DOI: 10.5061/dryad.k98sf7mdg
title: Data from: Integrated species distribution models to account for sampling biases and improve range wide occurrence predictions

description: Aim Species distribution models (SDMs) that integrate presence-only and
 presence-absence data offer a promising avenue to improve information on
 species’ geographic distributions. The use of such ‘integrated SDMs’ on a
 species range-wide extent has been constrained by the often-limited
 presence-absence data and by the heterogeneous sampling of the
 presence-only data. Here, we evaluate integrated SDMs for studying species
 ranges with a novel expert range map-based evaluation. We build a new
 understanding about how integrated SDMs address issues of estimation
 accuracy and data deficiency and thereby offer advantages over traditional
 SDMs. Location South and Central America. Time period 1979-2017. Major
 taxa studied Hummingbirds. Methods We build integrated SDMs by linking two
 observation models – one for each data type – to the same underlying
 spatial process. We validate SDMs with two schemes: i) cross-validation
 with presence-absence data and ii) comparison with respect to the species’
 whole range as defined with IUCN range maps. We also compare models
 relative to the estimated response curves and compute the association
 between the benefit of the data integration and the number of presence
 records in each data set. Results The integrated SDM accounting for the
 spatially varying sampling intensity of the presence-only data was one of
 the top-performing models in both model validation schemes. Presence-only
 data alleviated overly large niche estimates, and data integration was
 beneficial compared to modelling solely presence-only data for species
 that had few presence points when predicting the species’ whole range. On
 the community level, integrated models improved the species richness
 prediction. Main conclusions Integrated SDMs combining presence-only and
 presence-absence data are successfully able to borrow strengths from both
 data types and offer improved predictions of species’ ranges. Integrated
 SDMs can potentially alleviate the impacts of taxonomically and
 geographically uneven sampling and to leverage the detailed sampling
 information in presence-absence data.
description: A detailed methodology associated with the environmental
 variables and species data can be found from the references used in the
 original publication.
description: This data set is a collection of publicly available species and
 environmental data. They have been used to study populations and
 distributions of species and their associations with environment. To avoid
 risks on species populations, coordinates of the presence records for
 three species with near-threatened IUCN status and all checklist
 coordinates have been rounded to 0.01 degrees. The species are Little
 Woodstar (Chaetocercus bombus), Black-thighed Puffleg (Eriocnemis derbyi)
 and Hoary Puffleg (Haplophaedia lugens). ## Description of the data and
 file structure * File 1 Name:
 Cloud_cover/MODCF_intraannualSD_resampled_masked_NA30x30_americas.tif *
 File 1 Description: Intra-annual variability of cloud cover in 1 square
 kilometer resolution over the study area. * Folder 2 Name: Domains_new/ *
 Folder 2 Description: Folder contains geotif-file for each species showing
 the study domain, which is a buffer around species expert range map and
 points records. * File 3 Name:
 Elevation_range/topography_elevation_1KMmi_GMTEDmi_NA30x30_americas.tif *
 File 3 Description: Areal elevation from sea level in meters in 1 square
 kilometer resolution over the study area. * File 4 Name:
 Environment/Chelsa_SA.tif * File 4 Description: Chelsa Bioclim (v. 1.2) of
 annual mean temperature (cov1), mean annual diurnal range (cov2), mean
 annual precipitation sum (cov3) and precipitation seasonality (cov4) in 1
 square kilometer resolution. * File 5 Name:
 EVI/Annual_EVI_resampled_NA30x30_americas.tif * File 5 Description:
 Enhanced vegetation index in 1 square kilometer resolution. * File 6 Name:
 PA_observations/Juan_parra_checklists/Sites_8Feb2011.csv * File 6
 Description: List of checklist locations. * Columns: * Communities_IDComm:
 identifying number of communities * CommunityName: name of communities *
 LatDecDeg: latitude in geographical coordinates * LongDecDeg: longitude in
 geographical coordinates * Country: country * MinElevation_m: minimum
 elevation from sea level in meters * MaxElevation_m: maximum elevation
 from sea level in meters * AreaSampled: sampled area in square meters *
 AllSpp: number of species recorded * SppWithPhy: number of species
 recorded with phylogenetic information * SppWithMorph: number of species
 recorded with morphological information * File 7 Name:
 PA_observations/Juan_parra_checklists/SpeciesxSite8Feb2011.csv * File 7
 Description: List of species sightings in the checklist locations. *
 Columns: * Communities_IDComm: identifying number of communities * Spname:
 Species name * CommunityName: name of communities * Country: country *
 SpID: identifying number of species * Folder 8 Name:
 PO_observations_non_thinned/ * Folder 8 Description: Folder contains
 species point records as species-specific CSV files. * Columns: * lat:
 latitude in geographical coordinates * lon: longitude in geographical
 coordinates * Folder 9 Name: Study_area/ * Folder 9 Description: Folder
 contains study area (rectangle with the extent matching the extent of the
 union of all species domains) in geographical (WGS 84) and planar (laea)
 coordinate system. * File 10 Name:
 TRI/tri_1KMmd_GMTEDmd_resampled_masked_NA30x30_americas.tif * File 10
 Description: Topographical ruggedness index in 1 square kilometer
 resolution. * Folder 11 Name: Compiled_data_v9/ * Folder 11 Description:
 Folder contains two .RData-files for two example species, Coeligena
 lutitae (Buff-winged starfrontlet) and Ensifera ensifera (Sword-billed
 hummingbird). Rdata-files contain compiled data for the species:
 presence-only, presence-absence and background point information with
 environmental associations. * File 12 Name:
 Compiled_data_v9/'species_name'_not_thin... * File 12
 Description: .RData file contains species and environmental data along
 with prediction data across the whole study domain: * env_data: *
 covariates: environmental covariates over the whole study area *
 coordinates: cell coordinates over the whole study area * min_coordinates:
 minimum coordinates of the study area * offsets: possible offsets computed
 from expert range maps and species' elevational limits * ind_na:
 index for study cells which are not used for range predictions, such as
 sea areas * PO_training_data (presence points data): * covariates:
 environmental covariates over the presence and background points *
 coordinates: cell coordinates over the presence and background points *
 cov_mean: mean of each covariate for covariate standardization * cov_sd:
 standard deviation of each covariate for covariate standardization *
 min_coordinates: minimum coordinates of the study area * response: species
 presence status * weights: statistical weights * offset_expert: offset
 from the species' range map * offset_elevation: offset from the
 species' elevational limits * PA_training_data (checklist data): *
 covariates: environmental covariates over the presence and background
 points * coordinates: cell coordinates over the presence and background
 points * response: species presence status * offset_expert: offset from
 the species' range map * offset_elevation: offset from the
 species' elevational limits * proj_raster: coordinate reference
 system * scale_out: resolution at which background points are sampled *
 File 13 Name: Compiled_data_v9/INLA_list_6_'species_name'... *
 File 13 Description: contain data for running models for the whole data
 and for cross-validation folds along with spatial mesh-structure for
 running spatial spde-type INLA-models: * po_coordinates: presence and
 background point coordinates * po_covariates: presence and background
 point covariates * po_response: presence status * po_weights: statistical
 weights for the presence and background points * po_offset: offsets from
 species' range maps and elevational limits * pa_coordinates:
 checklist coordinates * pa_covariates: checklist covariates * pa_response:
 presence status in checklists * pa_offset: offsets from species'
 range maps and elevational limits * cov_mean: mean of each covariate for
 covariate standardization * cov_sd: standard deviation of each covariate
 for covariate standardization * spde_hull: geographical hull for defining
 SPDEs for the INLA model * spde_mesh: geographical mesh for defining SPDEs
 for the INLA model * pa_testing_folds: cross-validation folds for the
 checklists * po_training_folds: presence points used for model training ##
 Sharing/Access information Data was derived from the following sources: *
 Cloud cover: ,
 () * Elevation:
 ,
 () * Chelsa Bioclim:
 ,
 () *
 EVI:,
 () *
 Presence-absence species observations:
 ,
 (Parra, J. L. et al. 2019. Data from: Continental-scale 1km hummingbird
 diversity derived from fusing point records with lateral and elevational
 expert information. * Presence-only species observations: Originates from
 the Global Biodiversity Information Facility ()
 and accessed through Map of Life () on 7 June 2017.
 * TRI: ,
 () * Expert range
 map-based validation requires expert range maps which are not included in
 this repository. They can be downloaded from Map of Life
 (). ## Code/Software This code and software have
 been used to create the analysis and results of the study. All analysis
 were run with R 4.3.1 (RStudio Team (2020)). Inference is conducted with
 INLA (version 23.05.30, ). The R-workflow
 is run using the iSDM.R script, which calls specific R-scripts to
 associate species data with environmental variables, fit models with full
 data and cross-validation folds and validates the models. * File 1 Name:
 Cross_validation.R * File 1 Description: Run cross-validation with
 specific validation metrics for over all folds. * File 2 Name:
 Cross_validation_range_map.R * File 2 Description: Run range map-based
 validation. * File 3 Name: Data_model_summaries_all_species.R * File 3
 Description: Compute summaries of data distribution and range predictions
 for all study species. * File 4 Name: Data_model_summaries_cv_species.R *
 File 4 Description: Compute summaries of data distribution and range
 predictions for cross-validated study species. * File 5 Name: Figure1.R *
 File 5 Description: Draw figure 1 of the original publication. * File 6
 Name: Figure2.R * File 6 Description: Draw figure 2 of the original
 publication. * File 7 Name: Figure3.R * File 7 Description: Draw figure 3
 of the original publication. * File 8 Name: Get_data.R * File 8
 Description: A collective script, which calls specific data-scripts. *
 File 9 Name: Get_domain.R * File 9 Description: Create a study domain
 given the point records and expert range map. * File 10 Name:
 Get_env_data.R * File 10 Description: Read environmental rasters and
 create a list of data covering the whole species domain for making
 predictions. * File 11 Name: Get_species_pa_training_data.R * File 11
 Description: Read species checklists and create a presence-absence data
 set. * File 12 Name: Get_species_po_data.R * File 12 Description: Read
 species point records. * File 13 Name: Get_species_po_training_data.R *
 File 13 Description: Associate point records and background points with
 environmental variables. * File 14 Name: iSDM.R * File 14 Description:
 Script for running the whole analysis pipeline. * File 15 Name:
 occurrence.r * File 15 Description: Spatial thinning script for the point
 records. * File 16 Name: RunInference_cv_inla_non_spat.R * File 16
 Description: Fit non-spatial models for each cross-validation fold. * File
 17 Name: RunInference_cv_inla_spat.R * File 17 Description: Fit spatial
 models for each cross-validation fold. * File 18 Name:
 RunInference_cv_inla_spat_rsr.R * File 18 Description: Fit spatial models
 with restricted spatial latent effect for each cross-validation fold. *
 File 19 Name: RunInference_cv_inla_spat_samp.R * File 19 Description: Fit
 spatial models with additional spatial latent effect for each
 cross-validation fold. * File 20 Name: RunInference_inla_non_spat.R * File
 20 Description: Fit non-spatial models for whole data set. * File 21 Name:
 RunInference_inla_spat.R * File 21 Description: Fit spatial models for
 whole data set. * File 22 Name: RunInference_inla_spat_rsr.R * File 22
 Description: Fit spatial models with restricted spatial latent effect for
 whole data set. * File 23 Name: RunInference_inla_spat_samp.R * File 23
 Description: Fit spatial models with additional spatial latent effect for
 whole data set. * File 24 Name: Set_data_for_inference.R * File 24
 Description: Add features in the data set (INLA mesh and cross-validation
 folds). * File 25 Name: Validation_functions.R * File 25 Description:
 Functions for computing different validation metrics. * File 26 Name:
 Species_list.csv * File 26 Description: List of all study species. * File
 27 Name: Species_list_cross_validation.csv * File 27 Description: List of
 cross-validated species.