Effects of Local and Landscape Factors on Population Dynamics of a Cotton Pest

BACKGROUND: Many polyphagous pests sequentially use crops and uncultivated habitats in landscapes dominated by annual crops. As these habitats may contribute in increasing or decreasing pest density in fields of a specific crop, understanding the scale and temporal variability of source and sink effects is critical for managing landscapes to enhance pest control. METHODOLOGY/PRINCIPAL FINDINGS: We evaluated how local and landscape characteristics affect population density of the western tarnished plant bug, Lygus hesperus (Knight), in cotton fields of the San Joaquin Valley in California. During two periods covering the main window of cotton vulnerability to Lygus attack over three years, we examined the associations between abundance of six common Lygus crops, uncultivated habitats and Lygus population density in these cotton fields. We also investigated impacts of insecticide applications in cotton fields and cotton flowering date. Consistent associations observed across periods and years involved abundances of cotton and uncultivated habitats that were negatively associated with Lygus density, and abundance of seed alfalfa and cotton flowering date that were positively associated with Lygus density. Safflower and forage alfalfa had variable effects, possibly reflecting among-year variation in crop management practices, and tomato, sugar beet and insecticide applications were rarely associated with Lygus density. Using data from the first two years, a multiple regression model including the four consistent factors successfully predicted Lygus density across cotton fields in the last year of the study. CONCLUSIONS/SIGNIFICANCE: Our results show that the approach developed here is appropriate to characterize and test the source and sink effects of various habitats on pest dynamics and improve the design of landscape-level pest management strategies

A Research Agenda for Helminth Diseases of Humans: Modelling for Control and Elimination

Mathematical modelling of helminth infections has the potential to inform policy and guide research for the control and elimination of human helminthiases. However, this potential, unlike in other parasitic and infectious diseases, has yet to be realised. To place contemporary efforts in a historical context, a summary of the development of mathematical models for helminthiases is presented. These efforts are discussed according to the role that models can play in furthering our understanding of parasite population biology and transmission dynamics, and the effect on such dynamics of control interventions, as well as in enabling estimation of directly unobservable parameters, exploration of transmission breakpoints, and investigation of evolutionary outcomes of control. The Disease Reference Group on Helminth Infections (DRG4), established in 2009 by the Special Programme for Research and Training in Tropical Diseases (TDR), was given the mandate to review helminthiases research and identify research priorities and gaps. A research and development agenda for helminthiasis modelling is proposed based on identified gaps that need to be addressed for models to become useful decision tools that can support research and control operations effectively. This agenda includes the use of models to estimate the impact of large-scale interventions on infection incidence; the design of sampling protocols for the monitoring and evaluation of integrated control programmes; the modelling of co-infections; the investigation of the dynamical relationship between infection and morbidity indicators; the improvement of analytical methods for the quantification of anthelmintic efficacy and resistance; the determination of programme endpoints; the linking of dynamical helminth models with helminth geostatistical mapping; and the investigation of the impact of climate change on human helminthiases. It is concluded that modelling should be embedded in helminth research, and in the planning, evaluation, and surveillance of interventions from the outset. Modellers should be essential members of interdisciplinary teams, propitiating a continuous dialogue with end users and stakeholders to reflect public health needs in the terrain, discuss the scope and limitations of models, and update biological assumptions and model outputs regularly. It is highlighted that to reach these goals, a collaborative framework must be developed for the collation, annotation, and sharing of databases from large-scale anthelmintic control programmes, and that helminth modellers should join efforts to tackle key questions in helminth epidemiology and control through the sharing of such databases, and by using diverse, yet complementary, modelling approaches

Validation and Comparison of a Model of the Effect of Sea-Level Rise on Coastal Wetlands

Models are used to project coastal wetland distribution under future sea-level rise scenarios to assist decision-making. Model validation and comparison was used to investigate error and uncertainty in the Sea Level Affecting Marshes Model, a readily available model with minimal validation, particularly for wetlands beyond North America. Accurate parameterisation is required to improve the performance of the model, and indeed any spatial model. Consideration of tidal attenuation further enhances model performance, particularly for coastal wetlands located within estuaries along wave-dominated coastlines. The model does not simulate vegetation changes that are known to occur, particularly when sedimentation exceeds rates of sea-level rise resulting in shoreline progradation. Model performance was reasonable over decadal timescales, decreasing as the time-scale of retrospection increased due to compounding of errors. Comparison with other deterministic models showed reasonable agreement by 2100. However, given the uncertainty of the future and the unpredictable nature of coastal wetlands, it is difficult to ascertain which model could be realistic enough to meet its intended purpose. Model validation and comparison are useful for assessing model efficacy and parameterisation, and should be applied before application of any spatially explicit model of coastal wetland response to sea-level rise

Fuzzy-Logic Cognitive Mapping: Introduction and Overview of the Method

Lack of information and large uncertainties can constrain the effectiveness and acceptability of environmental models. Fuzzy-logic cognitive mapping (FCM) is an approach that deals with these limitations by incorporating existing knowledge and experience. It is a soft-knowledge approach for system modeling, where components of a system and their relationships are identified and semi-quantified in a participatory way. Its usefulness has been manifested through applications in a variety of disciplines, including engineering, information technology, business, and medicine. This chapter introduces FCM as a simple, transparent, and flexible participatory method to model complex social-ecological systems based on expert and stakeholder knowledge. It describes the evolution of FCM to environmental modeling due to its ability to facilitate public participation, data generation, and systems thinking. Numerous actors can be involved when studying environmental issues: experts, scientists, decision makers, and other stakeholders. Thus, a wide range of opinions and perceptions can be taken into account, providing a platform for discussion and negotiation among different actors. Moreover, data that is otherwise inaccessible can be gathered through FCM. Finally, one of the most significant characteristics of the method is the possibility to study causal relationships and feedback loops. In this way, FCM supports decision-making by simulation and scenario studies