Suchergebnisse

Xylella fastidiosa is an important insect‐vectored bacterial plant pathogen with a wide host range, causing significant economic impact in the agricultural and horticultural industries. Once restricted to the Americas, severe European outbreaks have been discovered recently in Italy, Spain, France, and Portugal. The Italian outbreak, detected in Puglia in 2013, has spread over 100 km, killing millions of olive trees, and is still expanding. To date, quantified assessment of important epidemiological parameters useful for risk assessment and management, such as transmission rates, symptomless periods, and time to death in field populations, has been lacking. This is due to the emergent and novel nature of the outbreak and length of time needed to monitor the course of disease progression. To address this, we developed a Bayesian method to infer epidemiological parameters by fitting and comparing compartmental epidemiological models to short snapshots of disease progression observed in multiple field plots. We estimated that each infected tree with symptoms is able to infect around 19 trees per year (95% credible range 14–26). The symptomless stage was estimated to have low to negligible infectivity and to last an average of approximately 1.2 years (95% credible range 1.0–1.3 years). Tree desiccation was estimated to occur approximately 4.3 years (95% credible range 4.0–4.6 years) after symptom appearance. However, we were unable to estimate the infectiousness of desiccated trees from the data. Our method could be used to make early estimates of epidemiological parameters in other emerging disease outbreaks where symptom expression is slow. ; All authors are funded by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 727987 – XF‐ACTORS 'Xylella fastidiosa Active Containment Through a multidisciplinary‐Oriented Research Strategy'. Field monitoring data was supported by Grant Agreement No. 635646 – POnTE 'Pest Organisms threaten Europe'. Support was also funded by grant agreement no. 734353 – CURE‐XF 'Capacity Building and Raising Awareness in Europe and in Third Countries to Cope with Xylella fastidiosa'.

As the pressure to take action against global warming is growing in urgency, scenarios that incorporate multiple social, economic and environmental drivers become increasingly critical to support governments and other stakeholders in planning climate change mitigation or adaptation actions. This has led to the recent explosion of future scenario analyses at multiple scales, further accelerated since the development of the Intergovernmental Panel on Climate Change (IPCC) research community Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). While RCPs have been widely applied to climate models to produce climate scenarios at multiple scales for investigating climate change impacts, adaptation and vulnerabilities (CCIAV), SSPs are only recently being scaled for different geographical and sectoral applications. This is seen in the UK where significant investment has produced the RCP-based UK Climate Projections (UKCP18), but no equivalent UK version of the SSPs exists. We address this need by developing a set of multi-driver qualitative and quantitative UK-SSPs, following a state-of-the-art scenario methodology that integrates national stakeholder knowledge on locally-relevant drivers and indicators with higher level information from European and global SSPs. This was achieved through an intensive participatory process that facilitated the combination of bottom-up and top-down approaches to develop a set of UK-specific SSPs that are locally comprehensive, yet consistent with the global and European SSPs. The resulting scenarios balance the importance of consistency and legitimacy, demonstrating that divergence is not necessarily the result of inconsistency, nor comes as a choice to contextualise narratives at the appropriate scale.

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021. ; Understanding the dispersal of Xylella fastidiosa is essential for effective management of the disease. In Puglia, Italy, surveillance is focused on buffer and containment zones established at the edge of the infected region with the aim of containing further spread. Success of this strategy will strongly depend on whether these zones are wide enough to form a barrier to long distance dispersal of the bacterium. In this presentation, I will describe our progress towards estimating the dispersal range of Xylella in Puglia using a generic spatial epidemiological model adapted to the biology of the pathosystem. The model simulates the spread of the disease across a heterogeneous landscape depending on the location and timing of introduction, the distribution of host plants, the rate of infection growth in infected olive groves and both short and long distance dispersal. Long distance dispersal seems to be a crucial feature of the Xylella epidemic, causing rapid spread of the disease over large areas but in an unpredictable manner. To calibrate the model, we used Approximate Bayesian Computation to compare model simulations to Xylella surveillance data and remote sensing of severe damage. This allows us to contrast a simple spread scenario with more complex scenarios such as anisotropic dispersal in the direction of prevailing winds and spatial variation in disease transmission. In doing so we characterise the spread and estimate the year of introduction. Finally, I will discuss potential for using the model to simulate management strategies and new outbreaks in other regions, using the UK as a case study ; Support from XF-Actors & CURE-XF (EU H2020), EFSA, BRIGIT (BBSRC, Defra & Scottish Government) & Scottish Plant Health Centre.

Many ecosystem services (ES) models exist to support sustainable development decisions. However, most ES studies use only a single modelling framework and, because of a lack of validation data, rarely assess model accuracy for the study area. In line with other research themeswhich have high model uncertainty, such as climate change, ensembles of ES models may better serve decision-makers by providing more robust and accurate estimates, as well as provide indications of uncertainty when validation data are not available. To illustrate the benefits of an ensemble approach, we highlight the variation between alternative models, demonstrating that there are large geographic regionswhere decisions based on individual models are not robust.We test if ensembles are more accurate by comparing the ensemble accuracy of multiple models for six ES against validation data across sub-Saharan Africa with the accuracy of individual models. We find that ensembles are better predictors of ES, being 5.0–6.1%more accurate than individualmodels.We also find that the uncertainty (i.e. variation among constituent models) of the model ensemble is negatively correlated with accuracy and so can be used as a proxy for accuracy when validation is not possible (e.g. in data-deficient areas or when developing scenarios). Since ensembles are more robust, accurate and convey uncertainty, we recommend that ensemble modelling should be more widely implemented within ES science to better support policy choices and implementation. ; The UK Ecosystem Services for Poverty Alleviation program (ESPA; www.espa.ac.uk) and 'EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models and the Spanish Government through María de Maeztu excellence accreditation 2018-2021. ; http://www.elsevier.com/locate/scitotenv ; am2021 ; Zoology and Entomology

Risks posed to bees from neonicotinoid seed treatments (clothianidin, thiamethoxam, imidacloprid) led in 2013 to the European Union instigating a moratorium for their use on mass-flowering crops, including oilseed rape in the UK. This restriction did allow for the continued use of these seed treatments, in particular clothianidin, on non-flowering crops like winter wheat. To determine the impacts of the moratorium, we assessed neonicotinoid concentrations pre- (2014) and post- (2015−17) moratorium in 347 honey samples collected across Great Britain. While the probability of detecting clothianidin declined immediately following the moratorium, detection rates remained constant over the following three years (mean = 0.10 ppb, maximum = 2.8 ppb). In contrast, after three years thiamethoxam residues entirely disappeared while detection of imidacloprid was infrequent but persistent over the whole period. For those hives where neonicotinoids were detected, there was no evidence that the concentrations in the honey declined over the three years following the ban. Using metabarcoding approaches, we identified plants foraged upon by honeybees during the production of honey. After the moratorium came into effect, the highest neonicotinoid residues were associated with honey produced by foraging on both oilseed rape and several wild plants found in arable field margins. Concerns about soil persistence and uptake by non-target flowering plants ultimately led to a full European Union ban in 2018. Our results suggest that before this full ban came into effect, the use of clothianidin on non-flowering crops maintained a low-level probability of encountering this neonicotinoid within honey. However, these concentrations were low and would have been unlikely to pose significant risks to honeybees.

Due to concerns over negative impacts on insect pollinators, the European Union has implemented a moratorium on the use of three neonicotinoid pesticide seed dressings for mass-flowering crops. We assessed the effectiveness of this policy in reducing the exposure risk to honeybees by collecting 130 samples of honey from bee keepers across the UK before (2014: N = 21) and after the moratorium was in effect (2015: N = 109). Neonicotinoids were present in about half of the honey samples taken before the moratorium, and they were present in over a fifth of honey samples following the moratorium. Clothianidin was the most frequently detected neonicotinoid. Neonicotinoid concentrations declined from May to September in the year following the ban. However, the majority of post-moratorium neonicotinoid residues were from honey harvested early in the year, coinciding with oilseed rape flowering. Neonicotinoid concentrations were correlated with the area of oilseed rape surrounding the hive location. These results suggest mass flowering crops may contain neonicotinoid residues where they have been grown on soils contaminated by previously seed treated crops. This may include winter seed treatments applied to cereals that are currently exempt from EU restrictions. Although concentrations of neonicotinoids were low (<2.0 ng g-1), and posed no risk to human health, they may represent a continued risk to honeybees through long-term chronic exposure.

Due to concerns over negative impacts on insect pollinators, the European Union has implemented a moratorium on the use of three neonicotinoid pesticide seed dressings for mass flowering crops. We assessed the effectiveness of this policy in reducing the exposure risk to honeybees by collecting 130 samples of honey from bee keepers across the UK before (2014: N = 21) and after the moratorium was in effect (2015: N = 109). Neonicotinoids were present in about half of the honey samples taken before the moratorium, and they were present in over a fifth of honey samples following the moratorium. Clothianidin was the most frequently detected neonicotinoid. Neonicotinoid concentrations declined from May to September in the year following the ban. However, the majority of post-moratorium neonicotinoid residues were from honey harvested early in the year, coinciding with oilseed rape flowering. Neonicotinoid concentrations were correlated with the area of oilseed rape surrounding the hive location. These results suggest mass flowering crops may contain neonicotinoid residues where they have been grown on soils contaminated by previously seed treated crops. This may include winter seed treatments applied to cereals that are currently exempt from EU restrictions. Although concentrations of neonicotinoids were low (<2.0 ng g-1), and posed no risk to human health, they may represent a continued risk to honeybees through long-term chronic exposure.

Our current global food system – from food production to consumption, including manufacture, packaging, transport, retail and associated businesses – is responsible for extensive negative social and environmental impacts which threaten the long-term well-being of society. This has led to increasing calls from science–policy organizations for major reform and transformation of the global food system. However, our knowledge regarding food system transformations is fragmented and this is hindering the development of co-ordinated solutions. Here, we collate recent research across several academic disciplines and sectors in order to better understand the mechanisms that 'lock-in' food systems in unsustainable states.

Faced with environmental degradation, governments worldwide are developing policies to safeguard ecosystem services (ES). Many ES models exist to support these policies, but they are generally poorly validated, especially at large scales, which undermines their credibility. To address this gap, we describe a study of multiple models of five ES, which we validate at an unprecedented scale against 1675 data points across sub-Saharan Africa. We find that potential ES (biophysical supply of carbon and water) are reasonably well predicted by the existing models. These potential ES models can also be used as inputs to new models for realised ES (use of charcoal, firewood, grazing resources and water), by adding information on human population density. We find that increasing model complexity can improve estimates of both potential and realised ES, suggesting that developing more detailed models of ES will be beneficial. Furthermore, in 85% of cases, human population density alone was as good or a better predictor of realised ES than ES models, suggesting that it is demand, rather than supply that is predominantly determining current patterns of ES use. Our study demonstrates the feasibility of ES model validation, even in data-deficient locations such as sub-Saharan Africa. Our work also shows the clear need for more work on the demand side of ES models, and the importance of model validation in providing a stronger base to support policies which seek to achieve sustainable development in support of human well-being.

Faced with environmental degradation, governments world-wide are developing policies to safeguard ecosystem services (ES). Many ES models exist to support these policies, but they are generally poorly validated, especially at large scales, which undermines their credibility. To address this gap, we describe a study of multiple models of five ES, which we validate at an unprecedented scale against 1,675 data points across sub-Saharan Africa. We find that potential ES (biophysical supply of carbon and water) are reasonably well predicted by existing models. These potential ES models can also be used as inputs to new models for realised ES (use of charcoal, firewood, grazing resources and water), by adding information on human population density. We find that increasing model complexity can improve estimates of both potential and realised ES, suggesting that developing more detailed models of ES will be beneficial. Furthermore, in 85% of cases, human population density alone was as good or a better predictor of realised ES than ES models, suggesting that it is demand, rather than supply that is predominantly determining current patterns of ES use. Our study demonstrates the feasibility of ES model validation, even in data-deficient locations such as sub-Saharan Africa. Our work also shows the clear need for more work on the supply-side of ES models, and the importance of model validation in providing a stronger base to support policies which seek to achieve sustainable development in support of human well-being.

Faced with environmental degradation, governments worldwide are developing policies to safeguard ecosystem services (ES). Many ES models exist to support these policies, but they are generally poorly validated, especially at large scales, which undermines their credibility. To address this gap, we describe a study of multiple models of five ES, which we validate at an unprecedented scale against 1675 data points across sub-Saharan Africa. We find that potential ES (biophysical supply of carbon and water) are reasonably well predicted by the existing models. These potential ES models can also be used as inputs to new models for realised ES (use of charcoal, firewood, grazing resources and water), by adding information on human population density. We find that increasing model complexity can improve estimates of both potential and realised ES, suggesting that developing more detailed models of ES will be beneficial. Furthermore, in 85% of cases, human population density alone was as good or a better predictor of realised ES than ES models, suggesting that it is demand, rather than supply that is predominantly determining current patterns of ES use. Our study demonstrates the feasibility of ES model validation, even in data-deficient locations such as sub-Saharan Africa. Our work also shows the clear need for more work on the demand side of ES models, and the importance of model validation in providing a stronger base to support policies which seek to achieve sustainable development in support of human well-being.