Metrics for evaluating representation target

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Aug 30, 2018 - target = 30%. MTA = 75%. (b). 0 ... http://orcid.org/0000‐0002‐3126‐3888. Caitlin D. Kuempel ... Retrieved from http://www.environment.gov.au/.
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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

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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

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 | 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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JANTKE et al.

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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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JANTKE et al. 100

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

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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

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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