Spatial and temporal hot spots of Aedes albopictus abundance inside and outside a South European metropolitan area

Aedes albopictus is a tropical invasive species which in the last decades spread worldwide, also colonizing temperate regions of Europe and US, where it has become a public health concern due to its ability to transmit exotic arboviruses, as well as severe nuisance problems due to its aggressive daytime outdoor biting behaviour. While several studies have been carried out in order to predict the potential limits of the species expansions based on eco-climatic parameters, few studies have so far focused on the specific effects of these variables in shaping its micro-geographic abundance and dynamics. The present study investigated eco-climatic factors affecting Ae. albopictus abundance and dynamics in metropolitan and sub-urban/rural sites in Rome (Italy), which was colonized in 1997 and is nowadays one of the most infested metropolitan areas in Southern Europe. To this aim, longitudinal adult monitoring was carried out along a 70 km-transect across and beyond the most urbanized and densely populated metropolitan area. Two fine scale spatiotemporal datasets (one with reference to a 20m circular buffer around sticky traps used to collect mosquitoes and the second to a 300m circular buffer within each sampling site) were exploited to analyze the effect of climatic and socio-environmental variables on Ae. albopictus abundance and dynamics along the transect. Results showed an association between highly anthropized habitats and high adult abundance both in metropolitan and sub-urban/ rural areas, with “small green islands” corresponding to hot spots of abundance in the metropolitan areas only, and a bimodal seasonal dynamics with a second peak of abundance in autumn, due to heavy rains occurring in the preceding weeks in association with permissive temperatures. The results provide useful indications to prioritize public mosquito control measures in temperate urban areas where nuisance, human-mosquito contact and risk of local arbovirus transmission are likely higher, and highlight potential public health risks also after the summer months typically associated with high mosquito densities

Noncooperative and Cooperative Transmission Schemes with Precoding and Beamforming

The next generation mobile networks are expected to provide multimedia applications with a high quality of service. On the other hand, interference among multiple base stations (BS) that co-exist in the same location limits the capacity of wireless networks. In conventional wireless networks, the base stations do not cooperate with each other. The BSs transmit individually to their respective mobile stations (MS) and treat the transmission from other BSs as interference. An alternative to this structure is a network cooperation structure. Here, BSs cooperate with other BSs to simultaneously transmit to their respective MSs using the same frequency band at a given time slot. By doing this, we significantly increase the capacity of the networks. This thesis presents novel research results on a noncooperative transmission scheme and a cooperative transmission scheme for multi-user multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM). We first consider the performance limit of a noncooperative transmission scheme. Here, we propose a method to reduce the interference and increase the throughput of orthogonal frequency division multiplexing (OFDM) systems in co-working wireless local area networks (WLANs) by using joint adaptive multiple antennas(AMA) and adaptive modulation (AM) with acknowledgement (ACK) Eigen-steering. The calculation of AMA and AM are performed at the receiver. The AMA is used to suppress interference and to maximize the signal-to-interference-plus-noise ratio (SINR). The AM scheme is used to allocate OFDM sub-carriers, power, and modulation mode subject to the constraints of power, discrete modulation, and the bit error rate (BER). The transmit weights, the allocation of power, and the allocation of sub-carriers are obtained at the transmitter using ACK Eigen-steering. The derivations of AMA, AM, and ACK Eigen-steering are shown. The performance of joint AMA and AM for various AMA configurations is evaluated through the simulations of BER and spectral efficiency (SE) against SIR. To improve the performance of the system further, we propose a practical cooperative transmission scheme to mitigate against the interference in co-working WLANs. Here, we consider a network coordination among BSs. We employ Tomlinson Harashima precoding (THP), joint transmit-receive beamforming based on SINR (signal-to-interference-plus-noise-ratio) maximization, and an adaptive precoding order to eliminate co-working interference and achieve bit error rate (BER) fairness among different users. We also consider the design of the system when partial channel state information (CSI) (where each user only knows its own CSI) and full CSI (where each user knows CSI of all users) are available at the receiver respectively. We prove analytically and by simulation that the performance of our proposed scheme will not be degraded under partial CSI. The simulation results show that the proposed scheme considerably outperforms both the existing noncooperative and cooperative transmission schemes. A method to design a spectrally efficient cooperative downlink transmission scheme employing precoding and beamforming is also proposed. The algorithm eliminates the interference and achieves symbol error rate (SER) fairness among different users. To eliminate the interference, Tomlinson Harashima precoding (THP) is used to cancel part of the interference while the transmit-receive antenna weights cancel the remaining one. A new novel iterative method is applied to generate the transmit-receive antenna weights. To achieve SER fairness among different users and further improve the performance of MIMO systems, we develop algorithms that provide equal SINR across all users and order the users so that the minimum SINR for each user is maximized. The simulation results show that the proposed scheme considerably outperforms existing cooperative transmission schemes in terms of the SER performance and complexity and approaches an interference free performance under the same configuration. We could improve the performance of the proposed interference cancellation further. This is because the proposed interference cancellation does not consider receiver noise when calculating the transmit-receive weight antennas. In addition, the proposed scheme mentioned above is designed specifically for a single-stream multi-user transmission. Here, we employ THP precoding and an iterative method based on the uplink-downlink duality principle to generate the transmit-receive antenna weights. The algorithm provides an equal SINR across all users. A simpler method is then proposed by trading off the complexity with a slight performance degradation. The proposed methods are extended to also work when the receiver does not have complete Channel State Informations (CSIs). A new method of setting the user precoding order, which has a much lower complexity than the VBLAST type ordering scheme but with almost the same performance, is also proposed. The simulation results show that the proposed schemes considerably outperform existing cooperative transmission schemes in terms of SER performance and approach an interference free performance. In all the cooperative transmission schemes proposed above, we use THP to cancel part of the interference. In this thesis, we also consider an alternative approach that bypasses the use of THP. The task of cancelling the interference from other users now lies solely within the transmit-receive antenna weights. We consider multiuser Gaussian broadcast channels with multiple antennas at both transmitter and receivers. An iterative multiple beamforming (IMB) algorithm is proposed, which is flexible in the antenna configuration and performs well in low to moderate data rates. Its capacity and bit error rate performance are compared with the ones achieved by the traditional zero-forcing method

Ecological and environmental factors affecting the risk of tick-borne encephalitis in Europe, 2017 to 2021

Background: Tick-borne encephalitis (TBE) is a disease which can lead to severe neurological symptoms, caused by the TBE virus (TBEV). The natural transmission cycle occurs in foci and involves ticks as vectors and several key hosts that act as reservoirs and amplifiers of the infection spread. Recently, the incidence of TBE in Europe has been rising in both endemic and new regions. Aim: In this study we want to provide comprehensive understanding of the main ecological and environmental factors that affect TBE spread across Europe. Methods: We searched available literature on covariates linked with the circulation of TBEV in Europe. We then assessed the best predictors for TBE incidence in 11 European countries by means of statistical regression, using data on human infections provided by the European Surveillance System (TESSy), averaged between 2017 and 2021. Results: We retrieved data from 62 full-text articles and identified 31 different covariates associated with TBE occurrence. Finally, we selected eight variables from the best model, including factors linked to vegetation cover, climate, and the presence of tick hosts. Discussion: The existing literature is heterogeneous, both in study design and covariate types. Here, we summarised and statistically validated the covariates affecting the variability of TBEV across Europe. The analysis of the factors enhancing disease emergence is a fundamental step towards the identification of potential hotspots of viral circulation. Hence, our results can support modelling efforts to estimate the risk of TBEV infections and help decision-makers implement surveillance and prevention campaigns

Climatic Factors Driving Invasion of the Tiger Mosquito (Aedes albopictus) into New Areas of Trentino, Northern Italy

Background:The tiger mosquito (Aedes albopictus), vector of several emerging diseases, is expanding into more northerly latitudes as well as into higher altitudes in northern Italy. Changes in the pattern of distribution of the tiger mosquito may affect the potential spread of infectious diseases transmitted by this species in Europe. Therefore, predicting suitable areas of future establishment and spread is essential for planning early prevention and control strategies.Methodology/Principal Findings:To identify the areas currently most suitable for the occurrence of the tiger mosquito in the Province of Trento, we combined field entomological observations with analyses of satellite temperature data (MODIS Land Surface Temperature: LST) and human population data. We determine threshold conditions for the survival of overwintering eggs and for adult survival using both January mean temperatures and annual mean temperatures. We show that the 0°C LST threshold for January mean temperatures and the 11°C threshold for annual mean temperatures provide the best predictors for identifying the areas that could potentially support populations of this mosquito. In fact, human population density and distance to human settlements appear to be less important variables affecting mosquito distribution in this area. Finally, we evaluated the future establishment and spread of this species in relation to predicted climate warming by considering the A2 scenario for 2050 statistically downscaled at regional level in which winter and annual temperatures increase by 1.5 and 1°C, respectively.Conclusions/Significance:MODIS satellite LST data are useful for accurately predicting potential areas of tiger mosquito distribution and for revealing the range limits of this species in mountainous areas, predictions which could be extended to an European scale. We show that the observed trend of increasing temperatures due to climate change could facilitate further invasion of Ae. albopictus into new areas. © 2011 Roiz et al.Peer Reviewe

Development of a hybrid model to interpolate monthly precipitation maps incorporating the orographic influence

[EN] This paper proposes an interpolation model for monthly rainfall in large areas of complex orography. It has been implemented in the Iberian Peninsula (continental territories of Spain and Portugal), Balearic and Canary Islands covering a territory of almost 600.000km(2). To do this a data set that comprises a total number of 11,822 monthly precipitation series has been created (11,042 provided by the Spanish Meteorological Agency and 780 provided by the National Water Resources Information System of the Portuguese Water Institute). The data set covers the period from October 1940 until September 2005. The interpolation model has been based on the assumption of two different components on monthly precipitation. The first component reflects local and seasonal characteristics and 24 different mean monthly precipitation maps (12) and SDs maps (12) compose it. It considers the varying influence of physiographic variables such as altitude and orientation. The second precipitation component reflects the synoptic pattern that dominated each month of the series and it is composed by series of anomalies of monthly precipitation (780). Anomalies have been interpolated by means of ordinary kriging once local spatial continuity was assumed. Gridded maps of each variable have been developed at 200m resolution following a hybrid methodology that implements two different interpolation techniques. The first technique applies a regression analysis to derive maps depending on altitude and orientation; the second one is a weighting technique to consider the non-linearity of the precipitation/altitude dependence. Cross validation has been applied to estimate the goodness of both techniques. Results show an average annual precipitation of 655mm/year. Although this figure is only 4% less than the estimate of MAGRAMA (2004), regional and local differences are highlighted when the spatial distribution is considered. The model constitutes a comprehensive implementation considering the availability of historical records and the need of avoiding slow calculations in large territories.Ministry of Economy, Industry and Competitiveness, Grant/Award Number: CGL2014-52571-RÁlvarez-Rodríguez, J.; Llasat, M.; Estrela Monreal, T. (2019). Development of a hybrid model to interpolate monthly precipitation maps incorporating the orographic influence. International Journal of Climatology. 39(10):3962-3975. https://doi.org/10.1002/joc.6051S396239753910AEMET.2011Atlas Climático Ibérico. (Iberian Climate Atlas) VV.AA. Agencia Estatal de Meteorología. Ministerio de Medio Ambiente. ISBN: 978‐84‐7837‐079‐5. Available at:http://www.aemet.es/documentos/es/conocermas/publicaciones/Atlas-climatologico/Atlas.pdf[Accessed 14th February 2018]Álvarez‐Rodríguez J.2011.Estimación de la distribución espacial de la precipitación en zonas montañosas mediante métodos geoestadísticos (Analysis of spatial distribution of precipitation in mountainous areas by means of geostatistical analysis). PhD Thesis. Polytechnic University of Madrid Higher Technical School of Civil EngineeringÁlvarez-Rodríguez, J., Llasat, M. C., & Estrela, T. (2017). Analysis of geographic and orographic influence in Spanish monthly precipitation. International Journal of Climatology, 37, 350-362. doi:10.1002/joc.5007Barros, A. P., Kim, G., Williams, E., & Nesbitt, S. W. (2004). Probing orographic controls in the Himalayas during the monsoon using satellite imagery. Natural Hazards and Earth System Sciences, 4(1), 29-51. doi:10.5194/nhess-4-29-2004Barstad, I., Grabowski, W. W., & Smolarkiewicz, P. K. (2007). Characteristics of large-scale orographic precipitation: Evaluation of linear model in idealized problems. Journal of Hydrology, 340(1-2), 78-90. doi:10.1016/j.jhydrol.2007.04.005Creutin, J. D., & Obled, C. (1982). Objective analyses and mapping techniques for rainfall fields: An objective comparison. Water Resources Research, 18(2), 413-431. doi:10.1029/wr018i002p00413Daly, C., Neilson, R. P., & Phillips, D. L. (1994). A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain. Journal of Applied Meteorology, 33(2), 140-158. doi:10.1175/1520-0450(1994)0332.0.co;2Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., … Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28(15), 2031-2064. doi:10.1002/joc.1688Daly, C., Slater, M. E., Roberti, J. A., Laseter, S. H., & Swift, L. W. (2017). High-resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset. International Journal of Climatology, 37, 124-137. doi:10.1002/joc.4986Dhar, O. N., & Nandargi, S. (2004). Rainfall distribution over the Arunachal Pradesh Himalayas. Weather, 59(6), 155-157. doi:10.1256/wea.87.03Falivene, O., Cabrera, L., Tolosana-Delgado, R., & Sáez, A. (2010). Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example. Computers & Geosciences, 36(4), 512-519. doi:10.1016/j.cageo.2009.09.015Fiering, B., & Jackson, B. (1971). Synthetic Streamflows. Water Resources Monograph. doi:10.1029/wm001Gambolati, G., & Volpi, G. (1979). A conceptual deterministic analysis of the kriging technique in hydrology. Water Resources Research, 15(3), 625-629. doi:10.1029/wr015i003p00625Gómez-Hernández, J. J., Cassiraga, E. F., Guardiola-Albert, C., & Rodríguez, J. Á. (2001). Incorporating Information from a Digital Elevation Model for Improving the Areal Estimation of Rainfall. geoENV III — Geostatistics for Environmental Applications, 67-78. doi:10.1007/978-94-010-0810-5_6Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228(1-2), 113-129. doi:10.1016/s0022-1694(00)00144-xHanson, C. L. (1982). DISTRIBUTION AND STOCHASTIC GENERATION OF ANNUAL AND MONTHLY PRECIPITATION ON A MOUNTAINOUS WATERSHED IN SOUTHWEST IDAHO. Journal of the American Water Resources Association, 18(5), 875-883. doi:10.1111/j.1752-1688.1982.tb00085.xLloyd, C. D. (2005). Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain. Journal of Hydrology, 308(1-4), 128-150. doi:10.1016/j.jhydrol.2004.10.026Marquı́nez, J., Lastra, J., & Garcı́a, P. (2003). Estimation models for precipitation in mountainous regions: the use of GIS and multivariate analysis. Journal of Hydrology, 270(1-2), 1-11. doi:10.1016/s0022-1694(02)00110-5Martínez-Cob, A. (1996). Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain. Journal of Hydrology, 174(1-2), 19-35. doi:10.1016/0022-1694(95)02755-6Mitáš, L., & Mitášová, H. (1988). General variational approach to the interpolation problem. Computers & Mathematics with Applications, 16(12), 983-992. doi:10.1016/0898-1221(88)90255-6Naoum, S., & Tsanis, I. K. (2004). Orographic Precipitation Modeling with Multiple Linear Regression. Journal of Hydrologic Engineering, 9(2), 79-102. doi:10.1061/(asce)1084-0699(2004)9:2(79)Ninyerola, M., Pons, X., & Roure, J. M. (2006). Monthly precipitation mapping of the Iberian Peninsula using spatial interpolation tools implemented in a Geographic Information System. Theoretical and Applied Climatology, 89(3-4), 195-209. doi:10.1007/s00704-006-0264-2Pebesma, E. J. (2004). Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30(7), 683-691. doi:10.1016/j.cageo.2004.03.012Rotunno, R., & Ferretti, R. (2001). Mechanisms of Intense Alpine Rainfall. Journal of the Atmospheric Sciences, 58(13), 1732-1749. doi:10.1175/1520-0469(2001)0582.0.co;2Singh, P., Ramasastri, K. S., & Kumar, N. (1995). Topographical Influence on Precipitation Distribution in Different Ranges of Western Himalayas. Hydrology Research, 26(4-5), 259-284. doi:10.2166/nh.1995.0015Tabios, G. Q., & Salas, J. D. (1985). A COMPARATIVE ANALYSIS OF TECHNIQUES FOR SPATIAL INTERPOLATION OF PRECIPITATION. Journal of the American Water Resources Association, 21(3), 365-380. doi:10.1111/j.1752-1688.1985.tb00147.xTHIESSEN, A. H. (1911). PRECIPITATION AVERAGES FOR LARGE AREAS. Monthly Weather Review, 39(7), 1082-1089. doi:10.1175/1520-0493(1911)392.0.co;2Tobin, C., Nicotina, L., Parlange, M. B., Berne, A., & Rinaldo, A. (2011). Improved interpolation of meteorological forcings for hydrologic applications in a Swiss Alpine region. Journal of Hydrology, 401(1-2), 77-89. doi:10.1016/j.jhydrol.2011.02.010Weber, D., & Englund, E. (1992). Evaluation and comparison of spatial interpolators. Mathematical Geology, 24(4), 381-391. doi:10.1007/bf00891270Weber, D. D., & Englund, E. J. (1994). Evaluation and comparison of spatial interpolators II. Mathematical Geology, 26(5), 589-603. doi:10.1007/bf02089243World Climate Programme.1985. World Meteorological Organization. Review of Requirements for Area‐Averaged Precipitation Data Surface‐Based and Space‐Based Estimation Techniques Space and Time Sampling Accurancy and Error; Data Exchange. Boulder Colorado EE.UU. 17–1

A simple and efficient GIS tool for volume calculations of submarine landslides

A numeric tool is presented for calculating volumes of topographic voids such as slump scars of landslides, canyons or craters (negative/concave morphology), or alternatively, bumps and hills (positive/convex morphology) by means of digital elevation models embedded within a geographical information system (GIS). In this study, it has been used to calculate landslide volumes. The basic idea is that a (singular) event (landslide, meteorite impact, volcanic eruption) has disturbed an intact surface such that it is still possible to distinguish between the former (undisturbed) landscape and the disturbance (crater, slide scar, debris avalanche). In such cases, it is possible to reconstruct the paleo-surface and to calculate the volume difference between both surfaces, thereby approximating the volume gain or loss caused by the event. I tested the approach using synthetically generated land surfaces that were created on the basis of Shuttle Radar Topography Mission data. Also, I show the application to two real cases, (1) the calculation of the volume of the Masaya Slide, a submarine landslide on the Pacific continental slope of Nicaragua, and (2) the calculation of the void of a segment of the Fish River Canyon, Namibia. The tool is provided as a script file for the free GIS GRASS. It performs with little effort, and offers a range of interpolation parameters. Testing with different sets of interpolation parameters results in a small range of uncertainty. This tool should prove useful in surface studies not exclusively on earth

Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field

Long-term carbon loss in fragmented Neotropical forests

Tropical forests play an important role in the global carbon cycle, as they store a large amount of carbon (C). Tropical forest deforestation has been identified as a major source of CO2 emissions, though biomass loss due to fragmentation—the creation of additional forest edges—has been largely overlooked as an additional CO2 source. Here, through the combination of remote sensing and knowledge on ecological processes, we present long-term carbon loss estimates due to fragmentation of Neotropical forests: within 10 years the Brazilian Atlantic Forest has lost 69 (±14) Tg C, and the Amazon 599 (±120) Tg C due to fragmentation alone. For all tropical forests, we estimate emissions up to 0.2 Pg C y−1 or 9 to 24% of the annual global C loss due to deforestation. In conclusion, tropical forest fragmentation increases carbon loss and should be accounted for when attempting to understand the role of vegetation in the global carbon balance.This study was part of the project ‘Biodiversity conservation in a fragmented landscape at the Atlantic Plateau of São Paulo’ (BIOTA/Caucaia and BioCAPSP) funded by FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo, project no. 99/05123-4, 01/13309-2, 02/02125-0, 02/02126-7), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, project no. 690144/01-6), Fundação O Boticário de Proteção à Natureza, and by BMBF (German Federal Ministry of Education and Research, project n. 01LB0202). J.P.M. and M.C.R. thank the Brazilian Science Council (Conselho Nacional de Desenvolvimento Científico) for his research fellowship (process no. 307934/2011-0 and 312045/2013-1, respectively). A.H. and S.P. were supported by the ERC advanced grant 233066. M.M. has been supported by BMBF (project n. 01LB0202), and the Department of Ecological Modelling of the Helmholtz Centre for Environmental Research (UFZ). We thank Birgit Felinks for the support during the Mata Atlântica project. Florian Hartig provided valuable comments on an earlier version of this manuscript. S.P. has been funded by the Helmholtz Association of German Research Centres within the project ‘Biomass and Bioenergy systems’. A.H. was also supported by the Helmholtz-Alliance Remote Sensing and Earth System Dynamics. A.H. thanks C. Wissel and H. Bossel for supporting the FORMIND project over the years