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The current and future consequences of anthropogenic impacts such as climate change and habitat loss on ecosystems will be better understood and therefore addressed if diverse ecological data from multiple environmental contexts are more effectively shared. Re-use requires that data are readily available to the scientific scrutiny of the research community. A number of repositories to store shared data have emerged in different ecological domains and developments are underway to define common data and metadata standards. Nevertheless, the goal is far from being achieved and many challenges still need to be addressed. The definition of best practices for data sharing and re-use can benefit from the experience accumulated by pilot collaborative projects. The Euromammals bottom-up initiative has pioneered collaborative science in spatial animal ecology since 2007. It involves more than 150 institutes to address scientific, management and conservation questions regarding terrestrial mammal species in Europe using data stored in a shared database. In this manuscript we present some key lessons that we have learnt from the process of making shared data and knowledge accessible to researchers and we stress the importance of data management for data quality assurance. We suggest putting in place a pro-active data review before data are made available in shared repositories via robust technical support and users’ training in data management and standards. We recommend pursuing the definition of common data collection protocols, data and metadata standards, and shared vocabularies with direct involvement of the community to boost their implementation. We stress the importance of knowledge sharing, in addition to data sharing. We show the crucial relevance of collaborative networking with pro-active involvement of data providers in all stages of the scientific process. Our main message is that for data-sharing collaborative efforts to obtain substantial and durable scientific returns, the goals should not only consist in the creation of e-infrastructures and software tools but primarily in the establishment of a network and community trust. This requires moderate investment, but over long-term horizons.

Monitoring agricultural production in vulnerable developing countries is important for food security assessment and requires near real-time (NRT) information on crop growing conditions for early detection of possible production deficits. The public online ASAP system (Anomaly hot Spots of Agricultural Production) is an early warning decision support tool based on weather data and direct observation of crop status as provided by remote sensing. Although decision makers and food security analysts are the main targeted user groups, all the information is fully made available to the public in a simple and well documented online platform. The information further contributes to multi-agency early warning products such as the GEOGLAM Crop Monitor for Early Warning and food security assessments following the IPC-Cadre Harmonisé framework.Low resolution remote sensing (1 km) and meteorological (5-25 km) data are processed automatically every 10 days and vegetation anomaly warnings are triggered at the first sub-national administrative level. The severity of the warnings is based on the observed land surface phenology and three main derived indicators computed at the 1 km grid level: a proxy of the current season biomass production (the cumulative value of the Normalized Difference Vegetation index from the start of season); an indicator of precipitation deficit (the Standardized Precipitation Index at the 3 month scale); and a water-balance model output (the Water Requirement Satisfaction Index).Warning maps and summary information are published on a web GIS every ten days and then further analyzed by analysts every month. This results in the identification of hotspot countries with potentially critical crop or rangelands production conditions.In addition to the hotspots analysis and the warning explorer, users can also zoom in to the parcel level thank to the so called High Resolution Viewer, a web interface based on Google Earth Engine that allows to visualize Sentinels (1 and 2) and Landsat imagery, plot temporal profiles and perform basic anomaly operation (e.g. current year NDVI anomaly with respect to a reference year). In the near future it is planned to make the anomaly warnings available also at the second sub-national level and to integrate meteorological forecasts in the warning system.

Much research on large herbivore movement has focused on the annual scale to distinguish between resident and migratory tactics, commonly assuming that individuals are sedentary at the within-season scale. However, apparently sedentary animals may occupy a number of sub-seasonal functional home ranges (sfHR), particularly when the environment is spatially heterogeneous and/or temporally unpredictable. The roe deer (Capreolus capreolus) experiences sharply contrasting environmental conditions due to its widespread distribution, but appears markedly sedentary over much of its range. Using GPS monitoring from 15 populations across Europe, we evaluated the propensity of this large herbivore to be truly sedentary at the seasonal scale in relation to variation in environmental conditions. We studied movement using net square displacement to identify the possible use of sfHR. We expected that roe deer should be less sedentary within seasons in heterogeneous and unpredictable environments, while migratory individuals should be seasonally more sedentary than residents. Our analyses revealed that, across the 15 populations, all individuals adopted a multi-range tactic, occupying between two and nine sfHR during a given season. In addition, we showed that (i) the number of sfHR was only marginally influenced by variation in resource distribution, but decreased with increasing sfHR size; and (ii) the distance between sfHR increased with increasing heterogeneity and predictability in resource distribution, as well as with increasing sfHR size. We suggest that the multirange tactic is likely widespread among large herbivores, allowing animals to track spatio-temporal variation in resource distribution and, thereby, to cope with changes in their local environment. Migration · Residency · Sub-seasonal functional home range · Plasticity · Roe deer

The increasing frequency and severity of extreme climatic events and their impacts are being realized in many regions of the world, particularly in smallholder crop and livestock production systems in Sub-Saharan Africa (SSA). These events underscore the need for timely early warning. Satellite Earth Observation (EO) availability, rapid developments in methodology to archive and process them through cloud services and advanced computational capabilities, continue to generate new opportunities for providing accurate, reliable, and timely information for decision-makers across multiple cropping systems and for resource-constrained institutions. Today, systems and tools that leverage these developments to provide open access actionable early warning information exist. Some have already been employed by early adopters and are currently operational in selecting national monitoring programs in Angola, Kenya, Rwanda, Tanzania, and Uganda. Despite these capabilities, many governments in SSA still rely on traditional crop monitoring systems, which mainly rely on sparse and long latency in situ reports with little to no integration of EO-derived crop conditions and yield models. This study reviews open-access operational agricultural monitoring systems available for Africa. These systems provide the best-available open-access EO data that countries can readily take advantage of, adapt, adopt, and leverage to augment national systems and make significant leaps (timeliness, spatial coverage and accuracy) of their monitoring programs. Data accessible (vegetation indices, crop masks) in these systems are described showing typical outputs. Examples are provided including crop conditions maps, and damage assessments and how these have integrated into reporting and decision-making. The discussion compares and contrasts the types of data, assessments and products can expect from using these systems. This paper is intended for individuals and organizations seeking to access and use EO to assess crop conditions who might not have the technical skill or computing facilities to process raw data into informational products.

The most common framework under which ungulate migration is studied predicts that it is driven by spatio–temporal variation in plant phenology, yet other hypotheses may explain differences within and between species. To disentangle more complex patterns than those based on single species/ single populations, we quantified migration vari-ability using two sympatric ungulate species differing in their foraging strategy, mating system and physiological constraints due to body size. We related observed variation to a set of hypotheses. We used GPS-collar data from 537 individuals in 10 roe Capreolus capreolus and 12 red deer Cervus elaphus populations spanning environmental gra-dients across Europe to assess variation in migration propensity, distance and tim-ing. Using time-to-event models, we explored how the probability of migration varied in relation to sex, landscape (e.g. topography, forest cover) and temporally-varying environmental factors (e.g. plant green-up, snow cover). Migration propensity varied across study areas. Red deer were, on average, three times more migratory than roe deer (56% versus 18%). This relationship was mainly driven by red deer males which were twice as migratory as females (82% versus 38%). The probability of roe deer migration was similar between sexes. Roe deer (both sexes) migrated earliest in spring. While territorial male roe deer migrated last in autumn, male and female red deer migrated around the same time in autumn, likely due to their polygynous mating system. Plant productivity determined the onset of spring migration in both species, but if plant productivity on winter ranges was sufficiently high, roe deer were less likely to leave. In autumn, migration coincided with reduced plant productivity for both species. This relationship was stronger for red deer. Our results confirm that ungulate migration is influenced by plant phenology, but in a novel way, that these effects appear to be modulated by species-specific traits, especially mating strategies.

Monitoring crop and rangeland conditions is highly relevant for early warning and response planning in food insecure areas of the world. Satellite remote sensing can obtain relevant and timely information in such areas where ground data are scattered, non-homogenous, or frequently unavailable. Rainfall estimates provide an outlook of the drivers of vegetation growth, whereas time series of satellite-based biophysical indicators at high temporal resolution provide key information about vegetation status in near real-time and over large areas. The new early warning decision support system ASAP (Anomaly hot Spots of Agricultural Production) builds on the experience of the MARS crop monitoring activities for food insecure areas, that have started in the early 2000's and aims at providing timely information about possible crop production anomalies. The information made available on the website (https://mars.jrc.ec.europa.eu/asap/) directly supports multi-agency early warning initiatives such as for example the GEOGLAM Crop Monitor for Early Warning and provides inputs to more detailed food security assessments that are the basis for the annual Global Report on Food Crises. ASAP is a two-step analysis framework, with a first fully automated step classifying the first sub-national level administrative units into four agricultural production deficit warning categories. Warnings are based on rainfall and vegetation index anomalies computed over crop and rangeland areas and are updated every 10 days. They take into account the timing during the crop season at which they occur, using remote sensing derived phenology per-pixel. The second step involves the monthly analysis at country level by JRC crop monitoring experts of all the information available, including the automatic warnings, crop production and food security-tailored media analysis, high-resolution imagery (e.g. Landsat 8, Sentinel 1 and 2) processed in Google Earth Engine and ancillary maps, graphs and statistics derived from a set of indicators. Countries with potentially critical conditions are marked as minor or major hotspots and a global overview is provided together with short national level narratives., Highlights • Earth Observation based global monitoring of crop and rangeland conditions for food security analysis • Timely early warning thanks to frequent updates (10 days for climate anomalies, 30 days for agricultural hotspot analysis) • Public online webGIS with downloadable information • Country level agricultural production hotspot identification and short narratives for non-remote sensing experts

Birds of prey, as top predators, play a key role in ecosystem functioning by regulating prey populations and, by means of cascade effects, promoting biodiversity. This makes them adequate sentinels of ecosystem health. Here we analyse the relationship between the occurrence of breeding short-toed snake eagle (Circaetus gallicus) and both the richness of potential prey species and landscape characteristics by taking into account two different spatial scales (i.e. nest-site scale and landscape scale). The short-toed snake eagle offers an interesting case study for investigating the relationships between top predators, prey diversity, and habitats, because it is an extremely specialised raptor that feeds on mesopredators, mostly snakes. Additionally, short-toed snake eagles are mainly threatened by changes in agriculture and land use in Europe, which have reduced the extent of suitable hunting habitats, and by the decrease in snake populations. Our study was conducted in the Latium Region (central Italy) in 2007, where most of the Italian breeding population is concentrated. By means of habitat selection analyses using generalised linear models, our results showed that the species selected breeding areas characterised by low elevations, rugged slopes, and high snake species richness at the nest-site scale (1 km2 ). At the landscape scale (25 km2), the best model showed that birds selected areas characterised by lower elevations for nesting, with a tendency towards intermediate values of wood cover and high snake species richness. Our study highlights the strong relationship between snake species richness and the occurrence of breeding eagles at both spatial scales, with optimal breeding sites located closer to hunting areas than expected by chance. This study provides further support for the role of short-toed snake eagles as sentinel species for Mediterranean habitats, and highlights the link between the location of nesting sites and the occurrence of human-modified landscapes characterised by high prey richness.

This study develops a methodology to identify hot spots of critical forage supply in nomadic pastoralist areas, using the Afar Region, Ethiopia, as a special case. It addresses two main problems. First, it makes a spatially explicit assessment of fodder supply and demand extracted from a data poor environment. Fodder supply is assessed by combining rainfall-based production functions and rule-based assessment for prevailing land use. Fodder demand is based on a data consistency check of livestock statistics concerning herd size, composition and geographical distribution. Second, individual herd movements have to be evaluated jointly in concurrent migration patterns to assess local pressures on fodder resources. We, therefore, apply a transition model that relates stock levels to seasonal migration routings for all Afar sub-clans jointly so as to localize the hot spots where feed demand exceeds forage supply. Critical areas come to the fore, especially, near fringes of Highlands and in the southern part of the Afar. A sensitivity test shows that ‘Baseline’ scenario is close to the ‘Best’ but under ‘Worst’, the Afar region would fall into despair. We conclude that the model is a useful tool to inform policy makers on critical areas in the Afar region. View Full-Text

Real time monitoring of vegetation conditions is of paramount importance, particularly for food insecure countries, to detect possible crop and pastures production drops as early as possible. This monitoring is classically based on remote sensing indicators of vegetation conditions (typically NDVI and rainfall produced every ten days at a resolution of 1 km or more) and many web portals now offer anomaly maps and time profiles derived from these indicators; however, timely and coherent interpretation of this coarse resolution information at global scale remains challenging. With the ASAP system (Anomaly hot Spots of Agricultural Production) we propose a two-step analysis to provide every month warning of production deficits in water-limited agricultural systems. The first step is fully automated and classifies each Gaul 1 unit (i.e. first sub-national administrative level) according to a warning scale. These warnings are triggered only during the crop/pasture growing season, as derived from a remote sensing based phenology. For each Gaul 1 unit, the classification takes into consideration its fraction of agricultural area affected by a severe anomaly for two rainfall-based indicators and one biomass indicator, as well as timing in the crop cycle at which the anomaly occurs. In the second step, agricultural analysts check the automatic warnings to identify the countries which qualify as “hot spot” because of their potentially critical conditions. The system elaborates the warnings for the whole globe but the analysts focus on 80 food insecure countries in Africa, Asia and America. In their evaluation, the analysts are assisted by graphs and maps automatically generated in the first step, agriculture and food security-tailored media analysis and high resolution imagery (e.g. Landsat 8, Sentinel 1 and 2) processed with Google Earth Engine. Maps and statistics, accompanied by short narratives are then published on the website and can be used directly by food security analysts with no expertise in crop monitoring with remote sensing, or can contribute to global early warning bulletins such as the GEOGLAM Early Warning Crop Monitor, which synthesizes every month crop conditions analysis from various institutions.

Partial migration, when only part of the population migrates seasonally while the other part remains resident on the shared range, is the most common form of migration in ungulates. Migration is often defined by spatial separation of seasonal ranges and consequently, classification of individuals as migrants or residents is usually only based on geographic criteria. However, the underlying mechanism for migration is hypothesized to be movement in response to spatiotemporal resource variability and thus, migrants are assumed to travel an “ecological distance” (ED) or shift their realized ecological niches. While ecological and geographic distances should be related, their relationship may depend on landscape heterogeneity. Here, we tested the utility of ecological niche theory to both classify migratory individuals and to understand the underlying ecological factors for migratory behavior. We developed an integrative approach combining measures in geographic and ecological niche space and used this to classify and explain migratory behavior of 71 annual roe deer (Capreolus capreolus) movement trajectories in five European study areas. First, to assess the utility of the ED concept for classifying migratory behavior, we tested whether roe deer sought the same ecological conditions year-round or moved to different ecological conditions by measuring the annual ED travelled and the seasonal niche overlap using multivariate statistics. Comparing methods to classify migrants and residents based on geographic and ecological niche space, we found that migratory roe deer switched between seasons both in geographic and in ecological dimensions. Second, we tested which seasonal ecological factors separated resident from migrant niches using discriminant analysis and which broad-scale determinants (e.g., spatiotemporal forage variation and population density) predicted migration probability using generalized linear models. Our results indicated that factors describing forage and topographic variability discriminated seasonal migrant from resident niches. Determinants for predicting migration probability included the temporal variation (seasonality) and also the spatial variability of forage patches. Last, we also found suggestive evidence for a positive relationship between population density and migration probability. By applying the ecological niche concept to the study of partial migration in ungulates, our work underlines that partial migration is a form of behavioral plasticity. Key words: behavioral plasticity; Capreolus capreolus; ecological distance; large herbivores; niche switching; Normalized Difference Vegetation Index; partial migration; realized niche; spatiotemporal variation; ungulates

International audience; Supplemental feeding for ungulates is a widespread practice in many human-dominated landscapes across Europe and North America, mainly intended to seasonally support populations. Surprisingly, little consideration was given so far to the effect of supplemental feeding on ungulate spatial ecology at a large scale, in management and conservation studies. Analyses of the main ecological drivers influencing the use of supplemental feeding sites by ungulates across a gradient of abiotic and biotic factors are currently lacking. We conducted a large-scale assessment of ecological and management drivers of use of feeding station sites in roe deer (Capreolus capreolus), a small cervid widely distributed across Europe that is particularly sensitive to winter severity. We tested four competing hypotheses by comparing the time spent at feeding station sites by 180 individual Global Positioning System-collared roe deer from nine populations spanning a wide latitudinal and altitudinal gradient. We found that roe deer used feeding station sites highly opportunistically in response to winter severity across its range. The harshest weather conditions at the northern range limit or the highest elevations provoked an intense use of feeding station sites, which typically peaked at the end of winter, in accordance with the adverse weather and nutritional condition hypotheses. Consistently, milder winters corresponded to a reduced and/or more homogeneous use of supplemental feeding. In general, intensively used feeding station sites heavily conditioned spatial behavior of roe deer. Importantly, biotic factors such as the presence of competitors decreased roe deer use of supplemental feeding station sites. Our results emphasize the importance of this human-induced alteration to resource distribution, especially in the context of the rapidly occurring climate change that is modifying resource availability for ungulate populations.

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.

Human–wildlife conflicts are common across Africa. In Mozambique, official records show that wildlife killed 265 people during 27 months (July 2006 to September 2008). Crocodile Crocodylus niloticus, lion Panthera leo, elephant Loxodonta africana and hippopotamus Hippopotamus amphibius caused most deaths but crocodiles were responsible for 66%. Crocodile attacks occurred across Mozambique but 53% of deaths occurred in districts bordering Lake Cabora Bassa and the Zambezi River. Hippopotamus attacks were also concentrated here. Lion attacks occurred mainly in northern Mozambique and, while people were attacked by elephants across the country, 67% of deaths occurred in northern Mozambique. Attacks by lions, elephants or hippopotamuses were relatively rare but additional data will probably show that attacks by these species are more widespread than the preliminary records suggest. Buffalo Syncerus caffer, hyaena Crocuta crocuta and leopard Panthera pardus were minor conflict species. Good land-use planning, a long-term solution to many conflicts, is particularly relevant in Mozambique, where the crocodile and hippopotamus populations of protected areas are often in rivers that border these areas, and cause conflicts outside them, and where people commonly live within protected areas. Poverty may prompt fishermen to risk crocodile attack by entering rivers or lakes. The high incidence of conflicts near Limpopo and South Africa’s Kruger National Parks (both within the Great Limpopo Transfrontier Conservation Area) highlights the problems created for people by facilitating the unrestricted movement of wildlife between protected areas across their land.

This study presents the main results and the methodology used in the creation of the atlas of the risk of desertification in Italy. A desertified area was defined as an unproductive area for agricultural or forestry use, due to soil degradation processes. An area at risk of desertification was a tract of the earth's surface which is vulnerable or sensitive to the processes of desertification. In a vulnerable land, environmental characteristics are close to that of a desertified area, but some factors (e.g. vegetation cover or irrigation), successfully mitigate the desertification process. On the other hand, sensitive land is a surface where the process leading to desertification is active, although the land is not yet unproductive. The DPSIR (Driving force-Pressure-State-Impact-Response) framework has been adopted as a reference. Using the national soil information system and socio-economic layers, an atlas of indicators of desertification risks was created, which was organized into different soil degradation systems. 51.8 per cent of Italy was considered to be at potential risk of desertification. Some 21.3 per cent of Italy (41.1 per cent of the area at potential risk) featured land degradation phenomena. Specifically, 4.3 per cent of Italy is already unproductive; 4.7 per cent is sensitive and 12.3 per cent is vulnerable. In the territory at potential risk of desertification, unproductive lands, plus areas vulnerable or sensitive to soil erosion, are at least the 19 per cent. Areas affected by aridity also sum up to 19 per cent. Salt-affected soils in Italy are estimated to cover >1 million ha. Irrigation can mitigate soil aridity and salinization, nevertheless, only about 15 per cent of the sensitive and vulnerable lands of southern Italy are actually equipped with irrigation networks. Copyright © 2009 John Wiley & Sons, Ltd.

Monitoring crop and natural vegetation conditions is highly relevant, particularly in the food insecure areas of the world. Data from remote sensing image time series at high temporal and medium to low spatial resolution can assist this monitoring as they provide key information about vegetation status in near real-time over large areas. The Software for the Processing and Interpretation of Remotely sensed Image Time Series (SPIRITS) is a stand-alone flexible analysis environment created to facilitate the processing and analysis of large image time series and ultimately for providing clear information about vegetation status in various graphical formats to crop production analysts and decision makers. In this paper we present the latest functional developments of SPIRITS and we illustrate recent applications. The main new developments include: HDF5 importer, Image re-projection, additional options for temporal Smoothing and Periodicity conversion, computation of a rainfall-based probability index (Standardized Precipitation Index) for drought detection and extension of the Graph composer functionalities.In particular,. The examples of operational analyses are taken from several recent agriculture and food security monitoring reports and bulletins. We conclude with considerations on future SPIRITS developments also in view of the data processing requirements imposed by the coming generation of remote sensing products at high spatial and temporal resolution, such as those provided by the Sentinel sensors of the European Copernicus programme.

International audience; Breeding dispersal, defined as the net movement between successive breeding sites, remains a poorly understood and seldom reported phenomenon in mammals, despite its importance for population dynamics and genetics. In large herbivores, females may be more mobile during the breeding season, undertaking short-term trips (excursions) outside their normal home range. If fertilisation occurs, leading to gene flow of the male genome, this behaviour could be considered a form of breeding dispersal from a genetic point of view. Here, we investigated ranging behaviour of 235 adult roe deer using intensive GPS monitoring in six populations across Europe within the EURODEER initiative. We show that excursions are common from June to August among females, with 41.8 % (vs. 18.1 % of males) making at least one excursion. Most individuals performed only one excursion per season and departure dates for females were concentrated in time, centred on the rutting period, suggesting a link with reproduction. The distance females travelled during excursions was significantly greater than the site-specific average diameter of a male home range, while travel speed decreased once they progressed beyond this diameter, indicating search behaviour or interaction with other male(s) outside the resident male's territory. Because adults are normally highly sedentary, the potential for mating with relatives is substantial; hence, we conclude that rut excursions could be an alternative tactic enabling females to avoid mating with a closely related male. To understand better the ultimate drivers at play, it will be crucial to explore the genetic causes and consequences of this behaviour.

Monitoring vegetation conditions is a critical activity for assessing food security in the Horn of Africa. Remote sensing from space offers a unique opportunity to obtain consistent and timely information over large and often inaccessible areas where field observations are scattered, non-homogenous, or frequently unavailable. In this study we outline a method to assess objectively the performance and characteristics of seasonal vegetation development solely on the basis of time series of the fraction of absorbed photosynthetically active radiation (FAPAR) derived from Satellite Pour l’Observation de la Terre SPOT-VEGETATION (SPOT-VGT) imagery. Key phenological indicators such as the start and end of growing periods are derived from a statistical analysis of the time series to characterize the spatial and temporal evolution of successive seasons. These indicators are then utilized to compute a proxy of the seasonal gross primary production (GPP) as the cumulative FAPAR during the growing season. Vegetation condition and associated risk of food deficit for specific seasons and locations are finally derived from the comparison of the seasonal GPP proxy with its average value computed over the whole time series. The impact on vegetation of the severe drought experienced by the Horn of Africa between late 2010 and late 2011 is discussed., JRC.H.4-Monitoring Agricultural Resources

A wildlife tracking data management system must include the capability to explicitly deal with the spatial properties of movement data. GPS tracking data are sets of spatiotemporal objects (locations), and the spatial component must be properly managed. You will now extend the database built in Chaps. 2, 3 and 4, adding spatial functionalities through the PostgreSQL spatial extension called PostGIS. PostGIS introduces spatial data types (both vector and raster) and a large set of SQL spatial functions and tools, including spatial indexes. This possibility essentially allows you to build a GIS using the capabilities of relational databases. In this chapter, you will start to familiarise yourself with spatial SQL and implement a system that automatically transforms the GPS coordinates generated by GPS sensors from a pair of numbers into spatial objects.

Early warning monitoring systems in food-insecure countries aim to detect unfavourable crop and pasture conditions as early as possible during the growing season. This manuscript describes a procedure to estimate the probability of experiencing an end-of-season biomass production deficit during the on-going season based on a statistical analysis of Earth Observation data. A 15-year time series of the Fraction of Absorbed Photosynthetically Active Radiation from the SPOT-VEGETATION instrument is used to characterize the climatological development of vegetation, its variability and its current status. Forecasts of overall seasonal performances, expressed in terms of the probability of experiencing a critical deficit at the end of the growing season, are updated regularly whenever a new satellite observation is made available. Results and performances of the method are discussed for croplands and pastures in the Sahel.

Animals move in and interact with complex environments that can be characterised by a set of spatial layers containing environmental data. Spatial databases can manage these different data sets in a unified framework, defining spatial and non-spatial relationships that simplify the analysis of the interaction between animals and their habitat. A large set of analyses can be performed directly in the database with no need for dedicated GIS or statistical software. Such an approach moves the information content managed in the database from a ‘geographical space’ to an ‘animal’s ecological space’. This more comprehensive model of the animals’ movement ecology reduces the distance between physical reality and the way data are structured in the database, filling the semantic gap between the scientist’s view of biological systems and its implementation in the information system. This chapter shows how vector and raster layers can be included in the database and how you can handle them using (spatial) SQL. The database built so far in Chaps. 2, 3, 4 and 5 is extended with environmental ancillary data sets and with an automated procedure to intersect these layers with GPS positions.

GPS positions are used to describe animal movements and to derive a large set of information, for example, about animals’ behaviour, social interactions and environmental preferences. GPS data are related to (and must be integrated with) many other sources of information that together can be used to describe the complexity of movement ecology. This can be achieved through proper database data modelling, which depends on a clear definition of the biological context of a study. Particularly, data modelling becomes a key step when database systems manage many connected data sets that grow in size and complexity: it permits easy updates of the database structure to accommodate the changing goals, constraints and spatial scales of studies. In this chapter’s exercise, you will extend your database (see Chap. 2) with two new tables to integrate ancillary information useful to interpreting GPS data: one for GPS sensors and the other for animals.

Tracking data can potentially be affected by a large set of errors in different steps of data acquisition and processing. Erroneous data can heavily affect analysis, leading to biased inference and misleading wildlife management/conservation suggestions. Data quality assessment is therefore a key step in data management. In this chapter, we especially deal with biased locations, or ‘outliers’. While in some cases incorrect data are evident, in many situations, it is not possible to clearly identify locations as outliers because although they are suspicious (e.g. long distances covered by animals in a short time or repeated extreme values), they might still be correct, leaving a margin of uncertainty. In this chapter, different potential errors are identified and a general approach to managing outliers is proposed that tags records rather than deleting them. According to this approach, practical methods to find and mark errors are illustrated on the database created in Chaps. 2, 3, 4, 5, 6 and 7.

This chapter introduces the Pl/R extension, a very powerful alternative to integrate the features offered by R in the database in a gapless workflow. Pl/R is a loadable procedural language that allows the use of the R engine and libraries directly inside the database, thus embedding R scripts into SQL statements and database functions and triggers. Among many advantages, Pl/R avoids unnecessary data replication, allows the use of a single SQL interface for complex scripts involving R queries and offers a tight integration of data analysis and management processes into the database. In this chapter, you will have a basic overview of the potential of Pl/R for the study of GPS locations. You will be introduced to the use of Pl/R, starting with exercises involving simple calculations in R (logarithms, median and quantiles), followed by more elaborated exercises designed to compute the daylight times of a given location at a given date, or to compute complex home range methods.

In the previous chapters, you have exclusively worked with GPS position data. We showed how to organise these data in databases, how to link them to environmental data and how to connect them to R for further analysis. In this chapter, we introduce an example of data recorded by another type of sensor: acceleration data, which can be measured by many tags where they are associated with the GPS sensors and are widely used to interpret the behaviour of tagged animals. The general structure of these data and an overview of possibilities for analysis are given. In the exercise for this chapter, you will learn how to integrate an acceleration data set into the database created in the previous chapters and link it with other information from the database. At the end, the database is extended with acceleration data and with an automated procedure to intersect these layers with GPS positions.

When position data are received from GPS sensors, they are not explicitly associated with any animal. Linking GPS data to animals is a key step in the data management process. This can be achieved using the information on the deployments of GPS sensors on animals (when sensors started and ceased to be deployed on the animals). In the case of a continuous data flow, the transformation of GPS positions into animal locations must be automated in order to have GPS data imported and processed in real-time. In the exercise for this chapter, you extend the database built in Chaps. 2 and 3 with two new tables, gps_sensors_animals and gps_data_animals, and a set of dedicated database triggers and functions that add the necessary tools to properly manage the association of GPS positions with animals.

In recent years, new wildlife tracking and telemetry technologies have become available, leading to substantial growth in the volume of wildlife tracking data. In the future, one can expect an almost exponential increase in collected data as new sensors are integrated into current tracking systems. A crucial limitation for efficient use of telemetry data is a lack of infrastructure to collect, store and efficiently share the information. Large data sets generated by wildlife tracking equipment pose a number of challenges: to cope with this amount of data, a specific data management approach is needed, one designed to deal with data scalability, automatic data acquisition, long-term storage, efficient data retrieval, management of spatial and temporal information, multi-user support and data sharing and dissemination. The state-of-the-art technology to meet these challenges are relational database management systems (DBMSs), with their dedicated spatial extension. DBMSs are efficient, industry-standard tools for storage, fast retrieval and manipulation of large data sets, as well as data dissemination to client programs or Web interfaces. In the future, we expect the development of tools able to deal at the same time with both spatial and temporal dimensions of animal movement data, such as spatiotemporal databases.

The state-of-the-art technical tool for effectively and efficiently managing tracking data is the spatial relational database. Using databases to manage tracking data implies a considerable effort for those who are not already familiar with these tools, but this is necessary to be able to deal with the data coming from the new sensors. Moreover, the time spent to learn databases will be largely paid back with the time saved for the management and processing of the data. In this chapter, you are guided through how to set up a new database in which you will create a table to accommodate the test GPS data sets. You create a new table in a dedicated schema. We describe how to upload the raw GPS data coming from five sensors deployed on roe deer in the Italian Alps into the database and provide further insight into time-related database data types and into the creation of additional database users. The reference software platform used is the open source PostgreSQL with its spatial extension PostGIS. This tool is introduced with its graphical interface pgAdmin. All the examples provided (SQL code) and technical solutions proposed are tuned on this software, although most of the code can be easily adapted for other platforms. The book is focused on the applications of spatial databases to the specific domain of movement ecology: to properly understand the content of this guide and to replicate the proposed database structure and tools, you will need a general familiarity with GIS, wildlife tracking and database programming.

Monitoring the impact of drought on vegetation conditions is a critical activity for assessing food security in semi-arid and arid countries in Africa. Remote sensing from space offers a unique opportunity to obtain consistent and timely information over large and inaccessible areas where field observations are scattered, inhomogeneous or frequently unavailable. In this study we outline a remote sensing method to estimate the probability of experiencing a biomass production deficit during the on-going season based on the statistical analysis of Earth Observation data. The method is based on the extraction of vegetation phenology from SPOT-VEGTATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and the subsequent computation of seasonally cumulated FAPAR as a proxy of vegetation gross primary production. Forecast of the overall seasonal performances, expressed in terms of probability of experiencing a critical deficit, are updated regularly whenever a new satellite observation is made available. Examples of application in the Sahel are discussed in this contribution.

International audience; 1. Because many large mammal species have wide geographical ranges, spatially distant populations may be confronted with different sets of environmental conditions. Investigating how home range (HR) size varies across environmental gradients should yield a better understanding of the factors affecting large mammal ecology. 2. We evaluated how HR size of a large herbivore, the roe deer (Capreolus capreolus), varies in relation to seasonality, latitude (climate), weather, plant productivity and landscape features across its geographical range in Western Europe. As roe deer are income breeders, expected to adjust HR size continuously to temporal variation in food resources and energetic requirements, our baseline prediction was for HR size to decrease with proxies of resource availability. 3. We used GPS locations of roe deer collected from seven study sites (EURODEER collaborative project) to estimate fixed-kernel HR size at weekly and monthly temporal scales. We performed an unusually comprehensive analysis of variation in HR size among and within populations over time across the geographical range of a single species using generalized additive mixed models and linear mixed models, respectively. 4. Among populations, HR size decreased with increasing values for proxies of forage abundance, but increased with increases in seasonality, stochastic variation of temperature, latitude and snow cover. Within populations, roe deer HR size varied over time in relation to seasonality and proxies of forage abundance in a consistent way across the seven populations. Thus, our findings were broadly consistent across the distributional range of this species, demonstrating a strong and ubiquitous link between the amplitude and timing of environmental seasonality and HR size at the continental scale. 5. Overall, the variability in average HR size of roe deer across Europe reflects the interaction among local weather, climate and seasonality, providing valuable insight into the limiting factors affecting this large herbivore under contrasting conditions. The complexity of the relationships suggests that predicting ranging behaviour of large herbivores in relation to current and future climate change will require detailed knowledge not only about predicted increases in temperature, but also how this interacts with factors such as day length and climate predictability.

To date, the processing of wildlife location data has relied on a diversity of software and file formats. Data management and the following spatial and statistical analyses were undertaken in multiple steps, involving many time-consuming importing/exporting phases. Recent technological advancements in tracking systems have made large, continuous, high-frequency datasets of wildlife behavioural data available, such as those derived from the global positioning system (GPS) and other animal-attached sensor devices. These data can be further complemented by a wide range of other information about the animals' environment. Management of these large and diverse datasets for modelling animal behaviour and ecology can prove challenging, slowing down analysis and increasing the probability of mistakes in data handling. We address these issues by critically evaluating the requirements for good management of GPS data for wildlife biology. We highlight that dedicated data management tools and expertise are needed. We explore current research in wildlife data management. We suggest a general direction of development, based on a modular software architecture with a spatial database at its core, where interoperability, data model design and integration with remote-sensing data sources play an important role in successful GPS data handling.

ISAMUD (Integrated System for Analysis and Management of Ungulate Data) is an integrated and modular software platform developed to manage GPS collar data for wildlife management. It is based on an open source spatial database (PostgreSQL and PostGIS) and includes open source data management, geo-statistical analysis and Web services modules (R, QGIS, GRASS, MapServer, Ka-Map) and a proprietary database front-end (MS Access). It is able to store, retrieve, update, analyze, visualize and disseminate GPS telemetry data in an efficient and consistent way, with a high degree of automatization.