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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.

Winter losses in honey bee colonies, Apis mellifera, raise concerns both for their critical role in crop pollination and for the sustainability of beekeeping. Beekeepers struggle to limit losses due to a lack of indicators to measure colony health and predict mortality in winter under field conditions. Due to the critical role of social thermoregulation for winter colony survival and brood production, this study aims to assess whether monitoring nest temperature during the winter may provide indicators of the state of health and survival of honey bee colonies. Under field conditions, we monitored the nest temperature of 31 hives distributed along a thermal gradient spanning over different European climates (including France, Germany, and Greece) over the winter 2022–2023. We installed 1,083 temperature sensors at multiple points inside the hives, with an average of 35 ± 1.1 sensors per hive (mean ± SD). We collected a total of 26,322,085 temperature data measurements for which we computed the time series of minimum, median, maximum temperature, and thermal amplitude. We found that the thermal amplitude within the nest is an indicator of colony health, effectively distinguishing between surviving and dying colonies with an accuracy of 96.8 %. Moreover, we found that nest temperature enables the detection of collapsing phases with an accuracy of 83.9 % and up to one month before the death of the colony. Finally, we showed that monitoring nest temperature in winter can help detect brood presence and size, an important health indicator for beekeeping. We suggest that monitoring nest temperature could represent a relevant field indicator of honey bee colony health for understanding the success or failure of overwintering. This indicator could help beekeepers anticipate colony mortalities to limit the winter colony losses observed worldwide.

High winter mortality of honey bees (Apis mellifera) has been observed in temperate regions over the past 30 years. Several biotic and abiotic stressors associated with winter colony losses have been identified, but the mechanisms and interactions underlying their effects remain unclear. We reviewed the effects of stressors on key overwintering biological traits, distinguishing between individual and colony traits. We found that disturbances at the level of individual traits can be amplified when transmitted to colony traits. By analyzing these cascading effects, we propose a concept of a feedback loop mechanism of winter mortality. We found that population size, social thermoregulation and honey reserve are integrative traits and can predict overwintering failure. Furthermore, we identified social thermoregulation as a good candidate for an early warning indicator. We therefore discuss existing tools for monitoring hive temperature to help mitigate the current high winter mortality of honey bees and support the sustainability of beekeeping.

Abstract Microclimatic and macroclimatic data differ, and microclimatic data are most useful at short time intervals (30‐min scales rather than daily/monthly scales). We developed an end‐to‐end system to acquire such data at a high temporal resolution, transfer them to a server through an Internet of Things network using low‐cost technologies, and make them available with dynamic graphical visualizations and download capabilities. The system has been used for 2 years to monitor environmental variables in contrasting agroecosystems in France, Bolivia and Kenya. It has been proven to be reliable and supports the mismatch between macro‐ and microclimates. This low‐cost Internet of Things system can capture the microclimate in contrasting environments with accuracy comparable to commercial solutions, and great flexibility in data processing. It thus constitutes a possible solution in an academic context and has the potential to be used by a broad audience of scientists interested in capturing environmental variables in real time and at a high temporal frequency.

While many insects are in decline due to global warming, the effect of rising temperatures on crop insect pests is uncertain. A capacity to understand future changes in crop pest populations remains critical to ensure food security. Using temperature-dependent mathematical models of the development of four maize stemborers in temperate and tropical regions, we evaluated the potential impacts of different climate change scenarios on development time. While recognizing the limitations of the temperature-dependent development rate approach, we found that global warming could either be beneficial or detrimental to pest development, depending on the optimal temperature for the development of the species and scenarios of climate change. Expected responses range from null development to 1.5 times faster development than expected today. These results suggest that in the medium term, the studied species could benefit from global warming with an accelerated development, while in the long term, their development could either be delayed or accelerated, which may impact their dynamics with implications on maize cultivation.

Monitoring accurately temperature is a key issue in biological studies involving living experimental material. It is especially true for insects which body temperature is mostly controlled by environmental temperature, with profound consequences of a few degrees variation on most physiological processes such as survival, development, fecundity, and mobility. If programmable rearing units can be purchased, it remains important to monitor and store temperature information acquired inside the rearing unit to ensure that observed phenomena are not the result of unintended and not scarily noticeable changes in temperature, and to account for the effect of temperature variation in statistical analysis. As most laboratories involved in insect rearing dispose of a large number of rearing units, the technical solution should meet the monitoring needs while being affordable and adaptable to various experimental designs. For that purpose, we designed a low cost (below 100€) and open source automatic temperature monitoring system for rearing units in laboratory. Key features providing advantage over pre-existing methods include: • Highly configurable temperature monitoring and life-time storage capacity • Email alerts based on configurable user-defined threshold • Automatic configurable reports in the form of dashboards Method name: Automatic system for monitoring temperature in laboratory insect rearing units, Keywords: Raspberry Pi, Temperature, Sensor, Insects

Abstract Phenological variability among populations is widespread in nature. A few predictive phenological models integrate intrapopulational variability, but none has ever explored the individual strategies potentially occurring within a population. The “pace-of-life” syndrome accounts for such individual strategies, but has yet to be explored under a phenological context. Here we integrated, for the first time, the slow-fast thermal strategies stemming from the “pace-of-life” into a mechanistic predictive framework. We obtained 4619 phenological observations of an important crop pest in the Bolivian Andes by individually following 840 individuals under five rearing temperatures and across nine life stages. The model calibrated with the observed individual “pace-of-life” strategies showed a higher accuracy in phenological predictions than when accounting for intrapopulational variability alone. We further explored our framework with generated data and suggest that ectotherm species with a high number of life stages and with slow and/or fast individuals should exhibit a greater variance of populational phenology, resulting in a potentially longer time window of interaction with other species. We believe that the “pace-of-life” framework is a promising approach to improve phenological prediction across a wide array of species.

The interactions among insect communities influence the composition of pest complexes that attack crops and, in parallel, their natural enemies, which regulate their abundance. The lepidopteran stemborers have been the major maize pests in Kenya. Their population has been regulated by natural enemies, mostly parasitoids, some of which have been used for biological control. It is not known how a new exotic invasive species, such as the fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera, Noctuidae), may affect the abundance and parasitism of the resident stemborers. For this reason, pest and parasitism surveys have been conducted, before and after the FAW invaded Kenya, in maize fields in 40 localities across 6 agroecological zones (AEZs) during the maize-growing season, as well as at 3 different plant growth stages (pre-tasseling, reproductive, and senescence stages) in 2 elevations at mid-altitude, where all maize stemborer species used to occur together. Results indicated that the introduction of the FAW significantly correlated with the reduction of the abundance of the resident communities of maize stemborers and parasitoids in maize fields; moreover, the decrease of stemborer density after the arrival of FAW occurred mostly at both reproductive and senescent maize stages. It also suggests a possible displacement of stemborers by FAW elsewhere; for example, to other cereals. However, since this study was conducted only three years after the introduction of the FAW, further studies will need to be conducted to confirm such displacements.

Background Most carabid beetles are particularly sensitive to local habitat characteristics. Although in China grasslands account for more than 40% of the national land, their biodiversity is still poorly known. The aim of this paper is to identify the main environmental characteristics influencing carabid diversity in different types of grassland in northern China. Methods We investigated the influence of vegetation (plant biomass, cover, density, height and species richness), soil (bulk density, above ground litter, moisture and temperature) and climate (humidity, precipitation and temperature) on carabid community structure (species richness, species composition and functional diversity—measured as body size, movement and total diversity) in three types of grasslands: desert, typical and meadow steppes. We used Canonical correspondence analysis to investigate the role of habitat characteristics on species composition and eigenvector spatial filtering to investigate the responses of species richness and functional diversities. Results We found that carabid community structure was strongly influenced by local habitat characteristics and particularly by climatic factors. Carabids in the desert steppe showed the lowest richness and functional diversities. Climate predictors (temperature, precipitation and humidity) had positive effects on carabid species richness at both regional and ecosystem levels, with difference among ecosystems. Plant diversity had a positive influence on carabid richness at the regional level. Soil compaction and temperature were negatively related to species richness at regional level. Climatic factors positively influenced functional diversities, whereas soil temperature had negative effects. Soil moisture and temperature were the most important drivers of species composition at regional level, whereas the relative importance of the various environmental parameters varied among ecosystems. Discussion Carabid responses to environmental characteristics varied among grassland types, which warns against generalizations and indicates that management programs should be considered at grassland scale. Carabid community structure is strongly influenced by climatic factors, and can therefore be particularly sensitive to ongoing climate change.

Carabid communities are influenced by landscape features. Chinese steppes are subject to increasing desertification processes that are changing land-cover characteristics with negative impacts on insect communities. Despite those warnings, how land-cover characteristics influence carabid communities in steppe ecosystems remains unknown. The aim of this study is to investigate how landscape characteristics drive carabid abundance in different steppes (desert, typical, and meadow steppes) at different spatial scales. Carabid abundances were estimated using pitfall traps. Various landscape indices were derived from Landsat 8 Operational Land Imager (OLI) images. Indices expressing moisture and productivity were, in general, those with the highest correlations. Different indices capture landscape aspects that influence carabid abundance at different scales, in which the patchiness of desert vegetation plays a major role. Carabid abundance correlations with landscape characteristics rely on the type of grassland, on the vegetation index, and on the scale considered. Proper scales and indices are steppe type-specific, highlighting the need of considering various scales and indices to explain species abundances from remotely sensed data.

Intra- and interspecific interactions within communities of species that utilize the same resources are characterized by competition or facilitation. The noctuid stemborers, Busseola fusca and Sesamia calamistis, and the crambid stemborer, Chilo partellus were the most important pests of maize in sub-Saharan Africa before the recent “invasion” of fall armyworm (FAW), Spodoptera frugiperda, which currently seriously limits maize yields in Africa. This new pest is interacting with the stemborer community at the larval stage in the use of maize resources. From previous works on the influence of temperature on the larval intra- and interspecific resources utilization within the community of Lepidoptera stemborers involving B. fusca, S. calamistis, and C. partellus, there is a need to update these studies by adding the new pest, S. frugiperda, in order to understand the effect of temperature on the larval interactions of all these four species under the context of climate change. The influence of temperature on intra- and interspecific larval interactions was studied using artificial stems kept at different constant temperatures (15 °C, 20 °C, 25 °C, and 30 °C) in an incubator and assessing survival and relative growth rates of each species in single and multi-species experiments. After the inclusion of FAW into the experiments, with regard to relative growth rates, both intra- and interspecific competition was observed among all four species. With regard to survival rates, cannibalism can also explain the intra- and interspecific interactions observed among all four species. Interspecific competition was stronger between the stemborers than between the FAW and the stemborers. Similar to lepidopteran stemborers, temperature affected both survival and relative growth rates of the FAW as well. Regardless of the temperature, C. partellus was superior in interspecific interactions shown by higher relative growth and survival rates. The results suggest that the FAW will co-exist with stemborer species along entire temperature gradient, though competition and/or cannibalism with them is weak. In addition, temperature increases caused by climate change is likely to confer an advantage to C. partellus over the fall armyworm and the other noctuids.

Sources of infestation are the key elements to be considered in the development of habitat management techniques for the control of maize stemborers. Several wild plants, grasses mostly, have been identified that serve as hosts for stemborers and their parasitoids during the off-season when maize is not present in the field. However, their abundance is much lower in wild plants compared to cultivated fields. Thus, the role of wild plants as a reservoir for cereal stemborers and their parasitoids is still controversial, particularly in agro-ecosystems with reduced wild habitat. We studied the occurrence of different maize stemborers and associated parasitoids in maize stem residues and wild grasses during non-cropping seasons as potential carry-over populations to subsequent early season maize plants. Surveys were conducted in the central region of Kenya during long and short dry seasons in maize residues and wild grasses as well as during the two rainy seasons in maize plants at earlier and late whorl stages during the years of 2017 and 2018. Wild habitat had a higher species diversity than maize residues habitat, but maize residues had a higher abundance of maize stemborer species, such as Busseola fusca, Sesamia calamistis, and Chilo partellus, and of associated parasitoid species (i.e., Cotesia flavipes and Cotesia sesamiae) than wild plants. Our surveys, complemented by field parasitoid releases of C. flavipes and C. sesamiae, indicated that maize residues constitute a better refugia reservoir not only of the maize stemborers but also of C. flavipes and C. sesamiae during non-cropping seasons as compared to wild plants and, thus, might constitute in this region the main source of both stemborers and C. flavipes/C. sesamiae carry-over in maize plants during the subsequent cropping season. Thus, systematic destruction of maize residues would not help the biological control of lepidopteran stemborers. This is particularly true in areas with reduced wild habitat.

Grazing areas management is of utmost importance in the Andean region. In the valleys of the Bolivian Cordillera Real near La Paz, pastoralism constitutes the traditional way for people to insure food security and economical sustainability. In these harsh mountains, unique and productive wetlands sustained by glacial water streams are of utmost importance for feeding cattle herds during the dry season. After the colonization by the Spanish, a shift in livestock species has been observed, with the introduction of exotic species such as cows and sheep, resulting in a different impact on pastures compared to native camelid species-llamas and alpacas. Here we explored some of the social-economical and environmental drivers that motivate Bolivian pastoralists to prefer exotic over native livestock species, based on 36 household surveys in the Cordillera Real. We constructed a Partial Least Squares Structural Equation Model in order to assess the relationships between these drivers. Our results suggest that the access to market influenced pastoralists to reshape their herd composition, by increasing the number of sheep. They also suggest that community size increased daily grazing time in pastures, therefore intensifying the grazing pressure. At a broader scale, this study highlights the effects of some social-economical and environmental drivers on mountain herding systems.

In Ecuador, the debate on the future of family farming is still very important because of the high vulnerability of peasant communities, particularly in the Andean region, and of the worrying food situation in rural areas. In this context, the fight against crop pests, identified by the FAO as a priority to reduce food insecurity of farmers in the South, appears to be a major challenge for farming communities of the Ecuadorian sierra. However, the success of this struggle still remains limited because of the weak coordination of the farmers. Based on fieldwork conducted in the province of Cotopaxi, this paper points out the determinants of the lack of collective action against crop pests as it opens a discussion on the need of a greater collaboration between environmental and social sciences in order to consider the most effective strategies to strengthen the farmers coordination in the fight against crop pests and to increase their food security.

Worldwide, the theory and practice of agricultural extension system have been dominated for almost half a century by Rogers' "diffusion of innovation theory". In particular, the success of integrated pest management (IPM) extension programs depends on the effectiveness of IPM information diffusion from trained farmers to other farmers, an important assumption which underpins funding from development organizations. Here we developed an innovative approach through an agent-based model (ABM) combining social (diffusion theory) and biological (pest population dynamics) models to study the role of cooperation among small-scale farmers to share IPM information for controlling an invasive pest. The model was implemented with field data, including learning processes and control efficiency, from large scale surveys in the Ecuadorian Andes. Our results predict that although cooperation had short-term costs for individual farmers, it paid in the long run as it decreased pest infestation at the community scale. However, the slow learning process placed restrictions on the knowledge that could be generated within farmer communities over time, giving rise to natural lags in IPM diffusion and applications. We further showed that if individuals learn from others about the benefits of early prevention of new pests, then educational effort may have a sustainable long-run impact. Consistent with models of information diffusion theory, our results demonstrate how an integrated approach combining ecological and social systems would help better predict the success of IPM programs. This approach has potential beyond pest management as it could be applied to any resource management program seeking to spread innovations across populations.

Climate changes are occurring rapidly at both regional and global scales. Farmers are faced with the challenge of developing new agricultural practices to help them to cope with unpredictable changes in environmental, social, and economic conditions. Under these conditions, adaptive management requires a farmer to learn by monitoring provisional strategies and changing conditions, and then incrementally adjust management practices in light of new information. Exploring adaptive management will increase our understanding of the underlying processes that link farmer societies with their environment across space and time, while accounting for the impacts of an unpredictable climate. Here, we assessed the impacts of temperature and crop price, as surrogates for climate and economic changes, on farmers' adaptive management in crop pest control using an agent-based modeling approach. Our model simulated an artificial society of farmers that relied on field data obtained in the Ecuadorian Andes. Farmers were represented as heterogeneous autonomous agents who interact with and influence each other, and who are capable of adapting to changing environmental conditions. The results of our simulation suggest that variable temperatures led to less effective pest control strategies than those used under stable temperatures. Moreover, farmers used information gained through their own past experience or through interactions with other farmers to initiate an adaptive management approach. At a broader scale, this study generates more than an increased understanding of adaptive management; it highlights how people depend on one another to manage common problems.