DOI: 10.5068/d1qt17
title: A new approach to geostatistical synthesis of historical records reveals capuchin spatial responses to climate and demographic change

description: The recent proliferation of GPS technology has transformed animal movement
 research. Yet, time-series data from this recent technology rarely span
 beyond a decade, constraining longitudinal research. Long-term field sites
 hold valuable historic animal location records, including hand-drawn maps
 and semantic descriptions. Here, we introduce a generalized workflow for
 converting such records into reliable location data to estimate home
 ranges, using 30 years of sleep-site data from 11 white-faced capuchin
 (Cebus imitator) groups in Costa Rica. Our findings illustrate that
 historic sleep locations can reliably recover home range size and
 geometry. We showcase the opportunity our approach presents to resolve
 open questions that can only be addressed with very long-term data,
 examining how home ranges are affected by climate cycles and demographic
 change. We urge researchers to translate historical records into usable
 movement data before this knowledge is lost; it is essential to understand
 how animals are responding to our changing world.
description: # 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](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](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](https://doi.org/10.17617/3.HUKMS6)