All the faces of the Alpine bear: integrating multi-source data in an individual-based spatial relational database for the brown bear (Ursus arctos) in the Alps.
The Alpine bear (Ursus arctos) population in Trentino, Italy, is probably one of the most surveyed large carnivore populations in Europe. Since the first reintroductions in the early 2000s, it has been continuously monitored and genetically sampled through the joint effort of various authorities. Th...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Conference Object |
| Language: | English |
| Published: |
University of Ljubljana
2018
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| Subjects: | |
| Online Access: | http://hdl.handle.net/11572/296616 https://lifewithbears.eu/conference/ |
| Summary: | The Alpine bear (Ursus arctos) population in Trentino, Italy, is probably one of the most surveyed large carnivore populations in Europe. Since the first reintroductions in the early 2000s, it has been continuously monitored and genetically sampled through the joint effort of various authorities. This long-term monitoring, combined with the advent of new technologies, has produced a comprehensive and ecologically precious dataset. However, the high amount of data has been generated by different sources, making the combined use of the data difficult. In order to reduce the loss of data significance, an accurate work of data integration has been done. As a result, an individual-based spatial relational database has been implemented, pooling a large amount of multi-source georeferenced data available in the Central Alps. Specifically, the spatial database contains the GPS location data (n≈ 50.000 fixes) coming from sensors deployed on 16 bears and information (n≈ 9.000 records) concerning genetics, kinship, damages, VHF radio tracking, sighting, and tracks regarding 144 bears (including the individuals equipped with GPS collars).The spatial database was implemented in PostgreSQL with the extension PostGIS. The database benefits fully from the software capabilities, such as data integrity, data consistency, storage capacity, reduced data redundancy, long-term storage, and advanced permission policy (allowing the sharing with partner institutions). Moreover, the high interoperability of PostgreSQL allowed the creation of a gapless workflow with both data analysis software (such as R) and GIS environments. The applications of this individual-based spatial relational database are manifold. From individual-based models considering inherited behavioral traits as potential drivers of movement parameters, survival, and reproductive success, to the creation of finely detailed life-history for each monitored individual. |
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