Rangeland Ecosystems Research Articles

Understory Vegetation Response to Thinning Pinyon-Juniper Woodlands

Portions of the western United States, extending from west Texas to eastern Oregon, are thin to densely populated with juniper (Juniperus spp.) woodlands. Managing tree densities, such as through mechanical thinning, has numerous ecological implications for rangeland watersheds and wildlife habitat. The objective of this study was to determine vegetation response to juniper removal in a pinyon (Pinus edulis Engelm)− and juniper (Juniperus monosperma Engelm)− dominated landscape in north-central New Mexico. Herbaceous cover and standing crop were collected from six 1.00- to 1.35-ha catchments situated within the New Mexico State University (NMSU)−Santa Fe Ranch, Santa Fe County, New Mexico, before treatment and at 5 and 10 yr following juniper removal. About 70% of mature juniper trees were cut in 2009 in three of the catchments (treated) while the remaining catchments were left as controls (untreated). Herbaceous cover and standing crop were measured in 2009 (pretreatment), 2014, and 2019 (post treatment) to test our hypothesis that juniper reductions would significantly increase herbaceous standing crop. After 10 yr, gravel, grass, and forb cover were comparable in the treated and the untreated, whereas bare soil cover was significantly decreased in the treated (30% ± 2.5) compared with the untreated (42% ± 2.5). Litter cover in the treated was higher (18% ± 2.6) than the untreated (5% ± 2.6). Herbaceous standing crop significantly increased in the treated compared with the untreated. Standing crop of grass in the treated was two to three times greater than the untreated. Forb standing crop was not influenced by treatment but increased over time in both treatments. Study findings provide land managers with critical information regarding one-seed juniper clearing effects on herbaceous vegetation response in the warm-climate rangeland ecosystems of the southwestern United States.

Influence of Pastoral Settlements Gradient on Vegetation Dynamics and Nutritional Characteristics in Arid Rangelands

An expansion of pastoral settlements in arid rangelands can increase pressure on fragile ecosystems. High stocking rates and inappropriate rangeland management can cause large, difficult-to-reverse changes in the composition and structure of rangeland ecosystems. This study aims to understand changes in vegetation composition (cover, density, biomass, richness, diversity) and nutritional characteristics of range vegetation along a gradient of increasing pastoral population in arid rangelands of Pakistan. Three sites were selected for sampling within three areas classified by their distance from settlement: Near, Away, Far (9 sites in total) belt transects (10 × 150 m). A total of 90 transects (30 each in classified sites) of size 10 × 150 m were placed at a distance of 100 m apart from each distance group. Results identified 28 species from nine plant families. We found a significant difference in vegetation characteristics along the gradient. Vegetation diversity increased along the settlement gradient, highest being in the far sites. Density, plant cover and biomass were greatest at the sites near pastoral communities and decreased as the distance from the settlements increased. Palatable species were characterized by low density and diversity near herder’s villages, but values increased as the distance from the villages increased. An increase in shrubs was observed near pastoral settlements, resulting in higher plant cover, biomass and density in these sites. This study identified anthropogenic impacts on rangeland structure and composition and found large shifts near communities. The frequent monitoring of vegetation resources is important, and the development of sustainable conservative strategies are recommended to ensure harmonious coexistence of pastoral populations in arid rangelands.

Effects of grazing and fire management on rangeland soil and biocrust microbiomes

Biocrusts play important roles in rangeland ecosystems by protecting soil surfaces and fixing carbon and nitrogen. Their responses to rangeland management practices, however, are poorly understood. Here, we characterised the impacts of cattle grazing and fire management (controlled 2- or 4-yearly burning versus no fire) on the diversity and composition of biocrust and associated soil bacterial communities (0–1 cm depth) in a long-term (30–60 years) field experiment in the Northern Territory, Australia. Both experiments were replicated on two soil types (vertosol, calcarosol). For the grazing experiment, we also characterised samples from 0 to 10 cm depth. Significant effects of grazing on bacterial community composition were only detected in the vertosol, where it was generally associated with enrichments of cyanobacterial taxa in the 0–1 cm samples, and more varied responses in 0–10 cm samples. In contrast, despite some minor reductions in the relative abundances of Bacillus populations in more frequently burned sites (late season 2-yearly burning), we did not observe any significant impacts of fire management on the overall composition of bacterial communities. Our findings indicate that the presence of livestock in rangelands increases the proportional representation of cyanobacteria within biocrust and associated soil microbiomes, and that these communities, at least from a taxonomic perspective, are not strongly impacted by fire management.

Pathways to coexistence with dingoes across Australian farming landscapes

IntroductionAgriculture and biodiversity conservation are both vitally important human activities that overlap geographically and are often in conflict. Animal agriculture has been implicated in species loss and the degradation of ecosystems due to land clearing, overgrazing, and conflicts with large carnivores such as dingoes (Canis dingo). This paper explores the potential for transformation in Australian commercial livestock production from human-dingo conflict towards social-ecological coexistence.MethodA qualitative model that depicts transformative change was developed from field observations and twenty-one in-depth interviews with livestock producers, conservation researchers, grazing industry representatives and policy makers across Australia. The model articulates the current state of dingo management and the drivers of system change.ResultsSeven pathways are described to catalyse transformation from routine lethal management of dingoes towards a future vision that embeds mutually beneficial coexistence. Central to transformation is the adoption by livestock producers of preventive non-lethal innovations supported by a new farming movement, Predator Smart Farming, that balances livestock grazing and wildlife conservation values to unlock the resilience of landscapes, animals (domesticated and wild) and livelihoods. Other key pathways include targeted research, capacity building, outreach and knowledge sharing networks; institutional (policy, legislation, and economic incentives) and cultural change; public awareness raising and advocacy to reduce lethal control; and greater involvement of Indigenous Australians in decisions relating to wildlife management.DiscussionThe seven transition pathways are discussed in relation to how they can collectively foster coexistence with dingoes in extensive rangelands grazing systems. International examples of interventions are used to illustrate the types of successful actions associated with each pathway that could inform action in Australia. The findings have implications for coexistence with large carnivores in rangeland ecosystems globally.

Spatial variability of herbage yield, grazing capacity and plant diversity in a tropical savannah rangeland ecosystem

To improve ruminant livestock production, evaluation of rangelands must be a routine. Rangeland evaluation gives information about the vegetation structure, biomass yield and quality. The Guinea savannah rangelands of Ghana lack research that characterises the spatial variability of herbage yield and quality. It was hypothesised that there is spatial heterogeneity in herbage yield, grazing capacity and plant diversity in the Guinea savannah rangelands of Ghana. The objective was to evaluate the spatial structure of herbage production and grazing capacity in the Guinea savannah rangelands of Ghana for sustainable livestock production. Data were collected from 105 sampling sites and integrated into geo-statistics, using ordinary kriging interpolation to generate herbage yield and grazing capacity estimates. Herbage yield and grazing capacity ranged from 0.63 t ha−1 to 13.43 t ha−1 and 0.18 LU ha−1 to 3.79 LU ha−1 respectively. The root mean square error and the average standard error values were close (2.38 and 2.51 respectively for herbage yield and 0.67 and 0.71 respectively for grazing capacity). Species diversity using the Shannon’s index ranged from 1.13 to 2.40. There was spatial heterogeneity in herbage yield, grazing capacity and species diversity in Ghana’s Guinea savannah rangelands with some parts needing effective site-specific improvement strategies for sustainable livestock production.

Modeling Weather Effects on Plant Production in the California Annual Grassland

Weather drives plant community assembly and seasonal plant production in most rangeland ecosystems. Previous models in the California Annual Grassland have indicated that plant production is largely determined by the thermal accumulation, or degree days, between the start and end of the growing season. These models, however, were relatively limited in spatial and temporal scope and did not include seasonal parameters commonly used in more current production-system models. The purpose of this study is to develop models that account for the annual and seasonal effects of weather inputs on plant production. Weather variables for these models included seasonal effects of accumulated degree days, total precipitation, average temperature, season length, past year's precipitation, and past year's plant production. Our results indicated that sorting precipitation years into low, moderate, and high groupings resulted in better production-model fit compared with a full-suite analysis of all precipitation years. In addition, we found that precipitation, temperature, past year's precipitation, and past plant production significantly improved model fit over models that considered only accumulated degree days and season length. Linking these models to seasonal forecasting applications across western rangeland ecosystems could significantly enhance management of grazing production systems in this environment.

Precipitation, Vegetation, and Groundwater Relationships in a Rangeland Ecosystem in the Chihuahuan Desert, Northern Mexico

For this study, conducted in a semiarid (318 mm) rangeland setting in the Chihuahuan Desert region in northern Mexico, we evaluated the seasonal and interannual variability of precipitation, vegetation, and groundwater relations. Between 2012 and 2014, a series of soil and water conservation practices (e.g., land imprinting, contour furrows, and planting of native shrub species) were conducted in several areas within the 2500 ha study site. Since 2014, the site has been gradually instrumented to monitor several hydrologic variables, including rainfall, soil water content, and groundwater. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII) vegetation indices were used to evaluate vegetation conditions between 2007 and 2021, before and after the treatment. Soil water content and groundwater began to be monitored in 2014 and 2016, respectively. Study results show that NDVI and NDII values were higher in the years following the treatment. A negative trend in NDVI values was observed in the years before restoration and reversed in the post-treatment years. The relatively low levels of soil water content obtained every year followed a seasonal response to precipitation inputs characterized by a quick rise and decline at the 0.2 m depth and a more gradual rise and decline for sensors at 0.5 m and 0.8 m depths. A positive trend in groundwater levels has been observed since the onset of monitoring in 2016, with seasonal groundwater levels rising between 0.7 m and 1.3 m for most years, except for 2020, when levels dropped 1 m. The yearly recharge of the aquifer ranged between 102 mm and 197 mm. The conservation practices employed have positively affected the state of the rangeland ecosystem. The upward trends in NDVI, NDII, and groundwater levels observed in the post-treatment years were partly attributed to improved land conditions. The findings of this study contribute to the improved understanding of land use and environmental relations in summer precipitation-dominated rangeland ecosystems.

Spatial E-PSR modelling for ecological sensitivity assessment for arid rangeland resilience and management

In semi-arid regions, land vulnerability and food security are crucial challenges for the scientific community, as millions of livestock keepers depend on rangeland ecosystem services. However, the Aravalli range is a fragile ecotonal arid ecosystem of western India, which acts as a barrier against the desertification of the Thar Desert. Besides increasing biotic pressure, land cover change, precipitation variability, and climate water deficits have a high potential to render the exploitation of resources unsustainable. This study is on the urgent basis addressing the sensitivity, fragility, and vulnerability status for rangeland ecosystem conservation management practices. Based on 17 distinct environmental indicators under five themes: topography, geo-climatic, socio-economic, ecological, and edaphic, which measure their susceptibility. Then, they evaluate their characteristics and sensitivity responses, as well as the resilience capabilities of the areas, utilizing the integrative methodology of the E-PSR (Ecosystem – Pressure, Sensitivity, and Resilience) model using a weightage decision matrix geostatistical approach. As a result, the study area is categorised into four eco-sensitive areas: I, II, III, and IV; they have an area of 4.1%, 62.5%, 32.2%, and 1.2%, respectively. As well, vulnerability reasons, i.e., human intervention, regulations, and management strategies for the arid rangeland ecosystem were discussed. To achieve eco-sensitivity and ecosystem stability in this study area, the government and conservationists must manage invasive species dominance and adjust buffer zones in reserve areas. In addition, study suggests using cutting-edge-technology for monitoring. In sum, this study helps governments and ecologists understand arid mountainous environments, their sensitivity and management.

Maxent Modeling for Predicting Habitat Suitability and Potential Distribution of Plateau Pika (Ochotona curzoniae) on the Qinghai-Tibet Plateau, China

The plateau pika (Ochotona curzoniae) is a keystone species in the alpine rangeland ecosystem on the Qinghai–Tibet Plateau (QTP). However, it has been considered a pest because these mammals dig holes and make bare patches, which reduces grassland productivity. Accordingly, there is a trade-off required between biodiversity conservation and pest management. Habitat assessment is one of the critical approaches in small mammal management and protection. Although a few papers have reported the distribution of plateau pika and predicted its potential distribution under climate change at local scales in some areas, little is known about the synergistic effect of climate change and human disturbance on the wider distribution of this mammal on the QTP. In this study, we recorded 219 points of plateau pika presence on the QTP via Global Positioning System and used the Maxent model to predict potential suitable habitats. The results indicated that, under the current climate, the potential distribution of plateau pika is mainly located in southern Gansu Province, eastern Qinghai Province, and northwestern Sichuan Province, accounting for 4.44% of the total area of the QTP. The most important factors in defining habitat suitability and limiting the distribution of plateau pika include normalized difference vegetation index (29.1%), human footprint (21.6%), seasonal precipitation (11.2%), and elevation (7.8%). In the future, in the 2030s and 2050s, the total area of suitable habitat on the QTP is projected to decrease, while the habitable region in northwest Sichuan and eastern Qinghai will likely increase.

Fish sonar image recognition algorithm based on improved YOLOv5.

Fish stock assessment is crucial for sustainable marine fisheries management in rangeland ecosystems. To address the challenges posed by the overfishing of offshore fish species and facilitate comprehensive deep-sea resource evaluation, this paper introduces an improved fish sonar image detection algorithm based on the you only look once algorithm, version 5 (YOLOv5). Sonar image noise often results in blurred targets and indistinct features, thereby reducing the precision of object detection. Thus, a C3N module is incorporated into the neck component, where depth-separable convolution and an inverse bottleneck layer structure are integrated to lessen feature information loss during downsampling and forward propagation. Furthermore, lowercase shallow feature layer is introduced in the network prediction layer to enhance feature extraction for pixels larger than $ 4 \times 4 $. Additionally, normalized weighted distance based on a Gaussian distribution is combined with Intersection over Union (IoU) during gradient descent to improve small target detection and mitigate the IoU's scale sensitivity. Finally, traditional non-maximum suppression (NMS) is replaced with soft-NMS, reducing missed detections due to occlusion and overlapping fish targets that are common in sonar datasets. Experiments show that the improved model surpasses the original model and YOLOv3 with gains in precision, recall and mean average precision of 2.3%, 4.7% and 2.7%, respectively, and 2.5%, 6.3% and 6.7%, respectively. These findings confirm the method's effectiveness in raising sonar image detection accuracy, which is consistent with model comparisons. Given Unmanned Underwater Vehicle advancements, this method holds the potential to support fish culture decision-making and facilitate fish stock resource assessment.

How resilient are US rangeland ecosystems?

While “resilience” has become a buzzword in agriculture and land management circles, notably as a framework for the response to climate change, there has not been a clear path as to how to organize and deploy a range of resilience-related ideas, tools, and practices to improve climate change response. Generic statements about the need for improved resilience are common, and programs to help enhance resilience are common as a basis for policy development and implementation. These initiatives include references to a range of “climate-smart practices” that should be encouraged, but because they are national strategies, they seldom go beyond general principles. Land management requires a level of spatiotemporal precision in decision-making, planning, and application that calls for much more than broad principles and generic practice. This paper reviews the concepts that have made resilience an important part of land management goals across policy, programs, and application; demonstrates how those concepts can be organized and applied to decision-making to respond to climate change on rangelands; and finally, proposes some approaches that can help improve the value of a resilience-based approach.

Measuring Smoke Emissions from Prescribed Rangeland Burning in the Flint Hills Region Using Unmanned Aircraft Systems

Highlights Smoke samples collected with UAS during rangeland prescribed burns were used to develop representative smoke emission factors for rangeland fires. The head fire emission factors of PM2.5 and NOx were consistent with the low end of the range of flaming emission factors in the literature. The combination of high air temperatures and high absolute humidity resulted in higher levels of PM2.5 and O3 in smoke. Abstract.Prescribed burning is an ecological process critical to maintaining and improving rangeland ecosystems. Smoke impacts related to prescribed burning have been the subject of intense discussion and public debate. The objectives of this study were to collect accurate smoke emissions data using unmanned aircraft systems (UAS) and use the data to develop smoke emission factors representative of prescribed rangeland fires in the tallgrass prairie Flint Hills region of Kansas and Oklahoma. The emission factors will be used to improve the input parameters used in smoke modeling tools. Four prescribed burns on unique burn units were sampled for this study. Smoke emission factors were determined using the carbon mass balance method. Average emission factors for head fires were: PM2.5, 11.3±10.8g/kg fuel; NOx as NO, 1.4±0.9 g/kg fuel; CO2, 1569±28 g/kg fuel; CH4, 6.8±4.3 g/kg fuel; NMHC as propane, 3.3±2.5 g/kg fuel; and VOC as propane, 4.5±3.5 g/kg fuel. Compared with head fires, back fires tended to produce lower emissions of PM2.5 but higher emissions of NOx and VOC. Green, high-moisture vegetation present during the growing season fires in September resulted in a larger head fire PM2.5 emission factor compared to April fires. Generally, the combination of high air temperature and high absolute humidity resulted in high PM2.5 and O3 in smoke. Conducting prescribed fires under conditions of cool air temperature and low absolute humidity can reduce the generation of PM2.5 and O3 in smoke, as long as these conditions fall within the prescribed range for the burn. Keywords: Carbon mass balance, Drone, Fire, Grass, Ozone, Pasture, Prescribed burning, Rangeland management, Rangelands, Smoke emission factor, Tallgrass prairie, Unmanned aircraft system.

Plant Community Transformations in Desert Rangeland Ecosystems – Evidence from the North-Western Caspian Lowland

Abstract—The article presents the results of long-term monitoring of successions, and the production dynamics in desert rangeland phytocoenoses over an 8-year period (2014–2021). It shows the current state of vegetative cover of the Chernozemelskaya sandy plain (north-west of the Caspian lowland, Republic of Kalmykia) following forest reclamation of fine-grained sands, carried out in the 1970s–1990s. The centers of soil deflation (aeolian erosion) differed by the degree of soil and vegetative cover transformation and the methods of the reclamation of open sands. Currently, a secondary vegetative cover has been formed on the previously identified deflation patches. The expansion and evolution of the current species composition suggests the development of secondary succession. The features of the synusial structure of desert phytocenoses reflect the state of plant communities. The relationship of the productivity of model pastures (shrub Callygonum aphyllum, subshrub Krascheninnikovia ceratoides, herbaceous species Stipa lessingiana, Artemisia lerchiana, Alyssum deserforum, Bromus tectorum, Carex stenophylla, Koeleria macrantha, Poa bulbosa) with precipitation in warm and cold periods of the growing season is analyzed. Under changing temporal and spatial climate dynamics, the successional processes are greatly influenced by wildfires and anthropogenic impact. It has been established that secondary successions under heavy transformation of the agro-pasture landscape develop more slowly. Thus, the natural and landscape environment of the current deflation centres in the rangelands of the North-Western Caspian region is an important factor determining the direction of succession.

Comparing Actual and Potential Productions of Rangeland Ecosystems of Kohgiluyeh and Boyer-Ahmad Province in Different Rangeland Conditions

Comparing Actual and Potential Productions of Rangeland Ecosystems of Kohgiluyeh and Boyer-Ahmad Province in Different Rangeland Conditions

Remotely sensed dry matter productivity and soil moisture content as potential predictors of arid rangeland wildfires: A case study of Kgalagadi District, Botswana

Fire is a critical tool for managing rangeland ecosystems; however, the increasing wildfire occurrence poses a considerable danger to rangeland ecosystem continuity. Predicting fire occurrence and mapping wildfire danger is critical in managing highly flammable rangelands. To identify potential remotely sensed variables for wildfire prediction, this study employed a Random Forest (RF) classifier using selected environmental variables to assess their possible use for wildfire prediction in Kgalagadi District, Botswana. The study used 107,883 active fire points from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor from 2015 to 2021. Datasets of remotely sensed Dry Matter Productivity (DMP), Soil Moisture (SM), Land Surface Temperature (LST), Live Fuel Moisture Content (LFMC), and Dead Fuel Moisture Content (DMFC) were analysed in ArcMap 10.7 Esri©. The RF model developed gave an Out of Bag (OOB) error of 9.91% and an overall accuracy of 90.15% for classifying fires and non-fire points using the test dataset. The results also showed a Kappa coefficient of 0.803, with 88.25% and 91.76% producer and user accuracies, respectively, for classifying fire points. The DMP was the most significant variable with Mean Decrease Accuracy (MDA)= 1,055.20 and Mean Decrease Gini (MDG)= 9.328.62), followed by SM (MDA= 828.39 and MDG= 15,745). The LFMC and DMFC were found to be weak in detecting fires. It is recommended that field studies be carried out in the study area to calibrate these to improve their contribution to accurate fire prediction, as most literature shows that they are significant in fire prediction.

Disease and weather induce rapid shifts in a rangeland ecosystem mediated by a keystone species (Cynomys ludovicianus).

Habitat loss and changing climate have direct impacts on native species but can also interact with disease pathogens to influence wildlife communities. In the North American Great Plains, black-tailed prairie dogs (Cynomys ludovicianus) are a keystone species that create important grassland habitat for numerous species and serve as prey for predators, but lethal control driven by agricultural conflict has severely reduced their abundance. Novel disease dynamics caused by epizootic plague (Yersinia pestis) within prairie dog colonies have further reduced prairie dog abundances, in turn destabilizing associated wildlife communities. We capitalized on a natural experiment, collecting data on prairie dog distributions, vegetation structure, avian abundance, and mesocarnivore and ungulate occupancy before (2015-2017) and after (2018-2019) a plague event in northeastern Wyoming, USA. Plague decimated black-tailed prairie dog populations in what was then the largest extant colony complex, reducing colony cover in the focal area from more than 10,000 ha to less than 50 ha. We documented dramatic declines in mesocarnivore occupancy and raptor abundance post-plague, with probability of occupancy or abundance approaching zero in species that rely on prairie dogs for a high proportion of their diet (e.g., ferruginous hawk [Buteo regalis], American badger [Taxidea taxus], and swift fox [Vulpes velox]). Following the plague outbreak, abnormally high precipitation in 2018 hastened vegetation recovery from prairie dog disturbance on colonies in which constant herbivory had formerly maintained shortgrass structure necessary for certain colony-associates. As a result, we observed large shifts in avian communities on former prairie dog colonies, including near-disappearance of mountain plovers (Charadrius montanus) and increases in mid-grass associated songbirds (e.g., lark bunting [Calamospiza melanocorys]). Our research highlights how precipitation can interact with disease-induced loss of a keystone species to induce drastic and rapid shifts in wildlife communities. Although grassland taxa have co-evolved with high spatiotemporal variation, fragmentation of the remaining North American rangelands paired with higher-than-historical variability in climate and disease dynamics are likely to destabilize these systems in the future.

Clustering Arid Rangelands Based on NDVI Annual Patterns and Their Persistence

Rangeland ecosystems comprise more than a third of the global land surface, sustaining essential ecosystem services and livelihoods. In Spain, Southeast Spain includes some of the driest regions; accordingly, rangelands from Murcia and Almeria provinces were selected for this study. We used time series metrics and the Hurst Exponent from rescale range and detrended fluctuation analysis to cluster different rangeland dynamics to classify temporally and spatially diverse rangelands. The metrics were only calculated for three time periods that showed significant NDVI changes: March to April, April to July, and September to December. Detrended fluctuation analysis was not previously employed to cluster vegetation. This study used it to improve rangeland classification. K-means and unsupervised random forest were used to cluster the pixels using time series metrics and Hurst exponents. The best clustering results were obtained when unsupervised random forest was used with the Hurst exponent calculated with detrended fluctuation analysis. We used the Silhouette Index to evaluate the clustering results and a spatial comparison with topographical data. Our results show that adding the Hurst exponent, calculated with detrended fluctuation analysis, provided a better classification when clustering NDVI time series, while classifications without the Hurst exponent or with the Hurst exponent calculated with the rescale range method showed lower silhouette values. Overall, this shows the importance of using detrending when calculating the Hurst exponent on vegetation time series, and its usefulness in studying rangeland dynamics for management and research.

Insecticidal Management of Rangeland Grasshoppers Using a Remotely Piloted Aerial Application System

Grasshoppers are integral parts of rangeland ecosystems but also have the potential to reach population densities high enough (outbreaks) to cause serious economic damage from forage loss and affect adjacent crops. The objective of this study was to investigate the efficacy of treating grasshopper population hotspots with a liquid insecticide using a remotely piloted aerial application system (RPAAS), as opposed to fixed-wing aircraft, which is the most common method currently in use. A liquid insecticide, Sevin XLR PLUS (containing carbaryl), was applied on replicated 4.05-hectare (10-acre) plots with an RPAAS on a ranch in New Mexico. Our results demonstrated that Sevin XLR PLUS significantly suppressed grasshopper populations over a 14-day period (normalized population reduction was 79.11 ± 8.35% SEM) and quite rapidly (mostly by day 3) compared to untreated controls. These results are comparable to those achieved with fixed-wing aircraft. The RPAAS covered the whole test area in a single flight in approximately 5 min, making these population hotspot treatment applications relatively rapid, potentially more cost-effective, and more targeted in comparison to fixed-wing aircraft. Before adoption as an application method option, further research is recommended on using an RPAAS to cover larger areas in combination with using diflubenzuron-based insecticides, which are often preferred.

Estimating Rangeland Fine Fuel Biomass in Western Texas Using High-Resolution Aerial Imagery and Machine Learning

Rangeland fine fuel biomass is a key factor in determining fire spread and intensity, while the accuracy of biomass estimation is limited due to inherent heterogeneity in rangeland ecosystems. In this study, high spatial resolution (0.23 m) images were used to classify fuel types and predict rangeland fine fuel biomass in west Texas based on the random forest algorithm. Two biomass models, including one with the fuel type, original spectral bands, and vegetation indices as explanatory variables, and the other that contained a combination of the fuel type, original spectral bands, vegetation, and texture indices as explanatory variables, were assessed. Furthermore, the biomass models were also examined by upscaling the remote sensing images from high to medium (30 m) spatial resolution with the spectral curves derived from Landsat images. The fuel type map had an accuracy of more than 95%, and herbaceous fuel types were kept for estimating fine fuel biomass. The results showed that around 76% and 80% of biomass variances were explained by models without texture indices and with texture indices, respectively. The fuel type and the normalized difference vegetation index (NDVI) were two significant input variables influencing fine fuel biomass for both models and adding texture indices contributed to the improvement of model accuracy. An upscaling analysis for biomass estimation using medium spatial resolution imagery showed that approximately 60% of the variance in biomass was explained by the model. The addition of fractional vegetation cover improved the model performance by explaining an additional 5% of the variance in biomass estimation. These findings indicate that high spatial resolution images have the potential to effectively estimate rangeland fuel types and fine fuel biomass, which can be helpful for mapping the spatial distribution of fine fuels to aid in monitoring and fire management on rangelands.

Effects of Ski-Resort Activities and Transhumance Livestock Grazing on Rangeland Ecosystems of Mountain Zireia, Southern Greece

The objective of the present study was to assess the impacts in time of the ski-resort infrastructure and transhumance livestock grazing on floristic composition, diversity, and rangeland health indices related to ecosystem stability and function. The study was carried out at a site under the pressure of ski resorts and livestock grazing (Ano Trikala) and a site only under the pressure of livestock grazing (Sarantapicho), both located at Mt Zireia, Southern Greece. The plant cover was measured at each site, and the floristic composition was calculated and classified into four functional groups: grasses, legumes, forbs, and woody species. Species richness, ecosystem function and stability landscape indices, diversity indices, and forage value were calculated. According to the results, the development of the ski resort in Ano Trikala had a neglectable negative impact on plant cover (reduced by 5%), while it had a minor impact on species richness and floristic diversity. Livestock grazing had a positive impact on maintaining plant cover in high values. These results suggest that livestock grazing can counterbalance the effects of ski resorts and related activities on plant cover and floristic diversity. Besides the relatively limited effects on the vegetation community, the ski resort significantly negatively impacted landscape composition, function, and stability. Forage value was 25% lower close to the ski resort, mainly due to the significantly lower percentage of legumes. Transhumance livestock grazing should be used as a management tool in ski-resort areas, as it benefits floristic diversity.