44 results found
Holt J, Leach AW, 2019, Linguistic variables as fuzzy sets to model uncertainty in the combined efficacy of multiple phytosanitary measures in pest risk analysis, ECOLOGICAL MODELLING, Vol: 406, Pages: 73-79, ISSN: 0304-3800
van der Gaag DJ, Holt J, Leach AW, et al., 2019, Model of the probability of pest transfer to a site suitable for establishment following their arrival on imported fruit, cut-flower or vegetable produce, CROP PROTECTION, Vol: 117, Pages: 135-146, ISSN: 0261-2194
Mumford JD, Leach AW, Benedict MQ, et al., 2018, Maintaining quality of candidate strains of transgenic mosquitoes for studies in containment facilities in disease endemic countries, Vector-Borne and Zoonotic Diseases, Vol: 18, Pages: 31-38, ISSN: 1530-3667
Transgenic mosquitoes are being developed as novel components of area-wide approaches to vector-borne disease control. Best practice is to develop these in phases, beginning with laboratory studies, before moving to field testing and inclusion in control programs, to ensure safety and prevent costly field testing of unsuitable strains. The process of identifying and developing good candidate strains requires maintenance of transgenic colonies over many generations in containment facilities. By working in disease endemic countries with target vector populations, laboratory strains may be developed and selected for properties that will enhance intended control efficacy in the next phase, while avoiding traits that introduce unnecessary risks. Candidate strains aiming toward field use must consistently achieve established performance criteria, throughout the process of scaling up from small study colonies to production of sufficient numbers for field testing and possible open release. Maintenance of a consistent quality can be demonstrated by a set of insect quality and insectary operating indicators, measured over time at predetermined intervals. These indicators: inform comparability of studies using various candidate strains at different times and locations; provide evidence of conformity relevant to compliance with terms of approval for regulated use; and can be used to validate some assumptions related to risk assessments covering the contained phase and for release into the environment.
Rindorf A, Mumford J, Baranowski P, et al., 2017, Moving beyond the MSY concept to reflect multidimensional fisheries management objectives, Marine Policy, Vol: 85, Pages: 33-41, ISSN: 0308-597X
Maximising the long term average catch of single stock fisheries as prescribed by the globally-legislated MSY objective is unlikely to ensure ecosystem, economic, social and governance sustainability unless an effort is made to explicitly include these considerations. We investigated how objectives to be maximised can be combined with sustainability constraints aiming specifically at one or more of these four sustainability pillars. The study was conducted as a three-year interactive process involving 290 participating science, industry, NGO and management representatives from six different European regions. Economic considerations and inclusive governance were generally preferred as the key objectives to be maximised in complex fisheries, recognising that ecosystem, social and governance constraints are also key aspects of sustainability in all regions. Relative preferences differed between regions and cases but were similar across a series of workshops, different levels of information provided and the form of elicitation methods used as long as major shifts in context or stakeholder composition did not occur. Maximising inclusiveness in governance, particularly the inclusiveness of affected stakeholders, was highly preferred by participants across the project. This suggests that advice incorporating flexibility in the interpretation of objectives to leave room for meaningful inclusiveness in decision-making processes is likely to be a prerequisite for stakeholder buy-in to management decisions.
Holt J, Leach AW, Johnson S, et al., 2017, Bayesian Networks to Compare Pest Control Interventions on Commodities Along Agricultural Production Chains., Risk Analysis, Vol: 38, Pages: 297-310, ISSN: 0272-4332
The production of an agricultural commodity involves a sequence of processes: planting/growing, harvesting, sorting/grading, postharvest treatment, packing, and exporting. A Bayesian network has been developed to represent the level of potential infestation of an agricultural commodity by a specified pest along an agricultural production chain. It reflects the dependency of this infestation on the predicted level of pest challenge, the anticipated susceptibility of the commodity to the pest, the level of impact from pest control measures as designed, and any variation from that due to uncertainty in measure efficacy. The objective of this Bayesian network is to facilitate agreement between national governments of the exporters and importers on a set of phytosanitary measures to meet specific phytosanitary measure requirements to achieve target levels of protection against regulated pests. The model can be used to compare the performance of different combinations of measures under different scenarios of pest challenge, making use of available measure performance data. A case study is presented using a model developed for a fruit fly pest on dragon fruit in Vietnam; the model parameters and results are illustrative and do not imply a particular level of fruit fly infestation of these exports; rather, they provide the most likely, alternative, or worst-case scenarios of the impact of measures. As a means to facilitate agreement for trade, the model provides a framework to support communication between exporters and importers about any differences in perceptions of the risk reduction achieved by pest control measures deployed during the commodity production chain.
Levontin P, Baranowski P, Leach AW, et al., 2017, On the role of visualisation in fisheries management, Marine Policy, Vol: 78, Pages: 114-121, ISSN: 1872-9460
Environmental change has focused the attention of scientists, policy makers and the wider public on the uncertainty inherent in interactions between people and the environment. Governance in fisheries is required to involve stakeholder participation and tobe more inclusive in its remit, which is no longer limited to ensuring a maximum sustainable yield from a single stock but considers species and habitat interactions, as well as social and economic issues. The increase in scope, complexity and awareness of uncertainty in fisheries management has brought methodological and institutional changes throughout the world. Progress towards comprehensive, explicit and participatory risk management in fisheries depends on effective communication. Graphic design and data visualisation have been underused in fisheries for communicating science to a wider range of stakeholders. In this paper, some of the general aspects of designing visualisations of modeling results are discussed and illustrated withexamples from the EU funded MYFISH project. These infographicswere tested in stakeholder workshops, and improved through feedbackfrom that 2process. It is desirable to convey not just modelling results but a sense of how reliable various models are. A survey was developed to judge reliability of different components of fisheries modelling: the quality of data, the quality of knowledge, model validation efforts, and robustness to key uncertainties. The results of these surveys were visualized for ten different models, and presented alongside the main case study.
Whitlock RE, Kopra J, Pakarinen T, et al., 2016, Mark-recapture estimation of mortality and migration rates for sea trout (Salmo trutta) in the northern Baltic sea, ICES JOURNAL OF MARINE SCIENCE, Vol: 74, Pages: 286-300, ISSN: 1054-3139
Holt J, Leach A, Mumford JD, et al., 2016, Development of probabilistic models for quantitative pathway analysis of plant pest introduction for the EU territory, Parma, Italy, Publisher: European Food Safety Authority, 2016:EN-1062
This report demonstrates a probabilistic quantitative pathway analysis model that can be used in risk assessment for plant pest introduction into EU territory on a range of edible commodities (apples, oranges, stone fruits and wheat). Two types of model were developed: a general commodity model that simulates distribution of an imported infested/infected commodity to and within the EU from source countries by month; and a consignment model that simulates the movement and distribution of individual consignments from source countries to destinations in the EU. The general pathway model has two modules. Module 1 is a trade pathway model, with a Eurostat database of five years of monthly trade volumes for each specific commodity into the EU28 from all source countries and territories. Infestation levels based on interception records, commercial quality standards or other information determine volume of infested commodity entering and transhipped within the EU. Module 2 allocates commodity volumes to processing, retail use and waste streams and overlays the distribution onto EU NUTS2 regions based on population densities and processing unit locations. Transfer potential to domestic host crops is a function of distribution of imported infested product and area of domestic production in NUTS2 regions, pest dispersal potential, and phenology of susceptibility in domestic crops. The consignment model covers the several routes on supply chains for processing and retail use. The output of the general pathway model is a distribution of estimated volumes of infested produce by NUTS2 region across the EU28, by month or annually; this is then related to the accessible susceptible domestic crop. Risk is expressed as a potential volume of infested fruit in potential contact with an area of susceptible domestic host crop. The output of the consignment model is a volume of infested produce retained at each stage along the specific consignment trade chain.
Kempf A, Mumford J, Levontin P, et al., 2016, The MSY concept in a multi-objective fisheries environment - Lessons from the North Sea, Marine Policy, Vol: 69, Pages: 146-158, ISSN: 1872-9460
One of the most important goals in current fisheries management is to maintain or restore stocks above levels that can produce the maximum sustainable yield (MSY). However, it may not be feasible to achieve MSY simultaneously for multiple species because of trade-offs that result from interactions between species, mixed fisheries and the multiple objectives of stakeholders. The premise in this study is that MSY is a concept that needs adaptation, not wholesale replacement. The approach chosen to identify trade-offs and stakeholder preferences involved a process of consulting and discussing options with stakeholders as well as scenario modelling with bio-economic and multi-species models. It is difficult to intuitively anticipate the consequences of complex trade-offs and it is also complicated to address them from a political point of view. However, scenario modelling showed that the current approach of treating each stock separately and ignoring trade-offs may result in unacceptable ecosystem, economic or social effects in North Sea fisheries. Setting FMSY as a management target without any flexibility for compromises may lead to disappointment for some of the stakeholders. To treat FMSY no longer as a point estimate but rather as a “Pretty Good Yield” within sustainable ranges was seen as a promising way forward to avoid unacceptable outcomes when trying to fish all stocks simultaneously at FMSY. This study gives insights on how inclusive governance can help to reach consensus in difficult political processes, and how science can be used to make informed decisions inside a multi-dimensional trade-off space.
Quinlan MM, Mengersen K, Mumford J, et al., 2016, Beyond Compliance A Production Chain Framework for Plant Health Risk Management in Trade, Publisher: Chartridge Books Oxford, ISBN: 9781911033103
A Production Chain Framework for Plant Health Risk Management in Trade M. Megan Quinlan, Kerrie Mengersen, John Mumford, Adrian Leach, Johnson Holt, Rebecca Murphy. M. MEGAN QUINLAN, KERRIE MENGERSEN, JOHN ...
Maria Rincon M, Mumford JD, Levontin P, et al., 2016, The economic value of environmental data: a notional insurance scheme for the European anchovy, ICES Journal of Marine Science, Vol: 73, Pages: 1033-1041, ISSN: 1054-3139
Anchovy population dynamics in the Gulf of Cádiz are governed by environmental processes. Sea surface temperature, intense easterly winds, and discharges from the Guadalquivir River have been identified as key factors determining early life stage mortality in this anchovy stock. We have constructed an environment-based recruitment model that simulates the abundance of juveniles under alternative parameters representing plausible biological hypotheses. We are able to evaluate how modelling environment-based recruitment can affect stock assessment and how responding to environmental information can beneﬁt ﬁshery management to allow greater average catch levels through the application of harvest control rules (HCRs) based on environmental conditions. While the environment-based rules generally increase allowable catch levels the variance in catch levels also increases, detracting from the improved value based only on average yield. In addition to changes in revenue, the probability of stock collapse is also reduced by using environmental factors in HCRs. To assess the value of these management systems we simulate a notional insurance scheme, which applies a value to both average yields and uncertainty. The value of the information-driven rules can be determined by comparing the relevant premiums payable for equal levels of insurance cover on revenue within each speciﬁc management regime. We demonstrate the net value of incorporating environmental factors in the management of anchovies in the Gulf of Cádiz despite the increased variability in revenue. This could be an effective method to describe outcomes for both commercial ﬁsheries and ecosystem management policies, and as a guide to management of other species whose dynamics are predictable based on in-season observations.
Romakkaniemi A, Apostolidis C, Bal G, et al., 2015, Best practices for the provision of prior information for Bayesian stock assessment, Copenhagen, Denmark, Publisher: ICES, . ICES Cooperative Research Report No 328
This manual represents a review of the potential sources and methods to be applied when providing prior information to Bayesian stock assessments and marine risk analysis. The manual is compiled as a product of the EC Framework 7 ECOKNOWS project (www.ecoknows.eu).The manual begins by introducing the basic concepts of Bayesian inference and the role of prior information in the inference. Bayesian analysis is a mathematical formalization of a sequential learning process in a probabilistic rationale. Prior information (also called ”prior knowledge”, ”prior belief”, or simply a ”prior”) refers to any existing relevant knowledge available before the analysis of the newest observations (data) and the information included in them. Prior information is input to a Bayesian statistical analysis in the form of a probability distribution (a prior distribution) that summarizes beliefs about the parameter concerned in terms of relative support for different values.Apart from specifying probable parameter values, prior information also defines how the data are related to the phenomenon being studied, i.e. the model structure. Prior information should reflect the different degrees of knowledge about different parameters and the interrelationships among them. Different sources of prior information are described as well as the particularities important for their successful utilization. The sources of prior information are classified into four main categories: (i) primary data, (ii) literature, (iii) online databases, and (iv) experts. This categorization is somewhat synthetic, but is useful for structuring the process of deriving a prior and for acknowledging different aspects of it.A hierarchy is proposed in which sources of prior information are ranked according to their proximity to the primary observations, so that use of raw data is preferred where possible. This hierarchy is reflected in the types of methods that might be suitable –
Leach AW, Levontin P, Holt J, et al., 2014, Identification and prioritization of uncertainties for management of Eastern Atlantic bluefin tuna (Thunnus thynnus), Marine Policy, Vol: 48, Pages: 84-92, ISSN: 0308-597X
In recent decades there has been steady progress towards a risk-based management approach for fisheries. An important first step in a risk analysis framework is scoping to identify, describe and catalog the sources of uncertainty that might have an impact on a fishery. This paper introduces a methodology based on a range of tools to formalize the process of elicitation of uncertainties, from both experts and stakeholders, for the International Commission for the Conservation of Atlantic Tunas (ICCAT). ICCAT is a regional fisheries management organization responsible for the conservation of tunas and other highly migratory fish in the Atlantic Ocean and its adjacent seas. The aim of the elicitation was to identify and prioritize uncertainties for inclusion in Operating Models for Management Strategy Evaluation (MSE). The tool presented in this paper supports the qualitative prioritization of uncertainties, while also visualizing the degree of consensus among stakeholders on particular issues. Perceptions of uncertainty in fisheries often vary widely among scientists, industry and other interest groups, so tools that can facilitate inclusion and representation of different opinions are useful where decision-making depends on broad agreement and more generally, where effective management depends on commitment from stakeholders.
Holt J, Leach AW, Schrader G, et al., 2013, Eliciting and combining decision criteria using a limited palette of utility functions and uncertainty distributions: illustrated by application to Pest Risk Analysis, Risk Analysis, Vol: 34, Pages: 4-16, ISSN: 0272-4332
Utility functions in the form of tables or matrices have often been used to combine discretely-rated decision-making criteria. Matrix elements are usually specified individually, so no one rule or principle can be easily stated for the utility function as a whole. A series of five matrices are presented which aggregate criteria two at a time using simple rules which express a varying degree of constraint of the lower rating over the higher. A further nine possible matrices were obtained by using a different rule either side of the main axis of the matrix to describe situations where the criteria have a differential influence on the outcome. Uncertainties in the criteria are represented by three alternative frequency distributions from which the assessors select the most appropriate. The output of the utility function is a distribution of rating frequencies that is dependent on the distributions of the input criteria. In Pest Risk Analysis (PRA), seven of these utility functions were required to mimic the logic by which assessors for the European and Mediterranean Plant Protection Organisation (EPPO) arrive at an overall rating of pest risk. The framework enables the development of PRAs which are consistent and easy to understand, criticise, compare and change. When tested in workshops, PRA practitioners thought that the approach accorded with both the logic and the level of resolution which they used in the risk assessments
Kehlenbeck H, Cannon R, Breukers A, et al., 2012, A protocol for analysing the costs and benefits of plant health control measures, Bulletin OEPP/EPPO Bulletin, Vol: 42, Pages: 81-88, ISSN: 0250-8052
Schrader G, Macleod A, Petter F, et al., 2012, Consistency in Pest Risk Analysis - How can it be achieved and what are the benefits?, Bulletin OEPP/EPPO Bulletin, Vol: 42, Pages: 3-12, ISSN: 0250-8052
Mengersen K, Quinlan MM, Whittle PJL, et al., 2012, Beyond Compliance: project on an integrated systems approach for pest risk management in South East Asia, Vol: 42, Pages: 109-116, ISSN: 0250-8052
Gavriel S, Gazit Y, Leach A, et al., 2012, Spatial patterns of sterile Mediterranean fruit fly disperal, Entomologia Experimentalis et Applicata, Vol: 142, Pages: 17-26
Leach AW, Levontin P, Holt J, et al., 2012, Preliminary Assessment of Uncertainties in GBYP, ICCAT Collective Volume of Scientific Papers
Holt J, Leach AW, Knight JD, et al., 2012, Tools for visualising and integrating pest risk assessment ratings and uncertainties, Bulletin OEPP/EPPO Bulletin, Vol: 42, Pages: 35-41, ISSN: 0250-8052
Leach AW, Mumford JD, 2011, Pesticide Environmental Accounting: A decision-making tool estimating external costs of pesticides, Journal für Verbraucherschutz und Lebensmittelsicherheit, Vol: 6, Pages: 21-26, ISSN: 1661-5751
In cost-benefit analyses of pesticide use an area-based measure of both costs and benefits is needed for spatial analysis of net benefits. The pesticide environmental accounting (PEA) tool provides a monetary estimate of environmental and health impacts per hectare-application of pesticide (Leach and Mumford 2008). The model combines the Environmental Impact Quotient method (rating human health and eco-toxicological behaviour of specific pesticides) with absolute estimates of external pesticide costs in the UK, USA and Germany. The model converts external costs of a pesticide to other countries using GDP per capita and % GDP from agriculture. For many countries, resources are not available for intensive assessments of external pesticide costs. Economic and policy applications include rationalising pesticide choice, estimating impacts of pesticide reduction policies or calculating benefits from technologies that replace pesticides [sterile insect technique (SIT) or biological pesticides such as Metarhizium]. PEA is a logical integration of diverse data and approaches. The assumptions provide transparency and consistency but at the cost of specificity and precision, a reasonable trade-off for a method that provides both comparative estimates of pesticide impacts and area-based assessments of absolute impacts. The method has been applied to cost-benefit analyses of SIT in fruit flies (two species) and pesticide choice in Desert Locust (DL) campaigns in Africa. An example of external cost calculations for sugar beet herbicides in Europe is presented. There are also planned uses in public health mosquito control.
Mumford JD, Booy O, RHA B, et al., 2010, Invasive species risk assessment in Great Britain, Aspects of Applied Biology, Vol: 104, Pages: 49-54, ISSN: 0265-1491
Carracso LR, Mumford JD, MacLeod A, et al., 2010, Unveiling human-assisted dispersal mechanisms in invasive alien insects: integration of spatial stochastic simulation and phenology models, Ecological Modelling, Vol: 221, Pages: 2068-2075
Mumford JD, Leach AW, Levontin P, et al., 2009, Insurance mechanisms to mediate economic risks in marine fisheries., ICES Journal of Marine Science, Vol: 66, Pages: 950-959
Leach AW, Mullié WC, Mumford JD, et al., 2009, Spatial and historical analysis of pesticide externalities in locust control in Senegal., Spatial and historical analysis of pesticide externalities in locust control in Senegal., Rome, Publisher: FAO
Leach AW, Mumford JD, 2008, Pesticide Environmental Accounting: A method for assessing the external costs of individual pesticide applications, ENVIRONMENTAL POLLUTION, Vol: 151, Pages: 139-147, ISSN: 0269-7491
Mumford JD, Leach AW, Levontin P, et al., 2008, The potential for insurance to mediate economic risks in marine fisheries, Corvalis, Oregon, USA, Fourteenth Biennial Conference of the International Institute of Fisheries Economics & Trade (IIFET), Publisher: The International Institute of Fisheries Economics & Trade
Leach AW, Mumford JD, Enkerlin W, 2007, Cost-Benefit Analysis Model: A Tool for Area-wide Fruit Fly Management, Vienna, Austria, Publisher: International Atomic Energy Agency
Waage JK, Mumford JD, Leach AW, et al., 2007, Responsibility and cost sharing options for quarantine plant health, Responsibility and cost sharing options for quarantine plant health, London, United Kingdom, Publisher: Department for Environment, Food and Rural Affairs
Leach AW, Mumford JD, 2005, A time-based simulation model of cocoa pod generation, pod age-structure and vegetative flushing: a virtual grove for pest and disease management., Malaysian International Cocoa Conference
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