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This study presents a new global baseline of mangrove extent for 2010 and has been released as the first output of the Global Mangrove Watch (GMW) initiative. This is the first study to apply a globally consistent and automated method for mapping mangroves, identifying a global extent of 137,600 km 2 . The overall accuracy for mangrove extent was 94.0% with a 99% likelihood that the true value is between 93.6–94.5%, using 53,878 accuracy points across 20 sites distributed globally. Using the geographic regions of the Ramsar Convention on Wetlands, Asia has the highest proportion of mangroves with 38.7% of the global total, while Latin America and the Caribbean have 20.3%, Africa has 20.0%, Oceania has 11.9%, North America has 8.4% and the European Overseas Territories have 0.7%. The methodology developed is primarily based on the classification of ALOS PALSAR and Landsat sensor data, where a habitat mask was first generated, within which the classification of mangrove was undertaken using the Extremely Randomized Trees classifier. This new globally consistent baseline will also form the basis of a mangrove monitoring system using JAXA JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR-2 radar data to assess mangrove change from 1996 to the present. However, when using the product, users should note that a minimum mapping unit of 1 ha is recommended and that the error increases in regions of disturbance and where narrow strips or smaller fragmented areas of mangroves are present. Artefacts due to cloud cover and the Landsat-7 SLC-off error are also present in some areas, particularly regions of West Africa due to the lack of Landsat-5 data and persistence cloud cover. In the future, consideration will be given to the production of a new global baseline based on 10 m Sentinel-2 composites.

A regime shift is an abrupt, substantial, and persistent change in the state of a system. We show that a regime shift in the September Arctic sea‐ice extent (SIE) occurred in 2007. Before 2007, September SIE was declining approximately linearly. In September 2007, SIE had its largest year‐to‐year drop in the entire 46‐year satellite record (1979–2024). Since 2007, September SIE has fluctuated but exhibits no long‐term trend. The regime shift in 2007 was caused by significant export and melt of older and thicker sea ice over the previous 2–3 years, as documented in other studies. We test alternatives to the traditional linear model of declining September SIE, and discuss possible explanations for the lack of a trend since 2007. Plain Language Summary The Arctic Ocean freezes in winter and melts back in summer, but not completely—some sea ice always remains in the ocean at the end of the melt season in September. However, the area of that remaining sea ice has greatly declined since satellites began monitoring it in 1979. We show that the decline of September sea‐ice area has not been steady over the years, as it is commonly portrayed. After declining from 1979 to 2006, there was a huge loss of sea ice in September 2007, followed by 18 years of ups and downs but no long‐term trend. We say that a regime shift occurred in 2007 because the behavior of the September sea‐ice area changed from declining to stable. We propose a simplified way of looking at the 46‐year record of September sea‐ice area, and we discuss possible explanations for the lack of a trend since 2007. Key Points A regime shift occurred in 2007 in the September Arctic sea‐ice extent, from a downward trend (1979–2006) to no trend (2007–2024) The period of no trend is inconsistent with a linear model of decline during 1979–2024, so an alternative description should be used

This study presents an updated global mangrove forest baseline for 2010: Global Mangrove Watch (GMW) v2.5. The previous GMW maps (v2.0) of the mangrove extent are currently considered the most comprehensive available global products, however areas were identified as missing or poorly mapped. Therefore, this study has updated the 2010 baseline map to increase the mapping quality and completeness of the mangrove extent. This revision resulted in an additional 2660 km2 of mangroves being mapped yielding a revised global mangrove extent for 2010 of some 140,260 km2. The overall map accuracy was estimated to be 95.1% with a 95th confidence interval of 93.8–96.5%, as assessed using 50,750 reference points located across 60 globally distributed sites. Of these 60 validation sites, 26 were located in areas that were remapped to produce the v2.5 map and the overall accuracy for these was found to have increased from 82.6% (95th confidence interval: 80.1–84.9) for the v2.0 map to 95.0% (95th confidence interval: 93.7–96.4) for the v2.5 map. Overall, the improved GMW v2.5 map provides a more robust product to support the conservation and sustainable use of mangroves globally.

Aim We studied global variation in beta diversity patterns of lake macrophytes using regional data from across the world. Specifically, we examined (1) how beta diversity of aquatic macrophytes is partitioned between species turnover and nestedness within each study region, and (2) which environmental characteristics structure variation in these beta diversity components. Location Global. Methods We used presence–absence data for aquatic macrophytes from 21 regions distributed around the world. We calculated pairwise-site and multiple-site beta diversity among lakes within each region using Sørensen dissimilarity index and partitioned it into turnover and nestedness coefficients. Beta regression was used to correlate the diversity coefficients with regional environmental characteristics. Results Aquatic macrophytes showed different levels of beta diversity within each of the 21 study regions, with species turnover typically accounting for the majority of beta diversity, especially in high-diversity regions. However, nestedness contributed 30–50% of total variation in macrophyte beta diversity in low-diversity regions. The most important environmental factor explaining the three beta diversity coefficients (total, species turnover and nestedness) was elevation range, followed by relative areal extent of freshwater, latitude and water alkalinity range. Main conclusions Our findings show that global patterns in beta diversity of lake macrophytes are caused by species turnover rather than by nestedness. These patterns in beta diversity were driven by natural environmental heterogeneity, notably variability in elevation range (also related to temperature variation) among regions. In addition, a greater range in alkalinity within a region, likely amplified by human activities, was also correlated with increased macrophyte beta diversity. These findings suggest that efforts to conserve aquatic macrophyte diversity should primarily focus on regions with large numbers of lakes that exhibit broad environmental gradients.

Questions How does the spatial configuration of sampling units influence recorded plant species richness values at small spatial scales? What are the consequences of these findings for sampling methodology and rarefaction analyses? LocationSix semi-natural grasslands in Western Eurasia (France, Germany, Bulgaria, Hungary, Italy, Turkey). MethodsIn each site we established six blocks of 40cm x280cm, subdivided into 5cm x5cm micro-quadrats, on which we recorded vascular plant species presence with the rooted (all sites) and shoot (four sites) presence method. Data of these micro-quadrats were then combined to achieve larger sampling units of 0.01, 0.04 and 0.16m(2) grain size with six different spatial configurations (square, 4:1 rectangle, 16:1 rectangle, three variants of discontiguous randomly placed micro-quadrats). The effect of the spatial configurations on species richness was quantified as relative richness compared to the mean richness of the square of the same surface area. ResultsSquare sampling units had significantly lower species richness than other spatial configurations in all countries. For 4:1 and 16:1 rectangles, the increase of rooted richness was on average about 2% and 8%, respectively. In contrast, the average richness increase for discontiguous configurations was 7%, 17% and 40%. In general, increases were higher with shoot presence than with rooted presence. Overall, the patterns of richness increase were highly consistent across six countries, three grain sizes and two recording methods. ConclusionsOur findings suggest that the shape of sampling units has negligible effects on species richness values when the length-width ratio is up to 4:1, and the effects remain small even for more elongated contiguous configurations. In contrast, results from discontiguous sampling units are not directly comparable with those of contiguous sampling units, and are strongly confounded by spatial extent. This is particularly problematic for rarefaction studies where spatial extent is often not controlled for. We suggest that the concept of effective area is a useful tool to report effects of spatial configuration on richness values, and introduce species-extent relationships (SERs) to describe richness increases of different spatial configurations of sampling units.

Estimating extended brain sources using EEG/MEG source imaging techniques is challenging. EEG and MEG have excellent temporal resolution at millisecond scale but their spatial resolution is limited due to the volume conduction effect. We have exploited sparse signal processing techniques in this study to impose sparsity on the underlying source and its transformation in other domains (mathematical domains, like spatial gradient). Using an iterative reweighting strategy to penalize locations that are less likely to contain any source, it is shown that the proposed iteratively reweighted edge sparsity minimization (IRES) strategy can provide reasonable information regarding the location and extent of the underlying sources. This approach is unique in the sense that it estimates extended sources without the need of subjectively thresholding the solution. The performance of IRES was evaluated in a series of computer simulations. Different parameters such as source location and signal-to-noise ratio were varied and the estimated results were compared to the targets using metrics such as localization error (LE), area under curve (AUC) and overlap between the estimated and simulated sources. It is shown that IRES provides extended solutions which not only localize the source but also provide estimation for the source extent. The performance of IRES was further tested in epileptic patients undergoing intracranial EEG (iEEG) recording for pre-surgical evaluation. IRES was applied to scalp EEGs during interictal spikes, and results were compared with iEEG and surgical resection outcome in the patients. The pilot clinical study results are promising and demonstrate a good concordance between noninvasive IRES source estimation with iEEG and surgical resection outcomes in the same patients. The proposed algorithm, i.e. IRES, estimates extended source solutions from scalp electromagnetic signals which provide relatively accurate information about the location and extent of the underlying source. •A new inverse imaging strategy suitable for estimating extended sources from EEG/MEG is proposed.•The sparsity of the source is exploited in multiple domains using an iterative method.•No thresholding is required to obtain extended-source solutions.•The proposed algorithm can estimate the source extent within reasonable error bounds.•A potential application of the method is to estimate the source extent in epilepsy patients.

Seagrass meadows globally are under pressure with worldwide loss and degradation, but there is a growing recognition of the global importance of seagrass ecosystem services, particularly as a major carbon sink and as fisheries habitat. Estimates of global seagrass spatial distribution differ greatly throughout the published literature, ranging from 177 000 to 600 000 km2 with models suggesting potential distribution an order of magnitude higher. The requirements of the Paris Climate Agreement by outlining National Determined Contributions (NDC's) to reduce emissions is placing an increased global focus on the spatial extent, loss and restoration of seagrass meadows. Now more than ever there is a need to provide a more accurate and consistent measure of the global spatial distribution of seagrass. There is also a need to be able to assess the global spread of other seagrass ecosystem services and in their extension, the values of these services. In this study, by rationalising and updating a range of existing datasets of seagrass distribution around the globe, we have estimated with Moderate to High confidence the global seagrass area to date as 160 387 km2, but possibly 266 562 km2 with lower confidence. We break this global estimate down to a national level with a detailed analysis of the current state of mapped distribution and estimates of seagrass area per country. Accurate estimates, however, are challenged by large areas remaining unmapped and inconsistent measures being used. Through the examination of current global maps, we are able to propose a pathway forward for improving mapping of this important resource. More accurate measure of global #seagrass distribution, critical for assessing current state and trends

Maslakov, A.A.; Shabanov, P.A.; Shabanova, N.N.; Baranskaya, A.V., 2024. Arctic coastal erosion threats to indigenous communities of Eastern Chukotka (Bering Strait): Physical causes and social consequences. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 1036-1040. Charlotte (North Carolina), ISSN 0749-0208. The Bering Strait is the region of compact settlement of indigenous peoples of the North – Chukchi, Eskimo (Yupiks and Inupiats), Kereks, etc. Traditional lifestyle of the locals is closely related with hunting on marine mammals and fishing, therefore all communities in the region are confined to the contemporary coastline. Such phenomena are typical for the whole Bering Strait region including Eastern Chukchi Peninsula, Western Alaska, and Aleutian islands. Recent global climate change has the greatest effect on the Arctic, where air temperature has been increasing three times higher than the world average for the last 60 years. Air warming causes sea ice extent shrinking that has negative effect on coastal environments' stability. The Bering Strait communities experience conditions when both previously stable coasts shift to erosion, and erosional coast sections show accelerated retreat. Fall storms cause the most damage to coastal infrastructure and households. In our study we estimated hydrometeorological forcing to coastal zone via presented wind-wave energy potential. In 1980-2023 this parameter had been growing in 2 times that explains the intensified coastal erosion in this region. Local authorities struggle with negative consequences of this phenomenon by installing coastal protection facilities, but their effectiveness is low because of hard logistics and moderate financial resources. The experience from the American side of the Bering Strait shows that deteriorating conditions in the coastal zone finally led to coastal community relocation as it happened with Kivalina community in Alaska. Accelerated coastal erosion caused by sea ice extent decline threatens to many communities of the Arctic. This raises the problem to a global level. Therefore, joint scholar, governmental and indigenous collaboration is needed to develop strategy on mitigating negative consequences of climate change in the Arctic.

Aim Forecasting changes in species distribution under future scenarios is one of the most prolific areas of application for species distribution models (SDMs). However, no consensus yet exists on the reliability of such models for drawing conclusions on species’ distribution response to changing climate. In this study, we provide an overview of common modelling practices in the field and assess the reliability of model predictions using a virtual species approach. Location Global. Methods We first review papers published between 2015 and 2019. Then, we use a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and random forest) to assess the estimated and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions. Results Most SDM papers relied on single models (65%) and small samples (N < 50, 62%), used presence‐only data (85%), binarized models' output (74%) and used a split‐sample validation (94%). Our simulation reveals that the split‐sample validation tends to be over‐optimistic compared to the real performance, whereas spatial block validation provides a more honest estimate, except when datasets are environmentally biased. The binarization of predicted probabilities of presence reduces models’ predictive ability considerably. Sample size is one of the main predictors of the real model accuracy, but has little influence on estimated accuracy. Finally, the inclusion of ecologically irrelevant predictors and the violation of modelling assumptions increases estimated accuracy but decreases real accuracy of model projections, leading to biased estimates of range contraction and expansion. Main conclusions Our ability to predict future species distribution is low on average, particularly when models’ predictions are binarized. A robust validation by spatially independent samples is required, but does not rule out inflation of model accuracy by assumption violation. Our findings call for caution in the application and interpretation of SDM projections under different climates.

Floods are among the most severe and impacting natural disasters. Their occurrence rate and intensity have been significantly increasing worldwide in the last years due to climate change and urbanization, bringing unprecedented effects on human lives and activities. Hence, providing a prompt response to flooding events is of crucial relevance for humanitarian, social and economic reasons. Satellite remote sensing using synthetic aperture radar (SAR) offers a great deal of support in facing flood events and mitigating their effects on a global scale. As opposed to multi-spectral sensors, SAR offers important advantages, as it enables Earth’s surface imaging regardless of weather and sunlight illumination conditions. In the last decade, the increasing availability of SAR data, even at no cost, thanks to the efforts of international and national space agencies, has been deeply stimulating research activities in every Earth observation field, including flood mapping and monitoring, where advanced processing paradigms, e.g., fuzzy logic, machine learning, data fusion, have been applied, demonstrating their superiority with respect to traditional classification strategies. However, a fair assessment of the performance and reliability of flood mapping techniques is of key importance for an efficient disasters response and, hence, should be addressed carefully and on a quantitative basis trough synthetic quality metrics and high-quality reference data. To this end, the recent development of open SAR datasets specifically covering flood events with related ground-truth reference data can support thorough and objective validation as well as reproducibility of results. Notwithstanding, SAR-based flood monitoring still suffers from severe limitations, especially in vegetated and urban areas, where complex scattering mechanisms can impair an accurate extraction of water regions. All such aspects, including classification methodologies, SAR datasets, validation strategies, challenges and future perspectives for SAR-based flood mapping are described and discussed.