Jamie Kirkpatrick argued that biodiversity conservation in fragmented landscapes is hampered by misapplication of island biogeography theory, which posits that species richness of islands is positively related to island size and negatively related to island isolation. The species-level mechanisms underpinning these relationships were later elaborated in metapopulation theory. Several empirical manipulations from around the world supported the predicted outcomes of fragmentation processes. The corollary, that small habitat fragments lose species, lack viability and thus contribute little to biodiversity conservation, has become a pervasive paradigm in design of protected area networks, environmental regulation and impact assessment, and ecological restoration practice. Jamie challenged the foundations of these applications as an 'ecological myth', citing evidence from his studies in Tasmania's midlands on the enduring conservation values of small fragments. Here, we investigated patterns and decadal-scale changes in plant species diversity in woodland fragments on the Cumberland plain, a southeastern Australian landscape fragmented by land clearing for agriculture similar to 200 years ago and now undergoing rapid urbanisation. After accounting for variations in pre-survey rainfall and sampling season, we found that, changes in species composition, loss of native species or addition of non-native species over 20 years were largely independent of patch size and connectivity, despite possible signals of past effects of patch geometry. Relationships varied between land tenure types, suggesting a dependence on land management legacies or biophysical properties that vary between tenures. Our study adds to the large, diverse and reliable body of empirical evidence that small isolated patches can have conservation values that larger more connected patches do not. We recommend policies and practices that: (1) embody a strong evidenced-based approach to conservation, development and restoration decisions that accounts for site-specific contributions of patches to landscape biodiversity, irrespective of patch size and connectivity; and (2) reject 'ecological myths' derived from misapplications of ecological theory that ignore its fundamental assumptions.

The IUCN Red List of Threatened Species, the most widely used global system for assessing species' extinction risk, has become a foundational source of information for conservation management, policy and research. Since the adoption of quantitative extinction risk criteria more than three decades ago, the Red List has expanded substantially in scope and influence, informing decisions ranging from species conservation and protected area designation to international agreements, corporate risk assessments and global biodiversity indicators. Given its central role, maintaining scientific rigour, transparency and trust in the Red List system is essential. Feedback from users, emerging from evolving applications and scientific advances, has shaped the Red List throughout. At the same time, the Red List has been subject to recurring critiques, some of which stem from persistent misconceptions about its purpose, design and appropriate use. To address these, we review the history and development of the Red List system, clarify the strengths of its design, delineate the contexts for which the system was and was not intended, and elucidate the circumstances under which it may be modified. Finally, we outline pathways for researchers and users to contribute to ongoing improvements and discuss potential future directions for evolution of the Red List.

Alpine and subalpine ecosystems are threatened by changing climate and disturbance regimes because they exist under extreme geographical and climatic conditions. Understanding the threats to and risk status of alpine and subalpine ecosystems is vital to guide their conservation, inform monitoring programs and identify the relative impact of activities currently degrading these ecosystems. We applied the IUCN Red List of Ecosystems criteria to 15 alpine and subalpine ecosystem types across Australian mountains. We found that seven ecosystem types were classified as threatened and three as Near Threatened, together accounting for 83% of the alpine and subalpine region. The primary threats were climate change and associated changes in fire regimes. There were insufficient data to quantify the environmental or biotic integrity of six ecosystem types, and one ecosystem type was listed as Data Deficient overall. Our study highlighted key knowledge gaps in the integrity of alpine and subalpine systems that limited our capacity to reliably assess risk to these ecosystems. We recommend expanding on-ground monitoring of ecosystem integrity to improve understanding of risks, key threats and actions to mitigate them. To reduce risks to these ecosystems, climate change must be aggressively mitigated, collateral pressures from invasive plants and animals must be reduced, and critical knowledge gaps that will allow conservation action prioritisation must be quickly filled. These strategies should be co-ordinated, resourced and enacted urgently in cross-jurisdictional Alps-wide work plans.

Coastal Upland Swamps of the Sydney Basin Bioregion, Australia, are highly biodiverse upland mires. Swamp vegetation organises into five communities, thought to occupy distinct positions along a hydrological gradient. Longwall mining reduces the duration of root zone saturation, but lack of quantitative understanding of hydrological niche dimensions limits our ability to predict the impacts of drawdown in terms of observed vegetation communities. Here, we use indicator species to quantify the hydrological niche of their corresponding plant communities and predict the impacts of mining-related drawdown. We first modelled the relative occurrence frequency of 20 vascular plant indicator species (four per community) from 11 unmined sites across four swamps and then used this model to predict their frequencies at a mine-impacted swamp. We quantified the hydrological niche for each community using the average number of days per year of root zone saturation (<= 30 cm below surface) from time series soil moisture data. Inferred community hydrological niches were broadly consistent with qualitative ecological understanding, with estimated optimal mean days per year saturated ranging from Restioid heath at 17 [<= 16, 49] (mean +/- [95% uncertainty]) days to Ti-tree thicket at 348 [315, >= 353] days, with Banksia thicket 45 [16, 111], Sedgeland 187 [139, 264] and Cyperoid heath 243 [198, 290] communities intermediate. At the impacted Swamp, the model underpredicted wetter- and overpredicted drier-adapted community indicator species, suggesting vegetation has not yet reached hydrological equilibrium. Results suggest potential for using the modelled hydrological niche of wetland plant communities to predict community-level impacts of hydrological change in upland swamps.

AimTo compare field-based evidence of plant and animal responses to fire with remotely sensed signals of fire heterogeneity and post-fire biomass recovery.LocationSouth-eastern Australia; New South Wales.Time Period2019-2022.Major Taxa StudiedA total of 982 species of plants and animals, in eight taxonomic groups: amphibians, birds, fish, insects, mammals, molluscs, plants and reptiles.MethodsWe collated 545,223 plant and animal response records from 47 field surveys of 4613 sites that focussed on areas burnt in 2019-2020. For each site, we calculated remotely sensed signals of fire heterogeneity and post-fire biomass recovery, including the delayed recovery index. Meta-regression analyses were conducted separately for species that declined after fire (negative effect sizes) and species that increased after fire (positive effect sizes) for each buffer size (250 m, 500 m, 1 km, 1.5 km, 2 km and 2.5 km radius).ResultsWe found that species exposed to homogenous high-severity fire (i.e., low fire heterogeneity) were more likely to exhibit decreased abundance/occurrence or inhibited recovery. Areas with delayed recovery of biomass also had significant negative on-ground responses, with lower abundance or occurrence in areas where biomass recovery was slower.Main ConclusionsThe fire heterogeneity index and the delayed recovery index are suitable for inclusion in monitoring and reporting systems for tracking relative measures over time, particularly when field survey data is not available at the landscape scales required to support reporting and management decisions. Locations with remotely sensed signals of delayed recovery should be prioritised for protection against further disturbances that may interfere with the recovery process. Research attention must next focus on how cumulative fire heterogeneity patterns of successive fires affect the post-fire recovery dynamics to further inform the application of remote sensing indicators as management tools for biodiversity conservation.

The goal of the EcoVeg approach is to fully describe and classify the diversity of the Earth's terrestrial ecosystems based on vegetation and ecological processes. The EcoVeg approach was used to develop the International Vegetation Classification (IVC) and various national classifications, which integrate patterns of vegetation growth form, structure, and floristics with ecological and biogeographic drivers at multiple spatial scales, from global formations to local plant communities. The approach remains unique among terrestrial ecological classifications in providing types at these scales. However, as a terrestrial typology, lack of context with respect to freshwater, marine and subterranean realms limited its clarity. Further, growth forms and structure were limited to readily observable features, which excluded important functional traits. The release by the International Union for Conservation of Nature (IUCN) of the Global Ecosystem Typology (GET) presented an opportunity to revisit the EcoVeg approach because GET has a conceptually robust, scalable, and spatially explicit functional approach for all of earth's ecosystems (terrestrial, freshwater, marine, subterranean). Here, we briefly introduce the EcoVeg approach and the GET, and then outline a biome‐based revision to EcoVeg and the IVC that builds on the strengths of GET for global terrestrial types and the IVC for continental to local terrestrial types. The outcome is a revised IVC that we rename the ecosystem‐based International Vegetation Classification (eIVC). As with GET, the eIVC has a conceptual foundation based on realms and transitional realms, but it focuses on the terrestrial and transitional terrestrial (wetland) realms. It then fully implements terrestrial biome concepts across all the upper levels based on the integration of vegetation with global ecosystem processes and properties. Interoperable compatibility with GET is reflected in the fact that 84% of the global ecosystem types are largely equivalent, which facilitates the linkage of GET with the continental to local ecosystem types of the eIVC. The revisions that now form the eIVC will enhance collaborative development of ecosystem types across the globe and provide more robust opportunities for co‐application of the eIVC and GET in the terrestrial realm for management, conservation, and restoration.

Climate change has pervasive impacts on Earth’s ecosystems, but the diversity and complexity of ecosystems makes estimating the severity of impacts and the resulting risk of collapse difficult. In this perspective, we conceptualise the challenge of understanding how climate change alters ecosystems, and how to reliably measure those changes in ecosystem risk assessments, focussing on the IUCN Red List of Ecosystems. We propose solutions to resolve these challenges – using diverse teams, conceptual models, diverse using data sources including projections, learning from analogous ecosystems, and evaluating uncertainties – and we identify research gaps to bridge these challenges. Together, these solutions will improve our capacity to produce reliable assessments of collapse risk under climate change to inform timely and effective ecosystem conservation.

Antarctica, Earth’s least understood and most remote continent, is threatened by human disturbances and climate-related changes, underscoring the imperative for biodiversity inventories to inform conservation. Antarctic ecosystems support unique species and genetic diversity, deliver essential ecosystem services and contribute to planetary stability. We present Antarctica’s first comprehensive ecosystem classification and map of ice-free lands, which host most of the continent’s biodiversity. We used latent variables in factor analyses to partition continental-scale abiotic variation, then biotic variation represented in spatial models, and finally recognised regional-scale variation among biogeographic units. This produced a spatially explicit hierarchical classification with nine Major Environment Units (Tier 1), 33 Habitat Complexes (Tier 2) and 269 Bioregional Ecosystem Types (Tier 3) mapped at 100 m resolution and aligned with ‘level 4’ of the IUCN Global Ecosystem Typology. This comprehensive ecosystem inventory provides foundational data to inform protected area designation under the Antarctic Treaty’s Environmental Protocol and track risks to Antarctic ecosystems. Its tiered structure and workflow accommodate data scarcity and facilitate updates, promoting robustness as knowledge builds.

Global fire regime change is threatening terrestrial biodiversity. Understanding how these changes affect biota is essential to protect biodiversity now and into the future. A targeted examination of the mechanisms through which fire influences populations will help achieve this by enabling comparisons and connections across taxa. Here, we develop a cross-taxa framework that identifies mechanisms through which fire regimes influence terrestrial species populations over different time scales, and traits on which those mechanisms depend. We focus on amphibians, birds, fungi, insects, mammals, plants, and reptiles. First, we identify key mechanisms through which fire regimes influence species populations across different taxonomic groups. Second, we link these mechanisms to functional traits that influence the relevance to different species. Third, we identify traits that shape the vulnerability-or conversely, resilience-of species populations to frequent, high-intensity, and large wildfires that are emerging as a threat in many parts of the world. Finally, we highlight how this integrative framework can be useful for understanding and identifying fire-related threats common to different taxa across the globe and for guiding future research on fire-related population change.

Effective ecosystem conservation for biodiversity and human well-being relies on accurate information. Consistent approaches to classifying, describing, and assessing ecosystems can improve understanding of ecological processes, threats, and management. We explored how the International Union for Conservation of Nature (IUCN) Global Ecosystem Typology-a global classification framework based on ecosystem function-could support the development of a classification of ecosystems for the Tiwi Islands, Australia, by incorporating scientific information and Indigenous Tiwi knowledge to facilitate environmental management and conservation. We synthesized ecosystem information from previous research, field data, reports, and Tiwi knowledge authorities to develop a classification, descriptions, and a map of 14 terrestrial ecosystem types. These ecosystem types were defined and described based on ecological processes and were broader yet largely congruent with existing vegetation classifications. Including functional properties accounted for variation in the vegetation physiognomy exhibited by dynamic and disturbance-prone ecosystems, such as savannas. Because we considered Tiwi knowledge authorities and the IUCN Global Ecosystem Typology, our inventory included ecosystem types that were typically omitted from previous classifications, which should allow for more comprehensive assessments and management. Relating the new ecosystem typology to the IUCN Global Ecosystem Typology will facilitate comparisons among similar ecosystems, regarding, for example, effective threat abatement options. Describing the biota and processes opens new avenues for monitoring. More collaborative work is needed to explore how Western scientific ecosystem inventories operate alongside and in connection with management of Tiwi murrakupuni enacted by Tiwi people. Given the ongoing loss of biodiversity, ecosystem management must draw on information across domains, scales, and knowledge systems. We demonstrated an approach to this task and provided socioecologically relevant ecosystem information.

The classification of freshwater ecosystems is essential for effective biodiversity conservation and ecosystem management, particularly with increasing threats. We developed an automated approach to mapping and classifying freshwater ecosystem functional groups based on the IUCN Global Ecosystem Typology (GET), offering a scalable, dynamic and efficient alternative to current manual methods. Our method leveraged remote sensing data and thresholding algorithms to classify ecosystems into distinct ecosystem functional groups, accounting for challenges such as the temporal and spatial complexities of dynamic freshwater ecosystems and inconsistencies in manual classification. Unlike traditional approaches, which rely on manual cross-referencing to adapt existing maps and contain subjective biases, our system is repeatable, transparent and adaptable to new incoming satellite data. We demonstrate the applicability of this method in the Paroo–Warrego region of Australia (~14,000,000 ha), highlighting the automated classification’s capacity to process large areas with diverse ecosystems. Although some functional groups require static datasets due to current limitations in satellite data, the overall approach had high accuracy (84%). This work provides a foundation for future applications to other freshwater ecosystems around the world, underpinning biodiversity management, monitoring and reporting worldwide.

An understanding of fire-response traits is essential for predicting how fire regimes structure plant communities and for informing fire management strategies for biodiversity conservation. Quantification of these traits is complex, encompassing several levels of data abstraction scaling up from field observations of individuals, to general categories of species responses. We developed the Fire Ecology Database to accommodate this complexity. Its conceptual framework is underpinned by a flexible data pipeline enabling links between fire-related trait data and event information at individual, population, and community levels. Key features include: (a) concise and documented trait and method vocabularies; (b) documented uncertainty in observations and aggregation; and (c) documented origin of data including field observations, laboratory experiments, and expert elicitation. We demonstrated application of our framework using data from new field surveys and existing data sets in New South Wales, Australia. The database includes 14 traits for 6,287 plant species derived from 8,936 field work records from 2007 to 2018, 7,054 field records from surveys after 2019, and 48,306 records from 301 existing sources.

The combined pressures of climate change and anthropogenic disturbance are increasingly pushing species toward extinction. However, many species remain unassessed for extinction risk, posing challenges to managers and decision makers when extreme events, such as megafires, impact large numbers of species. This has led to an increased need for rapid assessments, which can accelerate extinction risk assessments and help to ensure species receive timely conservation actions. In Australia, the 2019-2020 Black Summer fires had extensive impacts on native endemic flora, necessitating a prioritisation process to identify the species most in need of conservation interventions or extinction risk assessments. We used rapid assessments to identify priority species for full extinction risk assessments and compared how well the rapid assessments, with minimal information, predicted extinction risk in species that received a full assessment. Some 260 species received rapid assessments and 131 of these received full assessments. We found that 84 % of species identified as threatened by full assessments had been accurately identified as such during the rapid assessments. Rapid assessments also accurately predicted the specific threatened category in 53 % of cases compared to full assessments, however accuracy decreased with extinction risk (67 % for Critically Endangered, 54 % for Endangered, 11 % for Vulnerable). Our results show that rapid assessments can be a reliable and informative predictor of extinction risk and may be particularly useful in emergency circumstances. Recognising that effective conservation action relies on comprehensive and up-to-date threat listings, our results show the value of rapid assessments during biodiversity crises and highlight their utility to drive conservation actions.

The extent of severe fires is projected to increase with climate change. Furthermore, changes to the fire regime, including the frequency, severity or seasonality of fire, can reduce resilience and promote persistent changes in ecosystem state. Wet sclerophyll forests are found in potentially dynamic mosaics of rainforest and dry sclerophyll forests and contain species from both these contrasting community types. As such, they create an opportunity to study alternative state theory in which states are mediated by fire regimes. To assess the resilience of wet sclerophyll forests to extreme fire events we specifically asked; do mortality rates and recruitment after fire differ between sclerophyllous and non-sclerophyllous components of wet sclerophyll forests, how do these impacts differ along gradients of fire severity and frequency, and is there evidence of positive fire feedback loops, and if so what levels of fire severity and frequency thresholds influence state shifts towards dry sclerophyll forest? We surveyed all canopy (upper and mid canopy) and grass species, to represent three key plant groups; Eucalyptus trees, non-sclerophyllous trees and grasses. We found strong evidence that fire frequency and severity determined the initial trajectory of wet sclerophyll forest recovery. Key findings showed that extreme fire severity can have significant impacts on non-sclerophyllous tree mortality, with an average of 72% of trees killed, much greater than in Eucalyptus species (mean mortality = 9%). However, our findings also highlighted the importance of analysing past fire regime variables, with sites experiencing 4-5 fires in 60 years also experiencing mortality rates of above 75% for non-sclerophyllous trees. Our results support the conclusion that a long multi-decadal fire-free interval is essential for these recovering wet sclerophyll forests, both to rebuild the resilience of their non-sclerophyllous biota and to reduce the risk of recurrent high severity fires in future.

Climate change is one of the most important drivers of ecosystem change, the global-scale impacts of which will intensify over the next 2 decades. Estimating the timing of unprecedented changes is not only challenging but is of great importance for the development of ecosystem conservation guidelines. Time of emergence (ToE) (point at which climate change can be differentiated from a previous climate), a widely applied concept in climatology studies, provides a robust but unexplored approach for assessing the risk of ecosystem collapse, as described by the C criterion of the International Union for Conservation of Nature's Red List of Ecosystems (RLE). We identified 3 main theoretical considerations of ToE for RLE assessment (degree of stability, multifactorial instead of one-dimensional analyses, and hallmarks of ecosystem collapse) and 4 sources of uncertainty when applying ToE methodology (intermodel spread, historical reference period, consensus among variables, and consideration of different scenarios), which aims to avoid misuse and errors while promoting a proper application of the framework by scientists and practitioners. The incorporation of ToE for the RLE assessments adds important information for conservation priority setting that allows prediction of changes within and beyond the time frames proposed by the RLE.

Protected and conserved areas (PCAs) are key ecosystem management tools for conserving biodiversity and sustaining ecosystem services and social cobenefits. As countries adopt a 30% target for protection of land and sea under the Global Biodiversity Framework of the United Nations Convention on Biological Diversity, a critical question emerging is, which 30%? A risk-based answer to this question is that the 30% that returns the greatest reductions in risks of species extinction and ecosystem collapse should be protected. The International Union for Conservation of Nature (IUCN) Red List protocols provide practical methods for assessing these risks. All species, including humans, depend on the integrity of ecosystems for their well-being and survival. Africa is strategically important for ecosystem management due to convergence of high ecosystem diversity, intense pressures, and high levels of human dependency on nature. We reviewed the outcomes (e.g., applications of ecosystem red-list assessments to protected-area design, conservation planning, and management) of a symposium at the inaugural African Protected Areas Congress convened to discuss roles of the IUCN Red List of Ecosystems in the design and management of PCAs. Recent progress was made in ecosystem assessment, with 920 ecosystem types assessed against the IUCN Red List criteria across 21 countries. Although these ecosystems spanned a diversity of environments across the continent, the greatest thematic gaps were for freshwater, marine, and subterranean realms, and large geographic gaps existed in North Africa and parts of West and East Africa. Assessment projects were implemented by a diverse community of government agencies, nongovernmental organizations, and researchers. The assessments have influenced policy and management by informing extensions to and management of formal protected area networks supporting decision-making for sustainable development, and informing ecosystem conservation and threat abatement within boundaries of PCAs and in surrounding landscapes and seascapes. We recommend further integration of risk assessments in environmental policy and enhanced investment in ecosystem red-list assessment to fill current gaps.

AbstractNumerous spatiotemporal species distribution modeling frameworks are now available to the ecological practitioner. This study compared three such frameworks accessible in the R programming language: generalized additive models with spatiotemporal smooths as implemented by mgcv, spatiotemporal generalized linear mixed models based on nearest neighbor Gaussian processes as implemented by starve, and spatiotemporal generalized linear mixed models based on the stochastic partial differential equations approach as implemented by sdmTMB. The primary focus was to compare the inferences obtained from applying these frameworks to the case study of the orange‐footed sea cucumber, Cucumaria frondosa, on the Scotian Shelf off Nova Scotia, Canada. Each model was fit to catch data (2000–2019) from Fisheries and Oceans Canada's annual Research Vessel and Snow Crab surveys. Environmental covariates were sourced from high‐resolution data layers, including physical oceanographic, bathymetric, and seafloor morphometric datasets. The three models captured variability in sea cucumber distribution that would have been overlooked without a spatiotemporal approach. Although their predictions were similar, including within C. frondosa spatial reserves, the models provided different inferences regarding covariate effects. This suggests that while practitioners primarily interested in mapping species distributions need only apply the most familiar framework, those most concerned with identifying predictive environmental covariates may benefit from comparing the output from multiple approaches. Employing multiple approaches can also serve as a validation technique.

The Kunming-Montreal Global Biodiversity Framework (GBF) of the UN Convention on Biological Diversity set the agenda for global aspirations and action to reverse biodiversity loss. The GBF includes an explicit goal for maintaining and restoring biodiversity, encompassing ecosystems, species and genetic diversity (goal A), targets for ecosystem protection and restoration and headline indicators to track progress and guide action 1 . One of the headline indicators is the Red List of Ecosystems 2 , the global standard for ecosystem risk assessment. The Red List of Ecosystems provides a systematic framework for collating, analysing and synthesizing data on ecosystems, including their distribution, integrity and risk of collapse 3 . Here, we examine how it can contribute to implementing the GBF, as well as monitoring progress. We find that the Red List of Ecosystems provides common theory and practical data, while fostering collaboration, cross-sector cooperation and knowledge sharing, with important roles in 16 of the 23 targets. In particular, ecosystem maps, descriptions and risk categories are key to spatial planning for halting loss, restoration and protection (targets 1, 2 and 3). The Red List of Ecosystems is therefore well-placed to aid Parties to the GBF as they assess, plan and act to achieve the targets and goals. We outline future work to further strengthen this potential and improve biodiversity outcomes, including expanding spatial coverage of Red List of Ecosystems assessments and partnerships between practitioners, policy-makers and scientists.

Ecosystem risk assessments estimate the likelihood of major transformations (ecosystem collapse) over a specified time frame. They require an understanding of the biotic and abiotic processes that drive declines. Relative Severity and Extent of Decline quantify essential dimensions of ecosystem degradation as part of the International Union for the Conservation of Nature (IUCN) Red List of Ecosystems risk assessment protocol. These flexible and powerful concepts are operationalised through ecosystem-specific indicators of functional decline. Here, we examine trade-offs in risk assessment between direct, yet data-demanding indicators and indirect indicators that are more widely applicable with global data sets. Using a case study of multiple tropical glacier ecosystems, we compared estimates of risk based on a direct indicator of functional decline (ice mass) with those based on an indirect indicator (bioclimatic suitability). The direct estimate of Relative Severity was based on the projected changes in ice mass using a glacier ice mass balance and dynamics model, while the indirect estimate was calculated from the expected changes in suitability based on a correlative habitat suitability model parameterised with current occurrence records. For reference, we calculated probability of ecosystem collapse from simulations of the ice mass balance and dynamics model. We found that the indirect indicator systematically underestimated risks of ecosystem collapse compared to the direct indicator and returned a different rank order of risks across glaciers due to prominent discrepancies in some units. Small and isolated glaciers located outside the tropical Andes are uniformly exposed to high levels of degradation and have high probabilities of collapse before 2080, whereas tropical Andean glaciers exhibit different rates of degradation, but are expected to undergo very severe degradation before 2100. For these larger units a detailed analysis of spatial differences in future projections could inform regional and local strategies for future monitoring, management and conservation action that can benefit people and nature. Evaluating Relative Severity and Extent of Decline over time and with different ecosystem-specific indicators allowed us to describe trends across a group of functionally similar ecosystem types and compare their performance in assessment units of different size and risk of collapse. The methods could be applied to other ice or snow-dependent ecosystems, while the case study should be instructive for development of risk indicators in many other ecosystem types.