A report on the effects of connectivity, conservation features and
targets on reserve system design in Tasmania, Australia
Abstract
The implementation of a carefully designed protected area network is vital to conserving some of
Tasmania’s most biodiverse ecosystems. Using systematic conservation planning this report
discusses the costs involved in designing a reserve system for Tasmania under different conservation
scenarios. The cost of the parameters: boundary length modifier (BLM), conservation feature and
conservation target were manipulated using Marxan. The scenarios were investigated by comparing
the results to that of an initial conservation plan with the aim of conserving 30% of ecosystem and
plant species with a BLM of 0. Generally, cost tended to increase with BLM and conservation targets
while the price of conserving different conservation features were quite similar. In this case it was
concluded that a cost effective reserve system could be designed using a reasonably low BLM (~75),
conserving a range of ecosystem and species types and by acquiring a practical target (~30%).
Conservation of the most biodiversity possible for minimal cost is a priority for modern
conservationists.
Introduction
Tasmania’s vast landscape offers natural beauty and a home to a diverse range of organisms.
Anthropogenic use can threaten this biodiversity, making the need for protection and
conservation more important. Systematic conservation planning allows conservation planners to
designate, manage and monitor areas for “the protection and maintenance of biological
diversity, and of natural and associated cultural resources” IUCN (1994) (Pressey et al. 2007). By
measuring and mapping biodiversity and identifying the conservation goals for a region, the
review and implementation of existing and new reserves can be carried out (Margules & Pressey
2000). As of June 2011, Tasmania currently has an estimated 800 terrestrial reserves which
cover 3 million hectares (Parks & Wildlife Service 2014).
The connectivity of a reserve system can help protect and maintain the population of species
through conserving the different areas that it may utilise during its lifetime. Mobile organisms
are not often restricted to one ecosystem their entire lives and so understanding which
ecosystems are connected to each other is important for spatial planning (Alagador et al. 2012).
By clumping reserves and decreasing the boundary length a reserve system can become more
connected, capturing relationships within and between species and improving the
representativeness of the protected area (van Teeffelen et al. 2006). While scattered reserve
systems can be representative and efficient they are often more difficult to manage and police
on the ground due to issues in locating boundaries.
In conservation planning any habitat or species requiring protection is referred to as a
conservation feature. Ideally one would want to conserve all conservation features possible but
this is not feasible due to high expenditure and use of land for other purposes. Conservation
features include biodiversity features such as major ecosystem types, plant and species. By
conserving major ecosystem types one can conserve the ecosystem services offered by not only
its inhabitants but also the abiotic factors on which they rely (Chan et al. 2006). Alternatively
one could consider conserving specific plant or animal species which are important for the
biodiversity of the area (Hamilton et al. 2012). A representative reserve system that captures a
sample of all habitats and important species is ideal. As it is also not feasible to conserve 100%
of each conservation feature, conservation targets are set to determine how much should be
conserved.
An adequate conservation target is one that contains enough of every habitat and species to
ensure that it persists through time. This can be achieved in conservation planning by ensuring
that a minimum target amount of the conservation features are within the reserve system. For
this to be possible one needs to consider the distribution of the chosen habitats and species as
those with narrow distribution ranges may prove more difficult to protect (Guisan et al. 2013).
Targets that are easily met usually involve an overlap of by-products of securing other targets at
a low cost. Those which are more difficult to meet incur a higher cost and tend to require more
effort to conserve fewer or no other features (Moilanen & Arponen 2011). A feasible target is
one that encourages the persistence of biodiversity features at a reasonable cost.
The aim of this report is to model a protected area network for Tasmania which minimises the
overall cost of conserving the most biodiversity possible. This report outlines how the protected
area network changes under different conservation plans in comparison to an initial
conservation plan aimed at conserving 30% of ecosystems and plants species’ distribution. This
is investigated through three research questions:
1. How does changing connectivity, through the parameter of Boundary Length Modifier
(BLM), affect the cost of reaching the set target? (Target= 0.3 of ecosystems and plants).
What BLM is most efficient?
2. How does changing the conservation features for protection affect the cost of reaching
the set target? Conservation features include: ecosystems, plants and animals. What
features are the most costly to meet the set target? (Target=0.3, BLM=0)
3. How does changing the target percentage of the conservation feature you want affect
the cost of reaching set target? What target is ideal? In combination, what target and
BLM are most cost efficient?
Methods
Study Subjects
The island state of Tasmania, south of the mainland of Australia, was chosen for this
study. With an approximate size of 68,500 km2 and temperate climate, Tasmania’s
ecosystems are highly biodiverse yet vulnerable (McQuillan et al. 2009). Some areas of the
Tasmanian forest generate the highest biomass per hectare and boast the highest levels of
inland marine biodiversity worldwide (McQuillan et al. 2009). Although approximately 22.6%
of Tasmania is already reserved, some vegetation communities such as grassland and
heathland are underrepresented in the reserve systems (McQuillan et al. 2009). With over
500 grassland and heathland species listed as vulnerable throughout Tasmania, the need for
an adequate reserve system to conserve such species is paramount.
Plant species that were identified as conservation features for this report were chosen for a
number of reasons. Such reasons include preserving those species which are endemic to
Tasmania, those which have a poor conservation status, those that are associated with
particular habitat types and those that offer valuable ecosystem services.
Five species of frog were chosen as conservation features in the hopes of minimising the
spread of the Chytrid fungus and to secondarily further maintain healthy Tasmanian frog
populations with the low levels of the Chytrid fungus in Tasmania (Pauza et al. 2010). It has
been found that gravel roads and small patches of forest increase the spread of this fungus
which has already been the suspected cause of various frog species extinctions (Pauza et al.
2010). One crayfish species was also selected as a conservation feature due to its
conservation status and its function as an ecosystem indicator within highly vulnerable and
diverse inland marine habitats (McQuillan et al. 2009, Richardson et al. 1998).
Data
The distribution of ecosystems was attained from the National Vegetation Information
System from the Executive Steering Committee for Australian Vegetation Information,
Canberra, Australia.
The distribution of species was attained from the Department of Sustainability,
Environment, Water, Population and Communities (2013). Available from
http://www.environment.gov.au/sprat
The plants of Tasmania used in the parameter named ‘plants’ include: Acacia axillaris,
Amphibromus fluitans, Barbarea australis, Caladenia caudata, Carex tasmanica,
Colobanthus curtisiae, Epacris acuminata, Epacris glabella, Glycine latrobeana,
Xanthorrhoea bracteata
Animals of Tasmania used in the parameter named ‘animals’ include: Crinia nimbus,
Engageus granulatus, Engaeus orramakunna Engaeus spinicaudatus. Engaeus yabbimunna
The reserved areas managed in Tasmania were determined through the IUCN category I-IV.
Tasmania was divided into 2857 Planning Units each with a size of 5km x 5km. The cost to
purchase each planning unit is the value of the unimproved land and was determined using
the Department of Primary Industries, Parks, Water and Environment’s Unimproved land
value in Tasmania (spatial data). Office of the Valuer-General, Tasmanian Government,
Hobart (2010).
Figure 1: Maps of Tasmania generated using the software Arc Map. Top left: the various different ecosystems found in
Tasmania according to National Vegetation Information System from the Executive Steering Committee for Australian
Vegetation Information, Canberra, Australia. Top right: the selected species of plant distribution throughout Tasmania
according to the Department of Sustainability, Environment, Water, Population and Communities (2013). Bottom left: the
selected species of animals distribution throughout Tasmania according to the Department of Sustainability, Environment,
Water, Population and Communities (2013). Bottom right: the cost to purchase each planning unit, the warmer the colour
the more expensive the planning unit, costs determined using spatial unimproved land cost data from the Department of
Primary Industries, Parks, Water and Environment.
Analysis Tools
The program ARC Map (ESRI, 2011) was used to process the input files of our data sets.
Zonae Cognito (Segan et al. 2011) acted as an interface to Marxan (Ball et al. 2009) and was
used to manipulate the parameters of our target conservation plan. Marxan was used to run
the parameters and generate 100 of the most cost effective scenario solutions.
Parameters
Our baseline parameters for all research questions were as follows unless stated otherwise:

Boundary Length Modifier (BLM) = 0

Conservation features= ecosystems and plants

Conservation target= 0.3
Planning units were 5km2 and existing reserved areas were blocked out as already protected
areas.
To investigate connectivity the parameter of BLM was manipulated. Simulations with BLMs
of 0, 50, 75, 100, 150 250, 500, 1000, 2000, 3000, 4000 and 5000 were run. The BLM range
from 0-100 was then honed in on to determine the point at which the highest BLM could be
obtained with the lowest price.
To investigate the cost of conserving different conservation features different combinations
of conservation features were run. These included runs with plants only, plants and
animals, plants and ecosystems, animals only, animals and ecosystems, ecosystems only and
the final scenario of plants, animals and ecosystems.
To investigate the cost of conserving different conservation targets, simulations of 10%,
30%, 50%, 75% and 100% were run. The parameter of BLM was also then manipulated to a
value of 75 with each of the conservation feature combinations. A BLM of approximately 75
was found to be suitable while investigating connectivity.
Results
It was found that as the parameter of Boundary Length Modifier increased, the connectivity
decreased. A non-linear relationship was observed with a general trend of increasing cost
with increased BLM (Figure 2a). However on a finer scale, cost slightly decreased from a
BLM of 0 to 75 before increasing with larger BLMs (Figure 2b). Increasing BLM also increased
the number of planning units selected.
Figure 2a: The relationship between the parameter BLM (Boundary Length Modifier) and cost in billions for a Tasmanian
reserve system with a conservation target of 30% of ecosystems and plants.
Figure 2b: An excerpt of Figure 1a showing the relationship between the parameter BLM (Boundary Length Modifier) and
cost in billions for a Tasmanian reserve system with a conservation target of 30% of ecosystems and plants.
The cost of conserving different combinations of conservation features was found to vary
(Figure 3). Conserving all conservation features was found to be the most costly while
conserving plants and animals was the least costly. The cost of conserving ecosystems only
was more expensive than conserving only plants or only animals.
An increase in cost with the increase of the conservation target was observed along with an
increase in targets not being met (Figure 4). Many species and ecosystem targets were
unable to be met by Marxan with a proposed target of 75% and 100%. The cost of having a
BLM of 75 was similar to that of the costs of having a BLM 0(Figure 4).
Figure 3: The cost in billions to conserve 30% of various combinations of conservation features in a Tasmanian
reserve system.
Figure 4: The relationship between the target proportion of the conservation feature conserved and the cost in
billions for a Boundary Length Modifier value of 0 and 75.
Discussion
Connectivity
As expected, an increase in price was observed with decreasing BLM and therefore
increasing connectivity. This is due to the fact that in order to meet the target the model
must now consider the selection of individual planning units in relation to other planning
units, and not just select 30% of conservation features for the lowest cost. Also larger more
clumped areas would need to be protected as opposed to smaller more scattered areas. The
relationship however between cost and connectivity was found to be non-linear (Figure
1). We found that the connectivity of the network can be increased with a minimal increase
to cost when selecting a BLM of 75(Figure 2). By manipulating the parameter of boundary
length we are manipulating the edge to area ratio. As previous studies have found
decreasing this ratio decrease edge effects and promotes connectivity, which has found to
generally increase species diversity (Debinski & Holt 2000). Although the result of edge
habitat to the ecosystems diversity and function varies as it is largely dependent on other
underlying mechanistic factors that modulate edge effects (Murcia 1995). For this study it is
more costly to increase connectivity when buying land, however the maintenance and
upkeep of various unconnected reserves may also have added costs in the long term.
Conservation Features
The change in cost was investigated by selecting different combinations of conservation
features for the model to preserve. It was noted that not all animal and plant species were
necessarily conserved by the model when conserving ecosystems for the lowest price. This
idea is best highlighted in the difference in cost between conserving plants, animals and
ecosystems in comparison to conserving only ecosystems. The distribution of conservation
features, seen in Figure 5, further highlights that ecosystems perhaps cannot be used as a
cost effective substitution to conserving plants and animal species.
The idea of conserving ecosystems as a cost effective proxy to conserving plant and animal
species was also briefly investigated. Noticeably conserving animal species was found to be
more costly than other combinations. This may be due to the requirement of an inland
water habitat. These planning units are expensive to purchase and may be due to
Tasmanian inland fisheries which contribute a substantially to the economy of the state
(McQuillan et al. 2009).
A constraint on planning reserves based on a species approach, in which decisions are based
on species distributions, is that adequate knowledge about the species life history is
required to correctly conserve the right amount of area to support its population. The
ecosystem approach to reserve planning aims to conserve the functionality of the
ecosystem which will ensure the conservation of particular species within the ecosystem
(Walker 1995). While the ecosystem approach does not require understanding of particular
species, it has the risk of the unknown, in regards to which species may or may not persist
(Walker 1995). As it appears that not all our selected species are conserved upon
conserving ecosystems we would recommend a combination of the species and ecosystem
approach. In doing this you reap the benefits of a species approach as you conserve
particular species but still gain the benefits of an ecosystem approach by conserving
ecosystem services and other unknown species residing in the ecosystem.
Conservation Targets
As expected and increase in cost with an increased target percentage was observed. Figure
4 clearly shows that the relationship is not linear with the gradient being the lowest
between 10% and 30%. This supports the idea of conserving 30% of species economically as
you conserve more with a smaller increase to cost. As we found when investigating
connectivity, a BLM of 75 increased connectivity with only a slight increase in cost. This
parameter was found to attribute a similar cost results to a BLM of 0 (Figure 4). Thus we
would recommended the parameters of a BLM 75 and 30% conservation target to apply to
the model. The theory of proportional representation of conservation features is that at a
certain proportion, biological diversity will be maintained (Stewart et al. 2007). Whilst there
is no universal ideal proportion identified, extensive studies in fisheries has found 20-50% a
good target to set and with most agreeing that 10-12% is too low a target for maintenance
of conservation feature (Stewart et al. 2007). The use of the proportional representation
does however rely on the original data and knowledge of species as well as the
management regimes in place making it difficult to draw comparisons to other reserves
(Stewart et al. 2007).
Concluding Remarks
It is important to acknowledge that not all of the planning units up for reserve selection are
in a natural state. Many areas that have urban development or agricultural uses, which are
generally costly to purchase, may also carry further difficulties in regeneration. A future
study could identify the planning units where intensive, costly regeneration is required and
planning units that carry too much economic value to reserve. These areas could be blocked
from selection, along with already reserved areas, to better develop a reserve that is more
feasible in a “real world” context.
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