SignificanceDecades of research have fostered the now-prevalent assumption that noncrop habitat facilitates better pest suppression by providing shelter and food resources to the predators and parasitoids of crop pests. Based on our analysis of the largest pest-control database of its kind, noncrop habitat surrounding farm fields does affect multiple dimensions of pest control, but the actual responses of pests and enemies are highly variable across geographies and cropping systems. Because noncrop habitat often does not enhance biological control, more information about local farming contexts is needed before habitat conservation can be recommended as a viable pest-suppression strategy. Consequently, when pest control does not benefit from noncrop vegetation, farms will need to be carefully comanaged for competing conservation and production objectives.
Managing agricultural landscapes to support biodiversity and ecosystem services is a key aim of a sustainable agriculture. However, how the spatial arrangement of crop fields and other habitats in landscapes impacts arthropods and their functions is poorly known. Synthesising data from 49 studies (1515 landscapes) across Europe, we examined effects of landscape composition (% habitats) and configuration (edge density) on arthropods in fields and their margins, pest control, pollination and yields. Configuration effects interacted with the proportions of crop and non-crop habitats, and species' dietary, dispersal and overwintering traits led to contrasting responses to landscape variables. Overall, however, in landscapes with high edge density, 70% of pollinator and 44% of natural enemy species reached highest abundances and pollination and pest control improved 1.7-and 1.4-fold respectively. Arable-dominated landscapes with high edge densities achieved high yields. This suggests that enhancing edge density in European agroecosystems can promote functional biodiversity and yield-enhancing ecosystem services.
Arthropods and pathogens damage leaves in natural ecosystems and may reduce photosynthesis at some distance away from directly injured tissue. We quantified the indirect effects of naturally occurring biotic damage on leaf-level photosystem II operating efficiency (Phi(PSII)) of 11 understory hardwood tree species using chlorophyll fluorescence and thermal imaging. Maps of fluorescence parameters and leaf temperature were stacked for each leaf and analyzed using a multivariate method adapted from the field of quantitative remote sensing. Two tree species, Quercus velutina and Cercis canadensis, grew in plots exposed to ambient and elevated atmospheric CO(2) and were infected with Phyllosticta fungus, providing a limited opportunity to examine the potential interaction of this element of global change and biotic damage on photosynthesis. Areas surrounding damage had depressed Phi(PSII )and increased down-regulation of PSII, and there was no evidence of compensation in the remaining tissue. The depression of Phi(PSII) caused by fungal infections and galls extended >2.5 times further from the visible damage and was approximately 40% more depressed than chewing damage. Areas of depressed Phi(PSII) around fungal infections on oaks growing in elevated CO(2) were more than 5 times larger than those grown in ambient conditions, suggesting that this element of global change may influence the indirect effects of biotic damage on photosynthesis. For a single Q. velutina sapling, the area of reduced Phi(PSII) was equal to the total area directly damaged by insects and fungi. Thus, estimates based only on the direct effect of biotic agents may greatly underestimate their actual impact on photosynthesis.
Accurately computing solar irradiance on external facades is a prerequisite for reliably predicting thermal behavior and cooling loads of buildings. Validation of radiation models and algorithms implemented in building energy simulation codes is an essential endeavor for evaluating solar gain models. Seven solar radiation models implemented in four building energy simulation codes were investigated: (1) isotropic sky, (2) Klucher, (3) Hay-Davies, (4) Reindl, (5) Muneer, (6) 1987 Perez, and (7) 1990 Perez models. The building energy simulation codes included: EnergyPlus, DOE-2.1E, TRNSYS-TUD, and ESP-r. Solar radiation data from two 25 days periods in October and March/April, which included diverse atmospheric conditions and solar altitudes, measured on the EMPA campus in a suburban area in Duebendorf, Switzerland, were used for validation purposes. Two of the three measured components of solar irradiances - global horizontal, diffuse horizontal and direct-normal - were used as inputs for calculating global irradiance on a south-west façade. Numerous statistical parameters were employed to analyze hourly measured and predicted global vertical irradiances. Mean absolute differences for both periods were found to be: (1) 13.7% and 14.9% for the isotropic sky model, (2) 9.1% for the Hay-Davies model, (3) 9.4% for the Reindl model, (4) 7.6% for the Muneer model, (5) 13.2% for the Klucher model, (6) 9.0%, 7.7%, 6.6%, and 7.1% for the 1990 Perez models, and (7) 7.9% for the 1987 Perez model. Detailed sensitivity analyses using Monte Carlo and fitted effects for N-way factorial analyses were applied to assess how uncertainties in input parameters propagated through one of the building energy simulation codes and impacted the output parameter. The implications of deviations in computed solar irradiances on predicted thermal behavior and cooling load of buildings are discussed
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