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Targeting global conservation funding to limit
immediate biodiversity declines
, Arne O. Mooers
, Daniel C. Miller
, Nate Nibbelink
, David Redding
, Tyler S. Kuhn
J. Timmons Roberts
, and John L. Gittleman
Odum School of Ecology, University of Georgia, Athens, GA 30602;
Departamento de Ciencias Biológicas, Universidade Estadual de Santa Cruz, CEP
45662-900, Bahia, Brazil;
Biological Sciences, Simon Fraser University, Burnaby, BC, Canada V6E 1S5;
School of Natural Resources and Environment, University
of Michigan, Ann Arbor, MI 48109;
Department of Genetics, Evolution and Enviornment, University College London, London WC1E 6BT, United Kingdom;
Center for Environmental Studies, Brown University, Providence, RI 02912
Edited* by Peter H. Raven, Missouri Botanical Garden, St. Louis, Missouri, and approved May 13, 2013 (received for review December 14, 2012)
Inadequate funding levels are a major impediment to effective global
biodiversity conservation and are likely associated with recent fail-
ures to meet United Nations biodiversity targets. Some countries are
more severely underfunded than others and therefore represent
urgent ﬁnancial priorities. However, attempts to identify these highly
underfunded countries have been hampered for decades by poor and
incomplete data on actual spending, coupled with uncertainty and
lack of consensus over the relative size of spending gaps. Here, we
assemble a global database of annual conservation spending. We
then develop a statistical model that explains 86% of variation in
conservation expenditures, and use this to identify countries where
funding is robustly below expected levels. The 40 most severely
underfunded countries contain 32% of all threatened mammalian
diversity and include neighbors in some of the world’s most biodiver-
sity-rich areas (Sundaland, Wallacea, and Near Oceania). However,
very modest increases in international assistance would achieve
a large improvement in the relative adequacy of global conservation
ﬁnance. Our results could therefore be quickly applied to limit imme-
diate biodiversity losses at relatively little cost.
aced with the recent failure of the Convention on Biological
Diversity (CBD) signatories to “signiﬁcantly reduce” rates of
biodiversity loss by 2010 (1), the world conservation community
must urgently decide how to target its next efforts to halt the
current extinction crisis. The CBD parties repeatedly listed lack of
ﬁnancial resources as one of the main barriers to meeting CBD
goals in the run-up to the 2010 failure (2). Academic studies have
also documented the global inadequacy of conservation spending
and its relationship to increased rates of species imperilment (3–
7). To improve the chances of fulﬁlling the new 2011–2020 stra-
tegic goals (8), and in particular the goal of effecting a rapid and
substantial reduction in the rate of biodiversity loss, the main
funding institutions need to target additional ﬁnance (3, 4, 7, 9).
To target the allocation of global conservation ﬁnance effec-
tively, assessments of relative underfunding across countries are
essential (10–12). The short time period remaining to achieve the
new strategic goals also implies that underfunding assessments are
urgent. However, 20 y after the original Rio agreement, most
countries are still unable to quantify the relative adequacy of their
levels of conservation ﬁnance, or use widely differing criteria and
even guesswork to do so (4, 9, 12, 13). Even baseline data on
current conservation spending by country have proved difﬁcult to
collate and are highly incomplete (4, 9–13). Biodiversity declines
have progressed rapidly (1), and further delays in improving ﬁ-
nance are likely to lead to even greater global extinction risks, the
opposite of what is needed to make progress on Aichi biodiversity
targets (4, 8, 14). We therefore need tools that can rapidly and
consistently estimate current levels of underfunding by country but
are also robust to current uncertainties in data and knowledge.
Here, we ﬁrst assemble the most complete database of global
conservation spending yet published, including country-speciﬁc
data for $19.8 billion (bn) annually of major conservation funding
(at current values; SI Appendix), ﬂowing from a broad range of
international donors, domestic governments, and other impor-
tant sources (Methods Summary and SI Appendix). We then create
a statistical model that uses current conservation prioritization
factors to explain 86% of the variation in global spending pat-
terns across countries for the period 2001–2008. Finally, we es-
tablish relative levels of funding adequacy across countries and
highlight countries where biodiversity conservation seems most
severely underfunded, by comparing known current levels of
spending with the model’s expectation of spending. We also test
the underfunding assessments for sensitivity to the widely rec-
ognized uncertainties in conservation ﬁnance data (4, 12, 13),
and to choice of allocation model.
A recent assessment suggested that global funding would need
to increase by at least an order of magnitude to meet CBD bio-
diversity targets (without suggesting how that funding should be
distributed among countries) (3). However, such a large increase
may not be politically achievable in time to meet 2020 targets, in
which case we would need to know how to proportionally allocate
a limited pool of resources (15). Our model is therefore designed
to estimate proportional levels of underfunding, making it appli-
cable to the targeting of any size of change in global conservation
The model is based on four main considerations known to be
important in prioritizing global conservation spending (10–12, 16–
21): threatened biodiversity, cost, cost effectiveness (the likelihood
of investment success), and the size of the area to be conserved
(Table 1 and Methods Summary). We develop a politically equita-
ble biodiversity measure, the threatened global biodiversity fraction
(GBF) (Fig. 1A and SI Appendix, Fig. S2), that considers countries
responsible for stewarding the fraction of total global biodiversity
found within their borders (22) (SI Appendix,Fig.S3). Raw GBF is
calculated as the sum of all range fractions in each country, using
global Mammalia—a major target of biodiversity funding (23)—as
our biodiversity surrogate (SI Appendix
). We developed GBF
rather than use simple species counts (7, 10, 11) because species are
often distributed very unevenly between countries, and yet simple
counts allocate equal responsibility irrespective of proportional
Author contributions: A.W., A.O.M., and D.R. designed research; A.W., D.C.M., and J.T.R.
performed research; N.N. contributed new reagents/analytic tools; A.W., D.C.M., and T.S.K.
analyzed data; and A.W., A.O.M., D.C.M., N.N., D.R., T.S.K., J.T.R., and J.L.G. wrote the paper.
The authors declare no conﬂict of interest.
*This Direct Submission article had a prearranged editor.
Freely available online through the PNAS open access option.
Data deposition: The full dataset has been deposited with Dryad (doi 10.5061/dryad.p69t1).
To whom correspondence should be addressed. E-mail: email@example.com.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
July 16, 2013
no. 29 www.pnas.org/cgi/doi/10.1073/pnas.1221370110
distributions. Our ﬁnal measure, threatened GBF, weights raw
GBF by risk of extinction (24) (SI Appendix).
The likel ihoo d of investment success (cost effectiveness) at
the country level should be strongly associated with governance
quality (7, 16, 18, 20), so we tested several possible governance
indicators as potential spending drivers (SI Appendix). We also
tested three possible cost measures and, ﬁnally, country size and
the extent of protected areas as candidate drivers of conservation
budget allocation decisions (Methods Summary and SI Appendix).
Our model premise is therefore that global biodiversity con-
servation spending patterns represent mu ltiple integrated pro-
fessional views abo ut what const itutes effective conservation
investment, with some variation in allocations due to political
and historica l preferences (4, 20, 25, 26) and, importantly, var-
iation due to lack of information on t he summed global spending
patterns themselves. To identify some of the political and his-
tori cal biases that might be driving departures from the model,
we also tested post hoc for largely donor-driven biases in re-
gion al allocation (26), and for reduced funding to Islamic (pre-
dominantly Muslim) countries, particularly Islamic countries in
the Arab world and central Asia (Afghanistan and neighbors)
Results and Discussion
We estimate that the total annual expenditure on global biodiversity
was approximately $21.5bn for 2001–2008 (2005 US dollars, non-
market ﬂows; SI Appendix). Of this amount, approximately $17bn
could be traced to country level (2005 US dollars, $19.8bn at current
values). The unknown $4.5bn largely represents government
spending by Mediterranean countries and spending by local com-
munities (Fig. 1B and SI Appendix). However, the ﬁnal analysis of
ﬁnancial shortfalls was carried out on $16bn, excluding $1bn of
nongovernmental organization (NGO) spending due to inconsistent
geographic coverage (SI Appendix). Traceable NGO ﬂows were
strongly correlated with other donor ﬂows (r = 0.85), suggesting this
omission is unlikely to have biased results (SI Appendix).
A total of $14.5bn of the $16bn analyzed represented domestic
spending, allocated among the four World Bank income catego-
ries (upper, upper-middle, lower-middle, and lower income) in the
proportions 94%, 4%, 2%, and 0.5% (SI Appendix). These data
suggest that domestic spending by developing countries is only
about 10% of previous estimates (27) (SI Appendix). A further
∼$1bn annual expenditure represented international biodiversity
aid. The major biodiversity aid donors were the Global Environ-
ment Facility (22% of biodiversity aid spending) and the World
Bank (19%; see Dataset S1 and data deposition for all donor ex-
penditure). The largest bilateral donors for biodiversity were the
United States (7.5%) and Germany (5%). The $1bn ﬁgure is based
on an explicit categorization of 75,000 aid projects and is again
appreciably lower than the broader “biodiversity-related aid”
often reported by aid donors (7) (SI Appendix). The remaining
$0.5bn is from other sources including conservation trust funds
For the drivers of spending, our best-ﬁtting model explained
86% of the variance in bio diversity conservation investment. We
found that more threatened biodiversity, l arger area requiring
conservati on (both country area and percentage protected area
within country), higher costs, and higher GDP all drove higher
spending (Table 1), exp laining 76% of the variation (deviance)
on their own (SI Appendix). An additional 10% of variation is
explained by two governance indicators: spending increased
nonlinearly in countr ies with better “government effectiveness”
(better policy formulation and implementation; Table 1 and SI
) (28). Once other variables inclu ding govern-
ment effectiveness were controlled for, spending was higher in
countries that had been more politically unstable in 2001– 2008
(Table 1 and SI Appendix,Fig.S1).
Fig. 1B highlights how far actual spending departed from ex-
pected spending in each country (the residual), and Table 2 shows
the 40 most severely underfunded countries (see SI Appendix,Table
S1, for all countries). Owing to the imprecision of ﬁnancial data (4,
Table 1. The best-ﬁtting model to explain global conservation
spending across countries
Predictor Slope t* P
Biodiversity 0.29 2.56 0.012
Country size 0.39 3.60 0.00005
Government effectiveness Spline 10.71 <0.000001
Political stability Spline 3.79 0.003
NPL (cost) 0.52 2.53 0.013
% land protected 0.46 5.81 <0.000001
GDP 0.36 2.64 0.010
GDP (quadratic) 0.15 1.95 0.054
Information-theoretic analysis was used but for reader information, we
include t and P values (n = 121, α = 0.05
*F value and approximate p
for splines) and standardized partial β coefﬁcients for comparability. See
Methods Summary for data transformations.
Fig. 1. (A) Levels of threatened global biodiversity (measured as threatened
mammal GBF; see text and SI Appendix) stewarded by each country. Color
coding is in blocks of 0.5 SDs, with white and blue showing very low and low
threatened diversity (<0.25 SD, −0.25–0.25 SD); yellow, medium diversity; and
the four red colors, high diversity (0.75 SD to >2.3 SD, darker reds indicating
higher values). (B) Underfunding levels from the predictor model (darker col-
ors indicate worse underfunding, in blocks of 20 countries). Somalia was not
analyzed but is probably also highly underfunded (SI Appendix).
Waldron et al. PNAS
July 16, 2013
12, 13), a reasonable policy interpretation would be that countries
with bigger negative residuals (shown as darker colors in Fig. 1B)
form a broad group of highly underfunded countries under current
priorities. We do not regard small differences between individual
country rankings as robustly interpretable. Data were still too
sparse to estimate shortfalls for all 198 countries, but we were able
to determine relative spending adequacy for 124, including all of
the world’s top-50 biodiversity nations (measured using our GBF
index) except Japan and Somalia (SI Appendix).
Table 2 also shows raw dollar differences between expected and
observed spending. All countries are likely underfunded in terms of
conservation (3, 4, 7), so the dollar values shown are unlikely to be
sufﬁcient to halt biodiversity decline by 2020 (8) and above-average
spending should in no way be seen as “overfunding.” However, in
the event that the large amount of extra funding needed to fully
achieve Aichi targets (3, 8) is delayed, deﬁciencies presented here
could be used in a rapid global triage approach (10, 11, 15) as
a proportional guide to the approximate funding improvements
appropriate for each country when resources are limited.
Policymakers should be particularly concerned by highly
underfunded countries that steward high amounts of threatened
biodiversity, so we further extracted all countries found in both
the bottom quartile of relative funding and the top quartile of
threatened biodiversity (measured as threatened mammal GBF).
These were Chile, Malaysia, the Solomon Islands, and Venezuela.
Highly underfunded countries are often neighbors (Fig. 1B),
creating areas where underfunding affects taxa across their entire
ranges. This trend is of particular concern in the geographical
grouping of Malaysia–Indonesia–Australia, a region that holds
a very large amount of threatened biodiversity (Fig. 1A). There is
also a pattern of underfunding in arid and semiarid lands across
Central Asia, Northern Africa, and the Middle East, suggesting
the possibility of global degradation of these biomes.
Table 2. The most highly underfunded countries for biodiversity conservation
1 Iraq 100 100 −0.7
2 Djibouti 100 100 −0.65
3 Angola 100 100 −3.59
4 Kyrgyzstan 100 100 −2.06
5 Guyana 100 100 −4.74
6 Solomon Islands 99.6 100 −0.4
7 Malaysia 98.8 100 −53.3
8 Eritrea 99.2 100 −0.8
9 Chile 98.8 100 −55.44
10 Algeria 100 100 −13.34
11 Senegal 98.8 100 −20.98
12 Trinidad and Tobago 98.4 100 −4.38
13 Vanuatu 97.2 100 −0.6
14 Uzbekistan 96 100 −1.12
15 Morocco 98.4 100 −8.36
16 Slovenia 94.8 100 −6.19
17 Finland 93.6 100 −69.76
18 Congo 91.6 100 −1.35
19 Yemen 95.2 100 −1.33
20 Comoros 92 100 −0.07
21 Ivory Coast 93.2 100 −7.02
22 Mauritania 92.4 100 −1.95
23 Bhutan 86 100 −4.75
24 Slovakia 83.2 100 −9.98
25 Mongolia 90.4 100 −4.34
26 Iceland 70.4 96.4 −30.36
27 Colombia 85.2 89.3 −72.73
28 Venezuela 76 100 −25.02
29 Armenia 80.8 100 −2.44
30 Moldova 72.4 100 −0.34
31 Indonesia 66.4 100 −24.14
32 Jordan 62 96.4
33 Azerbaijan 64.4 100 −1.24
34 Sudan 63.6 89.3 −2.14
35 Botswana 58 96.4 −11.41
36 France 64.8 96.4 −355.49
37 Sri Lanka 51.6 75 −6.08
38 Australia 62 71.4 −275.36
39 China 39.6 75 −75.31
40 Austria 46.4 89.3 −53.08
The 40 most highly underfunded countries are shown, in rank order, along with the percentage of times
that they ranked in the bottom 40 when data were perturbed (column 3) or the model was varied (column 4).
The last column shows the difference between expected and observed spending in $US millions. See SI Ap-
pendix for all countries analyzed.
www.pnas.org/cgi/doi/10.1073/pnas.1221370110 Waldron et al.
The positive inﬂuence of political instability on spending was
only detectable when government effectiveness was also included
in the model (otherwise spending tended to be lower in less
stable countries). Instability considerations may therefore only
represent a minor priority adjustment to the general pattern on
investing more in better-governed countries. Indeed, a sizeable
fraction of the countries identiﬁed as highly underfunded have
suffered recent (and in some cases ongoing) armed conﬂicts, e.g.,
Iraq, Somalia (SI Appendix), and several countries in central
Asia, North and West Africa, and South East Asia (29), sug-
gesting that a net donor reticence to investing in countries in
conﬂict still exists overall (29). Globally, countries in conﬂict
have high levels of both biodiversity and threat (29, 30). Donor
reticence therefore deserves careful consideration because re-
moval of funding may make a bad situation even worse.
Table 2, perhaps surprisingly, includes some developed coun-
tries, e.g., Finland, France, Iceland, Australia, and Austria. We note
that, toward the end of the study period and in forward-looking
budget plans, many of those countries made very large increases in
their biodiversity funding allocations (SI Appendix), although in the
case of Australia and France such increases are still smaller than the
modeled shortfalls in Table 2. We discuss developed country pat-
terns further in SI Appendix. We also modelled developed and
developing/emerging countries separately. The results were ex-
tremely similar to the all-country analysis presented here, with the
same developing countries being listed as highly underfunded
whether or not developed countries were included in the model
(with the one exception of China; SI Appendix).
When testing for political and historical biases, we found that
predominantly Islamic Arab/central Asian countries had only
49% of the funding that countries in the rest of the world re-
ceived for similar levels of biodiversity, size, cost, and governance
(t = −3.31, P = 0.001; SI Appendix). Donors are the main source
of funding for these countries and so an underfunding pattern
may reﬂect donor bias. The pattern may also help explain the
severe underfunding of arid biomes globally and deserves further
investigation. There was a similar but weaker pattern of reduced
funding with increasing percentage of Muslim population glob-
ally (SI Appendix ). Regional funding differences were detectable,
but were dropped from the model when terms related to the
predominance of Islam were included (SI Appendix).
Decision makers may need to further investigate subnational or
country-speciﬁc investment contexts when targeting allocations at
a ﬁner scale (16, 31). They should also be aware that the in-
vestment efﬁciency of current institutional weightings for factors
such as cost effectiveness (governance) remains largely untested in
the scientiﬁc literature. Nevertheless, underfunding patterns un-
der the model remain surprisingly consistent even when gover-
nance terms are omitted (e.g., 75% of the countries in Table 2
remain unchanged; SI Appendix). No country-level breakdown was
available for the estimated $2bn spent by local developing-country
communities annually on conservation (32) and no quantitative
sensitivity test of how this might affect the results was possible.
Over the longer term, scientists and policymakers will achieve
better funding, more comprehensive data, and more sophisticated
allocation tools. For example, mathematical efﬁciency algorithms
have been developed, principally suggesting how to allocate re-
sources for the purchase and capitalization of new protected areas,
e.g., by 2030–2040 (16). Theoretically, it should be possible to develop
a similar but broader algorithm to estimate efﬁcient funding alloca-
tions for all conservation actions globally, including ﬁnance needs for
existing protected area maintenance, future land purchases, and the
full range of conservation activities outside protected areas. Never-
theless, such an approach will require comprehensive and accurate
global data, extensive testing of whether the conclusions are sensitive
to the precisely speciﬁed priorities and weightings, and global polit-
ical consensus on exact weightings, a currently infeasible combination
of conditions (16, 20, 33). Developing and applying such an algorithm
could therefore take several years.
In the meantime, rapid methods that work robustly within
current uncertainties could signiﬁcantly reduce short-term bio-
diversity losses (14) and also reduce the need for future expendi-
tures (4), especially if the methods also reﬂect current institutional
priorities. Our estimates of relative underfunding levels proved
robust to possible data inaccuracies and competing allocation
models (see Methods Summary, Table 2, and SI Appendix, Table
S1, for qualitative robustness). Judicious application of the under-
funding patterns revealed here may therefore reduce short-term
biodiversity losses with appreciably greater efﬁciency than would
current spending patterns.
Short-term biodiversity losses may indeed be substantial if fund-
ing patterns are not improved: the 40 most highly underfunded
countries in our analysis steward 32% of all threatened global bio-
diversity (threatened mammal GBF), including many of the species
that moved into a higher category of extinction risk between 1996
and 2008 (1). However, most of these highly underfunded countries
are developing nations, where only a modest absolute dollar in-
vestment would generate a large correction in relative underfunding
(Table 2 and SI Appendix,TableS1). Our results therefore suggest
that international conservation donors have the opportunity to act
now, in a swift and coordinated fashion, to reduce an immediate
wave of further biodiversity declines at relatively little cost.
We collated a database of country-level conservation funding ﬂows from
multiple sources including government, donors, trust funds, and self-funding
via user payments, and then calculated average annualized spending 2001–
2008 (in constant 2005 US dollars). Formally speaking, global conservation ﬁ-
nance data represent an unknowable population for statistical modeling (4, 9,
13), and the database represents a very large sample, an order of magnitude
larger and more representative than previous comparable work (10, 11) (SI
Appendix). We created candidate regression models using threatened mam-
mal GBF, country area, percent protected area, gross domestic product (GDP),
the cost measures national price level (NPL), and the conservation action unit
(the recurrent cost of maintaining 100 km
of protected area for 1 y; SI Ap-
pendix), ﬁve possible governance indicators and an Island term, and then used
information-theoretic approaches to test model ﬁts. Diagnostic plots sug-
gested nonlinearities (especially in governance and GDP responses) and non-
normality, so we ln(x + constant)-transformed all variables except NPL and
percent protected area, added several generalized additive mixed models with
cubic splines to the candidate model set, and tested possible quadratic terms.
Residuals were tested for spatial autocorrelation by semivariogram plots and
by adding several possible spatial covariance structures and comparing Akaike
information criterion (AIC) values. There was no strong collinearity and no
spatial autocorrelation in the residuals. Relative funding adequacy was de-
ﬁned as the residuals from the model, scaled by total spending. We repeated
the regression 1,000 times with perturbed spending data, drawing each per-
turbed amount from a random normal distribution with mean of the original
value and 1 SD = 25% of the original value. We also reran the analysis for a ll 26
models that provided a medium to good ﬁt(ΔAIC < 10). We tested post hoc for
improvement in ﬁt when percentage Muslim population (globally or in Arab/
central Asian countries only) and/or political region were added to the model.
See SI Appendix and Dataset S1 for further details.
ACKNOWLEDGMENTS. We thank colleagues at Simon Fraser Univer sity and
T. Brooks for methodological discussion and comments and several referees
for comments on previous versions of this manuscript. The work was
supported in part by a Natural Sciences and Engineering Research Council
Canada Discovery grant (to A.O.M.), the Odum School of Ecology (A.W. and
J.L.G.), and the MacArthur Foundation through the Advancing Conservation
in a Social Context research initiative (D.C.M. and J.T.R.).
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