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West Nile Virus (WNV) is now endemic in many European countries, causing hundreds of human cases every year, with a high spatial and temporal heterogeneity. Previous studies have suggested that spring temperature might play a key role at shaping WNV transmission. Specifically, warmer temperatures in April-May might amplify WNV circulation, thus increasing the risk for human transmission later in the year. To test this hypothesis, we collated publicly available data on the number of human infections recorded in Europe between 2011 and 2019. We then applied generalized linear models to quantify the relationship between human cases and spring temperature, considering both average conditions (over years 2003-2010) and deviations from the average for subsequent years (2011-2019). We found a significant positive association both spatial (average conditions) and temporal (deviations). The former indicates that WNV circulation is higher in usually warmer regions while the latter implies a predictive value of spring conditions over the coming season. We also found a positive association with WNV detection during the previous year, which can be interpreted as an indication of the reliability of the surveillance system but also of WNV overwintering capacity. Weather anomalies at the beginning of the mosquito breeding season might act as an early warning signal for public health authorities, enabling them to strengthen in advance ongoing surveillance and prevention strategies.

International audience; Ljungan virus (LV), which belongs to the Parechovirus genus in the Picornaviridae family, was first isolated from bank voles (Myodes glareolus) in Sweden in 1998 and proposed as a zoonotic agent. To improve knowledge of the host association and geographical distribution of LV, tissues from 1685 animals belonging to multiple rodent and insectivore species from 12 European countries were screened for LV-RNA using reverse transcriptase (RT)-PCR. In addition, we investigated how the prevalence of LV-RNA in bank voles is associated with various intrinsic and extrinsic factors. We show that LV is widespread geographically, having been detected in at least one host species in nine European countries. Twelve out of 21 species screened were LV-RNA PCR positive, including, for the first time, the red vole (Myodes rutilus) and the root or tundra vole (Alexandromys formerly Microtus oeconomus), as well as in insectivores, including the bicolored white-toothed shrew (Crocidura leucodon) and the Valais shrew (Sorex antinorii). Results indicated that bank voles are the main rodent host for this virus (overall RT-PCR prevalence: 15.2%). Linear modeling of intrinsic and extrinsic factors that could impact LV prevalence showed a concave-down relationship between body mass and LV occurrence, so that subadults had the highest LV positivity, but LV in older animals was less prevalent. Also, LV prevalence was higher in autumn and lower in spring, and the amount of precipitation recorded during the 6 months preceding the trapping date was negatively correlated with the presence of the virus. Phylogenetic analysis on the 185 base pair species-specific sequence of the 5 ' untranslated region identified high genetic diversity (46.5%) between 80 haplotypes, although no geographical or host-specific patterns of diversity were detected.

Unmanned Vehicles (UV) are used daily for different applications around the world. However, most of the software and technologies on which they are based are closed and proprietary systems. The presentation will start by introducing the history of Unmaned Vehicle technologies, with some unknown historical info, and the different types of Unmaned Vehicles that exist. The aim of the presentation is to demonstrate the integration of free and open source systems starting from the hardware, specially the autopilot component, through some payloads and finishing with the software solutions. Different software solutions for diverse aims will be showed and compared, i.e., first the different software used to configure the UV and manage the mission and later the possible software used to manage the outputs from the mission.

Aedes koreicus is a mosquito species native to Asia that has recently successfully invaded new areas in several European countries. Here, we provide important data on Ae. koreicus establishment in Pécs (Southern Hungary). Mosquito surveillance was carried out weekly between 2016 and 2019 at 10 different sites located throughout the city from May to September. We conducted a statistical analysis to evaluate the most important abiotic factors driving Ae. koreicus abundance. We then calibrated a previously developed temperature-dependent mathematical model to the recorded captures to evaluate mosquito abundance in the study area. We found that too high summer temperatures negatively affect mosquito abundance. The model accurately replicated the observed capture patterns, providing an estimate of Ae. koreicus density for each breeding season, which we interpolated to map Ae. koreicus abundance throughout Pécs. We found a negative correlation between mosquito captures and human density, suggesting that Ae. koreicus does not necessarily require humans for its blood meals. Our study provides a successful application of a previously published mathematical model to investigate Ae. koreicus population dynamics, proving its suitability for future studies, also within an epidemiological framework.

Background In the Netherlands, Aedes albopictus has been found each year since 2010 during routine exotic mosquito species surveillance at companies that import used tires. We developed habitat suitability models to investigate the potential risk of establishment and spread of this invasive species at these locations. Methods We used two methodologies: first, a species distribution model based on the maximum entropy modelling approach (MaxEnt) taking into consideration updated occurrence data of the species in Europe, and secondly, a spatial logic conditional model based on the temperature requirements of the species and using land surface temperature data (LST model). Results Suitability assessment obtained with the MaxEnt model at European level accurately reflect the current distribution of the species and these results also depict moderately low values in parts of the Netherlands, Belgium, Denmark, the British islands and southern parts of Scandinavia. Winter temperature was the variable that contributed most to the performance of the model (47.3%). The results of the LST model showed that: (i) coastal areas are suitable for overwintering of eggs; (ii) large areas in the northern part of the country have a low suitability for adult survival; and (iii) the entire country is suitable for successful completion of the life-cycle if the species is introduced after the winter months. Results of the LST model revealed that temperatures in 2012 and 2014 did not limit the overwintering of eggs or survival of adults at the locations where the species was found. By contrast, for the years 2010, 2011 and 2013, overwintering of eggs at these locations is considered unlikely. Conclusions Results using two modelling methodologies show differences in predicted habitat suitability values. Based on the results of both models, the climatic conditions could hamper the successful overwintering of eggs of Ae. albopictus and their survival as adults in many areas of the country. However, during warm years with mild winters, many areas of the Netherlands offer climatic conditions suitable for developing populations. Regular updates of the models, using updated occurrence and climatic data, are recommended to study the areas at risk.

Mosquitoes represent a considerable nuisance and are actual/potential vectors of human diseases in Europe. Costly and labour-intensive entomological monitoring is needed to correct planning of interventions aimed at reducing nuisance and the risk of pathogen transmission. The widespread availability of mobile phones and of massive Internet connections opens the way to the contribution of citizen in complementing entomological monitoring. ZanzaMapp is the first mobile &ldquo, mosquito&rdquo, application for smartphones specifically designed to assess citizens&rsquo, perception of mosquito abundance and nuisance in Italy. Differently from other applications targeting mosquitoes, ZanzaMapp prioritizes the number of records over their scientific authentication by requesting users to answer four simple questions on perceived mosquito presence/abundance/nuisance and geo-localizing the records. The paper analyses 36,867 ZanzaMapp records sent by 13,669 devices from 2016 to 2018 and discusses the results with reference to either citizens&rsquo, exploitation and appreciation of the app and to the consistency of the results obtained with the known biology of main mosquito species in Italy. In addition, we provide a first small-scale validation of ZanzaMapp data as predictors of Aedes albopictus biting females and examples of spatial analyses and maps which could be exploited by public institutions and administrations involved in mosquito and mosquito-borne pathogen monitoring and control.

Aedes koreicus is a mosquito species native to Asia that has recently successfully invaded new areas in several European countries. Here, we provide important data on Ae. koreicus establishment in P&eacute, cs (Southern Hungary). Mosquito surveillance was carried out weekly between 2016 and 2019 at 10 different sites located throughout the city from May to September. We conducted a statistical analysis to evaluate the most important abiotic factors driving Ae. koreicus abundance. We then calibrated a previously developed temperature-dependent mathematical model to the recorded captures to evaluate mosquito abundance in the study area. We found that too high summer temperatures negatively affect mosquito abundance. The model accurately replicated the observed capture patterns, providing an estimate of Ae. koreicus density for each breeding season, which we interpolated to map Ae. koreicus abundance throughout P&eacute, cs. We found a negative correlation between mosquito captures and human density, suggesting that Ae. koreicus does not necessarily require humans for its blood meals. Our study provides a successful application of a previously published mathematical model to investigate Ae. koreicus population dynamics, proving its suitability for future studies, also within an epidemiological framework.

The use of database technologies as a tool for implementing data for quantitative ecological studies and biodiversity conservation planning has recently attracted the attention of the biological community. Despite the fact that the number of biodiversity data sets is quickly rising, online databases of small mammals are still scarce, especially for Mediterranean ecosystems. We implemented the first standardized and accessible georeferenced European database of small mammal occurrences, abundances, and functional traits. Data derived from owl pellets was obtained from different sources, including original field surveys, publications, gray literature, existing databases, and museum collections. The OpenMICE database covers the years 1972 to 2017 and includes nearly 50,000 individuals from 23 species (13 Rodentia and 10 Eulipotyphla) at 190 sites in south-central Italy. Our specific goals in compiling this data set were as follows: (1) to make data that is usually accessible to a restricted audience widely available; (2) to identify the gaps in knowledge about small mammal communities and guide future sampling and conservation efforts; and (3) to gain a first insight into small mammal diversity and abundance in the study area. The potential applications of our spatial relational database are many, from individual-based to community-based models as potential indicators of environmental changes at different geographical scales. Given the long-term support for data storage, the OpenMICE database could be further expanded to include other geographical contexts and implemented with new information and traits. We would appreciate that researchers cite this paper if using all or part of the data set. We also request that researchers and teachers inform us of how they are using the data. We intend to keep it up to date as novel studies become available (see Data Availability).

Synthesizing and properly visualizing data in 2D systems is a key issue when aiming at explaining spatial patterns by spatial processes.

The assessment of species diversity in relatively large areas has always been a challenging task for ecologists, mainly because of the intrinsic difficulty to judge the completeness of species lists and to undertake sufficient and appropriate sampling. Since the variability of remotely sensed signal is expected to be related to landscape diversity, it could be used as a good proxy of diversity at species level. It has been demonstrated that the relation between species and landscape diversity measured from remotely sensed data or land use maps varies with scale. While traditional metrics supply point descriptions of diversity, generalized entropy’s framework offers a continuum of possible diversity measures, which differ in their sensitivity to rare and abundant reflectance values. In this paper, we aim at: (i) discussing the ecological background beyond the importance of measuring diversity based on generalized entropy and (ii) providing a test on an Open Source tool with its source code for calculating it. We expect that the subject of this paper will stimulate discussions on the opportunities offered by Free and Open Source Software to calculate landscape diversity indices.

Accurate and high resolution bathymetric data is a necessity for a wide range of coastal oceanographic research topics. Active sensing methods, such as ship-based soundings and Light Detection and Ranging (LiDAR), are expensive and time consuming solutions. Therefore, the significance of Satellite-Derived Bathymetry (SDB) has increased in the last ten years due to the availability of multi-constellation, multi-temporal, and multi-resolution remote sensing data as Open Data. Effective SDB algorithms have been proposed by many authors, but there is no ready-to-use software module available in the Geographical Information System (GIS) environment as yet. Hence, this study implements a Geographically Weighted Regression (GWR) based SDB workflow as a Geographic Resources Analysis Support System (GRASS) GIS module (i.image.bathymetry). Several case studies were carried out to examine the performance of the module in multi-constellation and multi-resolution satellite imageries for different study areas. The results indicate a strong correlation between SDB and reference depth. For instance, case study 1 (Puerto Rico, Northeastern Caribbean Sea) has shown an coefficient of determination (R2) of 0.98 and an Root Mean Square Error (RMSE) of 0.61 m, case study 2 (Iwate, Japan) has shown an R2 of 0.94 and an RMSE of 1.50 m, and case study 3 (Miyagi, Japan) has shown an R2 of 0.93 and an RMSE of 1.65 m. The reference depths were acquired by using LiDAR for case study 1 and an echo-sounder for case studies 2 and 3. Further, the estimated SDB has been used as one of the inputs for the Australian National University and Geoscience Australia (ANUGA) tsunami simulation model. The tsunami simulation results also show close agreement with post-tsunami survey data. The i.mage.bathymetry module developed as a part of this study is made available as an extension for the Open Source GRASS GIS to facilitate wide use and future improvements.

Given the growing risk of arbovirus outbreaks in Europe, there is a clear need to better describe the distribution of invasive mosquito species such as Aedes albopictus. Current challenges consist in simulating Ae. albopictus abundance, rather than its presence, and mapping its simulated abundance at a local scale to better assess the transmission risk of mosquito-borne pathogens and optimize mosquito control strategy. During 2014–2015, we sampled adult mosquitoes using 72 BG-Sentinel traps per year in the provinces of Belluno and Trento, Italy. We found that the sum of Ae. albopictus females collected during eight trap nights from June to September was positively related to the mean temperature of the warmest quarter and the percentage of artificial areas in a 250 m buffer around the sampling locations. Maps of Ae. albopictus abundance simulated from the most parsimonious model in the study area showed the largest populations in highly artificial areas with the highest summer temperatures, but with a high uncertainty due to the variability of the trapping collections. Vector abundance maps at a local scale should be promoted to support stakeholders and policy-makers in optimizing vector surveillance and control.

International audience; Ungulate populations exhibiting partial migration present a unique opportunity to explore the causes of the general phenomenon of migration. The European roe deer Capreolus capreolus is particularly suited for such studies due to a wide distribution range and a high level of ecological plasticity. In this study we undertook a comparative analysis of roe deer GPS location data from a representative set of European ecosystems available within the EURODEER collaborative project. We aimed at evaluating the ecological factors affecting migration tactic (i.e. occurrence) and pattern (i.e. timing, residence time, number of migratory trips). Migration occurrence varied between and within populations and depended on winter severity and topographic variability. Spring migrations were highly synchronous, while the timing of autumn migrations varied widely between regions, individuals and sexes. Overall, roe deer were faithful to their summer ranges, especially males. In the absence of extreme and predictable winter conditions, roe deer seemed to migrate opportunistically, in response to a tradeoff between the costs of residence in spatially separated ranges and the costs of migratory movements. Animals performed numerous trips between winter and summer ranges which depended on factors influencing the costs of movement such as between-range distance, slope and habitat openness. Our results support the idea that migration encompasses a behavioural continuum, with one-trip migration and residence as its end points, while commuting and multi-trip migration with short residence times in seasonal ranges are intermediate tactics. We believe that a full understanding of the variation in tactics of temporal separation in habitat use will provide important insights on migration and the factors that influence its prevalence.

Agricultural and forest monitoring is a valued instrument needed by public authorities (PA) for determining land uses, planning natural resources management and collecting taxes. Remote Sensing (RS...

In this study, we investigated the relationship between (D/H)1, (D/H)2 and δ(13)C of ethanol and δ(18)O of water in wine, and variables describing the climate and the geography of the production area, using exploratory visualisation tools, regression analysis and linear modelling. For the first time, a large amount of data (around 4000 wine samples collected over 11 years in Italy) and all the official isotopic parameters, as well as a large number of significant climatic and geographical descriptors (date of harvest, latitude, longitude, elevation, distance from the sea, amount of precipitation, maximum daily temperature, minimum daily temperature, mean daily temperature, δ(18)O and δ(2)H of precipitation) were considered. δ(18)O, followed by (D/H)1, was shown to have the strongest relationship with climate and location. The dominant variables were latitude, with a negative relationship, δ(18)O and δ(2)H of precipitation and temperature, both with positive relationships. The identified correlations and models could be used to predict the isotopic composition of authentic wines, offering increased possibilities for detecting fraud and mislabelling.

Background\ud West Nile Virus (WNV) is an emerging global health threat. Transmission risk is strongly related to the abundance of mosquito vectors, typically Culex pipiens in Europe. Early-warning predictors of mosquito population dynamics would therefore help guide entomological surveillance and thereby facilitate early warnings of transmission risk. \ud \ud Methods\ud We analysed an 11-year time series (2001 to 2011) of Cx. pipiens mosquito captures from the Piedmont region of north-western Italy to determine the principal drivers of mosquito population dynamics. Linear mixed models were implemented to examine the relationship between Cx. pipiens population dynamics and environmental predictors including temperature, precipitation, Normalized Difference Water Index (NDWI) and the proximity of mosquito traps to urban areas and rice fields. \ud \ud Results\ud Warm temperatures early in the year were associated with an earlier start to the mosquito season and increased season length, and later in the year, with decreased abundance. Early precipitation delayed the start and shortened the length of the mosquito season, but increased total abundance. Conversely, precipitation later in the year was associated with a longer season. Finally, higher NDWI early in the year was associated with an earlier start to the season and increased season length, but was not associated with abundance. Proximity to rice fields predicted higher total abundance when included in some models, but was not a significant predictor of phenology. Proximity to urban areas was not a significant predictor in any of our models. Predicted variations in start of the season and season length ranged from one to three weeks, across the measured range of variables. Predicted mosquito abundance was highly variable, with numbers in excess of 1000 per trap per year when late season temperatures were low (average 21°C) to only 150 when late season temperatures were high (average 30°C). \ud \ud Conclusions\ud Climate data collected early in the year, in conjunction with local land use, can be used to provide early warning of both the timing and magnitude of mosquito outbreaks. This potentially allows targeted mosquito control measures to be implemented, with implications for prevention and control of West Nile Virus and other mosquito borne diseases.

Due to the difficulties of field-based species data collection at wide spatial scales, remotely sensed spectral diversity has been advocated as one of the most effective proxies of ecosystem and species diversity. It is widely accepted that the relationship between species and spectral diversity is scale dependent. However, few studies have evaluated the impacts of scale on species diversity estimates from remote sensing data. In this paper we tested the species versus spectral relationship over very large scales (extents) with a varying spatial grain using floristic data of North America. Spectral diversity explained a low amount of variance while spatial extent of the sampling units (floras) explained a high amount of variance based on results from our variance partitioning analyses. This leads to the conclusion that spectral diversity must be carefully related to species diversity, explicitly taking into account potential area effects.

Losses in biodiversity critically impact the ability of ecosystems to provide critical services ranging from carbon sequestration and food production to the maintenance of soil fertility. The maintenance of biodiversity is thus essential for human well-being and a sustainable future. Since landscape diversity often relates to species biodiversity, considering several ecological levels from species community diversity to genetic diversity, measuring landscape heterogeneity, is an efficient and relatively cheap way of providing biodiversity estimates over large geographical areas. In this study we will demonstrate the power of using remotely sensed data to estimate landscape heterogeneity and locate diversity hotspots, allowing effective management and conservation of the landscape.

Heat requirements for grapevine varieties have been widely used to characterize potential growing regions for viticulture. One of the most common landscape classifications is based on the Winkler Index (WI) values, defined as the total summation of daily average air temperature above 10 °C from April 1 to October 31 in the Northern Hemisphere. Mapping WI is commonly performed by spatial interpolation of temperature data collected with weather station (WS) networks. However, in complex terrain such as the European Alps, these are usually irregularly and sparsely distributed or unavailable. This renders traditional geospatial interpolation approaches unreliable. As an alternative, thermal remote sensing data, which are intrinsically spatialized, can be used. The aims of this paper are the following: (1) to provide time series of WI-value maps from 2003 to 2010, by means of the Moderate-resolution Imaging Spectroradiometer (MODIS) land surface temperature (MOD11A1 and MYD11A1) daily data and (2) to calibrate them using ground truth data, collected by two WS networks, (3) leading to a classification of the study area into WI regions. WI values from WS data and from MODIS data exhibit a strong linear regression, close to the 1 : 1 prediction with an offset of 42.02 and a slope of 0.90. These coefficients have been used to calibrate the original map, before the following classification. The study area was found to have growing degree days that correspond to four Winkler Regions. The use of MODIS data represents a robust and straightforward method for selectively detecting areas suitable for grapevine varieties, particularly in regions with sparsely distributed WSs.

The assessment of species diversity in relatively large areas has always been a challenging task for ecologists, mainly because of the intrinsic difficulty to judge the completeness of species lists and to undertake sufficient and appropriate sampling. Since the variability of remotely sensed signal is expected to be related to landscape diversity, it could be used as a good proxy of diversity at species level. It has been demonstrated that the relation between species and landscape diversity measured from remotely sensed data or land use maps varies with scale. However, Free and Open Source tools (allowing an access to the source code) for assessing landscape diversity at different spatial scales are still lacking today. In this paper, we aim at: i) providing a theoretical background of the mostly used diversity indices stemmed from information theory that are commonly applied to quantify landscape diversity from remotely sensed data and ii) proposing a free and robust Open Source tool ( r.diversity ) with its source code for calculating diversity indices (and allowing an easy potential implementation of new metrics by multiple contributors globally) at different spatial scales from remotely-sensed imagery or land use maps, running under the widely used Open Source program GRASS GIS. r.diversity can be a valuable tool for calculating landscape diversity in an Open Source space given the availability of multiple indices at multiple spatial scales with the possibility to create new indices directly reusing the code. We expect that the subject of this paper will stimulate discussions on the opportunities offered by Free and Open Source Software to calculate landscape diversity indices.

The causal relationship between species diversity and environmental (landscape) heterogeneity has been a long-lasting interest among ecologists. In fact, environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with higher environmental heterogeneity can host more species due to their higher number of available niches. In particular, entropy (also referred to as “heterogeneity”) measured by the spatial variation of remotely sensed spectral signals has been proposed as a proxy for species diversity. The aim of this book chapter is to review the main spatial algorithms for measuring landscape heterogeneity based on remote sensing.

Aerial photographs provide the basis for developing indices of landscape composition and structure as sensitive measures of large-scale environmental change over relatively long periods of time. In view of this, proper image rectification is needed to enable geometrically unbiased application of landscape metrics in order to obtain meaningful results. It is also particularly important to provide researchers with image rectification tools within an open source environment, in order to: (i) guarantee free and robust tools for processing remote sensing data, (ii) facilitate customization, and (iii) provide useful support via forums and email lists. In this paper we provide a complete description of a robust and freely licensed toolchain for orthorectifying images, which is available in the open source software GRASS GIS. We will first sketch the theoretical background behind rectification and then illustrate the workflow of the orthorectification procedure in GRASS GIS.

Heat requirements for grapevine varieties have been widely used to characterize potential growing regions for viticulture. One of the most important indices is the Winkler Index (WI) defined as the total summation of daily average air temperature above 10 °C from 1st of April to 31th of October in the Northern hemisphere [1]. Mapping of the WI is commonly based on temperature data from meteorological stations. However, in complex terrain such as the European Alps, these are usually irregularly and sparsely distributed or unavailable. This renders traditional geospatial interpolation approaches difficult to become reliable. As an alternative, thermal remote sensing data, which are intrinsically spatialised, can be used. The aim of this work was to provide time series of Winkler Index maps from 2003 to 2010, by means of the MODIS Land Surface Temperature (LST) data and to validate the maps using ground truth data, collected by two weather station networks.