Changes in agroecosystem management (e.g. landscape diversity, management intensity) affect the natural control of pests. The effects of agricultural change on this ecosystem service, however, are not universal and the mechanisms affecting it remain to be understood. As biological control is effectively the product of networks of interactions between pests and their natural enemies, food web analysis provides a versatile tool to address this gap of knowledge. The proposed project will utilize a molecular food web approach and examine, for the first time, how changes in plant fertilisation and landscape complexity affect quantitative aphid-parasitoid-hyperparasitoid food webs on a species-specific level to unravel how changes in food web interactions affect parasitoid aphid control. Based on the fieldderived data, cage experiments will be conducted to assess how parasitoid diversity and identity affect parasitoid interactions and pest control, complementing the field results. The work proposed here will take research on parasitoid aphid control one step further, as it will provide a clearer understanding of how plant fertilization affects whole aphid-parasitoid food webs in both simple and complex landscapes, allowing for further improvements in natural pest control.
Almond in California represents an agroecosystem pollinated solely by a single species, the European honey bee, a species that is becoming increasingly difficult and expensive to manage due to substantial, unpredictable mortality. Therefore, sustainable and high output production require a more integrated approach that diversifies sources of pollination. For this purpose, detailed data of our understanding how diversity can stabilize pollination are required. The project will identify alternative wild pollinator species and collect high quality data contributing to our understanding of how diversity (pollen and insects) can bolster honey bee pollination during stable and unstable climatic conditions. The research will be carried out on almond orchards in Northern California known to be either pollinator species rich (up to 30 species) or depauperate (honey bees only). The replicated extremes in pollinator diversity represent a unique opportunity to study the effects of diversity on pollination in real agroecosystems combined with laboratory and glasshouse experiments. The overall goal is to provide basic research that is essential for our general understanding of how insect diversity can affect high-quality pollination under land use and climate change.
To understand impacts of climate and land use changes on biodiversity and accompanying ecosystem stability and services at the Mt. Kilimanjaro, detailed understanding and description of the current biotic and abiotic controls on ecosystem C and nutrient fluxes are needed. Therefore, cycles of main nutrients and typomorph elements (C, N, P, K, Ca, Mg, S, Si) will be quantitatively described on pedon and stand level scale depending on climate (altitude gradient) and land use (natural vs. agricultural ecosystems). Total and available pools of the elements will be quantified in litter and soils for 6 dominant (agro)ecosystems and related to soil greenhouse gas emissions (CO2, N2O, CH4). 13C and 15N tracers will be used at small plots for exact quantification of C and N fluxes by decomposition of plant residues (SP7), mineralization, nitrification, denitrification and incorporation into soil organic matter pools with various stability. 13C compound-specific isotope analyses in microbial biomarkers (13C-PLFA) will evaluate the changes of key biota as dependent on climate and land use. Greenhouse gas (GHG) emissions and leaching losses of nutrients from the (agro)ecosystems and the increase of the losses by conversion of natural ecosystems to agriculture will be evaluated and linked with changing vegetation diversity (SP4), vegetation biomass (SP2), decomposers community (SP7) and plant functional traits (SP5). Nutrient pools, turnover and fluxes will be linked with water cycle (SP2), CO2 and H2O vegetation exchange (SP2) allowing to describe ecosystem specific nutrient and water characteristics including the derivation of full GHG balances. Based on 60 plots screening stand level scale biogeochemical models will be tested, adapted and applied for simulation of key ecosystem processes along climate (SP1) and land use gradients.