Vegetation Cover Types Research Articles

Multiphasic movement and step-selection patterns of dispersed tigers in the central Indian landscape.

Large carnivores play a crucial role in the ecosystem, though their conservation needs a landscape-level approach due to their wide range of habitats and dispersal events. The study of tigers in a human-dominated landscape matrix and their adaptation and adjustment of movement behaviours during the dispersal phase is essential for long-term conservation planning and management policy. We studied the dispersal event of five VHF/GPS collared individuals during 2009-2020. We investigated movement parameters (step length), and the effects of anthropogenic pressures (distance from village), distance from water and vegetation cover, on behavioural phase under a Hidden Markov Model framework. We also tested the effects of distance from village, vegetation cover, and habitat types on animal movement using an integrated Step Selection Function framework. The mean step length (405.44±10.63 m/hr) varied widely by different time of day. Displacement was high during the night (665.28±21.36 m/hr) compared to day (434.16±17.37 m/hr). Tigers moved fast (872.7m; 95% CI 839.1-906.3m) with longer step length and a directional turning angle in non-forested areas (i.e. the human-dominated landscape), likely to avoid conflict with humans. Individuals distinctly exhibited two behavioural states: encamping (~32% of the time) and travelling (~68% of the time). Further, they avoided the human-dominated landscape and mostly remained in and forested areas, especially during nighttime. Our study is the first attempt to understand behavioural transition of dispersal tigers and their habitat selection. Lesser anthropogenic disturbance and high vegetation cover positively influenced the tiger dispersal, while water availability did not affect their state transitional probability. Additionally, dispersers showed high affinity towards forested land during nighttime for travelling.The findings of this study show the importance of functional corridors and stepping stones (mostly encamping areas), and also provide baseline knowledge for integrated landscape management planning and policymaking for the long-term survival of tigers in metapopulation framework.

Changes in salinity and vegetation growth under different land use types during the reclamation in coastal saline soil

The ecological construction in coastal saline-alkali areas urgently needs to be explored in terms of water-salt regulation management and functional irrigation. In this paper, the extremely severe saline soil in the eastern coastal area of the North China Plain was selected for vegetation rehabilitation by drip irrigation. Through two-year field experiments, the spatial and temporal soil salinity-water dynamics of three land use types (LUTS) under two irrigation strategies (IIS) were systematically studied. The results revealed: (1) The soils in the understory grassland and shrub land root zones remained stable for desalination, with the average ECe decreasing to 0.69 dS/m and 0.71 dS/m under autumn irrigation at surface, and 0.66 dS/m and 0.85 dS/m under winter irrigation. And a slight salt accumulation occurred in the bare land in stage IV. (2)The soil surface moisture increased, and the bulk density decreased significantly with drip irrigation. The final moisture of understory grassland and shrub land was 3.85 and 2.97 times that of the bare land layer at 0–10 cm, 2.55 and 1.97 times at 10–20 cm, and 1.61 times and 1.47 times at 20–40 cm, respectively. (3)Due to quick salt rinsing, Salix matsudana and Hibiscus maintained a high survival rate, and the germination of understory vegetation further increased the vegetation coverage. Meanwhile perennial understory herbs gradually became the dominant species, which positively effects on the maintenance of soil low-salt environment. (4)There were significant differences in SWC and ECe between autumn and winter irrigation treatment during stage I and stage II, indicating that irrigation strategies only impact on soil water-salt movement in the early stage. While there was no significant difference between understory grassland and shrub land, indicating that the research about the effects of vegetation cover type on water-salt transport should take a longer time scale.

Vegetation phenology as a key driver for fire occurrence in the UK and comparable humid temperate regions

Background Fire activity in the UK and comparable regions of northwest Europe is generally out of phase with peak fire weather conditions. Aims Here, we assess the potential effect of phenology on fire occurrence patterns for the UK. Methods We examined fire occurrence and vegetation phenology in the UK for 2012–2023, mapped onto the main fire-affected vegetation cover types within distinct precipitation regions, allowing the fire occurrence for fuels in different phenological phases to be explored across distinct ‘fuel’ types and regions. Key results The UK’s fire regime is characterised by burning in semi-natural grasslands and evergreen dwarf shrub ecosystems in early spring when vegetation is still dormant. During the high-greenness phase in late spring and summer, fire activity is reduced by a factor of 5–6 despite typically elevated fire weather conditions within that period. Conclusions and implications Semi-natural vegetation in the UK is very resistant to burning during the high-greenness phase. However, this ‘fire barrier’ is diminished during severe drought episodes, which are predicted to become more extreme in the coming decades. Incorporating phenology information into models therefore has great potential for improving future fire danger and behaviour predictions in the UK and comparable humid temperate regions.

Forest Fire Hazard Forecasting Based on Google Earth Engine Open Satellite Data

Introduction. Forest fires cause significant damage to both the natural fund and the national economy. In recent years, their harmful influence has increased due to global warming, and in Ukraine also due to the armed conflict. Thus, traditional methods of patrolling (ground and air) are not only costly, but also dangerous due to mines and possible shelling of border areas. Therefore, the role of surveillance using satellite systems is increasing. Space monitoring is more efficient and covers a larger area of ??the Earth's surface. Another important advantage is open access to information. The purpose of the paper is to build a mathematical model for determining fire danger based on climatic and biophysical satellite data for the forests of Ukraine, as well as a similar climatic zone with the possibility of further scaling to other climatic regions and types of vegetation cover. To adhere to the principles of open science, Google Earth Engine (GEE), a cloud-based platform that provides open access to dynamic collections of pre-processed Earth remote sensing results, was chosen. Results. Climatic and biophysical data for the forests of Ukraine for the years 2017-2020 were collected using the tools of the Python library for working with GEE data. Further, the obtained data were processed by two methods: linear (PCA) and non-linear (UMAP) in order to obtain statistically independent attributes. Both obtained datasets were subjected to statistical processing using the Bayesian method. Finally, for each point on the map for which information was collected, an indicator was calculated that predicted fire danger if the obtained value was greater than 1 and its absence in the opposite case. The resulting model showed its efficiency on training data. On the test dataset (data on Polish forests for the same period), the results turned out to be worse, in particular, the model using PCA did not predict absence of fire danger, and the model using UMAP generally showed lower performance. This can be due to both the imperfection of the model and the small size of the test dataset or factors unrelated to natural processes (in particular the human factor). Conclusions. An approach to forest fires forecasting based on satellite data is proposed. The obtained results indicate that the model is already effective at this stage, although machine learning methods have not yet been applied. However, it needs further improvement, so work on the model will be continued. Along with improving the quality of forecasts, attention will be paid to the geographic expansion and the creation of a web application. Keywords: fire danger in forests, satellite data, correlation, PCA, UMAP, hard-to-reach areas.

Beyond the surface: microbiological and biochemical attributes as indicators of soil quality in Atlantic Forest ecosystem

The objective of this study was to determine the biochemical and microbiological attributes in areas with different vegetation covers (mature forest, secondary forest, and sugar cane monoculture) and seasonal changes, and to compare the sensitivity of these attributes types (mycorrhizal, enzymatic, and carbon attributes) in response to changes in vegetation cover and season. Soil samplings were conducted over two years (one per semester, during the dry and wet periods of 2007 and 2008) in three types of areas: (I) mature forest; (II) secondary forest; and (III) sugar cane crop. The enzymatic activity of fluorescein diacetate, dehydrogenase, saccharase, basal carbon respiration, the carbon content of microbial biomass, the microbial coefficient, total and easily extracted glomalin soil-related proteins, mycorrhizal colonization, the number of glomerospores, total organic carbon, fulvic and humic acid content, and the fulvic/humic acid ratio were analyzed. In general, mycorrhizal parameters were more sensitive in distinguishing areas with different types of vegetation cover. Additionally, carbon stocks in areas of mature and secondary forest were higher compared to areas cultivated with sugar cane. The study reveals that mycorrhizal properties, such as root colonization and GRSP fractions, are more sensitive indicators of soil health than soil C attributes, providing a cost-effective alternative for evaluating soil health in the Atlantic Forest. The confirmed hypothesis supports the effectiveness of microbiological activity and C dynamics in discerning significant changes between sugar cane cultivation and forested areas. The study underscores the vital role of conserving forested areas, serving as crucial carbon reservoirs, and emphasizes the potential for enhanced sustainability through conservation practices and rapid soil function recovery in secondary forests within the Atlantic Forest.

Influences of climate change on carbon and water fluxes of the ecosystem in the Qinling Mountains of China

Climate change is one of the foremost challenges confronting the contemporary world. Carbon and water cycles and their interconnections of ecosystems are playing a pivotal role in assessing the impacts of climate change on ecosystems. The Qinling Mountains of China represent a focal area for research on global climate change and regional adaptation strategies. Based on historical and CMIP6 (Coupled Model Intercomparison Project Phase 6) future climatic data, the Biome-BGC model, which was sufficiently optimized with the Biome-BGC-PEST package, was used to simulate the historical and future trends of GPP (Gross primary productivity), ET (Evapotranspiration), and WUE (Ecosystem water use efficiency) in the Qinling Mountains, so as to elucidate the dynamics and spatiotemporal variations of carbon and water fluxes amidst climate change. The results showed that the GPP, ET, and WUE of the Qinling Mountains heterogeneously distributed in space, due to the dramatic disparities in carbon and water fluxes among different vegetation types in the Qinling Mountains. Among the various vegetation types, evergreen needle-leaved forest had the highest annual mean value of WUE. In the historical period of 1979-2018, deciduous broad-leaved forest had the highest annual mean value of GPP and ET, while evergreen needle-leaved forest would dominate in the future (2021-2100). For the annual GPP and ET flux variations, different vegetation cover types followed varying peak patterns in the Qinling Mountains. Deciduous broad-leaved forest, evergreen needle-leaved forest, and shrub meadow showed ’single-peak’ pattern annually, whereas the crop area displayed ’double-peak’ pattern due to the different growing seasons of crops. There also were temporal variations for the carbon and water fluxes within the same vegetation type. Future projections suggested a continuous increase in GPP and ET fluxes, underscoring the consistent role of the Qinling Mountains in water resource conservation and carbon sink amid evolving climatic conditions.

Technical note: Surface fields for global environmental modelling

Abstract. Climate change has resulted in more frequent occurrences of extreme events, such as flooding and heavy snowfall, which can have a significant impact on densely populated or industrialised areas. Numerical models are used to simulate and predict these extreme events, enabling informed decision-making and planning to minimise human casualties and to protect costly infrastructure. LISFLOOD is an integrated hydrological model underpinning the European Flood Awareness System and Global Flood Awareness System (EFAS and GloFAS, respectively), developed by the Copernicus Emergency Management Service (CEMS). The CEMS_SurfaceFields_2022 dataset is a new set of high-resolution surface fields at 1 and 3 arcmin resolution (approximately 2 and 6 km at the Equator, respectively) based on a wide variety of high-resolution and up-to-date data sources. The 1 arcmin fields cover Europe, while the surface fields at 3 arcmin cover the global land surface (excluding Antarctica). The dataset encompasses (i) catchment morphology and river networks, (ii) land use, (iii) vegetation cover type and properties, (iv) soil properties, (v) lake information, and (vi) water demand. This paper details the complete workflow used to generate the CEMS_SurfaceFields_2022 fields, including the data sources and methodology. Whilst created together with upgrades to the open source LISFLOOD code, the CEMS_SurfaceFields_2022 fields can be used independently for a wide range of applications, including as input to hydrological, Earth system, or environmental models or for carrying out general analyses across spatial scales, ranging from global and regional levels to local levels (especially useful for regions outside Europe), expected to improve the accuracy, detail and realism of applications.

Building Vulnerability to Landslides: Broad-Scale Assessment in Xinxing County, China.

This study develops a model to assess building vulnerability across Xinxing County by integrating quantitative derivation with machine learning techniques. Building vulnerability is characterized as a function of landslide hazard risk and building resistance, wherein landslide hazard risk is derived using CNN (1D) for nine hazard-causing factors (elevation, slope, slope shape, geotechnical body type, geological structure, vegetation cover, watershed, and land-use type) and landslide sites; building resistance is determined through quantitative derivation. After evaluating the building susceptibility of all the structures, the susceptibility of each village is then calculated through subvillage statistics, which are aimed at identifying the specific needs of each area. Simultaneously, different landslide hazard classes are categorized, and an analysis of the correlation between building resistance and susceptibility reveals that building susceptibility exhibits a positive correlation with landslide hazard and a negative correlation with building resistance. Following a comprehensive assessment of building susceptibility in Xinxing County, a sample encompassing different landslide intensity areas and susceptibility classes of buildings was chosen for on-site validation, thus yielding an accuracy rate of the results as high as 94.5%.

Response of the subalpine bunchgrasses to wildfires and its effects in the relative abundance of the volcano rabbit in the Ajusco-Chichinautzin Mountain Range.

The volcano rabbit (Romerolagus diazi) is a lagomorph endemic to the central mountains of the Trans-Mexican Volcanic Belt and is classified as threatened at extinction risk. It is a habitat specialist in bunchgrass communities. The annual wildfires that occur throughout its distribution range are a vulnerability factor for the species. However, the effects of wildfires on volcano rabbit populations are not fully understood. We evaluated the occupancy and change in the volcano rabbit relative abundance index in the burned bunchgrass communities of the Ajusco-Chichinautzin Mountain Range during an annual cycle of wildfire events. Additionally, we assessed the factors that favor and limit occupation and reoccupation by the volcano rabbit using the relative abundance index in burned plots as an indicator of these processes. The explanatory factors for the response of the volcano rabbit were its presence in the nearby unburned bunchgrasses, the height of three species of bunchgrass communities, the proportion of different types of vegetation cover within a 500 m radius around the burned plots, heterogeneity of the vegetation cover, and the extent of the wildfire. Statistical analyses indicated possible reoccupation in less than a year in burned bunchgrass communities adjacent to unburned bunchgrass communities with volcano rabbits. The relative abundance index of volcano rabbits was not favored when the maximum height of the Muhlenbergia macroura bunchgrass community was less than 0.77 m. When the vegetation around the burned plots was dominated by forest (cover >30% of the buffer) and the fire was extensive, the number of latrines decreased per month but increased when the bunchgrass and shrub cover was greater around the burned plots. While the statistical results are not conclusive, our findings indicate a direction for future projects, considering extensive monitoring to obtain a greater number of samples that contribute to consolidating the models presented.

Effects of Vegetation Cover Varying along the Hydrological Gradient on Microbial Community and N-Cycling Gene Abundance in a Plateau Lake Littoral Zone

The lake littoral zone is periodically exposed to water due to water level fluctuations, driving the succession and distribution of littoral vegetation covers, which complexly affect nutrient biogeochemical transformation. However, the combined effects of water level fluctuations and other environmental factors on microbial characteristics and functions at the regional scale remain unclear. In this study, typical vegetation cover types along various water levels were chosen to investigate the effects of water level and vegetation cover on the microbial community and functional genes in the Lake Erhai littoral zone. The results showed that water level fluctuations influenced oxygen and nitrogen compound contents due to oxic–anoxic alternations and intensive material exchange. Meanwhile, vegetation cover affected the organic matter and total nitrogen content through plant residues and root exudation supplying exogenous carbon and nitrogen. Along the hydrological gradient, the high microbial diversity and abundant microbes related to nitrogen cycling were observed in interface sediments. It was attributed to the alternating aerobic–anaerobic environments, which filtered adaptable dominant phyla and genera. The abundances of amoA AOA, nirS, and amx were higher than those of the other genes and were strongly related to flooding days and water content. In conclusion, water level fluctuations and vegetation type jointly affect microbial community structure and nitrogen-related functional genes.

Using Sentinel-2 Imagery to Measure Spatiotemporal Changes and Recovery across Three Adjacent Grasslands with Different Fire Histories

As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity of wildfires. Recognizing the growing need to monitor the impacts of these smaller, low-severity fires, we leveraged Sentinel-2 imagery to reveal important inter- and intra-annual variation in grasslands before and after fires. Specifically, we explored three methodological approaches: (1) the complete time series of the normalized burn ratio (NBR), (2) annual summary metrics (mean, fifth percentile, and amplitude of NBR), and (3) maps depicting spatial patterns in these annual NBR metrics before and after fire. We also used a classification of pre-fire vegetation to stratify these analyses by three dominant vegetation cover types (grasses, shrubs, and trees). We applied these methods to a unique study area in which three adjacent grasslands had diverging fire histories and showed how grassland recovery from a low-severity intentional burn and a high-severity wildfire differed both from each other and from a reference site with no recent fire. On the low-severity intentional burn site, our results showed that the annual NBR metrics recovered to pre-fire values within one year, and that regular intentional burning on the site was promoting greater annual growth of both grass and shrub species, even in the third growing season following a burn. In the case of the high-severity wildfire, our metrics indicated that this grassland had not returned to its pre-fire phenological signals in at least three years after the fire, indicating that it may be undergoing a longer recovery or an ecological shift. These proposed methods address a growing need to study the effects of small, intentional burns in low-biomass ecosystems such as grasslands, which are an essential part of mitigating wildfires.

The different vegetation types responses to potential evapotranspiration and precipitation in China

Global climate change is considered one of the greatest environmental threats in the world. It is expected to significantly change the global hydrological cycle. The two main water cycle components, potential evapotranspiration (PET) and precipitation (P), are closely related to vegetation dynamics. In this study, the partial correlation analysis method was used to analyzed the relationship between Normalized Difference Vegetation Index (NDVI) and climate factors (PET and P) based on grid cells. PET was calculated by FAO-56 Penman–Monteith method. Moreover, we also investigated the NDVI and climate factors in different vegetation cover types. The results showed that grassland, forest and cropland in China were positively correlated with PET and P. The time scales of the maximum partial correlation coefficients between NDVI and PET of grassland vegetation were mostly longer than 5–6 months. These time scales were longer than the time scales related to P. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China, whereas the spatial distribution of related time scales was the opposite. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China. However, the spatial distribution of related time scales was the opposite. The correlations between NDVI and PET, P of cropland vegetation and the time scales related to PET had clear spatial heterogeneity. The time scale of the correlation between NDVI and P for cropland in the northern China was about 2 months. P had a strong influence on the growth of various types of vegetation in the study area, and grassland vegetation was affected by P over the shortest time scale. We compare and analyze the results of this study with other related studies. These results provide a reference for exploring the dynamic changes in different vegetation types and factors impacting them.

Impacts of Wildlife Artificial Water Provisioning in an African Savannah Ecosystem: A Spatiotemporal Analysis

The use of artificial water points for wildlife in African savannah ecosystems has been widely criticised for affecting the distribution of wildlife and initiating changes in the heterogeneity of natural landscapes. We examined the spatiotemporal variations in the landscape before and after the installation of an artificial water point by integrating the analysis of vegetation and soil spectral response patterns with a supervised learning random forest model between 2002 and 2022 in Chobe Enclave, Northern Botswana. Our results revealed that the study area is characterised by animal species such as Equus quagga, Aepyceros melampus, and Loxodonta africana. The findings also showed that the main vegetation species in the study area landscape include Combretum elaeagnoides, Vachellia luederitzii, and Combretum hereroense. The artificial water point induced disturbances on a drought-vulnerable landscape which affected vegetation heterogeneity by degrading the historically dominant vegetation cover types such as Colophospermum mopane, Dichrostachys cinerea, and Cynodon dactylon. The immediate years following the artificial water point installation demonstrated the highest spectral response patterns by vegetation and soil features attributed to intense landscape disturbances due to abrupt high-density aggregation of wildlife around the water point. Landscapes were strongly homogenised in later years (2022), as shown by overly overlapping spectral patterns owing to an increase in dead plant-based material and senescent foliage due to vegetation toppling and trampling. The landscape disturbances disproportionately affected mopane-dominated woodlands compared to other vegetation species as indicated by statistically significant land cover change obtained from a random forest classification. The woodlands declined significantly (p < 0.05) within 0–0.5 km, 0.5–1 km, 1–5 km, and 5–10 km distances after the installation of the water point. The results of this study indicate that continuous nonstrategic and uninformed use of artificial water points for wildlife will trigger ecological alterations in savannah ecosystems.

Remote sensing of vegetation and soil moisture content in Atlantic humid mountains with Sentinel-1 and 2 satellite sensor data

The satellite monitoring of vegetation moisture content (VMC) and soil moisture content (SMC) in Southern European Atlantic mountains remains poorly understood but is a fundamental tool to better manage landscape moisture dynamics under climate change. In the Atlantic humid mountains of Portugal, we investigated an empirical model incorporating satellite (Sentinel-1 radar, S1; Sentinel-2 optical, S2) and ancillary predictors (topography and vegetation cover type) to monitor VMC (%) and SMC (%). Predictors derived from the S1 (VV, HH and VV/HH) and S2 (NDVI and NDMI) are compared to field measurements of VMC (n = 48) and SMC (n = 48) obtained during the early, mid and end of summer. Linear regression modelling was applied to uncover the feasibility of a landscape model for VMC and SMC, the role of vegetation type models (i.e. native forest, grasslands and shrubland) to enhance predictive capacity and the seasonal variation in the relationships between satellite predictors and VMC and SMC. Results revealed a significant but weak relationship between VMC and predictors at landscape level (R2 = 0.30, RMSEcv = 69.9 %) with S2_NDMI and vegetation cover type being the only significant predictors. The relationship improves in vegetation type models for grasslands (R2 = 0.35, RMSEcv = 95.0 % with S2_NDVI) and shrublands conditions (R2 = 0.52, RMSEcv = 45.3 %). A model incorporating S2_NDVI and S1_VV explained 52 % of the variation in VMC in shrublands. The relationship between SMC and satellite predictors at the landscape level was also weak, with only the S2_NDMI and vegetation cover type exhibiting a significant relationship (R2 = 0.28, RMSEcv = 18.9 %). Vegetation type models found significant associations with SMC only in shrublands (R2 = 0.31, RMSEcv = 9.03 %) based on the S2_NDMI and S1_VV/VH ratio. The seasonal analysis revealed however that predictors associated to VMC and SMC may vary over the summer. The relationships with VMC were stronger in the early summer (R2 = 0.31, RMSEcv = 90.1 %; based on S2_NDMI) and mid (R2 = 0.37, RMSEcv = 70.8 %; based on S2_NDVI), butnon-significant in the end of summer. Similar pattern was found for SMC, where the link with predictors decreases from the early summer (R2 = 0.33, RMSEcv = 16.0 %; based on S1_VH) and mid summer (R2 = 0.30, RMSEcv = 17.8 %; based on S2_NDMI) to the end of summer (non-significant). Overall, the hypothesis of a universal landscape model for VMC and SMC was not fully supported. Vegetation type models showed promise, particularly for VMC in shrubland conditions. Sentinel optical and radar data were the most significant predictors in all models, despite the inclusion of ancillary predictors. S2_NDVI, S2_NDMI, S1_VV and S1_VV/VH ratio were the most relevant predictors for VMC and, to a lesser extent, SMC. Future research should quantify misregistration effects using plot vs. moving window values for the satellite predictors, consider meteorological control factors, and enhance sampling to overcome a main limitation of our study, small sample size.

Effects of rainfall patterns, vegetation cover types and antecedent soil moisture on run‐off and soil loss of typical Luvisol in southern China

AbstractIdentifying the effect of precipitation, vegetation cover and underlying surface conditions on run‐off and soil loss is essential for understanding the mechanism of water erosion. The study site is located in the Shiqiaopu watershed of Hubei Province, China. The long‐term (2017–2021) monitoring data for this study included rainfall characteristics, antecedent soil moisture, run‐off and soil loss in four run‐off plots with four vegetation cover types (tea garden, soybean and rape, peanut and rape, and vetiver zizanioides). K‐means clustering and discriminant analysis were used to classify rainfall patterns. The improved redundancy analysis was used to quantitatively explore the relative importance of rainfall characteristics, vegetation cover and antecedent soil moisture to run‐off and soil loss. The results showed that the rainfall patterns were mainly divided into three categories: A (medium duration, small rainfall, medium rain intensity, high frequency), B (long duration, large rainfall, light rain intensity, low frequency) and C (short duration, medium rainfall, heavy rain intensity, medium frequency). The average run‐off coefficient, run‐off depth of different rainfall patterns were C > B > A, and the cumulative run‐off depth and soil loss under the A rainfall pattern were the largest. For the run‐off plots with four vegetation cover types, vetiver zizanioides had the best effect on run‐off and sediment reduction, while peanut and rape had the worst effect. The driving factor that contributed the most to the run‐off depth was vegetation cover (19.36%), and rainfall characteristics explained the most to the soil loss (11.65%). We also found that although antecedent soil moisture had a small explanation rate for soil loss, it was significantly correlated with the run‐off depth under the vegetation cover of the tea garden. Therefore, regional soil erosion should be combined with the importance of driving factors to take comprehensive control measures.

Integration of Indigenous Research Methodologies, Traditional Ecological Knowledge and molecular scatology in an assessment of mesocarnivore presence, diet and habitat use on Yurok Ancestral Lands.

Partnerships between Tribes and researchers in wildlife monitoring and application of Traditional Ecological Knowledge (TEK) have taken a variety of forms, and some scholars have noted a need for culturally sensitive approaches. Guided by Indigenous Research Methodologies, this research is coupled with Yurok TEK, or hlkelonah 'ue-megetohl ('to take care of the earth'), enabling an applied, culturally sensitive approach in partnership with the Yurok Tribe. We present results from a molecular scatology study of wildlife within the ancestral territory of the Yurok Tribe. Scats were collected opportunistically on road transects. All samples (N = 132) were analysed via DNA barcoding and results matched to documented 'Oohl 'we-toh (Yurok language) names to determine the depositor species (N = 8). Though there were four focal mesocarnivore species in our study, only bobcat (Chmuuek; Lynx rufus) and gray fox (Wergers; Urocyon cinereoargenteus) were detected as depositor species. Post hoc analyses were conducted to explore distribution, habitat use and selection in a use-availability context, and food habits of these two species. We found almost complete separation of bobcat and gray fox use of transects, as well as indication of partitioning of vegetation cover types and food. We demonstrate an integrated framework of Western and Indigenous sciences that allows the Indigenous researcher to transcend structured academic disciplinary boundaries. Our approach can be modified for partnerships between Tribes, agencies, academics and students for wildlife monitoring in broader geographic regions in various research applications.

Livelihood adaptation to changes in River Ekulu depth, channel width and riverbank vegetation cover type by household breadwinners in selected communities in Enugu, Nigeria

Nigeria and most other developing countries need to explore using socio-ecological approaches to address some ecosystem challenges. Changes in river depth, channel width and riverbank vegetation cover type of River Ekulu in Enugu, Nigeria, their causes and effects on biodiversity and livelihood adaptation by household breadwinners, who depended on the river for livelihood activities, were investigated. Snowball sampling technique was adopted. Causes of changes in the river depth, channel width and riverbank vegetation cover type and their effects on biodiversity and livelihood adaptation were obtained from literature with Google Forms administered to purposively selected experts. Factor component analysis was used to select the significant ones. Aerial images of the river were captured with a drone. Questionnaire was used to elicit information from the target household breadwinners to corroborate the images or otherwise, the causes and the effects of the changes on biodiversity and livelihood adaptation. Data were analyzed to test seven null hypotheses bordering on the objectives of the study. Results showed that the observed changes led to adverse effect on biodiversity and the attendant imperative livelihood adaptation. Factors causing the changes in the river depth, width and riverbank vegetation cover type were climate change (factor weight, fw, 0.44–0.54) and increase in population density (fw 0.46–0.63). Recommendations include conscientisation of government and riverine neighbourhood dwellers on the observed ecological changes, their causes and effects, and ways-forward on policies and practices.

Multi-Temporal Analysis of the Impact of Summer Forest Dynamics on Urban Heat Island Effect in Yan’an City

In this study, MODIS land products and China land cover datasets were used to extract normalized difference vegetation index, land surface temperature, and vegetation cover type in Yan’an City during the summers of 2017–2022. On this basis, analysis of spatial change and correlation were carried out as a way to study the mitigation effect on urban heat islands in Yan’an City with forest. The study showed that: (1) The coverage of normalized difference vegetation index over 0.4 in summer in Yan’an City increased from 59.38% to 69.12%, and the vegetation showed good growth conditions. It has a spatial distribution pattern of more in the south and less in the north. (2) The proportion of the urban heat island in Yan’an City increased from 15.51% to 16.86%. Urban heat island intensity fluctuated year by year, with the maximum urban heat island intensity of 6.26 °C appearing in 2019. It has a spatial distribution pattern of less in the south and less in the north. The transition rate of temperature field grade from low to high is 73.32%, and the transition rate to low is only 0.31%. (3) There is a negative correlation between land surface temperature and normalized difference vegetation index in Yan’an City. Vegetation has a mitigating effect on the UHI and the best cooling effect among the vegetation is shown by forest. The cooling effect of forest in Yan’an City is attenuated by an increase in distance, and the effective range is greater than 1000 m. In this study, the regulation effect of forest on the urban heat island was obtained by digging deeper into the intrinsic connection between spatial change in vegetation cover and land surface temperature change in Yan’an City. It provides an important reference for the formulation of meteorological protection policy as well as the promotion of sustainable development of the urban ecological environment and is of guiding significance for future urban planning and ecological construction.

Vegetation diversity and structure influence small‐mammal communities in native and restored northern mixed grasslands

AbstractCurrent grassland restoration strategies aim to recreate grassland vegetation communities, and often rely on high‐diversity native seeding to promote vegetation diversity. Questions remain concerning the influence of vegetation richness and diversity on grassland fauna. Small‐mammal communities are integral parts of grassland ecosystems, but their responses to restoration are often mixed or overlooked. During July 2014 to 2016, we used Sherman live traps to survey grassland small‐mammal communities of 24 study sites in northeastern South Dakota and southeastern North Dakota, USA, to better understand their responses to vegetation cover type, diversity, richness, and site‐specific vegetation structure. Sites represented a vegetation species richness gradient and 3 vegetation cover types including low‐diversity restorations planted with dense nesting cover (DNC) seed mix, high‐diversity seeded restorations, and unseeded reference grasslands. Small‐mammal abundance was highest at low‐diversity DNC restoration sites and lowest in reference grassland. Small‐mammal diversity was highest at high‐diversity restoration sites and lowest at low‐diversity DNC restoration sites. Models assessing the influence of vegetation structure on the abundance of focal taxa differed. Deer mice (Peromyscus spp.) were negatively influenced by percent native vegetation cover, and voles (Microtus spp.) showed yearly variation and were influenced positively by litter depth and negatively by vegetation richness. Small‐mammal communities of low‐diversity DNC restorations differed from reference sites, but high‐diversity restorations were not different from reference or low‐diversity DNC sites. Thirteen‐lined ground squirrel (Ictidomys tridecemlineatus) abundance was higher at reference and high‐diversity restored sites, while low‐diversity DNC sites had higher deer mice abundance. Results indicate small mammals are unlikely to respond uniformly to vegetation characteristics, and diversity of seed mixes used in grassland restoration is likely to influence grassland small‐mammal communities.

Detection Probability and Bias in Machine-Learning-Based Unoccupied Aerial System Non-Breeding Waterfowl Surveys

Unoccupied aerial systems (UASs) may provide cheaper, safer, and more accurate and precise alternatives to traditional waterfowl survey techniques while also reducing disturbance to waterfowl. We evaluated availability and perception bias based on machine-learning-based non-breeding waterfowl count estimates derived from aerial imagery collected using a DJI Mavic Pro 2 on Missouri Department of Conservation intensively managed wetland Conservation Areas. UASs imagery was collected using a proprietary software for automated flight path planning in a back-and-forth transect flight pattern at ground sampling distances (GSDs) of 0.38–2.29 cm/pixel (15–90 m in altitude). The waterfowl in the images were labeled by trained labelers and simultaneously analyzed using a modified YOLONAS image object detection algorithm developed to detect waterfowl in aerial images. We used three generalized linear mixed models with Bernoulli distributions to model availability and perception (correct detection and false-positive) detection probabilities. The variation in waterfowl availability was best explained by the interaction of vegetation cover type, sky condition, and GSD, with more complex and taller vegetation cover types reducing availability at lower GSDs. The probability of the algorithm correctly detecting available birds showed no pattern in terms of vegetation cover type, GSD, or sky condition; however, the probability of the algorithm generating incorrect false-positive detections was best explained by vegetation cover types with features similar in size and shape to the birds. We used a modified Horvitz–Thompson estimator to account for availability and perception biases (including false positives), resulting in a corrected count error of 5.59 percent. Our results indicate that vegetation cover type, sky condition, and GSD influence the availability and detection of waterfowl in UAS surveys; however, using well-trained algorithms may produce accurate counts per image under a variety of conditions.