This image service contains high-resolution land cover data for the states of Nebraska, South Dakota, and North Dakota. These data are a digital representation of land cover derived from 1-meter aerial imagery from the USDA National Agriculture Imagery Program (NAIP.) The year of NAIP used for each state was 2014.Data are intended for use in rural areas and therefore do not include land cover in cities and towns. Land cover classes (tree cover, other land cover, or water) were mapped using an object-based image analysis approach and supervised classification. These data are designed for conducting geospatial analyses and for producing cartographic products. In particular, these data are intended to depict the _location of tree cover in the county. The mapping procedures were developed specifically for agricultural landscapes that are dominated by annual crops, rangeland, and pasture and where tree cover is often found in narrow configurations, such as windbreaks and riparian corridors. Because much of the tree cover in agricultural areas of the United States occurs in windbreaks and narrow riparian corridors, many geospatial datasets derived from coarser-resolution satellite data (such as Landsat), do not capture these landscape features. This dataset is intended to address this particular data gap. These data can be downloaded by county at the Forest Service Research Data Archive. Nebraska: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0038 South Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0068 North Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0067 A Kansas dataset was also developed using the same methods and is located at: Kansas data download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0052 Kansas map service: https://data-usfs.hub.arcgis.com/documents/high-resolution-tree-cover-of-kansas-2015-map-service/explore

The Land Cover vector tile layer is primarily for visualisation. For a downloadable dataset, use Land Cover 2017.Land Cover 2017 is the companion dataset to Impervious Surfaces 2017, and classifies land cover into five surface types: • Grass (open space without trees and shrubs) (Class Name = 1)• Scrub/shrub (rough grass, rushes, low profile vegetation often around wetlands) (Class Name = 4)• Sand/Gravel/Bare Earth (Class Name = 5)• High vegetation (trees and shrubs) (Class Name = 6)• Water (Class Name = 7)Legend:Image processing was conducted by Lynker Analytics using machine learning techniques on Auckland Council’s most recent orthophotography—7.5 cm pixel resolution (2017) covering all the region’s urban settlements and beyond; and 50 cm pixel resolution (2010-2012) covering the region’s remaining, predominantly rural areas—to produce two separate (urban and rural) land cover datasets.A copy of the full background report can be obtained from the Regional Planning team in Auckland Council’s Healthy Waters department.

Land use and land cover estimates based on 50 randomly selected 1-km2 segments across the urban-rural gradients within the 25-km radius around each city center of Atlanta, Charlotte, and Raleigh using the National Land Cover Database.

The 2015 Kansas Land Cover Patterns Level III map represents Phase 2 of a two-phase mapping initiative occurring over a three-year period. The map is designed to be explicitly comparable to the 1990 and 2005 Kansas Land Cover Patterns maps. Using a similar methodology to produce the 2015 Kansas Land Cover Patterns map provides opportunities to identify and examine how the Kansas landscape has changed over time.The Modified Level III map was produced from multi-seasonal Landsat 8 and MODIS NDVI imagery acquired during the 2014 and 2015 growing seasons. The map contains seventeen land use/land cover classes and has a positional accuracy and spatial resolution appropriate for producing 1:50,000 scale maps. The MMU varies by land cover class and ranges between 0.22 to 5.12 acres.The 2015 Kansas Land Cover Patterns map represents Phase II of a two-phase mapping initiative occurring over a three-year period. During Phase 2, subclasses were mapped to produce a Modified Level III map of Kansas using a combination of 250-meter resolution time-series MODIS NDVI imagery, Landsat imagery and the 2015 Cropland Data Layer. The formal accuracy assessment reports the map to have an overall accuracy level of 81%. User and Producer accuracies vary by land cover class and rural classes have higher accuracy levels (37-92%) than urban classes (48-78%). Users are encouraged to reference the reported accuracy levels in this report and/or metadata when using the 2015 Kansas Land Cover Patterns Level III map. Download KLCP 2015 Level IIIDownload KLCP 2015 Level IDownload KLCP 2005 Level IDownload KLCP 1990 Level I

Land Cover 2017 is the companion dataset to Impervious Surfaces 2017, and classifies land cover into five surface types:• Grass (open space without trees and shrubs) (Class Name = 1)• Scrub/shrub (rough grass, rushes, low profile vegetation often around wetlands) (Class Name = 4)• Sand/Gravel/Bare Earth (Class Name = 5)• High vegetation (trees and shrubs) (Class Name = 6)• Water (Class Name = 7)Image processing was conducted by Lynker Analytics using machine learning techniques on Auckland Council’s most recent orthophotography—7.5 cm pixel resolution (2017) covering all the region’s urban settlements and beyond; and 50 cm pixel resolution (2010-2012) covering the region’s remaining, predominantly rural areas—to produce two separate (urban and rural) land cover datasets.A copy of the full background report can be obtained from the Regional Planning team in Auckland Council’s Healthy Waters department.

This data publication contains 2014 high-resolution land cover data for each of the 93 counties within Nebraska. These data are a digital representation of land cover derived from 1-meter aerial imagery from the National Agriculture Imagery Program (NAIP). There is a separate file for each county. Data are intended for use in rural areas and therefore do not include land cover in cities and towns. Land cover classes (tree cover, other land cover, or water) were mapped using an object-based image analysis approach and supervised classification.These data are designed for conducting geospatial analyses and for producing cartographic products. In particular, these data are intended to depict the location of tree cover in the county. The mapping procedures were developed specifically for agricultural landscapes that are dominated by annual crops, rangeland, and pasture and where tree cover is often found in narrow configurations, such as windbreaks and riparian corridors. Because much of the tree cover in agricultural areas of the United States occurs in windbreaks and narrow riparian corridors, many geospatial datasets derived from coarser-resolution satellite data (such as Landsat), do not capture these landscape features. This dataset is intended to address this particular data gap.This metadata file contains documentation for the entire set of land cover county files. Individual metadata documents containing detailed information specific to each county (e.g., bounding coordinates) are included with each raster dataset.

The FGGD global land cover map is a global raster datalayer with a resolution of 5 arc-minutes. Each pixel contains a class value representing the dominant land cover type found in the pixel. The method is described in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al.

The 2005 Kansas Land Cover Patterns Level I map represents Phase 1 of a two-phase mapping initiative occurring over a three-year period. The map is designed to be explicitly comparable to the 1990 Kansas Land Cover Patterns map. Using a similar methodology to produce the 2005 Kansas Land Cover Patterns map provides opportunities to identify and examine how the Kansas landscape has changed over a 15-year period. The map contains eleven land use/land cover classes. The positional accuracy and spatial resolution of the map are appropriate for producing 1:50,000 scale maps. The map is not intended to define precise boundaries between landscape features and while the source data has a spatial resolution of 30 m x 30 m, the minimum map unit varies by land cover class and ranges between 0.22 to 5.12 acres (see below). The formal accuracy assessment reports the map to have an overall accuracy level of 90.72%. User and Producer accuracies vary by land cover class and rural classes have higher accuracy levels (88-95%) than urban classes (48-78%). Users are encouraged to reference the reported accuracy levels in this report and/or metadata when using the 2005 Kansas Land Cover Patterns map. Download KLCP 2015 Level IIIDownload KLCP 2015 Level IDownload KLCP 2005 Level IDownload KLCP 1990 Level I

This data set reports on dissolved nutrient concentrations, as well as dissolved oxygen, alkalinity, conductivity, turbidity, and pH measured in water samples collected from nine streams located in the state of Brasilia, Brazil, between September, 2004 and December, 2006. Streams were located in different land cover types including natural (forest), rural (agricultural), and developed landscapes. In addition, water samples from wells, lysimeters, surface runoff, and precipitation were collected from four sites, 2 natural and 2 rural, and analyzed for nutrient concentrations. Streams were sampled every 2-4 weeks; rain water was collected approximately monthly during the wet season and once during a dry season; wells and lysimeters were sampled monthly; and surface runoff collections were event based. There are three comma-delimited data files with this data set.

Land use and land cover estimates across the urban-rural gradients of Atlanta, Charlotte, and Raleigh based on visual interpretation of 2012 National Agriculture Imagery Program imagery.

1) Data Introduction • The Land-Use Scene Classification Dataset is an image dataset built to classify land-use types in different regions based on Landsat satellite imagery.

2) Data Utilization (1) Characteristics of the Land-Use Scene Classification Dataset: • The images are collected from a diverse range of geographic environments, including urban, rural, coastal, and forested areas, making the dataset suitable for evaluating domain generalization performance. • It is based on low-resolution Landsat satellite images, yet designed to effectively distinguish various terrain and structural patterns even with limited spatial resolution.

(2) Applications of the Land-Use Scene Classification Dataset: • Development of land-use classification models: The dataset can be used to train deep learning models that automatically classify land-use types such as residential areas, roads, and farmlands from satellite imagery. • GIS-based land-use change analysis: It can support geographic information system (GIS) research to analyze land-use pattern changes over time and infer spatial utilization trends.

Private Figshare link: https://figshare.com/s/b7ba65734bfa1ec8adbb DOI: 10.6084/m9.figshare.5594629. (ZIP)

This data publication contains multiple maps of Puerto Rico scanned at 600 dots per inch: full map scans, scans clipped to mapped areas only, and georeferenced scans of 1:10,000-scale land-use maps from 1950-1951 that were produced by the Rural Land Classification Program of Puerto Rico, a project led by Dr. Clarence F. Jones of Northwestern University. These historical maps classified land use and land cover into 20 different classes, including 13 different types of crops, two classes of forests, four classes of grasslands and other areas, which is a general class for non-rural areas. This package includes maps from 76 out of the 78 municipalities of Puerto Rico, covering 422 quadrangles of a 443-quadrangle grid for mainland Puerto Rico. It excludes the island municipalities of Vieques and Culebra, Mona Island and minor outlying islands.The Rural Land Classification Program of Puerto Rico produced 430 1:10,000-scale maps. That program also produced one island-wide land-use map with more generalized delineations of land use. Previously, Kennaway and Helmer (2007) scanned and georeferenced the island-wide map, and they converted it to vector and raster formats with embedded georeferencing and classification. This data publication contains the higher-resolution maps, which will provide more precise historical context for forests. It will better inform management efforts for the sustainable use of forest lands and to build resilience and resistance to various future disturbances for these and other tropical forest landscapes.

The maps were scanned and georeferenced to help with the planning and application process for the USDA Forest Service (USDA) Forest Legacy Program, a competition-based program administered by the USDA Forest Service in partnership with State agencies to encourage the protection of privately owned forest lands through conservation easements or land purchases. Geospatial products and maps will also be used by personnel at the Department of Natural and Environmental Resources and partners in Non-Governmental Organizations working with the Forest Stewardship Program. This latter program provides technical assistance and forest management plans to private landowners for the conservation and effective management of private forests across the US. The information will provide local historical context on forest change patterns that will enhance the recommendations of forest management practices for private forest landowners. These data will also be useful for urban forest professionals to understand the land legacies as a basis for planning green infrastructure interventions.

Data depict the rural areas of Puerto Rico around 1951 and how they were classified by geographers then. Having it georeferenced allows managers, teachers, students, the public and scientists to compare how these classifications have changed throughout the years. It will allow more precise identification and mapping of the past land use of present forests, forest stand age, and the past juxtaposition of different land uses relative to each other. These factors can affect forest species composition, biodiversity and ecosystem services. Forest stand age, past land-use type and past disturbance type, forest example, help gauge current forest structure, carbon storage, or rates of carbon accumulation. Another example of how the maps are important is for understanding how watersheds have changed through time, which helps assess how forest ecosystem services related to hydrology evolve. These maps will also help gauge how the forests of Puerto Rico are responding to recent disturbances, and how past disturbances over a range of scales relate to these responses.For more information on the Rural Land Classification Program of Puerto Rico, generated maps, and the island-wide land-use map, please see Jones (1952), Jones and Berrios (1956), as well as Kennaway and Helmer (2007).

Areal and percentage of land use land cover class of study area for the year 2006.

The land use / land cover data set of Heihe River Basin in 2011 is the Remote Sensing Research Office of Institute of cold and drought of Chinese Academy of Sciences. Based on the remote sensing data of landsatm and ETM in 2011, combined with field investigation and verification, a 1:100000 land use / land cover image and vector database of Heihe River Basin is established.
The data set mainly includes 1:100000 land use graph data and attribute data in the middle reaches of Heihe River Basin.
The land cover data of 1:100000 (2011) in Heihe River Basin and the previous land cover are classified into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural residents, industrial and mining land and unused land) and 25 second-class categories by the same hierarchical land cover classification system. The data type is vector polygon and stored in shape format. Land cover classification attributes:
Level 1 type level 2 type attribute code spatial distribution location
Cultivated land: plain dry land 123 is mainly distributed in basin, piedmont, river alluvial, proluvial or lacustrine plain (poor irrigation conditions due to water shortage).
The upland and land 122 is mainly distributed in the hilly area, and generally, the plot is distributed on the gentle slope of the hill, as well as on the top of the ridge and the base.
The dry land 121 is mainly distributed in the mountainous area, the hillside (gentle slope, hillside, steep slope platform, etc.) and the Piedmont belt below 4000 m above sea level.
Woodland: there are woodland (Arbor) 21 mainly distributed in high mountains (below 4000 meters above sea level) or middle mountain slopes, valley slopes, mountain tops, plains, etc.
Shrub land 22 is mainly distributed in the higher mountain area (below 4500m), most of which are hillside, valley and sandy land.
Sparse forest land 23 is mainly distributed in mountainous areas, hills, plains and sandy land, Gobi (Loamy, sandy conglomerate) edge.
Other forest lands 24 are mainly distributed around the oasis ridge, riverside, roadside and rural residential areas.
Grassland: high cover grassland 31 is generally distributed in mountainous area (gentle slope), hilly area (steep slope), river beach, Gobi, sandy land, etc.
The middle cover grassland 32 is mainly distributed in dry areas (low-lying land next door and land between Sandy Hills, etc.).
Low cover grassland 33 mainly grows in dry areas (loess hills and sand edge).
Water area: channel 41 is mainly distributed in plain, inter Sichuan cultivated land and inter mountain valley.
Lake 42 is mainly distributed in low-lying areas.
Reservoir pond 43 is mainly distributed in plain and valley between rivers, surrounded by residential land and cultivated land.
Glaciers and permanent snow cover 44 are mainly distributed on the top of (over 4000) mountains.
The beach land 46 is mainly distributed in the valley, piedmont, plain lowland, the edge of river lake basin and so on.
Residential land: urban land 51 is mainly distributed in plain, mountain basin, slope and gully platform.
Rural residential land 52 is mainly distributed in oasis, cultivated land and roadside, tableland, slope, etc.
Industrial and mining land and traffic land 53 are generally distributed in the periphery of cities and towns, more developed traffic areas and industrial mining areas.
Unused land: sand 61 is mostly distributed in the basin, both sides of the river, the river bay and the periphery of the mountain front Gobi.
Gobi 62 is mainly distributed in the Piedmont belt with strong wind erosion and sediment transport.
Salt alkali 63 is mainly distributed in relatively low and easy to accumulate water, dry lakes and lakeside.
Swamp 64 is mainly distributed in relatively low and easy to accumulate water.
Bare soil 65 is mainly distributed in the arid areas (mountain steep slopes, hills, Gobi), and the vegetation coverage is less than 5%.
Bare rock 66 is mainly distributed in the extremely dry stone mountain area (windy, light rain).
The other 67 are mainly distributed in the exposed rocks formed by freezing and thawing over 4000 meters, also known as alpine tundra. Projection parameters: Projection ALBERS Units METERS Spheroid Krasovsky Parameters: 25 00 0.000 /* 1st standard parallel 47 00 0.000 /* 2nd standard parallel 105 00 0.000 /* central meridian 0 0 0.000 /* latitude of projection's origin 0.00000 /* false easting (meters) 0.00000 /* false northing (meters)

Land use/land cover change of study area for the years 1991, 2006 and 2021.

This raster dataset depicts a final version of the Coarse Filter Vegetation Map as a 30 meter grid with 61 cover types, 51 of which are natural or semi-natural land cover, for the nine county San Francisco Bay Area Region, California. This dataset includes only vegetation types that were reclassified to Rural Residential based on parcels less than 10 acres in size. This data was compiled from data sourced from the United States Department of Agriculture Forest Service, The Nature Conservancy and the California Department of Forestry and Fire.

Transition matrix of land use/land cover change (2006–2021).

This archive contains derived and supporting data products to support:

Crawford CL*, Wiebe RA, Yin H, Radeloff VC, and Wilcove DS. 2024. Effects of cropland abandonment on biodiversity. Nature Sustainability. In press.

*Contact Christopher L. Crawford at ccrawford@alumni.princeton.edu with any questions.

A public Zenodo archive of the Github repository containing analysis scripts developed for this project (https://github.com/chriscra/biodiversity_abandonment) can be found here: 10.5281/zenodo.13777205

This analysis builds on: Crawford, C. L., Yin, H., Radeloff, V. C. & Wilcove, D. S. Rural land abandonment is too ephemeral to provide major benefits for biodiversity and climate. Science Advances 8, 1–13 (2022). Data and scripts from Crawford et al. 2022 are archived and publicly available at Zenodo (https://doi.org/10.1126/sciadv.abm8999).

The annual land cover maps (1987-2017, 30 meter resolution) that underlie our analysis were developed on Google Earth Engine using publicly available Landsat satellite imagery (Yin et al. 2020, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2020.111873).These annual land cover maps, along with other derived data that were produced by Crawford et al. 2022, are archived and publicly available at Zenodo (https://doi.org/10.5281/zenodo.5348287).

This archive includes important derived data products created for Crawford et al. 2024. Note that these and other project data are described in detail in util/_util_files.R (https://github.com/chriscra/biodiversity_abandonment). This is a convenience script that loads many of the relevant input and derived data that are used throughout the project. The primary required data files for reproducing this work are archived here, but "_util_files.R" also includes information about where additional files can be accessed (if external, e.g., https://doi.org/10.5281/zenodo.5348287) or created across the various .R and .Rmd files in this repository (e.g., "habitats.Rmd" chunk {r land-cover-of-abn-pixels}).

Naming conventions for sites and raster files follow Crawford et al. 2022, as described here: https://doi.org/10.5281/zenodo.5348287

Site file names correspond to the following geographic locations:belarus = Vitebsk, Belarus / Smolensk, Russiabosnia_herzegovina = Bosnia & Herzegovinachongqing = Chongqing, Chinagoias = Goiás, Braziliraq = Iraqmato_grosso = Mato Grosso, Brazilnebraska = Nebraska / Wyoming, USAorenburg = Orenburg, Russia / Uralsk, Kazakhstanshaanxi = Shaanxi/Shanxi, Chinavolgograd = Volgograd, Russiawisconsin = Wisconsin, USA

This archive includes the following files:

site_df.csv

crop_to_abn_iucn_observed.zip

crop_to_abn_iucn_potential.zip

max_abn_lcc_iucn.zip

max_abn_lcc_iucn_potential.zip

lcc_iucn_habitat.zip

lcc_iucn_habitat_potential.zip

frag_df.csv

frag_hypo_no_abn_2017_df.csv

iucn_lc_crosswalk.csv

habitat_age_req_coded.csv

centroids_df.csv

aoh_l.parquet

aoh_feols.parquet

aoh_start_end_l.parquet

aoh_change_df.parquet

aoh_est_change_tmp_all.csv

aoh_obs_change_tmp_all.csv

taxonomy_df.parquet

final_species_list.csv

trait_mod_df_modx1.rds

site_df.csv

A list of site names and related metadata describing our study sites, taken from https://zenodo.org/records/5348287

Derived habitat rasters:

crop_to_abn_iucn_observed.zip (Calculation 1a)crop_to_abn_iucn_potential.zip (Calculation 1b)max_abn_lcc_iucn.zip (Calculation 2a)max_abn_lcc_iucn_potential.zip (Calculation 2b)lcc_iucn_habitat.zip (Calculation 3a)lcc_iucn_habitat_potential.zip (Calculation 3b)

These maps show IUCN Level 2 habitat types (Jung et al. 2020) interpolated onto the land cover classes in the Yin et al. (2020) abandonment maps at multiple spatial and temporal extents, which serve as inputs for the three primary calculations in our manuscript. Accompanying each calculation is a corresponding map for a scenarios in which no abandoned croplands were recultivated over the course of the time series (marked as "potential"). Each .zip file contains maps for each of 11 sites.

Calculation 1. This calculation isolates the direct effect of abandonment on habitat availability, by comparing the habitat provided before and after abandonment. These "crop_to_abn_iucn" maps show IUCN Level 2 habitats in cropland pixels that experienced abandonment, including the abandonment period as well as the immediately preceding period of cultivation (to allow for a proper before and after comparison). As a result, these maps show only habitat provided by croplands when they were actively cultivated, abandoned, or, where appropriate, recultivated, which allows for a proper before and after comparison. These maps are created in the script "cluster/noncrop_precrop_mask.R".

Calculation 2. This calculation considered changes in habitat that took place exclusively in pixels that experienced abandonment at some point during the time series (following Calculation 1), but expanded to track changes across our entire time series, from 1987 through 2017, in order to account for any land cover that was cleared for agriculture prior to abandonment. These "max_abn_lcc_iucn" maps therefore show IUCN Level 2 habitat types for each pixel that was abandoned at any point during the time series, across the full time series. These maps were created in the script "habitats.Rmd" code chunks {r mask-lcc-iucn-habitat-to-abn} and {r *potential_max}.

Calculation 3. This calculation tracks habitat area provided by every pixel throughout the entire spatial and temporal extent (1987-2017), in order to place abandonment into the context of broader land-cover change dynamics like ongoing cropland expansion taking place alongside of abandonment. These "lcc_iucn" maps therefore show the IUCN Level 2 habitat types for each pixel at each site in each year of our time series. These maps were created in the script "habitats.Rmd" code chunks {r lcc-iucn-habitat-composite} and {r *potential-lcc-full} and the script "cluster/potential_full_iucn.R".

Some analyses require these .tif files (manipulated as SpatRasters using {terra}, https://rspatial.org/terra/) to be converted to tabular format (data.tables, via {data.table} (https://rdatatable.gitlab.io/data.table/) and saved as .parquet files (via {arrow}, https://arrow.apache.org/docs/r/). This can be accomplished via scripts "cluster/save_spatraster_as_dt.R" and "cluster/save_parquet.R."

frag_df.csvfrag_hypo_no_abn_2017_df.csv

These tabular files contain derived fragmentation statistics calculated using the {landscapemetrics} R package (https://r-spatialecology.github.io/landscapemetrics/). The second file contains metrics for a scenario in which no croplands were abandoned through the year 2017, in order to assess the effect cropland abandonment on landscape configuration. Each file contains 11 columns:

"layer" -- the spatial raster layer for which the metric is calculated, corresponding to a year.

"level" -- the level at which the metric is calculated, in our case, the land cover "class."

"class" -- corresponding the to land cover class for which the metric is calculated (1 = non-vegetation, 2 = woody vegetation [i.e., forest], 3 = cropland, and 4 = herbaceous vegetation [i.e., grassland]).

"id" -- An unused field containing NA values.

"metric" -- the specific term used for each metric by {landscapemetrics} ("area_mn", "clumpy", or "para_mn").

"value" -- the numerical value of the statistic.

"name" -- the name of the landscape metric being calculated ("patch area," "clumpiness index," or "perimeter-area ratio").

"type" -- the broad type of metric being calculated ("area and edge metric," "aggregation metric," or "shape metric").

"function_name" -- the name of the {landscapemetrics} function used to calculate the statistic.

"site" -- the site (out of 11 study sites) for which this statistic was calculated.

"year" -- the year corresponding to the metric statistic, between 1987-2017 (including 1986-2018 for Nebraska and 1987-2018 for Wisconsin)Additional details on these metrics can be found at https://r-spatialecology.github.io/landscapemetrics/.

The spatial IUCN data underlying our analyses (species range maps) are available upon request from BirdLife International (http://datazone.birdlife.org/species/requestdis) and IUCN (https://www.iucnredlist.org/resources/spatial-data-download). Tabular species assessment data (including habitat and elevation preferences) are freely available from IUCN (https://www.iucnredlist.org/). Here we share three IUCN-related data files that serve as important inputs throughout our analyses:

iucn_lc_crosswalk.csv

This tabular file outlines the crosswalk between the 4 land cover classes in Yin et al. 2020 and the IUCN Level 2 habitat types mapped by Jung et al. 2020. It contains five columns:

"map_code" -- the habitat code corresponding to Jung et al. (2020).

"Coarse_Name" -- the broad Level 1 habitat grouping.

"lc" -- the corresponding land cover type from Yin et al. (2020) (1 = non-vegetation, 2 = woody vegetation [i.e., forest], 3 = cropland, and 4 = herbaceous vegetation [i.e., grassland]).

"IUCNLevel" -- the full IUCN Level 2 habitat type name.

"code" -- the IUCN Level 2 habitat code.

habitat_age_req_coded.csv

This tabular file lists whether each species was determined (by R. Alex Wiebe [AW] and Christopher L. Crawford [CLC]) to be a "mature forest obligate" (i.e., requiring forest older than 30 years, our time series length) or not. Species determined to be "mature forest obligate" species were excluded from our final analysis. The file includes 11 columns:

"vert_class" -- Vertebrate class ("bird" or "mam" [mammal])

"binomial" -- Species' binomial scientific name containing genus and species.

"common_names" -- Species' common names listed by IUCN.

"mature_forest_obl" -- Whether a species is determined to be a "mature forest obligate" species (1) or not (0). Some species are marked as 0.9, 0.75, 0.25, or 0.1 as an indication of some uncertainty, but