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Received: 31 January 2018 Revised: 20 August 2018 Accepted: 30 August 2018 DOI: 10.1111/ddi.12853
BIODIVERSITY LETTER
Metrics for evaluating representation target achievement in protected area networks Kerstin Jantke1
| Caitlin D. Kuempel2,3
Alienor L. M. Chauvenet2,3 1
Center for Earth System Research and Sustainability, Research Unit Sustainability and Global Change, Universität Hamburg, Hamburg, Germany
2
Centre for Biodiversity and Conservation Science, School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia 3
ARC Centre of Excellence for Environmental Decisions, School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia
4
| Jennifer McGowan2,3
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| Hugh P. Possingham2,3,4 Abstract Global conservation targets (e.g. Aichi Target 11) have helped drive a dramatic expan‐ sion of the global protected area (PA) network. Credible metrics have an important role to play in evaluating and expanding PAs to achieve conservation outcomes and objectives. For metrics to be useful and adopted, they need to be transparent, easy to understand, and easy to implement. We present two complementary metrics, “mean protection gap” and “mean target achievement”, for evaluating representation target achievement in PA networks along with the R package “ConsTarget” that cal‐ culates and plots both metrics. We use Australia’s proposed Commonwealth Marine
The Nature Conservancy, Arlington, Virginia
Reserve network as a case study to demonstrate the application of these metrics. We
Correspondence Kerstin Jantke, Universität Hamburg, Hamburg, Germany. Email:
[email protected]
sentative PA networks in line with Aichi target 11’s goals.
Funding information Australian Research Council; Centre of Excellence for Environmental Decisions, Australian Research Council, Grant/Award Number: CE11001000104; Deutsche Forschungsgemeinschaft, Grant/Award Number: JA 2710/1‐1
representation
recommend the metrics be used to evaluate the progress towards building repre‐
KEYWORDS
Aichi target 11, bioregions, conservation target, metric, protected area networks,
Editor: Enrico Di Minin
1 | I NTRO D U C TI O N
protected). Yet, we know this alone is not a sufficient indicator for conservation achievement (Barnes, Glew, Wyborn, & Craigie, 2018;
The global protected area (PA) estate has increased rapidly in the
Tittensor et al., 2014; Watson et al., 2016) because it ignores the
past decades (UNEP‐WCMC & IUCN, 2016). This has been partly
other key components of the target, namely, how well the network
driven by international agreements such as the Convention on
represents important biodiversity features (e.g., ecoregions or spe‐
Biological Diversity’s Aichi Target 11 (CBD, 2010), which calls
cies), and the connectivity performance of the PAs estate (Saura,
for 17% of terrestrial and 10% of marine areas to be in “effective
Bastin, Battistella, Mandrici, & Dubois, 2017).
and equitably managed, ecologically representative and well con‐
Recent evaluations of terrestrial and marine PA networks show
nected” PAs by 2020. Much of the progress reporting towards Aichi
that despite increased coverage, representation goals are not being
target 11 focuses on coverage (e.g., percentage of the land or sea
met (Klein et al., 2015; Kuempel, Chauvenet, & Possingham, 2016;
Diversity and Distributions. 2018;1–6.
wileyonlinelibrary.com/journal/ddi © 2018 John Wiley & Sons Ltd | 1
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JANTKE et al.
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McGowan & Possingham, 2015; Venter et al., 2014). Credible met‐
((
rics, such as Protection Equality (PE; Barr et al., 2011; Chauvenet, MPG =
Kuempel, McGowan, Beger, & Possingham, 2017), can play an im‐
et al. (2017) unveils how equally PA networks represent biodiversity features, it does not explicitly address representation targets. For example, a perfect PE score can be achieved by protecting 1% of each conservation feature or 100%. Importantly, for metrics to be adopted by conservation professionals, they need to be transpar‐ ent, easy to understand and use, and directly linked to policy goals (https://www.bipindicators.net/national-indicator-development). Towards this end, we build on the work by Sutcliffe, Klein, Pitcher, and Possingham (2015), who developed the mean percent‐ age gap metric to evaluate the effectiveness of biological surrogates
(
1−
Pi Ai
))
) ,0
T
,
N
1…N
portant role in evaluating and expanding the global PA estate to bet‐ ter achieve representation goals. While the PE metric by Chauvenet
i ∑
max
where Pi is the protected amount of each feature of interest i (e.g., ecoregions, habitats or species), A i is the total amount of feature i, T is the fixed proportional target protection level (e.g. T = 0.17 for a 17% PA coverage target for all features), and N is the total number of features. The max function ensures that the nu‐ merator does not become negative for features that overachieve the target. The metric gives a value between 0 and 1, with 0 in‐ dicating no gap, i.e., all biodiversity features meet or exceed the specified target, and 1 indicating 100% gap to the target, i.e., no feature is protected at all.
in spatial conservation prioritization. The metric determines the av‐ erage shortfall in conservation target achievement. For example, if all features miss their target by 20%, or if a third of the features miss their target by 60%, then the percentage gap is 20%. If all tar‐ gets are met, the percentage gap is 0%. We adapt and reframe this metric to measure progress in meeting global representation targets, like Aichi Target 11, and introduce a new metric called mean target achievement. The two complementary metrics allow planners to point out either the gaps or the achievements in PA coverage in a
2.2 | Mean target achievement Since current reporting on progress towards global conservation targets focuses on achievements rather than missed opportunities (i.e., gaps; see e.g., UNEP‐WCMC & IUCN 2016), we also present the mean target achievement (MTA) metric. MTA calculates the degree of conservation target achievement for all biodiversity features of interest in a conservation plan or reserve system. It is given by:
single metric, while also considering ecological representation. We (( (
demonstrate the application of these metrics using Australia’s pro‐ posed Commonwealth Marine Reserve network as a case study. We
MTA =
provide an R package to facilitate the calculation and plotting of both
i ∑ 1…N
min
Pi Ai
))
T
N
) ,1 ,
metrics. where Pi is the amount of the features of interest i under protection, Ai is the total amount of feature i, T is the fixed proportional target
2 | M E TR I C S
protection level, and N is the total number of features. The metric value ranges from 0 if no features are protected to 1 if all targets
2.1 | Mean protection gap The mean percentage gap metric by Sutcliffe et al. (2015) calculates the number of species that miss a conservation target and the aver‐ age amount by which the targets are missed. It is given by: ( i ∑
Pi 0.3
1…N
N
MTA and MPG are complementary such that: MPG + MTA = 1.
) ∗ 100,
where “Pi is the amount that species i is less than the 30% conserva‐ tion target” and N is the total number of species. We reformulate the problem in a more general manner as mean protection gap (MPG) to prevent possible confusion over how to de‐ termine the value of Pi, and enable straightforward calculation of the metric for multiple conservation targets. The mean protection gap (MPG) metric determines the mean conservation target shortfall across all biodiversity fea‐ tures. It calculates the coverage of biodiversity features in a conservation plan or reserve system and derives the mean gap in protection for achieving a specified conservation target. It is given by:
are met.
3 | C A S E S T U DY O F AU S TR A LI A' S PRO P OS E D CO M M O N W E A LTH M A R I N E R E S E RV E N E T WO R K To illustrate the use of these two metrics, we analysed the represen‐ tation of bioregions in Australia’s proposed Commonwealth Marine Reserve network. We evaluated how well the network achieves two bioregional target levels: (a) 10% targets, which reflect the near‐term requirements of Aichi Target 11; and (b) 30% targets set by marine scientists at the World Parks Congress (Sydney, 2014) and the IUCN World Conservation Congress (Hawaii, 2016). The proposed Commonwealth Marine Reserve network cov‐ ers the Coral Sea, North, North‐west, South‐west, and Temperate
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JANTKE et al.
TA B L E 1 Protected area coverage of marine bioregions across Australia's Commonwealth Marine Reserve and within each of the five regions Conservation target: 10% coverage of each bioregion
Region
Marine protected area coverage (%)
Number of bioregions
Mean protection gap (MPG) (%)
Mean target achievement (MTA) (%)
Conservation target: 30% coverage of each bioregion Mean protection gap (MPG) (%)
Mean target achievement (MTA) (%)
Commonwealth Marine Reserve
43.3
53
14
86
25
75
Coral Sea
100.0
6
0
100
0
100
North
19.6
16
21
79
36
64
North‐west
37.1
15
5
95
15
85
South‐west
36.0
10
0
100
9
91
Temperate East
26.1
11
55
45
67
33
East marine regions, therefore only bioregions within these ma‐
100
rine regions were considered. We used the Integrated Marine and
90
Coastal Regionalisation of Australia (IMCRA, version 4) to define bioregional classifications. In areas where provincial bioregions were subdivided into mesoscale bioregions, the finer, mesoscale bioregions were used, resulting in a total of 53 bioregional fea‐ tures. To derive MPG and MTA metrics, we calculated the per‐ centage of area protected of each bioregion by intersecting equal
80
Protected amount (%)
the bioregional features, focusing on provincial and mesoscale
area projections of bioregions with the 2017 proposed zoning
70 60 50 40 30 20
plan (Australian Government, 2017), including PAs of all IUCN
100
there is a 14% MPG across bioregions at the 10% target level
90
(Table 1, Figure 1a). This is driven in part by protection shortfalls
80
tire network rises to 25% when bioregional targets are set at 30% (Figure 1b). To further evaluate the performance of the proposed PA net‐ work, we calculated MTA for the five regions independently by
Protected amount (%)
are protected by the Commonwealth Marine Reserve network,
50 40
10
MTA also accounts for the protection offered to features that fall short of their targets which would not be captured when report‐ ing only on the percentage of features that meet a target (e.g., bi‐
target = 30%
30 20
area protected, several bioregions have zero protection (Figure 2).
(b)
60
age. We found that the North and Temperate East regions never even though these regions have 19.6% and 26.1% of their total
Marine bioregions
70
incrementally increasing target levels from 1%–50% PA cover‐ reach 100% MTA for even the lowest targets (Figure 3) because
target = 10% MTA = 86%
0
We found that while in total, 43.3% of the five marine regions
MPGs of 21% and 55% respectively (Figure 2). The MPG for the en‐
MPG = 14%
10
categories.
for bioregions in the North and Temperate East regions, which had
(a)
MPG = 25%
0
MTA = 75%
Marine bioregions
F I G U R E 1 Graphic illustration of metrics for 10% (a) and 30% (b) protected area coverage targets of marine bioregions across Australia's Commonwealth Marine Reserves. X‐axis contains 53 bioregions, ordered from lowest to highest amount protected
nary yes/no achievement; Figure 4). For example, a PA coverage target of 30% is met in 60% of the South‐west region’s bioregions in our case study. However, MTA is as high as 91% for this region
4 | CO N C LU S I O N
(Figure 4b), because MTA accounts for the considerable protection in some bioregions, e.g. 27% protection of the Eucla bioregion, that
Reports on target achievement for the growing PA estate require
falls short of the specified 30% target but still contributes to over‐
transparent and repeatable performance metrics. Our intention is
all biodiversity goals.
to present a tailored set of metrics oriented towards promoting a
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F I G U R E 2 Graphic illustration of mean protection gap (MPG) and mean target achievement (MTA) for a 10% protected area coverage target of marine bioregions in five Australian regions
100
Coral Sea
Mean target achievement (%)
90
North-west
80
in PA coverage for representation. Mean Protection Gap and Mean Target Achievement add to the growing metric toolkit to assess protected area evaluation and design (e.g., Chauvenet et al., 2017;
70 60
South-west
Kemp, Jenkins, Smith, & Fulton, 2012; Roberts, Valkan, & Cook,
North
2015). For example, the Commonwealth Marine Reserve network
2018; Saura et al., 2017; Soykan & Lewison, 2015; Sutcliffe et al.,
50
far surpasses the recommended 10% Aichi target level for cover‐
40
age. However, the MPG and MTA metrics quantify its shortfalls in
30
Temperate East
20 10 0
more rigorous and nuanced evaluation of the progress being made
the representation of bioregions that would otherwise remain unde‐ tected. As many species and habitats require varying targets based on their attributes (e.g., threat status or abundance, McGowan,
0
10
20
30
40
50
Protected area coverage target (%)
F I G U R E 3 Mean target achievement (MTA) values across increasing protected area coverage targets (1%–50%) within the five Australian regions of the Commonwealth Reserve Network
Smith, Di Marco, Clarke, & Possingham, 2017), further improvement to these metrics should incorporate unique and varying targets for features. We recommend these metrics be used to evaluate the pro‐ gress towards building representative PA networks in line with Aichi target 11’s goals.
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South−west, Coral Sea
(a)
North−west
90
C O N FL I C T S O F I N T E R E S T The authors certify that there are no conflicts of interest relating to the publication of this article.
Mean target achievement (%)
80
North 70
DATA AC C E S S I B I L I T Y
60
The R package “ConsTarget”, a primer for using the package, and the case study data can be accessed here: https://github.com/
50
KerstinJantke/ConsTarget. Temperate East
40
ORCID
30
Kerstin Jantke
20 10 0
0
100
10
20
30
40
50
60
70
80
Percentage of bioregions achieving target
(b)
90
100
North−west
Mean target achievement (%)
70
North
50 40 30
Temperate East
20 10 0
0
10
20
30
40
50
60
70
80
Percentage of bioregions achieving target
90
100
F I G U R E 4 Comparison of mean target achievement (MTA) to the percentage of bioregions meeting the target. Figure shows both measures for a 10% (a) and 30% (b) protected area coverage target for marine bioregions in five regions across Australia's Commonwealth Marine Reserves
AC K N OW L E D G E M E N T S We thank three anonymous reviewers for their valuable comments. K.J. was supported by a German Research Foundation (DFG) research fellowship (JA 2710/1‐1). A.L.M.C, C.D.K, and J.M. were supported by H.P.P.’s Australian Research Council (ARC) Laureate Fellowship. This research was supported by an ARC Centre of Excellence grant (CE11001000104).
http://orcid.org/0000-0003-1609-9706
Jennifer McGowan
http://orcid.org/0000-0001-9061-3465
Alienor L. M. Chauvenet Hugh P. Possingham
http://orcid.org/0000-0002-3743-7375 http://orcid.org/0000-0001-7755-996X
REFERENCES
90
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Caitlin D. Kuempel
Coral Sea
South−west
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http://orcid.org/0000-0002-3126-3888
Australian Government. (2017). Commonwealth marine reserves review. Retrieved from http://www.environment.gov.au/ marinereservesreview/resources Barnes, M. D., Glew, L., Wyborn, C., & Craigie, I. D. (2018). Prevent per‐ verse outcomes from global protected area policy. Nature Ecology & Evolution, 2, 759–762. Barr, L. M., Pressey, R. L., Fuller, R. A., Segan, D. B., McDonald‐Madden, E., & Possingham, H. P. (2011). A new way to measure the world's protected area coverage. PLoS ONE, 6(9), e24707. CBD (2010). 2011–2020 Strategic plan, convention on biological diversity. Montreal, QC: CBD. Chauvenet, A. L. M., Kuempel, C. D., McGowan, J., Beger, M., & Possingham, H. P. (2017). Methods for calculating protection equal‐ ity for conservation planning. PLoS ONE, 12(2), e0171591. Kemp, J., Jenkins, G. P., Smith, D. C., & Fulton, E. (2012). Measuring the performance of spatial management in marine protected areas. In R. N. Gibson, R. J. A. Atkinson, J. D. M. Gordon, & R. N. Hughes (Eds.), Oceanography and marine biology: An annual review, Vol. 50 (pp. 287– 314). Boca Raton, FL: CRC Press‐Taylor & Francis Group. Klein, C. J., Brown, C. J., Halpern, B. S., Segan, D. B., McGowan, J., Beger, M., & Watson, J. E. M. (2015). Shortfalls in the global protected area network at representing marine biodiversity. Scientific Reports, 5, 17539. Kuempel, C. D., Chauvenet, A. L. M., & Possingham, H. P. (2016). Equitable representation of ecoregions is slowly improving despite strategic planning shortfalls. Conservation Letters, 9(6), 422–428. McGowan, J., & Possingham, H. P. (2015). Submission to the commonwealth marine reserves review. Technical Report, ARC Centre of Excellence for Environmental Decisions, The University of Queensland. McGowan, J., Smith, R. J., Di Marco, M., Clarke, R. H., & Possingham, H. P. (2017). An evaluation of marine important bird and biodiversity areas in the context of spatial conservation prioritization. Conservation Letters, 11(3), e12399. Roberts, K. E., Valkan, R. S., & Cook, C. N. (2018). Measuring progress in marine protection: A new set of metrics to evaluate the strength of marine protected area networks. Biological Conservation, 219, 20–27. Saura, S., Bastin, L., Battistella, L., Mandrici, A., & Dubois, G. (2017). Protected areas in the world's ecoregions: How well connected are they? Ecological Indicators, 76, 144–158.
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Soykan, C. U., & Lewison, R. L. (2015). Using community‐level metrics to monitor the effects of marine protected areas on biodiversity. Conservation Biology, 29(3), 775–783. Sutcliffe, P. R., Klein, C. J., Pitcher, C. R., & Possingham, H. P. (2015). The effectiveness of marine reserve systems constructed using different surrogates of biodiversity. Conservation Biology, 29(3), 657–667. Tittensor, D. P., Walpole, M., Hill, S. L. L., Boyce, D. G., Britten, G. L., Burgess, N. D., … Ye, Y. (2014). A mid‐term analysis of progress to‐ ward international biodiversity targets. Science, 346(6206), 241–244. UNEP‐WCMC, & IUCN (2016). Protected planet report 2016. Cambridge, UK and Gland, Switzerland: UNEP‐WCMC. Venter, O., Fuller, R. A., Segan, D. B., Carwardine, J., Brooks, T., Butchart, S. H. M., … Watson, J. E. M. (2014). Targeting global protected area expansion for imperiled biodiversity. PLoS Biology, 12(6), 7. Watson, J. E. M., Darling, E. S., Venter, O., Maron, M., Walston, J., Possingham, H. P., … Brooks, T. M. (2016). Bolder science needed now for protected areas. Conservation Biology, 30(2), 243–248.
B I O S K E TC H The team around Prof. Hugh P. Possingham from The University of Queensland, Australia, and The Nature Conservancy focuses on providing solution‐oriented research for biodiversity conser‐ vation. The scientists work in partnerships with governments, non‐governmental organizations, and industry to solve the world’s most important conservation problems. Author contributions: All authors conceived the ideas and devel‐ oped the metrics. C.D.K. provided the case study. K.J. led the writing. All authors contributed to writing and revisions.
How to cite this article: Jantke K, Kuempel CD, McGowan J, Chauvenet ALM, Possingham HP. Metrics for evaluating representation target achievement in protected area networks. Divers Distrib. 2018;00:1–6. https://doi. org/10.1111/ddi.12853