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Item does not contain fulltext, We give a survey of applications of mixed Hodge theory to the study of isolated singularities. A summary of mixed Hodge theory is followed by some examples. The formalism of vanishing cycles is realized on the Hodge level by a sheaf complex with three filtrations, making the application to the cohomology of the Milnor fibre possible. The approaches by algebraic analysis and by motivic integration are discussed, and the spectrum with its properties is considered. The paper ends with a treatment of Du Bois singularities.

[EN] With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 di erent combinations of a global climate model (GCM)¿regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO3, NH4, PO4) in mid- (2041¿2070) and long-term (2071¿2100) periods with respect to the baseline (1983¿2012). BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between di erent GCM¿RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources.

[EN] Climate change is likely to strongly affect both the qualitative and quantitative characteristics of water resources. However, while potential impacts of climate change on water availability have been widely studied in the last decades, their implication for water quality have been just poorly explored since now. Accordingly, an integrated assessment based on Bayesian Networks (BNs) was implemented in the Zero river basin (Northern Italy) to capture interdependencies between future scenarios of climate change with water quality alterations (i.e. changes in nutrients loadings). Bayesian Networks were used as integrative tool for structuring and combining the information available in existing hydrological models, climate change projections, historical observations and expert opinion producing alternative risk scenarios to communicate the probability (and uncertainty) of changes in the amount nutrients (i.e. NO3-, NH4+, PO43-) delivered from the basin under different climate change projections (i.e. RCP 4.5 and 8.5) The model predictive accuracy and uncertainty were evaluated through a cross comparison with existing observed data and hydrological models' simulations (i.e. SWAT) available for the ease study and, in addition, sensitivity analysis was performed to identify key input variables, knowledge gaps in model structurers and data. Simulated scenarios show that seasonal changes in precipitation and temperature are likely to modify both the hydrology and nutrient loadings of the Zero river with a high probability of an increase of freshwater discharge, runoff and nutrient loadings in autumn and a decrease in spring and summer with respect to the current conditions 1983-2012. Greater increase for both river flow and nutrients loadings are predicted under the medium and long term RCP8.5 scenarios. Diffuse pollution sources play a key role in determining the amount of nutrients loaded: both NH4+ and PO43- loadings are mainly influenced by changes in hydrological v

One of the current challenges for applied ecologists is to understand how to manage/restore agroecosystems in a sustainable and cost-effective way. The intermediate landscape complexity hypothesis (ILCH) predicts that the effectiveness of agri-environmental measures (AES) on biodiversity and ecosystem services recovery is often largest in landscapes of intermediate complexity. This hypothesis has rarely been tested in savanna-like permanent agroecosystems. Focusing on pollinators, we test the ILCH at the regional scale in Mediterranean olive orchards, one of the most important permanent agroecosystems in the world. We inferred abundance of cavity-nesting pollinators in 40 paired olive orchards (extensively vs. intensively managed herbaceous cover) in 20 localities selected across a landscape complexity gradient. We also studied how different magnitudes in local management switches may affect pollinators by considering organic and intensive fields as management extremes in olive orchards. We used 208 trap nests for solitary bees to measure colonization rates. Additionally, we conducted pollinator surveys to ascertain that colonization rate was a representative proxy for pollinator activity. Our results showed that (a) changes in colonization rates due to local herb cover management peaked at intermediate landscape complexity, with extensively managed fields rendering higher colonization rates. (b) Organic fields had higher colonization rates than their control farms regardless of landscape complexity. (c) There was a highly significant correlation between nest colonization rates and density of pollinators foraging on flowers, which suggests that colonization rate is a good estimator of pollinator activity. Policy implications. The maintenance of ground herb cover (main agri-environmental measure in olive orchards) is a cost-effective investment allowing recuperation of pollinators when targeting olive farms located in landscapes of intermediate complexity. Additional

23 september 2016, Item does not contain fulltext