Aim: Although global trade is implicated in biological invasions, the assumption that trade networks explain the large-scale distributions of non-native species remains largely untested. We addressed ...this by analysing relationships between global trade networks and plant pest invasion. Location: Forty-eight countries in Europe and the Mediterranean. Time period: Current. Major taxa studied: Four hundred and twenty-two non-native plant pests (173 invertebrates, 166 pathogens, 83 plants). Methods: Ten types of connectivity index were developed, representing potential roles of trade networks, air transport links, geographical proximity, climatic similarity and source country wealth in facilitating invasion. Generalized linear mixed models (GLMMs) identified the connectivity index that best explained both historical and recent invasion. Then, more complex GLMMs were developed including connectivity through trade networks for multiple commodities relevant for pests (live plants, forest products, fruit and vegetables and seeds) and species' transport associations with those commodities. Results: Total import volumes, species' global prevalence and connectivity measures based on air transport, geographical distance or climate did not explain invasion as well as connectivity through global trade networks. Invasion was strongly promoted by agricultural imports from countries in which the focal species was present and that were climatically similar to the importing country. However, live plant imports from nearby countries provided a better explanation of the most recent invasions. Connectivity through multiple trade networks predicted invasion better than total agricultural trade, and there was support for our hypothesis that species known to be transported with a particular network had greater sensitivity to its connectivity. Main conclusions: Our findings show that patterns of invasion are governed to a large extent by global trade networks connecting source areas for non-native species and the dispersal of those species through multiple trade networks. This enhances potential for developing a predictive framework to improve risk assessment, biosecurity and surveillance for invasions.
Summary
Species distribution modelling (SDM) is widely used in ecology, and predictions of species distributions inform both policy and ecological debates. Therefore, methods with high predictive ...accuracy and those that enable biological interpretation are preferable. Gaussian processes (GPs) are a highly flexible approach to statistical modelling and have recently been proposed for SDM. GP models fit smooth, but potentially complex response functions that can account for high‐dimensional interactions between predictors. We propose fitting GP SDMs using deterministic numerical approximations, rather than Markov chain Monte Carlo methods in order to make GPs more computationally efficient and easy to use.
We introduce GP models and their application to SDM, illustrate how ecological knowledge can be incorporated into GP SDMs via Bayesian priors and formulate a simple GP SDM that can be fitted efficiently. This model can be fitted either by learning the hyperparameters or by using a fixed approximation to them. Using a subset of the North American Breeding Bird Survey data set, we compare the out‐of‐sample predictive accuracy of these models with several commonly used SDM approaches for both presence/absence and presence‐only data.
Predictive accuracy of GP SDMs fitted by Laplace approximation was greater than boosted regression trees, generalized additive models (GAMs) and logistic regression when trained on presence/absence data and greater than all of these models plus MaxEnt when trained on presence‐only data. GP SDMs fitted using a fixed approximation to hyperparameters were no less accurate than those with MAP estimation and on average 70 times faster, equivalent in speed to GAMs.
As well as having strong predictive power for this data set, GP SDMs offer a convenient method for incorporating prior knowledge of the species' ecology. By fitting these methods using efficient numerical approximations, they may easily be applied to large data sets and automatically for many species. An r package, GRaF, is provided to enable SDM users to fit GP models.
•Europe is at risk of further emergence of arboviruses transmitted by Culicoides biting midges.•Culicoides transmission of zoonotic arboviruses may represent a route of incursion into ...Europe.•Introduction of arboviruses transmitted from person to person by Culicoides is unlikely to lead to major disease outbreaks.•Sustained transmission by Culicoides is unlikely, but involvement of other vectors could lead to establishment.
The emergence of multiple strains of bluetongue virus (BTV) and the recent discovery of Schmallenberg virus (SBV) in Europe have highlighted the fact that exotic Culicoides-borne arboviruses from remote geographic areas can enter and spread rapidly in this region. This review considers the potential for this phenomenon to impact on human health in Europe, by examining evidence of the role of Culicoides biting midges in the zoonotic transmission and person-to-person spread of arboviruses worldwide. To date, the only arbovirus identified as being primarily transmitted by Culicoides to and between humans is Oropouche virus (OROV). This member of the genus Orthobunyavirus causes major epidemics of febrile illness in human populations of South and Central America and the Caribbean. We examine factors promoting sustained outbreaks of OROV in Brazil from an entomological perspective and assess aspects of the epidemiology of this arbovirus that are currently poorly understood, but may influence the risk of incursion into Europe. We then review the secondary and rarely reported role of Culicoides in the transmission of high-profile zoonotic infections, while critically reviewing evidence of this phenomenon in endemic transmission and place this in context with the presence of other potential vector groups in Europe. Scenarios for the incursions of Culicoides-borne human-to-human transmitted and zoonotic arboviruses are then discussed, along with control measures that could be employed to reduce their impact. These measures are placed in the context of legislative measures used during current and ongoing outbreaks of Culicoides-borne arboviruses in Europe, involving both veterinary and public health sectors.
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.
Satellite-based land cover mapping plays an important role in understanding changes in ecosystems and biodiversity. There are global land cover products available, however for region specific studies ...of drivers of infectious disease patterns, these can lack the spatial and thematic detail or accuracy required to capture key ecological processes. To overcome this, we produced our own Landsat derived 30 m maps for three districts in India's Western Ghats (Wayanad, Shivamogga and Sindhudurg). The maps locate natural vegetation types, plantation types, agricultural areas, water bodies and settlements in the landscape, all relevant to functional resource use of species involved in infectious disease dynamics. The maps represent the mode of 50 classification iterations and include a spatial measure of class stability derived from these iterations. Overall accuracies for Wayanad, Shivamogga and Sindhudurg are 94.7 % (SE 1.2 %), 88.9 % (SE 1.2 %) and 88.8 % (SE 2 %) respectively. Class classification stability was high across all three districts and the individual classes that matter for defining key interfaces between human habitation, forests, crop, and plantation cultivation, were generally well separated. A comparison with the 300 m global ESA CCI land cover map highlights lower ESA CCI class accuracies and the importance of increased spatial resolution when dealing with complex landscape mosaics. A comparison with the 30 m Global Forest Change product reveals an accurate mapping of forest loss and different dynamics between districts (i.e., Forests lost to Built-up versus Forests lost to Plantations), demonstrating an interesting complementarity between our maps and the % tree cover Global Forest Change product. When studying infectious disease responses to land use change in tropical forest ecosystems, we recommend using bespoke land cover/use classifications reflecting functional resource use by relevant vectors, reservoirs, and people. Alternatively, global products should be carefully validated with ground reference points representing locally relevant habitats.
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•Global land cover and use maps are unsuitable for zoonotic disease studies.•We produced maps reflecting functional resource use by vectors, reservoirs and people.•Local training and multiple map iterations ensured spatially stable and high accuracies.•We compared our map with ESA CCI land cover and Hansen Global Forest Change.•Regional maps represent the landscape components better than global products.
Debates over whether climate change could lead to the amplification of Lyme disease (LD) risk in the future have received much attention. Although recent large-scale disease mapping studies project ...an overall increase in Lyme disease risk as the climate warms, such conclusions are based on climate-driven models in which other drivers of change, such as land-use/cover and host population distribution, are less considered.
The main objectives were to project the likely future ecological risk patterns of LD in Europe under different assumptions about future socioeconomic and climate conditions and to explore similarity and uncertainty in the projected risks.
An integrative, spatially explicit modeling study of the ecological risk patterns of LD in Europe was conducted by applying recent advances in process-based modeling of tick-borne diseases, species distribution mapping, and scenarios of land-use/cover change. We drove the model with stakeholder-driven, integrated scenarios of plausible future socioeconomic and climate change the Shared Socioeconomic Pathway (SSPs) combined with the Representative Concentration Pathways (RCPs).
The model projections suggest that future temperature increases may not always amplify LD risk: Low emissions scenarios (RCP2.6) combined with a sustainability socioeconomic scenario (SSP1) resulted in reduced LD risk. The greatest increase in risk was projected under intermediate (RCP4.5) rather than high-end (RCP8.5) climate change scenarios. Climate and land-use change were projected to have different roles in shaping the future regional dynamics of risk, with climate warming being likely to cause risk expansion in northern Europe and conversion of forest to agriculture being likely to limit risk in southern Europe.
Projected regional differences in LD risk resulted from mixed effects of temperature, land use, and host distributions, suggesting region-specific and cross-sectoral foci for LD risk management policy. The integrated model provides an improved explanatory tool for the system mechanisms of LD pathogen transmission and how pathogen transmission could respond to combined socioeconomic and climate changes. https://doi.org/10.1289/EHP4615.
The enormous global burden of vector-borne diseases disproportionately affects poor people in tropical, developing countries. Changes in vector-borne disease impacts are often linked to human ...modification of ecosystems as well as climate change. For tropical ecosystems, the health impacts of future environmental and developmental policy depend on how vector-borne disease risks trade off against other ecosystem services across heterogeneous landscapes. By linking future socio-economic and climate change pathways to dynamic land use models, this study is amongst the first to analyse and project impacts of both land use and climate change on continental-scale patterns in vector-borne diseases. Models were developed for cutaneous and visceral leishmaniasis in the Americas-ecologically complex sand fly borne infections linked to tropical forests and diverse wild and domestic mammal hosts. Both diseases were hypothesised to increase with available interface habitat between forest and agricultural or domestic habitats and with mammal biodiversity. However, landscape edge metrics were not important as predictors of leishmaniasis. Models including mammal richness were similar in accuracy and predicted disease extent to models containing only climate and land use predictors. Overall, climatic factors explained 80% and land use factors only 20% of the variance in past disease patterns. Both diseases, but especially cutaneous leishmaniasis, were associated with low seasonality in temperature and precipitation. Since such seasonality increases under future climate change, particularly under strong climate forcing, both diseases were predicted to contract in geographical extent to 2050, with cutaneous leishmaniasis contracting by between 35% and 50%. Whilst visceral leishmaniasis contracted slightly more under strong than weak management for carbon, biodiversity and ecosystem services, future cutaneous leishmaniasis extent was relatively insensitive to future alternative socio-economic pathways. Models parameterised at narrower geographical scales may be more sensitive to land use pattern and project more substantial changes in disease extent under future alternative socio-economic pathways.
Species distribution modelling is widely used in epidemiology for mapping spatial patterns and the risk of introduction of diseases and vectors and also for predicting how exposure may alter given ...future environmental change, motivated by the high societal impact and the multiple environmental drivers of disease outbreaks. Although pathogens and vectors have historically been sparsely recorded, monitoring systems and media sources are generating novel, online data sources on occurrence. Moreover, increasing ecological realism is being incorporated into distribution modelling techniques, focussing on dispersal, biotic interactions and evolutionary constraints that shape species distributions alongside abiotic factors and biases in recording effort, common to pathogens and vectors and wildlife species. Considering pathogens and arthropod vector systems with high impact on plant, animal and human health, the present review describes how biological records for vectors and pathogens arise, introduces the concepts behind distribution models and illustrates the potential for ecologically realistic distribution models to yield insight into the establishment and spread of pathogens. Because distribution modellers aim to provide policy makers with evidence and maps for planning and evaluation of disease mitigation measures, we highlight factors that currently constrain direct translation of models to policy. Disease distributions will be better understood and mapped in the future given improved occurrence data access and integration and combined (correlative and mechanistic) modelling approaches that are developed iteratively in concert with stakeholders.
Vector-borne diseases (VBDs), such as dengue, Zika, West Nile virus (WNV) and tick-borne encephalitis, account for substantial human morbidity worldwide and have expanded their range into temperate ...regions in recent decades. Climate change has been proposed as a likely driver of past and future expansion, however, the complex ecology of host and vector populations and their interactions with each other, environmental variables and land-use changes makes understanding the likely impacts of climate change on VBDs challenging. We present an environmentally driven, stage-structured, host–vector mathematical modelling framework to address this challenge. We apply our framework to predict the risk of WNV outbreaks in current and future UK climates. WNV is a mosquito-borne arbovirus which has expanded its range in mainland Europe in recent years. We predict that, while risks will remain low in the coming two to three decades, the risk of WNV outbreaks in the UK will increase with projected temperature rises and outbreaks appear plausible in the latter half of this century. This risk will increase substantially if increased temperatures lead to increases in the length of the mosquito biting season or if European strains show higher replication at lower temperatures than North American strains.
Haemorrhagic septicaemia (HS) is an economically important disease affecting cattle and buffaloes and the livelihoods of small-holder farmers that depend upon them. The disease is caused by ...Gram-negative bacterium, Pasteurella multocida, and is considered to be endemic in many states of India with more than 25,000 outbreaks in the past three decades. Currently, there is no national policy for control of HS in India. In this study, we analysed thirty year (1987-2016) monthly data on HS outbreaks using different statistical and mathematical methods to identify spatial variability and temporal patterns (seasonality, periodicity). There was zonal variation in the trend and seasonality of HS outbreaks. Overall, South zone reported maximum proportion of the outbreaks (70.2%), followed by East zone (7.2%), Central zone (6.4%), North zone (5.6%), West zone (5.5%) and North-East zone (4.9%). Annual state level analysis indicated that the reporting of HS outbreaks started at different years independently and there was no apparent transmission between the states. The results of the current study are useful for the policy makers to design national control programme on HS in India and implement state specific strategies. Further, our study and strategies could aid in implementation of similar approaches in HS endemic tropical countries around the world.
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