School of Agriculture, Food and Ecosystem Sciences - Research Publications

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    Using Remote Sensing to Estimate Understorey Biomass in Semi-Arid Woodlands of South-Eastern Australia
    Riquelme, L ; Duncan, DH ; Rumpff, L ; Vesk, PA (MDPI, 2022-05)
    Monitoring ground layer biomass, and therefore forage availability, is important for managing large, vertebrate herbivore populations for conservation. Remote sensing allows for frequent observations over broad spatial scales, capturing changes in biomass over the landscape and through time. In this study, we explored different satellite-derived vegetation indices (VIs) for their utility in estimating understorey biomass in semi-arid woodlands of south-eastern Australia. Relationships between VIs and understorey biomass data have not been established in these particular semi-arid communities. Managers want to use forage availability to inform cull targets for western grey kangaroos (Macropus fuliginosus), to minimise the risk that browsing poses to regeneration in threatened woodland communities when grass biomass is low. We attempted to develop relationships between VIs and understorey biomass data collected over seven seasons across open and wooded vegetation types. Generalised Linear Mixed Models (GLMMs) were used to describe relationships between understorey biomass and VIs. Total understorey biomass (live and dead, all growth forms) was best described using the Tasselled Cap (TC) greenness index. The combined TC brightness and Modified Soil Adjusted Vegetation Index (MSAVI) ranked best for live understorey biomass (all growth forms), and grass (live and dead) biomass was best described by a combination of TC brightness and greenness indices. Models performed best for grass biomass, explaining 70% of variation in external validation when predicting to the same sites in a new season. However, we found empirical relationships were not transferrable to data collected from new sites. Including other variables (soil moisture, tree cover, and dominant understorey growth form) improved model performance when predicting to new sites. Anticipating a drop in forage availability is critical for the management of grazing pressure for woodland regeneration, however, predicting understorey biomass through space and time is a challenge. Whilst remotely sensed VIs are promising as an easily-available source of vegetation information, additional landscape-scale data are required before they can be considered a cost-efficient method of understorey biomass estimation in this semi-arid landscape.
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    Permanent removal of livestock grazing in riparian systems benefits native vegetation
    Jones, CS ; Duncan, DH ; Rumpff, L ; Robinson, D ; Vesk, PA (ELSEVIER, 2022-01)
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    Understanding the spatiotemporal dynamics of understorey biomass in semi-arid woodlands of south-eastern Australia
    Riquelme, L ; Rumpff, L ; Duncan, DH ; Vesk, PA (CSIRO PUBLISHING, 2022)
    When managing grazing pressure for conservation, understanding forage dynamics is essential. In south-eastern Australia, ongoing grazing is inhibiting regeneration in several semi-arid woodland communities. Western grey kangaroos (Macropus fuliginosus (Desmarest, 1817)) have been identified as a key component of total grazing pressure. They are thought to switch from grass to lower-quality browse, including tree seedlings, when grass biomass falls below 400 kg ha−1. One static threshold may not adequately capture the spatial and temporal hazard associated with kangaroo grazing, and this study aimed to explore how grassy biomass varies across a case-study landscape. Understorey biomass and species composition data were collected in the field on seven occasions between December 2016 and May 2019. We used Generalised Linear Mixed Models (GLMMs) to describe the influence of environmental and herbivory variables on total (live and dead) understorey, live understorey, and grass (live and dead) biomass. Canopy cover showed the strongest influence on understorey biomass, with more biomass found in open sites than in woodland. Understorey biomass levels were lowest in summer and autumn. Grass biomass, in particular, fell below the 400 kg ha−1 forage-switch threshold in wooded areas during this time. We anticipate that an increased understanding of understorey biomass dynamics will inform managers as to when and where to focus management efforts to promote regeneration and sustained recovery of these semi-arid woodlands. Results of this study suggest that conducting management efforts before the summer/autumn decline in understorey biomass, particularly in woodlands, is critical in reducing the browsing risk to seedlings.
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    Predicting reliability through structured expert elicitation with repliCATS (Collaborative Assessments for Trustworthy Science)
    Fraser, H ; Bush, M ; Wintle, B ; Mody, F ; Smith, ET ; Hanea, A ; Gould, E ; Hemming, V ; Hamilton, DG ; Rumpff, L ; Wilkinson, DP ; Pearson, R ; Singleton Thorn, F ; Ashton, R ; Willcox, A ; Gray, CT ; Head, A ; Ross, M ; Groenewegen, R ; Marcoci, A ; Vercammen, A ; Parker, TH ; Hoekstra, R ; Nakagawa, S ; Mandel, DR ; van Ravenzwaaij, D ; McBride, M ; Sinnott, RO ; Vesk, PA ; Burgman, M ; Fidler, F (Early Release, 2021-02-22)

    Replication is a hallmark of scientific research. As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce a new technique to evaluating replicability, the repliCATS (Collaborative Assessments for Trustworthy Science) process, a structured expert elicitation approach based on the IDEA protocol. The repliCATS process is delivered through an underpinning online platform and applied to the evaluation of research claims in social and behavioural sciences. This process can be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period. Pilot data suggests that the accuracy of the repliCATS process meets or exceeds that of other techniques used to predict replicability. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to assist with problems like understanding the limits of generalizability of scientific claims. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.