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Zonation analysis types and setups
Atte Moilanen, Joona Lehtomäki, Heini Kujala,
Federico Montesino Pouzols, Jarno Leppänen &
Laura Meller
C-BIG - Conservation Biology Informatics Group
Dept. of Biosciences, University of Helsinki
http://cbig.it.helsinki.fi
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Contents
1. Basic analyses
2. Connectivity
3. Uncertainty
4. Community/ecosystem level approaches
5. Advanced analyses
6. Data size issues
7. Limitations of Zonation
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Zonation - Basic analyses
1. Identification of “optimal” reserve networks
2. Identification of least valuable areas
3. Expansion of conservation area networks
4. Evaluation of conservation areas
o
Replacement cost
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B1. Identification of optimal reserves
• The most ”traditional” function of any reserve
•
selection method
Basic question: Where to protect?
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B1: Optimal areas = basic Zonation analysis
These
schematics
are in the
manual
Look for areas with highest priority
Several
examples in
the tutorial
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Zonation
• Produces a hierarchical zoning of a landscape
• looking for priority sites for conservation
• aiming at species persistence
Top fraction of the landscape
2%
2-5%
5-10%
10-25%
25-50%
50-80%
80-100%
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Basic output 1
• Map of landscape showing the ranking
(cell removal levels)
Area needed to achieve 30% of sp
distributions
Top fraction of the landscape
2%
2-5%
5-10%
10-25%
25-50%
50-80%
80-100%
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Basic output 2
• Curves: performance of features (or groups) at
different levels of cell removal (these curves
correspond 1-1 to the rank map)
10% top fraction
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B2. Identification of least valuable areas
• Ecologically least valuable = where economic
activity disturbs biodiversity least = impact
avoidance
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B2: least valuable = basic Zonation analysis
Look for areas with lowest priority
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Balancing between competing land-uses
carbon (+)
agri (-)
biodiversity (+)
urban (-)
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... all can be put in the same analysis
… produces separation of conservation and other land uses
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Alternative land uses
Note negative weights
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B4. Extending conservation area networks
• Usually conservation does not start from
•
•
scratch
Uses hierarchical mask layer to enforce
hierarchy into ranking
When present PAs are at top rank category,
highest ranked areas outside PAs identify
optimal and balanced CAN expansion
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Extending conservation area networks
Force
Inclusion
of PAs
Tutorial
example:
do_rs.bat
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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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Proposed extension
from present 6.3% PAs
• All major taxa
• 2315 endemic species
• Entire country at 0.7 km2
resolution
• Existing reserves missed
28% of species
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B3. Evaluating a proposed network of sites
• Comparison between ”what is” and ”what
could be, ideally”
• Zonation specific way: replacement cost
analysis
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Replacement cost analysis
• Situation where areas need to be forcibly
included to or excluded from the final solution
• Eg. evaluation of existing or proposed reserves
• Forced inclusion of poor areas entails a cost to
biodiversity
• Broader interpretation: difference between
ideal free and constrained solution
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B4: Evaluating a conservation area network
Tutorial
example:
do_rs.bat
(compare
with and
without
mask)
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Replacement cost analysis
Optimal solution
Forced solution
Proposed
reserves
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Replacement cost analysis
1. Calculate biologically optimal solution
2. Force in areas that must to be protected
or force out areas that cannot be protected
3. Re-optimize under constraint and calculate
the difference in cost/benefit
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Replacement cost analysis:
New Zealand revisited
Leathwick et al. Conservation Letters, 200
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Proportion of species distribution protected
Replacement cost analysis
Performance curve for
ideal solution
Curve for
forced solution
COST
= loss in
biological value
Proportion of cells removed
Leathwick et al. Conservation Letters, 200
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Replacement cost analysis:
New Zealand EEZ
Cost
Benefit
loss for fishermen
species protected
Existing reserves
18.1%
29.8%
Proposed by fisheries
0.2%
11.9%
19.9%
31.1%
1.6%
28.6%
Network
Zonation software
no costs
Zonation software
cost-adjusted
Leathwick et al. Conservation Letters, 2008
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5. Connectivity
• Zonation includes many ways of accounting for
connectivity
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Accounting for Connectivity
•
•
•
•
•
Qualitative /structural
1.
2.
Edge removal
Boundary length penalty: do_blp.bat
Feature-specific
3.
4.
5.
Distribution smoothing: do_ds.bat
Boundary Quality Penalty: do_bqp.bat
Neighborhood Quality Penalty: do_nqp.bat
Interaction connectivity
6.
7.
Pairwise interaction
Matrix connectivity (many-to-one interaction)
Path-like connectivity; corridors
8.
In Zonation 4 – coming soon!
Connectivity in environmental space
9.
In Zonation 4 – coming soon!
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Accounting for connectivity:
Distribution smoothing
Original distribution
Connectivity distribution
Tutorial example: do_ds.bat
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Accounting for connectivity:
Distribution smoothing
No aggregation
Using distribution smoothing
Top 20% (color) is scrappy
Top 20% well connected
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Accounting for connectivity:
Boundary quality penalty (BQP)
• Species-specific decrease in local quality due
to proximity of reserve boundary
• Forces connectivity only where needed.
• Allows fragmentation where it does not hurt
Tutorial example: do_bqp.bat
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Accounting for connectivity:
Small effect of neighboring
habitat loss
Strong effect of neighboring
habitat loss
1.0
1.0
Local value remaining
Local value remaining
Boundary quality penalty (BQP)
0.8
0.6
0.4
focal
cell
0.2
0.0
0.0
small buffer
0.2
0.4
0.6
0.8
1.0
Proportion of neighboring cells lost
0.8
0.6
0.4
large buffer
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Proportion of neighboring cells lost
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Accounting for connectivity:
Boundary Quality Penalty (BQP)
Moilanen and Wintle 2006
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Accounting for connectivity:
Boundary Quality Penalty (BQP)
Distributions
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Accounting for connectivity:
Neighborhood Quality Penalty (NQP)
NQP = extension of BQP to operate on planning units
(e.g catchments), linked to tree structures (freshwater
systems)
Connectivity responses upstream and downstream
Tutorial example: do_nqp.bat
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Directed freshwater connectivity
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Freshwater planning
accounting for hydrological connectivity of catchments
+ condition
Rivers in New Zealand
Core-area Zonation
+ connectivity
Moilanen, Leathwick & Elith. Freshwater Biology 2008.
Leathwick et al. Biological Conservation 2010.
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Interaction connectivity
connectivity between two distributions
•
Between two
features
(Rayfield et al. 2009)
•
Same species;
different time
steps
(Carrol et al. 2010)
•
Positive
(resourceconsumer)
or negative
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Single-species prioritization
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Ecological interactions in Zonation, phase 1
Inter- and intraspecies connectivities result in aggregated
solution
Conservation areas for the Marten in Canada
Rayfield et al. 2009.
Ecological Modelling.
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3. Uncertainty in inputs
• Uncertainty is pervasive in ecological data,
including spatial data about occurrence levels of
biodiversity features
Simple solution: give low weigth to uncertain
data
• 2 types of uncertainty analyses:
• Robustness
• Opportunity
• Requires additional rasters: distribution of
uncertainty in feature distributions
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Uncertainty analysis
LOW
HIGH
HIGH
IMPORTANT
AVOID
LOW
Certainty of information
Conservation value
NEGATIVE
SURPRISES
POSITIVE
SURPRISES
Robustness
requirement
Opportunity
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Uncertainty analysis
Tutorial
example:
do_uc.bat
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Uncertainty analysis:
Distribution discounting
Distribution
model
Error or relative
uncertainty surface
Discounted
distribution
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4. Community-level analysis
Species are common biodiversity features, but
many analyses can be productively done at
the level of community / ecosystem /
environment type
Tutorial example: do_comm_similarity.bat
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Community-level analysis
The catch:
communities /
ecosystems are not
completely dissimilar,
which should be
accounted for!
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Combined communities and species analysis
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Similarity expansion
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Community classification and similarity expansion:
Riverine communities in New Zealand
WITH similarity expansion WITHOUT similarity expansion
•
•
•
•
Macroinvertebrate and
fish species used for
modelling community
turnover (GDM)
Similarity expansion
Or, matrix connectivity
accounting for similarity
Directed connectivity
(NQP)
Leathwick et al. Biological
Conservation 2010.
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5.1. Prioritization across multiple administrative
regions
• Different administrative regions may have
•
•
different priorities
But connectivity extends across borders
Representation of species may be considered
across multiple spatial scales
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Administrative units analysis
Prioritization across multiple administrative regions
Global priorities with connectivity
• Hunter Valley, Australia
• 7 species
• Two imaginary
administrative regions:
Eastland and Westland
Tutorial example:
do_admu.bat
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Administrative units analysis
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Administrative units analysis
Prioritization across multiple administrative regions
Prioritization separately for Eastland and Westland
NOTE: changed priorities and edge effects
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Administrative units analysis
Prioritization across multiple administrative regions
MODE 1: Weak local administrative priorities: weight local but
representation global
Eastland w = 2
Westland w = 2
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MODE 2: Strong local administrative priorities ;
Weights & Representation both local and global
Eastland w = 1
”Boxland” w = 1
Westland w = 2
Weight selection important!
Area of admin unit should influence weight!
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5.2. Balancing representation and retention
• Representation = what is out there right now
• Retention = what will remain across the
landscape depending on our actions
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5.3. Dynamic landscapes and climate change
• The world is not static
• Trick to model dynamics in Zonation (static
approach): enter pattern data for multiple
time steps
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Balancing representation and retention
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5.4 Planning for climate change
•
•
Based on pairwise
interactions
between successive
time steps
Account for
uncertainty
Carroll et al. 2010,
Global Change
Biology 14: 891-904.
Kujala et al. 2013, PLoS
ONE 8(2): e53315
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Interaction connectivity through time:
Resilient reserve networks to climate change,
Pacific Northwest, USA
Interaction:
Current + NF
Interaction:
Current + DF
Distant future projections
Carroll et al. 2010, Global Change Biology 14: 891-904.
Current distributions
Near future
projections
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5.5. Habitat maintenance or restoration
• These are the two other common forms of
conservation. Can management or restoration
be targeted using Zonation?
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Bird habitat restoration
Victoria, Australia
• Multiple time steps
• Maturation of
•
•
•
restored habitat
Suitability for birds
Connectivity
Complicated!
Thomson et al. 2009.
Ecol.Appl.
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5.6. Note on combined analyses
• The previous analyses illustrate setups for
particular purposes. It is possible to combine
data and analysis settings in innovative ways
to answer complex planning needs! Pick
influences from multiple simpler analyses and
combine them!
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6. Analysis size issues
• It is useful to be able to estimate the amout of
RAM memory needed for an analysis.
=> Can you do your analysis with your
computer?
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Increased memory capacity in Zonation v. 3.1
• Multithreaded 64 bit software is fast and allows
processing large data sets
• Landscapes with up to ~50 million grid cells of effective
•
•
data
Up to 25 000 species or other biodiversity features in
grid format
Example: 8 GB memory (of available RAM):
• ~280 features/species X 5M cells map
• Data effectively limited by size of RAM memory
• Memory is inexpensive but run time increases!
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7. What is Zonation not well suited for?
• Different software have different suitability in
• What problems they solve directly
• What problems they can approximately solve
indirectly
• How well they are documented and supported
• How easy they are to use
• How big data they can feasibly handle
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7. What is Zonation v4 not well suited for?
• it does not operate directly on vector-based
•
•
•
•
•
polygons
it does not do species distribution modelling
it is not a dynamic stochastic simulation
it does not convert bad data into good, it does
not generate the data for you
it does not solve complicated planning
problems with minor effort
it does not directly allocate alternative actions