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Abstract

Conservation funds are grossly inadequate to address the plight of threatened species. Government and conservation organizations faced with the task of conserving threatened species desperately need simple strategies for allocating limited resources. The academic literature dedicated to systematic priority setting usually recommends ranking species on several criteria, including level of endangerment and metrics of species value such as evolutionary distinctiveness, ecological importance, and social significance. These approaches ignore 2 crucial factors: the cost of management and the likelihood that the management will succeed. These oversights will result in misallocation of scarce conservation resources and possibly unnecessary losses. We devised a project prioritization protocol (PPP) to optimize resource allocation among New Zealand's threatened-species projects, where costs, benefits (including species values), and the likelihood of management success were considered simultaneously. We compared the number of species managed and the expected benefits gained with 5 prioritization criteria: PPP with weightings based on species value; PPP with species weighted equally; management costs; species value; and threat status. We found that the rational use of cost and success information substantially increased the number of species managed, and prioritizing management projects according to species value or threat status in isolation was inefficient and resulted in fewer species managed. In addition, we found a clear trade-off between funding management of a greater number of the most cost-efficient and least risky projects and funding fewer projects to manage the species of higher value. Specifically, 11 of 32 species projects could be funded if projects were weighted by species value compared with 16 projects if projects were not weighted. This highlights the value of a transparent decision-making process, which enables a careful consideration of trade-offs. The use of PPP can substantially improve conservation outcomes for threatened species by increasing efficiency and ensuring transparency of management decisions.
Contributed Paper
Optimal Allocation of Resources among Threatened
Species: a Project Prioritization Protocol
LIANA N. JOSEPH,‡ RICHARD F. MALONEY,† AND HUGH P. POSSINGHAM
The Ecology Centre, School of Integrative Biology, University of Queensland, St Lucia 4072 Australia
†Threatened Species Development, Ecosystems and Species Unit, Research and Development Group, Department of Conservation,
Christchurch, New Zealand
Abstract: Conservation funds are grossly inadequate to address the plight of threatened species. Government
and conservation organizations faced with the task of conserving threatened species desperately need simple
strategies for allocating limited resources. The academic literature dedicated to systematic priority setting
usually recommends ranking species on several criteria, including level of endangerment and metrics of species
value such as evolutionary distinctiveness, ecological importance, and social significance. These approaches
ignore 2 crucial factors: the cost of management and the likelihood that the management will succeed.
These oversights will result in misallocation of scarce conservation resources and possibly unnecessary losses.
We devised a project prioritization protocol (PPP) to optimize resource allocation among New Zealand’s
threatened-species projects, where costs, benefits (including species values), and the likelihood of management
success were considered simultaneously. We compared the number of species managed and the expected
benefits gained with 5 prioritization criteria: PPP with weightings based on species value; PPP with species
weighted equally; management costs; species value; and threat status. We found that the rational use of cost and
success information substantially increased the number of species managed, and prioritizing management
projects according to species value or threat status in isolation was inefficient and resulted in fewer species
managed. In addition, we found a clear trade-off between funding management of a greater number of the
most cost-efficient and least risky projects and funding fewer projects to manage the species of higher value.
Specifically, 11 of 32 species projects could be funded if projects were weighted by species value compared with
16 projects if projects were not weighted. This highlights the value of a transparent decision-making process,
which enables a careful consideration of trade-offs. The use of PPP can substantially improve conservation
outcomes for threatened species by increasing efficiency and ensuring transparency of management decisions.
Keywords: conservation planning, conservation priorities, cost–benefit analysis, probability of success, species
management, species values, threat status
Asignaci´
on ´
Optima de Recursos entre Especies Amenazadas: un Protocolo de Priorizaci´
on de Proyectos
Resumen: Los fondos para la conservaci´
on son insuficientes para atender la dif´
ıcil situaci´
on de las especies
amenazadas. Las organizaciones gubernamentales y de conservaci´
on encargadas de la tarea de conservar
especies amenazadas desesperadamente requieren de estrategias simples para la asignaci´
on de recursos limi-
tados. La literatura acad´
emica dedicada a la definici´
on sistem´
atica de prioridades generalmente recomienda
clasificar a las especies considerando varios criterios, incluyendo el nivel de peligro y medidas del valor de las
especies como la unicidad evolutiva, la importancia ecol´
ogica y el significado social. Estos m´
etodos ignoran
dos factores cruciales: el costo del manejo y la probabilidad de que el manejo sea exitoso. Estas omisiones
resultar´
an en la asignaci´
on err´
onea de recursos de conservaci´
on escasos y, posiblemente, en p´
erdidas innece-
sarias. Dise˜
namos un protocolo de priorizaci´
on de proyectos (PPP) para optimizar la asignaci´
on de recursos
entre los proyectos sobre especies amenazadas de Nueva Zelanda, en el que se consideraron simult´
aneamente
los costos, los beneficios (incluyendo valor de las especies) y la probabilidad de ´
exito del manejo. Comparamos
email l.joseph@uq.edu.au
Paper submitted November 19, 2007; revised manuscript accepted August 4, 2008.
328
Conservation Biology, Volume 23, No. 2, 328–338
C
2008 Society for Conservation Biology
DOI: 10.1111/j.1523-1739.2008.01124.x
Joseph et al. 329
el n´
umero de especies manejadas y los beneficios esperados obtenidos con cinco criterios de priorizaci´
on: PPP
con ponderaciones basadas en el valor de las especies; PPP con ponderaciones equitativas de especies; costos de
manejo; valor de las especies y estatus de amenaza. Encontramos que el uso racional de la informaci´
on de
costo y ´
exito increment´
o sustancialmente el n´
umero de especies manejadas, y la priorizaci´
on de proyectos
de manejo de acuerdo con el valor de las especies o el estatus de amenaza fue insuficiente y result´
oen
menos especies manejadas. Adicionalmente, encontramos una clara desventaja entre el financiamiento de la
mayor´
ıa de los proyectos m´
as rentables y menos riesgosos y el financiamiento de menos proyectos para mane-
jar las especies de mayor valor. Espec´
ıficamente, 11 de 32 proyectos pudieran ser financiados si los proyectos
fueran ponderados por el valor de las especies en comparaci´
on de 16 proyectos si no hubiera ponderaci´
on.
Esto resalta el valor de un proceso transparente de toma de decisiones, que permite una consideraci´
on
cuidadosa de los pros y contras. El uso de PPP puede mejorar sustancialmente los resultados de la conser-
vaci´
on de especies amenazadas al incrementar la eficiencia y asegurar la transparencia de las decisiones de
manejo.
Palabras Clave: an´
alisis de costo-beneficio, estatus de amenaza, manejo de especies, planificaci´
on de la con-
servaci´
on, prioridades de conservaci´
on, probabilidad de ´
exito, valores de las especies
Introduction
The resources available for conserving the world’s bio-
diversity are grossly inadequate for the task. The exist-
ing expenditure is just a fraction of the predicted to-
tal cost of maintaining global biodiversity (James et al.
2001; Balmford et al. 2003). Currently, only a small pro-
portion of species recognized as threatened with ex-
tinction are managed for recovery (Baillie et al. 2004).
In the years 1989–1991, 54% of U.S. funding was de-
voted to conservation of just 1.8% of all U.S. threatened
species (Metrick & Weitzman 1996). Similarly, in 2006
only 22% of New Zealand’s threatened species were man-
aged, and many of these were inadequately managed to
ensure persistence (Joseph et al. 2008). Given this short-
fall in available resources, it is essential that the capital
available for management of threatened species be spent
wisely.
Formal methods for setting threatened-species priori-
ties are often conducted by identifying species that are
highly threatened or valuable and ranking them accord-
ing to one or both criteria. The management of species
near the top of the rank-ordered list is then funded (Miller
et al. 2006). A number of frameworks exist for ranking
species based on criteria such as the level of endanger-
ment (Master 1991; Carter et al. 2000), evolutionary dis-
tinctiveness (Faith 1994; Vane-Wright et al. 1994; N. J.
Isaac, S. T. Turvey, B. Collen, C. Waterman, and J. E.
Baillie. 2007. Mammals on the EDGE: conservation pri-
orities based on threat and phylogeny. Public Library of
Science ONE 2:e296.), a combination of these and so-
ciopolitical significance (Rodr´
ıguez et al. 2004), ecologi-
cal importance, and potential for recovery (Marsh et al.
2007). Understanding how to value species and the ur-
gency required for management is essential for making
good decisions. Nevertheless, any method that considers
only these criteria and ignores financial and technical con-
straints will result in fewer species being managed and
potentially unnecessary extinctions (IUCN 2001; Possing-
ham et al. 2002; Mace et al. 2006; Miller et al. 2006).
A species of low threat status or value may be cheap
to secure, whereas management of a highly threatened
or valuable species may be costly and have little po-
tential to reduce extinction risk. By failing to consider
the cost of management, the technical capacity to man-
age, and the potential for species recovery, standard ap-
proaches to priority setting make the implicit assump-
tion that the conservation budget is large enough to
fund all projects. This incorrect assumption results in
misallocation of scarce conservation resources and po-
tentially more extinctions. Simultaneously considering
species value and financial and technical constraints en-
sures that cost-efficient management is given priority and
will maximize conservation outcomes.
The Noah’s Ark framework (Metrick & Weitzman 1998;
Weitzman 1998) provides a cost-efficient solution to
the problem of threatened-species resource allocation.
The framework considers the benefits (i.e., increase in
the probability of a species persisting under a recovery
project), costs of the recovery project, species contri-
bution to diversity (i.e., distinctiveness), and value of
the species (i.e., utility of a species). On the basis of
mathematical theory (i.e., knapsack theory, Kellerer et al.
2004), with a slightly flexible total budget of a reasonable
size, the Noah’s Ark framework presents an optimal or
near-optimal solution (i.e., a greedy solution, Martello &
Toth 1990; Weitzman 1998; Hartmann & Steel 2006).
Although the Noah’s Ark framework provides an ap-
propriate methodology for considering costs and ben-
efits of conservation, it fails to consider a factor that is
crucial for making sound decisions to maximize conserva-
tion outcomes: the probability that the management will
succeed. A management action that is likely to succeed
should be given a higher priority than an action that is
Conservation Biology
Volume 23, No. 2, 2009
330 Cost-Efficient Conservation Priorities
likely to fail. Managers often have a good understanding
of their confidence in management projects, but these
judgments have never been included in a formal decision-
making process. Thus, we developed the Noah’s Ark
framework to include the likelihood that management
will succeed.
In addition, we extended the Noah’s Ark concept and
applied it to a genuine management problem. Only theo-
retical examples are described in the literature (Weitz-
man 1993). We developed the theoretical framework
into an operational Project Prioritization Protocol (PPP)
that requires setting conservation objectives (Nicholson
& Possingham 2006) and targets (see Sanderson 2006),
clarification of realistic working definitions of benefits,
costs and values, and careful consideration of the effects
of data limitations. We applied the PPP to the problem of
minimizing the number of extinctions of species over 50
years with a fixed budget. We used the framework in a
case study of the allocation of conservation resources for
managing the threatened species of New Zealand, includ-
ing a representative range of terrestrial and freshwater
taxonomic groups.
Our aim was to investigate efficiency of the PPP in
choosing the optimal set of management actions to max-
imize the overall benefit to a region’s threatened species
under financial constraints. We compared 5 priority-
setting methods: PPP with weightings based on species
value, PPP with species weighted equally, management
costs, species value, and threat status. We examined their
efficiency with 4 metrics of success: number of species
managed; expected benefit gained by managing the set
of species; summed value of the set of species managed;
and summed expected value gained by managing the set
of species. In summary we assessed the gains in efficiency
in spending afforded by properly incorporating informa-
tion about species values and the management costs, ben-
efits, and likelihood of management success.
Methods
Case Study: Management of the Threatened Species of New
Zealand
New Zealand is experiencing a massive biodiversity cri-
sis. Over the last 100 years, there have been 33 docu-
mented extinctions, including 16 birds, 9 terrestrial inver-
tebrates, and 6 vascular plants (King 1984; Hitchmough
et al. 2005). Of the more than 90,000 indigenous species,
about 2,000 are listed as threatened and another 3,000 are
data deficient under New Zealand’s threatened-species
classification system (Hitchmough et al. 2005). The De-
partment of Conservation has a total budget of approxi-
mately NZ$32 million/year (Department of Conservation
2006) specifically to improve the status of threatened
species.
Project Prioritization Protocol
The PPP consisted of 9 steps: (1) define objectives, (2)
list biodiversity assets (in this case, threatened species),
(3) weight assets, (4) list management projects, (5) cal-
culate the costs of each project, (6) predict the benefit
to species generated by each project, (7) estimate likeli-
hood of success, (8) state constraints, and (9) combine
information on costs, values, benefits and likelihood of
success to rank projects according to benefits per unit
dollar and choose set of projects.
STEP 1: DEFINE OBJECTIVES
To optimally allocate resources among management
projects, it is essential to clearly state the objectives (Poss-
ingham et al. 2001; Sanderson 2006). The general goal set
by New Zealand’s Department of Conservation Threat-
ened Species Program is to improve security of the great-
est possible number of unique species. The term security
refers to initial security from extinction in the wild. Secu-
rity is achieved when available evidence indicates there
is a viable population (i.e., one or more spatially discrete
populations) that is stable and will be able to recover in
the future once key agents of decline have been removed
or mitigated. The uniqueness of a species is measured as
its taxonomic distinctiveness (see step 3).
STEP 2: LIST BIODIVERSITY ASSETS
Biodiversity assets are the components of biodiversity
that one wishes to secure from extinction. In our case
study, biodiversity assets were species that have 95%
probability of being secure in 50 years if they are not man-
aged. We assumed that species that are not likely to be se-
cure in 50 years are those that are currently listed in one of
the following threat categories of New Zealand’s threat-
ened species classification system (Hitchmough et al.
2005): nationally critical (NC), nationally endangered
(NE), nationally vulnerable (NV), serious decline (SD),
gradual decline (GD), sparse (S), and range restricted
(RR).
To illustrate our protocol, we used 32 species listed
on New Zealand’s list of threatened species that repre-
sented a broad range of threat categories (15 NC, 10 NE,
1 NV, 2 SD, 2 GD, 1 S, and 1 RR), taxonomic groups
(4 frogs, 1 bat, 7 birds, 3 freshwater fish, 3 reptiles, 4 ter-
restrial invertebrates, and 10 vascular plants), measures
of taxonomic distinctiveness (number of close relatives,
see step 3), and potential management actions (e.g., cap-
tive breeding and translocation, fencing, predator con-
trol). The common and scientific names of the case-study
species are in Table 1.
STEP 3: WEIGHT ASSETS
Social, political, or biological values may be incorporated
into PPP by weighting species (see step 9). Species may
Conservation Biology
Volume 23, No. 2, 2009
Joseph et al. 331
Table 1. A list of the project parameters (benefit, B;cost,C; and probability of success, S) and species parameters (taxonomic distinctiveness, W; and threat status) that were used to calculate
weighted and unweighted project efficiency.
Weighted Unweighted
efficiency efficiency Taxonomic Probability
Common name Scientific name 1e12 1e9 Benefit distinctiveness Cost ($) of success Threat status
Dactylanthus Dactylanthus taylorii 218502 927 0.70 0.236 755,103 1.00 4 serious decline
Maud Island frog Leiopelma pakeka 47849 550 0.70 0.087 1,273,311 1.00 2 nationally endangered
Shrubby tororaro Muehlenbeckia astonii 43206 473 0.40 0.091 760,615 0.90 3 nationally vulnerable
Hamilton’s frog Leiopelma hamiltoni 39012 448 0.60 0.087 1,338,631 1.00 1 nationally critical
North Island brown kiwi Apteryx mantelli 26854 120 0.95 0.224 7,910,292 1.00 4 serious decline
Climbing everlasting daisy Helichrysum dimorphum 22112 942 0.35 0.023 371,438 1.00 2 nationally endangered
Hochstetter’s frog Leiopelma hochstetteri 22099 254 0.10 0.087 354,472 0.90 6 sparse
New Zealand shore plover Thinornis novaeseelandiae 15296 355 0.40 0.043 855,261 0.76 1 nationally critical
Pittosporum patulum 12902 294 0.95 0.044 3,229,002 1.00 2 nationally endangered
Oreomyrrhis sp. 12746 869 0.70 0.015 724,823 0.90 2 nationally endangered
nov [ =O.aff rigida]
Pachycladon exilis 12573 515 0.95 0.024 884,866 0.48 1 nationally critical
Archey’s frog Leiopelma archeyi 11168 128 0.70 0.087 4,909,753 0.90 1 nationally critical
Canterbury mudfish Neochanna burrowsius 8425 173 0.95 0.049 891,340 0.16 2 nationally endangered
Carmichaelia hollowayi 3721 637 0.70 0.006 751,950 0.68 1 nationally critical
Poa spania 3370 743 0.70 0.005 847,626 0.90 1 nationally critical
Chatham Island Haematopus chathamensis 2768 223 0.95 0.012 1,798,486 0.42 1 nationally critical
oystercatcher
Kaki Himantopus novaezelandiae 1962 102 0.95 0.019 8,851,848 0.95 1 nationally critical
Cardamine cf. bilobate 1647 216 0.95 0.008 874,373 0.20 1 nationally critical
Black robin Petroica traverse 1311 107 0.40 0.012 1,534,592 0.41 1 nationally critical
Pygmy button daisy Leptinella nana 704 86 0.60 0.008 297,571 0.04 2 nationally endangered
Cook Strait giant weta Deinacrida rugosa 592 509 0.40 0.001 785,586 1.00 7 range restricted
Big-nose galaxias Galaxias macronansus 525 28 0.95 0.019 2,047,005 0.06 5 gradual decline
Long-tailed bat Chalinolobus tuberculate 332 31 0.95 0.011 6,210,151 0.21 2 nationally endangered
Carabid beetle Zecillenus tillyardi 325 117 0.70 0.003 1,242,714 0.21 1 nationally critical
Lowland long jaw galaxid Galaxias cobitinis 283 15 0.95 0.019 1,757,261 0.03 1 nationally critical
Orange-fronted parakeet Cyanoramphus malherbi 266 6 0.95 0.041 14,453,678 0.10 1 nationally critical
Mohua Mohoua ochrocephala 188 26 0.40 0.007 5,762,489 0.38 2 nationally endangered
Grand skink Oligosoma grande 159 58 0.95 0.003 7,754,153 0.48 1 nationally critical
Otago Skink Oligosoma otagense 159 58 0.95 0.003 7,754,153 0.48 1 nationally critical
Short horned grasshopper Sigaus minutus 49 33 0.90 0.001 3,795,354 0.14 5 gradual decline
Chevron skink Oligosoma homalonotum 35 13 0.70 0.003 7,888,632 0.14 2 nationally endangered
Robust grasshopper Brachapsis robustus 19 8 0.90 0.002 5,636,964 0.05 2 nationally endangered
Minimum 19 6 0.10 0.0012 297,571 0.03 7 range restricted
Maximum 218502 942 0.95 0.2357 14,453,678 1.00 1 nationally critical
Mean or median 23651500.73 0.01673,259,484 0.56 2 nationally endangered
(denoted by )
Coefficient of variation 0.33 1.06 0.67
Fold difference 11661 146 10 203 49 36 7
Conservation Biology
Volume 23, No. 2, 2009
332 Cost-Efficient Conservation Priorities
be weighted on the basis of factors such as cultural signif-
icance, economic importance, evolutionary significance,
ecological function, and endemicity, or species may be
treated equally (i.e., no weights). If more than one type
of value is used to weight species, the values can be com-
bined by summing their weighted scores. The Noah’s
Ark framework combines 2 species values: distinctive-
ness and species utility. We considered only distinctive-
ness and assumed the utility of species was the same for
all species (i.e., equal to zero).
The weighting factor, Wi, quantified the taxonomic
distinctiveness of species i, where distinctiveness was
inversely related to the number of relatives of species
i. For example, a species that has many close relatives
(e.g., robust grasshopper) was given a small weight,
whereas a species that has few close relatives (e.g.,
North Island Brown Kiwi) received a large weight. We
expressed taxonomic distinctiveness as the inverse of
the product of the number of branches at the genus,
family, and order nodes (i.e., a modified version of the
method of Daniels et al. [1991]). We believe taxonomic
distinctiveness is well characterized for our purposes by
this method because it delivers a metric that is contin-
uous and encapsulates diversity at the species, genus,
and family levels. This method is rapid and inexpensive
and can be applied to a simple, widely available, and
partially resolved taxonomic system, such as Linnaean
taxonomy.
By weighting species by their taxonomic distinctive-
ness, we sacrificed species richness for taxonomic di-
versity (Solow et al. 1993). We maintained the order of
species while reducing the magnitude of the weighting
so that less emphasis was placed on taxonomic diver-
sity and more on species richness by taking the xth root
of measure of distinctiveness (Solow et al. 1993). The
choice of xdepended on the relative value of taxonomic
diversity and species richness; we used x=2because
it provided a balance between conserving the greatest
number of unique species and simply the greatest quan-
tity of species. Therefore, we measured the taxonomic
distinctiveness for species, Wi,asthexth root of the in-
verse of the product of the number of branches, b,atthe
genus, family, and order nodes, n:
Wi=x
1
n
b,(1)
where x= 2. We used the on-line version of the An-
nual Checklist (http://www.catalogueoflife.org/annual-
checklist/2007/) of the Catalogue of Life (Bisby et al.
2007) to estimate the number of branches at each node.
The Catalogue of Life is incomplete and some of the
species or genera are not listed. When a species or genus
of interest was absent from the list, we added this taxon
to the total number of branches.
STEP 4: LIST MANAGEMENT PROJECTS
We asked experts to choose an appropriate project for
each species. A project was the minimum set of all neces-
sary actions for obtaining a reasonable (95%) probability
of securing the species over 50 years. Projects had 4 com-
pulsory components (outcome monitoring, services and
support, project management, and infrastructure) and at
least one optional intervention (e.g., captive breeding,
translocation, pest animal control, weed control, legal
actions, and education). Experts clearly described a pre-
cise location, intensity, and duration of management for
each action. Because there was a one-to-one match of
projectsandspecies,weusedtheindexifor both.
STEP 5: ESTIMATE COST
The cost of each project, Ci, was estimated each year
and converted to current dollar values. Costs included
all future outlays, whereas past outlays (e.g., the cost
of building captive-breeding facilities that are now avail-
able for use) were not considered. We calculated the net
present value of the cost to fund each project over 50
years as
Ci=
50
t
ci,t
(1 +r)t,(2)
where cis the cost of project iin year tand ris a discount
rate derived from the present value of future spending
(i.e., discount factor). For example, if it cost $10,000/year
to control stoats now, $395,881 in today’s dollars (r=
0.01) would be needed for stoat control over 50 years.
Effectively, our choice of the discounting rate (e.g., r
>0) resulted in the cost of actions in the future being
cheaper in today’s dollars. In the case in which an action
benefited more than one species, the cost of the action
among the species projects could be shared; thus, actions
that benefited multiple species were favored.
STEP 6: ESTIMATE BENEFITS
The impact of project ion the species probability of being
secure was recorded as the biodiversity benefit, Bi.Se-
curity probabilities are related to extinction probabilities
and may be calculated from stochastic population mod-
els (Burgman et al. 2001; Drechsler & Burgman 2004) or
estimated directly with expert knowledge (Cullen et al.
2001, 2005). Because knowledge available for many of
the species is not sufficient to estimate parameters for
population viability models, we used experts to estimate
security probabilities directly. The biodiversity benefit of
project i,Bi, is the difference between the probability of
the species being secure in 50 years with (Pi) and without
(P0) management:
Bi=PiP0.(3)
Conservation Biology
Volume 23, No. 2, 2009
Joseph et al. 333
STEP 7: ESTIMATE LIKELIHOOD OF SUCCESS
We asked experts to state the probability that each
project, i, could be implemented successfully, Mi,and
the probability that, if implemented successfully, it would
be reasonably (95%) successful in securing the species,
Ni. Only actions that would have a direct impact on the
probability of security (e.g., translocation) were assessed,
whereas essential actions that would not have direct ef-
fects (i.e., compulsory actions such as infrastructure and
service support) were assumed to have 100% probabil-
ity of succeeding. Total probability of success of each
project, Si, was Si=MiNi.
To aid in estimation of the probabilities of implemen-
tation success, we asked experts to provide probabil-
ities on a series of possible limitations to the success
of the projects. Factors that may contribute to failure
of the project included operational (e.g., isolation, dif-
ficulty moving around terrain, capacity, unpredictable,
prohibitive weather), legal (e.g., resource-management
acts, building codes, health and safety, animal ethics),
and political and social (e.g., public attitude, conflict-
ing land use, iwi views, access permission) constraints.
Experts estimated the probability of successfully imple-
menting each action of project i, and we combined the
probabilities to calculate the total probability of success-
ful implementation of the full project, Mi:
Mi=
J
j=1
Kij,(4)
where Kij is the probability that action jof project i
works.
Experts directly estimated the probability of technical
success, Nj, of each action in the project through demon-
strated evidence and confidence in project choice. Ex-
perts based their estimates on knowledge of the propor-
tion of successes and failures of past implementation and
said whether their opinions were based on information
relevant to the same or other species, habitats, threats,
and scales. We combined the probability of technical suc-
cess for each action to calculate the total probability of
technical success for the ith project, Nias follows:
Ni=
J
j=1
Lij,(5)
where Lij is the probability that action jof project isuc-
ceeds.
STEP 8: STATE CONSTRAINTS
The primary constraint on the resource-allocation prob-
lem was the total budget available for management of
threatened species. We focused on 32 species, and the
total budget was proportional to the budget available for
all 2000 threatened species. The total budget available
for the management of 2000 threatened species in the
2005–2006 financial year (i.e., $32,000,000) was equiva-
lent to an annual budget of $512,000. Allocation of this
amount among 32 species projects resulted in a budget
of $20,269,096 for the 50-year period (with r=0.01
discounting).
STEP 9: CHOOSE SET OF PROJECTS
The Noah’s Ark framework ranks species or species
projects on the basis of ranking criterion, R,whichis
a cost-efficiency metric:
Ri=Wi×pi
Ci
,(6)
where pis analogous to our biodiversity benefits, B,
and is defined as the change in survivability of a species
iand Wis the sum of distinctiveness and species utility.
We modified the cost-efficiency measure to include the
likelihood of success of a project and called the modified
metric the project efficiency, E,ofprojecti.TheEiwas
calculated as:
Ei=Wi×Bi×Si
Ci
,(7)
where Wiis the species weights, Biis the biodiversity
benefits, Siis probability of success, and Ciis the cost of
project i.
COMPARISON OF PRIORITY SETTING METHODS
We used 4 metrics to measure efficiency: number of
species managed; expected benefit gained by managing
the set of species (i.e., sum of the product of the bene-
fit and the probability of success); uniqueness of the set
of species managed (i.e., summed uniqueness values);
and expected uniqueness gained by managing the set
of species (i.e., the sum of the product of the benefit,
the probability of success, and the taxonomic distinctive-
ness). We compared the efficiency of prioritizing man-
agement projects by their project efficiency with species
weighted by taxonomic distinctiveness with 4 other crite-
ria: project efficiency when species are weighted equally,
cost, taxonomic distinctiveness, and threat status.
Project efficiency with species weighted by taxonomic
distinctiveness was calculated with the methodology de-
scribed earlier (hereafter referred to as weighted project
efficiency). We ranked species projects in order of de-
creasing weighted project efficiency values: small val-
ues were ranked lower than high values. The project
efficiency when species were weighted equally was cal-
culated in the same manner; however, species weights
were considered equal to each other and equal to one
(hereafter referred to as unweighted project efficiency).
Conservation Biology
Volume 23, No. 2, 2009
334 Cost-Efficient Conservation Priorities
We ranked species projects in decreasing order of the
unweighted project efficiency value. The procedure for
calculating project costs is outlined earlier. We ranked
projects in increasing order of price. Taxonomic distinc-
tiveness was calculated with the methods described ear-
lier. Projects were ranked on the basis of the taxonomic
distinctiveness of each species: species with few close rel-
atives were ranked higher than those with many close rel-
atives. To rank projects by species threat status, we used
the threat categories and species status of New Zealand’s
Classification Species List (Hitchmough et al. 2005). In
this system species are listed in 1 of 8 categories of threat.
We assumed an order of decreasing extinction risk from
NC, NE, NV, SD, GD, S, and RR to not threatened. We
ranked projects in order of decreasing extinction risk of
the species. To distinguish among species within threat
categories, we ranked projects by their cost: inexpensive
projects were given a higher ranking. We selected cost
of management because it is an intuitive and important
consideration and information required to rank manage-
ment projects by cost is readily available (compared with
probability of success or benefits).
Table 2. Species projects ranked with the weighted project efficiency (columns 1 & 2) and priority ranks obtained with the 4 other priority-setting
methods: unweighted project efficiency, cost, taxonomic distinctiveness, and threat status.
Weighted Unweighted Cost Distinctiveness Threat
Species efficiency rank efficiency rank rank rank status rank
Dactylanthus 126127
Maud Island frog 2615420
Shrubby tororaro 397326
Hamilton’s frog 4101657
North Island Brown Kiwi 518 30 2 28
Climbing everlasting daisy 61313 17
Hochstetter’s frog 7132631
New Zealand Shore Plover 8111010 3
Pittosporum patulum 91221 9 21
Oreomyrrhis sp.nov(=O. aff rigida)10
3417 18
Pachycladon exilis 1171212 5
Archey’s frog 12 17 23 711
Canterbury mudfish 13 1613819
Carmichaelia hollowayi 14 5524 1
Poa spania 15 4925 2
Chatham Island Oystercatcher 16 141918 10
Kaki 17 21 31 14 14
Cardamine cf. bilobata 18 151122 4
Black Robin 19 20 1719 8
Pygmy button daisy 20 22 121 16
Cook Strait giant weta 21 8832 32
Big-nose galaxias 22 27 2015 29
Long-tailed bat 23 26 26 20 24
Carabid beetle 24 19 1426 6
Lowland long jaw galaxid 25 29 1816 9
Orange-fronted Parakeet 26 32 32 11 15
Mohua 27 28 25 23 23
Grand skink 28 23 27 27 12
Otago skink 29 24 28 28 13
Short-horned grasshopper 30 25 22 31 30
Chevron skink 31 30 29 29 25
Robust grasshopper 32 31 24 30 22
Species selected with a fixed budget of $20,269,096.
Results
The management project parameters (costs, benefit, and
probability of success) and species parameters (taxo-
nomic distinctiveness and threat status) spanned a wide
range and generated informative project efficiency values
for species management projects (Table 1).
Order of Species Projects
The order of species projects varied considerably for each
of the 5 priority-setting methods (Table 2). For the pro-
jected available budget of $20,269,096, a different set of
projects was selected by each of the priority-setting meth-
ods. The first 4 methods (both project efficiency, cost and
distinctiveness methods) selected comparatively similar
sets of projects, whereas the threat-status ranking crite-
rion’s set of projects was noticeably different. For threat-
status ranking, 3 projects were selected in common with
both of the project efficiency and cost methods, whereas
these 3 methods shared 9 of a possible 11 projects in
common.
Conservation Biology
Volume 23, No. 2, 2009
Joseph et al. 335
Figure 1. Returns on spending budgets up to $50 million over 50 years for each of the 5 priority-setting methods:
project efficiency with species weighted by taxonomic distinctiveness (weighted E); project efficiency when species
are weighted equally (unweighted E); cost; taxonomic distinctiveness (distinctiveness); and threat status (threat).
Returns are measured as (a) number of species managed, (b) expected benefit gained by managing the set of
species (i.e., summed product of benefit and probability of success), (c) uniqueness of the set of species managed
(i.e., summed taxonomic distinctiveness), and (d) expected uniqueness gained by managing the set of species (i.e.,
product of the benefit, probability of success, and taxonomic distinctiveness).
Number of Species Funded
The number of species projects that may be funded with
a range of budgets (Fig. 1a) and the projected budget
(Table 3) differed for each of the 5 priority-setting meth-
ods. The rank order produced by prioritizing projects
Table 3. The returns on the spending of $20,269,096 over 50 years for each of the 5 priority-setting methods.
Efficiency Weighted Unweighted Taxonomic Threat
metrics efficiency efficiency Cost distinctiveness status
Number of species managed 11 16 20 8 11
Expected benefit gained (BS) 6.09 7.39 6.86 4.18 4.02
Uniqueness of species managed (W) 0.96 0.82 0.83 0.95 0.31
Expected uniqueness gained (WBS) 0.61 0.42 0.38 0.59 0.15
Returns are measured as number of species managed; expected benefit gained by managing the set of species (i.e., the summed product of
benefit, Band probability of success, S); uniqueness of the set of species managed (i.e., summed taxonomic distinctiveness, W); and expected
uniqueness gained by managing the set of species (i.e., the summed product of the benefit, B; probability of success, S; and taxonomic
distinctiveness, W).
on the basis of cost always resulted in the most projects
being funded for any given budget. Ranking projects
with the unweighted project efficiency criterion was
the second-most efficient method. Ranking species by
taxonomic distinctiveness always resulted in the fewest
species managed.
Conservation Biology
Volume 23, No. 2, 2009
336 Cost-Efficient Conservation Priorities
Expected Benefit Gained
The expected benefit gained by funding projects with
the projected budget (Table 3) and across the range of
budgets (Fig. 1b) differed among the 5 methods. The rank
order generated by prioritizing projects on the basis of
the unweighted project efficiency metric was always the
most efficient because it generated the highest expected
benefit for a given budget. Prioritizing projects on the ba-
sis of threat status or taxonomic distinctiveness produced
the lowest expected benefit.
Uniqueness of Species Managed
Interestingly, ranking projects by taxonomic distinctive-
ness did not produce a rank order of species with the
highest uniqueness for the majority of budgets (Fig.
1c), including the projected available budget (Table 3).
For some budgets, 3 of the priority selection methods
(cost, weighted and unweighted project efficiency crite-
ria) generated a rank order that selected a set of species
with a higher summed uniqueness value than ranking
by taxonomic distinctiveness. Generally, ranking species
projects by the weighted project efficiency produced a
rank order with the highest summed uniqueness. Con-
versely, ranking species by threat status consistently se-
lected a set of species with the lowest summed unique-
ness.
Expected Uniqueness Gained
Across the range of budgets, the expected gains in
uniqueness was maximized if projects were ranked by the
weighted project efficiency (Table 3 & Fig. 1d). Ranking
projects on the basis of taxonomic distinctiveness gen-
erated the second-highest expected gains in uniqueness.
For larger budgets, the unweighted project efficiency cri-
terion was equally as efficient as the weighted index;
however, for smaller budgets, this method generated
lower expected gains in uniqueness. Ranking projects
by the species threat status performed poorly across all
budgets because this ranking method produced very low
expected gains in uniqueness.
Discussion
The PPP provided a means for prioritizing management to
maximize conservation outcomes for threatened species
when budgets were limited. Efficiency in spending was
improved by considering project costs, benefits, and like-
lihood of success. The unweighted project efficiency cri-
terion resulted in the maximum benefits gained for all
budgets. The expected gains in value (i.e., uniqueness)
from projects were maximized by simultaneously con-
sidering values, benefits, costs, and the probability of
management success.
Inadequate Priority-Setting Methods: Threat Status and
Values
Methods for prioritizing projects that focus exclusively
on threat status resulted in management of fewer species
and procurement of less benefit to threatened species in
general. The projected available budget for threatened
species management was enough to fund only 11 species
projects when ranked by threat status, compared with
the 16 projects that would be funded if species were
ranked with the unweighted project efficiency ranking
criterion. In addition, ranking species by threat status ig-
nored species distinctiveness and hence selected projects
for a set of species that were not highly valued.
Likewise, to prioritize projects efficiently, it is not suf-
ficient to consider a species value, such as taxonomic
distinctiveness, in isolation. Ranking species by their
taxonomic distinctiveness resulted in appreciably fewer
projects (8 projects) being funded with the available
budget when compared with either of the project ef-
ficiency ranking methods (weighted project efficiency
=11 projects and unweighted project efficiency =16
projects). In addition, the expected benefit gained was
substantially lower than the unweighted project effi-
ciency method (taxonomic distinctiveness =4.18, un-
weighted project efficiency =7.39). Significantly, for the
majority of budgets, ranking projects by taxonomic dis-
tinctiveness did not maximize the summed taxonomic
distinctiveness for the set of priority projects; in many
cases, ranking projects by their cost or unweighted
and weighted project efficiency scores performed bet-
ter. Nevertheless, the expected uniqueness gained when
ranking species on taxonomic distinctiveness was only
slightly lower than the optimal choice of ranking with
weighted project efficiency for all budgets. Prioritizing
projects by species distinctiveness produced gains in
uniqueness by giving high priority to a few species with
high taxonomic distinctiveness rather than, as with the
weighted project efficiency method, a greater number of
species that are less taxonomically distinct.
Values and Objectives
A trade-off exists between funding the management of
a greater number of the most cost-efficient and least
risky projects and funding fewer projects to manage
the species of higher value (i.e., uniqueness). The un-
weighted project-efficiency method resulted in the man-
agement of 5 additional species because it ignored
species distinctiveness (i.e., unweighted project effi-
ciency =16 species compared with weighted project
efficiency =11 species). Five of 32 species is 15.6% of
the case-study species; this is a considerable proportion,
equivalent to 312 of the 2000 species listed as threatened
in New Zealand.
Specifically, the unweighted project-efficiency method
ranked the species such that 6 additional species,
Conservation Biology
Volume 23, No. 2, 2009
Joseph et al. 337
including 3 plants (Carmichaelia hollowayi,Cardamine
cf. bilobata,andPoa spania), a bird (Chatham Island
Oystercatcher), a fish (Cantebury mudfish), and an inver-
tebrate (Cook Strait giant weta) could be funded at the
expense of the highly taxonomically distinct North Island
brown kiwi. The additional 6 species were not individ-
ually taxonomically distinct (median =0.007), but they
were all relatively cheap to manage (mean =$991,560,
CV =0.40) and had high benefits (mean =0.78, CV =
0.28) or a high potential for management success (mean
=0.56, CV =0.63).
To maximize the expected value (in this case, taxo-
nomic distinctiveness) of a set of species, it is essential to
weight management benefits by the species value. Nev-
ertheless, to what degree should efficiency (e.g., number
of species managed or total expected benefit) be sacri-
ficed for the preferential allocation of resources to highly
valued species? The North Island Brown Kiwi has high
taxonomic distinctiveness (W=0.224); hence, it is highly
valued by the weighted project efficiency criterion. Nev-
ertheless, is funding this species worth sacrificing 6 other
species, even if each is individually relatively taxonom-
ically indistinct? Managers need to think clearly about
their intention for weighting species by their uniqueness
or any other value and the ramifications of the form of
metric selected. The PPP provides a transparent approach
to project selection that promotes scrutiny of decisions
such as this.
Department of Conservation policy
New Zealand’s Department of Conservation is using the
PPP to enable efficient allocation of resources among
management projects for its work on threatened species
and ecosystems. Initial prioritization is aimed at maximiz-
ing the number of species secured from extinction. Ad-
ditional objectives include restoring threatened species
to self-sustaining populations and maximizing ecolog-
ical integrity. They have used PPP to assess manage-
ment projects and generate a rank-ordered list of species
projects to guide senior managers in resource allocation
decisions for more than 2000 species. The process has
taken <1.5 years and 105 experts have been consulted.
We do not believe this process is beyond the capabil-
ities of any national government department, including
mega-diverse countries, where data are limited. If there is
enough data to list the species on threatened species lists,
then there should be enough data to rank management
projects with project prioritization protocol.
Conclusion
Our protocol for prioritizing management actions inte-
grates biological data, financial and technical constraints,
and social and/or biological values. The framework pro-
vides a systematic, transparent, and repeatable method
for prioritizing actions to minimize the number of extinc-
tions. Clearly stating the steps used to make decisions
presents an opportunity to scrutinize and improve the
decision-making process; initiates a forum for the explicit
examination of management principles and limitations,
including the development of unambiguous working ob-
jectives; and reveals knowledge gaps and uncertainty in
the system. We demonstrated that to select management
actions that maximize conservation outcomes, it is insuf-
ficient to prioritize species based solely on threat status or
species value. Correspondingly, return on investment of
conservation dollars is substantially improved by incorpo-
rating management costs, benefits, and likelihood of man-
agement success. Consequently, the number of species
managed and the expected overall benefit to threatened
species is increased remarkably.
Acknowledgments
We thank S. O’Connor, P. Cromarty, A. Holzapfel, L. Hart-
ley,J.Terry,B.Kappers,J.Davis,N.Singers,L.Adams,
E. Edwards, G. Taylor, J. Reardon, T. Stephens, D. God-
den and the more than 100 threatened species experts
for their contributions and support. This work was sup-
ported by the New Zealand Department of Conserva-
tion and the Applied Environmental Decision Analysis
research hub that is funded by the Commonwealth Envi-
ronment Research Facilities program.
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Conservation Biology
Volume 23, No. 2, 2009
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... Ready-to-use prioritization frameworks that help decision-makers allocate funds to actions, post megafire, are critical for guiding action for recovering species as quickly as possible. They also allow for transparent and robust decision making processes, ensuring that the limited resources allocated to conservation are spent efficiently and costeffectively (Joseph et al. 2009;Waldron et al. 2017). We provide a decision-support framework that explores the considerations needed to prioritize conservation actions needed immediately after a megafire. ...
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Due to climate change, megafires are increasingly common, and have sudden, extensive impacts on many species over vast areas, leaving decision‐makers uncertain about how best to prioritize recovery. Here, we provide a decision‐support framework to prioritize conservation actions to improve species outcomes immediately after a megafire. The framework selects complementary locations to extend actions across all impacted species' habitats. We then assess the conservation advantages of this approach by comparing it to a site‐richness approach (i.e., identifying areas that can cost‐effectively recover the most species in any one location). Using the 2019‐2020 Australian megafires as a case study, we show that 290 threatened species have likely been severely impacted and likely require immediate conservation action to ensure their survival. Our framework identified 179 subregions, found mostly in south‐east Australia, as key locations to extend actions that benefit multiple species. We compare our complementarity‐based prioritization with a conventional site‐richness approach and demonstrate cost savings of more than AUD$300 million to reduce 95% of threats across all species. In addition to cost efficiencies, our complementarity‐based prioritization spreads post‐fire management actions across a wider proportion of the study area compared with site richness (43% versus 37% of the landscape managed, respectively), and ensures more of each species' range is managed (average 90% versus 79% of every species' habitat managed). In addition to wildfire response, our complementarity‐based management allocation framework can be used to prioritize conservation actions that will best mitigate threats impacting species following other environmental disasters like floods and drought, all of which are likely to increase in intensity and frequency under future climate change. This article is protected by copyright. All rights reserved
... Global conservation efforts advocate for the establishment of networks of marine reserves covering 30% of the oceans by 2030 (IUCN, 2016;CBD, 2020). While countries work to legally develop comprehensive national marine spatial plans (Frazão Santos et al., 2020), it is important that local and regional-scale efforts establish marine reserves that achieve maximum conservation benefits while considering user compliance (Carwardine et al., 2008;Joseph et al., 2009;Wenger et al., 2018). We assessed the effect of data resolution on the efficiency of a marine spatial plan for an overexploited system which requires balancing the recovery of its marine resources and biodiversity with the interest of the area's stakeholders (Dalton et al., 2010;Gurney et al., 2015). ...
Article
Establishing marine reserves is particularly challenging in highly populated coastal areas where stakeholders compete for resources and space, such as the Mediterranean Sea. While large-scale conservation planning is useful, there is a need for finer-grained assessments at local and regional scales. Yet fine scale environmental and socioeconomic data are not always available. Here, we evaluate the influence of the spatial resolution of biodiversity and socioeconomic data on the selection of priority areas for conservation in Montgrí, the Illes Medes and the Baix Ter Natural Park, Spain. We used varying levels of habitat data, from fine-scale maps created using detailed bathymetry and underwater surveys to less detailed ones using existing data or broader classifications. We also used different estimates of the cost of protection, from a combined recreational and artisanal fishing cost obtained through local consultation and in situ mapping to simple indirect measures such as distance to port. Our results reveal that conservation planning scenarios that do not use fine-scale bathymetry are ineffective at protecting biodiversity and only represent 40% or less of the habitats. Scenarios that only include recreational fishing, which is limited to certain planning units in the park, perform very poorly at minimizing cost, highlighting the need to use cost estimates that include all planning units. We conclude that local-scale data will often be needed to support and guide local-scale efforts to expand or establish new marine reserves.
... In this section we give a brief overview of the methodology, but greater detail is available elsewhere (Martin et al., 2009;Clemen and Reilly, 2013). Throughout this section, we use the Project Prioritization Protocol (PPP) for threatened species funding allocation in New Zealand and New South Wales as an illustration to give a "real world example" for each step (Joseph et al., 2009;Brazill-Boast et al., 2018). Before PPP was deployed, the allocation of funding to threatened species was largely ad hoc, determined by individuals without public transparency, focussed almost entirely on the species most likely to go extinct or of an iconic nature, and with no explicit objective and hence no measurable outcome. ...
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Scientific knowledge and advances are a cornerstone of modern society. They improve our understanding of the world we live in and help us navigate global challenges including emerging infectious diseases, climate change and the biodiversity crisis. However, there is a perpetual challenge in translating scientific insight into policy. Many articles explain how to better bridge the gap through improved communication and engagement, but we believe that communication and engagement are only one part of the puzzle. There is a fundamental tension between science and policy because scientific endeavors are rightfully grounded in discovery, but policymakers formulate problems in terms of objectives, actions and outcomes. Decision science provides a solution by framing scientific questions in a way that is beneficial to policy development, facilitating scientists’ contribution to public discussion and policy. At its core, decision science is a field that aims to pinpoint evidence-based management strategies by focussing on those objectives, actions, and outcomes defined through the policy process. The importance of scientific discovery here is in linking actions to outcomes, helping decision-makers determine which actions best meet their objectives. In this paper we explain how problems can be formulated through the structured decision-making process. We give our vision for what decision science may grow to be, describing current gaps in methodology and application. By better understanding and engaging with the decision-making processes, scientists can have greater impact and make stronger contributions to important societal problems.
... However, limited financial resources and competing stakeholder interests constrain the geographic area that can reasonably be protected. The process of identifying potential regions for designation as protected area (PA) should therefore be undertaken thoroughly and strategically ( [2,3], see [4] for a review). The striking obstacle is that biodiversity is very complex and difficult to characterize [5], and surveying biodiversity in its entirety is nearly impossible. ...
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Because it is impossible to comprehensively characterize biodiversity at all levels of organization, conservation prioritization efforts need to rely on surrogates. As species distribution maps of relished groups as well as high-resolution remotely sensed data increasingly become available, both types of surrogates are commonly used. A good surrogate should represent as much of biodiversity as possible, but it often remains unclear to what extent this is the case. Here, we aimed to address this question by assessing how well bird species and habitat diversity represent one another. We conducted our study in Romania, a species-rich country with high landscape heterogeneity where bird species distribution data have only recently started to become available. First, we prioritized areas for conservation based on either 137 breeding bird species or 36 habitat classes, and then evaluated their reciprocal surrogacy performance. Second, we examined how well these features are represented in already existing protected areas. Finally, we identified target regions of high conservation value for the potential expansion of the current network of reserves (as planned under the new EU Biodiversity Strategy for 2030). We found a limited reciprocal surrogacy performance, with bird species performing slightly better as a conservation surrogate for habitat diversity than vice versa. We could also show that areas with a high conservation value based on habitat diversity were represented better in already existing protected areas than areas based on bird species, which varied considerably between species. Our results highlight that taxonomic and environmental (i.e., habitat types) data may perform rather poorly as reciprocal surrogates, and multiple sources of data are required for a full evaluation of protected areas expansion.
... While previous ocean impact assessments were used to inform protected area design (Jones et al., 2020;Klein et al., 2013) and guide decision-making around which management activities were most costeffective (Klein et al., 2010), trait-based vulnerability assessments can provide improved information for species-level conservation, which is often the scale at which managers operate. For example, such assessments will be critical for prioritizing actions for species conservation, whether focused on a species that has different and multiple stressors operating at different life-history stages (Hamilton et al., 2017;Hazlitt et al., 2010;Klein et al., 2017), or on determining which management actions would secure the most threatened species (Joseph et al., 2009). ...
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Marine species and ecosystems are widely affected by anthropogenic stressors, ranging from pollution and fishing to climate change. Comprehensive assessments of how species and ecosystems are impacted by anthropogenic stressors are critical for guiding conservation and management investments. Previous global risk or vulnerability assessments have focused on marine habitats, or on limited taxa or specific regions. However, information about the susceptibility of marine species across a range of taxa to different stressors everywhere is required to predict how marine biodiversity will respond to human pressures. We present a novel framework that uses life‐history traits to assess species' vulnerability to a stressor, which we compare across more than 44,000 species from 12 taxonomic groups (classes). Using expert elicitation and literature review, we assessed every combination of each of 42 traits and 22 anthropogenic stressors to calculate each species' or representative species group's sensitivity and adaptive capacity to stressors, and then used these assessments to derive their overall relative vulnerability. The stressors with the greatest potential impact were related to biomass removal (e.g., fisheries), pollution, and climate change. The taxa with the highest vulnerabilities across the range of stressors were mollusks, corals, and echinoderms, while elasmobranchs had the highest vulnerability to fishing‐related stressors. Traits likely to confer vulnerability to climate change stressors were related to the presence of calcium carbonate structures, and whether a species exists across the interface of marine, terrestrial, and atmospheric realms. Traits likely to confer vulnerability to pollution stressors were related to planktonic state, organism size, and respiration. Such a replicable, broadly applicable method is useful for informing ocean conservation and management decisions at a range of scales, and the framework is amenable to further testing and improvement. Our framework for assessing the vulnerability of marine species is the first critical step toward generating cumulative human impact maps based on comprehensive assessments of species, rather than habitats.
... year period, aligning with DOC's species management timeframe (Joseph et al. 2009). We parameterised a status quo model using DOC field data, collected between 1997 and 2017. ...
Thesis
Decision making for threatened species recovery can be complex: there is often a diverse range of stakeholders holding values that may be conflicting, data are typically deficient and imperfect, and there is uncertainty about the outcomes of proposed actions. Yet in this pressured and challenging context, decisions must still be made. Conservationists therefore need the right tools to address these complexities, and structured decision making (SDM) is effective in this space. Here, I demonstrate the utility of SDM and its component tools to assist recovery planning for Aotearoa-New Zealand’s rarest indigenous breeding bird, tara iti (New Zealand fairy tern, Sternula nereis davisae). My PhD aims to advance (i) the way we approach decisions via inclusivity and expression of values, (ii) the way we make decisions by recognising objectives, creating alternatives and making explicit trade-offs, and (iii) the way we use data to support these decisions by analysing and interpreting biased or imperfect datasets. Values drive decisions, and I first demonstrate how SDM, a values-focused approach, can be used to meaningfully integrate stakeholder values such as mātauranga Māori (Māori [indigenous New Zealander] knowledge/perspective) into conservation decisions and provide a basis for co-management between different peoples. Second, I analyse a seabird translocation trial, showing how creative thinking about alternatives can help better achieve conservation objectives. Third, I show how the application of SDM resulted in a new management recommendation that balanced across multiple objectives and was evidence-based. This was the first action after a decade of inaction and communication breakdown between stakeholders. Finally, I use a decision tree and counterfactuals to analyse the efficacy of tara iti egg management, showing how these tools can help navigate complex and biased monitoring data sets to improve future decisions. This thesis provides a detailed real-world example of how SDM can be applied effectively to a complex conservation problem, and highlights the importance of clear, values-focused thinking and inclusive approaches in species recovery.
Article
Despite accelerating biodiversity loss, conservation efforts suffer from serious resource deficiencies. This requires conservation managers to strike a challenging balance between direct action, which is needed to avoid further biodiversity loss, and research and monitoring, which provide the information needed for more effective action. Here, we explore different types of management tasks in action plans for legally protected species at risk of extinction in Canada, and determine the proportion of effort allocated to research and monitoring. We found that 46% of management tasks in action plans were research and monitoring, largely involving area-based management planning (12%), population monitoring (12%), and research examining conservation actions and techniques (11%). Of the management tasks that were categorized as actions, the largest number (35%) were related to education and awareness. The proportion of management tasks allocated to research and monitoring were highest for species with higher risk of extinction (i.e., Endangered vs. No Status). This suggests that either less is known about these species or that fear of negative outcomes for especially imperiled species may deter necessary actions. Our findings underscore the need to carefully examine the value of collecting new information and consider the optimal allocation of resources between action and research and monitoring to maximize the recovery of species at risk of extinction.
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The Species 2000 & ITIS Catalogue of Life is planned to become a comprehensive catalogue of all known species of organisms on Earth. Rapid progress has been made recently and this, the tenth edition of the Annual Checklist, contains 1,257,735 species. The present Catalogue is compiled with sectors provided by 77 taxonomic databases from around the world. Many of these contain taxonomic data and opinions from extensive networks of specialists, so that the complete work contains contributions from more than 3,000 specialists from throughout the taxonomic profession. Species 2000 and ITIS teams peer review databases, select appropriate sectors and integrate the sectors into a single coherent catalogue with a single hierarchical classification. It is planned to introduce alternative taxonomic treatments and alternative classifications, but an important feature is that for those users who wish to use it, a single preferred catalogue, based on peer reviews, will continue to be provided.
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Conservation biologists struggle to decide how many animals to save. In this article, I outline 18 approaches to setting population target levels (PTLs) for animals, with rules of thumb and analytical recommendations for each approach. Minimally viable populations, the most common target level, are necessary but not sufficient for most efforts, given the range of values that bear on conservation. Reference ecosystems, either extant or historical, are key for setting practical target levels. Setting PTLs sufficient for conserved populations to be animals in all respects (including functional, social, landscape, ethical, aesthetic, and spiritual aspects) is a critical consensus point. In many cases densities as well as overall population size will need to be specified. I suggest a four-tiered system of setting incrementally higher population target levels such that conservation provides first for demographic sustainability, then ecological integrity, then sustainable use, and finally restoration of historical numbers of wildlife, based on times when human beings had less impact on the planet than we do today.
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We develop and apply a four-dimensional priority-setting process for the conservation of threatened birds in Venezuela. The axes that we consider are extinction risk, degree of endemicity, taxonomic uniqueness and public appeal. Alhough the first three are relatively objective measures of biological attributes, the last one is a subjective judgement of the likelihood that conservation actions in favour of a species may succeed. By grouping higher priority species according to their geographical distribution within Venezuela, we generate a list of the top priorities to save the country's threatened birds, both species- and bioregion-based. The highest priority species are northern-helmeted curassow Pauxi pauxi, Andean condor Vultur gryphus, red siskin Carduelis cucullata and plain-flanked rail Rallus wetmorei, followed by eight high priority birds, wattled guan Aburria aburri, yellow-shouldered parrot Amazona barbadensis, scissor-tailed hummingbird Hylonympha macrocerca, rusty-faced parrot Hapalopsittaca amazonina, northern screamer Chauna chavaria, torrent duck Merganetta armata, rusty-flanked crake Laterallus levraudi, and military macaw Ara militaris. Northern Venezuela stands out as a significantly higher conservation priority than the south. The Andean Cordillera, Central Coastal Cordillera, Paria Peninsula-Turimiquire Massif Complex, and Sierra de Perijá are the highest priority bioregions, followed by Lara-Falcón Arid Lands and Maracaibo Lake Basin. A final set of combined priorities was determined by integrating all top ranking species and bioregions. Our approach is relatively simple and readily applicable to other taxa and regions.
Chapter
Approximation algorithms and in particular approximation schemes like PTAS and FPTAS were already introduced in Section 2.5 and 2.6, respectively. The main motivation in these sections was to illustrate the basic concept of constructing simple approximation schemes. The focus was put on algorithms where both the correctness and the required complexities were easy to understand without having to go deeply into the details of complicated technical constructions. Hence, an intuitive understanding about the basic features of approximation should have been brought to the reader which is a necessary prerequisite to tackle the more sophisticated methods required to improve upon the performance of these simple algorithms.