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Chlorophyll fluorescence is a well-established method to estimate chlorophyll content in leaves. A popular fluorescence-based meter, the Opti-Sciences CCM-300 Chlorophyll Content Meter (CCM-300), utilizes the fluorescence ratio F735/F700 and equations derived from experiments using broadleaf species to provide a direct, rapid estimate of chlorophyll content used for many applications. We sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use. We linked CCM-300 measurements of broadleaf, conifer, and graminoid samples in 2018 and 2019 to high-performance liquid chromatography (HPLC) and/or spectrophotometric (Spec) analysis of the same leaves. We observed a significant difference between the CCM-300 and HPLC/Spec, but not between HPLC and Spec. In comparison to HPLC, the CCM-300 performed better for broadleaves (r = 0.55, RMSE = 154.76) than conifers (r = 0.52, RMSE = 171.16) and graminoids (r = 0.32, RMSE = 127.12). We observed a slight deterioration in meter performance between years, potentially due to meter calibration. Our results show that the CCM-300 is reliable to demonstrate coarse variations in chlorophyll but may be limited for cross-plant functional type studies and comparisons across years. [ABSTRACT FROM AUTHOR]

Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with foliar nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the contiguous United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree‐years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) across the continent. Within‐species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first continental‐scale study sheds light on the relationship between seed production and foliar nutrients, highlighting the potential of using combined Light Detection And Ranging (LiDAR) and hyperspectral remote sensing to evaluate forest regeneration. The fact that both ISP and CSP decline in the presence of high foliar P levels has immediate application in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales. [ABSTRACT FROM AUTHOR]

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling. Detailed maps of tree species can be used for forest analysis, biogeographic research and ecosystem monitoring. This study provides individual tree species maps for 24 sites in the United States National Ecological Observatory Network (NEON), covering 80 tree species and 100 million individuals. [ABSTRACT FROM AUTHOR]

Summary: Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear.We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits.While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross‐validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability.These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications. [ABSTRACT FROM AUTHOR]

This article details the construction of a Grounded Theory to explain the concept of enhancing professional learning through mobile devices. The research data was delimited to the behaviours and beliefs of Aboriginal and Torres Strait Islander pre-service teachers enrolled in two community-based initial teacher education programs in very remote communities in Australia. Four educational uses of mobile devices were identified: accessing content, handling administration, collaborating for academic support and sharing personal encouragement. The use of mobile devices enabled adults to choose times of study, choose places of study, complete assessment relevant to their course and achieve a career goal. Three elements that impact the educational use of mobile devices were identified (i.e. context, precursors and catalyst). Seven categories underlie the concept of enhancing professional learning through mobile devices: fostering access, facilitating customisation, promoting collaboration, supporting relevance, completing the course, empowering agency and enabling networking.

Plant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade‐offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long‐table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the 'rtry' R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, 'rtry' avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. 'rtry' is therefore easy to use even for beginners in R. Notably, 'rtry' does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While 'rtry' primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The 'rtry' package is available on the Comprehensive R Archive Network (CRAN; https://cran.r‐project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI‐BGC‐Functional‐Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy offers great potential to characterize plant traits at fine resolution across broad regions and then assess controls on their variation across spatial resolutions. We applied permutational partial least‐squares regression to map seven key foliar chemical and morphological traits using NASA's Airborne Visible/Infrared Imaging Spectrometer‐Next Generation (AVIRIS‐NG) for six sites spanning a climatological gradient in the Western Ghats of India. We studied the variation of trait space at spatial resolutions from the plot level (4 m), community level (30 and 100 m) to the ecosystem level (1,000 m). We observed a consistent pattern of trait space across different resolutions, with one axis defined by foliar nitrogen and leaf mass per area (LMA) and another axis representing leaf structure and defense defined by fiber, lignin, and total phenolics. We also observed consistent directionality of environment‐trait correlations across resolutions with generally higher predictive capacity of our environment‐traits models at coarser resolutions. Among the seven traits, total phenolics, fiber, and lignin were strongly influenced by environmental factors (model R2 > 0.5 at 1,000 m). Calcium, sugars, and nitrogen were significantly affected by site conditions, incorporating site as a fixed effect largely improved model performance. LMA showed little dependence on environmental factors or site conditions, suggesting a stronger influence of species composition and site history on LMA variation. Our results show that reliable trait‐trait relationships can be identified in coarse resolution imagery, but that local scale trait‐trait relationships (resolutions finer than 30 m) are not sensitive to broad‐scale abiotic/biotic factors. Plain Language Summary: We combined field leaf measurements and airborne hyperspectral images to map seven key foliar functional traits across the Western Ghats in India. We analyzed the traits at different spatial resolutions, from plot‐level (4 m), community level (30 and 100 m) to ecosystem‐level (1,000 m). We found that the trait‐trait relationship remains the same at different resolutions, which shows that we can learn a lot about the trait relationships by using images that aren't very detailed. We also looked at how traits are linked to the environment. Traits like chemicals that help plants defend themselves, as well as physical defenses like fiber and lignin, were strongly influenced by broad climate conditions. We could predict these traits well at 1,000 and 100 m. Other traits like calcium, sugars, and nitrogen were more affected by where the plants were growing, and we could predict them well when we considered the specific location. However, leaf mass per area didn't seem to depend much on the environment or location, which means it's probably influenced more by species composition and site history. When we looked at traits on a finer scale (30 m), our predictions weren't as accurate, suggesting that other things might also be affecting traits at those levels. Key Points: We mapped seven key plant functional traits for six sites along the Western Ghats of India‐a diverse but under‐represented areaOur study showed that the trait‐trait relationship (or trait space) is well conserved during the spatial aggregation from 4 to 1,000 mMost traits could be predicted well at 1,000 m and moderately at 100 m by environmental parameters but not at local scales finer than 30 m [ABSTRACT FROM AUTHOR]