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Rain‐triggered landslides cause significant socioeconomic loss and long‐term ecological impact, disrupting topsoil and seed banks and creating unfavourable conditions for vegetation establishment. Although hazard management effectively reduces landslide risk, most management plans primarily focus on the societal impacts (loss of property and human lives), while environmental impacts related to landslide occurrence and recurrence are often overlooked. The role of different vegetation types in mitigation strategies remains uncertain due to the time and spatial constraints of traditional field study methods. Therefore, the role of vegetation in landslide restoration should be reconsidered. In this study, we investigated post‐landslide vegetation recovery patterns, analysed the duration of vegetation recovery, identified factors influencing these patterns, examined the role of forests in occurrence and recurrence probability and explored the implications of these findings for each element within an integrated management framework for landslide restoration. We utilised long‐term landslide inventory data (1924–2018) covering the entire city of Hong Kong (~1100 km2) and combined it with structural data derived from airborne LiDAR scanning in 2020 to capture landslide recovery. Our findings revealed significant differences in recovery trajectories between the main bodies and foot areas of scars, with the former taking approximately 46 years to recover and the latter recovering in about 38 years. Scar age, average wind speed, elevation and proximity to the forest were identified as key factors influencing structural recovery on scars, while the proximity to forest also regulated recovery rate of scars. We observed decreased landslide occurrence in forested areas, with recurrent landslides primarily appearing on older, unrecovered scars on barren hillsides. Synthesis and applications: Our research highlights the dynamic patterns, recovery time and drivers of vegetation recovery on landslide scars; and the higher probability of landslide occurrence in non‐forest areas and increased recurrence on bare, low vegetated scars. This information provides valuable insights for informing restoration efforts and managing landslide‐prone areas. Such knowledge is crucial for developing effective mitigation strategies and enhancing the resilience of vulnerable ecosystems facing climate change and increased frequency of extreme weather events. [ABSTRACT FROM AUTHOR]

Ecosystem degradation is a key challenge that human society faces, as ecosystems provide services that are tied to human well‐being. Particularly, mangrove ecosystems provide important services to communities but are suffering heavy degradation, loss and potential collapse due to anthropogenic activities. The IUCN Red List of Ecosystems is a transparent and consistent framework for assessing ecosystems' risk of collapse and is increasingly used to inform legislation and ecosystem management globally.Satellite data have become increasingly common in environmental monitoring due to their extensive spatial and temporal coverage. Here, recent advances in analyses using satellite‐derived data were implemented to reassess the conservation status of the 'Rakhine mangrove forest on mud', an important intertidal ecosystem in Myanmar, extending a previous national Red List assessment that assessed the ecosystem as Critically Endangered.By incorporating additional data sources and analyses, the extended assessment produced more robust results and reduced the uncertainty in the previous assessment. Overall, the ecosystem was assessed as Critically Endangered (range: Vulnerable to Critically Endangered) as a result of historical mangrove extent loss. Recent losses and biotic disruptions were also observed, which would have led to the ecosystem being assessed as Vulnerable.While the final outcome of the Red List assessment remained at Critically Endangered due to the historical state of the mangroves pre‐dating the temporal coverage from satellite data, the uncertainty of the ecosystem's status was reduced, and the reassessment highlighted the recent areal changes and mangrove degradation that has occurred.The importance of conducting reassessments when new data become available is discussed, and a template for future mangrove Red List assessments that use satellite data as their primary source of information to improve the robustness of their results is presented. [ABSTRACT FROM AUTHOR]

Aim: Understanding the considerable variability and drivers of global leaf photosynthetic capacity [indicated by the maximum carboxylation rate standardized to 25°C (Vc,max25)] is an essential step for accurate modelling of terrestrial plant photosynthesis and carbon uptake under climate change. Although current environmental conditions have often been connected with empirical and theoretical models to explain global Vc,max25 variability through acclimatization and adaptation, long‐term evolutionary history has largely been neglected, but might also explicitly play a role in shaping the Vc,max25 variability. Location: Global. Time period: Contemporary. Major taxa studied: Terrestrial plants. Methods: We compiled a geographically comprehensive global dataset of Vc,max25 for C3 plants (n = 6917 observations from 2157 species and 425 sites covering all major biomes world‐wide), explored the biogeographical and phylogenetic patterns of Vc,max25, and quantified the relative importance of current environmental factors and evolutionary history in driving global Vc,max25 variability. Results: We found that Vc,max25 differed across different biomes, with higher mean values in relatively drier regions, and across different life‐forms, with higher mean values in non‐woody relative to woody plants and in legumes relative to non‐leguminous plants. The values of Vc,max25 displayed a significant phylogenetic signal and diverged in a contrasting manner across phylogenetic groups, with a significant trend along the evolutionary axis towards a higher Vc,max25 in more modern clades. A Bayesian phylogenetic linear mixed model revealed that evolutionary history (indicated by phylogeny and species) explained nearly 3‐fold more of the variation in global Vc,max25 than present‐day environment (53 vs. 18%). Main conclusions: These findings contribute to a comprehensive assessment of the patterns and drivers of global Vc,max25 variability, highlighting the importance of evolutionary history in driving global Vc,max25 variability, hence terrestrial plant photosynthesis. [ABSTRACT FROM AUTHOR]

Summary: To what degree plant ecosystems thermoregulate their canopy temperature (Tc) is critical to assess ecosystems' metabolisms and resilience with climate change, but remains controversial, with opinions from no to moderate thermoregulation capability.With global datasets of Tc, air temperature (Ta), and other environmental and biotic variables from FLUXNET and satellites, we tested the 'limited homeothermy' hypothesis (indicated by Tc & Ta regression slope < 1 or Tc < Ta around midday) across global extratropics, including temporal and spatial dimensions.Across daily to weekly and monthly timescales, over 80% of sites/ecosystems have slopes ≥1 or Tc > Ta around midday, rejecting the above hypothesis. For those sites unsupporting the hypothesis, their Tc–Ta difference (ΔT) exhibits considerable seasonality that shows negative, partial correlations with leaf area index, implying a certain degree of thermoregulation capability. Spatially, site‐mean ΔT exhibits larger variations than the slope indicator, suggesting ΔT is a more sensitive indicator for detecting thermoregulatory differences across biomes. Furthermore, this large spatial‐wide ΔT variation (0–6°C) is primarily explained by environmental variables (38%) and secondarily by biotic factors (15%).These results demonstrate diverse thermoregulation patterns across global extratropics, with most ecosystems negating the 'limited homeothermy' hypothesis, but their thermoregulation still occurs, implying that slope < 1 or Tc < Ta are not necessary conditions for plant thermoregulation. See also the Commentary on this article by Drake, 238: 921–923. [ABSTRACT FROM AUTHOR]

Landslides are common in tropical and subtropical regions with hilly terrains and heavy rainstorms, and can cause significant economic, ecological, and social impacts. Natural forest succession is usually slow on landslides due to poor soil structure and lack of woody plant seeds, and often comes with a higher risk of repeated landslides. Active forest restoration has been increasingly suggested as an effective alternative to restore exposed landslide scars. Here, we evaluated the effectiveness of using different seed coatings and surface soil treatments on seed germination and seedling establishment of pioneer and late‐successional tree species on landslide scars in Hong Kong. Our results show that biochar‐dominant seed coating boosted seed germination rate by an additional 9.33% (Standard error = 0.04), while clay‐dominant seed coating did not show significant effects. The effects on seedling survival were not significant in both seed coating and surface soil treatments. Our results also show that large seed and later successional species have significantly higher germination rates than pioneer species and are able to successfully establish on landslide sites. These results suggest that direct seeding using a biochar seed coat with large seeds or late‐successional species is a useful method to enhance tree seed germination—an essential first step to restore the forests after landslide disturbances, with potential to be extended to other humid tropical and subtropical forests. [ABSTRACT FROM AUTHOR]

Background: 'Megafire' is an emerging concept commonly used to describe fires that are extreme in terms of size, behaviour, and/or impacts, but the term's meaning remains ambiguous. Approach: We sought to resolve ambiguity surrounding the meaning of 'megafire' by conducting a structured review of the use and definition of the term in several languages in the peer‐reviewed scientific literature. We collated definitions and descriptions of megafire and identified criteria frequently invoked to define megafire. We recorded the size and location of megafires and mapped them to reveal global variation in the size of fires described as megafires. Results: We identified 109 studies that define the term 'megafire' or identify a megafire, with the term first appearing in the peer‐reviewed literature in 2005. Seventy‐one (~65%) of these studies attempted to describe or define the term. There was considerable variability in the criteria used to define megafire, although definitions of megafire based on fire size were most common. Megafire size thresholds varied geographically from > 100–100,000 ha, with fires > 10,000 ha the most common size threshold (41%, 18/44 studies). Definitions of megafire were most common from studies led by authors from North America (52%, 37/71). We recorded 137 instances from 84 studies where fires were reported as megafires, the vast majority (94%, 129/137) of which exceed 10,000 ha in size. Megafires occurred in a range of biomes, but were most frequently described in forested biomes (112/137, 82%), and usually described single ignition fires (59% 81/137). Conclusion: As Earth's climate and ecosystems change, it is important that scientists can communicate trends in the occurrence of larger and more extreme fires with clarity. To overcome ambiguity, we suggest a definition of megafire as fires > 10,000 ha arising from single or multiple related ignition events. We introduce two additional terms – gigafire (> 100,000 ha) and terafire (> 1,000,000 ha) – for fires of an even larger scale than megafires. [ABSTRACT FROM AUTHOR]

The International Union for the Conservation of Nature (IUCN) Red List of ecosystems and Red List of threatened species are global standards for assessing risks of ecosystem collapse and species extinction. However, misconceptions of the Red List assessment process, along with its technically demanding nature, can result in the misapplication of their criteria, leading to inconsistent and potentially unreliable assessments. To address this problem, we developed redlistr, an R package aiding in the production of consistent species and ecosystem Red List assessments. Redlistr's features include methods to calculate 1) area from spatial data, 2) range size metrics, 3) rates of change of distributions or populations, and 4) distribution or population at another time from these rates. A key feature of the package is the systematic approach used to eliminate geometric uncertainty when estimating area of occupancy. Here, we develop two case studies to demonstrate the functionalities of redlistr with typical workflows for both species and ecosystems. Redlistr was developed to be accessible to users with a broad range of experience in programming for spatial and temporal data analysis, and sufficiently flexible to allow users to parameterise functions and select equations to fit their purposes. The package specifically aims to assist researchers and conservation practitioners to conduct robust and transparent risk assessments of ecosystems and species under the IUCN Red List criteria but is also useful for other studies requiring analyses of range size, area change and calculations of rates of change. [ABSTRACT FROM AUTHOR]

The cover image is based on the Research Article I Vegetation regeneration on natural terrain landslides in Hong Kong: Direct seeding of native species as a restoration tool i by Ying Ki Law et al., https://doi.org/10.1002/ldr.4492. [Extracted from the article]

Anthropogenic and natural disturbances can cause degradation of ecosystems, reducing their capacity to sustain biodiversity and provide ecosystem services. Understanding the extent of ecosystem degradation is critical for estimating risks to ecosystems, yet there are few existing methods to map degradation at the ecosystem scale and none using freely available satellite data for mangrove ecosystems. In this study, we developed a quantitative classification model of mangrove ecosystem degradation using freely available earth observation data. Crucially, a conceptual model of mangrove ecosystem degradation was established to identify suitable remote sensing variables that support the quantitative classification model, bridging the gap between satellite-derived variables and ecosystem degradation with explicit ecological links. We applied our degradation model to two case-studies, the mangroves of Rakhine State, Myanmar, which are severely threatened by anthropogenic disturbances, and Shark River within the Everglades National Park, USA, which is periodically disturbed by severe tropical storms. Our model suggested that 40% (597 km2) of the extent of mangroves in Rakhine showed evidence of degradation. In the Everglades, the model suggested that the extent of degraded mangrove forest increased from 5.1% to 97.4% following the Category 4 Hurricane Irma in 2017. Quantitative accuracy assessments indicated the model achieved overall accuracies of 77.6% and 79.1% for the Rakhine and the Everglades, respectively. We highlight that using an ecological conceptual model as the basis for building quantitative classification models to estimate the extent of ecosystem degradation ensures the ecological relevance of the classification models. Our developed method enables researchers to move beyond only mapping ecosystem distribution to condition and degradation as well. These results can help support ecosystem risk assessments, natural capital accounting, and restoration planning and provide quantitative estimates of ecosystem degradation for new global biodiversity targets. [ABSTRACT FROM AUTHOR]