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Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity


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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 km² of 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.
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Anthropogenic modication 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 modication remain poorly quantied 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 modication. 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. OPEN
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
modication of remaining forests, which reduces ecosys-
tem integrity and diminishes many of the benets that these
forests provide2,3. This is worrying since modication is poten-
tially as signicant 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 scientic
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 Desertica-
tion (UNCCD), and Framework Convention on Climate Change
Ecosystem integrity is foundational to all three of the Rio
Conventions (UNFCCC, UNCCD, CBD)6.Asdened by Parrish
et al.7, it is essentially the degree to which a system is free from
anthropogenic modication of its structure, composition, and
function. Such modication causes the reduction of many eco-
system benets, and is often also a precursor to outright
deforestation8,9. Forests largely free of signicant modication
(i.e., forests having high ecosystem integrity), typically provide
higher levels of many forest benets than modied 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
biodiversity1518. Industrial-scale logging, fragmentation by
infrastructure, farming (including cropping and ranching) and
urbanization, as well as less visible forms of modication such as
over-hunting, wood fuel extraction, and changed re or hydro-
logical regimes19,20, all degrade the degree to which forests still
support these benets, as well as their long-term resilience to
climate change10. There can be trade-offs, however, between the
benets best provided by less-modied forests (e.g., regulatory
functions such as carbon sequestration) and those production
services that require some modication (e.g., timber production).
These trade-offs can, at times, result in disagreement among
stakeholders as to which forest benets should be prioritized21.
In recent years, easily accessible satellite imagery and new
analytical approaches have improved our ability to map and
monitor forest extent globally2224. However, while progress has
been made in developing tools for assessment of global forest
losses and gains, consistent monitoring of the degree of forest
modication has proved elusive25,26.
Technical challenges include the detection of low intensity and
unevenly distributed forest modication, the wide diversity of
changes that comprise forest modication, 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 forests2730.
Some binary measures of forest modication, 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 modication within remaining forests,
which we aimed to do with this assessment.
Human activities inuence the integrity of forests at multiple
spatial scales, including intense, localized modications 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 conguration that alter landscape-
level connectivity. Previous studies have quantied several of these
aspects individually2729, but there is a need to integrate them to
measure and map the overall degree of modication considering
these landscape-level anthropogenic inuences at each site. Here, we
integrate data on forest extent dened 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 modication 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 Earths 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,andlow) while noting that the underlying continuous
index enables much ner 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 modication caused by human activity is both highly
pervasive and highly variable across the globe (Fig. 2). We found
Observed human
human pressure
Forest Landscape
Integrity Index
Loss of forest
Pressures that can be directly
Pressures associated with
observed pressures
Ratio of current to potential
forest connectivity
Tree cover
Infrastructure Potential
Change in
Tree cover
Apply to current
forest extent
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 ner 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 dene 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 classied as having high
integrity (Fig. 3;Table1).Moreover,eveninthiscategoryof
high integrity 36% still showed at least a small degree of human
modication. The remaining 59% (25.6 million km2)ofthe
forest was classied 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
dened by33 not a single biogeographical realm of the world
had more than half of its forests in the high category (Fig. 3;
Table 1).
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 signicant
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-Pacic, the largest
tracts of forest with high integrity are in New Guinea. Smaller but
still very signicant 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
Mekong subregion.
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
(Fig. 3;Table1).
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,
A1 A2
Low (0) High (10)
B1 B2
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 A1C1 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 modied (Table 2). Even though
they are quite modied, 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 modication, and changes in forest
connectivity, our analysis paints a sobering picture of the extent
of human impacts on the worlds 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 quantication 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
modication 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
B -Palearctic
C -Oceania
D -Neotropic
E -Afrotropic
A -Nearctic
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 (ag). The size of the pie charts indicates the relative size of the forests
within each realm (ag), and hshows all the worlds forest combined.
Table 1 Brief title: Forest Landscape Integrity Index scores for each biogeographic realm.
Biogeographic realm Historical
forest area
forest area
Proportion of
FLII High Medium Low
(9.610) (69.6) (06)
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 modication
and the resulting losses of forest benets.
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
types3436, 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 benets5. 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 rened 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
Russian (9.02)
Canada (8.99)
Brazil (7.52)
United States (6.65)
China (7.14)
Dem Rep of the Congo (7.56)
Australia (7.22)
Uganda (4.36)
Guinea (4.9)
Nigeria (6.2)
Ethiopia (7.16)
Cote d'lvoire (3.64)
South Sudan (9.45)
Madagascar (4.63)
Gabon (9.07)
Rep of the Congo (8.89)
Cameroon (8)
Zambia (7.5)
Tanzania (7.13)
Mozambique (6.93)
Central African Rep (9.28)
Angola (8.35)
Suriname (9.39)
Guyana (9.58)
Ecuador (7.66)
Paraguay (6.39)
Chile (7.37)
Argentina (7.21)
Venezuela (8.78)
Mexico (6.82)
Bolivia (8.47)
Peru (8.86)
Colombia (8.26)
Papua New Guinea (8.84)
New Zealand (7.12)
Sweden (5.35)
Norway (6.98)
France (4.52)
Germany (2.28)
Finland (5.08)
Poland (2.24)
Spain (4.23)
Ukraine (3.3)
Italy (3.65)
Burma (7.18)
Indonesia (6.6)
India (7.09)
Japan (5.8)
Malaysia (5.01)
Thailand (6)
Philippines (5.91)
Laos (5.59)
Vietnam (5.35)
Turkey (6.39)
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 countrys
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medium integrity, with > 50% falling in these two broad cate-
gories in every biogeographical realm. These levels of human
modication 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-modied
forests should focus on securing any remaining fragments of
forests in good condition, proactively protecting those forests
most vulnerable to further modication8and planning where
restoration efforts might be most effective3941. 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 benets 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 modication 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 modication10,47. We urgently
need SMART (specic, 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 specic 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 exibility 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-dened 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 countrys 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
socio-cultural landscapes).
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 Earths
remaining forests is likely even more severe than our ndings
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 9.610) Medium (score 69.6) Low (score 06)
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
IV (habitat/species
management area)
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
reected 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 difcult 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 res 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 res from natural res at a
global scales58 we have taken a strongly conservative approach to
re in our calculations, treating all tree cover loss in 10 km pixels
where re 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 re return fre-
quencies, or causing re in previously re-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 quantiable processes over
the recent past (since 2000). In some areas modication that
occurred prior to this (e.g., historical logging) is not detectable by
our methods but may have inuenced 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
denition 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
difcult 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 signicant 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 denitions and
the relative weights of the global parameters we use can be
adjusted to t 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, specically: 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 reecting 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 20012018,
except for losses categorized by Curtis and colleagues24 as those likely to be tem-
porary in nature (i.e., those due to re, 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 re, see Discussion). Specically, for pixel i:
Pi¼exp β1Ii
þexp β2Ai
þexp β3Hi
 ð1Þ
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 res and invasive species, pollution,
and livestock grazing6466. 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:
 ð2Þ
where w
is the weighting given to the modication arising from short-range
pressure, as a function of distance from the source pixel, and v
is the weighting
given to the modication 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).
wi;j¼αexpðλdi;jÞ½for di;j5km
wi;j¼0½for di;j>5kmð3Þ
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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 re 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):
vi;j¼γ½for di;j6km
vi;j¼γ12 di;j
=6½for 6km <di;j12km
vi;j¼0½for 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 specied 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 nal 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
quantied 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,
2n) is given by:
 ð6Þ
where F
is the forest extent is a binary variable indicating if forested (1) or not (0)
and G
is the weight assigned to the distance between pixels iand j.G
uses a
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 Conguration (CC
) of forest extent in pixel i was calculated using the
nal forest extent map and compared to the Potential Conguration (PC) of forest
extent without extensive human modication, so that areas with naturally low
connectivity, e.g., coasts and natural vegetation mosaics, are not penalized. PC was
calculated from a modied 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 Conguration (LFC) for every pixel as:
ðÞ ð7Þ
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 modied 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
xed 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:
FLIIi¼10=3½ð3minð3;½PiþQiþLFCiÞÞ ð8Þ
where FLII
ranges from 0 to 10, forest areas with no modication 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 dened, mapped, and summarized to give
an overview of broad patterns of integrity in the worlds forests. The three cate-
gories were dened as follows.
High Forest Integrity (scores 9.6) Interiors and natural edges of more or less
unmodied 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 modied 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, signicant timber removals, scattered stand-replacement events
such as swidden and/or moderate changes to re 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
climate change).
Low Fores t Integrity (score 6.0): Diverse range of heavily modied 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, sufcient to greatly simplify the structure and
species composition and possibly result in signicant 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
climate change).
The numerical category boundaries were derived by inspecting FLII scores for a
wide selection of benchmark locations whose forest integrity according to the
category denitions 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,orestablished, 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.
Data availability
The authors declare that all data supporting the ndings of this study are available at The datasets used to develop the Forest Landscape
Integrity Index can be found at the following websites: tree cover and loss http://, tree cover loss driver,
potential forest cover
coverage ESA-CCI Land Cover Open
Street Maps, croplands
release-of-gfsad-30-meter-cropland-extent-products/, surface water https://global-, protected areas
Code availability
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
nancially supported by UKAID from the UK government via the Forest Governance,
Markets, and Climate Programme.
Author contributions
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.
Competing interests
The authors declare no competing interests.
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... We used the global Forest Landscape Integrity Index (FLII) (Grantham et al., 2020), to test if cumulative impacts of anthropogenic forest modification affect nest survival. The FLII integrates four spatial datasets on: (1) current forest extent, (2) observed anthropogenic pressures at <3 km and <12 km scales, (3) inferred anthropogenic pressures associated with forest edges, and (4) loss of forest connectivity (Grantham et al., 2020). ...
... We used the global Forest Landscape Integrity Index (FLII) (Grantham et al., 2020), to test if cumulative impacts of anthropogenic forest modification affect nest survival. The FLII integrates four spatial datasets on: (1) current forest extent, (2) observed anthropogenic pressures at <3 km and <12 km scales, (3) inferred anthropogenic pressures associated with forest edges, and (4) loss of forest connectivity (Grantham et al., 2020). FLII scores (range 0-10) are at a pixel resolution of 300 m and represent three broad categories of forest integrity: low (≤6.0); ...
... FLII scores (range 0-10) are at a pixel resolution of 300 m and represent three broad categories of forest integrity: low (≤6.0); medium (>6.0 and <9.6); and high integrity (≥9.6) (Grantham et al., 2020). We used the FLII value for each pixel around each nest as an empirical index of local forest quality. ...
Conservation assessments of threatened species are often limited by scarce data and parameter uncertainty. Predictive models, designed to incorporate this uncertainty, may be the only tool available to inform conservation assessments for data‐deficient species, but they are used surprisingly rarely for this purpose. The swift parrot Lathamus discolor is the only critically endangered bird to be listed in Australia based on population viability analysis (PVA). We aimed to evaluate the accuracy of the 2015 conservation assessment, which used sparse information, by incorporating new detailed and long‐term data. First, we updated a range of life history parameter estimates, and then we repeated the same PVA as per the original conservation assessment. This process confirmed our earlier finding that swift parrot nests were more likely to survive in places with high mature forest cover. We identify that high forest landscape integrity and abundant hollow‐bearing trees best predict nest daily survival rates. Based on the updated PVA, we predict a 92.3% population decline over three generations (11 years). This supported the predictions of the original conservation assessment, and the main benefit of the additional data was improved confidence in projections (the magnitude and direction of the population decline were similar between the original and updated PVAs). Our results demonstrate that meaningful trends can be inferred for species with imperfect information about their life history. Using predictive models like PVAs can help managers identify which life history parameters impact most on demographic trends. This information can guide targeted data collection so that ‘draft’ models can be later updated to improve certainty around population predictions. Demographic models may form the basis of conservation assessments for species with limited data but are relatively underused tools. We evaluate a conservation assessment for a critically endangered bird by repeating a population viability analysis (PVA) with more than twice the data. Our updated PVA results support the predictions of the accurate but less precise original assessment. We highlight that decline is faster than previously estimated (92.3% over 11 years) and that nest survival is predicted by forest landscape integrity and hollow‐bearing trees. We conclude that PVAs based on sparse or imperfect data can predict meaningful trends for threatened species.
... OpenStreetMap 1 (OSM) is a community-driven effort to provide free and open access to global spatial data. Volunteered geographic information, which leverages local knowledge to map the geometries and attributes of both natural and urban features, is widely used for humanitarian crises 2,3 , city planning 4 , scientific studies 5,6 , and navigation systems 7 . For example, OSM building footprints as well as streets, roads, rivers, and basic community services have been used to support urban planning and land administration, especially in parts of the world with little traditional data availability 8 . ...
... Other branches are longer, up to a length of 17. An important branch of this tree is the branch in which the decision tree learns that buildings that are (1) small, (2) of non-residential land use, (3) are not within a 60 m range of Category 1 (residential) roads, (4) do not have a building name, (5) do not have 'misc' land use, (6) are not within a 60 range of a Category 4 (service) road, (7) are not have the value 'miscellaneous' in the 'building' tag, (8) does not have the value 'commercial' in the building tag, (9) does not have the value 'amenity' in the building tag, and (10) does not have the value 'office' in the building tag tend towards residential buildings (710 non-residential and 121,496 residential buildings). Based on the Gini index, the decision tree decides this node to be a leaf node, thus classifying such buildings as non-residential buildings. ...
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Having accurate building information is paramount for a plethora of applications, including humanitarian efforts, city planning, scientific studies, and navigation systems. While volunteered geographic information from sources such as OpenStreetMap (OSM) has good building geometry coverage, descriptive attributes such as the type of a building are sparse. To fill this gap, this study proposes a supervised learning-based approach to provide meaningful, semantic information for OSM data without manual intervention. We present a basic demonstration of our approach that classifies buildings into either residential or non-residential types for three study areas: Fairfax County in Virginia (VA), Mecklenburg County in North Carolina (NC), and the City of Boulder in Colorado (CO). The model leverages (i) available OSM tags capturing non-spatial attributes, (ii) geometric and topological properties of the building footprints including adjacent types of roads, proximity to parking lots, and building size. The model is trained and tested using ground truth data available for the three study areas. The results show that our approach achieves high accuracy in predicting building types for the selected areas. Additionally, a trained model is transferable with high accuracy to other regions where ground truth data is unavailable. The OSM and data science community are invited to build upon our approach to further enrich the volunteered geographic information in an automated manner.
... Online tools are being developed to enable users to tailor the global assumptions, weights, and criteria. [18]. ...
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... Despite the critical role of equatorial forests in the global carbon cycle and biodiversity conservation, recent decades have seen extensive tropical deforestation and degradation, with losses driven largely by logging and agricultural expansion [1][2][3]. Human-modified forests and secondary growth forests now account for the majority of forest cover [4]. Forest restoration is intended to mitigate damage from anthropogenic impacts by reinstating tree cover where forests occurred naturally. ...
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Current policy is driving renewed impetus to restore forests to return ecological function, protect species, sequester carbon and secure livelihoods. Here we assess the contribution of tree planting to ecosystem restoration in tropical and sub-tropical Asia; we synthesize evidence on mortality and growth of planted trees at 176 sites and assess structural and biodiversity recovery of co-located actively restored and naturally regenerating forest plots. Mean mortality of planted trees was 18% 1 year after planting, increasing to 44% after 5 years. Mortality varied strongly by site and was typically ca 20% higher in open areas than degraded forest, with height at planting positively affecting survival. Size-standardized growth rates were negatively related to species-level wood density in degraded forest and plantations enrichment settings. Based on community-level data from 11 landscapes, active restoration resulted in faster accumulation of tree basal area and structural properties were closer to old-growth reference sites, relative to natural regeneration, but tree species richness did not differ. High variability in outcomes across sites indicates that planting for restoration is potentially rewarding but risky and context-dependent. Restoration projects must prepare for and manage commonly occurring challenges and align with efforts to protect and reconnect remaining forest areas. The abstract of this article is available in Bahasa Indonesia in the electronic supplementary material. This article is part of the theme issue ‘Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration’.
With the concern for biodiversity conservation and ecological sustainability, the traditional method of assessing conservation management based on single elements such as timber production, stand structure, and forest area is no longer applicable. Especially for China, where the primary goal of national park conservation management is to “protect and maintain the authenticity and integrity of natural ecosystems”, it is necessary to construct a set of indicators that can comprehensively evaluate the effectiveness of natural ecosystem conservation management. Therefore, in this study, we take Jiuzhaigou and Giant Panda National Park, in Sichuan, China, as examples, and construct a set of ecological integrity evaluation indices system from the perspective of attributes of forest ecosystems. Initially, we screened the candidate indicators based on the coefficient of variation, distribution range, and redundancy analysis and identified 16 indicators as the evaluation indices of ecological integrity. Next, the model fit and the interrelationship of the components of the ecological integrity model were verified using a combination of structural equation modeling and actual data. The model validation results showed that: (1) CMIN/DF = 1.66, RMSEA = 0.058, GFI = 0.954, and other model fit metrics have reached the best reference standard, indicating that the ecological integrity model overall fit is high. (2) In the parameter estimation results of the model observed and latent variables, the ecological integrity model explained 96%, 85% and 89% of the variance in species composition, stand structure and ecological processes, respectively, indicating that the evaluation indices selected can effectively predict the ecological integrity of forests. In addition, we used the factor loadings in the structural equation model to calculate the weights of the indices and used the weighted sum to calculate the ecological integrity. A one-way ANOVA was conducted to compare the differences among the ecological integrity of forests of different origins, forest types and life forms. The relative importance and linear regression were used to elucidate the main factors affecting forest ecological integrity. The results showed that (1) except for the scrub community, the mean value of forest ecological integrity was >0.45, which was in the middle-to-upper level. (2) the ecological integrity of forests of different origins and different forest types showed a changing pattern: natural forest > plantation forest, mixed forest > pure forest. (3) Stand age and elevation strongly influenced the ecological integrity of forests, with relative contribution rates of 64.54% and 11.88%, respectively.
Forested Indigenous lands typically maintain high levels of forest integrity. A new study found that this is particularly true for Indigenous lands within tropical protected areas. Better recognising the importance of Indigenous lands is key to new global conservation goals.
Generation the Distance Matrix (DMx) is an important aspect that influences the correct solution of the routing problem in the dynamic variant. In the case of a frequent changing of points number and location, a continuous and effective update of the data is required, e.g., from more and more popular services such as Mapping APIs. The time-consuming nature of this process, which may extend the planning process, was emphasized. The article discusses the possibility of estimating the distance matrix based on the correction of the “haversine” distance. Method for the generation and updating of the DMx was proposed. The influence of update progress on some optimization algorithms was investigated. The research was carried out on the example of the real VRP problem. It was found that even a partial DMx update can significantly reduce the discrepancy between the VRP optimization results.
Nowadays, younger generation is much more exposed to technology than previous generations used to. The recent advances in artificial intelligence (AI) and particularly natural language processing (NLP) and understanding (NLU) make it possible to reinforce and widespread the adoption of AI chatbots in education not only to help students in their administrative affairs or in academic advising but also in assisting them and monitoring their performance during their learning experience. This paper presents a review of the different methods and tools devoted to the design of chatbots with an emphasis on their use and challenges in the education field. Additionally, this paper focuses on language-related challenges and obstacles that hinder the implementation of English, Arabic, and other languages of chatbots. To show how AI chatbots benefit education, a use case is described where chatbot has been used to assess students’ feedback regarding a machine learning course evaluation.
The study focused on assessing the forest health dynamics of Similipal Tiger Reserve (STR), Odisha, India, covering an area of 2707 sq km, using Harmonic optimization, Autocovariance, Autocorrelation and Autoregressive (AR) model in Google Earth Engine (GEE) cloud. Landsat 7/8 surface reflectance tier 1 images were acquired during 2000–2019 for three forest cover types such as dense forest, moderately dense forest, and open forest. The forest attribute through long-term NDVI time series data was estimated from stacked Landsat images through harmonic regression. Forest phenology response to climatic variables such as NDVI was evaluated by CHIRPS precipitation measure through the autocovariance and autocorrelation analysis. Then AR model explained the changes in harmonic parameters with the amplitude and phase criteria for predicting the fitting values. In this analysis, the Northern part of the STR region found a very good relationship with the NDVI and precipitation level. Long-term lag periods had sound correlation between precipitation and NDVI. The highest lag 5 days correlation was 0.28 for the open forest and highest correlation for lag 30 was 0.62 also for open forest. Our research successfully utilized and recommended NDVI phenology distribution technique for forest cover monitoring and mapping in the RS-based GEE platform.
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The forests of Central Africa contain some of Earth's few remaining intact forests. These forests are increasingly threatened by infrastructure development, agriculture, and unsustainable extraction of natural resources (e.g. minerals, bushmeat, and timber), all of which is leading to deforestation and forest degradation, particularly defaunation, and hence causing declines in biodiversity and a significant increase in carbon emissions. Given the pervasive nature of these threats, the global importance of Central African forests for biodiversity conservation, and the limited resources for conservation and sustainable management, there is a need to identify where the most important areas are to orientate conservation efforts. We developed a novel approach for identifying spatial priorities where conservation efforts can maximize biodiversity benefits within Central Africa's most intact forest areas. We found that the Democratic Republic of Congo has the largest amount of priority areas in the region, containing more than half, followed by Gabon, the Republic of Congo and Cameroon. We compared our approach to one that solely prioritizes forest intactness and one that aims to achieve only biodiversity representation objectives. We found that when priorities are only based on forest intactness (without considering biodiversity representation), there are significantly fewer biodiversity benefits and vice versa. We therefore recommend multi-objective planning that includes biodiversity representation and forest intactness to ensure that both objectives are maximized. These results can inform various types of conservation strategies needed within the region, including land-use planning, jurisdictional REDD + initiatives, and performance related carbon payments, protected area expansion, community forest management, and forest concession plans.
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Intact Forest Landscapes (IFLs) are critical strongholds for the environmental services that they provide, not least for their role in climate protection. On the basis of information about the distributions of IFLs and Indigenous Peoples’ lands, we examined the importance of these areas for conserving the world's remaining intact forests. We determined that at least 36% of IFLs are within Indigenous Peoples’ lands, making these areas crucial to the mitigation action needed to avoid catastrophic climate change. We also provide evidence that IFL loss rates have been considerably lower on Indigenous Peoples’ lands than on other lands, although these forests are still vulnerable to clearing and other threats. World governments must recognize Indigenous Peoples’ rights, including land tenure rights, to ensure that Indigenous Peoples play active roles in decision‐making processes that affect IFLs on their lands. Such recognition is critical given the urgent need to reduce deforestation rates in the face of escalating climate change and global biodiversity loss.
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Human activities are altering natural areas worldwide. While our ability to map these activities at fine scales is improving, a simplistic binary characterization of habitat and non‐habitat with a focus on change in habitat extent has dominated conservation assessments across different spatial scales. Here, we provide a metric that captures both habitat loss, quality and fragmentation effects which, when combined, we call intactness. We identify nine categories of intactness of the world's terrestrial ecoregions based on changes in intactness across a 16‐year period. We found that highly impacted and degraded categories are predominant (74%) and just 6% of ecoregions are on improving trajectories. It is essential that management of degrading processes be targeted in international agendas in order to ensure that Earth's remaining intact ecosystems are effectively conserved and restored in order to achieve effective conservation outcomes.
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Remotely sensed maps of global forest extent are widely used for conservation assessment and planning. Yet, there is increasing recognition that these efforts must now include elements of forest quality for biodiversity and ecosystem services. Such data are not yet available globally. Here we introduce two data products, the Forest Structural Condition Index (SCI) and the Forest Structural Integrity Index (FSII), to meet this need for the humid tropics. The SCI integrates canopy height, tree cover, and time since disturbance to distinguish short, open-canopy, or recently deforested stands from tall, closed-canopy, older stands typical of primary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar. The FSII overlays a global index of human pressure on SCI to identify structurally complex forests with low human pressure, likely the most valuable for maintaining biodiversity and ecosystem services. These products represent an important step in maturation from conservation focus on forest extent to forest stands that should be considered “best of the last” in international policy settings.
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Reducing the rate of global biodiversity loss is a major challenge facing humanity¹, as the consequences of biological annihilation would be irreversible for humankind2–4. Although the ongoing degradation of ecosystems5,6 and the extinction of species that comprise them7,8 are now well-documented, little is known about the role that remaining wilderness areas have in mitigating the global biodiversity crisis. Here we model the persistence probability of biodiversity, combining habitat condition with spatial variation in species composition, to show that retaining these remaining wilderness areas is essential for the international conservation agenda. Wilderness areas act as a buffer against species loss, as the extinction risk for species within wilderness communities is—on average—less than half that of species in non-wilderness communities. Although all wilderness areas have an intrinsic conservation value9,10, we identify the areas on every continent that make the highest relative contribution to the persistence of biodiversity. Alarmingly, these areas—in which habitat loss would have a more-marked effect on biodiversity—are poorly protected. Given globally high rates of wilderness loss¹⁰, these areas urgently require targeted protection to ensure the long-term persistence of biodiversity, alongside efforts to protect and restore more-degraded environments.
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Large herbivores, such as elephants, can have important effects on ecosystems and biogeochemical cycles. Yet, the influence of elephants on the structure, productivity and carbon stocks in Africa’s rainforests remain largely unknown. Here, we quantify those effects by incorporating elephant disturbance in the Ecosystem Demography model, and verify the modelled effects by comparing them with forest inventory data from two lowland primary forests in Africa. We find that the reduction of forest stem density due to the presence of elephants leads to changes in the competition for light, water and space among trees. These changes favour the emergence of fewer and larger trees with higher wood density. Such a shift in African’s rainforest structure and species composition increases the long-term equilibrium of aboveground biomass. The shift also reduces the forest net primary productivity, given the trade-off between productivity and wood density. At a typical density of 0.5 to 1 animals per km², elephant disturbances increase aboveground biomass by 26–60 t ha⁻¹. Conversely, the extinction of forest elephants would result in a 7% decrease in the aboveground biomass in central African rainforests. These modelled results are confirmed by field inventory data. We speculate that the presence of forest elephants may have shaped the structure of Africa’s rainforests, which probably plays an important role in differentiating them from Amazonian rainforests.
Humans have influenced the terrestrial biosphere for millennia, converting much of Earth’s surface to anthropogenic land uses. Nevertheless, there are still some ecosystems that remain free from significant direct human pressure (and as such, considered ‘‘intact’’), thereby providing crucial habitats for imperilled species and maintaining the ecosystem processes that underpin planetary life-support systems. Our analyses show that, between 2000 and 2013, 1.9 million km2—an area approximately the size of Mexico—of land relatively free of human disturbance became highly modified. This loss has profound implications for the biodiversity that require intact land for their continued survival and for people who rely on the services that intact ecosystems provide. Our results showcase the urgent need to safeguard Earth’s last intact ecosystems and suggest that greater efforts are needed to ameliorate human pressures.
The conservation community must be able to track countries’ progress in protecting wetlands, reefs, forests and more, argue James Watson and colleagues. The conservation community must be able to track countries’ progress in protecting wetlands, reefs, forests and more, argue James Watson and colleagues.
The interventions required to reduce deforestation differ widely across the tropics