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Old world fruit bats are important seed dispersers of forest plants as well as of commercial fruit crops. Bats scatter seeds across landscapes and also aggregate (clump) seeds under feeding and roosting trees. In agroforestry landscapes, bat frugivory and seed dispersal can result in simultaneous accrual of socio‐economic benefits and costs to humans, which may be further affected by human health risks from zoonotic spillover through human–bat interactions. In our study, we used an integrated approach to assess socio‐economic benefits (from seed dispersal) and costs (from frugivory) from bats, in relation to latent zoonotic risk. We carried out the study in five agroforestry landscapes along India's Western Ghats, for selected commercial fruit crops (cashew, areca, banana and other pulpy/fleshy fruits). Specifically, we hypothesized that people's perceptions of benefits from fruit bats would be correlated with measured extents of clumped seed dispersal in fruit plantations. We conducted ecological studies to investigate the effects of fruit crop type and plantation attributes on clump‐dispersal, and in turn, on perceived 'net benefits' assessed from interviews with plantation‐associated people. We then explored associations between perceived socio‐economic net benefits and spatial data on disease risk factors for fruit bat‐linked henipavirus transmission. We found that the extent of bat‐mediated clump‐dispersal was highest for cashew, especially in isolated plantations. People's perceptions of benefits from fruit bats matched the measured extents of clump‐dispersal of cashew and areca. These benefits came with some costs from scatter‐dispersal and damage to other fleshy fruits from frugivory by bats. Interestingly, we did not detect tradeoffs between perceived net benefits from bats and disease risk, which is of significance for bat conservation and its implications for human well‐being. Overall, our results highlight that bat‐mediated seed dispersal needs to be sustained as an important ecosystem service, despite some latent zoonotic risk, in the Anthropocene [ABSTRACT FROM AUTHOR]

Zoonoses disproportionately affect tropical communities and are associated with human modification and use of ecosystems. Effective management is hampered by poor ecological understanding of disease transmission and often focuses on human vaccination or treatment. Better ecological understanding of multi-vector and multi-host transmission, social and environmental factors altering human exposure, might enable a broader suite of management options. Options may include "ecological interventions" that target vectors or hosts and require good knowledge of underlying transmission processes, which may be more effective, economical, and long lasting than conventional approaches. New frameworks identify the hierarchical series of barriers that a pathogen needs to overcome before human spillover occurs and demonstrate how ecological interventions may strengthen these barriers and complement human-focused disease control. We extend these frameworks for vector-borne zoonoses, focusing on Kyasanur Forest Disease Virus (KFDV), a tick-borne, neglected zoonosis affecting poor forest communities in India, involving complex communities of tick and host species. We identify the hierarchical barriers to pathogen transmission targeted by existing management. We show that existing interventions mainly focus on human barriers (via personal protection and vaccination) or at barriers relating to Kyasanur Forest Disease (KFD) vectors (tick control on cattle and at the sites of host (monkey) deaths). We review the validity of existing management guidance for KFD through literature review and interviews with disease managers. Efficacy of interventions was difficult to quantify due to poor empirical understanding of KFDV–vector–host ecology, particularly the role of cattle and monkeys in the disease transmission cycle. Cattle are hypothesised to amplify tick populations. Monkeys may act as sentinels of human infection or are hypothesised to act as amplifying hosts for KFDV, but the spatial scale of risk arising from ticks infected via monkeys versus small mammal reservoirs is unclear. We identified 19 urgent research priorities for refinement of current management strategies or development of ecological interventions targeting vectors and host barriers to prevent disease spillover in the future. Author summary: Zoonotic diseases (caused by pathogens passed between animals and people) disproportionately affect tropical communities. Management to prevent human cases is often hindered by a poor ecological understanding of zoonotic disease systems, which may involve complex communities of vector (e.g., ticks) and host species involved in pathogen transmission. Thus, management usually focuses on human medical treatments such as vaccination. However, there may be other alternative interventions targeted at vectors or wildlife hosts which may be more effective, economical, or long lasting. Furthermore, more conventional human-focused intervention strategies could be improved through better ecological understanding of the disease system. Here, we refine a new framework developed to identify barriers in the disease system that a pathogen needs to overcome in order to spillover to cause human disease cases, to include additional barriers pertinent to vector-borne pathogens. We apply this refined framework to Kyasanur Forest Disease (KFD), a potentially deadly tick-borne infection from India which affects poor forest communities in the Western Ghats and demonstrate how it can be used to identify key knowledge gaps that that need to be addressed in order to evaluate the effectiveness of existing management and to design new interventions. [ABSTRACT FROM AUTHOR]

Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global "One Health" initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014–2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014–2018). Consistent with suggestions that KFD is an "ecotonal" disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological risk factors; and tailoring the spatial grain and outputs to the scale of forest use, and public health interventions. We argue this inter-disciplinary approach to risk prediction is applicable across zoonotic diseases in tropical settings. Author summary: Worldwide, impacts of zoonotic diseases, that cycle between animals and people, are concentrated in tropical communities and often linked to the way people use and change ecosystems. Interventions for zoonotic diseases could be targeted better using risk maps based on computer models that integrate social and ecological risk factors across degraded ecosystems. However, such predictive models often perform poorly at local scales, incorporate narrow ranges of risk factors, and are disconnected from policy, managers and interventions. Co-production brings together stakeholders and knowledge, across the human health, animal health and environmental sectors, aligning with the OneHealth Initiative, to develop more informative predictive tools for zoonotic diseases. Through co-production, we develop predictive models for a fatal tick-borne disease, Kyasanur Forest Diseases (KFD) that is spreading across the degraded Western Ghats forest in India. These models incorporating contextual risk factors identified by stakeholders, accurately predicted patterns in human cases of KFD (2014–2018) in Shivamogga district, Karnataka State, and identified new hotspots of infection during the subsequent 2019 outbreak. Landscapes at highest risk encompassed diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Co-production resulted in outbreak data that reflected where exposure occurred in the landscape and outputs of value for targeting of interventions, matched to the scale of forest use and public health interventions. [ABSTRACT FROM AUTHOR]