Linking cost efficiency evaluation with population viability analysis
to prioritize wetland bird conservation actions
Esther Sebastián-González a,b,⇑, José Antonio Sánchez-Zapata a, Francisco Botella a, Jordi Figuerola b,
Fernando Hiraldo b, Brendan A. Wintle c
a
Ecology Area, Department of Applied Biology, Miguel Hernández University, Ctra. Beniel Km 3.2, E-03312 Orihuela, Alicante, Spain
Doñana Biological Station – CSIC, Américo Vespucio s/n, E-41092 Sevilla, Spain
c
School of Botany, University of Melbourne, E-3010 Victoria, Australia
b
Keywords:
Conservation planning
Efficiency
Prioritization
Risk assessment
Threat status
Uncertainty
Cost-benefit
a b s t r a c t
Prioritizing management actions for wildlife conservation is a difficult task due to the large number of
problems relative to available conservation resources and uncertainty about the benefits arising from
numerous potential management actions. In this study we use a cost-efficiency protocol to evaluate
and prioritize eight different management actions for waterbird community in wetlands throughout
southeastern Spain. The protocol generated an action priority ranking based on the costs and predicted
benefits of the actions in terms of waterbird carrying capacity. Action prioritization outcomes were also
evaluated using population viability analysis models for two of the study species. Removal of dead bird
carcasses to prevent disease outbreaks was identified as the most cost efficient action. Removing lead pellets from the sediment was the least efficient strategy. Our approach highlights the role of detailed risk
assessment as a form of quality control on the simpler prioritization protocols. We recommend a twostep prioritization protocol based on (i) a rapid, usually simpler prioritization approach for the bulk of
species or values being managed, and (ii) a more sophisticated risk assessment for a subset of the species
of interest for which detailed risk assessments are tractable. This process strikes a balance between
sophistication and practicality.
.
1. Introduction
Conserving and managing sites to protect species demands
evaluation judgments because such actions often involve a choice
among a set of alternatives. Competing management options might
be prioritized in order to maximize species persistence. The budget
for conservation is generally small relative to the number and magnitude of conservation problems and it is seldom possible to deal
with all the identifiable problems. Therefore, managers have to decide how their budget can be spent most effectively (Naidoo et al.,
2006; Wilson et al., 2006; Polasky, 2008).
An increasing number of researchers attempt to quantify the
possible gains in conservation efficiency by including information
about economic costs of locations in reserve selection (e.g., Polasky
et al., 2001; Williams et al., 2003; Juutinen et al., 2004; Strange
⇑ Corresponding author at: Ecology Area, Department of Applied Biology, Miguel
Hernández University, Avda. Universidad s/n, E-03202 Elche, Alicante, Spain. Tel.:
+34 965 22 21 23.
E-mail addresses: esebastian@umh.es (E. Sebastián-González), toni@umh.es
(J.A.
Sánchez-Zapata), paco.botella@umh.es (F.
Botella), jordi@ebd.csic.es
(J.
Figuerola),
hiraldo@ebd.csic.es
(F.
Hiraldo), b.wintle@unimelb.edu.au
(B.A. Wintle).
et al., 2006). These studies emphasize the value of incorporating
biodiversity benefits such as increased habitat area or increased
species abundance, and costs in an integrated approach with the
aim of giving priority to the most cost-efficient options and, therefore maximizing net expected benefits of conservation investments. Cost-efficiency analysis are based on different approaches
such as the design of reserve networks (Jantke and Schneider,
2010), the prioritization of species for conservation (Marsh et al.,
2007) or the selection of management actions (Rodonini and
Boitani, 2007). However, the bulk of the literature around systematic conservation planning focuses on the situation involving binary
investment or zoning choices (e.g. reserve/non-reserve,
restore/not-restore). Simple and workable strategies for maximizing the net benefits (in biological terms) obtained from multiple
competing management actions across multiple environmental
values (e.g. species, vegetation types, wilderness values) are in
the early stages of development (e.g. Polasky et al., 2008; Joseph
et al., 2009). Both Polasky et al. (2008) and Joseph et al. (2009)
provide a compelling case for utilizing cost-efficiency in conservation planning. However, in the case of Polasky et al. (2008) the
economic modeling approach utilized is challenging to implement
without modeling skills that are not necessarily accessible to all
practitioners (e.g. Polasky et al., 2008). In the case of Joseph et al.
(2009) the subjective estimates of biodiversity benefit (expert
opinion about extinction probabilities) are not transparent, and if
applied in most circumstances would not make the most of available data and ecological modeling tools.
Here we attempt to strike a balance between ecological realism
and practicality, by presenting a cost-efficiency action prioritization case study that exploits a simple cost-efficiency definition
combined with available ecological data and models. Our approach
utilizes available time-series data on species responses to habitat
change and seasonal variation to underpin estimates of the
biodiversity benefits arising from proposed management actions.
Biodiversity benefit in this case study is measured in terms of increases to focal population abundances resulting from proposed
management actions. We illustrate our approach by prioritizing
non-spatial habitat restoration options for the extensive, speciesrich complex of wetlands in south-eastern Spain. Our approach is
twofold. First we implement a cost-efficiency analysis to identify
the habitat restoration and management investments that bring
the greatest expected net benefit (in terms of increased abundance
of 25 waterbird species) for our fixed budget. We then attempt to
evaluate our prioritization strategy by comparing the prioritized
ranking of actions determined by the cost-efficiency analysis
across all species with a more detailed analysis of the consequences of management options based on population viability
analysis (sensu Akçakaya, 2000; Wintle et al., 2005a; Bekessy
et al., 2009) for a subset of well-studied species. Combining population viability analysis (based on metapopulation modeling) with
cost-efficiency prioritization approaches is novel in conservation
planning (Newbold and Siikamaki, 2009; Wakamiya and Roy,
2009). Our protocol can be seen broadly as a prioritization based
on cost-efficiency analysis across the full range of species of interest, followed by a quality control step that evaluates the likely outcomes for a subset of the species using population viability
analysis. While the approach does not guarantee optimality in an
economic sense, we believe that it is an appropriate trade-off
between the practicality of simple cost-efficiency analysis (sensu
Joseph et al., 2009) and the elegance and rigor of population risk
assessment that cannot be practically applied to all of the species
of interest.
Our specific aims were to (i) describe a management action prioritization approach in which the biodiversity benefits of candidate
actions are measured using commonly available ecological data
and models, (ii) illustrate how population viability analysis modeling can be integrated with a cost-efficiency prioritization scheme,
and (iii) produce a cost-efficiency prioritized ranking of possible actions for managing threatened wetland bird populations in Spain.
2. Methods
2.1. Case-study: waterbirds in a wetland network
The study was conducted in the Vega Baja Valley in Southeast
Spain (Fig. 1). It covers an area of 95,840 ha with a landscape dominated by citrus fruit trees, vegetables, palm trees and housing
developments. In the 1980s, an inter-river water transfer was built
to bring water for irrigation purposes. Since then, more than 2600
ponds have been constructed to store the water received, and the
area has turned into a mosaic of crop fields and artificial wetlands
(Sánchez-Zapata et al., 2005).
Apart from the ponds there is also an important network of natural and semi-natural wetlands. Some of them enjoy regional environmental protection, as well as the international status of SPAs and
RAMSAR sites. This complex includes Salines (Torrevieja-La Mata,
Santa Pola, San Pedro del Pinatar), large water reservoirs (El Hondo,
La Pedrera) and temporary ponds (Clot de Glvany). The wetlands
complex holds one of the most important populations of breeding
waterbirds in southern Europe, including some globally endangered
species such as marbled teal (Marmaronetta angustirostris), whiteheaded duck (Oxyura leucocephala) or audouin’s gull (Larus audouini). During the twentieth century several factors, such as the creation of new irrigated lands and the urbanization of traditionally
irrigated lands, have changed the use of natural resources, giving rise
to a growing imbalance between water resources and irrigation demands and a loss of fertile soil and other environmental and cultural
values of this traditionally agricultural area (Martínez-Fernández
et al., 2000). Actually fresh water and land management is subject
to many different public and private interests including biodiversity
conservation, public use, agriculture, hunting, fishing, industrial salt
production and housing. Furthermore, climate is semiarid and water
resources are heavily exploited including local resources (basin and
ground waters), water transferred from neighbor basins (Tajo-Segura rivers) and desalination, resulting into important socio-political
conflicts. This background reinforces the need for providing sound
scientific-based proposals for conservation under a cost-efficiency
approach.
2.2. Cost-efficiency analysis
Our stated objective was to achieve the maximum increase in the
abundance of 25 wetland bird species across the region within the
available budget. In order to achieve this, we ranked management
actions according to cost-efficiency (greatest gain in abundance
per unit cost (€)) and then chose as many of the most cost efficient
set of actions as could be purchased within the available budget.
Some actions aim to decrease mortality (e.g. removing pellets from
a natural park), while other actions seek to increase carrying capacity (e.g. installing floating devices). To evaluate the impact of these
measures, we selected both common and endangered species which
were likely to be affected by the management actions.
Candidate management actions were determined by eight experts. The selection of the management options depended on the
availability of information about costs and possible benefits to
the waterbirds. Eight candidate strategies were selected. We included both proposed and existing actions in the analysis. We
incorporated some actions that have already been performed because they could be used in other similar wetlands or again in
the same wetland, so it is important to evaluate their effectiveness.
We considered all the management actions as additive. Actions
that affected the carrying capacity included:
(i) Installation of floating devices in the irrigation ponds to provide waterbirds with resting and breeding sites. The number
of devices per pond depended on the size of the pond. We
evaluated the installation of devices with vegetation in
ponds without natural vegetation and vice verse. We estimated the benefit on the basis of the installation of ponds
in 30% of the total number of irrigation ponds at the area.
(ii) and (iii) Change in the construction design of the irrigation
ponds. We have distinguished two types of ponds: LDP (constructed with Low Density Polyethylene) and HDP ponds
(constructed with High Density Polyethylene). LDP ponds
are lined by a layer of gravel to protect the plastic from solar
radiation that can damage them, and this cover provides the
pond with a more natural appearance. HDP materials allow
the ponds to have higher slopes and to store more water.
For these reasons, LDP ponds hold more abundant and richer
waterbirds, vegetation and macroinvertebrates communities
than HDP ponds (Sánchez-Zapata et al., 2005; Abellán et al.,
2006; Sebastián-González et al., 2010). We have evaluated
the conversion of 30% (ii) or 60% (iii) of the HDP to LDP
ponds.
Fig. 1. Study area. The figure represents the natural and artificial wetlands at the study area and their location.
(iv) Construction of a permanent pond at the Clot de Galvany.
This wetland often dries completely in summer and the construction of these temporary ponds has allowed some species to stay all year around at the wetland. It consists in a
small wetland filled with water coming from a nearby sewage system. This action has already been carried out.
(v) Installation of floating devices at the Salinas. Waterbirds
breeding at the Salinas often suffer from a high chick predation. Thus, the installation of this type of devices offers predator-free breeding sites for some species. This type of
devices has already been installed in the Salinas de San
Pedro.
(vi) Installation of artificial islands at the Salinas. Another strategy for avoiding high predation rates at the salinas is the
installation of artificial islands constructed with stones and
soil. These islands have been installed at the Salinas de La
Mata-Torrevieja.
Actions that influence survival include:
(vii) Cleaning the Hondo and the Salinas de Santa Pola from lead
pellets to avoid lead poisoning.
(viii) Develop a prevention protocol to reduce biological contamination outbreaks at El Hondo (being botulism the most
important one). The protocol consists in a weekly patrol to
collect dead animals that are one of the main causes of the
amplification of outbreaks.
The cost of each action was calculated in Euros (€) and estimated for a 10-year period. The costs were based on the accomplished actions and estimations derived from small actions. The
price for the floating devices at the ponds and salinas was calculated for the construction and the installation of the devices. The
price of the devices at the Salinas was the real cost of the action
performed in 2008 at the Salinas de San Pedro (Concejalia de Medio
Ambiente de la Región de Murcia, pers. com.), while the price for
the devices at the ponds was estimated using the cost of five
experimental devices constructed by a NGO in 2007 (Sánchez,
pers.com.). The cost was calculated for the renewal of the devices
every 5 years, which was the average life of the experimental devices. For the calculation of the price of the transformation of the
ponds construction design we took in account that the edges of
LDP ponds slope more gently and, therefore require a larger surface
area to store the same amount of water. We included both the
price of the extra water loss by evaporation and the money the
owner does not earn because they cultivate less area. LDP ponds
are cheaper to construct but have to be renewed with a higher frequency. We considered both the higher price of HDP ponds and the
higher renewal frequency of LDP ponds in the calculation of the
costs. Moreover, as both the artificial permanent pond at the Clot
and the artificial islands at the Salinas are actions already accomplished and the price is the total cost of the action (Aranda, pers.
com.). For calculating the price of the lead cleaning we used the
price of two executed cleaning actions at the study area. We used
the same cleaning system used in the executed actions. As the cost
increases with the amount of pellets present, we stratified the
costs of cleaning in all the affected area using previous information
about pellet concentration in the affected wetlands (Terrones Contreras, 2006). Finally, the costs of the protocol to control for biological outbreaks include the costs of the work force (two people,
5 months a year, 20 h a week) using stipulated salaries at the
sector.
We first estimated the population size of each species in every
wetland. As we did not have information about the total population size at the irrigation ponds we constructed species-specific
Generalized Linear Models (GLMs) in R (R Development Core Team,
http://www.r-project.org) to estimate the abundance. We followed
the steps given in Wintle et al. (2005b) to create individual specieshabitat models. We used information about waterbird surveys in 7
consecutive years (2003–2009) and in around 200 ponds
(Sebastián-González et al., 2010). We included information about
the size and location of the pond, the presence/absence of different
types of vegetation and the construction design. We finally estimated the total population for each of the species in the 2600
ponds existing at the study area (see Appendix A, Supplementary
material for a more detailed explanation and Appendix B for the results of the model). To estimate the population size at species level
for the rest of the wetlands we used an average of the population
given by the official surveys during the same years.
To calculate the benefits of the installation of floating devices at
the ponds and the lead cleaning we used extrapolations of previous
experiences at small scale. We used the predictions given by the
habitat models to estimate the benefit of the change in the construction design in all the ponds at the study area. The term benefit
is here defined as the increase in expected species abundance (as
predicted by the statistical model). For the construction of a permanent pond at the Clot and the artificial island at the salinas
we used real data from previous experiences, while for evaluating
the effect of the biological outbreaks we used the average mortality
rates in the last three biological contamination outbreaks at the
Hondo to estimate the number of pairs that would survive if there
were not biological contamination outbreaks episodes. In the case
of contamination outbreaks, we assume that the mortality associated with individual outbreaks is additive. We calculated the benefit (increased abundance relative to doing nothing) after 10 years
in order to make outcomes comparable, despite the fact that some
benefits derive from increasing carrying capacity and some focus
on reducing mortality.
We calculated for each action i, the total benefit (Bi) as the sum
of the number of pairs in 10 years over the execution of the action
for all the species,
X
Bi ¼
bij ;
where bij is the benefit of the action i in terms of the expected abundance of species j.
For each action j and species i we included a correction factor
that represents the likelihood of success (Lij) that ranged between
1 (actions with the maximum probability of success) and 0 (actions
without any probability of success). This factor accounted for the
variability in the data and for the feasibility of the action, and
can be viewed as a measure of uncertainty (see Appendix C in Supplementary material for the list of the correction factors used). The
benefit of each action on each species was multiplied by this factor
to provide an expected benefit accounting for the probabilistic
uncertainty about whether or not actions will be successful. Therefore, the total expected benefit arising from each action was estimated as
Bi ¼
X
ðbij x Lij Þ:
The value of the correction factor varied depending on the experts’ confidence on the probability that each action could be
implemented successfully. This factor was high for already accomplished actions because the benefit is based on real data about the
benefit for the species. It was also high when we could calculate a
confidence interval and use the bounds of the interval for the evaluation of alternative scenarios. When the data availability was low,
the factor was also low because the risk of failure is higher.
We ranked the actions on the basis of a cost-efficiency criterion.
We calculated the efficiency E of each action j as
Ej ¼ C j =Bj
where Cj is the cost of the action over 10 years. The actions were
ranked from the lowest to the highest Ej.
We evaluated alternative scenarios for management using two
approaches. When available from the data, we used the upper
(Lmax) and lower (Lmin) limits of the estimates. We used this approach for the action considering changes in the construction design of the ponds, and for the control of biological outbreaks. The
second approach used the likelihood of success for each project.
We estimated the maximum benefit by considering the correction
factor to be maximal (Lij = 1). For the minimum benefit we reduced
the factor to the half. This approach was used for the rest of the actions evaluated.
In order to reflect the relatively high social importance of highly
threatened species compared with relatively common species, we
weighted each species by its threat status (Madroño et al., 2004).
Multiplicative weighting applied in the analyses were: (1) if the
species is cataloged as non-threatened, 1.5 if it is cataloged as almost endangered, (2) if it is vulnerable, (3) for endangered species
and (4) for critically endangered ones.
2.3. Population viability analysis
We modeled the population viability of a subset of the species
under each of the management actions, to provide a separate line
of evidence about the relative performance of management options
and to ensure that certain options were not highly detrimental to
the viability of the species that could plausibly be modeled in this
way. Due to the availability of data and expertise, we were able to
model the 100-year viability of the black-winged stilt (Himantopus
himantopus) and the little grebe (Tachybaptus ruficollis) in southeastern Spain using a spatially-explicit, stage-structured, stochastic model based on habitat suitability maps and on demographic
data (see Appendix C in Supplementary material for a further
explanation of the model). The demographic component of the
model included density dependence, stage and sex structure,
demographic and environmental stochasticity. The population
model was used to estimate the expected minimum population
size (EMP: McCarthy and Thompson, 2001) for the two modeled
species over the 100 years of the simulation under each investment
option. EMP was used as the basis of comparison between actions
(sensu Wintle et al., 2005a) and to evaluate the congruence between the population viability analysis and the simpler evaluation
approach based on the correlative habitat models. We used the
population viability analysis program RAMAS/GIS, which is
designed to link landscape data from a geographic information
system with a metapopulation model (Akçakaya, 1995; Akçakaya
et al., 1995).
3. Results
3.1. Action ranking
Our protocol provided information at species level about the effect of the implementation of each management action. The cost of
the actions was very variable, but the range of potential effects was
also high (Table 1). Some actions produced small increases in the
populations and affected few species (e.g. 15 new pairs and one
species for the construction of floating islands) while others had
widespread effects (e.g. 846 pairs and 20 species for the installation of floating devices at the ponds). In general, the control of biological contamination outbreaks was the measure that affected a
higher number of individuals and species. The cleaning of the El
Hondo Park from lead pellets and the two actions based on the irrigation ponds also had high benefits in terms of net increase in the
number of pairs after 10 years.
The ranked order of the cost-efficiency evaluation changed
depending on the approach used (Table 2). For most of the analyzed scenarios, removing pellets from the Hondo was the least
efficient strategy. Investment in the irrigation ponds also seemed
to be relatively inefficient. The control of biological contamination
outbreaks was the most efficient action, while the two management actions affecting the Salinas were also relatively efficient.
The ranked order of the actions varied when using Lmin. Neither
the rank obtained using Lmax nor the one obtained when weighting
the results by threat status of the involved species, changed the
overall ranking of actions.
3.2. Population viability analysis
The expected minimum population size (EMP) for the two study
species varied depending on the management strategy used
(Table 3). All the strategies resulted in an increase in EMP. The
scenario with the highest EMP was produced by the control of
biological contamination outbreaks for both species. As the population at the irrigation ponds is large, the two actions affecting
these artificial wetlands also produced an important increase in
the population of the species. The sensitivity analysis performed
showed that the model for the black-winged stilt was affected by
the inclusion of demographic stochasticity, while the model for
the grebe suffered from the strongest variations when removing
the correlation of the stochastic events in the vital parameters
(see Appendix D in Supplementary material). Changes in the rest
of the parameters did not produce large variations in the EMP of
the model, considering a variation large when predicted population size is double. There were also no changes in the ranking of
Table 2
Cost-efficiency (E) calculated as the cost of the action divided by the expected net
benefit for each of the management actions (in terms of the change in number of
breeding pairs compared with the status quo for all species), under four criteria:
average estimated benefit, maximum benefit (L max), minimum benefit (Lmin) and
weighted by species’ threatened degree (T.D.). Final ranking of the action for the given
prioritization strategy is shown in brackets. The negative sign appears because the
variability in the population of some species at the ponds is high and the lowest
confidence interval shows a reduction in the population.
Management action
Average
30% floating devices
30% ponds LDP
60% ponds LDP
Pond clot
Floating islands
Artificial island
Lead cleaning
Biological outbreaks
3311 (5)
12,833 (7)
9829 (6)
1556 (4)
592 (3)
351 (2)
22,831 (8)
77 (1)
L max
845 (5)
4534 (6)
5128 (7)
622 (4)
300 (3)
255 (2)
13,334 (8)
43 (1)
Lmin
T.D.
6622 (5)
—15,453 (8)
118,051 (7)
3111 (3)
592 (2)
703 (3)
45,662 (6)
338 (1)
1933 (5)
12,691 (7)
9712 (6)
1521 (4)
592 (3)
207 (2)
19,341 (8)
23 (1)
management actions between the PVA and the full prioritization
protocol (Table 3).
4. Discussion
4.1. Prioritization protocol
The analysis of management actions’ effectiveness can be an
important tool in planning conservation strategies. However, a rigorous evaluation of the outcomes of the cost-efficiency analysis is
necessary to ensure that the result is not simply an artifact of particular settings and assumptions made during the analysis. Several
factors affected the final prioritized rank order obtained using our
approach. Variation in the estimated likelihood of success and the
weights assigned to species influenced the rank of the actions,
making our results similar to previous studies (Joseph et al.,
2009; van Teeffelen et al., 2008). This highlights the importance
of well defined management objectives and careful elicitation of
social and political preferences (e.g. how much more important it
is to people to protect threatened species rather than just increase
overall waterbird abundance; Wilherea et al., 2008; Wikberg et al.,
2009).
Action prioritization is likely to be sensitive to error in the estimation of action costs (Bryan, 2010). The cost of some of the actions was estimated with previous pilot studies and the final
value depends on the accuracy of the estimations. For example,
the budget for cleaning the Hondo may vary if a different technique
is used for cleaning. We also did not include the costs of maintaining the actions in the prioritization protocol because this information is difficult to obtain. These costs should be considered to
ensure the success of the management in time. Furthermore, all
the costs and benefits were estimated for a 10-years study period
to allow tractable estimation of costs. Nonetheless, the rank for
Table 1
Costs of each of the management actions over 10 years and the expected number of pairs arising from that action after 10 years (summed across all species). Values for the
number of pairs, species and threatened species are those estimated to be affected by the proposed management actions. Maintenance cost reflects the economic cost of
maintaining the action over 10 years after implementation. Number of pairs was calculated as a consequence of a higher carrying capacity or as a reduction in the mortality for all
the studied species. The baseline scenario represents the total number of pairs, species and threatened species currently present at the study area (without taking any action).
Management action
Baseline scenario
30% floating devices
30% ponds LDP
60% ponds LDP
Pond clot
Floating islands
Artificial island
Lead cleaning
Biological outbreaks
Cost (€)
2.800.000
3.500.000
7.000.000
44.892
8.999
66.150
20.000.000
100.000
Maintenance cost
Number of pairs
Number of species
Number of threatened species
Low
None
None
Medium
Low
Low
None
Included cost
4397
845
273
712
29
15
188
876
1.303
25
20
11
11
16
1
3
12
17
10
7
3
3
1
0
1
3
6
Table 3
Management action prioritization rank for the black-winged stilt and the little grebe under the population viability analysis (Rank 1) and under our protocol (Rank 2). The
expected minimum population (EMP) size is given for 100 years from now, as is the estimated benefit in terms of expected increase in abundance above the ‘do nothing scenario’
predicted by the statistical models used in the prioritization for all species (‘benefit’). Note that the rank is only based on expected benefits and that cost information has not been
included for the comparisons. The installation of floating and artificial islands could not be evaluated using PVAs because none of the modeled species used these facilities.
Little grebe
Black-winged stilt
Management
EMPa
Rank 1
Benefit
Rank 2
EMPa
Rank 1
Benefit
Rank 2
30% floating devices
30% ponds LDP
60% ponds LDP
Pond clot
Lead cleaning
Biological outbreaks
861
859
870
865
861
881
4
6
2
3
4
1
91.6
44.3
100.5
3.1
64.5
141.3
3
5
2
6
4
1
2012
2024
2028
1999
2006
2053
4
3
2
6
5
1
41.3
97.7
220.6
2.5
143.8
756.4
5
4
2
6
3
1
some of the actions may change using a different time period. For
example, a park free of lead would continue to increase the survival rate of the species using the wetland until the lead pellets become unavailable when the sediment covers them. In general,
there are so many factors affecting the final cost of an action that
managers often need to generalize to make the calculations feasible. Even if in this study we did not evaluate the effect of changes
in the cost of the actions, costs are crucial in determining the final
ranking of the actions and more complex methods are available for
this part of the evaluation (Naidoo et al., 2006). Moreover, in such a
framework it could happen that the budget available for conservation is bigger or smaller than the cost of the prioritized management action. When the budget is small, then other projects with
smaller costs could be executed, following the prioritized order.
If the budget is high, we can perform several actions, as the effect
of each action has been estimated as additive with this method. Besides, the possible complementary effect of the projects has not
been evaluated with this framework but should be considered for
the action selection (i.e. Leathwick et al., 2010). The benefits obtained from the management actions might be synergic. Therefore,
as we are assuming that the actions are additive, we are probably
underestimating the benefits of the projects. Moreover, some projects, such as the installation of floating devices or the change in
the construction design of the ponds may be expanded or diminished depending on the budget if necessary.
Other factor affecting the benefit that is difficult to evaluate but
that should be considered is the suitability of an action for a specific habitat and place. As it happened with the islands at the Salinas, that were a response to previous predation problems. Our
protocol also assumes that all the species are substitutable. It evaluates actions by means of the benefit given by any species. Again,
including a parameter concerning species complementarity would
deal with this problem, but it is beyond the scope of this study.
Managers interested in cost-efficiency evaluations should also
be aware of the importance of the quality of the data used in the
assessment (Cabeza and Moilanen, 2001; Hegland et al., 2010). In
many cases information about the effectiveness of management
actions is not very well understood. The inclusion of likelihood of
success (L) goes part way to catering for this form of uncertainty.
However, the quality of decisions made using cost-efficiency analysis still rests squarely on the quality of the ecological and economic data and knowledge underpinning them. If data are sparse
and knowledge is poor about a particular species, ecosystem or
management strategy, then the use of cost-efficiency analysis
should be couched in a decision theory that deals with uncertainty.
At the very least, analysts should explore the consequences of
misinformation using sensitivity analysis or robustness analysis
(Ben-Haim, 2006). Our methods here do not reduce the need for,
or importance of good ecological (Arponen et al., 2010) and
economic information. They do, however, provide a means to
ensure logical consistency in decisions made on the basis of
cost-efficiency, given the available information.
Our analysis also provides insights into the relative efficiency of
investing in various habitat types. In general, investing in rich areas
with big populations may produce higher benefits and there is also
a clear bias in our results for the actions that affect the richest wetlands. For example, the most efficient action was the control of biological contamination outbreaks, predominantly in the Hondo, the
most important wetland for waterbirds at the area (Martí and del
Moral, 2003, 2004). Moreover, in most of the scenarios, the less
efficient actions were those proposed for the irrigation ponds,
which are less suitable habitat for a variety of reasons, including
low protection from predation.
The secondary evaluation of the actions using PVAs reinforced
the feasibility of the protocol in this case. PVA provides managers
with predictions about the actual variable of management interest;
the probability that species will persist in the study region over a
specified period, or in the case of EMP, the lowest abundance
expected for the species of interest over the simulation period
(Akçakaya, 2000; Wintle et al., 2005a; Bekessy et al., 2009). In contrast, prioritization made on the basis of predictions about carrying
capacity alone, rely on a set of implicit assumptions about the relationship between carrying capacity and the long-term viability of
the species of interest. Therefore, PVAs are more ‘proximal’ to the
management objective of interest and management evaluations
based on this approach may be more likely to achieve their objectives. Nonetheless, the realism of PVA hinges on the availability of
relevant biological information which may be difficult to obtain.
There is a need for long-term studies that can provide reliable
information about the survival rates of the species so that PVA
can be used more widely and reliably in conservation action prioritization. Moreover, PVA necessarily operates on species information. If we need to evaluate actions affecting communities, we
would have to perform PVAs for each of the species at the assemblage and understand something about the interactions between
species within a community, which may not be possible in most
cases. Unfortunately, community and ecosystem models are not
yet applicable in most conservation planning and prioritization
frameworks as they tend not to be quantitative.
4.2. Management implications
The waterbird community in the south-east of Spain benefited
differentially under the range of proposed management investments: Some actions affected a high number of species while others focused on only a few of them. The cost-efficiency protocol
provides clarity around the selection of management actions by
providing a ranking that directly reflects the aim of managers; to
maximize water bird abundance within the available budget. If
the aim was slightly different, for example, to maximize the number of species maintained above a minimum population size, then
the analysis would be slightly different (and more complex).
In our analysis, the most efficient action was the control of biological contamination outbreaks and therefore this action should
be prominent in the minds of regional managers. The main outbreak in the system is botulism, even though other agents like
toxic algae blooms may produce massive mortalities among waterbirds, further favoring botulism outbreaks (Lopez-Rodas et al.,
2008). However, removing corpses of dead animals would reduce
the spread of the bacteria or the infection of animals preying on
fly larvae with high toxin loads and reduce the probability of any
biological outbreak (Rocke and Friend, 1999); even though a better
water quality would also be important.
The constructions of artificial and floating islands in the Salinas
were also ranked in a good position. These actions were partially
successful because they were carried out after detecting high chick
mortality rates for some of the species already breeding in the wetlands. Both actions were very efficient and can be a good example
to be followed in similar situations. Similarly, the success of the
artificial pond construction at the Clot de Galvany was related to
the water availability problems at the study area as a result of
overexploitation. The objective of this action was not to increase
bird numbers ‘‘per se’’ by creating new artificial conditions or
areas, but to restore the former environmental conditions destroyed by human activities.
The less efficient actions where those affecting irrigation ponds.
The ponds are used as breeding, foraging, resting and wintering
sites by some waterbird species (Sánchez-Zapata et al., 2005;
Sebastián-González et al., 2010). Most of the species are widespread at the study area, but some of them are of conservation concern (Madroño et al., 2004). Besides, the analyzed actions affecting
these artificial wetlands had in general high economic costs. Nevertheless, for the action affecting the construction design of the
ponds, the estimation of the costs is not easy because LDP ponds
are not constructed anymore. Even if LDP ponds are not constructed, their design could be easily copied and used, therefore,
the best proposal would be to motivate from the regional government the construction of ponds with softer slopes that benefit the
establishment of the vegetation and their use by waterbirds, by
offering economic benefits to the farmers.
5. Conclusions
Developing a method for prioritizing management options that
is, both ecologically and economically, rigorous and practical to
implement is a major challenge. The appropriate trade-off between
rigor and practicality depends on the availability of time and
expertise for the problem at hand. Many management agencies
may not have the expertise and time to implement the most rigorous and sophisticated methods. Our protocol retains the elegant
simplicity of existing cost-efficiency analysis, while adding slightly
more rigorous ecological modeling than has currently be employed
in similarly large action prioritization studies. We believe that the
opportunity to include population demographic information and
predictions in prioritization processes is worthwhile when the data
and expertise exist to support it. The challenge of making rigorous
ecological models a routine part of conservation management
planning and prioritization remains one of the most important
challenges facing conservation biologists.
Acknowledgements
We want to thank the researchers and students at the School of
Botany (University of Melbourne) for many interesting comments.
M. Bode and T. Regan helped with the PVA. S. Gilard helped with
the preparation of the maps for the PVA. The following people,
organizations and natural parks provided important information
and comments: M. Ferrández, G. Ballesteros, J. Sánchez, J.L.
Echevarrias, S. Polo, ANSE, A. Bonet, J. Jiménez, P.M. Mújica, the
Conselleria de Medio Ambiente, Agua, Urbanismo y Vivienda, the
directors and technicians of the Salinas de La Mata-Torrevieja, Salinas de San Pedro del Pinatar and Clot de Galvany. E. S.-G. benefited
from a FPU grant from the Spanish Ministry of Education. The Servicio de Biodiversidad de la Generalitat Valenciana also funded this
research. BW was supported by AEDA via a grant from the Commonwealth Department. Two anonymous reviewers made constructive critiques and comments improving the quality of this
paper.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.biocon.2011.06.015.
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