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Abstract Antarctic moss beds are sensitive to climatic conditions, and both their survival and community composition are particularly influenced by the availability of liquid water over summer. As Antarctic regions increasingly face climate pressures (e.g., changing hydrology and heat waves), advancing capabilities to efficiently and non‐destructively monitor water content in moss communities becomes a key research priority. Because of the complexity induced by multiple micro‐climatic drivers and its fragility, tracking the evolution and responses of moss bed moisture requires monitoring methods that are non‐intrusive, efficient, and spatially significant, such as the use of unoccupied aerial systems (UAS). In this study, we combine a multi‐species drying laboratory experiment with short‐wave infrared (SWIR) spectroscopy analyses to first develop a Random Forest regression Model (RFM) capable of predicting Antarctic moss turf water content (~5% error). The RFM was then applied to UAS‐borne SWIR imaging data (900–1700 nm,

Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications.

Mapping Antarctic Specially Protected Areas (ASPAs) remains a critical yet challenging task, especially in extreme environments like Antarctica. Traditional methods are often cumbersome, expensive, and risky, with limited satellite data further hindering accuracy. This study addresses these challenges by developing a workflow that enables precise mapping and monitoring of vegetation in ASPAs. The processing pipeline of this workflow integrates small unmanned aerial vehicles (UAVs)—or drones—to collect hyperspectral and multispectral imagery (HSI and MSI), global navigation satellite system (GNSS) enhanced with real-time kinematics (RTK) to collect ground control points (GCPs), and supervised machine learning classifiers. This workflow was validated in the field by acquiring ground and aerial data at ASPA 135, Windmill Islands, East Antarctica. The data preparation phase involves a data fusion technique to integrate HSI and MSI data, achieving the collection of georeferenced HSI scans with a resolution of up to 0.3 cm/pixel. From these high-resolution HSI scans, a series of novel spectral indices were proposed to enhance the classification accuracy of the model. Model training was achieved using extreme gradient boosting (XGBoost), with four different combinations tested to identify the best fit for the data. The research results indicate the successful detection and mapping of moss and lichens, with an average accuracy of 95%. Optimised XGBoost models, particularly Model 3 and Model 4, demonstrate the applicability of the custom spectral indices to achieve high accuracy with reduced computing power requirements. The integration of these technologies results in significantly more accurate mapping compared to conventional methods. This workflow serves as a foundational step towards more extensive remote sensing applications in Antarctic and ASPA vegetation mapping, as well as in monitoring the impact of climate change on the Antarctic ecosystem.

Abstract Background Antarctic bryophytes (mosses and liverworts) are resilient to physiologically extreme environmental conditions including elevated levels of ultraviolet (UV) radiation due to depletion of stratospheric ozone. Many Antarctic bryophytes synthesise UV-B-absorbing compounds (UVAC) that are localised in their cells and cell walls, a location that is rarely investigated for UVAC in plants. This study compares the concentrations and localisation of intracellular and cell wall UVAC in Antarctic Ceratodon purpureus, Bryum pseudotriquetrum and Schistidium antarctici from the Windmill Islands, East Antarctica. Results Multiple stresses, including desiccation and naturally high UV and visible light, seemed to enhance the incorporation of total UVAC including red pigments in the cell walls of all three Antarctic species analysed. The red growth form of C. purpureus had significantly higher levels of cell wall bound and lower intracellular UVAC concentrations than its nearby green form. Microscopic and spectroscopic analyses showed that the red colouration in this species was associated with the cell wall and that these red cell walls contained less pectin and phenolic esters than the green form. All three moss species showed a natural increase in cell wall UVAC content during the growing season and a decline in these compounds in new tissue grown under less stressful conditions in the laboratory. Conclusions UVAC and red pigments are tightly bound to the cell wall and likely have a long-term protective role in Antarctic bryophytes. Although the identity of these red pigments remains unknown, our study demonstrates the importance of investigating cell wall UVAC in plants and contributes to our current understanding of UV-protective strategies employed by particular Antarctic bryophytes. Studies such as these provide clues to how these plants survive in such extreme habitats and are helpful in predicting future survival of the species studied.

The terrestrial flora of Antarctica’s frozen continent is restricted to sparse ice-free areas and dominated by lichens and bryophytes. These plants frequently battle sub-zero temperatures, extreme winds and reduced water availability; all influencing their ability to survive and grow. Antarctic mosses, however, can have canopy temperatures well above air temperature. At midday, canopy temperatures can exceed 15°C, depending on moss turf water content. In this study, the optimum temperature of photosynthesis was determined for six Antarctic moss species: Bryum pseudotriquetrum, Ceratodon purpureus, Chorisodontium aciphyllum, Polytrichastrum alpinum, Sanionia uncinata, and Schistidium antarctici collected from King George Island (maritime Antarctica) and/or the Windmill Islands, East Antarctica. Both chlorophyll fluorescence and gas exchange showed maximum values of electron transport rate occurred at canopy temperatures higher than 20°C. The optimum temperature for both net assimilation of CO2 and photoprotective heat dissipation of three East Antarctic species was 20–30°C and at temperatures below 10°C, mesophyll conductance did not significantly differ from 0. Maximum mitochondrial respiration rates occurred at temperatures higher than 35°C and were lower by around 80% at 5°C. Despite the extreme cold conditions that Antarctic mosses face over winter, the photosynthetic apparatus appears optimised to warm temperatures. Our estimation of the total carbon balance suggests that survival in this cold environment may rely on a capacity to maximize photosynthesis for brief periods during summer and minimize respiratory carbon losses in cold conditions.

Biogeographic patterns of globally widespread species are expected to reflect regional structure, as well as connectivity caused by occasional long-distance dispersal. We assessed the level and drivers of population structure, connectivity, and timescales of population isolation in one of the most widespread and ruderal plants in the world — the common moss Ceratodon purpureus. We applied phylogenetic, population genetic, and molecular dating analyses to a global (n = 147) sampling data set, using three chloroplast loci and one nuclear locus. The plastid data revealed several distinct and geographically structured lineages, with connectivity patterns associated with worldwide, latitudinal “bands.” These imply that connectivity is strongly influenced by global atmospheric circulation patterns, with dispersal and establishment beyond these latitudinal bands less common. Biogeographic patterns were less clear within the nuclear marker, with gene duplication likely hindering the detection of these. Divergence time analyses indicated that the current matrilineal population structure in C. purpureus has developed over the past six million years, with lineages diverging during the late Miocene, Pliocene, and Quaternary. Several colonization events in the Antarctic were apparent, as well as one old and distinct Antarctic clade, possibly isolated on the continent since the Pliocene. As C. purpureus is considered a model organism, the matrilineal biogeographic structure identified here provides a useful framework for future genetic and developmental studies on bryophytes. Our general findings may also be relevant to understanding global environmental influences on the biogeography of other organisms with microscopic propagules (e.g., spores) dispersed by wind.

Climate change is affecting Antarctica and minimally destructive long-term monitoring of its unique ecosystems is vital to detect biodiversity trends, and to understand how change is affecting these communities. The use of automated or semi-automated methods is especially valuable in harsh polar environments, as access is limited and conditions extreme. We assessed moss health and cover at six time points between 2003 and 2014 at two East Antarctic sites. Semi-automatic object-based image analysis (OBIA) was used to classify digital photographs using a set of rules based on digital red, green, blue (RGB) and hue-saturation-intensity (HSI) value thresholds, assigning vegetation to categories of healthy, stressed or moribund moss and lichens. Comparison with traditional visual estimates showed that estimates of percent cover using semi-automated OBIA classification fell within the range of variation determined by visual methods. Overall moss health, as assessed using the mean percentages of healthy, stressed and moribund mosses within quadrats, changed over the 11 years at both sites. A marked increase in stress and decline in health was observed across both sites in 2008, followed by recovery to baseline levels of health by 2014 at one site, but with significantly more stressed or moribund moss remaining within the two communities at the other site. Our results confirm that vegetation cover can be reliably estimated using semi-automated OBIA, providing similar accuracy to visual estimation by experts. The resulting vegetation cover estimates provide a sensitive measure to assess change in vegetation health over time and have informed a conceptual framework for the changing condition of Antarctic mosses. In demonstrating that this method can be used to monitor ground cover vegetation at small scales, we suggest it may also be suitable for other extreme environments where repeat monitoring via images is required.

LiDAR data and derived canopy height models can provide useful information about mangrove tree heights that assist with quantifying mangrove above-ground biomass. This study presents a validated method for quantifying mangrove heights using LiDAR data and calibrating this against plot-based estimates of above-ground biomass. This approach was initially validated for the mangroves of Darwin Harbour, in Northern Australia, which are structurally complex and have high species diversity. Established relationships were then extrapolated to the nearby West Alligator River, which provided the opportunity to quantify biomass at a remote location where intensive fieldwork was limited. Relationships between LiDAR-derived mangrove heights and mean tree height per plot were highly robust for Ceriops tagal, Rhizophora stylosa and Sonneratia alba (r2 = 0.84–0.94, RMSE = 0.03–0.91 m; RMSE% = 0.07%–11.27%), and validated well against an independent dataset. Additionally, relationships between the derived canopy height model and field-based estimates of above-ground biomass were also robust and validated (r2 = 0.73–0.90, RMSE = 141.4 kg–1098.58 kg, RMSE% of 22.94–39.31%). Species-specific estimates of tree density per plot were applied in order to align biomass of individual trees with the resolution of the canopy height model. The total above-ground biomass at Darwin Harbour was estimated at 120 t ha−1 and comparisons with prior estimates of mangrove above-ground biomass confirmed the accuracy of this assessment. To establish whether accurate and validated relationships could be extrapolated elsewhere, the established relationships were applied to a LiDAR-derived canopy height model at nearby West Alligator River. Above-ground biomass derived from extrapolated relationships was estimated at 206 t ha−1, which compared well with prior biomass estimates, confirming that this approach can be extrapolated to remote locations, providing the mangrove forests are biogeographically similar. The validated method presented in this study can be used for reporting mangrove carbon storage under national obligations, and is useful for quantifying carbon within various markets.

In recent times, the use of Unmanned Aerial Vehicles (UAVs) as tools for environmental remote sensing has become more commonplace. Compared to traditional airborne remote sensing, UAVs can provide finer spatial resolution data (up to 1 cm/pixel) and higher temporal resolution data. For the purposes of vegetation monitoring, the use of multiple sensors such as near infrared and thermal infrared cameras are of benefit. Collecting data with multiple sensors, however, requires an accurate spatial co-registration of the various UAV image datasets. In this study, we used an Oktokopter UAV to investigate the physiological state of Antarctic moss ecosystems using three sensors: (i) a visible camera (1 cm/pixel), (ii) a 6 band multispectral camera (3 cm/pixel), and (iii) a thermal infrared camera (10 cm/pixel). Imagery from each sensor was geo-referenced and mosaicked with a combination of commercially available software and our own algorithms based on the Scale Invariant Feature Transform (SIFT). The validation of the mosaic’s spatial co-registration revealed a mean root mean squared error (RMSE) of 1.78 pixels. A thematic map of moss health, derived from the multispectral mosaic using a Modified Triangular Vegetation Index (MTVI2), and an indicative map of moss surface temperature were then combined to demonstrate sufficient accuracy of our co-registration methodology for UAV-based monitoring of Antarctic moss beds.

As interest in improving urban air quality grows, phytoremediation—amelioration through plants—is an increasingly popular method of targeting particulate matter (PM), one of the most harmful pollutants. Decades of research has proven that plants effectively capture PM from air; however, more information is needed on the dynamics of PM accumulation. Our study evaluated the effects of meteorological conditions on the dynamics of PM deposition, wash off and resuspension using four Australian tree species growing under natural conditions near a busy highway. Accumulation of PM on foliage was analyzed over the short term (daily changes) and over a longer time period (weekly changes). The results obtained were correlated with ambient concentrations of PM2.5 and PM10, rain intensity and wind strength. The highest accumulation of PM was recorded for Eucalyptus ovata (100.2 µg cm−2), which also had the thickest wax layer while the lowest was for Brachychiton acerifolius (77.9 µg cm−2). PM accumulation was highly changeable, with up to 35% different PM loads on the foliage from one day to the next. Importantly these dynamics are hidden in weekly measurements. Changes in PM deposition on the leaves was mostly affected by rain and to a lesser extent by wind, but the extent of the effect was species specific. The large PM fraction (10−100 µm) was the first to be removed from leaves, while the smallest PM fraction (0.2−2.5 µm) was retained for longer. Precipitation affects also PM retained in waxes, which until now were believed to be not affected by rain. This work demonstrates important interactions between PM load and weather, as well as adding to the small inventory of Australian native tree PM accumulation data.

Urbanisation largely consists of removing native vegetation. Plants that remain interact with air quality in complex ways. Pollutants can be detrimental to plant growth; plants sometimes reduce air quality, yet some species also improve it through phytoremediation. A common pollutant of concern to human health in urban areas is particulate matter (PM), small particles of solid or liquid. Our study compared roadside moss turfs with leaves of a common Australian tree species, Pittosporum undulatum, in their ability to capture PM along an urban gradient. We sampled nine sites, three in each of three levels of urbanisation: low, medium, and high according to road type (freeway, suburban road, quiet peri-urban road). In addition, we deployed a PM monitor over a two-week period in one site of each urban level to provide concentrations of PM2.5. We used chlorophyll fluorescence (Fv/Fm; maximum quantum yield of photosystem II) as a measure of plant stress. We extracted PM in three size fractions using a filtration and washing technique with water and chloroform. Site averages for moss turfs were between 5.60 and 33.00 mg per g dry weight for total PM compared to between 2.15 and 10.24 mg per g dry weight for the tree leaves. We found that moss was more sensitive to increasing urbanisation, both in terms of trapping proportionately more PM than the leaves, and also in terms of photosynthetic stress, with moss Fv/Fm declining by a site average of 40% from low to high urban “class„ (0.76 to 0.45). Our study highlights the stressors potentially limiting moss persistence in cities. It also demonstrates its ability to trap PM, a trait that could be useful in urban applications relating to urban greening or air quality.

Solar induced chlorophyll fluorescence (SIF) emissions of photosynthetically active plants retrieved from space-borne observations have been used to improve models of global primary productivity. However, the relationship between SIF and photosynthesis in diurnal and seasonal cycles is still not fully understood, especially at large spatial scales, where direct measurements of photosynthesis are unfeasible. Motivated by up-scaling potential, this study examined the diurnal and seasonal relationship between SIF and photosynthetic parameters measured at the level of individual leaves. We monitored SIF in two plant species, avocado (Persea Americana) and orange jasmine (Murraya paniculatta), throughout 18 diurnal cycles during the Southern Hemisphere spring, summer and autumn, and compared them with simultaneous measurements of photosynthetic yields, and leaf and global irradiances. Results showed that at seasonal time scales SIF is principally correlated with changes in leaf irradiance, electron transport rates (ETR) and constitutive heat dissipation (YNO; p < 0.001). Multiple regression models of correlations between photosynthetic parameters and SIF at diurnal time scales identified leaf irradiance as the principle predictor of SIF (p < 0.001). Previous studies have identified correlations between photosynthetic yields, ETR and SIF at larger spatial scales, where heterogeneous canopy architecture and landscape spatial patterns influence the spectral and photosynthetic measurements. Although this study found a significant correlation between leaf-measured YNO and SIF, future dedicated up-scaling experiments are required to elucidate if these observations are also found at larger spatial scales.

Green roofs help ameliorate some of the adverse social, economic and environmental effects of urbanisation. However, green roofs are harsh environments for plants, as they must cope with shallow soils, low nutrient availability, high solar radiation, low water availability and high pollution/disturbances. The effect of shade plants on vegetation survivability was investigated using green roof mesocosms with four different native species pairs (shade plant and a target ground cover plant). To examine the effect of shading and competition on plant growth and survival, plant pairs were subjected to four treatments; naturally shaded with a shade plant shading the target plant, artificially shaded with an artificial plant shading the target plant, unshaded natural which had a trimmed shade plant providing no shade to the target plant and an unshaded treatment with the target plant being the sole occupant of the mesocosm. The experiment ran for 11 months with measurements taken monthly to record growth and visual health of the target plant. Soil moisture and biomass data was collected at the end of the experiment. Overall, natural shade treated plants had the highest biomass while unshaded plants had the lowest biomass. Contrary to our predictions, the shaded artificial and the unshaded natural had similar moderate biomass. This similarity suggests that while shading had a positive influence on plant growth, there was also a positive influence of growing with a shade plant which is not accounted for by shading. The results highlight the complexity of biotic relations between plants and emphasises that the presence of a nurse plants can be benefit to the survival and growth of other species within a green roof ecosystem.

Breastfeeding, additional children, and partner relationship predicted postpartum depression among 59 Latinas who had an infant who was 6 months old or younger. The most powerful predictor was conflict with partner. Counselors working with Latinas experiencing postpartum depression should explore the partner relationship, particularly relationship conflict. (Contains 2 tables.)

The authors investigated the psychometric appropriateness of the Valuing/ Commitment to Education scale (A. M. Gloria, 1993), the Rosenberg Self-Esteem Scale (M. Rosenberg, 1965), and the Educational Self-Efficacy Scale (A. M. Gloria, 1993) for use with European American, Latina/o, and Native American college freshmen. Strong to moderate reliabilities were found, as was initial evidence of validity across groups. (Contains 4 tables.)

The relationships of self-beliefs, social support, and university comfort with the academic persistence decisions and first-year grade point averages of 527 first semester female undergraduates were examined. Data were gathered in 56 classes or group meetings. These three constructs predicted academic persistence decisions, with social support as the strongest predictor, followed by self-beliefs, and then university comfort. Although there were no differences between Euro-American women and women of color on initial academic persistence decisions, Euro-American women had higher first-year GPAs than did the women of color. Research-informed practice and policy implications for increasing the persistence decisions and academic success of first-year female college students are discussed. (Contains 2 tables.)

This study examined the relationships among academic stress, valuing of education, self-esteem, and educational self-efficacy for first-semester, female undergraduates and the interaction of race/ethnicity with these variables. Conducted at a large, southwestern university, this study was part of a comprehensive examination of psychosocial factors related to academic persistence decisions of 876, first-year college undergraduates, 549 (62.7%) of whom were women. The women's mean age was 18.27 years (SD = 1.56). Self-reported race/ethnicity revealed that 377 (68.3%) were Euro-American, 25 (4.6%) Asian American, 77 (14%) Latino, 7 (1.3%) international, 12 (2.2%) African American, 19 (3.5%) Native American, and 32 (5.8%) were biracial. The majority of the women were from in state (n = 339, 61.7%), and most were either living with family (n = 160, 29.1%) or on campus (n = 329, 59.9%). Self-reported high school grade point average (GPA) was 3.49 (SD = .39). Participants were recruited through more than 50 sections of 100-level classes and through ethnic and racial minority student organizations. Students were told that participation was voluntary, that participation was not related to their class grade, and that their responses would be kept confidential. A lottery gift of $100, $50, or $25 was offered to encourage participation. Approximately 90% of 1st-year female undergraduates who were recruited chose to participate and completed the survey packet. In addition to the demographic variables, the five constructs examined were personal and family valuing of education, self-esteem, educational self-efficacy, and academic stress. Overall, the findings present an interesting pattern. Mothers' education, fathers' education, family income, and high school GPAs were positively related to educational self-efficacy. The findings suggest that educational self-efficacy, personal valuing of education, family valuing of education, academic stress, and self-esteem are important for college women, in general, and do not necessarily interact with race/ ethnicity. The results also have implications for counseling practice and future research. The findings suggest that self-beliefs are more influential in determining college women's levels of educational self-efficacy than is family valuing of education and are also related to less academic stress. Future research, both quantitative and qualitative, should reexamine and extend the issues raised in this study.

This study examined the relationships among self-esteem, body image, and health-related behaviors of 267 female and 156 male first-year college students. Data were collected in 23 classrooms. Instruments included a demographic sheet, the Objectified Body Consciousness Scale, the Weight and Appearance Visual Analogue Scales, the Contour Drawing Rating Scale, the Rosenberg Self Esteem Scale, and a measure of physical fitness/health-related behaviors. Self-esteem was consistently related to body image dissatisfaction for women, and women consistently exhibited a more negative body image than did men. Even when both men and women were consistent exercisers, the women had poorer body image. Finally, for both men and women, more positive physical fitness/health-related behaviors were positively related to self-esteem and body image.

Investigated the relationship of sex, gender role attitudes, and sexual orientation to perceptions of rape. College students responded to scenarios depicting the rape of heterosexual and homosexual males and females. Men assigned more blame to victims (particularly male victims) than did women. Traditional gender role attitudes positively related to victim blame and to more negative attitudes toward homosexuals. (SM)

The decisions of 83 American Indian undergraduates to drop out of school were investigated based on the influence of their self-beliefs, social support, and comfort. Although all three accounted for academic nonpersistence decisions, social support was the strongest predictor. Discusses implications for increasing academic persistence of students, such as fostering mentoring relationships and providing interventions to increase social support. (Author/JDM)

Surveyed students at the U.S. Naval Academy, U.S. Air Force Academy, Reserve Officer Training Corp, and a civilian university about attitudes toward gender roles and authoritarianism. Military students had the most traditional authoritarian beliefs and gender role attitudes, with Naval Academy males holding the most traditional attitudes toward women compared to Naval Academy females and civilian females and males. (EV)

Examines the influence of social support, university comfort, and self-beliefs on the persistence decisions of African American (N=98) undergraduates attending a predominantly White university. Each construct predicted persistence, with university comfort and social support the strongest predictors. A positive perception of the university environment was related to higher cultural congruity and perceived mentorship. (Author/MKA)

Upper-class undergraduates (n=740) beginning graduate students in counseling (n=78) and mental health professionals (n=45) were surveyed regarding attitudes toward rape victims. Results show that male undergraduates had the most negative attitudes toward rape victims, and female professionals the most favorable. Male participants held more negative attitudes toward victims of rape than did their female counterparts, regardless of professional status. (Author/MKA)