Koala Forever Noosa Securing the Future of Wild Koalas and Wild Koala Eco-Tourism in the Noosa Biosphere Reserve

Koala Forever Noosa Securing the future of wild koalas and wild koala eco-tourism in the Noosa Biosphere Reserve

Does landscape connectivity translate into genetic connectivity of koalas? Prepared for the Noosa Biosphere Reserve Foundation Ltd

By the University of the Sunshine Coast, Detection Dogs for Conservation Katrin Hohwieler, Sally Chudleigh, Dr Celine Frere and Dr Romane Cristescu October 2020

Interim report Vegetation Sally Chudleigh connectivity mapping:

Position: Spatial analyst

Date: 29/10/2020

Signature:

Genetic analyses: Katrin Hohwieler

Position: PhD

Date: 29/10/2020

Signature:

Draft 1: Dr Romane Cristescu

Position: Postdoc Research Fellow

Date: 29/10/2020

Signature:

Review: Dr Celine Frere Position: A. Prof Date: Signature: 29/10/2020

Noosa Biosphere Reserve Foundation Koala Report 2 | P a g e

Disclaimer

This report was prepared in accordance with the scope of work agreed with the Noosa Biosphere Reserve Foundation Ltd (the Client) and is subject to the specific time, cost and other constraints as defined by the scope of work.

To prepare this report, USC relied on information supplied by the Client, and does not accept responsibility for the accuracy or completeness of this information. USC also relied on information gathered at particular times and under particular conditions, and does not accept responsibility for any changes or variances to this information which may have subsequently occurred. Accordingly, the authors of the report provide no guarantee, warranty or representation in respect to the accuracy, adequacy or completeness of the information, whether generally or for use or reliance in specific circumstances. To the extent permitted by law, the authors exclude any liability, including any liability for negligence, for any loss, damage, injury, illness howsoever caused, including (with limitation) by the use of, or reliance upon, the information, and whether arising from errors or omissions or otherwise.

This report is subject to copyright protection and the copyright owner reserves its rights.

Noosa Biosphere Reserve Foundation Koala Report 3 | P a g e

Table of Contents

Disclaimer ...... 3

1. Introduction ...... 6

2. Project aims and objectives ...... 8

3. Methods...... 10

3.1. Vegetation connectivity mapping ...... 10

3.1.1. Concepts behind the methodology ...... 10

3.1.2. Vegetation analysis ...... 14

3.1.3. Landscape permeability matrix ...... 19

3.1.4. Corridor identification using Linkage Mapper ...... 21

3.1.5. Evaluation and prioritisation of corridor linkages ...... 22

3.2. Scat sampling ...... 27

3.3. Genetic analyses...... 29

3.4. Data analysis ...... 30

3.5. Limitations ...... 32

4. Results ...... 34

4.1. Vegetation connectivity mapping ...... 34

4.1.1. Vegetation analysis ...... 34

4.1.2. Landscape permeability matrix ...... 39

4.1.3. Corridor Identification using Linkage Mapper ...... 41

4.1.4. Evaluation and prioritisation of corridor linkages ...... 41

4.2. Scat sampling ...... 58

4.3. Genetic analyses...... 63

Noosa Biosphere Reserve Foundation Koala Report 4 | P a g e

4.3.1. Population genetic structure using fastSTRUCTURE ...... 63

4.3.2. sPCA ...... 64

4.3.3. Assessing connectivity between patches using FST and assessing general diversity within and amongst clusters ...... 67

5. Discussion ...... 70

Determining already established vegetation corridors and nodes ...... 70

Identifying key connected koala populations ...... 70

Identifying key locations where koala and vegetation connectivity needs to be recovered 71

Identifying key locations suitable for sustainable wild koala eco-tourism and recreation .. 72

6. Acronyms and glossary ...... 75

7. References ...... 79

Noosa Biosphere Reserve Foundation Koala Report 5 | P a g e

1. Introduction

Fragmentation is one of the major drivers of biodiversity loss. Over the past 35 years alone, habitat fragmentation has reduced species biodiversity to as little as 25% of its pre-industrial value across five continents (Haddad et al. 2015). Fragmentation can result in 1) a decrease in population size, through division and increase threats in, or moving between, fragmented habitats resulting in higher mortality, 2) alteration of the genetic makeup of the population through disruption of natural dispersal processes and small population size, reducing the fitness of individuals, and the evolutionary adaptive potential of the species, and 3) increase risk of population extirpation, as isolated population can be lost through stochastic events (e.g. drought, fire) and the lack of connectivity prevents recolonisation. For all these reasons, it is important that habitat connectivity is kept or, in case it has been lost, is restored. Restoration is becoming so critical in the current world that, starting in 2021, the United Nations has declared a UN Decade on Ecosystem Restoration.

Fragmentation issues particularly affect species such as the koala (Phascolarctos cinereus), that have already lost a large proportion of their habitat and are often found in parts of the landscape humans favour for agriculture and urbanisation (Lunney and Matthews 1997). Despite its iconic status and overall threats being well-known, fine scale koala distribution and population fragmentation are rarely known, thus impairing effective management. As a result, the widespread decline of koalas has not been halted or slowed down (McAlpine et al. 2015). Koalas have suffered population declines of up to 80% in parts of their range (Rhodes 2015) and are now considered vulnerable to extinction across most of their current range (Shumway et al. 2015). In particular, habitat fragmentation poses a significant threat to koalas through high mortality by vehicle collisions (Gonzalez-Astudillo et al. 2017) and vulnerability to genetic erosion. Indeed, low genetic diversity and inbreeding in koalas has been linked to negative fitness consequences including testicular abnormalities (Cristescu et al. 2009), and lower body condition, reproductive success and sperm quality .

Noosa Biosphere Reserve Foundation Koala Report 6 | P a g e

Similarly to other species, the main ways fragmentation impacts koalas are: increase vulnerability to injury and mortality through more travelling time on open area between patches (facing threats such as cars, dogs) and decrease genetic diversity which underpins adaptation to changing environments (disease, heat waves). Lino et al. (2019) found that even though mammals living in highly fragmented habitat are generally at risk of genetic diversity loss, forest-dependent, arboreal, and herbivorous mammals are especially sensitive - all of which being characteristic of the koala. In addition, recent study has shown that fragmentation of koala populations alters their movement behaviour since they have to move further distances in the search of food to satisfy their nutritional needs (Rus et al. 2020). Increase movement will translate to higher energy budget, this can have a disproportionate detrimental impact on a specialised species, with such a poor diet it is described as “living on the nutritional edge”.

Connectivity is a measure of the ability of organisms to move through the landscape.

Corridors are defined as “narrow, continuous strips of habitat that structurally connect two otherwise non-contiguous habitat patches” (Kindlmann and Burel 2008).

The general assumption, and defining framework for connectivity mapping, is that organisms do not venture into non-habitat (Kindlmann and Burel 2008), but instead stay in larger patches of habitat linked by corridors. Corridors functioning as landscape linkages must be wide enough to support ecosystem processes and enable fauna to move between larger reserves over an extended period of time (Hess et al. 2001). While there is no maximum recommended corridor width, the wider a corridor the less disturbance and edge effects would be expected and an increased probability that habitat and feeding requirements would be met (Bennett 2003). Pinch points, also called choke points or bottlenecks, are parts of a corridor that might be compromised and therefore might hinder connectivity. Pinch points normally occur when there

Noosa Biosphere Reserve Foundation Koala Report 7 | P a g e

is high resistance or high human density found in the environment. In such spots, loss of a small amount of habitat could for example disproportionally compromise connectivity and could identify areas for restauration and protection. Desktop assessment are standard methods of quantifying landscape connectivity (e.g. CIRCUITSCAPE, Linkage Mapper).

How the habitat fragmentation – connectivity continuum influences population genetics is an area of active research. According to the most basic landscape genetic model, isolation by distance, genetic similarity among populations (or individuals) is correlated with geographic or Euclidean distance (Wright 1943, Kindlmann and Burel 2008). This is particularly true at larger scale, such as across a State. However, at a smaller scale, such as a local government area (LGA), the connectivity of vegetation (not just Euclidian distance) is assumed to represent the potential for populations to be connected, where more vegetation corridors are linked to higher gene flow.

Here we set out to test this assumption. We use a state-of-the-art corridor mapping to identify vegetation connectivity, and how this translates into koala population connectivity. It is crucial for conservation management and budgeting to identify if results produced by modelling assumptions are a good representation of the reality of how real animals behave. This in turn can inform restoration strategies such as prioritising areas for investment in restoration.

To do this, the genetic connectivity of the population was measured. To determine if a corridor between two patches of koala habitat actually connects koala populations, we can investigate the gene flow between those two. If patches that are connected by corridor(s) do not result in measurable gene flow, the corridor is ineffective. If genetic connectivity exists, we can assume that the corridors work.

2. Project aims and objectives

The overarching objective of this Project is to help secure the long-term survival of koalas in the Noosa Biosphere Reserve (Biosphere) by understanding landscape and population connectivity, and to develop an initial blueprint for wild koala eco-tourism within the Biosphere. This project is a collaboration between Detection Dogs for Conservation at the

Noosa Biosphere Reserve Foundation Koala Report 8 | P a g e

University of the Sunshine Coast and Noosa Parks Association Inc, in partnership with WWF- Australia and supported by the Noosa Biosphere.

Key stages of the project reported in this report are points a. to c., while points d. and e. will be mostly part of another report: a. Determining whether already established vegetation corridors are being utilised by koalas Geographic information system (GIS) analysis of the Noosa Biosphere vegetation landscape was used to identify vegetation corridors. b. Identifying key connected koala populations This built on the output of stage 1 as well as USC’s Noosa Biosphere Reserve genetic dataset collected through past and current project using specially trained koala scat detection dogs. This will enable determination of whether koalas are using identified vegetation corridors through the estimation of gene-flow. c. Identifying key locations where koala and vegetation connectivity needs to be recovered From steps 1 and 2, USC identified where populations of koalas are not connected due to (1) lack of vegetation corridor and (2) unidentified threats preventing koala crossing (e.g. wild dogs, road fragmentation – lack of underpasses). This information can then be used for targeted management actions (e.g. baiting, retrofitting, rehabilitation, privately owned nature refuges, Land for Wildlife). d. Identifying key locations suitable for sustainable wild koala eco-tourism and recreation Based on the output of stages 1, 2 and 3, and in consultation with QPWS, Noosa Council, and Tourism Noosa, USC and NPA will jointly identify key locations suitable for sustainable wild koala eco-tourism and recreation.

Noosa Biosphere Reserve Foundation Koala Report 9 | P a g e

e. Development of an initial blueprint for a Noosa wild koalas eco-tourism and recreation strategy Based on the output of stage 4, NPA, in consultation with QPWS, Noosa Council, Tourism Noosa, and USC, will develop an initial blueprint for koala-based eco-tourism and recreation in the current and pending perpetually protected conservation estate of the Noosa Biosphere Reserve.

3. Methods

3.1. Vegetation connectivity mapping

3.1.1. Concepts behind the methodology

We used Linkage Mapper, a GIS tool designed to support regional wildlife habitat connectivity analyses and automate mapping of wildlife habitat corridors. It investigates landscape connectivity defined as “the degree to which the landscape impedes or facilitates movement among resource patches” (Taylor et al. 1993). Linkage Mapper uses vector maps of core habitat areas and raster maps1 of permeability to movement to identify and map least-cost linkages between core areas.

We make the following assumptions that it is preferable that koala corridors:

 link identified koala habitat nodes via least energetic ‘cost’ paths (i.e. difficulty and mortality risk’),

1 Raster analysis was based on a 10m cell resolution covering Noosa Shire Council (NSC) LGA and includes a 10km buffer into neighbouring LGAs (Gympie Regional Council and Sunshine Coast Council).

Noosa Biosphere Reserve Foundation Koala Report 10 | P a g e

 contain well connected vegetation and/or stepping stones to facilitate koala movement (ensuring the gap crossing distance threshold is met i.e. the maximum distance a species will cross between connectivity elements (such as scattered trees), which limits the distances of open ground (gaps) individuals will move across,  minimise landscape impediments such as built-up areas, large cropping areas and major infrastructure where, even though koalas might readily move through, they are exposed to more threats such as dogs, vehicle strikes etc.,  are wide to allow for the dispersal of individuals through the area of interest,  are short and follow least cost resistance pathways to maintain functionality (landscape permeability is high and cost distances are kept to a minimum where possible),  provide links to important core habitats based on centrality, climate change refugia, known populated areas etc.

Identifying and prioritising koala corridors within the Noosa Biosphere was achieved through five main steps;

1. Analysis of vegetation containing koala preferred tree species to identify;  high connectivity of koala habitat tree species  koala habitat nodes 2. Landscape permeability matrix to approximate the energetic ‘cost’ (i.e. difficulty and mortality risk) of movement for koalas through different parts of the landscape including;  high connectivity of vegetation containing koala preferred tree species  other vegetation or land cover (e.g. plantations, exotic, horticulture, cleared etc.)  urban areas  transport infrastructure  water barriers such as estuaries, large lakes and rivers

Noosa Biosphere Reserve Foundation Koala Report 11 | P a g e

3. Identification of a koala corridor network throughout the Noosa Biosphere using least cost modelling techniques that follow paths of least resistance by;  following well connected vegetation containing koala preferred feed tree;s  avoiding or minimising landscape impediments where possible (i.e. built-up areas, major roads, water etc). 4. Evaluation and relative importance of corridors by analysing;  quality of linkages  pinch points (i.e. constrictions, a.k.a. bottlenecks or choke points) and cost distances  centrality  overall relative conservation priority in the landscape in providing links to important core habitat (based on centrality, climate change refugia, mean resistance etc) 5. Field verification and data gap analysis using a koala scat detection dog

An outline of the method is provided in Figure 1 and explained in more detail in the following sections.

Noosa Biosphere Reserve Foundation Koala Report 12 | P a g e

Figure 1: Flow diagram of methodology

Noosa Biosphere Reserve Foundation Koala Report 13 | P a g e

3.1.2. Vegetation analysis

The availability, condition (configuration) and connectivity of koala preferred trees are considered important in evaluating both koala habitat nodes and koala movement or steppingstones through the landscape.

Availability of koala preferred tree species

Vegetation containing a high relative abundance of koala feed/browse tree species was identified through ranking Regional Ecosystems (RE’s). Noosa and District Landcare Group have established a list of koala food and habitat trees for the Noosa Region and categorised tree species into ‘koala favourites’ and ‘other food and habitat species’. Trees listed as ‘koala favourites’ were categorised as primary, while ‘other food and habitat species’ were categorised as secondary tree species. Additional tree species formerly listed by Noosa and District Landcare were included as categorised as supplementary tree species.

Table 1: Koala feed/browse trees in the Noosa Region (Noosa and District and Landcare Group)

Species Common Name

Primary (favourites)

Eucalyptus microcorys Tallowwood

Eucalyptus propinqua Small-fruited Grey Gum

Eucalyptus robusta Swamp Mahogany

Eucalyptus tereticornis Blue / Forest Red Gum

Eucalyptus resinifera Red Mahogany / Messmate

Secondary (other food and habitat species)

Corymbia citriodora ssp variegata Spotted Gum

Eucalyptus crebra Narrow-leaved Ironbark

Noosa Biosphere Reserve Foundation Koala Report 14 | P a g e

Eucalyptus grandis Flooded Gum / Rose Gum

Eucalyptus racemosa Scribbly Gum

Eucalyptus bancroftii Tumbledown Gum

Eucalyptus siderophloia Grey Ironbark

Corymbia intermedia Pink Bloodwood

Eucalyptus pilularis Blackbutt

Supplementary Eucalyptus moluccana Gum-topped Box

Corymbia tessellaris Moreton Bay Ash

Eucalyptus fibrosa Broad-leaf Ironbark

Eucalyptus Seeana Narrow-leaved red gum

Eucalyptus sideroxylon Red Ironbark

Eucalyptus salignus Sydney Blue Gum

Lophostemon confertus Brush Box

Melaleuca quinquenervia Paperbark Tea tree

Eucalyptus camaldulensis River red gum

RE’s were ranked using a similar approach adopted by O2 Ecology for modelling koala habitat in the Gympie Region. A combination of Corveg data (data collected at sites across Queensland for ground-truthing and validating RE mapping) and descriptions from the Regional Ecosystem Description Database (REDD) version 9.0 (Queensland Herbarium 2015) where Corveg data was not available. The relative abundance of trees that are koala preferred species and number of koala preferred species dominant in the canopy (T1) and subcanopy (T2) for each RE were used in the ranking system to account for dominant tree species in each RE, the presence and combination of primary, secondary and supplementary tree species and the number of dominant feed trees in the RE.

The following ratings were applied to each category of koala food tree:

Noosa Biosphere Reserve Foundation Koala Report 15 | P a g e

 Primary = ‘9’  Secondary = ‘3’  Supplementary = ‘1’

Corveg information was imported and formatted in excel where it could be analysed and referenced to a lookup table with ratings for each preferred koala tree species. The applied rating (i.e. 9, 3 or 1) for each dominant tree species found within each RE was multiplied by the proportion of sites for that RE where that tree species was found. This was applied to both T1 and T2. The values for all primary tree species for each RE were added together and the mean calculated. Hence the maximum value for ‘primary’ tree species can only be 9 i.e. 100% of sites contain the primary tree species as a dominant canopy species. This process was repeated for both secondary and supplementary tree species. The mean values for all categories were then added together to give an overall rating. The maximum koala habitat value in one stratum of an RE would be 13 (i.e. 9 + 3 + 1).

In addition, the number of dominant preferred koala tree species was incorporated to further rank REs as koalas prefer to select from a range of tree species to fulfil their nutritional requirements. The number of tree species was incorporated for primary and secondary tree species only (not supplementary). For each preferred primary tree species within an RE, a value of ‘1’ was added to its total value, while for each number of secondary tree species, a value of ‘0.5’ was added. This was applied to both T1 and T2.

Condition

In addition to the availability of koala tree species, the condition (including density) of vegetation is also thought to be an important factor in safe koala movement. Koalas moving on the ground between sparse or very scattered trees are more at risk from predators. Hence configuration was also incorporated into the overall biophysical or connectivity value.

Noosa Biosphere Reserve Foundation Koala Report 16 | P a g e

Condition was ranked by using vegetation condition mapping undertaken by Eco Logical Pty Ltd in 2016 where vegetation was classified as remnant, contiguous remnant2, dense regrowth, scattered regrowth, scattered trees or exotic3.

Remnant or contiguous RE generally have good tree cover, and is usually in better condition than regrowth, with less disturbance and edge effects. Dense regrowth may still have good tree coverage, however it is generally more disturbed and can have more edge effects such as weed encroachment. Sparse regrowth or scattered trees do not provide much tree cover for koalas whom are susceptible to predators when moving over greater distances between trees.

Hence, condition of vegetation was incorporated into rating vegetation containing koala trees species. Refer to Table 2 for ratings. A higher rating will result in a higher connectivity value when performing a sum of focal statistics later discussed.

Table 2: Vegetation condition ratings

Vegetation type Rating Remnant or contiguous RE 3 Dense regrowth 2 Sparse regrowth or scattered trees 1

Patch size and shape are also important factors when defining steppingstones or continuous vegetation links. The size of remnant patches of vegetation is an important indicator of overall ecological significance and large areas are considered to represent greater significance because they are:

 more representative of overall biodiversity values;  more resilient to the effects of disturbance; and  constitute a greater proportion of the overall vegetation resource (Department of Environment and Heritage Protection 2014).

2 vegetation (identified by fine scale vegetation mapping) with remnant-like quality located immediately adjacent to designated remnant vegetation 3 Note that vegetation condition has only been undertaken for the Noosa LGA and scattered trees have not been identified for the Sunshine Coast Council and Gympie Regional Council.

Noosa Biosphere Reserve Foundation Koala Report 17 | P a g e

Connectivity

Habitat connectivity is a measure of the ability of organisms to move through an environment, between patches of habitat.

There is limited information on what the gap crossing threshold between patches is for the koala. In a systematic literature review, Doerr et al. (2010) calculated a mean gap-crossing threshold of 106 m and an interpatch-crossing threshold of 1100 m for limited data of bird and mammal species inhabiting wooded habitats. This indicates that many species are unable to cross open areas that exceed 106 m and many species are unable to disperse between patches of habitat separated by >1100 m, even where structural connectivity exists between the patches.

In this analysis, 100 m between patches was considered an appropriate distance for koalas moving from one patch to another.

Identifying areas of high connectivity

Areas of high connectivity were identified by performing a ‘weighted’ connectivity analysis that considers composition, condition (configuration) and connectivity of vegetation containing preferred koala tree species.

Hence, a weighted connectivity analysis using sum of focal statistics was performed using a 50m neighbourhood analysis i.e. a total distance of 100m between patches. Connectivity was classified into 3 classes using natural breaks and assigned an overall permeability value as displayed in Table 3. ‘Very high’ connectivity areas are generally large, well-connected remnant vegetation patches containing koala preferred tree species and are highly permeable landscapes for koala movement.

High connectivity areas are then incorporated into an overall resistance or permeability matrix discussed later.

Noosa Biosphere Reserve Foundation Koala Report 18 | P a g e

Table 3: High Connectivity Areas

Weighted Landscape Connectivity Permeability Value Value Very High 1 High 2 Medium 3

Identifying koala habitat nodes

Vegetation analysis was also used to identify koala habitat nodes. Koala habitat nodes were identified by their ability to support a koala population of significant size and with minimal disturbance and/or edge effects. They are generally considered secure and are unlikely to be subject to land use conflicting with conservation outcomes. Links between identified habitat nodes were the basis for corridor design.

Koala habitat nodes were identified as large tracts of vegetation (at least 50 hectares of remnant, contiguous RE or dense regrowth) with minimal edge disturbance (at least 200m wide) containing a high relative abundance of preferred koala trees.

Koala habitat nodes were further evaluated by running circuit scape to evaluate ‘centrality’ and a linkage priority tool set which considers other core values such as climate refugia, size, shape etc.

3.1.3. Landscape permeability matrix

Each cell in a landscape permeability map is attributed with a value reflecting the energetic cost, difficulty, or mortality risk of moving across that cell.

Landscape permeability was produced by combining high connectivity areas identified earlier with:

Noosa Biosphere Reserve Foundation Koala Report 19 | P a g e

 other land covers or land uses (i.e. native vegetation containing little to no koala feed/browse trees, cleared land, plantations, exotic, horticulture etc.),  landscape impediments (such as built-up areas, and transport infrastructure),  hard barriers (such as large expanses of water and estuaries).

Manmade barriers such as linear infrastructure, fences and urban fragmentation and natural barriers such as large water bodies, cliffs and escapements impede movement across a landscape and can affect the efficiency of wildlife corridors. Built-up areas present physical impediments to movement such as large cleared areas, buildings, and fences, as well as threats from domestic dog attacks and high traffic.

Transport infrastructure (particularly highways and secondary roads) are impediments to koala movement due to the risk of vehicle strike. However, in some areas, koala exclusion fencing, if maintained, can guide koalas through safe passageways under highways or bridges. Hence, potential passageways (some retrofitted) have been identified and incorporated into the model as they are not physical barriers as such and potentially could be retrofitted to assist koala movement over physical barriers.

Natural barriers such as large expanses of water can be a physical barrier as koalas are unlikely to swim over large distances. Although, koalas have been known to traverse or use pedestrian footbridges such as the one over Weyba Creek and hence this has been incorporated into the model.

A rating between 1-100 was applied to reflect landscape permeability for koala movement. Table 4 displays land cover permeability ratings.

Noosa Biosphere Reserve Foundation Koala Report 20 | P a g e

Table 4: Land cover and permeability value

Permeability Land cover Value (1-100) 1 (very high) Very high functional connectivity (biophysical rating) between koala feed/browse trees (within interpatch threshold of 100m) 2 High functional connectivity between koala feed/browse trees (within interpatch threshold) 3 High - Medium functional connectivity between koala feed/browse trees 5 Native vegetation containing minimal or no koala feed trees (outside interpatch threshold) 10 Cleared areas (outside interpatch threshold) or potential passageway over transport or water barriers 12 Exotic/ plantation/ horticulture 15 Urban 20 Local Urban Road 25 Local Connector Road 33 Secondary Road 50 (very low) Highway 4 100 (barrier) Large expanses of water (e.g. lakes, rivers, estuaries)

3.1.4. Corridor identification using Linkage Mapper

Euclidean distance, the straight-line distance between two points, is the simplest way to measure distances between habitat nodes. However, the calculation of Euclidean distances ignores behaviour and other factors that come into play in functioning corridors. Least-cost modelling was therefore employed as species will endure ‘costs’ when moving through a

4 underpasses or passageways under bridges or highways and footbridges were assigned a rating of 10

Noosa Biosphere Reserve Foundation Koala Report 21 | P a g e

landscape and the optimal solution will be to provide corridors for which the cost distance between two patches is the least accumulated cost.

As fauna moves away from specific core areas, cost-weighted distance analyses produce maps of total movement resistance accumulated. Cost distance maps for each node are added, normalised and mosaicked to produce a least-cost corridor map. The cost distance between two habitat nodes is the least accumulated cost associated with a single path (the least-cost path) between identified habitat nodes.

There is very limited research on the cost distance threshold for koala movement between habitat nodes. As we did not specify any maximum corridor lengths or costs, our connectivity analysis always identified a ‘best’ linkage, regardless of whether movement through the linkage zone would actually be possible. Therefore, there is a need to evaluate, prioritise and validate these linkages on the ground for koala presence and movement.

3.1.5. Evaluation and prioritisation of corridor linkages

Core centrality value

Circuit scape was run to identify the current flow centrality which is a measure of how important a link or core area is for keeping the overall network connected. Core centrality values are represented in Figure 10 and are later incorporated into calculating the overall relative conservation priority of each linkage in a landscape.

Linkages have a higher score than the others because its loss would disconnect more than one core area from the rest of the network. This also applies to nodes, where a high centrality score means the loss of a node would disconnect the network more than the loss of a node with a lower score.

Noosa Biosphere Reserve Foundation Koala Report 22 | P a g e

Quality of linkages

Linkage quality can be described by two metrics calculated for each link using cost weighted distance (CWD) ratios. These include;

 CWD to Euclidean distance ratio  CWD to least cost path ratio

Pinch points

Corridors functioning as landscape linkages must be wide enough to support ecosystem processes and enable fauna and flora to move between larger reserves over an extended period of time (Hess & Fischer 2001). There is no maximum recommended corridor width rather increasing width is the most effective option for increasing corridor function (Department of Environment and Conservation 2004; Bond 2003; Bennett 2003). Increased corridor width reduces disturbance and edge effects and increases the probability that habitat and feeding requirements will be met (Bennett 2003).

Once corridors have been mapped using Linkage Mapper, Pinch Point Mapper runs Circuit scape within the resulting corridors. This produces current maps that identify and map pinch points (i.e. constrictions, a.k.a. bottlenecks or choke points) in least-cost corridors.

Pinch points can be due to lack of low resistance land cover types, high human density, transportation networks, or any combination of these factors. Identifying pinch points where loss of a small area could disproportionately compromise connectivity can be used to identify areas for protection and potential restoration.

Overall relative conservation priority

The relative conservation priority of each linkage in a landscape was analysed by running a tool called ‘Linkage Priority’ (an extension to Linkage Mapper) released in September 2017.

The Linkage Priority Tool is based on weighted combinations among many factors (see Figure 2).

Noosa Biosphere Reserve Foundation Koala Report 23 | P a g e

The lower set of factors on the diagram estimate the relative priority of the two cores at either end of a linkage. These factors include the shape, mean resistance value, size, and expert opinion. An assumption is then made that a linkage which connects two important core areas is a higher conservation priority than one that connects two marginal core areas. The Tool calculates this relative value for every linkage.

This output is combined with the other higher-level factors (top row) that relate directly to linkage priority, including the permeability of each linkage (i.e., the mean resistance values along the least cost path), the proximity, the centrality (i.e. how central the linkage is to the entire network), and an expert opinion option. The expert opinion option is implemented via a table of each linkage as a row, and a relative value of each linkage based on expert opinion, or other factors, such as demographic analyses.

Figure 2: Overall relative conservation priority

Noosa Biosphere Reserve Foundation Koala Report 24 | P a g e

In March 2018, Ecosure modelled climate change impacts on biodiversity in the Noosa Coastal Zone. The species distribution modelling platform MaxEnt was used to generate predicted occurrence maps for selected BVGs and species under current and projected climate scenarios. Substantial trends from 1990 to 2090 in maxent modelling show that the koala is predicted to persist, and probability of occurrence will increase, especially in western areas of the coastal zone (note that the model has low goodness of fit).

Koalas are susceptible to heat stress, so are extremely vulnerable to increased extreme temperature events. Drought, fire, loss of habitat/fragmentation are also climate change risks.

It is important that connections to climate refugia are protected and this was therefore incorporated into the model. It must be noted however, that climate refugia has only been modelled for the coastal section of Noosa and may present some bias towards the coast. Figure 3 represents climate refugia for koalas in 2090.

The overall weights assigned for running the linkage priority tool are listed below;

Core area values (CAV) calculations;  Resistance weight = 20%  Size weight = 20%  Area/perimeter weight = 20%  Expert core area value weight = 0% (not available)  Current flow centrality weight = 20%  Other core area value weight (climate refugia) = 20%

Corridor Specific priority (CSP) options (using default parameters);  Closeness weight = 33%  Permeability weight = 33%  Core are value weight = 34%  Expert corridor important value weight = 0% (not available)  Climate envelope = 0 % (not available)  Proportion of Top CSP values to keep = 2%

Noosa Biosphere Reserve Foundation Koala Report 25 | P a g e 490,000 505,000 7,110,000 7,110,000

Tin AD a K O n IN KI N R a Toolara SF C

r e

GYMPIE Great Sandy NP

K Goomboorian NP IN K IN RO AD

Lake Cooloola

Cooloola (Noosa River) RR

D A O R KIN KIN E I

Kin Kin 7,095,000 7,095,000

Noosa River Lake Cootharaba Teewah Boreen Woondum NP

Cooloothin CP

Mount Pinbarren NP E IVE V R I D Woondum CP O R L OUIS BA Z Z D NOOSA N O N

N Cooran Pinbarren Ringtail SF I K

C Great Sandy RR

M Six Mile Creek CP

Lake Cooroibah

POUND ROAD Pomona Yurol SF YU RO L FO RE Cooroibah CP Tuchekoi NP ST D R IV

E Tewantin NP

BRUC WAY Noosa Heads E HIGH IGH Harry Spring CP WA CE H YO L D BRU Tewantin EL M ST R B Noosaville EE OR E Doonella Lake T CO OY C NOO OAD SA R KM 7,080,000 AN Weyba Creek CP 7,080,000 S Sunshine Beach RO EENIE CREEK ROAD AD

Cooroy

E V I BRUCE HIGHWAY R D

Y A H Mount Cooroy CP R CREE TE LI K L EL RO A B A W OY D OR E O UM Lake Weyba C U N E D I M Noosa NP RAN D GE A U RO O A R M D SA O O O U N N DI T N A U I M N Tuchekoi CP U E A SK D R Y A T O E R R R I H DAVID LOW WAY N RT IA O L G ILW SUNSHINE COAST R C EN O I K AD R ND Eumundi E West Cooroy SF U Peregian Beach E M K U Eumundi CP E D R Mount Eerwah CP A O O A R

D A NY U B Mapleton FR Mapleton NP

490,000 505,000 Legend

Railway Highway Major Watercourse LGA 10km buffer Koala refugia 2090 High : 0.840439 Road Network Secondary Road Lake Core habitat (over 50 hectares) Freeway/ Motorway Local Connector Road Protected Area Estate Potential corridor network Low : 0.0254072

LGA Boundary

Koala Landscape Connectivity and 0 1 2 3 4 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig013_refugia.mxd Kilometers USC and Noosa Biosphere

1:130,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Koala Climate Refugia Grid: GDA 1994 MGA Zone 56

26 Aug 2018 26 Year 2090 Figure 3 Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science)

The model has additional capability to include expert opinions on koala node and corridor values such as population analysis etc and it is recommended that these be included for future model runs.

3.2. Scat sampling

3.2.1. Koala scat survey

Koala scat surveys were conducted between September 2018 and July 2019. Across the shire, we targeted areas of known koala presence to maximise the number of sampled individuals. Sites were searched for koala scats using specially trained koala scat detection dogs, which has proven to be a very efficient method (Cristescu et al. 2015a). Surveys were led non- systematically, which we refer to as casual surveys, where the detection dog was allowed to search freely without directions or constrains given through the handler (Cristescu et al. 2019).

The casual surveys, in contrast to systematic surveys [i.e. 30 trees, or the equivalent of the Spot Assessment Technique “SAT” surveys (Phillips and Callaghan 2011)] cannot be compared in space and time, nor give insight into utilisation of the habitat. The casual surveys are however an excellent and fast way to determine 1) whether koala scats are present at a specific site (for example, this survey type is widely used to inform or test koala habitat mapping) and 2) sample fresh scats. This method is indeed designed to maximise the chance of detecting koala scat / fresh scat presence in the minimum amount of time. It also allows for coverage of larger areas than systematic surveys.

The detection dogs work under strict Animal Ethics approvals (USC: ANA16113, ANA1494 and ANS1752) and QLD Government wildlife permits allowing the DDC to perform koala surveys using detection dogs and collect scats for genetic analysis (SPP WA0000738, WIF418590017, WISP18590117 and WITK18570117).

3.2.2. Dogs utilised for koala scat detection and dog handlers

We deployed three dogs during the koala scat surveys in Noosa LGA:

Noosa Biosphere Reserve Foundation Koala Report 27 | P a g e

 Billie-Jean, trained on only very fresh scats,  Baxter, trained on scats of all ages and  Maya, trained on scats of all ages.

Billie-Jean was handled by Katrin Hohwieler and Riana Gardiner, while Baxter was handled by Kirralee McDougall and Kye McDonald, and Maya was handled by Anthony Schultz. Surveys conducted with Billie-Jean exclusively indicate presence when fresh scats are found but scat absence cannot be classified as koala absence, because Billie-Jean is trained to ignore old koala scats.

3.2.3. Scat identification

When a detection dog signalled that a koala scat was present, the handler visually confirmed the scat identification, recorded the location with a hand-held GPS and classified the scat by age (Table 5) to help estimate how recently a koala had utilised the area as well as to decide whether to sample the scat for molecular analyses.

Table 5: Scat age categories.

Scat age categories Characteristics – approximate age 1 Extremely fresh (covered in mucus) – 1 day old or less 2 Fresh (shiny, smelly) – days old 3 Medium fresh (shine, or smells when broken) – weeks old 4 Old (no shine, no smell) – months old 5 Very old and discoloured – many months to years old Note: It has been estimated that koala scats can persist in the environment for up to four years (Rhodes et al. 2011).

Typical koala scats (Figure 4) have the following characteristics (Triggs 1996):

 symmetrical and bullet-shaped (not jelly-bean shaped);  generally, about 1.5 cm long by 0.5 cm wide (adult koala scat size);  even-sized and especially fine particles;  absence of insect parts (koalas do not eat insects); and

Noosa Biosphere Reserve Foundation Koala Report 28 | P a g e

 very compact.

Figure 4 Koala scats, freshest (Category 1) on the right.

3.3. Genetic analyses

The best DNA quality is found in very fresh koala scats (Schultz et al. 2018). Therefore, we only collected scats that were estimated to be less than one week old (categories 1 and 2, Table 1), still presenting a shiny mucus layer and a strong smell. Scats were collected in a sterile 50 ml tube without direct skin contact to avoid contamination and further degradation of DNA. Samples were kept on ice and transferred to a -20°C freezer at the end of each field day. The location of each sample was recorded using a handheld Garmin GPS (Alpha ® 100, Garmin Ltd., Olathe).

Genotyping

We followed the protocol of Schultz et al. (2018) to isolate DNA from koala scats. We used the QIAamp DNA Stool Mini Kit (Qiagen) with an adapted version of the protocol as described in the same article. After the production of this extraction kit was discontinued, we used the QIAamp PowerFecal Pro DNA Kit (Qiagen) with an additional one-hour incubation step after adding the buffer to the faecal sample. The DNA aliquots were genotyped for Single Nucleotide Polymorphisms (SNPs) by Diversity Arrays Technology (DArT, Canberra, (Jaccoud et al. 2001)). We used the DArTcap method, which involves a selective step to genotype only

Noosa Biosphere Reserve Foundation Koala Report 29 | P a g e

specific markers from the DArTseq representation. To achieve this, we designed koala specific “capture probes” that bind to restriction fragments in the representations carrying the specific DArTseq markers, aiming for approximately 2,000 SNPs.

Duplicate identification

The identification of duplicate samples and therefore the identification of individuals in the data set is a crucial step when using non-invasively collected samples from the wild. Because DNA from scats shows high levels of degradation (Taberlet and Luikart 1999), missing values and genotyping errors are challenges for duplicate identification and must be considered. Therefore, we firstly identified how many SNPs have been sequenced per sample. In a pairwise comparison, we then investigated how many SNPs two samples have in common and how many are mismatched. We used data from previously genotyped koalas with known identity and relatedness from Schultz et al. (2018) to investigate how many SNPs are likely to be mismatched between two samples of the same individual (duplicate samples). We determined that shared SNPs of duplicate samples match 80% or more. Two samples that show >20% mismatch likely stem from different individuals and are considered distinct.

Filtering

We filtered our set of SNPs to increase data quality with the following criteria: loci with reproducibility of <90%, call rate of <70% and minor allele frequency of <1% were removed from the dataset, as well as individuals with more than 30% missing data and secondary loci. Filtering was done in R studio using the R package dartR (Gruber et al. 2018).

3.4. Data analysis

3.4.1. Running a standard structure analysis using the program fastSTRUCTURE

Population structure of koalas in Noosa LGA was assessed using fastSTRUCTURE (Raj et al. 2014), a software that is utilising the same variational Bayesian framework as the

Noosa Biosphere Reserve Foundation Koala Report 30 | P a g e

STRUCTURE software (Pritchard et al. 2000) but can provide faster estimations of ancestry proportion. We set the potential number of populations k to be between one and five with a simple prior setting.

3.4.2. Running an sPCA using the adegenet R package to reveal cryptic structure.

Because the performance of classical Bayesian clustering methods, as implemented in fastSTRUCTURE, is dependent on the level of genetic differentiation, results are not sensitive to cryptic structure due to the time-lag of genetic differentiation signatures (Landguth et al. 2010, Epps and Keyghobadi 2015). We therefore further assessed population structure applying a spatial principal component analysis (sPCA), a spatially explicit multivariate method that explores non-random spatial distributions of genetic variation (Jombart et al. 2008). This function is implemented in the R package adegenet (Jombart 2008). We used a neighbourhood by distance approach with a minimum distance of d=0 meters since different individuals can be found in the same spot. Maximum distance was set to d=10,000 meters, which reflects the likely maximum dispersal distance (Dique et al. 2003). The screeplot distribution of eigenvalues gives an indication as to how many principal components to interpret, with the eigenvalues giving further information on global and local patterns, with highly positive eigenvalues indicating strong variance and highly positive spatial autocorrelation (i.e., global structure). If global structure is present, we find a high degree of spatial autocorrelation. Local structures indicate genetic dissimilarity and We tested significance of global and local patterns using the spca_randtest function included in the adegenet package (Montano and Jombart 2017) with 999 . We tested each principal component separately for significance using the Moran’s test as described in Jombart (2017). The results were mapped as the lagged scores of the sPCA which are computed from averaged values of scores of neighbouring locations, which helps to smooth effects and makes interpretation easier (Jombart 2017). Individual PCs were extracted and mapped, and we also used the colorplot function to display multiple components together. Only three components can be visualised at a time because a combination of red, green, and blue is used for this plot (Jombart et al. 2008).

Noosa Biosphere Reserve Foundation Koala Report 31 | P a g e

3.4.3. Dividing landscape into according patches and calculating pairwise Fst/genetic distance between those patches

We visually assessed the results of the sPCA and identified four potential clusters of koalas in Noosa. To assess if there are any further signs for a dysconnectivity between those clusters, we calculated pairwise FST, F’ST (Meirmans 2006) and percentage of molecular variance by applying an analysis of molecular variance (AMOVA) with 999 permutations. We tested for isolation-by-distance using a Mantel test to assess the relationship between the genetic and geographic distance. We calculated Heterozygosity (HO and HE) as well as the inbreeding coefficient FIS for each of the clusters to investigate potential differences between them. All analyses was done using GenAlEx 6.5 (Peakall and Smouse 2012). In GenAlEx, F’ST calculated via AMOVA is equivalent to G’ST. We calculated FIS using the formula

Fis = (He – Ho) / He

It has to be noted that the number of individuals within each cluster varied (Individuals cluster 1: 45, cluster 2: 42, cluster 3: 21, cluster 4: 16) which can present a limitation to the results.

3.5. Limitations

Vegetation connectivity mapping

While the modelled least-cost paths represent the theoretical best corridors within a landscape, there is no guarantee that these corridors will be used by the intended species. It is difficult to accurately predict behaviour; however, the modelling carried out here is a viable step within the bigger conservation picture within the NSC LGA.

Vegetation condition mapping was not available for Gympie and Sunshine Coast Council areas, so scattered regrowth and trees were not represented. This may under-represent the actual connectivity in these regions.

Climate refugia was only mapped for the coastal region of Noosa and may present bias towards these areas.

Noosa Biosphere Reserve Foundation Koala Report 32 | P a g e

Sampling

Most sites were surveyed on only one occasion (at the exclusion of the sites re-visited to collect fresh scats); therefore, the results presented here provide a snapshot of the population during this period and it can be noted that evidence of koalas is likely to change seasonally.

Detection dogs are a powerful method to study koala presence / absence and their use could greatly improve our ability to protect and conserve the koala. However, results of accuracy and efficiency of detection dogs will vary with both the dogs’ and the handlers’ abilities. Constant training and testing is required and conducted by the DDC handlers and dogs.

The rate at which scats decay may also vary significantly between sites due to varying ground layer structure, composition, moisture, sunlight, local weather events and invertebrate activity (Rhodes et al. 2011, Cristescu et al. 2012). Decomposed scats may lose their unique scent mark and the dog may no longer detect it – however this has not been proven yet (Cristescu et al. 2015b).

Failure to detect koala scats in an area does not necessarily indicate koalas are not using the area. Failure to detect koala scats may suggest either of the following:

 Koalas are not present in the area (i.e. true absence);  Koalas occur in the area, however, scats were not detected (false negative) because: o scats were present at some stage but decayed and disappeared from the environment before the survey was conducted, o the dog did not detect the scat; and/or, o the dog indicated the presence of a scat, but it was too decayed (fragments only, no scat) to be confirmed.

Genetics

Compared to high quality samples (e.g. biopsies/swabs), scat DNA is degraded and presents multiple extraction difficulties (due to inhibitors present from the koala dietary component of the scat and the low quantity of material present). However, here we were able to alleviate

Noosa Biosphere Reserve Foundation Koala Report 33 | P a g e

some of these limitations by designing a new next generation genotyping method (DArTcap, see methods), which enabled the genotyping of numerous loci. Next generation genotyping from scats however is a cutting-edge technology and as such is still an area of active research, results might be updated and refined as the field continues its rapid evolution.

4. Results

4.1. Vegetation connectivity mapping

4.1.1. Vegetation analysis

Availability of koala preferred tree species

Vegetation containing a very high or high relative abundance of koala preferred tree species is represented in Figure 5 5 and was used as the basis for analysing connectivity and identifying koala habitat nodes described in the following sections.

5 Gympie’s koala habitat mapping (2015) was based on a native vegetation extent layer created by using RE V9 (remnant only) and mature regrowth (2012) as a base and refined using aerial photography interpretation of 2014 ortho-photos. Sunshine Coast koala habitat mapping is based on Qld Koala habitat mapping (2010).

Noosa Biosphere Reserve Foundation Koala Report 34 | P a g e 480,000 490,000 500,000 510,000 7,110,000 7,110,000

Tin AD a K O n IN KI N R a C

r Toolara SF e

k GYMPIE

Great Sandy NP Goomboorian NP K IN K IN RO AD

Lake Cooloola

Cooloola (Noosa River) RR 7,100,000 7,100,000

D A O R KIN KIN E I

Kin Kin

Noosa River Teewah Lake Cootharaba Boreen Woondum NP

Cooloothin CP 7,090,000 7,090,000 Traveston Mount Pinbarren NP E IVE V R I D Woondum CP O R L OUIS BA ZZ D NOOSA N O N

N Cooran Pinbarren Ringtail SF I K

C Great Sandy RR

M Six Mile Creek CP

Lake Cooroibah

POUND ROAD Pomona Yurol SF YU RO L FO RE Cooroibah CP Tuchekoi NP ST D R IV

E Tewantin NP

B RUC AY Noosa Heads E HIGH HW Harry Spring CP WAYO HI G LD BRUCE Tewantin EL M ST R B Noosaville EE OR E Doonella Lake T CO OY C NOO OAD SA R KM 7,080,000 AN Weyba Creek CP 7,080,000 S Sunshine Beach RO EENIE CREEK ROAD AD Cooroy

E V I BRUCE HIGHWAY R D

Y A H Mount Cooroy CP R E CREE T LI K L EL RO A B A W OY D GYMPIE OR E O UM Lake Weyba C U N E D I M RAN Noosa NP GE U RO A M D D O A U O N R MEMORIAL DRIVE T SA A O I O N Tuchekoi CP N WE I A SK S D R Y T N T E U E R D U EU M R I A DAVID LOW WAY N RO M IA U L G TH R R N O C O A LW D D R NI Eumundi E West Cooroy SF E I Peregian Beach K R E I D O K N Eumundi CP A R U Mount Eerwah CP D O M A U

D E

7,070,000 SUNSHINE COAST 7,070,000 SUNSHINEB COAST

Y A Mapleton FR R O A Mapleton NP D Noosa RR 480,000 490,000 500,000 510,000 Legend

Koala Evidence ") USC Scat Dog - O2 ecology Railway Highway Lake Vegetation containing koala preferred feed browse trees ") ALA ") Wildnet/SEQc Road Network Secondary Road Protected Area Estate Other native vegetation cover ") Council Freeway/ Motorway ") Azwh point locations Local Connector Road LGA Boundary ") GRC ") Koala tracker april 2016 Major Watercourse LGA 10km buffer ") USC Scat Dog

Koala Landscape Connectivity and 0 1 2 3 4 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig02_vegkoala.mxd Kilometers USC and Noosa Biosphere

1:130,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Vegetation containing preferred Grid: GDA 1994 MGA Zone 56 5 26 Aug 2018 26 Koala feed/browse trees Figure Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science)

Condition

Figure 6 represents vegetation condition.

Identifying areas of high connectivity

High connectivity appears to correlate with koala evidence, although there are some areas in which koalas are found in heavily built-up areas with minimal connectivity. This is particularly evident in the Sunshine Beach area.

Refer to Figure 7.

Identifying Koala Habitat Nodes

A total of 125 koala habitat nodes (i.e. large tracts of vegetation (at least 50 hectares of remnant, contiguous RE or dense regrowth) with minimal edge disturbance (at least 200m wide) containing a high relative abundance of koala preferred trees) were identified within the Noosa LGA and 10km beyond. 71 nodes are within the Noosa LGA and 54 nodes are within 10km of the Noosa LGA boundary. Refer to “Core habitat (over 50 ha)” in Figure 7.

Noosa Biosphere Reserve Foundation Koala Report 36 | P a g e 480,000 490,000 500,000 510,000 7,110,000 7,110,000

Tin AD a K O n IN KI N R a C

r Toolara SF e

k GYMPIE

Great Sandy NP Goomboorian NP K IN K IN RO AD

Lake Cooloola

Cooloola (Noosa River) RR 7,100,000 7,100,000

D A O R KIN KIN E I

Kin Kin

Noosa River Teewah Lake Cootharaba Boreen Woondum NP

Cooloothin CP 7,090,000 7,090,000 Traveston Mount Pinbarren NP E IVE V R I D Woondum CP O R L OUIS BA ZZ D NOOSA N O N

N Cooran Pinbarren Ringtail SF I K

C Great Sandy RR

M Six Mile Creek CP

Lake Cooroibah

POUND ROAD Pomona Yurol SF YU RO L FO RE Cooroibah CP Tuchekoi NP ST D R IV

E Tewantin NP

E V I BRUCE HIGHWAY R D

D E

7,070,000 SUNSHINE COAST 7,070,000 SUNSHINEB COAST

Y A Mapleton FR R O A Mapleton NP D Noosa RR 480,000 490,000 500,000 510,000 Legend

Koala Evidence ") USC Scat Dog - O2 ecology Railway Highway Lake Vegetation containing koala preferred feed browse trees ") ALA Remnant or contigous RE (3) ") Wildnet/SEQc Road Network Secondary Road Protected Area Estate ") Council Freeway/ Motorway Dense regrowth (2) ") Azwh point locations Local Connector Road LGA Boundary ") GRC Sparse regrowth or scattered trees (1) ") Koala tracker april 2016 Major Watercourse LGA 10km buffer ") USC Scat Dog

Koala Landscape Connectivity and 0 1 2 3 4 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig03_config.mxd Kilometers USC and Noosa Biosphere

1:130,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Condition of vegetation containing Grid: GDA 1994 MGA Zone 56 6 26 Aug 2018 26 preferred Koala feed/browse trees Figure Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science) )" 475,000 490,000 505,000 D D D

") ")"))" D ")")") )" )"") ") "))")")")" )"" ") ")")") ") )""")"))""))""))")" )")")" )" )")"")")"))") ))")")")")" )" Great Sandy NP """))")") )")" )" )")" ")")")")")")") )" )" ")")"))"")") ")") "))" D ")") )" ")") D D ")"))"") Sheep Island CP )")"") ")") ") )" " )")" " ) ")") )" )") D ") ")"))")"Noosa)" Heads Lake Cooloomera D )"")"))"")")")") )" )" D )" )")")")")" ")") ) ") "))")")")")" )" " "))")" )")")")")" N ") E ) )")" )")") )" o AD ") )")")")")")")")" Noosa") NP") o R )""))")"" )" ") s A )" )"")""))" )")" a ") ")") ") )")" )")" ") Riv P ") "))")")")" )" ))" e "))")") ") )" r )")""))")" )" ") ") r SA )" ") "))" )" ive O )" )" )" a R O )")" ")")") )"" )" ") Noos N )" )") ")") ")") )" )" )" ") " )")" )" ")") Noosaville") ")")) ") ") ") ") NOOSA DRIVE )" )" ") )" )" )"") )" ") ")")") " )" ") ") ")")" "))"") )")" ") ))" )" ") )" ") )")"") ")" ")") "))" )" Keyser") Island CP ") )")")" ) ") ")") )" ")") Great Sandy NP ") )" )" ")") )" ")")")"))" )" )" )"") )" ") ")") )" )"")")")")") ")") ") ")") ")")") Weyba CreekT CP")") ") "))" "))" ") )" ") inan")")"") ") "))"")a))" ")") ")") )" )" ")D")") ")")"))"") D") K RO ") )")")")"")C)" E D AD )" "r ") ") E )" )" )e") )" D ")") Sunshine Beach DD ")")")" ")")")") R ")")") ") GYMPIE ")")")")"))")")" )"")e") C "))" D GYMPIE ")")") ") k )" )" ") )""))")"") ")") ") D )"A )" D)" ")")") ")") ")") ") E ") ") )"")")") "")") ")") I V " ") D " )" ) )" N )" I ) D )")" ") E D

7,110,000 ") 7,110,000 EENI ") E D "") E C)"R" ") ") L D A )")"")")"))" ") )"E")EK )"R")OAD ")") ") D O D ") "))" )" O EENIE CREEK R " "))" )" D)")"") )"")"))" ") )" )"D ) ")) D W ") ")") )" " D ) W Toolara SF )" D )")" ") "))" )" "))")" AD A )" )")" ")" O Y ") D )" ") )" R D )")" "))" ")") )" IN ") D )" ") ")"")") )" KIN K ") ") ") ") D ") ")")")") )" ")") D ") DD )"") )"") ")DD") D Inset 1 - GoomboorianLake Weyba NP ") KIN KIN ROAD WALTER HAY DRIVE Noosa Heads "))" DD )" Area D D)")" ") )" ") ") )" ") Lake Cooloola Cooloola (Noosa River) RR

D A O R IN N K KI

IE P M Y G Woondum SF Kin Kin Lake Cootharaba 7,095,000 7,095,000

Noosa River Teewah Boreen Traveston SF Woondum NP

NOOSA Cooloothin CP Traveston Mount Pinbarren NP IVE DR O Woondum CP LOUIS BAZ Z

Cooran Pinbarren Ringtail SF Great Sandy RR Six Mile Creek CP

O L Lake Cooroibah da D an ng B K a C R r U Pomona Yurol SF ee C k E M H I C G K Cooroibah CP H Tuchekoi NP IN W NO IVE A N DR Y Tewantin NP Harry Spring CP Noosa Heads BRU CE HIGHW Tewantin er AY EL iv M B R S E ary TR Noosaville E RO D C M E OO Y A K Doonella Lake T C NOOSA R O M AN Weyba Creek CP 7,080,000 S Sunshine Beach 7,080,000 RO AD Cooroy E V k I bba Cree BRUCE HIGHWAY a D R Y A O D I R Y D T O A A U K H C HE K O Mount Cooroy CP R E R E R C E Lake Weyba N LI EK T Y I L L L E RO E B A A E W Y D UM L O E DAVID LOW WAY O U W R M L O ND A R O C I U V T RA H NG Noosa NP Y E R M R S O O K A A Y D U M R N IN T G A I C Tuchekoi CP N R A E ROAD E H SUNSHINE COAST R T T K OR E R LW R O NI IA A KE L DI R D UN Eumundi OA West Cooroy SF M D Peregian Beach U E Eumundi CP Brooloo Mount Eerwah CP

Mapleton FR Mapleton NP 475,000 490,000 505,000 Legend

Koala Evidence ") 2000-2010 Railway Highway Lake Core habitat (over 50 hectares) )" 2014 - current ") pre 2000 Road Network Secondary Road Protected Area Estate High Connectivity ") 2012 - 2014 Freeway/ Motorway Very High ! unknown Local Connector Road LGA 10km buffer ") 2010-2012 High D USC koala scat dog - absent Major Watercourse LGA Boundary Medium

Koala Landscape Connectivity and 0 1 2 3 4 5 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig04_high_connect_.mxd Kilometers USC and Noosa Biosphere

1:160,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Grid: GDA 1994 MGA Zone 56 7 15 Oct 15 2018 High Connectivity Figure Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science)

4.1.2. Landscape permeability matrix

Figure 8 shows the overall landscape permeability within the Noosa LGA and surrounding area.

Noosa Biosphere Reserve Foundation Koala Report 39 | P a g e 470,000 480,000 490,000 500,000 510,000 7,120,000 7,120,000

Great Sandy NP

Sheep Island CP Noosa Heads Lake Cooloomera

N E o AD Noosa NP os R a R A ive P r A er S Riv OO Noosa N Noosaville

Keyser Island CP Great Sandy NP

Weyba CreekTinan CP a C K ROA NOOSA r E D NOOSA e E Sunshine Beach e R GYMPIE D GYMPIE k C E A I V N I 7,110,000 E D 7,110,000 D EENIE C E A REEK ROAD L IE O O EEN CR K R EE W Toolara SF W

OAD A R Y IN KIN K

Inset 1 - GoomboorianLake Weyba NP Noosa HeadsKIN KIN ROAD WALTER HAY DRIVE Area

Lake Cooloola Cooloola (Noosa River) RR 7,100,000 7,100,000

D A O R IN N K KI

IE P M Y G Woondum SF Kin Kin Lake Cootharaba

Noosa River Teewah Boreen Traveston SF Woondum NP

NOOSA Cooloothin CP 7,090,000 7,090,000 Traveston Mount Pinbarren NP IVE DR O Woondum CP LOUIS BAZ Z

Cooran Pinbarren Ringtail SF Great Sandy RR Six Mile Creek CP

Mapleton FR Mapleton NP 7,070,000 470,000 480,000 490,000 500,000 510,000 Legend

Railway Landscape permeability value Cleared area outside 100m interpatch threshold or passageway (10) Local connector Road (25) Very high connectivity or biophysical value (1) Water Plantation, exotic or horticulture outside interpatch 100m threshold (12) Secondary Road (33) High connectivity or biophysical value (2) Protected Area Estate Urban Area (15) Highway (50) Medium connectivity or biophysical value (3) LGA Boundary Local rd/ street in urban area (20) Large lake, estuary or major river (100) Other native vegetation outside 100m interpatch threshold (5) LGA 10km buffer

Koala Landscape Connectivity and 0 0.5 1 2 3 4 5 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig05_permability.mxd Kilometers USC and Noosa Biosphere

1:160,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Landscape Grid: GDA 1994 MGA Zone 56 8 26 Aug 2018 26 Permeability Figure Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science)

4.1.3. Corridor Identification using Linkage Mapper

Corridors were displayed using a 1km cost weighted ‘width’ cut-off represented in Figure 6, while Figures 9a to 9e show specific areas of Noosa. Where corridors are wide there is generally higher connectivity and less movement restrictions.

4.1.4. Evaluation and prioritisation of corridor linkages

A total of 243 linkages were identified between different pairs of koala habitat nodes across the landscape in the Noosa Region and beyond of which 135 are within the Noosa LGA.

Core centrality value

Highest centrality values for koala habitat nodes (dark green) are concentrated in 3 distinct areas all of which are generally within protected areas. They include sections of;

 Yurol State Forest, Tewantin National Park and Harry Spring Conservation Park  Woondum National Park  Toolara State Forest and Great Sandy National Park

Linkages between these 3 areas generally have a higher centrality flow score.

Core centrality values are represented in Figure 10.

Noosa Biosphere Reserve Foundation Koala Report 41 | P a g e 475,000 490,000 505,000

Lake Cooloomera N oo s a Riv e r

Great Sandy NP 7,110,000 7,110,000

Toolara SF AD RO IN KIN K

Goomboorian NP KIN KIN ROAD

Lake Cooloola Cooloola (Noosa River) RR

D A O R N KI GYMPIE IN K E I P M Y G Woondum SF Kin Kin Lake Cootharaba 7,095,000 7,095,000 Noosa River Teewah Boreen Mary River Traveston SF Woondum NP

NOOSA Cooloothin CP Traveston E Dagun Mount Pinbarren NP DRIV AZZO MEDDLETON ROAD Woondum CP LOU IS B

Cooran Pinbarren Ringtail SF Great Sandy RR Amamoor Six Mile Creek CP

O L D Lake Cooroibah B R M U Pomona C C Yurol SF E K H I N I G NO Cooroibah CP H Tuchekoi NP N eek W D R IVE a Cr A ng Y da Tewantin NP an K Kandanga UCE Noosa Heads BR HIGH Harry Spring CP WAY Tewantin BRUCE EL HIG M H ST W R Noosaville E O OAD A E OR Y N OSA R Doonella Lake Y T CO O Weyba Creek CP

7,080,000 Sunshine Beach 7,080,000 AD RO M A a Cooroy S E r K O y V a e k E O I bb Cr e D N R a A R IL N Y O iv W I D R e D I O Y TU O r N A C K R H H E U T Mount Cooroy CP R D H CRE M E A I E E U T S L K U O L R L O E K BE A D M E R U A Y Y N M O D Lake Weyba DAVID LOW WAY R R W Y O I R U E I O A N C N L GE M Noosa NP L G RO A A O C D D V R A U O N Y E R E A T R K S A A O R O IN M O Tuchekoi CP N A I A D D R U N T EUM E R IA L R Eumundi OA Peregian Beach West Cooroy SF D D Imbil SF 1 A O Brooloo R Mount Eerwah CP H T R O B W U L I N N DAVID LOW WAY E Y I K A ND R EUMU O A D North Arm Eumundi CP Noosa RR Noosa CP Mapleton FR SUNSHINE COAST OAD R Coolum Beach M LU O CO A 7,065,000 IN 7,065,000 D N A

Y Coolum Creek CP Mapleton NP Mount Coolum NP roo a ch M y Ri ve

r Marcoola 475,000 490,000 505,000 Legend

Potential Linkage Highway Protected Area Estate Potential koala corridor network High : 1000 Railway Secondary Road LGA Boundary

Road Network Local Connector Road LGA 10km buffer Low : 0 Freeway/ Motorway Major Watercourse Core habitat (over 50 hectares)

Lake

Koala Landscape Connectivity and 0 0.5 1 2 3 4 5 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig06_corridor_.mxd Kilometers USC and Noosa Biosphere

1:177,277 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Potential linkges between Grid: GDA 1994 MGA Zone 56 9 30 Aug 2018 30 koala habitat nodes Figure Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science) 480,000 485,000 490,000

KIN KIN ROAD 7,105,000 7,105,000

Goomboorian NP

K IN K IN R O A D N

E U S LOOLA W A OO AY V C AL E

D Toolara SF

N E U S A V A BATES ROAD L E R O 7,100,000 A 7,100,000 D

GYMPIE KIN KIN ROAD W AH PU N G A LANE

S I S T E R T R E E C R E E K

GYMPIE A GYMPIE D

Kin Kin 7,095,000 7,095,000

NOOSA

Woondum NP

AD RO BA RA HA OT CO 7,090,000 7,090,000

Traveston Mount Pinbarren NP TR AV E S T Woondum CP O Tewantin NP N D D R A A OA O D G R O R R E S E D IN N N K RID A KIN GE L A E P E N V IN L O I BA M R RRE B N R O D OAD A P O T ZZ A Cooran Pinbarren B IS U O L

Six Mile Creek CP Ringtail SF

Tuchekoi NP 480,000 485,000 490,000 Legend

") OA D Koala Evidence 2000-2010 Major Watercourse LGA Boundary Potential Linkage KIN KI N R N E U S A V A KIN K IN ROAD L E

R COOLOOLA DWAY O )" A 2014 - current A ") O D R pre 2000 Lake LGA 10km buffer Cost distance threshold (300) S E B A T WA HPUNGA LANE b ") 2012 - 2014 ! Potential koala corridor network Kin Kin Lake Cootharaba unknown Protected Area Estate Core habitat (over 50 hectares) D

O R A High : 1000 B A a AR TH ") O V E O RI 2010-2012 C Vegetation containing koala O D Traveston ZZ D USC koala scat dog - absent IS BA U O L O L Noosa preferred feed browse trees D BR U B C R E U H d C Pomona E I H G Heads Low : 0 H I G W A H W Y Notes: Sites circled white are USC scat dog records AY Tewantin Cooroy B R U C E H D IG A

H O R c W A S A SUNRISE ROA D O Y NO N DI M U e U Koala Landscape Connectivity and D E A O R H RT O W IL BU N C 0 0.5 1 1.5 2 2.5 N D I K E K R EEK ROA D U B A C M Y YA NDIN U R M Corridors in the Noosa Biosphere E R A TH O R V N O A A L D D

O DAVID LO W WAY R A

A USC and Noosa Biosphere D E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig06_corridor_tiles_.mxd Kilometers 1:60,000 at A3 µ Map Projection: Transverse Mercator Potential corridor network Horizontal Datum: GDA 1994 Figure 9 - a Grid: GDA 1994 MGA Zone 56 truncated to 1k cost distance

30 Aug 2018 30 Woondum - Kin Kin

Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science) 495,000 500,000 505,000 7,110,000 7,110,000 GYMPIE

Toolara SF

Y A

A L O O L O

C 7,105,000 7,105,000

Great Sandy NP

WAY LA O O L O O C

Lake Cooloola

Cooloola (Noosa River) RR

BATES ROAD 7,100,000 7,100,000

A L S NOOSA Y

W Noosa River LO L A G

LAKE FLAT ROAD 7,095,000 7,095,000

D Lake Cootharaba A

H T Teewah O O C

Boreen

E IV R D O Z Z J A UNCTI O B N IS U R O O L AD

495,000 500,000 505,000 Legend

") OA D Koala Evidence 2000-2010 Major Watercourse LGA Boundary Potential Linkage KIN KI N R N E U S A V A KIN K IN ROAD L E

O DAVID LO W WAY R A

A USC and Noosa Biosphere D E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig06_corridor_tiles_.mxd Kilometers 1:60,000 at A3 µ Map Projection: Transverse Mercator Potential corridor network Horizontal Datum: GDA 1994 Figure 9 - b Grid: GDA 1994 MGA Zone 56 truncated to 1k cost distance

30 Aug 2018 30 Cootharaba

G R E E NR IDG E P INB ARREN ROAD Cooran Pinbarren E IV R D O Z A Z IS B Six Mile Creek CP L OU

A Ringtail SF

AD T O I POUND ROAD R M 7,085,000 EK 7,085,000 RE M COLES C U S Yurol SF Pomona

HILL STREET

YURO L FO RE Tuchekoi NP ST D RI VE

RESERVE STREET AD RO N Tewantin NP IO T C E N N O

N D O A

M O

O R

P O R L D E B E R U N CE AY O HIGHW I P BRUCE HIGHWAY ON RAMP

M ID D E LE LM ST C RE R E E E IV T EK R R C IE D O NOOSA UDG E R A D 7,080,000 7,080,000 E IV R D D AL ON D C A

E K Y R A R IVER L MA ROA D Cooroy AD O R IN BRUCE HIGHWAY NTA BLACK MOU MYALL STREET

MAPLE STREET

BRUCE HIGHWAY D O A E R ILL LAWNV GYMPIE

D OA K R E K N E IL E W R C D 7,075,000 O I 7,075,000 L A R L T O BE H Y R SK O N Y OR O R O L I C Y N E G C C D R L E E O K R O AD

Tuchekoi CP

S K Y AD ary R R O M iv I R e N H r G T OR C ILW R I KEN E West Cooroy SF UND E UM K E R O

D SUNSHINE COAST Mount Eerwah CP 7,070,000 7,070,000

Mapleton NP

Mapleton FR 480,000 485,000 490,000 Legend

") OA D Koala Evidence 2000-2010 Major Watercourse LGA Boundary Potential Linkage KIN KI N R N E U S A V A KIN K IN ROAD L E

O DAVID LO W WAY R A

A USC and Noosa Biosphere D E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig06_corridor_tiles_.mxd Kilometers 1:60,000 at A3 µ Map Projection: Transverse Mercator Potential corridor network Horizontal Datum: GDA 1994 Figure 9 - c Grid: GDA 1994 MGA Zone 56 truncated to 1k cost distance

30 Aug 2018 30 West Cooroy - Pomona

A O Teewah R A B

H T Boreen O O C

Lake Cootharaba

JU NCTI O N R O AD Great Sandy NP Noosa River

Cooloothin CP 7,090,000 7,090,000

E IV R D O Z Z BA I S U E LO V

N I K C M

Ringtail SF

Great Sandy RR

Lake Cooroibah 7,085,000 7,085,000 NOOSA

Yurol SF Cooroibah CP

Tewantin NP

Harry Spring CP Sheep Island CP

POINCIANA AVENUE Tewantin

Goat Island (Noosa River) CP E V I

D Noosaville Doonella Lake D L GIB A CO GOODCHAP STREET SON N O ROADKeyser Island CP RO O Y 7,080,000 NOO 7,080,000 D SA ROA C D A B M E E C K K LA M AN S ROAD BECKMANS ROAD STREET REEF

AD EENIE CREEK RO AD O R A S Cooroy O O Noosa NP N I D N U

U MYALL STREET

D Y A H R BRUCE HIGHWAY Mount Cooroy CP E T L A W NA NDR WUST ROAD OYA RO WUST ROAD B A R D D B U A E C O D E E R H RIS D Lake Weyba N D IG U I H S N A G W O A T R O Y A N S E SUNSHINECOAST R O

7,075,000 U SUNSHINECOAST 7,075,000 M O O U N N A I DI D RA ND NG U E ROAD M EU

495,000 500,000 505,000 Legend

") OA D Koala Evidence 2000-2010 Major Watercourse LGA Boundary Potential Linkage KIN KI N R N E U S A V A KIN K IN ROAD L E

O DAVID LO W WAY R A

A USC and Noosa Biosphere D E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig06_corridor_tiles_.mxd Kilometers 1:60,000 at A3 µ Map Projection: Transverse Mercator Potential corridor network Horizontal Datum: GDA 1994 Figure 9 - d Grid: GDA 1994 MGA Zone 56 truncated to 1k cost distance

30 Aug 2018 30 Tewantin

Great Sandy NP

D A O M R C K RK IN A N Sheep Island CP P O N Noosa Heads DR IVE

POINCIANA AVENUE DE Tewantin A R

A Goat Island (Noosa River) CP S O H r NO I Noosa Rive LT Doonella Lake ON TERR ACE TE Noosaville NOOSA DRIVE MPIE RRACE GY MARY STREET WEYBA ROAD GIBSON ROAD Keyser Island CP BRYAN STREET GIBSON ROAD 7,080,000 GOODCHAP STREET 7,080,000

Weyba Creek CP

L E HEATHLAND DRIVE LANGURA STREET S K ROAD L E IE E Sunshine Beach R WALTER HAY DRIVE D R C IV E BE ANS E I CKM RO N A E D E EENI E CREEK ROAD AD EENIE AD LINKS DRIVE RO C R K RO SA EE O O N I Tewantin NP D N U M U E Y

NOOSA W

V I R D Y A H R E T L A W

Lake Weyba 7,075,000 7,075,000 Noosa NP

W O L ID V SUNSHINE COAST A SUNSHINE COAST D E M U M O U N T A IN A R T E R IA L R O A D

E MU M Peregian Beach OU DOONAN NT B AI VERRIERDALE ROAD RI N DG T AR E R EAS T OAD E R IA L R O A D

505,000 510,000 Legend

") OA D Koala Evidence 2000-2010 Major Watercourse LGA Boundary Potential Linkage KIN KI N R N E U S A V A KIN K IN ROAD L E

O DAVID LO W WAY R A

A USC and Noosa Biosphere D E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig06_corridor_tiles_.mxd Kilometers 1:40,000 at A3 µ Map Projection: Transverse Mercator Potential corridor network Horizontal Datum: GDA 1994 Figure 9 - e Grid: GDA 1994 MGA Zone 56 truncated to 1k cost distance

30 Aug 2018 30 Noosa Heads - Peregian Beach

Great Sandy NP 7,110,000 7,110,000

Toolara SF AD RO IN KIN K

Goomboorian NP KIN KIN ROAD

Lake Cooloola Cooloola (Noosa River) RR

D A O R IN N K KI IE P M Y GYMPIE G Woondum SF Kin Kin Lake Cootharaba 7,095,000 7,095,000 Noosa River Teewah Boreen Mary River Traveston SF Woondum NP

NOOSA Cooloothin CP Traveston Mount Pinbarren NP IVE Dagun DR O MEDDLETON ROAD Woondum CP LOUIS BA ZZ

Cooran Pinbarren Ringtail SF Great Sandy RR Amamoor Six Mile Creek CP

O L D Lake Cooroibah B R U Pomona M C Yurol SF E C H K I I G NN Cooroibah CP H Tuchekoi NP O ek W N IVE re A D R a C Y ng nda Tewantin NP Ka Noosa Heads Kandanga Harry Spring CP Tewantin BRUCE EL HIG M H ST Noosaville W RE D A E OROY OA Doonella Lake Y T CO NOOSA R Weyba Creek CP 7,080,000 Sunshine Beach 7,080,000 D OA M R Cooroy A a S E r K O y E BRUCE HIGHWAY IV Y e k O ba Cr e N A b D I R a A R L N W D W Y O iv I R e O D Y I W T r A U O R N C K H O HE T U H Mount Cooroy CP R L R M D C E E S D I E U T I A L K E K L R L E OA U E V O Y B D M A E Y U A R R O N M Lake Weyba R D W D I O I U Y N O R E C AN G G M L E R Noosa NP L O O A C A R D D U V A E O N Y E R K T A A R R S A O I O Tuchekoi CP O N M A I N A D D R N T EUM U E R I AL RO West Cooroy SF Eumundi AD Peregian Beach D Imbil SF 1 A Brooloo O Mount Eerwah CP R H T R O B

W U IL N N KE Y DI A UN R EU M O A D North Arm Eumundi CP Noosa RR Noosa CP Mapleton FR AD O SUNSHINE COAST R Coolum Beach M U OL CO A 7,065,000 IN 7,065,000 D N A

Y Coolum Creek CP

Mapleton NP arooc M h y R SUNSHINE MOTORWAY iv e

r Marcoola Mount Coolum NP 475,000 490,000 505,000 Legend

Railway Highway Lake Current flow centrality (linkage) 256 - 350 Core centrality value 634 - 1086 566 - 1469 (high) 1814 - 3115 (high) Road Network Secondary Road Protected Area Estate 165 - 255 394 - 633 Freeway/ Motorway 351 - 565 1087 - 1813 Local Connector Road LGA Boundary 37 - 164 (low) 124 - 393 (low)

Major Watercourse LGA 10km buffer

Koala Landscape Connectivity and 0 0.5 1 2 3 4 5 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig07_centrality.mxd Kilometers USC and Noosa Biosphere

1:170,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Koala node centrality value and Grid: GDA 1994 MGA Zone 56 10 26 Aug 2018 26 linkage centrality flow values Figure Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science)

Quality of linkages

Figure 11 represents the quality of least cost paths using the ‘Cost weight distance (CWD) to Euclidean distance ratio’. This ratio indicates how difficult it is to move between habitat nodes relative to how close they are. For the highest possible quality linkage, the cost-weighted distance is equivalent to Euclidean distance, and the ratio is 1.

Noosa Biosphere Reserve Foundation Koala Report 49 | P a g e 475,000 490,000 505,000

N o os a Ri ve r

Great Sandy NP 7,110,000 7,110,000

Toolara SF AD RO IN KIN K

Goomboorian NP KIN KIN ROAD

Lake Cooloola Cooloola (Noosa River) RR

D A O R N KI IN GYMPIE K IE P M Y G Woondum SF Kin Kin Lake Cootharaba 7,095,000 7,095,000 Noosa River Teewah Boreen Mary River Traveston SF Woondum NP

NOOSA Cooloothin CP Traveston Mount Pinbarren NP IVE Dagun DR O MEDDLETON ROAD Woondum CP LOUIS BA Z Z

Cooran Pinbarren Ringtail SF Great Sandy RR Amamoor Six Mile Creek CP

O L D Lake Cooroibah B R U Pomona M C Yurol SF E C k H K e I I re G NN Cooroibah CP H Tuchekoi NP O a C W N IVE ng A DR nda Y Ka Tewantin NP Kandanga Harry Spring CP Noosa Heads Tewantin BRUCE EL HIG M H ST W R Noosaville E D A E OROY N OA Doonella Lake Y T CO OOSA R Weyba Creek CP 7,080,000 Sunshine Beach 7,080,000 D OA M R Cooroy A a S E r K O y E BRUCE HIGHWAY IV ba reek N O b C D R a A R IL N W D Y O iv I R e O I D Y r A T O R N UC K H HE T U H Mount Cooroy CP R R M D C E E S I E U T A L K K L R E L E O U E A E O B D M A DAVID LOW WAY Y Y U R R O N M Lake Weyba R D W I O I U Y N O R E C AN G G M L E R Noosa NP L O O A C A R D D U V A E O N Y E R K T A A R R S A O I O Tuchekoi CP O N M A I N A D D R N T EUM U E R I AL RO West Cooroy SF Eumundi AD Peregian Beach D Imbil SF 1 A Brooloo O Mount Eerwah CP R H T R O B

W U IL N N DAVID LOW WAY KE Y DI A UN R EU M O A D North Arm Eumundi CP Noosa RR Noosa CP SUNSHINE COAST Mapleton FR AD O R Coolum Beach M LU O CO A 7,065,000 IN 7,065,000 D N A

Y Coolum Creek CP

Mapleton NP Mount Coolum NP

475,000 490,000 505,000 Legend

Railway Highway Lake Core habitat (over 50 hectares) 2-2.5

Road Network Secondary Road Protected Area Estate Linkage quality (CW to Euc Dist Ratio) 2.5-3 Freeway/ Motorway 1-1.5 (high quality) Local Connector Road LGA Boundary 3-3.5 1.5-2 Major Watercourse LGA 10km buffer >3.5

Koala Landscape Connectivity and 0 0.5 1 2 3 4 5 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig08_linkage quality_cw_euc_ratio.mxd Kilometers USC and Noosa Biosphere

1:170,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Linkage quality - Cost weight to Grid: GDA 1994 MGA Zone 56 11 26 Aug 2018 26 Euclidean Distance Ratio Figure Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science)

Figure 12 represents the quality of least cost paths using the ‘Cost weight distance (CWD) to least cost path ratio’, which tells you how many times more difficult it is for the koala to traverse the path as compared to the ideal path where the cost is the same as the true distance. It gives the average resistance encountered along the optimal path between habitat nodes. Higher values represented in red indicate higher cost of movement along the path of least resistance and lower values represented in green indicate higher quality of linkages along the least-cost path.

Quality of linkages are high amongst the three centrality areas mentioned previously as connectivity is relatively high and resistances are minimal.

Linkages in the Noosa Heads region is of poor quality which is most likely attributed to limited vegetation connectivity and energetic costs of moving through built-up areas and across main roads. Threats from vehicle strike and dog attacks are high when koalas are moving through these areas. Lake Weyba river system also creates a water barrier for koala movement although koalas are known to use the Weyba pedestrian footbridge (to what extent is unknown).

Linkages are poor between koala nodes either side of the estuary, the Noosa River and associated lakes (i.e. Lake Cootharaba and Lake Cooroibah. There is limited information on the koala population east of the Noosa River System.

Other poor-quality linkages are generally found to the south and west of the Noosa LGA where nodes are far apart, and permeability is reduced due a combination of limited koala habitat vegetation cover, agricultural areas, transport networks (i.e. the Bruce Highway) and large rivers (i.e. the Mary River).

Quality of linkages will be further investigated by field surveys using koala scat detection dogs.

Noosa Biosphere Reserve Foundation Koala Report 51 | P a g e 475,000 490,000 505,000

N o os a Ri ve r

Great Sandy NP 7,110,000 7,110,000

Toolara SF AD RO IN KIN K

Goomboorian NP KIN KIN ROAD

Lake Cooloola Cooloola (Noosa River) RR

D A O R N KI IN GYMPIEGYMPIE K IE P M Y G Woondum SF Kin Kin Lake Cootharaba 7,095,000 7,095,000 Noosa River Teewah Boreen Mary River Traveston SF Woondum NP

NOOSA Cooloothin CP Traveston Dagun Mount Pinbarren NP Woondum CP MEDDLETON ROAD IVE R NOOSA D NOOSA Cooran Pinbarren O ZZ Ringtail SF BA Great Sandy RR S UI Amamoor Six Mile Creek CP LO

Y Coolum Creek CP

Mapleton NP Mount Coolum NP

475,000 490,000 505,000 Legend

Railway Highway Lake LGA 10km buffer Linkage quality (Cwd to path length ratio) 2.5-3 1-1.5 (high) Road Network Secondary Road Protected Area Estate LGA Boundary 3-3.5 Freeway/ Motorway 1.5-2 Local Connector Road Core habitat (over 50 hectares) >3.5 2-2.5 Major Watercourse

Koala Landscape Connectivity and 0 0.5 1 2 3 4 5 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig09_linkage quality_cwd_pathlength_ratio.mxd Kilometers USC and Noosa Biosphere

1:170,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Linkage quality - Grid: GDA 1994 MGA Zone 56 12 25 Aug 2018 25 CWD to Path Length Ratio Figure Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science)

Pinch points

Pinch Point Mapper was performed using a cost weighted ‘width’ cut-off of 300m for adjacent pairs of nodes (Figure 13) to identify pinch points between all nodes.

Pinch points are found throughout linkages in the Noosa Heads region (namely along Noosa Drive, Cooyar Street and across Eenie Creek Road). Other pinch points are found in linkages between Woondum NP and Lake Cootharaba where connectivity of vegetation containing koala preferred trees is limited.

Global pinch points were also identified for the entire network of core areas and corridors (Figure 14). This provides a current flow centrality measure to evaluate the importance of linkages and pinch points for maintaining connectivity in the entire landscape and is an additional measure of centrality that complements pairwise pinch point analysis.

Weyba Creek and the passageway under the western side of Monks Bridge also present barriers to movement but are important in keeping koala nodes in the Noosa Heads region connected to the entire corridor network.

Global pinch points are also found linking the three important centrality core areas i.e. on the southern end of Lake Cootharaba, linkages between Woondum National Park and Tuchekoi National Park, and West Cooroy State Forest to the southern section of Yurol State Forest.

Noosa Biosphere Reserve Foundation Koala Report 53 | P a g e 475,000 490,000 505,000

N o os a R

r Great Sandy NP

Sheep Island CP Noosa Heads Lake Cooloomera

E N AD R Noosa NP oo A s a P R ive r A r e OS Riv O Noosa N Noosaville

NOOSA DRIVE

Keyser Island CP Great Sandy NP Weyba Creek CP Tinan a EK ROAD C E Sunshine Beach ) r R ) e C D e E A GYMPIE k I ) V N I E D D EENIE E 7,110,000 C 7,110,000 A ) REEK ROAD L EENIE CREEK RO ) O ) W W Toolara SF A AD Y RO IN KIN K ) )

Lake Weyba Goomboorian NP KIN KIN ROAD )WALTER HAY DRIVE

Lake Cooloola Cooloola (Noosa River) RR

D A O R IN N K KI E I P M Y G Woondum SF Kin Kin Lake Cootharaba 7,095,000 7,095,000

Noosa River Teewah Boreen Traveston SF Woondum NP

NOOSA Cooloothin CP Traveston Mount Pinbarren NP IVE DR O Woondum CP LOUIS BA Z Z

Cooran Pinbarren Ringtail SF Great Sandy RR Six Mile Creek CP

O L Lake Cooroibah da D an ng B K a R Cr U Pomona Yurol SF ee C k E M H C I G K Cooroibah CP H Tuchekoi NP IN W NO IVE A N DR Y Tewantin NP Harry Spring CP Noosa Heads BRU CE HIGH Tewantin er WA EL iv Y M B R S E ary TR Noosaville E RO D C M E OO Y OA K Doonella Lake T C NOOSA R ) M AN Weyba Creek CP 7,080,000 S Sunshine Beach 7,080,000 RO ) ) AD ) ) Cooroy )) E V k I abba Cre e BRUCE HIGHWAY D R Y A ) O )D R D I Y A T O A U K H O C HE K Mount Cooroy CP R R ) E RE E Lake Weyba N I C E Y L T I L K L E L E RO E B A U A L W Y D O M E DAVID LOW WAY L O U W R M A O N R O D V C I U T RA Y H N Noosa NP GE M R RO A S A O K D U M Y R N IN T G A I C Tuchekoi CP N R AD A E RO SUNSHINE COAST R E H T K T E R OR R O ILW IA A EN L D I K RO ND Eumundi A West Cooroy SF U D Peregian Beach M U Eumundi CP Brooloo E Mount Eerwah CP

Mapleton NP Mapleton FR 475,000 490,000 505,000 Legend ) Possible passageway Highway Major Watercourse LGA Boundary Pinch points (adjacent pairs) 0.02 - 0.05 0.08-1 (Critical pinch point) Railway Secondary Road Lake LGA 10km buffer 0 - 0.02 0.05 - 0.08 Road Network Local Connector Road Protected Area Estate Core habitat (over 50 hectares) None

Freeway/ Motorway

Koala Landscape Connectivity and 0 1 2 3 4 5 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig010_pinchpoints_adj_pairs_.mxd Kilometers USC and Noosa Biosphere

1:160,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Pinch points - Grid: GDA 1994 MGA Zone 56 3 15 Oct 15 2018 adjacent pairs (nodes) Figure 1 Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science) 475,000 490,000 505,000

Great Sandy NP Sheep Island CP Noosa Heads Lake Cooloomera E AD Noosa NP R N A o P o s a er SA Riv Riv O e Noosa NO r Noosaville

Keyser Island CP Great Sandy NP Weyba Creek CP

EK ROAD Tinan E a R Sunshine Beach C C E re I e GYMPIE EENIE k EEN GYMPIEAD CREEK ROAD EENIE C RO 7,110,000 REEK 7,110,000

Toolara SF

AD Y RO A

IN W

KIN K W E O V L

D DRI I Y V A A D H

T L Lake Weyba A KIN KIN ROAD W Goomboorian NP

Lake Cooloola Cooloola (Noosa River) RR

D A O R IN N K KI E I P M Y G Woondum SF Kin Kin Lake Cootharaba 7,095,000 7,095,000

Noosa River Teewah Boreen Traveston SF Woondum NP

NOOSA Cooloothin CP Traveston Mount Pinbarren NP IVE DR O Woondum CP LOUIS BAZ Z

Cooran Pinbarren Ringtail SF Great Sandy RR Six Mile Creek CP

O L Lake Cooroibah da D an ng B K a C R r U Pomona Yurol SF ee C k E M H I C G K Cooroibah CP H Tuchekoi NP IN W NO IVE A N DR Y Tewantin NP Harry Spring CP Noosa Heads BRU CE HIGH Tewantin er WA EL iv Y M B R S E ary TR Noosaville E D C M E OROY A K Doonella Lake T CO NOOSA R O M AN Weyba Creek CP 7,080,000 S Sunshine Beach 7,080,000 RO AD Cooroy E V k I bba Cree BRUCE HIGHWAY a D R Y A O D I R Y D T O A A U K H C HE K O R E Mount Cooroy CP R CRE E Lake Weyba N LI EK T Y I L L L E R E E OA A W Y B D U L O M E DAVID LOW WAY O U W R M L O ND A R O C I U V T RA H N Noosa NP Y GE M RO R S A O A K Y D U M R N IN T G A I C Tuchekoi CP N R AD A E RO SUNSHINE COAST R E T K TH E R OR R O ILW IA A EN L D I K RO ND Eumundi AD Peregian Beach West Cooroy SF U M U Eumundi CP Brooloo E Mount Eerwah CP

Mapleton NP Mapleton FR 475,000 490,000 505,000 Legend

Railway Highway Lake Core habitat (over 50 hectares)

Road Network Secondary Road Protected Area Estate Pinch points (one to all centrality) High : 856 (critical global pinch point) Freeway/ Motorway Local Connector Road LGA Boundary

Major Watercourse LGA 10km buffer Low : 0

Koala Landscape Connectivity and 0 0.5 1 2 3 4 5 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig011_pinchpoints_centrality_.mxd Kilometers USC and Noosa Biosphere

1:160,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Global Pinch points - Grid: GDA 1994 MGA Zone 56 4 15 Oct 15 2018 All to one - centrality Figure 1 Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science)

Overall relative conservation priority

Figure 15 represents the relative conservation priority in the landscape. Dark green corridors represent important areas for conserving and are highlighted for field surveys discussed in the next section. Corridors linking the 3 important node regions have high priorities as well as linkages from Noosa Heads. Corridors linking nodes from the south and west of Noosa LGA

(such as Mapleton Forest Reserve and West Cooroy SF) have less priority.

Noosa Biosphere Reserve Foundation Koala Report 56 | P a g e 475,000 490,000 505,000

Lake Cooloomera Great Sandy NP N oo sa Riv e r Sheep Island CP Noosa Heads DE RA PA Tewantin A Noosa NP S r O ve O Noosa Ri N Great Sandy NP Noosaville Doonella Lake Keyser Island CP Weyba Creek CP Sunshine Beach

7,110,000 EEN 7,110,000 IE CREEK ROAD Tewantin NP Toolara SF AD RO IN KIN K E IV R D

Y A H

R E Insert 1 - T Lake Weyba L Noosa Heads A DAVID LOW WAY KIN KIN ROAD W GoomboorianRegion NP

Lake Cooloola Cooloola (Noosa River) RR

D A O R N KI IN GYMPIE K IE P M Y G Woondum SF Kin Kin Lake Cootharaba 7,095,000 7,095,000 Noosa River Teewah Boreen Mary River Traveston SF Woondum NP

NOOSA Cooloothin CP Traveston Dagun Mount Pinbarren NP RIVE O D BAZZ MEDDLETON ROAD Woondum CP LOU IS

Cooran Pinbarren Ringtail SF Great Sandy RR Amamoor Six Mile Creek CP

O L D Lake Cooroibah B M R U Pomona C C Yurol SF K E I H N I N Cooroibah CP G ON H Tuchekoi NP D eek W R IVE Inset 1 a Cr A ng Y da Tewantin NP an K Kandanga UCE H Noosa Heads BR IGH Harry Spring CP WAY Tewantin BRUCE EL HIG M H ST Noosaville W RE O OAD A E OR Y N OOSA R Doonella Lake Y T CO Weyba Creek CP

7,080,000 Sunshine Beach 7,080,000 AD RO M Cooroy A a S

r O Y y K ba Creek E O A b D N N A R I W a L I Y O i W R v D e W I O NANDROYA ROAD T O r N U O R C K U HE L T D Mount Cooroy CP M H R C E D A I LI EK U O S L R V E OA E K B D E R Y A Y M O WALTER HAY DRIVE Lake Weyba D Y R R O U E I O L N C M Noosa NP L G A D O V C A R O U N Y E R R E A T K S A A O R O IN M Tuchekoi CP O I N A D A D N R EUMU T E R IAL Eumundi RO Peregian Beach West Cooroy SF D A Imbil SF 1 A D O Brooloo R Mount Eerwah CP H T R O B W U IL N N Y E I K A ND R EUM U O A D North Arm Eumundi CP Noosa RR Noosa CP Mapleton FR AD SUNSHINE COAST O DAVID LOW WAY SUNSHINE COAST R Coolum Beach M LU O CO A

7,065,000 IN 7,065,000 D N A

Y Coolum Creek CP

Mapleton NP roo a ch M y Ri ve

r Marcoola Mount Coolum NP 475,000 490,000 505,000 Legend

Railway Highway Lake Core habitat (over 50 hectares)

Road Network Secondary Road Protected Area Estate Relative Conservation Priority High : 1 Freeway/ Motorway Local Connector Road LGA Boundary

Major Watercourse LGA 10km buffer Low : 0

Koala Landscape Connectivity and 0 1 2 3 4 5 Corridors in the Noosa Biosphere

E:\sjc\jobs\corr_biosphere\GIS\Maps\Working(Optional)\final\fig014_priority_.mxd Kilometers USC and Noosa Biosphere

1:180,000 at A3 µ Map Projection: Transverse Mercator Horizontal Datum: GDA 1994 Relative Grid: GDA 1994 MGA Zone 56 5 27 Aug 2018 27 Conservation Priority Figure 1 Data source: GRC:Koala habitat mapping (2015), DES: Koala Planning areas (2010 for SCC area only), Protected Areas of Qld (May 2018). DNRME: Baseline Roads and Tracks (June 2018), Lakes - Qld (2014), Major Watercourse lines (2014), LGA Boundaries (July 2018), © State of Queensland (Department of Natural Resources and Mines, Department of Environment and Science)

4.2. Scat sampling

Between September 2018 and July 2019, a total of 186 koala scat surveys were conducted. Of these, 101 sites were positive for (fresh) scats. The remaining 85 survey sites were negative and no scats were found. Figure 16 and Figure 17 show the results of the presence/ absence surveys.

Noosa Biosphere Reserve Foundation Koala Report 58 | P a g e

Figure 16: Presence absence surveys conducted in Noosa (note that all surveys between 2015 and 2020 are included here)

Noosa Biosphere Reserve Foundation Koala Report 59 | P a g e

Figure 17: Presence absence surveys conducted in the Yurol / Ringtail area of Noosa (note that all surveys between 2015 and 2020 are included here)

Noosa Biosphere Reserve Foundation Koala Report 60 | P a g e

During the surveys, a total of 174 fresh scats were collected for genetic analyses. An additional 28 samples from surveys conducted in 2017 were added to further boost the sample size as well as spread across the landscape. Therefore, a total of 202 samples were processed for genotyping. Refer to Figure 18.

Noosa Biosphere Reserve Foundation Koala Report 61 | P a g e

Figure 18: All genetic samples in Noosa (note that all surveys between 2015 and 2020 are included here)

Noosa Biosphere Reserve Foundation Koala Report 62 | P a g e

4.3. Genetic analyses

We extracted DNA from all 202 samples. A total of 10 extractions failed due to poor sample quality. The remaining samples were genotyped, further filtered for quality and after duplicate identification, a total of 124 unique individuals were identified. After data filtering for SNP quality, 1076 SNPs were retained for subsequent analysis.

4.3.1. Population genetic structure using fastSTRUCTURE

The results of the analysis of population genetic structure with fastSTRUCTURE indicate that the sampled individuals likely belong to two ancestral populations. Figure 19 shows a graph of a range of hypothetical numbers of populations that were tested; the peak of the line marks the most likely number of populations, given our data.

Number of tested populations -0.862 0123456 -0.864

-0.866

-0.868

-0.87

-0.872

-0.874

-0.876

-0.878

Figure 19: The most likely number of populations of koalas in Noosa, given our data, is two. This is indicated by the peak of the curve that presents likelihoods that were calculated through the population genetic structure program fastSTRUCTURE.

Noosa Biosphere Reserve Foundation Koala Report 63 | P a g e

We then mapped the individuals and visualised their admixture as extracted from the fastSTRUCTURE results. Figure 20 shows the distribution of individuals and the proportion of their genetic identity associated with each of the two ancestral populations.

Figure 20: Map representing the degree of belonging of each koala to two ancestral populations

4.3.2. sPCA

The eigenvalue plot (Figure 21) and screeplot (Figure 22) of the sPCA revealed that the first four global components could play an important role. The permutation tests on the sPCA indicated significant global structure but no local structure. The first four global principal

Noosa Biosphere Reserve Foundation Koala Report 64 | P a g e

components were tested separately and were all found to be significant. No significant local structure was identified. The sPCA plots of the first four global principal components are shown in Figure 21. All four plots show distinct clines across the landscape. The plot of the first PC strongly resembles the results of fastSTRUCTURE genetic structure analysis, with a differentiation between individuals in the eastern part of Noosa and the koalas west of Tewantin. The following PCs are less strong yet still significant. The plot of PC2 shows a cline between koalas in the centre of Noosa and Woondum as well as West Cooroy. The third PC shows differentiation between koalas in Woondum and West Cooroy and the plot of PC four indicates a cline between koalas around Tewantin/Doonan and the surrounding locations. The colour plot of the first three components combined showed a division of the koala population into four clusters (Figure 23).

Figure 21: Significant principal components of an sPCA are indicated by large but sharply dropping eigenvalues. Here, the first four global eigenvalues, presented in red, show steep declines and indicate that the first four principal components are significant.

Noosa Biosphere Reserve Foundation Koala Report 65 | P a g e

A B

C D

Figure 22: The first four global principal components are significant and here we mapped the lagged scores of these principal components, which shows important cryptic structure in the Noosa Koala populations. Each square represents an individual koala and large black squares are well differentiated from large white squares but small squares indicate weaker differentiation. The first principal component (A) shows similar results to the fastSTRUCTURE output of population genetic structure for the most likely number of populations - two. This would be the strongest differentiation, followed by less strong – yet significant cryptic global structure (B, C and D).

Noosa Biosphere Reserve Foundation Koala Report 66 | P a g e

Figure 23: Cryptic structure of the koalas in the Noosa Biosphere

4.3.3. Assessing connectivity between patches using FST and assessing general diversity within and amongst clusters

Based on the previous results, individuals were divided into four clusters: Cluster 1 includes 45 koalas in the urban and peri-urban area of Noosa Heads and Tewantin; Cluster 2 includes 42 koalas of Yurol and Ringtail State Forest and Ringtail Forest Reserve; Cluster 3 includes 21 koalas found mainly in Woondum National Park and Cluster 4 includes 16 individuals sampled in West Cooroy State Forest and Tuchekoi. The Mantel test showed a significant (P = 0.013) but weak (Mantels correlation coefficient rxy = 0.093) relationship between the geographic and genetic distance of the koalas across all clusters, indicating that the geographic distance between individuals does only weakly explain genetic differentiation (refer to Figure 24).

Noosa Biosphere Reserve Foundation Koala Report 67 | P a g e

GGD vs GD 3500.000

3000.000

2500.000

2000.000

1500.000 Genetic DIstance DIstance Genetic

1000.000

500.000 y = 0.0068x + 1780.7 R² = 0.0087 0.000 0 5000 10000 15000 20000 25000 30000 35000 40000 Geographic Distance (in meters)

Figure 24: Relationship between the geographic and genetic distance of the koalas across all clusters

Pairwise FST values were significant (P = 0.01) and ranged from 0.006 to 0.026, pairwise F’ST values ranged from 0.009 to 0.042; a full output is given in Table 6. Only two percent (2%) of the molecular variance was attributed to occur among populations, 29% among individuals and the majority, 69%, within individuals. Heterozygosity and FIS varied amongst clusters with higher FIS and lower HO values found in koalas of the urban cluster 1 than in the other clusters (Table 7).

Noosa Biosphere Reserve Foundation Koala Report 68 | P a g e

Table 6: AMOVA results of pairwise FST values and F’ST corrected for small sample size after Meirmans (2006) of four clusters of koalas in Noosa Shire, Queensland. Cluster 1 includes 45 koalas in the urban and peri-urban area of Noosa Heads and Tewantin; Cluster 2 includes 42 koalas of Yurol and Ringtail State Forest and Ringtail Forest Reserve; Cluster 3 includes 21 koalas found mainly in Woondum National Park and Cluster 4 includes 16 individuals sampled in West Cooroy State Forest and Tuchekoi.

FST 1 2 3 4

1 0.000

2 0.012 0.000

3 0.023 0.011 0.000 4 0.026 0.021 0.006 0.000

F’ST 1 2 3 4

1 0.000

2 0.020 0.000

3 0.036 0.018 0.000 4 0.042 0.034 0.009 0.000

Table 7: Heterozygosity and FIS

Clusters HO ± SD HE ± SD FIS 1 0.217 ± 0.003 0.366 ± 0.003 0.41 2 0.230 ± 0.003 0.366 ± 0.003 0.37 3 0.286 ± 0.004 0.362 ± 0.004 0.21 4 0.259 ± 0.004 0.348 ± 0.004 0.26 ALL 0.248 ± 0.002 0.361 ± 0.002 0.31

Noosa Biosphere Reserve Foundation Koala Report 69 | P a g e

5. Discussion

Determining already established vegetation corridors and nodes

Geographic information system (GIS) analysis of the Noosa Biosphere vegetation landscape was used to identify vegetation corridors and nodes. The GIS mapping identified that there were a total of 125 koala habitat nodes within the Noosa LGA and 10km beyond; 71 nodes are within the Noosa LGA and 54 nodes are within 10km of the NSC LGA boundary. A total of 243 linkages were identified between different pairs of koala habitat nodes across the landscape in the Noosa Region and beyond, of which 135 are within the Noosa LGA.

Three high value vegetation areas (made of multiple nodes) were identified, located within Yurol State Forest, Tewantin National Park and Harry Spring Conservation Park; Woondum National Park; and Toolara State Forest and Great Sandy National Park. A fourth high value vegetation area was identified in the peri urban area of Noosa Heads and Tewantin.

Identifying key connected koala populations

Working with detection dogs trained to detect koala scats, we performed surveys targeting the high value areas identified through the GIS mapping. A total of 202 scat samples were analysed for DNA, from which 125 individuals were identified. The surveys confirmed that koalas used some of the high value areas, with individuals divided as follows: Cluster 1 in the urban and peri-urban area of Noosa Heads and Tewantin (45 koalas); Cluster 2 in Yurol and Ringtail State Forest and Ringtail Forest Reserve (42 koalas); Cluster 3 mainly in Woondum National Park (21 koalas), additionally, Cluster 4 in West Cooroy State Forest and Tuchekoi (16 koalas). Toolara State Forest and Great Sandy National Park were not confirmed as high value areas in term of koala use, but one should note that the remoteness and difficult access also make extensive surveys of the area logistically difficult.

The GIS analysis showed that connectivity or linkage between the three high value areas (Yurol State Forest, Tewantin National Park and Harry Spring Conservation Park; Woondum National Park; and Toolara State Forest and Great Sandy National Park) was of a high quality. The

Noosa Biosphere Reserve Foundation Koala Report 70 | P a g e

genetic results supported the GIS analyses that the connectivity existed both for the vegetation and for the koalas, as the three high value areas were pooled together as one breeding population.

Identifying key locations where koala and vegetation connectivity needs to be recovered

The GIS analysis showed that, in comparison to the three high value areas, linkage in the Noosa Heads region is of poor quality which is most likely attributed to limited vegetation connectivity the built landscape as well as threats from vehicle strike and dog attacks. Again, the genetic data supports the observed lack of vegetation connectivity. The genetic analysis identified that there is likely to be two koala populations within the Biosphere. Indeed, although connectivity was confirmed between Cluster 2 - Yurol and Ringtail State Forest and Ringtail Forest Reserve; Cluster 3 - Woondum National Park and Cluster 4 - West Cooroy State Forest and Tuchekoi, very low connectivity could be found between these clusters and Cluster 1 - Noosa Heads and Tewantin. The identified two distinct ancestor populations might be due to either the population having different origins and restricted geneflows between them, of the restricted gene flow having enabled for population differentiation through for example a bottleneck and genetic drift in the smaller Noosa Head population.

Global pinch points, which present barriers to koala movement within the landscape, were identified for the entire network of core areas and corridors within the Noosa Biosphere. Global pinch points are candidates for immediate conservation actions, because the loss of them can lead to a collapse of connectivity in habitat networks (Dutta et al. 2016).

Global pinch points where connectivity improvement could be prioritised are:

 Weyba Creek and the passageway under the western side of Monks Bridge, as this barrier between the Noosa Heads region and the rest of the Biosphere region is preventing gene flow (as seen with the genetic structure).  pinch points between Woondum National Park and Tuchekoi National Park, and West Cooroy State Forest to the southern section of Yurol State Forest are also supported by

Noosa Biosphere Reserve Foundation Koala Report 71 | P a g e

the identification of cryptic genetic structure between genetic clusters in these nodes. This might reflect that gene flow is insufficient to maintain genetic connectivity in the long term.

Recommendations:

 Study what mitigation infrastructures could be introduced to facilitate koala safe passage in the Weyba Creek / Monks Bridge area.  Undertake revegetation and conservation protection measures along high priority corridors and in key pinch point locations.  Consideration of incorporating koala movement corridors into the Noosa Trail or other networks to support the protection of these corridors whilst promoting outdoor and eco- tourism.

Identifying key locations suitable for sustainable wild koala eco-tourism and recreation

A review of the available data has been undertaken to identify potential eco- tourism hotspots. Initial discussions have been held with the Noosa Parks Association to determine potential eco- tourism locations. Broader consultation was impacted due to COVID-19 restrictions but will be completed when restrictions ease, and this report updated and reissued accordingly. A potential hotspot has been identified at Harry Springs Conservation Park as shown in Figure 25. In comparison to other locations, this area has a high density of koalas (refer to Figure 25); is within one of the high value habitat nodes. The site is situated away from existing tourism hotspots such as Noosa Heads and promotes a nature-based tourism option which is easily accessible to tourists. There are potential opportunities for collaboration with other stakeholders including the Department of Environment and Science to establish minimum impact walking trails. Car parking facilities and other amenities would need to be considered, especially due to the fact no clearing should be promoted in this high koala value area.

Noosa Biosphere Reserve Foundation Koala Report 72 | P a g e

Further data analysis will need to be undertaken to confirm the population characteristics of the koalas at this location and the connectivity in the landscape.

The last of our deliverables, “Development of an initial blueprint for a Noosa wild koalas eco- tourism and recreation strategy”, is a step that will heavily rely on consultation with QPWS, Noosa Council, Tourism Noosa, and USC, and any other appropriate stakeholders, and will also proceed once COVID restrictions allows it.

Recommendations:

 Once the eco-tourism location has been chosen, develop a strategy to involve all required partners to deliver low-impact infrastructure to facilitate access to wild koalas.  Develop an information trail for the koala walk. In particular, seek funding to pioneer new technology to monitor koalas passively via mobile phones, to enhance the tourism experience of seeing koalas in the wild. Such technology can not only inform tourists of koala whereabouts, but also enrich the experience by providing each koala history, and linking this to educational material. The experience is transformed into an interactive adventure. The community is also engaged to become the guardians of the koalas by being trained to monitor their health and breeding status.  Development of a web-based application to identify location of koalas and provide data for the community, council and other stakeholders and for use in eco-tourism for cycling, walking and other recreational activities.

Noosa Biosphere Reserve Foundation Koala Report 73 | P a g e

Figure 25 A potential location for eco-tourism at Harry Springs Conservation Park

Noosa Biosphere Reserve Foundation Koala Report 74 | P a g e

6. Acronyms and glossary

Allele: a variant of a gene. The size of an allele can vary in size (e.g. between one nucleotide to hundreds of nucleotides). At the population level, variation in alleles are used to estimate patterns of genetic diversity.

Na = the number of alleles found for one segment (in this report specifically, one specific SNP), averaged across all SNPs, per population;

Ne = the number of effective alleles, this measure is the number of equally frequent alleles it would take to achieve a given level of gene diversity. That is, it enables comparison of populations where the number and distributions of alleles differ.

DDC: Detection Dogs for Conservation at the University of the Sunshine Coast.

DNA: Deoxyribonucleic acid, a molecule carrying genetic information.

F-statistics (fixation index): is the basic method used to measure the amount of subdivision in populations, and consists of three measures, FIS, FST, and the less commonly used FIT. These measures relate to the amounts of heterozygosity at various levels of a population structure: individual (I), subpopulation (S) and total (T).

FST estimates the amount of structuring of a population into subpopulations, and can range from 0 to 1 (where 0 means complete sharing of genetic material and 1 means no sharing).

In this report, F’ST, the standardised FST (produced by dividing FST by the maximum value it can obtain, given the observed within-population diversity) was also calculated to enable comparisons of our results to other studies.

FIS, also called inbreeding coefficient, is the proportion of the variance in the subpopulation contained in an individual and can range from -1 to 1 (the closest to 1, the higher the degree of inbreeding). Note that inbreeding can not only result from non-random matings (matings between cousins for example), but also from small isolated populations, where all individuals are more closely related than large populations.

Noosa Biosphere Reserve Foundation Koala Report 75 | P a g e

Genotype: in diploid species (species with two sets of chromosomes - paternal and maternal copies), genotype is often used to refer to the particular pair of alleles that are carried by an individual. A genotype is described as homozygous if it features two identical alleles and as heterozygous if the two alleles differ. The process of determining a genotype is called genotyping.

Heterozygosity: refers to the presence of two different alleles within a diploid individual, here it refers to the presence of two different nucleotides at a specific SNP locus. Commonly, at the population level, two measures of average heterozygosity (calculated for all SNP loci and all individuals) are reported:

HO = observed heterozygosity, the calculated level of heterozygosity from the allele frequencies of the population under study,

HE = expected heterozygosity, the level of heterozygosity that could be expected based on observed allele frequencies if the population was at the Hardy-Weinberg equilibrium (HWE).

The comparison between observed and expected level of heterozygosity is a measure of interest:

 A lower observed heterozygosity compared to the expected heterozygosity can be a sign of inbreeding.  A higher observed heterozygosity compared to the expected heterozygosity can be due to the mixing of two previously isolated populations.

HWE: Hardy-Weinberg Equilibrium is a principle that is used to examine, based on observed genotype frequencies (see observed / expected heterozygosity), whether a population is experiencing forces such as natural selection, non-random mating, genetic drift, and gene flow. The Hardy-Weinberg Equilibrium states that in the absence of these forces, the genetic variation in a population will remain constant from one generation to the next. Therefore, if a population of interest is found not to be at the Hardy-Weinberg Equilibrium, underlying causes can be explored.

Noosa Biosphere Reserve Foundation Koala Report 76 | P a g e

I: Shannon's index mutual information, is commonly used to describe diversity at the genetic level because of its ability to be integrated and compared to community-level diversity data.

IR: Internal Relatedness is a measure of inbreeding at the individual level (as opposed to population level, such as FIS). It is calculated from heterozygosity data and does not require a pedigree (pedigrees are difficult to obtain in wild populations). Internal relatedness is currently the most widespread used index for inbreeding and its main strength is that allele frequencies are incorporated into the measure.

Inbreeding occurs when individuals are more likely to mate with relatives than with randomly chosen individuals in the population. Inbreeding increases the probability that offspring are homozygous, which can lead to lower fitness.

Locus (plural loci): refers to a specific position in the genetic material (such as in a chromosome), for example where a SNP is detected.

Microsatellite: refers to a part of the genome where many repetitions of a certain DNA motif (ranging in length from 1–6 or more base pairs) occur, typically 5 to 50 times. This part of the DNA is non-coding (also referred to as Junk DNA) and is highly variable (mutation rate is high). This makes microsatellites popular for estimating gene flow between populations, but not accurate for estimating genetic diversity or inbreeding. Genetic diversity and inbreeding are more accurately described by hundreds of genetic markers spread across the entire genome. This is to ensure genetic diversity be measured form genetic markers (e.g. single nucleotide polymorphisms, SNPs) which include both coded and non-coded part of the genome.

Nucleotide: A nucleotide is the basic structural unit and building block for DNA. These building blocks are hooked together to form a chain of DNA. There are four types of bases (part of DNA that stores information) in DNA. They are called: Adenine (A), Cytosine (C), Guanine (G) and Thymine (T).

PCR: Polymerase Chain Reaction, a technique in molecular genetics that permits the analysis of any short sequence of DNA even in samples containing only minute quantities of DNA, such as scats.

Noosa Biosphere Reserve Foundation Koala Report 77 | P a g e

QKC: Queensland Koala Crusaders Inc. qPCR (quantitative, or real-time, PCR): a molecular technique based on PCR (see above) that allows for the quantification of DNA in real time.

SD: Standard Deviation

SEQ: South East Queensland

SNP: Single Nucleotide Polymorphism is the most common type of genetic variation. Each SNP represents a difference in a single DNA building block, called a nucleotide (there are four nucleotides: A, C, T and G).

USC: University of the Sunshine Coast

Noosa Biosphere Reserve Foundation Koala Report 78 | P a g e

7. References

Bennett, A. 2003. Linkages in the landscape: the role of corridors and connectivity in wildlife conservation. IUCN, Gland, Switzerland and Cambridge, UK. 254 pp. Cristescu, R., V. Cahill, K. Handasyde, K. Carlyon, D. Whisson, G. Johnson, C. Herbert, A. N. Wilton, B. L. J. Carlsson, and D. Cooper. 2009. Inbreeding and testicular abnormalities in a bottlenecked population of koalas, Phascolarctos cinereus. Wildlife Research 36:299-308. Cristescu, R., K. Goethals, P. B. Banks, F. Carrick, and C. Frère. 2012. Persistence and detectability of fecal pellets in different environment and the implication for pellet based census of fauna. International Journal of Zoology 2012, Article ID 631856:doi:10.1155/2012/631856. Cristescu, R. H., E. Foley, A. Markula, G. Jackson, D. Jones, and C. Frere. 2015a. Accuracy and efficiency of detection dogs: a powerful new tool for koala conservation and management. Scientific Reports 5. Cristescu, R. H., E. Foley, A. Markula, G. Jackson, D. Jones, and C. Frère. 2015b. Accuracy and efficiency of detection dogs: a powerful new tool for koala conservation and management. Scientific Reports 5. Cristescu, R. H., R. L. Miller, and C. H. Frère. 2019. Sniffing out solutions to enhance conservation: How detection dogs can maximise research and management outcomes, through the example of koalas. Australian Zoologist 030:null. Dique, D. S., J. Thompson, H. J. Preece, D. L. de Villiers, and F. N. Carrick. 2003. Dispersal patterns in a regional koala population in south-east Queensland. Wildlife Research 30:281-290. Dutta, T., S. Sharma, B. H. McRae, P. S. Roy, and R. J. R. E. C. DeFries. 2016. Connecting the dots: mapping habitat connectivity for tigers in central India. 16:53-67. Epps, C. W., and N. Keyghobadi. 2015. Landscape genetics in a changing world: disentangling historical and contemporary influences and inferring change. Molecular Ecology 24:6021-6040. Gonzalez-Astudillo, V., R. Allavena, A. McKinnon, R. Larkin, and J. Henning. 2017. Decline causes of Koalas in South East Queensland, Australia: a 17-year retrospective study of mortality and morbidity. Scientific Reports 7:42587. Gruber, B., P. J. Unmack, O. F. Berry, and A. Georges. 2018. DARTR: An R package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Molecular Ecology Resources 18:691-699. Haddad, N. M., L. A. Brudvig, J. Clobert, K. F. Davies, A. Gonzalez, R. D. Holt, T. E. Lovejoy, J. O. Sexton, M. P. Austin, C. D. Collins, W. M. Cook, E. I. Damschen, R. M. Ewers, B. L. Foster, C. N. Jenkins, A. J. King, W. F. Laurance, D. J. Levey, C. R. Margules, B. A. Melbourne, A. O. Nicholls, J. L. Orrock, D.-X. Song, and J. R. Townshend. 2015.

Noosa Biosphere Reserve Foundation Koala Report 79 | P a g e

Habitat fragmentation and its lasting impact on Earth’s ecosystems. Science Advances 1:e1500052. Hess, G. R., R. A. J. L. Fischer, and u. planning. 2001. Communicating clearly about conservation corridors. 55:195-208. Jaccoud, D., K. Peng, D. Feinstein, and A. Kilian. 2001. Diversity Arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Research 29:e25-e25. Jombart, T. 2008. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403-1405. Jombart, T. 2017. A tutorial for the spatial Analysis of Principal Components (sPCA) using adegenet 2.1. 0. Imperial Collger London. Jombart, T., S. Devillard, A.-B. Dufour, and D. J. H. Pontier. 2008. Revealing cryptic spatial patterns in genetic variability by a new multivariate method. 101:92-103. Kindlmann, P., and F. Burel. 2008. Connectivity measures: a review. Landscape Ecology 23:879-890. Landguth, E. L., S. A. Cushman, M. K. Schwartz, K. S. McKelvey, M. Murphy, and G. Luikart. 2010. Quantifying the lag time to detect barriers in landscape genetics. Molecular Ecology 19:4179-4191. Lino, A., C. Fonseca, D. Rojas, E. Fischer, and M. J. R. Pereira. 2019. A meta-analysis of the effects of habitat loss and fragmentation on genetic diversity in mammals. Mammalian Biology 94:69-76. Lunney, D., and A. Matthews. 1997. The changing roles of state and local government in fauna conservation outside nature reserves: A case study of koalas in New South Wales. Pages 97-106 in P. Hale and D. Lamb, editors. Conservation Outside Nature Reserves. Centre for Conservation Biology, University of Queensland, Brisbane. McAlpine, C., D. Lunney, A. Melzer, P. Menkhorst, S. Phillips, D. Phalen, W. Ellis, W. Foley, G. Baxter, D. de Villiers, R. Kavanagh, C. Adams-Hosking, C. Todd, D. Whisson, R. Molsher, M. Walter, I. Lawler, and R. Close. 2015. Conserving koalas: a review of the contrasting regional trends, outlooks and policy challenges. Biological Conservation 192:226-236. Meirmans, P. G. 2006. Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evolution 60:2399-2402. Montano, V., and T. Jombart. 2017. An Eigenvalue test for spatial principal component analysis. BMC bioinformatics 18:562. Peakall, R., and P. E. Smouse. 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28:2537-2539.

Noosa Biosphere Reserve Foundation Koala Report 80 | P a g e

Phillips, S., and J. Callaghan. 2011. The Spot Assessment Technique: a tool for determining localised levels of habitat use by Koalas Phascolarctos cinereus. Australian Zoologist 35:774-780. Pritchard, J. K., M. Stephens, and P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945-959. Raj, A., M. Stephens, and J. K. Pritchard. 2014. fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets. Genetics 197:573-589. Rhodes, J. R., Beyer, H. L., Preece, H.J. and McAlpine, C.A. . 2015. South East Queensland koala population modelling study. Page 88 in UniQuest, editor., Brisbane. Rhodes, J. R., D. Lunney, C. Moon, A. Matthews, and C. A. McAlpine. 2011. The consequences of using indirect signs that decay to determine species’ occupancy. Ecography 34:141-150. Rus, A. I., C. McArthur, V. S. A. Mella, and M. S. Crowther. 2020. Habitat fragmentation affects movement and space use of a specialist folivore, the koala. Animal Conservation n/a. Schultz, A. J., R. H. Cristescu, B. L. Littleford-Colquhoun, D. Jaccoud, and C. H. Frere. 2018. Fresh is best: Accurate SNP genotyping from koala scats. Ecology and Evolution 8:3139-3151. Shumway, N., D. Lunney, L. Seabrook, and C. McAlpine. 2015. Saving our national icon: An ecological analysis of the 2011 Australian Senate inquiry into status of the koala. Environmental Science & Policy 54:297-303. Taberlet, P., and G. Luikart. 1999. Non-invasive genetic sampling and individual identification. Biological Journal of the Linnean Society 68:41-55. Triggs, B. 1996. Tracks, scats and other traces: A field guide to Australian mammals. Oxford University Press, South Melbourne. Wright, S. 1943. Isolation by distance. Genetics 28:114-138.

Noosa Biosphere Reserve Foundation Koala Report 81 | P a g e