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Anticipating future impacts of climate warming and aridification on drylands requires understanding how coexisting woody plant species respond to climate variability. However, we lack knowledge of the growth resilience capacity of Mediterranean shrubs. Do coexisting trees and shrubs differ in their response to climate? Do coexisting shrub species have comparable post‐drought growth resilience? This study was conducted in two Mediterranean shrublands with sparse trees in semi‐arid north‐eastern Spain. We selected sites situated in formerly agricultural or grassland areas in two regions subjected to semi‐arid Mediterranean climate conditions. We sampled six shrubs' species (Juniperus phoenicea, Juniperus oxycedrus, Pistacia lentiscus, Pistacia terebinthus, Rhamnus lycioides and Rhamnus alaternus) and one tree species (Pinus halepensis) to measure their radial growth and to reconstruct their past growth patterns using dendrochronology. We quantified climate–growth relationships of trees and shrubs, as well as the growth resilience capacity after drought events of the six shrubs. Growth patterns differed between species but a prevalent trade‐off between growth and longevity was found. Growth responses to climate were comparable between species but differed between sites. Most species responded positively to precipitation and negatively to temperature. The most negative correlations of growth with drought severity were found at the driest Valcuerna site. Shrubs differed in their resilience capacity. Across sites, species were more resistant and resilient in Alcubierre, the wettest site. The shrubs P. terebinthus and R. lycioides were more resistant and resilient than the rest of species, which required longer growth recovery times and accumulated stronger growth reductions, particularly J. phoenicea. Synthesis. These results highlight the importance of drought as a driver of growth in Mediterranean scrublands. Growth resilience strategies differed between species with junipers being less resilient to drought than other coexisting shrubs. Further research should investigate how this difference in post‐drought resilience is related to functional traits, particularly those related with plant water‐use strategies. [ABSTRACT FROM AUTHOR]

Laetoli, Tanzania is one of the most important palaeontological and palaeoanthropological localities in Africa. We report on a survey of the extant terrestrial gastropod faunas of the Laetoli-Endulen area, examine their ecological associations and re-examine the utility of Pliocene fossil molluscs in palaeoenvironmental reconstruction. Standardised collecting at 15 sites yielded 7302 individuals representing 58 mollusc species. Significant dissimilarities were found among the faunas of three broad habitat types: forest, woodland/bushland and open (grassland and scattered, xeric shrubland). Overall, more species were recorded in the woodland/bushland sites than in the forest sites. Open sites were less diverse. Environmental factors contributing most strongly to the separation of habitat types were aridity index and elevation. The results are supplemented with new mollusc data from the Mbulu Plateau south of Lake Eyasi, and compared to the list of species cumulatively recorded from the Ngorongoro area. Some regional variation is apparent and historical factors may explain the absence of some fossil taxa from Laetoli today. Differences in seasonality separated upland forest sites on the Mbulu plateau from those at Lemagurut at Laetoli. Indicator species were identified for each habitat. These included several large-bodied species analogous to the Laetoli Pliocene fossil species that were then used for palaeoenvironmental reconstruction. Based on the estimated aridity index, and adopting the widely used United Nations Environment Programme (UNEP) global climate classification, the four stratigraphic subunits of the Upper Laetolil Beds (3.6–3.85 Ma) would be placed in either the UNEP's Dry Sub-humid or Semi-arid climate classes, whereas the Upper Ndolanya Beds (2.66 Ma) and Lower Laetolil Beds (3.85-<4.36 Ma) would be assigned to the Humid and Semi-arid climate classes respectively. Pliocene precipitation at Laetoli is estimated as 847–965 mm per year, refining previous estimates. This is close or slightly higher than the present mean annual precipitation, and is likely to have corresponded to a mosaic of forest, woodland and bushland within a grassland matrix consistent with other reconstructions. [ABSTRACT FROM AUTHOR]

Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area. [ABSTRACT FROM AUTHOR]

Coastal mallee shrubland wildfires present challenges for accurately predicting fire spread sustainability and rate of spread. In this study, we assess the fuel drivers contributing to coastal mallee shrubland fires. A review of shrubland fire behaviour models and fuel metrics was conducted to determine the current practice of assessing shrubland fuels. This was followed by workshops designed to elicit which fuel structural metrics are key drivers of fire behaviour in coastal mallee shrublands. We found that height is the most commonly used fuel metric in shrubland fire models due to the ease of collection in situ or as a surrogate for more complex fuel structures. Expert workshop results suggest that cover and connectivity metrics are key to modelling fire behaviour in coastal mallee shrublands. While height and cover are frequently used in fire models, we conclude that connectivity metrics would offer additional insights into fuel drivers in mallee shrublands. Future research into coastal mallee fire behaviour should include the measurements of fuel height, cover, and horizontal and vertical connectivity. [ABSTRACT FROM AUTHOR]

Knowledge on mesoclimatic zonation and microclimatic variations within mountain forest ecosystems is crucial for understanding regional species turnover and effects of climate change on these systems. The temperate mountain forests in the Andean region of South America are among the largest and contiguous natural deciduous forest areas in the world. Due to their pronounced disturbance regime and different successional stages, a climatic zonation combined with the characterisation of its microclimatic variation is important to identify thresholds of species occurrences. We used micro-loggers to measure air temperature and relative humidity for one year at 40 measurement locations along longitudinal and elevation gradients in mountain forests in Northern Patagonia, Argentina. Our results unveil mesoclimatic patterns within these forests characterised by variations in temperature and vapour pressure deficit along the elevational gradient in general, but also at different times of the year. For example, Austrocedrus chilensis and Nothofagus dombeyi forests differed mainly by temperature and its diurnal range in the warmest months of the year. Also, differences between forest stands and gaps were more pronounced in the warmest months of the year and at lower elevations, with up to 2.5 K higher temperatures in the second half of the day in gaps. We found clear indications that shrubland of Nothofagus antarctica representing a successional stage after disturbances alters the mesoclimatic pattern, favouring forest fire ignition. Such mesoclimatic variations have a major influence on tree species turnover and ecological processes within these forest ecosystems. The findings contribute to our understanding of the complex interplay between topography, climate, and vegetation in shaping the spatial patterns of species occurrences. [ABSTRACT FROM AUTHOR]

In recent years, Geoscience Australia has undertaken a successful continental-scale validation program, targeting Landsat and Sentinel analysis-ready data surface reflectance products. The field validation model used for this program was successfully built upon earlier studies, and the measurement uncertainties associated with these protocols have been quantified and published. As a consequence, the Australian earth observation community was well-prepared to respond to the United States Geological Survey (USGS) call for collaborators with the 2021 Landsat 8 (L8) and Landsat 9 (L9) underfly. Despite a number of challenges, seven validation datasets were captured across five sites. As there was only a single 100% overlap transit across Australia, and the country was amidst a strong La Niña climate cycle, it was decided to deploy teams to the two available overpasses with only 15% side lap. The validation sites encompassed rangelands, chenopod shrublands, and a large inland lake. Apart from instrument problems at one site, good weather enabled the capture of high-quality field data allowing for meaningful comparisons between the radiometric performance of L8 and L9, as well as the USGS and Australian Landsat analysis-ready data processing models. Duplicate (cross-calibration) spectral sampling at different sites provides evidence of the field protocol reliability, while the off-nadir view of L9 over the water site has been used to better compare the performance of different water and atmospheric correction processing models. [ABSTRACT FROM AUTHOR]

Environmental niche modeling (ENM) has emerged as a promising tool for identifying grass species with potential for rangeland restoration. This approach can detect suitable areas and environments where these species can be planted. In this study, we employed ENM to estimate the potential distribution range of 50 grass species of the grasslands and shrublands of northern Mexico. The outcome of the ENM served to identify grass species with potential for restoration in Mexico, especially those not commonly used for that purpose in the past. Results suggested the possibility of selecting seven grass species with the potential for revegetating degraded grasslands, nine for shrublands, and six for alkaline soils. This research provides insights into the environmental adaptations of different grass species distributed in the rangelands of northern Mexico. Ecologists, conservation planners, researchers, and range managers could use these outcomes and the maps of the potential distribution ranges as supportive information to conduct effective restoration efforts. In turn, this can assist in increasing the probability of success of future rangelands restoration programs, which are often costly in terms of financial investments and labor. [ABSTRACT FROM AUTHOR]

Background: In Italy, malaria was endemic until the 1970s, when it was declared eradicated by WHO. Nowadays, with the persistence of competent mosquito populations, the effect of climate change, and increased possibility of importing malaria parasites from endemic counties due to growing migration, a malaria resurgence in Italy has become more likely. Hence, enhancing the understanding of the current distribution of the Anopheles maculipennis complex and the factors that influence the presence of this malaria vector is crucial, especially in Northern Italy, characterised by a high density of both human population and livestock. Methods: To assess the presence and abundance of malaria vectors, a 4-year field survey in the plain areas of Lombardy and Emilia-Romagna region in Italy was conducted. Every sampling point was characterised in space by the land use in a 500-m radius and in time considering meteorological data collected in the short and long time periods before sampling. We combined the results of a linear regression model with a random forest analysis to understand the relative importance of the investigated niche dimensions in determining Anopheles mosquito presence and abundance. Results: The estimated normalised variable importance indicates that rice fields were the most important land use class explaining the presence of Anopheles, followed by transitional woodlands and shrubland. Farm buildings were the third variable in terms of importance, likely because of the presence of animal shelters, followed by urbanised land. The two most important meteorological variables influencing the abundance of Anopheles in our study area were mean temperature in the 24 h before the sampling date and the sum of degree-days with temperature between 18 °C and 30 °C in the 14 days before the sampling date. Conclusions: The results obtained in this study could be helpful in predicting the risk of autochthonous malaria transmission, based on local information on land cover classes that might facilitate the presence of malaria vectors and presence of short- and medium-term meteorological conditions favourable to mosquito development and activity. The results can support the design of vector control measures through environmental management. [ABSTRACT FROM AUTHOR]

Previous studies have shown that climate change has significant cumulative effects on vegetation growth. However, there remains a gap in understanding the characteristics of cumulative climatic effects on different vegetation types and the underlying driving mechanisms. In this study, using the normalized difference vegetation index data from 1982 to 2015, along with accumulated temperature, precipitation, and solar radiation data, we quantitatively investigated the intra-annual cumulative effects of climatic factors on global vegetation biomes across climatic zones. We also explored the underlying driving mechanisms. The results indicate that precipitation has a longer intra-annual cumulative effect on vegetation, with effects lasting up to 12 months for large percentages of most vegetation biomes. The cumulative effect of solar radiation is mostly concentrated within 0–6 months. Temperature has a shorter cumulative effect, with no significant cumulative effect of temperature on large percentages of tree-type vegetation. Compared to other vegetation types, evergreen broadleaf forests, close shrublands, open shrublands, savannas, and woody savannas exhibit more complex cumulative climatic effects. Each vegetation type shows a weak-to-moderate correlation with accumulated precipitation while exhibiting strong-to-extremely-strong positive correlations with accumulated temperature and accumulated solar radiation. The climate-induced regulations of water, heat, and nutrient, as well as the intrinsic mechanisms of vegetation's tolerance, resistance, and adaptation to climate change, account for the significant heterogeneity of cumulative climatic effects across vegetation biomes in different climatic zones. This study contributes to enriching the theoretical understanding of the relationship between vegetation growth and climate change. It also offers crucial theoretical support for developing climate change adaptation strategies and improving future "vegetation-climate" models. [ABSTRACT FROM AUTHOR]

As a predominant ecosystem-providing area and distinctive landscape in arid regions, an oasis plays an important role in maintaining land stability, human production, and daily activities. Studying the dynamics of oasis and its driving factors is vital to supporting arid regions' sustainable development. As a typical mountain–desert–oasis landscape, the Turpan–Hami (Tuha) Basin, located in Xinjiang, China, includes complex interactions among different land types. For this study, we revealed the spatio-temporal patterns and transition processes of the oasis using a complex network method between 1990 and 2020 in the Tuha Basin. In the oasis transition network, the degree value, betweenness centrality, and average path length were calculated to express the transition relationship, key oasis type, and oasis structural stability, respectively. Six factors related to climate change and human actives were selected to investigate the driving forces behind oasis transitions, including the average temperature and precipitation in the growing season, the total power of agricultural machinery (TAMP), the production of raw coal (PRC), the total output value of the plantation industry (TPI), and the population (Pop). Our results show that the oasis area of the Tuha Basin, including the natural oasis and artificial oasis, all grew from 1990 to 2020, with the natural oasis expanding more than the artificial oasis. The transitions between oasis types became more frequent as the area of oasis land types increased throughout the study period. Grassland acted as the most important oasis type in the network, with the highest betweenness centrality, but its importance declined due to the increasing complexity of the oasis transition network from 1990 to 2020. The transitions between oasis types became simpler, and the oasis structural stability decreased. Through driving force analysis, the oasis changes showed a positive correlation with the temperature (p-value < 0.05, r = 0.88), and urbanization and industrialization factors prompted transitions to built-up areas and cropland from grassland and shrubland. In summary, our results suggest that to create a harmonious symbiotic relationship between the natural environment in dryland and human activities, preventing grassland degradation and excessive reclamation of land cover is an available way. Meanwhile, the protection of shrubland and water resources is also important. This study provided reference and theory support for promoting the sustainable development of oases. [ABSTRACT FROM AUTHOR]

Climate change induces changes in plant communities according to species' climatic requirements. These changes can be assessed by community climatic disequilibrium (CD), which corresponds to the difference between the climate inferred from the climatic requirements of the species in a community (community‐inferred climate, CIC) and the local observed climate.We assessed changes in CIC and CD during a long‐term climatic manipulation (warming and drought treatments), embedded within the ongoing trends of climate change, in a Mediterranean shrubland (NE Iberian Peninsula) during 1999–2014. We used plant censuses, species distribution and climate layers since 1979 to create a multivariate environmental space where CIC and CD trends were estimated for 1999–2014.CD consistently decreased, concomitant with an overall climate change‐derived increase in aridity (higher temperature and lower precipitation). CIC significantly changed during 1999–2014, reflecting the reshuffling of the community composition due to an increase in the abundance of species distributed in warmer, drier and more seasonal localities. Overall, treatments simulating greater climate change did not accelerate the decrease in CD. However, a trend of steeper diminution of CD was observed under warming treatment. In turn, under drought treatment the species less adapted to seasonality and aridity became less abundant. This community tracking of climate did not follow yearly climatic variability; instead, it was detected by the CD trend at the decadal scale.Synthesis. The procedure developed to measure CD reflects demographic behaviours, thus providing a reliable method to assess the impact of climate change on species and communities. The study demonstrates the current, rapid tracking of Mediterranean woody plant communities to climate change. This tracking results from changes in the abundance of species according to their climatic requirements, inferred from their distribution. However, inertia in demographic processes implies that plant communities do not immediately fit current climate change at local level, as supported by the minor effect of experimentally accentuated climate change. [ABSTRACT FROM AUTHOR]

Shrubs are important ecological barriers in desert regions and an important component of global carbon estimation. However, the shrubland in deserts has been hardly presented, although many high-quality land cover datasets with a 10 m scale based on remote-sensing data have been publicly released products. Therefore, the underestimation of carbon storage is inevitable with the absence of desert shrublands. The existing land-cover datasets have been analyzed and compared, and it has been found that the reason for missing the shrubland in deserts is mainly indued by the absence of shrubland samples, which are easy to neglect and difficult to retrieve. In this study, we developed a semi-automatic method to extract shrubland samples in deserts as the updated input for the machine-learning method. Firstly, the initial samples of desert shrublands were identified from the very high spatial-resolution (0.3~0.5 m) imagery on GEE, and the maximum NDVI from Sentinel-2 was used for double-checking. Secondly, a feature-based method was used to learn the feature from the initial samples and a similarity-based searching method was employed to automatically expand the samples. Finally, the expanded samples and their corresponding time-series satellite images were inputted into different machine-learning methods at a large region (1.63 × 106 km2) for extracting the shrubland in the desert. It was found that different combinations of feature variables and time-series combinations have different impacts on the overall accuracy (OA) of the classification results, as well as the performance of identifying and classifying the different land-cover types. Compared to the existing global-scale land-cover products, the proposed method can better identify the shrubland in deserts and show better overall accuracy. [ABSTRACT FROM AUTHOR]

The knowledge of the effects of fuel dryness on fire occurrence is critical for sound forest fire management planning, particularly in a changing climate. This study aimed to analyze the monthly distributions of MODIS active fire (AF) detections and their relationships with a fuel dryness index (FDI) based on satellite-derived weather and vegetation greenness. Monthly AF distributions showed unimodal distributions against FDI, which were described using generalized Weibull equations, fitting a total of 19 vegetation types and ecoregions analyzed in Mexico. Monthly peaks of fire activity occurred at lower FDI values (wetter fuels) in more hygrophytic ecosystems and ecoregions, such as wet tropical forests, compared to higher fire activity in higher FDI values (drier fuels) for the more arid ecosystems, such as desert shrublands. In addition, the range of fuel dryness at which most monthly fire activity occurred was wider for wetter vegetation types and regions compared to a narrower range of fuel dryness for higher monthly fire occurrence in the more arid vegetation types and ecoregions. The results from the current study contribute towards improving our understanding of the relationships between fuel dryness and fire occurrence in a variety of vegetation types and regions in Mexico. [ABSTRACT FROM AUTHOR]

Elephants have been structuring woodlands along the Chobe Riverfront, changing them to shrublands. Along with the loss of woodland cover, impala populations seem to be increasing along the Chobe Riverfront. We tested the prediction that elephant-induced loss in woodland cover has increased preferred habitats for impalas along the Chobe Riverfront. We found that home range sizes of impala breeding herds did not differ among seasons at all spatial scales. Impala breeding herds were more selective at broader spatial scales than finer spatial scales in all seasons. Impala breeding herds selected shrublands more than the other habitats throughout the year. The Baikiaea woodland and the riparian woodland were the most avoided habitats, with mixed combretum shrublands and relationships between habitat selectivity and group size, inter-herd encounter and habitat fidelity. The results of our study suggest that impala breeding herds used elephant-transformed habitats throughout the year. Season did not influence habitat selectivity, suggesting that the preferred habitat had higher required resources relative to the other habitats throughout the year. [ABSTRACT FROM AUTHOR]

Through their rooting profiles and water demands, plants affect the distribution of water in the soil profile. Simultaneously, soil water content controls plant development and interactions within and between plant communities. These plant-soil water feedbacks might vary across plant communities with different rooting depths and species composition. In semiarid environments, understanding these differences will be essential to predict how ecosystems will respond to drought, which may become more frequent and severe with climate change. In this study, we tested how plant-soil water feedbacks responded to drought in two contrasting ecosystem types—grassland and shrubland—in the coastal foothills of southern California. During years 5–8 of an ongoing precipitation manipulation experiment, we measured changes in plant communities and soil moisture up to 2 m depth. We observed different water use patterns in grassland and shrubland communities with distinct plant functional types and water use strategies. Drought treatment did not affect perennial, deep-rooted shrubs because they could access deep soil water pools. However, mid-rooted shrubs were sensitive to drought and experienced decreased productivity and die-off. As a result, water content actually increased with drought at soil depths from 50–150 cm. In grassland, biomass production by annual species, including annual grasses and forbs, declined with drought, resulting in lower water uptake from the surface soil layer. An opportunistic "live fast, die young" life strategy allowed these species to recover quickly once water availability increased. Our results show how drought interacts with plant community composition to affect the soil water balance of semiarid ecosystems, information that could be integrated into global scale models. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a very important variable in the global water cycle, carbon cycle, and energy cycle. However, there are still some uncertainties in existing ET products. Therefore, this paper evaluates the uncertainty of three widely used global ET products (ERA5-Land, GLDAS-Noah, and MERRA-2) based on the three-cornered hat (TCH) method, and generates a new ET product based on this. The new product is a long-series global monthly ET dataset with a spatial resolution of 0.25° × 0.25° and a time span of 21 years. The results show that ERA5-Land (8.46 mm/month) has the lowest uncertainty among the three ET products, followed by GLDAS-Noah (8.81 mm/month) and MERRA-2 (11.78 mm/month). The new product (TCH) captures ET trends in different regions as well as validating against in situ flux observations, and it exhibits better performance than the re-analysis dataset (ERA5-Land) in vegetation classifications such as evergreen needle-leaf forest, grassland, open shrubland, savanna, and woody savanna. The linear trend analysis of the new product shows a significant decreasing trend in south-eastern South America and southwestern parts of Africa, and an increasing trend in almost all other regions, including eastern North America, north-eastern South America, western Europe, north-central Africa, southern Asia, and south-eastern Oceania. [ABSTRACT FROM AUTHOR]

Vegetation of the Northern Hemisphere plays a vital role in global ecosystems and the carbon cycle. Variations in precipitation profoundly affect vegetation productivity, plant growth, and species communities. Precipitation frequency directly controls soil moisture availability, which has an impact on the vegetation carbon sink. However, it is unclear how precipitation frequency affects the vegetation productivity of different land cover types in different seasons. In this study, the sensitivities of the gross primary production (GPP) of six vegetation types (forest, cropland, grassland, shrubland, tundra and barren land) in response to the frequency of five categories of precipitation (trace: 0.1–5 mm/day, small: 5–10 mm/day, moderate: 10–15 mm/day, heavy: 15–20 mm/day, and very heavy: >20 mm/day) were analyzed based on the XGBoost model. The results showed that, between 1982 and 2015, precipitation frequency declined in most land cover types but increased significantly in the pan-Arctic. Differences in the sensitivity to precipitation frequency were observed between seasons and precipitation categories in northern latitudes. The GPP values of forest and barren land vegetation were less sensitive to precipitation frequency than grassland, shrubland and tundra. This may be related to different vegetation community structures and underlying surfaces and gradually increasing drought resistance capability. The sensitivity to precipitation frequency declined for moderate and heavy precipitation in cropland, but it increased in winter. As the frequency of trace precipitation diminishes in winter, the sensitivity of each vegetation type reduces by an average of 0.03%/decade. Conversely, the sensitivities to small and moderate rain increase by 0.01%/decade and 0.02%/decade, respectively, for ecosystems such as cultivated land, forests, and shrubs. However, shrubs and tundra exhibit distinct behaviors, where shifts in precipitation frequency align directly with trends in sensitivity. These results show that the frequency of precipitation significantly affects vegetation productivity and has different sensitivities, and vegetation shows different feedback mechanisms in the face of environmental changes. [ABSTRACT FROM AUTHOR]

Aim: Earth observation‐based estimates of land–atmosphere exchange of carbon are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. Temperature, soil water content and gross primary production are the main drivers of ecosystem respiration (Reco), and the main aims of this study are to develop an Reco model driven by long‐term global‐scale Earth observations and to study Reco spatiotemporal dynamics 1982–2015. Location: Global scale. Time Period: 1982–2015. Major Taxa Studied: Terrestrial ecosystems. Methods: We parameterized and applied a global Reco model for 1982–2015 using novel Earth observation‐based data. We studied the relationships between Reco measured at field sites globally and land surface temperature, gross primary production and soil water content. Trends 1982–2015 were quantified, and the contributions from terrestrial regions to the spatiotemporal variability were evaluated. Results: The Reco model (LGS‐Reco) captured the between‐site and intra‐ and interannual variability in field‐observed Reco and soil respiration well in comparison with other Earth observation‐based products. The global annual Reco was on average 105.6 ± 2.3 Pg C for 1982–2015, which is close to 105 Pg C according to residuals of the carbon exchange processes within the global carbon budgets. The trend in global terrestrial Reco 1982–2015 was 0.19 ± 0.02 Pg C y−1, with the strongest positive trends found in cropland areas, whereas negative trends were primarily observed for savannah/shrublands of Southern Africa and South America. Trends were especially strong during the eighties and nineties, but substantially smaller 1998–2015. Main Conclusions: The LGS‐Reco model revealed a substantial increase in global Reco during recent decades. However, the growth rates of global Reco were slower during 1998–2015, partially explaining the reduced growth rates of atmospheric CO2 during this period. The LGR‐Reco product may be an essential source for studying carbon sources and sinks and functioning of the Earth system. [ABSTRACT FROM AUTHOR]

This study endeavors to produce a comprehensive land cover map for Morocco, addressing the absence of such a detailed map in the country. Our research encompasses ecological and climatic aspects specific to Morocco, while the methods used can be adapted to various regions and countries, considering their unique climatic conditions and land cover types. A combination of MODIS and Landsat datasets was employed to create a 5 km resolution Land Use and Land Cover (LULC) map for the entire nation. The process involved the aggregation and advanced processing of these datasets using surface processes algorithms. The resulting LULC map is the first of its kind for Morocco, shedding light on land cover distribution nationwide. It shows that approximately 13.5% of the country is covered by forests, predominantly in the Atlas and Rif mountains, Rabat–Sale, and the southern regions. Grasslands occupy over 16% of the study area, mainly in the north-east and west. Urban areas, including major cities like Casablanca, Rabat, and Marrakech, span nearly 3400 km². Moreover, large areas of shrublands and bare lands are evident across the country, while agricultural lands account for almost 20% of the national territory, mainly in the interior plains and north-western Atlantic coast. This study forms a crucial basis for ecological and climatic research in Morocco and serves as a valuable reference for various disciplines such as agriculture, natural resource management, and climate modeling. The mapping of biophysical parameters for each land cover class is a key feature of our research, and these parameters will be instrumental in a subsequent study examining the impact of urban development on surface climate in Morocco. Overall, our study underscores the importance of understanding biophysical parameters in addressing environmental and societal challenges. [ABSTRACT FROM AUTHOR]

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m2 in the YRB and increased at rates of 4.64 g C/m2/a1 (R2 = 0.86, p < 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts. [ABSTRACT FROM AUTHOR]

Two new species and two new subspecies of the satyrine butterfly genus Punargentus Heimlich, P. atusparia sp. n., P. heimlichi sp. n., P. blanchardi libertas ssp. n. and P. atusparia yupania ssp. n., are described from north-central Peru (Ancash, Huánuco and La Libertad). The affinities of the new taxa are evaluated based on molecular data obtained using target enrichment and COI barcoding. Punargentus is fully supported and consists of two branches, the P. lamna clade with four species and P. blanchardi clade with three species, two of which are described here. Species in the P. blanchardi clade occur in Andean shrubland, at altitudes between 2800-3600 m, whereas those in the P. lamna clade occur in puna grassland at 3600-4600 m, with occasional overlaps. Based on their habitat preferences, none of the newly described species are in need of immediate conservation measures. The genus Etcheverrius Herrera is reinstated, with E. chiliensis (Guérin-Méneville) and E. tandilensis (Köhler), comb. n. [ABSTRACT FROM AUTHOR]

Simple Summary: Rockrose (Cistus laurifolius L.) is a pyrophyte species with allelopathic effects that colonizes acid forest soils in the Mediterranean basin. Its continuous spread contributes to increasing wildfire risk, even more so in the current context of forecasts of increasingly intense and prolonged heat waves and droughts. Its mechanized collection and transformation into biofuels contributes to climate change mitigation, the economic development of rural areas and wildfire risk reduction. Biomass storage is an essential requirement in the supply chain of bio-refineries and biomass plants. This research aims to evaluate the influence of long-term outdoor storage (1 year) of baled rockrose shrub biomass on the quality of biofuels (30 mm milled material and pellets), on pre-treatment processes and on combustion emissions in an industrial boiler. After storage, no significant differences were observed in the biomass pre-treatment processes or in the emissions in an industrial combustion boiler. Biomass weight loss was 12% after storage. Some quality biofuels parameters improved, with the reduction in ash content being the most prominent aspect, which allowed 30 mm of milled material to be classified as I1, according to ISO 17225-9:2022, and rockrose pellets as class I3 for industrial use, according to the ISO 17225-2-2021 standard. Biomass storage is an essential requirement in the supply chain of bio-refineries and power plants. This research aims to evaluate the influence of long-term outdoor storage (1 year) of baled rockrose (Cistus laurifolius L.) shrub biomass on biofuel's quality, pre-treatment processes and on combustion emissions in an industrial boiler. The raw material was obtained from different rockrose shrublands in north central Spain. A total of 233 tWM (tones of wet matter) of biomass were used to produce biofuels (30 mm of milled biomass and Ø 8 mm pellets) in the pre-treatment pilot plants at CEDER-CIEMAT. The combustion tests were conducted in an industrial moving grate boiler with a thermal power of 50 MWth, in a 17 MWe power plant. Outdoor storage improved some biofuel quality parameters, mainly the reduction in ash content, which allowed 30 mm of milled material to be classified as class I1 (ISO 17225-9:2022) and pellets as class I3 (ISO 17225-2-2021). No significant differences were observed in the total specific mass flow and energy consumption in the pre-treatment processes. The combustion tests had similar results, with the emissions being below the limits established in the directive (EU) 2015/2193. The results obtained indicated that the 1-year outdoor storage of rockrose-baled biomass, under Mediterranean conditions, was feasible for its subsequent use as biofuel. [ABSTRACT FROM AUTHOR]

Multiyear periods (≥4 years) of extreme rainfall are increasing in frequency as climate continues to change, yet there is little understanding of how rainfall amount and heterogeneity in biophysical properties affect state changes in a sequence of wet and dry periods. Our objective was to examine the importance of rainfall periods, their legacies, and vegetation and soil properties to either the persistence of woody plants or a shift toward perennial grass dominance and a state reversal. We examined a 28‐year record of rainfall consisting of a sequence of multiyear periods (average, dry, wet, dry, average) for four ecosystem types in the Jornada Basin. We analyzed relationships between above ground net primary production (ANPP) and rainfall for three plant functional groups that characterize alternative states (perennial grasses, other herbaceous plants, dominant shrubs). A multimodel comparison was used to determine the relative importance of rainfall, soil, and vegetation properties. For perennial grasses, the greatest mean ANPP in mesquite‐ and tarbush‐dominated shrublands occurred in the wet period and in the dry period following the wet period in grasslands. Legacy effects in grasslands were asymmetric, where the lowest production was found in a dry period following an average period, and the greatest production occurred in a dry period following a wet period. For other herbaceous plants, in contrast, the greatest ANPP occurred in the wet period. Mesquite was the only dominant shrub species with a significant positive response in the wet period. Rainfall amount was a poor predictor of ANPP for each functional group when data from all periods were combined. Initial herbaceous biomass at the plant scale, patch‐scale biomass, and soil texture at the landscape scale improved the predictive relationships of ANPP compared with rainfall alone. Under future climate, perennial grass production is expected to benefit the most from wet periods compared with other functional groups with continued high grass production in subsequent dry periods that can shift (desertified) shrublands toward grasslands. The continued dominance by shrubs will depend on the effects that rainfall has on perennial grasses and the sequence of high‐ and low‐rainfall periods rather than the direct effects of rainfall on shrub production. [ABSTRACT FROM AUTHOR]

In this study, we conducted a global assessment of the sensitivity of vegetation greenness (VGS) to precipitation and to the estimated Lagrangian precipitation time series of oceanic (PLO) and terrestrial (PLT) origin. The study was carried out for terrestrial ecosystems consisting of 9 biomes and 139 ecoregions during the period of 2001–2018. This analysis aimed to diagnose the vegetative response of vegetation to the dominant component of precipitation, which is of particular interest considering the hydroclimatic characteristics of each ecoregion, climate variability, and changes in the origin of precipitation that may occur in the context of climate change. The enhanced vegetation index (EVI) was used as an indicator of vegetation greenness. Without consideration of semi-arid and arid regions and removing the role of temperature and radiation, the results show the maximum VGS to precipitation in boreal high-latitude ecoregions that belong to boreal forest/taiga: temperate grasslands, savannas, and shrublands. Few ecoregions, mainly in the Amazon basin, show a negative sensitivity. We also found that vegetation greenness is generally more sensitive to the component that contributes the least to precipitation and is less stable throughout the year. Therefore, most vegetation greenness in Europe is sensitive to changes in PLT and less to PLO. In contrast, the boreal forest/taiga in northeast Asia and North America is more sensitive to changes in PLO. Finally, in most South American and African ecoregions, where PLT is crucial, the vegetation is more sensitive to PLO, whereas the contrast occurs in the northern and eastern ecoregions of Australia. [ABSTRACT FROM AUTHOR]

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm−2yr−1 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) hosts a variety of vegetation types ranging from broadleaved and needle-leaved forests at the lower altitudes and mesic areas to alpine grassland at the higher altitudes and xeric areas. Accurate and detailed mapping of the vegetation distribution on TP is essential for an improved understanding of climate change effects on terrestrial ecosystems. Yet, existing land cover datasets of TP are either provided at a low spatial resolution or have insufficient vegetation types to characterize certain unique TP ecosystems, such as the alpine scree. Here, we produced a 10 m resolution TP land cover map with 12 vegetation classes and 3 non-vegetation classes for the year 2022 (referred as TP_LC10-2022) by leveraging state-of-the-art remote sensing approaches including the Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and 4 machine learning models using Google Earth Engine platform. Our dataset TP_LC10-2022 achieved an overall classification accuracy of 86.5% with a Kappa coefficient of 0.854. By comparing with 4 existing global land cover products, TP_LC10-2022 showed significant improvements in terms of reflecting local-scale vertical variations in the southeast TP region. Moreover, we found that alpine scree occupied 13.99% of the TP region which was ignored in existing land cover datasets, and that shrublands occupied 4.63% of the TP region characterized by distinct forms of deciduous shrublands and evergreen shrublands largely determined by topography and missed in existing land cover datasets. Our dataset provides a solid foundation for further analyses which need accurate delineation of these unique vegetation types in TP. The TP_LC10-2022 and the sample dataset are freely available at https://doi.org/10.5281/zenodo.8228112 (Huang et al., 2023b) and https://doi.org/10.5281/zenodo.8227942 (Huang et al., 2023a) respectively. Additionally, the classification map can be viewed through https://cold-classifier.users.earthengine.app/view/tplc10-2022. [ABSTRACT FROM AUTHOR]

Aim: Studies investigating the determinants of plant invasions rarely examine multiple factors and often only focus on the role played by native plant species richness. By contrast, we explored how vegetation structure, landscape features and climate shape non‐native plant invasions across New Zealand in mānuka and kānuka shrublands. Location: New Zealand. Method: We based our analysis on 247 permanent 20 × 20‐m plots distributed across New Zealand surveyed between 2009 and 2014. We calculated native plant species richness and cumulative cover at ground, understorey and canopy tiers. We examined non‐native species richness and mean species ground cover in relation to vegetation structure (native richness and cumulative cover), landscape features (proportion of adjacent anthropogenic land cover, distance to nearest road or river) and climate. We used generalized additive models (GAM) to assess which variables had greatest importance in determining non‐native richness and mean ground cover and whether these variables had a similar effect on native species in the ground tier. Results: A positive relationship between native and non‐native plant species richness was not due to their similar responses to the variables examined in this study. Higher native canopy richness resulted in lower non‐native richness and mean ground cover, whereas higher native ground richness was associated with higher native canopy richness. Non‐native richness and mean ground cover increased with the proportion of adjacent anthropogenic land cover, whereas for native richness and mean ground cover, this relationship was negative. Non‐native richness increased in drier areas, while native richness was more influenced by temperature. Main Conclusions: Adjacent anthropogenic land cover seems to not only facilitate non‐native species arrival by being a source of propagules but also aids their establishment as a result of fragmentation. Our results highlight the importance of examining both cover and richness in different vegetation tiers to better understand non‐native plant invasions. [ABSTRACT FROM AUTHOR]

We estimated the amount of carbon (C) stored in terrestrial ecosystems of the Chilean Patagonia and the proportion within protected areas. We used existing public databases that provide information on C stocks in biomass and soils. Data were analysed by ecosystem and forest type in the case of native forests. Our results show that some ecosystems have been more extensively studied both for their stocks in biomass and soils (e.g. forests) compared with others (e.g. shrublands). Forests and peatlands store the largest amount of C because of their large stocks per hectare and the large area they cover. The total amount of C stored per unit area varies from 261.7 to 432.8 Mg C ha−1, depending on the published value used for soil organic C stocks in peatlands, highlighting the need to have more precise estimates of the C stored in this and other ecosystems. The mean stock in national parks (508 Mg C ha−1) is almost twice the amount stored in undisturbed forests in the Amazon. State and private protected areas contain 58.9% and 2.1% of the C stock, respectively, playing a key role in protecting ecosystems in this once pristine area. [ABSTRACT FROM AUTHOR]

Protected Areas (PAs) play a crucial role in the conservation and appreciation of the biological, ecological and socio-cultural elements of the landscape. These areas, in line with their creation objectives, provide various environmental services such as watershed protection, landscape and biodiversity preservation, and regulation of ecosystem processes. Additionally, they offer opportunities for research, education, and environmental training. Researchers from the Facultad de Agronomía, UNLPam are currently conducting two projects related to the theme of fire in Parque Nacional Lihué Calel, focusing on the impact of recurrent wildfires and the assessment of prescribed burns. Undertaking such investigations within a national park creates an interdisciplinary and interinstitutional working space, addressing various topics with the aim of transforming gathered information into practical management tools. Collaboration with PA technicians is essential to achieve the proposed objectives and serves as the link between information generation and effective implementation. [ABSTRACT FROM AUTHOR]

Land cover change is one of the major problems facing the world, and as human needs increase, so the inappropriate use of land, climate change, precipitation variability, temperature rise, and the frequency of extreme natural disasters are the primary factors influencing land cover change. We investigate how the land cover in Khar Yamaat Nature Reserve and its buffer zone changed between 2014 and 2019. Using Landsat satellite images between September 2014 and September 2019, the land cover change was classified into three ways using the pixel based supervised classification method: Maximum likelihood classification, K-Nearest neighbor, and Minimum distance classifier. Based on common plant species and environmental factors, land cover categories were divided into nine categories: mountain steppe, dry steppe, shrubland, degraded area, agriculture land, sand, lake, river, and meadow. To detect land cover changes, the "Maximum likelihood classification" that accurately depicts the spatial distribution of each land cover class and has a high statistical significance was chosen. During the six-year period from 2014 to 2019, the area of dry steppes decreased by 42.232 hectares (42.8%), mountain steppes 3.797 hectares (3.84%), rivers 1.000 hectares (1.1%), meadows 1.581 hectares (1.6%), and agriculture 723 hectares (0.7%), while degraded area increased by 39.480 hectares. For 2014 and 2019, the overall accuracy was 95.96 (k=0.94) and 93.39 (k=0.93). [ABSTRACT FROM AUTHOR]

The lack of information regarding biodiversity status hampers designing and implementing conservation strategies and achieving future targets. Northern Pakistan consists of a unique ecoregion mosaic which supports a myriad of environmental niches for anuran diversity in comparison to the deserts and xeric shrublands throughout the rest of the country. In order to study the niche suitability, species overlap and distribution patterns in Pakistan, we collected observational data for nine anuran species across several distinct ecoregions by surveying 87 randomly selected locations from 2016 to 2018 in Rawalpindi District and Islamabad Capital Territory. Our model showed that the precipitation of the warmest and coldest quarter, distance to rivers and vegetation were the greatest drivers of anuran distribution, expectedly indicating that the presence of humid forests and proximity to waterways greatly influences the habitable range of anurans in Pakistan. Sympatric overlap between species occurred at significantly higher density in tropical and subtropical coniferous forests than in other ecoregion types. We found species such as Minervarya spp., Hoplobatrachus tigerinus and Euphlyctis spp. preferred the lowlands in proximal, central and southern parts of the study area proximal to urban settlements, with little vegetation and higher average temperatures. Duttaphrynus bengalensis and D. stomaticus had scattered distributions throughout the study area with no clear preference for elevation. Sphaerotheca pashchima was patchily distributed in the midwestern extent of the study area as well as the foothills to the north. Microhyla nilphamariensis was widely distributed throughout the study area with a preference for both lowlands and montane terrain. Endemic frogs (Nanorana vicina and Allopaa hazarensis) were observed only in locations with higher elevations, higher density of streams and lower average temperatures as compared to the other seven species sampled. It is recommended to provide legal protection to amphibians of Pakistan, especially endemic species, through revision in the existing wildlife laws. We suggest studying the effectiveness of existing amphibian tunnels and corridors or designing new ones tailored to the needs of our species to prevent their local extinction due to ongoing or proposed urban development which might affect their dispersal and colonization. [ABSTRACT FROM AUTHOR]

Climate change forecasts indicate that the frequency and intensity of extreme climate events will increase in the future; these changes will have important effects on ecosystem stability and function. An important arid region of the world, Central Asia has ecosystems that are extremely vulnerable to extreme weather events. However, few studies have investigated the resistance and resilience of this region's ecosystems to extreme weather events. In this study, first, the extreme drought/wet threshold was calculated based on the 113-year (1901–2013) standardized precipitation–evapotranspiration index (SPEI); second, moderate resolution imaging spectroradiometer (MODIS) remote sensing data were applied to calculate ecosystem water use efficiency (WUE) and quantify ecosystem resistance and resilience after different extreme climate events; and finally, differences in the changes of various ecosystem types before and after climate events were assessed. The results showed the following: (1) The average SPEI was 0.073, and the thresholds of extreme wetness and drought were 0.91 and −0.67, respectively. Central Asia experienced extreme wet periods in 2002 and 2003 and a drought period in 2008. (2) Suitable wetness levels can increase the resistance of an ecosystem; however, continuous wetness reduces ecosystem resistance, as does drought. Wet areas had strong resilience after wet events, and arid areas had strong resilience after drought events. (3) During both wet and drought years, the transition between shrubland and grassland caused changes in ecosystem resistance and resilience. These findings are important for understanding the impact of future climate change on ecosystem stability. [ABSTRACT FROM AUTHOR]

Over the past decades, benefiting from the development of computing capacity and the free access to Landsat and Sentinel imagery, several fine-resolution global land cover (GLC) products (with a resolution of 10 m or 30 m) have been developed (GlobeLand30, FROM-GLC30, GLC_FCS30, FROM-GLC10, European Space Agency (ESA) WorldCover and ESRI Land Cover). However, there is still a lack of consistency analysis or comprehensive accuracy assessment using a common validation dataset for these GLC products. In this study, a novel stratified random sampling GLC validation dataset (SRS_Val) containing 79,112 validation samples was developed using a visual interpretation method, significantly increasing the number of samples of heterogeneous regions and rare land-cover types. Then, we quantitatively assessed the accuracy of these six GLC products using the developed SRS_Val dataset at global and regional scales. The results reveal that ESA WorldCover achieved the highest overall accuracy (of 70.54% ± 9%) among the global 10 m land cover products, followed by FROM-GLC10 (68.95% ± 8%) and ESRI Land Cover (58.90% ± 7%) and that GLC_FCS30 had the best overall accuracy (of 72.55% ± 9%) among the global 30 m land cover datasets, followed by GlobeLand30 (69.96% ± 9%) and FROM-GLC30 (66.30% ± 8%). The mapping accuracy of the GLC products decreased significantly with the increased heterogeneity of landscapes, and all GLC products had poor mapping accuracies in countries with heterogeneous landscapes, such as some countries in Central and Southern Africa. Finally, we investigated the consistency of six GLC products from the perspective of area distributions and spatial patterns. It was found that the area consistencies between the five GLC products (except ESRI Land Cover) were greater than 85% and that the six GLC products showed large discrepancies in area consistency for grassland, shrubland, wetlands and bare land. In terms of spatial patterns, the totally inconsistent pixel proportions of the 10 m and 30 m GLC products were 23.58% and 14.12%, respectively, and these inconsistent pixels were mainly distributed in transition zones, complex terrains regions, heterogeneous landscapes, or mixed land-cover types. Therefore, the SRS_Val dataset well supports the quantitative evaluation of fine-resolution GLC products, and the assessment results provide users with quantitative metrics to select GLC products suitable for their needs. [ABSTRACT FROM AUTHOR]

Kotawaringin Barat is a high-risk area for forest and land fires; a total of 564.13 km2 of forest land was burned from 2015 to 2022, the majority of which spread to peatlands. The goal of this contribution is to use the information value method (IVM) to construct forest and land fire spatial susceptibility maps for the Kotawaringin Barat regency. MODIS hotspots from 2016 to 2020 were used as the dependent variable, with six independent variables included in the modeling. According to the data, there were 925 hotspots detected in Kotawaringin Barat between 2016 and 2020. The areas closest to rivers and roads are more susceptible to forest and land fires, while the areas closest to settlements are safer. Flat slopes have an IVM of 0.697, while peatlands have an IVM of 0.667, making them the most susceptible to forest and land fires. Furthermore, the most susceptive land covers are swamps (IVM = 1.071) and shrublands (IVM = 0.024). According to the IVM model of susceptibility mapping, Kotawaringin Barat is categorized as very high (18.32%) and high (27.97%) risk. About 33.57% of the study area is classified as moderately susceptible, while the remaining 20.14% is classified as low risk. The accuracy of the IVM for forest and land fires is 66.87% (AUC), indicating that the model can be used for susceptibility assessments particularly for very high to high susceptibility areas. [ABSTRACT FROM AUTHOR]

(1) Land surface models require inputs of temperature and moisture variables to generate predictions of gross primary production (GPP). Differences between leaf and air temperature vary temporally and spatially and may be especially pronounced under conditions of low soil moisture availability. The Sentinel-3 satellite mission offers estimates of the land surface temperature (LST), which for vegetated pixels can be adopted as the canopy temperature. Could remotely sensed estimates of LST offer a parsimonious input to models by combining information on leaf temperature and hydration? (2) Using a light use efficiency model that requires only a handful of input variables, we generated GPP simulations for comparison with eddy-covariance inferred estimates available from flux sites within the Integrated Carbon Observation System. Remotely sensed LST and greenness data were input from Sentinel-3. Gridded air temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. We chose the years 2018–2019 to exploit the natural experiment of a pronounced European drought. (3) Simulated GPP showed good agreement with flux-derived estimates. During dry conditions, simulations forced with LST performed better than those with air temperature for shrubland, grassland and savanna sites. (4) This study advances the prospect for a global GPP monitoring system that will rely primarily on remotely sensed inputs. [ABSTRACT FROM AUTHOR]

Despite the fact that gross primary productivity (GPP) and water use efficiency (WUE) have been widely used as indicators to evaluate the water-carbon cycle, uncertainties exist in the patterns of GPP and WUE responses to climate variability along different aridity gradients. In this study, the aridity index was used to divide China into four arid-humid zones. The spatiotemporal variability of multiple vegetation types GPP and WUE in response to climate change under different arid-humid zones were investigated based on remote sensing data. The results indicated that the increasing trend of WUE in the four arid-humid zones of China was less pronounced than GPP from 2001 to 2021. The GPP value decreased gradually from the humid to the arid zone, and the WUE value in the arid zone was slightly higher than in the semi-arid zone. The GPP of all vegetation types in China showed a tendency to increase, while shrubland and wetland WUE tended to decrease. The major vegetation types (e.g., forest, cropland and grassland) in each aridity gradient contributed to the changes in local GPP and WUE. However, in individual arid-humid zones, wetland and shrubland also exhibited high GPP and WUE values that were not inferior to forest and cropland. Temperature and precipitation were the main climatic factors responsible for the increase in vegetation GPP in different aridity gradients, with a higher positive correlation for temperature than precipitation. WUE showed a distinct positive and negative correlation with the thermal factors (temperature and net radiation) and the moisture factors (precipitation and relative humidity); this pattern was more pronounced in the humid and semi-humid zones. Net radiation and precipitation may be the main climatic factors causing a slight upward trend in WUE across the arid-humid zones, while the decrease in shrubland and wetland WUE may be related to relative humidity and precipitation. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) in shrublands is an important component of global carbon cycling. However, there is a dearth of large-scale systematic observations of SOC stocks at different soil depths, and it remains uncertain whether and how the relative importance of biotic and abiotic variables in regulating SOC stocks changes with soil depth. Here, we quantified large-scale patterns and controlling factors of SOC storage per area (SOCD, kg m−2) for both topsoils (0–30 cm) and subsoils (30–100 cm) by taking full advantage of a consistent stratified random sampling study of one-meter soil profiles across 1211 sites in Chinese shrublands. We found that subsoils stored about 53.30% of total SOCD, falling into the range of previously reported values for terrestrial ecosystems. SoilGrids250m model-derived assessments overestimated SOCD by 13.72 and 65.49% for topsoils and subsoils, respectively. The effects of climate means and seasonality on SOCD were equally strong in both topsoils and subsoils. The predominant effects of edaphic properties on SOCD were more robust in subsoils than in topsoils. Belowground biomass of shrublands was the only significant predictor of topsoil SOCD, but it did not predict subsoil SOCD accurately. These findings have refined our understanding of the pivotal role of shrublands in SOC storage and sequestration potential and could serve as an ecologically valuable baseline for large-scale improvement and validation of depth-dependent SOC dynamics for multilayer SOC modules in Earth Systems Models. [ABSTRACT FROM AUTHOR]

Western juniper (Juniperus occidentalis Hook.) woodlands have persisted for millennia in semiarid parts of the northern Great Basin, USA, providing critical habitat for plant and animal species. Historical records suggest that the establishment of western juniper is strongly associated with regional climatic variability. For example, the abundance of western juniper pollen and macrofossils measured in lake sediment cores increased rapidly in the mid-1500s, concurrent with a regional increase in winter precipitation. However, little is known about how climatic factors interact with landscape structure to control the spatial distribution of western juniper at fine scales and at lower treelines. We used tree rings to reconstruct a spatially distributed history of establishment for 421 western juniper trees across 130 ha on Horse Ridge in central Oregon. Establishment occurred between 845 and 1961 CE, but most trees established after the mid-1550s. The pronounced sixteenth century pulse of establishment represents a transition from more open wooded shrublands to persistent woodlands and coincides with an increase in cool-season moisture and generally cool summer temperatures. Ancient trees that established before this were limited to certain microsites, suggesting that local topoedaphic conditions influenced juniper woodland demography and distributions, although we could not identify consistent environmental drivers. In the future, warmer and drier growing season conditions and a potential increase in wildfire activity may broadly limit western juniper recruitment and its distribution across the region, but at finer scales landscape features that buffer climate change impacts or provide fire safe niches may serve as refugia. [ABSTRACT FROM AUTHOR]

Abiotic factors are usually considered key drivers of species distribution at macro scales, while biotic interactions are mostly used at local scales. A few studies have explored the role of biotic interactions at macro scales, but all considered a limited number of species and obligate interactions. We examine the role of biotic interactions in large‐scale SDMs by testing two main hypotheses: (1) biotic factors in SDMs can have an important role at continental scale; (2) the inclusion of biotic factors in large‐scale SDMs is important also for generalist species. We used a maximum entropy algorithm to model the distribution of 177 bat species in Africa calibrating two SDMs for each species: one considering only abiotic variables (noBIO‐SDMs) and the other (BIO‐SDMs) including also biotic variables (trophic resource richness). We focused the interpretation of our results on variable importance and response curves. For each species, we also compared the potential distribution measuring the percentage of change between the two models in each pixel of the study area. All models gave AUC >0.7, with values on average higher in BIO‐SDMs compared to noBIO‐SDMs. Trophic resources showed an importance overall higher level than all abiotic predictors in most of the species (~68%), including generalist species. Response curves were highly interpretable in all models, confirming the ecological reliability of our models. Model comparison between the two models showed a change in potential distribution for more than 80% of the species, particularly in tropical forests and shrublands. Our results highlight the importance of considering biotic interactions in SDMs at macro scales. We demonstrated that a generic biotic proxy can be important for modeling species distribution when species‐specific data are not available, but we envision that a multi‐scale analysis combined with a better knowledge of the species might provide a better understanding of the role of biotic interactions. [ABSTRACT FROM AUTHOR]

Fire is a key disturbance affecting plant biodiversity patterns and evolution. Although a wide range of studies have shown important impacts of fire on vegetation, most have focused on taxonomic diversity, with less emphasis on other aspects of biodiversity, such as functional and phylogenetic diversity. Therefore, we assessed the recovery of biodiversity facets across different times since the last fire in semiarid shrublands in Northeast Iran. We quantified changes in plant biodiversity facets, including taxonomic, functional, and phylogenetic diversity, and the diversity of seven functional traits in five ecologically comparable sites that have experienced wildfire disturbances at short-term (1 and 4 year sites) and long-term (10 and 20 year sites) intervals, in com- parison to an unburnt site. Our results showed significant changes in all biodiversity facets related to the year since the last fire, with a significant increase in biodiversity and diversity of functional traits under long-term rather than short-term conditions, and in comparison to the unburned site. We conclude that wildfire influences the presence of plant species with distant functional and evolutionary relatedness and causes an increase in plant species and diversity of functional traits de- pending on time intervals. Therefore, wildfire can promote positive effects on the recovery of bio- diversity aspects and the evolution of vegetation in semiarid shrublands. [ABSTRACT FROM AUTHOR]

This paper reports two experimental fires conducted at field-scale in Corsica, across a particular mountain shrubland. The orientation of the experimental plots was chosen in such a way that the wind was aligned along the main slope direction in order to obtain a high intensity fire. The first objective was to study the high intensity fire behavior by evaluating the propagation conditions related to its speed and intensity, as well as the geometry of the fire front and its impact on different targets. Therefore, an experimental protocol was designed to determine the properties of the fire spread using UAV cameras and its impact using heat flux gauges. Another objective was to study these experiments numerically using a fully physical fire model, namely FireStar3D. Numerical results concerning the fire dynamics, particularly the ROS, were also compared to other predictions of the FireStar2D model. The comparison with experimental measurements showed the robustness of the 3D approach with a maximum difference of 5.2% for the head fire ROS. The fire intensities obtained revealed that these experiments are representative of high intensity fires, which are very difficult to control in the case of real wildfires. Other parameters investigated numerically (flame geometry and heat fluxes) were also in fairly good agreement with the experimental measurements and confirm the capacity of FireStar3D to predict surface fires of high intensity. [ABSTRACT FROM AUTHOR]

To reveal the spatio-temporal variation characteristics of vegetation net primary productivity(NPP)in the Haihe River Basin and the driving mechanisms of its spatio-temporal heterogeneity, we studied the spatio-temporal pattern evolution characteristics of vegetation NPP in the Haihe River Basin from 2000 to 2020 using coefficient of variation, trend analysis, and Hurst index, based on MOD17A3HGF dataset. Driving factors from natural and human aspects and their influence were also quantitatively explored for vegetation NPP variations using correlation analysis and the Geodetector model. The results were as follows:(1)The temporal variation of vegetation NPP in the Haihe River Basin presented a significant growth trend form 2000 to 2020, with a rate of 1.73 Tg C·a-1, and the annual average NPP was 326.75 g C·m-2·a-1; the average NPP of coniferous forests, broad-leaved forest, shrub, grassland and farmland were 313.59, 385.28, 353.03, 320.12, 295.22 g C·m-2·a-1.(2)In terms of spatial distribution, the high-value areas of vegetation NPP were mainly located in the northwest mountains, and the low-value areas were mainly located in the southeast plain; the overall situation of vegetation NPP was relatively stable, with an average coefficient of variation of 0.17; the future trend of NPP was anti-continuity and might decrease.(3)The correlation analysis showed that vegetation NPP was negatively correlated with temperature, and positively correlated with precipitation that was the main climate factor; NPP showed an “increase-decrease” trend with elevation and slope increasing; the conversion of farmland to grassland resulted in the largest increment of NPP at 732.22×10-3 Tg C.(4)From 2000 to 2020, the average influence value of different drivers was 0.2, and the order was precipitation > elevation > slope > humidity > temperature > sunshine duration > land use > wind speed. Overall, the results indicated that vegetation NPP in the Haihe River Basin was improving during the study period; productivity varied among different vegetation types, with the strongest being broad-leaved forest and shrubland; the dominant factors affecting the spatial distribution of vegetation NPP were precipitation, elevation and slope, and the influence of human factors was lower than that of natural factors. The results of this study provide some scientific reference and decision basis for the treatment of ecological environment such as soil erosion and vegetation degradation in the Haihe River Basin. [ABSTRACT FROM AUTHOR]

This study aims to assess, compare, and attribute the effects due to separate and combined land use/land cover (LULC) and climate changes on hydrological processes in a tropical catchment. The Soil and Water Assessment Tool (SWAT) model is set up and calibrated for a small contributing sub-basin of the Tana River Basin (TRB) in Kenya. The model is then applied to simulate the hydrological components (i.e., streamflow (FLOW), evapotranspiration (ET), soil water (SW), and water yield (WYLD)) for different combinations of LULC and climate scenarios. Land use data generated from Land Satellite 5 Thematic Mapper (Landsat 5TM) images for two different periods (1987 and 2011) and satellite-based precipitation data from the African Rainfall Climatology version 2 (ARC2) dataset are utilized as inputs to the SWAT model. The Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R2), percent bias (PBIAS), and the ratio of root mean square error to the standard deviation (RSR) for daily streamflow were 0.73, 0.76, 3.16%, and 0.51 in calibration period, respectively, and 0.45, 0.54, 12.53%, and 0.79 in validation period, respectively, suggesting that the model performed relatively good. An analysis of the LULC data for the catchment showed that there was an increase in agricultural, grassland, and forested land with a concomitant decrease in woodland and shrubland. Simulation results revealed that change in climate had a more significant effect on the simulated parameters than the change in LULC. It is shown that changes in LULC only had very minor effects in the simulated parameters. The monthly mean FLOW and WYLD decreased by 0.02% and 0.11%, respectively, while ET and SW increased by a monthly mean of 0.2% and 2.2%. Varying the catchment climate and holding the land use constant reduced FLOW, ET, SW, and WYLD by an average monthly mean of 43.2%, 21%, 13%, and 70%, respectively, indicating that climate changes have more significant effects on the catchment hydrological processes than changes in LULC. Thus, it is necessary to evaluate and identify the isolated and combined effects of LULC and climatic changes when assessing impacts on the TRB's hydrological processes. [ABSTRACT FROM AUTHOR]

In this paper, the patterns of the occurrences of fire incidents over sub-Saharan Africa are studied on the basis of satellite data. Patterns for the whole sub-Saharan Africa are contrasted with those for northern sub-Saharan Africa and southern-hemisphere Africa. This paper attempts to unravel linear trends and overriding oscillations using regression and spectral techniques. It compares fire patterns for aggregated vegetation with those for specific types, which are savannahs, grasslands, shrublands, croplands, and forests, to identify key trend drivers. The underlying cyclic trends are interpreted in light of climate change and model projections. Considering sub-Saharan Africa, northern sub-Saharan Africa, and southern-hemisphere Africa, we found declining linear trends of wildfires with overriding cyclic patterns that have a period of ∼5 years, seemingly largely driven by savannahs, grasslands, and croplands. [ABSTRACT FROM AUTHOR]

Fire is an important disturbance factor in shrublands, grasslands, and savannas. It alters the habitat of a multitude of species and, under natural dynamics, is a major determinant of landscape vegetation patterns. Here, we evaluate the effects of different wildfire regimes on the abundance of sun spiders in the Cerrado-Pantanal ecotone. To study how different fire regimes affect the number of individual sun spiders, we considered the frequency of fire occurrences in the last 20 years and classified locations as high frequency or low frequency. We also classified the time of the last fire in 2020 as occurring in the first or second half of the year. In addition, we compared the number of individual sun spiders before and after fire. We found no effects of fire frequency and period when the fire occurred in 2020, but the number of individual sun spiders was higher after wildfires. Although ground-dwelling are considered fire sensitive, some can employ strategies to tolerate fire so that they are able to not only survive, but also reproduce in fire-prone landscapes. Thus, we suggest that sun spiders are resilient, can explore sites under different fire regimes, and can be considered pyrophilous species. [ABSTRACT FROM AUTHOR]

The capacity to forecast the effects of climate change on biodiversity largely relies on identifying traits capturing mechanistic relationships with the environment through standardized field experiments distributed across relevant spatial scales. The effects of short‐term experimental manipulations on local communities may overlap with regional climate gradients that have been operating during longer time periods. However, to the best of our knowledge, there are no studies simultaneously assessing such long‐term macroecological drivers with local climate manipulations.We analysed this issue with springtails (Class Collembola), one of the dominant soil fauna groups, in a standardized climate manipulation experiment conducted across six European countries encompassing broad climate gradients. We combined community data (near 20K specimens classified into 102 species) with 22 eco‐morphological traits and reconstructed their phylogenetic relationships to track the evolution of adaptations to live at different soil depths, which is key to cope with desiccation. We then applied joint species distribution models to investigate the combined effect of the regional aridity gradient with the local experimental treatment (drought and warming) over the assembly of springtail communities and tested for significant trait–environment relationships mediating their community‐level responses.Our results show (1) a convergent evolution in all three major collembolan lineages of species adapted to inhabit at different soil strata; (2) a clear signature of aridity selecting traits of more epigeic species at a biogeographical scale and (3) the association of short‐term experimental drought with traits related to more euedaphic life‐forms.The hemiedaphic condition would be the plesiomorphic state for Collembola while the adaptations for an epigeic life would have been secondarily gained. Epigeic springtails are not only more resistant to drought, but also have a higher dispersal capacity that allows them to seek more favourable micro‐habitats after experiencing drier conditions. The observed relative edaphization of the springtail communities after short‐term experimental drought may thus be a transient community response.The disparity between macroecological trends and fast community‐level responses after climate manipulations highlights the need of simultaneously assessing long‐term and short‐term drivers at broad spatial scales to adequately interpret trait–environment relationships and better forecast biodiversity responses to climate change. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

As an alpine area, surface fires significantly affect the fragile ecosystems of the Qinghai-Tibetan Plateau (QTP). However, no studies on the accuracy of any remote sensing burned area (BA) products on the QTP has not been evaluated, resulting in higher uncertainties in BA mapping. To address this, we used 56-view medium-resolution Landsat images of the QTP from 2001 to 2020 to verify the MCD64A1 and FireCCI51 products by stratified sampling. The results showed that MCD64A1 was more accurate than FireCCI51 for identifying small-scale (<2 km2) BAs within the QTP, whereas FireCCI51 was more accurate for identifying medium-scale (2–5 km2) and large-scale (>5 km2) BAs. Additionally, MCD64A1 was more accurate than FireCCI51 for identifying BAs in forests, FireCCI51 was more accurate than MCD64A1 for identifying BAs in grasslands, shrublands, and wetlands. These results can aid fire risk assessment in the region to improve ecosystem stewardship and inform further improvements in BA products. [ABSTRACT FROM AUTHOR]

Population growth and environmental shifts have elevated the pressure on land use and cover (LULC), necessitating vital management and adaptive strategies to preserve the balance between ecosystem services and human well-being in watersheds. It's pivotal to understand the implications of human-induced shifts from natural to human-dominated surroundings This study utilized Google Earth Engine (GEE) to analyze 31 years of LULC changes in Letaba watershed. Using GEE's random forest, LULC classes were mapped with 93% to 99% accuracy across four timeframes (1990, 2000, 2010, and 2021). Trends revealed declining water bodies (-4%), bare surfaces (-2%), natural forest (-3%), and grassland (-3%), while shrublands, plantations, and built-up areas increased at annual rates of 41%, 24%, and 47% respectively. This transformation reflects population-driven shifts, necessitating adaptive strategies. Given the importance of plantations for income, embracing climate-smart agriculture could ensure long-term food and environmental security, thus addressing the evolving dynamics in Letaba watershed. [ABSTRACT FROM AUTHOR]

Background and Aims Clonality is a key life-history strategy promoting on-spot persistence, space occupancy, resprouting after disturbance, and resource storage, sharing and foraging. These functions provided by clonality can be advantageous under different environmental conditions, including resource-paucity and fire-proneness, which define most mediterranean-type open ecosystems, such as southwest Australian shrublands. Studying clonality–environment links in underexplored mediterranean shrublands could therefore deepen our understanding of the role played by this essential strategy in open ecosystems globally. Methods We created a new dataset including 463 species, six traits related to clonal growth organs (CGOs; lignotubers, herbaceous and woody rhizomes, stolons, tubers, stem fragments), and edaphic predictors of soil water availability, nitrogen (N) and phosphorus (P) from 138 plots. Within two shrubland communities, we explored multivariate clonal patterns and how the diversity of CGOs, and abundance-weighted and unweighted proportions.of clonality in plots changed along with the edaphic gradients. Key Results We found clonality in 65 % of species; the most frequent were those with lignotubers (28 %) and herbaceous rhizomes (26 %). In multivariate space, plots clustered into two groups, one distinguished by sandy plots and plants with CGOs, the other by clayey plots and non-clonal species. CGO diversity did not vary along the edaphic gradients (only marginally with water availability). The abundance-weighted proportion of clonal species increased with N and decreased with P and water availability, yet these results were CGO-specific. We revealed almost no relationships for unweighted clonality. Conclusions Clonality is more widespread in shrublands than previously thought, and distinct plant communities are distinguished by specific suites (or lack) of CGOs. We show that weighting belowground traits by aboveground abundance affects the results, with implications for trait-based ecologists using abundance-weighting. We suggest unweighted approaches for belowground organs in open ecosystems until belowground abundance is quantifiable. [ABSTRACT FROM AUTHOR]

The characterization of aboveground biomass is important in forest management planning, with various objectives ranging from prevention of forest fires to restoration of burned areas, especially in fire-prone regions such as NW Spain. Although remotely sensed data have often been used to assess the recovery of standing aboveground biomass after perturbations, the data have seldom been validated in the field, and different shrub fractions have not been modelled. The main objective of the present study was to assess different vegetation parameters (cover, height, standing AGB and their fractions) in field plots established in five areas affected by wildfires between 2009 and 2016 by using Sentinel-2 spectral indices and LiDAR metrics. For this purpose, 22 sampling plots were established in 2019, and vegetation variables were measured by a combination of non-destructive measurement (cover and height) and destructive sampling (total biomass and fine samples of live and dead fractions of biomass).The structural characterization of gorse shrublands was addressed, and models of shrub cover—height, total biomass, and biomass by fraction and physiological condition—were constructed, with adjusted coefficients of determination ranging from 0.6 to 0.9. The addition of LiDAR data to optical remote sensing images improved the models. Further research should be conducted to calibrate the models in other vegetation communities. [ABSTRACT FROM AUTHOR]