1

Atte Moilanen, Joona Lehtomäki, Heini Kujala, Federico M. Pouzols, Jarno Leppänen, Laura Meller & Victoria Veach

C-BIG - Biodiversity Conservation Informatics GroupDept. of Biosciences, University of Helsinki

http://cbig.it.helsinki.fi

Conservation resource allocation and

the Zonation framework

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1. Introduction to conservation resource allocation

2.Zonation• Illustrative example• Operational principle and features• More examples

Introduction: contents 1h+

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ConservationResourceallocation

01

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• To identify different (spatial) allocations of conservation resources (actions) best possible long-term conservation outcome

(population sizes, persistence)

• Limited resources prioritization

• Spatial allocation, various forms of land use: protection, management, restoration, offsetting, competing uses

• What are the consequences and interactions between different (possibly complementary) actions

Objective

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• Often: species• Many others:

• Habitat types and properties (e.g. suitability) • Communities• Ecological processes• Ecosystem services• Vegetation classes• Functional traits• Genetic information• Socio-cultural factors

• Surrogates, pervasive: complete information usually missing

Biodiversity features

6

• Fundamental quantities of spatial population biology:1. Area: the available habitat (spatial amount)2. Quality: resource density (e.g. micro-climate)3. Aggregation: spatial (network) structure of the

habitat

Area and quality determine the carrying capacity Aggregation affects the local dynamics and

occupancy

3 key dimensions

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3 key dimensions:

Qua

lity

AreaAggregation

Fundamental quantities

Fundamental quantities ofspatialpopulation biology

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• Spatial distributions and local occurrence levels of biodiversity features (species, communities etc...)

• Connectivity and minimum population size requirements

• Habitat loss and degradation, landscape change• Climate change• Availability of conservation resources• Socio-political constraints• Pervasive uncertainties about biological facts and

economic realities, sparse data

Conservation prioritization: Relevant factors

10CRA is not the only part of the puzzle –

Social Dimension!

Knight et al. Cons Biol. 2006.

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More about Spatial Conservation Prioritization

+ Recent review:

Kukkala, A. & A. Moilanen. 2013. The core concepts of spatial prioritization in systematic conservation planning. Biological Reviews, 88: 443-464.

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The Zonation framework and software

02

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Illustrative example: evaluation of the proposed benthic protection areas of

New Zealand

Leathwick et al. 2008. Novel methods for the design and evaluation of marine protected areas in offshore waters.

Conservation Letters, 1: 91-102.

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Aim: evaluate proposed New Zealand’s Benthic Protected Areas (BPAs)

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Marine protection areas of New Zealand: Data

• 1.59 million 1 km2 grid cells

• 100 demersal fish species

• Habitat models based on 21000 experimental

trawls

• ~20 environmental variables

• Locations of commercial trawls = cost data

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Basic Zonation output 1

• Map of priority rank

Cell rank0 - 50%50 - 75%75 - 90%90 - 100% (= 10% best)

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Basic output 2: representation of features with different ranks

Endemic weighted higher

With equal weights

10% of total area

Prop

ortio

n of

feat

ure

dist

ributi

on p

rote

cted

Rank (proportion of landscape not under conservation action)

18Pr

opor

tion

of s

peci

es d

istrib

ution

pro

tect

ed

Proportion of cells removed

Replacement cost analysis for proposed reserve areas

LOSS = COST

Performance curve for”ideal” solution

Curve forforced solution

Rank (proportion of landscape not under conservation action)

Prop

ortio

n of

feat

ure

dist

ributi

on p

rote

cted

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Influence of cost

0 10 20 30 40 50

01

02

03

04

05

0

Fishing opportunity cost (%)

Co

nse

rva

tion

be

ne

fit (

%)

10% geographic protectionno cost constraint

full cost constraint

modif ied cost constraints

BPAs - 16.6% geographic protection

20% geographic protection

no cost constraint

full cost constraint

modif ied cost constraints

Fishing opportunity cost [%]

Cons

erva

tion

bene

fit, %

of a

ll

ProposedBPAs

Zonation,Full cost

Zonation,Ideal free solution

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Zonation

operational principle

and features

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Zonation

• Produces a hierarchical zoning of a landscape • looking for priority sites for conservation • indirectly aiming at species persistence• using large grids

2%

2-5%

5-10%

10-25%

25-50%

50-80%

80-100%

Top fraction of the landscape

2%

2-5%

5-10%

10-25%

25-50%

50-80%

80-100%

Top fraction of the landscape

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Zonation

• Persistence by considering:• Habitat quantity, quality and connectivity• For multiple biodiversity features simultaneously

(species, communities, ecoregions, functional traits, etc.)

• Can optimize:• Return on investment (ROI)• Targets

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• Basic input:• Spatial distributions of biodiversity features as static patterns in raster

maps:• Presence• Abundance• Probability

• Many more optional inputs: uncertainty, PAs, interactions, etc.

• Produces 2 main outputs:• Spatial priority ranking for conservation (map)• Performance curves (x-y plots)

• Zonation is not about:• GIS processing• PVAs, dynamic models, etc.

Zonation: inputs and outputs

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Zonation: inputs and outputs

Input data

GIS

Experts

Ecologicalknowledge

Features

Weights

Costs

Connectivity

Higher/lowerpriority areas for

conservation

Performance/potential for proctection

Data collection

Data preparation

Data analysis Inference/ Decision

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A general Zonation workflow

Basic output 1

Basic output 2

Lehtomäki, J., M

oilanen, A., 2013. M

ethods and workflow

for spatial conservation prioritization using Zonation. Env. M

odel. & Sof. 47, 128-137.

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Basic output 1

• Landscape map showing the ranking

2%

2-5%

5-10%

10-25%

25-50%

50-80%

80-100%

Top fraction of the landscape

2%

2-5%

5-10%

10-25%

25-50%

50-80%

80-100%

Top fraction of the landscape

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Basic output 2

• Performance: curves of representation of features (or groups) at different rank levels

10% top fraction

Rank (proportion of landscape not protected)Prop

ortio

n of

feat

ure

dist

ributi

on p

rote

cted

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Additional basic output (3): Post-processing analyses

For example:

• Comparison of different solutions

• Connected sets of sites with similar species compositions can be connected into management landscapes

• Tutorial example: do_ppa.bat

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Zonation - Basic analyses

1. Identification of optimal reserve areas

2. Identification of least valuable areas

3.Evaluation of conservation areas

4.Expansion of conservation areas

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Major Zonation Features

• Species/feature weighting• Species-specific connectivity • Handles uncertainty and costs• Combined species and community level prioritization• Balancing alternative land uses• Landscape condition and retention analysis• Prioritization across multiple administrative regions

• Direct link: GIS distribution modeling Zonation

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• Improved detection of errors in setups• Manage and monitor multiple analyses• Post-process and explore output• Explore transformed layers used in computations• Explore all output curves interactively• Import/export publication-quality maps• Simple interface for comparing/merging maps

New Graphical User Interface,much improved for Zv3.1

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Zonation strategy summarized

Minimize loss of weighted range-size rarity

=

Maximize retention of weighted range-sizenormalized (rarity corrected) featurerichness

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in other words

Zonation produces a complementarity-based balanced priority ranking through the landscape.

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Zonation Meta-algorithm

1.Start from full landscape

2.Determine cell that has least marginal value and remove it

3.Update occurrence levels of features (in the remaining landscape)

4.Repeat (2 and 3) until no cells remain

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0.02 0.05 0.075

0.025 0.115 0.16

0.1 0.2 0.255

0.0510 0.0765

0.0255 0.1174 0.1632

0.102 0.2041 0.2602

0.0523 0.0785

0.1204 0.1675

0.1047 0.2094 0.2670

0.0828

0.1270 0.1767

0.1104 0.2209 0.2817

0.1385 0.1927

0.1204 0.2409 0.3072

0.1575 0.2191

0.2739 0.3493

0.2601

0.3252 0.41460.4395 0.56041.0

4 10 15

5 23 32

20 40 51

Absolute valueNormalized values

&Removal sequence

Cell removal

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37

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= definition of marginal loss in conservation value

= different rules implement different conceptions of conservation value, how is it aggregated across space, time and features?

Cell removal rule

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• Determines how marginal loss is aggregated when a cell is lost

• Four alternatives• Core-area Zonation (CAZ)• Additive benefit function (ABF)• Targeting benefit function (TBF)• Generalized benefit function (GBF)

• These alternatives• Have different aims• Value representation differently

Cell removal rule

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Cell removal rules

• Core-area Zonation• Cell value is the maximum biological value within

the cell, across all features/species• Cell with the smallest (max) value will be removed

• Additive benefit function• Cell value is the sum of value across species within

the cell• Cell with the smallest sum value will be removed

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Cell removal rules

0.600.05

0.100.30

0.050.15

0.250.50

0.60 0.30

0.15 0.50

0.65 0.40

0.20 0.75

Core-area Zonation

Additive benefit function

0.63160.0588

0.10530.3529

0.26320.5882

0.6316 0.3529

0.5882

0.6904 0.4582

0.8514

0.70590.0909

0.29410.9091

0.7059

0.9091

0.7968

1.2032

1.01.0

1.0

2.0

Species 2

Species 1

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Zonation: Cell removal principles

“More rare”“More important”

“less prop.remains”

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i

jij

ji c

wSqi

)(maxmin for which cell remove

over cells i over spp j

weight of sp j

proportion of remaining distribution of sp j in cell i in remaining landscape S

cost of site i

Core-Area Zonation (CAZ) emphasizes the most valuable feature in the cell

CAZ valuationof site i

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• ABF uses a power function, which has a smooth shape, and can replicate, for example, the species-area curve

• loss of representation => loss of value

• GBF has a more flexible shape (incl. sigmoids)

Cell removal rules:

Additive benefit function & Generalized BF

proportion of distribution remaining

0.0 0.2 0.4 0.6 0.8 1.0

valu

e V

j

0.0

0.2

0.4

0.6

0.8

1.0

Rj

Vj

Sum over species-specific loss ΔVj; free trade between spp;implicitly emphasizes locations with many species (richness)

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Cell removal rules:

Finnish breeding birds – CAZ vs. ABF

Number of species< 3030 - 6060 - 9090 - 120> 120

Additive benefit functionCore-area Zonation

Cell Ranking0 - 50 %50 - 75 %75 - 90 %90 - 100 %No Data

som

ewha

t

emph

asizes

rar

ity

som

ewha

t

emph

asizes

richn

ess

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Other cell removal rules

Target-based planning

•Below target: 0 value•Above target: power function

Generalized benefit functions

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What can be done using Zonation?

Some Zonation study summaries

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Aligning conservation priorities in Madagascar

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Plan of extension of Madagascar protected areas to 10%

• Most extensive example of conservation prioritization at the time

• + Extensive surrogacy analysis

Kremen, Cameron, Moilanen, Phillips, Thomas et al. 2008 Science 320: 222-226.

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Bird habitat restorationVictoria, Australia

• Multiple time steps• Maturation of

restored habitat• Suitability for birds• Connectivity

Thomson et al. 2009. Ecol. Appl.

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Urban analysis around Melbourne

Extending reserves Guiding placement of green areas

Gordon et al. 2009. Landscape & Urban Planning

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Ecological interactions in Zonation, phase 1Inter- and intraspecies connectivities

Conservation for the Marten in Canada

Rayfield et al. 2009. Ecological Modelling

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Core-area Zonation

Freshwater planning accounting for hydrological connectivity of catchments

Rivers in New Zealand

Moilanen, Leathwick & Elith. Freshwater Biology 2008.Leathwick et al. Biological Conservation 2010.

+ condition

+ connectivity

56Balancing between competing land-uses

biodiversity (+)

agri (-)

urban (-)

carbon (+)

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... all can be put in the same analysis

Moilanen, A., B.J. Anderson, F. Eigenbrod, A. Heinemeyer, D. B. Roy, S. Gillings, P. R. Armsworth, K. J. Gaston, and C.D. Thomas. 2011. Balancing alternative land uses in conservation prioritization. Ecological Applications, 21: 1419-1426.

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Administrative units analysis

• Admin. areas have different priorities• Balancing national & global priorities• Local, global or compromise analyses• Striking edge artifacts!• Need for ”Collaboration in conservation”

Moilanen, A., and Arponen A. 2011b. Administrative regions in conservation: balancing local priorities with regional to global preferences in spatial planning. Biological Conservation, 144: 1719-1725. Moilanen, A., Anderson, B.J., Arponen, A., Pouzols, F.M., and C.D. Thomas. 2012. Edge artefacts and lost performance in national versus continental conservation priority areas. Diversity and Distributions, 19: 171-183.

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Administrative units analysisWestern hemisphere mammals, birds and amphibians

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Largest landscape (at the time...):

Arponen, A., J. Lehtomäki, J. Leppänen, E. Tomppo, and A. Moilanen. 2012. Effects of connectivity and spatial resolution of analyses on conservation prioritization across large extents. Conservation Biology, 26: 294–304.

• Spatial planning and connectivity in Finnish forests• Entire country up to 1ha resolution• Up to 28 million grid cells with data

The Academy of Finland, EU FP7 SCALES,

the European Research Council ERC, Finnish

Ministry of Environment;

the Finnish Natural Heritage Services

Univ. York: Chris Thomas, Aldina Franco, Regan Early

Barbara Anderson

Univ. Melbourne: Mark Burgman, Brendan Wintle, Jane Elith

Finnish Environment Institute Risto Heikkinen, Raimo Heikkilä

NIWA & DOC, New-Zealand John Leathwick

Berkeley/Princeton Alison Cameron, Claire Kremen

Israel Univ. Techn. Yakov Ben-Haim

Royal Melbourne Univ. Techn. Sarah Bekessy, Ascelin Gordon

CSIRO, Australia Simon Ferrier

Univ. Queensland, Australia Hugh Possingham, Kerrie Wilson

Klamath conservation Carlos Carroll

Special thanks to