DOI: 10.5061/dryad.qfttdz0jm
title: Data from: Effects of input data sources on species distribution model predictions across species with different distributional ranges

description: Species distribution models (SDMs) are a popular tool in theoretical and
 quantitative ecology and constitute the most widely used modelling
 framework in global change science and biodiversity conservation. As main
 data sources, SDMs require georeferenced biodiversity observations as a
 response or dependent variable (e.g. species occurrence, species richness,
 etc) and geographic layers of environmental information as predictors or
 independent variables (e.g. climate, land cover, vegetation indices
 derived from remote sensing, etc). However, although SDMs have become one
 of the most important quantitative tools for addressing regular and timely
 biodiversity assessments worldwide, these techniques are still subject to
 different sources of uncertainty that have been unequally assessed. Thus,
 despite the uncertainty related to niche-based or distribution-based
 models has been addressed at different stages in the modelling process, an
 analysis of the effect of uncertainty coming from alternative data sources
 on the predictive ability of SDMs is still
 limited. Citizen-collected species occurrence data (e.g. eBird)
 are often used for fitting SDMs when data from standardized and
 expert-supported surveys (e.g. Atlases) are unavailable. On the other
 hand, macroclimate variables are much more commonly used as predictors in
 SDMs than other sources of information coming from remote sensing data. We
 assessed the effects of using different data sources (in both response and
 predictor variables) on SDM performance across a wide range of bird
 species with contrasting distributional ranges in the Iberian Peninsula
 (Portugal and Spain). To do that, an SDM ensemble-forecasting approach was
 implemented by using bird data from two different data sources: the
 semi-structured eBird project and standardized Atlases. We fitted SDMs
 with three predictor types: macroclimate, remotely sensed ecosystem
 functional attributes (EFAs) from vegetation indices and their
 combination. Species were grouped into four range-size classes. We also
 used different evaluation metrics to better assess the uncertainty of
 model predictions. We then applied generalized linear mixed-effects models
 to test the effect on the model performance of input data sources across
 distributional range sizes while accounting for different accuracy
 metrics. Pairwise comparisons between range projections were used to
 assess their spatial similarity. Our models demonstrated the
 usefulness and complementarity of different input data sources when
 modelling species distribution across different distributional ranges.
 Citizen science and remote sensing data contribute to updating the
 knowledge of the distribution of the most threatened bird species by
 increasing the model accuracy. These findings highlight the need to
 integrate different data sources to improve the model predictions at a
 regional scale. Our framework also underlines that model uncertainty
 should be examined more exhaustively at the early stages of the modelling
 process.
description: Methods for processing the data: 
 <em><strong>1) The Iberian bird species occurrences
 dataset (10km):</strong></em> Bird occurrence data were
 collected from two different data sources of biodiversity: i) a
 standardized dataset based on national Bird Atlases (Atlas); and ii) a
 citizen science (i.e. non-standardized) dataset based on the EOD - eBird
 Observation Dataset from the Global Biodiversity Information Facility –
 GBIF (eBird). To reduce the potential geographical errors in species
 records that can strongly influence the results of models, we filtered the
 original dataset removing duplicates and positional/spatial errors such as
 outliers using R and QGIS programs, and nomenclature errors and taxa
 misidentification supported by expert knowledge. In addition, we
 harmonized the species records in grid cells with a resolution higher than
 10 km before the modelling procedures. We adjusted eBird data to the
 spatial resolution of both Atlases (10-km UTM square) to standardize input
 data and make both datasets comparable, also matching the predictive
 variable spatial resolution (10-km UTM square). We also matched the eBird
 data to the years and months within the Atlas data (1999-2012), from late
 February to mid-August, the breeding season in the Iberian Peninsula.
 Additionally, to perform subsequent comparisons, we built a full dataset
 from the combination of both Atlas and eBird datasets. This full dataset
 represents the best knowledge of bird distribution in Iberia as it
 includes all occurrences recorded in the two datasets (Species name,
 Species code, and Data source are provided). To assess the effect of
 species distributional range (from narrow-ranged to widespread species) on
 modelling performance, we grouped the species records from the full
 dataset in four sets of size classes based on the number of occurrences at
 10-km UTM squares: i) Class I (10 – 100); Class II (101 – 500); ii) Class
 III (501 – 1000); Class IV (&gt; 1000). We also considered the
 conservation categories of species in the IUCN Red List to assess the
 potential effects of model uncertainty on decision-making for bird
 conservation. Therefore, each species always belongs to the same group (n
 = 236) because the full dataset represents the actual distributional range
 of each species. The number of 10-km UTM squares, and accuracy metrics
 (AUC and BoyceI) per each species and by type of predictor (climate, EFAs,
 and combined climate+EFAs) were also provided.
 <em><strong>2) CHELSA bioclimate variables
 (IP-10km):</strong></em> Nineteen (bio-) climate predictors
 from monthly temperature and rainfall data were derived from the platform
 https://chelsa-climate.org/. This climatic dataset was obtained for
 historical conditions (1979 to 2013) from the CHELSA V1.2 database at a
 spatial resolution of 30 arc-sec (~1-km pixel size), to be later
 aggregated at 10-km UTM square. Based on the pairwise correlations and the
 collinearity assessment from initial nineteen (bio-) climate variables,
 three temperature-related (bio4 — Temperature Seasonality; bio8 — Mean
 Temperature of Wettest Quarter; bio9 — Mean Temperature of Driest Quarter)
 and two precipitation (bio16 — Precipitation of Wettest Quarter; bio17 —
 Precipitation of Driest Quarter) variables were selected to build this
 climate dataset for the Iberian Peninsula.
 <strong><em>3) MODIS EVI-based EFAs
 (IP-10km):</em></strong> Satellite remote sensing data from
 the Moderate Resolution Imaging Spectrometer (MODIS) on board the Terra
 satellite platform were used to derive remotely sensed ecosystem
 functioning attributes (EFAs) to characterize species habitat dynamics as
 a counterpoint/complement of climate data. To compute EFAs, we used the
 MODIS Enhanced Vegetation Index (EVI) (MOD13Q1.v006; 232 m pixel every 16
 days) as a proxy of vegetation greenness, biomass, and leaf area index for
 the 2000–2012 time period. For that, we used the Google Earth Engine (GEE)
 cloud-based platform to derive originally eleven metrics of the EVI
 seasonal dynamics. These statistical measures were calculated for each
 complete year. To capture the multi-year normal conditions of each EFA
 variable, thus reducing the effect of stochastic interannual climatic
 fluctuations, we computed the overall mean. EFAs were exported from GEE at
 1 km squares of final spatial resolution to be later aggregated at 10 km
 UTM squares. Similarly as in the case of climate variables, and based on
 the pairwise correlations and the collinearity assessment, we selected
 five remotely sensed EFAs as descriptors of species habitat dynamics for
 the Iberian Peninsula: EVI annual mean (EVImean as surrogate of annual
 total amount of primary production), EVI annual minimum (EVImin as an
 indicator of the annual extremes), EVI seasonal standard-deviation (EVIsd
 as descriptor of variations between seasons), and dates of maximum
 (EVIdmax) and minimum (EVIdmin) EVI (indicators of phenology - growing
 season). <em><strong>4) Combined bioclimate
 and EFA dataset (IP-10km):</strong></em> This file represents
 the most significant five (and uncorrelated) predictors selected from the
 previous partial climate (Temperature Seasonality - bio4, Precipitation of
 Wettest Quarter - bio16, Precipitation of Driest Quarter - bio17) and
 EFA-based models (EVI annual mean - EVImean, EVI seasonal
 standard-deviation - EVIsd) (CLIM_EFAs) at 10km pixel size for the Iberian
 Peninsula.
description: # Data from: Effects of input data sources on species distribution model
 predictions across species with different distributional ranges
 [https://doi.org/10.5061/dryad.qfttdz0jm](https://doi.org/10.5061/dryad.qfttdz0jm) To perform and replicate this study, this dataset provides all needed files (as tables) to fit SDMs: i) the Iberian bird species occurrences at 10km UTM square as a response or dependent variable;  ii) the geographic layers of environmental information at 10km UTM square for the Iberian Peninsula as predictors or independent variables, such as climate data, ecosystem functioning attributes (EFAs) and the combined climate and EFA data. The dataset is provided by four **.csv* files named as: *1) The_Iberian_bird_species_occurrences_dataset_10km.csv* *2) CHELSA_bioclimate_variables_IP10km.csv* *3) MODIS_EVI-based_EFAs_IP10km.csv* *4) Combined_bioclimate_EFA_dataset_IP10km.csv* Recommended citation for this dataset: Arenas-Castro, S. et al. (2024), Data from: Effects of input data sources on species distribution model predictions across species with different distributional ranges, Dryad, Dataset, DOI: [https://doi.org/10.5061/dryad.qfttdz0jm](https://doi.org/10.5061/dryad.qfttdz0jm) ## DATA-SPECIFIC INFORMATION FOR: ### The Iberian bird species occurrences dataset (10km) 1\. Number of variables: 5 2\. Number of cases/rows: 1506 3\. Variable List: * Data source: Atlas, eBird * Type of predictor: CLIM, EFAs, CLIM_EFAs * Size class: I, II, III, IV * IUCN category: EN, LC, NT, NT/LC, VU * Type of metric: AUC, BoyceI 4\. Missing data codes: N/A ### CHELSA bioclimate variables (IP-10km) 1\. Number of variables: 5 2\. Number of cases/rows: 5883 3\. Variable List: * Temperature Seasonality (bio4), ºC * Mean Temperature of Wettest Quarter (bio8), ºC * Mean Temperature of Driest Quarter) (bio9), ºC * Precipitation of Wettest Quarter (bio16), kg m-2 month-1 * Precipitation of Driest Quarter) (bio17), kg m-2 month-1 4\. Missing data codes: N/A ### MODIS EVI-based EFAs (IP-10km) 1\. Number of variables: 5 2\. Number of cases/rows: 5883 3\. Variable List: * EVI annual mean (EVImean), surrogate of annual total amount of primary production, unitless * EVI annual minimum (EVImin) as an indicator of the annual extremes, unitless * EVI seasonal standard-deviation (EVIsd), as descriptor of variations between seasons, unitless * Dates of maximum (EVIdmax) and minimum (EVIdmin) EVI, indicators of phenology - growing season, unitless 4\. Missing data codes: N/A ### Combined bioclimate and EFA dataset (IP-10km) 1\. Number of variables: 5 2\. Number of cases/rows: 5883 3\. Variable List: * Temperature Seasonality (bio4), ºC * Precipitation of Wettest Quarter (bio16), kg m-2 month-1 * Precipitation of Driest Quarter) (bio17), kg m-2 month-1 * EVI annual mean (EVImean), surrogate of annual total amount of primary production, unitless * EVI seasonal standard-deviation (EVIsd), as descriptor of variations between seasons, unitless 4\. Missing data codes: N/A