Vegetation Cover Types Research Articles

Sun-induced fluorescence as a proxy for primary productivity across vegetation types and climates

Abstract. Sun-induced chlorophyll a fluorescence (SIF) retrieved from satellites has shown potential as a remote sensing proxy for gross primary productivity (GPP). However, to fully exploit the potential of this signal, the robustness and stability of the SIF–GPP relationship across vegetation types and climates must be assessed. For this purpose, current studies have been limited by the availability of SIF datasets with sufficient spatial resolution to disentangle the signal between different vegetation cover types. To overcome this limitation, this analysis uses GOME-2 (Global Ozone Monitoring Experiment-2) SIF retrievals, downscaled to a resolution of 0.05∘ (∼5 km) to explore the relationship between SIF and FLUXCOM GPP (GPPFX), a data-driven dataset of primary productivity obtained by upscaling flux-tower measurements. The high resolution of the downscaled SIF (SIFDS) dataset allows the relationships to be broken down by vegetation cover for separate climate zones, thus enabling a confrontation between GPP and SIF at fine granularity. This analysis first investigates the spatial and temporal relationships between FLUXCOM GPP and downscaled SIF at a global scale. A reasonably strong linear relationship is generally observed between SIFDS and GPPFX in all vegetation categories, and an analysis of covariance (ANCOVA) shows that the spatial response is similar between certain plant traits, with some distinction between herbaceous and woody vegetation and notable exceptions, such as equatorial broadleaf forests. Geographical regions of non-linearity suggest where SIFDS could potentially provide information about ecosystem dynamics that are not represented in the FLUXCOM GPP dataset. With the demonstration of downscaled SIF as a proxy for GPP, the response of SIFDS to short-term fluctuations in several meteorological variables is analysed and the most significant short-term environmental driving and limiting meteorological variables determined. Vegetation groupings of similar SIF–meteorological response reinforce the vegetation categorisations suggested by the ANCOVA. This comparative exploration of two of the most recent products in carbon productivity estimation shows the value in downscaling SIF data, provides an independent probe of the FLUXCOM GPP model, enhances our understanding of the global SIF–GPP spatio-temporal relationship with a particular focus on the role of vegetation cover, and explores the similarity of the SIF and GPP responses to meteorological fluctuations. Additional analyses with alternative SIF and GPP datasets support these conclusions.

Are vegetation influences on Arctic–boreal snow melt rates detectable across the Northern Hemisphere?

The timing and rate of northern high latitude spring snowmelt plays a critical role in surface albedo, hydrology, and soil carbon cycling. Ongoing changes in the abundance and distribution of trees and shrubs in tundra and boreal ecosystems can alter snowmelt via canopy impacts on surface energy partitioning. It is unclear whether vegetation-related processes observed at the ecosystem scale influence snowmelt patterns at regional or continental scales. We examined the influence of vegetation cover on snowmelt across the boreal and Arctic region across a ten-year reference period (2000–2009) using a blended snow water equivalent (SWE) data product and gridded estimates of surface temperature, tree cover, and land cover characterized by the dominant plant functional type. Snow melt rates were highest in locations with a late onset of melt, higher temperatures during the melt period, and higher maximum SWE before the onset of melt. After controlling for temperature, melt onset, and the maximum SWE, we found snow melt rates were highest in evergreen needleleaf forest, mixed boreal forest, and herbaceous tundra compared to deciduous needleleaf forest and deciduous shrub tundra. Tree canopy cover had little effect on snowmelt rate within each land cover type. While accounting for the influence of vegetative land cover type is necessary for predictive understanding of snowmelt rate variability across the Arctic–Boreal region. The relationships differed from observations at the ecosystem and catchment scales in other studies. Thus highlighting the importance of spatial scale in identifying snow-vegetation relationships.

Spatial-Temporal Changes of Carbon Source/Sink in Terrestrial Vegetation Ecosystem and Response to Meteorological Factors in Yangtze River Delta Region (China)

As an important part and the core link of a terrestrial ecosystem, terrestrial vegetation is the main means for human to regulate climate and mitigate the increase in atmospheric CO2 concentration. The Yangtze River Delta (YRD) region is an urban agglomeration with the strongest comprehensive strength among developing countries (China). In the context of global climate change, a rapid, comprehensive, and detailed understanding of the spatio-temporal characteristics and variation tendency of the net ecosystem productivity (NEP) of vegetation and its response to climate during the rapid development of the YRD region is important for protecting ecological land, strengthening land management, and optimizing urban planning. The monthly mean temperature and rainfall data from 63 meteorological stations, the MODIS net primary productivity product, and a land cover product in the YRD region were used to estimate the NEP from 2000 to 2019 based on the soil respiration model, and the correlation between NEP and meteorological factors (such as temperature and rainfall) was analyzed. The results showed that: (1) From 2000 to 2019, the carbon sink area was much larger than the carbon source area in terrestrial vegetation in the Yangtze River Delta, the mean NEP of the vegetation ecosystem in the past 20 years was 253.2 g C·m−2·a−1, and the spatial distribution presented a trend that was higher in the south and lower in the north, higher in the east and lower in the west, and that gradually increased from northwest to southeast; moreover, the NEP of mountain areas was generally higher than that of river courses and urban surroundings. The interannual fluctuation of NEP was small, but presented a slightly increasing trend, and the interannual variation of NEP was significantly correlated with the maximum NEP in this region. (2) The carbon sink capacity of different vegetation cover types was (from strong to weak): forestlands > grasslands > wetlands ≈ croplands. (3) The area with the NEP change rate (θslope) > 0 accounted for 69.0%; however, there was certain spatial difference, the proportions of the areas with θslope < 0 were (from large to small) 14.50% (Zhejiang Province, China), 9.10% (Anhui Province, China), 6.65% (Jiangsu Province, China), and 0.79% (Shanghai, China). In terms of the individual changes of these provinces and municipalities, Shanghai > Zhejiang Province > Jiangsu Province ≈ Anhui Province. (4) There was a correlation between NEP and the annual mean temperature and annual precipitation in some regions.

Assessing Rangeland Sensitivity to Degradation in North Tibet

Rangelands are the dominant ecosystems in the Tibetan Plateau and the most critical livestock production resources. However, a large quantity of rangelands has degraded in North Tibet, with declined forage yield and invaded unpalatable weeds. Thus, identifying potential degradation is essential to manage rangelands sustainably in the future. In this study, we aimed to use an environmentally sensitive area index (ESAI) that integrated the conditions of vegetation, soil, climate, and management to assess rangeland degradation in North Tibet and identify the major drivers of degradation by the random forest model. The ESAI contained 16 indicators of the selected four categories, which were input by layers from geographic information system data. The results revealed that 33% of the study area was susceptive to rangeland degradation, in which alpine desert-steppes in the west, with the highest mean ESAI scores of 1.38, were the most sensitive areas. According to the degrees of degradation, only 9% of rangelands in the east had low mean values of ESAI (< 1.22), which were mostly located in the alpine meadow. The importance ranking for rangeland degradation based on the random forest model indicated that the highest importance was vegetation cover type, followed by grazing intensity and aridity with more than 60% mean decrease accuracy. Besides, field measurements showed that the adjusted correlation coefficient of multiple regression of four vegetation degradation indicators was 0.86, illustrating that the assessment as a proxy for rangeland degradation sensitivity is feasible. This study provides an important approach to evaluating potential rangeland degradation. Therefore, the assessment model is recommended for regional-level decision makers to manage rangeland degradation with full consideration of local physical conditions.

Developing and classifying urban biomes as a basis for nature-based solutions

Urbanization is a major driver of environmental change, which calls for multifunctional and comprehensive actions such as Nature-based Solutions (NbS). They are “inspired and supported by nature… and must benefit biodiversity and support the delivery of a range of ecosystem services”. But what nature should one aim for? We tested the hypothesis that local vegetation may not always be the best source of inspiration, as environmental changes impact both extant conditions and species suitability for restored ecosystems. We analyzed the megacity of São Paulo, where laws promote the use of species from the local Atlantic Forest biome. We trained a Linear Discriminant Analysis to classify the Brazilian biomes and predicted the biomes' correspondence considering city's vegetation cover and climate. With 80% accuracy, the model predicted correspondence with the Atlantic Forest in 57% of the city, while 43% is better represented by the Cerrado, a dense Tropical Savanna biome. Cerrado species are naturally adapted to higher insolation, temperature and more seasonal precipitation, and they can parallel the ecosystem services of the Atlantic Forest. To help guide NbS implementation, we consider four “urban biomes”: Atlantic Forest, Seasonally Flooded Atlantic Forest, Cerrado, and the Seasonally Flooded Cerrado, whose dynamics seem to depend mainly on changes in the proportion of dense vegetation cover. We then discuss possible examples of NbS in the city. ResultsAccording to the PCA (Tables S3-S6), the main difference between the classification of the green spaces classified is the higher proportion of forest cover in those classified as Mata Atlantica, with almost any influence from other vegetation cover types (Fig. 5E). This pattern is still consistent when vegetation cover is evaluated outside the largest green spaces where the presence of even small forest patches across the urban fabric results in the Mata Atlântica category DiscussionThe urban ecosystem restoration could benefit from such natural and dynamic processes in which the Cerrado could act as a transient urban biome in the way of restoring the urban forests through soil horizon development and species succession followed by the densification of the vegetation whenever this planting scheme is possible. ConclusionsThese transitions among urban biomes may occur as local conditions change with the implementation of NbS, and the results point to the effectiveness of increasing the vegetation density wherever possible.

UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest

Development of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and mapping of vegetation by image classification remains limited in the Antarctic environment due to the complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of Hope Bay, on the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA) approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images obtained by sensors with low spectral resolution.

An Impact of Short-Term Climate Oscillations in the Late Pleniglacial and Lateglacial Interstadial on Sedimentary Processes and the Pedogenic Record in Central Poland

Environmental studies based on analyses of fluvio-aeolian successions with paleosols in central Poland, forming the central part of the European Sand Belt, are presented. Paleogeographic reconstruction was based on high-resolution analyses of four sites using sedimentological and paleopedological methods as well as forty-four optically stimulated luminescence and fourteen radiocarbon dating measurements. Age-identified individual lithological and soil units were first correlated between sites, emphasizing the differences between them. The results were then correlated with Greenland ice-core stratigraphic units reflecting global environmental changes in the Late Pleniglacial and Lateglacial interstadial, thus ranging from GS-2.1a, through the GI-1 complex (seven subunits), to GS-1. Studies revealed considerable sensitivity of fluvio-aeolian succession to climate changes and oscillations. Climate ameliorations are recorded in fossil soil horizons developed beneath different types of vegetation cover. We detected that the climate cooling GI-1d (the Older Dryas) was not the main phase of dune formation as had been claimed earlier. It is postulated that dunes in the extraglacial zone were formed mainly in GI-1c2 (the Early Allerød). Preexisting dunes were transformed in GS-1 (the Younger Dryas) and in the Early Holocene, locally interrupted by soil formation.

Losses of Tree Cover in California Driven by Increasing Fire Disturbance and Climate Stress

AbstractForests provide natural climate solutions for sequestering carbon and mitigating climate change, yet are increasingly threatened by increasing temperature and disturbance. Understanding these threats requires accurate information on vegetation dynamics and their drivers, which is currently lacking in many regions experiencing rapid climate change such as California. To address this, we combined remote sensing observations with geospatial databases to develop annual maps of vegetation cover (tree, shrub, and herbaceous) and disturbance type (fire, harvest, and forest die‐off) in California at 30 m resolution from 1985 to 2021. Considering both changes in cover fraction and areal extent, California lost 4,566 km2 of its tree cover area (6.7% relative to initial cover) since 1985. Substantial gains in tree cover area during the 1990s were more than offset by fire‐driven declines since 2000, resulting in greater shrub and herbaceous cover area. Tree cover loss occurred in all ecoregions but was most severe in the southern mountains, where losses from wildfire were not compensated by regrowth in undisturbed areas. Fires and tree cover area loss generally occurred where summer temperatures were greater than 17.5°C, whereas net tree cover gain often occurred in cooler areas, suggesting that ongoing climate warming is threatening forests in many areas. California's vegetation is undergoing rapid transformation, with disturbance rates and climate change posing substantial potential risks to the integrity of California's terrestrial carbon sink.

Polycyclic aromatic hydrocarbons (PAHs) in surface soils of tropical reef islands in China under external plant and soil introduction: Occurrence, sources, risks, and relationships with soil properties, vegetation cover, and soil source

This study explored the levels, sources, and risks of PAHs in soils from Yongle Atoll (YLA) and Xuande Atoll (XDA) of the Xisha Islands (XSIs) in the South China Sea, China, under different vegetation cover types and soil sources. The results clearly showed that the levels of 16 US EPA priority PAHs (Σ16PAHs) are relatively low in XDA and YLA, with concentrations ranging from not detected (ND) to 151 ng/g (average 15.7 ng/g) and ND to 5.8 ng/g (average 2.1 ng/g), respectively. Three- and four-ring PAHs (62.3% and 53.8%) were widely distributed in YLA and XDA. The average concentration of Σ16PAHs in soils with shrub cover was 1.4, 1.8, 4.8, and 5.0 times higher than that in soils with herbaceous cover, vegetable cover, arbor cover, and no plant cover, respectively. Source analysis using binary diagnostic ratios and the positive matrix factorization (PMF) model suggested that PAHs have similar sources (gasoline/coal combustion, coke production, and biomass combustion), but different contributions in native soil and introduced soil. Moreover, diesel-related vehicular emission was identified to be an additional source of PAHs in native soil. Pearson's correlations revealed strong relationships between PAHs and organic matter or total organic carbon. The cancer risk of PAHs varied among different vegetation cover types and soil sources, following the orders herbaceous cover > vegetable cover > shrub cover > arbor cover > no plant cover and introduced soil > mixed soil > native soil. Nevertheless, the risk remained lower than the risk threshold (10−6), suggesting low carcinogenesis risk in the two atolls. Our findings provide new evidence for the introduction of external vegetation/soil acting as a driver of changes in the characteristics of PAHs in islands, and also underline the negligibility of the PAH increase in soils in the South China Sea, China, from the perspective of health hazards.

Flight initiation distance and refuge in urban birds

Risk-taking in birds is often measured as the flight initiation distance (FID), the distance at which individuals take flight when approached by a potential predator (typically a human). The ecological factors that affect avian FID have received great attention over the past decades and meta-analyses and comparative analyses have shown that FID is correlated with body mass, flock size, starting distance of the approaching human, density of potential predators, as well as varying along rural to urban gradients. However, surprisingly, only few studies (mainly on reptiles and mammals) have explored effects of different types of refugia and their availability on animal escape decisions.We used Bayesian regression models (controlling for the phylogenetic relatedness of bird species) to explore changes in escape behaviour recorded in European cities in relationship to the birds' distance to the nearest refuge and distance fled to the refuge. In our analyses, we also included information on the type of refuge, built-up and vegetation cover, starting distance, flock size, urbanization level, and type of urban habitat. We found that birds preferred tree refuges over artificial and bush refuges. Birds escaped earlier if the distance to the nearest refuge of any type was longer and if birds fled longer distances to the refuge. FID was shorter when birds used bushes as refugia or landed on the ground after flushing compared to using artificial refugia. Similarly, the distance fled to a refuge was shortest when using bushes, and increased when escaping to artificial substrates and trees. Birds were more timid in suburban than core areas of cities, cemeteries than parks, and in areas with higher bush cover but lower cover of built-up areas and trees. Our findings provide novel information regarding the importance of refuge proximity and type as factors affecting the escape behaviour of urban birds.

Effects of terrestrial dissolved organic matter on a bloom of the toxic cyanobacteria, Raphidiopsis raciborskii

The concentration of coloured terrestrial dissolved organic matter (tDOM) from vegetation appears to be increasing in lakes in some regions of the world, leading to the term brownification. The light attenuating effect of coloured tDOM on phytoplankton growth has been a major focus of attention, but the phytotoxic effects of tDOM, particularly on cyanobacterial blooms, are less well understood. This mesocosm study tested whether coloured tDOM, leached from the leaves of a Eucalyptus tree species, inhibited a naturally occurring bloom of the toxic cyanobacterium, Raphidiopsis raciborskii, in a reservoir over a 10 day period. The study found that tDOM leachate, measured as dissolved organic carbon (DOC), inhibited photosynthesis and growth of both R. raciborskii, as well as species present at lower densities, i.e. other cyanobacteria and diatoms. However, the effect was greater at higher tDOM input loads. The photosynthetic yield (Fv/Fm) of cyanobacteria decreased rapidly in treatments with 5.9 and 25 mg L−1 DOC addition, compared to the control (reservoir water with background DOC concentration of 6.85 ± 1.09 mg L−1). tDOM had no measurable effect in the 2 and 3.3 mg L−1 DOC addition treatments. By day 5, cell densities of cyanobacteria, including R. raciborskii, and diatoms, in treatments with 5.9 and 25 mg L−1 DOC addition were significantly lower than the control with no tDOM addition, and this effect continued throughout the experiment. This is despite the leachate addition increasing phosphate concentrations which counteracted the low background concentrations of phosphate. Light attenuation and dissolved oxygen (DO) levels were also affected by the tDOM addition, but were only significantly lower in the 25 mg L−1 DOC treatment compared with the control. DOC, dissolved organic nitrogen (DON) and dissolved organic phosphorus (DOP) concentrations all decreased in the tDOM addition treatments over the first 3 days, as the microbial cell densities increased. The components of the tDOM that decreased over time were determined by 1H NMR spectroscopy in the 25 mg L−1 DOC treatment. After 5 d, the relative concentrations of fatty acids, sugars and gallic acid decreased by around 60%, while concentrations of flavonoids and myo-inositol decreased by 45 and 35% respectively. This study suggests that phytotoxic compounds in tDOM can suppress cyanobacterial blooms, despite the increased nutrient inputs. This has implications for predicting the future likelihood of cyanobacterial blooms in lakes and reservoirs with climate-change driven changes in flow events, and other changes in the amount and types of vegetation cover. Revegetation of riparian zones, resulting in increased tDOM into waterways, may also be beneficial in reducing cyanobacterial blooms.

Downscaling Microwave Soil Moisture Products with SM-RDNet for Semiarid Mining Areas

Surface soil moisture (SM), as a crucial ecological element, is significant to monitor in semiarid mining areas characterized by aridity and little rainfall. The passive microwave remote sensing, which is not affected by weather, provides more accurate SM information, but the resolution is too coarse for mining areas. The existing downscaling method is usually pointed to natural scenarios like agricultural fields rather than mining areas with high-intensity mining. In this paper, combined with geoinformation related to SM, we designed a convolutional neural network (SM-Residual Dense Net, SM-RDNet) to downscale SMAP/Sentinel-1 Level-2 radiometer/radar soil moisture data (SPL2SMAP_S SM) into 10 m spatial resolution. Based on the in-site measured data, the root mean square error (RMSE) was utilized to verify the downscaling accuracy of SM-RDNet. In addition, we analyzed its performance for different data combinations, vegetation cover types and the advantages compared with random forest (RF). Experimental results show that: (1) The downscaling from the 3 km product with the combination of auxiliary data NDVI + DEM + slope performs best (RMSE 0.0366 m3/m3); (2) Effective data combinations can improve the downscaling accuracy at the range of 0.0477–0.1176 m3/m3 (RMSE); (3) The SM-RDNet shows better spatial completeness, details and accuracy than RF (RMSE improved by 0.0905 m3/m3). The proposed SM-RDNet can effectively obtain the fine-grained SM in semiarid mining areas. Our method bridges the gap between coarse-resolution microwave SM products and ecological applications of small-scale mining areas, and provides data and technical support for future research to explore how the mining effect SM in semiarid mining areas.

Topographic and vegetation drivers of thermal heterogeneity along the boreal-grassland transition zone in western Canada: Implications for climate change refugia.

Climate change refugia are areas that are relatively buffered from contemporary climate change and may be important safe havens for wildlife and plants under anthropogenic climate change. Topographic variation is an important driver of thermal heterogeneity, but it is limited in relatively flat landscapes, such as the boreal plain and prairie regions of western Canada. Topographic variation within this region is mostly restricted to river valleys and hill systems, and their effects on local climates are not well documented. We sought to quantify thermal heterogeneity as a function of topography and vegetation cover within major valleys and hill systems across the boreal–grassland transition zone.Using iButton data loggers, we monitored local temperature at four hills and 12 river valley systems that comprised a wide range of habitats and ecosystems in Alberta, Canada (N = 240), between 2014 and 2020. We then modeled monthly temperature by season as a function of topography and different vegetation cover types using general linear mixed effect models.Summer maximum temperatures (T max) varied nearly 6°C across the elevation gradient sampled. Local summer mean (T mean) and maximum (T max) temperatures on steep, north‐facing slopes (i.e., low levels of potential solar radiation) were up to 0.70°C and 2.90°C cooler than highly exposed areas, respectively. T max in incised valleys was between 0.26 and 0.28°C cooler than other landforms, whereas areas with greater terrain roughness experienced maximum temperatures that were up to 1.62°C cooler. We also found that forest cover buffered temperatures locally, with coniferous and mixedwood forests decreasing summer T mean from 0.23 to 0.72°C and increasing winter T min by up to 2°C, relative to non‐forested areas.Spatial predictions of temperatures from iButton data loggers were similar to a gridded climate product (ClimateNA), but the difference between them increased with potential solar radiation, vegetation cover, and terrain roughness.Species that can track their climate niche may be able to compensate for regional climate warming through local migrations to cooler microsites. Topographic and vegetation characteristics that are related to cooler local climates should be considered in the evaluation of future climate change impacts and to identify potential refugia from climate change.

Characteristics of soil carbon emissions and bacterial community composition in peatlands at different stages of vegetation succession

Microorganisms are important components of soil ecosystems and play an important role in material cycles. Northern peatlands are important ecosystems in middle-high latitude regions. In peatlands, different vegetation successions occur with changes in groundwater levels. The overall carbon emission of peat bogs is related to the carbon stability of the surrounding environment. Unraveling the assembly and distribution of bacterial communities at different succession stages in peatland is essential to understanding the soil nutrient cycle. In this study, we investigated the characteristics of soil carbon emissions and the composition of subsurface microorganisms under six different succession stages. The highest carbon emission was observed in mossy peatlands, and their soil enzyme activity was closely related to the aboveground vegetation cover type. The succession pattern of ground vegetation was the main driver of soil microorganisms. The abundance of the dominant Proteobacteria decreased with increasing soil depth, while the opposite trend was observed for Chloroflexi. Furthermore, the community structure of microorganisms became progressively simpler and looser as soil water content decreased. The bacterial alpha diversity was driven by soil dissolved organic carbon and Fe, and the beta diversity was driven mainly by soil water content. The bacteria presented a random distribution in a nutrient-rich soil environment and shifted to deterministic distribution with decreasing water and nutrient contents. The balance between taxonomic diversity and dispersal limitation mediates species coexistence in the soil microbiome. This study provides new insights into the soil environment at different stages of succession in peatlands.

Fusion of airborne hyperspectral and LiDAR canopy-height data for estimating fractional cover of tall woody plants, herbaceous vegetation, and other soil cover types in a semi-arid savanna ecosystem

ABSTRACT Vegetation in semi-arid regions is often a spatially heterogeneous mix of bare soil, herbaceous vegetation, and woody plants. Detecting the fractional cover of woody plants versus herbaceous vegetation, in addition to non-photosynthetically active cover types such as biological soil crusts, dry grasses, and bare ground, is crucial for understanding spatial patterns and temporal changes in semi-arid savanna ecosystems. With and without the inclusion of LiDAR-derived canopy height information, we tested different configurations of unsupervised linear unmixing classification of hyperspectral data to estimate fractional cover of tall woody plants, herbaceous vegetation, and other non-photosynthetically active cover types. To perform this analysis, we used 1 m resolution hyperspectral and LiDAR data collected by the NEON airborne observation platform at the Santa Rita Experimental Range in Arizona. Our results show that both hyperspectral and canopy height information are needed in the linear unmixing algorithm to correctly identify tall woody plants versus herbaceous vegetation. However, including height data is not always sufficient to be able to separate the two vegetation types: selecting the optimum number of endmembers is also crucial for separating these two types of photosynthetically active vegetation. In comparison to our reference dataset, fractional cover accuracy was highest for the woody plant class based on two accuracy assessment methods, although the fractional cover of dense woody plant is likely underestimated because linear unmixing also detects sub-canopy elements. The accuracy of the herbaceous vegetation class was lower than the other classes, likely because of the presence of both active and senesced grasses. Our study presents the first use of both hyperspectral and canopy height information to separately detect fractional cover of woody plants versus herbaceous vegetation in addition to other bare soil cover types that should be useful for fractional cover classification in other semi-arid savanna ecosystems where hyperspectral and LiDAR data are available.

How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?

Maps of the distribution and abundance of dominant plants derived from satellite data are essential for ecological research and management, particularly in the vast semiarid shrub-steppe. Appropriate application of these maps requires an understanding of model accuracy and precision, and how it might vary across space, time, and different vegetation types. For a 113 k Ha burn area, we compared modeled maps of different vegetation cover types created from satellite data to ‘benchmark” models based on intensive field sampling (∼1500–2000 plots resampled annually for 5 years) for three new satellite-derived models: USDA Rangeland Analysis Platform (RAP), the USGS Rangeland Condition Monitoring Assessment and Projection (RCMAP), and USGS fractional estimate of exotic annual grass cover (USGS-fractional-EAG). We assessed out-of-sample point accuracy and asked if and how accuracy changed each year due to vegetation shifts, new images, and model improvements (i.e. model versions). We also assessed how map agreement between satellite-based and field-based models changed with scale of application, topography, and time since fire.Accuracy and map agreement varied considerably among the vegetation types and across time and space (r2 ranging from 0 to 0.53), and some of the variability was predictable. All models tended to over or underestimate cover when field-measured cover was relatively low or high, respectively, i.e. a “false moderating effect”. Accuracy was greater and improved with newer versions of RAP (+0.05 to 0.29 r2) compared to RCMAP and USGS fractional model estimates, and in some cases was greater than field-based models. Variability in map agreement tended to decrease with larger areas sampled (particularly in areas >12 km), and this scale dependency was more evident in RAP and USGS-fractional-EAG models. Creating a “fair” basis for comparison of spatial models of low-statured semiarid vegetation derived from satellite compared to field data is not trivial because scaling the field data to the scale of large satellite pixels (or downscaling satellite-based models to field scale) requires modeling and associated model uncertainty. Accuracy can vary considerably and understanding the variation can help guide application of the models to the appropriate time, place, and variables.

Diversity and distribution pattern of scorpions from the Ouarsenis massif of Tissemsilt, North-West Algeria

Abstract. Chedad AW, Hammou MA, Chelghoum H, Chedad A, Amara OO, El Bouhissi M, Dahmani W, Sadine SE. 2022. Diversity and distribution pattern of scorpions from the Ouarsenis massif of Tissemsilt, North-West Algeria. Biodiversitas 23: 2444-2450. Ecological barriers such as mountains are important factors leading to fragmentation of the range of different species and the important cause of vicariance events. Ouarsenis massif (Tissemsilt, North-West Algeria) is an important protected Park. This paper presents a preliminary study of scorpion diversity and assesses this biodiversity change in this Mountain range along a gradient of many ecological descriptors, including elevation, vegetation cover, slope, and soil types. During 2020 and 2021, we sampled 179 specimens of scorpions by direct hunting from the eight biotopes in Ouarsenis massif. Only adult individuals were used for identification after being killed and kept in 70% alcohol. Based on morphological criteria, we identified eight species distributed in two genera and two families. The family Buthidae was represented by five species with the highest relative abundance (RA) was Buthus tunetanus (RA: 47.49%). Furthermore, the family Scorpionidae was represented by three species with the highest RA was Scorpio maurus (RA: 25.14%). The Shannon index (H’: 2.71 bits) indicated that Ouarsenis massif region had a high level of scorpion diversity and the value of evenness (E: 0.90) reflected the high equilibrium between the effectiveness of sampled species. The main determinant descriptor of scorpion distribution was the elevation and soil types, while the vegetation cover could be considered an ancillary factor.

Soil macroinvertebrate communities: A world‐wide assessment

AbstractAimMacroinvertebrates comprise a highly diverse set of taxa with great potential as indicators of soil quality. Communities were sampled at 3,694 sites distributed world‐wide. We aimed to analyse the patterns of abundance, composition and network characteristics and their relationships to latitude, mean annual temperature and rainfall, land cover, soil texture and agricultural practices.LocationSites are distributed in 41 countries, ranging from 55° S to 57° N latitude, from 0 to 4,000 m in elevation, with annual rainfall ranging from 500 to &gt;3,000 mm and mean temperatures of 5–32°C.Time period1980–2018.Major taxa studiedAll soil macroinvertebrates: Haplotaxida; Coleoptera; Formicidae; Arachnida; Chilopoda; Diplopoda; Diptera; Isoptera; Isopoda; Homoptera; Hemiptera; Gastropoda; Blattaria; Orthoptera; Lepidoptera; Dermaptera; and “others”.MethodsStandard ISO 23611‐5 sampling protocol was applied at all sites. Data treatment used a set of multivariate analyses, principal components analysis (PCA) on macrofauna data transformed by Hellinger’s method, multiple correspondence analysis for environmental data (latitude, elevation, temperature and average annual rainfall, type of vegetation cover) transformed into discrete classes, coinertia analysis to compare these two data sets, and bias‐corrected and accelerated bootstrap tests to evaluate the part of the variance of the macrofauna data attributable to each of the environmental factors. Network analysis was performed. Each pairwise association of taxonomic units was tested against a null model considering local and regional scales, in order to avoid spurious correlations.ResultsCommunities were separated into five clusters reflecting their densities and taxonomic richness. They were significantly influenced by climatic conditions, soil texture and vegetation cover. Abundance and diversity, highest in tropical forests (1,895 ± 234 individuals/m2) and savannahs (1,796 ± 72 individuals/m2), progressively decreased in tropical cropping systems (tree‐associated crops, 1,358 ± 120 individuals/m2; pastures, 1,178 ± 154 individuals/m2; and annual crops, 867 ± 62 individuals/m2), temperate grasslands (529 ± 60 individuals/m2), forests (232 ± 20 individuals/m2) and annual crops (231 ± 24 individuals/m2) and temperate dry forests and shrubs (195 ± 11 individuals/m2). Agricultural management decreased overall abundance by ≤54% in tropical areas and 64% in temperate areas. Connectivity varied with taxa, with dominant positive connections in litter transformers and negative connections with ecosystem engineers and Arachnida. Connectivity and modularity were higher in communities with low abundance and taxonomic richness.Main conclusionsSoil macroinvertebrate communities respond to climatic, soil and land‐cover conditions. All taxa, except termites, are found everywhere, and communities from the five clusters cover a wide range of geographical and environmental conditions. Agricultural practices significantly decrease abundance, although the presence of tree components alleviates this effect.

Analysis of spatial and temporal changes of vegetation cover and its driving forces in the Huainan mining area.

The Huainan mining area is rich in coal resources and has sparse vegetation and many collapsed waterways. Large-scale and long-term underground coal mining has led to a fragile ecological environment in the mining area, and it is urgent to solve the contradiction between coal development and ecological environmental protection. The Huainan mining area was selected as the research object, and the vegetation cover was extracted using 10-phase Landsat multispectral remote sensing images from 1989 to 2021 to analyze its spatial and temporal changes and driving forces to provide a scientific basis for the guided restoration of the ecological environment in the region. Combined with the image dichotomous model, regression slope, correlation coefficient, and standard deviation of vegetation cover grid points in different time series, standard deviation ellipse, and center of gravity migration, we analyzed the spatial and temporal variation pattern of vegetation cover for 33 years and revealed the responses of temperature, precipitation, population density, GDP, and afforestation area to vegetation cover. Results show the following: (1) from 1989 to 2021, the overall vegetation cover in the study area tended to decrease with 36.48% of the areas increasing and 63.52% of the areas decreasing, primarily in the very low and medium range; (2) the center of gravity of different types of vegetation cover generally shifted from north to south during 33 years; (3) climate and social activities had a substantial effect on the spatial heterogeneity of the vegetation cover in the study area. There is significant spatial heterogeneity in the effects of climate and social activities on the vegetation in the study area with human activities negatively correlating with vegetation cover. Mining activities are the primary driver of the evolution of regional vegetation cover, with climate change serving as a secondary driver.

Amblyomma mixtum free-living stages: Inferences on dry and wet seasons use, preference, and niche width in an agroecosystem (Yopal, Casanare, Colombia).

The formulation of effective control strategies for any pest species generally involves the study of habitat use and preference and niche width in anthropogenically transformed natural landscapes. We evaluated whether the use, habitat preference, and niche range of the Amblyomma mixtum tick changed between stages, habitats, and seasonality (dry-wet seasons 2019) on a farm in Yopal (Casanare, Colombia). The presence and relative abundance of free-living larvae, nymphs, and adults was quantified in four different habitats according to the type of vegetation cover (Riparian Forest, Cocoa Crop, King Grass Crop, and Star Grass Paddock). Habitat availability was estimated, environmental variables were analyzed, and various indices of habitat use and preference, and niche width were calculated. A. mixtum’s habitat use and preference, and niche width changed between stages, habitat types, and time of the year. The total abundance of A. mixtum was an order of magnitude greater in the dry season than the wet season. In the dry season, all stages used all habitats, while A. mixtum adults used all the habitats in both seasons. In the dry season, nymphs and larvae preferred three out of the four habitats, while adults preferred the King Grass Crop. In the wet season, nymphs and larvae preferred two habitats, whereas the adults preferred the King Grass Crop. The value of the niche width index was high for larvae, nymphs, and adults in the dry season, while it was high only for adults in the wet season. Thus, A. mixtum’s vast environmental tolerance and niche breadth allows the species to use and colonize changing habitats (unstable or temporary) with fluctuating environmental conditions (e.g., King Grass Crop), potentially keeping a stable population over time and making it an extremely resistant species. However, the wet flooding season in Yopal may exceed A. mixtum’s stages’ tolerances.