Detecting animals in African Savanna with UAVs and the crowds

Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub-decimeter resolution color images. The system achieves a high recall rate and a human operator can then eliminate false detections with limited effort. Our system provides good perspectives for the development of data-driven management practices in wildlife conservation. It shows that the detection of large mammals in semi-arid Savanna can be approached by processing data provided by standard RGB cameras mounted on affordable fixed wings UAVs

Divergent selection in trailing- versus leading-edge populations of Biscutella laevigata

Background and Aims Knowledge on how climate-induced range shifts might affect natural selection is crucial to understand the evolution of species ranges. Methods Using historical demographic perspectives gathered from regional-scale phylogeography on the alpine herb Biscutella laevigata, indirect inferences on gene flow and signature of selection based on AFLP genotyping were compared between local populations persisting at the trailing edge and expanding at the leading edge. Key Results Spatial autocorrelation revealed that gene flow was two times more restricted at the trailing edge and genome scans indicated divergent selection in this persisting population. In contrast, no pattern of selection emerged in the expanding population at the leading edge. Conclusions Historical effects may determine different architecture of genetic variation and selective patterns within local populations, what is arguably important to understand evolutionary processes acting across the species range

Biogeoinformatics

GIScience methods and geodata may contribute to the elaboration of efficient decision‐making approaches to favor the conservation of plant/animal genetic diversity and to favor the advancement of our understanding of mechanisms controlling the evolution of species (adaptation to local environment in particular). Since 2001, a group of landscape genetics at LASIG/EPFL immersed in the evolutionary biology community. A sustainable collaboration with biologists, geneticists, vets, geneticitsts, etc. enabled a real appropriation of the domain (molecular ecology). These transdisciplinary efforts - versus punctual unsignificant interdisciplinary periods of service exchange - permitted to extract the best from GIScience - spatial analysis and geocomputation in particular - to serve evolutionary biology

Current challenge in landscape genomics: what about the environmental counterpart of high-throughput genomic data?

Landscape genomics aims to identify genomic regions having adaptive significance by combining genomic and environmental data using regression methods. As regards its genetic component, next-generation high throughput sequencing technologies became available at the beginning of the 2000s. Compared with the traditional DNA sequencing technologies developed in the late 1970s, they provide information characterizing much larger parts of the genome, are faster, and contribute in major discoveries and applications in medical research and evolution. But what about the environmental component of landscape genomics? Are the spatial and temporal resolutions of environmental variables improving compared with their genetic counterpart? Yes to a certain extent, but the scale at which adaptation of species to their local environment is investigated determines the richness and cost of available variables. On a large geographic scale, there is a plethora of data sets of very good quality and low cost. On a local scale, there is a plethora of cutting-edge sensors to be distributed in the field, and of multi-spectral satellite images whose use also results in high costs. In this context, multi-scale variables derived from high resolution digital elevation models provide reliable surrogates for topo-climatic variables, offering suitable alternatives for inclusion in landscape genomic models

Biogeoinformatics of livestock genomic resources

In 2008, FAO and WAAP published a report on production environment descriptors for animal genetic resources. One of the main conclusions was that it was necessary to quickly systematize the recording of breeds’ geographical coordinates worldwide in order to enable links to any kind of information available in other geo-referenced databases. Today we must recognize that this recommendation has hardly been applied and that even important projects on genomic resources did not take care of characterizing sampled animals with their precise location. Indeed, livestock conservation tasks require complementary data on population and evolutionary genetics, on animal husbandry practices, but also data characterizing the socio-economic and environmental conditions of the regions where animals are bred. Only the integration of these different information levels is likely to facilitate and optimize the processes used to establish priorities in the conservation of livestock genetic resources. In addition, in conjunction with molecular data the use of geographical coordinates enables the implementation of livestock landscape genomics to seek regions of the genome influencing the ability of animals to cope with environmental variations. This approach can be used to identify key traits involved in parasite resistance, to conserve the adaptive potential of local breeds, and even to increase adaptability in industrial breeds. In both cases computer science and biogeoinformatics have a more important role than ever. New knowledge will be extracted from the present data tsunami constituted by the advent of high throughput molecular data, new sources of high resolution environmental data, new sources of socio-economic data, etc. only if innovative, transdisciplinary and efficient computing tools are developed. But for biogeoinformatics can keep its promises, an important challenge for the livestock genomics resources community for the next decade remains to enforce the recording of geographical coordinates of any sampled animals as a standard rule

Combining biotechnologies and GIScience to contribute to sheep and goat genetic resource conservation

Geographic Information Science methods and tools are likely to help to extract useful and so far unknown information from large spatially explicit genetic datasets to understand the distribution of diversity among and within sheep and goat breeds. Considering the vast quantity of data collected within the Econogene project, exploratory data analysis methods were chosen as mean of investigation

Geomedicine: opportunities of using spatial information to move toward more precision in public health - spatial approaches and clusters: an introduction for clinicians

In this presentation we introduce basic knowledge about the use of located health data to detect clusters of disease prevalence. Most often, geographic maps are produced to *represent* health data. Medical information is transmitted through thematic choropleth (or not) maps. For instance administrative units – that can be surface or points - are colored according to the variable of interest. Today we will stress the importance of analysing health data by explicitly including geographic characteristics (distances, co-location) and also the potential and power of spatial statistics to detect specific patterns in the geographic distribution of disease occurrences (make visible the invisible). A classic example using clusters is the map produced by John Snow in 1854 showing the number of deaths caused by a cholera outbreak in London. Looking at a detail of Snow's original map, it is possible to realise how he graphically represented the number of deaths, with short bold lines representing death occurrences placed on the street at the addresses where it happened (this is what we name now georeferencing); and together the lines form histograms. The cluster of death people is an effect observed on the territory, and the existence of this cluster depends on an infected water pump located at the same place, and this is the cause. How can this spatial dependence be detected and measured? The main objective is to identify patterns in the geographic space. So we need to determine whether the variable of interest is randomly distributed or spatially dependent, and to check if the patterns observed are robust to random permutations. Finally we also need to explore the data to find out what is the range of influence of this spatial dependence. Here I will explain the functioning of one among several measures of spatial autocorrelation named Moran’s. Let us consider a cloud of points distributed in the geographic space and focus on a first point of the dataset around which we decide to use a neighborhood of 5km defining spatial weighting. The mean of the values of the variable of interest for all points located within this neighborhood will be compared with the value of the central point. Then the algorithm will move to the next point and do the same for all points in the dataset. We obtain two distributions of observed versus weighted values and then we process a linear regression between these 2 variables to obtain the coefficient of regression, which is equivalent to Moran’s I. After standardization, we obtain a Moran’s scattergram.The distribution of points among the quadrats of the scattergram defines 4 classes which correspond to the types of relationships between observed values and weighted values at all locations. E.g. High-high (red) = high observed value and high weighted value. Moran’s I translates the global relationship between points and their neighborhood, but the class membership provides a local information to be displayed on the map. Then we need to check if the Moran’s I obtained is statistically significant. The question is to know whether the spatial structure observed and quantified by the Moran’s I persists when BMI values are randomly distributed among all locations? (permutations are run by means of Monte-Carlo method). Moran’s I is calculated again after each run of random permutations and after each run feeds the histogram. A pseudo p-value is calculated on the basis of the number of random configurations that produce a Moran’s I higher or equal to the observed one. The white dots on the map thus correspond to a random situation showing a neutral space without spatial dependence. When using the local version of Moran’s I named LISA for Local Indicators of Spatial Association, the pseudo p-value obtained can be mapped to show the level of significance of the local spatial autocorrelation. This opportunity is interesting because it allows to introduce subtleties in the interpretation of the clusters obtained. Finally, it is important to keep in mind that spatial statistics like Moran’s I or Getis-Ord Gi are exploratory approaches and that it is always necessary to test several spatial lags to possibly identify different explanatory factors. To conclude we want to say that the measure of spatial dependence is key to detect and visualize spatial patterns in health data because spatial statistics can reveal signals that remain often hidden using thematic mapping. On the basis of the clusters highlighted by these exploratory methods, it is possible to formulate hypotheses about possible environmental or socio-economic causes and to test them with the help of confirmatory statistics «Ideas come from previous explorations» John Tukey said in a paper published in 1980 in The American Statistician, a paper entitled «We Need Both Exploratory and Confirmatory». First explore and then confirm was the reasoning applied by John Snow to detect deaths "hot spots" in London, which then allowed him to hypothesize that a particular water pump was infected, and finally to take public health steps to check the cholera epidemic

High performance computation of landscape genomic models integrating local indices of spatial association

Since its introduction, landscape genomics has developed quickly with the increasing availability of both molecular and topo-climatic data. The current challenges of the field mainly involve processing large numbers of models and disentangling selection from demography. Several methods address the latter, either by estimating a neutral model from population structure or by inferring simultaneously environmental and demographic effects. Here we present Sam$\beta$ada, an integrated approach to study signatures of local adaptation, providing rapid processing of whole genome data and enabling assessment of spatial association using molecular markers. Specifically, candidate loci to adaptation are identified by automatically assessing genome-environment associations. In complement, measuring the Local Indicators of Spatial Association (LISA) for these candidate loci allows to detect whether similar genotypes tend to gather in space, which constitutes a useful indication of the possible kinship relationship between individuals. In this paper, we also analyze SNP data from Ugandan cattle to detect signatures of local adaptation with Sam$\beta$ada, BayEnv, LFMM and an outlier method (FDIST approach in Arlequin) and compare their results. Sam$\beta$ada is an open source software for Windows, Linux and MacOS X available at \url{http://lasig.epfl.ch/sambada}Comment: 1 figure in text, 1 figure in supplementary material The structure of the article was modified and some explanations were updated. The methods and results presented are the same as in the previous versio

Population density and water balance influence the global occurrence of hepatitis E epidemics

In developing countries, the waterborne transmission of hepatitis E virus (HEV), particularly the genotypes 1 and 2, leads to the onset of large recurrent outbreaks. In order to identify the geographical regions at higher risk of HEV epidemics and the conditions most favorable for the transmission of the virus, we compiled a dataset of HEV outbreaks and used it to obtain models of HEV distribution across the planet. The main three variables that best predict the geographical distribution of HEV outbreaks at global scale are population density, annual potential evapotranspiration and precipitation seasonality. At a regional scale, the probability of illness due to HEV in the Ganges watershed is negatively correlated with the river discharge and positively correlated with the number of reported outbreaks. Similarly, the temporal occurrence of HEV outbreaks in the region is negatively correlated with the discharge of the Ganges river. Combined, our findings suggest that population density and water balance are the main parameters influencing the occurrence of HEV epidemics