrestrial systems, but remain relatively unexplored in marine mammals. Infectious disease‐induced MMEs (ID MMEs) have not been reported ubiquitously among marine mammal species, indicating that intrinsic (host) and/or extrinsic (environmental) ecological factors may influence this heterogeneity. We assess the occurrence of ID MMEs (1955–2018) across extant marine mammals (n = 129) in relation to key life‐history characteristics (sociality, trophic level, habitat breadth) and environmental variables (season, sea surface temperature [SST] anomalies, El Niño occurrence). Our results show that ID MMEs have been reported in 14% of marine mammal species (95% CI 9%–21%), with 72% (n = 36; 95% CI 56%–84%) of these events caused predominantly by viruses, primarily morbillivirus and influenza A. Bacterial pathogens caused 25% (95% CI 14%–41%) of MMEs, with only one being the result of a protozoan pathogen. Overall, virus‐induced MMEs involved a greater number of fatalities per event compared to other pathogens. No association was detected between the occurrence of ID MMEs and host characteristics, such as sociality or trophic level, but ID MMEs did occur more frequently in semiaquatic species (pinnipeds) compared to obligate ocean dwellers (cetaceans; χ² = 9.6,p = .002). In contrast, extrinsic factors significantly influenced ID MMEs, with seasonality linked to frequency (χ² = 19.85,p = .0002) and severity of these events, and global yearly SST anomalies positively correlated with their temporal occurrence (Z = 3.43,p = 2.7e‐04). No significant association was identified between El Niño and ID MME occurrence (Z = 0.28,p = .81). With climate change forecasted to increase SSTs and the frequency of extreme seasonal weather events, epizootics causing MMEs are likely to intensify with significant consequences for marine mammal survival.