Vegetation Patterns Research Articles

Dynamic simulation study of soil erosion intensity on slopes with different vegetation patterns in pisha sandstone area

The study of soil erosion is significant to soil and water conservation and ecological construction planning. CA, a dynamic modeling method, has been widely used to simulate the future changes of soil erosion intensity. In this study, three slopes with different vegetation patterns (uniform distribution, aggregated distribution, and random distribution) and a bare slope in the Baojiagou watershed in the Pisha sandstone area were taken as the study objects. Based on the soil erosion intensity grade maps of slopes with different vegetation patterns under the three periods of rainfall, the soil erosion intensity grade maps of the four slopes were simulated by combining multi-criteria evaluation (MCE) and Markov chain (CA-Markov). Through tests, the Kappa coefficients of the simulated maps of uniform distribution, random distribution, aggregated distribution and a bare slope reached 80.15%, 75.41%, 75.92% and 76.48%, respectively. The results revealed that the soil erosion types on slopes with different vegetation patterns were transferred differently, and fewer erosion areas were generally transferred into highly eroded ones, with moderately and slightly eroded areas as the main transfer flows. After simulating the grade map of soil erosion intensity on slopes with different vegetation patterns, it was found that the slopes with uniform and random vegetation distribution primary displayed the transfer from highly eroded areas to less eroded ones, and the sedimentary area was enlarged, while the slope with aggregated vegetation distribution and the bare slope mainly presented the transfer from less eroded areas to highly eroded ones. The sedimentary area was diminished. Therefore, compared with the bare slope, the slopes with uniform and random vegetation distribution exerted a better inhibitory effect on the soil erosion degree of hydraulically eroded slopes, and the inhibitory effect of aggregated vegetation distribution-type slope was the weakest. The study results provide critical information for planning and implementing soil conservation measures in the study area.

Ecological Environment Assessment and Driving Mechanism Analysis of Nagqu and Amdo Sections of Qinghai-Xizang Highway Based on Improved Remote Sensing Ecological Index

The ecological environment along the Qinghai-Xizang highway is an important part of the construction of the ecological civilization in the Xizang region, and current research generally suffers from difficulties in data acquisition, low timeliness, and failure to consider the unique "alpine saline" environmental conditions in the study area due to the unique geographical environment of the Qinghai-Xizang plateau. Based on the GEE platform and the unique geographical environment of the study area, the remote sensing ecological index (RSEI) was improved, and a new saline remote sensing ecological index (SRSEI) applicable to the alpine saline region was constructed by using principal component analysis as an ecological environment quality evaluation index. The spatial distribution pattern and temporal variation trend of ecological environment quality along the Qinghai-Xizang Highway Nagqu-Amdo section were analyzed at multiple spatial and temporal scales using the ArcGIS 10.3 platform and geographic probes, and the driving mechanisms of eight control factors, including natural and human-made, on the spatial and temporal changes in SRSEI were investigated. The results showed that:① compared with RSEI, SRSEI was more sensitive to vegetation and had a stronger discriminatory ability in areas with sparse vegetation and severe salinization, which is suitable for ecological quality evaluation in alpine saline areas. ② The spatial scale of ecological environment quality in the study area had obvious geographical differentiation, and the areas with poor ecological quality were mainly concentrated in the northern Amdo County, whereas the areas with excellent and good quality grades were mainly distributed in the central-western and southeastern Nagqu areas. On the temporal scale, the ecological environment of the study area as a whole showed an improvement trend over 32 years, and the vegetation cover in the central-western and southeastern areas increased significantly, which had a strong improvement effect on the ecological environment. The improvement area was 1 425.98 km2, accounting for 99.82%. The mean value of SRSEI was 0.49, with an overall fluctuating upward trend and an average increase of 0.015 7 a-1. ③ The land use pattern was the most driving influence factor in the change of ecological environment quality in the study area, with an average q value of 0.157 6 over multiple years, and the influence of environmental factors was low. The multi-factor interaction results showed that the ecological environment in the study area was the result of multiple factors acting together, all factors had synergistic enhancement under the interaction, the influence of human factors was gradually increasing, and the interaction of the net primary productivity (NPP) of vegetation and land use pattern was the main interactive control factor of ecological environment quality in the study area. This study can provide a theoretical basis for ecological environmental protection and sustainable development along the Nagqu to Amdo section.

Efficacy of Multiseason Sentinel-2 Imagery for Classifying and Mapping Grassland Condition

Assessing the condition of ecosystems is imperative for understanding their degree of degradation and managing their conservation. The increasing availability of remote sensing products offers unprecedented opportunities for mapping vegetation with high detail and accuracy. However, mapping complex ecosystems, like grasslands, remains challenging due to their heterogeneity in vegetation composition and structure. Furthermore, degraded ecosystems affected by invasive vegetation present different condition levels within vegetation classes, limiting the accuracy of classifications and condition assessment. Here, we evaluated the capacity of Sentinel-2 multispectral time series imagery as an input for classifying different levels of cover within a vegetation class to detect the subtle differences needed to assess the condition of degraded ecosystems. Our study was conducted in the iron-grasslands of South Australia, a perennial tussock grassland dominated by iron-grasses (Lomandra spp.) and severely affected by invasive annual grasses. We developed random forest models to discriminate classes defined by the cover of iron-grasses, wild oats (Avena barbata), and woodland (training points = 250). We tested the importance of data seasonality, spatial resolution, spectral bands, and vegetation indices. The combination of spatial, temporal, and spectral detail produced the best classification results. Random forest classifications performed best at 10 m resolution, suggesting that detailed resolution outweighs spectral detail for discriminating vegetation patterns in systems with high spatial heterogeneity. The model at 10 m resolution combining all periods and all variables (spectral bands and vegetation indices) produced a mean kappa coefficient of 56% and a mean overall accuracy of 67%. The dry season imagery and vegetation indices emerged as the most informative, suggesting that vegetation classes presented different phenological properties critical for their discrimination. Our study contributes to mapping complex ecosystems, facilitating the discrimination of different levels of condition in grasslands degraded by invasive species, and thus benefits the conservation of native grasslands and other ecosystems.

Weed Flora Composition in Upland Direct Seeded Rice in Brahmaputra River Ecotones and Edges in Majuli District, Assam

Background: The River Brahmaputra is one of the largest braided rivers in the world, where the island and bars (“Chapories”) are formed almost annually by erosion and deposition, which are good agricultural lands, as most of the depositions are extremely fertile and possess ready water source in close proximity. With an aim to depict the weed vegetation pattern in upland direct seeded rice ecosystem in such an ecotone zone, Chapories of Majuli district were surveyed during 2021 and 2022. Methods: One square meter quadrates were plotted in between 20 to 65 days after sowing (before flowering) of rice to study the weed status in the critical period of crop weed competition in four locations. Result: Study recorded altogether 55 species of weedy nature in four locations with as many as 39 broadleaved species belonging to 25 families, 3 sedges and 13 grasses, which represented a great diversity with several unusual species unique to chapori areas. Isolation of location - 4 by water channel has contributed in increasing dissimilarity with respect to weed flora, though river water might have had great role in distribution of weeds especially the stubble borne perennial species. The cumulative IVI of grass species was also 30.4 per cent higher in this isolated location than the non-grass species, unlike the locations that had land connectivity with shore areas.

A Comprehensive Analysis of Vegetation Dynamics and Their Response to Climate Change in the Loess Plateau: Insight from Long-Term kernel Normalized Difference Vegetation Index Data

The Loess Plateau (LP) is a typical climate-sensitive and ecologically delicate area in China. Clarifying the vegetation–climate interaction in the LP over 40+ years, particularly pre- and post-Grain to Green Program (GTGP) implementation, is crucial for addressing potential climate threats and achieving regional ecological sustainability. Utilizing the kernel Normalized Difference Vegetation Index (kNDVI) and key climatic variables (precipitation (PRE), air temperature (TEM), and solar radiation (SR)) between 1982 and 2022, we performed an extensive examination of vegetation patterns and their reaction to changes in climate using various statistical methods. Our findings highlight a considerable and widespread greening on the LP from 1982 to 2022, evidenced by a kNDVI slope of 0.0020 yr−1 (p < 0.001) and a 90.9% significantly increased greened area. The GTGP expedited this greening process, with the kNDVI slope increasing from 0.0009 yr−1 to 0.0036 yr−1 and the significantly greened area expanding from 39.1% to 84.0%. Over the past 40 years, the LP experienced significant warming (p < 0.001), slight humidification, and a marginal decrease in SR. Post-GTGP implementation, the warming rate decelerated, while PRE and SR growth rates slightly accelerated. Since the hurst index exceeded 0.5, most of the vegetated area of the LP is expected to be greening, warming, and humidification in the future. In the long term, 75% of the LP vegetated area significantly benefited from the increase in PRE, especially in relatively dry environments. In the LP, 61% of vegetated areas showed a positive correlation between kNDVI and TEM, while 4.9% exhibited a significant negative correlation, mainly in arid zones. SR promoted vegetation growth in 23% of the vegetated area, mostly in the eastern LP. The GTGP enhanced the sensitivity of vegetation to PRE, increasing the area corresponding to a significant positive correlation from 15.3% to 59.9%. Overall, PRE has emerged as the dominant climate driver for the vegetation dynamics of the LP, followed by TEM and SR. These insights contribute to a comprehensive understanding of the climate-impact-related vegetation response mechanisms, providing guidance for efforts toward regional sustainable ecological development amid the changing climate.

A case study on the early stage of Pinus nigra invasion and its impact on species composition and pattern in Pannonic sand grassland

Alien woody species are successful invaders, frequently used for afforestation in regions like semi-arid lands. Shrubs and trees create important microhabitats in arid areas. Understorey vegetation in these habitats has unique species composition and coexistence. However, the impact of solitary woody species on understorey vegetation is less understood. This study evaluated the effect of native (Juniperus communis) and invasive solitary conifers (Pinus nigra) on surrounding vegetation, where individuals were relatively isolated (referred to as solitary conifers). The field study conducted in Pannonic dry sand grassland in 2018 recorded plant and lichen species presence around six selected solitary conifers. Composition and pattern of understorey vegetation were assessed using 26 m belt transects with 520 units of 5 cm × 5 cm contiguous microquadrats. Compositional diversity (CD) and the number of realized species combinations (NRC) were calculated from the circular transects. Results showed native conifer J. communis created more complex, organized microhabitats compared to alien P. nigra. CD and NRC values were significantly higher under native conifers than invasive ones (p = 0.045 and p = 0.026, respectively). Native species also had more species with a homogeneous pattern than the alien species. Alien conifers negatively affected understorey vegetation composition and pattern: some species exhibited significant gaps and clusters of occurrences along the transects under P. nigra. Based on our study, the removal of invasive woody species is necessary to sustain habitat diversity.

Rich specialized insect damage on Pliocene leaves from the Mahuadanr Valley (India) growing under a warm climate with weak seasonality.

Plant-insect interactions play a crucial role in shaping terrestrial ecosystems, influencing abundance and distribution of plant species. In the present study, we investigated leaf-mining patterns on fossil leaves from Pliocene strata of the Mahuadanr Valley, Jharkhand, eastern India, deposited under a seasonal tropical climate, and reported complex interactions between plants and insects. We identified 11 distinct mining morphotypes. These morphotypes were mainly found on Dipterocarpaceae, Fabaceae, Lauraceae, and Moraceae; similar mining traces were also observed in the contemporary vegetation surrounding the fossil site. Although mining richness was relatively high, only 2.6% of all leaves in the fossil assemblage were mined. We compared mining richness and abundance values with previously reported values for galling. While richness was slightly lower for galling, almost 50% of all fossil leaves were galled. A literature survey on mining and galling patterns in modern vegetation suggests that there is no global explanation for richness of mining or gall-inducing insects. Thus, low nutrient availability in the ancient forest, dominance of semideciduous leaves with hard texture, and different habitats in the same forest ecosystem, such as well-drained forests and riparian stands, may all have favored different types of specialized plant-insect interactions.

UAV Thermal Mapping as a New Tool to Detect Subsurface Moisture and Document Baseline Environmental Conditions

AbstractThe California High‐Speed Rail is currently planning a rail corridor through the San Gabriel Mountains. The alignment will require tunnels up to approximately 600 m deep and crossing through the San Gabriel Mountains in the SR 14 area. This study utilized repeated optical and infrared unmanned aerial vehicle (UAV) aerial imagery to document the baseline environmental conditions overlying one of the future high‐speed rail tunnels. Images provide vegetation and soil moisture patterns along the surface route of the corridor for future comparison when the tunneling segments are bored. Analysis of reflectance data revealed subsurface water flow in the Bear Creek‐Sand Canyon Creek Study Area. Using UAVs is considered an effective new tool to detect large‐scale and discrete environmental conditions, perhaps undocumented or unobservable, via standard mapping tools. Setting detailed baseline conditions is important to identify changes that may occur because of future tunneling.

Linking African herbivore community enamel isotopes and environments: challenges, opportunities, and paleoecological implications.

Paleoenvironmental reconstructions of fossil sites based on isotopic analyses of enamel typically rely on data from multiple herbivore taxa, with the assumption that this dietary spectrum represents the community's isotopic range and provides insights into local or regional vegetation patterns. However, it remains unclear how representative the sampled taxa are of the broader herbivore community and how well these data correspond to specific ecosystems. Verifying these underlying assumptions is essential to refining the utility of enamel isotopic values for paleoenvironmental reconstructions. This study explores potential links between modern herbivore community carbon isotopic enamel spectra, biome types, and climate in sub-Saharan Africa. This region is one of the most comprehensively isotopically sampled areas globally and is of particular relevance to hominin evolution. Our extensive data compilation reveals that published enamel isotopic data from sub-Saharan Africa typically sample only a small percentage of the taxa documented at most localities and that some biome types (e.g., subtropical savannas) are dramatically overrepresented relative to others (e.g., forests) in these modern data sets. Multiple statistical analyses, including linear models and cluster analyses, revealed weak relationships of associated mammalian herbivore enamel isotopic values, biome type, and climate parameters. These results confound any simple assumptions about how community isotopic profiles map onto specific environments, highlighting the need for more precise strategic approaches in extending isotopic frameworks into the past for paleoecological reconstructions. Developing more refined modern analogs will ultimately allow us to more accurately characterize the isotopic spectra of paleo-communities and link isotopic dietary signatures to specific ecosystems.

Deep-Learning-Based Automatic Extraction of Aquatic Vegetation from Sentinel-2 Images—A Case Study of Lake Honghu

Aquatic vegetation is an important component of aquatic ecosystems; therefore, the classification and mapping of aquatic vegetation is an important aspect of lake management. Currently, the decision tree (DT) classification method based on spectral indices has been widely used in the extraction of aquatic vegetation data, but the disadvantage of this method is that it is difficult to fix the threshold value, which, in turn, affects the automatic classification effect. In this study, Sentinel-2 MSI data were used to produce a sample set (about 930 samples) of aquatic vegetation in four inland lakes (Lake Taihu, Lake Caohai, Lake Honghu, and Lake Dongtinghu) using the visual interpretation method, including emergent, floating-leaved, and submerged vegetation. Based on this sample set, a DL model (Res-U-Net) was used to train an automatic aquatic vegetation extraction model. The DL model achieved a higher overall accuracy, relevant error, and kappa coefficient (90%, 8.18%, and 0.86, respectively) compared to the DT method (79%, 23.07%, and 0.77) and random forest (78%,10.62% and 0.77) when utilizing visual interpretation results as the ground truth. When utilizing measured point data as the ground truth, the DL model exhibited accuracies of 59%, 78%, and 91% for submerged, floating-leaved, and emergent vegetation, respectively. In addition, the model still maintains good recognition in the presence of clouds with the influence of water bloom. When applying the model to Lake Honghu from January 2017 to October 2023, the obtained temporal variation patterns in the aquatic vegetation were consistent with other studies. The study in this paper shows that the proposed DL model has good application potential for extracting aquatic vegetation data.

Vegetation Greening and Its Response to a Warmer and Wetter Climate in the Yellow River Basin from 2000 to 2020

Vegetation greening is time-dependent and region-specific. The uncertainty of vegetation greening under global warming has been highlighted. Thus, it is crucial to investigate vegetation greening and its response to climate change at the regional scale. The Yellow River Basin (YRB) is a vital ecological barrier in China with high ecological vulnerability and climatic sensitivity. The relationship between vegetation greening and climate change in the YRB and the relative contribution of climate change remain to be explored. Using the Enhanced Vegetation Index (EVI) and meteorological observation data, the spatiotemporal patterns of vegetation greening across the YRB in response to climate change at the basin and vegetation sub-regional scales from 2000 to 2020 were analyzed. The impact of human activities on regional greening was further quantified. Results showed that approximately 92% of the basin had experienced greening, at average annual and growing season rates of 0.0024 and 0.0034 year–1, respectively. Greening was particularly prominent in the central and eastern YRB. Browning was more prevalent in urban areas with a high intensity of human activities, occupying less than 6.3% of the total basin, but this proportion increased significantly at seasonal scales, especially in spring. Regional greening was positively correlated with the overall warmer and wetter climate, and the partial correlation coefficients between EVI and precipitation were higher than those between EVI and temperature. However, this response varied among different seasonal scales and vegetation sub-regions. The combined effects of climate change and human activities were conducive to vegetation greening in 84.5% of the YRB during the growing season, while human activities had a stronger impact than climate change. The relative contributions of human activities to greening and browning were 65.15% and 70.30%, respectively, mainly due to the promotion of ecological rehabilitation programs and the inhibition of urbanization and construction projects.

Cooling effects in urban communities: Parsing green spaces and building shadows

The thermal environment in urban communities significantly impacts the comfort and well-being of residents. Reducing the surface temperature in these areas is critical for improving the quality of life of residents and mitigating the urban heat island effect. Various factors, including buildings, roads, and green spaces, can influence the cooling effects in community areas. However, there is currently a lack of research focusing on examining the relationship between the surface temperature and community characteristics in urban settings. In this paper, using highly urbanized Beijing as an example, 1-m spatial resolution imagery (GaoFen-2) was used to extract 241 communities with different vegetation and building characteristics. High-resolution surface temperature data, calculated using the Google Earth Engine (GEE), facilitated the investigation of the cooling efficiency of surfaces in communities. We then evaluated the contributions of the green space ratio, building shadows, and green space pattern to the surface temperature. This study revealed the following: (1) an inverse nonlinear relationship was found between the green space ratio and surface temperature. The cooling efficiency decreased by 2/3 when the green space ratio exceeded 0.43–0.46. (2) The proportions of green spaces and building shadows were the primary drivers of cooling, accounting for more than 85% of the overall cooling effect. (3) In communities with high-rise buildings, the cooling effect of building shadows was greater than that of vegetation patterns. This study could provide a reference for renovating existing communities and planning green spaces in newly developed urban areas.

Alpine vegetation community patterns in the Khumbu region, Nepalese Himalaya

ABSTRACT The Himalayan alpine zone (HAZ)—a high-altitude zone above approximately 4,100 m.a.s.l., is projected to experience strong eco-environmental changes with climate change. As plants expand their range in this region, other processes are likely to be impacted; for example, flows and stores of water. A first vital step in conceptualizing HAZ ecohydrology is to understand the distribution pattern of HAZ vegetation communities. Satellite remote sensing provides one means of doing so, but the often patchy distribution of alpine vegetation creates challenges when using coarse-grained satellite data whose pixels are typically coarser than the grain of vegetation pattern. Here we use fine spatial resolution satellite imagery from WorldView-2 (2 m2 per pixel) coupled with elevation model data from the Copernicus GLO-30 product to produce a land cover classification for the HAZ. Field data captured during in situ surveys in the Gokyo valley, Nepal, were used to drive and then test a random forest classifier. Grassy meadows and dwarf shrubs belonging to the Rhododendron and Juniperus families dominate the ecology of the alpine zone in this region, so we created three vegetation classes for mapping indicative major plant communities dominated by these species. We found that altitude and aspect were dominant drivers of vegetation distribution in the HAZ and that the average vegetation cover of Rhododendron spp. and Juniperus spp. reduces with increasing altitude, as expected. South- and east-facing slopes were dominated by Juniperus spp., whereas north- and west-facing slopes were dominated by Rhododendron spp., and the growth extent of Rhododendron spp. (between 4,010 and 4,820 m.a.s.l.) and meadow (between 4,010 and 4,680 m.a.s.l.) were vertically wider than that of Juniperus spp. (between 4,010 and 4,660 m.a.s.l.). Results from this study demonstrate the vegetation distribution pattern in HAZ at the plant community level and provide an impetus for further studies that seek to understand ecohydrological interactions between dwarf plants and water flows and stores in the HAZ.

Bioturbating megafauna limits the expansion of Cymodocea nodosa seagrass meadows

In seagrass systems, there is a realization that biotic interactions affecting sediment stability might be as important as abiotic stressors in shaping the growth and distribution patterns of seagrass vegetation. However, few studies have looked at the interaction between seagrass and large (mega) bioturbating species, although through sediment reworking activity they may have the capacity to cause large scale impact on seagrass establishment. We assessed the effect of bioturbation by mobile megabioturbators (species lager than 5 cm) on subtidal seagrass (Cymodocea nodosa) using a manipulative field experiment where access by megabioturbators was restricted for 4 months during the growth season. There was a negative effect of megabioturbators on both the biomass and density of young seagrass ramets extending outwards from a mature seagrass meadow, but megabioturbators did not have a notable effect on seagrass growth in the meadows. Interestingly, the exclusion of megabioturbators in all tested habitats significantly modified the infaunal community composition, indicating the presence of complexity in biotic interactions and the possibility of indirect effects caused by the megabioturbators. Overall, megabioturbators have direct and indirect impact in seagrass habitats, playing a significant role in seagrass distribution patterns.

Tracking the Temporal Changes in Land Surface Temperature, Vegetation, and Built-up Patterns in Rizal Province, Philippines using Landsat Imagery

The Rizal Province was subjected to a series of natural and human-induced disturbances throughout the years. Currently, the area is undergoing urbanization which in turn results in shifts in the extent of impervious surfaces that can intensify heat-related health concerns, increase energy consumption for cooling, and alter local weather patterns. This study uses remote sensing images from to quantify the various environmental considerations that remain undocumented and unmapped for areas caused by changes in land use and land cover from Landsat Collection 1- Level 1 (Landsat 4-5 ™ C1- Level 1 & Landsat 8 OLI/ TIRS C1 Level 1) and calculated three parameters namely, (i) Land surface temperature (LST), (ii) Normalized Difference Vegetation Index (NDVI), and (iii) the Normalized Difference Built-up Index (NDBI). The results showed the following: (i) an increase in the vegetation cover from 1993-2020 showed a decrease in LST from 29.34°C to 24.03°C, (ii) the relationship between LST and NDBI is directly proportional, whereas an inversely proportional relationship can be observed between LST and NDVI, and (iii) there is a fluctuating LST due to the changes in the land cover of the study site for almost three decades. This implicates the extensive shift in the ambient temperature of Rizal which further emphasizes the effects of the modification in certain land use land cover classifications, especially in vegetation cover and urban development. This highlights how human-induced and natural factors significantly contribute to the release of heat and ambient temperature, thus, accentuating the need for sustainable urban planning.

Habitat simplification affects functional group structure along with taxonomic and phylogenetic diversity of temperate‐zone ant assemblages over a ten‐year period

Biodiversity is declining at various scales due to habitat simplification. Nevertheless, there is scarce information on how the biotic and abiotic changes linked to simplification affect several diversity dimensions, such as taxonomic, functional, and phylogenetic diversities. This study investigated whether transforming natural oak forests into induced grasslands affected species diversity, functional group structure, and phylogenetic diversity of ant assemblages inhabiting a temperate forest in central Mexico. We placed over 1000 pitfall traps in five sampling events covering a ten‐year period. We used Hill numbers to evaluate species diversity differences between vegetation types and patterns over time. Ant species were classified into stress‐related functional groups, which were analyzed for their association with vegetation types and changes to their proportional abundance over time. We calculated the standardized effect size of the mean nearest taxon distance to quantify the evolutionary history and test for non‐random patterns within vegetation types and sampling years. Species richness did not differ between vegetation types, yet grasslands showed greater diversity for the q = 1 and q = 2 orders. , We also found three ant species as bioindicators for each type of vegetation. Regarding functional structure, cold climate specialists were associated with oak forests. In contrast, generalist species were predominant in induced grasslands. Higher phylogenetic diversity with an overdispersed structure was associated with oak forest, whereas lower phylogenetic diversity and a clustered pattern were found in induced grassland. These results indicate that habitat simplification may not affect the number of ant species, but rather increases their relative abundance and reorganizes the functional and phylogenetic structure in the ecosystem, particularly shifting towards the dominance of evolutionary closely related species and broad‐stress‐tolerant groups. These results highlight the importance of integrating further dimensions of diversity to properly evaluate the reassembly dynamics after habitat simplification, and understand the mechanisms driving this biodiversity loss.

Spatial vegetation pattern formation and transition of an extended water–plant model with nonlocal or local grazing

Spatial vegetation pattern formation and transition of an extended water–plant model with nonlocal or local grazing

Evaluation of climate change impact on plants and hydrology

Climate change (CC) is the menace of the hour impacting every facet of human existence. Regional CC and its impact studies are crucial in that they contribute to global change. The current study aims to investigate the prevalence of CC in Charsadda, Pakistan and its impact on vegetation and hydrology of the region to understand microclimate variability contribution to global CC. Utilizing local climate data for 20 years (2001–2020), Modified Mann-Kendall and Sen’s Slope statistics were employed to determine monthly and seasonal trends in climate variables. Significant changing climate variables were regressed on Moderate resolution Imaging Spectroradiometer (MODIS) satellite dataset viz. normalized difference vegetation index (NDVI). Due to the prominent climate factor impacting vegetation, NDVI was further correlated to MODIS land surface temperature (LST). Floods being the conspicuous climate calamity were mapped for 2005 and 2010 using satellites Landsat 5 and 7 dataset viz. normalized difference water index (NDWI) with flood risk assessment by watershed delineation. The findings revealed significant (p < 0.05) variability in climate variables (average monthly and summer maximum temperature, and average monthly and summer precipitation) that are driving CC and impacting vegetation and hydrology in the region. Temperature and solar radiation affect NDVI adversely while precipitation and relative humidity has positive impact on vegetation. NDVI varied greatly spatiotemporally, often increasing but worsening in some areas (Shabqadar, Abazai, Palai and Charsadda city with NDVI = 0.1–0.3) of the study region as a result of extreme weather events. Temporally, NDVI improved with an overall positive trend with a stage (2007–2016) of noticeable zigzag fluctuation. Spatial grids with higher LST (>40°C) were either devoid of or with sparse NDVI (<0.3) presenting global warming as peril to vegetation. NDWI maps (2005, 2010 floods) indicate that after floods wreaked havoc on the region altering the vegetation pattern revealing heavy irregular precipitation as the next to temperature in jeopardizing vegetation of the region. Lower elevation regions along the Swat and Kabul Rivers with a greater risk of flooding were identified by watershed delineation. The study suggests that local governments and stakeholders implement CC mitigation strategies and plans for vegetation restoration, flood alerts with post-flood management for regional sustainable development.

Vegetation Patterns during the Last 132,000 Years: A Synthesis from Twelve Eifel Maar Sediment Cores (Germany): The ELSA-23-Pollen-Stack

Seven published and four new pollen records from well-dated sediment cores from six Pleistocene and Holocene maar structures located in the Eifel, Germany, are combined to a pollen stack that covers the entire last 132,000 years. This stack is complemented by new macroremain data from one additional sediment core. The pollen data included into the stack show consistently that the Eifel was covered by a dense forest during the Eemian, early Marine Isotope Stage (MIS) 3, and the Holocene. While other European records indeed indicate a warming, the early MIS 3 fully developed forest remains a unique feature in central European pollen records. Comparison to orbital parameters and insolation hints to warm and humid, however, not fully interglacial conditions, which are also visible in speleothem growth throughout Europe. With the cooling trend towards the glacial maxima of MIS 4 and 2, tree pollen declined, with recovering phases during MIS 5c and 5a, as well as during all MIS 3 interglacials. During the colder stadials, steppe vegetation expanded. For MIS 5 and 4, we defined six new landscape evolution zones based on pollen and macroremains.

Self-organization as a mechanism of resilience in dryland ecosystems

Self-organized spatial patterns are a common feature of complex systems, ranging from microbial communities to mussel beds and drylands. While the theoretical implications of these patterns for ecosystem-level processes, such as functioning and resilience, have been extensively studied, empirical evidence remains scarce. To address this gap, we analyzed global drylands along an aridity gradient using remote sensing, field data, and modeling. We found that the spatial structure of the vegetation strengthens as aridity increases, which is associated with the maintenance of a high level of soil multifunctionality, even as aridity levels rise up to a certain threshold. The combination of these results with those of two individual-based models indicate that self-organized vegetation patterns not only form in response to stressful environmental conditions but also provide drylands with the ability to adapt to changing conditions while maintaining their functioning, an adaptive capacity which is lost in degraded ecosystems. Self-organization thereby plays a vital role in enhancing the resilience of drylands. Overall, our findings contribute to a deeper understanding of the relationship between spatial vegetation patterns and dryland resilience. They also represent a significant step forward in the development of indicators for ecosystem resilience, which are critical tools for managing and preserving these valuable ecosystems in a warmer and more arid world.