Heterogeneous Vegetation Research Articles

Vegetation biomass and topography are associated with seasonal habitat selection and fall translocation behavior in Arctic hares.

Habitat selection theory suggests that environmental features selected at coarse scales reveal fundamental factors affecting animal fitness. When these factors vary across seasons, they may lead to large-scale movements, including long-distance seasonal migrations. We analyzed the seasonal habitat selection of 25 satellite-tracked Arctic hares from a population on Ellesmere Island (Nunavut, Canada) that relocated over 100 km in the fall. Since no other lagomorph is known to perform such extensive movements, this population offered an ideal setting to test animal movement and habitat selection theory. On summer grounds hares selected low elevation areas, while on winter grounds they selected high vegetation biomass, high elevation, and steep slopes. During fall relocation, they alternated between stopover and traveling behavioral states (ratio 2:1). Stopover locations were characterized by higher vegetation heterogeneity and lower rugosity than traveling locations, while vegetation biomass and elevation interacted to explain stopover locations in a more complex way. The selected combination of environmental features thus varied across seasons and behavioral states, in a way broadly consistent with predictions based on the changing food and safety needs of hares. Although causality was not demonstrated, our results improve our understanding of long-distance movements and habitat selection in Arctic hares, as well as herbivore ecology in the polar desert. Results also provide strong support to animal movement and habitat selection theory, by showing how some important hypotheses hold when tested in a species phylogenetically distinct from most animal models used in this research field.

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.

Three Decades of Wetland Methane Surface Flux Modeling by Earth System Models‐Advances, Applications, and Challenges

AbstractEarth System Models (ESMs) simulate the exchange of mass and energy between the land surface and the atmosphere, with a key focus on modeling natural greenhouse gas feedbacks. Methane is the second most important greenhouse gas after carbon dioxide. There are growing concerns over the rapidly increasing methane concentration in the atmosphere, underscoring the need for accurate global modeling of its emissions using ESMs. Of the multitude of sources of methane globally, wetlands are the largest natural emitters for methane, leading to significant efforts targeting their representation in ESMs with a special focus on their methane emissions. In this review, we first provide a historical overview of including wetland‐methane components in ESMs and how methane modeling approaches have evolved over time. Second, we discuss recent modeling advancements that show promise for improvements in methane emissions predictions, namely the coupling of surface and atmospheric modules of ESMs, the representation of microtopography and transport mechanisms, the resolution of microbial processes at different spatial‐temporal scales, and the improved mapping of wetland area extent across the different wetland types. Third, we shed light on the different challenges hindering accurate estimations of wetland‐methane emissions, as shown by the consistent discrepancy between bottom‐up and top‐down models' predictions. Finally, we emphasize that more detailed representation of biogeochemistry and dynamic hydrology while resolving the within‐wetland vegetation heterogeneity should improve model predictions, especially when coupled with expanding ground‐based measurement networks and high‐resolution remote sensing mapping of methane‐relevant variables, such as water elevation, water table depth, and methane concentration.

Characterization of carbon fluxes, stock and nutrients in the sacred forest groves and invasive vegetation stands within the human dominated landscapes of a tropical semi-arid region

In the semi-arid plains of Southern India, outside the protected area network, sacred groves forests and the barren lands invaded by Prosopis juliflora are reckoned to be the major greenery, but have homogenous and heterogeneous vegetation respectively. This study attempted to compare 50 Sacred Groves Stands (SGS) and 50 monodominant Prosopis juliflora Stands (PJS) for the functional diversity, evenness, floral diversity, carbon stock and dynamics, carbon-fixing traits, dendrochronology of trees, soil nutrient profiles, and soil erosion. Quadrat sample survey was adopted to record stand density, species richness, abundance, basal area and leaf area index; composite soil samples were collected at depths 0–30 cm for nutrient profiling (N, P, K, and OC). Photosynthesis rate (µmole co2 m2/sec), air temperature (°c), leaf intracellular co2 concentration (ppm), ambient photosynthetic active radiation (µmole m2/sec), transpiration rate (m. mole H2O m2/sec) were determined for the 51 tree species existed in SGS and PJS using Plant Photosynthesis system. Structural Equation Model (SEM) was applied to derive the carbon sequestering potential and photosynthetic efficiency of eight dominant tree species using vital input parameters, including eco-physiological, morphological, and biochemical characterization. The Revised Universal Soil Loss Equation (RUSLE) model, in conjunction with ArcGIS Pro and ArcGIS 10.3, was adopted to map soil loss. Carbon source/sink determinations inferred through Net Ecosystem Productivity (NEP) assessments showed that mature SGS potentially acted as a carbon sink (0.06 ± 0.01 g C/m2/day), while matured PJS acted as a carbon source (−0.34 ± 0.12 g C/m2/day). Soil erosion rates were significantly greater (29.5 ± 13.4 ton/ha/year) in SGS compared to PJS (7.52 ± 2.55 ton/ha/year). Of the eight selected tree species, SEM revealed that trees belonging to the family Fabaceae [Wrightia tinctoria (estimated coefficient: 1.28, p = 0.02) > Prosopis juliflora (1.22, p = 0.01) > Acacia nilotica (1.21, p = 0.03) > Albizia lebbeck (0.97, p = 0.01)] showed comparatively high carbon sequestering ability.

Microhabitat characterization in the home range of the Mule deer (Odocoileus hemionus) in arid zones

Knowing the home range of a species helps to identify the resources it needs to survive and reproduce and how this behavior is expressed spatially. Within home ranges, core areas are the sites where the main resources are abundant. The microhabitat is a spatial area composed of variables that can affect individual behavior. In this sense, the characterization of this inner part of the home range can contribute significantly to understanding the elements that these areas offer compared to the rest of the habitat of a population. This work characterized the home range and areas outside it, as well as the core areas of female mule deer on a microhabitat scale in the Chihuahuan Desert, México. The structure and composition of the vegetation were characterized according to three habitat use hierarchies: interior of the core areas and zones within and outside the home ranges of seven female mule deer. A Principal Component Analysis (PCA) was performed, and a hierarchical clustering was used to relate the variables. The variation in structure and composition in each hierarchy was evaluated by performing multivariate permutation tests. Twenty-five plant species were recorded in the transects. The PCA showed the most similar use hierarchies are the core and home range inner areas. The core area presents higher density and cover-dominance values, and the zone outside the home range showed high values of distance to the individual closest to the central point and greater variation in this parameter. The MANOVA indicated a significant variation in vegetation structure and composition in relation to use hierarchies. Significant differences in vegetation structure and composition were found at the microhabitat level between the core area of activity and the zones within and outside the home range. The core area has a greater structural complexity of vegetation, with greater plant coverage-abundance and density; this suggests that the core area is located in a more competitive and saturated environment. Outside the home range, the microhabitat has greater spatial heterogeneity of vegetation, with greater distance and variation of plant cover. Future research could address the spatial (micro-macro) and temporal scales to better understand the ecological dynamics of the species in different habitat use hierarchies.

Upland Sandpipers select for later time since fire and experience high nest survival in grasslands managed with patch-burn grazing

Abstract Upland Sandpipers (Bartramia longicauda) are a grassland obligate shorebird that nests in dense vegetation structure near recently disturbed areas and could benefit from management practices that promote heterogenous vegetation structure. Upland Sandpipers primary breeding range is generally managed for livestock production using traditional practices that lack patchy disturbances to facilitate higher levels of structural heterogeneity. Patch-burn grazing (PBG) could be an alternative management practice for Upland Sandpiper conservation for its use to create areas of dense vegetation structure near recently disturbed areas. However, limited information is available regarding nest production of Upland Sandpipers within a PBG framework. To assess the compatibility of PBG with Upland Sandpiper conservation, we estimated nest site selection and survival of Upland Sandpiper nests on private lands managed with PBG in the unglaciated plains region of North Dakota. We located 59 nests from 2017 to 2020. Upland Sandpipers avoided 1 year since fire (YSF) patches and selected for 2 and 3 YSF patches for nest sites. Additionally, nest site selection decreased with increased bare ground and at intermediate distances to the nearest Upland Sandpiper nest. Upland Sandpipers experienced high overall nest survival during the study with 51 of 59 nests successfully hatching, which limited our ability to make inferences between daily survival rates and variables of interest. Our findings suggest that PBG can provide suitable nesting cover for Upland Sandpipers in later YSF patches despite annual prescribed fire and livestock grazing during the nesting season. PBG seems a suitable grassland management strategy that should be included in conservation planning within Upland Sandpiper’s breeding distribution.

The complexity of goats’ feeding behaviour: An overview of the research in the tropical low deciduous forest

This overview describes a series of studies on the feeding behaviour of goats in the low deciduous forest (LDF) and some of the factors that affect such behaviour. Those multidisciplinary studies included field trials as well as controlled pen trials. Field observations were performed with the direct observation method (DOM) and by establishing a bite code grid adapted to the LDF, and the pen studies used a cafeteria strategy using leaves harvested from different plants of the LDF. Studies exploring the selection of feed resources used different selection indexes. Field observations showed that goats consumed >90 plant species from the heterogeneous vegetation, but only a few (7 - 15 species) represented >90% of the daily intake, and that differed between rainy and dry seasons. Although the LDF vegetation allows goats to match their crude protein requirements in both seasons, dietary energy was the limiting factor, and the condensed tannin (CT) content of the harvested diet was always high. Field and cafeteria studies suggested that goats optimize their diet with the resources available as they prefer to consume leaves with lower fibrous content while plant secondary metabolites (PSM) such as condensed tannins (CT) seemed less influential. Field and pen studies confirmed that CT blocking with polyethylene glycol did not modify the intake and selection of the CT-rich foliage which could be related to tannin-binding salivary proteins which were identified as an innate feature of the local goats. While pen studies showed that browsing experience helped goats select feeds of better digestibility and lower PSM content, helping them optimize their nutrition, field studies showed that adult goats consumed more plant species, performed larger bites, at higher strata than kids, while the proportion of life forms consumed was similar. Most field and pen studies showed that goats generally do not consume CT-rich foliage as a therapeutic self-medication strategy against gastrointestinal nematodes (GIN), instead, a prophylactic strategy seemed feasible. This behavioural feature could aim at different goals: (i) to avoid excess nitrogen in the rumen, (ii) to maintain the CP:energy balance (reducing energy loss due to urea urinary excretion), and (iii) to avoid exsheathment of GIN L3 (affecting their life-cycle). The DOM showed that goats consume small quantities of plants containing toxic compounds and they shift from browsing early in the morning to grazing some hours later, and both strategies warrant further research. The DOM also helped to relate plant life forms consumed with the degree of GIN infection, which deepens our understanding of GIN epidemiology in heterogeneous vegetation ecosystems. From these findings on the feeding behaviour of goats in the LDF, new research opportunities were proposed.

Leaf properties estimation enhancement over heterogeneous vegetation by correcting for terrestrial laser scanning beam divergence effect

The need for an accurate description of forest structure is increased as it is a key for monitoring carbon storage, energy, and mass exchange, and different ecosystem functions. The leaf area index (LAI), the leaf area density (ul), and the leaf inclination distribution function (LIDF) are regarded as important and critical keys allowing the characterization of the forest structure. The dependency between these properties and the heterogeneity of the forest makes their estimation challenging and delicate. In this context, despite the expanding body of works reporting forest structure properties retrieval using infinitesimal footprint size terrestrial LiDAR system (TLS) data, there seem to be few works evaluating the influence of the TLS beam divergence effect (i.e. its size increase with distance from the sensor), on the estimation accuracy, even though the influence is not negligible. In this study, we aim to jointly estimate LAI/ul and LIDF using non-infinitesimal footprint size TLS data based on forward/inverse radiative transfer (RT) modeling. Our approach assumes a uniform distribution of azimuth angles enabling us to attain valuable insights into inclination angles and leaf area density while simplifying our computational efforts. The forward modeling allows an understanding of the interaction between vegetation structure properties and TLS light beams, while the inversion allows retrieving the properties given the TLS data. The inversion technique is based on the maximum likelihood estimator (MLE). Our problem turns out to be a non-linear and high-dimensional cost function to be optimized. To tackle this complexity, the global optimization technique, the shuffled complex evolution (SCE-UA), is adapted to find the globally optimal solution. Our developed approach is validated with simulated and actual forest TLS point clouds acquired from Estonian Pine, Birch, and Spruce stands. Our findings using simulated data prove that our estimates are in agreement with the actual ul and LIDF values with (MAEul∈0.00120.0043) and (RMSEul∈0.01370.0224). In the actual case, RMSELAI for Pine, Birch and Spruce stands are 0.1066, 0.014, and 0.070, respectively. In all cases, our approach outperforms state-of-the-art techniques.

Turbulence characteristics in partially vegetated open channels with alternating sparse and dense patches

A large amount of vegetation in nature exists in the form of heterogeneous vegetation patches, and variations in vegetation characteristics significantly affect water flow structures. The objective of this study is to investigate the effect of alternating sparse and dense patches on turbulence characteristics. Multiple sets of heterogeneous vegetation scenarios were designed for numerical simulation analysis, and a comparison was made with homogeneous vegetation. Results indicate that compared with that of homogeneous vegetation, the arrangement of heterogeneous vegetation alters the distribution of flow velocities in the vegetation zone and the main channel, promoting material exchange between these regions. The vegetation density difference between sparse and dense vegetation patches in heterogeneous vegetation effect on the main channel increases with larger vegetation density differences, but the magnitude of the effect is limited and generally remains within 10% of the vegetation width. In the balance equation of turbulent kinetic energy, the terms are influenced differently by changes in vegetation density. As vegetation density difference increases, the convective term gradually increases, while the production and diffusion terms exhibit a “lower in the middle, higher at both ends” pattern. The dissipation term demonstrates a reduction effect at low-density differences, gradually increases with larger differences, and ultimately exhibits an amplification effect. Furthermore, this study determines that using data from mid-height to represent the entire cross section for heterogeneous vegetation may result in a maximum error of up to 11%.

Clustered-map probabilistic cellular automata for fire propagation in the Brazilian Cerrado with heterogeneous vegetation and wind interference

<abstract><p>Wildfires pose a significant threat to both biodiversity and human communities, and understanding their behavior and the rate at which they burn through different vegetation types is crucial for effective management and conservation. In this research, we present a comprehensive analysis of wildfire behavior and vegetation burning rates in the unique ecosystem of Sete Cidades National Park. To achieve this, we adopt a qualiquantitative approach that combines both qualitative and quantitative methodologies, considering the multifaceted variables at play, including wind conditions, various vegetation types, and the dynamics of fire progression. We conducted an extensive dataset comprising $ 100 $ simulations for each of three distinct scenarios, ensuring robustness in our data for statistical analysis. By incorporating qualitative data obtained through field observations and expert opinions, we gain a deeper understanding of the contextual nuances specific to Sete Cidades National Park. This approach enriches the interpretation of our quantitative results, providing valuable context and real-world relevance. Our materials include a cellular automaton lattice with $ 200 \times 200 $ cells, representing the diverse landscape of the study area. We used MATLAB to visualize this landscape, generating distinct representations of the scenarios. Our findings reveal the distribution of different vegetation types across these scenarios, emphasizing the resilience of Rupestrian Cerrado, the diversity of Typical Cerrado, and the importance of Riparian Forest in preserving aquatic ecosystems. This research contributes to the broader understanding of wildfire management, considering the interdisciplinary aspects of environmental science, forestry, and meteorology. By integrating knowledge from diverse fields, we provide a holistic analysis that can inform effective conservation strategies and wildfire management practices.</p></abstract>

Effects of initial vegetation heterogeneity on competition of submersed and floating macrophytes

<p>Non-spatial models of competition between floating aquatic vegetation (FAV) and submersed aquatic vegetation (SAV) predict a stable state of pure SAV at low total available limiting nutrient level, <italic>N</italic>, a stable state of only FAV for high <italic>N</italic>, and alternative stable states for intermediate <italic>N</italic>, as described by an S-shaped bifurcation curve. Spatial models that include physical heterogeneity of the waterbody show that the sharp transitions between these states become smooth. We examined the effects of heterogeneous initial conditions of the vegetation types. We used a spatially explicit model to describe the competition between the vegetation types. In the model, the FAV, duckweed (<italic>L. gibba</italic>), competed with the SAV, Nuttall's waterweed (<italic>Elodea nuttallii</italic>). Differences in the initial establishment of the two macrophytes affected the possible stable equilibria. When initial biomasses of SAV and FAV differed but each had the same initial biomass in each spatial cell, the S-shaped bifurcation resulted, but the critical transitions on the <italic>N</italic>-axis are shifted, depending on FAV:SAV biomass ratio. When the initial biomasses of SAV and FAV were randomly heterogeneously distributed among cells, the vegetation pattern of the competing species self-organized spatially, such that many different stable states were possible in the intermediate <italic>N</italic> region. If <italic>N</italic> was gradually increased or decreased through time from a stable state, the abrupt transitions of non-spatial models were changed into smoother transitions through a series of stable states, which resembles the Busse balloon observed in other systems.</p>

A Multiple-Scattering Microwave Radiative Transfer Model for Land Emission With Vertically Heterogeneous Vegetation Coverage

A Multiple-Scattering Microwave Radiative Transfer Model for Land Emission With Vertically Heterogeneous Vegetation Coverage

Land surface emissivity retrieval from SDGSAT-1: comparison of LSE products with different spatial resolutions

ABSTRACT Land surface emissivity (LSE) represents an essential parameter for understanding land change and surface energy balance. While numerous algorithms have been proposed to retrieve LSE, they are seldom applied to remote sensing data with high spatial resolution. The SDGSAT-1 satellite provides an opportunity to retrieve LSE with high spatial resolution that can be regarded as references for validating LSE products generated from coarse-spatial-resolution satellite data. However, no study has yet explored LSE-retrieval based on SDGSAT-1. This research seeks to fill this gap through evaluating the radiometric calibration accuracy of SDGSAT-1, using five methods to retrieve pixel-level emissivity of Guangzhou, and comparing such results with emissivity generated from other satellites (e.g. Landsat 8, ECOSTRESS, and MODIS) that transited Guangzhou during the similar period. Our results indicated that SDGSAT-1 performed well in radiometric calibration. Heterogeneous vegetation and man-made surfaces registered significant emissivity differences in varying spatial resolutions, whereas relatively homogeneous water bodies registered smaller differences. Decreasing spatial resolution amplifies the emissivity differences of the same land cover among distinctive sensors. The five emissivity-retrieval methods performed exceptionally well based on SDGSAT-1, registering average absolute differences less than 0.026 as compared with Landsat 8; with decreasing spatial resolutions, the maximum difference between SDGSAT-1 and MODIS LSE product reached 0.124.

Heterogeneity in habitat and microclimate delay butterfly community tracking of climate change over an elevation gradient

Gradients in community diversity and composition rarely track rates of warming, prompting efforts to understand the factors causing non-equilibrium responses to climatic change and their implications for conservation. Here, we test the roles of fine-resolution habitat heterogeneity and microclimate in delaying butterfly community responses to warming over a mountain elevation gradient. We assess species diversity and Community Temperature Index (CTI) in 2004–2005 and 2017 across 120 transect sites in the Sierra de Guadarrama (Spain), modelling temperatures based on topography, and vegetation structure based on LiDAR. A humped elevation gradient in species diversity was maintained over time. However, diversity in the later period was more positively related to vegetation heterogeneity, and sites with reduced rates of warming and greater forest cover maintained or increased their diversity. Site CTI declines with increasing elevation, showing little evidence of systematic change over the gradient between periods, although CTI increased most in locations with the greatest rates of spring microclimatic warming. Our results show that delays in community tracking of climatic conditions could result partly from positive effects of habitat and topographic heterogeneity providing microclimatic buffering against warming to existing communities; although barriers to colonization could also delay diversity increases and community thermophilization at high elevations. We suggest that protecting and managing complex habitats with high community diversity, and identifying localized microclimates that buffer populations against negative effects of warming, are more immediate conservation priorities over elevation gradients than efforts to ensure that communities track prevailing rates of warming.

Dealing with imperfect data for invasive species detection using multispectral imagery

Detection and monitoring invasive species can provide valuable ecological information to guide management decisions. Multispectral imagery remote sensing may be an ideal tool to address this problem by providing accurate and affordable repeat imagery. However, developing training datasets for remote sensing imagery can be riddled with issues such as matching a training site to a single pixel in target imagery or mixed pixels caused by low invader densities. The multitarget multiple-instance spectral match filter (MTMI-SMF) algorithm accounts for such errors and has low computation costs. Here, we evaluated MTMI-SMF for invasive species detection with National Agriculture Imagery Program (NAIP), RapidEye, and Landsat imagery using Brazilian peppertree (Schinus terebinthifolia) in the Everglades National Park as a case study. MTMI-SMF detected Brazilian peppertree with an area under the curve (AUC) of 0.85 for Landsat, 0.82 for RapidEye, and 0.71 NAIP imagery. Invader classification was developed using a threshold calculated from MTMI-SMF image detection confidence values and compared to a random forest classification. MTMI-SMF and random forest classifications had similar overall accuracies with NAIP imagery performing the lowest (an average of 73.6% and 88.9%, respectively) and Landsat performing the highest (an average of 90.9% and 91.4%, respectively). However, due to the relatively rare nature of Brazilian peppertree across this landscape, the overall classification accuracy was not fully representative of detection capabilities. MTMI-SMF outperformed random forest for user accuracy and had comparable producer accuarcy across all three image sources. Classifications primarily relied on the brighter signature of Brazilian peppertree due to the limited number of spectral bands in the multispectral imagery. As a result, confusion was caused by heterogeneous vegetation communities or shrub habitats, which most likely could be resolved with the increased availability of satellite hyperspectral datasets. Our study indicates that MTMI-SMF, a machine-learning approach that allows flexibility in training data, can detect highly problematic invaders using multispectral imagery. This pipeline could be readily adapted to other invaders and ecosystems, as well as different remote sensing image sources, and has low computational requirements. Our study demonstrates that remote sensing technologies and multiple-instance learning algorithms can provide managers with critical tools for tackling the ever-growing, costly, and challenging problem of tracking invasive species' plant spread.

Spatiotemporal Variation in Vegetation and Its Driving Mechanisms in the Southwest Alpine Canyon Area of China

The Southwest Alpine Canyon Area (SACA), a well-known ecological vulnerability region, plays a very important role in China. Identifying the driving force of the spatial heterogeneity of vegetation and the response of interannual vegetation changes to climate change and human activities would be helpful for ecosystem management. Based on the NDVI dataset, the study analyzed the trend of NDVI change from 2000 to 2019 using the Theil–Sen trend analysis and the Mann–Kendal significance test, detected the driving forces of the spatial heterogeneity of NDVI by the means of the geographical detector, and analyzed the relative contribution of climate change and human activities to interannual NDVI changes using residual analysis model. The results showed that, in terms of the spatial distribution, the pattern of NDVI showed that it is higher in the southeast and lower in the northwest region of the SACA. Elevation was the dominant factor influencing the spatial heterogeneity of NDVI, with the explanatory power of 64%, much larger than other factors, and vegetation type, temperature, precipitation, land use type, and soil type were the main factors. In addition, the explanatory power of the dual factor interaction was higher than that of the single factor effect, which showed two kinds of interaction relationships: bivariate enhancement and nonlinear enhancement. In terms of the temporal variation, 85.59% of the study area showed an increasing trend, and only 14.41% of the area showed a decreasing trend. The main factor affecting NDVI changes was human activities, and climate change was the secondary factor, with relative contributions of 71.35% and 28.65%, respectively. The study will promote a better understanding of the complex mechanisms of vegetation changes and provide scientific recommendations for the prevention of vegetation degradation and vegetation restoration in the SACA.

Dynamic Plant–Herbivore Interactions between Bison Space Use and Vegetation Heterogeneity in a Tallgrass Prairie

Investigations into plant–herbivore interactions are of importance for understanding grassland ecosystem dynamics. Our research quantified the effects of vegetation heterogeneity at a patch scale of 30 m on bison space use in a tallgrass prairie through the analyses of the resource utilization function. In addition, we assessed the vegetation heterogeneity associated with bison locations by comparing the patch-scale vegetation characteristics between areas with high and low bison space use through Mann–Whitney U tests. Furthermore, we simulated the interactions between bison and vegetation patches (2 × 2 m) during the early growing season for the lowland topographic positions using agent-based modeling (ABM) as a preliminary study of linking bison foraging site selection with vegetation responses to bison grazing dynamically. The bison grazing strategy in the ABM of the grassland system was adjusted to ensure consistency in the vegetation pattern variations related to bison space use between the simulation and the empirical on-the-ground observations. The results indicated the following: (1) The effects of the patch-scale vegetation heterogeneity on the bison foraging site selection varied across the seasons, which were most evident in the middle of the growing season. (2) A relatively high level of bison space use generally resulted in diverse grassland canopies with high variability and interspersion. (3) From the ABM of the grassland system, it can be implied that bison select patches with high quality and quantity at the beginning of the growing season; as the vegetation quality and quantity improve overall, the bison graze randomly. This pattern was confirmed by observations of the bison foraging site selection in our study site. The ABM proved to be valuable in exploring and elucidating the underlying mechanisms of the grassland dynamics with a native North American grazer.

The Relationship Between Soil-Transmitted Helminth Infections and Environmental Factors in Puerto Iguazú, Argentina: Cross-Sectional Study.

Soil-transmitted helminths (STHs) are widely distributed throughout the world. Various factors, including the environment, socioeconomic characteristics, and access to water and sanitation, play an important role in the spread and persistence of these parasites within communities. They, in turn, affect the growth and development of members of the community, especially children. Studies in the northern provinces of Argentina have shown variable prevalence of STHs, but the factors associated with their presence have not been completely elucidated. This cross-sectional study aimed to identify the socioeconomic and environmental factors related to STH infection in indigenous villages located in Puerto Iguazú (Misiones), Argentina. Between 2018 and 2019, stool samples were collected from individuals ≥1 year residing in 3 villages: Mini-Marangatú, Yriapú, and Fortín Mbororé. Standard parasitological methods were used to determine STH prevalence. Standardized questionnaires were used to assess participants' habits, customs, and household characteristics, and environmental data were obtained through satellite imagery. Multilinear regression with Akaike information criterion stepwise variables was used to explore relevant associations. A total of 342 individuals from the 3 villages participated in this study. The prevalence of STHs varied across villages: 89.6% (43/48), in Mini-Marangatú, 80.8% (101/125) in Yriapú, and 68.5% (115/169) in Fortín Mbororé. Notably, there was a significant difference in hookworm infection among the villages (P=.02). The analysis highlighted the significant influence of specific environmental factors on STH presence and spatial distribution, particularly in relation to hookworm infection. Vegetation patterns represented by the Vegetation Heterogeneity Index, created ad hoc for this study, emerged as a critical factor, with 2 significant predictors related to it (P=.002 and P=.004) alongside impervious surface density with a significant predictor (P<.001). The multilinear regression model yielded a high F test score (F108=4.75, P<.001), indicating a strong fit (R2=0.5465). Furthermore, socioeconomic factors, including walking barefoot in houses with dirt floors and overcrowding, were significantly correlated with hookworm infection intensity (P<.001 and P=.001, respectively). We also used the multilinear regression model to calculate hookworm infection intensity (F110=21.15, P<.001; R2=0.4971). Our study underscores the complexity of STH transmission, as villages with similar living conditions and environmental characteristics displayed varied STH prevalence and spatial distribution. Specific environmental factors, such as vegetation pattern and impervious surface density, played major roles in STH presence, demonstrating the crucial relationship between environmental factors and hookworm infection distribution. Moreover, our findings emphasize the significant influence of socioeconomic factors on hookworm infection intensity. By gaining insights into this complex interplay, our research contributes to a better understanding of STH transmission characteristics, thereby informing targeted public health interventions for effective control.

Untangling fuel, weather and management effects on fire severity: Insights from large-sample LiDAR remote sensing analysis of conditions preceding the 2019-20 Australian wildfires

Evaluation of fire severity reduction strategies requires the quantification of intervention outcomes and, more broadly, the extent to which fuel characteristics affect fire severity. However, investigations are currently limited by the availability of accurate data on fire severity predictors, particularly relating to fuel. Here, we used airborne LiDAR data collected before the 2019-20 Australian Black Summer fires to investigate the contribution of fuel structure to fire severity under a range of weather conditions. Fire severity was estimated using the Relative Burn Ratio calculated from Sentinel-2 optical remote sensing imagery. We modelled the effects of various fuel structure estimates and other environmental predictors using Random Forest models. In addition to variables estimated at each observation point, we investigated the influence of surrounding landscape characteristics using an innovative method to estimate fireline progression direction. Our models explained 63–76% of fire severity variance using parsimonious predictor sets. Fuel cover in the understorey and canopy, and vertical vegetation heterogeneity, were positively associated with fire severity. Up-fire burnt area and recent planned and unplanned fire reduced fire severity, whereby unplanned fire provided a longer-lasting reduction of fire severity (up to 15 years) than planned fire (up to 10 years). Although fuel structure and land management effects were important predictors, weather and canopy height effects were dominant. By mapping continuous interactions between weather and fuel-related variables, we found strong evidence of diminishing fuel effects below 20–40% relative air humidity. While our findings suggest that land management interventions can provide meaningful fire severity reduction, they also highlight the risk of warmer and drier future climates constraining these advantages.

Machine Learning in the Analysis of Carbon Dioxide Flow on a Site with Heterogeneous Vegetation

The article presents the results of studies of carbon dioxide flow in the territory of section No. 5 of the Eurasian Carbon Polygon (Russia, Republic of Bashkortostan). The gas analyzer Sniffer4D V2.0 (manufactured in Shenzhen, China) with an installed CO2 sensor, quadrocopter DJI MATRICE 300 RTK (manufactured in Shenzhen, China) were used as control devices. The studies were carried out on a clear autumn day in conditions of green vegetation and on a frosty November day with snow cover. Statistical characteristics of experimental data arrays are calculated. Studies of the influence of temperature, humidity of atmospheric air on the current value of CO2 have been carried out. Graphs of the distribution of carbon dioxide concentration in the atmospheric air of section No. 5 on autumn and winter days were obtained. It has been established that when building a model of CO2 in the air, the parameters of the process of deposition by green vegetation should be considered. It was found that in winter, an increase in air humidity contributes to a decrease in gas concentration. At an ambient temperature of 21 °C, an increase in humidity leads to an increase in the concentration of carbon dioxide.