Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – http://www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.

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While the potential of agroforestry products to contribute to rural livelihoods is well-recognized, the quantification of their yields, incomes, and value for domestic consumption (VDC) and knowledge about their relationships with biodiversity are still scarce. This information is crucial for choosing the best strategy for growing cocoa in tropical landscapes while conserving biodiversity and enhancing ecosystem services. We analyzed the contribution of cocoa agroforestry farming to the incomes and domestic consumption of small farmers¿ families in 179 cocoa agroforestry systems (CAFS) (254 ha) in five Central American countries. The two hypotheses were: (1) agroforestry products are as important as cocoa in contributing to livelihoods, (2) the typology of CAFS determines the relationships between socioeconomic indicators and yield, biodiversity, and structure of the shade canopy, as well as the relationships between plant species richness and cocoa yield. We quantified the yields of agroforestry products and their contribution to net income, cash flow, and family benefits and developed a typology of CAFS production to evaluate relationships for each CAFS cluster. The main agroforestry products other than cocoa were bananas, oranges, peach palm, other fruits, and timber, which generated modest cash incomes but high VDC at low cash costs, thus contributing to family savings and food security. Timber volumes and harvest rates were low but significant increase was deemed feasible. The contribution of the set of agroforestry products to family benefits was similar or higher than cocoa, depending on the typology of the CAFS. Intensified highly diverse-dense CAFS demonstrated remarkably higher yields, net income, cash flow, and family benefits, and had more synergetic relationships than extensive CAFS and traditional highly diverse-dense CAFS, which showed more trade-offs. Our findings point to intensified highly diverse-dense CAFS as feasible for farming within a land-sparing strategy. Further research is needed to better understand the mechanisms that could regulate synergies or trade-offs to improve this type of intensification. (Résumé d'auteur

The assessment of crop yield losses is needed for the improvement of production systems that contribute to the incomes of rural families and food security worldwide. However, efforts to quantify yield losses and identify their causes are still limited, especially for perennial crops. Our objectives were to quantify primary yield losses (incurred in the current year of production) and secondary yield losses (resulting from negative impacts of the previous year) of coffee due to pests and diseases, and to identify the most important predictors of coffee yields and yield losses. We established an experimental coffee parcel with full-sun exposure that consisted of six treatments, which were defined as different sequences of pesticide applications. The trial lasted three years (2013–2015) and yield components, dead productive branches, and foliar pests and diseases were assessed as predictors of yield. First, we calculated yield losses by comparing actual yields of specific treatments with the estimated attainable yield obtained in plots which always had chemical protection. Second, we used structural equation modeling to identify the most important predictors. Results showed that pests and diseases led to high primary yield losses (26%) and even higher secondary yield losses (38%). We identified the fruiting nodes and the dead productive branches as the most important and useful predictors of yields and yield losses. These predictors could be added in existing mechanistic models of coffee, or can be used to develop new linear mixed models to estimate yield losses. Estimated yield losses can then be related to production factors to identify corrective actions that farmers can implement to reduce losses. The experimental and modeling approaches of this study could also be applied in other perennial crops to assess yield losses.