Matt White - Academia.edu (original) (raw)

Papers by Matt White

Research paper thumbnail of Mapping the Current Condition of River red gum (Eucalyptus camaldulensis Dehnh.) Stands Along the Victorian Murray River Floodplain

Research paper thumbnail of Soil radiometrics: Field and remotely-sensed data sets for model building and validation

Research paper thumbnail of Defining reference conditions and determining change using discrete vegetation attributes at landscape scales

Research paper thumbnail of Plant functional traits reflect different dimensions of species invasiveness

Ecology, 2021

This is the author manuscript accepted for publication and has undergone full peer review but has... more This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Research paper thumbnail of Habitat Condition Assessment System: a new way to assess the condition of natural habitats for terrestrial biodiversity across whole regions using remote sensing data

Methods in Ecology and Evolution, 2016

Consistent and repeatable estimation of habitat condition for biodiversity assessment across larg... more Consistent and repeatable estimation of habitat condition for biodiversity assessment across large areas (i.e. regional to global) with limited field observations presents a major challenge for remote sensing (RS). RS can describe what a site looks like and how it behaves (using time series), but is unable to distinguish anthropogenic impacts from natural dynamics. Consequently, it is possible to mistake a heavily degraded habitat for a natural habitat, for example a logged forest may appear identical to an intact open woodland. This problem is compounded by the existence of multiple natural states in any given environment, and spatial variation in the natural composition and structure of vegetation as a function of variation in environment. Uncertainty in assessing habitat condition from RS is often further exacerbated by sparseness in the spatial coverage of training data. We describe a novel generic, RS‐based algorithm called Habitat Condition Assessment System, designed to address the above sources of uncertainty and to be highly flexible in its application. It allows for variability in the definition of condition and in the type and quantity of input data employed. Here, we demonstrate the mechanics of the new algorithm in a simple worked example and its practical application in a case study using inferred ‘natural‐only’ reference data, reflective remotely sensed data, and associated environmental data, to map condition for Australia at a 0·01° resolution. We assess the behaviour and shortcomings of the method, and compare the national case study with estimates from two existing national data sets, and field measured condition data observed at 16 967 sites across the State of Victoria. The modelled predictions outperform both of the existing national data sets, explaining 52% of the variability in field observations for well‐sampled cells (relative to 8% and 12% for the existing products). The methodology can potentially address some of the key pitfalls of condition modelling and could be applied in other regions with sufficient coverage of reference data. The approach also has good potential to be extended to work with reference data for which condition is measured on a continuous scale, for example from field‐based condition assessment initiatives.

Research paper thumbnail of Combining functional traits, the environment and multiple surveys to understand semi-arid tree distributions

Journal of Vegetation Science, 2018

This is the author manuscript accepted for publication and has undergone full peer review but has... more This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Research paper thumbnail of Testing the utility of species distribution modelling using Random Forests for a species in decline

Research paper thumbnail of Appendix A. TESS analysis for Brown Treecreeper and Superb Fairy-wren

TESS analysis for Brown Treecreeper and Superb Fairy-wren.

Research paper thumbnail of Appendix B. FRAGSTATS class aggregation statistics for tree cover in east and west

FRAGSTATS class aggregation statistics for tree cover in east and west.

Research paper thumbnail of Appendix C. Building CIRCUITSCAPE isolation-by-resistance and isolation-by-distance models

Building CIRCUITSCAPE isolation-by-resistance and isolation-by-distance models.

Research paper thumbnail of Clustering analysis of vegetation data

Vegetation may be described as the plant life of a region. The study of patterns and processes in... more Vegetation may be described as the plant life of a region. The study of patterns and processes in vegetation at various scales of space and time is useful in understanding landscapes, ecological processes, environmental history and predicting ecosystem attributes such as productivity. Generalized vegetation descriptions, maps and other graphical representations of vegetation types have become fundamental to land use planning and management. They are widely used as biodiversity surrogates in conservation assessments and can provide a useful summary of many non-vegetation landscape elements such as animal habitats, agricultural suitability and the location and abundance of timber and other forest resources. We use clustering or classification of vegetation data to obtain such descriptions, maps and other representations. Clustering vegetation data is well known machine learning problem which aims to partition the data set into subsets, so that the data in each subset share some common...

Research paper thumbnail of Potential impacts of a changing climate on selected terrestrial ecosystems in northern Victoria

Research paper thumbnail of Response matrix used to train artifical neural network models

Research paper thumbnail of Detecting outliers in species distribution data

Journal of Biogeography, 2017

Aim Species distribution data play a pivotal role in the study of ecology, evolution, biogeograph... more Aim Species distribution data play a pivotal role in the study of ecology, evolution, biogeography and biodiversity conservation. Although large amounts of location data are available and accessible from public databases, data quality remains problematic. Of the potential sources of error, positional errors are critical for spatial applications, particularly where these errors place observations beyond the environmental or geographical range of species. These outliers need to be identified, checked and removed to improve data quality and minimize the impact on subsequent analyses. Manually checking all species records within large multispecies datasets is prohibitively costly. This work investigates algorithms that may assist in the efficient vetting of outliers in such large datasets. Location We used real, spatially explicit environmental data derived from the western part of Victoria, Australia, and simulated species distributions within this same region. Methods By adapting species distribution modelling (SDM), we developed a pseudo-SDM approach for detecting outliers in species distribution data, which was implemented with random forest (RF) and support vector machine (SVM) resulting in two new methods: RF_pdSDM and SVM_pdSDM. Using virtual species, we compared eight existing multivariate outlier detection methods with these two new methods under various conditions. Results The two new methods based on the pseudo-SDM approach had higher true skill statistic (TSS) values than other approaches, with TSS values always exceeding 0. More than 70% of the true outliers in datasets for species with a low and intermediate prevalence can be identified by checking 10% of the data points with the highest outlier scores. Main conclusions Pseudo-SDM-based methods were more effective than other outlier detection methods. However, this outlier detection procedure can only be considered as a screening tool, and putative outliers must be examined by experts to determine whether they are actual errors or important records within an inherently biased set of data.

Research paper thumbnail of Predicting Landscape-Genetic Consequences of Habitat Loss, Fragmentation and Mobility for Multiple Species of Woodland Birds

Research paper thumbnail of The Mallee vegetation of north western Victoria

Proceedings of the Royal Society of Victoria, 2006

Mallee vegetation is a vegetation formation that once covered more than 15% of the State of Victo... more Mallee vegetation is a vegetation formation that once covered more than 15% of the State of Victoria. Mallee vegetation straddles an extensive region that lies between the maritime temperate eucalypt woodlands and heathlands and the inland arido-temperate non-eucalypt woodlands and semi-succulent shrublands. In Victoria a number of broad types of Mallee vegetation has been identified within the typological framework of the Ecological Vegetation Class. Mallee Ecological Vegetation Classes are principally delimited by biophysical and physiognomic attributes of the site and the surrounding landscape.

Research paper thumbnail of Coastal wetlands of Victoria, south-eastern Australia: providing the inventory and condition information needed for their effective management and conservation

Aquatic Conservation: Marine and Freshwater Ecosystems, 2014

ABSTRACT The effective management and conservation of coastal wetlands requires an appropriate ty... more ABSTRACT The effective management and conservation of coastal wetlands requires an appropriate typology to underpin classification and mapping, adequate inventory information, and a robust assessment of ecological condition and threats. Extensive and floristically diverse coastal wetlands occur along much of the coast of Victoria (south-eastern Australia), but there are serious deficiencies in all these information requirements.Previously unanalysed data from the Victorian Biodiversity Atlas were used to revise the typology currently applied to coastal saltmarsh in Victoria. To supplement the single unit currently used for State-endorsed mapping and inventory (EVC 9 Coastal Saltmarsh Aggregate), seven new Ecological Vegetation Classes are proposed to better reflect the floristic and structural diversity of coastal saltmarsh in south-eastern Australia. Coastal saltmarsh is currently allocated the lowest conservation status (‘least concern’) across much of Victoria, and it is recommended that this be upgraded variously to the higher categories of ‘endangered’, ‘vulnerable’, or ‘rare’.A State-wide inventory using the new typology, prepared using recently flown, high-resolution aerial photographs and extensive ground-truthing (212 person-days), indicated that there were 19 212 ha of coastal saltmarsh of all types, 5177 ha of mangroves, and 3227 ha of EVC 10 Estuarine Wetland (a wetland type dominated by Juncus kraussii) in Victoria.On-ground assessments undertaken across 30 geographic sectors of the coast indicated that coastal wetlands were confronted by a wide range of anthropogenic threats, which in many cases were quite different from those outlined in prior reviews of Australian wetland systems. Weed invasions were especially problematic, not so much of the exotic and highly publicized Spartina in the lower levels of tidal wetlands but from a wide range of exotic taxa in more elevated saltmarshes (e.g. tall wheat grass Lophopyrum ponticum). Copyright © 2014 John Wiley & Sons, Ltd.

Research paper thumbnail of Extending site-based observations to predict the spatial patterns of vegetation structure and composition

Context: Conservation planning and land management are inherently spatial processes that are most... more Context: Conservation planning and land management are inherently spatial processes that are most effective when implemented over large areas. Objectives: Our objectives were to (i) use existing plot data to aggregate species inventories to growth forms and derive indicators of vegetation structure and composition and ii) generate spatially-explicit, continuous, landscape scaled models of these discrete vegetation indicators, accompanied by maps of model uncertainty. Method: Using a case study from New South Wales, Australia, we aggregated floristic observations from 7234 sites into growth forms. We trained ensembles of artificial neural networks (ANN) to predict the distribution of these indicators over a broad region covering 11.5 million hectares. Importantly, we show spatially explicit models of uncertainty so that end-users have a tangible and transparent means of assessing models. Results: Our key findings were firstly, widely available site-based floristic records can be used...

Research paper thumbnail of Responding to the biodiversity impacts of a megafire: A case study from south‐eastern Australia’s Black Summer

Diversity and Distributions, 2021

Research paper thumbnail of Rangeland condition metrics for the Gobi Desert, derived from stakeholder evaluations

Disclaimer This publication may be of assistance to you but the State of Victoria and its employe... more Disclaimer This publication may be of assistance to you but the State of Victoria and its employees do not guarantee that the publication is without flaw of any kind or is wholly appropriate for your particular purposes and therefore disclaims all liability for any error, loss or other consequence which may arise from you relying on any information in this publication.

Research paper thumbnail of Mapping the Current Condition of River red gum (Eucalyptus camaldulensis Dehnh.) Stands Along the Victorian Murray River Floodplain

Research paper thumbnail of Soil radiometrics: Field and remotely-sensed data sets for model building and validation

Research paper thumbnail of Defining reference conditions and determining change using discrete vegetation attributes at landscape scales

Research paper thumbnail of Plant functional traits reflect different dimensions of species invasiveness

Ecology, 2021

This is the author manuscript accepted for publication and has undergone full peer review but has... more This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Research paper thumbnail of Habitat Condition Assessment System: a new way to assess the condition of natural habitats for terrestrial biodiversity across whole regions using remote sensing data

Methods in Ecology and Evolution, 2016

Consistent and repeatable estimation of habitat condition for biodiversity assessment across larg... more Consistent and repeatable estimation of habitat condition for biodiversity assessment across large areas (i.e. regional to global) with limited field observations presents a major challenge for remote sensing (RS). RS can describe what a site looks like and how it behaves (using time series), but is unable to distinguish anthropogenic impacts from natural dynamics. Consequently, it is possible to mistake a heavily degraded habitat for a natural habitat, for example a logged forest may appear identical to an intact open woodland. This problem is compounded by the existence of multiple natural states in any given environment, and spatial variation in the natural composition and structure of vegetation as a function of variation in environment. Uncertainty in assessing habitat condition from RS is often further exacerbated by sparseness in the spatial coverage of training data. We describe a novel generic, RS‐based algorithm called Habitat Condition Assessment System, designed to address the above sources of uncertainty and to be highly flexible in its application. It allows for variability in the definition of condition and in the type and quantity of input data employed. Here, we demonstrate the mechanics of the new algorithm in a simple worked example and its practical application in a case study using inferred ‘natural‐only’ reference data, reflective remotely sensed data, and associated environmental data, to map condition for Australia at a 0·01° resolution. We assess the behaviour and shortcomings of the method, and compare the national case study with estimates from two existing national data sets, and field measured condition data observed at 16 967 sites across the State of Victoria. The modelled predictions outperform both of the existing national data sets, explaining 52% of the variability in field observations for well‐sampled cells (relative to 8% and 12% for the existing products). The methodology can potentially address some of the key pitfalls of condition modelling and could be applied in other regions with sufficient coverage of reference data. The approach also has good potential to be extended to work with reference data for which condition is measured on a continuous scale, for example from field‐based condition assessment initiatives.

Research paper thumbnail of Combining functional traits, the environment and multiple surveys to understand semi-arid tree distributions

Journal of Vegetation Science, 2018

This is the author manuscript accepted for publication and has undergone full peer review but has... more This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Research paper thumbnail of Testing the utility of species distribution modelling using Random Forests for a species in decline

Research paper thumbnail of Appendix A. TESS analysis for Brown Treecreeper and Superb Fairy-wren

TESS analysis for Brown Treecreeper and Superb Fairy-wren.

Research paper thumbnail of Appendix B. FRAGSTATS class aggregation statistics for tree cover in east and west

FRAGSTATS class aggregation statistics for tree cover in east and west.

Research paper thumbnail of Appendix C. Building CIRCUITSCAPE isolation-by-resistance and isolation-by-distance models

Building CIRCUITSCAPE isolation-by-resistance and isolation-by-distance models.

Research paper thumbnail of Clustering analysis of vegetation data

Vegetation may be described as the plant life of a region. The study of patterns and processes in... more Vegetation may be described as the plant life of a region. The study of patterns and processes in vegetation at various scales of space and time is useful in understanding landscapes, ecological processes, environmental history and predicting ecosystem attributes such as productivity. Generalized vegetation descriptions, maps and other graphical representations of vegetation types have become fundamental to land use planning and management. They are widely used as biodiversity surrogates in conservation assessments and can provide a useful summary of many non-vegetation landscape elements such as animal habitats, agricultural suitability and the location and abundance of timber and other forest resources. We use clustering or classification of vegetation data to obtain such descriptions, maps and other representations. Clustering vegetation data is well known machine learning problem which aims to partition the data set into subsets, so that the data in each subset share some common...

Research paper thumbnail of Potential impacts of a changing climate on selected terrestrial ecosystems in northern Victoria

Research paper thumbnail of Response matrix used to train artifical neural network models

Research paper thumbnail of Detecting outliers in species distribution data

Journal of Biogeography, 2017

Aim Species distribution data play a pivotal role in the study of ecology, evolution, biogeograph... more Aim Species distribution data play a pivotal role in the study of ecology, evolution, biogeography and biodiversity conservation. Although large amounts of location data are available and accessible from public databases, data quality remains problematic. Of the potential sources of error, positional errors are critical for spatial applications, particularly where these errors place observations beyond the environmental or geographical range of species. These outliers need to be identified, checked and removed to improve data quality and minimize the impact on subsequent analyses. Manually checking all species records within large multispecies datasets is prohibitively costly. This work investigates algorithms that may assist in the efficient vetting of outliers in such large datasets. Location We used real, spatially explicit environmental data derived from the western part of Victoria, Australia, and simulated species distributions within this same region. Methods By adapting species distribution modelling (SDM), we developed a pseudo-SDM approach for detecting outliers in species distribution data, which was implemented with random forest (RF) and support vector machine (SVM) resulting in two new methods: RF_pdSDM and SVM_pdSDM. Using virtual species, we compared eight existing multivariate outlier detection methods with these two new methods under various conditions. Results The two new methods based on the pseudo-SDM approach had higher true skill statistic (TSS) values than other approaches, with TSS values always exceeding 0. More than 70% of the true outliers in datasets for species with a low and intermediate prevalence can be identified by checking 10% of the data points with the highest outlier scores. Main conclusions Pseudo-SDM-based methods were more effective than other outlier detection methods. However, this outlier detection procedure can only be considered as a screening tool, and putative outliers must be examined by experts to determine whether they are actual errors or important records within an inherently biased set of data.

Research paper thumbnail of Predicting Landscape-Genetic Consequences of Habitat Loss, Fragmentation and Mobility for Multiple Species of Woodland Birds

Research paper thumbnail of The Mallee vegetation of north western Victoria

Proceedings of the Royal Society of Victoria, 2006

Mallee vegetation is a vegetation formation that once covered more than 15% of the State of Victo... more Mallee vegetation is a vegetation formation that once covered more than 15% of the State of Victoria. Mallee vegetation straddles an extensive region that lies between the maritime temperate eucalypt woodlands and heathlands and the inland arido-temperate non-eucalypt woodlands and semi-succulent shrublands. In Victoria a number of broad types of Mallee vegetation has been identified within the typological framework of the Ecological Vegetation Class. Mallee Ecological Vegetation Classes are principally delimited by biophysical and physiognomic attributes of the site and the surrounding landscape.

Research paper thumbnail of Coastal wetlands of Victoria, south-eastern Australia: providing the inventory and condition information needed for their effective management and conservation

Aquatic Conservation: Marine and Freshwater Ecosystems, 2014

ABSTRACT The effective management and conservation of coastal wetlands requires an appropriate ty... more ABSTRACT The effective management and conservation of coastal wetlands requires an appropriate typology to underpin classification and mapping, adequate inventory information, and a robust assessment of ecological condition and threats. Extensive and floristically diverse coastal wetlands occur along much of the coast of Victoria (south-eastern Australia), but there are serious deficiencies in all these information requirements.Previously unanalysed data from the Victorian Biodiversity Atlas were used to revise the typology currently applied to coastal saltmarsh in Victoria. To supplement the single unit currently used for State-endorsed mapping and inventory (EVC 9 Coastal Saltmarsh Aggregate), seven new Ecological Vegetation Classes are proposed to better reflect the floristic and structural diversity of coastal saltmarsh in south-eastern Australia. Coastal saltmarsh is currently allocated the lowest conservation status (‘least concern’) across much of Victoria, and it is recommended that this be upgraded variously to the higher categories of ‘endangered’, ‘vulnerable’, or ‘rare’.A State-wide inventory using the new typology, prepared using recently flown, high-resolution aerial photographs and extensive ground-truthing (212 person-days), indicated that there were 19 212 ha of coastal saltmarsh of all types, 5177 ha of mangroves, and 3227 ha of EVC 10 Estuarine Wetland (a wetland type dominated by Juncus kraussii) in Victoria.On-ground assessments undertaken across 30 geographic sectors of the coast indicated that coastal wetlands were confronted by a wide range of anthropogenic threats, which in many cases were quite different from those outlined in prior reviews of Australian wetland systems. Weed invasions were especially problematic, not so much of the exotic and highly publicized Spartina in the lower levels of tidal wetlands but from a wide range of exotic taxa in more elevated saltmarshes (e.g. tall wheat grass Lophopyrum ponticum). Copyright © 2014 John Wiley & Sons, Ltd.

Research paper thumbnail of Extending site-based observations to predict the spatial patterns of vegetation structure and composition

Context: Conservation planning and land management are inherently spatial processes that are most... more Context: Conservation planning and land management are inherently spatial processes that are most effective when implemented over large areas. Objectives: Our objectives were to (i) use existing plot data to aggregate species inventories to growth forms and derive indicators of vegetation structure and composition and ii) generate spatially-explicit, continuous, landscape scaled models of these discrete vegetation indicators, accompanied by maps of model uncertainty. Method: Using a case study from New South Wales, Australia, we aggregated floristic observations from 7234 sites into growth forms. We trained ensembles of artificial neural networks (ANN) to predict the distribution of these indicators over a broad region covering 11.5 million hectares. Importantly, we show spatially explicit models of uncertainty so that end-users have a tangible and transparent means of assessing models. Results: Our key findings were firstly, widely available site-based floristic records can be used...

Research paper thumbnail of Responding to the biodiversity impacts of a megafire: A case study from south‐eastern Australia’s Black Summer

Diversity and Distributions, 2021

Research paper thumbnail of Rangeland condition metrics for the Gobi Desert, derived from stakeholder evaluations

Disclaimer This publication may be of assistance to you but the State of Victoria and its employe... more Disclaimer This publication may be of assistance to you but the State of Victoria and its employees do not guarantee that the publication is without flaw of any kind or is wholly appropriate for your particular purposes and therefore disclaims all liability for any error, loss or other consequence which may arise from you relying on any information in this publication.