Data from: Complex patch geometries maximize species richness at the expense of forest specialists
Data files
Feb 08, 2024 version files 59.98 KB
Abstract
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.
README: Complex patch geometries maximize species richness at the expense of forest specialists
https://doi.org/10.5061/dryad.wdbrv15w6
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: slclements@miami.edu
B. Additional Author
Name: Dunia Villalobos Alpízar
Institution: N/A
Address: Copal, Agua Buena, San Vito de Coto Brus, Puntarenas, Costa Rica
Email: duniavillaalpi@gmail.com
C. Principal Investigator
Name: Christopher A. Searcy
Institution: University of Miami
Address: CORAL GABLES, FL, USA
Email: cas383@miami.edu
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. https://doi.org/10.5061/dryad.wdbrv15w6
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
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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 > 20 % of identified individuals were found in the matrix)
* Specialist Abundance: The abundance of species we defined as “specialists” (if > 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 (> 72 years) or new growth (< 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
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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
Methods
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. Diasporus vocator and Diasporus diastema were collapsed into Diasporus spp. due to difficulties in discerning the two species both visually and by call surveys. Additionally, the rhodopis-species group contains seven small leaf litter frogs in the genus Craugastor that are notoriously difficult to identify in the field, although most were presumably Craugastor stejnegerianus 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.