Human dependency on natural capital and ecosystem services (ES) present significant risks to wellbeing and economic prosperity, which are poorly understood. While tools, such as the Exploring Natural Capital Opportunities, Risks and Exposure (ENCORE) database, provide valuable global dependency information, translating these insights into country-specific contexts remains challenging. Here, we present the Natural Capital Dependency Framework (NCDF), which advances dependency analysis by mapping specific relationships between ecosystem types and economic activities within countries. The NCDF uses existing data, and integrates global ecosystem classifications, and environmental and economic accounting standards to map direct dependency pathways with unprecedented specificity. Application to Australia and the Maldives demonstrates the framework's capability to reveal both direct and indirect dependencies despite vast differences in the granularity of available data. Direct nature-economy dependencies, concentrated in the primary industries (agriculture, fisheries, forestry, water, mining), are underpinned by 45 Australian and 21 Maldivian ecosystem types, predominantly through provisioning ES. Indirectly, all industries in both nations depend on ecosystems within three degrees of separation. Beyond economic dependencies, communities directly depend on ecosystems for essential health and safety services. By specifying dependency relationships in the context of national ecosystem information, the NCDF enables more accurate risk assessment and targeted policy responses – transforming natural capital dependency from an intangible problem to actionable national intelligence.

Detecting changes in coral and seagrass habitat composition from satellite imagery places exceptionally high demands on sensor design due to low underwater reflectance signals and variable water column conditions. Recent multispectral satellite-based attempts to assess such changes across large spatial extents illustrate this challenge through an inability to reliably distinguish live coral from algae, often resulting in broad confidence intervals. This study quantifies the radiometric sensitivity requirements for detecting 10% absolute changes in live coral and seagrass fractional cover from spaceborne imaging spectroscopy using representative parameters for an aquatic imaging spectrometer. The analysis combined representative benthic spectra with realistic, management-relevant co-occurrence scenarios informed by extensive regional knowledge and field measurements from Fiji, Australia, and the Solomon Islands to evaluate detection performance across depths from 0 to 30 m. We show that live coral is the most demanding of the benthic targets evaluated because of its low reflectance and spectral similarity to algal cover types, requiring SNRs of approximately 300–700 to detect 10% absolute changes in cover at depths up to 10 m. In contrast, the greater spectral separation between seagrass and adjacent sandy substrates allows detection of 10% absolute changes in cover to depths exceeding 20 m in clear water. These results highlight the importance of high radiometric sensitivity and contiguous spectral sampling for future aquatic imaging spectrometers intended to monitor benthic change. Approaches that increase effective SNR, such as ground motion compensation (GMC), can extend the depth and confidence with which changes in benthic composition are detected, supporting a transition from broad-area habitat mapping toward quantitative monitoring of benthic change from space.

Monitoring coral reef and seagrass ecosystems is essential for managing the health of coastal marine environments, requiring detailed information on benthic composition and extent. Traditional in situ monitoring programs are critical for assessing reef health, but are limited in scale, covering only small areas, while conventional remote sensing approaches extend monitoring coverage scale, but provide limited benthic composition detail. Hyperspectral remote sensing has emerged as a solution, offering detailed spectral information capable of discerning diverse benthic classes across increased spatial extents. This review examines recent literature on hyperspectral remote sensing applications for monitoring the composition and spatial extent of coral reefs and seagrass meadows. Research demonstrates that important composition features present distinct spectral signatures, enabling separation of coral from turf algae and seagrass from macroalgae at pixel sizes below 5 m. However, spectral mixing complicates classification at larger pixel sizes, and the use of satellites remains relatively untested. While hyperspectral remote sensing can technically map benthic composition across large spatial scales (>100 km2), several limitations impede widespread adoption, including: lack of management relevant spatial data, restricted technology access, spectral mixing challenges and absence of standardized workflows and spectral libraries. A critical transition is needed from basic mapping exercises toward monitoring functional ecosystem health aspects through derived metrics with direct management relevance. Hyperspectral technology offers strong potential for mapping coral reef and seagrass habitat composition and extent, but achieving routine monitoring value demands continued methodological refinement, improved data accessibility, and enhanced integration into ecological frameworks.

Reef systems span spatial scales from 10s to 100s and even 1000s of kilometres, with substantial spatial variability across these scales. Managing and predicting the future of coral reefs requires insights into reef functioning at all spatial scales. However, investigations of reef functioning often consider individual reefs as the smallest unit (10s of kilometres), despite substantial spatiotemporal variability occurring within-reefs (100s of meters). We developed C∼scape, a coral metacommunity modelling framework that integrates the demography of corals with population-level responses to physical and environmental spatial layers, to simulate a mosaic of interacting coral communities across a heterogenous seascape. Coral communities are linked using biophysical connectivity modelling. Coral community growth is modelled with a logistic growth model, with the intrinsic growth parameter determined from taxa-specific Integral Projection Models to incorporate demographic mechanisms. Site-specific coral habitat parameters, derived from satellite-based geomorphic and benthic habitat maps, define the maximum coral cover and are used to modulate community growth spatially and temporally as a function of the available space suitable for corals. These parameters are a proxy for the many interacting physical and environmental factors — e.g., depth, light, wave exposure, temperature, and substrate type — that drive within-reef variability in coral demography. Using a case study from the Great Barrier Reef, we show that modulating community growth using site-specific habitat parameters enables more accurate hindcasts of coral cover dynamics, while overlooking within-reef variability may lead to misleading conclusions about metacommunity dynamics. More generally, C∼scape provides a valuable framework for predicting spatiotemporal dynamics of coral communities within and between reefs, offering a mechanistic approach to test a range of management and restoration options.### Competing Interest StatementThe authors have declared no competing interest.

Natural systems exhibit high spatial variability across multiple scales. Models that can capture ecosystem dynamics across space and time by explicitly incorporating major biological mechanisms are crucial, both for management and for ecological insight. In the case of coral reef systems, much focus has been on modelling variability between reefs, despite substantial variability also existing within reefs. We developed C~scape, a coral metacommunity modelling framework that integrates the demography of corals with population-level responses to physical and environmental spatial layers, to facilitate spatiotemporal predictions of coral dynamics across reefs at fine (100s of metres to kilometres) scales. We used satellite-derived habitat maps to modulate community growth spatially, as a proxy for the many interacting physical and environmental factors-e.g., depth, light, wave exposure, temperature, and substrate type-that drive within-reef variability in coral demography. With a case study from the Great Barrier Reef, we demonstrate the model's capability for producing hindcasts of coral cover dynamics and show that overlooking within-reef variability may lead to misleading conclusions about metacommunity dynamics. C~scape provides a valuable framework for exploring a range of management and restoration scenarios at relevant spatial scales.

Fire regimes are changing globally, leading to an increased need for management interventions to protect human lives and interests, potentially conflicting with biodiversity conservation. We conceptualized 5 major aspects of the process required to address threats to flora and used this conceptual model to examine and identify areas for improvement. We focused on threat identification, policy design, and action implementation. We illustrated the application of the conceptual model through a case study in southeastern Australia, where policies have been designed to prevent hazard reduction burns from exposing threatened flora to high-frequency fire (HFF). We examined whether threatened species have been accurately identified as threatened by HFF, species were accounted for in key policies, and implementation of the policy reduced the incidence of HFF for target species. Species were mostly identified accurately as being threatened by HFF, and, broadly, the policy effectively minimized the threat from HFF. However, 96 species did not have HFF identified as a threat, and another 36 were missing from the policy entirely. Outcomes regarding the reduction of threat from HFF since policy introduction were species specific, despite an average increase in fire interval of 2 years. Despite the policy, over half (55%) the species studied have been affected by HFF since the policy was introduced. Although relatively minor improvements could optimize threat identification and policy design, the mixed success of action implementation highlights limitations that warrant further investigation. Our conceptual model enabled us to make clear and targeted recommendations for how different aspects of the policy could be improved and where further work is needed. We propose the conceptual model can be useful in a variety of contexts.

Coral reefs underpin the environmental, social, and economic fabrics of much of the world's tropical coast. Yet, the fine-scale distribution and composition of coral reefs have never been reported consistently across the planet. Here, we present new area estimates enabled by global geomorphic zone and benthic substrate maps at 5 m pixel resolution. We revise global coral reef estimates to 348,361 km2 of shallow coral reefs and 80,213 km2 (46,237–106,319 km2, 95% confidence interval) of coral habitat. The mapping used more than 1.5 million training samples supported by 480+ data contributions to deploy a coral reef classification of over 100 trillion pixels from the Sentinel-2 satellites and the Planet Dove CubeSat constellation. The publicly available maps are accessible via the Allen Coral Atlas and Google Earth Engine and are already being used by thousands of people to improve the conservation, management, and research of coral reef ecosystems.

Evidence-based environmental management is urgently required to enable progress towards global conservation commitments. To fill large-scale biodiversity knowledge gaps, governments, organisations and communities must adopt innovative ecological monitoring techniques. The emergence of environmental DNA (eDNA) survey methods, as well as the increased availability of in situ ocean observation and satellite remote sensing data, offers opportunities to overcome significant logistic and financial challenges previously associated with traditional biodiversity monitoring. Here, a 1.5 year-long eDNA survey reveals the spatial and temporal presence of ‘critically endangered’ scalloped hammerhead sharks (Sphyrna lewini) in Apra Harbor, Guam. The survey confirms the presence of S. lewini in the region, and shows eDNA detections are most frequent at the beginning of the dry season, in January. Combined analysis of eDNA results with open (freely available) ecological data identifies that S. lewini presence is associated with increases in water velocity, turbidity and wind direction, as well as decreases in temperature and wind speed. Additionally, the data indicate S. lewini are most frequent at sampling sites characterised by high turbidity, shallow depths, and large distances from the harbor entrance. Information on local distribution and habitat preferences generated here is fundamental to the establishment of management protocols that aim to minimise negative anthropogenic impacts on S. lewini in Apra Harbor. The work provides an example of contemporary marine monitoring for the efficient generation of biodiversity data; an approach that will enable informed management decisions and help to reduce the risk and rate of species extinctions throughout the next decade.

Background Climate change is driving global fire regimes toward greater extremes, potentially threatening plant species that are adapted to historic fire regimes. Successful conservation of threatened plant species depends upon improving our understanding of how they respond to these changing fire regimes in fire prone regions. The 2019–2020 Australian megafires burnt at very high to extreme severity across an unprecedented extent and overlaid a complex history of prescribed burns and wildfires, providing an ideal foundation to study the consequences of multiple fire regime elements. We examined the recruitment response of Pomaderris bodalla , one of many threatened obligate-seeding shrub species growing in wet sclerophyll (mesic) forest in south-east Australia. We surveyed seedling recruitment at sites across a gradient of fire severity and frequency. Our aims were to (i) confirm in vitro results that suggest a positive relationship with fire severity; (ii) determine the species response to fire frequency and (iii) identify the nature of the effect of fire severity and fire frequency in combination. Results We found that recruitment had a positive response to fire severity, peaking at high severity sites as soil temperatures reached optimal levels for dormancy-break but declining, while still remaining positive, at moderate and extreme severity sites. The pattern of response matched in vitro studies, which had established that physically dormant P. bodalla seeds had minimal dormancy broken at low fire-related temperatures, peak dormancy broken at high fire-related temperatures and heat-induced mortality at extreme temperatures. Fire frequency had an overall negative effect on recruitment, with fewer recruits at more frequently burnt sites and this effect appeared to be additive with fire severity. Conclusion Our findings indicate that increased fire frequency poses an ongoing threat to P. bodalla and similar obligate-seeding shrub species. The hump-shaped relationship with fire severity suggests that future large-scale extreme fires will cause seed mortality-induced reduction in recruitment, with the potential to exacerbate the negative effects of high fire frequency. Informed management of threatened species requires detailed knowledge of species responses to multiple fire regime elements, and novel fire response traits like seed dormancy can provide beneficial insights for robust conservation strategies.

Comparisons of recent global forest products at higher resolutions that are only available annually have shown major disagreements among forested areas in highly fragmented landscapes. A holistic reductionist framework and colourimetry were applied to create a chorologic typology of environmental indicators to map forest extent with an emphasis on large-scale performance, interpretability/communication, and spatial–temporal scalability. Interpretation keys were created to identify forest and non-forest features, and a set of candidate tree cover indices were developed and compared with a decision matrix of prescribed criteria. The candidate indices were intentionally limited to those applying only the visible and NIR bands to obtain the highest possible resolution and be compatible with commonly available multispectral satellites and higher resolution sensors, including aerial and potentially UAV/drone sensors. A new High-Resolution Tree Cover Index (HRTCI) in combination with the Green band was selected as the best index based on scores from the decision matrix. To further improve the performance of the indices, the chorologic typology included two insolation indices, a water index and a NIR surface saturation index, to exclude any remaining spectrally similar but unrelated land cover features such as agriculture, water, and built-up features using a process of elimination. The approach was applied to the four seasons across a wide range of ecosystems in south-eastern Australia, with and without regionalisation, to identify which season produces the most accurate results for each ecoregion and to assess the potential for mitigating the spatial–temporal scaling effects of the Modifiable Spatio-Temporal Unit Problem. Autumn was found to be the most effective season, yielding overall accuracies of 94.19% for the full extent, 95.79% for the temperate zone, and 95.71% for the arid zone. It produced the greatest spatial agreement between two recognised global products, the GEDI forest heights extent and the ESA WorldCover Tree cover class. The performance, transparency, and scalability of the approach should provide the basis for a framework for globally relatable forest monitoring.

A remote sensing method that integrates virtual sampling from formalized visual interpretations is proposed to facilitate land cover mapping and enhance its accuracy, with an emphasis on spatial and temporal scalability. Indices are widely used for mapping and monitoring surface water across space and time; however, they typically display some kind of limitation across different environments and seasons. A decision matrix framework based on observations derived from interpretation keys was designed to compare the performance of existing indices alongside a set of newly developed indices. This comparison helped to shortlist indices that warranted further evaluation and accuracy assessment to identify effective indices for global inter-seasonal surface water extent mapping. Additional visual inspections were conducted for criteria that remained unresolved by the decision matrix to examine index consistency across the seasons in a wide range of geographic settings around the world, and further reduce the shortlist. An accuracy assessment was performed for three new shortlisted indices. On a global scale, CAWI (Comprehensive Automatic Water Index) was the best-performing index. Its distinct binary data distribution provides the possibility of regional automatic Otsu thresholding. CAWI was determined to be compatible for Sentinel-2 and Landsat 8 sensors, providing the highest possible spatial resolution as well as the longest time series for retrospective analyses with freely available multispectral imagery. Two alternative indices were identified for sensors limited to the visible and NIR bands. The first index, CATWIC (Clear and Turbid Water Index Combination), split the classification of water into two components, with one index for generally clear water and another index for turbid water. The second, NDCHRWI (Normalized Difference Colourimetric High Resolution Water Index), applied the hue angle from a normalized difference RGB. Masking indices based on modified HSV Saturation equations were developed to reduce misclassification due to other high reflectance features. The indices' overall accuracies, respectively, were: 94.97%, 94.51%, and 94.85%. This study concludes with recommendations for the application of different indices for sensors possessing shortwave infrared bands and for sensors limited to the visible and NIR bands, with a simple stratification of six zones for Global Surface Water monitoring.

Tidal wetlands are expected to respond dynamically to global environmental change, but the extent to which wetland losses have been offset by gains remains poorly understood. We developed a global analysis of satellite data to simultaneously monitor change in three highly interconnected intertidal ecosystem types—tidal flats, tidal marshes, and mangroves—from 1999 to 2019. Globally, 13,700 square kilometers of tidal wetlands have been lost, but these have been substantially offset by gains of 9700 km 2 , leading to a net change of −4000 km 2 over two decades. We found that 27% of these losses and gains were associated with direct human activities such as conversion to agriculture and restoration of lost wetlands. All other changes were attributed to indirect drivers, including the effects of coastal processes and climate change.

Globally marine-terrestrial interfaces are highly impacted due to a range of human pressures. Seagrass habitats exist in the shallow marine waters of this interface, have significant values and are impacted by a range of pressures. Cumulative risk analysis is widely used to identify risk from multiple threats and assist in prioritizing management actions. This study conducted a cumulative risk analysis of seagrass habitat associated with the Australian continent to support management actions. We developed a spatially explicit risk model based on a database of threats to coastal aquatic habitat in Australia, spanning 35,000 km of coastline. Risk hotspots were identified using the model and reducing the risk of nutrient and sediment pollution for seagrass habitat was assessed. Incorporating future threats greatly altered the spatial-distribution of risk. High risk from multiple current threats was identified throughout all bioregions, but high risk from climate change alone manifested in only two. Improving management of nutrient and sediment loads, a common approach to conserve seagrass habitat did reduce risk, but only in temperate regions, highlighting the danger of focusing management on a single strategy. Monitoring, management and conservation actions from a national and regional perspective can be guided by these outputs.

Wildfires in 2019-2020 broke global records for extent and severity, affirming the arrival of the megafire era. Frequent megafires reflect changes to fire regimes that can negatively impact species and ecosystems. Here, we offer what we believe to be the first comprehensive analysis of megafire impacts on southeastern Australian vegetation communities, combining remote-sensing data, fire-history records, and plant trait-derived fire interval thresholds. In our study area, fires burned over 5.5 million ha. We found that one-third of all native vegetation in this region has burned too frequently following the megafires, particularly impacting fire-sensitive vegetation (for example, rainforests). This represents a single-year increase of 36% in the vegetation at risk of interval squeeze (vegetation transitions driven by altered fire regimes) compared to the previous 59 years combined. We demonstrate that megafires can overrun recently burned vegetation and infiltrate refugia, reducing fire intervals beyond the persistence thresholds of plant species and increasing the risk of ecosystem collapse. Averting this will require innovative approaches to fire management. However, if climate change is not addressed, ecosystem collapse may be unavoidable especially for ecosystems adapted to infrequent, high-severity fire.

Assessments of the status of tidal flats, one of the most extensive coastal ecosystems, have been hampered by a lack of data on their global distribution and change. Here we present globally consistent, spatially-explicit data of the occurrence of tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation. More than 1.3 million Landsat images were processed to 54 composite metrics for twelve 3-year periods, spanning four decades (1984–1986 to 2017–2019). The composite metrics were used as predictor variables in a machine-learning classification trained with more than 10,000 globally distributed training samples. We assessed accuracy of the classification with 1,348 stratified random samples across the mapped area, which indicated overall map accuracies of 82.2% (80.0–84.3%, 95% confidence interval) and 86.1% (84.2–86.8%, 95% CI) for version 1.1 and 1.2 of the data, respectively. We expect these maps will provide a means to measure and monitor a range of processes that are affecting coastal ecosystems, including the impacts of human population growth and sea level rise.

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R-2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha(-1). Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.

BACKGROUND AND AIMS:Seed germination is strongly influenced by environmental temperatures. With global temperatures predicted to rise, the timing of germination for thousands of plant species could change, leading to potential decreases in fitness and ecosystem-wide impacts. The thermogradient plate (TGP) is a powerful but underutilized research tool that tests germination under a broad range of constant and alternating temperatures, giving researchers the ability to predict germination characteristics using current and future climates. Previously, limitations surrounding experimental design and data analysis methods have discouraged its use in seed biology research. METHODS:Here, we have developed a freely available R script that uses TGP data to analyse seed germination responses to temperature. We illustrate this analysis framework using three example species: Wollemia nobilis, Callitris baileyi and Alectryon subdentatus. The script generates >40 germination indices including germination rates and final germination across each cell of the TGP. These indices are then used to populate generalized additive models and predict germination under current and future monthly maximum and minimum temperatures anywhere on the globe. KEY RESULTS:In our study species, modelled data were highly correlated with observed data, allowing confident predictions of monthly germination patterns for current and future climates. Wollemia nobilis germinated across a broad range of temperatures and was relatively unaffected by predicted future temperatures. In contrast, C. baileyi and A. subdentatus showed strong seasonal temperature responses, and the timing for peak germination was predicted to shift seasonally under future temperatures. CONCLUSIONS:Our experimental workflow is a leap forward in the analysis of TGP experiments, increasing its many potential benefits, thereby improving research predictions and providing substantial information to inform management and conservation of plant species globally.

Abstract Broad‐scale biodiversity monitoring relies, at least in part, on the efforts of citizen, or community, scientists. To ensure robust inferences from citizen science data, it is important to understand the spatial pattern of sampling effort by citizen scientists and how it deviates from an optimal pattern. Here, we develop a generalized workflow to estimate the optimal distribution of sampling effort for inference of species diversity (e.g., species richness, Shannon diversity, and Simpson's diversity) patterns using the relationship between species diversity and land cover. We used data from the eBird citizen science project that was collected across heterogeneous landscapes in Florida (USA) to illustrate this workflow across different grain sizes. We found that a relatively small number of samples are needed to meet 95% sampling completeness when diversity estimation is focused on dominant species: 43, 64, 96, 123, 172, and 176 for 5 × 5, 10 × 10, 15 × 15, 20 × 20, 25 × 25, and 30 × 30‐km2 grain sizes, respectively. In contrast, three to five times more samples are necessary to infer species diversity when estimation is focused on rare species. However, in both cases, the optimal distribution of effort was spatially heterogeneous, with more effort needed in regions of higher diversity. Our results highlight the potential of citizen science data to make informed comparisons of species diversity in space and time, as well as how sampling effort inherently depends on monitoring goals, such as whether dominant or rare species are targeted. Our general workflow allows for the quantification of sampling effort needed to estimate species diversity with citizen science data and can guide future adaptive sampling by citizen science participants.