François Rebaudo

Obstacles to integrated pest management adoption in developing countries

Significance Integrated pest management (IPM) has been the dominant crop protection paradigm promoted globally since the 1960s. However, its adoption by developing country farmers is surprisingly low. This article reports 51 potential reasons why, identified and prioritized by hundreds of IPM professionals and practitioners around the world. Stakeholders from developing countries prioritized different adoption obstacles than those from high-income countries. Surprisingly, a few of the obstacles prioritized in developing countries appear to be overlooked by the literature. We suggest that a more vigorous analysis and discussion of the factors discouraging IPM adoption in developing countries may accelerate the progress needed to bring about its full potential. Despite its theoretical prominence and sound principles, integrated pest management (IPM) continues to suffer from anemic adoption rates in developing countries. To shed light on the reasons, we surveyed the opinions of a large and diverse pool of IPM professionals and practitioners from 96 countries by using structured concept mapping. The first phase of this method elicited 413 open-ended responses on perceived obstacles to IPM. Analysis of responses revealed 51 unique statements on obstacles, the most frequent of which was “insufficient training and technical support to farmers.” Cluster analyses, based on participant opinions, grouped these unique statements into six themes: research weaknesses, outreach weaknesses, IPM weaknesses, farmer weaknesses, pesticide industry interference, and weak adoption incentives. Subsequently, 163 participants rated the obstacles expressed in the 51 unique statements according to importance and remediation difficulty. Respondents from developing countries and high-income countries rated the obstacles differently. As a group, developing-country respondents rated “IPM requires collective action within a farming community” as their top obstacle to IPM adoption. Respondents from high-income countries prioritized instead the “shortage of well-qualified IPM experts and extensionists.” Differential prioritization was also evident among developing-country regions, and when obstacle statements were grouped into themes. Results highlighted the need to improve the participation of stakeholders from developing countries in the IPM adoption debate, and also to situate the debate within specific regional contexts.

Community-Based Participatory Research Helps Farmers and Scientists to Manage Invasive Pests in the Ecuadorian Andes

Participatory research has not been a conspicuous methodology in developing nations for studying invasive pests, an increasing threat to the sustainable development in the tropics. Our study presents a community-based monitoring system that focuses on three invasive potato tuber moth species (PTM). The monitoring was developed and implemented by young farmers in a remote mountainous area of Ecuador. Local participants collected data from the PTM invasion front, which revealed clear connection between the abundance of one of the species (Tecia solanivora) and the remoteness to the main market place. This suggests that mechanisms structuring invasive populations at the invasion front are different from those occurring in areas invaded for longer period. Participatory monitoring with local people may serve as a cost-effective early warning system to detect and control incipient invasive pest species in countries where the daily management of biological resources is largely in the hands of poor rural people.

Modeling invasive species spread in complex landscapes: the case of potato moth in Ecuador

Tropical mountains have a long history of human occupation, and although vulnerable to biological invasions, have received minimal attention in the literature. Understanding invasive pest dynamics in socio-ecological, agricultural landscapes, like the tropical Andes, is a challenging but timely issue for ecologists as it may provide developing countries with new tools to face increasing threats posed by these organisms. In this work, road rehabilitation into a remote valley of the Ecuadorian Andes constituted a natural experiment to study the spatial propagation of an invasive potato tuber moth into a previously non-infested agricultural landscape. We used a cellular automaton to model moth spatio-temporal dynamics. Integrating real-world variables in the model allowed us to examine the relative influence of environmental versus social landscape heterogeneity on moth propagation. We focused on two types of anthropogenic activities: (1) the presence and spatial distribution of traditional crop storage structures that modify local microclimate, and (2) long-distance dispersal (LDD) of moths by human-induced transportation. Data from participatory monitoring of pest invasion into the valley and from a larger-scale field survey on the Ecuadorian Andes allowed us to validate our model against actual presence/absence records. Our simulations revealed that high density and a clumped distribution of storage structures had a positive effect on moth invasion by modifying the temperature of the landscape, and that passive, LDD enhanced moth invasion. Model validation showed that including human influence produced more precise and realistic simulations. We provide a powerful and widely applicable methodological framework that stresses the crucial importance of integrating the social landscape to develop accurate invasion models of pest dynamics in complex, agricultural systems.

An agent-based modeling framework for integrated pest management dissemination programs

The study of how people acquire and diffuse information among heterogeneous populations has a rich history in the social sciences. However, few approaches have been developed to better understand how information diffusion patterns and processes affect resource management in complex socio-ecological systems. This is a timely issue for crop protection diffusion programs, which have a larger place than ever on the international policy agenda due to the growing number of challenges related to controlling agricultural pests. To assess the impact of heterogeneous farmer behaviors (receptivity toward IPM practices) and types of information diffusion (either active or passive) on the success of integrated pest management (IPM) programs, we developed a socio-ecological model coupling a pest model (population growth and dispersion) with a farmer behavioral model (pest control and diffusion of pest management practices). The main objective of the model was to provide insights to explore effective IPM information diffusion strategies at the farmer community level. Our simulations revealed 1) that passive IPM information diffusion among agents seemed to be more effective to control pests over the community of agents than active diffusion and 2) that increasing levels of agent heterogeneity would significantly slow down pest control dynamics at the community level, but to a lower extent in the case of passive IPM information diffusion. Our findings therefore suggest that IPM diffusion programs should focus their efforts in developing methods to create purposefully the conditions for social learning as a deliberate pest control mechanism, while taking into account potential limitations related to the commonly reported farmer heterogeneity. Our study further stresses the need to develop a comprehensive and empirically based framework for linking the social and ecological disciplines across space and time in agricultural system management. While we specifically focus on pest infestation levels and IPM information diffusion strategies in this study, our approach to understand information diffusion within heterogeneous human populations in interaction with environmental features would be applicable to a much wider range of both social and resource management issues.

Changes in the distribution of multispecies pest assemblages affect levels of crop damage in warming tropical Andes

Climate induced species range shifts might create novel interactions among species that may outweigh direct climatic effects. In an agricultural context, climate change might alter the intensity of competition or facilitation interactions among pests with, potentially, negative consequences on the levels of damage to crop. This could threaten the productivity of agricultural systems and have negative impacts on food security, but has yet been poorly considered in studies. In this contribution, we constructed and evaluated process-based species distribution models for three invasive potato pests in the Tropical Andean Region. These three species have been found to co-occur and interact within the same potato tuber, causing different levels of damage to crop. Our models allowed us to predict the current and future distribution of the species and therefore, to assess how damage to crop might change in the future due to novel interactions. In general, our study revealed the main challenges related to distribution modeling of invasive pests in highly heterogeneous regions. It yielded different results for the three species, both in terms of accuracy and distribution, with one species surviving best at lower altitudes and the other two performing better at higher altitudes. As to future distributions our results suggested that the three species will show different responses to climate change, with one of them expanding to higher altitudes, another contracting its range and the other shifting its distribution to higher altitudes. These changes will result in novel areas of co-occurrence and hence, interactions of the pests, which will cause different levels of damage to crop. Combining population dynamics and species distribution models that incorporate interspecific trade-off relationships in different environments revealed a powerful approach to provide predictions about the response of an assemblage of interacting species to future environmental changes and their impact on process rates.

SimAdapt: an individual-based genetic model for simulating landscape management impacts on populations

Summary 1. Simulation models are essential tools in landscape genetics to study how genetic processes are affected by landscape heterogeneity. However, there is still a need to develop different simulation approaches in landscape genetics, so that users may dispose of additional programs to explore further the impact of land-use and land-cover changes on population genetics. 2. We developed a spatially explicit, individual-based, forward-time, landscape-genetic simulation model combined with a landscape cellular automaton to represent evolutionary processes of adaptation and population dynamics in changing landscapes, using the NetLogo environment. 3. This simulation model represents a unique tool for scientists and scholars looking for a practical and pedagogical framework to explore both empirical and theoretical situations.

Development of a viral biopesticide for the control of the Guatemala potato tuber moth Tecia solanivora

The Guatemala potato tuber moth Tecia solanivora (Povolny) (Lep. Gelechiidae) is an invasive species from Mesoamerica that has considerably extended its distribution area in recent decades. While this species is considered to be a major potato pest in Venezuela, Colombia, and Ecuador, currently no specific control methods are available for farmers. To address this issue we developed a biopesticide formulation to be used in integrated pest management of T. solanivora, following three steps. First, search for entomopathogenic viruses were carried out through extensive bioprospections in 12 countries worldwide. As a result, new Phthorimaea operculella granulovirus (PhopGV) isolates were found in T. solanivora and five other gelechid species. Second, twenty PhopGV isolates, including both previously known and newly found isolates, were genetically and/or biologically characterized in order to choose the best candidate for a biopesticide formulation. Sequence data were obtained for the ecdysteroid UDP-glucosyltransferase (egt) gene, a single copy gene known to play a role in pathogenicity. Three different sizes (1086, 1305 and 1353 bp) of egt were found among the virus isolates analyzed. Unexpectedly, no obvious correlation between egt size and pathogenicity was found. Bioassays on T. solanivora neonates showed a maximum of a 14-fold difference in pathogenicity among the eight PhopGV isolates tested. The most pathogenic PhopGV isolate, JLZ9f, had a medium lethal concentration (LC(50)) of 10 viral occlusion bodies per square mm of consumed tuber skin. Third, we tested biopesticide dust formulations by mixing a dry carrier (calcium carbonate) with different adjuvants (magnesium chloride or an optical brightener or soya lecithin) and different specific amounts of JLZ9f. During laboratory experiments, satisfactory control of the pest (>98% larva mortality compared to untreated control) was achieved with a formulation containing 10 macerated JLZ9f-dead T. solanivora larvae per kg of calcium carbonate mixed with 50 mL/kg of soya lecithin. The final product provides an interesting alternative to chemical pesticides for Andean farmers affected by this potato pest.

Simulating Population Genetics of Pathogen Vectors in Changing Landscapes: Guidelines and Application with Triatoma brasiliensis

Background Understanding the mechanisms that influence the population dynamics and spatial genetic structure of the vectors of pathogens infecting humans is a central issue in tropical epidemiology. In view of the rapid changes in the features of landscape pathogen vectors live in, this issue requires new methods that consider both natural and human systems and their interactions. In this context, individual-based model (IBM) simulations represent powerful yet poorly developed approaches to explore the response of pathogen vectors in heterogeneous social-ecological systems, especially when field experiments cannot be performed. Methodology/Principal Findings We first present guidelines for the use of a spatially explicit IBM, to simulate population genetics of pathogen vectors in changing landscapes. We then applied our model with Triatoma brasiliensis, originally restricted to sylvatic habitats and now found in peridomestic and domestic habitats, posing as the most important Trypanosoma cruzi vector in Northeastern Brazil. We focused on the effects of vector migration rate, maximum dispersal distance and attraction by domestic habitat on T. brasiliensis population dynamics and spatial genetic structure. Optimized for T. brasiliensis using field data pairwise fixation index (FST) from microsatellite loci, our simulations confirmed the importance of these three variables to understand vector genetic structure at the landscape level. We then ran prospective scenarios accounting for land-use change (deforestation and urbanization), which revealed that human-induced land-use change favored higher genetic diversity among sampling points. Conclusions/Significance Our work shows that mechanistic models may be useful tools to link observed patterns with processes involved in the population genetics of tropical pathogen vectors in heterogeneous social-ecological landscapes. Our hope is that our study may provide a testable and applicable modeling framework to a broad community of epidemiologists for formulating scenarios of landscape change consequences on vector dynamics, with potential implications for their surveillance and control.

Genetic variation in aggregation behaviour and interacting phenotypes in Drosophila

Aggregation behaviour is the tendency for animals to group together, which may have important consequences on individual fitness. We used a combination of experimental and simulation approaches to study how genetic variation and social environment interact to influence aggregation dynamics in Drosophila. To do this, we used two different natural lines of Drosophila that arise from a polymorphism in the foraging gene (rovers and sitters). We placed groups of flies in a heated arena. Flies could freely move towards one of two small, cooler refuge areas. In groups of the same strain, sitters had a greater tendency to aggregate. The observed behavioural variation was based on only two parameters: the probability of entering a refuge and the likelihood of choosing a refuge based on the number of individuals present. We then directly addressed how different strains interact by mixing rovers and sitters within a group. Aggregation behaviour of each line was strongly affected by the presence of the other strain, without changing the decision rules used by each. Individuals obeying local rules shaped complex group dynamics via a constant feedback loop between the individual and the group. This study could help to identify the circumstances under which particular group compositions may improve individual fitness through underlying aggregation mechanisms under specific environmental conditions.

Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures

A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies.