When should we save the most endangered species?
Howard B. Wilson,
* Liana N
Alana L. Moore
Hugh P. Possingham
The University of Queensland,
St Lucia, QLD 4072, Australia
Wildlife Conservation Society,
2300 Southern Boulevard, The
Bronx, NY 10460, USA
University of Melbourne, Parkville,
Victoria 3010, Australia
The University of Queensland,
St Lucia, QLD 4072, Australia
At the heart of our efforts to protect threatened species, there is a controversial debate about whether to
give priority to cost-effective actions or whether focusing solely on the most endangered species will
ultimately lead to preservation of the greatest number of species. By framing this debate within a decision-
analytic framework, we show that allocating resources solely to the most endangered species will typically not
minimise the number of extinctions in the long-term, as this does not account for the risk of less endangered
species going extinct in the future. It is only favoured when our planning timeframe is short or we have a
long-term view and we are optimistic about future conditions. Conservation funding tends to be short-term
in nature, which biases allocations to more endangered species. Our work highlights the need to consider
resource allocation for biodiversity over the long-term; Ôpreventive conservationÕ, rather than just short-term
Anti-triage, decision theory, endangered species, time-frames.
Ecology Letters (2011)
Conservation budgets are limited and inadequate to conserve the
worldÕs biodiversity and there is increasing pressure for prudent
investment (Balmford et al. 2003; Hoffmann et al. 2010). However,
conservation practitioners and the public alike are often polarised
as to what constitutes the wise use of a limited budget. On one
side, proponents of triage, the process of prioritising the allocation
of limited resources to maximise conservation returns, claim that
resources are limited and the threatened species problem is
sufﬁciently large that, to maximise the number of species that we
save, the management of species must be prioritised based on
concepts of cost-efﬁciency (Weitzman 1998a; Possingham et al.
2002; Bottrill et al. 2008; McCarthy et al. 2008; Joseph et al. 2009;
Schneider et al. 2010). This may, under certain circumstances, result
in the decision to not invest in managing highly endangered
species. Iconic or charismatic species, for example, often receive a
disproportionate amount of spending (Male & Bean 2005; Christie
2006), whereas the majority of threatened species receive no, or
very little, funding (Male & Bean 2005). This represents an
opportunity cost; a large number of species could be individually
managed with the money that has been allocated to a few.
Resigning some endangered species to extinction is, however,
socially and politically unpalatable. The alternative point of view is
that this is a defeatistsÕstrategy and that allows policy makers to
give up on highly threatened species that may be expensive to
manage (Mittermeier et al. 1998; Pimm 2000; Marris 2007;
Jachowski & Kesler 2009). The adversaries of the triage philosophy
propose that focusing on the most urgent species now will
maximise our chances of saving the greatest number of species in
These two philosophies are not mutually exclusive. Focusing on the
most urgent species may also be the most cost-effective strategy. Here,
we uncover the conditions under which spending money on the most
endangered of all threatened species may succeed in conserving the
greatest number or nearly the greatest number of species. To do this
we place the debate within a decision-analytic framework, where we
model a set of species and determine the optimal strategy for
allocating resources for a range of conditions. We show that allocating
resources solely to the most endangered species will typically not
minimise the number of extinctions in the long-term as this does not
account for the risk of less endangered species going extinct in the
future. It is only favoured when our planning time frame is short or
we have a long-term view and we are optimistic about future
MATERIALS AND METHODS
We deﬁned a model in which every species is in one of three states:
extinct, endangered or recovered. Species that have gone extinct stay
extinct indeﬁnitely, species in the recovered category remain recovered
indeﬁnitely. At every time step, a species iin an endangered state can
(in this order): recover with probability r
if management intervention
is enacted at a cost h
; recover by chance with a low generic probability
q; or go extinct with probability, p
. After each time step, any species
still in an endangered state goes through the same process. The
recovery by chance or serendipitous event can represent a multitude
of different possibilities; including a reduction of anthropogenic
pressures on that species, technical advances or evolving immunity to
a disease. This has been proposed as one argument for focusing
resources on highly endangered species, namely that urgency is a
catalyst for scientiﬁc innovation (Pimm 2000) and that scientists
should retain hope for breakthroughs that could lead to recovery
(Jachowski & Kesler 2009).
The probability of species ibeing extant at time t,A
, is the sum of
being in states endangered or recovered at t. This probability will
depend on the species parameters p
. No management is when
= 0. So, the increase in the probability of being extant or beneﬁt B
at time tas a consequence of spending on recovery is: B
,q, 0). The benefit to species iafter ttime steps is:
Ecology Letters, (2011) doi: 10.1111/j.1461-0248.2011.01652.x
2011 Blackwell Publishing Ltd/CNRS
The ﬁrst quantity in equation (1) is the probability of recovery via
intervention alone plus recovery via serendipity if intervention fails
)q), summed over all time steps up to time t(multiplied by
the factor in square brackets). The second quantity is the probability
of being in an endangered state at time t, which is the probability of
not having recovered or died: (1 )r
. The third and
fourth quantities are the same except for the case where no resources
are spent on recovery (i.e. r=0).
The cumulative cost at time tis:
and the cost-effectiveness, R
, is the beneﬁt divided by the cost.
), where c
, which is the
expected total cost of a successful recovery (the cost of a recovery
programme divided by the likelihood of success). As tﬁ¥:R
(1 )q)(1 ⁄c
)(1 )[1 )(1 )r
)(1 )q)(1 )p
)] ⁄[1 )(1 )q)
)]). Two key parameters were estimated for 32 endangered
species from New Zealand (Joseph et al. 2009), the probability of
and the expected total cost of a successful recovery,
). These parameters spanned a range of values from highly
endangered, but expensive to recover, to a smaller probability of
extinction, but less costly to recover (Fig. 1). The data for the 32
endangered species are listed in table 1 in Joseph et al. 2009, from
which we also use the cost (=h
), the probability of success (=r
the beneﬁt, B
. The annual probability of extinction used here, p
calculated as 1 )(1 )p
, where p
is the probability of
extinction within 50 years (=B
). The recovery probability, r
the probability of success, although we assume here that the whole
recovery plan can be enacted in 1 year.
We wished to determine how to allocate resources between these
species solely to maximise the number of species extant at time
, for a given budget. We also explored a different objective
function; maximise the total number of species alive in every year up
to time horizon t
. This places value on a species being alive for some
time, even if they are not alive at the time horizon. An objective such
as this may be particularly politically palatable, as species alive now
may ÔbeneﬁtÕcitizens now, even if the species subsequently go extinct.
This objective function is equivalent to maximising the total number
of Ôspecies yearsÕor, equivalently, maximise the sum of the number of
species alive in each year up to time horizon t
. In this case, the
beneﬁt at time tis PB
, where the sum is over j=1tot, and the cost
is the same as before (see Supporting Information). We have not
employed a discount factor, so that species alive in any year have the
same value. Whether discounting is relevant here, and what level and
type of discounting to use, is contentious for such problems with a
social concern (Heal 1998; Weitzman 1998a; Moore et al. 2008),
although there is evidence that people would rather have beneﬁts now
as opposed to the future. None of our results would be qualitatively
changed if a discount factor was employed, although there would be
more emphasis on keeping species alive in the short-term.
Consider a ﬁxed allocation of resources, i.e. the cost-efﬁciency of
directing resources solely at one species when compared with another.
Maximising the total number of species extant at t
for a ﬁxed budget,
is achieved by spending on those species with the highest cost-
. When we ignored the possibility of fortuitous
serendipitous events (i.e. q=0) and we considered only one time step
=1), then R
(Materials and Methods). When the
timeframe was short, the beneﬁt gained was the probability of
extinction (without management) divided by the expected cost, and
the optimal strategy was to allocate resources to the species with the
(Fig. 1a), i.e. species that have high extinction probabil-
ities and low expected recovery cost. In the short-term, at least for
species with equal cost, the beneﬁt was weighted to species that are
more endangered, as there is little beneﬁt to recovering a species with
a low probability of extinction as it would be very likely to survive
even without management. However, even for short-time horizons,
when there are differential costs between species, allocating resources
based on risk of extinction alone will not maximise the number of
As the time horizon of the planning process increased, the optimal
strategy was to allocate resources to the species with the lowest expected
cost of recovery (Fig. 1b). When r
= 1 (and q=0) and species only
differed in the cost of recovery, then R
=[1 )(1 )p
. When t
is large, even species with a low risk of extinction were likely to become
extinct within the timeframes considered and R
converged to 1 ⁄h
converging quicker for higher p
„1, then R
, Materials and Methods). As the time horizon increased, species with
a high probability of extinction, but also high cost became relatively less
cost-effective. There was a switch to spending on less endangered and
easier to recover species, and a focus on maximising the total number of
recovered species as opposed to minimising the number of short-term
extinctions (Fig. 1c). When this switch occurred depended on the
precise distribution of costs and extinction probabilities (see also Hartig
& Drechsler 2008). Spending on less endangered species meant a short-
term loss for a long-term gain; in the short-term, there would be
relatively fewer species extant when compared with spending on more
endangered species, whereas at longer time periods, there would
be relatively more species extant (Fig. 2). A focus on a longer planning
horizon changed some of the allocation of resources away from the
more endangered New Zealand species to the less endangered, but
cheaper to recover species (Fig. 1d). The extent of this difference
(i.e. which species are funded) between short- and long-time horizon
strategies depends on the precise speciﬁcs of species costs and
probabilities of extinction.
When there was optimism about the future, i.e. q>0, for short-
time horizons the optimal allocation between species remained the
same, but the benefit gained was weighted by the probability of no
serendipitous event; R
(Materials and Methods). For
longer time horizons, there were beneﬁts to keeping as many species
extant as possible in the hope that they will recover. The result is a
species allocation intermediate between short (Fig. 1a) and long
(Fig. 1b) time horizons (Fig. 3a). An objective function that seeks to
maximise the total number of species alive in every year up to time
also results in allocating resources to more endangered
species in a very similar way to optimism about the future (Fig. 3b).
When resources can be switched from one species to another
dynamically with time, determining the optimal solution required
2H. B. Wilson et al. Letter
2011 Blackwell Publishing Ltd/CNRS
Stochastic Dynamic Programming (Supporting Information).
We found very similar results to when looking at a ﬁxed allocation
of resources; namely, when there was a long time to the time horizon,
then resources were spent on less endangered, but cheaper to recover
species, but as the time horizon approached, resources were switched
to spending on more endangered, but expensive to recover species.
The only scenarios when resources were directed ﬁrst at highly
endangered, expensive species were when the original time horizon
was short or when the budget was very large compared with the cost
of recovering all species (so that all species could be recovered).
Our results highlight two basic strategies: for short time horizons,
minimise the number of extinctions in the short-term by allocating
resources to the species with the highest ratio of the probability of
extinction to cost; for long-time horizons, recover as many species as
possible by allocating resources based on the lowest expected cost of
recovery. Allocating resources to the most endangered species will not
typically maximise the number of species saved, as this does not take
into account the risk of less-endangered species going extinct in the
future. This result has parallels in the dynamic conservation planning
literature (e.g. Naidoo et al. 2006), although conservation planning also
incorporates concepts such as dependencies and complementarity,
and in reserve site selection studies that include the likelihood of
unprotected land parcels being converted to alternative uses other
than conservation (Costello & Polasky 2004; Meir et al. 2004; Harrison
et al. 2008); analogous to an unrecovered species going extinct here.
Allocating resources to the most endangered species will be closer to
optimal when: (1) the time-period considered is short; (2) we are
optimistic about the future conditions for management of biodiversity;
(3) if the conservation resources are large relative to the number of
threatened species; and (4) if we value species that are alive now even if
they then go extinct. Conditions (1) and (2) do not act synergistically
though, as optimism over future conditions is only beneﬁcial when
considering long time-scales. More generally, these conditions are
unlikely as: (1) long time-frames must be considered if species are to be
conserved in perpetuity; (2) the threats to biodiversity are increasing and
conservation efforts for threatened species are not sufﬁcient (Stokstad
2010), despite major gains in conservation spending and international
Figure 1 The allocation of resources for strategies with different time horizons. (a) Contour plots of lines of equal cost-effectiveness for a 1-year time horizon with no species
recovery by chance. All points (or species) that fall on the same line have equal cost-effectiveness, species below the line are more cost-effective and species above are less cost-
effective. (b) A 100-year time horizon with no species recovery by chance. (c) Schematic showing how the allocation of funds moves from more endangered, but expensive to
recover species when only a short-time horizon is considered (the area under the straight diagonal line with hatching at 45
) to less endangered, but cheaper to recover species
when a longer time horizon is considered (the area under the curve with hatching at 135
). A linear scale for the probability of extinction is used to more clearly show the
differences. Species in the cross-hatched area in the bottom right are allocated resources under both time horizon scenarios. The graph assumes a uniform distribution of
species across the parameter space, so that the total area hatched under each curve is proportional to the total number of species-allocated resources (there is an equal budget
for both strategies). (d) The New Zealand species allocated resources under a ﬁxed budget of $80 million. All species under the curves are funded. The straight line assumes a
planning horizon of 1 year and the curve a 100-year planning horizon. The points on graphs a, b and d are 32 endangered species from New Zealand (Joseph et al. 2009), and
the expected recovery cost is in NZD$M.
Letter Saving endangered species 3
2011 Blackwell Publishing Ltd/CNRS
targets for protected areas; and (3) resources for conservation are grossly
inadequate (Balmford et al. 2003; Hoffmann et al. 2010).
There are other arguments for directing resources to highly
endangered species not considered here. These principally include
that citizens often place greater value on charismatic species; many of
the most endangered species are large animals with large ranges, and
so conservation of their habitat spills over into conservation beneﬁts
for other species, and conservation budgets focused on the most
charismatic will tend to grow with time. Assigning a cultural,
economic or ecological value to a species is a notoriously difﬁcult
task. However, if each species was assigned a value or weighting, w
then these can be simply included within our framework by weighting
the cost-effectiveness of each species, R
, i.e. R
exactly as in the NoahÕs Ark framework (Weitzman 1998b). The result
is that more resources would be directed to the more valued species.
Some endangered species will indeed act as umbrella species, but this
is not a trait restricted solely to charismatic species. Furthermore,
many conservation actions are species-speciﬁc and site-speciﬁc, as
endangered species often do not overlap in range and do not
necessarily require the same management actions. Finally, growing
conservation budgets are also not restricted to charismatic species
(Joseph et al. 2011). Consequently, some of these issues will direct
more funding to highly endangered species, whereas others are not
restricted to highly endangered species or may not applicable. None of
them invalidate the general results presented here.
One last point concerns uncertainty and risk. A possible justiﬁcation
for spending on highly endangered species is that protecting species
that are most likely to go extinct now is a risk-averse strategy.
By keeping more species alive in the short-term, it provides the best
environment to take advantage of future unseen events. We have
attempted to look at this through the use of a probability of future
unseen events helping to recover a species by chance, which results in
a greater weighting of resources to more endangered species.
However, there are clearly more issues around uncertainty than
Conservation funding tends to be short-term in nature, which biases
allocations to more endangered species. However, our results show
that, as in medicine (Messonnier et al. 1999), more emphasis should be
Figure 2 The number of species extant under different resource allocation
scenarios. An $80 million budget is allocated in year 1 based on three different
allocation scenarios for the New Zealand species, and each curve then represents
the number of species extant each year over the next 100 years. (a) The full line
allocates resources to the species with the highest cost-effectiveness assuming a
100-year planning horizon (typically species with low expected recovery costs).
(b) The dotted, light line allocates resources assuming a planning horizon of only
1 year (species that have high extinction probabilities and low expected recovery
costs; the highest p
ﬁrst). (c) The dashed, heavy line (lowest) allocates resources
to the most endangered species ﬁrst (highest probability of extinction ﬁrst). In each
year, species still in the endangered state go extinct with probability p
. The curves
are the average over 1000 simulations. The variances around these curves are very
small, so for clarity are not shown.
Figure 3 Contour plots of lines of equal cost-effectiveness. (a) The effects of
positive events in the future. The full lines assume a time horizon of 100 years and
no species recovery by chance, and the dotted lines assume a time horizon of
100 years and a probability of recovery by chance of 0.05 each year. The effect of
serendipity is to allocate more resources to species with a high probability
of extinction and high recovery cost at the expense of species with a lower
probability of extinction and lower recovery cost. (b) The effect of a different
objective function. All the lines are for a time horizon of 100 years with no species
recovery by chance. The full lines are for an objective function which maximises the
number of species alive at 100 years. The dotted lines are for an objective function
that maximises the total number of species alive in every year up to 100 years. The
effect of valuing extent species even if they do not survive to the time horizon is to
allocate more resources to species with a high probability of extinction and high
recovery cost at the expense of species with a lower probability of extinction and
lower recovery cost, as in (a). The expected recovery costs are in NZD$M and the
diamond points are 32 endangered species from New Zealand.
4H. B. Wilson et al. Letter
2011 Blackwell Publishing Ltd/CNRS
placed on long-term Ôpreventive conservationÕrather than short-term
Ôﬁre-ﬁghtingÕ. Allocating resources to the most endangered of all
threatened species, regardless of cost, may be a logical consequence of
short-term thinking and great optimism about the future.
We thank Richard Fuller and Madeleine Bottrill for helpful comments.
The work was supported by the Centre for Applied Environmental
Decision Analysis, a Commonwealth Environmental Research Facility
Hub funded by the Australian Government Department of
Environment, Water, Heritage and the Arts.
HPP designed the study; HBW and AM contributed to the research,
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Additional Supporting Information may be found in the online
version of this article:
Appendix S1 Derivation of the benefit function with the alternative
objective function and details of the the stochastic dynamic
As a service to our authors and readers, this journal provides
supporting information supplied by the authors. Such materials are
peer-reviewed and may be reorganized for online delivery, but are not
copy edited or typeset. Technical support issues arising from
supporting information (other than missing ﬁles) should be addressed
to the authors.
Editor, Stephen Polasky
Manuscript received 21 March 2011
First decision made 1 May 2011
Manuscript accepted 9 June 2011
Letter Saving endangered species 5
2011 Blackwell Publishing Ltd/CNRS