README: A new approach to geostatistical synthesis of historical records reveals capuchin spatial responses to climate and demographic change

Raw sleep site data:

Here we present two chunks of data that were created in the process of converting early historical data to something that could be linked to GPS locations so that the full process can be understood. We did not provide the more recent data for conservation reasons, as we want to protect the locations of habituated primates. Scientists with a legitimate reason to see the full data set can contact Susan Perry for more information.

Sleepsites_1997_oddshift.csv

SleepsitesAA1990-95_bjbshift.csv

The file Sleepsites_1997_oddshift.csv is from the first season in which electronic devices (Psion Observers) were used to record data in the field (though these were still supplemented with field notes having hand-drawn maps to help us find sleep sites the following morning, as this was our first field season working with RR group, so its home range had not yet been mapped). The file 1993AAsubsetData.xlsx is a chunk of the larger file SleepsitesAA1990-95_bjbshift.csv that is mentioned in the R code. This is from the first 3 years of the project when we focused exclusively on the AA group, recording data on microcassette recorders and writing additional notes about sleep site locations in field notebooks.

Originally there were more columns, including (a) values for a 50 m grid system (which we abandoned when it became clear that we could rarely pinpoint the precise grid cell at that level of granularity), (b) columns indicating whether we saw the monkeys curl up in their sleep trees, or at least were with them during the last hour before sundown, (c) extensive comments about how certain we were about the specific grid cell assigned and listing other possible values (when we were considering listing all possible grid cell values), and (d) comments containing notes from Susan about which sleep sites needed to be double checked via older versions of our maps, and then checked in the field by finding those points and taking a GPS reading. When this became possible, we corrected the Grid100 value. The versions presented here use only the columns that are used in the present analysis, after we had refined our data processing workflow as described in the manuscript.

• date: the date representing the evening on which we watched the monkeys settle into their sleep tree for the night.
• location: a description of where the monkeys slept
• group: the group of monkeys monitored on that date
• Grid100: the number assigned to the grid cell in the 100 m2 grid. Note that the numbers in the entire grid published in the code repository have been systematically altered to protect these threatened monkeys, so that their sleep sites cannot be easily located by non-researchers.
• certainty?: As described in the manuscript, a score of 0 indicates that we can confidently assign the sleep site to that grid cell. A score of 1 means that the sleep site lies within a radius of 1 away from the grid cell assigned, i.e. could be within that cell or one of the 8 neighboring grid cells (corresponding to 300 meters squared). A score of 2 means that the sleep site was within 2 grid cells of the one chosen (corresponding to 500 meters squared), etc.

Demographic data:

annual_group_sizes.csv

This file reports the group size (including members of all age-sex classes) for each social group in each year, derived from the Lomas Barbudal Monkey Project demographic database, using only census days in which trained observers were present in the group for at least 6 hours.

Raw GPS data:

For conservation reasons, i.e., to protect the animals from the pet trade and poachers, these data will not be made public. They are archived at Movebank, and can be accessed here, pending permission of the curator (Susan Perry):

https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study3389013696

Description of the relationship between the data and the code used to analyze it:

To analyze these data, please refer to the code found at the following location:

 https://doi.org/10.17617/3.HUKMS6

The descriptions of sleep sites were transformed to location data using the quadrant centroids in which they fell, using a map that had a 100x100m2 grid. The code for this and other procedures described below can be found in 01_georeferencing/scripts/00_hist_slp_data_processing.R.

All location data (georeferenced data, GPS-tracking data, and GPS sleep-site data) were used to estimate home ranges using the ctmm package in R. To do this, one can follow 02_validation/scripts for annotated steps within the 01 and 02 scripts.

Next, the home ranges using sleep site data were validated, using the scripts 03-06, by comparing them to home ranges estimated from tracking data. Then home ranges estimated from sleep site data, once validated, were used in the case study models to draw predictions about the effects of ENSO and group size on home range area. To generate the tables necessary for these models, run the 07 script within the 02_validation/scripts folder. The statistical models and plots for the case study are found in the 03_case_study_models folder. For more details please see the README.md file within the Edmond code repository linked above.

Code/Software

The code used to process these raw data is housed in the following repository:

 https://doi.org/10.17617/3.HUKMS6