SUPPLEMENTARY MATERIAL FOR
Moilanen, A. Leathwick, J.R and J. Elith. A method for spatial freshwater
conservation prioritization. Freshwater Biology.
APPENDIX S1
MORE DETAILED DESCRIPTION OF THE ZONATION METHOD AND
IMPLEMENTATION OF CONNECTIVITY IN IT
Adapting Zonation to river systems
Zonation is a framework and software for spatial conservation prioritization using species
distributions defined on large grids, which allows direct linkages from GIS-based
environmental information to statistical species distribution modelling to conservation
prioritization (Moilanen et al., 2005; Moilanen & Wintle, 2007). The Zonation metaalgorithm starts from the full landscape and proceeds by iterative removal of grid cells, at
each step removing that which results in the smallest marginal loss in conservation value
and retaining the most important until late in the removal process (Moilanen, 2007).
Three approaches can be used to guide this process of cell removal, i.e., core-area
Zonation, additive benefit function Zonation and target-based planning. These methods
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differ in how they balance tradeoffs between rarity, species richness (accounting for
complementarity) and local habitat quality (Moilanen, 2007), and depending on the
system these may give different results for mean performance across species,
conservation performance for the worst-off species and the relative fractions of high and
medium quality sites included in a solution.
Output from Zonation includes (i) a map of the ranking of conservation priority for the
landscape of interest, (ii) performance curves that describe the proportion of the original
distribution of each species remaining as a function of fraction of the landscape reserved,
and (iii) species-specific distributions of site quality in a given top fraction of the
landscape. Additional features of Zonation include options for: taking into account
variation in costs of protection; weighting of species to reflect their varying conservation
priorities; species-specific methods for handling connectivity requirements (Moilanen et
al., 2005; Moilanen & Wintle, 2006, 2007; Mikusinski et al., 2008); uncertainty analysis
to take account of uncertainty in species predictions (Moilanen et al., 2006a, b); and
estimation of replacement costs (Cabeza & Moilanen, 2006) allowing evaluation of the
importance of existing or proposed conservation areas. Version 1 of the Zonation
software and its user manual is downloadable from the web (Moilanen & Kujala, 2006).
We give an example here of how cell removal is implemented in the core-area rule to
provide an illustration of the prioritisation methodology. Heuristically, the core-area
Zonation cell removal rule embodies the following principles. (i) Of two otherwise equal
locations, the one with a lower occurrence level for the most important species is
removed first. (ii) Assuming two otherwise equal locations, one with the occurrence of a
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lower-weight species is removed before the location with a high-priority species. (iii)
Assuming two identical locations with identical occurrence levels for two different
species, the one is removed that contains a species with a higher fraction of original
distribution remaining across the rest of the landscape – the species that has already lost
more has higher priority. (iv) Of two otherwise identical locations, one with higher cost is
removed first. The fundamental concept of complementarity (Margules & Pressey, 2000)
is inherent in this process.
Mathematically, the cell to remove next is the one having smallest
 i  max
j
Qij ( S ) w j
ci
 max
j
pij w j
p c
i S
,
(1)
ij i
where wj is the weight (or priority) of species j, ci is the cost of adding cell i to the reserve
network, and pij is the predicted occurrence level (probability, abundance, density or
occurrence) of species j in unit i. Zonation operates in terms of fractions of species
distributions residing in individual grid cells. Qij(S) is the proportion of the remaining
distribution of species j located in cell i in the remaining set of cells, S. Thus, when a part
of the distribution of a species is removed (unit i is removed from S), the proportion
located in each remaining cell goes up. This means Zonation tries to retain core areas of
all species until the end of cell removal, even if the species is initially widespread and
common. Species can be differentially weighted to enable priority protection of some.
The additive benefit function cell removal (Eq. 3, below) rule treats conservation value as
linearly additive across species, implying that species richness influences prioritization
relatively more than with the core-area rule (above). See Moilanen (2007) for detailed
3
descriptions of the additive benefit function and targeting benefit function cell removal
rules.
Whilst we understand that a grid-based system is not intuitively natural for freshwater
systems, statistical modelling is natural on grids that have equal-sized elements, and the
features that Zonation provides are particularly useful in a grid-based planning context.
We therefore focussed on adapting our data and analyses to suit a grid-based approach,
while allowing the distinctive ecological features of freshwater ecosystems to be
accommodated.
In particular, we modified the terrestrial/marine version by first altering the software to
allow it to work with planning units rather than individual grid cells. This change allows
the use of catchment or sub-catchment based units (Peres & Terborgh, 1995; Allan,
Erickson & Fay, 1997), enforcing an initial degree of connectivity between grid cells and
preventing the removal of isolated cells, i.e. a planning unit is either completely protected
(selected) or it is left vulnerable to anthropogenic (mis-)use. Second, we adapted the
software to accommodate the connectivity inherent in river systems, describing this with
a network topology (tree-hierarchy) of river segments, which reflects the flow of water.
This latter feature was implemented using the planning units, each of which was given a
unique identification number. For each planning unit connection we simply entered
couplets consisting of the identifier of the contributing planning unit and its
corresponding receiving unit, this allowing complete specification of the connection
hierarchy.
Note that the overall philosophy behind the Zonation approach to conservation
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prioritization is rather different from that embodied in Systematic Conservation Planning
(SCP) as described by Margules & Pressey (2000). In SCP, protection targets for species
are first specified, and then a minimum cost solution is sought that achieves these targets.
By contrast, Zonation requires no targets to be specified, and one quantifies the ideal
expected balance between species via specification of species weights, cell removal rules
and connectivity responses. The actual species protection levels and the optimal pattern at
any landscape fraction then arise from the Zonation process as guided by these
considerations. Importantly, rather than producing a single solution as in SCP, Zonation
defines a set of nested solutions with a rank given for all cells in the landscape. Sites are
therefore given an informative estimate of their position in a broader ranking of priority,
rather than simply being flagged as requiring protection, or not. Particularly when
coupled with replacement cost analysis, this provides a clear means of evaluating the
overall importance of all individual areas.
Modifying connectivity in Zonation
Connectivity is a critical component of any reserve network design, because it influences
dispersal, colonization and population levels at sites. It is generally accepted that a very
fragmented reserve system would be both awkward to implement and biologically
unreasonable (Possingham, Ball & Andelman, 2000; Moilanen & Wintle, 2006).
According to a fundamental tenet of spatial (meta)population dynamics, of two sites with
otherwise identical features, the one with higher connectivity would have higher
population densities (Hanski, 1998). Three methods have been used to implement
connectivity requirements in Zonation, i.e. boundary length penalties, distribution
5
smoothing, and boundary quality penalties. Before explaining how we defined
connectivity in this study, we first explain why the connectivity variants typically used in
terrestrial or marine planning are inappropriate for use in rivers.
Boundary length penalties are the most commonly used method for inducing reserve
network aggregation in terrestrial planning (e.g., Possingham et al., 2000; Önal & Briers,
2002; Fischer & Church, 2003; Cabeza et al., 2004). Essentially, a penalty term is added
to the objective function of optimization, and the size of the penalty goes up as the ratio
of reserve network edge to area increases, thus enforcing reserve network aggregation.
However, this technique is not species-specific, and it requires assumptions to be made
about both the perimeter and boundary length of the proposed reserve, and such
measurements are largely irrelevant as descriptors of the essentially linear components
that make up river networks.
Distribution smoothing (Moilanen et al., 2005; Moilanen & Wintle, 2006, 2007) utilizes a
species-specific parameter, which describes the species' dispersal ability or its scale of
landscape use. Essentially, small features of the predicted species distribution are
smoothed out and the selection is based on averaged population densities over larger
areas. Distribution smoothing corresponds structurally to the use of a metapopulation
connectivity measure (see Moilanen & Nieminen 2002) in conservation planning.
Distribution smoothing is unsuitable for use in rivers because it fails to take into account
the directional, channel-constrained nature of their connectivity. For example, it could
indicate equal connection between a focal site and other sites both 10km upstream and
downstream in the same river, as well as to a location in a completely different river
6
system running in parallel 10km away. Obviously connectivity between rivers with no
hydrological connection is not realistic for most freshwater species.
Boundary quality penalties (for details see Moilanen & Wintle 2007) assume that the
population density (or probability of occurrence) at a location depends on both local
quality and the quality in the neighbourhood of the site. Locations close to the edge of a
reserve will suffer from reduced quality due to edge effects and the negative influence of
reduced connectivity on spatial dynamics. According to the boundary quality penalty, the
loss of a site will result in loss of conservation value both at that site, and in its
surrounding neighbourhood. The boundary quality penalty, as applied by Moilanen &
Wintle (2007), uses a species-specific radius and response that define the way individual
species are influenced by loss of adjacent habitat. Boundary quality penalties are not
directly applicable to river systems for the same reason as distribution smoothing, i.e.
they lack ability to realistically represent hydrological connection and directional flow.
The boundary quality penalty concept can however be extended to apply to river systems
simply via a redefinition of the concept of neighbourhood to accommodate the meaning
of neighbourhood for aquatic species. Here, it is natural to assume that a site (segment of
river) is connected both upstream and downstream within the same river. The
neighbourhood of the site is the river and all its tributaries upstream and the main river
stem downstream from the focal site. Thus, we call the general method of handling
connectivity that we use here the neighbourhood quality penalty. In a river context, loss
of a particular river segment results in both a local loss, plus losses in both the upstream
and downstream neighbourhoods of that segment. However, the upstream and
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downstream losses will be species-specific, reflecting the different ecological and life
history requirements of species, including the need to migrate to the sea for diadromous
species, sensitivity to water quality, connection to upstream breeding habitat and so on.
Here we define the responses of species to connectivity loss via two functions, one each
for responses to upstream and downstream loss (Fig. 1 in the main paper). The x-axis of
the function describes the fraction of original connectivity lost (upstream or downstream)
for the location, while the y-axis describes the fraction of the original local value
remaining as upstream or downstream connectivity is lost. For example, if an upstream
tributary of a focal site is removed, a fraction of the upstream connectivity of the site is
lost (curves 2 to 5, Fig. 1, main paper), resulting in a loss of value at the site. The overall
loss for any species is calculated as the product of the respective up- and downstream
losses. Whatever the functional cause of a negative effect from lost connectivity, it would
be modelled with the species-specific loss functions, under the assumption that if a
location is left vulnerable to anthropogenic degradation, benefits of connectivity to
species may in the future be completely lost. Note that some species might even benefit
from lost connectivity (the function would go above one), but such species would likely
be of little conservation concern. Also, note that none of the responses assumed in Fig. 1
of the main paper are extremely strong; if a species is very sensitive to loss of water
quality and connectivity, then even a small loss in connectivity could cause complete loss
of local quality, which would be modelled using a function that declines to zero.
The details of the Neighbourhood quality penalty implemented for directional freshwater
connectivity here are given in the main paper.
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