We hope that the Env-DATA System saves you many time-consuming steps in linking tracking data to environmental conditions, making it easier to test hypotheses and leaving you with more resources to design novel studies, collect high-quality data, and put the results in an interesting and realistic biological context.
After you access your results,
- Store a copy of the original results in a safe place. The readme file contains critical information that you will likely need to return to later, including the specifics of your original request, links to the environmental data products accessed, units and citation requirements.
- Take care in reading the data. For example, variables may have different nodata values, and categorical variables may need to be interpreted based on keys provided in the readme.
- Note that opening results in Excel and then saving the file can corrupt timestamps and truncate data values.
The annotated data are estimates that reflect the resolution and accuracy of the original datasets. The environmental variables available through Env-DATA come from many different products and providers. To understand the quality of these products,
- Review the readme files that come with your results for more about the products and their QA/QC procedures, including the "Source link" and "Related websites" provided for each product you have used.
- When comparing the results from different products and variables, including QC variables, consider differences in the source resolution and chosen interpolation method used.
When planning analysis and assessing what variability and trends are biologically meaningful, take into account the resolution of the environmental datasets as well as the tracking data. For example, weather conditions from global atmospheric models do not describe local conditions and so are only an approximation of what animals actually experienced. If animal locations have an accuracy of 1 km, annotating 30-m elevation land use in heterogeneous environments might not be useful.
Here are explanations for commonly asked questions about Env-DATA results:
This happens most commonly when annotation is requested for animals that have no associated locations. Make sure the data are correctly imported and linked to animals using deployments. Verify what data are deployed in the study by downloading the data or reference data, viewing the data on the map or comparing deployments and available data in the Deployment Manager. You can also choose to annotate data for tags rather than animals to include undeployed records.
Missing data, or "NaN" values, occur when
- The source file has periods or regions with nodata values, for example, if you request a vegetation index for a period when the animal was flying over the ocean, or sea surface temperature for a period when the animal was on land. Satellite imagery will contain more no data values in cloudier regions; likewise for locations near the poles, there may be missing data during times of year when it is nearly always dark.
- Vertical interpolation of weather data will return NaN values for records where height-above-ellipsoid is near or below sea level. Request similar surface-level variables to get values for these records.
- The requested records fall outside the temporal or spatial range of the dataset: See our data products summary for the area and time covered by each product. For products that are available to the "present", there is a lag between today and when today's measurements are available as processed data files, so if you have live data feeds, the most recent few weeks or months will commonly return NaNs for these products (see an explanation from ECMWF).
Solutions: See what obvious explanations there might be for your results as described above. You can also try requesting using a different interpolation method—as described in Env-DATA Interpolation Methods (look for "missing data"), nearest neighbour or inverse distance weighted interpolation will give a valid result even if some of the neighboring data values are missing, while bilinear interpolation will not. In addition, some products include quality control variables that might indicate reasons for missing data.
A good resource with instructions recommended by NARR's support team is here. See the section on "geographic wind direction" and if you are using a spreadsheet program like Excel, see their warning under "two argument arctangent function" to make sure the equations work correctly.
If you use Excel to view your results, be aware that it can corrupt timestamps and truncate values in the annotation results. See here to learn how to avoid this problem.
Dodge S, Bohrer G, Weinzierl R, Davidson SC, Kays R, Douglas D, Cruz S, Han J, Brandes D, Wikelski M. 2013. The Environmental-Data Automated Track Annotation (Env-DATA) System: linking animal tracks with environmental data. Movement Ecology. 1:3. https://doi.org/10.1186/2051-3933-1-3
The time it takes to process your request will depend on many variables: the spatial and temporal resolution of your dataset and the requested environmental attributes, the number of other requests being processed by EnvDATA and the environmental data providers, and whether others have recently requested the same environmental data. This means that even similar requests may have different wait times. To reduce the chance of extended wait times, see these tips for submitting requests.
If you receive an email saying or request has failed, have not gotten results within a week, or have questions about the results not addressed above, please send the access key for the request to firstname.lastname@example.org so we can take a closer look. The access key is shown in the third paragraph of the readme file, which can also be found by going to Env-DATA > Show My Requests and clicking on Details for the request, as shown here:
Movebank study name: Golden Eagles in Alaska
Annotated Animal IDs: 12345
Requested on Tue Oct 27 20:00:33 CET 2015
Access key: 5485945721593819135
Requested by: user name
For more details about how the Env-DATA Track Annotation Service works, see Dodge et al. 2013.
Interpreting results and FAQ