DOI: 10.5281/zenodo.15991086
title: Ecological niche models for mapping cultural ecosystem services (CES)

description: Description

This dataset includes the inputs and outputs generated in the spatial modeling of CES using social media data for eight mountain parks in Spain and Portugal (Aigüestortes, Sierra de Guadarrama, Ordesa, Peneda-Gerês, Picos de Europa, Sierra de las Nieves, Sierra Nevada and Teide). This spatial modeling is addressed in the article in preparation entitled: "What drives cultural ecosystem services in mountain protected areas? An AI-assisted answer using social media."

The variables used as inputs to generate the models come from different sources:

-CES presence points come from social media photos (Flickr and Twitter) labeled using AI models and validated by experts. The models used for automatic labeling were Dino v2 and OPENAI's GPT 4.1 model. Consensus was sought on the labels from these two label sources, which showed F1 values above 0.75, and these labels were used as presence data.

The environmental variables used are mainly derived from:

-  OpenStreetMap (OSM) https://www.openstreetmap.org/

- Variables derived from remote sensing

- Topographic variables

- Current and future climate variables derived from CHELSA (https://chelsa-climate.org/)

- Landscape metrics (calculated with Fragstats software https://www.fragstats.org/)

- Viewshed 

- Land use and land cover maps (https://land.copernicus.eu/en/products/corine-land-cover)

The models were generated with the maximum entropy (MaxEnt) algorithm using the biomod2 R package, leveraging its suitability for presence-only data, low sample sizes, and mixed predictor types. To address sampling bias, we generated 10 pseudo-absence replicates based on the “target-group background” method. Models were evaluated using AUC-ROC and True Skill Statistic (TSS), with performance validation via 10-fold cross-validation, resulting in 100 runs per model. Ensemble models were created from runs with AUC-ROC > 0.6, using the median for spatial projections of CES and the coefficient of variation to estimate uncertainty. We implemented two modelling approaches: one assuming consistent CES preferences across parks, and another assuming park-specific preferences shaped by local environmental contexts.

 

Table 1. Categories used in social media photo tagging: Stoten, based on the scientific framework proposed by Moreno-Llorca et al. (2020) (https://doi.org/10.1016/j.scitotenv.2020.140067). 








Stoten





Cultural





Fauna/Flora





Gastronomy





Nature & Landscape





Not relevant





Recreational





Religious





Rural tourism





Sports





Sun and beach





Urban






 

Table 2. Table of contents of the dataset








Folder

 
 
 
 
 


format



Description





Inputs



Base layers



by National Park



100-meter grid



grid_wgs84_atrib



.shp



100 x 100 meter grid for each of the studied national  parks that cover the study area



 
 
 


Biosphere Reserve



MAB_wgs84



.shp



Biosphere reserve layers present in each of the national parks studied



 
 
 


Municipality

 


Municipality



.shp



Layers of municipalities that overlap with the park area, biosphere reserve, Natura 2000 and the socioeconomic influence area with a 100-meter buffer



 
 
 


National park limit



National_park_limit



.shp



Boundaries of each of the national parks studied



 
 
 


Natura 2000

 


RN2000



.shp



Layers of the Natura 2000 for each of the national parks studied



 
 
 


Socioeconomic influence area



AIS



.shp



Area of socioeconomic influence of each of the parks studied



 
 
 
 
 


Readme



.txt



File containing layer metadata, including download locations and descriptions of shape attributes.



 
 


by National Park



Accessibility

 
 


.tif



Accessibility variables that include routes, streets, parking, and train tracks



 
 
 


Climate

 
 


.tif



Chelsea-derived climate variable layers and solar radiation layers



 
 
 


Ecosystem functioning

 


.tif



Layers derived from remote sensing that are related with the functional attributes of ecosystems



 
 
 


Ecosystem structure

 


.tif



Landscape and spectral diversity metrics



 
 
 


Geodiversity

 
 


.tif



Topographic and derived variables



 
 
 


Land use Land cover

 


.tif



Layers related to land use and cover



 
 
 


Tourism and Culture

 


.tif



Layers related to infrastructure associated with tourism such as bars, restaurants, lodgings and places of cultural interest such as monuments



 


Scripts



Modeling to get output data



Biomod_modelling_by_park



.R



Script used for modeling CES using data from social media by fitting one ENM for each park and CES.



 
 
 


Biomod_modelling_all_parks



.R



Script used for modeling CES using data from social media by fitting one ENM for each CES.



 
 


Modeling to get output data



Downloading and processing variables



EFAS



EFAs code



.js



GEE scripts used to download the Ecosystem Functional Attributes  (EFAs) (Paruelo et al.2001; Alcaraz-Segura et al. 2006) derived from Sentinel 2 dataset  for each of the national parks studied



 
 
 
 


OSM



1) Download layers



.py



Python scripts used to download the OpenStreetMap layers of interest for each of the national parks studied.



 
 
 
 
 


2) Join layers



.py



Scripts used to merge OSM layers belonging to the same category. e.g., primary, secondary, and tertiary highways.



 
 
 
 
 


3) Count point



.py



Scripts used to count the number of points in each of the 100 grid cells for each park, used in case of point type data



 
 
 
 
 


4) Presence and absence



.py



Scripts used to assess presence in each of the cells of the 100-square grid for each park, used in the case of data types such as points, lines, and polygons.



 
 
 
 


Remote sensing



Canopy



.js



GEE scripts used to download the canopy (https://gee-community-catalog.org/projects/canopy/) downloaded and cropped for each of the national parks studied



 
 
 
 
 


ESPI



.js



GEE scripts used to download the ESPI index (Ecosystem Service Provision Index) downloaded and cropped for each of the national parks studied



 
 
 
 
 


European disturbance map



.js



GEE scripts used to download European disturbance maps (//https://www.eea.europa.eu/data-and-maps/figures/biogeographical-regions-in-europe-2)

downloaded and cropped for each of the national parks studied



 
 
 
 
 


LST



.js



GEE scripts used to download LST maps (from Landsat Collection)

downloaded and cropped for each of the national parks studied



 
 
 
 
 


Night lights



.js



GEE scripts used to download nighttime light maps (https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_ANNUAL_V22)

downloaded and cropped for each of the national parks studied



 
 
 
 
 


Population density



.js



GEE scripts used to download population density maps (https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Population_Density)

downloaded and cropped for each of the national parks studied



 
 
 
 
 


Soil groups



.js



GEE scripts used to download Hydrologic Soil Group maps (https://gee-community-catalog.org/projects/hihydro_soil/)

downloaded and cropped for each of the national parks studied



 
 
 
 
 


Solar radiation



.js



GEE scripts used to download solar radiation maps (https://globalsolaratlas.info/support/faq)

downloaded and cropped for each of the national parks studied



 
 
 
 


RGB diversity



Seasonal KMeans clustering



.js



GEE scripts were used to calculate seasonal clusters using Sentinel 2 RGB bands with GEE's .wekaKMeans algorithm. These layers were downloaded and cropped for each of the national parks studied.



 
 
 
 
 


Colour diversity analysis



.R



R script used to calculate spectral diversity (Shannon, Simpson and inverse Simpson) using the cluster layers and RGB bands derived from Sentinel 2.



 
 
 
 


Post processing



Align_and_Clip_rasters



.py



Python scripts used to align and clip the downloaded layers to a 100-meter grid reference layer for each of the national parks studied.





Outputs



CES projections

 
 
 


proj_Aiguestortes_Sports_ensemble



.tif



Spatial projections for the best models obtained for each CES and park








References: 

Alcaraz-Segura, D., Paruelo, J., and Cabello, J.  2006: Identification of current ecosystem functional types in the Iberian Peninsula, Global Ecol. Biogeogr., 15, 200–212, https://doi.org/10.1111/j.1466-822X.2006.00215.x

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M., 2017. Climatologies at high resolution for the earth’s land surface areas. Sci Data 4, 170122. https://doi.org/10.1038/sdata.2017.122 

Lobo, J.M., Jiménez-Valverde, A., Hortal, J., 2010. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33, 103–114. https://doi.org/10.1111/j.1600-0587.2009.06039.x

Paruelo, J. M., Jobbágy, E. G., and Sala, O. E. 2001: Current Distribution of Ecosystem Functional Types in Temperate South America, Ecosystems, 4, 683–698, https://doi.org/10.1007/s10021-001-0037-9

Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026

Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J., Ferrier, S., 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19, 181–197. https://doi.org/10.1890/07-2153.1

Thuiller, W., Georges, D., Gueguen, M., Engler, R., Breiner, F., Lafourcade, B., Patin, R., 2023. biomod2: Ensemble Platform for Species Distribution Modeling.

Sillero, N., Arenas-Castro, S., Enriquez‐Urzelai, U., Vale, C.G., Sousa-Guedes, D., Martínez-Freiría, F., Real, R., Barbosa, A.M., 2021. Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling. Ecological Modelling 456, 109671. https://doi.org/10.1016/j.ecolmodel.2021.109671

Valavi, R., Guillera-Arroita, G., Lahoz-Monfort, J.J., Elith, J., 2022. Predictive performance of presence-only species distribution models: a benchmark study with reproducible code. Ecological Monographs 92, e01486. https://doi.org/10.1002/ecm.1486