A global synthesis reveals biodiversity-mediated benefits for crop production

Human land use threatens global biodiversity and compromises multiple ecosystem functions critical to food production. Whether crop yield-related ecosystem services can be maintained by a few dominant species or rely on high richness remains unclear. Using a global database from 89 studies (with 1475 locations), we partition the relative importance of species richness, abundance, and dominance for pollination; biological pest control; and final yields in the context of ongoing land-use change. Pollinator and enemy richness directly supported ecosystem services in addition to and independent of abundance and dominance. Up to 50% of the negative effects of landscape simplification on ecosystem services was due to richness losses of service-providing organisms, with negative consequences for crop yields. Maintaining the biodiversity of ecosystem service providers is therefore vital to sustain the flow of key agroecosystem benefits to society

A global synthesis reveals biodiversity-mediated benefits for crop production

Human land use threatens global biodiversity and compromises multiple ecosystem functions critical to food production. Whether crop yield-related ecosystem services can be maintained by a few dominant species or rely on high richness remains unclear. Using a global database from 89 studies (with 1475 locations), we partition the relative importance of species richness, abundance, and dominance for pollination; biological pest control; and final yields in the context of ongoing land-use change. Pollinator and enemy richness directly supported ecosystem services in addition to and independent of abundance and dominance. Up to 50% of the negative effects of landscape simplification on ecosystem services was due to richness losses of service-providing organisms, with negative consequences for crop yields. Maintaining the biodiversity of ecosystem service providers is therefore vital to sustain the flow of key agroecosystem benefits to society. [Abstract copyright: Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

The idea that noncrop habitat enhances pest control and represents a win–win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win–win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies

Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

The idea that noncrop habitat enhances pest control and represents a win–win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win–win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Hardware-Assisted Cross-Generation Prediction of GPUs under Design

This paper introduces a predictive modeling framework for GPU performance. The key innovation underlying this approach is that performance statistics collected from representative workloads running on current generation GPUs can effectively predict the performance of next-generation GPUs. This is useful when simulators are available for the next-generation device, but simulation times are exorbitant, rendering early design space exploration of microarchitectural parameters and other features infeasible. When predicting performance across three Intel GPU generations (Haswell, Broadwell, Skylake), our models achieved impressively low out-of-sample-errors ranging from 7.45% to 8.91%, while running 29 481 to 44 214 times faster than cycle-accurate simulations. A detailed ranking of the most impactful features selected for these models provides an insight as to which microarchitectural subsystems have the greatest impact on performance from one generation to the next

HALWPE: Hardware-Assisted Light Weight Performance Estimation for GPUs

This paper presents a predictive modeling framework for GPU performance. The key innovation underlying this approach is that performance statistics collected from representative workloads running on current generation GPUs can effectively predict the performance of next-generation GPUs. This is useful when simulators are available for the next-generation device, but simulation times are exorbitant, rendering early design space exploration of microarchitectural parameters and other features infeasible. When predicting performance across three Intel GPU generations (Haswell, Broadwell, Skylake), our models achieved low out-of-sample-errors ranging from 7.45% to 8.91%, while running 30,000-45,000 times faster than cycle-Accurate simulation

Hardware-Assisted Cross-Generation Prediction of GPUs under Design

This paper introduces a predictive modeling framework for GPU performance. The key innovation underlying this approach is that performance statistics collected from representative workloads running on current generation GPUs can effectively predict the performance of next-generation GPUs. This is useful when simulators are available for the next-generation device, but simulation times are exorbitant, rendering early design space exploration of microarchitectural parameters and other features infeasible. When predicting performance across three Intel GPU generations (Haswell, Broadwell, Skylake), our models achieved impressively low out-of-sample-errors ranging from 7.45% to 8.91%, while running 29 481 to 44 214 times faster than cycle-accurate simulations. A detailed ranking of the most impactful features selected for these models provides an insight as to which microarchitectural subsystems have the greatest impact on performance from one generation to the next