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Prioritizing global conservation efforts. Nature

The Ecology Centre, Schools of Integrative Biology and Physical Sciences, The University of Queensland, Brisbane, Queensland 4072, Australia.
Nature (Impact Factor: 41.46). 04/2006; 440(7082):337-40. DOI: 10.1038/nature04366
Source: PubMed
ABSTRACT
One of the most pressing issues facing the global conservation community is how to distribute limited resources between regions identified as priorities for biodiversity conservation. Approaches such as biodiversity hotspots, endemic bird areas and ecoregions are used by international organizations to prioritize conservation efforts globally. Although identifying priority regions is an important first step in solving this problem, it does not indicate how limited resources should be allocated between regions. Here we formulate how to allocate optimally conservation resources between regions identified as priorities for conservation--the 'conservation resource allocation problem'. Stochastic dynamic programming is used to find the optimal schedule of resource allocation for small problems but is intractable for large problems owing to the "curse of dimensionality". We identify two easy-to-use and easy-to-interpret heuristics that closely approximate the optimal solution. We also show the importance of both correctly formulating the problem and using information on how investment returns change through time. Our conservation resource allocation approach can be applied at any spatial scale. We demonstrate the approach with an example of optimal resource allocation among five priority regions in Wallacea and Sundaland, the transition zone between Asia and Australasia.

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© 2006 Nature Publishing Group
Prioritizing global conservation efforts
Kerrie A. Wilson
1
, Marissa F. McBride
1
, Michael Bode
1
& Hugh P. Possingham
1
One of the most pressing issues facing the global conservation
community is how to distribu te limited resources b etween
regions identified as priorities for biodiversity conservation
1–3
.
Approaches such as biodiversity hotspots
4
, endemic bird areas
5
and ecoregions
6
are used by international organizations to priori-
tize conservation efforts globally
7
. Although identifying priority
regions is an important first step in solving this problem, it does
not indicate how limited resources should be allocated between
regions. Here we formulate how to allocate optimally conservation
resources between regions identified as priorities for conserva-
tion
the conservation resource allocation problem’. Stochastic
dynamic programming is used to find the optimal schedule of
resource allocation for small problems but is intractable for large
problems owing to the “curse of dimensionality”
8
. We identify two
easy-to-use and easy-to-interpret heuristics that closely approxi-
mate the optimal solution. We also show the importance of both
correctly formulating the problem and using information on how
investment returns change through time. Our conser vation
resource allocation approach can be applied at any spatial scale.
We demonstrate the ap proach with an example of optimal
resource allocation among five priority regions in Wallacea and
Sundaland, the transition zone between Asia and Australasia.
Conservation organizations allocate resources to areas that have
been identified as priorities for conservation investment
3,7
. These
priority regions are identified using information on relative bio-
diversity values, past or present threats to these values, and current
levels of protection
9
. Species richness, or endemic species richness, is
typically used to estimate the biodiversity value of a region
10
. The
relative cost of conservation in different regions is ignored in the
identification of priority regions despite evidence that its inclusion
improves the cost-effectiveness of conservation prioritization
11–15
.
Some international organizations rank these regions in terms of
their priority for funding, but the approaches used to derive these
rankings are not solutions of a properly formulated problem
4,6,16
.If
the objective is to maximize the total number of species conserved,
then this objective is unlikely to be achieved if regions are prioritized
only on the basis of species richness. This is because regions that are
highly threatened but marginally less species-rich may lose many
species before being considered for conservation investment. Like-
wise, if the relative cost of investing in different regions is not taken
into account, resources may be directed to expensive regions when
the same amount of resources might have conserved more species if
invested in regions with lower land-acquisition and management
costs. The efficient allocation of conservation resources will be
achieved only if the problem includes data on biodiversity, threat
and cost, and is rigorously formulated.
Allocation of conservation resources, like any problem in decision
theory, requires a broad goal, a specific objective, a set of constraints,
a set of possible actions that form a strategy, and an understanding of
the system dynamics provided by equations that link the actions
and constraints to the objective
1
(see Methods). Here, the goal is to
maximize biodiversity conservation through the creation of reserves,
given ongoing habitat destruction and the constraint of a fixed
budget. The best strategy
how much money to spend in each region
each year
depends on endemic species richness, forest conversion
rates (and the uncertainty associated with these rates), land cost and
initial conditions (area of land currently reserved, converted or
otherwise; see Table 1 and Supplementary Table). This decision
theoretic formulation provides an explicit and transparent statement
of the problem, which addresses the essential features of conservation
resource allocation: biodiversity values, threats, costs, investment
returns and data uncertainty.
We find an optimal resource allocation schedule using stochastic
dynamic programming (SDP)
8,17
. The SDP algorithm finds the
optimal allocation decision each year given the current state of the
system and possible events in the future
17
. Applying SDP to problems
with more than a few regions is computationally intractable, so we
investigate whether myopic heuristics (‘rules of thumb that look only
one time-step ahead) can approximate the optimal solution. The two
heuristics that we propose as approximations are ‘maximize shor t-
term gain’ and ‘minimize short-term loss’ (see Methods). We
compare the performance of these heuristic s to priority sett ing
appro aches base d on simple rankings, usin g a case study from
Southeast Asia.
To illustrate our conservation resource allocation approach, we
first compare the allocation of resources between two regions,
LETTERS
Table 1 | Biodiversity, threat and cost data for the five priority regions
Priority region Area
(km
2
)
Forested area
(km
2
) in 1997
Reserved area
(km
2
) in 2003
No. of endemic bird
species
Conversion rate
(% yr
21
)
Cost
(US$ km
22
)
Actual Rank Actual Rank Actual Rank
Sumatra 475,746 164,303 84,901 18 4 2.3 2 95 2
Borneo 735,372 426,975 173,989 29 2 2.1 3 110 3
Sulawesi 187,530 79,509 68,150 67 1 2.4 1 76 1
Java/Bali 138,787 19,464 8,770 24 3 1.7 4 782 4
Southern peninsular Malaysia 131,598 58,500 29,221 4 5 1.2 5 2,746 5
1
The Ecology Centre, Schools of Integrative Biology and Physical Sciences, The University of Queensland, Brisbane, Queensland 4072, Australia.
Vol 440|16 March 2006|doi:10.1038/nature04366
337
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© 2006 Nature Publishing Group
Borneo and Sumatra, using the parameters in Table 1. We find that
the optimal schedule is to allocate all resources to Sumatra for over a
decade. Once all species occurring in Sumatra are conserved, invest-
ment is scheduled for Borneo. For this initial case study, the heuristic
that minimizes shor t-term loss most closely approximates the
optimal solution (Fig. 1). However, if there is uncertainty regarding
our ability to invest in a region for the whole planning period, for
example if funding ceases unexpectedly, then maximizing short-term
gains is likely to result in the greatest number of species conserved.
When we modify the problem to include a random probability
of investment ceasing, the optim al allocation schedule more
closely reflects the heuristic that maximizes short-term gain. The
heuristic that maximizes short-term gain allocates funding to both
regions simultaneously, in proportion to the marginal returns from
investment (Fig. 2).
To explore the sensitivity of our results to the parameters, we assess
each approach using different combinations of relative threat and
relative endemic species richness for two hypothetical regions. Both
heuristics perform well, but the heuristic that minimizes short-term
loss is most similar to the optimal SDP solution and outperforms the
other heuristic for most parameter sets (Fig. 3). The heuristic that
maximizes short-term gain performs best when the threat levels of
the two regions are similar, and performs poorly when the regions
have very different threat levels but similar endemic species richness
(Fig. 3a). The heuristic that minimizes short-term loss performs
slightly worse than the optimal solution when both the relative level
of threat and the endemic species richness of the two regions are very
similar (Fig. 3b). If the annual budget is increased, land parcels are
reserved at a fas ter rate. This mitigates forest conversion and,
consequently, the difference between the approaches in the number
of species conserved is reduced. When the relative cost of land
acquisition in each region is varied, the results are similar to those
in Fig. 3 once the axes are adjusted to a species gain per dollar basis.
We next evaluate the performance of the heuristic algorithms,
which we have shown to be close to optimal, for five priority regions
from Southeast Asia. We compare the results of the algorithms with
rankings based on endemic species richness, threat and cost (Table 1
and Fig. 4). This case study is used to i llustrate our resource
allocation approach, which can be applied to any number of priority
regions for which a schedule for resource allocation is required. The
approach can also be applied at any spatial scale, from global level
problems to those at a local level. Our decision theory approach
recommends initially investing all resources in Sulawesi and no other
place until all the species occurring in Sulawesi are conser ved. Only
after this should investment proceed in Sumatra, Borneo and Java.
After investment in Sulawesi ceases, the heuristic that maximizes
short-term gain recommends roughly equal investment in Sumatra,
Borneo and Java, and investment is scheduled last for Malaysia
(Fig. 4a).
By contrast, ranking the five regions based on indiv idual criteria
does not provide an obvious schedule for resource allocation
(Table 1). The three ranking criteria suggest that Sulawesi is the
highest priority for conservation investment and Malaysia is the
lowest. On the basis of only endemic bird richness, the rankings
would recommend that investment should occur first in Sulawesi,
second in Borneo, third in Java, fourth in Sumatra, and last in
Malaysia. Although these simple rankings are not widely different
from the results of our decision theory approach, there are discrep-
ancies, which would occur more frequently for problems involving
more regions. In addition, these rankings do not indicate the fraction
of funds to allocate between the regions (nor whether funds should
be allocated totally to a particular region or distributed between
them). For example, the rankings could mean that investment should
be directed towards Sulawesi until its species are conserved or,
alternatively, that investments should be in proportion to the relative
number of endemic species occurring in these regions. It is also not
clear how these criteria should be combined to provide an allocation
schedul e that will maximize the protection of biodiversity. For
example, if priority is determined only by endemic species richness,
ignoring cost and threat, then Borneo’s priority is overestimated.
Similar confusion can arise if we prioritize only on threat or cost.
We have formulated the conservation resource allocation problem
in a clear and transparent manner that involves defining an objective,
identifying management action s, acknowledging constraints and
incorporating uncertainty. Our problem formulation has five main
simplifications. First, we have not accounted for the spatial variability
within the priority regions and, consequently, the particular parcels
where resources should be allocated are not identified. Second, our
economic model is very simple. Third, we have not accounted for
the temporally heterogeneous nature of land availability for reser-
vation
18
. Fourth, we have used endemic bird species as a surrogate for
biodiversity and assumed that numbers of endemic species reserved
Figure 1 | Proportion of endemic species reserved through time in Borneo
and Sumatra.
The average proportion is calculated by four different
resource allocation approaches: SDP, maximizing short-term gain,
minimizing short-term loss, and random allocation between regions. The
annual budget is US$ 1 million and assumes no pre-existing reserves. The
minimize short-ter m loss heuristic most closely approximates the optimal
SDP solution; however, the maximize short-term gain heuristic results in the
greatest number of endemic species reserved at early time steps.
Figure 2 | Proportion of the total area of Borneo and Sumatra reserved
through time.
The average proportion of the total area reserved is
calculated by three different resource allocation approaches. a, SDP. b, The
heuristic that maximizes short-term gain. c, The heuristic that minimizes
short-term loss. The annual budget is US$ 1 million and assumes no
pre-existing reserves. The approach that maximizes short-term gain tends to
allocate resources to both regions simultaneously, whereas the other, slightly
superior, approaches allocate sequentially between regions.
LETTERS NATURE|Vol 440|16 March 2006
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© 2006 Nature Publishing Group
follows a species–area relationship. Last, we have assumed that the
amount of resources invested in a region is directly proportional to
the p robability of species persisting. These assumptions can be
relaxed w ithin the framework that we present.
There is a need for computationally feasible and understandable
algorithms that can deliver near-optimal solutions for large con-
servation resource allocation problems. The simple heuristics that we
explore were developed to solve a properly formulated problem,
perform surprisingly well relative to the optimal SDP solution, and
are superior to simple ranking approaches. Minimizing short-term
loss most closely approximates the optimal allocation schedule and
maximizing short-term gain is close to optimal despite ignoring
threat, although it underperforms if threat levels are very different.
Under extreme uncertainty, maximizing short-term gain is the most
risk-averse approach as it provides a buffer against an uncertain
investment future.
We recommend that conservation organizations maximize short-
term gain, unless the regions of concern have similar endemic species
richness and ver y different levels of threat. In such circumstances,
resource allocation should minimize short-term loss. We argue that
conservation investments should be evaluated as any investment is
evaluated: that is, with a clearly defined objective and an assessment
of how well the returns from the investment meet this objective.
Responsible conservation organizations and international agencies
should consider embracing a dec ision theoretic approach when
scheduling the allocation of conservation resources.
METHODS
The first step in formulating the conservation resource allocation problem is to
define a quantifiable objective. Our objective is to maximize the number of
endemic species remaining across all regions when habitat conversion ceases
because there is no unreserved or unconverted land (se e Supplementary
Methods ‘Problem formulation’). We assume that, for each region, the number
of endemic species conserved per unit area is a monotonically decreasing
function of the area reserved. Therefore, our conservation returns in each region
diminish with increasing investment. We model this relationship using a species–
area curve
19–21
(see Supplementary Methods (ii)). In principle, any relationship
between area and endemic species conserved could be used. The next step in
formulating the problem is determining what actions are possible, in this case
what fraction of the budget to allocate to each region each year. Our decisions are
thus constrained by a fixed annual budget. Although we recognize that funds for
conservation can be directed towards many kinds of activity (for example,
restoration programs, the purchase of forestry concessions and species recovery
programs), we focus on the acquisition of land for reservation. We assume that
each land parcel can be classified as reserved, available for reservation or
converted (anthropogenically altered and assumed no longer suitable habitat
for the endemic species of the region). Threat is modelled by assuming that a
constant proportion of available parcels in each region are converted each year.
To incorporate the uncertainty associated with parcel loss
22–24
(see Supplemen-
tary Methods (iii)), conversion is represented as a stochastic process with a
binomial distribution. We estimate the cost of reservation in each region using
statistical models
12,25
(see Supplementary Methods (iv)).
We use SDP and two myopic heuristics to determine how many parcels to
reserve in each region each year. We compare these results to a random
acquisition process. Once the optimal solution is obtained, we forward-simulate
the resulting a cquisition schedule 10,000 times for each parameter set to
calc ulate the expected number of species conserved. The solutions found
using SDP are optimal in the face of uncertainty
18,26–28
. Owing to an exponen-
tially increasing state space, however, ‘the curse of dimensionality’ limits SDP to
problems with few regions. The maximize short-term gain heuristic selects
parcels for reservation that result in the greatest increase in the number of
endemic species conserved. This heuristic ignores threat and is myopic, con-
sidering only the short-term future when selecting the next parcel to reserve and
not all possible futures as the SDP algorithm does. The minimize short-term loss
heuristic is also myopic: it selects parcels that will minimize the expected loss of
species from the system in the next time step (see Supplementary Methods (i)).
Received 27 September; accepted 24 October 2005.
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Figure 4 | Proportion of five priority regions from Southeast Asia reserved
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Supplementary Information is linked to the online version of the paper at
www.nature.com/nature.
Acknowledgements We thank C. Elkin, T. Martin and E. Game for comments on
the manuscript; and P. Kareiva, S. Polasky, R. L. Pressey, S. Andelman and
B. Murdoch for discussions. The work was supported by The University of
Queensland and grants from the Australian Research Council (to H.P.P,
M. A. McCarthy and R. L. Pressey).
Author Information Reprints and permissions information is available at
npg.nature.com/reprintsandpermissions. The authors declare no competing
financial interests. Correspondence and requests for materials should be
addressed to H.P.P. (h.possingham@uq.edu.au) or K.A.W.
(k.wilson2@uq.edu.au).
LETTERS NATURE|Vol 440|16 March 2006
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  • Source
    • "Conservation focused planning that is based on costs and conservation benefits can demonstrate best possible outcomes for conservation objectives and provides at the same time a measure for the loss of possible conservation benefits when factors other than cost-efficiency have to be considered. Investment in conservation actions where the rates of return on investment are forecast to be highest is therefore an approach that is increasingly being applied in conservation decision making (Ando et al., 1998; Armsworth and Roughgarden, 2001; Joseph et al., 2009; Murdoch et al., 2007; Polasky et al., 2008; Possingham et al., 2001; Wilson et al., 2006). Several approaches have been developed to inform terrestrial investment decisions to improve water quality, which all have advantages and disadvantages in different contexts. "
    [Show abstract] [Hide abstract] ABSTRACT: Runoff from human land-uses is one of the most significant threats to some coastal marine environments. Initiatives to reduce that runoff usually set runoff reduction targets but do not give guidance on how to prioritize the different options that exist to achieve them. This paper demonstrates an easy to interpret economic framework to prioritise investment for conservation projects that aim to reduce pollution of marine ecosystems caused by runoff from agricultural land-uses. We demonstrate how to apply this framework using data on project cost, benefit and feasibility with a subset of projects that have been funded to reduce runoff from subcatchments adjacent to the Great Barrier Reef. Our analysis provides a graphical overview of the cost-effectiveness of the investment options, enables transparent planning for different budgets, assesses the existence of trends in the cost-effectiveness of different categories, and can test if the results are robust under uncertainty in one or more of the parameters. The framework provided solutions that were up to 4 times more efficient than when omitting information on cost or benefit. The presented framework can be used as a benchmark for evaluating results from a range of prioritisation processes against the best possible conservation outcomes.
    Full-text · Article · May 2016 · Environmental Science & Policy
  • Source
    • "Resource constraints are a major limitation for conservation (Wilson et al., 2006). Identifying priority areas for conservation is thus useful to ensure that resources are efficiently utilized. "
    Full-text · Article · Apr 2016 · Marine Pollution Bulletin
  • Source
    • "Incorporating multiple interacting threats into the selection of management actions for the conservation of biodiversity is important if we are to avoid poor conservation outcomes (Ban et al., 2014; MantykaPringle et al., 2015). Integrating costs also allows us to achieve more for our conservation dollar and consequently make greater gains for conservation overall (Naidoo et al., 2006; Wilson et al., 2006). While it is inappropriate to take our management action rankings too literally, they provide an indication of which actions are the best and most cost-effective in terms of conserving freshwater biodiversity in areas where the negative effects of climate change and/or land-cover change are likely to be high. "
    [Show abstract] [Hide abstract] ABSTRACT: Freshwater ecosystems are declining under climate change and land-use change. To maximize the return on investment in freshwater conservation with limited financial resources, managers must prioritize management actions that are most cost-effective. However, little is known about what these priorities may be under the combined effects of climate and land-cover change. We present a novel decision-making framework for prioritizing conservation resources to different management actions for the conservation of freshwater biodiversity. The approach is novel in that it has the ability to model interactions, rank management options for dealing with conservation threats from climate and land-cover change, and integrate empirical data with expert knowledge. We illustrate the approach using a case study in South East Queensland (SEQ), Australia under climate change, land-cover change and their combined effects. Our results show that the explicit inclusion of multiple threats and costs results in quite different priorities than when costs and interactions are ignored. When costs are not considered, stream and riparian restoration, as a single management strategy, provides the greatest overall protection of macroinvertebrate and fish richness in rural and urban areas of SEQ in response to climate change and/or urban growth. Whereas, when costs are considered, farm/land management with stream and riparian restoration are the most cost-effective strategies for macroinvertebrate and fish conservation. Our findings support riparian restoration as the most effective adaptation strategy to climate change and urban development, but because it is expensive it may often not be the most cost-efficient strategy. Our approach allows for these decisions to be evaluated explicitly.
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