This study evaluates the economic implications of hospital building materials in relation to hydro-climatological changes and their impacts on land use and land cover (LULC) dynamics in Khyber Pakhtunkhwa, Pakistan, from 2010 to 2013. Our objectives were to analyze temperature, precipitation, and flood patterns, assess their effects on building material costs, and examine subsequent LULC changes through Remote Sensing and GIS techniques. Using MODIS satellite imagery and meteorological data, we found significant correlations between climatic variables and construction costs, with temperatures ranging from 15°C to 38°C and monthly rainfall peaking at 611mm in 2011. Climate Impact Assessment revealed that extreme weather events, particularly flooding affecting 30,633 km² in 2013, substantially influenced building material durability and costs. Resource availability analysis showed that rangeland area decreased from 30,522 km² to 25,435 km², affecting local construction material sourcing. Water discharge data demonstrated seasonal variations correlating with flooding events, with peak discharge reaching 16,844 m³/s, directly impacting construction site accessibility and material transportation costs. The study found that flood-prone areas experienced up to 25% higher construction costs due to necessary flood-resistant materials and design modifications. These findings highlight the critical relationship between climatic factors, resource availability, and hospital construction costs, providing valuable insights for construction planners and healthcare facility managers to develop cost-effective and climate-resilient building strategies.

In this study, we investigated the effects of nocturnal variation in grazing behavior and stock density on the distribution of cattle dung in a sloped rotational grazing system in Japan. The specific objectives were to determine the influence of watering sites on dung distribution at night and the relationship between stock density and dung distribution. The study was conducted on a 2.56-ha steeply sloped pasture that was divided into four sub-paddocks for rotational grazing. Drone imagery and the YOLO deep learning algorithm were used to automate the mapping of more than 400 cattle dung pats per grazing session, with a mean average precision of 0.75. The YOLO algorithm demonstrated high accuracy in identifying dung that was up to 4 days old. Our analyses revealed that the distance to a watering site influenced fecal distribution during the day, but had a weaker and insignificant effect at night. With low-density grazing, dung pats showed a clumped distribution across the entire pasture, whereas at higher stock density the distribution approached randomness. The formation of four dung hotspots was observed in the pasture, although these spots were not consistently used throughout subsequent grazing periods. The cattle tended to migrate from more elevated foraging areas to more level grazing areas for rest, resulting in a loose connection between foraging and resting sites, and this behavior may have a bearing on the lack of successive utilization of these fecal hotspots. Subsequent research must examine whether these patterns persist across seasonal variations and different grazing lands. Despite this need for validation, our findings can provide insights for guiding sustainable nutrient redistribution and grassland management on sloped terrain through the management of stock density and grazing schedules.

Woody plant encroachment (WPE), the advancement of woody plants into rangelands, threatens rangelands globally and negatively impacts ecosystem services important to humans such as causing losses in cattle production and grassland biodiversity. In the Southern Great Plains, Juniperus virginiana L. has become a pervasive and dominant encroaching woody plant. We examined the effect of J. virginiana on rangeland herbaceous vegetation in central Oklahoma. In 2022, we collected herbaceous vegetation data from J. virginiana-encroached grasslands using a balanced, paired design within J. virginiana-dominated and grass-dominated transects. We analyzed the data using linear or generalized linear mixed models. We found that within J. virginiana-dominated transects there was two times less grass and forb cover than in grass-dominated transects. Within J. virginiana-dominated transects, grass, and forb cover were highest at the edge of the tree. We also detected significant differences in woody cover, green herbaceous cover, and average litter depth between J. virginiana-dominated and grass-dominated transects. These findings align with previous studies that indicate that WPE could limit herbaceous vegetation, which could negatively impact cattle production and pollinators. Reducing woody plant density in rangelands is likely necessary to improve cattle production and pollinator habitat, but the occurrence of woody plants in rangelands can also increase cattle production by buffering temperature extremes. Land managers must determine what their objectives are to determine how to manage WPE appropriately. Utilizing land management strategies that promote multiple ecosystems services such as patch burn grazing could be an important tool to manage woody plants effectively.

Microhistology analyses are well-supported techniques useful for studying herbivore diet composition, but they involve the correct identification and validated relative quantification of the plant species consumed, as well as the knowledge of possible biases inherent to methods. We analyzed two methods for quantifying the relative frequency of plant cuticles using an experimental procedure from laboratory-prepared samples. The factors affecting the precision of quantification were analyzed, the minimum sampling effort to detect all plants in the experimental mixture was estimated and the practical application on a complex real sample discussed, in order to describe a simple, standardized, and reliable procedure for studies of herbivore diet composition in Mediterranean ecosystems. Ten random mixtures were prepared with combinations of 40 known plant species, in different number and dry weight percentages. A total of 100 microscopic fields, distributed across 5 slides per sample, were analyzed using both quantification methods. Statistical analysis concluded that the two methods differed significantly. Higher precision was achieved with the method based on annotating the presence/absence of each species in each microscopic field, instead of the total number of times each plant was detected in each field. The important predictors of accuracy were the complexity of the sample and the proportion of plant material in the mixtures. The number of slides and microscopic fields did not significantly impact the quantification results, and no additional species were identified beyond 25 fields in the most complex samples. Due to its greater reliability, shorter analysis time, and reduced visual effort, the first method proved to be more efficient. This method is validated and useful in diet studies of overabundant populations of deer and extensive and transhumant livestock to evaluate the impacts of herbivores on ecosystems and generate sustainable management criteria in the use of pastures toward environmental and economic sustainability.

Unsustainable land use leading to soil degradation has become one of the most pressing global environmental issues, threatening the foundation of human existence. Vegetation restoration can restore degraded soils by increasing soil organic carbon (SOC), soil stability, and soil water content (SWC), creating a favorable living environment. However, few studies have simultaneously examined the impacts of vegetation restoration on these three factors. This study collects soil samples from croplands (control), grasslands, shrublands, and forestlands at depths of 0–20, 20–40, and 40–60 cm to investigate the effects of different vegetation types on SOC, soil stability, and SWC. A comprehensive soil quality index (CSQI), derived from a weighted summation method, indicates the overall impacts of vegetation restoration on these factors. The results showed that vegetation restoration significantly increased SOC stocks and mean weight diameter only in the 0–20 cm layer, with no significant difference among the vegetation types. Shrublands and forestlands significantly increased SWC in the 0–20 cm layer, while all vegetation types decreased SWC in the 20–40 and 40–60 cm layers, with grasslands showing the least reduction. This is primarily because shrublands and forestlands require more water to sustain their higher biomass. Across the entire profile (0–60 cm), grasslands exhibited the highest CSQI, indicating they have the most beneficial effect on the soil ecological environment. The main factors influencing soil CSQI variation in the 0–60 cm depth include aggregate composition, soil nitrogen, and litter biomass. Therefore, grasslands emerge as the optimal vegetation type in the central of the Loess Plateau, effectively sequestering SOC and enhancing soil stability while minimizing soil water consumption.

Several ecotypes within a group of introduced, warm-season, perennial bunchgrasses labeled Old World Bluestems (OWB) have become invasive throughout much of Texas. Specifically, Kleberg bluestem (Dichanthium annulatum Forssk.) and yellow bluestem (Bothriochloa ischaemum L.) in South Texas have become a dense monoculture that reduces biodiversity of both plants and wildlife while also being poor fodder for domestic livestock. Reduction of Kleberg and yellow bluestem is desired, but there has been limited research on effective combinations of grassland management practices. We conducted two 4 × 5 factorial experiments at six locations across Bee, Kleberg, and San Patricio counties in South Texas. Primary treatments (summer fire, glyphosate and native reseeding, or nicosulfuron + metsulfuron methyl, and a control [no treatment]) were combined with secondary treatments (plowing, plowing and native reseeding, mowing, or fertilization, and a control [no treatment]). In both experiments, herbage mass was not different among primary treatments at any sample collection date, whereas plowed treatments had reduced herbage mass until the spring (Experiment 1) or summer (Experiment 2) after treatments were completed. Plowing reduced canopy cover until the summer after treatment when canopy cover was not different among treatments in both experiments. Among primary treatments, only glyphosate herbicide application reduced canopy cover. All management practices applied in this study either had negative effects or provided only short-term (1–1.5 yr) reduction in OWB. Treatments that were most effective in reducing OWB cover and herbage mass included plowing, mowing, and glyphosate application and were also detrimental to desirable plant species so may not be desirable to land managers. Limited success of treatments applied in this study reinforces the challenge to reduce the invasive tendencies of introduced forage species such as OWB.

Managing invasive annual grasses in sagebrush steppe communities with a shrub overstory can be challenging because herbicides, commonly in liquid formulations, applied to control annual grasses may be intercepted by shrub canopies. Poor control of annual grasses and damage to wildlife-important shrubs, such as sagebrush, may occur if herbicides are intercepted by shrubs. Aerial broadcast application of imazapic, a pre-emergent herbicide, in granular formulation may overcome these challenges in annual grass-invaded rangelands with a shrub overstory. However, granular imazapic effectiveness at controlling annual grasses and effects on shrubs have not been investigated in rangelands with a shrub overstory. We investigated the effects of aerial application of granular imazapic (124 g ai·ha^–1) in the summer (pre-emergent) at five sites for two growing seasons postapplication. Granular imazapic substantially reduced annual grass cover and density in plant communities with a shrub overstory and did not negatively impact sagebrush. Control of invasive annual grasses with granular imazapic did not increase the density of perennial herbaceous vegetation, likely because of low perennial vegetation abundance prior to treatment and slow response to increased resources. Although granular imazapic application resulted in slight increases in perennial forb and Sandberg bluegrass cover, it does not appear to be an effective stand-alone treatment to shift herbaceous vegetation from annual- to perennial-dominated, at least not in plant communities with low abundance of co-occurring perennial grasses and forbs. However, its ability to temporarily reduce invasive annual grasses without damaging the shrub overstory may make it a valuable tool in efforts to restore perennial dominance that include additional treatments and seeding perennial grasses and forbs. More intensive treatment and seeding combinations need to be tested before they can be recommended for practice.

Despite providing numerous ecosystem services, black-tailed prairie dogs (Cynomys ludovicianus) can negatively impact livestock production, presenting a challenge for rangeland management. Lethal control along public-private boundaries is one approach to balance competing stakeholder desires. A novel approach to reduce but not eliminate prairie dogs on public land (“density control”) was proposed to increase forage availability for livestock while maintaining other prairie dog-associated ecosystem services. Little research on this approach exists, but we posit that where boundary management leads to population reduction but not elimination (the case on many U.S. Forest Service National Grasslands), boundary management is one form of density control. We reviewed the literature on boundary management and density control, and then evaluated boundary management as one form of density control using a before-after control impact design in the Thunder Basin National Grassland of Wyoming. We found scant literature describing either management approach; resources reporting efficacy were typically management documents not found in traditional literature searches. Boundary management did not reduce adult prairie dog density relative to untreated areas (β_treatment = 0.28, 95% CI [–0.28, 0.85]), but pup numbers were lower following treatment (β_treatment = –1.43, 95% CI [–2.12, –0.79]). Bird communities and overall plant biomass were largely unaffected by treatment, although forb biomass was 5x higher on sites that experienced treatment. Forbs often increase within the months following prairie dog reductions; this paired with high numbers of prairie dogs on treated areas in the following spring indicate treatment was temporarily effective but that prairie dogs rapidly re-colonized. Studies of these management approaches are rare and difficult to access by managers, which is concerning because we found little support for positive impacts (i.e., increased forage) of density control at local scales. While it may be effective for small colonies, boundary management that results in partial lethal control (density control) may be economically and ecologically ineffective.

Geospatial products such as fractional vegetation cover maps often report overall, pixel-wise accuracy, but decision-making with these products often occurs at coarser scales. As such, data users often desire guidance on the appropriate spatial scale to apply these data. We worked toward establishing this guidance by assessing RCMAP (Rangeland Condition Monitoring Assessment and Projection) accuracy relative to a series of high-resolution predictions of component cover. We scale the 2-m and RCMAP predictions to various focal window sizes scales ranging from 30 to 1 500 m using focal averaging. We also evaluated variation in scaling effects on error at ecoregion and pasture (mean area of 1 050 ha) scales. Our results demonstrate increased accuracy at broader windows, across all components, and most increases in accuracy level off at ∼200–600 m scales. At the scale with highest accuracy, cross-component average correlation (r) increased by 6.5%, and root mean square error (RMSE) was reduced 46.4% relative to 30-m scale data. Scaling-related improvements to accuracy were greatest in components such as shrub and tree with more spatially heterogeneous cover and in ecoregions with more spatially heterogenous cover. When components were aggregated at the pasture scale, r increased 10% and RMSE decreased 34.3% on average relative to the 30-m scale. Our results provide empirical data on the scale dependence of error, which fractional cover data users may consider alongside their needs when using these data. Although the general principle remains that remotely sensed products are intended to address landscape-scale questions, our analysis indicates that applying data at finer than landscape spatial scales and grouping even a handful of pixels resulted in lowered error compared to pixel-level comparisons. Our results quantify the trade-offs between data granularity and error related to scale for fractional vegetation cover.

Rangeland management necessitates addressing complex and dynamic social-ecological challenges and opportunities at scales appropriate to target landscapes. We combine geospatial and climate data in an agent-based system dynamics model to simulate temporally and spatially scalable rangeland human-environment-animal-forage relationships. Modeling highly variable grazing system elements requires both adaptability and mathematical realism. Agent-based modeling makes it possible to test alternative approaches to real-world scenarios without taking risks associated with actual experimentation on working operations. Our agent-based model, Ecological Comanagement of Rangelands, or ECo-Range, allows managers to simulate cattle grazing scenarios by setting environmental conditions and management decisions that affect simulation outcomes. In this sense, ECo-Range is not just a product of scientific inquiry, but a tool to be used for collaborative discovery, as it illuminates relationships among environmental conditions, management decisions, and ecological and livestock outcomes for modeled landscapes. ECo-Range explores the complexities of grazing management, embracing rather than excluding variability and heterogeneity inherent in rangeland social-ecological systems. We first present ECo-Range, explaining the model's relevance to the fields of simulation modeling and rangeland management. We then present a case study on the Colorado Front Range as proof of concept to test the utility, validity, and applicability of ECo-Range as a learning tool to explore scenarios related to government-owned landscapes that necessitate comanagement approaches to cattle grazing.

This study presents a comprehensive assessment of groundwater quality across the urban-rural continuum of Lahore and Sheikhupura districts in Pakistan, employing an innovative approach that integrates geospatial analysis, multicriteria decision analysis (MCDA), remote sensing-based land use/land cover (LULC) classification, and simulated treatment scenarios. Utilizing a Geostatistical analytical hierarchy process (AHP) within a Geographic Information System (GIS) framework, we developed a groundwater quality index to evaluate contamination levels across 68 sampling points, while incorporating rangeland distribution analysis through remote sensing techniques. The study area encompassed diverse land use patterns, ranging from densely populated urban centers to rural agricultural lands and rangeland ecosystems. Our analysis revealed significant spatial variations in groundwater quality, with urban areas in Lahore district, particularly Lahore Cantt and Model Town tehsils, exhibiting higher levels of contamination compared to rural and rangeland areas in Sheikhupura district. The integration of LULC analysis revealed that rangeland areas, which constitute approximately 15% of the study area, showed distinct groundwater quality patterns, with generally lower contamination levels but specific vulnerabilities to certain pollutants. To address these water quality issues, we simulated various treatment scenarios, including reductions in heavy metals, chemical parameters, and specific contaminants such as arsenic, total dissolved solids (TDS), and hardness. The results demonstrated that tertiary treatment approaches, especially those targeting TDS and hardness, yielded the most substantial improvements in water quality. A 65% reduction in TDS and a 45% decrease in hardness led to significant enhancements, with some locations showing index value reductions of over 40%. Notably, our arsenic-specific treatment scenario revealed that a 90% reduction in arsenic levels could be sufficient for most locations, offering a potentially cost-effective approach to addressing this critical contaminant. The integration of GIS-based AHP, MCDA techniques, and remote sensing analysis proved instrumental in identifying contamination hotspots, understanding land use impacts, and evaluating the effectiveness of various treatment scenarios. This study's outcomes provide valuable guidance for policymakers and water management authorities in developing targeted, location-specific strategies for sustainable groundwater management across urban, rural, and rangeland landscapes.

Information about what ecological conditions are likely, causes or drivers of degradation, and potential management actions to restore degraded lands may support land conservation and restoration decisions. State-and-transition models (STMs) describe persistent plant and ecological conditions that are possible (the “state”) within a given abiotic setting and drivers or actions that can cause shifts between states (the “transitions”). These primarily conceptual models are widely used to inform resource and conservation decisions. Data-driven STMs have been developed for some lands, but not at regional or national scales. Here, we demonstrate a new repeatable workflow for developing data-driven STMs in the United States (US). The approach leverages predictive maps of Ecological Site Groups (ESGs), extensive field-based Federal monitoring databases, information from Ecological Site Description (ESD) STMs, soil erosion models, remotely sensed productivity, and other available spatial information (fire, land protection, and drought) to provide context and descriptions of the data-driven states, including likely drivers of transitions. Results of this workflow applied to one dryland ESG in the Upper Colorado River Basin in the southwestern US suggest that an Invaded state (16% of 1352 plots) and some occurrences of a Grassland state (30% of plots) are in a degraded or at-risk condition with reduced ecosystem services. The most common drivers of state transitions in the associated ESDs (n = 26) are related to livestock grazing and fire. The Invaded state in the ESG has evidence of degraded habitat quality and accelerated run-off while the Grassland state occurrences show reduced richness, productivity, and elevated erosion risk by wind. Areas subject to wildfire and with lower protection status had greater probability of Invaded state occurrence, generally supporting drivers in ESDs. The workflow presented here can serve as a template for describing ecological dynamics at regional scales, and support prioritization of land for conservation and climate adaptation activities.