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Human activities, especially conversion and degradation of habitats, are causing global biodiversity declines. How local ecological assemblages are responding is less clear[mdash]a concern given their importance for many ecosystem functions and services. We analysed a terrestrial assemblage database of unprecedented geographic and taxonomic coverage to quantify local biodiversity responses to land use and related changes. Here we show that in the worst-affected habitats, these pressures reduce within-sample species richness by an average of 76.5%, total abundance by 39.5% and rarefaction-based richness by 40.3%. We estimate that, globally, these pressures have already slightly reduced average within-sample richness (by 13.6%), total abundance (10.7%) and rarefaction-based richness (8.1%), with changes showing marked spatial variation. Rapid further losses are predicted under a business-as-usual land-use scenario; within-sample richness is projected to fall by a further 3.4% globally by 2100, with losse
ARTICLE doi:10.1038/nature14324
Global effects of land use on local
terrestrial biodiversity
Tim Newbold
*, Lawrence N. Hudson
*, Samantha L. L. Hill
, Sara Contu
, Igor Lysenko
, Rebecca A. Senior
{, Luca Bo
Dominic J. Bennett
{, Argyrios Choimes
, Ben Collen
, Julie Day
{, Adriana De Palma
, Sandra Dı
Susy Echeverria-London
, Melanie J. Edgar
, Anat Feldman
, Morgan Garon
, Michelle L. K. Harrison
, Tamera Alhusseini
Daniel J. Ingram
{, Yuval Itescu
, Jens Kattge
, Victoria Kemp
, Lucinda Kirkpatrick
{, Michael Kleyer
David Laginha Pinto Correia
, Callum D. Martin
, Shai Meiri
, Maria Novosolov
, Yuan Pan
, Helen R. P. Phillips
Drew W. Purves
, Alexandra Robinson
, Jake Simpson
, Sean L. Tuck
, Evan Weiher
, Hannah J. White
{, Robert M. Ewers
Georgina M. Mace
¨rn P. W. Scharlemann
& Andy Purvis
Human activities, especially conversion and degradation of habitats, are causing global biodiversity declines. How local
ecological assemblages are responding is less clear—a concern given their importance for many ecosystem functions and
services. We analysed a terrestrial assemblagedatabase of unprecedented geographic and taxonomic coverage to quantify
local biodiversity responses to land use and related changes. Here we show that in the worst-affected habitats, these
pressures reduce within-sample species richness by an average of76.5%, total abundance by 39.5% andrarefaction-based
richness by 40.3%. We estimate that, globally, these pressures have already slightly reduced average within-sample
richness (by 13.6%), total abundance (10.7%) and rarefaction-based richness (8.1%), with changes showing marked
spatial variation. Rapid further losses are predicted under a business-as-usual land-use scenario; within-sample
richness is projected to fall by a further 3.4% globally by 2100, with losses concentrated in biodiverse but economically
poor countries. Strong mitigation can deliver muchmore positive biodiversity changes (up to a 1.9% average increase) that
are less strongly related to countries’ socioeconomic status.
Biodiversity faces growing pressures from human actions, includ-
ing habitat conversion anddegradation, habitat fragmentation, climate
change, harvesting and pollution
. As a result, global assessments show
that species’ extinction risk is increasing on average while population
sizes are declining
. Such assessments have usually focused on data-
rich vertebrates, so might not reflect broader biodiversity
. Furthermore,
most have concentrated on the global status of species, whereas the
long-term security of many ecosystem functions and services – especially
in changing environments – is likely to depend upon local biodiver-
. Average trendsin local diversity remainunclear: analyses of tem-
poral changes in assemblages have suggested no systematic change in
species richness
, but the available times-series data might under-
represent transitions between land-use types
, and population time series
suggest vertebrate populations have declined sharply in recent decades
Spatialcomparisons provide an alternative source of evidence on how
human pressures affect biodiversity, assuming that differences in pres-
sures have caused observed biodiversity differences between otherwise
matched sites
. The prevalence of published spatial comparisons makes
it possible to go beyond particular taxa or regions
to develop global,
taxonomically representative models. Furthermore, the willingness of
many researchers to share their raw data makes it possible to consider
multiple aspects of biodiversity, rather than the single, simple metrics
of most existing models
, which cannot capture all key aspects of
We present the most geographically and taxonomically represent-
ative models to date of how several aspects of the composition and
diversity of terrestrial assemblages respond to multiple human pres-
sures. The explanatory variables in our models most directly measure
land use and infrastructure, but might correlate
with two other im-
portant pressures, harvesting and invasive species, for which compar-
able high-resolution spatial data are unavailable globally. We exclude
climatechange effects because they are not captured well by spatialcom-
parisons. We use our models to infer past net changes in assemblages
since the year 1500, project future changes over this century under dif-
ferentsocioeconomicscenarios of land use,and relate projected national
changes in local biodiversity to socioeconomic variables and natural
Our models of local within-sample species richness (hereafter ‘rich-
ness’), rarefaction-basedspecies richness (hereafter ‘rarefied richness’),
total abundance, compositional turnover and average organism size are
*These authors contributed equally to this work.
United Nations Environment Programme World Conservation Monitoring Centre, 219 Huntingdon Road, Cambridge CB3 0DL, UK.
Computational Science Laboratory, Microsoft Research Cambridge, 21
Station Road, Cambridge CB1 2FB, UK.
Department of Life Sciences, Natural History Museum, Cromwell Road, London SW7 5BD, UK.
Department of Life Sciences, Imperial College London, Silwood Park,
London SL5 7PY, UK.
Department of Biosciences, College of Science, Swansea University, Singleton Park, Swansea SA2 8PP, UK.
Department of Genetics, Evolution and Environment, Centre for
Biodiversity and Environment Research, University College London, Gower Street, London WC1E 6BT, UK.
Instituto Multidisciplinario de Biologı
´a Vegetal (CONICET-UNC) and FCEFyN, Universidad
Nacional de Co
´rdoba, Casilla de Correo 495, 5000 Co
´rdoba, Argentina.
Deptartment of Zoology, Faculty of Life Sciences, Tel-Aviv University, 6997801 Tel Aviv, Israel.
Max Planck Institute for
Biogeochemistry, Hans Kno
¨ll Straße 10, 07743 Jena, Germany.
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
Ecology Group, Institute of Biology and Environmental Sciences, University of Oldenburg, D-26111 Oldenburg, Germany.
Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, UK.
Biology Department, University of Wisconsin–Eau Claire, Eau Claire, Wisconsin 54701, USA.
School of Life Sciences, University of Sussex, Brighton BN1 9QG, UK. {Present addresses: Department of
Animal and Plant Sciences, University of Sheffield, Alfred Denny Building, Western Bank, Sheffield S10 2TN, UK (R.A.S.); Department of Earth Science and Engineering, Imperial College London, London
SW7 2AZ, UK and Institute of Zoology, Zoological Society of London, London NW1 4RY, UK (D.J.B.); College of Life and Environmental Sciences, Hatherly Laboratories, University of Exeter, Prince of Wales
Road, Exeter EX4 4PS, UK (J.D.); School of Life Sciences, University of Sussex, Brighton BN1 9QG, UK (D.J.I.); School of Biological and Ecological Sciences,University of Stirling, Stirling FK9 4LA, UK (L.K.);
School of Biological Sciences, Queen’s University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK (H.J.W.).
G2015 Macmillan Publishers Limited. All rights reserved
based on among-site comparisons of ecological assemblage composi-
tion collated from the literature as part of the PREDICTS project
The data set consisted of 1,130,251 records of abundance and 320,924
of occurrence or species richness at 11,525 sites (2–360 sites per study,
median 15; Fig. 1a). These data, from 284 publications (see Methods),
represent 26,953 species (1.4% of the number formallydescribed
) and
13 of the 14 terrestrial biomes (Extended Data Fig. 1). Each site was
scored for six putative pressures: land use
and use intensity
, land-
use history
, human population density
, proximity to roads
accessibility from the nearest large town. Random effects in our mod-
els accounted for study-level differences in response variables and sam-
pling methods, and for the within-study spatial arrangement of sites.
Effects of pressure on site-level diversity
Local richness, rarefied richness and total abundance were most strongly
influenced by land use and land-use intensity: they were substantially
lower in mostother land-usetypes than in primaryvegetation, especially
in intensivelyused areas (Fig. 1; see SupplementaryInformation for sta-
tistics and coefficientestimates). These results extend those of previous,
geographically or taxonomically restricted, meta-analyses (for example,
refs 11, 22). Other variables were weaker as main effects, but showed
stronger effects in interaction (Extended Data Fig. 2) and were often
significant overall (see Supplementary Information). Richness and total
abundance tended to be slightly lower at the highest human population
densities, and richness was lower nearer to roads andin more accessible
sites (Fig. 1). Differences in richness were not driven solely by differ-
ences in abundance. Rarefied richness
(see Methods for details) showed
weaker but mostly similar patterns, although the effects of variables other
than land use and land-use intensity were not significant (Extended
Data Fig. 3a, b). Under the worst combinations of pressures, our models
estimated richness, rarefied richness and total abundance to be 76.5%,
40.3% and 39.5% lower, respectively, than in minimally affected sites.
Effects of pressures on vertebrate, invertebrate and plant richness were
statistically indistinguishable (P.0.05; results not shown). The mod-
elled coefficients were robust to efforts to correct for any publication
bias (Extended Data Fig. 4). As with all studies based on data from the
literature, unpublished data are almostunrepresented. Coefficients were
also robust under cross-validation (Extended Data Fig. 3c, d), and the
model residuals showed little spatial autocorrelation (Extended Data
Fig. 5).
The importance of secondary vegetation for conservation is a hotly
debated topic
, and an important one, given that this land use will
soon become the most widespread type
. We find that the answer
depends strongly on the secondary vegetation’s maturity: early-stage
communities tend to be less diverse than those in primary vegetation
and are compositionallydistinct, but thesedifferences aremuch reduced
in mature secondary vegetation (Figs 1 and 2; we caution though that
not all data sources clearly distinguished mature secondary from prim-
ary vegetation). This successional rise in diversity accords with a recent
meta-analysis of plant communities over time
Net changes in diversity provide an incomplete view of the effects of
human activities on biodiversity because they ignore the replacement
of original species by newcomers
. We therefore analysed how land use
affects similarity in species composition between sites. Communities
under the same land use were, as expected, the most similar (Fig. 2a).
Across land uses, communities in primary vegetation were most like
those in secondary vegetation, while plantation forest, pasture and crop-
land communities formed a different, human-dominated cluster (Fig. 2b).
Anthropogenic pressures can affectecosystem functions and services
more strongly than changes in species diversity would imply, if species’
responses depend on their traits
. Large size is often linked to species’
and is important for some ecosystem processes
. We com-
bined abundance data with species’ average sizes to calculate site-level
community-weighted mean plant height and animal mass. As in local
, mean plant height was lower in human-dominated land uses
than in primary and secondary vegetation, and tended to decline with
increasing human population density (Fig. 1d). Most field studies focused
on particular plant taxa, so this difference does not simply reflect tree
removal. Average animal mass did not change consistently with land
use or human population density, but increased with proximity to roads
(Fig. 1d).
Models like ours that substitute space for time ignore time lags in
biotic changes, which can be important
. We also a ssume that land uses
Richness difference (%)
bLand-use intensity:
Light and intense
Abundance difference (%)
CWM size difference (%)
Plant height
Animal mass
Figure 1
Locations of sites and responses of four metrics of local
diversity to human pressures. a, Sites used in the models. bd, Responses
of richness (b), total abundance (c) and community-weighted mean
(CWM) organism size—plant height (crosses) and animal mass (triangles)—
(d) to anthropogenic variables. Error bars show 95% confidence intervals.
Primary, primary vegetation; YSV, young secondary vegetation;
ISV, intermediate secondary vegetation; MSV, mature secondary vegetation;
plantation, plantation forest. Land-use intensity is categorized as minimal
(circle), light (triangle), intense (diamond), or combined light and intense
(square). HPD, human population density
; PR, proximity to roads
2log(distance to nearest road)); and ACC, accessibility to humans
2log(travel time to nearest major city)), are shown as fitted effects from a
model with no interactions between continuous effects and land use, at the
lowest (L), median (M) and highest (H) values in the data set. Sample sizes
are given in full in the Methods.
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are situated randomly within studies relative to sites’ intrinsicsuitability
for biodiversity. Adding global data on other important pressures as
they become available, and also incorporating climate change, will give
a more complete picture of human effects on local biodiversity.
Global effects on local diversity to date
By applying our model for within-sample species richness—the most
widely used and understood biodiversity measure—to maps of current
pressure variables
, we estimated the global pattern of net local changes
to date in plot-level richness (Fig. 3; we did not estimate total richness
within the 0.5u30.5ugrid cells). Human-dominated areas are inferred
to have lost much more local diversity than have regions where more
natural vegetation remains. The worst-affected cells showed a 31% reduc-
tion in average localrichness—probably enough to alter ecosystem func-
tioning substantially
. Local richness increased in 1.7% of cells (by #4.8%).
Total abundance and, less strongly, rarefied richness showed broadly
similar patterns (Extended Data Fig. 6).
We applied our models to global spatial estimates of how land use
and human population changed from 1500–2005 (ref. 26) (see Methods)
to infer the global history of local biodiversity change. Here we focus on
within-sample species richness because of its wide use and easy inter-
pretation. Our inferences incorporate uncertainty in model parameter
estimates, but not in the trajectories of the pressures themselves(which
have not been assessed
) nor effects of changes in roads and accessi-
bility, for which temporal estimates could not be obtained.
Richness is estimated to have declined most rapidly in the 19th and
20th centuries (Fig. 4), with other metrics showing similar responses
(Extended Data Fig. 6). By 2005, we estimate that land use and related
pressures had reduced local richness by an average of 13.6% (95% con-
fidence interval (CI): 9.1–17.8%) and total abundance by 10.7% (95%
CI: 3.8% gainto 23.7% reduction) compared with what they would have
been in the absence of human effects. Approximately 60% of the
decline in richness was independent of effects on abundance; average
rarefied richness has fallen by 8.1% (95% CI: 3.5–12.9%). Although
these confidence limits omit uncertainty in the projections of land use
and otherpressures, thereis less uncertainty in estimates of current pres-
sure levels than in changes over time
Our inferences contrast withtwo recent analyses of community time
, which suggested no overall trend in local diversity, and with the
Living Planet Index
, which, based on vertebrate population time se-
ries, reports a much more rapid decline in abundance than we infer.
Although time series potentially provide a more direct view of tem-
poral trends thanour space-for-time approach, the available data might
under-represent transitions between land-use types
. However, our ap-
proach may underestimate additions of species through climate change
and species invasion (although accessibility and proximity to roads may
partly capture the latter
Urban 12 0 1114212
49 711 16 716 41 2
32 12 8 14 7 26 16 4
59 10 11 9 54 771
44 13 23 40 9 14 16 1
39 10 35 23 11 811 1
29 24 10 13 10 12 7 0
189 29 39 44 59 32 49 12
Figure 3
Net change in local richness caused
by land use and related pressures by 2000.
Projections used an IMAGE reference scenario
The baseline landscape was assumed to be entirely
uninhabited, unused primary vegetation. Shown
using a Lambert Cylindrical Equal-Area projection
at 0.5u30.5uresolution.
Figure 2
Similarity in assemblage composition as a function of land use.
a, Average dissimilarity of species composition (1 2Sørenson Index) between
pairs of sites within and among land uses (shown relative to the similarity
between pairs of primary-vegetation sites); blue and red colours indicate,
respectively, more or less similar composition; numbers indicate numbers of
studies within which comparisons could be made. b, Clustering of land-use
types based on average compositional dissimilarity; urban sites were excluded
owing to the small sample size.
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Global and national projections to 2095
Global changes in local diversity from 2005 to 2095 were projected using
estimated land use and human population from the four Intergovern-
mental Panel on Climate Change Representative Concentration Path-
way (RCP) scenarios
, which correspond to different intensities of
global climate change (Table 1). Although these estimates have limita-
, they are the most consistent available, are widely used
, and
are consistent with the historical estimates
. However, they—like all
other global land-use projections—include no estimate of uncertainty;
therefore, each of our projections must be viewed as the predicted bio-
diversity outcome under one particular set of land-use assumptions.
Projected net changes in average local diversity to 2095 vary widely
among scenarios (Fig. 4 and Extended Data Fig. 6). The scenario with
the least climate change (IMAGE 2.6) yields the second-worst out-
come for biodiversity, because it assumes rapid conversion of primary
vegetation, especiallyin the tropics, to crops and biofuels
(Table 1 and
Extended Data Fig. 7). These projections do not imply that low-emission
scenarios must entail large losses of biodiversity, but insteadreflect that
scenario’s mitigation strategy. Indeed, in MiniCAM 4.5 (where mit-
igation is through carbon markets, crop improvements and diet shifts,
Table 1) average richness is projected to increase (though other divers-
ity metrics respond more weakly, Extended Data Fig. 6).The worst bio-
diversity outcomes arise from the scenario with most climate change
(MESSAGE 8.5) in which rapid human population growth drives wide-
spread agricultural expansion (Table 1 and Extended Data Fig. 7). This
scenario, which has been characterized as ‘business-as-usual’
, most
closely matches recent trends in emissions
and gives the worst out-
comes even though our projections omit direct climate effects on local
The global projections hide wide regional and national variation
(Fig. 5 and Extended Data Fig. 8).Projections for 2095 under business-
as-usual (MESSAGE 8.5) are strongly inequitable, presenting serious
challenges for bothsustainable development and global conservation of
biodiversity (Fig. 5a). Under this scenario, European and North Amer-
ican countries, typically with a high Human Development Index, low
native biodiversity and widespread historical land conversion, are mostly
projected to gain in local richness by 2095. More naturally biodiverse
but less economically developed Southeast Asian and especially sub-
Saharan African countries, with more natural and semi-natural hab-
itat, will suffer the greatest losses (Fig. 5a and Extended Data Fig. 8f).
Such globally inequitable outcomes might be avoidable. The bestsce-
nario for biodiversity(MiniCAM 4.5; Fig. 4) yielded country-level out-
comes that are relatively independent of Human Development Index,
native species richness (Fig. 5b) and past changes (Extended Data Fig. 8e).
For local richness, outcomes under MiniCAM4.5 were better than
MESSAGE 8.5 for 93% of countries worldwide (Fig. 5c).
Under AIM 6.0, most Afrotropical countries are projected to gain in
local richness but heavy losses are inferred for the Indo-Malay region
(Extended Data Fig. 8). Projectionsunder IMAGE 2.6 are spatially sim-
ilar to those under MESSAGE 8.5. The land-use change caused by the
biofuels-based strategy in IMAGE 2.6 is projected to have a major neg-
ative effect overall on terrestrial biodiversity (Extended Data Fig. 8).
Many assessments of the state of biodiversity have focused on global
metrics such as rates of speciesextinction
, but resilient deliveryof eco-
system functions and services is more likely to depend on local divers-
. Our models suggest land-use changes and associated pressures
strongly reduce local terrestrial biodiversity, and we estimate global
average reductions to date of 13.6% in within-sample species richness,
10.7% in total abundance and 8.1% in rarefaction-based species rich-
ness (Figs 3 and 4). Climate change, which we could not include in our
Net richness change (%)
1500 1600 1700 1800 1900 2000 2100
AIM 6.0
Figure 4
Projected net change in local richness from 1500 to 2095. Future
projections were based on the four RCP scenarios (Table 1). Historical
(shading) and future (error bars) uncertainty is shown as 95% confidence
intervals, rescaled to zero in 2005. The baseline for projections is a world
entirely composed of uninhabited, unused primary vegetation; thus, the value
at 1500 is not constrained to be zero because by then non-primary land
uses were present (and in some regions widespread). The global average
projection for MESSAGE8.5 does not join the historical reconstructionbecause
that scenario’s human population projections start in 2010 and because
human population and plantation forest extent have not been harmonized
among scenarios.
Table 1
Key features of the four RCP scenarios
Scenario Land use (see also Extended Data Fig. 7) Climate and energy Human population
IMAGE 2.6 Agriculture moves from developed to developing
countries. Large increase in area of biofuel plantations.
Urban extent assumed constant.
Increased energy efficiency. Increased use of carbon
capture and storage, nuclear, renewable energy
and biofuels. Approximately 1 uC temperature increase
by 2100 compared to pre-industrial.
10.1 billion by 2100 (UN
Medium variant, 2010)
MiniCAM 4.5 Carbon pricing leads to preservation of primary forest
and expansion of secondary forest. Crop yield
increases, improved agricultural efficiency and dietary
shifts lead to decreases in cropland and pasture
areas. Small increase in area of biofuel plantations.
Urban extent assumed constant.
Decline in overall energy use. Decreased use of fossil
fuels and increase in nuclear and renewable energy,
and in carbon capture and storage. Moderate increase
in use of biofuels, but limited by availability of biomass.
Approximately 1.75 uC temperature increase by 2100.
Peaks above 9 billion around
2065, then declines to 8.7
billion by 2095.
AIM 6.0 Urban area increases owing to human population
growth. Cropland area increases to meet food
demand. Pasture area declines strongly.
Approximately 2.5 uC temperature increase by 2100. 9.1 billion by 2100
(UN Medium variant, 2004)
MESSAGE 8.5 Increasing crop yields and intensification account
for much of the increased production required, but
area of cropland and, to a lesser extent, pasture
increases rapidly. Small increase in area of biofuel
plantations. Urban area increases owing to
increased population.
Small improvements in efficiency leading to high
demand for energy. Conventional oil and gas become
scarce, leading to shift in favour of unconventional
and carbon-intensive fossil fuels. Moderate increase in
use of biofuels. Approximately 4 uC increase in
temperature by 2100.
12 billion by 2100.
Land-use and human population assumptions are detailed in ref. 26, energy assumptions in refs 40–42, and climate implications in ref. 43.
G2015 Macmillan Publishers Limited. All rights reserved
framework, is likely to exacerbate losses, especially under business-as-
, although direct effects of climate change will increase local
diversity in some regions
It is important to remember that the habitat conversion and assoc-
iated changes that reduced local biodiversity had largely positive con-
sequences for people; agricultural intensification underpinned many
countries’ development. However, benefits have not been shared equally
among or within countries
. Losses of local species richness exceeding
20% are likely to substantially impair the contribution of biodiversity to
ecosystem function and services, and thus to human well-being
estimate that reductions in averageplot-level species richness currently
exceed this level for 28.4% of grid cells, increasing to 41.5% of cells by
2095 under business-as-usual (note that we do not estimate or project
total richness across the cell). Importantly, our projections suggest that
such widespread large losses are not inevitable. With concerted action
and the right societal choices, global sustainability of local biodiversity
may be an achievable goal.
Online Content Methods, along with any additional Extended Data display items
and SourceData, are available in theonline version of the paper;references unique
to these sections appear only in the online paper.
Received 9 July 2014; accepted 12 February 2015.
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Richness change 2005−2095 (%)
0.3 0.4 0.5 0.6 0.7 0.8 0.9
Human Development Index
Richness change 2005−2095 (%)
0.3 0.4 0.5 0.6 0.7 0.8 0.9
Human Development Index
Richness change (%) MINICAM 4.5
−30 −20 −10 0 10
Richness chan
e (%) MESSAGE 8.5
Figure 5
Biodiversity projections at the country level. a,b, Country-level
projections of average net local richness change between 2005 and 2095 under
the worst (a, MESSAGE 8.5) and best (b, MiniCAM 4.5) RCP scenarios for
biodiversity, shown in relation to countries’ Human Development Index.
Colours indicate biogeographic realms; colour intensity reflects natural
vertebrate species richness (more intense colour representshigher richness);
point diameter is proportional to (log) country area. c, Correlation between
projected richness changes under the MiniCAM 4.5 and MESSAGE 8.5
scenarios, with dashed line showing equality; colours as in aand b; colour
intensity is proportional to the Human Development Index (more intense
colour represents higher index).
G2015 Macmillan Publishers Limited. All rights reserved
2 APRIL 2015 | VOL 520 | NATURE | 49
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Supplementary Information is available in the online version of the paper.
Acknowledgements We thank all the many researchers who have made their data
available to us; S. Butchart and Birdlife International for sharing bird body-size data;
F. Gilbert for hoverflybody-size data; the IMAGE,HYDE, MESSAGE and MiniCAM teams,
especially R. Alkemade, M. Bakkenes and A. Thomson for sharing additional data from
their integrated assessment models; D. Tittensor for statistical advice; C. Sleep and
S. Patlola at the Natural History Museum in London for IT support with the database;
members of the GARD initiative ( for help
with estimating the reptile species richnessmap; K. Jones, J. Tylianakis,M. Crawleyand
E. J. Milner-Gulland for discussion,N. Burgess for commentson a draft of the paper. We
also thank C. D. Thomas and two anonymous reviewers for very helpful comments on
the manuscript.This study is part of the PREDICTS(Projecting Responsesof Ecological
Diversity in Changing Terrestrial Systems) project, which is supported by the UK
Natural Environment Research Council (NERC, grant number: NE/J011193/1), the
Biotechnology and Biological Sciences Research Council (grant number: BB/
F017324/1) a Hans Rausing PhD scholarship. The study was also supported by the
TRY initiative on plant traits, whose database is maintained at Max-Planck-Institute for
Biogeochemistry, Jena, Germany, and which is supported by DIVERSITAS, IGBP, the
Global Land Project, NERC, the French Foundation for Biodiversity Research, and GIS
‘Climat, Environnement et Socie
´’ France. This is a contribution from the Imperial
College Grand Challenges in Ecosystem and the Environment Initiative.
Author Contributions T.N.,L.N.H., S.L.L.H., S.C., I.L.,B.C., D.W.P., R.M.E., G.M.M., J.P.W.S.
and A.P. designed the project and this study; T.N., L.N.H., I.L., R.A.S., L.B., J.P.W.S. and
A.P. performed the analyses; T.N., L.N.H., S.L.L.H., S.C., D.J.B., A.C., B.C., J.D., A.D.P.,
S.E.-L., M.G.,M.L.K.H., T.A., D.J.I., V.K., L.K., D.L.P.C., C.D.M., Y.P., H.R.P.P., A.R., J.S., H.J.W.
and A.P. collatedthe assemblage composition data; T.N., L.N.H.,S.L.L.H., S.C., A.D.P.,I.L.,
H.R.P.P., J.P.W.S. and A.P. designed the data-collection protocols and database; R.A.S.,
S.D., M.J.E., A.F., Y.I., J.K., M.K., S.M. and E.W. made substantial contributions to the trait
data used in theanalyses and S.L.T. to thesite-level environmental data; R.A.S., A.F.,Y.I.,
S.M., and M.N. generated the maps of species richness used in the model projections;
T.N., L.N.H. and A.P. wrote the manuscript with contributions from G.M.M., L.B., D.W.P.,
R.M.E., A.D.P., H.R.P.P., S.L.L.H., R.A.S., B.C., S.D., A.F., Y.I., J.K., M.K., S.M., J.P.W.S and
S.L.T.; T.N. and L.N.H. contributed equally to the study.
Author Information Reprints and permissions information is available at The authors declare no competing financial interests.
Readers are welcome to comment on the online version of the paper.
Correspondence and requests for materials should be addressed to
T.N. (
G2015 Macmillan Publishers Limited. All rights reserved
Data collation. Between March 2012 and April 2014 we collated among-site com-
parisonsof ecological assemblage composition from published studies(or from un-
publisheddatasets where the methods have been published) examining the effect of
human activitieson more than one named taxon. A full descriptionof how the data
set was assembled and curated is presented elsewhere
. We define sites to be in the
same study if they were sampled using the same methodology and the data were
reported in the same paper; therefore, some publications contain multiple studies.
After six monthsof broad searching, we targeted effortstowards under-represented
taxa, habitat types, biomes and regions. We accepted data only from published or
in-press papers, or data collected usinga published methodology, and we required
that the data providers agreed to our making their data publicly available at the
end of the PREDICTS (Projecting Responses of Ecological Diversity In Changing
Terrestrial Systems) project in 2015. We accepted data only where abundance,
occurrence or richness had been measured at two or more samplinglocations and/
or times, and where all sites were sampled using the same sampling procedure and
with either the same effort or site-specific data on effort. We used geographical
coordinates preferentially from the paper or supplied by data providers; but where
coordinates were not thus available, we geo-referenced themfrom maps in the papers.
The final data set came from 378 studies
and two unpublished datasets (M. E.
Hanley, 2005 and 2011) that were collected with published methods
Studies compared from 2 to 360 sites (median 515; 82% had $5 sites); most
sampledspecies from multiplefamilies but fewer thanhalf sampled multipleorders.
Over 70% of sites were from studies that sampled entire communities within a
taxonomic group rather than a target list of species. Removing studies having a
target list did not substantially alter model coefficients (results not shown) and
increased the projected global net average loss of local species richness until 2005
by 0.6%. Sites varied in the maximum linear extent sampled (median 106 m; inter-
quartile range 50 m to 354 m). Model coefficients for the approximately 50% of
studies that reported maximum linear extent were robust to its inclusion in the
models (results not shown).
The great majority of listed taxa were species level, although many could not be
given explicit species identifications (for example, morpho-species)
; henceforth
we refer to distinct taxa in our data set as species. We matched taxonomic names
given in the source paper to the Catalogue of Life 2013 Annual Checklist (COL)
obtaining the full taxonomic classification. In order to relatethe taxonomic names
to species-level trait databases, we generated, for each taxon, a ‘best-guess’ Latin
binomial as: (a) the taxon name from COL if the COL query returned a species-
level identification; (b)the first two words of the text returned by the COL query if
this was a sub-speciesdesignation; (c) the first two words of the taxon name in the
source publication if the COL query returned neither a speciesor sub-species name,
and the taxon name in the source publication contained two or more words. Taxa
that met none of these criteria were not matched to trait data, but were included in
the calculation of richness and total abundance, and for estimating turnover in
community composition among sites.
The resulting data set containeddata for 26,953 species at 11,525 sites. For many
high-diversity taxa, thedatabase contains data for more than 1% of the number of
species thought to have been formally described (Extended Data Fig. 1a). The dis-
tribution of sites among major biomes is roughly proportional to the amount of
terrestrialnet primary productivity (NPP) fixed within each biome (ExtendedData
Fig. 1b).
Site-level composition and diversity. We computed four site-level biodiversity
metrics: within-sample species richness, total abundance, rarefaction-basedrichness
and community-weighted mean organism size. These were calculated as follows.
Within-sample species richness was calculated as the number of differently-named
taxa recorded ata given site in a standardised samplingunit (a measure also known
as species density
). We gave precedence to the author’s classification of species,
even where a search of global databases revealed potential synonymies, because
only certain taxonomic groups could be reliably matched to accepted taxonomies.
This measure of richness is appropriate for conservation questions but among-
site differences could be due to effects on numbers of individuals as well as to
changes in the shapeof the species accumulation curve
. We therefore also calcu-
lated rarefaction-based species richness by taking 1,000 random samples of nin-
dividuals from each site, wherenis the smallest totalnumber of individuals recorded
at any sitewithin its study,and calculatingthe mean speciesrichness acrosssamples.
This index could only be calculated for sites where, in addition to the criteria above
being met, abundance was recorded as number of individuals. Rarefied species
richness was rounded to the nearest integer for analysis with Poisson errors.
Total abundance was calculated as the sum of the measures of abundance of all
taxa at a site; we were thus unable to estimateabundance for siteswhere only species
occurrence or overall richness or diversity had been recorded (17% of sites). Some
abundance metrics—those not reported as densities per unit time, distance, area
or volume sampled—were sensitive to sampling effort. When a study reported any
of these metrics and sampling effortvaried among sites withina study, we corrected
the raw abundance measurements for the sampling effort expended at each sam-
pling locationand time. This was done by rescaling the sampling effortswithin each
study so that the most heavily sampled site had a value of one (to prevent intro-
ducing additional heterogeneity into the modelled values), and then dividing the
raw abundance measurements by this relative sampling effort.
Community-weighted meanorganism size wascalculated as the arithmetic mean
of log-transformed heightof plants (availablefor 4,235 speciesin our data set) or the
log-transformed body mass or volume of vertebrates, beetles and hoverflies (5,236
species) present at a site, weighted by abundance
. Plant height data were taken
from the TRY database
; for 61 species where plant vegetative height data were
unavailable, we estimated it from generative height from a regression across the
2,554 specieswith estimatesof both traits (R
50.91). Dataon vertebrate body mass
were taken from the PanTHERIA database for mammals
, from BirdLife Inter-
national’s World Bird Database for birds, and from a wide range of published and
grey-literature sources for amphibians
. Length data for reptiles were take n from
and unpublished (S. Meiri and A. Feldman, unpublished data) sources,
and converted to estimates of body mass using published length-mass allome-
. Arthropod size data (beetles and hoverflies) were collated from pub-
lished sources
. Beetle length and amphibian snout-vent length values were
raised to the power three so that they had the same dimensionality as the other
animal size measures. For both plant height and vertebrate body mass, missing
values were interpolated as the average values for congeners, since both of these
traits are strongly conserved phylogenetically (Pagel’s l50.98, 0.997, 0.93, 0.89
for plant height, vertebrate body mass, beetle body length and hoverfly thorax vol-
ume, respectively).
Human pressure data. While manyhuman pressures can affect local biodiversity,
we focus on those that can be obtained for sites aroundthe world and for which, as
far as possible, spatiotemporal data are available for 1500–2095; this focus enables
us to useour statistical models as a basis forprojecting responses through time.Each
site was assigned to one of eight land-use classes based on the description of the
habitat given in the source paper (see Extended Data Table 1 for definitions): pri-
mary vegetation, secondary vegetation (subdivided into mature, intermediate or
young secondary vegetation), plantation forest, cropland, pasture and urban
classes were selected to match the land-use classification adopted in the Intergov-
ernmental Panel on Climate Change Representative Concentration Pathways sce-
to facilitate the projection of our models onto these scenarios. Sites were
also assigned to a level of human intensity of use (minimal, light or intense) within
each major land-use class, also based on the description of the habitat in the source
paper (see Extended Data Table 1 for definitions). The factors that determined
this level depended on the land-use class (for example, bushmeat extraction and
limited logging in primary and secondary vegetation, or stocking density and
chemical inputs in pasture; Extended Data Table 1). Sites that could not be clas-
sified for land-use and use intensity were excluded from the analyses. The final
dataset contained the following numbers of sites in each land use and land-use
intensity level:primary vegetation, minimal use, 1,546 (from 183 studies);light use,
860 (76 studies); intense use, 449 (33 studies); mature secondary vegetation, min-
imal use, 198 (52 studies); light/intense use, 213 (23 studies); intermediate second-
ary vegetation, minimal use, 404 (55 studies); light/intense use, 269 (30 studies);
young secondary vegetation, minimal use, 431 (50 studies); light/intense use, 331
(34 studies); plantation forest, minimal use, 356 (47 studies); light use, 402 (42
studies); intenseuse, 238 (29 studies); cropland, minimal use, 427 (45 studies); light
use, 632 (43 studies); intense use, 703 (36 studies); pasture, minimal use, 525 (43
studies); light use, 434 (52 studies); intense use, 174 (23 studies); and urban, min-
imal use, 174 (23 studies); light use, 244 (26 studies); intense use, 195 (18 studies).
We overlaid our sites with available globaldata sets to obtain site-level estimates
of human population density
, distance to the nearest road
and estimated travel
time to nearest population centre with greater than 50,000 inhabitants
tance to nearest road, the map of roads was first projected onto a Berhmann equal-
area projection. These operations were carriedout using Python code implemented
using the arcpy Python module in ArcMap version 10.0 (ref. 388). In the main
figures, the inverses of distanceto roads and travel time to major population centre
(proximity to roads and accessibility) were presented so that high values corre-
sponded to higher hypothesized human effect. To estimate the history of human
use of the landscapes within which sites were located, we calculated the number of
years since the 30-arc-second grid cell containing each site became 30% covered
by human land uses (cropland, pasture and urban), according to the HYDE model
Co-linearity among variables describing anthropogenic change was low; the highest
correlation was between land use and human population density (Pearson R
Modelling site-level diversity, composition and turnover. The response of site-
level diversity to the measures of anthropogenic change was modelled using gen-
eralized linear mixed effects models, implemented in the lme4 package version
G2015 Macmillan Publishers Limited. All rights reserved
1.0-5 (ref. 44) in R version 3.0.2 (ref. 390). We first compared candidate random-
effects structures using the full candidate fixed-effects structure
. Random-
intercept terms considered in all models were the identity of the study from which
data were taken,to account for study-level differencesin the response variables and
samplingmethods used, and—within study—thespatial block in which the site was
located, to account for the spatial arrangement of sites. For models of species rich-
ness (within-sample and rarefied),we also fitted an observation-level random effect
(that is, site identity) to account for the overdispersion present
. We also con-
sidered random slopes, with respect to study, of each of the main fixed effects (land
use, land-use intensity, human population density, distance to nearest road, travel
time to nearest major city and time since the landscape was majority converted to
human uses). Random effects were retained or discarded based on the models’
Akaike Information Criterion values.
Once the best random-effects structure had been selected, we performed back-
ward stepwise model simplification to select the best fixed-effects structure (see
Supplementary Information)
. Human population density, distance to roads, tra-
vel time tonearest major city andtime since major human useof the landscape were
log transformed in the analyses, with a value of 1 added to human population den-
sity, travel time to nearest major city and time since majorlandscape conversion to
deal with zero values. These four variables were fitted as continuous effects, with
quadratic polynomials for human population, distance to roads and travel time to
nearest major city, and as a linear effect for time since human landscape conver-
sion. For variables fitted as quadraticpolynomials, we also tested linear effects dur-
ing the backward stepwise model selection. All continuous variables were rescaled
before analysis so that valuesranged between zero and one. Interaction terms were
tested first, and then removed to test the main effects. All main effects that were
part of significant interaction terms were retained in the final models regardless of
their significance as maineffects. Forthe model of community-weightedmean body
mass and plant height, because the number of sites with data was smaller than for
the other metrics, only land use (excluding urban sites, which were few), human
population density and distance to roads, and no interactions, were fitted (for the
model of plant height, sample sizes in each land use were: primary vegetation, 634
sites; secondary vegetation,851 sites; plantation forest, 222 sites; cropland, 72 sites;
pasture, 412 sites; and for the modelof animal mass: primary vegetation, 1728 sites;
secondary vegetation,805 sites; plantation forest, 602 sites; cropland,641 sites; pas-
ture, 440 sites).The decision whether or not to retain termswas based on likelihood
ratio tests. The coefficient estimates of the best models are shown in Fig. 1b–d and
Extended Data Fig. 2, and theformulae and statistical results are shown in the Sup-
plementary Information. To test for spatial autocorrelation in the residuals of the
final best models, we calculated Moran’s Ivalues and associated Pvalues, sepa-
rately for each study considered in the models, using the spdep package version
0.5-68 (ref. 393) in R; the distribution of Pvalues across studies was used as an
indication of whether spatial autocorrelation was likely to cause a problem. This
revealed that the residuals showed little spatial autocorrelation (Extended Data
Fig. 5). We used cross validation to assess the robustness ofmodel parameter esti-
mates, first based on dividing the studies randomly into ten equal-sized sets and
dropping each set in turn (ExtendedData Fig. 3c), and second based on leaving out
the studies from each biome in turn (Extended Data Fig. 3d).
Publication bias is a potential problem for any large-scale synthesis of data from
many publications. In standard meta-analyses,funnel plots
can be used to test for
any relationship between standard error and effect size, as a bias in effect sizes at
high standard error towardsmore positive or more negative effects indicates a likely
effect of publication bias. Creating funnel plots for our data was more complicated
because ours was a site-level analysis of raw diversity estimates rather than a tra-
ditional meta-analysis. Instead we generatedindividual models relating diversityto
land use for each study that sampled at least two sites within each of at least two
land-use types. We focused on land use because: (a) there were a small number of
sites included in mostwithin-study models; and (b) the original studies focused on
effects of land use, not generally on land-use intensity, human population density
or distance to roads, and thus any effect of publication bias would likely be seen in
the land-use coefficients. Funnel plots were generated by plotting, for each land-
use type, the estimated model coefficients against the associated standard errors
(Extended Data Fig. 4). There were some indications of an effect of publication
bias, with less certaincoefficient estimates tending to have more negativeestimates
for some of the land uses (Extended Data Fig. 4). However, study-level random
slopes of human-dominated land uses tended to be more negative for studies that
sampled more sites (Extended Data Fig. 4). It is important to emphasize that in a
site-level analysis likeours, studies with fewer sites have less weight in the models.
Modelled coefficient estimates were generallyrobust to the removal of these studies
(Extended Data Fig. 4). Basing projections on coefficient estimates from models
where small studies were excluded led to a less than 1% change in the estimated
global richness values (results not shown). As with all studies based on data from
the literature, we underrepresent unpublished data.
To model turnover of species composition between pairs of sites, we calculated
average dissimilarity
in the lists of present species (1 2Sørensen index) between
all pairs of sites within each study. For this analysis, we were only able to consider
studies with more than one site in at least one of the land-use types considered.
Once compositional similarity had been calculated for every pair of sites within
each study, the average compositional similarity was calculated for every pair of
land-use types consideredwithin each study (including comparisons between sites
in the same land-use type).Finally, the average compositionalsimilarity was calcu-
lated for each pair of land-usetypes across all studies. To visualize the clustering of
different land-usetypes in terms of community composition, we performed a hier-
archical complete-linkage cluster analysis on the compositional dissimilarity (that
is, 1 2similarity) matrix, usingthe hclust function in R version 3.0.2 (ref. 390). To
test whether differences in the average geographicdistance between pairs of sites in
different land-use combinations affected these results, we correlated average com-
positional similarity with average distance between sites, for all pairwise combina-
tions of landuse (including comparisons of a land-use type with itself). Correlations
between average distance and average community similarity were only very weakly
negative (R
50.001), suggesting they do not strongly distort the comparisons of
communitycomposition. However, the fact that someland uses tend to occur more
closely together than others could influence the diversity patterns seen in our
models, if some land uses are typically close to high-diversity habitats and so are
more likely to benefit from dispersal. For example, sites in secondary vegetation
and plantation forestwere closer, on average, to primary vegetationsites than were
those in cropland, pasture and urban (averagedistances to sites in primary vegeta-
tion were:other primary vegetation sites 57.38 km;mature secondary vegetation5
4.4 km; intermediate secondaryvegetation53.9 km; young secondaryvegetation5
6.9 km; plantation forest54.2 km; cropland 516.4 km; pasture 510.1 km; and
urban 511.4 km). Accounting for distance in such already complex models is not
computationally tractable. In making the projections, we therefore implicitly assume
that the average distances will not change (that is, that secondary vegetation and
plantation forests will remain closer to primary vegetation than cropland, pasture
and urban habitats).
Projecting the models onto spatial estimates of anthropogenic variables. We
projected the best overall models of richness (within-sample and rarefied), abun-
dance and community-weighted mean organism size onto estimates of land use,
land-use intensity and human population density at 0.5u30.5uresolution, using
historical estimates for 1500 to 2005, and four RCP scenarios of future changes
(IMAGE 2.6, MiniCAM 4.5, AIM 6.0 and MESSAGE 8.5; the names refer to the
integrated assessment models used and the numbers to the amount of radiative
forcing assumed in 2100)
. In the absence of global projections, proximity to
roads and accessibility were omitted from our projections.
Estimates of land use for both the historical reconstruction and the future sce-
narios were taken from the harmonized land-use data accompanying the scen-
. Estimates of the stage of secondary vegetation (young, intermediate or
mature) are not available directly in the RCP land-use data. However, these data
contain estimates of the transition each year between secondary vegetation and all
other land-use types. To convert this into an estimate of the proportion of second-
ary vegetation in each of the stages of maturity, we considered any transition to
secondary vegetation to result in secondary vegetation of age zero. Each year, this
age was then incremented by one. In the absence of better information, any tran-
sitions from secondary vegetation to any other land-use type were assumed to be
drawn evenly from the ages currently represented. For the purposes of the projec-
tions, secondary vegetation was considered to be young until an age of 30 years,
intermediatebetween 30 years and 100 years, and mature thereafter. We developed
C# code to convert land-use transitions into estimates of the stage structure of
secondary vegetation.
Gridded temporal estimates of human population density were directly avail-
able for the HYDE historical scenario and MESSAGE future scenario. Human
population trajectories in the MiniCAM model were resolved only to the level of
United Nations regions
; we therefore downscaled these to grid cells assuming no
temporal changein the spatial pattern of relativepopulation density within regions
compared to present day patterns
, which is the method used in other RCP-
scenario land-use models lacking human population data resolved to grid cells
Gridded estimates of human population from the MESSAGE model were down-
loaded from For the scenarios for which
human population projections were not available (IMAGE and AIM), we used
country-level estimates from the ‘medium’ scenario of the United Nations popu-
lation division
, which gives the closest global predictions of future human po-
pulation to those assumed by IMAGE and AIM
. These country-level estimates
were downscaled to grid cells using the same method as for MiniCAM’s regional
Land-use intensity was an important explanatory variable in our models, but
global maps of land-use intensity are not available. We therefore generated global
G2015 Macmillan Publishers Limited. All rights reserved
estimates of current land-use intensity based on a map of ‘Global Land Systems’
which divides coarseland-use types into sub-categories basedon levels of cropland
intensity, livestock densities and human population density. We mapped each
Global Land Systems class onto one or more relevant combinations of our classes
of land use and land-use intensity (Extended Data Table 2). The Global Land Sys-
tems data set has a spatial resolution of 5 arcmin. To calculate the proportion of
each 0.5u30.5ucelloccupied by each land use and land-use intensity combination
we calculated the proportionof 5-arcmin cells within each 0.5u30.5ucell contain-
ing matching GlobalLand Systems categories (see legend of Extended DataTable 2
for details).
To generate past and future estimates of land-use intensity, we modelled the
current proportion of each land-use type estimated to be under minimal, light or
intense levels of intensity within each grid cell (one model for each intensity level),
as a function of the prevalence of the land-use type within each cell and human
population density, with the relationships allowed to vary among the 23 United
Nations (UN) sub-regions (that is, we fitted interaction terms between UN sub-
region and both the prevalence of each land-use type and human population den-
sity). UN sub-region data were taken from the world borders shapefile version 0.3
( and converted to a
0.5u30.5uraster using ArcMap version10.0 (ref. 388). The models weredeveloped
using generalized linearmodels with a binomial distributionof errors, implemented
in the lme4package version 1.0-5(ref. 44) in R version 3.0.2 (ref. 390). The resulting
models explained between 30.6% and 76.7% of the deviance in estimated current
levels of intensity. Past and future land-use intensities were estimated by applying
the models to the same past and future estimates of land use and human popu-
lation density as above.
The scenarios gave the proportion of each grid cell estimated to be occupied by
each combinationof land use and land-use intensity.We did not attempt to resolve
human populationdensity within grid cells for our historical estimates or forecasts,
thereby assuming it to be spatially (but not temporally) constant within each cell.
The coefficients from the models of site-level diversity were thus applied to each
combinationof land use and intensity within each cell, with the same human popu-
lation density estimate acrossall combinations. All predictions were expressed as a
percentage net changecompared with a baseline before human land-use effects on
biodiversity, in which all land use was assumed to be primary vegetation of min-
imal intensity of use, and with a human population density of zero. Each cell’s
average value of net biodiversity change was calculated as the area-weighted mean
value across all land uses and intensities. Global average values were calculated as
mean values across all cells, weighted by cell area and an appropriate weighting
factor to account for the fact that cells have different baseline levels of diversity.
The weighting factors applied were: terrestrial vertebrate species richness in the
case of richness, and net primary production (NPP) in the case of total abundance.
No weighting factor was appliedfor projectionsof community-weighted meanplant
height. Terrestrial vertebrate species richness was estimated by overlaying extent-
of-occurrence range maps for mammals, birds, amphibians and reptiles, using
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Extended Data Figure 1
Taxonomic and geographic representativeness
of the data set used. a, The relationship between the number of species
represented in our data and the number estimated to have been described
47 major taxonomic groups. Lines show (from bottom to top) 0.1%, 1% and
10% representation of described species in our data set; magenta, invertebrates;
red, vertebrates; green,plants; blue, fungi; and grey, all other taxonomic
groups. b, The relationship across biomes
between the percentage of global
terrestrial net primary production and the number of sites in our data set;
A, tundra; B,boreal forests and taiga; C, temperate conifer forests; D,temperate
broadleaf and mixed forests; E,montane grasslands and shrublands;
F, temperate grasslands, savannahs and shrublands; G,Mediterranean forests,
woodlands and scrub; H,deserts and xeric shrublands; J, tropical and
subtropical grasslands, savannahs and shrublands; K, tropical and subtropical
coniferous forests; M,tropical and subtropical dry broadleaf forests; N, tropical
and subtropical moist broadleaf forests; P, mangroves; note that the flooded
grasslands and savannah biome is not represented in the data set; grey line
shows a 1:1 relationship.
G2015 Macmillan Publishers Limited. All rights reserved
Extended Data Figure 2
Detailed response of local diversity to human
pressures. ai, Modelled effects (controlling for land use and land-use
intensity) of human population density (HPD), distance to nearest road, time
since 30% conversion of a landscape to human uses (TSC) and time to nearest
population centre with greater than 50,000 inhabitants (ad), interactions
between pairs of these variables (e), and interactions between these variables
and land use (fi) on site-level diversity. ac,f,g, Within-sample species
richness; e,h,i, total abundance; and d, community-weighted mean vertebrate
body mass. Shaded polygons in adshow 95% confidence intervals. For clarity,
shaded polygons in fiare shown as 60.53s.e.m. Confidence intervals in eare
omitted. Rugs along the xaxes in the line graphs show the values of the
explanatory variables represented in the data set used for modelling. Only
significant effects areshown. Note that distance to nearest road and travel time
to major population centre measures are the raw (log-transformed) values
fitted in the models rather than the proximity to roads and accessibility values
(obtained as 1 minus the former values) presented in Fig. 1. Sample sizes are
given in full in the Methods.
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Extended Data Figure 3
Robustness of modelled effects of human
pressures. a, Effects of land use and land-use intensity on rarefaction-based
species richness. b, To test that any differences between these results and the
results for within-sample species richness presented in the main manuscript
were not because rarefied species richness could only be calculated with a
smaller data set, we also show modelled effects on within-sample species
richness with the same reduced data set. c,d, Cross-validated robustness of
coefficient estimates for land use and land-use intensity. Crosses show 95%
confidence intervals around the coefficient estimates under tenfold cross-
validation, excluding data from approximately 10% of studies at a time (c), and
under geographical cross-validation, excluding data from one biome at a
time (d); colours, points, error bars and land-use labels are as in Fig. 1 in the
main text. Sample sizes are given in full in the Methods.
G2015 Macmillan Publishers Limited. All rights reserved
Extended Data Figure 4
Tests of the potential for publication bias to
influence the richness models and projections. Left-hand panels
(a,d,g,j,m) show funnel plots of the relationship between the standard error
around coefficient estimates (inversely related to the size of studies) and the
coefficient estimates themselves for each coarse land-use type; there is evidence
for publication bias with respect to some of the land-use types, as indicated
by an absence of points on one or other side of zero for studies with large
standard errors (but note that small studies are down-weighted in the model).
Red points show studies with more than five sites in the land use in question
(ten for secondary vegetation and plantation forest because there were more
sites for these land uses and some studies withbetween five and ten sites showed
variable responses); horizontal dashed lines show the modelled coefficients for
each land use. Central panels (b,e,h,k,n) show the relationship between study
size (log-transformed total number of sites) and the random slope of the land
use in question with respect to study identity, from a random-slopes-and-
intercepts model. Where a significant relationship was detected using a linear
model, fitted values and 95% confidenceintervals are shown as a red dashed line
and red dotted lines, respectively. Conversely to what would be expected if
publication bias was present, where significant relationships between study
size and random slopeswere detected, these were negative(that is, larger studies
detected more negative effects). Right-hand panels (c,f,i,l,o) show the
robustness of modelled coefficients to removal of studies with few sites in a
given land use (black points in the left-hand panels). Left-hand error bars
show coefficient estimates for all studies and right-hand error bars show
coefficient estimates for studies with more than five sites in that land use (ten
for secondary vegetation and plantation forest).
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Extended Data Figure 5
Tests for spatial autocorrelation in the model
residuals. ad, For the four main modelled metrics of site-level diversity—
within-sample species richness (a), total abundance (b), community-weighted
mean plant-height (c) and community-weighted mean animal mass
(d)—histograms of Pvalues from sets of Moran’s tests for spatial
autocorrelation in the residuals of the best models for individual studies are
shown. The percentage of studies with significant spatial autocorrelation
(P,0.05; indicated by a vertical red line) is shown.
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Extended Data Figure 6
Current, past and future projections of all metrics
of local biodiversity. ad, Net change in local diversity caused by land use and
related pressures by 2000 under an IMAGE reference scenario
. Changes
in richness (a), rarefied richness (b), total abundance (c) and community-
weighted mean plant height (d) are shown. Note that the values used to divide
the colours are the same in all panels, but that the maximum and minimum
values are different, as indicated in the legends. eg, Historical and future
estimates of net change in localdiversity from 1500–2095, based on estimatesof
land-use, land-use intensity and human population density from the four RCP
scenarios (Table 1). Net changes in richness (e), total abundance (f) and
community-weighted mean plant height (g) are shown. Historical (shading)
and future (error bars) uncertainty shown as 95% confidence intervals, with
uncertainty rescaled to be zero in 2005 to show uncertainty in past and future
change separately. The global average projection for the MESSAGE scenario
does not directly join the historical reconstruction because projections start in
2010 (human population estimates are available at 15-year intervals) and
because human population (and thus land-use intensity) and plantation forest
extent have not been harmonized among scenarios. In panel e, the dashed
line shows projected diversity change under land-use change only (that is,
without land-use intensity and human population density, the projections of
which involved simplifying assumptions), and the dotted line shows
projections of rarefaction-based species richness.
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Extended Data Figure 7
Reconstructed and projected total global land-use areas under the RCP scenarios. a, Estimated total area of the major land-use types.
bf, Estimated total area of secondary vegetation in different stages of recovery.
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Extended Data Figure 8
Biodiversity projections at the country level.
ad, Country-levelprojections of net change in local richness between 2005 and
2095 under the four RCP scenarios (IMAGE2.6 (a), MiniCAM 4.5 (b), AIM 6.0
(c) and MESSAGE 8.5 (d)), shown in relation to the Human Development
Index (an indicator of education, life expectancy, wealth and standard of living)
in the most recent year for which data are available. e,f, Country-level
projections of net change in local richness between 2005 and 2095 under the
best- and worst-performing RCP scenarios in terms of biodiversity (MiniCAM
4.5 (e) and MESSAGE 8.5 (f), respectively), shown in relation to past change
in biodiversity from a baseline with no human land-use effects to 2005
according to the HYDE land-use reconstruction. Colours indicate
biogeographic realms (key in b); colour intensity reflects native vertebrate
species richness (more intensecolour represents higher species richness);
point size is proportional to (log) country area.
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Extended Data Table 1
Land use and land-use intensity classification definitions (from ref. 16)
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Extended Data Table 2
Conversion between Global Land Systems data set and our intensity classification for each major land-use type
To estimate proportional coverage of each intensity class for each land-use type in the 0.5u30.5ugrid cells used for projection, we calculated the number of finer-resolution Global Land Systems
cells with a
matching intensity class for the land-use type in question, as a proportion of Global Land Systems cells matching any intensity class for the land-usetype in question. For example, to calculate the proportion of
urban land that is under intense use, we divided the number of cells with a Global Land Systems classification of ‘urban’ by the number of cells classified as ‘urban’ or ‘peri-urban and villages’. None of the Global
Land Systems classes could inform about the intensity of plantation forest, and so we assumed that any plantation forest was composed of equal proportions under minimal, light and intense use.
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... China's forest cover is among the top ten countries in the world but has extensive forest ecosystem degradation, with complex and diverse consequences [5]. Anthropogenic pressures result in forest loss, fragmentation and degradation [6] and have led to substantial declines in biodiversity and increased landscape homogenization [7][8][9]. These negative trends are expected to continue (e.g., [8]). ...
... Anthropogenic pressures result in forest loss, fragmentation and degradation [6] and have led to substantial declines in biodiversity and increased landscape homogenization [7][8][9]. These negative trends are expected to continue (e.g., [8]). Forest restoration is an important way to slow (reduce) forest degradation and is relevant to human wellbeing [10]. ...
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The most visited provincially administered park in Hanzhong City, the South Lake Scenic Area, is degrading the Masson Pine forest communities. Determining and repairing the landscape degradation and impacts on recreational value due to the degraded community structure is essential for restoring the environment of the southern Qinling Mountains. By evaluating the degree, trend, and pattern (DTP) of impacts, we identified the degradation status of the plant community in the South Lake Scenic Area in the past 20 years. We show that the scenic area has experienced an increase in the degradation of vegetation cover in the last 20 years. The area of degraded vegetation cover is significantly larger than the area of improvement, and the overall area is changing, with fewer stable areas. The area of reduced forest cover in the South Lake Scenic Area from 2000–2010 and 2010–2020 has been expanding, and the area of forest land transferred to nonforest land from 2010–2020 has been accelerating compared with 2000–2010; the landscape pattern index has decreased year over year, fragmentation has become serious, landscape connectivity is declining, woodland patches are subject to human disturbance, and patch shapes are simplifying. Based on theories of natural succession and moderate disturbance, the Miyawaki method and interlogging are used to promote plant community renewal and biodiversity restoration. This is intended to shorten the natural succession process in the scenic area and to rapidly restore the ecological foundation of the scenic area. Recovery will meet the aesthetic and ecological values of the South Lake Scenic Area.
... Human activities have significantly altered much of the Earth's surface in recent years [5]. Increasing demands for food, water, and shelter from growing human populations have resulted in large-scale deforestation, expansion of urban construction areas [6], fragmentation and loss of agricultural land [7,8], and accelerated evolution of regional land use patterns globally [9], all of which have inevitably affected changes in and the sustainable development of land use systems [10][11][12]. In addition, the impact of human activities on LUCC has led to irreversible loss of biodiversity [9,13,14], isolation of habitat [15], changes in surface temperature [16], and soil erosion [17,18]. ...
... Increasing demands for food, water, and shelter from growing human populations have resulted in large-scale deforestation, expansion of urban construction areas [6], fragmentation and loss of agricultural land [7,8], and accelerated evolution of regional land use patterns globally [9], all of which have inevitably affected changes in and the sustainable development of land use systems [10][11][12]. In addition, the impact of human activities on LUCC has led to irreversible loss of biodiversity [9,13,14], isolation of habitat [15], changes in surface temperature [16], and soil erosion [17,18]. Irrational land use will also severely affect carbon sources [19], carbon sinks [20], habitat integrity [21], and food production [21,22], which in turn will exacerbate tensions between humans and the environment [23]. ...
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This study analyzed change and spatial patterns of land use in Shanxi from 2000 to 2020. The drivers of land use and cover change (LUCC) in cultivated lands, forest lands, grasslands, and rural construction areas were explored from four dimensions, including population, natural environment, location traffic, and economic development. The CA-Markov model was used to simulate the scenarios of natural growth (NG), ecological protection (EP), economic development (ED), food security (FS), ecological protection–economic development (EP-ED), and ecological protection–food security (EP-FS) in 2030. The results indicated that: (1) The conversion to built-up areas primarily dominated the LUCC processes, and their expansion was mainly to the detriment of the cultivated lands and grasslands during 2000–2020. (2) From 2000 to 2020, population, economy, and land productivity were the main factors of LUCC; the interaction of drivers for the increase of cultivated lands, forest lands, grasslands, and rural construction areas showed enhancement. (3) Under the NG, ED, and EP-ED scenarios, the rural construction areas would have increased significantly, while under the FS and EP-FS scenarios, the cultivated lands would only just have increased. These future land use scenarios can inform decision-makers to make sound decisions that balance socio-economic, ecological, and food security benefits.
... The increasing anthropogenic pressures on the forest ecosystem have had an impact on the abundance and diversity of plants, vertebrates, fungi, and beneficial insects. A growing body of literature on biodiversity has reported the decline of species richness and endemism rate in the intense agricultural and urban locations in the basin (Matson et al., 1997;Preiss et al., 1997;Lavorel et al., 1998;Donald et al., 2001;Benton et al., 2003;Lavergne et al., 2005;Falcucci et al., 2007;Newbold et al., 2015;García-Vega and Newbold, 2020). ...
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The Mediterranean Basin covers more than 2 million square kilometres and is surrounded by three continents: Africa, Asia, and Europe. It is home to more than 500 million people and is projected to reach 670 million by 2050. The basin is rich in species diversity, with a great wealth of endemism. The supply of ecosystem services is greatly challenged due to the trend of land use and land cover (LULC) change coupled with other global change drivers. The current study thoroughly reviewed the existing body of knowledge on the impacts of LULC change on forest ecosystem services. The LULC change is driven by synergetic factor combinations of urbanization, population increase, agricultural land abandonment and deforestation putting additional strain on forest ecosystem services. The review shows the potential impacts on biodiversity as well as ecosystem services such as wood and non-wood forest products, water resources, and carbon stock. Moreover, there is evidence showing the threats of LULC change to saproxylic beetle species, a key agent in the nutrient cycling process, posing a significant risk to a nutrient-deficient ecosystem. Therefore, there is a need to mitigate the challenges posed by LULC change and adapt forest management practices to impending changes to sustain the provision of ecosystem goods and services.
... However, understanding drivers of population trends (i.e. the direction of abundance change for a given species in a specific location) is challenging, partly because the factors that influence population trends are numerous and hard to measure 2 . For instance, whilst a wealth of ecological knowledge has been amassed with regards to how local-scale environmental change, like changes in land-use [3][4][5] and climate [6][7][8] , influences species population trends. Comparably, we know little about the role of socioeconomic factors and their ability to mitigate or magnify local-scale impacts on wildlife populations. ...
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Land-use and climate change have been linked to changes in wildlife populations, but the role of socioeconomic factors in driving declines, and promoting population recoveries, remains relatively unexplored. Here, we evaluate potential drivers of population changes observed in 50 species of some of the world’s most charismatic and functionally important fauna—large mammalian carnivores. Our results reveal that human socioeconomic development is more associated with carnivore population declines than habitat loss or climate change. Rapid increases in socioeconomic development are linked to sharp population declines, but, importantly, once development slows, carnivore populations have the potential to recover. The context- and threshold-dependent links between human development and wildlife population health are challenges to the achievement of the UN Sustainable development goals.
... A 2019 global assessment by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services estimated that up to one million species around the globe are at risk of extinction (Brondizio et al. 2019). Preventing this large-scale biodiversity decline will require action to reduce the intensity of its underlying drivers: habitat loss, degradation, and fragmentation (Newbold et al. 2015). These drivers collectively exacerbate the problem by impeding species movement, or ecological connectivity, which is necessary to allow individuals to access food and water, establish new territories, supplement existing populations, avoid predators, and to find breeding partners (Hilty et al. 2020). ...
Maintaining and restoring ecological connectivity is considered a global imperative to help reverse the decline of biodiversity. To be successful, practitioners need to be guided by connectivity modeling research that is rigorous and reliable for the task at hand. However, the methods and workflows within this rapidly growing field are diverse and few have been rigorously scrutinized. We propose three procedural steps that should be consistently undertaken and reported on in connectivity modeling studies in order to improve rigour and utility: (1) describe the type of connectivity being modeled, (2) assess the uncertainty and sensitivity of model parameters, and (3) validate the model outputs, ideally with independent data. We reviewed the literature to determine the extent to which studies included these three steps. We focused on studies that generated novel landscape connectivity outputs using circuit theory. Among 181 studies meeting our search criteria, 39% communicated the type of connectivity being modeled and 18% conducted some form of sensitivity or uncertainty analysis (or both). Only 19% of studies attempted to validate their connectivity model outputs and only 7% used fully independent data. Our findings highlight a clear need and opportunity to improve the rigour, reliability, and utility of connectivity modeling research. At a minimum, researchers should be transparent about which, if any, of these three steps were undertaken. This will help practitioners make more informed decisions and ensure limited resources for connectivity conservation and restoration are allocated appropriately.
... Here, we used their modelled mean estimates (following Newbold et al. (2015)) of relative percent biodiversity change for each land-system class for species abundance as a measure of the land use pressure ( Figure S2). ...
Protected areas are a key instrument for conservation. Despite this, they are vulnerable to risks associated with weak governance, land use intensification, and climate change. Using a novel hierarchical optimization approach, we identified priority areas for expanding the global protected area system to explicitly account for such risks whilst maximizing protection of all known terrestrial vertebrate species. We illustrate how reducing exposure to these risks requires expanding the area of the global protected area system by 1.6% while still meeting conservation targets. Incorporating risks from weak governance drove the greatest changes in spatial priorities for protection, while incorporating risks from climate change required the largest increase in global protected area. Conserving wide-ranging species required countries with relatively strong governance to protect more land when bordering nations with comparatively weak governance. Our results underscore the need for cross-jurisdictional coordination and demonstrate how risk can be efficiently incorporated into conservation planning. This article is protected by copyright. All rights reserved.
... The Landsat-7 images for the study period were obtained from the USGS and covered the period from 2013 to 2020. Referring to Global Land 30, Jiuzhaigou was divided into ten land cover types including agricultural land, forest, grass land, shrub, wetland, water body, construction land, barren land, snow, and ice (Newbold et al., 2015;Camara, 2020). We obtained samples from high-definition images using manual annotation and used a random forest algorithm to automatically extract land cover information in Jiuzhaigou (Svoboda et al., 2022). ...
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The effects of geohazards on the ecological environment and ecological spatial pattern have received wide attention from scholars. However, the positive role played by ecological restoration projects on the environment and in the reduction of geohazards has usually been neglected. Jiuzhaigou Valley Scenic Area is a world natural heritage area, has a high incidence of geohazards, and is a demonstration area for ecological restoration projects. Based on remote sensing technology, this paper adopted an InVEST model (Integrated Valuation of Ecosystem Services and Tradeoffs) and ecological landscape index to extract information about spatio-temporal changes in Jiuzhaigou from 2013 to 2020. This study utilized a quantitative analysis of the ecological recoverability of Jiuzhaigou in cases of artificial restoration and spontaneous restoration under different types of geohazards. Results showed that forests play a vital role in maintaining and controlling habitat quality; artificial restoration can significantly ameliorate the impact of geohazards on the scenic area. As of 2020, the forested scenic area recovered 3.868 k m 2 , and the habitat quality index rebounded to 98.88% of the historical high. The ecological restoration project significantly shortened the scenic area recover time of its ecosystem service capability.
... Our results showed that urbanization can reduce the network complexity and species richness of phyllosphere microbiota. It has been shown that with the intensification of agricultural land and urbanization, biodiversity is under increasing pressure from human activities (such as climate change and pollution [55] and global biodiversity is declining [56]. This may explain the significantly lower phyllosphere microbial richness of urban camphor trees than that of suburban camphor trees, and may partly explain the lower complexity of the phyllosphere bacterial networks of urban camphor trees. ...
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Studies on microbial communities associated with foliage in natural ecosystems have grown in number in recent years yet have rarely focused on urban ecosystems. With urbanization, phyllosphere microorganisms in the urban environment have come under pressures from increasing human activities. To explore the effects of urbanization on the phyllosphere microbial communities of urban ecosystems, we investigated the phyllosphere microbial structure and the diversity of camphor trees in eight parks along a suburban-to-urban gradient. The results showed that the number of ASVs (amplicon sequence variants), unique on the phyllosphere microbial communities of three different urbanization gradients, was 4.54 to 17.99 times higher than that of the shared ASVs. Specific microbial biomarkers were also found for leaf samples from each urbanization gradient. Moreover, significant differences (R2 = 0.133, p = 0.005) were observed in the phyllosphere microbial structure among the three urbanization gradients. Alpha diversity and co-occurrence patterns of bacterial communities showed that urbanization can strongly reduce the complexity and species richness of the phyllosphere microbial network of camphor trees. Correlation analysis with environmental factors showed that leaf total carbon (C), nitrogen (N), and sulfur (S), as well as leaf C/N, soil pH, and artificial light intensity at night (ALIAN) were the important drivers in determining the divergence of phyllosphere microbial communities across the urbanization gradient. Together, we found that urbanization can affect the composition of the phyllosphere bacterial community of camphor trees, and that the interplay between human activities and plant microbial communities may contribute to shaping the urban microbiome.
... Land-use change, while impacting specialist and generalist species, may also be advantageous to certain endemic species under certain circumstances. Land-use change is often seen in the light of the benefit to people due to agricultural intensification and development, though it results in biodiversity decline and the associated loss in ecosystem function and services to humans (Hooper et al., 2012;Newbold et al., 2015). Future studies need to systematically document the impacts of land-use change on . ...
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Open natural ecosystems like lateritic plateaus, are undergoing rapid transformation with very poor understanding of these impacts on the threatened and endemic biodiversity. The unprotected, low-elevation lateritic plateaus of the northern Western Ghats are case to the point, as they have high endemicity but remain unprotected under Indian law. We aimed to understand the impact of the conversion of the natural lateritic plateaus to agroforestry and paddy cultivation on biodiversity. We compared the prevalence of two species of endemic herpetofauna of the northern Western Ghats (Gegeneophis seschachari and Hemidactylus albofasciatus) and a widespread snake (Echis carinatus) and the composition of other rock-dwelling animals across 12 undisturbed plateau sites and 10 sites each in agroforestry plantations and abandoned paddies on plateaus using time-constrained searches. We had 5738 encounters with 38 different animal species/groups. We found that the abundance of large rocks, which were the most-preferred size class of rocks by animals, was higher in abandoned paddy compared to plateaus and orchards. However, the prevalence of H. albofasciatus and E. carinatus was highest on undisturbed plateaus. Contrastingly, the prevalence of G. seshachari was significantly higher in abandoned paddy than undisturbed plateau or orchards. Non-metric multi-dimensional analysis showed that the assemblage of rock-dwelling fauna differed significantly across the three land-use types. Despite being adapted to persist in extremely variable climates on lateritic plateaus, we find that multiple species/groups are vulnerable to land-use change. However, G. seshachari and a few other taxa appear to benefit from certain kinds of land-use change, highlighting the context-specificity in species responses. While multiple studies have determined the impacts of forest conversion to other land-uses, this is one of the first studies to determine the impacts of the conversion of rocky outcrops, thereby highlighting the conservation value of habitats that are often classified as wastelands.