Dryad
Published July 31, 2022 | Version v1
Software Open

Biodiversity and infrastructure interact to drive tourism to and within Costa Rica

Authors/Creators

  • 1. Stanford University
  • 2. Princeton University
  • 3. University of California, Davis
  • 4. SALO Sciences*
  • 5. Ministerio de Ambiente y Energía*
  • 6. Banco Central de Costa Rica*
  • 7. University of Washington
  • 8. SPRING*

Description

Significance Tourism accounts for roughly 10% of global gross domestic product, with nature-based tourism its fastest-growing sector in the past 10 years. Nature-based tourism can theoretically contribute to local and sustainable development by creating attractive livelihoods that support biodiversity conservation, but whether tourists prefer to visit more biodiverse destinations is poorly understood. We examine this question in Costa Rica and find that more biodiverse places tend indeed to attract more tourists, especially where there is infrastructure that makes these places more accessible. Safeguarding terrestrial biodiversity is critical to preserving the substantial economic benefits that countries derive from tourism. Investments in both biodiversity conservation and infrastructure are needed to allow biodiverse countries to rely on tourism for their sustainable development.

Notes

The data contains rasters and CSVs necessary to run the code associated with the Echeverri, Smith, et al. PNAS paper titled: "Biodiversity and infrastructure interact to drive tourism to and within Costa Rica". 

Scripts are available in: (https://github.com/jeffreysmith-jrs/natcapCR/blob/main/bin/tourismModel.R).

These are modified versions of publicly available data (cited in the main MS) that allow reproducibility of results.

The files and their descriptions are as followed:

  • arm_diversity.tif: Map showing combined species richness of amphibians, reptiles, and mammals (unit: # of species)
  • amphibians.tif: Map showing species richness of amphibians (unit: # of species)
  • reptiles.tif: Map showing species richness of reptiles (unit: # of species)
  • mammals.tif: Map showing species richness of mammals (unit: # of species)
  • abirdDiversity.tif: Map showing species richness of birds (unit: # of species)
  • abirdDiversity_te.tif: Map showing species richness of threatened and endemic bird species (see Costa_Rican_Birds_endemic_JZ.csv, unit: # of species)
  • tree-cover.tif: Map showing percent tree cover (unit: percentage)
  • distWater.tif: Map showing distance to water (unit: kilometers)
  • distRoads.tif: Map showing distance to roads (unit: kilometers)
  • distPAs.tif: Map showing distance to protected areas (unit: kilometers)
  • hoteDensity.tif: Map showing hotel density (see Hotels_revised_duplicates_deleted.csv, unit: unitless index)
  • fcover1.tif: Map showing percentage non-photosynthetic bare ground (unit: percentage)
  • fcover2.tif: Map showing percentage photosynthetic ground cover (unit: percentage)
  • fcover3.tif: Map showing percentage impervious ground cover (unit: percentage)
  • bio1.tif: Map showing mean annual temperature (unit: degrees Celsius * 10)
  • bio4.tif: Map showing temperature seasonality (unit: unitless)
  • bio12.tif: Map showing mean annual precipitation (unit: millimeters)
  • bio15.tif: Map showing precipitation seasonality (unit: millimeters)
  • Hotels_revised_duplicates_deleted.csv: CSV filed containing georeferenced locations of Costa Rican hotels
  • flickr.csv: CSV filed containing georeferenced locations of Flickr photos
  • checklist_uploads.csv: CSV filed containing georeferenced locations of eBird checklists
  • Costa_Rican_Birds_endemic_JZ.csv: CSV file containing data on which species are IUCN listed and/or endemic in Costa Rica compiled by paper co-author Jim Zook

In addition, we have included 6 result maps which allow for reproducibility of figures:

  • ebird_base.tif: Predicted tourism using model constructed with all predictor variables and eBird checklists (unit: index between 0 and 1)
  • ebird_noBio.tif: Predicted tourism using model constructed without biodiversity predictor variables and eBird checklists (unit: index between 0 and 1)
  • ebird_noInfra.tif: Predicted tourism using model constructed without infrastructure variables and eBird checklists (unit: index between 0 and 1)
  • flickr_base.tif: Predicted tourism using model constructed with all predictor variables and flickr photos (unit: index between 0 and 1)
  • flickr_noBio.tif: Predicted tourism using model constructed without biodiversity predictor variables and flickr photos (unit: index between 0 and 1)
  • flickr_noInfra.tif: Predicted tourism using model constructed without infrastructure variables flickr photos (unit: index between 0 and 1)

Funding provided by: National Aeronautics and Space Administration
Award Number: 80NSSC18K0434

Funding provided by: Gordon and Betty Moore Foundation
Award Number: NA

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Additional details

Related works

Is cited by
10.1073/pnas.2107662119 (DOI)
Is source of
10.5061/dryad.1ns1rn8w7 (DOI)