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Invasive species, climate change and the negative impacts of tourism are currently and globally recognised as the three most significant threats to Outstanding Universal Values of natural World Heritage listed areas. This study investigated the threat of incursion of invasive plant species associated with access roads in the Wet Tropics World Heritage Area in Queensland, Australia. We assessed richness, composition, and the spread of invasive plant species along roads and in the adjacent vegetation using 34 transects established in 2015 and remeasured in 2021. Species and foliage cover of all ground-layer plants were recorded. Thirty-one invasive species were recorded, and their richness was found to be significantly higher at the road edge (27 species) than in the adjacent vegetation (16 species). While invasive species richness decreased with distance from the road edge, their foliage cover did not show a consistent trend. Foliage cover for more than 73% of the recorded invasive species at the road edge was Urochloa decumbens) with 65% at the road edge, and Parramata grass (Sporobolus fertilis) in the adjacent vegetation with 64% foliage cover. The abundance of invasive species and the potential for a small set to dominate the road edge and spread into the adjoining environment, highlights the importance of timely surveillance and targeted control of invasive species at road edges to ensure that they do not spread to the adjacent vegetation.

Vegetation classification systems form a base for conservation management and the ecological exploration of the patterns and drivers of species’ distributions. A standardised system crossing administrative and geographical boundaries is widely recognised as most useful for broad-scale management. The Queensland Government, recognising this, uses the Regional Ecosystem (RE) classification system and accompanying mapping as a state-wide standardised vegetation classification system. This system informs legislation and policy at local, state and national levels, underpinning decisions that have wide-ranging implications for biodiversity and people’s livelihoods. It therefore needs to be robust from a scientific and legal perspective. The current approach in the RE system for identifying vegetation communities relies on expert-based class definition procedures. This is in contrast to best practice, which is based on quantitative procedures. This paper discusses the RE system in a global context and outlines the updated approach that incorporates quantitative class definition procedures, synthesises the research behind the updated approach and discusses its implications and implementation.

Soil disruption from open-cut mining practices can adversely impact microbial communities and the ecosystem services that they mediate. Despite this, assessment of impacts of soil disruption, and the subsequent recovery of microbial communities is rarely studied. Monitoring of ecological restoration success on mine sites has traditionally focused on vegetation; however, most plants rely, at least in part, on associations with soil fungi for enhanced nutrient and water acquisition. Here, we used high-throughput phylogenetic marker gene sequencing to characterize the diversity of soil fungal communities along a restoration chronosequence ranging from 3 to 23 years at a rehabilitated mine site. We used nonmined analogue sites as a baseline for comparative purposes and examined the associations of soil fungal communities with soil physicochemical and aboveground vegetation variables. Fungal richness on rehabilitated sites was significantly larger than on nonmined sites, suggesting that mixing of topsoil during stockpiling resulted in a composite microbial community. Fungal community composition was significantly influenced by edaphic variables and the length of rehabilitation, with mined sites becoming more similar to nonmined sites over time. Fungal populations associated with ectomycorrhizae were relatively more abundant than those associated with arbuscular mycorrhizae and declined in response to disturbance, but recovered over time on the woody dominated sites indicating a strong coupling of these fungi with aboveground vegetation. Our data indicate that soil fungal diversity is a useful bioindicator of soil restoration in mined sites and may complement more traditional vegetation-based surveys.

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

Plant communities in extensive landscapes are often mapped remotely using detectable patterns based on vegetation structure and canopy species with a high relative cover. A plot-based classification which includes species with low relative canopy cover and ignores vegetation structure, may result in plant communities not easily reconcilable with the landscape patterns represented in mapping. In our study, we investigate the effects on classification outcomes if we (1) remove rare species based on canopy cover, and (2) incorporate vegetation structure by weighting species’ cover by different measures of vegetation height. Using a dataset of 101 plots of savanna vegetation in north-eastern Australia we investigated first, the effect of removing rare species using four cover thresholds (1, 5, 8 and 10% contribution to total cover) and second, weighting species by four height measures including actual height as well as continuous and categorical transformations. Using agglomerative hierarchical clustering we produced a classification for each dataset and compared them for differences in: patterns of plot similarity, clustering, species richness and evenness, and characteristic species. We estimated the ability of each classification to predict species cover using generalised linear models. We found removing rare species at any cover threshold produced characteristic species appearing to correspond to landscape scale changes and better predicted species cover in grasslands and shrublands. However, in woodlands it made no difference. Using actual height of vegetation layer maintained vegetation structure, emphasised canopy and then sub-canopy species in clustering, and predicted species cover best of the height-measures tested. Thus, removing rare species and weighting species by height are useful techniques for identifying plant communities from plot-based classifications which are conceptually consistent with those in landscape scale mapping. This increases the confidence of end-users in both the classifications and the maps, thus enhancing their use in land management decisions.

Background Australia’s energy future is at the crossroads and the role of renewable sources is in focus. Biomass from sustainably managed forests provide a significant opportunity for electricity and heat generation and production of liquid fuels. Australia has extensive native forests of which a significant proportion are on private land. However, there is limited knowledge on the potential capacity of this resource to contribute to the expansion of a biomass for bioenergy industry. In addition, there are concerns on how to reconcile biomass harvesting with environmental protection. Methods We used regional ecosystem vegetation mapping for Queensland to stratify harvestable forests within the 1.8 m hectares of private native forests present in the Southeast Queensland bioregion in 2014. We used a dataset of 52,620 individual tree measurements from 541 forest inventory plots collected over the last 10 years. Tree biomass was estimated using current biomass allometric equations for Australia. Biomass potentially available from selective sawlog harvesting and silvicultural treatment across the bioregion was calculated and mapped. Results Current sawlog harvesting extracts 41.4% of the standing tree biomass and a biomass for bioenergy harvest would retain on average 36% of felled tree biomass on site for the protection of environmental and fauna habitat values. The estimated area extent of harvestable private native forests in the bioregion in 2013 was 888,000 ha and estimated available biomass for bioenergy in living trees was 13.6 million tonnes (t). The spotted gum (Corymbia citriodora subsp. variegata) forests were the most extensive, covering an area of 379,823 ha and with a biomass for bioenergy yield of 14.2 t∙ha−1 (with approximately 11.2 t∙ha−1 of the biomass harvested from silvicultural thinning and 3 t∙ha−1 recovered from sawlog harvest residual). Conclusions Silvicultural treatment of private native forests in the Southeast Queensland bioregion, has the capacity to supply a large quantity of biomass for bioenergy. The availability of a biomass for bioenergy market, and integration of sawlog harvesting and silvicultural treatment operations, could provide land owners with additional commercial incentive to improve the management of private native forests. This could potentially promote restoration of degraded forests, ecological sustainability and continued provision of wood products.

Open-cut mining severely disrupts landforms and soils, preventing or impeding the restoration of preexisting or functional ecosystems because essential properties of the original soils cannot immediately or easily be reinstated. We examined the soil physicochemical and bacterial characteristics of 21 coal-mined sites in subtropical Queensland, Australia, 3-23 years after establishment of native plant species relative to nonmined analogue sites. Soil disturbance significantly decreased total nitrogen, nitrate nitrogen, and especially total carbon (TC). The TC is projected to take 36 years to recover. Bacterial communities assessed by 16S ribosomal RNA sequencing showed greater species richness and evenness in rehabilitated as compared with nonmined soils, regardless of rehabilitation age. However, bacterial species composition was associated significantly with soil electrical conductivity, the plant density, and total stem cross-sectional area of woody vegetation. The bacterial communities on rehabilitated sites became progressively more similar to those of nonmined analogue sites over time. This work demonstrates that if topsoils are conserved carefully during mining and supplemented by inorganic fertilizer addition, vigorous plant growth and changes in bacterial community composition can occur soon after plant establishment. This will mitigate the effects of soil disturbance and accelerate the return to the chemical and biological attributes of nonmined analogue soils.

Summary The establishment success of woody plant species at 56 revegetation sites, four to 26 years old, across the Meandu open-cut coal mine in south-east Queensland was assessed. The revegetation process involved returning stockpiled topsoil, deep ripping and mechanical sowing of a mix of native seeds. Blakes Wattle (Acacia blakei) and less often Black Wattle (A. leiocalyx), both primarily derived from respread topsoil seed, dominate the vegetation canopy at 59% and 20% of revegetation sites, respectively. The additional sowing of seeds of many tree and shrub species within the sites has had limited success with most failing to persist or grow well. Revegetation management, for example selective thinning of acacias (Acacia spp.) saplings within the first 5 years is recommended to release the competition pressure on the poorly performing tree species. This will also allow opportunities for other less well represented shrub and herb species to persist. This study has shown that a range of tree and shrub species, including Eucalyptus spp., are performing poorly under the current revegetation regime, suggesting adjustments to revegetation species selection and/or methodologies are needed. The natural colonization of woody native species within the sites from nearby remnant vegetation is shown to be limited to only four species, and therefore is unlikely to significantly supplement the species diversity of the revegetation.

This paper presents insights from long-term monitoring into the dynamics of savanna plant species composition, specifically on the ground layer. Key questions are (1) how much variation is there from year to year in plant species abundance and composition? And (2) is there evidence of directional change in community composition? Five sites were located near Mareeba, north-eastern Australia, and sampled 18 times with fixed 500-m2 plots at the annual peak of plant species richness over 23 years. The sites were located in eucalypt communities across an altitudinal range of 460 m. Sampling involved systematic searching of all plant species visible above ground. The results show variation from year to year among satellite herbaceous species, with more than 50% of grasses and forbs observed at a given site only being detected at half or fewer of the 18 samplings. Herbaceous composition did not show clear signs of directional change relative to inter-annual variability. Over the 23 years, there were three very dry periods and 4 very wet years. Rainfall variation affected total cover more than plant species composition or richness. Several fires and episodes of grazing affected ground cover but did not have a lasting impact on cover or diversity. The cover and composition of the ground layer is consistently dominated by the core perennial grasses Themeda triandra Forssk. and Heteropogon triticeus (R.Br.) Stapf, with many species collectively contributing minimally to the overall cover but significantly to richness. Many herb species are persistent long-lived perennials which do not appear above ground every year. The number of naturalised plants in the ground layer was consistently low. Overall, the ground layer communities appear to be very stable in these woodlands that have been burnt 3–5 times in 23 years. High inter-annual variability of subdominant species is consistent with other studies, and emphasises the need for replication in time in ecological sampling.

Summary Reinstatement of a ‘self-sustaining native ecosystem’ is an increasingly common revegetation goal for open-cut mines in Australia. This is usually applied as a regulatory requirement for mine closure, with some mines aiming for a high standard of ‘ecological restoration’. There is a growing appreciation that ecological restoration outcomes of mine rehabilitation are unachievable within the life of a mine and that assessment methodologies are subjective and inadequate to provide the high degree of confidence needed for mine closure. Here we elaborate on the integration of the BioCondition assessment and the Ecosystem Dynamics Simulator (EDS) trajectory methodologies. We also demonstrate an alternative early relinquishment assessment criteria similar to that used for early recruitment of regrowth native vegetation that exhibit structural characteristics of undisturbed native vegetation into remnant status in Queensland. We used ten age cohorts of rehabilitation ranging from three to 22 years at Meandu coal mine, south-east Queensland. All the sites gained average BioCondition scores ranging from 19 to 44 out of possible score of 65. Growth trajectories indicated decline in species and basal area on all sites in the next three decades as the dense stock of short-lived acacia species senesce and die. None of the sites are projected to meet self-sustaining status by 2072, but there is evidence in some of a progression. All the sites except two (K2.3 and SW2) are projected to achieve remnant vegetation status by 2062. Applying the remnant vegetation criteria results in shorter timeframes and robustly evaluates whether sites are progressing towards the self-sustaining ecosystem mine closure goal.

Post-mining landscape reconstruction on open-cut coal mines aims to support restoration of self-sustaining native vegetation ecosystems that in perpetuity require no extra inputs relative to unmined analogs. Little is known about the soil moisture retention capacity of the limited layer of topsoil replaced (often

Summary The standard rehabilitation objective for open-cut mines in Queensland is to establish a self-sustaining native forest ecosystem. Consequently, mine regulators and managers need tools to project whether sites are likely or not to meet agreed completion criteria and to ensure timely remedial interventions. The Ecosystem Dynamics Simulator (EDS) is such a tool capable of modelling forest dynamics and projecting long-term growth of woody species only. Here, the model was applied to rehabilitation sites aged between 5 and 22 years in Meandu open-cut coal mine in southeast Queensland. EDS projected structural characteristics for trees (height, diameter, basal area, foliage projective cover and stem density) and tree species composition as a function of rehabilitation age. Projected stand growth attributes were assessed against BioCondition benchmarks developed from eucalypt (Eucalyptus/Corymbia) remnant forests adjacent to the mine. Growth trajectories indicated that sites with >30% eucalypt basal area composition were more likely to develop into eucalypt-dominated self-sustaining ecosystems compared with sites that were initially dominated by acacias (Acacia spp.). Projections suggested that some benchmark attributes such as number of large eucalypt trees would take more than 70 years to be met. The application of EDS provided a framework to support decisions on early remedial intervention and assess the risk associated with lease relinquishment.

SummaryEstimates of carbon stocks and their annual change for extensive Australian sub-tropical forests are based on indirect estimates or on data derived from temperate forests. We estimated live above-ground tree carbon (LAC) stocks at landscape level from 355 000 measurements of 94 127 tree stems from 604 permanently monitored plots representing 2.6 million ha of managed uneven-aged mixed-species native forests in sub-tropical Queensland. These plots were established between 1936 and 1998 and re-measured every 2 to 10 years up to 2011. Landscapes were represented by 16 broad vegetation groups growing across a mean annual rainfall range of 500 to 2000 mm. Landscape-mean LAC stocks varied from 20.8 ± 4.3 t C ha−1 in inland eucalypt woodlands to 146.4 ± 11.1 t C ha−1 in coastal wet tall open forests. Landscape maximum LAC stock, defined as the mean of maximum LAC stocks over the entire measurement history for a specified landscape under prevailing environmental conditions and disturbance regimes, includin...

Access trails that traverse native vegetation expose these ecosystems to potential invasion by non-native plant species, facilitated by a multitude of vectors that utilise these trails. This study examined the diversity and distribution patterns of non-native plant species occurring adjacent to forest trails that are also designated for horse riding use in National Parks in southeast Queensland. A total of 39 non-native species were recorded within 20 m of the trail edge. Observed species varied among the National Parks with 21 species recorded at Bellthorpe and 16 species at D'Aguilar, compared to one or two species in the rest of the parks. There was a rapid decrease in number of non-native species with increasing distance from the trail edge. Up to 25 species occurred directly adjacent to trails compared to nine species 5 m from the trails. Among the observed non-native species, only Sonchus oleraceus and Plantago major are currently known to be dispersed via horse dung, whereas 15 of the recorded spec...

Summary A quantitative ‘scorecard’ is essential to provide both mine regulators and managers with a robust way of assessing what is ‘good’ vegetation rehabilitation and whether it is adequate to satisfy the regulatory and legal requirements of mine closure criteria and community expectations. The BioCondition framework (Eyre et al. 2011a, http://www.ehp.qld.gov.au/ecosystems/biodiversity/biocondition.html) was applied as a scorecard to evaluate vegetation rehabilitation using largely locally native species at Meandu coal mine in Southeast Queensland. Benchmarks for vegetation condition attributes were developed from an amalgam of local reference vegetation types. To allow the appropriate, rather than aspirational evaluation of restoration for sites that were

The biodiversity-productivity relationship (BPR) is foundational to our understanding of the global extinction crisis and its impacts on ecosystem functioning. Understanding BPR is critical for the accurate valuation and effective conservation of biodiversity. Using ground-sourced data from 777,126 permanent plots, spanning 44 countries and most terrestrial biomes, we reveal a globally consistent positive concave-down BPR, showing that continued biodiversity loss would result in an accelerating decline in forest productivity worldwide. The value of biodiversity in maintaining commercial forest productivity alone - US$166 billion to 490 billion per year according to our estimation - is more than twice what it would cost to implement effective global conservation. This highlights the need for a worldwide reassessment of biodiversity values, forest management strategies, and conservation priorities.

This study assesses the use of pyroelectric infrared sensors combined with digital cameras to document visitor use patterns on a Horse Trail Network within an area of D'Aguilar National Park; a peri-urban bushland reserved for a range of purposes and used by several user-groups. Data were obtained from four cameras and comprised 7000 photographs over 1000 days. Forty-five percent of photographs were false triggers attributable to environmental factors and 42% were of confirmed users. An exercise aimed at assessing camera success revealed that in this study capture rates were in the order of 63% for cyclists, 82% for pedestrians, 90% of motor vehicles and 100% for horses. Sources of error can be minimised and primarily include the internal and external camera settings. Major advantages of infrared digital cameras include the portability of the technique, low cost, digital data format and discrimination of user types. In this case study, two-thirds of total observed visitor numbers occur on weekends and two...

Reviews of horse-riding impacts usually mention degradation of trails, watercourses and vegetation. These potential impacts are of current relevance within some south-east Queensland protected areas where the future of horse-riding is subject to studies on biophysical and social impacts. This study aimed to provide an indication of soil movement patterns on vehicular roads in response to three separate endurance riding events, which represent the most intense horse use of these roads with up to 300 passes in one day. A measure of the areal extent and depth of erosion was obtained from at least 16 sections of road at each event. Across the study, there was an average increase in eroded area of less than 2 per cent and decrease of erosion depth by less than 1 mm. Linear mixed effects modelling revealed that soil movement was weakly related to slope and field texture, where the steeper the slope and softer the surface the more movement. Soil movement could be predicted using pre-existing road characteristics alone and the number of horse passes was not significant in soil redistribution. While no other data on horse-related soil movement from such roads is known, this study supports the contention that harder surfaces (i.e. graded, built-up vehicular roads) would suffer less soil loss than other track types. There was no evidence to suggest that the combination of vehicle tracks and horse use compounded soil loss. Where other maintained vehicle tracks elsewhere within protected areas are like those studied here, it is expected that high-use horse-riding would have little impact on the road surface additional to other uses.

The first account of the lichen flora of the Cape York Peninsula bioregion is provided with 423 lichen taxa from 47 families and 127 genera recorded. The tropical savannas of Cape York Peninsula, which experience annual or biennial ground fires, are generally a harsh environment for lichen establishment and growth. The majority of eucalypts and Melaleuca viridiflora Sol. ex Gaertn. trees that dominate much of Cape York Peninsula vegetation regularly shed their bark and do not generally provide a suitable habitat for lichen establishment and persistence. However, even in these savanna habitats, some tree species provide suitable fire-safe substrates for lichens. The rainforest, riparian and coastal vegetation lichen flora is much more diverse, primarily because of the lack of fire but also because of more mesic conditions and persistent thin bark types. Most of the lichens (413 lichen species) survive on a number of bark types, while there are 51 species on rock substrates (10 exclusively on rocks). The diverse lichen flora is dominated by pantropical species, many of which are shared with New Guinea, Thailand and the Northern Territory. However, eleven lichen species are only known from Cape York Peninsula.

Increasing human population, economical development, and industrialization of our society lead to disturbance of natural ecosystems. To prevent long-term damage of the environment, the affected ecosystems need to be rehabilitated to a sustainable form after industrial operations cease. Environment rehabilitation is a long-term process that is complex and costly as it includes restoration of soil, water bodies, and reintroduction of plant, insect, and animal species. We present a wireless sensor network for evidence-based rehabilitation of disturbed natural ecosystems. Drawing on our case study of an open-cut surface mine in rural Australia, we show that microclimate data can provide insights into the efficiency of specific rehabilitation processes, disentangle the impact of microclimate on the rehabilitation success, and provide early indicators into potential rehabilitation problems. We worked with ecologists to provide domain-based advice on addressing a range of rehabilitation problems and developed a system that periodically generates reports on the rehabilitation status of areas of interest. The report incorporates data from expert surveys from the field and microclimate data from sensors, and provides recommendations to improve rehabilitation in under-performing areas. Such evidence-based assessment of rehabilitation is an important step towards ensuring compliance with set rehabilitation objectives, potentially leading to both more successful and less costly environment rehabilitation.

Naturalised non-native plants that become invasive pose a significant threat to the conservation of biodiversity in protected areas (areas dedicated and managed for long-term conservation of nature), economic productivity of agricultural businesses, and societal impacts including community, culture infrastructure and health. Quantifying the spread, potential dominance and invasion threat of these species is fundamental to effective eradication and development of threat mitigation policy. But this is often hampered by the lack of comprehensive data. This study used existing ecological survey data from 2548 sites and 64 758 Herbarium specimen records to document the status and abundance of non-native plants in two case study bioregions, Cape York Peninsula (CYP) and the Desert Uplands (DEU) in Queensland covering a total area of 186 697 km2. There were 406 non-native species in the CYP, 186 (45.6%) of which are known environmental weeds and 159 non-natives in DEU, of which 69 (43.5%) are environmental weeds. Inside the protected areas, there were 98 species of environmental weeds in CYP, 27 of which are listed as weeds of State significance (Weeds of National Significance (WONS), Queensland declared and non-declared pest plants categories). In DEU, there were 18 environmental weeds inside protected areas and none of them was listed as a weed of State significance. Non-native species that recorded foliage cover dominance in the ecological site data are generally recognised as environmental weeds in Queensland. The threat of weeds from outside of protected areas was serious, with 41 weeds of State significance found in CYP, five of which are WONS, and 25 weeds of State significance found in DEU, 10 of which are WONS.

The grasslands of Cape York Peninsula in monsoonal north Queensland, Australia represent extensive examples of this formation that have been relatively undisturbed by agricultural development. Fourteen grassland units are mapped on the basis of their relationship to soils and landforms. Nine grassland communities were derived from a floristic analysis of the site data, and are related to the mapping units. Half of the grassland map units have less than 10% of their area protected in conservation reserves. The annual forb sickle pod Senna obtusifolia is considered to be the most serious exotic weed at present, because it dominates large areas of unreserved Imperata cylindrica grassland, a community that does not occur extensively elsewhere in Australia. Wetland exotic grasses that are being actively promoted for agriculture pose a potential threat to the Oryza grasslands that occur over large areas of the seasonally flooded coastal plains. It is recommended that further exotic pasture species should not be grown on Cape York Peninsula unless their containment can be assured. Some grasslands appear to be maintained by fire. Data are provided that demonstrate the conversion of some grassland types to woodlands in the last 30 years, and it seems probable that the change is a result of altered fire regimes. Even if adequately reserved, appropriate fire management is required to maintain the grasslands of Cape York Peninsula.

Data from biological surveys are increasingly being used for making land use and management decisions, and as input in predictive models. Biased or inadequately sampled data sets may result in misleading predictions and lead to poor management decisions. Five methods for assessing the adequacy of sampling in vegetation surveys using Geographic Information Systems (GIS) were investigated. The methods were used and assessed on a 1:250,000 scale vegetation survey on Cape York Peninsula, Australia. For surveys in progress, the Intuitive Reliability Assessment (Method 1) and Sampling Intensity Index (Method 2) were found to be very useful at identifying undersampled plant assemblages and areas. Non-stratified Environmental Parameter Analysis (Method 3) and Environmental Domain Analysis (Method 5) gave only general assessment of areas requiring further sampling. For more specific identification of supplementary sampling sites, the complementary use of Methods 1 or 2 was required. However, Methods 3 and 5 together with Gradsect Evaluation using DOMAIN (Method 4), can assist in the planning of comprehensive and efficient sampling strategies for new surveys. The results of this study have application to a wide range of natural resource surveys.

Hierarchical agglomerative polythetic clustering of vegetation data for 51 sites from Brian Pastures Research Station, south-east Queensland, Australia, produced site and species groups that supported those formed during a previous intuitive mapping survey. On the basis of floristic composition, these analyses suggested the possible amalgamation of some map units. However, their segregation into separate units was justified on the basis of having distinct photopatterns and physiographic positions in the landscape. The classifications of the trees only (55 species) and woody plants only (115 species) data sets produced site groupings of high similarity to those constructed by the mapping survey. The addition of the herbaceous plants (266 additional species, of which 70% were annual species) tended to dominate the analyses, and produce site groupings that were less similar to the mapping groups. The distribution of many annual species appeared to be independent of the perennial plant species and underlying substrate. In wooded communities, herbaceous plants have only a minor influence on photopattern, which is the primary determinant of the mapping classification. Binary data retained a large percentage of the information contained in the quantitative data. The extra effort of gathering herbaceous data may not be justified for a broad scale mapping project, but is required for comprehensive nature conservation surveys, flora inventory, and vegetation monitoring purposes.

The timing of vegetation sampling in highly seasonal environments is one of the critical factors in determining the proportion of the flora captured in a single sampling. Four sites were located within a 20 km radius of Mareeba, north Queensland and sampled every three months for three years. The sites were located in a variety of eucalypt communities and across an altitudinal range from 380 to 840 m above sea level. In these eucalypt communities experiencing highly seasonal rainfall typical of the tropical savannas, vegetation sampling in the early dry season (May) maximises the diversity of flora recorded. The ANOVA analysis showed a significant effect of month of sampling for the number of ground taxa recorded (P < 0.005). There was significant variation (P < 0.005) in species diversity between the sites but in all four study sites the May sampling recorded greater than 84% of the total recorded flora, whereas the November samplings accounted for between 21% and 56% of the flora. This supports the experience of other researchers that a May sampling is near optimum for sampling the ground layer floristic diversity in tropical eucalypt woodlands. Most vegetation survey and mapping data are of necessity collected at less than ideal times of the year due to access and resource issues. Care must be exercised in using data collected in the dry season, as only a limited proportion of the total ground flora is likely to be recorded. Studies designed to capture the full florisitic inventory of species present in these highly seasonal environments need to budget resources and plan to access these environments in the late wet season.

Vegetation classification, survey and mapping provide key information underpinning the implementation of statewide vegetation management legislation and associated policies in Queensland. This paper summarises: (i) the Queensland Herbarium survey and mapping methods and land classification system and its role in vegetation management legislation; and, (ii) the current extent and rate of vegetation clearing by bioregion, sub-region and Broad Vegetation Group; (iii) and the amount of vegetation protected under legislated statewide bioregional and regional ecosystem thresholds. Information also is provided on the pre-clearing and current extent by 18 Broad Vegetation Groups and the area of non-remnant woody vegetation by bioregion. The implications for vegetation management are discussed, along with a comparison of clearing statistics derived from other studies that use different classification and mapping methodologies. The majority of Queensland has relatively continuous native vegetation cover (82% remnant native vegetation remaining in 1999). The productive soils of the southern part of the Brigalow Belt, lowlands in South-east Queensland, New England Tableland and Central Queensland Coast have been, however, extensively cleared with 7–30% of remnant vegetation remaining. Between 1997 and 1999, the annual rate of remnant clearing in Queensland was 4460 km2 of which over 60% occurred in the Brigalow Belt bioregion. A greater proportion of this recent clearing occurred in Broad Vegetation Groups that are associated with less fertile and/or more arid parts of the State compared with pre 1997 clearing. For bioregions and regional ecosystems where past clearing has been extensive, a substantial proportion (50–91%) of the remaining vegetation is protected by bioregional and regional ecosystem thresholds prescribed under statewide legislation and associated policies. For other bioregions and regional ecosystems, other factors such as rainfall, soil and areas of high conservation value are likely to play a larger role in determining the amount of vegetation protected. However, the effectiveness of the Queensland legislation cannot be assessed until regional planning processes have been completed and all criteria addressed.

Options for a new definition of, and key for, rainforest in Australia are provided. The definitions take a national perspective, and are based on the ecological characteristics of rainforest species and some structural and floristic characteristics. Rainforest plant species are defined as those adapted to regenerating under low-light conditions experienced under the closed canopy or in localised gaps caused by recurring disturbances which are part of the natural rainforest ecosystem, and are not dependent on fire for successful regeneration. Three definitions are provided which differ in the extent of inclusion of transitional and seral communities. The first definition recognises communities such as mixed forests as transitional to rainforests and therefore as separate communities. The second definition includes a minimal component of emergent non-rainforest species in rainforest in the recognition that the main floristic component and functioning of the communities cannot be distinguished. The third definition includes the late successional stages of transitional and seral communities in rainforest on the presumption that such communities include non-rainforest species which are close to senescence, and that these communities are essential for the long-term conservation of rainforest in areas where rainforest is vulnerable and subject to major disturbance, particularly by fire. The first definition is concluded to be the least ambiguous and arbitrary, and enables a consistent approach to rainforest management. Recognition of mixed forests as a distinctive and mappable vegetation type should be incorporated in a comprehensive conservation strategy inclusive of all ecosystem developmental stages.

A large floristic data set was collated from vegetation surveys of Mitchell (Astrebla spp.) grasslands in Queensland spanning more than 20 years. The data was ordinated using non-metric multi-dimensional scaling and a four-dimensional solution could be deciphered. The longest floristic gradient was clearly aligned with climatic variables associated with mean annual precipitation. The vector for mean annual temperature was orientated orthogonally to the primary vector and was strongly correlated through the ordination space. A grazing vector tentatively derived from the frequency of species known to be favoured by grazing in Mitchell grasslands was orthogonal to the major climatic trends. However, the suspected grazing influence is complicated by the potential influence of sites occurring on limited areas of limestone habitat representing marginal Mitchell grassland habitat. A vector derived from the binomial categorisation of landform into downs and alluvia was clearly aligned with the fourth dimension of the ordination. Short- and long-term rainfall history variables were generated for each site from local rainfall records. The oblique trajectory of these variables through the ordination space tends to suggest that they may be artefacts of sampling rather than a major independent influence on the character of Mitchell grasslands. Thus the results at the broad scale of this study do not conform to previous studies at the paddock scale that emphasise the influence of climatic fluctuations on the floristic character of Mitchell grasslands. The results suggest Mitchell grasslands will require conservation planning and action throughout their geographic range.

This paper provides a detailed review of the major vegetation classification and mapping systems used by the management agencies with primary responsibilities for forested land in Australia. It focuses on the clarification of vegetation units and methodologies used. The paper also provides a comparison of the different nomenclatures against a simplified standard to show how the different systems relate to each other. In Australia, different systems for classifying and describing forest vegetation have been developed by various forest land management agencies to suit their own situations. Most vegetation classification systems reviewed are similar in using floristics and structure as the two primary elements in classifying vegetation types, and all use growth form (physiognomy) to distinguish vegetation units. The classification and mapping systems for wood production purposes differ from those for conservation and environment purposes in several aspects—wood production classifications emphasise commercial tree species and/or attributes such as height, whereas conservation classifications emphasise ecology, vegetation coverage, and the importance of understorey species. There are three broad strategic approaches in the vegetation classification programs being undertaken by the major forest land management agencies in Australia: (1) conducting a single classification across the whole of the agencies’ land in a State; (2) conducting a vegetation classification at the regional level, but using the same methods in each region; and (3) using different methods depending on the specific objectives of individual studies. This paper highlights the value of accurate quantitative measurements in the field. For example, for the two key structural attributes of height and crown density, the measured raw data can be accommodated by a number of different classification schemes whereas if the raw data consists of only records by predetermined classes, then such accommodation is difficult and loses precision.