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Ag-based electrical contact materials are essential in low-voltage devices such as relays, switches, circuit breakers, and contactors. Historically, Ag-CdO composites have been preferred due to their superior electrical and thermal conductivities, resistance to arcing, and mechanical strength. However, the toxicity of Cd has led to increased restrictions on its use. With the aim of contributing to the development of a new environment-friendly, Ag-Zn2SnO4-based electrical contact material, the kinetics of the hot mechanochemical oxidation of a Ag-Sn-Zn solid solution obtained by mechanical alloying were investigated. The results indicated that the proposed synthesis route produces Ag-based composites with a homogeneous distribution of nanoscale Zn2SnO4 precipitates, which is unattainable through conventional material processing methods. This kinetic study established that the mechanochemical oxidation of the Ag-Sn-Zn solid solution follows the Johnson–Mehl–Avrami–Kolmogorov model. An analysis of the microstructure and the relationship between the activation energy “Ea” and the Avrami exponent “n” from experimental data fitting suggests that the primary mechanism for the oxidation of the Ag-Sn-Zn solid solution during the hot mechanochemical process is related to the three-dimensional oxide growth being limited by oxygen diffusion after its immediate initial nucleation.

Solar saline water splitting is a promising approach to sustainable hydrogen production, harnessing abundant solar energy and the availability of brine resources, especially in the Atacama Desert. Bischofite salt (MgCl2·6H2O) has garnered significant attention due to its wide range of industrial applications. Efficient hydrogen production in arid or hyper arid locations using bischofite solutions is a novel and revolutionary idea. This work studied the electrochemical performance of Mn2O3 electrodes using a superposition model based on mixed potential theory and evaluated the superficial performance of this electrode in contact with a 0.5 M bischofite salt solution focusing on the hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR) that occur during saline water splitting. The application of the non-linear superposition model provides valuable electrochemical kinetic parameters that complement the understanding of Mn2O3, this being one of the novelties of this work.

Photovoltaic technology has proven to be a reliable, economical, and clean energy source that is capable of adapting to diverse geographical conditions. However, factors such as soiling overshadow these qualities, thus leading to production losses and affecting the profitability of this technology. For these reasons, soiling is a highly studied topic, which involves considering the physicochemical characterization of the deposited material, mitigation strategies, effect predictions, and cleaning mechanisms. However, there is a relatively unexplored area related to the microbiological contribution to soiling. The surface of photovoltaic modules, along with the deposited material and local atmospheric factors, fosters favorable conditions for the colonization of microorganisms. These microorganisms influence the soiling mechanisms and optical properties of photovoltaic modules. This work presents a detailed characterization of the microbial diversity present in the soiling deposited on photovoltaic modules installed in the Atacama Desert. Two study sites were defined: Antofagasta and the Solar Platform of the Atacama Desert, which have warm and cold desert climates, respectively. Mineralogical characterization tests, heavy metal analyses, TOC, and inorganic element analyses were conducted on the deposited material. Additionally, the culturable isolates and the metagenomic DNA of the soiling samples and biofilms grown on standard PV glass were characterized using next-generation sequencing. The results show that the deposited soiling contained a microbiological component that had adapted to extreme desert conditions. The presence of the genera Arthrobacter, Kocuria, and Dietzia were identified in the culturable isolates from Antofagasta, while Arthrobacter and Dietzia were obtained from the Solar Platform of the Atacama Desert. The metagenomic DNA was mainly represented by the genera Pontibacter, Noviherbaspirillum, Massilia, Arthrobacter, Hymenobacter, and Deinococcus at Antofagasta. However, at the Solar Platform of the Atacama Desert, the analyzed samples presented DNA concentrations below 0.5 ng/µL, which made their preparation unviable. At the PSDA, the biofilms formed by the genera Peribacillus and Kocuria were identified, whereas the UA showed a greater abundance of bacteria that favored biofilm formation, including those that belonged to the genera Bacillus, Sporosarcina, Bhargavaea, Mesaobacillus, Cytobacillus, Caldakalibacillus, and Planococcus. Based on these results, we propose a soiling mechanism that considers the microbiological contribution to material cementation.

Abstract Effective management of rare species requires an understanding of spatial variation in abundance, which is challenging to estimate. We tested the efficacy of high‐resolution imagery to detect burrows of the Texas kangaroo rat (TKR; Dipodomys elator) as a means of estimating abundance across its geographic range. Specifically, we estimated burrow counts using an Unmanned Aerial System (UAS) to collect data from very high‐resolution Red–Green–Blue (RGB) imagery and estimate digital elevation (2.5‐mm pixel resolution) over active and inactive burrows located on mesquite mounds and anthropogenic features (roadsides, fences, etc.). In 2018, we identified 26 burrow locations on a private ranch in Wichita County, Texas, USA, and characterized burrows based on topography and vegetation density. We found that TKR burrows can only be identified with data of

Corrosion is an inevitable and persistent issue that affects various metallic infrastructures, leading to significant economic losses and safety concerns, particularly in areas near or in contact with saline solutions such as seawater. Green corrosion inhibitors are compounds derived from natural sources that are biodegradable in various environments, offering a promising alternative to their conventional counterparts. Despite their potential, green corrosion inhibitors still face several limitations and challenges when exposed to NaCl environments. This comprehensive review delves into these limitations and associated challenges, shedding light on the progress made in addressing these issues and potential future developments as tools in corrosion management. Explicitly the following aspects are covered: (1) attributes of corrosion inhibitors, (2) general corrosion mechanism, (3) mechanism of corrosion inhibition in NaCl, (4) typical electrochemical and surface characterization techniques, (5) theoretical simulations by Density Functional Theory, and (6) corrosion testing standards and general guidelines for corrosion inhibitor selection. This review is expected to advance the knowledge of green corrosion inhibitors and promote further research and applications.

In the United States, several land use and land cover (LULC) data sets are available based on satellite data, but these data sets often fail to accurately represent features on the ground. Alternatively, detailed mapping of heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences among scenes, landscape heterogeneity, and computational limitations. Various machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study were to evaluate bagging (Random Forest, RF), boosting (Gradient Boosting Machines [GBM] and extreme gradient boosting [XGB]), and stacking ensemble ML models. We used these techniques on a time series of Sentinel 2A data and NAIP orthoimagery to create a LULC map of a portion of Irion and Tom Green counties in Texas (USA). We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. We then compared accuracy based on random cross-validation without accounting for spatial autocorrelation and target-oriented cross-validation accounting for spatial structures of the training data set. Comparison of random and target-oriented cross-validation results showed that autocorrelation in the training data offered overestimation ranging from 2% to 3.5%. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base learners. We show that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use/land cover classification using geographic object-based image analysis.

Solar thermal plants commonly plan their operation for a period of approximately 25 years. Therefore, it is key to accurately predict and evaluate the damage and faults occurring for that long period. Among all components, we find that choosing the heat storage medium (molten salt) and the material (steel) used in the equipment and pipping plays a crucial role in the plant’s design. We tested the long-term corrosion effect of the molten salt pump in a low-melting-point ternary mixture composed of 30wt.%LiNO3+57wt.%KNO3+13wt.%NaNO3 over a period of more than 5 years, corresponding to 30,000 hours of operation in a pilot experimental facility built at the University of Antofagasta (Chile).

The context of global economic instability and armed conflicts has caused fossil fuels to reach unprecedented levels. In this sense, Concentrating Solar Power (CSP) technology has proven to be an excellent option to diversify the energy matrix dependent on fossil fuels, especially in places where there is a good quality solar resource, such as desert areas. These good qualities can be overshadowed by local phenomena such as soiling that can affect the viability of a project. In this paper, the effect of soiling on CSP mirrors installed in a cold desert climate in the Atacama Desert, Chile, is presented. During the 202 days (continuous) of outdoor exposure, atmospheric parameters such as humidity and temperature, as well as reflectance were evaluated daily. At the end of the experiment, the chemical composition of the material deposited on the mirror surface was analyzed by scanning electron microscopy (SEM) and elemental analysis (EDX). The characterization of the material detected the presence of Gypsum (soluble salt), which was shown to have the ability to cement the deposited material. The atmospheric parameters showed that the relative humidity at the site can exceed 60%, sufficient values to solubilize the Gypsum, producing the cementation process. The reflectance values showed that the effect of the cemented material, for 202 days of exposure, can reach a 47% loss of its reflective capacity. This means that the longer the exposure time, the greater the effect of the cementation and the more energy will be needed to clean the mirrors.

Abstract Background In certain unisexual flowers, non-functional sexual organs remain vestigial and unisexuality can be overlooked leading to the ambiguous description of the sexual systems. Therefore, to accurately describe the sexual system, detailed morphological and developmental analyses along with experimental crosses must be performed. Cylindropuntia wolfii is a rare cactus endemic to the Sonoran Desert in southern California and northern Baja California that was described as gynodioecious by morphological analysis. The aims of our project include accurately identifying the sexual system of C. wolfii using histological and functional studies and characterizing the developmental mechanisms that underlie its floral development. Methods Histological analyses were carried out on different stages of C. wolfii flowers and controlled crosses were performed in the field. Result Our results identified C. wolfii to be functionally dioecious. The ovule and anther development differed between staminate and pistillate flowers. In vivo pollen germination tests showed that the pollen of staminate and pistillate flowers were viable and the stigma and style of both staminate and pistillate flowers were receptive. This suggests that there are no genetic or developmental barriers in the earlier stages of pollen recognition and pollen germination. Conclusions Despite being functionally dioecious, we observed that functionally pistillate individuals produced fruits with a large number of aborted seeds. This implies that not only does this species have low reproductive success, but its small population sizes may lead to low genetic diversity.

Excess energy derived from photosynthesis can be used in plant microbial fuel cell (PMFC) systems as a sustainable alternative for the generation of electricity. In this study, the in situ performance of CAM (Crassulacean acid metabolism) plants in Calama, in the Atacama Desert, was evaluated for energy recovery using PMFCs with stainless steel AISI 316L and Cu as electrodes. The plant species evaluated included Aloe perfoliata, Cereus jamacaru, Austrocylindropuntia subulata, Agave potatorum, Aloe arborescens, Malephora crocea, and Kalanchoe daigremontiana. Among the plant species, Kalanchoe daigremontiana demonstrated significant potential as an in situ PMFC, showing a maximum cell potential of 0.248 V and a minimum of 0.139 V. In addition, the cumulative energy for recovery was about 9.4 mWh m−2 of the electrode. The use of CAM plants in PMFCs presents a novel approach for green energy generation, as these plants possess an inherent ability to adapt to arid environments and water-scarce areas such as the Atacama Desert climate.

The Puuc Biocultural State Reserve (PBSR) is a unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on maintaining low-impact productive activities coordinated by multiple communal and private landowners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and their relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000–2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (±19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000–2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing changed over the years after the creation of the PBSR in 2011. However, an emergent hotspot analysis shows that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012–2021 period was less common than prior to 2011, and these more recent hotspots have been confined to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, the results show that landowners outside the PBSR often clear forests with lower carbon density and species diversity than those inside the PBSR. This suggests that, compared to landowners outside the PBSR, landowners within the PBSR might be practicing longer fallow periods allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acted as a stabilizing forest management scheme during the 2012–2021 period, minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend continuing efforts to provide alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. Combining these models with continuous change detection datasets will allow for underlying ecological processes to be revealed and the generation of information better adapted to forest governance scales.

For more than three decades, the remote sensing scientific community has successfully generated predictive models of tropical forest attributes and ecological processes at the leaf, canopy, patch and landscape scale by linking field-measured data to remotely sensed spectral values, as well as other variables derived from remotely sensed data. The main interest of these applications is to help describe ecological and functional patterns occurring at larger geographic scales with sufficient accuracy and precision and enable scientists to better understand ecological processes, such as the relationship between atmospheric fluxes, plant structural and ecophysiological traits, soil attributes, anthropogenic use, species occurrence and animal movement. However, as the earth’s environment suffers from ever-increasing human use and abuse, detecting spatiotemporal changes in these variables has become a necessary decision-making tool in conservation action and natural resources’ management. Moving from modeling into the study of soil, plants, wildlife and socioecological processes using remotely sensed data requires the extrapolation of single time-step models to its application on a time series of data with the same expected accuracy. The challenges in this matter are not trivial, since changes in soil moisture conditions, cloud contamination, canopy and leaf-level geometry and physiology can affect the strength of the proposed models. In this context, the term ‘Operationalization’ refers to migration from single time-step models to time series but also refers to the design and implementation of user-friendly tools to increase the efficacy of communicating spatiotemporal trends to the users. [...]

Abstract During the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi‐resolution information for analysis. Climate change, land‐use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high‐density, and high‐resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) for Socio‐Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross‐site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio‐environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data.

The lesser prairie-chicken (Tympanuchus pallidicinctus) is a species of prairie grouse that occupies grassland ecosystems in the Southern and Central High Plains of the Great Plains. Reduced abundance and occupied ranges have led to increased conservation efforts throughout the species’ range. Habitat loss is considered the predominant cause of these declines. In the Southern High Plains of Texas and New Mexico, lesser prairie-chicken habitat corresponds to the Sand Shinnery Oak Prairie Ecoregion, which is comprised of a mixture of sand shinnery oak (Quercus havardii)-dominated grasslands, sand sagebrush (Artemisia filifolia)-dominated grasslands, and mixed grasslands. In sand shinnery oak–grassland communities, conversion to row-crop agriculture, continuous unmanaged livestock grazing, restriction of natural fire, invasive plant species (e.g., mesquite (Prosopis spp.)), extensive use of herbicides, energy development, and a variety of other factors have also negatively affected ecosystem extent and function. We integrated historical maps and remote sensing-derived information to measure trends in the extent and geographical distribution of sand shinnery oak prairies in eastern New Mexico and northwest Texas. Potential lesser prairie-chicken habitat was reduced by 56% from a potential of 43,258 km2 to 18,908 km2 in ~115 years (since pre-settlement). Our assessment indicated both mixed grasslands and sand shinnery oak-dominated grasslands were transformed from large parcels of existing vegetation communities to urban settlements, row crops, roads, and industrial land uses by the 1970s. Currently, potential habitat is highly fragmented and restricted to isolated locations in Texas and New Mexico, with an increasing dominance in mixed grasslands, especially in the southeastern portion of the lesser prairie-chicken range. Sand shinnery oak-dominated grasslands have been declining rapidly, from 69% of its potential extent in 1985, 65% in 1995, 54% in 2005, to 42% in 2015. Mixed grasslands drastically declined to 50% of its potential distribution by 1985. Since then, it has been stable until the 2005–2015 period when it declined to 45% of its potential extent. Based on the 2015 assessment, the current potential habitat for lesser prairie-chicken is estimated at 18,908 km2 (1,890,800 ha or 4.6 million acres), where 13,126 km2 corresponds to mixed grasslands and 5782 km2 corresponds to sand shinnery oak-dominated grasslands.

As part of the research for techniques to control the final energy reaching the receivers of central solar power plants, this work combines two contrasting methods in a novel way as a first step towards integrating such systems in solar plants. To determine the effective power reaching the receiver, the direct normal irradiance was predicted at ground level using a total sky camera, TSI-880 model. Subsequently, these DNI values were used as the inputs for a heliostat model (Fiat-Lux) to trace the sunlight’s path according to the mirror features. The predicted valuex of flux, obtained from these simulations, differ of less than 20% from the real values. This represents a significant advance in integrating different technologies to quantify the losses produced in the path from the heliostats to the central receiver, which are normally caused by the presence of atmospheric attenuation factors.

The urgency to preserve tropical forest remnants has encouraged the development of remote sensing tools and techniques to monitor diverse forest attributes for management and conservation. State-of-the-art methodologies for mapping and tracking these attributes usually achieve accuracies greater than 0.8 for forest cover monitoring; r-square values of ~0.5–0.7 for plant diversity, vegetation structure, and plant functional trait mapping, and overall accuracies of ~0.8 for categorical maps of forest attributes. Nonetheless, existing operational tropical forest monitoring systems only track single attributes at national to global scales. For the design and implementation of effective and integrated tropical forest monitoring systems, we recommend the integration of multiple data sources and techniques for monitoring structural, functional, and compositional attributes. We also recommend its decentralized implementation for adjusting methods to local climatic and ecological characteristics and for proper end-user engagement. The operationalization of the system should be based on all open-source computing platforms, leveraging international support in research and development and ensuring direct and constant user engagement. We recommend continuing the efforts to address these multiple challenges for effective monitoring.

The Atacama Desert receives the highest levels of solar irradiance in the world with an annual average of 2500 kWh/m2 for the global horizontal irradiance and 3500 kWh/m2 for the direct normal irradiance. One of the challenges is the large portion of ultraviolet light. This part of the spectrum be detrimental for the encapsulant materials, reducing their lifetime. To develop a module adapted to the Atacama Desert conditions, it is imperative to have standardized information from first-hand about the typical faults experienced by photovoltaic modules operating in the desert. This work reports on the design and implementation of the Inspection Data Collection Tool to evaluate crystalline silicon-based moules operating in desert climates. The tool brings together novel features of compatibility with current standards, efficient mobile-type instrumentation (equipment and tools), clear procedures/protocols for non-expert users and low development costs. A total of 95 modules were inspected to characterize failure/degradation issues. Three components of the solar modules were assessed: front cover glass, ethylene-vinyl acetate encapsulant and solar cells. Seven abnormalities were analyzed: Soiling, front cover glass discoloration, encapsulant delamination, hotspots, partial shading, cell fracture and faulty soldering. Soiling was the most common issue, showing correlation between dust deposition and location.

Tropical dry forests (TDFs) worldwide have an environment-sensitive phenological signal, which easily marks their response to the changing climatic conditions, especially precipitation and temperature. Using TDF phenological characteristics as a proxy, this study aims to evaluate their current continental response to climate change across the Americas. Here, we show that TDFs are resilient to water stress and droughts by increasing their rain use efficiency (RUE) in drier years and recovering to average RUE in the year following the drought. Additionally, we find that TDF productivity trends over the past 18 years are spatially clustered, with sites in the northern hemisphere experiencing increased productivity, while equatorial regions have no change, and the southern hemisphere exhibiting decreased productivity. The results indicate that the TDF will be resilient under future climatic conditions, particularly if there are increasing drought conditions.

Concentrated solar power (CSP) plants, in the context of thermal energy storage (TES) upgrades, need to provide a timely and effective response to the corrosion process that occurs due to the effect of high temperatures, where one of the main challenges is to control its effect, and thus the costs related to the materials used. Electrochemical impedance spectroscopy (EIS) and linear polarization resistance (LPR) were applied in this study as a corrosion monitoring technique. The electrochemical tests were carried out on the materials AISI304, AISI430, and HR-224 immersed in a mixture of ternary salt composed of 57 wt.% KNO3 + 13 wt.% NaNO3 + 30 wt.% LiNO3 at 550 °C during 100 h of exposure and subsequently compared with solar salt. The test was also carried out on the VM12 alloy in the ternary salt with lithium content at 100 and 1000 h of exposure at 550 °C. The corrosion tests show that the materials conform to a model of protective layer in which the same results were contrasted with the chemical corrosion mechanism of nitrate mixture. According to the results obtained in this research, electrochemical techniques could be an interesting option to control corrosion in CSP plants and reduce operational risks during operation.

The use of ceptometers and digital hemispherical photographs to estimate Plant Area Index (PAI) often include biases and errors from instrument positioning, orientation and data analysis. As an alternative to these methods, we used an In-Situ Monitoring LiDAR system that provides indirect measures of PAI and Plant Area Volume Density (PAVD) at a fixed angle, based on optimized principles and algorithms for PAI retrieval. The instrument was installed for 22 nights continuously from September 26 to October 17, 2013 during leaf-fall in an Aspen Parkland Forest. A total of 85 scans were performed (~4 scans per night). PAI measured decreased from 1.27 to 0.67 during leaf-fall, which is consistent with values reported in the literature. PAVD profiles allowed differentiating the contribution of PAI per forest strata. Phenological changes were captured in four ways: number of hits, maximum cumulative and absolute PAI values, time series of PAVD profiles and PAI values per forest strata. We also found that VEGNET IML Canopy PAI and MODIS LAI values showed a similar decreasing trend and differed by 2%–15%. Our results indicate that the VEGNET IML has great potential for rapid forest structural characterization and for ground validation of PAI/LAI at a temporal frequency compatible with earth observation satellites.

We present a commented taxonomic list of the reptiles found during herpetological surveys carried out in an isolated fragment of tropical dry forest located in the municipality of San Francisco, Zulia state, northwestern Venezuela between January-December 2011. We report a total of 24 species belonging to the order Squamata, distributed in 12 families and 21 genera. Colubridae is the most diverse family with six species, followed by Dipsadidae (four species), Boidae, Gymnophthalmidae, Phyllodactylidae and Teiidae with two species each, and finally Dactyloidae, Iguanidae, Elapidae, Gekkonidae, Sphaerodactylidae and Viperidae with a single species each. The species composition at this site matches what would be expected in a tropical dry forest in the region. Our study suggests that this isolated tropical dry forest fragment is the last refuge of the herpetofauna that once occupied much of the dry forests of the northern Maracaibo basin and should therefore be considered for conservation purposes.

The general goal of this study is to investigate and analyze patterns of ecophysiological leaf traits and spectral response among life forms (trees, shrubs and lianas) in the Cerrado ecosystem. In this study, we first tested whether life forms are discriminated through leaf level functional traits. We then explored the correlation between leaf-level plant functional traits and spectral reflectance.Serra do Cipo National Park, Minas Gerais, Brazil.Six ecophysiological leaf traits were selected to best characterize differences between life forms in the woody plant community of the Cerrado. Results were compared to spectral vegetation indices to determine if plant groups provide means to separate leaf spectral responses.Values obtained from leaf traits were similar to results reported from other tropical dry sites. Trees and shrubs significantly differed from lianas in terms of the percentage of leaf water content and Specific Leaf Area. Spectral indices were insufficient to capture the differences of these key traits between groups, though indices were still adequately correlated to overall trait variation.The importance of life forms as biochemical and structurally distinctive groups is a significant finding for future remote sensing studies of vegetation, especially in arid and semi-arid environments. The traits we found as indicative of these groups (SLA and water content) are good candidates for spectral characterization. Future studies need to use the full wavelength (400 nm-2500 nm) in order to capture the potential response of these traits. The ecological linkage to water balance and life strategies encourages these traits as starting points for modeling plant communities using hyperspectral remote sensing.

Agricultural expansion is causing deforestation in Minas Gerais, Brazil, converting savanna and tropical dry forest to farmland, and in 2012, Brazil's Forest Code was revised with the government reducing deforestation restrictions. Understanding the effects of policy change on rates and locations of natural ecosystem loss is imperative. In this paper, deforestation in Minas Gerais was simulated annually until 2020 using Dinamica Environment for Geoprocessing Objects (Dinamica EGO). This system is a state-of-the-art land use and cover change (LUCC) model which incorporates government policy, landscape maps, and other biophysical and anthropogenic datasets. Three studied scenarios: (i) business as usual, (ii) increased deforestation, and (iii) decreased deforestation showed more transition to agriculture from shrubland compared to forests, and consistent locations for most deforestation. The probability of conversion to agriculture is strongly tied to areas with the smallest patches of original biome remaining. Increases in agricultural revenue are projected to continue with a loss of 25% of the remaining Cerrado land in the next decade if profit is maximized. The addition of biodiversity value as a tax on land sale prices, estimated at over $750,000,000 USD using the cost of extracting and maintaining current species ex-situ, can save more than 1 million hectares of shrubland with minimal effects on the economy of the State of Minas Gerais. With environmental policy determining rates of deforestation and economics driving the location of land clearing, site-specific protection or market accounting of externalities is needed to balance economic development and conservation.

The increase of tree density in forests of the American Southwest promotes extreme fire events, understory biodiversity losses, and degraded habitat conditions for many wildlife species. To ameliorate these changes, managers and scientists have begun planning treatments aimed at reducing fuels and increasing understory biodiversity. However, spatial variability in tree density across the landscape is not well-characterized, and if better known, could greatly influence planning efforts. We used reflectance values from individual Landsat 8 bands (bands 2, 3, 4, 5, 6, and 7) and calculated vegetation indices (difference vegetation index, simple ratios, and normalized vegetation indices) to estimate tree density in an area planned for treatment in the Jemez Mountains, New Mexico, characterized by multiple vegetation types and a complex topography. Because different vegetation types have different spectral signatures, we derived models with multiple predictor variables for each vegetation type, rather than using a single model for the entire project area, and compared the model-derived values to values collected from on-the-ground transects. Among conifer-dominated areas (73% of the project area), the best models (as determined by corrected Akaike Information Criteria (AICc)) included Landsat bands 2, 3, 4, and 7 along with simple ratios, normalized vegetation indices, and the difference vegetation index (R2 values for ponderosa: 0.47, piñon-juniper: 0.52, and spruce-fir: 0.66). On the other hand, in aspen-dominated areas (9% of the project area), the best model included individual bands 4 and 2, simple ratio, and normalized vegetation index (R2 value: 0.97). Most areas dominated by ponderosa, pinyon-juniper, or spruce-fir had more than 100 trees per hectare. About 54% of the study area has medium to high density of trees (100–1000 trees/hectare), and a small fraction (4.5%) of the area has very high density (>1000 trees/hectare). Our results provide a better understanding of tree density for identifying areas in need of treatment and planning for more effective treatment. Our analysis also provides an integrated method of estimating tree density across complex landscapes that could be useful for further restoration planning.

While marijuana use rates are significantly high within college students, less is known about use patterns and correlates in Latinx groups. This study assessed the relationships between ever use and frequency of use of marijuana, polysubstance use, expectancies, motives, and mental illness symptoms among Latinx college students (n = 345). Results indicated more frequent marijuana use was positively associated with: enjoyment, celebration, social anxiety, low risk, sleep and availability and was negatively associated with: experimentation, boredom, altered perception, and anxiety. Other dual use with tobacco expectancies were also observed. These constructs should be explored prospectively and inform prevention and intervention efforts. [ABSTRACT FROM AUTHOR]