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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.

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.

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.