Implications of the 2019–2020 megafires for the biogeography and conservation of Australian vegetation

Australia's 2019–2020 'Black Summer' bushfires burnt more than 8 million hectares of vegetation across the south-east of the continent, an event unprecedented in the last 200 years. Here we report the impacts of these fires on vascular plant species and communities. Using a map of the fires generated from remotely sensed hotspot data we show that, across 11 Australian bioregions, 17 major native vegetation groups were severely burnt, and up to 67–83% of globally significant rainforests and eucalypt forests and woodlands. Based on geocoded species occurrence data we estimate that >50% of known populations or ranges of 816 native vascular plant species were burnt during the fires, including more than 100 species with geographic ranges more than 500 km across. Habitat and fire response data show that most affected species are resilient to fire. However, the massive biogeographic, demographic and taxonomic breadth of impacts of the 2019–2020 fires may leave some ecosystems, particularly relictual Gondwanan rainforests, susceptible to regeneration failure and landscape-scale decline

The impacts of extreme climatic events on wild plant populations

Despite growing evidence that species and ecosystems are responding to broad climatic trends globally, relatively little is known about the role that extreme climatic or weather events (ECEs) play in driving population and ecosystem change. The objective of this chapter is to provide an overview of the nature of ECEs and their impacts on the demography of wild plant populations in both terrestrial and aquatic ecosystems. We do this by drawing out some of the main lessons that have been learned from the past and contemporary study of ECEs, focusing primarily on case studies involving Australian vegetation, and then use these to identify potential phytosociological and evolutionary roles of extreme events within the context of anthropogenic climate change. We then discuss the contribution that genomics can make to our understanding of the demographic and evolutionary impact of historical ECEs on plant populations, and propose four key questions that are likely to shape future research in this field.</p

Why non-native grasses pose a critical emerging threat to biodiversity conservation, habitat connectivity and agricultural production in multifunctional rural landscapes

<b>Context</b>\ud \ud Landscape-scale conservation planning is key to the protection of biodiversity globally. Central to this approach is the development of <i>multifunctional rural landscapes</i> (MRLs) that maintain the viability of\ud natural ecosystems and promote animal and plant dispersal alongside agricultural land uses.\ud \ud <b>Objectives</b>\ud \ud We investigate evidence that <i>non-native grasses</i> (NNGs) in rangelands and other low-intensity agricultural systems pose a critical threat to landscape conservation initiatives in MRLs both in Australia and globally. Methods We first establish a simple socio-ecological model that classifies different rural landscape elements within typical MRLs based on their joint conservation and agro-economic value. We then quantify the impacts of eight Australian NNGs(Andropogon gayanus, Cenchrus ciliaris, Eragrostis curvula, Hyparrhenia hirta, Nassella neesiana, Nassella trichotoma, Phalaris aquatica and Urochloa mutica) on different landscape elements and then classify and describe the socio-ecological transformations that result at the MRL scale.\ud \ud <b>Results</b>\ud \ud Our data indicate that two broad classes of NNGs exist. The first reduces both conservation and agro-economic value (‘co-degrading’ species) of invaded landscapes, while the second improves agroeconomic value at the expense of conservation value (‘trade-off’ species). Crucially, however, both classes cause hardening of the landscape matrix, agricultural intensification, reduced habitat connectivity, and the loss of multi-value land use types that are vital for landscape conservation.\ud \ud <b>Conclusions</b>\ud \ud NNGs drive socio-ecological transformations that pose a growing threat to landscape-scale connectivity and conservation initiatives in Australia and globally. There is an urgent need for further research into the impacts of NNGs on habitat connectivity and biodiversity within multifunctional landscapes, and the socio-ecological goals that can be achieved when landscape transformation and degradation by these species is unavoidable

Component loadings of ten variables based on principal components analysis of site-level climatic and soil data.

1<p>Climatic data for the May to October growing season (1910–2010) (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049000#s2" target="_blank">methods</a>). Potential evapotranspiration (PET); atmospheric water balance (AWB) = precipitation- PET.</p>2<p>Component loadings above 0.400 or below – 0.400 are in bold.</p

Climatological data for the May–October growing season for bioregions in the study area.

<p>Data are based on 1910–2010 averages derived from the enhanced meteorological dataset hosted by The State of Queensland (Department of Environment and Resource Management) 2012 (<a href="http://www.longpaddock.qld.gov.au/silo/" target="_blank">http://www.longpaddock.qld.gov.au/silo/</a>). (a) Total precipitation (mm). (b) Mean temperature (°C). (c) Aridity Index. Abbreviations denote the individual bioregions, See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049000#pone-0049000-g001" target="_blank">Fig. 1</a> for full names. Bioregions are arranged along the <i>x</i> axis from most westerly (BHC) to most easterly (COAST).</p

Mean scores for each bioregion on first and second principal components derived from PCA of data from ten climatological and soil-related variables collected at each of the 34 study sites (see

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049000#s2" target="_blank"><b>methods</b></a><b>).</b> a) Mean scores on PC1. b) Mean scores on PC2. Variables with high (>0.40) loadings on PC1 and PC2 are shown beside the <i>y</i>-axis; upward and downward arrows indicate positive and negative loadings respectively. For example, in (a) average annual temperature (Tav) loads positively on PC1 while atmospheric water balance (AWB) loads negatively. The data show that PC1 is primarily related to overall aridity, which increases from the most mesic (South-Eastern Highlands) to most arid (Broken Hill Complex) bioregions, while PC2 primarily distinguishes between COAST and South-Eastern Highland bioregions based on soil characteristics. Abbreviations denote the individual bioregions; see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049000#pone-0049000-g001" target="_blank">Fig. 1</a> for full names. Bioregions are arranged along the <i>x</i> axis from most westerly (BHC) to most easterly (COAST). Climate and soil variable acronyms defined in the text except %N = percentage soil nitrogen; %C = percentage soil carbon; EC = soil electroconductivity.</p

Distribution of <i>Echium plantagineum</i> in Australia and location of collection sites across south-eastern Australia.

<p>(a) Herbarium records from the periods 1889–1910, 1911–1960 and 1961–2010 based on data obtained from Australia's Virtual Herbarium (2009 Council of Heads of Australasian Herbaria Inc) show the pattern of spread since introduction. (b) Distribution of study sites (depicted by black diamonds) across south-eastern Australia, grouped by IBRA bioregion (coloured). BHC = Broken Hill Complex, MDD = Murray Darling Depression, RIV = Riverina, NSS = NSW (New South Wales) South-Western Slopes, SEH = South-Eastern Highlands, SB = Sydney Basin, SEC = South-East Corner. Note that for all analyses SB and SEC were combined into a single coastal bioregion (COAST). The location of CSIRO Black Mountain Laboratories (S 35.27°, E 149.12°) where the glasshouse experiment was conducted is indicated.</p

Seed mass and seed mass variance derived from plants collected from six bioregions in the study area.

<p>(a) Mean site-level seed mass (g; 100 seeds) across all six bioregions based on the final data set (with small seeds removed). Site means are shown as filled circles while bioregion means (average of all sites within a bioregion) are shown as unfilled circles. (b) Estimated mean seed mass (±1 SE) for each bioregion based on linear mixed model analysis (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049000#s2" target="_blank">methods</a>) of final data set (square root transformed). Means sharing the same letter did not differ significantly at the 0.05 level. (c) Variance in seed mass for all study sites in each bioregion, determined as the variance in seed mass among seed-producing plants using the final data set. Abbreviations denote the individual bioregions, See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049000#pone-0049000-g001" target="_blank">Fig. 1</a> for full names. Bioregions are arranged along the <i>x</i> axis from most westerly (BHC) to most easterly (COAST).</p

Nonindigenous Plant Advantage in Native and Exotic Australian Grasses under Experimental Drought, Warming, and Atmospheric CO2 Enrichment

A general prediction of ecological theory is that climate change will favor invasive nonindigenous plant species (NIPS) over native species. However, the relative fitness advantage enjoyed by NIPS is often affected by resource limitation and potentially by extreme climatic events such as drought. Genetic constraints may also limit the ability of NIPS to adapt to changing climatic conditions. In this study, we investigated evidence for potential NIPS advantage under climate change in two sympatric perennial stipoid grasses from southeast Australia, the NIPS Nassella neesiana and the native Austrostipa bigeniculata. We compared the growth and reproduction of both species under current and year 2050 drought, temperature and CO2 regimes in a multifactor outdoor climate simulation experiment, hypothesizing that NIPS advantage would be higher under more favorable growing conditions. We also compared the quantitative variation and heritability of growth traits in populations of both species collected along a 200 km climatic transect. In contrast to our hypothesis we found that the NIPS N. neesiana was less responsive than A. bigeniculata to winter warming but maintained higher reproductive output during spring drought. However, overall tussock expansion was far more rapid in N. neesiana, and so it maintained an overall fitness advantage over A. bigeniculata in all climate regimes. N. neesiana also exhibited similar or lower quantitative variation and growth trait heritability than A. bigeniculata within populations but greater variability among populations, probably reflecting a complex past introduction history. We found some evidence that additional spring warmth increases the impact of drought on reproduction but not that elevated atmospheric CO2 ameliorates drought severity. Overall, we conclude that NIPS advantage under climate change may be limited by a lack of responsiveness to key climatic drivers, reduced genetic variability in range-edge populations, and complex drought-CO2 interactions