Anthropogenic modiﬁcation of forests means only
40% of remaining forests have high ecosystem
H. S. Grantham1✉, A. Duncan1, T. D. Evans1, K. R. Jones1, H. L. Beyer2, R. Schuster 3, J. Walston1, J. C. Ray4,
J. G. Robinson1, M. Callow1, T. Clements1, H. M. Costa1, A. DeGemmis1, P. R. Elsen 1, J. Ervin5, P. Franco 1,
E. Goldman6, S. Goetz 7, A. Hansen8, E. Hofsvang9, P. Jantz 7, S. Jupiter 1, A. Kang1, P. Langhammer10,11,
W. F. Laurance 12, S. Lieberman1, M. Linkie1, Y. Malhi 13, S. Maxwell2, M. Mendez1, R. Mittermeier10,
N. J. Murray 12,14, H. Possingham 15,16, J. Radachowsky1, S. Saatchi17, C. Samper1, J. Silverman1, A. Shapiro18,
B. Strassburg 19, T. Stevens1, E. Stokes1, R. Taylor6, T. Tear1, R. Tizard 1, O. Venter20, P. Visconti 21,
S. Wang1& J. E. M. Watson 1,2
Many global environmental agendas, including halting biodiversity loss, reversing land
degradation, and limiting climate change, depend upon retaining forests with high ecological
integrity, yet the scale and degree of forest modiﬁcation remain poorly quantiﬁed and
mapped. By integrating data on observed and inferred human pressures and an index of lost
connectivity, we generate a globally consistent, continuous index of forest condition as
determined by the degree of anthropogenic modiﬁcation. Globally, only 17.4 million km2of
forest (40.5%) has high landscape-level integrity (mostly found in Canada, Russia, the
Amazon, Central Africa, and New Guinea) and only 27% of this area is found in nationally
designated protected areas. Of the forest inside protected areas, only 56% has high
landscape-level integrity. Ambitious policies that prioritize the retention of forest integrity,
especially in the most intact areas, are now urgently needed alongside current efforts aimed
at halting deforestation and restoring the integrity of forests globally.
1Wildlife Conservation Society, Global Conservation Program, Bronx, New York 10460, USA. 2School of Earth and Environmental Sciences, University of
Queensland, Brisbane, Australia. 3Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada. 4Wildlife Conservation
Society Canada, 344 Bloor St W #204, Toronto, ON M5S 3A7, Canada. 5United Nations Development Programme, One United Nations Plaza, New York,
NY 10017, USA. 6World Resources Institute, Washington, DC, USA. 7Global Earth Observation & Dynamics of Ecosystems Lab, School of Informatics,
Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA. 8Landscape Biodiversity Lab, Ecology Department, Montana State
University, Bozeman, MT 59717, USA. 9Rainforest Foundation Norway, Mariboes gate 8, 0183 Oslo, Norway. 10 Global Wildlife Conservation, P.O. Box 129,
Austin, TX 78767, USA. 11 School of Life Sciences, Arizona State University, P.O. Box 874501, Tempe, AZ 85287, USA. 12 Centre for Tropical Environmental
and Sustainability Science, College of Science and Engineering, James Cook University, Cairns, QLD 4878, Australia. 13 Environmental Change Institute,
School of Geography and the Environment, University of Oxford, Oxford, UK. 14 College of Science and Engineering, James Cook University, Townsville,
Queensland, Australia. 15 School of Biological Sciences, University of Queensland, St. Lucia, QLD, Australia. 16 The Nature Conservancy, Arlington, VA, USA.
17 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA. 18 World Wide Fund for Nature Germany, Space+Science,
Berlin, Germany. 19 International Institute of Sustainability, Rio de Janeiro 22460-320, Brazil. 20 Natural Resource and Environmental Studies Institute,
University of Northern British Columbia, Prince George, Canada. 21 International Institute for Applied Systems Analysis, Laxenburg, Austria .
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Deforestation is a major environmental issue1, but far less
attention has been given to the degree of anthropogenic
modiﬁcation of remaining forests, which reduces ecosys-
tem integrity and diminishes many of the beneﬁts that these
forests provide2,3. This is worrying since modiﬁcation is poten-
tially as signiﬁcant as outright forest loss in determining overall
environmental outcomes4. There is increasing recognition of this
issue, for forests and other ecosystems, in synthesis reports by
global science bodies such as the global assessment undertaken by
the Intergovernmental Science-Policy Platform on Biodiversity
and Ecosystem Services5, and it is now essential that the scientiﬁc
community develop improved tools and data to facilitate the
consideration of levels of integrity in decision-making. Mapping
and monitoring this globally will provide essential information
for coordinated global, national, and local policy-making, plan-
ning, and action, to help nations and other stakeholders achieve
the Sustainable Development Goals (SDGs) and implement other
shared commitments such as the United Nations Convention on
Biological Diversity (CBD), Convention to Combat Desertiﬁca-
tion (UNCCD), and Framework Convention on Climate Change
Ecosystem integrity is foundational to all three of the Rio
Conventions (UNFCCC, UNCCD, CBD)6.Asdeﬁned by Parrish
et al.7, it is essentially the degree to which a system is free from
anthropogenic modiﬁcation of its structure, composition, and
function. Such modiﬁcation causes the reduction of many eco-
system beneﬁts, and is often also a precursor to outright
deforestation8,9. Forests largely free of signiﬁcant modiﬁcation
(i.e., forests having high ecosystem integrity), typically provide
higher levels of many forest beneﬁts than modiﬁed forests of the
same type10, including; carbon sequestration and storage11,
healthy watersheds12, traditional forest use13, contribution to
local and regional climate processes14, and forest-dependent
biodiversity15–18. Industrial-scale logging, fragmentation by
infrastructure, farming (including cropping and ranching) and
urbanization, as well as less visible forms of modiﬁcation such as
over-hunting, wood fuel extraction, and changed ﬁre or hydro-
logical regimes19,20, all degrade the degree to which forests still
support these beneﬁts, as well as their long-term resilience to
climate change10. There can be trade-offs, however, between the
beneﬁts best provided by less-modiﬁed forests (e.g., regulatory
functions such as carbon sequestration) and those production
services that require some modiﬁcation (e.g., timber production).
These trade-offs can, at times, result in disagreement among
stakeholders as to which forest beneﬁts should be prioritized21.
In recent years, easily accessible satellite imagery and new
analytical approaches have improved our ability to map and
monitor forest extent globally22–24. However, while progress has
been made in developing tools for assessment of global forest
losses and gains, consistent monitoring of the degree of forest
modiﬁcation has proved elusive25,26.
Technical challenges include the detection of low intensity and
unevenly distributed forest modiﬁcation, the wide diversity of
changes that comprise forest modiﬁcation, and the fact that many
changes are concealed by the forest canopy25. New approaches
are emerging on relevant forest indicators, such as canopy height,
canopy cover and fragmentation, and maps of different human
pressures, which are used as proxies for impacts on forests27–30.
Some binary measures of forest modiﬁcation, such as Intact
Forest Landscapes31 and wilderness areas32, have also been
mapped at the global scale and used to inform policy, but do not
resolve the degree of modiﬁcation within remaining forests,
which we aimed to do with this assessment.
Human activities inﬂuence the integrity of forests at multiple
spatial scales, including intense, localized modiﬁcations such as
road-building and canopy loss, more diffuse forms of change that
are often spatially associated with these localized pressures (e.g.,
increased accessibility for hunting, other exploitation, and selective
logging), and changes in spatial conﬁguration that alter landscape-
level connectivity. Previous studies have quantiﬁed several of these
aspects individually27–29, but there is a need to integrate them to
measure and map the overall degree of modiﬁcation considering
these landscape-level anthropogenic inﬂuences at each site. Here, we
integrate data on forest extent deﬁned as all woody vegetation taller
than 5 m, following23, observed human pressures (e.g., infra-
structure) which can be directly mapped using current datasets,
other inferred human pressures (e.g., collection of forest materials)
that occur in association with those that are observed but cannot be
mapped directly, and alterations in forest connectivity, to create the
Forest Landscape Integrity Index (FLII), that describes the degree of
forest modiﬁcation for the beginning of 2019 (Fig. 1). The result is a
globally applicable, continuous-measure map of landscape-level
forest integrity (hereafter, integrity), which offers a timely indicator
of the status and management needs of Earth’s remaining forests.
The results show there has been a huge loss of forest integrity. To
give a global overview we summarize the results according to three
simple, illustrative categories of integrity (which we term “high”,
“medium”,and“low”) while noting that the underlying continuous
index enables much ﬁner distinctions to be made for detailed ana-
lysis in diverse contexts. This reveals around 40% of remaining
forests have high forest integrity. Further, our methodological fra-
mework (Fig. 1) can be adapted to match local conditions at national
or subnational scales and for different weightings to be applied.
Forest modiﬁcation caused by human activity is both highly
pervasive and highly variable across the globe (Fig. 2). We found
Loss of forest
Pressures that can be directly
Pressures associated with
Ratio of current to potential
Apply to current
Fig. 1 Methods used to construct the Forest Landscape Integrity Index.
The Forest Landscape Integrity Index was constructed based on three main
data inputs: (1) observed pressures (infrastructure, agriculture, tree cover
loss), (2) inferred pressure modeled based on proximity to the observed
pressures, and (3) change in forest connectivity.
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31.2% of forests worldwide are experiencing some form of
observed human pressure, which included infrastructure, agri-
culture, and recent deforestation. Our models also inferred the
likely occurrence of other pressures, and the impacts of lost
connectivity, in almost every forest location (91.2% of forests),
albeit sometimes at very low levels. Diverse, recognizable patterns
of forest integrity can be observed in our maps at a range of
scales, depending on the principal forms and general intensity of
human activity in an area. Broad regional trends can be readily
observed, for example, the overall gradient of decreasing human
impact moving northwards through eastern North America
(Fig. 2), and ﬁner patterns of impact are also clearly evident,
down to the scale of individual protected areas, forest conces-
sions, settlements, and roads (Supplementary Fig. 2).
FLII scores range from 0 (lowest integrity) to 10 (highest).
We discretized this range to deﬁne three broad illustrative
categories: low (≤6.0); medium (>6.0 and <9.6); and high
integrity (≥9.6) by benchmarking against reference locations
worldwide (see Methods, Supplementary Table 4). Only 40.5%
(17.4 million km2) of the forest was classiﬁed as having high
integrity (Fig. 3;Table1).Moreover,eveninthiscategoryof
high integrity 36% still showed at least a small degree of human
modiﬁcation. The remaining 59% (25.6 million km2)ofthe
forest was classiﬁed as having low or medium integrity,
including 25.6% (11 million km2) with low integrity (Fig. 3;
Table 1). When we analyzed across biogeographical realms
deﬁned by33 not a single biogeographical realm of the world
had more than half of its forests in the high category (Fig. 3;
The biogeographical realms with the largest area of forest with
high integrity are the Paleartic, particularly northern Russia, and the
Neartic, in northern Canada, Rocky Mountains, and Alaska (Fig. 3).
There are also large areas of forest with high integrity in the Neo-
tropics, concentrated in the Amazon region, including within the
Guianas, Atlantic forest in Brazil, southern Chile, and parts of
Mesoamerica (Fig. 3,Table1). The Afrotropic realm has signiﬁcant
areas with high integrity, particularly within the humid forests of
central Africa (e.g., in Republic of Congo and Gabon) and in some
of the surrounding drier forest/woodland belts (e.g., in South Sudan,
Angola, and Mozambique) (Fig. 3). Some smaller patches occur in
West Africa and Madagascar. In tropical Asia-Paciﬁc, the largest
tracts of forest with high integrity are in New Guinea. Smaller but
still very signiﬁcant tracts of forest with high integrity are also
scattered elsewhere in each of the main forested regions, including
parts of Sumatra, Borneo, Myanmar, and other parts of the Greater
Concentrations of the forest with low integrity are found in
many regions including west and central Europe, the south-
eastern USA, island and mainland South-East Asia west of New
Guinea, the Andes, much of China and India, the Albertine Rift,
West Africa, Mesoamerica, and the Atlantic Forests of Brazil
(Fig. 3). The overall extent of forests with low integrity is
greatest in the Paleartic realm, followed by the Neotropics,
which are also those biogeographic realms with the largest
forest cover (Table 1). The Indo-Malayan realm has the highest
percentage with low integrity, followed by the Afrotropics
These patterns result in variation of forest integrity scores in ways
that allow objective comparisons to be made between locations and
at a resolution relevant for policy and management planning, such
as at national and sub-national scales. The global average FLII score
is 7.76 (Table 1), representing a medium level of integrity. However,
Low (0) High (10)
Forest Landscape Integrity Index
Fig. 2 Forest Landscape Integrity Index map. A global map of Forest Landscape Integrity for the start of 2019. Three regions are highlighted including (a)
Smoky Mountains National Park in Tennessee USA, (b) a region in Shan State Myanmar, and (c) Reserva Natural del Estuario del Muni in Equatorial
Guinea. Maps A1–C1 shows the Forest Landscape Integrity Index for these locations. A2, B2, and C2 are photographs from within these regions: (A2) the
edge of Smoky Mountains National Park; (B2) shows a logging truck passing through some partially degraded forest along a newly constructed highway in
Shan Stat; and, (C3) shows an intact mangrove forest within Reserva Natural del Estuario del Muni, near the border with Gabon. The stars in (a), (b), and
(c) indicate approximate location of where these photos were taken. All photos were taken by H.S.G.
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the average score across countries, disregarding their size, is 5.48,
suggesting that low scores dominate in many of the smaller coun-
tries, and indeed a quarter of forested countries have a national
average score < 4. National mean scores vary widely, ranging from
>9 in Guyana, French Guiana, Gabon, Sudan, and South Sudan to
<3 in Sierra Leone and many west European countries (see Fig. 4.
and Supplementary Table 5 for a full list of countries). Provinces
and other sub-national units vary even more widely (see Supple-
mentary Fig. 2 and Supplementary Table 6)
Over one-quarter (26.1%) of all forests with high integrity fall
within protected areas, compared to just 13.1% of low and 18.5%
of medium integrity forests respectively. For all forests that are
found within nationally designated protected areas (around 20%
of all forests globally), we found the proportions of low, medium,
and high integrity forests were 16.8%, 30.3%, and 52.8%,
respectively (Table 2). Within the different protected area cate-
gories, we typically found that there was more area within the
high integrity category versus the medium and low except for
Category V (protected landscape/seascape) (Table 2). However,
with 47.1% of forests within protected areas having low to
medium integrity overall, it is clear that forests considered pro-
tected are already often fairly modiﬁed (Table 2). Even though
they are quite modiﬁed, some of these forests might still have high
conservation importance, such as containing endangered species.
By providing a transparent and defensible methodological fra-
mework, and by taking advantage of global data on forest extent,
human drivers of forest modiﬁcation, and changes in forest
connectivity, our analysis paints a sobering picture of the extent
of human impacts on the world’s forests. This analysis enables the
changes that degrade many forest values to be visualized in a way
for policymakers and decision-makers to see where forests that
survive in good condition are found. By integrating data on
multiple human pressures that are known to modify forests, our
analysis moves global quantiﬁcation beyond the use of simple
categories, or solely using pressure indicators as proxies for
integrity, to a more nuanced depiction of this issue as a con-
tinuum, recognizing that not all existing forests are in the same
condition. Our analysis reveals that severe and extensive forest
modiﬁcation has occurred across all biogeographic regions of the
world. Consequently, indices only using forest extent may
inadequately capture the true impact of human activities on
F -Indo-Malay G -Australasia
H –All forest
Low integrity (0 - 6) Medium integrity (6 – 9.6)
Forest Landscape Integrity Index
High integrity (>9.6)
Fig. 3 Forest Landscape Integrity Index map categorized into three illustrative classes. The Forest Landscape Integrity Index for 2019 categorized into
three broad, illustrative classes and mapped across each biogeographic realm (a–g). The size of the pie charts indicates the relative size of the forests
within each realm (a–g), and hshows all the world’s forest combined.
Table 1 Brief title: Forest Landscape Integrity Index scores for each biogeographic realm.
Biogeographic realm Historical
FLII High Medium Low
(9.6–10) (6–9.6) (0–6)
km2km2% Mean km2%of
Afrotropic 9,071,897 7,362,740 81.2 7.34 2,450,953 33.3 2,903,483 39.4 2,008,304 27.3
Australasia 2,225,054 1,711,684 76.9 8.05 656,701 38.4 753,188 44 301,796 17.6
Indo-malayan 4,797,518 3,596,249 75.0 5.9 420,977 11.7 1,599,049 44.5 1,576,223 43.8
Neotropic 14,965,342 10,271,519 68.6 7.81 4,579,406 44.6 3,122,706 30.4 2,569,407 25
Oceania 30,746 23,389 76.1 7.66 5,279 22.6 14,331 61.3 3,780 16.2
Palearctic 16,524,088 12,172,668 73.7 8 5,571,997 45.8 3,910,629 32.1 2,690,042 22.1
Nearctic 9,756,589 7,794,117 79.9 7.84 3,716,855 47.7 2,257,518 29 1,819,744 23.3
Total 57,371,234 42,932,367 74.8 7.76 17,402,170 14,560,903 10,969,294
A summary of the Forest Landscape Integrity Index scores for each biogeographic realm globally, measuring the mean score, in addition to the area and proportion of realm for each category of integrity.
Scores are divided into three categories of integrity: high, medium, and low.
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forests, and are insensitive to many drivers of forest modiﬁcation
and the resulting losses of forest beneﬁts.
A plan is clearly needed to put in place retention strategies for
the remaining forests with high integrity, tailored towards the
context in each country or jurisdiction and its different forest
types34–36, because such areas are known to hold exceptional
value. Avoiding the loss of integrity is a better strategy than
aiming to restore forest condition after it is lost, because
restoration is more costly, has a risk of failure, and is unlikely to
lead to full recovery of beneﬁts5. For the forests with the highest
integrity to be retained they should ideally be mapped using
nationally appropriate criteria by the countries that hold them,
formally recognized, prioritized in spatial plans, and placed under
effective management (e.g., protected areas and other effective
conservation areas, lands under Indigenous control, etc.). These
forests must be protected from industrial development impacts
that degrade them through sensible public and private sector
policy that is effective at relevant scales13,37. Our global assess-
ment reveals where these places are found, and can be reﬁned at
more local scales where better data are available.
Around a third of global forests had already been cleared by
200038, and we show that at least 59% of what remains has low or
United States (6.65)
Dem Rep of the Congo (7.56)
Cote d'lvoire (3.64)
South Sudan (9.45)
Rep of the Congo (8.89)
Central African Rep (9.28)
Papua New Guinea (8.84)
New Zealand (7.12)
Forest area (km2)
Forest Landscape Integrity Index
High integrity (>9.6)
Medium integrity (6-9.6)
Low integrity (0-6)
0 3.00m 6.00m 9.00m
Fig. 4 Forest Landscape Integrity Index map categorized into three illustrative classes for each major forested country. The Forest Landscape Integrity
Index for 2019 categorized into three broad, illustrative classes for each major forested country in the world. (a) countries with a forest extent larger than 1
million km2, and (b) countries with forest extent between 1 million km2and 100,000 km2of forest. The size of the bar represents the area of a country’s
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medium integrity, with > 50% falling in these two broad cate-
gories in every biogeographical realm. These levels of human
modiﬁcation result partly from the large areas affected by rela-
tively diffuse anthropogenic pressures whose presence is inferred
near forest edges, and by lost connectivity. We also map a sur-
prising level of more localized, observed pressures, such as
infrastructure and recent forest loss, which are seen in nearly a
third of forested pixels worldwide.
Conservation strategies in these more heavily human-modiﬁed
forests should focus on securing any remaining fragments of
forests in good condition, proactively protecting those forests
most vulnerable to further modiﬁcation8and planning where
restoration efforts might be most effective39–41. In addition,
effective management of production forests is needed to sustain
yields without further worsening their ecological integrity42. More
research is required on how to prioritize, manage, and restore
forests with low to medium integrity41,43, and the FLII presented
here might prove useful for this, for example, by helping prioritize
where the best returns on investment are, in combination with
other sources of data (e.g., carbon)44.
Loss of forest integrity severely compromises many beneﬁts of
forests that are central to achieving multiple Sustainable Devel-
opment Goals and other societal targets45,46. Therefore, govern-
ments must adopt policies and strategies to retain and restore the
ecological integrity of their forests, whilst ensuring that the
solutions are also economically viable, socially equitable, and
politically acceptable within complex and highly diverse local
contexts. This is an enormous challenge and our efforts to map
the degree of forest modiﬁcation are designed both to raise
awareness of the importance of the issue, and to support imple-
mentation through target setting, evidence-based planning, and
enhanced monitoring efforts.
Whilst policy targets for halting deforestation are generally
precise and ambitious, only vague targets are typically stipulated
around reducing levels of forest modiﬁcation10,47. We urgently
need SMART (speciﬁc, measurable, achievable, realistic, and
time-bound) goals, targets, and indicators for maintaining and
restoring forest integrity that directly feeds into higher-level
biodiversity, climate, land degradation, and sustainable develop-
ment goals48. Forest speciﬁc targets could be included within an
over-arching target on ecosystems within the post-2020 Global
Biodiversity Framework, which is currently being negotiated
among Parties to the CBD49. This target needs to be outcome-
focused and address both the extent and the integrity of
ecosystems (e.g., using FLII for forests), in a way that enables
quantitative, measurable goals to be set and reported on, but
allows ﬂexibility for implementation between Parties. The index
we provide here could be easily updated annually and utilized by
nations as a way to report the state of their forests.
In addition to broader goals in global frameworks, the reten-
tion and restoration of forest integrity should also be addressed in
nationally-deﬁned goals embodied in, and aligned between,
Nationally Determined Contributions under the UNFCCC,
efforts to stop land degradation and achieve land degradation
neutrality under the UNCCD, and National Biodiversity Strategy
and Action Plans under the CBD. Since no single metric can
capture all aspects of a country’s environmental values, efforts to
conserve high levels of forest integrity should be complemented
by consideration of areas that support important values according
to other measures (e.g., Key Biodiversity Areas50 and notable
A key management tool for maintaining and improving forest
integrity is protected areas10. We found over a quarter of forests
with high integrity are within protected areas, showing that this
importance has been widely recognized by some national
authorities. However, we also found that nearly half of the forests
within protected areas have medium or low integrity. This result
aligns with other studies such as Jones et al.51 that found a third
of protected areas had high human pressure within them. Com-
pared with more restricted protected areas (e.g., category I), there
was a broad trend of decreasing forest integrity in protected area
categories that allows more human use, with particularly low
mean scores and high percentages of the forest with low integrity
in Category V (Protected Landscapes/Seascapes). The exception is
category VI, which includes indigenous and community protected
areas, some of which contain very extensive areas with low
human population pressure, and for which mean integrity scores
are comparable to those in category I. Some of these differences
probably represent differences at the time of establishment, so
time series or quasi-experimental methods are needed to clarify
the degree to which the various categories are effective in miti-
gating threats to integrity, as suggested by Fa et al.52.
The overall level and pervasiveness of impacts on Earth’s
remaining forests is likely even more severe than our ﬁndings
suggest, because some input data layers, despite being the most
comprehensive available, are still incomplete as there are lags
between increases in human pressures and our ability to capture
them in spatial datasets e.g., infrastructure53,54, (see also
Table 2 Brief title: Forest Landscape Integrity Index scores for different types of protected areas.
Protected area category Total forest FLII High (score 9.6–10) Medium (score 6–9.6) Low (score 0–6)
km2Mean km2% of protected
km2% of protected
km2% of protected
Ia (strict nature reserve) 439,082 9.27 304,329 69.31 106,703 24.3 28,049 6.39
Ib (wilderness area) 367,330 9.22 240,453 65.46 102,096 27.79 24,780 6.75
II (national park) 1,900,000 9.14 1,223,138 64.38 540,805 28.46 136,056 7.16
III (natural monument or feature) 113,805 8.49 54,476 47.87 40,021 35.17 19,308 16.97
838,707 8.69 432,828 51.61 268,027 31.96 137,850 16.44
V (protected landscape/seascape) 840,919 6.4 224,491 26.7 295,769 35.17 320,658 38.13
VI (Protected area with sustainable
use of natural resources)
1,472,278 9.21 1,026,169 69.7 344,617 23.41 101,491 6.89
Not Applicable / Not Assigned / Not
2,613,541 8.29 1,030,430 39.42 906,745 34.69 676,365 25.88
All Protected Areas 8,585,661 8.55 4,536,314 52.83 2,694,784 30.34 1,444,562 16.82
A summary of the Forest Landscape Integrity Index scores for each type of protected area designation based on the IUCN Protected Areas categories measuring mean score, in addition to the area and
proportion of realm for each category of integrity. Scores are divided into three categories of integrity: high, medium, and low.
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Supplementary Note 5 and Supplementary Fig. 1). For example,
roads and seismic lines used for natural resource exploration and
extraction in northern boreal regions of Canada, are not fully
reﬂected in global geospatial datasets (Supplementary Fig. 1; see
also55) The over-exploitation of high socio-economic value ani-
mals and plants may be quite varied across nations and region,
driven by complex social, cultural, economic and governance
factors e.g.56,57, which are difﬁcult to model spatially but as these
data become available, they could be included in further updates
of the index. Adding a temporal dimension of the index is an
important next step, as it will be possible to start to assess the
drivers and underlying caused leading to intact forest erosion
which clearly requires further research attention. Furthermore,
because natural ﬁres are such an important part of the ecology of
many forest systems (e.g., boreal forests) and it is not possible to
consistently identify anthropogenic ﬁres from natural ﬁres at a
global scales58 we have taken a strongly conservative approach to
ﬁre in our calculations, treating all tree cover loss in 10 km pixels
where ﬁre was the dominant driver as temporary, and not treating
such canopy loss as evidence of observed human pressure.
Varying these assumptions where human activity is shown to be
causing permanent tree cover losses, increasing ﬁre return fre-
quencies, or causing ﬁre in previously ﬁre-free systems would
result in lower forest extent and/or lower forest integrity scores in
some regions than we report.
We map forest integrity based on quantiﬁable processes over
the recent past (since 2000). In some areas modiﬁcation that
occurred prior to this (e.g., historical logging) is not detectable by
our methods but may have inﬂuenced the present-day integrity of
the forest so, in such cases, we may overestimate forest integrity.
This is another reason why our index should be considered as
conservative, and we, therefore, recommend that the index be
used alongside other lines of evidence to determine the absolute
level of the ecological integrity of a given area. Moreover, the
deﬁnition of forest in this study is all woody vegetation taller than
5 m, following23 and hence includes not only naturally regener-
ated forests but also tree crops, planted forests, wooded agro-
forests, and urban tree cover in some cases. Users should be
mindful of this when interpreting the results, especially when
observing areas with low forest integrity scores. Inspection of the
results for selected countries with reliable plantation maps59
shows that the great majority of planted forests have low forest
integrity scores, because they are invariably associated with dense
infrastructure, frequent canopy replacement, and patches of
We note our measure of forest integrity does not address past,
current, and future climate change. As climate change affects
forest conditions both directly and indirectly, this is a clear
shortfall and needs research attention. The same is true for
invasive species, as there are no globally coherent data on the
ranges of those invasive species that degrade forest ecosystems,
although this issue is indirectly addressed since the presence of
many invasive species is likely spatially correlated with the human
pressures that we use as drivers in our model27. We estimated the
likely occurrence of damage caused by inferred pressures using a
distance function; this function could be tailored to particular
contexts, such as the presence of high-value species or unusually
difﬁcult terrain, if training data were available. As global data
become available it would also be valuable to incorporate data on
other drivers of forest integrity loss. Future research might enable
the inclusion of governance effectiveness as a factor in our model,
because there are potentially contexts (e.g., well-managed pro-
tected areas and community lands, production forests under
“sustainable forest management”) where the impacts associated
with the human pressures we base our map on are at least par-
tially ameliorated42, and enhanced governance is also likely to be
a signiﬁcant component of some future strategies to maintain and
enhance forest integrity.
The framework we present is now being tailored for use at
smaller scales, ranging from regional to national and sub-national
scales, and even to individual management units, through the
development of a cloud-based online tool. Forest deﬁnitions and
the relative weights of the global parameters we use can be
adjusted to ﬁt local contexts and, in many cases, better local data
could be substituted, or additional variables incorporated. This
would not only increase the precision of the index in representing
local realities, but also the degree of ownership amongst national
and local policymakers and stakeholders whose decisions are so
important in determining forest management trajectories.
To produce our global Forest Landscape Integrity Index (FLII), we combined four
sets of spatially explicit datasets representing: (i) forest extent23; (ii) observed
pressure from high impact, localized human activities for which spatial datasets
exist, speciﬁcally: infrastructure, agriculture, and recent deforestation27; (iii)
inferred pressure associated with edge effects27, and other diffuse processes, (e.g.,
activities such as hunting and selective logging)27 modeled using proximity to
observed pressures; and iv) anthropogenic changes in forest connectivity due to
forest loss27 (see Supplementary Table 1 for data sources). These datasets were
combined to produce an index score for each forest pixel (300 m), with the highest
scores reﬂecting the highest forest integrity (Fig. 1), and applied to forest extent for
the start of 2019. We use globally consistent parameters for all elements (i.e.,
parameters do not vary geographically). All calculations were conducted in Google
Earth Engine (GEE)60.
Forest extent. We derived a global forest extent map for 2019 by subtracting from
the Global Tree Cover product for 200023 annual Tree Cover Loss 2001–2018,
except for losses categorized by Curtis and colleagues24 as those likely to be tem-
porary in nature (i.e., those due to ﬁre, shifting cultivation and rotational forestry).
We applied a canopy threshold of 20% based on related studies e.g.31,61, and
resampled to 300 m resolution and used this resolution as the basis for the rest of
the analysis (see Supplementary Note 1 for further methods).
Observed human pressures. We quantify observed human pressures (P) within a
pixel as the weighted sum of impact of infrastructure (I; representing the combined
effect of 41 types of infrastructure weighted by their estimated general relative
impact on forests (Supplementary Table 3), agriculture (A) weighted by crop
intensity (indicated by irrigation levels), and recent deforestation over the past 18
years (H; excluding deforestation from ﬁre, see Discussion). Speciﬁcally, for pixel i:
whereby the values of βwere selected so that the median of the non-zero values for
each component was 0.75. This use of exponents is a way of scaling variables with
non-commensurate units so that they can be combined numerically, while also
ensuring that the measure of observed pressure is sensitive to change (increase or
decrease) in the magnitude of any of the three components, even at large values of
I, A, or H. This is an adaptation of the Human Footprint methodology62. See
Supplementary Note 3 for further details.
Inferred human pressures. Inferred pressures are the diffuse effects of a set of
processes for which directly observed datasets do not exist, that include micro-
climate and species interactions relating to the creation of forest edges63 and a
variety of intermittent or transient anthropogenic pressures such as selective log-
ging, fuelwood collection, hunting; spread of ﬁres and invasive species, pollution,
and livestock grazing64–66. We modeled the collective, cumulative impacts of these
inferred effects through their spatial association with observed human pressure in
nearby pixels, including a decline in effect intensity according to distance, and
partitioning into stronger short-range and weaker long-range effects. The inferred
pressure (P′) on pixel ifrom source pixel jis:
is the weighting given to the modiﬁcation arising from short-range
pressure, as a function of distance from the source pixel, and v
is the weighting
given to the modiﬁcation arising from long-range pressures.
Short-range effects include most of the processes listed above, which together
potentially affect most biophysical features of a forest, and predominate over
shorter distances. In our model, they decline exponentially, approach zero at 3 km,
and are truncated to zero at 5 km (see Supplementary Note 4).
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where αis a constant set to ensure that the sum of the weights across all pixels in
the range is 1.85 (see below), λis a decay constant set to a value of 1 (see67 and
other references in Supplementary Note 4) and d
is the Euclidean distance
between the centers of pixels iand jexpressed in units of km.
Long-range effects include over-exploitation of high socio-economic value animals
and plants, changes to migration and ranging patterns, and scattered ﬁre and
pollution events. We modeled long-range effects at a uniform level at all distances
below 6 km and they then decline linearly with distance, conservatively reaching zero
at a radius of 12 km65,68 (and other references in Supplementary Note 4):
=6½for 6km <di;j≤12km
where γis a constant set to ensure that the sum of the weights across all pixels in the
range is 0.15 and d
is the Euclidean distance between the centers of pixels iand j,
expressed in kilometers.
The form of the weighting functions for short- and long-range effects and the sum
of the weights (α+γ) were speciﬁed based on a hypothetical reference scenario where
a straight forest edge is adjacent to a large area with uniform human pressure, and
ensuring that in this case total inferred pressure immediately inside the forest edge is
equal to the pressure immediately outside, before declining with distance. γis set to
0.15 to ensure that the long-range effects conservatively contribute no more than 5%
to the ﬁnal index in the same scenario, based on expert opinion and supported e.g.,
Berzaghi et al.69 regarding the approximate level of impact on values that would be
affected by severe defaunation and other long-range effects.
The aggregate effect from inferred pressures (Q) on pixel ifrom all npixels
within range (j=1to j=n) is then the sum of these individual, normalized,
distance-weighted pressures, i.e.,
Loss of forest connectivity. Average connectivity of forest around a pixel was
quantiﬁed using a method adapted from Beyer et al.70. The connectivity C
pixel isurrounded by n other pixels within the maximum radius (numbered j=1,
2…n) is given by:
is the forest extent is a binary variable indicating if forested (1) or not (0)
is the weight assigned to the distance between pixels iand j.G
normalized Gaussian curve, with σ=20 km and distribution truncated to zero at
4σfor computational convenience (see Supplementary Note 2). The large value of σ
captures landscape connectivity patterns operating at a broader scale than pro-
cesses captured by other data layers. C
ranges from 0 to 1 (C
Current Conﬁguration (CC
) of forest extent in pixel i was calculated using the
ﬁnal forest extent map and compared to the Potential Conﬁguration (PC) of forest
extent without extensive human modiﬁcation, so that areas with naturally low
connectivity, e.g., coasts and natural vegetation mosaics, are not penalized. PC was
calculated from a modiﬁed version of the map of Laestadius et al38. and resampled
to 300 m resolution (see Supplementary Note 2 for details). Using these two
measures, we calculated Lost Forest Conﬁguration (LFC) for every pixel as:
Values of CC
> 1 are assigned a value of 1 to ensure that LFC is not
sensitive to apparent increases in forest connectivity due to inaccuracy in estimated
potential forest extent –low values represent least loss, high values greatest loss
Calculating the Forest Landscape Integrity Index. The three constituent metrics,
LFC, P, and Q, all represent increasingly modiﬁed conditions the larger their values
become. To calculate a forest integrity index in which larger values represent less
degraded conditions we, therefore, subtract the sum of those components from a
ﬁxed large value (here, 3). Three was selected as our assessment indicates that
values of LFC +P+Q of 3 or more correspond to the most severely degraded
areas. The metric is also rescaled to a convenient scale (0-10) by multiplying by an
arbitrary constant (10/3). The FLII for forest pixel iis thus calculated as:
ranges from 0 to 10, forest areas with no modiﬁcation detectable using
our methods scoring 10 and those with the most scoring 0.
Illustrative forest integrity classes. Whilst a key strength of the index is its
continuous nature, the results can also be categorized for a range of purposes. In
this paper three illustrative classes were deﬁned, mapped, and summarized to give
an overview of broad patterns of integrity in the world’s forests. The three cate-
gories were deﬁned as follows.
High Forest Integrity (scores ≥9.6) Interiors and natural edges of more or less
unmodiﬁed naturally regenerated (i.e., non-planted) forest ecosystems, comprised
entirely or almost entirely of native species, occurring over large areas either as
continuous blocks or natural mosaics with non-forest vegetation; typically little
human use other than low-intensity recreation or spiritual uses and/or low-
intensity extraction of plant and animal products and/or very sparse presence of
infrastructure; key ecosystem functions such as carbon storage, biodiversity, and
watershed protection and resilience expected to be very close to natural levels
(excluding any effects from climate change) although some declines possible in the
most sensitive elements (e.g., some high value hunted species).
Medium Forest Integrity (scores > 6.0 but <9.6) Interiors and natural edges of
naturally regenerated forest ecosystems in blocks smaller than their natural extent
but large enough to have some core areas free from strong anthropogenic edge
effects (e.g., set-asides within forestry areas, fragmented protected areas),
dominated by native species but substantially modiﬁed by humans through a
diversity of processes that could include fragmentation, creation of edges and
proximity to infrastructure, moderate or high levels of extraction of plant and
animal products, signiﬁcant timber removals, scattered stand-replacement events
such as swidden and/or moderate changes to ﬁre and hydrological regimes; key
ecosystem functions such as carbon storage, biodiversity, watershed protection and
resilience expected to be somewhat below natural levels (excluding any effects from
Low Fores t Integrity (score ≤6.0): Diverse range of heavily modiﬁed and
often internally fragmented ecosystems dominated by trees, including (i)
naturally regenerated forests, either in the interior of blocks or at edges, that
have experienced multiple strong human pressures, which may include
frequent stand-replacing events, sufﬁcient to greatly simplify the structure and
species composition and possibly result in signiﬁcant presence of non-native
species, (ii) tree plantations and, (iii) agroforests; in all cases key ecosystem
functions such as carbon storage, biodiversity, watershed protection and
resilience expected to be well below natural levels (excluding any effects from
The numerical category boundaries were derived by inspecting FLII scores for a
wide selection of benchmark locations whose forest integrity according to the
category deﬁnitions was known to the authors, see text S6 and Table S4.
Protected areas analysis. Data on protected area location, boundary, and year of
the inscription were obtained from the February 2018 World Database on Protected
Areas71. Following similar global studies e.g.72, we extracted protected areas from
the WDPA database by selecting those areas that have a status of “designated”,
“inscribed”,or“established”, and were not designated as UNESCO Man and Bio-
sphere Reserves. We included only protected areas with detailed geographic infor-
mation in the database, excluding those represented as a point only. To assess the
integrity of the protected forest, we extracted all 300 m forest pixels that were at least
50% covered by a formally protected area and measured the average FLII score.
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
The authors declare that all data supporting the ﬁndings of this study are available at
www.forestlandscapeintegrity.com. The datasets used to develop the Forest Landscape
Integrity Index can be found at the following websites: tree cover and loss http://
earthenginepartners.appspot.com/science-2013-global-forest, tree cover loss driver
potential forest cover https://data.globalforestwatch.org/datasets/potential-forest-
coverage ESA-CCI Land Cover https://maps.elie.ucl.ac.be/CCI/viewer/index.php Open
Street Maps https://www.openstreetmap.org, croplands https://lpdaac.usgs.gov/news/
release-of-gfsad-30-meter-cropland-extent-products/, surface water https://global-
surface-water.appspot.com/, protected areas https://www.protectedplanet.net/en.
The code for Google Earth Engine is available upon any reasonable request.
Received: 18 May 2020; Accepted: 13 October 2020;
Published online: 08 December 2020
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We thank Peter Potapov, Dmitry Aksenov, and Matthew Hansen for comments and
advice. The research for this paper was in part funded by the John D. and Catherine T.
MacArthur Foundation, Trillion Trees (a joint venture between BirdLife International,
Wildlife Conservation Society, and WWF-UK), and other generous donors. It was also
ﬁnancially supported by UKAID from the UK government via the Forest Governance,
Markets, and Climate Programme.
Conceived and designed the study: H.S.G., T.E., and J.E.M.W., collected data and
developed the model: A.D., H.S.G., T.D.E., H.B., R.S., analyzed and interpreted the
results: A.D., H.S.G., T.E., H.L.B., R.S., K.R.J., J.C.R., J.E.M.W., wrote draft manuscript:
H.S.G., T.D.E., and J.E.M.W., contributed to the writing of the manuscript: A.D., K.R.J.,
H.L.B., R.S., J.W., J.C.R., J.G.R., M.C., T.C., H.M.C., A.D., P.R.E., J.E., P.F., E.G., SG, A.H.,
E.H., P.J., S.J., A.K., P.L., W.F.L., S.L., M.L., Y.M., S.M., M.M., R.M., NJ.M., H.P., J.R., S.S.,
C.S., J.S., A.S., B.S., T.S., E.S., R.T., T.T., R.T., O.V., P.V., S.W.
The authors declare no competing interests.
Supplementary information is available for this paper at https://doi.org/10.1038/s41467-
Correspondence and requests for materials should be addressed to H.S.G.
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ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-19493-3
10 NATURE COMMUNICATIONS | (2020)11:5978 | https://doi.org/10.1038/s41467-020-19493-3 | www.nature.c om/naturecommunications
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