Animal biotelemetry is driven by GPS Tracking Device
We are currently seeing a golden age of unprecedented progress and discovery in movement research. The innovation in Global Positioning System (GPS) technology, combined with systems for remote data transfer, has particularly favored tracking device-based animal telemetry to become a standard in wildlife tracking. Three main processes are responsible for this rapid advancement: (1) the development of new tracking techniques to quantify movement, (2) powerful data analysis tools to examine key questions about movement and its causes and consequences, and (3) conceptual and theoretical frameworks for integrating movement research. Many new tools have been developed recently to record the movement of various organisms vii with levels of accuracy, frequency, and duration that are much higher than they were just a few years ago. This has strongly improved our capacity to track insects, fish, and other relatively small organisms. However, arguably the most dramatic development in animal biotelemetry is driven by GPS-based devices, which have been applied mostly to track mammals and birds of relatively large ([100 g) body mass.
Although such devices remain limited to a small proportion of the world’s biota, they are now applicable to a large and increasing number of species, and have caused a dramatic shift of movement ecology from a data-poor to a data-rich research enterprise. The parallel development of bio-logging techniques that use accelerometers and various other sensors, as well as GIS and remote sensing techniques, have further enriched our ability to quantify the internal state, behaviour, and the external environment of the animal en route. Therefore, animal-borne GPS data is important for the advancement of the most active frontiers of movement research.
A recent prognosis of the state of the art of physics, as an example for scientific research more generally, has concluded that we are lucky to live in an era when both tools and ideas are getting strong. The same applies to movement research. Along these lines, the three main processes that drive the current rapid advancement of movement ecology should be strongly interlinked. This is precisely the contribution that this book attempts to make. In order to efficiently utilize the large volume of high-quality GPS tracker data, to take advantage of the power of advanced data analysis tools, and to facilitate the investigation of key hypotheses and the promotion of integrative theoretical frameworks, we first need to properly manage the data sets.
However, the quantity and complexity of GPS Tracking Device and other bio-logged data require a proper software architecture to be fully exploited and not wasted or, worse, misused. This blog is a guide to manage and process wildlife tracking data with an advanced software platform based on a spatial database. Although this requirement holds for data sets of all sizes, and is therefore relevant to all movement ecology projects, a key challenge is to manage the large and rapidly growing GPS tracking data sets that have been collected by many groups studying diverse species across different ecosystems. This marks the entrance of movement ecology to the Big Data era, which necessitates revolutionary improvements in data management and data analysis techniques, and resembles the bioinformatics revolution of genomics and proteomics in the previous decade (Mount 2004). Ecologists have been late to enter the information age, and will need to conquer the strong traditions that have developed over the long data-poor history of this discipline in order to utilize the huge scientific opportunities of the Big Data era (Hampton et al. 2013).
Although such devices remain limited to a small proportion of the world’s biota, they are now applicable to a large and increasing number of species, and have caused a dramatic shift of movement ecology from a data-poor to a data-rich research enterprise. The parallel development of bio-logging techniques that use accelerometers and various other sensors, as well as GIS and remote sensing techniques, have further enriched our ability to quantify the internal state, behaviour, and the external environment of the animal en route. Therefore, animal-borne GPS data is important for the advancement of the most active frontiers of movement research.
A recent prognosis of the state of the art of physics, as an example for scientific research more generally, has concluded that we are lucky to live in an era when both tools and ideas are getting strong. The same applies to movement research. Along these lines, the three main processes that drive the current rapid advancement of movement ecology should be strongly interlinked. This is precisely the contribution that this book attempts to make. In order to efficiently utilize the large volume of high-quality GPS tracker data, to take advantage of the power of advanced data analysis tools, and to facilitate the investigation of key hypotheses and the promotion of integrative theoretical frameworks, we first need to properly manage the data sets.
However, the quantity and complexity of GPS Tracking Device and other bio-logged data require a proper software architecture to be fully exploited and not wasted or, worse, misused. This blog is a guide to manage and process wildlife tracking data with an advanced software platform based on a spatial database. Although this requirement holds for data sets of all sizes, and is therefore relevant to all movement ecology projects, a key challenge is to manage the large and rapidly growing GPS tracking data sets that have been collected by many groups studying diverse species across different ecosystems. This marks the entrance of movement ecology to the Big Data era, which necessitates revolutionary improvements in data management and data analysis techniques, and resembles the bioinformatics revolution of genomics and proteomics in the previous decade (Mount 2004). Ecologists have been late to enter the information age, and will need to conquer the strong traditions that have developed over the long data-poor history of this discipline in order to utilize the huge scientific opportunities of the Big Data era (Hampton et al. 2013).
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