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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.

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.