This archive provides consistent global datasets on harmonized and use, biodiversity intactness index (BII), and biodiversity intactness footprint of agricultural production from 2000 to 2020.Global high-resolution harmonized land use (HHLU) maps reflect land use and vegetation states in seven (07) categories: (1) Primary-minimal use vegetation, (2) Primary vegetation, (3) Secondary vegetation, (4) Cropland, (5) Urban lands, (6) Pasture/Grazing lands, (7) Agroforestry.Land use Fraction (LUF) maps present land use proportions for each aggregated location for land use classes corresponding to HHLU (value range, 0-1). Primary minimal-use vegetation and Primary vegetation are combined into Primary vegetation.Biodiversity Intactness Index (BII) maps simulate terrestrial biodiversity integrity across all land use, ranging from 0 to 1.Biodiversity loss footprint (BII loss) tabular data allocates biodiversity loss footprints induced by agricultural production (crops and livestock), which estimates BII loss over 14 biomes, 193 countries and territories, 154 crops, and 09 livestock categories from 2000 to 2020.

Using the PREDICTS database of local biodiversity measures at thousands of sites around the world, we statistically modelled how total abundance of organisms and compositional similarity responded to land use and related pressures. We combined these models with spatio-temporal projections of explanatory variables (at 0.25 degrees spatial resolution) from the year 1970 to 2050 under five Shared Socioeconomic Pathways (SSPs) to project the Biodiversity Intactness Index (BII). Mean BII (weighted by cell area) was calculated at the country, subregion, interregion and global level. We used cross-validation (leaving one biome out in turn) to produce decadal upper and lower uncertainty margins for 1970-2050. These summary data were uploaded to the Natural History Museum's Biodiversity Trends Explorer on 2021-10-27. We have also provided mean values of some of the pressures, as changes in these contribute to changes in BII.

Superseded by: none (current version)

Excel files using output from the LandSHIFT model to calculate changes in BII in India for the four scenarios and the base year 2010.

This dataset is based on the GLOBIO3 approach, represented by the Mean Species Abundance (MSA) indicator. Due to historical data availability only a selective set of pressures (cropland and grazing) is included here. This dataset therefore gives an overestimation of remaining biodiversity or naturalness, as compared to other studies in which the GLOBIO approach was used for the more recent time periods, e.g. Environmental Data Compendium (http://www.compendiumvoordeleefomgeving.nl/) and the Global Biodiversity Outlook4 (https://www.cbd.int/gbo4/. GLOBIO3 is built on a set of equations linking environmental drivers and biodiversity impact (cause–effect relationships). Cause–effect relationships are derived from available literature using meta-analyses. GLOBIO3 describes biodiversity as the remaining mean species abundance (MSA) of original species, relative to their abundance in pristine or primary vegetation, which are assumed to be not disturbed by human activities for a prolonged period. MSA is similar to the Biodiversity Integrity Index (Majer and Beeston 1996) and the Biodiversity Intactness Index (Scholes and Biggs 2005) and can be considered as a proxy for the CBD indicator on trends in species abundance (UNEP 2004). The main difference between MSA and BII is that every hectare is given equal weight in MSA, whereas BII gives more weight to species rich areas. MSA is also similar to the Living Planet Index (Loh and others 2005), which compares changes in populations to a 1970 baseline, rather than to primary vegetation. It should be emphasized that MSA does not completely cover the complex biodiversity concept, and complementary indicators should be included, when used in extensive biodiversity assessments (Faith and others 2008).

The output of GLOBIO is expressed here as MSA, an indicator of naturalness or biodiversity intactness. It is defined as the mean abundance of original species relative to their abundance in undisturbed ecosystems. An area with an MSA of 100% means a biodiversity that is similar to the natural situation. An MSA of 0% means a completely destructed ecosystem, with no original species remaining. Global environmental drivers of biodiversity change are input for GLOBIO3. In this particular case, a simplified method is used since not all required drivers are available for the historical period. Therefore, only historical land use changes are the main driver here. Long term historical expansion of cropland, pasture (land used for grazing livestock, intensive and extensive) and built-up area (urban sprawl, growth of cities and towns) are taken from the HYDE 3.1 database (Klein Goldewijk et al. 2011). GLOBIO3 calculates the overall MSAi value by substracting the individual MSAX maps from the potential maximum available grid cell land area (and dividing with it so a fraction is obtained):

MSAi,t = (Gareai – 0.7* Croplandi,t – 0.3 *Pasturei,t – 0.95 * Built-upi,t)/Gareai

where i is a grid cell, t is (historical) time step, MSAi is the overall value for grid cell i, Gareai is the total available land area of grid cell i. Cropland, Pasture and Built-up are the corresponding historical land use areas at time step t. The multipliers are derived from expert judgment, indicating a very high negative impact on biodiversity (0.95), a severe impact (0.7) and a modest impact (0.3).

Biodiversity Intactness Index 2005 input data:Sanchez-Ortiz, Katia; Newbold, Tim; Purvis, Andy; De Palma, Adriana (2019): Global maps of Biodiversity Intactness Index (Sanchez-Ortiz et al., 2019 - bioRxiv ). figshare. Dataset. https://doi.org/10.6084/m9.figshare.7951415.v1 https://figshare.com/articles/dataset/Global_maps_of_Biodiversity_Intactness_Index_Sanchez-Ortiz_et_al_2019_-_bioRxiv_/7951415Human Footprint Index 2005 input data:Williams, Brooke et al. (2020), Change in terrestrial human footprint drives continued loss of intact ecosystems , Dryad, Dataset, https://doi.org/10.5061/dryad.3tx95x6d9Living Planet Index 2005 input data: LPI 2020. Living Planet Index database. 2020. < www.livingplanetindex.org/>. Downloaded on 21 July 2020.

Identifying the urban-rural gradient in biodiversity variation is crucial to better understand the response of biodiversity dynamics to global urbanization. However, there is a paucity of knowledge regarding the urban-rural gradient in biodiversity variation in global cities, largely due to the limitations of ground-based experiments. In this study, through statistical analyses of global gridded 100 m Biodiversity Intactness Index (BII) data, we find that the global mean BII is 0.40 ± 0.07 and 0.60 ± 0.11 for the urban and rural areas, respectively. Notably, this urban-rural BII difference is significant in Africa and Asia. There is a notable and pervasive pattern of increasing BII along the urban-rural gradient in global cities. The global mean BII demonstrates an increase from 0.37 ± 0.07 in urban center to 0.63 ± 0.12 in rural periphery. The difference in BII between the urban center and the rural periphery can reach 0.20 and 0.21 in Africa and Asia, respectively, representing 50% and 54% of the total BII in the urban center. In addition, generalized additive models (GAM) revealed nonlinear relationships between biodiversity intactness and key drivers: urban green space (UGS) exhibited threshold effects, with BII increasing significantly when vegetation indices exceeded critical levels (NDVI >0.40, EVI >0.20, VCF >60%; R²=0.58–0.72, p<0.001). Conversely, economic activity showed a strong negative association with BII (R²=0.87, p<0.001), stabilizing at higher GDP densities (>40 million/km²). The findings contribute to a deeper understanding of the large-scale urban-rural gradient in biodiversity variation and may prove beneficial for the conservation of urban biodiversity.

R code for calculating aggregated intactness scores for a focal region (e.g., ecoregion or country) and/or taxonomic group using the bii4africa dataset

The bii4africa dataset is presented in a multi-spreadsheet .xlsx file. The raw data spreadsheet (‘Scores_Raw’) includes 31,313 individual expert estimates of the impact of a sub-Saharan African land use on a species response group of terrestrial vertebrates or vascular plants. Estimates are reported as intactness scores - the remaining proportion of an ‘intact’ reference (pre-industrial or contemporary wilderness area) population of a species response group in a land use, on a scale from 0 (no individuals remain) through 0.5 (half the individuals remain), to 1 (same as the reference population) and, in limited cases, to 2 (two or more times the reference population). For species that thrive in human-modified landscapes, scores could be greater than 1 but not exceeding 2 to avoid extremely large scores biasing aggregation exercises. Expert comments are included alongside respective estimates.

This work supports the Wild Coast Project. This is a FEATURE LAYER for the accompanying Web Map.Rankings: All final data layers were ranked by quintiles. Quintiles represent a percentile ranking with 5classes. This allows the user to see an easily interpretable rank-score for each hexagon.Input layers include: 1) Human Disturbance Index2) Physical Intactness Index3) Habitat and Species Diversity Index4) Ecological Intactness Index. Additional Layers for Visualization Include: 1) Conservation Management Status2) Marine Protected AreasLayer Description: 1) The Human Disturbance Index based on three indices relevant to coastal access and density of build features including roads and buildings, summed in an additive model framework. Quintiles were again taken from the distribution generated from the model to represent a rank- score for the index. A value of 1 is low human disturbance where a value of 5 is high human disturbance.2) Physical Intactness Index is based on three indices articulating the built environment with a focus on the coast and coastal structures including counts of piers, jetty’s, harbors and percent of armored shoreline, and the intensity of the surrounding built environment through the built environment intensity index from CCA 2018, summed in an additive model framework. Quintiles were again taken from the distribution generated from the model to represent a rank- score for the index. A value of 1 is high physical intactness where a value of 5 is low physical intactness.3) Habitat and Species Diversity Index is solely based on the rarity -weighted index which is a combined spatial index of 40 habitats and 159 imperiled species that characterizes the relative biodiversity and conservation value across the landscape in terms of value of habitat type and imperiled species presence. This metric was derived at the 1km scale for the California Coastal Assessment. We used this same index and aggregated it to the scale of the ACE datasets at 2.5 sq. mile. Quintiles were again taken from the distribution generated from the model to represent a rank-score for the index. A value of 1 is low species and habitat diversity where a value of 5 is high species and habitat diversity.4) Ecological Intactness Index is based on four indicators of connectivity, natural landscape blocks, and counts of shorebird and marine mammal colonies and haul out areas, summed in an additive model framework. Quintiles were again taken from the distribution generated from the model to represent a rank-score for the index. A value of 1 is low ecological intactness where a value of 5 is high ecological intactness.All data was compiled from the following paper: Reynolds, M., Gleason, M. G., Heady, W., Easterday, K., & Morrison, S. A. The Importance of Identifying and Protecting Coastal Wildness. Frontiers in Conservation Science, 4, 1224618.

The contents of this dataset is the basis of the analysis conducted in the related publication. The scope of the study was to investigate the relationship between renewable energy consumption and the state of sustainable development across countries. The investigation was facilitated by clustering countries according to their sustainable development metrics, and correlations between the sustainable development indicator gaps for each country and their renewable energy shares.

These datasets represent a Human Industrial Footprint (HIF) index map and an Ecological Intactness Index (EII) map for Australia circa 2020-2024. The datasets are distributed in raster format (.tif) and have a spatial resolution of 100 m, mapped on an Australian Albers Equal Area projection (EPSG:3577).

The HIF was created by incorporating 16 nationally relevant pressure layers, also part of the dataset. The pressures used to compute the HIF were 1) intensive land uses, 2) buildings, 3) mining and quarrying, 4) human population density, 5) croplands, 6) pasturelands, 7) forestry plantations, 8) reservoirs and large dams, 9) farm dams, 10) roads, 11) railways, 12) energy transmission lines, 13) oil pipelines, 14) gas pipelines, 15) hiking trails, and 16) navigable waterways. Each pressure layer was assigned a relative score between 0 and 10 to make them comparable. The scored (scaled) pressure layers were then summed to obtain the final HIF map.

The HIF was used to derive the Ecological Intactness Index (EII). The EII is calculated using the HIF, with the intactness index value for each cell parameterised to: a) be proportional to habitat area when there is no habitat fragmentation; b) decline mono-tonically as fragmentation increases, and be sensitive to both the number of nearby patches and the separation between patches, and (c) to be proportional to habitat quality for a given total area of habitat and degree of fragmentation.

In the pressure layer folder, native and modified pasturelands are merged in the "pastures" pressure layer and paved and unpaved roads are in the "roads" layer.

The code to create these maps is also available through this repository. The code is an end‑to‑end GRASS GIS pipeline to rebuild the Human Industrial Footprint Index for continental Australia on a 100 m grid in Albers Australia Equal Area (EPSG:3577). It generates 16 pressure layers, applies hierarchical priority (Urban > Mining > Crops >Pasture), scales each 0–10, and exports individual layers plus the summed index as Cloud‑Optimised GeoTIFFs (COGs).

Acknowledgements

This research was funded by The Wilderness Society.

Contact

Further queries regarding these datasets can be directed to Ruben Venegas (r.venegas@uq.edu.au) and James Watson (james.watson@uq.edu.au).

This global spatial layer of contextual intactness aims to identify priority areas around the world where protection and management will best promote biodiversity persistence. This layer was derived by integrating both the condition of each focal location and the condition of all other locations expected to have supported shared species with the focal location prior to any habitat degradation. The contextual intactness of each location (grid cell) is the proportion of habitat predicted to have once supported a similar assemblage of species but is now in worse condition than the focal location. This was derived using the BILBI global biodiversity assessment system, by integrating: (1) an updated map of the terrestrial human footprint on natural systems, and; (2) generalized dissimilarity models of species assemblage turnover for terrestrial vertebrates, invertebrates, and plants. Lineage: Source Data The input datasets are: (1) an updated map of the terrestrial human footprint on natural systems to describe current habitat condition (Venter et al. 2016); (2) generalized dissimilarity models of species assemblage turnover for terrestrial vertebrates, invertebrates, and plants, derived using more than 100 million occurrence records from more than 400,000 species (Hoskins et al. 2019). These models enable the estimation of the similarity in species assemblages between any pair of locations, and subsequently the expected uniqueness of the biodiversity within any terrestrial location globally.

Preparation Method To determine the contextual intactness for each location across the globe, we combined the revised human footprint spatial layer and the predicted similarity in species assemblages between pairs of locations using the BILBI framework. For each grid cell i, we selected a spatially regular randomly positioned selection of n other grid cells j to compare to cell i . A sample of comparison cells is required because there are >200 million cells on the 1 km terrestrial grid of the planet, and comparing each grid cell with every other grid cell is computationally prohibitive. For this assessment, the number of other grid cells j was a minimum of 1% of the total grid cells within each of the world’s 7 biogeographic-realms (Antarctica being excluded) (Olson et al. 2001).

We then determined the expected similarity (sij) in species assemblages between cell i and each comparison cell j using the BILBI framework (Hoskins et al, 2019). The human footprint (HFP) value for cell i (HFPi) and all comparison cells j (HFPj) was also extracted. We then derived a histogram of the summed species assemblage similarity to grid cell i, within integer bands of the human footprint value for all the comparison cells j .

From this histogram, we then calculated: (a) the sum of the assemblage similarities to i where the comparison cell j had a higher human footprint to i, and; (b) the total sum of the all the assemblage similarities between i and j across all human footprint scores . The contextual intactness for grid cell i (CIi) was then calculated as the sum of assemblage similarities to i with a higher human footprint divided by the total sum of assemblage similarities to i:

This calculation was repeated for every terrestrial grid cell globally to derive a spatial map of contextual intactness for each taxonomic group (vertebrates, invertebrates, plants). The spatial layers for these three taxonomic groups were then averaged to derive a single contextual intactness layer for biodiversity, though future analyses could consider each taxonomic group separately.

References A. J. Hoskins et al., (2019) Supporting global biodiversity assessment through high-resolution macroecological modelling: Methodological underpinnings of the BILBI framework. bioRxiv, 309377. D. M. Olson et al. (2001) Terrestrial Ecoregions of the World: A New Map of Life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933-938. O. Venter et al., (2016) Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications 7, 12558.

Conservation of coastal biodiversity and associated ecosystem services requires protection and management for attributes of coastal wildness, which we define to include physical and ecological intactness and connectivity, native species and habitat diversity, and limited human disturbance. Coastal wildness is threatened by high demand for access to and development of coastal margins; sea level rise exacerbates this threat. As a case study, California (USA), a biodiversity hotspot, has a network of marine and terrestrial protected areas along the coast and strong coastal policy. While 35% of California’s coast has wildness attributes, only 9% of California’s coast is characterized as wild and also protected on both land and in the adjacent waters. A multi-tiered approach is needed to incorporate wild coast attributes into conservation planning and protection of coastal areas. A coastal wildness designation may be needed, as well as policies that manage for wildness attributes in existing protected areas. This dataset contains 4 layers which were used in the Wild Coast Project carried out by The Nature Conservancy to highlight the importance of identifying and protecting coastal wildness. Ecological Intactness, Physical Intactness, Habitat and Species Diversity Index, and Human Disturbance Index.The following authors are included: Mary G. Gleason, Mark D. Reynolds, Walter N. Heady, Kelly Easterday, Scott A. Morrison.

This work supports the Wild Coast Project. This is a FEATURE LAYER for the accompanying Web Map.Rankings: All final data layers were ranked by quintiles. Quintiles represent a percentile ranking with 5classes. This allows the user to see an easily interpretable rank-score for each hexagon.Input layers include: 1) Human Disturbance Index2) Physical Intactness Index3) Habitat and Species Diversity Index4) Ecological Intactness Index. Additional Layers for Visualization Include: 1) Conservation Management Status2) Marine Protected AreasLayer Description: 1) The Human Disturbance Index based on three indices relevant to coastal access and density of build features including roads and buildings, summed in an additive model framework. Quintiles were again taken from the distribution generated from the model to represent a rank- score for the index. A value of 1 is low human disturbance where a value of 5 is high human disturbance.2) Physical Intactness Index is based on three indices articulating the built environment with a focus on the coast and coastal structures including counts of piers, jetty’s, harbors and percent of armored shoreline, and the intensity of the surrounding built environment through the built environment intensity index from CCA 2018, summed in an additive model framework. Quintiles were again taken from the distribution generated from the model to represent a rank- score for the index. A value of 1 is high physical intactness where a value of 5 is low physical intactness.3) Habitat and Species Diversity Index is solely based on the rarity -weighted index which is a combined spatial index of 40 habitats and 159 imperiled species that characterizes the relative biodiversity and conservation value across the landscape in terms of value of habitat type and imperiled species presence. This metric was derived at the 1km scale for the California Coastal Assessment. We used this same index and aggregated it to the scale of the ACE datasets at 2.5 sq. mile. Quintiles were again taken from the distribution generated from the model to represent a rank-score for the index. A value of 1 is low species and habitat diversity where a value of 5 is high species and habitat diversity.4) Ecological Intactness Index is based on four indicators of connectivity, natural landscape blocks, and counts of shorebird and marine mammal colonies and haul out areas, summed in an additive model framework. Quintiles were again taken from the distribution generated from the model to represent a rank-score for the index. A value of 1 is low ecological intactness where a value of 5 is high ecological intactness.All data was compiled from the following paper: Reynolds, M., Gleason, M. G., Heady, W., Easterday, K., & Morrison, S. A. The Importance of Identifying and Protecting Coastal Wildness. Frontiers in Conservation Science, 4, 1224618.

This dataset was last updated 02/2017. This version includes a new tidal restrictions metric that assesses the effect of undersized culverts and bridges on tidal regime.The previous version (3.1) was updated on 05/2016 by incorporating a revised version of the land cover classification, DSLland Version 3.1, developed by UMass, which included the addition of The Nature Conservancy's Northeast lakes and ponds classification.

This dataset depicts the ecological integrity of locations (represented by 30 m grid cells) throughout the northeastern United States based on environmental conditions existing in approximately 2010. Ecological integrity is defined as the ability of an area (e.g., local site or landscape) to sustain important ecological functions over the long term. In particular, the functions include the long-term ability to support biodiversity and the ecosystem processes necessary to sustain biodiversity. The Index of Ecological Integrity (IEI) is expressed on a relative scale (0 to 1)* for ecosystems mapped on a modified version of the Northeast Terrestrial Habitat Map developed by the Nature Conservancy and the northeastern states. Ecosystems are the finest scale level of the ecological classification hierarchy. Classes include "Northeastern Interior Pine Barrens" and "Acidic Cliff and Talus". *NOTE - Data displayed in web mapper are values 0 - 100.

This version of ecological integrity includes two categories of landscape metrics:

• Intactness – the freedom from human impairment (anthropogenic stressors), measured as a combination of a number of stressor metrics.

• Resiliency – the capacity to recover from disturbance and stress, measured as a combination of the connectedness and similarity to neighboring natural areas.

This ecological integrity dataset is one of a larger set of results developed by the Designing Sustainable Landscapes project led by Professor Kevin McGarigal of UMass Amherst. Projected future ecological integrity for 2030 and 2080 are also being developed based on models of development (urban growth), climate change, and forest change. More information and detailed documentation for the Designing Sustainable Landscapes project, which includes many additional datasets, is available at: http://www.umass.edu/landeco/research/dsl/dsl.html.

More details about the calculation of the Index of Ecological Integrity are as follows. The basic building blocks of the index are a series of Ecological Settings, each of which is a spatial dataset encompassing the Northeastern U.S. The ecological settings represent a broad but carefully selected suite of biophysical variables representing the natural and anthropogenic environment at each location for each time step used in the Designing Sustainable Landscapes project. Each ecological setting is available as a separate spatial dataset. One of the key components is the DSLland dataset, which is a modified version of the Northeast Terrestrial Wildlife Habitat Map developed by The Nature Conservancy and the northeastern states. Other settings include variables such as temperature, soil depth, above-ground live biomass, extent of development, and traffic rate. A series of metrics, such as the intensity of urban development and the degree to which ecosystems are connected, are calculated from these ecological settings.The metrics are integrated in weighted linear combinations to calculate IEI based on the opinions of expert teams as to the importance of each metric in determining the ecological integrity of the different ecosystem types.

In the final IEI, results are re-scaled by ecosystem type to make comparisons more meaningful. For example, marshes are ranked relative to other marshes rather than in comparison to forests or other ecosystem types. Hence, IEI represents a cell’s percentile within its group, e.g., a cell of Laurentian-Acadian freshwater marsh with an IEI of 80 is in the top 20% of Laurentian-Acadian freshwater marshes.The specific metrics for IEI, each of which is available as a separate dataset, are the following:

Intactness Metrics: 1) Habitat loss – the intensity of habitat loss due to development in the neighborhood of each cell 2) Watershed habitat loss (aquatic metric) – the intensity of habitat loss due to development upstream of the cell 3) Road traffic – the intensity of traffic in the neighborhood of the cell 4) Mowing and plowing – the intensity of agriculture in the vicinity of the cell, reflecting mortality to organisms from mowing and plowing 5) Edge effects – the effects of human-induced edges on ecosystems 6) Watershed road salt (aquatic metric) – the density of upstream roads, a surrogate for road salt application rates 7) Watershed road sediment (aquatic metric) – the density of upstream roads, a surrogate for road sediment production rates 8) Nutrient enrichment (aquatic metric) – the intensity of residential and agricultural land uses upstream of each cell a surrogate for fertilizer application rates 9) Watershed imperviousness (aquatic metric) – the intensity of impervious surface (such as roads and buildings) upstream of the cell 10) Dams (aquatic metric) – the number and proximity of dams upstream of the cell 11) Biotic alterations – the intensity of development in the neighborhood of the cell, calculated separately as a surrogate for four effects: a) edge predators (such as raccoons and skunks), b) domestic predators (such as cats), c) invasive earthworms, and d) invasive plants

Resiliency Metrics: 1) Connectedness – the degree to which development and ecologically dissimilar sites interfere with connections between the cell and ecologically similar neighbors 2) Aquatic connectedness – the degree to which connections along streams and rivers are diminished by barriers such as dams and culverts 3) Similarity – the similarity (lack of contrast) between the environment of a cell and its surroundings (with higher similarity implying greater resilience)

Changes of BII in Pará between 2010 and 2030.

This dataset depicts the ecological integrity of locations (represented by 30 m grid cells) throughout the northeastern United States based on environmental conditions existing in approximately 2010 for aquatic systems.
The values for this dataset were extracted from the Index of Ecological Integrity, Region-wide, Version 3.2 for all aquatic systems. Updated 09/2017. The metadata for the original dataset is as follows:

This dataset was last updated 02/2017. This version includes a new tidal restrictions metric that assesses the effect of undersized culverts and bridges on tidal regime. The previous version (3.1) was updated on 05/2016 by incorporating a revised version of the land cover classification, DSLland Version 3.1, developed by UMass, which included the addition of The Nature Conservancy's Northeast lakes and ponds classification.

Ecological integrity is defined as the ability of an area (e.g., local site or landscape) to sustain important ecological functions over the long term. In particular, the functions include the long-term ability to support biodiversity and the ecosystem processes necessary to sustain biodiversity. The Index of Ecological Integrity (IEI) is expressed on a relative scale (0 to 1) for ecosystems mapped on a modified version of the Northeast Terrestrial Habitat Map developed by the Nature Conservancy and the northeastern states. Ecosystems are the finest scale level of the ecological classification hierarchy. Classes include "Northeastern Interior Pine Barrens" and "Acidic Cliff and Talus". This version of ecological integrity includes two categories of landscape metrics: • Intactness – the freedom from human impairment (anthropogenic stressors), measured as a combination of a number of stressor metrics. • Resiliency – the capacity to recover from disturbance and stress, measured as a combination of the connectedness and similarity to neighboring natural areas.

This ecological integrity dataset is one of a larger set of results developed by the Designing Sustainable Landscapes project led by Professor Kevin McGarigal of UMass Amherst. Projected future ecological integrity for 2030 and 2080 are also being developed based on models of development (urban growth), climate change, and forest change. More information and detailed documentation for the Designing Sustainable Landscapes project, which includes many additional datasets, is available at: http://www.umass.edu/landeco/research/dsl/dsl.html.

More details about the calculation of the Index of Ecological Integrity are as follows. The basic building blocks of the index are a series of Ecological Settings, each of which is a spatial dataset encompassing the Northeastern U.S. The ecological settings represent a broad but carefully selected suite of biophysical variables representing the natural and anthropogenic environment at each location for each time step used in the Designing Sustainable Landscapes project. Each ecological setting is available as a separate spatial dataset. One of the key components is the DSLland dataset, which is a modified version of the Northeast Terrestrial Wildlife Habitat Map developed by The Nature Conservancy and the northeastern states. Other settings include variables such as temperature, soil depth, above-ground live biomass, extent of development, and traffic rate. A series of metrics, such as the intensity of urban development and the degree to which ecosystems are connected, are calculated from these ecological settings.The metrics are integrated in weighted linear combinations to calculate IEI based on the opinions of expert teams as to the importance of each metric in determining the ecological integrity of the different ecosystem types. In the final IEI, results are re-scaled by ecosystem type to make comparisons more meaningful. For example, marshes are ranked relative to other marshes rather than in comparison to forests or other ecosystem types. Hence, IEI represents a cell’s percentile within its group, e.g., a cell of Laurentian-Acadian freshwater marsh with an IEI of 80 is in the top 20% of Laurentian-Acadian freshwater marshes.The specific metrics for IEI, each of which is available as a separate dataset, are the following:
Intactness Metrics: 1) Habitat loss – the intensity of habitat loss due to development in the neighborhood of each cell 2) Watershed habitat loss (aquatic metric) – the intensity of habitat loss due to development upstream of the cell 3) Road traffic – the intensity of traffic in the neighborhood of the cell 4) Mowing and plowing – the intensity of agriculture in the vicinity of the cell, reflecting mortality to organisms from mowing and plowing 5) Edge effects – the effects of human-induced edges on ecosystems 6) Watershed road salt (aquatic metric) – the density of upstream roads, a surrogate for road salt application rates 7) Watershed road sediment (aquatic metric) – the density of upstream roads, a surrogate for road sediment production rates 8) Nutrient enrichment (aquatic metric) – the intensity of residential and agricultural land uses upstream of each cell a surrogate for fertilizer application rates 9) Watershed imperviousness (aquatic metric) – the intensity of impervious surface (such as roads and buildings) upstream of the cell 10) Dams (aquatic metric) – the number and proximity of dams upstream of the cell 11) Biotic alterations – the intensity of development in the neighborhood of the cell, calculated separately as a surrogate for four effects: a) edge predators (such as raccoons and skunks), b) domestic predators (such as cats), c) invasive earthworms, and d) invasive plants.
Resiliency Metrics: 1) Connectedness – the degree to which development and ecologically dissimilar sites interfere with connections between the cell and ecologically similar neighbors 2) Aquatic connectedness – the degree to which connections along streams and rivers are diminished by barriers such as dams and culverts 3) Similarity – the similarity (lack of contrast) between the environment of a cell and its surroundings (with higher similarity implying greater resilience)

Aggregation of LandSHIFT land-use types.