David Keith

Professor David Keith

Professor of Botany

David's fields of research are vegetation dynamics, population and ecosystem modelling, and fire. 

david.keith@unsw.edu.au

Publications

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Lyons et al. 2018 A comparison of resampling methods for remote sensing classification and accuracy assessment

Abstract: Maps that categorise the landscape into discrete units are a cornerstone of many scientific, management and conservation activities. The accuracy of these maps is often the primary piece of information used to make decisions about the mapping process or judge the quality of the final map. Variance is critical information when considering map accuracy, yet commonly reported accuracy metrics often do not provide that information. Various resampling frameworks have been proposed and shown to reconcile this issue, but have had limited uptake. In this paper, we compare the traditional approach of a single split of data into a training set (for classification) and test set (for accuracy assessment), to a resampling framework where the classification and accuracy assessment are repeated many times. Using a relatively simple vegetation mapping example and two common classifiers (maximum likelihood and random forest), we compare variance in mapped area estimates and accuracy assessment metrics (overall accuracy, kappa, user, producer, entropy, purity, quantity/allocation disagreement). Input field data points were repeatedly split into training and test sets via bootstrapping, Monte Carlo cross-validation (67:33 and 80:20 split ratios) and k-fold (5-fold) cross-validation. Additionally, within the cross-validation, we tested four designs: simple random, block hold-out, stratification by class, and stratification by both class and space. A classification was performed for every split of every methodological combination (100’s iterations each), creating sampling distributions for the mapped area of each class and the accuracy metrics. We found that regardless of resampling design, a single split of data into training and test sets results in a large variance in estimates of accuracy and mapped area. In the worst case, overall accuracy varied between ~40–80% in one resampling design, due only to random variation in partitioning into training and test sets. On the other hand, we found that all resampling procedures provided accurate estimates of error, and that they can also provide confidence intervals that are informative about the performance and uncertainty of the classifier. Importantly, we show that these confidence intervals commonly encompassed the magnitudes of increase or decrease in accuracy that are often cited in literature as justification for methodological or sampling design choices. We also show how a resampling approach enables generation of spatially continuous maps of classification uncertainty. Based on our results, we make recommendations about which resampling design to use and how it could be implemented. We also provide a fully worked mapping example, which includes traditional inference of uncertainty from the error matrix and provides examples for presenting the final map and its accuracy.

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Murray et al. 2018 The role of satellite remote sensing in structured ecosystem risk assessments

Abstract: The current set of global conservation targets requires methods for monitoring the changing status of ecosystems. Protocols for ecosystem risk assessment are uniquely suited to this task, providing objective syntheses of a wide range of data to estimate the likelihood of ecosystem collapse. Satellite remote sensing can deliver ecologically relevant, long-term datasets suitable for analysing changes in ecosystem area, structure and function at temporal and spatial scales relevant to risk assessment protocols. However, there is considerable uncertainty about how to select and effectively utilise remotely sensed variables for risk assessment. Here, we review the use of satellite remote sensing for assessing spatial and functional changes of ecosystems, with the aim of providing guidance on the use of these data in ecosystem risk assessment. We suggest that decisions on the use of satellite remote sensing should be made a priori and deductively with the assistance of conceptual ecosystem models that identify the primary indicators representing the dynamics of a focal ecosystem.

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Rodríguez et al. 2015 A practical guide to the application of the IUCN Red List of Ecosystems criteria

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Keith et al. 2015 The IUCN red list of ecosystems: Motivations, challenges and applications

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Letten and Keith 2014 Phylogenetic and functional dissimilarity does not increase during temporal heathland succession

The compelling idea that closely related species should be less likely to coexist on account of their overlapping needs dates back to Darwin. It follows from Darwin's hypothesis that if species compete more intensely as communities mature, recently assembled communities (such as those emerging in the wake of a fire) will consist of closer relatives than older communities. Researchers from the Centre for Ecosystem Science tested this theory using a long-term dataset of community assembly in fire-prone heathland vegetation. Contrary to expectations, the relatedness of coexisting species tended to increase in the wake of fires, thus challenging this logical extention to one of ecology's oldest hypotheses.

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Keith et al. 2013 Scientific foundations for an IUCN Red List of ecosystems

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Enright et al. 2012 Fire ecology of Australian sclerophyllous shrubby ecosystems: heathlands, heathy woodlands and mallee woodlands

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Swab et al. 2012 Niche models tell half the story: spatial context and life-history traits influence species responses to global change

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Lindenmayer et al. 2012 Value of long-term ecological studies

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Fordham et al. 2012 Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming?

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Tozer and Keith 2012 Population dynamics of Xanthorrhoea resinosa Pers. over two decades: implications for fire management

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Maron et al. 2012 Faustian bargains? Restoration realities in the context of biodiversity offset policies

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Hayward and Keith 2012 The Scotia science symposium 2011

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Moir et al. 2012 A preliminary assessment of changes in plant-dwelling insects when threatened plants are translocated

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Keith et al. 2012 Functional traits: their roles in understanding and predicting biotic responses to fire regimes

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Moir et al. 2012 Considering extinction of dependent species during translocation, ex situ conservation, and assisted migration of threatened hosts

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Keith et al. 2012 Spatial analysis of risks posed by root rot pathogen, Phytophthora cinnamomi: implications for disease management

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Akçakaya et al. 2012 Commentary: IUCN classifications under uncertainty

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Nenzén et al. 2012 demoniche – an R-package for simulating spatially-explicit population dynamics

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Keith and Tozer 2012 The influence of fire, herbivores and rainfall on vegetation dynamics in the mallee: a long term experiment

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Campbell et al. 2012 Seed traits and seed bank longevity of wet sclerophyll forest shrubs

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Rodríguez et al. 2012 From Alaska to Patagonia: the IUCN Red List of the Continental Ecosystems of the Americas

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Keith and Tozer 2012 Vegetation dynamics in coastal heathlands of the Sydney Basin

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