Part III - Answers to questions about Zonation
and analysis setups
Atte Moilanen, Joona Lehtomäki, Heini Kujala,
Anni Arponen and Laura Meller
Conservation Biology Informatics Group
http://cbig.it.helsinki.fi
www.helsinki.fi/science/metapop
Finnish Centre of Excellence in Metapopulation Biology
Dept. Biological and Environmental sciences
P.O. Box 65, FI-00014 University of Helsinki, Finland.
The short introductions to these questions are on the question papers. Note that below species and
feature are used almost interchangeably. A conservation feature can be a species, habitat type,
ecosystem process, or whatever. Any such data can be used; species is often used as a naturalsounding shorthand while one really means a feature in general.
References are given after each question. These references illustrate the use of techniques relevant
for that particular question. Some general references:
Lehtomäki, J., and Moilanen 2013 Methods and workflow for spatial conservation prioritization using
Zonation . Environmental Modeling & Software, 47:128-137.
Moilanen, A., Kujala, H. and J. Leathwick. 2009. The Zonation framework and software for conservation
prioritization. Pp. 196-210 in Moilanen, Wilson and Possingham (Eds.), Spatial Conservation
Prioritization, Oxford University Press.
Moilanen, A., Franco, A.M.A., Early, R., Fox, R., Wintle, B., and C.D. Thomas. 2005. Prioritising multiple-use
landscapes for conservation: methods for large multi-species planning problems. Proc. R. Soc. Lond. B
Biol. Sci., 272: 1885-1891. [first Zonation paper]
Questions 1: Selecting best areas for conservation.
Q1.1 How does one select best areas for conservation from a Zonation solution? What about the
quality of the solution – where can you find information from?
The best areas are those that have highest rank in the map output (.rank.asc file). For example, best 6% are all
cells that have rank higher than 0.94. The quality of the solution is measured from the performance curves (at
the level of fraction chosen, like 6% remaining as in the previous example). If the absolute performance is good,
then the chosen areas include a big fraction of distributions of species. If performance is bad, chosen areas only
have a small fraction of distributions. Like, if 6% of landscape has a balanced mean representation of 50% of
distributions, then that is very good. If 6% of the landscape only include 10% of distributions, then 90% of
distributions would be outside protection - not very good.
Moilanen, A., Franco, A.M.A., Early, R., Fox, R., Wintle, B., and C.D. Thomas. 2005. Prioritising multiple-use
landscapes for conservation: methods for large multi-species planning problems. Proc. R. Soc. Lond. B
Biol. Sci., 272: 1885-1891.
Gordon, A., Simondson, D., White, M., Moilanen, A., and Bekessy, S.A. 2009. Integrating conservation planning
and land-use planning in urban landscapes. Landscape and Urban Planning, 91: 183-194.
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Q1.2 What is the meaning of the definition of marginal loss (cell removal rule) from the point of
view of the Zonation solution? Why do different cell removal rules lead to different solutions? As a
reminder, the removal rules were Core-Area Zonation CAZ; Additive Benefit Function analysis
ABF; Targeting benefit function analysis TBF; Generalized Benefit Function GBF.
The definition of marginal loss implements your conception of conservation value. For example, ABF (additive
benefit function) implies emphasis on locations that are species rich. CAZ (core-area Zonation) requires highquality locations for all species, even if such locations include otherwise species-poor locations. Targeting
analysis (TBF) simply wants everything represented with minimal cost, trying to keep species representations
above a pre-set target as long as possible?.
Moilanen, A., Franco, A.M.A., Early, R., Fox, R., Wintle, B., and C.D. Thomas. 2005. Prioritising multiple-use
landscapes for conservation: methods for large multi-species planning problems. Proc. R. Soc. Lond. B
Biol. Sci., 272: 1885-1891.
Moilanen, A. 2007. Landscape Zonation, benefit functions and target-based planning. Unifying reserve selection
strategies. Biological Conservation, 134: 571-579.
Moilanen, A. 2008a. Two paths to a suboptimal solution: once more about optimality in reserve selection.
Biological Conservation 141: 1919-1923.
Arponen, A., Heikkinen, R., Thomas, C.D. and A. Moilanen. 2005. The value of biodiversity in reserve selection:
representation, species weighting and benefit functions. Conservation Biology 19: 2009-2014. [for more
about the additive benefit function approach]
Q1.3 Can cost be accounted for in the prioritization process? What is the conceptual difference
between accounting for cost or not?
Cost can be accounted for via use of a cost layer. If cost is used, highest-priority areas are those with highest costefficiency for conservation. Note that such analysis may lead to cost being the driving factor of analysis - highrank areas may be those that simply are very cheap. For this reason, it would be wise to also do the no-cost
biodiversity-only analysis for a comparison. These two analyses are in a sense at the ends of a spectrum of
analyses in which cost is accounted for in differing degrees.
The following study shows how the influence of cost in the analysis can be tuned.
Leathwick, J. R., Moilanen, A., Francis, M., Elith, J., Taylor, P. Julian, K. and T. Hastie. 2008. Novel methods
for the design and evaluation of marine protected areas in offshore waters. Conservation Letters 1: 91102.
It is also possible to account for multiple opportunity costs: see
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
Q1.4 Can Zonation be used for targeting of habitat maintenance or restoration? What is the
interpretation of the input data in this case?
Yes it can. Protected or not, maintained or not, restored or not – these are all based on binary choice. Something
is done at a location or not. For example, if you have one restoration action available, your inputs would be (i) a
cost layer for restoration cost across the landscape (or across locations that could be restored), (ii) occurrence
level maps for features assuming conservation action is taken. Then the top fraction of the solution is the areas
that should be restored. Note that allocation of multiple alternative actions is a different question about which
there are comments in another question (Q11).
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Thomson, J.R., Moilanen, A., McNally, R., and P. Vesk. 2009. A quantitative method for prioritizing landscape
revegetation. Ecological Applications, 19: 817-828.
An alternative is to enter extra information (feature layers) about the feasibility and costs of
conservation management, see
Moilanen, A., J.R. Leathwick, and J. M. Quinn. 2011b. Spatial prioritization of conservation management.
Conservation letters, 4, 383-393.
Questions 2. Selecting best areas for competing (economic) land-uses.
Q2.1 How can one using Zonation select areas that are least useful for biodiversity (assuming the
areas are lost due to economic activity).
See answer to Q1.1. This is the same, except one chooses the areas with lowest ranks - these areas are least
important in terms of biodiversity.
Gordon, A., Simondson, D., White, M., Moilanen, A., and Bekessy, S.A. 2009. Integrating conservation planning
and land-use planning in urban landscapes. Landscape and Urban Planning, 91: 183-194.
Q2.2 Cost can be accounted for in this process. But, what if there are multiple costs, such as cost for
agriculture, cost for forestry, etc. Can multiple costs be accounted for? Is there any problem with
zv2 capabilities here?
Multiple costs can be accounted for by summing them to a single layer. However, this has the problem that the
true allocation of costs may be unbalanced inside the summed measure. Say, for example that conservation has
costs to both forestry and agriculture. In a summed measure, all cost could for example occur for forestry. The
potential for an unbalanced distribution of costs cannot be avoided when one cost measure only is used.
But, happily, Zv3 allows for multiple alternative costs, see:
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
Suitability for alternative land uses can be entered into Zonation analysis as grids that work like a biodiversity
feature layer – the only difference is that these layers are assigned negative weights (and inverted benefit
functions, possibly different connectivity responses). This way, Zonation balances conservation value and value
for other land uses in the prioritization. That is different from a cost-efficiency analysis which aims at
maximizing conservation value per cost unit.
Questions 3. Extending conservation area networks.
Most real life planning situations are about extending an existing conservation area network. The
analysis setup is for this problem is rather similar to that of selection of conservation areas, but there
are a few complications.
Q3.1. You have an existing conservation area network. Then there is data through the landscape.
How would you select extensions to the network using Zonation? What relevant considerations and
analysis features are there? How about connectivity in particular?
3
The key to designing extension of protected area networks in Zonation is that existing conservation areas are
“masked in”, meaning that they are forced to be in the top priority areas of the landscape. Then the next highest
priority areas are the proposed extensions of the network. A mask file needs to be produced and used to
implement this analysis.
Deciding how connectivity should be handled is the more complicated bit of this analysis. One could wish
internal connectivity for conservation areas. One could also wish that extensions should be well-connected to
existing conservation areas, which may need to act as colonization source areas. See Lehtomäki et al. for an
example of such a setup.
Kremen, C., A. Cameron, A. Moilanen, S. Phillips, C. D. Thomas, H. Beentje, J. Dransfeld, B. L. Fisher, F. Glaw,
T. Good, G. Harper, R.J. Hijmans, D. C. Lees, E. Louis Jr., R. A. Nussbaum, C. Raxworthy, A.
Razafimpahanana, G. Schatz, M. Vences, D. R. Vieites, P. C. Wright , M. L.Zjhra. 2008. Aligning
conservation priorities across taxa in Madagascar, a biodiversity hotspot, with high-resolution planning
tools. Science 320: 222-226.
Lehtomäki, J., Tomppo, E., Kuokkanen, P. Hanski, I., and A. Moilanen. 2009. Planning of forest conservation
areas using high-resolution GIS data and software for spatial conservation prioritization. Forest
Ecology and Management, 258: 2439-2449.
Questions 4. How about evaluating proposed conservation areas, or areas that will be set
outside of conservation use.
This is one common analysis type.
Q4.1. Someone has proposed a set of areas as the extension of the protected area network. Can you
evaluate the quality of these extensions with Zonation?
This is done using the technique of replacement cost. Replacement cost measures the inevitable loss of
conservation value due to restrictions placed on the solution. It is measured as (conservation value of ideal
unrestricted solution) - (conservation value of solution that has been restricted due to non-ecological reasons).
When evaluating proposed conservation areas, both existing and proposed areas are masked in. Say for example,
that these total 12% of the landscape. Then compare the quality of the restricted solution to the quality of the
unrestricted (no masked) solution at a 12% top fraction of the landscape. The difference between performance
curves tells the story.
Leathwick, J. R., Moilanen, A., Francis, M., Elith, J., Taylor, P. Julian, K. and T. Hastie. 2008. Novel methods
for the design and evaluation of marine protected areas in offshore waters. Conservation Letters 1: 91102.
Cabeza, M. and A. Moilanen. 2006. Replacement cost: a useful measure of site value for conservation planning.
Biological Conservation 132: 336-342.
Moilanen, A., A. Arponen, J. Stokland and M. Cabeza. 2009. Assessing replacement cost of conservation areas:
how does habitat loss influence priorities? Biological Conservation, 142: 575-585.
Areas removed from conservation can likewise be evaluated using the mask file, but in this case
forcibly excluding areas from the start.
Questions 5. About connectivity.
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Connectivity can be very important in prioritization. Different connectivity methods and analyses
are covered in the Z manual and publications. This topic is not closed - new setups or new
connectivity features might be discovered and implemented into Z.
Q.5.1 Make a list of connectivity features of Zonation. How can these features be used in the
selection of conservation areas or areas for development?
The list of connectivity features in Zonation includes
Method
Edge removal
Structural or
species-specific
structural
Summary & primary references
Removal only from the edges. Indirectly favors maintaining areas
structurally connected. Usually small influence on solution, but speeds up
calculations significantly.
Moilanen, A., Franco, A.M.A., Early, R., Fox, R., Wintle, B., and C.D.
Thomas. 2005. Prioritising multiple-use landscapes for
conservation: methods for large multi-species planning
problems. Proc. R. Soc. Lond. B Biol. Sci., 272: 1885-1891.
Boundary
length penalty
structural
Results in shorter edge length of remaining areas. Useful for
administrative purposes to reduce fragmentation.
Moilanen, A. and B. A. Wintle. 2007. The boundary quality penalty - a
quantitative method for approximating species responses to
fragmentation in reserve selection. Conservation Biology, 21: 355364.
Distribution
smoothing
Species-specific
A kernel-type (metapopulation) connectivity calculation. A parameter
sets the species-specific relevant scale. Transforms raw habitat layer to
connectivity layer.
Moilanen, A., Franco, A.M.A., Early, R., Fox, R., Wintle, B., and C.D.
Thomas. 2005. Prioritising multiple-use landscapes for
conservation: methods for large multi-species planning
problems. Proc. R. Soc. Lond. B Biol. Sci., 272: 1885-1891.
Moilanen, A. and B.A. Wintle. 2006. Uncertainty analysis favours
selection of spatially aggregated reserve structures. Biological
Conservation, 129: 427-434.
Boundary
quality penalty,
BQP.
Species-specific
More mechanistic form of connectivity where both radius of effect and
connectivity response function can be entered, per species or per species
group. Doubles memory usage.
Moilanen, A. and B. A. Wintle. 2007. The boundary quality penalty - a
quantitative method for approximating species responses to
fragmentation in reserve selection. Conservation Biology, 21: 355364.
Neighborhood
quality penalty,
NQP.
Species-specific,
operates on
linked planning
units
Generalization of BQP, suitable for, e.g., freshwater environments with
directional connectivity between planning units (catchments).
Moilanen, A., Leathwick, J.R., and J. Elith. 2008a. A method for
freshwater conservation prioritization. Freshwater Biology, 53:
577-592.
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Interaction
connectivity
Between two
species or more
generally, two
distributions
Leathwick, J.R., A. Moilanen, S. Ferrier and K. Julian. 2010.
Community-based conservation prioritization using a community
classification, and its application to riverine ecosystems. Biological
Conservation, 143: 984-991.
Kernel-type connectivity between two distributions. Those areas become
emphasized that are best connected. A negative variant of this
connectivity variant de-emphasizes areas that are connected - this could
be relevant for example to avoid pollution or to avoid locations where
invasive species can spread to.
Rayfield, B., Moilanen, A. and M.-J. Fortin. 2009. Incorporating
consumer-resource spatial interactions in reserve design.
Ecological Modelling, 220: 725-733.
Carroll, C., Moilanen, A., and J. Dunk. 2010. Optimizing resilience of
reserve networks to climate change: multispecies conservation
planning in the Pacific North-West USA. Global Change Biology,
16: 891-904
Matrix
connectivity
Many to one,
feature-specific
connectivity
Generalization of interaction connectivity, where many features (habitat
types) influence the connectivity of the focal feature. For example,
different forest types would all influence each other's connectivity to a
variable degree.
Lehtomäki, J., Tomppo, E., Kuokkanen, P. Hanski, I., and A. Moilanen.
2009. Planning of forest conservation areas using high-resolution
GIS data and software for spatial conservation prioritization.
Forest Ecology and Management, 258: 2439-2449.
Different connectivity components can typically be combined, although in the case of multiple transforms,
one has to carefully track what happens to each input layer. Of the methods above, BQP and NQP have the
property that they increase both memory usage and computation time substantially.
Note that multiple connectivity transforms can be entered per species. For example Rayfield et al. (2009)
used raw habitat, home-range scale connectivity and population level connectivity for the same species.
Questions 6. About uncertain inputs.
Q6.1. Make a list of different uncertainties/inaccuracies you can imagine there would be in data and
parameters.
Inaccuracy about
- where the species occur
- what is the density of the species at a location
- what is the distribution of the feature in the future (after habitat loss, climate change, restoration activity etc.)
- inaccuracy about the past distribution of the species (could influence species weighting)
- inaccuracy about the taxonomic status of the species (could influence species weighting)
- inaccuracy about the shape of the dispersal kernel of the species
- uncertain land cost, or cost of conservation at a location
- uncertainty of threats
- the list goes on endlessly
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Q6.2 How can uncertainties be accounted for in Zonation? Hint: there are specific techniques in
Zonation, but one can play with inputs as well.
(i) Distribution discounting is a species-specific uncertainty method. The importance of locations with
comparatively large uncertainty in the prediction becomes reduced (discounted). Requires a set of uncertainty
layers for species (features).
Moilanen, A., B.A. Wintle., J. Elith and M. Burgman. 2006a. Uncertainty analysis for regional-scale reserve
selection. Conservation Biology 20: 1688-1697.
Moilanen, A., M. Runge, J. Elith, A. Tyre, Y. Carmel, E. Fegraus, B. Wintle, M.
Burgman and Y. Ben-Haim. 2006b. Planning for robust reserve networks using
uncertainty analysis. Ecological Modelling, 119: 115-124.
(ii) Weights. A feature with large uncertainty can be given a reduced weight in analysis, due to the comparative
unreliability of the information.
Carroll, C., Moilanen, A., and J. Dunk. 2010. Optimizing resilience of reserve networks to climate change:
multispecies conservation planning in the Pacific North-West USA. Global Change Biology, 16: 891-904.
(iii) Multiple inputs can be entered for each feature; these could for example represent multiple different
predictions for the distribution of the species.
(iv) Sensitivity analysis is possible by entering different variants of the input data layers (as one can do for
practically any analysis).
Questions 7. What if the unit of biodiversity is community type?
Quantitative conservation planning often considers species as a unit of biodiversity. However, it is
often practical to use something else, such as community type or habitat type.
Q7.1. What benefits can you think of for using species as units of biodiversity? What kind of
problems could one face with the species-based approach? What benefits and problems do you
think are related to the community-based approach?
Benefits of the species approach:
(i) easy to understand
(ii) lots of ecological detail about species can be accounted for
(iii) connectivity is relatively easy to understand from the species perspective
Problems of the species approach:
(i) distributions of most species are not well understood and data on many species is simply missing, especially in
the tropics
(ii) operating on many poor-quality distribution models is statistically risky and unstable
(iii) extensive surveys can be very expensive
(iv) use of surrogates is necessary, although their effectiveness is uncertain
Benefits of the community/ecosystem approach:
(i) data is more easily available than for individual species
(ii) concepts such as landscape condition and retention are more naturally incorporated to the community
approach
Problems of the community/ecosystem approach:
(i) connectivity is not easily defined in the community context
(ii) one can argue that protecting communities ignores individual species and adequate conservation cannot be
guaranteed for all species
(iii) modelling and valuing community turnover (similarity) is somewhat complicated
(iv) the analysis is done at a higher level of abstraction, making the approach less easy to relate to.
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Q7.2. How would you set up a Zonation analysis based on community types? Do you need any
special input data? Can you account for species richness in such an analysis?
For a community level analysis, you need to classify the landscape into different communities.
For a community level analysis, you then need a community similarity matrix assigning a similarity value to all
possible pairs of community types. The matrix can be compiled, for example, utilizing a generalized dissimilarity
model (GDM; see Ferrier 2002). The matrix can be compiled, for example, in R with the package 'gdm'
http://www.biomaps.net.au/gdm/.
A meaningful method for assigning weights to community types is to weigh them by their species richness.
Weights are assigned in the first column of the biodiversity feature list file.
Ferrier, S. 2002. Mapping spatial pattern in regional conservation planning: where to go from here? Systematic
Biology 51: 331-363.
Leathwick, J.R., A. Moilanen, S. Ferrier and K. Julian. 2010. Community-based conservation prioritization
using a community classification, and its application to riverine ecosystems. Biological
Conservation, 143: 984-991.
(NB. Leathwick et al. did not use similarity expansion of Zonation v.3.0., but pre-processed data using GIS and
R. Following their protocol, it is possible to use the technique also with Zonation v.2.0.)
Lehtomäki, J., Tomppo, E., Kuokkanen, P. Hanski, I., and A. Moilanen. 2009. Planning of forest conservation
areas using high-resolution GIS data and software for spatial conservation prioritization. Forest
Ecology and Management, 258: 2439-2449.
Questions 8. Balancing representation in protected areas and retention in the full landscape
In conservation planning analyses, it is often assumed that no biodiversity value will remain in the
landscape matrix outside protected areas. In Zonation v3, it is possible to account for likely
retention of biodiversity value outside protected areas.
Q8.1. Can you come up with arguments to justify accounting for representation in protected areas
only in a conservation prioritization analysis? What about arguments to justify including retention
in the whole landscape? Why is the balance important?
Reasons for using representation only
(i) Considering only protected areas is less uncertain than predicting the development across an entire landscape
(ii) In regions where pressure for land conversion is high, assuming that the surrounding landscape contribute
nothing to overall biodiversity value may actually be a realistic assumption
Reasons for including retention in the analysis
(i) In most situations, habitats and conservation value are retained in the landscape even if they are not protected
(ii) Allows identification of areas where protection or other conservation action would make most difference and
areas where action would not affect much the biodiversity, which helps to plan conservation more cost-effectively
Balance between representation and retention: Most often one would like to account for both representation in
the protected area network and retention across the entire landscape. Use of only representation can hamper
effective conservation, as some biodiversity features are likely to be well retained also in non-protected areas.
Then again, relying only on retention is risky, as high uncertainty is inherent in assumptions about threat and
loss of habitat. The relative weighting of these sets of layers will determine how much emphasis is placed on
representation and how much on retention. A high weight on retention layers means that emphasis is placed on
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locations where conservation action can make a difference. A high weight on representation means emphasis on
locations where rarity and richness of features is highest based on known data.
Moilanen, A., J.R. Leathwick, and J. M. Quinn. 2011b. Spatial prioritization of conservation management.
Conservation letters, 4, 383-393.
Q8.2. Imagine you have two species, A and B. Species A is common in the region, but very
sensitive to disturbance and is likely to persist only in a reserve. Species B is not very widespread,
but its habitat is likely to remain all over the landscape even without protection. How would you
think Zonation would treat the two species if only representation was accounted for? How would
the situation change if you would include retention as well?
Using only representation, Zonation would prioritize a higher proportion of species B distribution. When
retention is included, the difference made with conservation action for species B is low, so action is directed more
towards species A. The result would depend on how much weight is given to retention in the analysis.
Questions 9. Dynamic landscapes and climate change
So far, analyses we have discussed have been about static landscapes - you feed in distributions that
are implicitly assumed to be static. However, landscapes may be dynamic due to natural succession
or human activity. Is this a problem?
Climate change is the second major threat for biodiversity, in addition to habitat loss and
fragmentation. One would wish to account for climate change in prioritization, but it is obviously
not very easy to do so.
Q9.1. How could dynamic landscapes be accounted for in Zonation? What kinds of issues are
related to or pronounced when considering dynamic landscapes?
One can simply enter predictions for the same feature at different time steps, like now, ten years from now, 30
years from now, etc. Then the Zonation solution becomes such that different features are represented in a
balanced manner at all time steps. Interaction connectivity can be used to link distributions at different time
steps. Thus, a dynamic landscape can be “faked” in the analysis.
Carroll, C., Moilanen, A., and J. Dunk. 2010. Optimizing resilience of reserve networks to climate change:
multispecies conservation planning in the Pacific North-West USA. Global Change Biology, 16: 891-904.
Rayfield, B., Moilanen, A. and M.-J. Fortin. 2009. Incorporating consumer-resource spatial interactions in
reserve design. Ecological Modelling, 220: 725-733.
Thomson, J.R., Moilanen, A., McNally, R., and P. Vesk. 2009. A quantitative method for prioritizing landscape
revegetation. Ecological Applications, 19: 817-828.
Issues:
(i) The cost is increased memory requirements because multiple distributions and possibly connectivity
distributions are entered per species
(ii) Requires quantitative estimates of habitat quality development, which makes the analysis data intensive and
complicated to set up
(iii) Uncertainty about habitat development in both space and time
Q9.2. Make a list of relevant considerations when trying to plan for climate change. What can be
accounted for in the context of Zonation?
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Climate change is a challenge for conservation planning. There are major uncertainties about the magnitude of
climate change and what happens to species and habitats following climate change. At a more mechanistic level,
there are uncertainties about the local persistence of species, and their ability to migrate to and establish at other
locations further away. There are uncertainties about local adaptation, evolution, species interactions, changes in
community structure, and the list goes on.
Carrol et al. (2010) describes a method that can be used in the context of climate change in Zonation. The
components it includes are
(i) Multiple distributions per species to account for the predicted time-evolution of the distributions following
climate change
(ii) Distribution discounting to emphasize locations for which predictions are more certain
(iii) Lower weights given to more speculative information. Future time steps and connectivity distributions are
taken as less reliable than the present distribution of the species.
Carroll, C., Moilanen, A., and J. Dunk. 2010. Optimizing resilience of reserve networks to climate change:
multispecies conservation planning in the Pacific North-West USA. Global Change Biology, 16: 891-904.
Moilanen, A. and B.A. Wintle. 2006. Uncertainty analysis favours selection of spatially aggregated reserve
structures. Biological Conservation, 129: 427-434.
Hodgson, J., C.D. Thomas, B.A. Wintle and A. Moilanen. 2009. Climate change, connectivity and conservation
decision making - back to basics. Journal of Applied Ecology, 46: 964-969.
Hodgson, J., Moilanen, A., Wintle, B.A., and C. D. Thomas. 2011. Habitat area, quality and connectivity:
striking the balance for efficient conservation. J. Applied Ecology, 48:148-152.
Questions 10. Planning for habitat maintenance or restoration.
So far we have been primarily discussing habitat protection. Habitat maintenance or restoration are
other common forms of conservation action. Can these be allocated using Zonation?
Q10.1. If you have one maintenance action or one restoration action, what then?
Enter the distributions of features as they would be if the entire landscape was restored. Then the top priority
areas are the ones that should become restored.
One can also develop separate GIS layers representing the feasibility and relevance of restoration at different
locations. These layers and respective cost layers can be entered as features into the prioritization analysis.
Q10.2. Restoration does not typically happen instantaneously. How can one account for the time
development of conservation features?
As with dynamic landscapes or climate change: enter predictions for multiple time steps following restoration
action. Interaction connectivity can be used to link time steps. See Thomson et al. for an example, which however
does not utilize interaction connectivity.
Thomson, J.R., Moilanen, A., McNally, R., and P. Vesk. 2009. A quantitative method for prioritizing landscape
revegetation. Ecological Applications, 19: 817-828.
Q10.3. Think about restoration more - it can actually be quite complicated. What complications can
you think of?
Well, at least there can be multiple alternative restoration actions that have different costs and different
consequences for different species. Zonation is not ideally suited for allocation of multiple actions. However, at
least the following is possible:
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Generate alternative scenarios, where different actions are applied in different parts of the landscape, and costs
are set correspondingly. Then run Zonation - this will answer the question, “where restoration should be done
under each particular scenario”. There is a further complication here in that Zonation operates on fractions of
distributions, where total distribution of each feature is normalized to sum to 1.0 (after which the number in the
cell is the fraction of the distribution in the cell). However, in restoration one is interested about the development
of features in absolute terms. This information can also be obtained from Zonation; fractions of distributions can
be multiplied with total predicted population size, which is computed from input distributions and output in the
curves file. See Thomson et al. (2009) again.
Question 11. What about analysis size?
One Mb, one megabyte is one million, 1000000 bytes. 1Gb is 1000 Mb. Storing one number, like
the occupancy level of a species in a cell, requires 4 bytes. The 32 bit windows environment can use
2GB of RAM memory (or 3GB, with a particular environment switch enabled). Some memory, like
200MB, should be counted for the operating system and other internal Zonation data. [The amount
of free RAM memory can be checked from the task manager btw.]
Q11.1. Assume you have a 1000 x 2000 cells landscape, of which 75% is “land” or effective cells.
(The rest 25% is missing data such as water or outside of the country.) How many features or
species can you include for analysis on a PC with a few GB of RAM memory? What is the effect of
having both a raw habitat quality layer and a connectivity layer for each species?
1000x2000 = 2M elements (grid cells). 2M elements is 2Mx4 bytes = 8Mb. 75% of area contains data => the layer
for one feature is 0.75*8Mb=6Mb.
Suppose that there are 2Gb ≈ 2000Mb of memory available (maybe your machine has 4 GB but half of it could
be used by other software). Count some off for the operating system and as internal data structured of Zonation,
say that you have 1800Mb available. 1800/6=300. The answer is, approximately 300 features can be used. If you
are keeping many programs open, this number will go down. In Windows, you can open the task manager to
check how much memory Zonation uses. Make sure there is sufficient RAM. If task manager indicates that
RAM has run out and the virtual memory is used, then computations will become untenably slow.
If both a connectivity layer and raw habitat layer is used for each species, then the number of species that can be
analyzed is halved. Including connectivity calculations to the analysis, especially BQP or NQP, further reduce
the number features that can be used.
2GB memory is actually the maximum limit that applies to 32 bit programs. Zonation v3.1 and later is a 64 bit
application and can use in principle as much RAM memory as available (as long as you use the 64 bits version of
Zonation). For example if your machine has 6GB of memory available you can process approximately 3 times
more features. This means that RAM memory size no longer is a major limitation. For very large landscapes the
Zonation process run time is most likely to become the factor limiting analysis size in practice.
Q11.2. Point distribution data. This data is stored as a list of (location, occurrence) pairs, not as
grids. How much extra memory would be needed for 1000 species with an average of 50 occurrence
locations per species. What is the conclusion here? (Note: penalty is that connectivity does not
operate on point distribution species.)
1000 (spp) x 50 (observation) x 3 (data elements, x, y, size) x 4bytes = 600 000 bytes = 0.6MB. Entering species as
point distributions uses next to nothing as memory. The penalty is that most connectivity features do not operate
on point distribution data. Locations with point occurrences are likely to be retained rather late in the
prioritization. Connectivity requirements set for map species may cause the solution to somewhat become built
around the locations with point distributions data for rare features.
Kremen, C., A. Cameron, A. Moilanen, S. Phillips, C. D. Thomas, H. Beentje, J. Dransfeld, B. L. Fisher, F. Glaw,
T. Good, G. Harper, R.J. Hijmans, D. C. Lees, E. Louis Jr., R. A. Nussbaum, C. Raxworthy, A.
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Razafimpahanana, G. Schatz, M. Vences, D. R. Vieites, P. C. Wright , M. L.Zjhra. 2008. Aligning
conservation priorities across taxa in Madagascar, a biodiversity hotspot, with high-resolution planning
tools. Science 320: 222-226.
Questions 12. Analysis across multiple administrative regions
Q12.1. Why would conservation planning processes benefit from an approach that accounts for
local as well as global conservation priority setting?
Conservation decisions are usually taken at national or regional levels, or even at the scale of individual land
parcels. Distributions of species and other biodiversity features are spread over multiple such administrative
units. Population dynamics and connectivity effects do not respect such administrative borders but extend across
them. It is therefore reasonable that the global conservation status of a biodiversity feature should influence its
conservation locally.
Q12.3. Compare strong and weak local weights. What are the differences? When would you prefer
weak local weights? What about strong local weights?
It is possible to account for variable local and global priorities in the Zonation analysis via locally variable
weights that are assigned to species or other biodiversity features. There are two alternative methods for doing
so.
The first relies on what we call weak local representation. The emphasis with this approach is in global
representation with locally varying weights; it allows a degree of flexibility between what features are
represented in which administrative regions. This approach would make sense in a planning process that spans
over multiple regions.
The second option, strong local representation, requires all features to be represented separately in each
administrative region, when at all possible. This is irrespective of how the local abundance of the feature
compares to global abundance.
Moilanen, A. and A. Arponen. Administrative regions in conservation: balancing local and global priorities in
spatial planning. Biological Conservation, 144: 1719-1725.
Moilanen, A., Anderson, B.J., Arponen, A., Pouzols, F.M., and C.D. Thomas. 2013. Edge artefacts and lost
performance in national versus continental conservation priority areas. Diversity and
Distributions, 2012: 1-13.
Question 13. Think about analyses that you think Zonation would not be best suited for.
(i) Zonation is a relatively high-level statistical-like approach that operates on distributions that have been input
as grids (raster maps). It is not a replacement for a very detailed single-species spatial PVA with lots of biological
realism implemented in a stochastic dynamic model (like RAMAS). Nevertheless, for single-species Zonation
analyses see:
Rayfield, B., Moilanen, A. and M.-J. Fortin. 2009. Incorporating consumer-resource spatial interactions in
reserve design. Ecological Modelling, 220: 725-733.
Sirkiä, S., Lehtomäki, J., Lindén,H., Tomppo, E. and A Moilanen. 2013. Spatial conservation prioritization of
capercaillie (Tetrao urogallus) lekking landscapes in South-Central Finland. Wildlife Biology, in press.
(ii) Zonation is not ideally suited for the allocation of multiple alternative conservation actions, although a staged
scenario analysis could probably be devised. (One in which actions are sequentially allocated one at a time, using
the result of the previous analysis as a starting point. There is no reference to this kind of a setup available.) (See
MARXAN with zones.)
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(iii) Computation times become a problem with BQP when the landscape is a few million elements. Otherwise
Zonation 3 can go up to some tens of millions of elements in analysis.
(iv) In sequential planning (or dynamic site selection, scheduling) the aim is to find actions that are good ones to
take, acknowledging that the landscape is dynamic, and that sites are becoming lost or available for conservation
in a stochastic manner. [Moilanen, A. and M. Cabeza. 2007. Accounting for habitat loss rates in sequential
reserve selection: simple methods for large problems. Biological Conservation, 136: 470-482.] Zonation is not
directly intended for such analysis, although someone might be able to come up with an imaginative analysis
setup that allows for a reasonable approximation. The capabilities of Zonation v3 may become useful here.
For other another approach and software to conservation resource allocation, see the RobOff sofware, which
focuses on the uncertain effects of multiple conservation actions (over time). This approach is relevant for
example when considering allocation to alternative habitat restoration options, or, as a more complicated case,
for design of compensation measures (offsets) for environmental damage caused by development.
Pouzols, F.M. and A. Moilanen. 2013. RobOff: software for analysis of alternative land-use options and
conservation actions. Methods in Ecology and Evolution, 4:426-432
Pouzols, F.M., Burgman, M.A., and A. Moilanen. 2012. Methods for allocation of habitat management,
maintenance, restoration and offsetting, when conservation actions have uncertain consequences. Biological
Conservation, 153: 41-50.
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