DOI: 10.5061/dryad.mw6m9065c
title: Fine-grain predictions are key to accurately represent continental-scale biodiversity patterns

description: Aim As global change accelerates, accurate predicti­­­ons of species
 distributions and biodiversity patterns are critical to limit biodiversity
 loss. Numerous studies have found that coarse-grain species distribution
 models (SDMs) perform poorly relative to fine-grain models because they
 mismatch environmental information with observations. However, it remains
 unclear how grain-size biases vary in intensity across space and time,
 possibly generating inaccurate predictions for specific regions, seasons
 or species. For example, coarse-grain biases may intensify in patchy,
 discontinuous landscapes. Such biases may accumulate to produce highly
 misleading estimates of continental and seasonal biodiversity patterns.
 Location: United States and Canada Time Period: 2004-2021 Major taxa
 studied: Birds (Aves) Methods We fit presence-absence SDMs characterizing
 the summer and winter distributions of 572 bird species native to the US
 and Canada across five spatial grains from 1 to 50 km, using observations
 from the eBird citizen science initiative. We combined these predictions
 to generate seasonal biodiversity estimates across the US and Canada,
 which we validated using observations from 322 independent sites. Results
 We find that in both seasons, 1km models more accurately predicted species
 presence, absence, and richness at local sites. Coarse-grain models (even
 at 3 km) consistently under-predicted range area, potentially missing
 important habitat. This bias intensified during summer (83-86% of species)
 when many birds have smaller ‘operational scales’ via localized home
 ranges while breeding. Biases were greatest in desert regions with
 patchier habitat and for range-restricted and habitat specialist species.
 Predictions based on coarse-grain models overpredicted avian diversity in
 the west and underpredicted it in the great plains, prairie pothole region
 and boreal zones. Main conclusions We demonstrate that coarse-grain models
 can bias seasonal and continental estimates of biodiversity patterns
 across space and time and that grain-related biases intensify during
 summer and in patchier landscapes, especially for range-restricted and
 habitat specialist species at risk of population declines.
description: # Fine-grain predictions are key to accurately represent continental-scale
 biodiversity patterns
 [https://doi.org/10.5061/dryad.mw6m9065c](https://doi.org/10.5061/dryad.mw6m9065c) ## Description of the data and file structure ## ## DATA FILES ### ebird\_prebreeding\_migration\_CEA.csv ### ebird\_nonbreeding\_CEA.csv ### ebird\_postbreeding\_migration\_CEA.csv ### ebird\_breeding\_CEA.csv Processed, filtered eBird data for March-May, December-February, September-November, or June-August (respectively, in order of files listed) with spatially linked environmental covariates (landcover, climate, and topographic variables) at all resolutions (1, 3, 5, 10, 50km, as suffix of each variable), as well as checklist-level temporal and effort covariates. eBird data is structured with rows for each checklist (sampling event) and columns for presence (1) or absence (0) for every species. Cells containing NA have no associated environmental information available at a given location. #### Column IDs Checklist_id: ID of observation Locality_id: Location ID y: vertical axis spatial coordinate x: horizontal axis spatial coordinate year: year of observation day_of_year: julian date of observation time_observations_started: time of day of observation duration_minutes: duration of observation in minutes effort_distance_km: distance traveled during observation number_observers: number of observers creating the observation \`Dendrocygna autumnalis` until `Passerina versicolor`: presence (1) or absence (0) for a given species bio1_1: mean temperature of climate where observation occurred, measured at 1km bio12_1: total annual precipitation in mm in climate where observation occurred, measured at 1km bio15_1: precipitation seasonality in climate where observation occurred, measured at 1km cloudsd_1: cloud cover variability in climate where observation occurred, measured at 1km evisum_1: enhanced vegetation index in climate where observation occurred, measured at 1km twi_1: terrain wetness index where observation occurred, measured at 1km tri_1: topographic roughness index where observation occurred, measured at 1km elev_1: elevation where observation occurred, measured at 1km pland_10_cropland_rainfed_1: percentage of 1km cell where observation occurred with rainfed cropland landcover type (type 1) pland_100_mosaic_tree_shrub_1: percentage of 1km cell where observation occurred with mosaic tree/shrub landcover type pland_11_cropland_rainfed_1: percentage of 1km cell where observation occurred with rainfed cropland landcover type (type 2) pland_110_mosaic_herbacious_1: percentage of 1km cell where observation occurred with mosaic herbaceous landcover type pland_12_cropland_rainfed_1: percentage of 1km cell where observation occurred with rainfed cropland landcover type (type 3) pland_120_shrubland_1: percentage of 1km cell where observation occurred with shrubland landcover type (type 1) pland_121_shrubland_1: percentage of 1km cell where observation occurred with shrubland landcover type (type 2) pland_122_shrubland_1: percentage of 1km cell where observation occurred with shrubland landcover type (type 3) pland_130_grassland_1: percentage of 1km cell where observation occurred with grassland landcover type pland_140_lichens_mosses_1: percentage of 1km cell where observation occurred with lichens/mosses landcover type pland_150_sparse_1: percentage of 1km cell where observation occurred with sparse landcover type (type 1) pland_152_sparse_1: percentage of 1km cell where observation occurred with sparse landcover type (type 2) pland_153_sparse_1: percentage of 1km cell where observation occurred with sparse landcover type (type 3) pland_160_flooded_freshwater_1: percentage of 1km cell where observation occurred with flooded freshwater landcover type pland_170_flooded_saltwater_1: percentage of 1km cell where observation occurred with flooded saltwater landcover type pland_180_flooded_shrub_1: percentage of 1km cell where observation occurred with flooded shrub landcover type pland_190_urban_1: percentage of 1km cell where observation occurred with urban landcover type pland_20_cropland_irrigated_1: percentage of 1km cell where observation occurred with irrigated cropland landcover type pland_200_barren_1: percentage of 1km cell where observation occurred with barren landcover type (type 1) pland_201_barren_1: percentage of 1km cell where observation occurred with barren landcover type (type 2) pland_202_barren_1: percentage of 1km cell where observation occurred with barren landcover type (type 3) pland_210_water_1: percentage of 1km cell where observation occurred with water landcover type pland_220_ice_1: percentage of 1km cell where observation occurred with ice landcover type pland_30_mosaic_cropland_1: percentage of 1km cell where observation occurred with mosaic cropland landcover type pland_40_mosaic_natural_veg_1: percentage of 1km cell where observation occurred with mosaic natural vegetation landcover type pland_50_evergreen_broadleaf_1: percentage of 1km cell where observation occurred with evergreen broadleaf landcover type pland_60_deciduous_broadleaf_1: percentage of 1km cell where observation occurred with deciduous broadleaf landcover type (type 1) pland_61_deciduous_broadleaf_1: percentage of 1km cell where observation occurred with deciduous broadleaf landcover type (type 2) pland_62_deciduous_broadleaf_1: percentage of 1km cell where observation occurred with deciduous broadleaf landcover type (type 3) pland_70_evergreen_needleleaf_1: percentage of 1km cell where observation occurred with evergreen needleleaf landcover type (type 1) pland_71_evergreen_needleleaf_1: percentage of 1km cell where observation occurred with evergreen needleleaf landcover type (type 2) pland_72_evergreen_needleleaf_1: percentage of 1km cell where observation occurred with evergreen needleleaf landcover type (type 3) pland_80_deciduous_needleleaf_1: percentage of 1km cell where observation occurred with deciduous needleleaf landcover type (type 1) pland_81_deciduous_needleleaf_1: percentage of 1km cell where observation occurred with deciduous needleleaf landcover type (type 2) pland_82_deciduous_needleleaf_1: percentage of 1km cell where observation occurred with deciduous needleleaf landcover type (type 3) pland_90_mixed_forest_1: percentage of 1km cell where observation occurred with mixed forest landcover type bio1_5 to pland_90_mixed_forest_3: same as above, but summarized to cells with grain size equaling the suffix of the column name in km ### habitat\_prediction\_surface\_1 ### habitat\_prediction\_surface\_3 ### habitat\_prediction\_surface\_5 ### habitat\_prediction\_surface\_10 ### habitat\_prediction\_surface\_50 Prediction surface for North America at the grid cell level (rows) with associated environmental features as columns (see list in methods) at 1, 3, 5, 10, or 50 km grain (given in file suffix). Cells containing NA have no associated environmental information available at a given location. #### Column IDs x: horizontal axis spatial coordinate y: vertical axis spatial coordinate elev: elevation of cell bio1: mean temperature of climate where observation occurred, measured at 1km bio12: total annual precipitation in mm in climate of cell, measured at 1km bio15: precipitation seasonality in climate of cell, measured at 1km cloudsd: cloud cover variability in climate of cell, measured at 1km evisum: enhanced vegetation index in climate of cell, measured at 1km twi: terrain wetness index of cell, measured at 1km tri: topographic roughness index of cell, measured at 1km elev: elevation of cell, measured at 1km pland_10_cropland_rainfed: percentage of 1km cell with rainfed cropland landcover type (type 1) pland_100_mosaic_tree_shrub: percentage of 1km cell with mosaic tree/shrub landcover type pland_11_cropland_rainfed: percentage of 1km cell with rainfed cropland landcover type (type 2) pland_110_mosaic_herbacious: percentage of 1km cell with mosaic herbaceous landcover type pland_12_cropland_rainfed: percentage of 1km cell with rainfed cropland landcover type (type 3) pland_120_shrubland: percentage of 1km cell with shrubland landcover type (type 1) pland_121_shrubland: percentage of 1km cell with shrubland landcover type (type 2) pland_122_shrubland: percentage of 1km cell with shrubland landcover type (type 3) pland_130_grassland: percentage of 1km cell with grassland landcover type pland_140_lichens_mosses: percentage of 1km cell with lichens/mosses landcover type pland_150_sparse: percentage of 1km cell with sparse landcover type (type 1) pland_152_sparse: percentage of 1km cell with sparse landcover type (type 2) pland_153_sparse: percentage of 1km cell with sparse landcover type (type 3) pland_160_flooded_freshwater: percentage of 1km cell with flooded freshwater landcover type pland_170_flooded_saltwater: percentage of 1km cell with flooded saltwater landcover type pland_180_flooded_shrub: percentage of 1km cell with flooded shrub landcover type pland_190_urban: percentage of 1km cell with urban landcover type pland_20_cropland_irrigated: percentage of 1km cell with irrigated cropland landcover type pland_200_barren: percentage of 1km cell with barren landcover type (type 1) pland_201_barren: percentage of 1km cell with barren landcover type (type 2) pland_202_barren: percentage of 1km cell with barren landcover type (type 3) pland_210_water: percentage of 1km cell with water landcover type pland_220_ice: percentage of 1km cell with ice landcover type pland_30_mosaic_cropland: percentage of 1km cell with mosaic cropland landcover type pland_40_mosaic_natural_veg: percentage of 1km cell with mosaic natural vegetation landcover type pland_50_evergreen_broadleaf: percentage of 1km cell with evergreen broadleaf landcover type pland_60_deciduous_broadleaf: percentage of 1km cell with deciduous broadleaf landcover type (type 1) pland_61_deciduous_broadleaf: percentage of 1km cell with deciduous broadleaf landcover type (type 2) pland_62_deciduous_broadleaf: percentage of 1km cell with deciduous broadleaf landcover type (type 3) pland_70_evergreen_needleleaf: percentage of 1km cell with evergreen needleleaf landcover type (type 1) pland_71_evergreen_needleleaf: percentage of 1km cell with evergreen needleleaf landcover type (type 2) pland_72_evergreen_needleleaf: percentage of 1km cell with evergreen needleleaf landcover type (type 3) pland_80_deciduous_needleleaf: percentage of 1km cell with deciduous needleleaf landcover type (type 1) pland_81_deciduous_needleleaf: percentage of 1km cell with deciduous needleleaf landcover type (type 2) pland_82_deciduous_needleleaf: percentage of 1km cell with deciduous needleleaf landcover type (type 3) pland_90_mixed_forest: percentage of 1km cell with mixed forest landcover type ### sdm\_bird\_outputs\_VS3.1\_complete.csv Species-level distribution model outputs. Each row is a species and columns contain model type information, including species modeled, modeling season, model grain (scale), model version, number of observations, estimated presence cells, model metrics (AUC, SPS threshold, TSS, sensitivity, specificity), associated file paths (may be NA if not relevant), and Random forest importance score for all model variables (any variable with \_imp suffix). Cells containing NA have no associated model or path or information for a given species. #### Column IDs common_name: common name of species sciname: scientific name of species season: season at which model was fit scale: grain size at which model was fit version: model version modName: model name modURL to elevOffset columns: not meaningful (all NAs) noPts: number of sampling points in model range_area: estimated range size from model prediction deltaAUC: not meaningful (all NAs) AUC: model area under the curve SPSThresh: model threshold value TSS: model true skill score Sensitivity: model sensitivity Specificity: model specificity modPathROR: file path for relative occurrence rate output modPathPA: file path for presence/absence output rangePath: file path for model range output ptsPath: file path for model points output ptsBgPath: file path for background points output confMatPath: file path for confusion matrix POPredsPath: file path for presence/absence predictions threshPredsPath: file path for thresholded predictions POPredsRasPath: file path for presence/absence raster threshPredsRasPath: file path for thresholded raster envVars: not meaningful (all NAs) year_imp: importance score for year day_of_year_imp: importance score for julian date time_observations_started_imp: importance score for time observations started duration_minutes_imp: importance score for duration of survey effort_distance_km_imp: importance score for distance traveled number_observers_imp: importance score for number of observers bio1_imp: importance score for mean temperature in climate bio12_imp: importance score for total precipitation in climate bio15_imp: importance score for precipitation seasonality cloudsd_imp: importance score for cloud cover variability evisum_imp: importance score for enhanced vegetation index twi_imp: importance score for terrain wetness index tri_imp: importance score for topographic roughness index elev_imp: importance score for elevation pland_10_cropland_rainfed_imp: importance score for cropland (rainfed) landcover (type 1) pland_100_mosaic_tree_shrub_imp: importance score for mosaic tree/shrub landcover pland_11_cropland_rainfed_imp: importance score for cropland (rainfed) landcover (type 2) pland_110_mosaic_herbacious_imp: importance score for mosaic-herbaceous landcover pland_12_cropland_rainfed_imp: importance score for cropland (rainfed) landcover (type 3) pland_120_shrubland_imp: importance score for shrubland landcover (type 1) pland_121_shrubland_imp: importance score for shrubland landcover (type 2) pland_122_shrubland_imp: importance score for shrubland landcover (type 3) pland_130_grassland_imp: importance score for grassland landcover pland_140_lichens_mosses_imp: importance score for lichens/mosses landcover pland_150_sparse_imp: importance score for sparse landcover (1) pland_152_sparse_imp: importance score for sparse landcover (2) pland_153_sparse_imp: importance score for sparse landcover (3) pland_160_flooded_freshwater_imp: importance score for flooded freshwater landcover pland_170_flooded_saltwater_imp: importance score for flooded saltwater landcover pland_180_flooded_shrub_imp: importance score for shrub landcover pland_190_urban_imp: importance score for urban landcover pland_20_cropland_irrigated_imp: importance score for irrigated cropland landcover pland_200_barren_imp: importance score for barren landcover (1) pland_201_barren_imp: importance score for barren landcover (2) pland_202_barren_imp: importance score for barren landcover (3) pland_210_water_imp: importance score for water landcover pland_220_ice_imp: importance score for ice landcover pland_30_mosaic_cropland_imp: importance score for mosaic cropland landcover pland_40_mosaic_natural_veg_imp: importance score for natural vegetation landcover pland_50_evergreen_broadleaf_imp: importance score for evergreen broadleaf landcover (1) pland_60_deciduous_broadleaf_imp: importance score for deciduous broadleaf landcover (1) pland_61_deciduous_broadleaf_imp: importance score for deciduous broadleaf landcover (2) pland_62_deciduous_broadleaf_imp: importance score for deciduous broadleaf landcover (3) pland_70_evergreen_needleleaf_imp: importance score for evergreen needleleaf landcover (1) pland_71_evergreen_needleleaf_imp: importance score for evergreen needleleaf landcover (2) pland_72_evergreen_needleleaf_imp: importance score for evergreen needleleaf landcover (3) pland_80_deciduous_needleleaf_imp: importance score for deciduous needleleaf landcover (1) pland_81_deciduous_needleleaf_imp: importance score for deciduous needleleaf landcover (2) pland_82_deciduous_needleleaf_imp: importance score for deciduous needleleaf landcover (3) pland_90_mixed_forest_imp: importance score for mixed forest landcover RDataPath: not meaningful (all NAs) rorOrigPath: not meaningful (all NAs) paOrigPath: not meaningful (all NAs) rangeOrigPath: not meaningful (all NAs) ptsBgOrigPath: not meaningful (all NAs) statsOrigPath: not meaningful (all NAs) ### species\_list.csv Index of all candidate species. Cells containing NA have no information about extinction for a given species. #### Column IDs species name: species name common name: common name Abbr: four letter species abbreviation Code: species rarity code- 1 (common) or 2 (uncommon) ebird_code: species six letter code TAXON_ORDER_CODE: order code category: taxonomic category range: description of range order: taxonomic order family: taxonomic family eBird species group: species group description waterbird: (not used in manuscript) extinction: (not used in manuscript) extinct year: (not used in manuscript) IW: (not used in manuscript) ## Sharing/access info  eBird data can be downloaded via the Cornell lab of Ornithology webiste at [www.ebird.org](http://www.ebird.org) Environmental data can be downloaded from CHELSA ([https://chelsa-climate.org/](https://chelsa-climate.org/)), MODIS ([https://lpdaac.usgs.gov/products/mod11a1v006/](https://lpdaac.usgs.gov/products/mod11a1v006/)) ESA CCI ([https://climate.esa.int/en/](https://climate.esa.int/en/)) and EarthEnv ([https://www.earthenv.org/](https://www.earthenv.org/)) AVONET trait data is available at [https://onlinelibrary.wiley.com/doi/full/10.1111/ele.13898](https://onlinelibrary.wiley.com/doi/full/10.1111/ele.13898) # CODE FILES 1\. modeling_ranger_scale.R - species distribution modeling workflow 2\. aggregate_scale.R - aggregating species-level predictions to biodiversity estimations 3\. patterns.R - trends across species, regions, and seasons, and generating all cross-species figures 4\. validations_sp_accuracy.R - code to run site-level validations 5\. simulation.R - code to run the simulation in fig. 1 (no data needed) 6\. ebird_exploration.R - code to explore how filtering/thinning alters ebird dataset for fig S1 and table S1 ## Code/software R 3.4.0 and Microsoft Excel