1
Conservation resource allocation and
the Zonation framework
Atte Moilanen, Joona Lehtomäki, Heini Kujala, Federico M.
Pouzols, Jarno Leppänen, Laura Meller & Victoria Veach
C-BIG - Biodiversity Conservation Informatics Group
Dept. of Biosciences, University of Helsinki
http://cbig.it.helsinki.fi
2
Introduction: contents 1h+
1. Introduction to conservation resource
allocation
2. Zonation
•
•
•
Illustrative example
Operational principle and features
More examples
3
01
Conservation
Resource
allocation
4
Objective
•
•
•
•
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
5
Biodiversity features
• 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
6
3 key dimensions
• 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
7
Quality
3 key dimensions:
Fundamental quantities
Fundamental
quantities of
spatial
population
biology
9
Conservation prioritization:
Relevant factors
• 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
10
CRA is not the only part of the puzzle –
Social Dimension!
Knight et al. Cons Biol. 2006.
11
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.
12
02
The Zonation
framework and
software
13
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.
14
Aim: evaluate proposed New Zealand’s
Benthic Protected Areas (BPAs)
15
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 rank
0 - 50%
50 - 75%
75 - 90%
90 - 100% (= 10% best)
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Proportion of feature distribution protected
Basic output 2: representation of features
with different ranks
Endemic weighted higher
With equal weights
10% of total area
Rank (proportion of landscape not under conservation action)
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Proportion
distributionprotected
Proportionofoffeature
speciesdistribution
protected
Replacement cost analysis
for proposed reserve areas
Performance curve for
”ideal” solution
Curve for
forced solution
Proportion
of cellsnotremoved
Rank (proportion
of landscape
under conservation action)
LOSS
= COST
19
50
20% geographic protection
40
Zonation,
Full cost
no cost constraint
modified cost constraints
Zonation,
Ideal free solution
30
full cost constraint
10% geographic protection
modified cost constraints
20
no cost constraint
full cost constraint
10
BPAs - 16.6% geographic protection
Proposed
BPAs
0
Conservation
benefit (%)
% of all
Conservation
benefit,
Influence of cost
0
10
20
30
40
50
Fishing
opportunity
cost [%]
20
Zonation
operational principle
and features
21
Zonation
• Produces a hierarchical zoning of a landscape
• looking for priority sites for conservation
• indirectly aiming at species persistence
• using large grids
Top fraction of the landscape
2%
2-5%
5-10%
10-25%
25-50%
50-80%
80-100%
22
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
23
Zonation: inputs and outputs
•
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.
24
Zonation: inputs and outputs
Data
collection
Input data
Data
preparation
Data analysis
Ecological
knowledge
Features
Weights
Inference/
Decision
Higher/lower
priority areas for
conservation
Experts
Costs
GIS
Connectivity
Performance/
potential for
proctection
A general Zonation workflow
25
Basic output 2
Lehtomäki, J., Moilanen, A., 2013. Methods and
workflow for spatial conservation prioritization using
Zonation. Env. Model. & Sof. 47, 128-137.
Basic output 1
26
Basic output 1
• Landscape map showing the ranking
Top fraction of the landscape
2%
2-5%
5-10%
10-25%
25-50%
50-80%
80-100%
27
Basic output 2
• Performance: curves of representation of
Proportion of feature distribution protected
features (or groups) at different rank levels
10% top fraction
Rank (proportion of landscape not protected)
28
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
29
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
30
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
31
New Graphical User Interface,
much improved for Zv3.1
• 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
32
Zonation strategy summarized
Minimize loss of weighted range-size rarity
=
Maximize retention of weighted range-size
normalized (rarity corrected) feature
richness
33
in other words
Zonation produces a complementaritybased balanced priority ranking
through the landscape.
34
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
35
Cell removal
Absolute value
Normalized values
&
Removal sequence
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10
15
0.02
0.05 0.0828
0.075
0.0523
0.0510
0.0785
0.0765
5
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0.025
0.0255
0.16
0.2601
0.1675
0.1767
0.1927
0.1632
0.115 0.2191
0.1575
0.1204
0.1270
0.1385
0.1174
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0.1
0.2
0.1204
0.1047
0.1104
0.102
0.2094
0.2209
0.2409
0.2739
0.3252
0.4395
0.2041
0.255
0.2670
0.2817
0.3072
0.3493
0.4146
0.5604
0.2602
1.0
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1
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0.078
0.042
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0.042
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0.042
0.078
0.042
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0.061
0.036
0.061
0.042
0.078
0.042
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0.042
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0.042
0.067
0.042
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0.042
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0.042
0.067
0.042
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0.103
0.042
0.078
0.042
0.078
0.103
0.042
0.025
0.036
0.042
0.078
0.025
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0.036
0.061
0.078
0.042
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0.025
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Cell removal rule
= definition of marginal loss in
conservation value
= different rules implement different
conceptions of conservation value,
how is it aggregated across space,
time and features?
41
Cell removal rule
• 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
42
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
43
Cell removal rules
Core-area Zonation
Species 1
0.60
0.6316
0.7059
0.30
0.3529
0.15
0.50
0.5882
0.9091
1.0
Species 2
0.7059
0.60
0.6316
0.05
0.0588
0.0909
0.1053
0.10
0.30
0.3529
0.05
0.15
1.0
0.25
0.2632
0.2941
0.50
0.5882
0.9091
1.0
Additive benefit function
0.65
0.6904
0.7968
0.40
0.4582
0.20
0.75
0.8514
1.2032
2.0
44
Zonation: Cell removal principles
“More important”
“More rare”
“less prop.
remains”
45
Core-Area Zonation (CAZ) emphasizes
the most valuable feature in the cell
proportion of remaining distribution of
sp j in cell i in remaining landscape S
weight of
sp j
qij ( S ) w j 

remove cell i for which min max

i
j
ci


over cells i
over spp j
CAZ valuation
of site i
cost of
site i
46
Cell removal rules:
Additive benefit function & Generalized BF
Sum over species-specific loss ΔVj; free trade between spp;
implicitly emphasizes locations with many species (richness)
•
1.0
value Vj
0.8
Vj
ABF uses a power
function, which has a
smooth shape, and can
replicate, for example,
the species-area curve
0.6
•
Rj
0.4
0.2
0.0
0.0
•
0.2
0.4
0.6
0.8
1.0
proportion of distribution remaining
loss of representation
=> loss of value
GBF has a more
flexible shape (incl.
sigmoids)
47
Cell removal rules:
Finnish breeding birds – CAZ vs. ABF
Core-area Zonation
Number of species
< 30
30 - 60
60 - 90
90 - 120
> 120
Cell Ranking
0 - 50 %
50 - 75 %
75 - 90 %
90 - 100 %
No Data
Additive benefit function
48
Other cell removal rules
Target-based planning
•Below target: 0 value
•Above target: power function
Generalized benefit functions
49
What can be done using Zonation?
Some Zonation study summaries
50
Aligning conservation priorities in
Madagascar
51
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.
52
Bird habitat restoration
Victoria, Australia
• Multiple time steps
• Maturation of
•
•
restored habitat
Suitability for birds
Connectivity
Thomson et al. 2009.
Ecol. Appl.
53
Urban analysis around Melbourne
Extending reserves
Guiding placement of green areas
Gordon et al. 2009. Landscape & Urban Planning
54
Ecological interactions in Zonation, phase 1
Inter- and intraspecies connectivities
Conservation for the Marten in Canada
Rayfield et al. 2009. Ecological Modelling
55
Freshwater planning
accounting for hydrological connectivity of catchments
+ condition
Core-area Zonation
Rivers in New Zealand
+ connectivity
Moilanen, Leathwick & Elith. Freshwater Biology 2008.
Leathwick et al. Biological Conservation 2010.
56
Balancing between competing land-uses
carbon (+)
agri (-)
biodiversity (+)
urban (-)
57
... 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.
58
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.
59
Administrative units analysis
Western hemisphere mammals, birds and amphibians
60
Largest landscape (at
the time...):
• Spatial planning and
connectivity in Finnish
forests
• Entire country up to 1ha
resolution
• Up to 28 million grid
cells with data
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.
Special thanks to
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