R E V I EW Detecting and attributing climate change effects on vegetation: Australia as a test case Laura J. Williams1 | Rachael V. Gallagher1 | Sami W. Rifai2 | Matthew A. Adeleye3 | Patrick J. Baker4 | David M. J. S. Bowman5 | Jake Eckersley6 | Jacqueline R. England7 | Michael-Shawn Fletcher8,9 | Pauline F. Grierson6 | Assaf Inbar1 | Jürgen Knauer1,10 | Clare M. Stephens1 | Raphaël Trouvé4 | Belinda E. Medlyn1 1Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia 2School of Biological Sciences, The University of Adelaide, Adelaide, SA, Australia 3Department of Geography, University of Cambridge, Cambridgeshire, UK 4School of Agriculture, Food, and Ecosystem Sciences, The University of Melbourne, Parkville, Australia 5Fire Centre, School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia 6School of Biological Sciences, The University of Western Australia, Perth, Australia 7CSIRO Environment, Clayton South, Victoria, Australia 8School of Geography, Earth and Atmospheric Sciences, The University of Melbourne, Australia 9ARC Centre of Excellence for Indigenous and Environmental Histories and Futures, James Cook University, Cairns, Queensland, Australia 10School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW, Australia Correspondence Laura J. Williams, Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia. Email: laura.williams@westernsydney.edu.au Societal Impact Statement Climate change is contributing to vegetation changes that threaten life support sys- tems. Yet, inherent climatic variability and past and present human actions—such as clearing, burning and grazing regimes—also alter vegetation and complicate under- standing of vegetation change. Australian ecosystems exemplify such complexity. To predict future vegetation changes, proactively guide management and ensure persis- tent drivers do not disrupt intended outcomes, we need to untangle the effects of these various change drivers on vegetation. Such attribution of change, which is rarely done, requires historical context, long-term datasets of vegetation and environ- mental drivers and integrating data with process-based understanding. Summary Climate change is expected to affect vegetation: associated rising atmospheric CO2, higher temperatures and more variable and extreme rainfall regimes can all cause major shifts in vegetation composition, structure and function. Such effects need to be detected to confirm understanding and to inform models that can predict future vegetation change and guide management efforts. However, many change drivers— some related to, and others distinct from, climate change—simultaneously affect veg- etation. These drivers include altered land management practices and shifts in fire and grazing regimes. Untangling the signals of climate-change-induced vegetation change from these other drivers of variation poses significant challenges. These chal- lenges are amplified in regions with high interdecadal climate variability and enduring legacies of shifting human activities. Here, we assess attempts to detect and attribute vegetation change across Australia, a continent that exemplifies such complexities. Disclaimer: The New Phytologist Foundation remains neutral with regard to jurisdictional claims in maps and in any institutional affiliations. Received: 29 July 2024 Revised: 4 July 2025 Accepted: 15 July 2025 DOI: 10.1002/ppp3.70090 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2025 The Author(s). Plants, People, Planet published by John Wiley & Sons Ltd on behalf of New Phytologist Foundation. Plants People Planet. 2025;1–25. wileyonlinelibrary.com/journal/ppp3 1 https://orcid.org/0000-0003-3555-4778 https://orcid.org/0000-0002-4680-8115 https://orcid.org/0000-0003-3400-8601 https://orcid.org/0000-0002-6034-5807 https://orcid.org/0000-0002-6560-7124 https://orcid.org/0000-0001-8075-124X https://orcid.org/0000-0002-7853-194X https://orcid.org/0000-0002-3371-1509 https://orcid.org/0000-0002-1854-5629 https://orcid.org/0000-0003-2135-0272 https://orcid.org/0000-0001-5861-963X https://orcid.org/0000-0002-4947-7067 https://orcid.org/0000-0002-7387-0563 https://orcid.org/0000-0002-2210-1035 https://orcid.org/0000-0001-5728-9827 mailto:laura.williams@westernsydney.edu.au https://doi.org/10.1002/ppp3.70090 http://creativecommons.org/licenses/by-nc/4.0/ http://wileyonlinelibrary.com/journal/ppp3 Funding information Ecological Society of Australia; Australian Research Council, Grant/Award Numbers: DP220103711, FL190100003, FL220100099 We develop a scheme to classify attribution efforts according to whether they con- sider (1) qualitative or quantitative evidence, (2) mechanistic explanations and (3) alternative plausible change drivers. While a significant body of evidence demon- strates vegetation change in Australia, we find that it is difficult to confidently attri- bute changes to recent climate shifts—noting that few studies have attempted to do so. Several recommendations emerge that may improve attribution worldwide, including explicitly considering attribution strength, committing to long-term moni- toring of vegetation and change drivers and recognising multiple drivers of change, especially past and present human influences. Finally, achieving the strongest level of attribution requires linking observations and mechanistic models. K E YWORD S anthropogenic climate change, attribution, change detection, disturbance regimes, historical legacies, land use change, process-based models, vegetation monitoring 1 | INTRODUCTION Terrestrial vegetation is a fundamental part of the Earth system, driving primary production and shaping the fluxes of energy and materials that underpin the benefits people derive from nature. Vegetation composition, structure and function influence biodiver- sity, carbon sequestration, surface water runoff, soil erosion, land surface temperatures and wildfire frequency (Ahlström et al., 2015; Poulter et al., 2014; Wilcox et al., 2022). Current and ongoing rapid climate change, driven by anthropogenic greenhouse gas emissions, will ultimately drive significant vegetation change and have major consequences for dependent organisms as well as the functioning of ecosystems. To maintain ecosystem services, we need to predict and proactively manage for climate-driven vegeta- tion change. However, there are still major uncertainties about the nature of this change. One critical question is the relative importance of the fer- tilising effect of rising atmospheric CO2 concentration, which is thought to drive large-scale greening (Walker et al., 2021), compared to the effect of increasing extremes of heat and drought, which may drive large-scale mortality events and increasingly severe fires (Hammond et al., 2022; Higuera & Abatzoglou, 2021; Pederson et al., 2014; Turco et al., 2023). Another open question is the degree to which species' current distributions reflect their climatic tolerances, and what might happen to populations that are increasingly exposed to climatic conditions beyond the range of their historical climate (Sexton et al., 2009). Attempts to study these processes and their interactions using mechanistic models confirm these uncertainties and the complexity of their dynamics (e.g., Abatzoglou et al., 2021; Hudiburg et al., 2013; McDowell et al., 2022), highlighting the need for further empirical research. One important way in which we can increase our understanding and predictive capacity is by measuring the climate-change-driven effects that have already taken place. Climate drivers have already changed substantially due to human activity. In 2024, atmospheric CO2 averaged 422 ppm, an increase of 50% above the pre-industrial concentration of 280 ppm, while global surface air temperature was 1.6�C warmer than the pre-industrial baseline (1850–1900) (climate. copernicus.eu). Other dimensions of anthropogenic climate change include more frequent and intense heatwaves, increased droughts and more frequent and intense precipitation events, which have all been observed since the 1950s (IPCC, 2023). These recent climatic changes are highly likely to have already affected vegetation (Harris et al., 2018; Hoffmann et al., 2019; Rosenzweig et al., 2008). Identify- ing the nature of these effects would help to reduce uncertainty about potential changes in the future. Monitoring to detect vegetation change is therefore a priority. However, detecting the signal of anthropogenic climate change in vegetation change is not straightforward for several reasons (Figure 1). First, the climate is inherently variable. Thus, detecting directional changes against background variability is challenging and requires long-term, high-resolution datasets, particularly in regions with high inter- and intra-annual climatic variability. Biological systems are also inherently variable, with successional dynamics following dis- turbance that can unfold over decades or centuries (Davis, 1983; Davis & Shaw, 2001). Moreover, climate change is not the only driver of vegetation change: a wide range of other human activities shape vegetation and have done so for millennia. Many regions have been subject to major transitions in land care and management practices due to waves of human colonisation. These impacts may vary across local to continental scales and variously reinforce or counteract the dynamics and systematic changes in vegetation that would hypotheti- cally occur in the absence of humans (Fletcher et al., 2024). These impacts are also cumulative, and the outcomes are path-dependent: current vegetation reflects unique historical pathways that are the result of interactions between vegetation and drivers in the past. Additional complications include confounding variables and the potential for interactions among drivers and the responses of vegeta- tion to be complex and nonlinear (Byrnes & Dee, 2025; Dudney et al., 2024; Oliver & Morecroft, 2014). 2 WILLIAMS ET AL. http://climate.copernicus.eu http://climate.copernicus.eu We argue that these complexities make it important to pay care- ful attention to attribution because understanding the full comple- ment of vegetation change drivers is needed to predict how and where vegetation will change and to manage these changes. Process- based models—key tools for predicting change—work by representing biological and ecological processes. Thus, these models rely on identi- fying the right processes and understanding their influence on vegeta- tion from first principles (Gustafson, 2013); empirical examples with strong attribution are needed to assess model performance and ensure predictions emerge via the correct pathways. Effective man- agement also relies on identifying the drivers of vegetation change. There may be scope to take strategic action to shape the rate or direc- tion of vegetation change where, for example, current management regimes are driving change (Prober et al., 2017). On the other hand, failure to recognise and address ongoing change drivers may thwart management outcomes. Emerging markets in nature repair and biodi- versity recovery are a case in point and further underscore the need to understand drivers of vegetation change lest markets incentivise actions that are ineffective or fail to achieve benefits that would not otherwise occur (i.e., fail to meet standards of additionality and integ- rity) (Bryan et al., 2016; Macintosh et al., 2024; West et al., 2023; zu Ermgassen et al., 2023). In this review, we aim to address current capacity to detect and attribute climate-change-driven vegetation change, drawing on data sources ranging from palaeoecological approaches to remote sensing, and focussing on the composition, structure and function of vegeta- tion in Australia. The Australian continent is a useful test case because it highlights some of the complexities in understanding and attributing vegetation change, including high climate variability (Nicholls et al., 1997; Peel et al., 2004) and diverse anthropogenic drivers of vegetation dynamics over tens of thousands of years (reviewed in e.g., Bergstrom et al., 2021; Bowman et al., 2013; Bradshaw, 2012; Fensham & Fairfax, 2002; Harris et al., 2018; Hoffmann et al., 2019; Hovenden & Williams, 2010; Hughes, 2003; Lunt, 2002). Of particular concern, several Australian ecosystems are thought to be especially vulnerable to change (Laurance et al., 2011), highlighting the need to accurately assess the drivers of change in these ecosystems. Here we (1) give an overview of the drivers of vegetation change in Australia; (2) summarise the empirical data sources available to detect change and (3) present a new scheme that classifies efforts to attribute change, and illustrate this scheme with examples of vege- tation change from across Australia. In doing so, we evaluate the strength of recent attribution attempts, particularly in demonstrating whether signals of elevated atmospheric CO2 or climate change are evident. By using Australia as a test case, we pinpoint key difficulties and challenges that may limit attribution worldwide and identify steps needed for improvement. 2 | A BRIEF OVERVIEW OF DRIVERS OF VEGETATION CHANGE IN AUSTRALIA The Australian continent has faced many drivers of vegetation change that have shifted from deep time to the present (Figure 2). Along with the rest of the globe, climate across Australia has been changing over recent decades (CSIRO & Bureau of Meteorology, 2024). From 1910 to 2024, Australia's climate has warmed on average by 1.5�C and the number of extreme heat events has increased. Changes in rainfall have differed across the continent: the highest declines have been observed in the south-west and south-east (16% and 9%, respectively, in the cool season since 1970) while rainfall has increased in the north (20% in the wet season since 1994). There has also been an increase in extreme fire weather, and the fire season has lengthened since the 1950s. These changes in cli- mate means and extremes, combined with the underlying rise in global atmospheric CO2, are forcing a dynamic system: Australia is subject to inherent high inter-decadal climatic variability, especially in F IGURE 1 The quest to understand how anthropogenic climate change, driven by rising atmospheric CO2 is affecting vegetation appears simple (shown in black). However, it is underpinned by complexity (shown in colour): climate and vegetation can each be characterised in different ways; change can encompass several dimensions; a suite of biological processes shape vegetation; current and historical change drivers other than climate, including disturbance agents (such as fire, grazers), also shape vegetation in ways that may interact with each other and climate; and perceptions of vegetation change depend on scale. WILLIAMS ET AL. 3 rainfall (Nicholls et al., 1997; Peel et al., 2004) and hydroclimatic con- ditions (Palmer et al., 2015). Since the late Pleistocene, there have been notably acute transi- tions in change drivers linked to the activities of humans. Indigenous people have shaped vegetation across the Australian continent over tens of millennia through the use of fire, producing landscapes and biodiversity that depend on human agency (Bliege Bird et al., 2008, 2018; Bowman, 1998; Fletcher, Hall, & Alexandra, 2021; Mariani et al., 2022; Russell-Smith et al., 2013). From 1788, British invasion and subsequent colonisation caused rapid shifts in land manage- ment: large swaths of the country were denied the care and man- agement they had received from Indigenous people (Fletcher, Romano, et al., 2021), and clearing of vegetation for settlement, agriculture and mining became a priority. Over time, attitudes to land management have evolved, notably with the emergence of the conservation movement in the 1960s. Woody vegetation neverthe- less continues to be cleared to expand agriculture (Fensham et al., 2011; Johansen et al., 2015) and, by 2023, one-fifth of Australia's bioregions (18 of 87), mostly in the southern and higher rainfall areas of Australia, were classified as predominantly cleared (Adams et al., 2023). Fire regimes are a dominant driver of vegetation structure, com- position and function. Since the last interglacial period, they have undergone profound shifts associated with climate change, the first arrival of humans to the continent and subsequent cultural shifts. Pre- human fire regimes were characterised by infrequent lightning igni- tions that could cause large fires with a mix of severities. Indigenous people shifted fire regimes to frequent, low-severity and typically small (pedestrian-scale) fires that created mosaics of vegetation types and ages (Bliege Bird et al., 2008; Bowman, 1998; Fletcher, Hall, & Alexandra, 2021; Gott, 2005; Jackson, 2022; Murphy et al., 2013). Following British invasion and colonisation, locally nuanced cultural fire regimes were lost from nearly all Australian landscapes (Bliege Bird et al., 2012; Fletcher, Romano, et al., 2021; Mariani et al., 2022; Price et al., 2012). In their place, fire regimes now include frequent and extensive fires, which have caused the loss of older habitat (Doherty et al., 2024). Modern fuel management regimes (prescribed burns) in forested areas of southern Australia are typically larger, more intense and occur in different seasons than cultural burning regimes (Duff et al., 2019; Howard et al., 2020; Morgan et al., 2020). Now, cat- astrophic fires are a major contributor to Australia's carbon emissions (van der Velde et al., 2021; Villalobos et al., 2023); these fires are F IGURE 2 In Australia, (a) many potential drivers of vegetation change have operated at various spatial scales from the past to the present. (b) Several approaches collect the time series data needed to detect change; these data sources vary in their spatial and temporal coverage. In (a), shading indicates the hypothesised influence, and ovals indicate the relative timing and frequency of events (under species introductions and losses, filled ovals indicate introductions and crosses indicate losses); these are intended to be broadly indicative. In (b), shading indicates the amount of data (darker = more), and dashed bars indicate the temporal resolution of data. 4 WILLIAMS ET AL. thought to be driven principally by a changing climate, but may be fuelled by increasing fuel loads potentially associated with altered management regimes (Abram et al., 2021; Nolan et al., 2021; van Oldenborgh et al., 2021). Many animal, plant and pathogen species have been introduced while native species have been lost, with implications for vegetation structure, composition and function. Megafaunal species, which browsed vegetation and dispersed seeds, were lost by ca. 40 ka BP (Adeleye, Andrew, et al., 2023; Johnson, 2009; Lopes Dos Santos et al., 2013). Since British colonisation, >10% of endemic terrestrial mammal species (28 of 273 species) have been lost and many more are threatened—most are small mammals, which acted as seed dis- persers, seed predators, pollinators and browsers (Woinarski et al., 2019). Some losses may instigate trophic cascades and affect vegetation structure and ecosystem function (e.g., Gordon et al., 2017; Gordon & Letnic, 2019; Stephenson et al., 2024). Coloni- sation saw the introduction of livestock such as cattle and sheep, with widespread impacts (reviewed in Lunt et al., 2007), as well as now- feral grazers and browsers including rabbits, goats, pigs, deer, horses, donkeys, buffalo and camels. The population size and influence of these feral species have varied over time, shaped by climate, vegeta- tion and human-induced control measures. For example, rabbit popu- lations reached plague proportions by the mid-19th century, have been periodically suppressed with the introduction and spread of viruses (myxoma in the 1950s and rabbit haemorrhagic disease virus in the mid-1990s) and have partly rebounded with the development of disease resistance (Mutze et al., 2014). Many plant species intro- duced to enhance agricultural production have become invasive. Some of these plant species have increased the size, intensity and frequency of fires through grass-fire feedback cycles, such as Cenchrus ciliaris L. (buffel grass) in arid and semi-arid central and northwest Australia, and Andropogon gayanus Kunth (gamba grass) in the tropical savannas of the northern Territory (Miller et al., 2010; Rossiter-Rachor et al., 2008; Schlesinger & Westerhuis, 2021). In summary, shifts in vegetation observed today may be the product of recently imposed change drivers as well as legacies from the past. Any effects of anthropogenic climate change on Australia's vegetation are occurring within this complex and variable milieu of drivers. 3 | DATA SOURCES FOR DETECTING VEGETATION CHANGE IN AUSTRALIA A range of methodological approaches produce data that may be used to detect vegetation change (Figure 2b). Each of these data sources gives a different perspective of vegetation change—offering views of different dimensions of vegetation and changes across different scales of space, time and biological organisation (Table 1). Each approach also has notable strengths and limitations—some of which become especially evident in the environments of Australia. Here we give a brief survey of approaches that yield time-series data and thus could potentially support quantitative attribution. Beyond these approaches, there is a notable body of work investigating vegetation change in Australia using ethnohistorical approaches (e.g., Gammage, 2011; Howitt, 1890; Mifsud et al., 2025; Prober et al., 2019; Rolls, 1981, 1999). 3.1 | Pollen, charcoal and stable isotope records Palaeoecological approaches, such as analyses of fossil plant remains, biomarkers and isotopes, are the only source of quantifiable data demonstrating changes in vegetation composition over centuries to millions of years. However, pollen and charcoal records only form in sedimentary environments. Thus, large regions of Australia lack such records. Palaeoecological records also typically lack the fine temporal resolution through to the present that would be needed to capture signals of anthropogenic climate change (Adeleye, Haberle, et al., 2023; Fletcher, Bowman, et al., 2021; Fletcher, Hall, & Alexandra, 2021). Nevertheless, there are some notable pollen records, particularly from southeastern Australia, which give long-term perspectives of vegetation change as well as insight into the relation- ship between vegetation change driven by past climate shifts and human interventions (e.g., Adeleye, Haberle, et al., 2023; Fletcher & Thomas, 2010; Hill, 1994; Lisé-Pronovost et al., 2019). Elsewhere, analyses of stable carbon isotopes in soil can reveal dynamics between forest and grassland where these vegetation types differ in their photosynthetic pathway (i.e., C3 versus C4 grasses, e.g., Bowman et al., 2007). Palaeoecological analyses can help quantify effect sizes of different drivers and thus may help in attributing observed changes as well as predicting rates and trajectories of future vegetation change (Adeleye, Haberle, et al., 2023; Beck et al., 2018; Fletcher et al., 2014). However, such approaches have significant limi- tations where change drivers are creating novel environments. 3.2 | Tree rings Tree ring analyses can inform understanding of vegetation dynamics and links with disturbance and climatic histories of particular regions over potentially thousands of years and at annual (or even seasonal) resolution. For example, dating of fire scars on conifers across North America has helped reconstruct fire histories that provide context for interpreting drivers of landscape and vegetation change (Margolis et al., 2022 and references therein). However, the angiosperm genera that dominate Australia's forests and woodlands (Acacia, Eucalyptus, Melaleuca, Allocasuarina) often have complex wood structures and poor delineation of annual growth rings. These traits can make it diffi- cult to accurately age many species and thus link, for example, changes in growth patterns and stand structures to events or regime shifts (e.g., drought, fire, insect attack). Nevertheless, climate recon- structions from tree ring studies of the widespread conifer Callitris as well as other long-lived conifers in Tasmania and Queensland have revealed extended drought and flood periods over several centuries (e.g., Allen et al., 2019, 2017; Haines et al., 2018; O'Donnell WILLIAMS ET AL. 5 T A B L E 1 Su m m ar y o f so m e o f th e m aj o r m et ho do lo gi ca la pp ro ac he s th at ca pt ur e hi st o ri ca lt im e- se ri es da ta ne ed ed to de te ct ve ge ta ti o n ch an ge in A us tr al ia . M et ho d T em po ra le xt en t T em po ra lg ra in Sp at ia le xt en t Sp at ia l gr ai n Sc al e o f bi o lo gi ca l o rg an is at io n T ax o no m ic re so lu ti o n V eg et at io n d im en si o n A p p lic ab le sy st em s P o lle n, ch ar co al an d is o to pe re co rd s 1 0 0 –1 0 0 ,0 0 0 + ye ar s C o ar se (1 0 –1 0 0 + ye ar s) C at ch m en t (r eg io na lt o lo ca l) C at ch m en t (r eg io na lt o lo ca l) P la nt co m m un it y F am ily to ge nu s (o r sp ec ie s) fo r po lle n; lo w (C 3 vs C 4 pa th w ay s) fo r st ab le is o to p es in so il C o m p o si ti o n , st ru ct u re Se d im en ta ry en vi ro n m en ts (la ke s, w et la n d s) fo r p o lle n an d ch ar co al T re e ri ng s 1 –3 ,0 0 0 + ye ar s A nn ua l St an d to la nd sc ap e In di vi du al o r st an d P la nt co m m un it y (o ne o r m o re tr ee sp ec ie s) Sp ec ie s St ru ct u re , fu n ct io n W o o d y ta xa w it h an n u al gr o w th ri n gs (m ai n ly se as o n al en vi ro n m en ts ) F o re st in ve nt o ry an d pe rm an en t gr o w th pl o ts 1 0 –1 0 0 ye ar s V ar ia bl e (1 –1 0 ye ar s) 1 to 1 0 0 s o f ha 0 .0 1 –5 0 ha T re e co m m un it y (s o m et im es lim it ed to co m m er ci al ly va lu ab le sp ec ie s) Sp ec ie s (s o m et im es sp ec ie s gr o up s) F u n ct io n , st ru ct u re F o re st s Lo ng -t er m ec o lo gi ca l m o ni to ri ng 1 0 –1 0 0 ye ar s V ar ia bl e (1 –5 ye ar s fo r m an ua l, su b- da ily fo r se ns o r- ba se d o bs er va ti o ns ) ha to la nd sc ap e V ar ia bl e (1 m 2 to 1 ha ) P la nt co m m un it y (s o m et im es se le ct iv e) to ec o sy st em Sp ec ie s C o m p o si ti o n , st ru ct u re , fu n ct io n A n y R em o te se ns in g up to ca .4 5 ye ar s (s at el lit e) ca . 7 0 ye ar s (a er ia l ph o to s) V ar ia bl e (d ai ly to de ca de s) P at ch (u nm an ne d ae ri al ve hi cl e) to gl o ba l( sa te lli te ) V ar ia bl e (c m to km ) In di vi du al pl an t to ec o sy st em N o ne (s o m et im es fa m ily to ge nu s) St ru ct u re , fu n ct io n , co m p o si ti o n A n y 6 WILLIAMS ET AL. et al., 2021; and references therein). This understanding of long-term variability and tree growth responses are important context for inter- preting potential impacts of more recent anthropogenic climate change. Regional-scale fire histories have also been developed by dat- ing fire scars in shrublands and woodlands using both Callitris and eucalypt species that, in turn provide a framework for assessing impacts of differing fire regimes on vegetation, especially where there is an absence of long-term monitoring (Gosper et al., 2013; O'Donnell et al., 2010). Increased capabilities for high resolution radiocarbon dating coupled with careful characterisation of growth patterns of species in different settings are now revealing new insights as to the underlying drivers of vegetation change, for example, establishing links between mangrove dynamics and changing hydrology (e.g., Goodwin et al., 2022), and interactions between insect attack and drought occurrence on growth of snow gums in southeast Australia (Brookhouse et al., 2024). 3.3 | Forest inventory and permanent growth plots In Australia, the longest forest inventory and permanent growth plot (PGP) datasets extend back to the early 20th Century. Strategic inven- tory data have been collected over a wide area of Australia's public forest estate since the 1930s. While individual plots are not revisited, strategic inventory plots can be analysed collectively to infer population-level changes. Forest management agencies also estab- lished PGPs, where individual trees are tagged and re-measured through time, to monitor change at the tree and stand level and thus characterise individual demographic parameters (e.g., growth, mortal- ity) (e.g., Bowman, Williamson, et al., 2014; Prior & Bowman, 2014). Two key limitations of forest inventory plots and PGPs are that they have historically focused on commercial timber species (primarily eucalypts in Australia) and have only sampled trees >10 cm DBH, reducing their utility in detecting changes in species composition. Experimental plots were also established to assess the consequences of silvicultural treatments, such as thinning or spacing. Many of the plots were abandoned in the 1980s and 1990s. However, those that were monitored for long time periods may reveal the influence of management treatments under changing environmental conditions (e.g., Horner et al., 2009; Trouvé et al., 2025, 2017). 3.4 | Long-term ecological monitoring Long-term ecological monitoring refers to repeated measurements of the same ecosystem, typically for 10 or more years (Strayer, 1986; Youngentob et al., 2013). Different long-term monitoring projects focus on different data types (Lindenmayer et al., 2014; Sparrow et al., 2020). Some projects survey floristics, revealing compositional shifts in vegetation communities at local to regional scales (e.g., Ashton, 2000). Others measure tree growth and mortality, similar to forest PGPs (e.g., Bradford et al., 2014), or track the demography of particular species (e.g., Connell & Green, 2000). Monitoring often targets responses to disturbance, such as fire, grazing and drought (Lindenmayer et al., 2014). High intrinsic variability at the relatively small spatial scales of many monitoring projects, coupled with the large influence of disturbance, can limit the power to detect long-term changes related to climate. Instrument-driven monitoring approaches have emerged over recent decades, such as eddy covariance measure- ments of fluxes from which near-continuous measures of ecosystem properties related to vegetation function can be derived. In Australia, a network of eddy covariance sites covers many of the continent's ecosystems (the OzFlux network, Beringer et al., 2016) and the lon- gest running sites now have measurements for >20 years. In contrast to long-term ecological monitoring, one-off observa- tions represent a single snapshot in time and cannot demonstrate directional or persistent vegetation change. For example, reports of large-scale tree mortality worldwide (e.g., Hammond et al., 2022) and in Australia (e.g. Brouwers et al., 2013; Duke et al., 2017; Losso et al., 2022; Wright et al., 2023), while notable and worrisome, may not be historically anomalous (Fensham et al., 2019; Godfree et al., 2019) and, because trees can sometimes recover, may be tran- sient (Losso et al., 2022). 3.5 | Remote sensing Remote sensing spans myriad sensor technologies that can be ground-based (e.g., photopoints, terrestrial laser scanning) or deployed on platforms ranging from unoccupied aerial vehicles to satellites. In Australia, aerial photographs from as early as the 1930s provide a rudimentary but useful characterisation of historical vegetation cover (e.g., Fensham & Fairfax, 1996; Harrington & Sanderson, 1994). Satellite-derived remote-sensing products give systematically retrieved “wall-to-wall” coverage of multispectral data, with some records extending to the early 1970s and more accessible records from the early 1980s (e.g., Landsat and the Advanced Very High Reso- lution Radiometer, AVHRR). Multispectral remote sensing has been used to map changes in land cover as well as vegetation indices that can approximate canopy leaf area (e.g., Rifai et al., 2022). Several newer remote-sensing techniques (e.g., solar-induced fluorescence, imaging spectroscopy, LiDAR) can detect properties more closely linked to vegetation structure, composition and function (Cavender- Bares et al., 2020; Jucker et al., 2023; Youngentob et al., 2012). They hold substantial future promise, but their broad-scale implementation is slow, and data are currently too limited in temporal and spatial scope to be of use in long-term detection and attribution of change. 4 | ATTRIBUTING DRIVERS TO VEGETATION CHANGES Most observed vegetation changes—in Australia and worldwide— could be the result of more than one driver. Attribution involves iden- tifying the relative contribution of these drivers and assigning statisti- cal confidence to their roles. Attribution of changes detected in WILLIAMS ET AL. 7 observational data (in contrast to experiments with known manipula- tions and controls) is typically probabilistic rather than definitive. However, some principles may help assert causality, including: the cause must occur before the effect; a plausible mechanism links the cause and effect; the magnitude of the effect is consistent with process-based understanding (noting that our understanding may be imperfect and frequently ignores or treats separately the influences of humans); and alternative explanations can be eliminated (Byrnes & Dee, 2025; Hill, 1965; Kimmel et al., 2021). These principles can be formalised into an attribution workflow for detecting and attributing vegetation change following those out- lined for climate, biodiversity change and other ecological responses (e.g., Dudney et al., 2024; Gonzalez et al., 2023; Hansen et al., 2016; Hegerl et al., 2010; Rosenzweig & Neofotis, 2013), as follows: 1. Develop a quantitative causal model (which may take any form from hypotheses with quantified effect sizes to a formal process-based model, such as a dynamic vegetation model) that predicts the mag- nitude of vegetation change from hypothesised drivers using process-based understanding derived from observations, experi- ments and meta-analyses. 2. Collect observations of vegetation through time alongside tempo- rally resolved data for specific hypothesised drivers of change. 3. Statistically analyse the vegetation data to detect change against background variability, such as the distribution of historical variability. 4. Compare vegetation data with the hypothesised drivers of change (using logic or quantitative techniques such as statistical or process-based models) to attribute drivers to observed changes— or to refute model hypotheses. While it is rare to have sufficient information to apply all steps of this formal workflow (Parmesan et al., 2013), attribution should be attempted. Imperfect attempts can still provide valuable insights. However, it is useful to be explicit about which principles have been applied. We posit that attribution efforts differ along three main axes: (i) whether the form of evidence is quantitative or qualitative, (ii) whether a process-based explanation has been investigated (mech- anistic or correlative) and (iii) the extent to which alternative plausible change drivers are considered (strong or partial). Based on these axes, we propose a simple scheme to classify attribution efforts into six classes (Table 2). In the following, we describe each of these six classes and illustrate them with examples of vegetation change that have been detected and attributed from across Australia (Figure 3, Table 3) and which draw upon various data sources outlined above (Section 3). In doing so, we survey the current strength of attribution in Australia, pinpoint the difficulties and challenges that limit attribution and iden- tify pathways toward improving attribution. 4.1 | Qualitative Correlative Qualitative Correlative attribution describes cases where changes are hypothesised to be consistent with a given change driver, but there is no direct evidence—qualitative or quantitative—to support or refute the effect of the change driver. For example, analyses of digitised aerial photographs from regions of northern Australia have revealed shifts in vegetation distributions: forest expanded into grasslands over 40 years in the Bunya Mountains of Queensland (Fensham & Fairfax, 1996); rainforest expanded into eucalypt forest over ca. 50 years in far north Queensland (Harrington & Sanderson, 1994; Tng et al., 2012); and rainforest expanded into savanna over ca. 50 years at several sites in the Northern Territory (Banfai & Bowman, 2006; Bowman, Walsh, & Milne, 2001; Brook & Bowman, 2006). The drivers of these observed vegetation shifts were hypothesised to include CO2 fertilisation, changes in rainfall patterns and/or altered fire regimes. However, none of these studies could for- mally attribute drivers to the observed vegetation changes because data on change drivers was lacking and/or vegetation data was of insufficient temporal resolution for statistical analysis. While not offering conclusive attribution, cases of Qualitative Correlative attribution can contribute important insights and generate hypotheses for further investigation. For example, a study of stable carbon isotopes in soil indicates boundaries between Triodia (C4 pho- tosynthetic pathway) grassland and Acacia aneura (C3 photosynthetic pathway) shrubland in the Tanami Desert of central Australia have remained stable for the past ca. 1,000 years (Bowman et al., 2007). TABLE 2 Classification of the degree of attribution in studies of vegetation change. Form of evidence Quantitative Qualitative Mechanistic certainty Process-based Strong Quantitative Strong.—Mechanistic model applied; effect size(s) of given driver(s) able to explain observations, other drivers not able to Qualitative Strong.—Evidence to support role of driver(s) and evidence to exclude role of other plausible drivers Partial Quantitative Partial.—Mechanistic model applied; effect size of given driver able to explain observations but other drivers not examined Qualitative Partial.—Evidence to support the role of one driver but other plausible drivers not examined Correlative Quantitative Correlative.—No mechanistic model applied (thus no process-based expectation of effect sizes), but correlation between driver and observations Qualitative Correlative.—Hypothesis only 8 WILLIAMS ET AL. This is despite the expectation that shifts from Indigenous to modern colonial fire management might have caused the shrubland to retreat. Pollen and charcoal records from western Tasmania indicate vegeta- tion distributions in this region are also stable, but this example reveals how human actions can modify climate-related effects on veg- etation (Fletcher & Thomas, 2010). Pollen records show that, over the past 18,000 years, rainforest and moorland boundaries remained static despite warming conditions, and charcoal values during this period are higher than in previous periods of comparable climate. Drawing on these patterns, Fletcher and Thomas (2010) hypothesise that recently arrived humans brought new sources of ignition that were independent of climate, and that vegetation boundaries resisted climate-induced shifts because they were developed and maintained by humans—that is, they argue that this region of western Tasmania is a cultural landscape. An example from northeastern Australia illustrates how land man- agement may overlay and interact with global changes to complicate change attribution. Burrows et al. (2002) examined tree growth and survival in eucalypt woodlands (savannas) at a network of inventory plots that were repeatedly surveyed for up to 17 years. They found that basal area increased through time, mostly due to tree growth rather than new establishment. The authors acknowledge a potential effect of elevated atmospheric CO2 and climate change. However, they postulated that elevated atmospheric CO2 and climate changes that promote plant growth would preferentially enhance grass at the expense of trees. Instead, they draw upon anecdotal observations of F IGURE 3 Approximate locations of the examples of observed vegetation changes in Australia discussed in Section 4. Examples are colour- coded by their attribution class: blue indicates Qualitative attribution and yellow indicates Quantitative contribution, with darker shades of each colour indicating stronger attribution (Table 2). Symbols indicate the main data sources used in change detection (top left of each example) and the attributed driver(s) (bottom right). See Table 3 for further details. WILLIAMS ET AL. 9 TABLE 3 Summary of some examples of vegetation changes observed in Australia and their attribution. Attribution class Data source Time scale System and location Changes observed Attribution Reference Qualitative Correlative Pollen Past 18,000 years Western Tasmania Increased charcoal; stable vegetation boundaries Fire regime, climate change (long-term warming) Fletcher and Thomas (2010) Qualitative Correlative Stable isotopes in soil Past ca. 1,000 years Tanami Desert, central Australia Stable boundary between Triodia grassland and Acacia aneura shrubland Soil fertility, fire regimes (increased soil fertility leads to more woody biomass, less grass and less fire) Bowman et al. (2007) Qualitative Correlative Forest inventory 1982-ca. 2000 Savanna, Queensland Stand structure (increased basal area) Grazing, fire regime Burrows et al. (2002) Qualitative Correlative Long-term monitoring 1926–2002 Chenopod shrubland, Koonamore, South Australia Increased number of trees and shrubs Rainfall (increased), grazing (rabbit removal) Foulkes et al. (2014), Sinclair (2005) Qualitative Correlative Long-term monitoring (eddy covariance) 2001–2018 Savanna, Northern Territory and temperate eucalypt forest, southern NSW Increased water use efficiency CO2 fertilisation, recovery from disturbance (insect outbreak), forest development Beringer et al. (2022) Qualitative Correlative Remote sensing (aerial) 1941–1994 1947–1997 1964–2004 Savanna, Northern Territory Rainforest expansion into savanna CO2 fertilisation, rainfall (increased), fire regime Banfai and Bowman (2006), Bowman, Walsh, and Milne (2001), Brook and Bowman (2006) Qualitative Correlative Remote sensing (aerial) 1951–1991 Montane grassland, Queensland Forest expansion into grasslands Fire regime Fensham and Fairfax (1996) Qualitative Correlative Remote sensing (aerial) 1943–1992 1949–2008 Wet tropics, Far North Queensland Rainforest expansion into eucalypt forest CO2 fertilisation, fire regime Harrington and Sanderson (1994), Tng et al. (2012) Qualitative Partial Remote sensing (aerial) 1941–2004 Savanna, Northern Territory Rainforest expansion into savanna Increased rainfall, CO2 fertilisation Bowman et al. (2010) Qualitative Strong Multiple (pollen, tree rings, historical records) Past 250 years Northwestern Tasmania Rainforest expansion Fire regime (cessation of Indigenous fire management) Fletcher, Hall, and Alexandra (2021) Qualitative Strong Multiple (long-term monitoring, tree rings, remote sensing, field experiments, historical records) 20th Century Callitris intratropica, savanna, Northern Territory Population collapse Fire regime Bowman et al. (2022) Quantitative Correlative Pollen Past 12,000 years Southeastern Australia Composition shifts including increased dominance of eucalypt woodlands and increasing woodland openness Climate change (long- term drying) Adeleye, Haberle, et al. (2023) Quantitative Correlative Tree rings Past ca. 1700 years Athrotaxis selaginoides and Lagarostrobos franklinii, northwestern Tasmania Increasing growth rates since mid-1900s Climate change (temperature) Allen et al. (2017) 10 WILLIAMS ET AL. fence lines, where vegetation was subject to different grazing man- agement, to hypothesise that observations of increased tree growth are most likely due to increased grazing intensity and reduced fire frequency. 4.2 | Qualitative Partial While still lacking quantitative data, other forms of evidence may support or refute a plausible change driver, such as accompanying experiments that demonstrate the effect of a change driver, or evi- dence for the timing of an event that is hypothesised to be a change driver (i.e., a single data point rather than quantified magnitude). We classify such attribution as Qualitative Partial. For example, in synthesising studies of rainforest expansion into tropical savanna in the Northern Territory, Bowman et al. (2010) refute the hypothesis that the changes were the result of fire regime change. They reached this conclusion because demographic studies provided evi- dence that the population of a dominant rainforest tree was stable in response to altered fire regimes (Prior et al., 2007). By eliminating one plausible change driver, Bowman et al. (2010) were able to attri- bute rainforest expansion to their remaining hypothesised change drivers: increases in rainfall and elevated atmospheric CO2. How- ever, additional evidence to distinguish between these two change drivers was lacking. 4.3 | Qualitative Strong Qualitative attribution may be considered Strongwhere alternative plau- sible change drivers are considered and evidence is presented to dis- criminate among them. Such cases often combine data from multiple TABLE 3 (Continued) Attribution class Data source Time scale System and location Changes observed Attribution Reference Quantitative Correlative Tree rings 1908–2018 Callitris columellaris, Western Australia and Northern Territory Highly variable tree growth Rainfall (amount and interannual variability) O'Donnell et al. (2021) Quantitative Correlative Forest inventory 1947–2000 Eucalyptus regnans, Victoria Increased mortality rates, decreased carrying capacity Climate change (increased temperature and vapour pressure deficit) Trouvé et al. (2025) Quantitative Correlative Forest inventory 1964–2019 Callitris-eucalypt forest, New South Wales Stand structure (more but smaller trees) Fire regime, rainfall Neumann et al. (2023) Quantitative Correlative Forest inventory 1965–2007 Floodplain forest, Victoria Tree mortality River regulation (human-induced drought) Horner et al. (2009) Quantitative Correlative Forest inventory 1971–2019 Tropical moist forest, Far North Queensland Increased mortality rates Climate change (increased vapour pressure deficit) Bauman et al. (2022) Quantitative Correlative Long-term monitoring 1944–2010 Alpine grassland and heathland, Bogong High Plains, Victoria Increased shrub cover, species-specific changes in forb cover, recent decreases in graminoid cover Rainfall (decreased), temperature (increased) Wahren et al. (2013) Quantitative Correlative Remote sensing (aerial and satellite) 1950–2016 Savanna, Northern Territory Fluctuation in woody cover but no systematic change Fluctuations attributable to rainfall and fire Prior et al. (2020) Quantitative Correlative Remote sensing (satellite) 2003–2017 Grasslands, southeast Australia Shift in C3:C4 grasses Rainfall (seasonal timing) Xie et al. (2022) Quantitative Partial Long-term monitoring (eddy covariance) 2001–2018 Savanna, Northern Territory Increased productivity and water use efficiency CO2 fertilisation, rainfall, temperature (increased) Hutley et al. (2022) Quantitative Partial Remote sensing (satellite) 1982–2019 Woody ecosystems, eastern Australia Greening of woody ecosystems CO2 fertilisation Rifai et al. (2022) WILLIAMS ET AL. 11 sources. For example, Fletcher, Hall, and Alexandra (2021) combined pollen and charcoal records, tree ageing from tree rings and a written historical account to examine vegetation change over the past 250 years in the Surrey Hills, northwestern Tasmania. Pollen analysis revealed a shift from grassland to temperate rainforest, consistent with a historical description of vegetation and supported by the timing of tree establishment based on dating of Nothofagus cunninghamii and Phyllocladus aspleniifolius stands using tree rings. Together, these data sources show that this vegetation change predated human-driven increases in elevated atmospheric CO2 and climate change. Fletcher, Hall, and Alexandra (2021) attribute the observed vegetation change to altered land management: grasslands were actively maintained by Indig- enous people through a regime of burning, which ceased with British colonisation. They concluded that as cultural burning ceased, rainforest tree species were able to successfully recruit and persist. This led to a transition from culturally maintained grasslands to closed rainforest over approximately two centuries. By drawing on multiple lines of evi- dence, this study builds a case through qualitative reasoning that the observed vegetation changes were driven by altered land management and that their timing is inconsistent with anthropogenic climate change. Synthesising evidence from multiple sources can reveal insights not evident from a single source alone. For example, the loss of popu- lations of a significant tree species from northern Australia appears consistent with climate change but, taken together, evidence from multiple sources suggests this change is largely attributable to human- altered fire regimes (Box 1). 4.4 | Quantitative Correlative Quantitative attribution involves comparing data on the magnitudes of change in vegetation and the hypothesised change driver. Quantitative Correlative attribution demonstrates a statistical correlation between a plausible change driver and patterns of vegetation change. The sophistication of analyses may vary substantially, with some methods better able to account for confounding variables (see e.g., Byrnes & Dee, 2025). There are many studies of vegetation change in Australia that could be considered Quantitative Correlative. For example, long tree- ring chronologies have been developed for the Tasmanian conifers: Athrotaxis selaginoides (King Billy pine) and Lagarostrobos franklinii (Huon pine). Each extends over 1,000 years, shows sensitivity to tem- perature, and shows accelerated growth beginning in the mid-1900s (Allen et al., 2014, 2017; Cook et al., 1991). These growth trends were attributed to increased temperatures based on correlations with instrumental records (Allen et al., 2014, 2017; Cook et al., 1991). However, the role of other potential drivers of growth trends, such as increasing atmospheric CO2 concentrations, were unclear. Moreover, comparing these two chronologies reveals some intriguing differ- ences: unusually rapid growth was detected from 1965 in the Huon Pine chronology (Cook et al., 1991) but ca. 25 years earlier in King Billy pine (Allen et al., 2017). Why the chronologies indicate that these species began growing faster at different times is unclear, but might relate to differences in sampling intensity, localised climate conditions, physiological differences between species and the effects of climatic drivers of tree growth other than temperature, such as water availabil- ity (Allen et al., 2017). Detecting systematic trends driven by climate change can be complicated by climatic variability, and the consequences of climatic shifts may differ among biomes. This is demonstrated by another tree-ring study: O'Donnell et al. (2021) examined five chronologies of Callitris columellaris spanning a climate gradient from southwest West- ern Australia through to the Northern Territory. They found ring widths were highly variable through time, and they did not document a directional trend in growth over the 100-year study period. Instead, they found growth was strongly correlated with rainfall, with different aspects of rainfall regimes explaining growth in different parts of the species' range: growth at semi-arid sites was strongly and linearly related to annual rainfall, while growth in the wet-dry (monsoon) tro- pics was sensitive to interannual variability in rainfall. These results suggest the sensitivity of productivity to climate drivers may differ among biomes and show how strong interannual climatic variability may overwhelm the capacity to detect systematic climate-driven trends in growth. Quantitative-Correlative studies vary in the extent to which alter- native plausible change drivers are considered. For example, Horner et al. (2009) examined 42 years of tree inventory data from an experi- mental spacing trial in floodplain forest in southeastern Australia. They found that tree mortality increased over time in the highest den- sity stands. Using change point analysis, they attributed the increase in mortality to human regulation of stream flow and reduced water table depth. Moreover, they discounted drought as the sole cause of mortality by demonstrating the recurrence of drought periods without concomitant pulses in mortality. This example also demonstrates how other human-related change drivers may mask or be related to climate change in a complex fashion. Several other examples reveal challenges in strengthening attribu- tion beyond Qualitative Correlative. For example, Neumann et al. (2023) examined growth and survival in the Pilliga Forest—semi-arid forest in inland eastern Australia dominated by Eucalyptus and Calli- tris—with permanent sample plot data consisting of five repeat cen- suses between 1964 and 2019. They found subtle changes in median tree basal area and stem density over this period across the plots, with a gradual 26% increase in basal area (24% in stem density) until 2000 and a subsequent small (9% in basal area, 7% in stem density) decrease to 2019. The decline in basal area between 2000 and 2019 was associated with a concomitant decline in rainfall, and thus, this change was attributed largely to drought. However, the relative stabil- ity of recent times contrasts with substantial variability during the post-colonisation period. Analyses of historical records and cut stumps (Lunt et al., 2006; Rolls, 1999; Whipp et al., 2012) suggest that the area was likely a widely spaced eucalypt woodland pre-colonisation, but was transformed by extensive clearing, followed by a recruitment pulse of Callitris. These stands now have similar basal area but much higher stem densities compared to pre-colonisation. The past disturbance regime may be continuing to shape stand 12 WILLIAMS ET AL. dynamics. Given the long time periods between inventory measure- ments, which limit statistical analysis, it is difficult to attribute changes observed in this ecosystem to a unique driver. Long and temporally resolved data sequences are needed to iden- tify systematic change. For example, studies of aerial photographs suggest woody cover has increased within the tropical savanna (Lehmann et al., 2009), but this inference is based on a few time points. A recent study (Prior et al., 2020) combined photographic records with satellite data to increase the number of data points from four to ten across the time period 1950 to 2016 and expand the spa- tial extent of the study area. The improved temporal resolution and extent allowed the authors to conduct statistical analyses that revealed woody cover fluctuated through time following patterns of recent rainfall and fire but—in contrast to earlier conclusions—showed no long-term, directional change. A related challenge is to distinguish internal vegetation dynamics from externally driven vegetation change. For instance, tree mortality BOX 1 MULTIPLE LINES OF EVIDENCE: A CASE STUDY OF CALLITRIS POPULATION COLLAPSE. Studies of Callitris intratropica R.T.Baker & H.G.Sm. popula- tion dynamics reveal a recent population collapse that has been attributed to changing fire regimes, largely indepen- dent of climate change. Evolutionary history, physiology and ecology of Calli- tris explain its current distribution. Callitris intratropica is a member of an ancient Gondwa- nan conifer group (Crisp et al., 2019). Callitris intratropica trees are slow-growing and live for up to 300 years. The species forms distinct annual tree rings, which enable ageing of trees (Baker et al., 2008; Pearson et al., 2011). Ecophysio- logical research demonstrates it is an extreme xerophyte (Brodribb et al., 2013). Despite occurring in flammable envi- ronments, it is classified as a fire-intolerant obligate seeder (Prior & Bowman, 2020), having few fire resistance and recovery traits, poor dispersal and short-lived seeds that are produced episodically (Bowman et al., 2018; Bowman, MacDermott, et al., 2014; Lawes et al., 2011). The species persists in savannas in localised grass-free groves (Trauernicht et al., 2012). Climate change and anthropogenic fire regimes in the late-Quaternary appear to have had a negligible effect on C. intratropica populations according to genetic analyses (Sakaguchi et al., 2013). In the 20th century, however, there have been widespread population collapses of this species (Bowman & Panton, 1993; Bowman, Price, et al., 2001; Edwards & Russell-Smith, 2009; Haynes, 1985; McVicar, 1922; Sharp & Bowman, 2004; Trauernicht et al., 2013; Yates & Russell-Smith, 2003). Dead C. intratropica trees are conspicuous because of their dura- ble termite-resistant timber (Gay & Evans, 1968). Field research has shown that populations of C. glaucophylla, a close relative of C. intratropica, rely on epi- sodic wet periods for recruitment in arid environments (Prior et al., 2018). By contrast, climate variation has little effect on demography of C. intratropica beyond dryness reducing tree growth (Baker et al., 2008; Bowman et al., 2011). Lines of evidence combine to show population decline consistent with anthropogenic changes in fire regimes. A multidisciplinary study was undertaken over two decades to resolve whether changed fire regimes could explain the widespread population collapse of C. intratropica (Bowman et al., 2022). This study was based on a comparison of Aboriginal management of a landscape in Arnhem Land and an adjacent landscape in Kakadu National Park that was ecologically comparable but no lon- ger managed by Aboriginal people. The study integrated a range of techniques including demographic surveys, longitudinal growth and survival analysis, field experiments, remote sensing of fire regimes and population modelling. It was found that Aboriginal fire regimes were characterised by frequent, patchy, low-intensity fires. In contrast, the areas sampled in Kakadu were frequently burned by large fires, had very few living C. intratropica, and had no regen- eration. Dendrochronological analysis, field surveys, field experiments and population modelling explained why a patchy, low intensity Aboriginal fire regime would benefit C. intratropica. Such mosaics with long unburned patches enable C. intratropica to establish dense stands of regener- ation that shade out grasses and develop a deep, fire- excluding litter mat (Trauernicht et al., 2012). Fires can degrade these stands, and continued exposure to fires causes the stand to die-out, thereby leaving clumps and eventually single dead stems. However, if unburned for several decades, a degraded stand can reinitiate another cohort of regeneration. Because contemporary fire regimes involve frequent large fires, there are few long-unburned stands where C. intratropica juveniles can establish. Fur- thermore, such frequent burning builds up grass biomass, making fire more intense and increasing the likelihood of killing juveniles and adults (Bowman et al., 2018; Bowman, MacDermott, et al., 2014). Combined, these processes drive a population collapse and cause a switch from patchy fires that generate unburned elements in the landscape to large frequent fires that produce more homogeneous habi- tats (Trauernicht et al., 2015, 2016) (Figure B1). There is emerging evidence that increasing temperatures are drying fuels earlier in the dry season, resulting in more intense fires than would have occurred in the late 20th Century (Bowman et al., 2024). This may be accelerating the loss of this fire sensitive savanna species. WILLIAMS ET AL. 13 naturally varies during stand development due to self-thinning, which can pose a challenge when interpreting changes in mortality rates over time. Trouvé et al. (2025) accounted for these internal dynamics when analysing mortality trends in Eucalyptus regnans forests in southeastern Australia. Using 63 years of inventory data from silvicul- tural experiments, they modelled both spatial and temporal variation in the self-thinning line. They found that mortality rates increased and carrying capacity decreased over time, and showed that these changes statistically correlated with rising temperatures. Case studies with sufficient data to statistically evaluate relation- ships can be especially powerful. Bauman et al. (2022) analysed tree mortality across a climate gradient in tropical forests, using an espe- cially rich forest inventory dataset of 20 0.5 ha plots established in the 1970s (Bradford et al., 2014). Across plots and species, they found that tree mortality risk had, on average, doubled over the past 35 years. Bauman et al. (2022) used gridded climate data to quantita- tively attribute increasing mortality risk to trends in atmospheric dry- ing (vapour pressure deficit, VPD). They also discounted some plausible alternative change drivers. For example, cyclones were ruled out as the sole explanation of the observed mortality patterns, and mortality risk did not increase with growth rate. However, they did not assess whether the effect size matched process-based understanding—and relevant processes remain uncertain. These tropi- cal forest inventory data are a rare case of having sufficient temporal extent and resolution to quantitatively evaluate a signal of climate change. Satellite records have revealed some nuanced changes in Australia's vegetation. For example, Xie et al. (2022) analysed a 15-year satellite record of enhanced vegetation index (EVI) across grasslands in southeastern Australia to detect change in C4 and C3 grasses by leveraging differences in their seasonal growth patterns. They found the total area covered by grasses has remained relatively constant, but the cover of C3 grasses had decreased and C4 grasses had increased over time—a trend corroborated by field observations. This observation is notable because modelling studies disagree on whether rising CO2 concentrations (Collatz et al., 1998; Luo et al., 2024) or temperature and water availability (Havrilla et al., 2023; Murphy & Bowman, 2007; Winslow et al., 2003) are the main drivers of C3-C4 distributions. Xie et al. (2022) also found these trends to be most strongly correlated with a shift in the seasonal tim- ing of rainfall (i.e., more rain in summer). Their study provides a good example of how attempts at attribution can lead to insights into the mechanisms of vegetation change. Nonetheless, process-based esti- mates of the magnitude of the effect size were absent from this study. 4.5 | Quantitative Partial Correlations between vegetation change and plausible mechanisms— as demonstrated in cases of Quantitative Correlative attribution—can give powerful indications of potential change drivers. However, even where data is sufficient for formal statistical analysis, the presence of statistical significance (or lack thereof) does not necessarily imply bio- logical importance. Incorporating process-based understanding is cru- cial in moving toward stronger attribution. This may be achieved by using models to delimit the expected magnitude and/or timing of effects based upon an understanding of the biological processes that shape an ecosystem. Quantitative Partial attribution describes cases where this is done; that is, where there is explicit consideration of the effect that would be anticipated from process-based understanding. Broad-scale (i.e., global, continental or large regional) remote- sensing studies often include quantitative attribution (Burrell et al., 2020; Zhu et al., 2016), but only occasionally do they evaluate whether observed effects match the magnitude anticipated from process-based understanding. For example, global studies of long- term increases (greening) and decreases (browning) in vegetation greenness indices derived from satellite remote sensing data (De Jong et al., 2011; Winkler et al., 2021) have revealed signals of vegetation change, but they have reached conflicting conclusions regarding the extent, magnitude and location of greening and browning trends over Australia (Burrell et al., 2020; Cortés et al., 2021; Higgins et al., 2023; Wang et al., 2020; Winkler et al., 2021; Yang et al., 2023; Zhu et al., 2016). This largely stems from differences in time periods of F IGURE B1 Callitris intratropica population dynamics on sand sheets of the Arnhem Plateau are influenced by many drivers. Fire regimes shaped by human actions currently have a dominant role in leading to alternative population outcomes. Under traditional Aboriginal fire regimes (a), frequent patchy, low-intensity fires in the landscape maintain a mosaic of groves and individual trees that fluctuate over time. Under modern colonial fire regimes (b), frequent large, higher-intensity fires abet the establishment of flammable grasses, which promote fires and lead to degraded groves, increased mortality, and, eventually, population collapse (following synthesis in Bowman et al., 2022). 14 WILLIAMS ET AL. analysis, oversights in data quality processing and statistical analysis (Cortés et al., 2021) and underlying differences in (model) attribution assumptions. For instance, the remote sensing record is short enough that patterns may be affected by a multi-year period of drying or wet- ting (e.g., the Millennium Drought, a decade-long drought that affected much of southern Australia from the late 1990s through 2010, the 2010–2011 La Niña and the 2017–2019 drought). Rifai et al. (2022) sought to address this by harmonising different NDVI records to analyse the change in NDVI over 38 years across the woody ecosystems of eastern Australia. They found patterns of greening and browning were highly variable at the decadal scale. But, after removing areas affected by disturbance (i.e., fire, deforestation), they detected a widespread greening trend—and the magnitude of this trend matched the CO2 fertilisation effect predicted from a process-based model. Thus, this is a good example of Quantitative Par- tial attribution. However, this and many other remote sensing studies (e.g., Xie et al., 2022) focus solely on global change drivers repre- sented in gridded datasets (e.g., precipitation, temperature and evapo- transpiration) and do not consider the effects of other potential drivers (e.g., grazing). Ground-based monitoring reveals trends and presents opportuni- ties for detailed attribution, but faces similar challenges in accounting for the full range of potential drivers. For example, Hutley et al. (2022) used 18 years of eddy covariance data to show that gross primary productivity and water-use efficiency increased over time at a savanna in the Northern Territory. A detailed process-based model was used to explore which climate drivers could potentially explain this trend, and found that changes in atmospheric CO2, temperature and rainfall over the period could all have contributed. However, they also noted that the site is recovering from historic cyclone damage, providing a potential alternative explanation for the trend. The fine temporal resolution and accumulating time series of the eddy covari- ance data facilitate attribution to climate drivers. Indeed, comparable data are also available for other sites, including an eucalypt forest in southern NSW for which formal attribution has not yet been attempted (Beringer et al., 2022). However, data to assess the effects of land management or historical legacies is typically limited at these monitoring sites. 4.6 | Quantitative Strong Attribution of the highest level—Quantitative Strong—requires a quan- titative and mechanistic approach that considers the effects of the range of alternative plausible change drivers. In the example of Quan- titative Partial attribution (Rifai et al., 2022), an observed vegetation change was shown to be consistent with the effect size of a plausible change driver (elevated CO2) anticipated from process-based under- standing, but the study did not formally evaluate other plausible explanations of the observed changes. Studies rarely (if ever) evaluate the influences of both global change drivers (e.g., CO2, elevated tem- perature, altered precipitation) and regional conditions and contexts, such as the influences of fire, livestock grazing, feral browsers and grazers and/or water management. Yet, this is exactly what is needed for the highest level of attribution. Quantitative Strong attribution quantifies the timing and magni- tude of the effects of plausible alternative change drivers, including interactions among them, so as to parse their independent and com- bined influences. Mechanistic models are central to achieving this standard of attribution because they are the main tool for quantifying process-based understanding of the functioning of plants and their interactions with the environment (Box 2). However, it is important to note that misattribution can still occur within any attribution class. For instance, plausible drivers may be missed because the process- based understanding of a system is incomplete (Grimm et al., 2020). There are no examples from our collective knowledge of studies of vegetation change in Australia that achieve this ‘gold standard’ of attribution. However, in the following, we outline two examples in Australia that may have the ingredients required: they have long-term data on vegetation and plausible change drivers as well as process- based expectations that could be formally combined with a process- based model. The first example comes from the longest-running monitoring site in Australia, Koonamore Reserve, which is an arid chenopod shrubland in South Australia that has been monitored using photo- points and permanent quadrats since 1926. The reserve has experi- enced pronounced shifts in grazing and browsing intensity over the past century—it was heavily overgrazed by sheep before being fenced in 1925, and populations of rabbits within the reserve have fluctuated with several periods of very heavy rabbit browsing up until the 1980s (Foulkes et al., 2014). The region also experiences highly variable rainfall at both annual and interdecadal timescales, with the period 1970–2010 being considerably wetter than 1920– 1970 (Foulkes et al., 2014). Counts of tree and shrub numbers in permanent quadrats within the reserve have revealed changes in the number of plants through time (Crisp, 1978; Crisp & Lange, 1976); in particular, large increases in several shrub species have been observed over the last 30 years (Foulkes et al., 2014; Sinclair, 2005). Previous studies classified as Qualitative Correlative attribution reported that reduced intensity of rabbit grazing along- side increasing rainfall may explain these changes, but found the influence of these two factors was difficult to separate (Foulkes et al., 2014). The temporal observations of vegetation and hypothe- sised dominant change drivers, as well as studies of grazing manipu- lations (Sinclair & Facelli, 2019), combine to form a rich, long-term dataset that could potentially support a detailed attribution study of vegetation change. A set of long-term monitoring plots located in alpine grassland and heathland offers a similar opportunity (Williams et al., 2014). The alpine region was used for summer cattle pasturing from the 1820s, and heavy grazing had caused significant degradation by the 1930s. Fenced and unfenced plots were established in the mid-1940s in the Bogong High Plains (Carr & Turner, 1959a, 1959b) and demonstrated that cattle grazing increased bare soil and prevented regeneration of palatable forbs and shrubs, while exclusion of cattle allowed gradual recovery of vegetation (Wahren et al., 1994). This evidence WILLIAMS ET AL. 15 contributed to the eventual banning of cattle grazing in the high coun- try in 2004. The major change drivers of concern in this region now include rising temperature and increasing fire frequency. Over time, monitoring has extended to plots in grassland, wetlands and snow- patch vegetation, as well as field experiments manipulating tempera- ture and fire (Camac et al., 2017; Jarrad et al., 2008; Wahren et al., 2013). Long-term, unburnt plots showed ongoing shrub encroachment, a decline in grass cover and variability in forb cover, over the period 1980–2010 (Wahren et al., 2013, 1994). These trends were mirrored in a warming experiment that commenced in 2004, with 1�C warming causing additional shrub expansion (Wahren et al., 2013). The observed vegetation changes were attributed to decreased annual rainfall and increased temperature on the grounds of significant statistical correlations (Wahren et al., 2013). However, the availability of long-term monitoring data in conjunction with experimental manipulation of grazing intensity, fire and temperature could allow for a formal attribution analysis to quantify the contribu- tion of individual drivers to vegetation change. BOX 2 GETTING TO QUANTITATIVE STRONG: ATTRIBUTION WITH MECHANISTIC MODELS. The key feature of Quantitative Strong attribution is that the magnitude of the observed change can be partitioned, on mechanistic grounds, into potential change drivers. Mecha- nistic models—that is, quantitative models that include rep- resentations of the main processes driving change—are an important tool to achieve attribution. For example, climate models are the main tool used to attribute observed variabil- ity in global temperatures to natural or anthropogenic causes (IPCC AR6). Similarly, for vegetation, Dynamic Global Vegetation Models (DGVMs) have been applied at a global scale to attribute spatio-temporal variation in terrestrial car- bon storage among three primary drivers, namely changes in atmospheric CO2, climate change and variability and changes in land use and land cover (Sitch et al., 2024). Coupled fire-vegetation models have also been applied to attribute drivers to variation in fire activity, untangling the interactive and sometimes compensatory effects of human activity and climate drivers on how much area is burned in different regions of the globe (Burton et al., 2024). Mecha- nistic models are important for Quantitative Strong attribu- tion because (i) they can be used to estimate the relative contributions of drivers, including those that are con- founded and their interactions, by conducting idealised sim- ulations with and without the influence of individual drivers (see, e.g., Bond et al., 2005), and (ii) they can draw on a wide range of evidence—not just the dataset to be attributed—to verify that the magnitude of observed change is consistent with expectations based on current scientific understanding. There are several requirements to apply mechanistic models to the attribution of vegetation change. First, time- series data documenting temporal trends in vegetation properties and the main potential drivers are needed. Some of the relevant driver datasets, such as time series of climate and atmospheric CO2, are readily available, but others, such as grazing pressure and fire management history, are often less readily available (or in some cases not available at all). One advantage of a model-based attribution approach is that it can help to identify these critical data needs. Second, we need a quantitative bio-physical understanding of how the main drivers affect vegetation processes and properties. Manipulative experiments that study the effects of individ- ual drivers are key to developing this understanding: for example, free-air CO2 enrichment experiments (Jiang et al., 2020), warming experiments (Wahren et al., 2013) and grazing exclusion experiments (Forrester et al., 2025) can be used to develop the mechanistic understanding required to represent these processes in a model. Natural experiments, when carefully leveraged, can be valuable for understanding phenomena that cannot be experimentally manipulated, such as extreme events or processes that are slow or episodic. Model predictions also need to be evaluated against observations, to build confidence that processes are suffi- ciently well represented to support attribution. Applying models for attribution, therefore, involves an iterative pro- cess of calibration, validation and updating (Figure B2). Although mechanistic models have not yet been used for attribution of vegetation change in Australia, most of the requirements to do so are in place, including suitable vege- tation models (e.g., Stephens et al., 2023; Wang et al., 2024), and should enable the adoption of model- based frameworks for attribution in the near future. F IGURE B2 The iterative process required for attribution with mechanistic models. Data from manipulative experiments, meta- analyses and observations can be used alongside theory to develop, calibrate and evaluate process-based models to ensure they sufficiently represent vegetation change. Limits in process-based understanding may be identified by insufficient observations to validate model outputs or mismatches between observations and model outputs, and can be used to guide the collection of new data to update models. Once model outputs are consistent with observations, models can be applied to estimate the contribution of individual drivers to the overall change. 16 WILLIAMS ET AL. 5 | SYNTHESIS: GLOBAL IMPLICATIONS AND RECOMMENDATIONS Australia presents a test case for attributing vegetation change to a changing climate across large geographic scales and across a range of climate zones. Globally, attempts to forecast and manage future vege- tation change should be informed by observations of past and ongo- ing change. Across the Australian continent, numerous studies report vegetation changes (Figure 3, Table 3), including forest expansion, vegetation thickening and greening, as well as tree mortality and for- est loss. The overall trend could be interpreted as one of increasing vegetation biomass with some pockets of loss that are often abrupt. However, it remains challenging to identify trends from the available data—and even more so to determine the role of climate in driving these trends. Drivers that have been attributed to the observed vegetation changes in Australia include land management, fire and grazing, in addition to climate drivers such as rainfall seasonality, drought and elevated atmospheric CO2 (Figure 3, Table 3). Over time, there has been a trend toward quantitative and stronger attribution, likely driven by accumulating data as well as advances in methods of data collection (see e.g., Section 3) and analysis. Yet, to date, few studies approach the upper left of the attribution classification scheme (Table 2), having both quantitative evidence and strong mechanistic certainty while also considering alternative plausible change drivers. This reflects the fact that attributing drivers to vegetation change— particularly attribution that approaches Quantitative Strong—is extremely difficult both theoretically and practically. Nevertheless, we advocate for explicitly considering the strength of attribution and striving for improved attribution because such understanding is needed to develop accurate predictions that, in turn, can support management and environmental markets. Several of the challenges that limit attribution in Australia cer- tainly exist elsewhere in the world, including a complex history of changing land use, short and patchy instrumental records and environ- mental conditions that limit the availability of palaeoecological records (e.g., pollen, tree rings). Similarly, some key recommendations from our review of Australia are likely applicable to improve detection and attribution in other parts of the world. A stronger commitment to long-term monitoring is needed if we are to detect trends against high background variability. For example, in regions with high levels of background variability, such as the high inter-decadal variability in rainfall across Australia, datasets stretching back several decades are particularly valuable. However, long intervals between observations (>10 years) significantly reduce the utility of data for detecting and attributing change. Different approaches to gathering observations (including those outlined in Section 3 and Table 2) have clear strengths and limitations relative to the steps needed for detection and attribution (Section 4). As long recognised (e.g., Delcourt et al., 1982), studies focused at different spatio- temporal scales may be best placed to detect vegetation changes in response to different change drivers. For example, regional to local scale studies might be able to fingerprint interactions among drivers of change that cannot be assessed with global datasets. Combining approaches can also lead to greater insight (Box 1). In addition to the observations of vegetation that are needed to detect changes, observations of the range of plausible change drivers (such as grazing and fire) are needed to attribute drivers to change. Moving toward stronger attribution requires consideration of plausi- ble change drivers beyond those that may be derived from the current suite of gridded datasets. Further, attempts to understand past and ongoing vegetation change must consider the influences of human activities. This includes recognising the underpinning purpose and practice of Indigenous care and management structures that, in the case of Australia, created and maintained environments for millennia (Section 2). Finally, a recurring thread that weaves throughout this review is the integral partnership between mechanistic models and observa- tions (Box 2). Moving toward more robust attribution of vegetation change and identifying the influence of climate requires the develop- ment of quantitative causal models whereby observations of change can be compared to predicted effects (Section 4). Ultimately, long- term observations of vegetation and change drivers in partnership with mechanistic models are needed to predict vegetation change, inform proactive management and give the best chance of sustaining the ecosystem services upon which humans rely. AUTHOR CONTRIBUTIONS B.E.M. initially conceived the idea for the symposium and subsequent paper. B.E.M., D.M.J.B., J.R.E., L.J.W., M.A.A., P.F.G., P.J.B., R.T. and S.W.R. contributed text to the initial draft. L.J.W. led manuscript development and writing, and all authors contributed to revisions and further manuscript development. ACKNOWLEDGEMENTS This paper arose from the symposium “Detecting and attributing change in Australian vegetation” convened by B.E.M., R.V.G. and L.J. W. and held at the Ecological Society of Australia meeting in Darwin, July 2023. We thank all participants in the symposium as well as Rachael Nolan and Aaranya Sekaran for helpful suggestions. We also thank Anthony Walker and three anonymous reviewers for constructive feedback that led us to focus this paper on attribution strength. This work was supported by an Australian Research Council (ARC) Laure- ate Fellowship (FL190100003) awarded to B.E.M., an ARC Laureate Fellowship (FL220100099) awarded to D.M.J.S.B., and an ARC Dis- covery Project (DP220103711) awarded to P.J.B. and R.T. Open access publishing facilitated by Western Sydney University, as part of the Wiley - Western Sydney University agreement via the Council of Australian University Librarians. CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest. DATA AVAILABILITY STATEMENT No new data were generated by this study. WILLIAMS ET AL. 17 ORCID Laura J. Williams https://orcid.org/0000-0003-3555-4778 Rachael V. Gallagher https://orcid.org/0000-0002-4680-8115 Sami W. Rifai https://orcid.org/0000-0003-3400-8601 Matthew A. Adeleye https://orcid.org/0000-0002-6034-5807 Patrick J. Baker https://orcid.org/0000-0002-6560-7124 David M. J. S. Bowman https://orcid.org/0000-0001-8075-124X Jake Eckersley https://orcid.org/0000-0002-7853-194X Jacqueline R. England https://orcid.org/0000-0002-3371-1509 Michael-Shawn Fletcher https://orcid.org/0000-0002-1854-5629 Pauline F. Grierson https://orcid.org/0000-0003-2135-0272 Assaf Inbar https://orcid.org/0000-0001-5861-963X Jürgen Knauer https://orcid.org/0000-0002-4947-7067 Clare M. Stephens https://orcid.org/0000-0002-7387-0563 Raphaël Trouvé https://orcid.org/0000-0002-2210-1035 Belinda E. Medlyn https://orcid.org/0000-0001-5728-9827 REFERENCES Abatzoglou, J. T., Battisti, D. S., Williams, A. P., Hansen, W. D., Harvey, B. J., & Kolden, C. A. (2021). Projected increases in western US forest fire despite growing fuel constraints. Communications Earth & Environment, 2(1), 227. https://doi.org/10.1038/s43247-021-00299-0 Abram, N. J., Henley, B. J., Sen Gupta, A., Lippmann, T. J. R., Clarke, H., Dowdy, A. J., Sharples, J. J., Nolan, R. H., Zhang, T., Wooster, M. J., Wurtzel, J. B., Meissner, K. J., Pitman, A. J., Ukkola, A. M., Murphy, B. P., Tapper, N. J., & Boer, M. M. (2021). Connections of cli- mate change and variability to large and extreme forest fires in south- east Australia. Communications Earth & Environment, 2(1), 8. https:// doi.org/10.1038/s43247-020-00065-8 Adams, V. M., Butt, N., Allen, S., Pressey, R. L., Engert, J. E., & Gallagher, R. V. (2023). Protected, cleared, or at risk: The fate of Australian plant species under continued land use change. Biological Conservation, 284, 110201. https://doi.org/10.1016/j.biocon.2023. 110201 Adeleye, M. A., Andrew, S. C., Gallagher, R., Van Der Kaars, S., De Deckker, P., Hua, Q., & Haberle, S. G. (2023). On the timing of mega- faunal extinction and associated floristic consequences in Australia through the lens of functional palaeoecology. Quaternary Science Reviews, 316, 108263. https://doi.org/10.1016/j.quascirev.2023. 108263 Adeleye, M. A., Haberle, S. G., Gallagher, R., Andrew, S. C., & Herbert, A. (2023). Changing plant functional diversity over the last 12,000 years provides perspectives for tracking future changes in vegetation com- munities. Nature Ecology & Evolution, 7(2), 224–235. https://doi.org/ 10.1038/s41559-022-01943-4 Ahlström, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., Kato, E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P., Wiltshire, A., Zaehle, S., & Zeng, N. (2015). The dominant role of semi- arid ecosystems in the trend and variability of the land CO 2 sink. Sci- ence, 348(6237), 895–899. https://doi.org/10.1126/science.aaa1668 Allen, K. J., Anchukaitis, K. J., Grose, M. G., Lee, G., Cook, E. R., Risbey, J. S., O'Kane, T. J., Monselesan, D., O'Grady, A., Larsen, S., & Baker, P. J. (2019). Tree-ring reconstructions of cool season tempera- ture for far southeastern Australia, 1731–2007. Climate Dynamics, 53(1–2), 569–583. https://doi.org/10.1007/s00382-018-04602-2 Allen, K. J., Cook, E. R., Buckley, B. M., Larsen, S. H., Drew, D. M., Downes, G. M., Francey, R. J., Peterson, M. J., & Baker, P. J. (2014). Continuing upward trend in Mt Read Huon pine ring widths – Temperature or divergence? Quaternary Science Reviews, 102, 39–53. https://doi.org/10.1016/j.quascirev.2014.08.003 Allen, K. J., Fenwick, P., Palmer, J. G., Nichols, S. C., Cook, E. R., Buckley, B. M., & Baker, P. J. (2017). A 1700-year Athrotaxis selagi- noides tree-ring width chronology from southeastern Australia. Den- drochronologia, 45, 90–100. https://doi.org/10.1016/j.dendro.2017. 07.004 Ashton, D. H. (2000). The Big Ash forest, Wallaby Creek, Victoria— changes during one lifetime. Australian Journal of Botany, 48(1), 1–26. https://doi.org/10.1071/BT98045 Baker, P. J., Palmer, J. G., & D'Arrigo, R. (2008). The dendrochronology of Callitris intratropica in northern Australia: annual ring structure, chro- nology development and climate correlations. Australian Journal of Bot- any, 56(4), 311. https://doi.org/10.1071/BT08040 Banfai, D. S., & Bowman, D. M. J. S. (2006). Forty years of lowland mon- soon rainforest expansion in Kakadu National Park. Northern Australia. Biological Conservation, 131(4), 553–565. https://doi.org/10.1016/j. biocon.2006.03.002 Bauman, D., Fortunel, C., Delhaye, G., Malhi, Y., Cernusak, L. A., Bentley, L. P., Rifai, S. W., Aguirre-Gutiérrez, J., Menor, I. O., Phillips, O. L., McNellis, B. E., Bradford, M., Laurance, S. G. W., Hutchinson, M. F., Dempsey, R., Santos-Andrade, P. E., Ninantay- Rivera, H. R., Chambi Paucar, J. R., & McMahon, S. M. (2022). Tropical tree mortality has increased with rising atmospheric water stress. Nature, 608, 528–533. https://doi.org/10.1038/s41586-022-04737-7 Beck, K. K., Fletcher, M.-S., Gadd, P. S., Heijnis, H., Saunders, K. M., Simpson, G. L., & Zawadzki, A. (2018). Variance and rate-of-change as early warning signals for a critical transition in an aquatic ecosystem state: A test case from Tasmania, Australia. Journal of Geophysical Research: Biogeosciences, 123(2), 495–508. https://doi.org/10.1002/ 2017JG004135 Bergstrom, D. M., Wienecke, B. C., van den Hoff, J., Hughes, L., Lindenmayer, D. B., Ainsworth, T. D., Baker, C. M., Bland, L., Bowman, D. M. J. S., Brooks, S. T., Canadell, J. G., Constable, A. J., Dafforn, K. A., Depledge, M. H., Dickson, C. R., Duke, N. C., Helmstedt, K. J., Holz, A., Johnson, C. R., … Shaw, J. D. (2021). Com- bating ecosystem collapse from the tropics to the Antarctic. Global Change Biology, 27(9), 1692–1703. https://doi.org/10.1111/gcb. 15539 Beringer, J., Hutley, L. B., McHugh, I., Arndt, S. K., Campbell, D., Cleugh, H. A., Cleverly, J., Resco De Dios, V., Eamus, D., Evans, B., Ewenz, C., Grace, P., Griebel, A., Haverd, V., Hinko-Najera, N., Huete, A., Isaac, P., Kanniah, K., Leuning, R., … Wardlaw, T. (2016). An introduction to the Australian and New Zealand flux tower network – OzFlux. Biogeosciences, 13(21), 5895–5916. https://doi.org/10.5194/ bg-13-5895-2016 Beringer, J., Moore, C. E., Cleverly, J., Campbell, D. I., Cleugh, H., De Kauwe, M. G., Kirschbaum, M. U. F., Griebel, A., Grover, S., Huete, A., Hutley, L. B., Laubach, J., Van Niel, T., Arndt, S. K., Bennett, A. C., Cernusak, L. A., Eamus, D., Ewenz, C. M., Goodrich, J. P., … Woodgate, W. (2022). Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network. Global Change Biology, 28(11), 3489–3514. https://doi.org/10.1111/ gcb.16141 Bliege Bird, R., Bird, D. W., Codding, B. F., Parker, C. H., & Jones, J. H. (2008). The “fire stick farming” hypothesis: Australian Aboriginal for- aging strategies, biodiversity, and anthropogenic fire mosaics. Proceed- ings of the National Academy of Sciences, 105(39), 14796–14801. https://doi.org/10.1073/pnas.0804757105 Bliege Bird, R., Bird, D. W., Fernandez, L. E., Taylor, N., Taylor, W., & Nimmo, D. (2018). Aboriginal burning promotes fine-scale pyrodiver- sity and native predators in Australia's Western Desert. Biological Con- servation, 219, 110–118. https://doi.org/10.1016/j.biocon.2018. 01.008 Bliege Bird, R., Codding, B. F., Kauhanen, P. G., & Bird, D. W. (2012). Aboriginal hunting buffers climate-driven fire-size variability in Australia's spinifex grasslands. Proceedings of the National Academy of 18 WILLIAMS ET AL. https://orcid.org/0000-0003-3555-4778 https://orcid.org/0000-0003-3555-4778 https://orcid.org/0000-0002-4680-8115 https://orcid.org/0000-0002-4680-8115 https://orcid.org/0000-0003-3400-8601 https://orcid.org/0000-0003-3400-8601 https://orcid.org/0000-0002-6034-5807 https://orcid.org/0000-0002-6034-5807 https://orcid.org/0000-0002-6560-7124 https://orcid.org/0000-0002-6560-7124 https://orcid.org/0000-0001-8075-124X https://orcid.org/0000-0001-8075-124X https://orcid.org/0000-0002-7853-194X https://orcid.org/0000-0002-7853-194X https://orcid.org/0000-0002-3371-1509 https://orcid.org/0000-0002-3371-1509 https://orcid.org/0000-0002-1854-5629 https://orcid.org/0000-0002-1854-5629 https://orcid.org/0000-0003-2135-0272 https://orcid.org/0000-0003-2135-0272 https://orcid.org/0000-0001-5861-963X https://orcid.org/0000-0001-5861-963X https://orcid.org/0000-0002-4947-7067 https://orcid.org/0000-0002-4947-7067 https://orcid.org/0000-0002-7387-0563 https://orcid.org/0000-0002-7387-0563 https://orcid.org/0000-0002-2210-1035 https://orcid.org/0000-0002-2210-1035 https://orcid.org/0000-0001-5728-9827 https://orcid.org/0000-0001-5728-9827 https://doi.org/10.1038/s43247-021-00299-0 https://doi.org/10.1038/s43247-020-00065-8 https://doi.org/10.1038/s43247-020-00065-8 https://doi.org/10.1016/j.biocon.2023.110201 https://doi.org/10.1016/j.biocon.2023.110201 https://doi.org/10.1016/j.quascirev.2023.108263 https://doi.org/10.1016/j.quascirev.2023.108263 https://doi.org/10.1038/s41559-022-01943-4 https://doi.org/10.1038/s41559-022-01943-4 https://doi.org/10.1126/science.aaa1668 https://doi.org/10.1007/s00382-018-04602-2 https://doi.org/10.1016/j.quascirev.2014.08.003 https://doi.org/10.1016/j.dendro.2017.07.004 https://doi.org/10.1016/j.dendro.2017.07.004 https://doi.org/10.1071/BT98045 https://doi.org/10.1071/BT08040 https://doi.org/10.1016/j.biocon.2006.03.002 https://doi.org/10.1016/j.biocon.2006.03.002 https://doi.org/10.1038/s41586-022-04737-7 https://doi.org/10.1002/2017JG004135 https://doi.org/10.1002/2017JG004135 https://doi.org/10.1111/gcb.15539 https://doi.org/10.1111/gcb.15539 https://doi.org/10.5194/bg-13-5895-2016 https://doi.org/10.5194/bg-13-5895-2016 https://doi.org/10.1111/gcb.16141 https://doi.org/10.1111/gcb.16141 https://doi.org/10.1073/pnas.0804757105 https://doi.org/10.1016/j.biocon.2018.01.008 https://doi.org/10.1016/j.biocon.2018.01.008 Sciences, 109(26), 10287–10292. https://doi.org/10.1073/pnas. 1204585109 Bond, W. J., Woodward, F. I., & Midgley, G. F. (2005). The global distribu- tion of ecosystems in a world without fire. New Phytologist, 165(2), 525–538. https://doi.org/10.1111/j.1469-8137.2004.01252.x Bowman, D., Bridle, K., Brook, B. W., Capon, S., Bellchambers, L., Garnett, S., Hero, J.-M., Hodgson, L., Hoffmann, A., & Hughes, L. (2013). Terrestrial Report Card 2013: Climate change impacts and adap- tation on Australian biodiversity. National Climate Change Adaptation Research Facility (NCCARF). Bowman, D. M. J. S. (1998). The impact of Aboriginal landscape burning on the Australian biota. New Phytologist, 140(3), 385–410. https://doi. org/10.1111/j.1469-8137.1998.00289.x Bowman, D. M. J. S., Boggs, G. S., Prior, L. D., & Krull, E. S. (2007). Dynam- ics of Acacia aneura—Triodia boundaries using carbon (14C and δ 13C) and nitrogen (δ15N) signatures in soil organic matter in central Australia. The Holocene, 17(3), 311–318. https://doi.org/10.1177/ 0959683607076442 Bowman, D. M. J. S., Borchers-Arriagada, N., Macintosh, A., Butler, D. W., Williamson, G. J., & Johnston, F. H. (2024). Climate change must be factored into savanna carbon- management projects to avoid maladap- tation: the case of worsening air pollution in western Top End of the Northern Territory, Australia. The Rangeland Journal, 46, RJ23049. https://doi.org/10.1071/RJ23049 Bowman, D. M. J. S., Haverkamp, C., Rann, K. D., & Prior, L. D. (2018). Dif- ferential demographic filtering by surface fires: How fuel type and fuel load affect sapling mortality of an obligate seeder savanna tree. Journal of Ecology, 106(3), 1010–1022. https://doi.org/10.1111/1365-2745. 12819 Bowman, D. M. J. S., MacDermott, H. J., Nichols, S. C., & Murphy, B. P. (2014). A grass–fire cycle eliminates an obligate-seeding tree in a trop- ical savanna. Ecology and Evolution, 4(21), 4185–4194. https://doi.org/ 10.1002/ece3.1285 Bowman, D. M. J. S., Murphy, B. P., & Banfai, D. S. (2010). Has global envi- ronmental change caused monsoon rainforests to expand in the Australian monsoon tropics? Landscape Ecology, 25(8), 1247–1260. https://doi.org/10.1007/s10980-010-9496-8 Bowman, D. M. J. S., & Panton, W. J. (1993). Decline of Callitris intratropica R. T. Baker & H. G. Smith in the Northern Territory: Implications for Pre- and Post-European Colonization Fire Regimes. Journal of Biogeog- raphy, 20(4), 373–381. https://doi.org/10.2307/2845586 Bowman, D. M. J. S., Price, O., Whitehead, P. J., & Walsh, A. (2001). The 'wilderness effect' and the decline of Callitris intratropica on the Arn- hem Land Plateau, northern Australia. Australian Journal of Botany, 49(5), 665. https://doi.org/10.1071/BT00087 Bowman, D. M. J. S., Prior, L. D., Tng, D. Y. P., Hua, Q., & Brodribb, T. J. (2011). Continental-scale climatic drivers of growth ring variability in an Australian conifer. Trees, 25(5), 925–934. https://doi.org/10.1007/ s00468-011-0567-5 Bowman, D. M. J. S., Walsh, A., & Milne, D. J. (2001). Forest expansion and grassland contraction within a Eucalyptus savanna matrix between 1941 and 1994 at Litchfield National Park in the Australian monsoon tropics. Global Ecology and Biogeography, 10(5), 535–548. https://doi. org/10.1046/j.1466-822X.2001.00252.x Bowman, D. M. J. S., Williamson, G. J., Johnston, F. H., Bowman, C. J. W., Murphy, B. P., Roos, C. I., Trauernicht, C., Rostron, J., & Prior, L. D. (2022). Population collapse of a Gondwanan conifer follows the loss of Indigenous fire regimes in a northern Australian savanna. Scien- tific Reports, 12(1), 9081. https://doi.org/10.1038/s41598-022- 12946-3 Bowman, D. M. J. S., Williamson, G. J., Keenan, R. J., & Prior, L. D. (2014). A warmer world will reduce tree growth in evergreen broadleaf for- ests: evidence from A ustralian temperate and subtropical eucalypt forests. Global Ecology and Biogeography, 23(8), 925–934. https://doi. org/10.1111/geb.12171 Bradford, M. G., Murphy, H. T., Ford, A. J., Hogan, D. L., & Metcalfe, D. J. (2014). Long-term stem inventory data from tropical rain forest plots in Australia: Ecological Archives E095-209. Ecology, 95(8), 2362–2000. https://doi.org/10.1890/14-0458R.1 Bradshaw, C. J. A. (2012). Little left to lose: deforestation and forest degra- dation in Australia since European colonization. Journal of Plant Ecol- ogy, 5(1), 109–120. https://doi.org/10.1093/jpe/rtr038 Brodribb, T. J., Bowman, D. M. J. S., Grierson, P. F., Murphy, B. P., Nichols, S., & Prior, L. D. (2013). Conservative water management in the widespread conifer genus Callitris. AoB Plants, 5, plt052. https:// doi.org/10.1093/aobpla/plt052 Brook, B. W., & Bowman, D. M. J. S. (2006). Postcards from the past: charting the landscape-scale conversion of tropical Australian savanna to closed forest during the 20th century. Landscape Ecology, 21(8), 1253–1266. https://doi.org/10.1007/s10980-006-0018-7 Brookhouse, M. T., Farrow, R., Meyer, J., McDougall, K., Ward-Jones, J., & Wright, G. T. (2024). Incidence and severity of Phoracantha-induced decline within high-elevation eucalypt woodlands are strongly associated with elevation and land management. Forest Ecology and Management, 561, 121872. https://doi.org/10.1016/j.foreco.2024. 121872 Brouwers, N., Matusick, G., Ruthrof, K., Lyons, T., & Hardy, G. (2013). Landscape-scale assessment of tree crown dieback following extreme drought and heat in a Mediterranean eucalypt forest ecosystem. Land- scape Ecology, 28(1), 69–80. https://doi.org/10.1007/s10980-012- 9815-3 Bryan, B. A., Runting, R. K., Capon, T., Perring, M. P., Cunningham, S. C., Kragt, M. E., Nolan, M., Law, E. A., Renwick, A. R., Eber, S., Christian, R., & Wilson, K. A. (2016). Designer policy for carbon and biodiversity co-benefits under global change. Nature Climate Change, 6(3), 301–305. https://doi.org/10.1038/nclimate2874 Burrell, A. L., Evans, J. P., & De Kauwe, M. G. (2020). Anthropogenic cli- mate change has driven over 5 million km2 of drylands towards desert- ification. Nature Communications, 11(1), 3853. https://doi.org/10. 1038/s41467-020-17710-7 Burrows, W. H., Henry, B. K., Back, P. V., Hoffmann, M. B., Tait, L. J., Anderson, E. R., Menke, N., Danaher, T., Carter, J. O., & McKeon, G. M. (2002). Growth and carbon stock change in eucalypt woodlands in northeast Australia: ecological and greenhouse sink implications. Global Change Biology, 8(8), 769–784. https://doi.org/10. 1046/j.1365-2486.2002.00515.x Burton, C., Lampe, S., Kelley, D. I., Thiery, W., Hantson, S., Christidis, N., Gudmundsson, L., Forrest, M., Burke, E., Chang, J., Huang, H., Ito, A., Kou-Giesbrecht, S., Lasslop, G., Li, W., Nieradzik, L., Li, F., Chen, Y., Randerson, J., … Mengel, M. (2024). Global burned area increasingly explained by climate change. Nature Climate Change, 14(11), 1186– 1192. https://doi.org/10.1038/s41558-024-02140-w Byrnes, J. E. K., & Dee, L. E. (2025). Causal Inference With Observational Data and Unobserved Confounding Variables. Ecology Letters, 28(1), e70023. https://doi.org/10.1111/ele.70023 Camac, J. S., Williams, R. J., Wahren, C.-H., Hoffmann, A. A., & Vesk, P. A. (2017). Climatic warming strengthens a positive feedback between alpine shrubs and fire. Global Change Biology, 23(8), 3249–3258. https://doi.org/10.1111/gcb.13614 Carr, S. G. M., & Turner, J. S. (1959a). The ecology of the Bogong High Plains. I. The environmental factors and the grassland communities. Australian Journal of Botany, 7(1), 12–33. https://doi.org/10.1071/ BT9590012 Carr, S. G. M., & Turner, J. S. (1959b). The ecology of the Bogong High Plains. II. Fencing experiments in grassland C. Australian Journal of Bot- any, 7, 34–63. Cavender-Bares, J., Gamon, J. A., & Townsend, P. A. (2020). Remote Sens- ing of Plant Biodiversity. Springer International Publishing. Collatz, G. J., Berry, J. A., & Clark, J. S. (1998). Effects of climate and atmo- spheric CO 2 partial pressure on the global distribution of C 4 grasses: WILLIAMS ET AL. 19 https://doi.org/10.1073/pnas.1204585109 https://doi.org/10.1073/pnas.1204585109 https://doi.org/10.1111/j.1469-8137.2004.01252.x https://doi.org/10.1111/j.1469-8137.1998.00289.x https://doi.org/10.1111/j.1469-8137.1998.00289.x https://doi.org/10.1177/0959683607076442 https://doi.org/10.1177/0959683607076442 https://doi.org/10.1071/RJ23049 https://doi.org/10.1111/1365-2745.12819 https://doi.org/10.1111/1365-2745.12819 https://doi.org/10.1002/ece3.1285 https://doi.org/10.1002/ece3.1285 https://doi.org/10.1007/s10980-010-9496-8 https://doi.org/10.2307/2845586 https://doi.org/10.1071/BT00087 https://doi.org/10.1007/s00468-011-0567-5 https://doi.org/10.1007/s00468-011-0567-5 https://doi.org/10.1046/j.1466-822X.2001.00252.x https://doi.org/10.1046/j.1466-822X.2001.00252.x https://doi.org/10.1038/s41598-022-12946-3 https://doi.org/10.1038/s41598-022-12946-3 https://doi.org/10.1111/geb.12171 https://doi.org/10.1111/geb.12171 https://doi.org/10.1890/14-0458R.1 https://doi.org/10.1093/jpe/rtr038 https://doi.org/10.1093/aobpla/plt052 https://doi.org/10.1093/aobpla/plt052 https://doi.org/10.1007/s10980-006-0018-7 https://doi.org/10.1016/j.foreco.2024.121872 https://doi.org/10.1016/j.foreco.2024.121872 https://doi.org/10.1007/s10980-012-9815-3 https://doi.org/10.1007/s10980-012-9815-3 https://doi.org/10.1038/nclimate2874 https://doi.org/10.1038/s41467-020-17710-7 https://doi.org/10.1038/s41467-020-17710-7 https://doi.org/10.1046/j.1365-2486.2002.00515.x https://doi.org/10.1046/j.1365-2486.2002.00515.x https://doi.org/10.1038/s41558-024-02140-w https://doi.org/10.1111/ele.70023 https://doi.org/10.1111/gcb.13614 https://doi.org/10.1071/BT9590012 https://doi.org/10.1071/BT9590012 present, past, and future. Oecologia, 114(4), 441–454. https://doi.org/ 10.1007/s004420050468 Connell, J. H., & Green, P. T. (2000). Seedling dynamics over thirty-two years in a tropical rain forest tree. Ecology, 81(2), 568–584. https:// doi.org/10.1890/0012-9658(2000)081[0568:SDOTTY]2.0.CO;2 Cook, E., Bird, T., Peterson, M., Barbetti, M., Buckley, B., D'Arrigo, R., Francey, R., & Tans, P. (1991). Climatic change in Tasmania inferred from a 1089-year tree-ring chronology of Huon pine. Science, 253(5025), 1266–1268. Cortés, J., Mahecha, M. D., Reichstein, M., Myneni, R. B., Chen, C., & Brenning, A. (2021). Where are global vegetation greening and brow- ning trends significant? Geophysical Research Letters, 48(6), e2020GL091496. https://doi.org/10.1029/2020GL091496 Crisp, M. D. (1978). Demography and survival under grazing of three Australian semi-desert shrubs. Oikos, 30(3), 520–528. https://doi.org/ 10.2307/3543347 Crisp, M. D., Cook, L. G., Bowman, D. M. J. S., Cosgrove, M., Isagi, Y., & Sakaguchi, S. (2019). Turnover of southern cypresses in the post- Gondwanan world: extinction, transoceanic dispersal, adaptation and rediversification. New Phytologist, 221(4), 2308–2319. https://doi.org/ 10.1111/nph.15561 Crisp, M. D., & Lange, R. T. (1976). Age structure, distribution and survival under grazing of the arid-zone shrub Acacia burkittii. Oikos, 27(1), 86– 92. https://doi.org/10.2307/3543436 CSIRO & Bureau of Meteorology. (2024). State of the Climate 2024. Gov- ernment of Australia. Davis, M. B. (1983). Quaternary history of deciduous forests of eastern North America and Europe. Annals of the Missouri Botanical Garden, 70(3), 550–563. https://doi.org/10.2307/2992086 Davis, M. B., & Shaw, R. G. (2001). Range Shifts and Adaptive Responses to Quaternary Climate Change. Science, 292(5517), 673–679. https:// doi.org/10.1126/science.292.5517.673 De Jong, R., De Bruin, S., De Wit, A., Schaepman, M. E., & Dent, D. L. (2011). Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115(2), 692– 702. https://doi.org/10.1016/j.rse.2010.10.011 Delcourt, H. R., Delcourt, P. A., & Webb, T. (1982). Dynamic plant ecology: the spectrum of vegetational change in space and time. Quaternary Sci- ence Reviews, 1(3), 153–175. https://doi.org/10.1016/0277-3791(82) 90008-7 Doherty, T. S., Macdonald, K. J., Nimmo, D. G., Santos, J. L., & Geary, W. L. (2024). Shifting fire regimes cause continent-wide transformation of threatened species habitat. Proceedings of the National Academy of Sciences, 121(18), e2316417121. https://doi.org/10.1073/pnas. 2316417121 Dudney, J., Dee, L., Heilmayr, R., Byrnes, J. & Siegel, K. (2024). A causal inference framework for climate change attribution in ecology. https://doi.org/10.22541/au.173349929.91948075/v1 Duff, T. J., Cawson, J. G., & Penman, T. D. (2019). Determining burnability: Predicting completion rates and coverage of prescribed burns for fuel management. Forest Ecology and Management, 433, 431–440. https:// doi.org/10.1016/j.foreco.2018.11.009 Duke, N. C., Kovacs, J. M., Griffiths, A. D., Preece, L., Hill, D. J. E., Van Oosterzee, P., Mackenzie, J., Morning, H. S., & Burrows, D. (2017). Large-scale dieback of mangroves in Australia. Marine and Freshwater Research, 68, 1816–1829. https://doi.org/10.1071/ MF16322 Edwards, A. C., & Russell-Smith, J. (2009). Ecological thresholds and the status of fire-sensitive vegetation in western Arnhem Land, northern Australia: implications for management. International Journal of Wildland Fire, 18(2), 127–146. https://doi.org/10.1071/ WF08008 Fensham, R., & Fairfax, R. (1996). The Disappearing Grassy Balds of the Bunya Mountains, South-Eastern Queensland. Australian Journal of Botany, 44(5), 543–558. https://doi.org/10.1071/BT9960543 Fensham, R. J., & Fairfax, R. J. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of find- ings for Australian vegetation history. Australian Journal of Botany, 50(4), 415–429. https://doi.org/10.1071/BT01032 Fensham, R. J., Laffineur, B., & Allen, C. D. (2019). To what extent is drought-induced tree mortality a natural phenomenon? Global Ecology and Biogeography, 28(3), 365–373. https://doi.org/10.1111/geb. 12858 Fensham, R. J., Powell, O., & Horne, J. (2011). Rail survey plans to remote sensing: vegetation change in the Mulga Lands of eastern Australia and its implications for land use. The Rangeland Journal, 33(3), 229– 238. https://doi.org/10.1071/RJ11007 Fletcher, M.-S., Bowman, D. M., Whitlock, C., Mariani, M., Beck, K. K., Stahle, L. N., Hopf, F., Benson, A., Hall, T., Heijnis, H., & Zawadzki, A. (2021). The influence of climatic change, fire and species invasion on a Tasmanian temperate rainforest system over the past 18,000 years. Quaternary Science Reviews, 260, 106824. https://doi.org/10.1016/j. quascirev.2021.106824 Fletcher, M.-S., Hall, T., & Alexandra, A. N. (2021). The loss of an indige- nous constructed landscape following British invasion of Australia: An insight into the deep human imprint on the Australian landscape. Ambio, 50(1), 138–149. https://doi.org/10.1007/s13280-020- 01339-3 Fletcher, M.-S., Romano, A., Connor, S., Mariani, M., & Maezumi, S. Y. (2021). Catastrophic bushfires, Indigenous fire knowledge and refram- ing science in southeast Australia. Fire, 4(3), 61. https://doi.org/10. 3390/fire4030061 Fletcher, M.-S., Romano, A., Nichols, S., Henriquez Gonzalez, W., Mariani, M., Jaganjac, D., & Sculthorpe, A. (2024). Lifting the veil: pyro- geographic manipulation and the leveraging of environmental change by people across the Vale of Belvoir, Tasmania. Australia. Frontiers in Environmental Archaeology, 3, 1386339. https://doi.org/10.3389/ fearc.2024.1386339 Fletcher, M.-S., & Thomas, I. (2010). The origin and temporal development of an ancient cultural landscape: Post-glacial vegetation history of western Tasmania. Journal of Biogeography, 37(11), 2183–2196. https://doi.org/10.1111/j.1365-2699.2010.02363.x Fletcher, M.-S., Wood, S. W., & Haberle, S. G. (2014). A fire-driven shift from forest to non-forest: evidence for alternative stable states? Ecol- ogy, 95(9), 2504–2513. https://doi.org/10.1890/12-1766.1 Forrester, D. I., England, J. R., Ng, E. L., Piper, M., Hodgkinson, K. C., Bray, S. G., Roxburgh, S. H., & Paul, K. I. (2025). Does grazing exclusion in Australia's rangelands affect biomass and debris carbon stocks? The Rangeland Journal, 47, RJ24028. https://doi.org/10.1071/RJ24028 Foulkes, J., de Preu, N., Sinclair, R., Thurgate, N., Sparrow, B., & White, A. (2014). Chenopd and acacia shrublands. In D. Lindenmayer, E. Burns, N. Thurgate, & A. Lowe (Eds.), Biodiversity and Environmental Change. CSIRO Publishing. Gammage, B. (2011). The Biggest Estate on Earth: How Aborigines made Australia. Allen & Unwin. Gay, F. J., & Evans, R. W. (1968). The status and termite durability of northern cypress pine. Australian Forestry, 32(2), 80–91. https://doi. org/10.1080/00049158.1968.10675464 Godfree, R. C., Knerr, N., Godfree, D., Busby, J., Robertson, B., & Encinas-Viso, F. (2019). Historical reconstruction unveils the risk of mass mortality and ecosystem collapse during pancontinental mega- drought. Proceedings of the National Academy of Sciences, 116(31), 15580–15589. https://doi.org/10.1073/pnas.1902046116 Gonzalez, A., Chase, J. M., & O'Connor, M. I. (2023). A framework for the detection and attribution of biodiversity change. Philosophical Transac- tions of the Royal Society B: Biological Sciences, 378(1881), 20220182. https://doi.org/10.1098/rstb.2022.0182 Goodwin, M. J., Verdon-Kidd, D. C., Hua, Q., English, N. B., Haines, H. A., & Allen, K. J. (2022). Hydroclimate proxies for eastern Australia using stable isotopes in grey mangroves (Avicennia marina). 20 WILLIAMS ET AL. https://doi.org/10.1007/s004420050468 https://doi.org/10.1007/s004420050468 https://doi.org/10.1890/0012-9658%282000%29081%5B0568:SDOTTY%5D2.0.CO;2 https://doi.org/10.1890/0012-9658%282000%29081%5B0568:SDOTTY%5D2.0.CO;2 https://doi.org/10.1029/2020GL091496 https://doi.org/10.2307/3543347 https://doi.org/10.2307/3543347 https://doi.org/10.1111/nph.15561 https://doi.org/10.1111/nph.15561 https://doi.org/10.2307/3543436 https://doi.org/10.2307/2992086 https://doi.org/10.1126/science.292.5517.673 https://doi.org/10.1126/science.292.5517.673 https://doi.org/10.1016/j.rse.2010.10.011 https://doi.org/10.1016/0277-3791(82)90008-7 https://doi.org/10.1016/0277-3791(82)90008-7 https://doi.org/10.1073/pnas.2316417121 https://doi.org/10.1073/pnas.2316417121 https://doi.org/10.22541/au.173349929.91948075/v1 https://doi.org/10.1016/j.foreco.2018.11.009 https://doi.org/10.1016/j.foreco.2018.11.009 https://doi.org/10.1071/MF16322 https://doi.org/10.1071/MF16322 https://doi.org/10.1071/WF08008 https://doi.org/10.1071/WF08008 https://doi.org/10.1071/BT9960543 https://doi.org/10.1071/BT01032 https://doi.org/10.1111/geb.12858 https://doi.org/10.1111/geb.12858 https://doi.org/10.1071/RJ11007 https://doi.org/10.1016/j.quascirev.2021.106824 https://doi.org/10.1016/j.quascirev.2021.106824 https://doi.org/10.1007/s13280-020-01339-3 https://doi.org/10.1007/s13280-020-01339-3 https://doi.org/10.3390/fire4030061 https://doi.org/10.3390/fire4030061 https://doi.org/10.3389/fearc.2024.1386339 https://doi.org/10.3389/fearc.2024.1386339 https://doi.org/10.1111/j.1365-2699.2010.02363.x https://doi.org/10.1890/12-1766.1 https://doi.org/10.1071/RJ24028 https://doi.org/10.1080/00049158.1968.10675464 https://doi.org/10.1080/00049158.1968.10675464 https://doi.org/10.1073/pnas.1902046116 https://doi.org/10.1098/rstb.2022.0182 Global and Planetary Change, 208, 103691. https://doi.org/10.1016/j. gloplacha.2021.103691 Gordon, C. E., Eldridge, D. J., Ripple, W. J., Crowther, M. S., Moore, B. D., & Letnic, M. (2017). Shrub encroachment is linked to extirpation of an apex predator. Journal of Animal Ecology, 86(1), 147– 157. https://doi.org/10.1111/1365-2656.12607 Gordon, C. E., & Letnic, M. (2019). Evidence that the functional extinction of small mammals facilitates shrub encroachment following wildfire in arid Australia. Journal of Arid Environments, 164, 60–68. https://doi. org/10.1016/j.jaridenv.2019.01.015 Gosper, C. R., Prober, S. M., & Yates, C. J. (2013). Multi-century changes in vegetation structure and fuel availability in fire-sensitive eucalypt woodlands. Forest Ecology and Management, 310, 102–109. https:// doi.org/10.1016/j.foreco.2013.08.005 Gott, B. (2005). Aboriginal fire management in south-eastern Australia: aims and frequency. Journal of Biogeography, 32(7), 1203–1208. https://doi.org/10.1111/j.1365-2699.2004.01233.x Grimm, V., Johnston, A. S. A., Thulke, H.-H., Forbes, V. E., & Thorbek, P. (2020). Three questions to ask before using model outputs for decision support. Nature Communications, 11(1), 4959. https://doi.org/10. 1038/s41467-020-17785-2 Gustafson, E. J. (2013). When relationships estimated in the past cannot be used to predict the future: using mechanistic models to predict landscape ecological dynamics in a changing world. Landscape Ecology, 28(8), 1429–1437. https://doi.org/10.1007/s10980-013-9927-4 Haines, H. A., Olley, J. M., English, N. B., & Hua, Q. (2018). Anomalous ring identification in two Australian subtropical Araucariaceae species per- mits annual ring dating and growth-climate relationship development. Dendrochronologia, 49, 16–28. https://doi.org/10.1016/j.dendro.2018. 02.008 Hammond, W. M., Williams, A. P., Abatzoglou, J. T., Adams, H. D., Klein, T., López, R., Sáenz-Romero, C., Hartmann, H., Breshears, D. D., & Allen, C. D. (2022). Global field observations of tree die-off reveal hotter-drought fingerprint for Earth's forests. Nature Communications, 13(1), 1761. https://doi.org/10.1038/s41467-022-29289-2 Hansen, G., Stone, D., Auffhammer, M., Huggel, C., & Cramer, W. (2016). Linking local impacts to changes in climate: a guide to attribution. Regional Environmental Change, 16(2), 527–541. https://doi.org/10. 1007/s10113-015-0760-y Harrington, G. N., & Sanderson, K. D. (1994). Recent contraction of wet sclerophyll forest in the wet tropics of Queensland due to invasion by rainforest. Pacific Conservation Biology, 1(4), 319–327. https://doi.org/ 10.1071/pc940319 Harris, R. M. B., Beaumont, L. J., Vance, T. R., Tozer, C. R., Remenyi, T. A., Perkins-Kirkpatrick, S. E., Mitchell, P. J., Nicotra, A. B., McGregor, S., Andrew, N. R., Letnic, M., Kearney, M. R., Wernberg, T., Hutley, L. B., Chambers, L. E., Fletcher, M.-S., Keatley, M. R., Woodward, C. A., Williamson, G., … Bowman, D. M. J. S. (2018). Biological responses to the press and pulse of climate trends and extreme events. Nature Cli- mate Change, 8(7), 579–587. https://doi.org/10.1038/s41558-018- 0187-9 Havrilla, C. A., Bradford, J. B., Yackulic, C. B., & Munson, S. M. (2023). Divergent climate impacts on C3 versus C4 grasses imply widespread 21st century shifts in grassland functional composition. Diversity and Distributions, 29(3), 379–394. https://doi.org/10.1111/ddi.13669 Haynes, C. D. (1985). The pattern and ecology of Munwag: Traditional Aboriginal fire regimes in north-central Arnhem Land. Proceedings of the Ecological Society of Australia, 13, 203–214. Hegerl, G. C., Hoegh-Guldberg, O., Casassa, G., Hoerling, M., Kovats, S., Parmesan, C., Pierce, D., & Stott, P. (2010). Good practice guidance paper on detection and attribution related to anthropogenic climate change. In T. F. Stocker, C. B. Field, D. Qin, V. Barros, G.-K. Plattner, M. Tignor, et al. (Eds.), Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Detection and Attribution of Anthro- pogenic Climate Change. Technical Support Unit. IPCC Working Group. Higgins, S. I., Conradi, T., & Muhoko, E. (2023). Shifts in vegetation activity of terrestrial ecosystems attributable to climate trends. Nature Geosci- ence, 16(2), 147–153. https://doi.org/10.1038/s41561-022-01114-x Higuera, P. E., & Abatzoglou, J. T. (2021). Record-setting climate enabled the extraordinary 2020 fire season in the western United States. Global Change Biology, 27(1), 1–2. https://doi.org/10.1111/gcb.15388 Hill, A. B. (1965). The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300. https:// doi.org/10.1177/003591576505800503 Hill, R. S. (1994). History of the Australian Vegetation: Cretaceous to Recent. University of Adelaide Press. Hoffmann, A. A., Rymer, P. D., Byrne, M., Ruthrof, K. X., Whinam, J., McGeoch, M., Bergstrom, D. M., Guerin, G. R., Sparrow, B., Joseph, L., Hill, S. J., Andrew, N. R., Camac, J., Bell, N., Riegler, M., Gardner, J. L., & Williams, S. E. (2019). Impacts of recent climate change on terrestrial flora and fauna: Some emerging Australian exam- ples. Austral Ecology, 44(1), 3–27. https://doi.org/10.1111/aec.12674 Horner, G. J., Baker, P. J., Mac Nally, R., Cunningham, S. C., Thomson, J. R., & Hamilton, F. (2009). Mortality of developing flood- plain forests subjected to a drying climate and water extraction. Global Change Biology, 15(9), 2176–2186. https://doi.org/10.1111/j.1365- 2486.2009.01915.x Hovenden, M. J., & Williams, A. L. (2010). The impacts of rising CO 2 con- centrations on Australian terrestrial species and ecosystems. Austral Ecology, 35(6), 665–684. https://doi.org/10.1111/j.1442-9993.2009. 02074.x Howard, T., Burrows, N., Smith, T., Daniel, G., & McCaw, L. (2020). A framework for prioritising prescribed burning on public land in West- ern Australia. International Journal of Wildland Fire, 29(5), 314. https:// doi.org/10.1071/WF19029 Howitt, A. W. (1890). The eucalypts of Victoria. Transactions of the Royal Society of Victoria, 1890, 82–120. Hudiburg, T. W., Luyssaert, S., Thornton, P. E., & Law, B. E. (2013). Interac- tive Effects of Environmental Change and Management Strategies on Regional Forest Carbon Emissions. Environmental Science & Technology, 47(22), 13132–13140. https://doi.org/10.1021/es402903u Hughes, L. (2003). Climate change and Australia: Trends, projections and impacts. Austral Ecology, 28(4), 423–443. https://doi.org/10.1046/j. 1442-9993.2003.01300.x Hutley, L. B., Beringer, J., Fatichi, S., Schymanski, S. J., & Northwood, M. (2022). Gross primary productivity and water use efficiency are increasing in a high rainfall tropical savanna. Global Change Biology, 28(7), 2360–2380. https://doi.org/10.1111/gcb.16012 IPCC. (2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I. In II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate. IPCC. Jackson, S. (2022). Enacting multiple river realities in the performance of an environmental flow in Australia's Murray-Darling Basin. Geographi- cal Research, 60(3), 463–479. https://doi.org/10.1111/1745-5871. 12513 Jarrad, F. C., Wahren, C.-H., Williams, R. J., & Burgman, M. A. (2008). Impacts of experimental warming and fire on phenology of subalpine open-heath species. Australian Journal of Botany, 56(8), 617–629. https://doi.org/10.1071/BT08018 Jiang, M., Medlyn, B. E., Drake, J. E., Duursma, R. A., Anderson, I. C., Barton, C. V. M., Boer, M. M., Carrillo, Y., Castañeda-Gómez, L., Collins, L., Crous, K. Y., De Kauwe, M. G., dos Santos, B. M., Emmerson, K. M., Facey, S. L., Gherlenda, A. N., Gimeno, T. E., Hasegawa, S., Johnson, S. N., … Ellsworth, D. S. (2020). The fate of car- bon in a mature forest under carbon dioxide enrichment. Nature, 580(7802), 227–231. https://doi.org/10.1038/s41586-020-2128-9 Johansen, K., Phinn, S., & Taylor, M. (2015). Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Goo- gle Earth Engine. Remote Sensing Applications: Society and Environment, 1, 36–49. https://doi.org/10.1016/j.rsase.2015.06.002 WILLIAMS ET AL. 21 https://doi.org/10.1016/j.gloplacha.2021.103691 https://doi.org/10.1016/j.gloplacha.2021.103691 https://doi.org/10.1111/1365-2656.12607 https://doi.org/10.1016/j.jaridenv.2019.01.015 https://doi.org/10.1016/j.jaridenv.2019.01.015 https://doi.org/10.1016/j.foreco.2013.08.005 https://doi.org/10.1016/j.foreco.2013.08.005 https://doi.org/10.1111/j.1365-2699.2004.01233.x https://doi.org/10.1038/s41467-020-17785-2 https://doi.org/10.1038/s41467-020-17785-2 https://doi.org/10.1007/s10980-013-9927-4 https://doi.org/10.1016/j.dendro.2018.02.008 https://doi.org/10.1016/j.dendro.2018.02.008 https://doi.org/10.1038/s41467-022-29289-2 https://doi.org/10.1007/s10113-015-0760-y https://doi.org/10.1007/s10113-015-0760-y https://doi.org/10.1071/pc940319 https://doi.org/10.1071/pc940319 https://doi.org/10.1038/s41558-018-0187-9 https://doi.org/10.1038/s41558-018-0187-9 https://doi.org/10.1111/ddi.13669 https://doi.org/10.1038/s41561-022-01114-x https://doi.org/10.1111/gcb.15388 https://doi.org/10.1177/003591576505800503 https://doi.org/10.1177/003591576505800503 https://doi.org/10.1111/aec.12674 https://doi.org/10.1111/j.1365-2486.2009.01915.x https://doi.org/10.1111/j.1365-2486.2009.01915.x https://doi.org/10.1111/j.1442-9993.2009.02074.x https://doi.org/10.1111/j.1442-9993.2009.02074.x https://doi.org/10.1071/WF19029 https://doi.org/10.1071/WF19029 https://doi.org/10.1021/es402903u https://doi.org/10.1046/j.1442-9993.2003.01300.x https://doi.org/10.1046/j.1442-9993.2003.01300.x https://doi.org/10.1111/gcb.16012 https://doi.org/10.1111/1745-5871.12513 https://doi.org/10.1111/1745-5871.12513 https://doi.org/10.1071/BT08018 https://doi.org/10.1038/s41586-020-2128-9 https://doi.org/10.1016/j.rsase.2015.06.002 Johnson, C. N. (2009). Ecological consequences of Late Quaternary extinc- tions of megafauna. Proceedings of the Royal Society B: Biological Sci- ences, 276(1667), 2509–2519. https://doi.org/10.1098/rspb.2008. 1921 Jucker, T., Gosper, C. R., Wiehl, G., Yeoh, P. B., Raisbeck-Brown, N., Fischer, F. J., Graham, J., Langley, H., Newchurch, W., O'Donnell, A. J., Page, G. F. M., Zdunic, K., & Prober, S. M. (2023). Using multi-platform LiDAR to guide the conservation of the world's largest temperate woodland. Remote Sensing of Environment, 296, 113745. https://doi. org/10.1016/j.rse.2023.113745 Kimmel, K., Dee, L. E., Avolio, M. L., & Ferraro, P. J. (2021). Causal assump- tions and causal inference in ecological experiments. Trends in Ecol- ogy & Evolution, 36(12), 1141–1152. https://doi.org/10.1016/j.tree. 2021.08.008 Laurance, W. F., Dell, B., Turton, S. M., Lawes, M. J., Hutley, L. B., McCallum, H., Dale, P., Bird, M., Hardy, G., Prideaux, G., Gawne, B., McMahon, C. R., Yu, R., Hero, J.-M., Schwarzkopf, L., Krockenberger, A., Douglas, M., Silvester, E., Mahony, M., … Cocklin, C. (2011). The 10 Australian ecosystems most vulnerable to tipping points. Biological Conservation, 144(5), 1472–1480. https://doi. org/10.1016/j.biocon.2011.01.016 Lawes, M. J., Richards, A., Dathe, J., & Midgley, J. J. (2011). Bark thickness determines fire resistance of selected tree species from fire-prone tropical savanna in north Australia. Plant Ecology, 212(12), 2057–2069. https://doi.org/10.1007/s11258-011-9954-7 Lehmann, C. E. R., Prior, L. D., & Bowman, D. M. J. S. (2009). Decadal dynamics of tree cover in an Australian tropical savanna. Austral Ecol- ogy, 34(6), 601–612. https://doi.org/10.1111/j.1442-9993.2009. 01964.x Lindenmayer, D., Burns, E., Thurgate, N., & Lowe, A. (Eds.). (2014). Biodi- versity and Environmental Change: Monitoring, Challenges and Direction. CSIRO Publishing. Lisé-Pronovost, A., Fletcher, M.-S., Mallett, T., Mariani, M., Lewis, R., Gadd, P. S., Herries, A. I. R., Blaauw, M., Heijnis, H., Hodgson, D. A., & Pedro, J. B. (2019). Scientific drilling of sediments at Darwin Crater, Tasmania. Scientific Drilling, 25, 1–14. https://doi.org/10.5194/sd-25- 1-2019 Lopes Dos Santos, R. A., De Deckker, P., Hopmans, E. C., Magee, J. W., Mets, A., Sinninghe Damsté, J. S., & Schouten, S. (2013). Abrupt vege- tation change after the Late Quaternary megafaunal extinction in southeastern Australia. Nature Geoscience, 6(8), 627–631. https://doi. org/10.1038/ngeo1856 Losso, A., Challis, A., Gauthey, A., Nolan, R. H., Hislop, S., Roff, A., Boer, M. M., Jiang, M., Medlyn, B. E., & Choat, B. (2022). Canopy die- back and recovery in Australian native forests following extreme drought. Scientific Reports, 12(1), 21608. https://doi.org/10.1038/ s41598-022-24833-y Lunt, I. D. (2002). Grazed, burnt and cleared: how ecologists have studied century-scale vegetation changes in Australia. Australian Journal of Botany, 50(4), 391–407mi. https://doi.org/10.1071/BT01044 Lunt, I. D., Eldridge, D. J., Morgan, J. W., & Witt, G. B. (2007). A framework to predict the effects of livestock grazing and grazing exclusion on conservation values in natural ecosystems in Australia. Australian Jour- nal of Botany, 55(4), 401. https://doi.org/10.1071/BT06178 Lunt, I. D., Jones, N., Spooner, P. G., & Petrow, M. (2006). Effects of European colonization on indigenous ecosystems: post-settlement changes in tree stand structures in Eucalyptus–Callitris woodlands in central New South Wales. Australia. Journal of Biogeogra- phy, 33(6), 1102–1115. https://doi.org/10.1111/j.1365-2699.2006. 01484.x Luo, X., Zhou, H., Satriawan, T. W., Tian, J., Zhao, R., Keenan, T. F., Griffith, D. M., Sitch, S., Smith, N. G., & Still, C. J. (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory. Nature Communications, 15(1), 1219. https://doi.org/10.1038/ s41467-024-45606-3 Macintosh, A., Butler, D., Larraondo, P., Evans, M. C., Ansell, D., Waschka, M., Fensham, R., Eldridge, D., Lindenmayer, D., Gibbons, P., & Summerfield, P. (2024). Australian human-induced native forest regeneration carbon offset projects have limited impact on changes in woody vegetation cover and carbon removals. Commu- nications Earth & Environment, 5(1), 149. https://doi.org/10.1038/ s43247-024-01313-x Margolis, E. Q., Guiterman, C. H., Chavardès, R. D., Coop, J. D., Copes-Gerbitz, K., Dawe, D. A., Falk, D. A., Johnston, J. D., Larson, E., Li, H., Marschall, J. M., Naficy, C. E., Naito, A. T., Parisien, M.-A., Parks, S. A., Portier, J., Poulos, H. M., Robertson, K. M., Speer, J. H., … Weisberg, P. J. (2022). The North American tree-ring fire-scar net- work. Ecosphere, 13(7), e4159. https://doi.org/10.1002/ecs2.4159 Mariani, M., Connor, S. E., Theuerkauf, M., Herbert, A., Kuneš, P., Bowman, D., Fletcher, M.-S., Head, L., Kershaw, A. P., Haberle, S. G., Stevenson, J., Adeleye, M., Cadd, H., Hopf, F., & Briles, C. (2022). Dis- ruption of cultural burning promotes shrub encroachment and unprec- edented wildfires. Frontiers in Ecology and the Environment, 20(5), 292– 300. https://doi.org/10.1002/fee.2395 McDowell, N. G., Sapes, G., Pivovaroff, A., Adams, H. D., Allen, C. D., Anderegg, W. R. L., Arend, M., Breshears, D. D., Brodribb, T., Choat, B., Cochard, H., De Cáceres, M., De Kauwe, M. G., Grossiord, C., Hammond, W. M., Hartmann, H., Hoch, G., Kahmen, A., Klein, T., … Xu, C. (2022). Mechanisms of woody-plant mortality under rising drought, CO2 and vapour pressure deficit. Nature Reviews Earth & Environment, 3(5), 294–308. https://doi.org/10.1038/s43017-022- 00272-1 McVicar, D. (1922). Reports concerning marketable timbers and forest prod- ucts of several regions of the north-west part of the state. WA Forests Department. Mifsud, B., Prior, L. D., Williamson, G. J., Corigliano, J., Hansen, C., Van Pelt, R., Pearce, S., Greenwood, T., & Bowman, D. M. J. S. (2025). Tas- mania's giant eucalypts: discovery, documentation, macroecology and conservation status of the world's largest angiosperms. Australian Jour- nal of Botany, 73(1), BT23088. https://doi.org/10.1071/BT23088 Miller, G., Friedel, M., Adam, P., & Chewings, V. (2010). Ecological impacts of buffel grass (Cenchrus ciliaris L.) invasion in central Australia - does field evidence support a fire-invasion feedback? The. Rangeland Jour- nal, 32(4), 353–365. https://doi.org/10.1071/RJ09076 Morgan, G. W., Tolhurst, K. G., Poynter, M. W., Cooper, N., McGuffog, T., Ryan, R., Wouters, M. A., Stephens, N., Black, P., Sheehan, D., Leeson, P., Whight, S., & Davey, S. M. (2020). Prescribed burning in south-eastern Australia: history and future directions. Australian Forestry, 83(1), 4–28. https://doi.org/10.1080/00049158. 2020.1739883 Murphy, B. P., & Bowman, D. M. J. S. (2007). Seasonal water availability predicts the relative abundance of C3 and C4 grasses in Australia. Global Ecology and Biogeography, 16(2), 160–169. https://doi.org/10. 1111/j.1466-8238.2006.00285.x Murphy, B. P., Bradstock, R. A., Boer, M. M., Carter, J., Cary, G. J., Cochrane, M. A., Fensham, R. J., Russell-Smith, J., Williamson, G. J., & Bowman, D. M. J. S. (2013). Fire regimes of Australia: a pyrogeo- graphic model system. Journal of Biogeography, 40(6), 1048–1058. https://doi.org/10.1111/jbi.12065 Mutze, G., Bird, P., Jennings, S., Peacock, D., De Preu, N., Kovaliski, J., Cooke, B., & Capucci, L. (2014). Recovery of South Australian rabbit populations from the impact of rabbit haemorrhagic disease. Wildlife Research, 41(7), 552. https://doi.org/10.1071/WR14107 Neumann, M., Eastaugh, C. S., & Adams, M. A. (2023). Recruitment, mortal- ity and growth in semi-arid conifer-eucalypt forest: Small trees insure against fire and drought. Journal of Biogeography, 50(2), 291–301. https://doi.org/10.1111/jbi.14522 Nicholls, N., Drosdowsky, W., & Lavery, B. (1997). Australian rainfall vari- ability and change. Weather, 52(3), 66–72. https://doi.org/10.1002/j. 1477-8696.1997.tb06274.x 22 WILLIAMS ET AL. https://doi.org/10.1098/rspb.2008.1921 https://doi.org/10.1098/rspb.2008.1921 https://doi.org/10.1016/j.rse.2023.113745 https://doi.org/10.1016/j.rse.2023.113745 https://doi.org/10.1016/j.tree.2021.08.008 https://doi.org/10.1016/j.tree.2021.08.008 https://doi.org/10.1016/j.biocon.2011.01.016 https://doi.org/10.1016/j.biocon.2011.01.016 https://doi.org/10.1007/s11258-011-9954-7 https://doi.org/10.1111/j.1442-9993.2009.01964.x https://doi.org/10.1111/j.1442-9993.2009.01964.x https://doi.org/10.5194/sd-25-1-2019 https://doi.org/10.5194/sd-25-1-2019 https://doi.org/10.1038/ngeo1856 https://doi.org/10.1038/ngeo1856 https://doi.org/10.1038/s41598-022-24833-y https://doi.org/10.1038/s41598-022-24833-y https://doi.org/10.1071/BT01044 https://doi.org/10.1071/BT06178 https://doi.org/10.1111/j.1365-2699.2006.01484.x https://doi.org/10.1111/j.1365-2699.2006.01484.x https://doi.org/10.1038/s41467-024-45606-3 https://doi.org/10.1038/s41467-024-45606-3 https://doi.org/10.1038/s43247-024-01313-x https://doi.org/10.1038/s43247-024-01313-x https://doi.org/10.1002/ecs2.4159 https://doi.org/10.1002/fee.2395 https://doi.org/10.1038/s43017-022-00272-1 https://doi.org/10.1038/s43017-022-00272-1 https://doi.org/10.1071/BT23088 https://doi.org/10.1071/RJ09076 https://doi.org/10.1080/00049158.2020.1739883 https://doi.org/10.1080/00049158.2020.1739883 https://doi.org/10.1111/j.1466-8238.2006.00285.x https://doi.org/10.1111/j.1466-8238.2006.00285.x https://doi.org/10.1111/jbi.12065 https://doi.org/10.1071/WR14107 https://doi.org/10.1111/jbi.14522 https://doi.org/10.1002/j.1477-8696.1997.tb06274.x https://doi.org/10.1002/j.1477-8696.1997.tb06274.x Nolan, R. H., Bowman, D. M. J. S., Clarke, H., Haynes, K., Ooi, M. K. J., Price, O. F., Williamson, G. J., Whittaker, J., Bedward, M., Boer, M. M., Cavanagh, V. I., Collins, L., Gibson, R. K., Griebel, A., Jenkins, M. E., Keith, D. A., Mcilwee, A. P., Penman, T. D., Samson, S. A., … Bradstock, R. A. (2021). What do the Australian black summer fires sig- nify for the global fire crisis? Fire, 4(4), 97. https://doi.org/10.3390/ fire4040097 O'Donnell, A. J., Cullen, L. E., Lachlan McCaw, W., Boer, M. M., & Grierson, P. F. (2010). Dendroecological potential of Callitris preissii for dating historical fires in semi-arid shrublands of southern Western Australia. Dendrochronologia, 28(1), 37–48. https://doi.org/10.1016/j. dendro.2009.01.002 O'Donnell, A. J., Renton, M., Allen, K. J., & Grierson, P. F. (2021). Tree growth responses to temporal variation in rainfall differ across a continental-scale climatic gradient. PLoS ONE, 16(5), e0249959. https://doi.org/10.1371/journal.pone.0249959 Oliver, T. H., & Morecroft, M. D. (2014). Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. WIREs Climate Change, 5(3), 317–335. https://doi.org/10.1002/wcc.271 Palmer, J. G., Cook, E. R., Turney, C. S. M., Allen, K., Fenwick, P., Cook, B. I., O'Donnell, A., Lough, J., Grierson, P., & Baker, P. (2015). Drought variability in the eastern Australia and New Zealand summer drought atlas (ANZDA, CE 1500–2012) modulated by the Interdecadal Pacific Oscillation. Environmental Research Letters, 10(12), 124002. https://doi.org/10.1088/1748-9326/10/12/124002 Parmesan, C., Burrows, M. T., Duarte, C. M., Poloczanska, E. S., Richardson, A. J., Schoeman, D. S., & Singer, M. C. (2013). Beyond cli- mate change attribution in conservation and ecological research. Ecol- ogy Letters, 16(s1), 58–71. https://doi.org/10.1111/ele.12098 Pearson, S., Hua, Q., Allen, K., & Bowman, D. M. J. S. (2011). Validating putatively cross-dated Callitris tree-ring chronologies using bomb- pulse radiocarbon analysis. Australian Journal of Botany, 59(1), 7. https://doi.org/10.1071/BT10164 Pederson, N., Dyer, J. M., McEwan, R. W., Hessl, A. E., Mock, C. J., Orwig, D. A., Rieder, H. E., & Cook, B. I. (2014). The legacy of episodic climatic events in shaping temperate, broadleaf forests. Ecological Monographs, 84(4), 599–620. https://doi.org/10.1890/13-1025.1 Peel, M. C., McMahon, T. A., & Finlayson, B. L. (2004). Continental differ- ences in the variability of annual runoff-update and reassessment. Journal of Hydrology, 295(1), 185–197. https://doi.org/10.1016/j. jhydrol.2004.03.004 Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J., Broquet, G., Canadell, J. G., Chevallier, F., Liu, Y. Y., Running, S. W., Sitch, S., & Van Der Werf, G. R. (2014). Contribution of semi-arid ecosystems to inter- annual variability of the global carbon cycle. Nature, 509(7502), 600– 603. https://doi.org/10.1038/nature13376 Price, O. F., Russell-Smith, J., & Watt, F. (2012). The influence of pre- scribed fire on the extent of wildfire in savanna landscapes of western Arnhem Land, Australia. International Journal of Wildland Fire, 21(3), 297. https://doi.org/10.1071/WF10079 Prior, L. D., & Bowman, D. M. J. S. (2014). Big eucalypts grow more slowly in a warm climate: evidence of an interaction between tree size and temperature. Global Change Biology, 20(9), 2793–2799. https://doi. org/10.1111/gcb.12540 Prior, L. D., & Bowman, D. M. J. S. (2020). Classification of post-fire responses of woody plants to include pyrophobic communities. Fire, 3 (2), 15. https://doi.org/10.3390/fire3020015 Prior, L. D., Bowman, D. M. J. S., & Brook, B. W. (2007). Growth and survival of two north Australian relictual tree species, Allosyncarpia ternata (Myrtaceae) and Callitris intratropica (Cupressaceae). Ecological Research, 22(2), 228–236. https://doi.org/10.1007/s11284-006-0011-2 Prior, L. D., Hua, Q., & Bowman, D. M. J. S. (2018). Demographic vulnera- bility of an extreme xerophyte in arid Australia. Australian Journal of Botany, 66(1), 26. https://doi.org/10.1071/BT17150 Prior, L. D., Whiteside, T. G., Williamson, G. J., Bartolo, R. E., & Bowman, D. M. J. S. (2020). Multi-decadal stability of woody cover in a mesic eucalypt savanna in the Australian monsoon tro- pics. Austral Ecology, 45(5), 621–635. https://doi.org/10.1111/aec. 12877 Prober, S. M., Gosper, C. R., Gilfedder, L., Harwood, T. D., Thiele, K. R., Williams, K. J., & Yates, C. J. (2017). Temperate eucalypt woodlands. In D. A. Keith (Ed.), Australian Vegetation (3rd ed.). Cambridge University Press. Prober, S. M., Raisbeck-Brown, N., Porter, N. B., Williams, K. J., Leviston, Z., & Dickson, F. (2019). Recent climate-driven ecological change across a continent as perceived through local ecological knowledge. PLoS ONE, 14(11), e0224625. https://doi.org/ 10.1371/journal.pone.0224625 Rifai, S. W., De Kauwe, M. G., Ukkola, A. M., Cernusak, L. A., Meir, P., Medlyn, B. E., & Pitman, A. J. (2022). Thirty-eight years of CO2 fertili- zation has outpaced growing aridity to drive greening of Australian woody ecosystems. Biogeosciences, 19(2), 491–515. https://doi.org/ 10.5194/bg-19-491-2022 Rolls, E. C. (1981). A million wild acres: 200 years of man and an Australian forest. Nelson. Rolls, E. C. (1999). Land of Grass: the Loss of Australia's Grasslands. Australian Geographical Studies, 37(3), 197–213. https://doi.org/10. 1111/1467-8470.00079 Rosenzweig, C., Karoly, D., Vicarelli, M., Neofotis, P., Wu, Q., Casassa, G., Menzel, A., Root, T. L., Estrella, N., Seguin, B., Tryjanowski, P., Liu, C., Rawlins, S., & Imeson, A. (2008). Attributing physical and biological impacts to anthropogenic climate change. Nature, 453(7193), 353– 357. https://doi.org/10.1038/nature06937 Rosenzweig, C., & Neofotis, P. (2013). Detection and attribution of anthro- pogenic climate change impacts. WIREs Climate Change, 4(2), 121– 150. https://doi.org/10.1002/wcc.209 Rossiter-Rachor, N. A., Setterfield, S. A., Douglas, M. M., Hutley, L. B., & Cook, G. D. (2008). Andropogon gayanus (Gamba Grass) invasion increases fire-mediated nitrogen losses in the tropical savannas of northern Australia. Ecosystems, 11(1), 77–88. https://doi.org/10. 1007/s10021-007-9108-x Russell-Smith, J., Cook, G. D., Cooke, P. M., Edwards, A. C., Lendrum, M., Meyer, C. M., & Whitehead, P. J. (2013). Managing fire regimes in north Australian savannas: applying Aboriginal approaches to contem- porary global problems. Frontiers in Ecology and the Environment, 11(s1), e55–e63. https://doi.org/10.1890/120251 Sakaguchi, S., Bowman, D. M. J. S., Prior, L. D., Crisp, M. D., Linde, C. C., Tsumura, Y., & Isagi, Y. (2013). Climate, not Aboriginal landscape burn- ing, controlled the historical demography and distribution of fire- sensitive conifer populations across Australia. Proceedings of the Royal Society B: Biological Sciences, 280(1773), 20132182. https://doi.org/ 10.1098/rspb.2013.2182 Schlesinger, C. A., & Westerhuis, E. L. (2021). Impacts of a single fire event on large, old trees in a grass-invaded arid river system. Fire Ecology, 17(1), 34. https://doi.org/10.1186/s42408-021-00121-4 Sexton, J. P., McIntyre, P. J., Angert, A. L., & Rice, K. J. (2009). Evolution and ecology of species range limits. Annual Review of Ecology, Evolu- tion, and Systematics, 40, 415–436. https://doi.org/10.1146/annurev. ecolsys.110308.120317 Sharp, B. R., & Bowman, D. M. J. S. (2004). Patterns of long-term woody vegetation change in a sandstone-plateau savanna woodland, North- ern Territory, Australia. Journal of Tropical Ecology, 20(3), 259–270. https://doi.org/10.1017/S0266467403001238 Sinclair, R. (2005). Long-term changes in vegetation, gradual and episodic, on the TGB Osborn Vegetation Reserve, Koonamore, South Australia (1926-2002). Australian Journal of Botany, 53(4), 283–296. https://doi. org/10.1071/BT04144 Sinclair, R., & Facelli, J. M. (2019). Ninety years of change on the TGB Osborn Vegetation Reserve, Koonamore: a unique research WILLIAMS ET AL. 23 https://doi.org/10.3390/fire4040097 https://doi.org/10.3390/fire4040097 https://doi.org/10.1016/j.dendro.2009.01.002 https://doi.org/10.1016/j.dendro.2009.01.002 https://doi.org/10.1371/journal.pone.0249959 https://doi.org/10.1002/wcc.271 https://doi.org/10.1088/1748-9326/10/12/124002 https://doi.org/10.1111/ele.12098 https://doi.org/10.1071/BT10164 https://doi.org/10.1890/13-1025.1 https://doi.org/10.1016/j.jhydrol.2004.03.004 https://doi.org/10.1016/j.jhydrol.2004.03.004 https://doi.org/10.1038/nature13376 https://doi.org/10.1071/WF10079 https://doi.org/10.1111/gcb.12540 https://doi.org/10.1111/gcb.12540 https://doi.org/10.3390/fire3020015 https://doi.org/10.1007/s11284-006-0011-2 https://doi.org/10.1071/BT17150 https://doi.org/10.1111/aec.12877 https://doi.org/10.1111/aec.12877 https://doi.org/10.1371/journal.pone.0224625 https://doi.org/10.1371/journal.pone.0224625 https://doi.org/10.5194/bg-19-491-2022 https://doi.org/10.5194/bg-19-491-2022 https://doi.org/10.1111/1467-8470.00079 https://doi.org/10.1111/1467-8470.00079 https://doi.org/10.1038/nature06937 https://doi.org/10.1002/wcc.209 https://doi.org/10.1007/s10021-007-9108-x https://doi.org/10.1007/s10021-007-9108-x https://doi.org/10.1890/120251 https://doi.org/10.1098/rspb.2013.2182 https://doi.org/10.1098/rspb.2013.2182 https://doi.org/10.1186/s42408-021-00121-4 https://doi.org/10.1146/annurev.ecolsys.110308.120317 https://doi.org/10.1146/annurev.ecolsys.110308.120317 https://doi.org/10.1017/S0266467403001238 https://doi.org/10.1071/BT04144 https://doi.org/10.1071/BT04144 opportunity. The Rangeland Journal, 41(3), 185. https://doi.org/10. 1071/RJ18022 Sitch, S., O'Sullivan, M., Robertson, E., Friedlingstein, P., Albergel, C., Anthoni, P., Arneth, A., Arora, V. K., Bastos, A., Bastrikov, V., Bellouin, N., Canadell, J. G., Chini, L., Ciais, P., Falk, S., Harris, I., Hurtt, G., Ito, A., Jain, A. K., … Zaehle, S. (2024). Trends and Drivers of Terrestrial Sources and Sinks of Carbon Dioxide: An Overview of the TRENDY Project. Global Biogeochemical Cycles, 38(7), e2024GB008102. https://doi.org/10.1029/2024GB008102 Sparrow, B. D., Edwards, W., Munroe, S. E. M., Wardle, G. M., Guerin, G. R., Bastin, J. F., Morris, B., Christensen, R., Phinn, S., & Lowe, A. J. (2020). Effective ecosystem monitoring requires a multi-scaled approach. Biological Reviews, 95(6), 1706–1719. https:// doi.org/10.1111/brv.12636 Stephens, C. M., Band, L. E., Johnson, F. M., Marshall, L. A., Medlyn, B. E., De Kauwe, M. G., & Ukkola, A. M. (2023). Changes in Blue/Green Water Partitioning Under Severe Drought. Water Resources Research, 59(11), e2022WR033449. https://doi.org/10.1029/2022WR033449 Stephenson, T., Hudiburg, T., Mathias, J. M., Jones, M., & Lynch, L. M. (2024). Do Tasmanian devil declines impact ecosystem function? Global Change Biology, 30(7), e17413. https://doi.org/10.1111/gcb. 17413 Strayer, D. (1986). An essay on long-term ecological studies. Bulletin of the Ecological Society of America, 67(4), 271–274. Tng, D. Y. P., Murphy, B. P., Weber, E., Sanders, G., Williamson, G. J., Kemp, J., & Bowman, D. M. J. S. (2012). Humid tropical rain forest has expanded into eucalypt forest and savanna over the last 50 years. Ecology and Evolution, 2(1), 34–45. https://doi.org/10.1002/ ece3.70 Trauernicht, C., Brook, B. W., Murphy, B. P., Williamson, G. J., & Bowman, D. M. J. S. (2015). Local and global pyrogeographic evidence that indigenous fire management creates pyrodiversity. Ecology and Evolution, 5(9), 1908–1918. https://doi.org/10.1002/ece3.1494 Trauernicht, C., Murphy, B. P., Portner, T. E., & Bowman, D. M. J. S. (2012). Tree cover–fire interactions promote the persistence of a fire- sensitive conifer in a highly flammable savanna. Journal of Ecology, 100(4), 958–968. https://doi.org/10.1111/j.1365-2745.2012.01970.x Trauernicht, C., Murphy, B. P., Prior, L. D., Lawes, M. J., & Bowman, D. M. J. S. (2016). Human-Imposed, Fine-Grained Patch Burning Explains the Population Stability of a Fire-Sensitive Conifer in a Frequently Burnt Northern Australia Savanna. Ecosystems, 19(5), 896–909. https://doi. org/10.1007/s10021-016-9973-2 Trauernicht, C., Murphy, B. P., Tangalin, N., & Bowman, D. M. J. S. (2013). Cultural legacies, fire ecology, and environmental change in the Stone Country of Arnhem Land and Kakadu National Park, Australia. Ecology and Evolution, 3(2), 286–297. https://doi.org/10.1002/ece3.460 Trouvé, R., Baker, P. J., Ducey, M., Robinson, A. P., & Nitschke, C. R. (2025). Global warming reduces the carrying capacity of the tallest angiosperm species (Eucalyptus regnans). Nature Communications. https://doi.org/10.1038/s41467-025-62635-x Trouvé, R., Nitschke, C. R., Robinson, A. P., & Baker, P. J. (2017). Estimat- ing the self-thinning line from mortality data. Forest Ecology and Man- agement, 402, 122–134. https://doi.org/10.1016/j.foreco.2017. 07.027 Turco, M., Abatzoglou, J. T., Herrera, S., Zhuang, Y., Jerez, S., Lucas, D. D., AghaKouchak, A., & Cvijanovic, I. (2023). Anthropogenic climate change impacts exacerbate summer forest fires in California. Proceed- ings of the National Academy of Sciences, 120(25), e2213815120. https://doi.org/10.1073/pnas.2213815120 van der Velde, I. R., van der Werf, G. R., Houweling, S., Maasakkers, J. D., Borsdorff, T., Landgraf, J., Tol, P., Van Kempen, T. A., Van Hees, R., Hoogeveen, R., Veefkind, J. P., & Aben, I. (2021). Vast CO2 release from Australian fires in 2019– 2020 constrained by satellite. Nature, 597(7876), 366–369. https:// doi.org/10.1038/s41586-021-03712-y van Oldenborgh, G. J., Krikken, F., Lewis, S., Leach, N. J., Lehner, F., Saunders, K. R., van Weele, M., Haustein, K., Li, S., Wallom, D., Sparrow, S., Arrighi, J., Singh, R. K., van Aalst, M. K., Philip, S. Y., Vautard, R., & Otto, F. E. L. (2021). Attribution of the Australian bush- fire risk to anthropogenic climate change. Natural Hazards and Earth System Sciences, 21(3), 941–960. https://doi.org/10.5194/nhess-21- 941-2021 Villalobos, Y., Canadell, J. G., Keller, E. D., Briggs, P. R., Bukosa, B., Giltrap, D. L., Harman, I., Hilton, T. W., Kirschbaum, M. U. F., Lauerwald, R., Liang, L. L., Maavara, T., Mikaloff-Fletcher, S. E., Rayner, P. J., Resplandy, L., Rosentreter, J., Metz, E. M., Serrano, O., & Smith, B. (2023). A Comprehensive Assessment of Anthropogenic and Natural Sources and Sinks of Australasia's Carbon Budget. Global Bio- geochemical Cycles, 37(12), e2023GB007845. https://doi.org/10. 1029/2023GB007845 Wahren, C.-H., Camac, J. S., Jarrad, F. C., Williams, R. J., Papst, W. A., & Hoffmann, A. A. (2013). Experimental warming and long-term vegeta- tion dynamics in an alpine heathland. Australian Journal of Botany, 61(1), 36–51. https://doi.org/10.1071/BT12234 Wahren, C.-H. A., Papst, W. A., & Williams, R. J. (1994). Long-Term Vege- tation Change in Relation to Cattle Grazing in Sub-Alpine Grassland and Heathland on the Bogong High-Plains: an Analysis of Vegetation Records From 1945 to 1994. Australian Journal of Botany, 42(6), 607– 639. https://doi.org/10.1071/BT9940607 Walker, A. P., De Kauwe, M. G., Bastos, A., Belmecheri, S., Georgiou, K., Keeling, R. F., McMahon, S. M., Medlyn, B. E., Moore, D. J. P., Norby, R. J., Zaehle, S., Anderson-Teixeira, K. J., Battipaglia, G., Brienen, R. J. W., Cabugao, K. G., Cailleret, M., Campbell, E., Canadell, J. G., Ciais, P., … Zuidema, P. A. (2021). Integrating the evi- dence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytologist, 229(5), 2413–2445. https://doi.org/10.1111/ nph.16866 Wang, B., Smith, B., Waters, C., Feng, P., & Liu, D. L. (2024). Modelling changes in vegetation productivity and carbon balance under future climate scenarios in southeastern Australia. Science of the Total Envi- ronment, 924, 171748. https://doi.org/10.1016/j.scitotenv.2024. 171748 Wang, S., Zhang, Y., Ju, W., Chen, J. M., Ciais, P., Cescatti, A., Sardans, J., Janssens, I. A., Wu, M., Berry, J. A., Campbell, E., Fernández- Martínez, M., Alkama, R., Sitch, S., Friedlingstein, P., Smith, W. K., Yuan, W., He, W., Lombardozzi, D., … Peñuelas, J. (2020). Recent global decline of CO2 fertilization effects on vegetation photosynthe- sis. Science, 370, 1295–1300. https://doi.org/10.1126/science. abb7772 West, T. A. P., Wunder, S., Sills, E. O., Börner, J., Rifai, S. W., Neidermeier, A. N., Frey, G. P., & Kontoleon, A. (2023). Action needed to make carbon offsets from forest conservation work for climate change mitigation. Science, 381(6660), 873–877. https://doi.org/10. 1126/science.ade3535 Whipp, R. K., Lunt, I. D., Spooner, P. G., & Bradstock, R. A. (2012). Changes in forest structure over 60 years: tree densities continue to increase in the Pilliga forests, New South Wales, Australia. Australian Journal of Botany, 60(1), 1–8. https://doi.org/10.1071/BT11191 Wilcox, B. P., Basant, S., Olariu, H., & Leite, P. A. M. (2022). Ecohydrologi- cal connectivity: A unifying framework for understanding how woody plant encroachment alters the water cycle in drylands. Frontiers in Envi- ronmental Science, 10, 934535. https://doi.org/10.3389/fenvs.2022. 934535 Williams, R., Papst, W., McDougall, K., Mansergh, I., Heinze, D., Camac, J., Nash, M., Morgan, J., & Hoffmann, A. (2014). Alpine ecosystems. In D. Lindenmayer, E. Burns, N. Thurgate, & A. Lowe (Eds.), Biodiversity and Environmental Change: Monitoring, Challenges and Direction. CSIRO Publishing. Winkler, A. J., Myneni, R. B., Hannart, A., Sitch, S., Haverd, V., Lombardozzi, D., Arora, V. K., Pongratz, J., Nabel, J. E. M. S., Goll, D. S., 24 WILLIAMS ET AL. https://doi.org/10.1071/RJ18022 https://doi.org/10.1071/RJ18022 https://doi.org/10.1029/2024GB008102 https://doi.org/10.1111/brv.12636 https://doi.org/10.1111/brv.12636 https://doi.org/10.1029/2022WR033449 https://doi.org/10.1111/gcb.17413 https://doi.org/10.1111/gcb.17413 https://doi.org/10.1002/ece3.70 https://doi.org/10.1002/ece3.70 https://doi.org/10.1002/ece3.1494 https://doi.org/10.1111/j.1365-2745.2012.01970.x https://doi.org/10.1007/s10021-016-9973-2 https://doi.org/10.1007/s10021-016-9973-2 https://doi.org/10.1002/ece3.460 https://doi.org/10.1038/s41467-025-62635-x https://doi.org/10.1016/j.foreco.2017.07.027 https://doi.org/10.1016/j.foreco.2017.07.027 https://doi.org/10.1073/pnas.2213815120 https://doi.org/10.1038/s41586-021-03712-y https://doi.org/10.1038/s41586-021-03712-y https://doi.org/10.5194/nhess-21-941-2021 https://doi.org/10.5194/nhess-21-941-2021 https://doi.org/10.1029/2023GB007845 https://doi.org/10.1029/2023GB007845 https://doi.org/10.1071/BT12234 https://doi.org/10.1071/BT9940607 https://doi.org/10.1111/nph.16866 https://doi.org/10.1111/nph.16866 https://doi.org/10.1016/j.scitotenv.2024.171748 https://doi.org/10.1016/j.scitotenv.2024.171748 https://doi.org/10.1126/science.abb7772 https://doi.org/10.1126/science.abb7772 https://doi.org/10.1126/science.ade3535 https://doi.org/10.1126/science.ade3535 https://doi.org/10.1071/BT11191 https://doi.org/10.3389/fenvs.2022.934535 https://doi.org/10.3389/fenvs.2022.934535 Kato, E., Tian, H., Arneth, A., Friedlingstein, P., Jain, A. K., Zaehle, S., & Brovkin, V. (2021). Slowdown of the greening trend in natural vegeta- tion with further rise in atmospheric CO2. Biogeosciences, 18(17), 4985–5010. https://doi.org/10.5194/bg-18-4985-2021 Winslow, J. C., Hunt, E. R., & Piper, S. C. (2003). The influence of seasonal water availability on global C3 versus C4 grassland biomass and its implications for climate change research. Ecological Modelling, 163(1–2), 153–173. https://doi.org/10.1016/S0304-3800(02) 00415-5 Woinarski, J. C. Z., Braby, M. F., Burbidge, A. A., Coates, D., Garnett, S. T., Fensham, R. J., Legge, S. M., McKenzie, N. L., Silcock, J. L., & Murphy, B. P. (2019). Reading the black book: The number, timing, dis- tribution and causes of listed extinctions in Australia. Biological Conser- vation, 239, 108261. https://doi.org/10.1016/j.biocon.2019.108261 Wright, B. R., Nipper, M., Nipper, N., Merson, S. D., & Guest, T. (2023). Mortality rates of desert vegetation during high-intensity drought at Uluru-Kata Tjuta National Park, Central Australia. Austral Ecology, 4, 699–718. https://doi.org/10.1111/aec.13290 Xie, Q., Huete, A., Hall, C. C., Medlyn, B. E., Power, S. A., Davies, J. M., Medek, D. E., & Beggs, P. J. (2022). Satellite-observed shifts in C3/C4 abundance in Australian grasslands are associated with rainfall pat- terns. Remote Sensing of Environment, 273, 112983. https://doi.org/ 10.1016/j.rse.2022.112983 Yang, H., Munson, S. M., Huntingford, C., Carvalhais, N., Knapp, A. K., Li, X., Peñuelas, J., Zscheischler, J., & Chen, A. (2023). The detection and attribution of extreme reductions in vegetation growth across the global land surface. Global Change Biology, 29(8), 2351–2362. https:// doi.org/10.1111/gcb.16595 Yates, C., & Russell-Smith, J. (2003). Fire regimes and vegetation sensitiv- ity analysis: an example from Bradshaw Station, monsoonal northern Australia. International Journal of Wildland Fire, 12(4), 349. https://doi. org/10.1071/WF03019 Youngentob, K. N., Likens, G. E., Williams, J. E., & Lindenmayer, D. B. (2013). A survey of long-term terrestrial ecology studies in Australia. Austral Ecology, 38(4), 365–373. https://doi.org/10.1111/j.1442- 9993.2012.02421.x Youngentob, K. N., Renzullo, L. J., Held, A. A., Jia, X., Lindenmayer, D. B., & Foley, W. J. (2012). Using imaging spectroscopy to estimate integrated measures of foliage nutritional quality: Imaging spectroscopy estimates of forage quality. Methods in Ecology and Evolution, 3(2), 416–426. https://doi.org/10.1111/j.2041-210X.2011.00149.x Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., … Zeng, N. (2016). Greening of the Earth and its drivers. Nature Climate Change, 6, 791–795. https://doi.org/10.1038/nclimate3004 zu Ermgassen, S. O. S. E., Devenish, K., Simmons, B. A., Gordon, A., Jones, J. P. G., Maron, M., Schulte to Bühne, H., Sharma, R., Sonter, L. J., Strange, N., Ward, M., & Bull, J. W. (2023). Evaluating the impact of biodiversity offsetting on native vegetation. Global Change Biology, 29(15), 4397–4411. https://doi.org/10.1111/gcb.16801 How to cite this article: Williams, L. J., Gallagher, R. V., Rifai, S. W., Adeleye, M. A., Baker, P. J., Bowman, D. M. J. S., Eckersley, J., England, J. R., Fletcher, M.-S., Grierson, P. F., Inbar, A., Knauer, J., Stephens, C. M., Trouvé, R., & Medlyn, B. E. (2025). Detecting and attributing climate change effects on vegetation: Australia as a test case. Plants, People, Planet, 1–25. https://doi.org/10.1002/ppp3.70090 WILLIAMS ET AL. 25 https://doi.org/10.5194/bg-18-4985-2021 https://doi.org/10.1016/S0304-3800(02)00415-5 https://doi.org/10.1016/S0304-3800(02)00415-5 https://doi.org/10.1016/j.biocon.2019.108261 https://doi.org/10.1111/aec.13290 https://doi.org/10.1016/j.rse.2022.112983 https://doi.org/10.1016/j.rse.2022.112983 https://doi.org/10.1111/gcb.16595 https://doi.org/10.1111/gcb.16595 https://doi.org/10.1071/WF03019 https://doi.org/10.1071/WF03019 https://doi.org/10.1111/j.1442-9993.2012.02421.x https://doi.org/10.1111/j.1442-9993.2012.02421.x https://doi.org/10.1111/j.2041-210X.2011.00149.x https://doi.org/10.1038/nclimate3004 https://doi.org/10.1111/gcb.16801 https://doi.org/10.1002/ppp3.70090 Detecting and attributing climate change effects on vegetation: Australia as a test case 1 | INTRODUCTION 2 | A BRIEF OVERVIEW OF DRIVERS OF VEGETATION CHANGE IN AUSTRALIA 3 | DATA SOURCES FOR DETECTING VEGETATION CHANGE IN AUSTRALIA 3.1 | Pollen, charcoal and stable isotope records 3.2 | Tree rings 3.3 | Forest inventory and permanent growth plots 3.4 | Long‐term ecological monitoring 3.5 | Remote sensing 4 | ATTRIBUTING DRIVERS TO VEGETATION CHANGES 4.1 | Qualitative Correlative 4.2 | Qualitative Partial 4.3 | Qualitative Strong 4.4 | Quantitative Correlative 4.5 | Quantitative Partial 4.6 | Quantitative Strong 5 | SYNTHESIS: GLOBAL IMPLICATIONS AND RECOMMENDATIONS AUTHOR CONTRIBUTIONS ACKNOWLEDGEMENTS CONFLICT OF INTEREST STATEMENT DATA AVAILABILITY STATEMENT ORCID REFERENCES