DOI: 10.5061/dryad.wdbrv15w6
title: Data from: Complex patch geometries maximize species richness at the expense of forest specialists

description: Habitat loss and fragmentation are the greatest threats to reptiles and
 amphibians (herpetofauna) around the globe, but especially in the
 Neotropics where high diversity and ongoing land-use change coincide.
 Persistence of biodiversity in fragmented systems relies both on
 characteristics of habitat patches, and on the permeability of the
 landscape that separates the patches (the ‘matrix’). We sought to
 understand: (a) how the herpetofauna community differs between forest
 reserves, patches, corridors, and matrices, and (b) the landscape
 characteristics that increase suitability of a habitat patch. We conducted
 herpetofauna surveys in patches, corridors, matrices, and preserves (54
 total sites) in a fragmented landscape in Costa Rica for three seasons. We
 recorded 1663 individuals of 52 species. We found that the herpetofauna
 community differed, and had lower richness and abundance, in the matrix
 compared to the other three habitat types. Patches and corridors supported
 a similar community to the forest preserves, demonstrating the
 conservation value of small forest remnants. Water body presence was an
 important predictor of richness and abundance in both patches and
 matrices. While total richness increased in patches with more edge, this
 was driven by the response of generalist species, whereas the prime
 indicator species of forest preserves decreased in patches with complex
 shapes. The differing response to landscape characteristics between
 specialist and generalist species demonstrates the importance of
 considering specific taxa when setting conservation goals, rather than
 using richness measures alone. Our findings can help guide preservation of
 forest fragments to optimize biodiversity conservation in mixed
 forest-pastoral landscapes.
description: Our study was centered around Las Cruces Biological Station
 (LCBS) in southern Costa Rica in Puntarenas Province (8.78°N, 82.96°W).
 Additional details on selected sites can be found in the published
 manuscript.  We completed herpetofauna surveys at 54
 sites (20 patches, 20 matrices, 10 corridors, 4 reference forests) in each
 of three field seasons: March 2019, June/July 2019, and March 2020. We
 employed three survey techniques to obtain representative samples of the
 entire herpetofaunal community. Diurnal surveys included a visual
 encounter survey (VES) and a leaf litter quadrat. Nocturnal surveys
 included a VES and a 10-min anuran call survey.  All
 individuals were identified to species when possible, with a few
 exceptions. <em>Diasporus vocator</em> and <em>Diasporus
 diastema</em> were collapsed into <em>Diasporus
 </em>spp. due to difficulties in discerning the two species both
 visually and by call surveys. Additionally, the
 <em>rhodopis</em>-species group contains seven small leaf
 litter frogs in the genus<em> Craugastor</em> that are
 notoriously difficult to identify in the field, although most were
 presumably <em>Craugastor stejnegerianus</em> based on our
 location. For our visual encounter surveys, we walked a
 100 m transect. For leaf litter quadrat surveys, we selected a 5x5-m area
 along our transects with leaf litter, and would rake through the leaves
 and grass to uncover any herpetofauna. For our anuran call surveys, we
 turned off our headlamps and sat in silence for 10 minutes while recording
 ambient sounds on a Tascam DR-40 recorder set to a sampling frequency of
 44.1 kHz (frog species at our sites have calls that range in dominant
 frequency from ~450 – 7500 Hz). To identify frog calls, we listened to
 each of our surveys while viewing them in Audacity 2.4.2, and every time a
 potential frog call was heard, we went through the spectrograms of all
 possible species from the region until we found one that matched the
 frequency range and spectrogram pattern. Calls were treated as
 presence/absence data because we could not determine whether multiple
 calls of the same species were a single or multiple individuals.
 Additional information on our survey techniques is available in the
 published manuscript.
description: # Complex patch geometries maximize species richness at the expense of
 forest specialists This
 README file was generated on 2024-01-31 by Stephanie Clements. GENERAL
 INFORMATION 1\. Title of Dataset: Complex patch geometries maximize
 species richness at the expense of forest specialists. 2\. Author
 Information       A. Corresponding Author        Name: Stephanie L.
 Clements        Institution: University of Miami        Address: CORAL
 GABLES, FL, USA        Email:         B.
 Additional Author        Name: Dunia Villalobos Alpízar      
  Institution: N/A  Address: Copal, Agua Buena, San Vito de Coto Brus,
 Puntarenas, Costa Rica        Email:  
       C. Principal Investigator       Name: Christopher A. Searcy       
  Institution: University of Miami        Address: CORAL GABLES, FL, USA  
      Email: 3\. Date of data collection (single
 date, range, approximate date): March 2019, June/July 2019, March 2020 4\.
 Geographic location of data collection: Las Cruces Biological Station and
 surrounding landscape, Costa Rica 5\. Information about funding sources
 that supported the collection of the data: Organization for Tropical
 Studies Pilot Research Award, Organization for Tropical Studies Thesis
 Grant, Sigma Xi Grant-in-Aid-of-Research Award, University of Miami
 Institute for Advanced Study of the Americas (UMIA) Pilot Research Grant,
 UMIA Research Fellowship, University of Miami Department of Biology Awards
 including the Aldridge Graduate Fellowship in Tropical Biology, Kushlan
 Fund, and Savage Fund.   SHARING/ACCESS INFORMATION 1\.
 Licenses/restrictions placed on the data: CC0  2\. Links to publications
 that cite or use the data: Clements, S. L., Villalobos Alpizar, D.,
 Searcy, C.A. (2024). Complex patch geometries maximize species richness at
 the expense of forest specialists. Biotropica. 3\. Links to other publicly
 accessible locations of the data: None 4\. Links/relationships to
 ancillary data sets: None 5\. Was data derived from another source? No
       A. If yes, list source(s): NA 6\. Recommended citation for this
 dataset: Clements, S. L., Villalobos Alpizar, D., Searcy, C.A. (2024).
 Complex patch geometries maximize species richness at the expense of
 forest specialists. Dryad Digital Repository.
   DATA & FILE OVERVIEW
   1\. File List:   A)  DataSets_BITR23188_Clementsetal2024.xls a.  Sheet 1
 – FinalData_FullMatrix b.  Sheet 2 – SppMatrixForCommunityAnalyses c. 
 Sheet 3 - Metadata   2\. Relationship between files, if important: Sheet 1
 is full dataset. Sheet 2 is modified dataset for community analysis only.
 Sheet 3 explains the 2 datasets.   3\. Additional related data collected
 that was not included in the current data package: None   4\. Are there
 multiple versions of the dataset? No       A. If yes, name of file(s) that
 was updated: NA            i. Why was the file updated? NA            ii.
 When was the file updated? NA  
 \#########################################################################
   DATA-SPECIFIC INFORMATION FOR: FinalData_FullMatrix (Sheet 1)   1\.
 Number of columns: 108   2\. Number of cases/rows: 54   3\. Column List:  
     * Site: Site Number where data was collected (C = Connected, D =
 Disconnected, P = Patch, M = Matrix, X = Corridor, Reference = Reference
 Forest)       * Species:  Columns B – AZ are species names found.        
 * Abundance: Total # of individuals at the site         * Ln(Abundance):
 Abundance after a natural log transformation for normality. Note that any
 column that says “Ln” was natural log transformed.         * Richness: The
 # of species found at the site         * RichnessWUnIDs: This species
 matrix does not include individuals that were not identified to species,
 but the “Richness with Un-ID’s” column reflects the richness at a site
 when taking those species into account. For example, if an unidentified
 lizard was seen at a site, but there were no other lizards recorded there,
 it would count as one additional richness.       * GeneralistAbundance:
 The abundance of species we defined as “generalists” (if &gt; 20 % of
 identified individuals were found in the matrix)         * Specialist
 Abundance: The abundance of species we defined as “specialists” (if &gt;
 80 % of identified individuals were found in the forests)   \* Generalist
 Richness: The # of species we defined as “generalists” within that site.  
 \* Specialist Richness: The # of species we defined as “specialists”
 within that site.           * EstSppRich: Estimated species richness
 (using second-order jackknife calculation)         * %SppObs: The richness
 divided by the estimated richness         * ArcSine(%SppObs): The %SppObs
 column after using an ArcSine transformation. Any columns with “ArcSine”
 in front indicate that the variable was transformed using an ArcSine
 transformation   \* HabitatType: The Habitat Type of the surveyed site
 (can be patch, matrix, corridor, or reference forest)   \* Location: This
 is the site number # without the habitat type (for example, C1 means
 connected 1, but that location has 3 habitat types, patch, corridor, and
 matrix)   \* Connected: Only for Patches – can be connected or
 disconnected. N/A for corridors, reference forests, and matrices   \*
 Latitude: Latitude of the site. More precise locality data are available
 upon request. Coordinate accuracy was decreased to protect threatened or
 endangered species in the dataset.   \* Longitude: Longitude of the site.
 More precise locality data are available upon request. Coordinate accuracy
 was decreased to protect threatened or endangered species in the dataset.
   \* Area (ha): The area of the site in hectares (only applicable for
 patches, corridors, and reference forests as the matrix is the space
 around the patch). The reference forests have 2 sites in each forest, so
 the second site does not have a separate area.   \* Perimeter (meters):
 The perimeter of the site in meters (only applicable for patches,
 corridors, and reference forests as the matrix is the space around the
 patch). The reference forests have 2 sites in each forest, so the second
 site in a given forest does not have a separate perimeter.   \* Shape
 Index: Perimeter divided by 2 multiplied by the square root of pi
 multiplied by the area. This is meant to account for the correlation
 between perimeter and area as discussed in Moser et al. 2002. A higher
 shape index means a more complex shape with a greater edge:area ratio.
 This is again only applicable for patches, corridors, and reference
 forests as the matrix is the space around the patch.   \*
 DistancetoReference(m): The distance from the site to the refence forest
 in meters as measured in ArcMap (we only measured this for the patches and
 matrix sites).   \* Reference Closest to: Which reference forest (1 or 2)
 the patch is closer to (patches only)   \* Road (No/Gravel/Dirt/Paved):
 Whether a road lies between the patch and the nearest reference forest,
 and if so, what type of road it is (patches only).   \* Reference
 Connected To: For Connected Patches only, which reference forest are they
 connected to via a corridor.   \* Water Body At Transect: Whether or not
 there was a water body present at the survey site (Y = Yes, N = No)   \*
 ElevationAtTransect: The elevation at the transect location (in meters)  
 \* SlopeAtTransect: The slope at the transect location   \*
 CanopyCover(mean): The mean Canopy Cover in a patch, corridor, or
 reference forest (as a %)   \* CanopyCover(mean)Decimal: The mean canopy
 cover converted to decimal in order to do the ArcSine Transformation in
 the following column (Any columns with “decimal” indicate that the
 variable was transformed to decimal)   * 
 CanopyCoverMeanAroundPatch80mBuffer: The mean canopy cover (%) in an 80
 meter buffer around the patch   \* CanopyCoverMeanAroundPatch100mBuffer:
 The mean canopy cover (%) in a 100 meter buffer around the patch   \*
 CanopyCoverMeanAroundCorridor80mBuffer: The mean canopy cover (%) in an 80
 meter buffer around the corridor (for the patch sites, it’s the mean
 canopy over in an 80 m buffer around the corridor that is connected to
 that patch). This is N/A for reference forests and matrix sites.   \*
 CorridorLength (km by hand): The length of the corridor traced by hand in
 Google Earth (in kilometers) (again, for the patch sites, it’s for the
 corridor that is connected to that patch)   \* CorridorWidthByHand(meters)
 at narrowest point: The width of the corridor (in meters) measured at the
 narrowest point in Google Earth (again, for the patch sites, it’s for the
 corridor that is connected to that patch)   \* ForestAge: The forest age
 at the site according to Zahawi et al. 2015, categorized as either old
 growth (&gt; 72 years) or new growth (&lt; 49 years). Not applicable for
 matrix sites.   \* Szn1Rich: Richness from the first season of surveys (to
 compare richness across survey seasons). Richness and Abundance are
 calculated for each of the 3 seasons in the next few columns.   \*
 CanopyAtTransectPt: The canopy cover (%) at the transect point for the
 matrix sites (because for patches, corridors, and reference forests, the
 canopy cover is measured for the full area of the patch, corridor, or
 reference forest).   \* Canopy80mBufferAroundTransectPt: The canopy cover
 (%) in an 80 m buffer around the transect point     4\. Missing data
 codes: N/A   5\. Specialized formats or other abbreviations used: None  
 \#########################################################################
   DATA-SPECIFIC INFORMATION FOR: SppMatrixForCommunityAnalyses (Sheet 2)  
 1\. Number of columns: 36   2\. Number of cases/rows: 54   3\. Column
 List:       * Name: Site Number where data collected (C = Connected, D =
 Disconnected, P = Patch, M = Matrix, X = Corridor, Reference = Reference
 Forest)       * Species: The rest of the columns are species names. While
 creating the community matrix for the community analyses, we combined
 certain species as deemed appropriate, or dropped species that were found
 at only one location. The decisions we made while creating the matrix for
 community analyses are available as supplemental materials with the
 published manuscript.       4\. Missing data codes: None   5\. Specialized
 formats or other abbreviations used: None