Kingborough case study

The Kingborough case study anchors the collective case study in the particular, providing an in-depth analysis of how biodiversity conservation was integrated into statutory planning within a specific LGA. This instrumental case study evaluated the effectiveness of biodiversity conservation actions at the local scale by investigating the extent of loss relative to gain, the role of offsets and the effectiveness of protection mechanisms imposed via planning permit conditions.

Kingborough is a LGA in southern Tasmania, located south of Hobart, has an area of 72,010 hectares and a population of 36,263 (section 8.1). This case was selected on the basis of the expectations around the information content, as Kingborough is one of Tasmania’s fastest growing LGAs, the growth area intersects with biodiversity and the rules being applied are recognised as the strongest in Tasmania. This case study has also been chosen as I have a detailed knowledge of many of the sites through my role as Environmental Planner with Kingborough Council and access to data not readily available to researchers.8 The Kingborough case study involved three components: an audit of biodiversity losses, gains and future risk; an audit of offsets; and, an audit of protection areas protected as a result of development approvals (Table 2.1). Each of these methods and their purpose is detailed hereafter.

2.2.1

Audit of biodiversity loss, gain and risk in the urban growth area

Purpose

Data on loss of biodiversity as a result of land use planning decisions is limited and consequently biodiversity loss and biodiversity gains from development regulated by the Land Use Planning and

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While this proximity to my case study has numerous potential benefits, I need to consider and address potential ethical issues associated with my proximity to the case. For example, what data is in the public arena, how do I access this data for research purposes, how do I maintain confidentiality where appropriate and how do I position myself and my experiences/perceptions/interpretations in relation to my research? Ethics committee approval, data agreements and non- disclosure of sensitive information are measures which have been implemented to address these issues.

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Approvals Act 1993 (LUPAA) remain largely unaccounted for and largely unknown.9 The audit of loss, gains and risk provides a comprehensive summary of native vegetation loss, gain and risk from 2000-2018 within the urban growth area (UGA) of Kingston/Blackmans Bay (Table 2.2).

Case selection

The audit was limited to the UGA as: (i) there was complete coverage and high-resolution satellite imagery for this area; (ii) the basis for and likelihood of loss was able to be reliably attributed to development regulated by LUPAA; and, (iii) 80% of native vegetation loss and 63% of individual tree removal subject to offsets over the period 2000-2018 occurred within the UGA.

Data collection, manipulation and analysis

Data sources for analysis of loss, gain and risk within the UGA were the TASVEG_Change_2005 dataset (Resource Management and Conservation 2006b), photo interpretation (PI) mapping of satellite imagery, and Council records on development applications. The data from development approvals and the MVEP were compared with satellite imagery from 2005-2015 to identify loss of native vegetation cover the UGA as identified in the Kingborough Land Use Strategy 2018 (Kingborough Council 2018). Native vegetation cover was used as a surrogate for biodiversity more broadly as it can be easily detected in satellite imagery and field verification has routinely identified native vegetation in the UGA as supporting a range of threatened species and threatened native vegetation communities. Mapped native vegetation cover comprised small remnant patches of native vegetation, including stands of trees, as well as more extensive areas of native vegetation. Individual tree loss was also included in the database, where this loss was subject to an offset.

Using these data, patches of native vegetation or canopy cover removed during the period 2000-2018 were identified and attributed with extent, zone, year of loss, type of loss, type of development, whether it was offset, the type of offset, the extent of offset and the regulations in effect at the time. Vegetation protected as an outcome of land use planning decisions was identified and attributed by protected status, development type, zone, offset type extent and regulations in effect. Vegetation cover identified as still remaining was attributed with extent, zone and level of risk based on a combination of zoning, tenure and protected status.

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While the Forest Practices Authority (FPA) monitors changes in the extent of native forest cover under the Permanent Native Forest Estate (PNFE) Policy, this monitoring is limited to loss approved under a certified forest practices plan (section 3.2.2) (Tasmanian Government 2017a). Consequently, loss arising from land use planning decisions is not included in the PNFE monitoring. Monitoring of vegetation change has also been undertaken by the Monitoring Vegetation Extent Project (MVEP), resulting in the TASVEG_Change_2005 dataset. The MVEP compared satellite imagery across a five-year period to detect forest and non-forest vegetation cover changes. Where possible, these changes were verified using information from other sources such as Forest Practices Plans and high-resolution imagery (Webb 2008). The MVEP and resultant TASVEG_Change_2005 dataset allow vegetation change to be identified for different land tenures and land uses, and for forest and non-forest vegetation, providing a useful snap-shot of native vegetation cover loss in the period 2000-2005 (Webb 2008). However the data are limited in that they do not identify the type of activity or development creating the loss, it has only been undertaken for a single 5 year period, non-forest change is difficult to detect and cloud-cover masks some changes. The data are also not considered reliable at the local or site scale (Webb 2008).

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Statistical tests were performed on both these databases, including one-way ANOVA and chi-square depending upon the type of data, to test the relationship between variables and establish whether there was a significant change in loss relative to gains over time and under different regulatory regimes.

In order to determine variation in loss relative to gain over time and under different regulatory regimes, I conducted a one-way ANOVA where the regulatory regime in effect was the factor and the extent of vegetation loss and extent of vegetation gain were the response variables. In order to determine whether there was a correlation between loss of particular values and regulatory regime in effect, I conducted a chi-squared analysis of the percentage of proposals involving loss of specified biodiversity values according to regulation in effect.

Limitations, errors and bias

The reliability of the loss, gain and risk analysis is a function of the accuracy of the PI mapping in differentiating non-native vegetation cover from native vegetation cover. While larger urban remnants are readily identifiable, determining when remnant vegetation becomes an urban garden with individual remnant trees is more difficult. Therefore, it is inevitable that some areas of remnant vegetation were excluded from analysis, and other areas more appropriately mapped as gardens, have been erroneously included. Individual trees are also excluded from spatial analysis of loss and risk. While valuable, PI mapping down to the scale of individual trees was impractical.

2.2.2

Audit of loss and gains subject to offsets

Purpose

To evaluate the contribution and effectiveness of offsets to biodiversity conservation at the local level, a database of all offsets within the Kingborough LGA for the period 2000-2018 was developed. The purpose of this database was twofold: (i) audit the extent of loss relative to gain across the LGA (sections 8.2 and 8.3); and, (ii) evaluate the effectiveness of these offsets in relation to the key offset principles of avoidance, additionality, equivalency, currency, location, timing and security (Brown et al. 2014; Gardner et al. 2013; Maron et al. 2016; Maron, Rhodes & Gibbons 2013; McKenney & Kiesecker 2010; Preston 2016; Webb 2009) (section 8.4). In order to explore the relationship between the use of offsets and regulatory regimes, I conducted chi-square analysis of the percentage of proposals using offsets and the offset mechanisms used relative to the regulations in effect. To evaluate the effectiveness of offsets I also conducted a chi-squared analysis of the percentage of proposals satisfying accepted offset principles of avoidance, additionality, equivalence, currency, location, timing and security.

Case selection

The audit of offsets was undertaken at the scale of the LGA to ensure it provided a comprehensive understanding of the extent and drivers of biodiversity loss and offset outcomes across the rural and urban landscape.

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Data collection, manipulation and analysis

Data on each development proposal involving an offset was obtained from Council records on development approvals for the period 2000-2018 and entered into an Excel spreadsheet, creating a comprehensive offset database (Table 2.2). Attributes in the database were the extent and type of offset mechanism, locality, zone, type of development, extent and type of values impacted, and evaluation of compliance with offset principles (Table 2.2). For the purposes of the loss and gain analysis, statistical tests were performed on both these databases. One-way ANOVA and chi-square were used depending upon the type of data.

Using the database of offsets, an audit of offsets across the LGA was undertaken in relation to the key offset principles of avoidance, additionality, equivalency, currency, location, timing and security. Results were exported into SPSS and Mini-tab for descriptive frequencies to explore relationships between attributes and offset outcomes and chi-square to test whether the relationships between categorical variables varied from random.

Limitations, errors and bias

The process of collating data on development approvals involving offsets into a single offset database was manual, involving content analysis of consultants reports, Council officer reports, the financial offsets register and legal agreements securing offset sites. Therefore, the quality and level of detail varied between cases. This limited analysis to attributes which were able to be reliably and consistently identified across all development applications.

2.2.3

Audit of areas protected through the development approval process

Purpose

To evaluate the effectiveness of the development approval process in achieving biodiversity outcomes, an audit was undertaken of conservation areas protected via a legally binding agreement as a result of conditions of approval for development. The audit involved field based compliance and ecological monitoring across 32% (177 hectares) of the conservation areas. The purpose of the audit was to: (i) determine compliance with the terms of the agreement, including implementation of management prescriptions; (ii) monitor the current extent and quality of identified biodiversity values protected by the agreement; and, (iii) evaluate the effectiveness of these agreements in contributing to biodiversity conservation.

Case selection

To prioritise agreements and properties for monitoring, the following criteria were developed:

(i) maximum variation in the agreements and sites monitored to obtain information about the importance of various circumstances for case process and outcome;

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(ii) sites with biodiversity values dependent upon the Kingborough area for their long-term persistence, including values largely confined in their total distribution to the municipal area or with most of their range within the municipal area;

(iii)the length of time Agreements had been in place, with priority given to agreements which had been in place for > 5 years as these are overdue for monitoring, and sites with Agreements established after 2010 excluded as they are considered to be too recent to warrant monitoring; and,

(iv)resource and time constraints.

Consistent with these criteria, the conservation areas monitored ranged in size from 0.09 hectares to 64 hectares, had an average size of 13 hectares and were located in a range of contexts from urban and agricultural landscapes to forested hills.

Data collection

A monitoring method was developed to measure compliance, ecological attributes and condition (see the following section on data manipulation and analysis and Appendix VII). The compliance measures were derived from the terms of the agreements and the ecological measures were derived from accepted assessment and monitoring methodologies in Tasmania including the Vegetation Condition Assessment (VCA) method (Michaels 2006), the Biophysical Naturalness method (Knight & Cullen 2010a), the Forest Conservation Fund Conservation Value Index (Eigenraam et al. 2006), the Department of Primary Industries, Parks, Water and Environment (DPIPWE) Technical Manual (Barker 2001), DPIPWE’s Land Manager’s Guide (Barnes & McCoull 2002), mature habitat method (Forest Practices Authority 2012), forty-spotted pardalote habitat plots (Bryant 2010) and the Habitat Hectares method (State of Victoria 2014).

With the exception of tree sampling, field data were collected from assessment zones. Determination of assessment zones was predominantly based on the VCA method (Michaels 2006), with:

(i) a zone being the spatial units within a site in which the ecological attributes are measured ; (ii) the size of the assessment zone being 1ha (a 56m radius circle plot from a central point) or a

number of 20 x 20m quadrats, except where distribution of trees is not uniform, where a 40 x 40m sample plot is used (State of Victoria 2014);

(iii)each zone located within a discrete area of native vegetation consisting of a single TASVEG vegetation community with an observed similar averaged condition across its extent;

(iv)the number of zones being relative to the size of the site, with a small change in condition warranting a separate assessment on a small site, whereas on a large site, it may be incorporated into an average score;

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(v) a different assessment required where there is a one category difference in four or more of the assessed site components or two categories difference in any one of the assessed site components; and,

(vi)zones not necessarily needing to be contiguous .

For tree sampling, the number of plots within the assessment zone was determined by the size of the zone and the variation across it, with a minimum of 3 plots in any uniform section and 15-30 trees in each plot, unless one or two plots covered most of the site, in which case all trees were measured (Reid & Stephen 2001). For large uniform forests, the total area of all plots was 2% of the total forest area (Reid & Stephen 2001).

Data manipulation and analysis

A database of all compliance and ecological monitoring was developed. Compliance with the terms of each agreement monitored was determined using a multi-point scale derived from Brown et al. (2013a), who attribute a score of ‘0’ where no effort was apparent to meet the terms of the Agreement, a score of ‘1’ where some effort was made but it fell short of what was required, a score of ‘2’ where substantial effort had been made but the requirements still were unsatisfied and a score of ‘3’ where the requirements were demonstrably met (Table 2.2). Ecological condition was determined using the VCA method and extent of values protected was derived from point and polygon data collected in the field and extrapolated using PI (Figure 2.2).

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Figure 2.2 Example of ecological monitoring results for one Part 5 Agreement site

2.2.4

Limitations, errors and bias

The Part 5 Reserve Estate audit was limited by observer error and bias. The use of accepted monitoring methods specific to the biodiversity surrogate being measured was one strategy to reduce observer error and bias. Testing these methods in the field with the supervisory team prior to data collection, and conducting early assessments with other suitably qualified people, also provided an opportunity to refine and calibrate data collection techniques and interpretation.