DOI: 10.5061/dryad.9zw3r22j1
title: Data for: The meta-analysis of the effects of spatial sampling bias correction on presence only species distribution models

description: This dataset contains information extracted from 70 studies identified
 through a systematic review of the peer-reviewed literature (Web of
 Science and SCOPUS databases both searched on the 13/02/2023) to evaluate
 the effect of spatial sampling bias correction methods in presence-only
 species distribution models.
description: Web of Science and SCOPUS databases were searched on the
 13/02/2023 using the following search string:
 ALL=(("species distribution*" OR SDM OR
 "environmental niche" OR ENM OR "resource selection"
 OR "habitat selection" OR suitability OR occurrence) AND
 ("presence-only" OR “presence data” OR
 "presence-background" OR “pseudo absence” OR opportunistic OR
 “citizen science” OR preferential OR maxent OR biomod))
 After removing duplicates, the search returned 8564 unique
 studies, and these were further filtered to remove studies that fell
 outside of the review subject area based on the title and abstract and
 then the remaining studies were filtered by content based on the criteria
 that they involved the building of SDMs using PO data (i.e. no absence
 information, including inferred absences from complete species lists) and
 that the study included a direct comparison between SDMs that attempted to
 correct models for SSB and models without this correction. To avoid
 ambiguity, studies were required to mention explicitly that a particular
 analytical approach was designed to account for SSB (e.g., not “filtering
 to reduce spatial autocorrelation”, which is ambiguous as to the cause of
 the spatial autocorrelation). This identified 70 studies from which
 information on the effect of SSB correction on model performance was
 extracted, along with metadata on species taxonomy, sample sizes of
 occurrence data, and details of the SDM methods and SSB correction
 approach used.
description: The file can be opened with any software capable of reading a
 .csv file.
description: --- title: Data for the meta-analysis of the effects of spatial sampling
 bias correction on presence-only species distribution models. output:
 pdf\_document: default html\_document: default -----------------------
 This dataset contains information extracted from 220 studies identified
 through a systematic review of the peer-reviewed literature (Web of
 Science and SCOPUS databases both searched on the 13/02/2023) to evaluate
 the usage and effect of spatial sampling bias correction methods in
 presence-only species distribution models. ## Description of the Data and
 file structure The dataset contains the following columns: * study_id =
 Unique number for identifying each study. * bibtexkey = Author/year key. *
 journal = Name of journal publishing the study. * title = Title of
 article. * volume = Volume number of published study. * year = Year of
 publication for study. * doi = doi of study. * focal_kingdom = Kingdom of
 focal species (all named, separated by '|'). * focal_phylum =
 Phylum of focal species (all named, separated by '|'). *
 focal_class = Class of focal species (all named, separated by
 '|'). * focal_order = Order of focal species (all named,
 separated by '|'). * focal_family = Family of focal species (all
 named, separated by '|'). * focal_genus = Genus of focal species
 (all named, separated by '|'). * focal_species = Species name
 for focal species (all named, separated by '|'). *
 species_common_name = Species common name in English (all named, separated
 by '|'). * number_species = Number of species included in
 analysis. * min_number_rec = Minimum number of records used in building
 species distribution models in the study. * max_number_rec = Maximum
 number of records used in building species distribution models in the
 study. * location = Geographic location of study. * grain = Spatial grain
 of analysis (if multiple, separated by '|'). * sdm_method =
 Species distribution model (SDM) method used (if multiple, separated by
 '|'). * n_bkgrd = Number of background points used in building
 SDMs (if multiple, separated by '|'). * bias_correction_method =
 Spatial sampling bias correction method used (if multiple, separated by
 '+'). * bias_correction_treatment = A numeric identifying
 treatments within studies, where multiple bias correction treatments are
 reported. * bias_correction_notes = Additional notes on spatial sampling
 bias correction method. * basisOfWeights = The approach used to create
 spatial sampling bias weights (i.e. for sampling background data). *
 covariates = Covariates included in model to correct for spatial sampling
 bias. * basisOfBuffer = The approach used to define a buffer to capture
 spatial sampling bias (i.e. for sampling background data). *
 comparison_to_uncorrected = Whether there was a comparison between models
 with and without spatial sampling bias correction. * performance_metric =
 The metric used to evaluate model predictive performance. * corrected_m =
 Reported mean value of the performance_metric for corrected models. *
 corrected_sd = Reported standard deviation of the performance_metric for
 corrected models. * corrected_n = Reported sample size for the corrected
 models. * corrected_lci = Reported lower 95% confidence interval for the
 corrected models. * corrected_uci = Reported upper 95% confidence interval
 for the corrected models. * uncorrected_m = Reported mean value of the
 performance_metric for uncorrected models. * uncorrected_sd = Reported
 standard deviation of the performance_metric for uncorrected models. *
 uncorrected_n = Reported sample size for the uncorrected models. *
 uncorrected_lci = Reported lower 95% confidence interval for the
 uncorrected models. * uncorrected_uci = Reported upper 95% confidence
 interval for the uncorrected models. * test_dataset = Whether the models
 were evaluated using internal or independent test data. * quant_metric =
 The quantile of metric reported (e.g. mean_sd, mean_95ci, median_iqr). *
 notes = Additional notes. * useInMa = Whether to use in meta-analysis
 (used where multiple performance_metrics (e.g. AUC, TSS) values have been
 extracted for the same model predictions). NA values in cells indicate
 that the information was not available or not relevant to that entry. ##
 Sharing/access Information The dataset contains data extracted from
 published literature and the literature sources are referenced therein. In
 most cases the data was extracted from the published study or
 supplementary material, but where this was not possible the study authors
 were contacted and asked to supply the raw data.