Reductions in connectivity and habitat quality drive local extinctions in a plant diversity hotspot

It is well documented that habitat loss is a major cause of biodiversity decline. However, the roles of the different aspects of habitat loss in local extinctions are less understood. Anthropogenic destruction of an area of habitat causes immediate local extinction but subsequently three additional gradual drivers influence the likelihood of delayed extinction: decreased habitat patch size, lower connectivity and habitat deterioration. We investigated the role of these drivers in local extinctions of 82 declining species in a UK biodiversity hotspot. We combined a unique set of ≈7000 vegetation surveys and habitat maps from the 1930s with contemporary species’ occurrences. We extrapolated from these surveys to the whole 2500-km2 study area using habitat suitability surfaces. The strengths of drivers in explaining local extinctions over this 70 year period were determined by contrasting connectivity, patch size and habitat quality loss for locations at which a species went extinct and those with persisting occurrences. Species’ occurrences declined on average by 60%, with half of local extinctions attributable to immediate habitat loss and half to the gradual processes causing delayed extinctions. On average, locations where a species persisted had a 73% higher contemporary connectivity than those suffering extinctions, but showed no differences in historical connectivity. Furthermore, locations with extinctions experienced a 37% greater decline in suitability associated with changes in habitat type. The strength of the drivers and the proportion of extinctions depended on the species’ habitat specialism, but were affected only minimally by life-history characteristics. In conclusion, we identified a hierarchy of drivers influencing local extinction: with connectivity loss being the strongest, suitability change being moderately important, but changes in habitat patch size having only weak effects. We suggest conservation efforts could be most effective by strengthening connectivity along with reducing habitat deterioration, which would benefit a wide range of species

Inventory and review of quantitative models for spread of plant pests for use in pest risk assessment for the EU territory

This report considers the prospects for increasing the use of quantitative models for plant pest spread and dispersal in EFSA Plant Health risk assessments. The agreed major aims were to provide an overview of current modelling approaches and their strengths and weaknesses for risk assessment, and to develop and test a system for risk assessors to select appropriate models for application. First, we conducted an extensive literature review, based on protocols developed for systematic reviews. The review located 468 models for plant pest spread and dispersal and these were entered into a searchable and secure Electronic Model Inventory database. A cluster analysis on how these models were formulated allowed us to identify eight distinct major modelling strategies that were differentiated by the types of pests they were used for and the ways in which they were parameterised and analysed. These strategies varied in their strengths and weaknesses, meaning that no single approach was the most useful for all elements of risk assessment. Therefore we developed a Decision Support Scheme (DSS) to guide model selection. The DSS identifies the most appropriate strategies by weighing up the goals of risk assessment and constraints imposed by lack of data or expertise. Searching and filtering the Electronic Model Inventory then allows the assessor to locate specific models within those strategies that can be applied. This DSS was tested in seven case studies covering a range of risk assessment scenarios, pest types and dispersal mechanisms. These demonstrate the effectiveness of the DSS for selecting models that can be applied to contribute to EFSA Plant Health risk assessments. Therefore, quantitative spread and dispersal modelling has potential to improve current risk assessment protocols and contribute to reducing the serious impacts of plant pests in Europe

Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

Xylella fastidiosa is an important plant pathogen that attacks several plants of economic importance. Once restricted to the Americas, the bacterium, which causes olive quick decline syndrome, was discovered near Lecce, Italy in 2013. Since the initial outbreak, it has invaded 23,000 ha of olives in the Apulian Region, southern Italy, and is of great concern throughout Mediterranean basin. Therefore, predicting its spread and estimating the efficacy of control are of utmost importance. As data on this invasive infectious disease are poor, we have developed a spatially-explicit simulation model for X. fastidiosa to provide guidance for predicting spread in the early stages of invasion and inform management strategies. The model qualitatively and quantitatively predicts the patterns of spread. We model control zones currently employed in Apulia, showing that increasing buffer widths decrease infection risk beyond the control zone, but this may not halt the spread completely due to stochastic long-distance jumps caused by vector dispersal. Therefore, management practices should aim to reduce vector long-distance dispersal. We find optimal control scenarios that minimise control effort while reducing X. fastidiosa spread maximally—suggesting that increasing buffer zone widths should be favoured over surveillance efforts as control budgets increase. Our model highlights the importance of non-olive hosts which increase the spread rate of the disease and may lead to an order of magnitude increase in risk. Many aspects of X. fastidiosa disease invasion remain uncertain and hinder forecasting; we recommend future studies investigating quantification of the infection growth rate, and short and long distance dispersal

Drivers of plant species’ potential to spread: the importance of demography versus seed dispersal

Understanding the ability of plants to spread is important for assessing conservation strategies, landscape dynamics, invasiveness and ability to cope with climate change. While long-distance seed dispersal is often viewed as a key process in population spread, the importance of inter-specific variation in demography is less explored. Indeed, the relative importance of demography vs seed dispersal in determining population spread is still little understood. We modelled species’ potential for population spread in terms of annual migration rates for a set of species inhabiting dry grasslands of central Europe. Simultaneously, we estimated the importance of demographic (population growth rate) vs long-distance dispersal (99th percentile dispersal distance) characteristics for among-species differences in modelled population spread. In addition, we assessed how well simple proxy measures related to demography (the number and survival of seedlings, the survival of flowering individuals) and dispersal (plant height, terminal velocity and wind speed during dispersal) predicted modelled spread rates. We found that species’ demographic rates were the more powerful predictors of species’ modelled potential to spread than dispersal. Furthermore, our simple proxies were correlated with modelled species spread rates and together their predictive power was high. Our findings highlight that for understanding variation among species in their potential for population spread, detailed information on local demography and dispersal might not always be necessary. Simple proxies or assumptions that are based primarily on species demography could be sufficient

Do ecosystem service maps and models meet stakeholders’ needs? A preliminary survey across sub-Saharan Africa

To achieve sustainability goals, it is important to incorporate ecosystem service (ES) information into decision-making processes. However, little is known about the correspondence between the needs of ES information users and the data provided by the researcher community. We surveyed stakeholders within sub-Saharan Africa, determining their ES data requirements using a targeted sampling strategy. Of those respondents utilising ES information (>90%; n=60), 27% report having sufficient data; with the remainder requiring additional data – particularly at higher spatial resolutions and at multiple points in time. The majority of respondents focus on provisioning and regulating services, particularly food and fresh water supply (both 58%) and climate regulation (49%). Their focus is generally at national scales or below and in accordance with data availability. Among the stakeholders surveyed, we performed a follow-up assessment for a sub-sample of 17 technical experts. The technical experts are unanimous that ES models must be able to incorporate scenarios, and most agree that ES models should be at least 90% accurate. However, relatively coarse-resolution (1–10 km2) models are sufficient for many services. To maximise the impact of future research, dynamic, multi-scale datasets on ES must be delivered alongside capacity-building efforts

What Can We Learn from In-Depth Analysis of Human Errors Resulting in Diagnostic Errors in the Emergency Department:An Analysis of Serious Adverse Event Reports

Introduction Human error plays a vital role in diagnostic errors in the emergency department. A thorough analysis of these human errors, using information-rich reports of serious adverse events (SAEs), could help to better study and understand the causes of these errors and formulate more specific recommendations. Methods We studied 23 SAE reports of diagnostic events in emergency departments of Dutch general hospitals and identified human errors. Two researchers independently applied the Safer Dx Instrument, Diagnostic Error Evaluation and Research Taxonomy, and the Model of Unsafe acts to analyze reports. Results Twenty-one reports contained a diagnostic error, in which we identified 73 human errors, which were mainly based on intended actions (n=69) and could be classified as mistakes (n=56) or violations (n=13). Most human errors occurred during the assessment and testing phase of the diagnostic process. Discussion The combination of different instruments and information-rich SAE reports allowed for a deeper understanding of the mechanisms underlying diagnostic error. Results indicated that errors occurred most often during the assessment and the testing phase of the diagnostic process. Most often, the errors could be classified as mistakes and violations, both intended actions. These types of errors are in need of different recommendations for improvement, as mistakes are often knowledge based, whereas violations often happen because of work and time pressure. These analyses provided valuable insights for more overarching recommendations to improve diagnostic safety and would be recommended to use in future research and analysis of (serious) adverse events

Seed bank dynamics govern persistence of Brassica hybrids in crop and natural habitats

Background and Aims: Gene flow from crops to their wild relatives has the potential to alter population growth rates and demography of hybrid populations, especially when a new crop has been genetically modified (GM). This study introduces a comprehensive approach to assess this potential for altered population fitness, and uses a combination of demographic data in two habitat types and mathematical (matrix) models that include crop rotations and outcrossing between parental species. Methods: Full life-cycle demographic rates, including seed bank survival, of non-GM Brassica rapa × B. napus F1 hybrids and their parent species were estimated from experiments in both agricultural and semi-natural habitats. Altered fitness potential was modelled using periodic matrices including crop rotations and outcrossing between parent species. Key Results: The demographic vital rates (i.e. for major stage transitions) of the hybrid population were intermediate between or lower than both parental species. The population growth rate (λ) of hybrids indicated decreases in both habitat types, and in a semi-natural habitat hybrids became extinct at two sites. Elasticity analyses indicated that seed bank survival was the greatest contributor to λ. In agricultural habitats, hybrid populations were projected to decline, but with persistence times up to 20 years. The seed bank survival rate was the main driver determining persistence. It was found that λ of the hybrids was largely determined by parental seed bank survival and subsequent replenishment of the hybrid population through outcrossing of B. rapa with B. napus. Conclusions: Hybrid persistence was found to be highly dependent on the seed bank, suggesting that targeting hybrid seed survival could be an important management option in controlling hybrid persistence. For local risk mitigation, an increased focus on the wild parent is suggested. Management actions, such as control of B. rapa, could indirectly reduce hybrid populations by blocking hybrid replenishment

Ensembles of ecosystem service models can improve accuracy and indicate uncertainty

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 themes which 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 regions where 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 individual models. 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. © 2020 The Author

Model Ensembles of Ecosystem Services Fill Global Certainty and Capacity Gaps

Sustaining ecosystem services (ES) critical to human wellbeing is hindered by many practitioners lacking access to ES models (‘the capacity gap’) or knowledge of the accuracy of available models (‘the certainty gap’), especially in the world’s poorer regions. We developed ensembles of multiple models at an unprecedented global scale for five ES of high policy relevance. Ensembles were 2-14% more accurate than individual models. Ensemble accuracy was not correlated with proxies for research capacity – indicating accuracy is distributed equitably across the globe and that countries less able to research ES suffer no accuracy penalty. By making these ES ensembles and associated accuracy estimates freely available, we provide globally consistent ES information that can support policy and decision making in regions with low data availability or low capacity for implementing complex ES models. Thus, we hope to reduce the capacity and certainty gaps impeding local to global-scale movement towards ES sustainability

Abiotic stress QTL in lettuce crop–wild hybrids: comparing greenhouse and field experiments

The development of stress-tolerant crops is an increasingly important goal of current crop breeding. A higher abiotic stress tolerance could increase the probability of introgression of genes from crops to wild relatives. This is particularly relevant to the discussion on the risks of new GM crops that may be engineered to increase abiotic stress resistance. We investigated abiotic stress QTL in greenhouse and field experiments in which we subjected Recombinant Inbred Lines from a cross between cultivated Lactuca sativa cv. Salinas and its wild relative L. serriola to drought, low nutrients, salt stress, and above ground competition. Aboveground biomass at the end of the rosette stage was used as a proxy for the performance of plants under a particular stress. We detected a mosaic of abiotic stress QTL over the entire genome with little overlap between QTL from different stresses. The two QTL clusters that were identified reflected general growth rather than specific stress responses and co-located with clusters found in earlier studies for leaf shape and flowering time. Genetic correlations across treatments were often higher among different stress treatments within the same experiment (greenhouse or field), than among the same type of stress applied in different experiments. Moreover, the effects of the field stress treatments were more correlated to those of the greenhouse competition treatments than to those of the other greenhouse stress experiments, suggesting that competition rather than abiotic stress is a major factor in the field. In conclusion, the introgression risk of stress tolerance (trans-)genes under field conditions cannot easily be predicted based on genomic background selection patterns from controlled QTL experiments in greenhouses. Especially field data will be needed to assess potential (negative) ecological effects of introgression of these transgenes into wild relatives