Aligning quantitative vegetation classification and landscape scale mapping: updating the classification approach of the Regional Ecosystem classification system used in Queensland
Abstract:Vegetation classification systems form a base for conservation management and the ecological exploration of the patterns and drivers of species' distributions. A standardised system crossing administrative and geographical boundaries is widely recognised as most useful for broad-scale management. The Queensland Government, recognising this, uses the Regional Ecosystem (RE) classification system and accompanying mapping as a state-wide standardised vegetation classification system. This system informs legislati… Show more
“…An overview of how State-based plot-based vegetation data (the Northern Territory's in this case), together with an assessment of how well data can be utilised for attribution to the National Vegetation Classification System, is provided by Lewis et al (2021b). Addicott et al (2021) provides a review of the updated classification approach of the Regional Ecosystem (RE) mapping program used in Queensland. The RE's are placed in the context of classification systems globally, and an explanation given as to how expert v. quantitatively derived vegetation classes are incorporated in mapping (Addicott et al 2021).…”
Section: This Special Issuementioning
confidence: 99%
“…Addicott et al (2021) provides a review of the updated classification approach of the Regional Ecosystem (RE) mapping program used in Queensland. The RE's are placed in the context of classification systems globally, and an explanation given as to how expert v. quantitatively derived vegetation classes are incorporated in mapping (Addicott et al 2021). The paper also provides an up-to-date review of classification and cluster evaluation methods in Australia and internationally.…”
Section: This Special Issuementioning
confidence: 99%
“…2.0-1, D. W. Roberts, see https://CRAN.R-project.org/ package=labdsv; vegclust, ver. 1.6.5, M. De Cáceres and S. K. Wiser, see https://cran.r-project.org/web/packages/ vegclust/index.html); JUICE, which handles large datasets (>100 000 plots) (Tichý 2002;Chytrý and Tichý 2018;Addicott et al 2021); PRIMER-e (see https://www.primere.com/), which does not require use of a command line (Lewis et al 2021a) and PATN (see https://patn.org/), which can also handle large datasets (>30 000 plots) (Belbin 1993;Luxton et al 2021). This complexity has likely been a barrier to numerical methods being used to ascribe vegetation types in Australia, although intuitive approaches may have benefits over numerical when the purpose is to create broad map units in wooded systems.…”
Section: Introductionmentioning
confidence: 99%
“…This complexity has likely been a barrier to numerical methods being used to ascribe vegetation types in Australia, although intuitive approaches may have benefits over numerical when the purpose is to create broad map units in wooded systems. In this case, herbaceous species will influence classification results but have a low correlation with remotely sensed patterns (Neldner and Howitt 1991;Addicott et al 2021). Complexity has also been a barrier to the use of numerical methods in policy and regulatory frameworks and can make the synthesis of historic with current datasets, and across jurisdictional boundaries difficult (Gellie and Hunter 2021;Luxton et al 2021;Muldavin et al 2021).…”
This editorial introduces the Australian Journal of Botany special issue ‘Vegetation science for decision-making’. Vegetation science and classification are crucial to understanding Australian landscapes. From the mulga shrublands of the arid interior to the monsoon rain forests of northern Australia, we have culturally and scientifically built upon the delineation of vegetation into recognisable and repeatable patterns. As remote sensing and database capacities increase, this improved capability to measure vegetation and share data also prompts collaboration and synthesis of complex, specialised datasets. Although the task faces significant challenges, the growing body of literature demonstrates a strong discipline. In Australia, purpose-driven products describe vegetation at broad scales (e.g. the National Vegetation Information System, the Terrestrial Ecosystem Research Network). At fine scales however (i.e. that of the vegetation community), no uniform framework or agreed protocols exist. Climate and landform dictate vegetation patterns at broad scales, but microtopography, microclimate and biotic processes act as filters at finer scales. This is the scale where climate-change impacts are most likely to be detected and effected; this is the scale at which a deeper understanding of evolutionary ecology will be achieved, and it is the scale at which species need to be protected. A common language and system for understanding Australian communities and impetus for collecting data at this scale is needed. In the face of ongoing climate and development pressures and an increasingly complex set of tools to manage these threats (e.g. offset policies, cumulative impact assessments), a nationally collaborative approach is needed. It is our hope that this special issue will help to achieve this.
“…An overview of how State-based plot-based vegetation data (the Northern Territory's in this case), together with an assessment of how well data can be utilised for attribution to the National Vegetation Classification System, is provided by Lewis et al (2021b). Addicott et al (2021) provides a review of the updated classification approach of the Regional Ecosystem (RE) mapping program used in Queensland. The RE's are placed in the context of classification systems globally, and an explanation given as to how expert v. quantitatively derived vegetation classes are incorporated in mapping (Addicott et al 2021).…”
Section: This Special Issuementioning
confidence: 99%
“…Addicott et al (2021) provides a review of the updated classification approach of the Regional Ecosystem (RE) mapping program used in Queensland. The RE's are placed in the context of classification systems globally, and an explanation given as to how expert v. quantitatively derived vegetation classes are incorporated in mapping (Addicott et al 2021). The paper also provides an up-to-date review of classification and cluster evaluation methods in Australia and internationally.…”
Section: This Special Issuementioning
confidence: 99%
“…2.0-1, D. W. Roberts, see https://CRAN.R-project.org/ package=labdsv; vegclust, ver. 1.6.5, M. De Cáceres and S. K. Wiser, see https://cran.r-project.org/web/packages/ vegclust/index.html); JUICE, which handles large datasets (>100 000 plots) (Tichý 2002;Chytrý and Tichý 2018;Addicott et al 2021); PRIMER-e (see https://www.primere.com/), which does not require use of a command line (Lewis et al 2021a) and PATN (see https://patn.org/), which can also handle large datasets (>30 000 plots) (Belbin 1993;Luxton et al 2021). This complexity has likely been a barrier to numerical methods being used to ascribe vegetation types in Australia, although intuitive approaches may have benefits over numerical when the purpose is to create broad map units in wooded systems.…”
Section: Introductionmentioning
confidence: 99%
“…This complexity has likely been a barrier to numerical methods being used to ascribe vegetation types in Australia, although intuitive approaches may have benefits over numerical when the purpose is to create broad map units in wooded systems. In this case, herbaceous species will influence classification results but have a low correlation with remotely sensed patterns (Neldner and Howitt 1991;Addicott et al 2021). Complexity has also been a barrier to the use of numerical methods in policy and regulatory frameworks and can make the synthesis of historic with current datasets, and across jurisdictional boundaries difficult (Gellie and Hunter 2021;Luxton et al 2021;Muldavin et al 2021).…”
This editorial introduces the Australian Journal of Botany special issue ‘Vegetation science for decision-making’. Vegetation science and classification are crucial to understanding Australian landscapes. From the mulga shrublands of the arid interior to the monsoon rain forests of northern Australia, we have culturally and scientifically built upon the delineation of vegetation into recognisable and repeatable patterns. As remote sensing and database capacities increase, this improved capability to measure vegetation and share data also prompts collaboration and synthesis of complex, specialised datasets. Although the task faces significant challenges, the growing body of literature demonstrates a strong discipline. In Australia, purpose-driven products describe vegetation at broad scales (e.g. the National Vegetation Information System, the Terrestrial Ecosystem Research Network). At fine scales however (i.e. that of the vegetation community), no uniform framework or agreed protocols exist. Climate and landform dictate vegetation patterns at broad scales, but microtopography, microclimate and biotic processes act as filters at finer scales. This is the scale where climate-change impacts are most likely to be detected and effected; this is the scale at which a deeper understanding of evolutionary ecology will be achieved, and it is the scale at which species need to be protected. A common language and system for understanding Australian communities and impetus for collecting data at this scale is needed. In the face of ongoing climate and development pressures and an increasingly complex set of tools to manage these threats (e.g. offset policies, cumulative impact assessments), a nationally collaborative approach is needed. It is our hope that this special issue will help to achieve this.
“…Within QLD communities are defined as regional ecosystems (RE) that are classified at a thematic level considered equivalent to association. Unlike traditional concepts of an association, which strongly emphasize floristics, REs in QLD are named based firstly on the bioregion (IBRA7; Thackway and Cresswell 1995) in which they occur, secondarily by geology, landform and soils and only thirdly by the most dominant stratum in terms of biomass (not height) and then dominant floristics within strata (Gellie et al 2018;Addicott et al 2021). The approach is mapping based and created predominantly through expert opinion, with more than 1300 types currently defined (Gellie et al 2018), although recently quantitative classification approaches are being implemented (Addicott et al 2018;Addicott et al 2021).…”
Aims: Ecosystems nationally at risk in Australia are listed under the Environmental Protection and Biodiversity Act (EPBC Act), and many cross State jurisdictional boundaries. The determination of these ecosystems across the State boundaries are based on expert knowledge. The International Vegetation Classification has the potential to be useful as a cross-jurisdictional hierarchy which also gives global perspective to ecosystems. Study Area: All bioregions that include Eucalyptus populnea as a dominant or major component of woodlands across the species known distribution. Methods: We use plot-based data (455 plots) from two states (Queensland and New South Wales) in eastern Australia and quantitative classification methods to assess the definition and description for the Poplar Box Woodland ecosystem type (hereafter “ecological community” or “community”) that is listed as endangered under the EPBC Act. Analyses were conducted using kR-CLUSTER methods to generate alliances. Within these alliances, analyses were undertaken to define associations using agglomerative hierarchical clustering and similarity profile testing (SIMPROF). We then explore how assigning this community into the IVC hierarchy may provide a mechanism for linking Australian communities, defined at the association and alliance levels, to international communities at risk. Results: We define three alliances and 23 associations based on the results of floristic analysis. Using the standard rule-set of the IVC system, we found that the IVC hierarchy was a useful instrument in correlating ecological communities across jurisdictional boundaries where different classification systems are used. It is potentially important in giving a broader understanding of communities that may be at risk continentally and globally. Conclusions: We conclude that the IVC hierarchy can incorporate Australian communities at the association level into useful units at higher levels, and provides a useful classification tool for Australian ecosystems.
Taxonomic reference: PlantNET (http://plantnet/10rbgsyd.nsw.gov.au/) [accessed June 2019].
Abbreviations: EPBC Act = Environmental Protection and Biodiversity Act; IVC = International Vegetation Classification; NMDS = non-metric multidimensional scaling; NSW = New South Wales; PCT = Plant Community Type; QLD = Queensland; RE = Regional Vegetation Community; SIMPER = similarity percentage analysis; SIMPROF = Similarity profile analysis.
Mapping vegetation communities requires considerable investment in field data collection, analysis and interpretation. The methods for data collection and analysis can significantly affect field time and the accuracy of the classifications. We test the ability of field data subsets and data pre-treatments to reproduce an intuitively derived vegetation classification within the Australian tropical savanna biome. The data subsets include all strata, upper strata, ground strata, and tree basal area. A range of multivariate techniques were used to describe patterns in the datasets as they related to the a priori vegetation classification. We tested the degree of floristic correlation among the data subsets and the extent to which several data transformations (square root, fourth root, presence or absence) improved the level of agreement between the numerically and the intuitively derived mapping units. Our results implied high redundancy in sampling both basal area and upper strata species cover, and the ground stratum was poorly correlated with the upper stratum. Across all statistical tests, the groups derived from analysis of square root-transformed upper stratum cover data were closely aligned with the expert classification. We propose that a numerical approach using an optimal dataset will produce a meaningful classification for vegetation mapping in poorly known Australian tropical savanna.
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