Climate Change and Insects

Climate change (CC) poses one of the foremost challenges for humanity in the 21st century [...].

Life history parameters of Bemisia tabaci MED (Hemiptera: Aleyrodidae) in the present and future climate of central Europe, predicted by physically realistic climatic chamber simulation

Whiteflies of the Bemisia tabaci species complex are among the most damaging insect pests in agriculture worldwide, causing damage by feeding on crop plants and by vectoring plant viruses. The species complex consists of over 35 cryptic species that differ in many aspects of their biology including the optimal environment, geographic distribution, and host range. Global warming and associated climate change resulting from human activities is expected to contribute to biological invasions. Bemisia tabaci species show fast adaptability to changes in agroecosystems and have a long record of biological invasions. Climate change driven increase in B. tabaci importance in agricultural systems of Europe has been predicted, but so far not experimentally tested. The present study evaluates the development of B. tabaci MED (=Mediterranean) in a climatic chamber simulation of the future climate in Luxembourg, chosen as a representative region for the Central Europe. Future climate predictions for the period 2061-2070 were derived from a multimodel ensemble of physically consistent regional climatic models. Results show a 40% shorter development time of this important pest in future climatic conditions, with an increase in fecundity by a third, and insignificant difference in mortality. Accelerated development, combined with its already established year-round presence in European greenhouses and predicted northward expansion of outdoor tomato production in Europe, means faster population build-up at the beginning of the outdoor cropping season with the potential of reaching economic importance. Benefits of simulating hourly diurnal cycle of physically consistent meteorological variables versus previous experiments are discussed.

Re-Visiting the Incidence of Environmental Factors on a Pre-Imaginal Population of the Red Gum Lerp Psyllid, Glycaspis brimblecombei Moore

The red gum lerp psyllid, Glycaspis brimblecombei Moore (Hemiptera: Aphalaridae), is an invasive pest of Eucalyptus trees worldwide, responsible for serious damage, including the death of plants. Knowledge about the incidence of climatic factors on the insect development are essential to define useful strategies for controlling this pest. To this aim, G. brimblecombei has been sampled by two different methods from April 2012 to February 2013 in eastern Sicily on Eucalyptus camaldulensis in nine different sites, where the main climatic data (air temperature, relative humidity, and precipitation) have been also registered. The Glycaspis brimblecombei population showed a similar trend in all nine sites, positively correlated only with air temperature, but a negative correlation has emerged with precipitation and relative humidity. The results show the need for a deeper understanding of the role played by other abiotic (such as different concentrations of CO2) and biotic (e.g., the antagonistic action of natural enemies, competition with other pests, etc.) factors. The greater sensitivity, even at low densities of psyllid, of sampling methods based on the random collection of a fixed number of leaves compared to methods based on the collection of infested leaves in a fixed time interval has been also outlined.

BotRisk: simulating the annual bunch rot risk on grapevines (Vitis vinifera L. cv. Riesling) based on meteorological data

The aim of the present investigations was to simulate the annual risk of bunch rot (Botrytis cinerea) on Vitis vinifera L. cv. Riesling grapes based on three long-term (n = 3 × 7 = 21 cases) assessment data sets originating from three Central European grape-growing regions. Periods when meteorological parameters were significantly (p < 0.01) correlated with the cumulative degree day (CDD_7;18;24) reaching 5% disease severity were determined by Window Pane analysis. Analyses revealed five critical weather constellations ("events") influencing annual epidemics: relatively low temperatures after bud break, dry conditions during flowering, high temperatures after flowering, and low temperatures and high precipitation sums during/after veraison were all associated with thermal-temporal early epidemics. Meteorological data in each of the five events served as input for the bunch rot risk model "BotRisk." The multiple linear regression model resulted in an adjusted coefficient of determination (R²_adj.) of 0.63. BotRisk enables (i) the simulation of the thermal-temporal position of the annual epidemic and, based on this, (ii) the classification of the annual bunch rot risk into three classes: low, medium, or high risk. According to leave-one-out cross-validation, 11 of 21 case studies were correctly classified. No systematic bias caused by location was observed, indicating that the transfer of the model into other locations with comparable climatic conditions could be possible. BotRisk (i) represents a novel viticultural decision support tool for crop cultural and chemical measures against bunch rot and (ii) enables an estimation of the bunch rot risk under changing environmental conditions.

Future Heat Waves in Different European Capitals Based on Climate Change Indicators

Changes in the frequency and intensity of heat waves have shown substantial negative impacts on public health. At the same time, climate change towards increasing air temperatures throughout Europe will foster such extreme events, leading to the population being more exposed to them and societies becoming more vulnerable. Based on two climate change scenarios (Representative Concentration Pathway 4.5 and 8.5) we analysed the frequency and intensity of heat waves for three capital cities in Europe representing a North-South transect (London, Luxembourg, Rome). We used indices proposed by the Expert Team on Sector-Specific Climate Indices of the World Meteorological Organization to analyze the number of heat waves, the number of days that contribute to heat waves, the length of the longest heat waves, as well as the mean temperature during heat waves. The threshold for the definition of heat waves is calculated based on a reference period of 30 years for each of the three cities, allowing for a direct comparison of the projected changes between the cities. Changes in the projected air temperature between a reference period (1971-2000) and three future periods (2001-2030 near future, 2031-2060 middle future, and 2061-2090 far future) are statistically significant for all three cities and both emission scenarios. Considerable similarities could be identified for the different heat wave indices. This directly affects the risk of the exposed population and might also negatively influence food security and water supply.

Winter honey bee colony losses, Varroa destructor control strategies, and the role of weather conditions: Results from a survey among beekeepers

Sets of treatments that were applied against varroa mites in the Luxembourgish beekeeper community were surveyed annually with a questionnaire between the winters 2010/11 and 2014/15. The average temperature and the precipitation sum of the month, when the respective varroa control method was applied were considered as co-variables when evaluating the efficacy of varroa control regimes. Success or failure of control regimes was evaluated based on the percentage of colonies lost per apiary in the winter following the treatment(s). Neither a positive nor a negative effect of formic acid (concentration 60%, w/v) on the colony losses could be found, irrespective of the weather conditions around the time of application. The higher concentration of 85% formic acid was linked with reduced colony losses when applications were done in August. Colony losses were reduced when Thymovar was applied in July or August, but applications in September were associated with increased losses compared with apiaries not treated with Thymovar during the same period. Apilife application in July as well as Apivar applications between July and September were associated with reduced colony losses. The removal of the drone brood and trickled oxalic acid application had beneficial effects when being done in April and December, respectively. Relatively warm (3.0±1.3°C) and wet (507.0±38.6mm/2months) conditions during the winter months December and January and relatively cool (17.2±1.4°C average monthly temperature) and wet (110.8±55.5mm/month) conditions in July were associated with elevated honey bee colony losses.

Short-term effects of air quality and thermal stress on non-accidental morbidity-a multivariate meta-analysis comparing indices to single measures

Air quality and thermal stress lead to increased morbidity and mortality. Studies on morbidity and the combined impact of air pollution and thermal stress are still rare. To analyse the correlations between air quality, thermal stress and morbidity, we used a two-stage meta-analysis approach, consisting of a Poisson regression model combined with distributed lag non-linear models (DLNMs) and a meta-analysis investigating whether latitude or the number of inhabitants significantly influence the correlations. We used air pollution, meteorological and hospital admission data from 28 administrative districts along a north-south gradient in western Germany from 2001 to 2011. We compared the performance of the single measure particulate matter (PM10) and air temperature to air quality indices (MPI and CAQI) and the biometeorological index UTCI. Based on the Akaike information criterion (AIC), it can be shown that using air quality indices instead of single measures increases the model strength. However, using the UTCI in the model does not give additional information compared to mean air temperature. Interaction between the 3-day average of air quality (max PM10, max CAQI and max MPI) and meteorology (mean air temperature and mean UTCI) did not improve the models. Using the mean air temperature, we found immediate effects of heat stress (RR 1.0013, 95% CI: 0.9983-1.0043) and by 3 days delayed effects of cold stress (RR: 1.0184, 95% CI: 1.0117-1.0252). The results for air quality differ between both air quality indices and PM10. CAQI and MPI show a delayed impact on morbidity with a maximum RR after 2 days (MPI 1.0058, 95% CI: 1.0013-1.0102; CAQI 1.0068, 95% CI: 1.0030-1.0107). Latitude was identified as a significant meta-variable, whereas the number of inhabitants was not significant in the model.

PESFOR-W: Improving the design and environmental effectiveness of woodlands for water Payments for Ecosystem Services

The EU Water Framework Directive aims to ensure restoration of Europe’s water bodies to “good ecological status” by 2027. Many Member States will struggle to meet this target, with around half of EU river catchments currently reporting below standard water quality. Diffuse pollution from agriculture represents a major pressure, affecting over 90% of river basins. Accumulating evidence shows that recent improvements to agricultural practices are benefiting water quality but in many cases will be insufficient to achieve WFD objectives. There is growing support for land use change to help bridge the gap, with a particular focus on targeted tree planting to intercept and reduce the delivery of diffuse pollutants to water. This form of integrated catchment management offers multiple benefits to society but a significant cost to landowners and managers.

New economic instruments, in combination with spatial targeting, need to be developed to ensure cost effective solutions – including tree planting for water benefits - are realised. Payments for Ecosystem Services (PES) are flexible, incentive-based mechanisms that could play an important role in promoting land use change to deliver water quality targets. The PESFOR-W COST Action will consolidate learning from existing woodlands for water PES schemes in Europe and help standardize approaches to evaluating the environmental effectiveness and cost-effectiveness of woodland measures. It will also create a European network through which PES schemes can be facilitated, extended and improved, for example by incorporating other ecosystem services linking with aims of the wider forests-carbon policy nexus.

A Threshold-Based Weather Model for Predicting Stripe Rust Infection in Winter Wheat

Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major threat in most wheat growing regions worldwide, which potentially causes substantial yield losses when environmental conditions are favorable. Data from 1999 to 2015 for three representative wheat-growing sites in Luxembourg were used to develop a threshold-based weather model for predicting wheat stripe rust. First, the range of favorable weather conditions using a Monte Carlo simulation method based on the Dennis model were characterized. Then, the optimum combined favorable weather variables (air temperature, relative humidity, and rainfall) during the most critical infection period (May-June) was identified and was used to develop the model. Uninterrupted hours with such favorable weather conditions over each dekad (i.e., 10-day period) during May-June were also considered when building the model. Results showed that a combination of relative humidity >92% and 4°C < temperature < 16°C for a minimum of 4 continuous hours, associated with rainfall ≤0.1 mm (with the dekad having these conditions for 5 to 20% of the time), were optimum to the development of a wheat stripe rust epidemic. The model accurately predicted infection events: probabilities of detection were ≥0.90 and false alarm ratios were ≤0.38 on average, and critical success indexes ranged from 0.63 to 1. The method is potentially applicable to studies of other economically important fungal diseases of other crops or in different geographical locations. If weather forecasts are available, the threshold-based weather model can be integrated into an operational warning system to guide fungicide applications.

Assessing climate change impacts on the rape stem weevil, Ceutorhynchus napi Gyll., based on bias- and non-bias-corrected regional climate change projections

Agricultural production is directly affected by projected increases in air temperature and changes in precipitation. A multi-model ensemble of regional climate change projections indicated shifts towards higher air temperatures and changing precipitation patterns during the summer and winter seasons up to the year 2100 for the region of Goettingen (Lower Saxony, Germany). A second major controlling factor of the agricultural production is the infestation level by pests. Based on long-term field surveys and meteorological observations, a calibration of an existing model describing the migration of the pest insect Ceutorhynchus napi was possible. To assess the impacts of climate on pests under projected changing environmental conditions, we combined the results of regional climate models with the phenological model to describe the crop invasion of this species. In order to reduce systematic differences between the output of the regional climate models and observational data sets, two different bias correction methods were applied: a linear correction for air temperature and a quantile mapping approach for precipitation. Only the results derived from the bias-corrected output of the regional climate models showed satisfying results. An earlier onset, as well as a prolongation of the possible time window for the immigration of Ceutorhynchus napi, was projected by the majority of the ensemble members.

Making air quality indices comparable--assessment of 10 years of air pollutant levels in western Europe

To address the incomparability of the large number of existing air quality indices, we propose a new normalization method that is suited to directly compare air quality indices based on the common European World Health Organization (WHO) air quality guidelines for NO2, O3, and PM10. Using this method, we compared three air quality indices based on the European guidelines, related them to another air quality index based on the relative risk concept, and used them to assess the air quality and its trends in northwest central Europe. The average air quality in the area of investigation is below the recommended European guidelines. The majority of index values exceeding this threshold are caused by PM10, which is also, in most cases, responsible for the degrading trends in air quality. Eleven out of 29 stations tested showed significant trends, of which eight indicated trends towards better air quality.

First Report of Wheat Leaf Rust in the Grand Duchy of Luxembourg and the Progress of its Appearance over the 2003-2008 Period

Wheat leaf rust caused by Puccinia triticina Eriks. was identified for the first time in 2000 in the Grand Duchy of Luxembourg on the basis of orange-to-brown, round-to-ovoid, erumpent uredinia (1 to 1.5 mm in diameter) scattered on the upper and lower leaf surfaces and producing orange-brown urediniospores that are subgloboid, approximately 20 μm in diameter, and with up to eight germ pore scattered in thick, echinulate walls. In a second phase, wheat was monitored weekly (starting from Zadoks growth stage 30, pseudo stem erection) during the 2003-2008 cropping seasons for wheat leaf rust. Disease severity (percentage of leaf area with symptoms) was recorded in four, replicated field experiments located in three villages (Diekirch District: Reuler; and Grevenmacher District: Burmerange and Christnach), which are representative of the different agroclimatological zones of Luxembourg. A significant difference in severity was observed between the sites (P < 0.01) and the years (P < 0.05). Over the 6-year period, Burmerange and Reuler consistently showed the highest and lowest disease severity, respectively. In 2003 and 2007, Burmerange (a southern site with the highest average spring temperatures of 13.6 and 14.0°C, respectively) showed the highest disease severity with 66 and 57%, respectively, whereas the lowest severity (<1% for both years) was observed in the north at Reuler (site with the lowest average spring temperatures of 12.0 and 12.4°C, respectively). Christnach, located midway between Reuler and Burmerange, showed an intermediate disease severity with 7% (2003) and 22% (2007). The disease appeared at growth stages 77 (late milk) and 87 (hard dough) in the period 2003-2005, but at an earlier stage (45, boots swollen) for 2006-2008 (P < 0.001). In 2005, low severity was recorded due to a severe drought during May, June, and July. A reason for this earlier appearance of leaf rust occurrences in the two districts may be related to an increase in the average spring temperature (average March to May temperature for Luxembourg was 8.3°C for the 1971-2000 period, 9.5°C for the 2003-2005 period, 9.9°C for the 2006-2008 period, 2007 was exceptional with 11.9°C, P < 0.01). In the past, cereal disease management strategies were oriented toward the control of predominant and yield-reducing diseases such as that caused by Septoria tritici Desm. Because the succession of mild winters and warm springs during the last 5 years allowed the early occurrence and the fast development of wheat leaf rust in the Grand Duchy of Luxembourg, it is advisable to take this disease into account in fungicide application schemes.

Operational application and improvements of the disease risk forecast model PROCULTURE to optimize fungicides spray for the septoria leaf blotch disease in winter wheat in Luxembourg

Abstract. The model PROCULTURE has been developed by the Université Catholique de Louvain – UCL (Belgium) to simulate the progress of the septoria leaf blotch disease on winter wheat during the cropping season. The model has been validated in Luxembourg for four years at four distinct representative sites. It is able to identify infection periods due to the causal agent Mycosphaerella graminicola on the last five leaf layers by combining meteorological data with phenological data from PROCULTURE's crop growth model component. The meteorological forcing consists of hourly time-series of air temperature, relative humidity and cumulative rainfall since the time of sowing, retrieved from automatic weather stations for hindcast and numerical weather prediction model outputs for the forecast periods. In order to improve the model, leaf wetness – which is one of the most important drivers for the spread of the disease – shall be added as an additional predictor. Therefore leaf wetness sensors were set up at four test sites during the 2007 growing season. To get a continuous spatial coverage of the country, it is planned to couple the PROCULTURE model offline to 12-hourly operational weather forecasts from an implementation of the Weather Research and Forecasting (WRF) model for Luxembourg at 1 km resolution. Because the WRF model does not provide leaf wetness directly, an artificial neural network (ANN) is used to model this parameter.

Reconstruction of daily solar UV irradiation from 1893 to 2002 in Potsdam, Germany

Long-term records of solar UV radiation reaching the Earth's surface are scarce. Radiative transfer calculations and statistical models are two options used to reconstruct decadal changes in solar UV radiation from long-term records of measured atmospheric parameters that contain information on the effect of clouds, atmospheric aerosols and ground albedo on UV radiation. Based on earlier studies, where the long-term variation of daily solar UV irradiation was derived from measured global and diffuse irradiation as well as atmospheric ozone by a non-linear regression method [Feister et al. (2002) Photochem Photobiol 76:281-293], we present another approach for the reconstruction of time series of solar UV radiation. An artificial neural network (ANN) was trained with measurements of solar UV irradiation taken at the Meteorological Observatory in Potsdam, Germany, as well as measured parameters with long-term records such as global and diffuse radiation, sunshine duration, horizontal visibility and column ozone. This study is focussed on the reconstruction of daily broad-band UV-B (280-315 nm), UV-A (315-400 nm) and erythemal UV irradiation (ER). Due to the rapid changes in cloudiness at mid-latitude sites, solar UV irradiance exhibits appreciable short-term variability. One of the main advantages of the statistical method is that it uses doses of highly variable input parameters calculated from individual spot measurements taken at short time intervals, which thus do represent the short-term variability of solar irradiance.

Screening and scenarios of traffic emissions at Trier, Germany

SCOPE AND BACKGROUND

In the course of the European Council Directive on permissible air pollutant limit values, valid starting from 2005 there is an urgent call for action, particularly for fine dust (PM10). Current investigations (Junk & Helbig 2003, Reuter & Baumüller 2003) show that the limit values in certain places in congested areas are exceeded. Only if it is possible to locate these Hot Spots purposeful measures to reduce the ambient air pollution can be conducted. For an efficient identification of these Hot Spots numerical computer models or establishing special measurements networks are too expensive. Using the statistical model STREET 5.0 (KTT 2003) a cost-effective screening of the air pollution situation caused by the traffic can be done.

METHODS

STREET is based on the 3-dimensional micro-scale non-hydrostatic flow- and dispersion model MISCAM (Eichhorn 1989). The results of over 100.000 different calculations with MISCAM are stored in a Database and used to calculate the emissions with STREET. In collaboration with the city council of Trier more than 150 streets were investigated, mapped, and calculated. A special urban climate measuring network supplies the necessary meteorological input data about the wind field and precipitation events in the valley of the Moselle. Information about road width and road orientation as well as building density was derived from aerial photographs. Traffic censuses and mobile air pollutants measurements supplied the remaining input data. We calculated the mean annual air pollutant concentrations for NO2, CO, SO2, O3, benzene as well as PM10.

RESULTS

A comparison of the model results with the values obtained from the stations of the central emission measuring network of Rhineland-Palatinate (ZIMEN, annual report 2002) shows very good agreements. The model was not only used to calculate the annual air pollutant but also for urban planning and management. The absolute level of the air pollutant is mainly dependent on the amount of traffic in the street canyons. Therefore four different case-scenarios with varying quantity of traffic were calculated and interpreted for each street. The results of the calculation show that on the basis of the mean values for both NO2 and benzene, it is not to be expected that the limits

PERSPECTIVES

Furthermore the model can be used to find the maximum tolerable numbers of cars for a street without exceeding the air pollutant thresholds.

Urban climate and air quality in Trier Germany

To conceptualize strategies for regional environmental management in the Trier region, extensive urban meteorological measurements were undertaken. Weather stations from the German Weather Service and the state Pollution Monitoring Network were used as well as a number of our automatic meteorological stations and a mobile platform (instrumented van). The bioclimatic conditions in the city of Trier are affected by the valley of the Moselle River. Both the wind field and the thermal stratification in the urban boundary layer showed local characteristics especially marked in the diurnal variation and monthly mean concentrations of the air pollutants nitrogen and sulfurdioxide (NO(x), SO(2)), ozone (O(3)) and particle matter (PM10). Catabatic flows from the side valleys partially reduce the urban heat island and increase the ozone concentration in the city in the evening during calm weather conditions. The impact-based air-quality index is mostly determined by a high PM10 concentration. Strategies to reduce air pollutions in the Trier region are discussed.