scholarly journals Large-scale trade-off between agricultural intensification and crop pollination services

2014 ◽  
Vol 12 (4) ◽  
pp. 212-217 ◽  
Author(s):  
Nicolas Deguines ◽  
Clémentine Jono ◽  
Mathilde Baude ◽  
Mickaël Henry ◽  
Romain Julliard ◽  
...  
Keyword(s):  
Agricultural Intensification ◽  
Large Scale ◽  
Crop Pollination ◽  
Pollination Services ◽  
Trade Off

scholarly journals On the inconsistency of pollinator species traits for predicting either response to agricultural intensification or functional contribution

2016 ◽  
Author(s):  
Ignasi Bartomeus ◽  
Daniel P. Cariveau ◽  
Tina Harrison ◽  
Rachael Winfree
Keyword(s):  
Ecosystem Function ◽  
Agricultural Intensification ◽  
Biodiversity Loss ◽  
Species Traits ◽  
Crop Pollination ◽  
Wild Bees ◽  
Ecological Processes ◽  
Pollination Services ◽  
Species Response ◽  
Pollination Systems

AbstractThe response and effect trait framework, if supported empirically, would provide for powerful and general predictions about how biodiversity loss will lead to loss in ecosystem function. This framework proposes that species traits will explain how different species respond to disturbance (i.e. response traits) as well as their contribution to ecosystem function (i.e. effect traits). However, predictive response and effect traits remain elusive for most systems. Here, we present detailed data on crop pollination services provided by native, wild bees to explore the role of six commonly used species traits in determining how crop pollination is affected by increasing agricultural intensification. Analyses were conducted in parallel for three crop systems (watermelon, cranberry, and blueberry) located within the same geographical region (mid-Atlantic USA). Bee species traits did not strongly predict species’ response to agricultural intensification, and the few traits that were weakly predictive were not consistent across crops. Similarly, no trait predicted species’ overall functional contribution in any of the three crop systems, although body size was a good predictor of per capita efficiency in two systems. So far, most studies looking for response or effect traits in pollination systems have found weak and often contradicting links. Overall we were unable to make generalizable predictions regarding species responses to land-use change and its effect on the delivery of ecosystem services. Pollinator traits may be useful for understanding ecological processes in some systems, but thus far the promise of traits-based ecology has yet to be fulfilled for pollination ecology.

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

scholarly journals Landscape effects on crop pollination services: are there general patterns?

2008 ◽  
Vol 11 (5) ◽  
pp. 499-515 ◽  
Author(s):  
Taylor H. Ricketts ◽  
James Regetz ◽  
Ingolf Steffan-Dewenter ◽  
Saul A. Cunningham ◽  
Claire Kremen ◽  
...  
Keyword(s):  
Crop Pollination ◽  
Pollination Services ◽  
Landscape Effects
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