DOI: 10.5061/dryad.0000000dg
title: Data from: Tiny patch, big value: A small dry forest patch supports wildlife conservation in Guanacaste, Costa Rica

description: Tropical dry forests are among the most threatened tropical ecosystems due
 to ongoing agricultural expansion and development. Despite this, small
 forest patches persist within fragmented landscapes, though their
 conservation value remains poorly understood. To evaluate the role of one
 such patch in supporting wildlife, we deployed camera traps across three
 zones (Dense Forest, Grassland, and Sparse Forest) within the Murren
 Reserve, a small dry forest patch on the southern coastline of Guanacaste,
 Costa Rica. Camera traps detected 16 vertebrate species, with
 opportunistic encounters adding 16 more. The most detected species
 included the white-nosed coati (Nasua narica), raccoon (Procyon lotor),
 and white-tailed deer (Odocoileus virginianus). Additionally, we recorded
 nationally endangered species, including the ocelot (Leopardus pardalis),
 puma (Puma concolor), and mantled howler monkey (Alouatta palliata), as
 well as nationally threatened species such as the spotted skunk (Spilogale
 angustifrons) and white-throated magpie-jay (Calocitta formosa).
 Extrapolated rarefaction curves suggest that further sampling could
 improve species richness estimates for the Dense Forest and Grassland.
 Although we did not detect a difference in species composition among zones
 (ANOSIM, R = 0.14, p = 0.26), a generalized linear mixed model indicated
 that zone explained 17% of the variation in species richness. These
 findings highlight the role that small dry forest patches play in
 conserving both threatened species and broader vertebrate communities. We
 also provide recommendations for future research to address current data
 gaps and improve long-term monitoring in fragmented landscapes. 
description: # Data from: Tiny patch, big value: A small dry forest patch supports
 wildlife conservation in Guanacaste, Costa Rica Dataset DOI:
 [10.5061/dryad.0000000dg](10.5061/dryad.0000000dg) ## Description of the
 data and file structure We evaluated vertebrate biodiversity across
 distinct zones within a small tropical dry forest by deploying camera
 traps in the Murren Reserve, Costa Rica. We placed cameras across three
 zones (Dense Forest, Grassland, and Sparse Forest) during the dry and wet
 seasons of 2024. Because of incorrect time and date metadata from images
 captured during the wet season, we focused most analyses on dry season
 data. We compared daily species richness and overall species composition
 between zones using generalized linear mixed models, dissimilarity
 analyses, and rarefaction curves. We also documented opportunistic
 vertebrate observations to supplement the camera data. *A note about the
 inclusion of location data of threatened species*:  The study site covers
 ~13 ha (about half a square mile), and the publication associated with
 this dataset already includes its coordinates. In this context, omitting
 GPS coordinates of the focal species would not meaningfully reduce the
 risk of illegal hunting. However, the site is privately owned, clearly
 marked as such, and bordered by a national park managed by government
 officials. These factors should, in theory, further reduce the risk of
 illegal hunting. ### Files and variables #### File:
 Full_Camera_Dataset_(Cleaned).csv **Description:** This is the primary
 data set used in our article. To clean the data, we first removed all
 "false captures" (i.e., videos with no animal). Then, we defined
 a unique capture event (UCE) as any instance in which a species had not
 been detected by the same camera within the previous 60 minutes. We
 counted each identifiable individual within a video as a separate UCE and,
 when multiple videos captured individuals within the same 60-minute
 window, we used the video with the highest number of identifiable
 individuals. Each row in this csv file represents one UCE.  #####
 Variables * Video_ID: Unique video ID assigned to a given video (i.e.,
 image) * Season: Season of camera deployment * Camera: Unique Camera ID
 assigned  * GPS_location: Location of camera deployment * Area: The zone
 where the camera was deployed (Dense Forest, Grassland, or Sparse Forest)
 * Date (MM/DD/YYYY): The date the video occurred * Time (military): The
 time the video occurred * Temperature (degree C): The temperature during
 the video * Class: The class organisms in the video * Species: The common
 name of the species ID'ed in the video  * Scientific_name: The
 scientific name of the species in the video * Species_Code: The first two
 letters of the genus and the first two letters of the specific epithet,
 entered as a four-letter code in all caps * Coder_name: The name of the
 author to enter the data (although several rows were cross-validated
 between multiple people) * Notes: Any misc. notes about the row #### File:
 Trapping_Data.csv **Description:** We derived this data from the
 "Full_Camera_Dataset_(Cleaned).csv" to create our rarefaction
 curves. The first row represents the total number of UCEs for that zone.
 Each row represents the number of UCEs for each species identified, in
 descending order. The cells among each of these rows do not represent a
 single species.  ##### Variables * Each of the columns represents one of
 the zones #### File: Analysis.R **Description:** The R code we wrote the
 our data analysis. ## Code/software We used R programming software version
 4.4.2 to conduct our analyses. For a detailed account of the data
 analysis, including packages used, please refer to our published article.