School of BioSciences - Research Publications
Permanent URI for this collection
Now showing 1 - 5 of 5
ItemSurvival and growth of a high-mountain daisy transplanted outside its local range, and implications for climate-induced distribution shifts.Sumner, EE ; Morgan, JW ; Venn, SE ; Camac, JS ; Pugnaire, F (Oxford University Press (OUP), 2022-04)Field transplant experiments can improve our understanding of the effects of climate on distributions of plants versus a milieu of biotic factors which may be mediated by climate. We use a transplant experiment to test how survival and growth of a mountain-top daisy (Podolepis robusta), when planted within and outside its current local range, varies as a function of individual plant size, elevation, aspect and the presence of other vegetation. We expected a home-site advantage for the species, with highest survival and growth within the species' current elevational limits, and a decline in vital rates above (due to physiological limitations) and below (due to competition with near-neighbours) these limits. Transplant survival during the beginning of the census was high (89 %), though by the third growing season, 36 % of initial transplants were remaining. Elevation had a significant negative effect on individual mortality rates; plants growing at higher elevations had a lower estimated hazard rate and thus, higher survival relative to those planted at elevations below the current lower limit of the distribution. By contrast, we detected no significant effect of elevation on growth rates. Small vegetation gaps had no effect on growth rates, though we found a negative, but non-significant, effect on mortality rates. Aspect had a very strong impact on growth. Plants transplanted to cool aspects had a significantly lower growth rate relative to transplants growing on a warm aspect. Conversely, aspect was not a significant predictor of individual mortality rates. Restrictions on the local distribution of P. robusta appear to be governed by mortality drivers at lower elevation and by growth drivers associated with aspect. We highlight that our ability to understand the drivers of distributions in current and future climates will be limited if contextual- and individual-level plant responses remain understudied.
ItemAusTraits, a curated plant trait database for the Australian floraFalster, D ; Gallagher, R ; Wenk, EH ; Wright, IJ ; Indiarto, D ; Andrew, SC ; Baxter, C ; Lawson, J ; Allen, S ; Fuchs, A ; Monro, A ; Kar, F ; Adams, MA ; Ahrens, CW ; Alfonzetti, M ; Angevin, T ; Apgaua, DMG ; Arndt, S ; Atkin, OK ; Atkinson, J ; Auld, T ; Baker, A ; von Balthazar, M ; Bean, A ; Blackman, CJ ; Bloomfeld, K ; Bowman, DMJS ; Bragg, J ; Brodribb, TJ ; Buckton, G ; Burrows, G ; Caldwell, E ; Camac, J ; Carpenter, R ; Catford, J ; Cawthray, GR ; Cernusak, LA ; Chandler, G ; Chapman, AR ; Cheal, D ; Cheesman, AW ; Chen, S-C ; Choat, B ; Clinton, B ; Clode, PL ; Coleman, H ; Cornwell, WK ; Cosgrove, M ; Crisp, M ; Cross, E ; Crous, KY ; Cunningham, S ; Curran, T ; Curtis, E ; Daws, M ; DeGabriel, JL ; Denton, MD ; Dong, N ; Du, P ; Duan, H ; Duncan, DH ; Duncan, RP ; Duretto, M ; Dwyer, JM ; Edwards, C ; Esperon-Rodriguez, M ; Evans, JR ; Everingham, SE ; Farrell, C ; Firn, J ; Fonseca, CR ; French, BJ ; Frood, D ; Funk, JL ; Geange, SR ; Ghannoum, O ; Gleason, SM ; Gosper, CR ; Gray, E ; Groom, PK ; Grootemaat, S ; Gross, C ; Guerin, G ; Guja, L ; Hahs, AK ; Harrison, MT ; Hayes, PE ; Henery, M ; Hochuli, D ; Howell, J ; Huang, G ; Hughes, L ; Huisman, J ; Ilic, J ; Jagdish, A ; Jin, D ; Jordan, G ; Jurado, E ; Kanowski, J ; Kasel, S ; Kellermann, J ; Kenny, B ; Kohout, M ; Kooyman, RM ; Kotowska, MM ; Lai, HR ; Laliberte, E ; Lambers, H ; Lamont, BB ; Lanfear, R ; van Langevelde, F ; Laughlin, DC ; Laugier-kitchener, B-A ; Laurance, S ; Lehmann, CER ; Leigh, A ; Leishman, MR ; Lenz, T ; Lepschi, B ; Lewis, JD ; Lim, F ; Liu, U ; Lord, J ; Lusk, CH ; Macinnis-Ng, C ; McPherson, H ; Magallon, S ; Manea, A ; Lopez-Martinez, A ; Mayfeld, M ; McCarthy, JK ; Meers, T ; van der Merwe, M ; Metcalfe, DJ ; Milberg, P ; Mokany, K ; Moles, AT ; Moore, BD ; Moore, N ; Morgan, JW ; Morris, W ; Muir, A ; Munroe, S ; Nicholson, A ; Nicolle, D ; Nicotra, AB ; Niinemets, U ; North, T ; O'Reilly-Nugent, A ; O'Sullivan, OS ; Oberle, B ; Onoda, Y ; Ooi, MKJ ; Osborne, CP ; Paczkowska, G ; Pekin, B ; Pereira, CG ; Pickering, C ; Pickup, M ; Pollock, LJ ; Poot, P ; Powell, JR ; Power, S ; Prentice, IC ; Prior, L ; Prober, SM ; Read, J ; Reynolds, V ; Richards, AE ; Richardson, B ; Roderick, ML ; Rosell, JA ; Rossetto, M ; Rye, B ; Rymer, PD ; Sams, M ; Sanson, G ; Sauquet, H ; Schmidt, S ; Schoenenberger, J ; Schulze, E-D ; Sendall, K ; Sinclair, S ; Smith, B ; Smith, R ; Soper, F ; Sparrow, B ; Standish, RJ ; Staples, TL ; Stephens, R ; Szota, C ; Taseski, G ; Tasker, E ; Thomas, F ; Tissue, DT ; Tjoelker, MG ; Tng, DYP ; de Tombeur, F ; Tomlinson, K ; Turner, NC ; Veneklaas, EJ ; Venn, S ; Vesk, P ; Vlasveld, C ; Vorontsova, MS ; Warren, CA ; Warwick, N ; Weerasinghe, LK ; Wells, J ; Westoby, M ; White, M ; Williams, NSG ; Wills, J ; Wilson, PG ; Yates, C ; Zanne, AE ; Zemunik, G ; Zieminska, K (NATURE PORTFOLIO, 2021-09-30)We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge.
ItemPredicting species and community responses to global change using structured expert judgement: An Australian mountain ecosystems case studyCamac, JS ; Umbers, KDL ; Morgan, JW ; Geange, SR ; Hanea, A ; Slatyer, RA ; McDougall, KL ; Venn, SE ; Vesk, PA ; Hoffmann, AA ; Nicotra, AB (WILEY, 2021-07-02)Conservation managers are under increasing pressure to make decisions about the allocation of finite resources to protect biodiversity under a changing climate. However, the impacts of climate and global change drivers on species are outpacing our capacity to collect the empirical data necessary to inform these decisions. This is particularly the case in the Australian Alps which have already undergone recent changes in climate and experienced more frequent large-scale bushfires. In lieu of empirical data, we use a structured expert elicitation method (the IDEA protocol) to estimate the change in abundance and distribution of nine vegetation groups and 89 Australian alpine and subalpine species by the year 2050. Experts predicted that most alpine vegetation communities would decline in extent by 2050; only woodlands and heathlands are predicted to increase in extent. Predicted species-level responses for alpine plants and animals were highly variable and uncertain. In general, alpine plants spanned the range of possible responses, with some expected to increase, decrease or not change in cover. By contrast, almost all animal species are predicted to decline or not change in abundance or elevation range; more species with water-centric life-cycles are expected to decline in abundance than other species. While long-term ecological data will always be the gold standard for informing the future of biodiversity, the method and outcomes outlined here provide a pragmatic and coherent basis upon which to start informing conservation policy and management in the face of rapid change and a paucity of data.
ItemHealth and economic costs of early and delayed suppression and the unmitigated spread of COVID-19: The case of AustraliaKompas, T ; Grafton, RQ ; Che, TN ; Chu, L ; Camac, J ; Devleesschauwer, B (PUBLIC LIBRARY SCIENCE, 2021-06-04)We compare the health and economic costs of early and delayed mandated suppression and the unmitigated spread of 'first-wave' COVID-19 infections in Australia in 2020. Using a fit-for-purpose SIQRM-compartment model for susceptible, infected, quarantined, recovered and mortalities on active cases, that we fitted from recorded data, a value of a statistical life year (VSLY) and an age-adjusted value of statistical life (A-VSL), we find that the economic costs of unmitigated suppression are multiples more than for early mandated suppression. We also find that using an equivalent VSLY welfare loss from fatalities to estimated GDP losses, drawn from survey data and our own estimates of the impact of suppression measures on the economy, means that for early suppression not to be the preferred strategy requires that Australia would have to incur more than 12,500-30,000 deaths, depending on the fatality rate with unmitigated spread, to the economy costs of early mandated suppression. We also find that early rather than delayed mandated suppression imposes much lower economy and health costs and conclude that in high-income countries, like Australia, a 'go early, go hard' strategy to suppress COVID-19 results in the lowest estimated public health and economy costs.
ItemHealth and Economic Costs of Early, Delayed and No Suppression of COVID-19: The Case of AustraliaKompas, T ; Grafton, Q ; Che, TN ; Chu, L ; Camac, J (Cold Spring Harbor Laboratory, 2020)We compare the health and economic costs of early (actual), delayed and no suppression of COVID-19 infections in 2020 in Australia. Using a fit-for-purpose compartment model that we fitted from recorded data, a value of a statistical life year (VSLY) and an age-adjusted value of statistical life (A-VSL), we find: (1) the economic costs of no suppression are multiples more than for early suppression; (2) VSLY welfare losses of fatalities equivalent to GDP losses mean that for early suppression to not to be the preferred strategy requires that Australians prefer more than 12,500–30,000 deaths to the economy costs of early suppression, depending on the fatality rate; and (3) early rather than delayed suppression imposes much lower economy and health costs. We conclude that in high-income countries, like Australia, a ‘go early, go hard’ strategy to suppress COVID-19 results in the lowest estimated public health and economy costs.