Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions
Data files
Feb 29, 2024 version files 52.82 MB
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ProcessedTrackingData_MakingBeterUseOfTrackingData_2023.csv
52.81 MB
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README.md
2.52 KB
Abstract
Understanding geographic ranges and species distributions is crucial for effective conservation, especially in the light of climate and land use change. However, the spatial, temporal and intraspecific resolution of digital accessible information on species distributions is often limited. Here, we suggest to make better use of high-resolution tracking data to address existing limitations of occurrence records such as spatial biases (e.g. lack of observations in parts of the geographic range), temporal biases (e.g. lack of observations during a certain period of the year), and insufficient information on intraspecific variability (e.g. lack of population- or individual-level variation). Addressing these gaps can improve our knowledge on geographic ranges, intra-annual changes in species distributions, and population-level differences in habitat and space use. We demonstrate this with tracking data and species distribution models (SDMs) of the Barnacle Goose, a migratory bird species wintering in western Europe and breeding in the Arctic. Our analyses show that tracking data can (1) supplement occurrence records from the Global Biodiversity Information Facility (GBIF) in remote areas such as the European and Russian Arctic, (2) improve information on the temporal use of wintering, staging and breeding areas of migratory species, and (3) provide insights into the differences of population-level responses to environmental variables. We recommend a broader use of tracking data to address the Wallacean shortfall (i.e. the incomplete knowledge on the geographic distribution of species) and to improve forecasts of biodiversity responses to climate and land use change (e.g. species vulnerability assessments). To avoid common pitfalls, we provide six recommendations for consideration during the research cycle when using tracking data in species distribution modelling, including steps to assess biases and integrate information on intraspecific variability in modelling approaches.
README: Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions
https://doi.org/10.5061/dryad.zw3r228fd
Description of the data and file structure
The uploaded data file contains processed tracking data from tracking datasets available for public download via Movebank. Using the package "movepub" (Desmet 2023) to Darwin Core standards, tracking data is sub-sampled to the first record for each hour.
Columns "license", "license holder" and "datasetID" provide information on the original tracking data set, giving the license under which it was published, the contact person for the study, and the DOI (if available respectively). In the absence of a "license", "license holder" or doi, "NA" values are provided, representing "not available".
Columns "institutionCode", "collectionCode" and "datasetName" provide information on the institute responsible for data storage (MPIAB, Max Planck Institute of Animal Behavior in all cases), the platform from which the data is retrieved (Movebank in all cases) and the name of the dataset as stored on Movebank or the Movebank data repository respectively.
Columns "occurrenceStatus", "organismID", "eventDate" and "samplingProtocol" are DwC columns providing information on whether it concerns presence or absence information (presence in all cases), to which individual animal the observation belongs, at which date the observation was made and whether it concerns a gps-observation (gps) or tag attachment (tag attachment) respectively.
Columns "decimalLatitude", "decimalLongitude", "geodeticDatum", and "coordinateUncertaintyInMeters" provide information on the latitude, longitude, geodetic datum and coordinate uncertainty (in m) of the observation respectively. In case of an tag attachment event, these values are set to "NA" (not applicable). "coordinateUncertaintyInMeters" is always set to "NA" as there is no estimation of coordiante uncertaitny for GPS-data.
Sharing/Access information
GBIF data were filtered based on coordinateUncertanty < 1000. GBIF Observation counts were subsequently plotted on a map with grid cells with a 0.3° spatial resolution.
Code/Software
The uploaded R script provides the code to construct SDMs for the Barents Sea and Svalbard Barnacle Goose population in the month July based on the provided processed tracking data. Code was run in R 4.3.0. All packages required are provided in the code.
Methods
Tracking data was obtained from the Movebank Data Repository using the following studies:
- van der Jeugd, H. P., Oosterbeek, K., Ens, B. J., Shamoun-Baranes, J., & Exo, K. (2014). Data from: Forecasting spring from afar? Timing of migration and predictability of phenology along different migration routes of an avian herbivore [Barents Sea data]. [dataset]. https://doi.org/10.5441/001/1.ps244r11
- Heim, W., Piironen, A., Heim, R. J., Piha, M., Seimola, T., Forsman, J. T., & Laaksonen, T. (2022). Data from: Effects of multiple targeted repelling measures on the behaviour of individually tracked birds in an area of increasing human-wildlife conflict [Csv]. Movebank Data Repository. https://doi.org/10.5441/001/1.VD7JB526
- Griffin, L. (2014). Data from: Forecasting spring from afar? Timing of migration and predictability of phenology along different migration routes of an avian herbivore [Svalbard data]. Movebank Data Repository. https://doi.org/10.5441/001/1.5K6B1364
- Garthe, S. (2023). FTZ Geese Wadden Sea [dataset]. Movebank Data Repository. (Downloaded 18-08-2023, contains barnacle goose data up to 02-2020).
Movebank data is subsequently converted to Darwin Core standards, using the package 'Movepub' (Desmet. 2023), after which redundant columns were removed.