Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest

Photosynthesis by terrestrial plants represents the majority of CO₂ uptake on Earth, yet it is difficult to measure directly from space. Estimation of gross primary production (GPP) from remote sensing indices represents a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, these have mostly been investigated with intermittent field campaigns or with narrow-band spectrometers in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400–900 nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components co-varied with carotenoid content and GPP, supporting the interpretation of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI). Although the entire 400–900 nm range showed additional spectral changes near the red edge, it did not provide significant improvements in GPP predictions. We found little seasonal variation in both normalized difference vegetation index (NDVI) and the near-infrared vegetation index (NIRv) in this ecosystem. In addition, we quantitatively determined needle-scale chlorophyll-to-carotenoid ratios as well as anthocyanin contents using full-spectrum inversions, both of which were tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using partial least-squares regression (PLSR) explained approximately 87 % of the variability in observed GPP. Our results linked the seasonal variation in reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400–900 nm associated with GPP seasonality in evergreen forests

Developing and Implementing a Sustainable, Integrated Weed Management Program for herbicide-resistant Poa annua in turfgrass

The ability of Poa annua L. to adapt to most turfgrass environments extends to its ability to develop resistance to commonly used herbicides. Herbicide resistant P. annua is of almost epidemic proportions. The loss of once viable chemical-based treatments pushes practitioners towards more expensive, and often less effective, control strategies. This management guide focuses on integrated weed management (IWM) practices for P. annua control and herbicide resistance—what it is and how to overcome it. Also discussed are resistance mechanisms and documentation of common occurrences of field-level resistance within much of the United States. Finally, a summary of some of the social and economic constraints that practitioners face in the implementation of IWM strategies for P. annua is discussed

Solar-Induced Fluorescence Detects Interannual Variation in Gross Primary Production of Coniferous Forests in the Western United States

Quantifying gross primary production (GPP), the largest flux of the terrestrial carbon cycle, remains difficult at the landscape scale. Evergreen needleleaf (coniferous) forests in the western United States constitute an important carbon reservoir whose annual GPP varies from year‐to‐year due to drought, mortality, and other ecosystem disturbances. Evergreen forest productivity is challenging to determine via traditional remote sensing indices (i.e., NDVI and EVI), because detecting environmental stress conditions is difficult. We investigated the utility of solar‐induced chlorophyll fluorescence (SIF) to detect year‐to‐year variation in GPP in four coniferous forests varying in species composition in the western United States (Sierra Nevada, Cascade, and Rocky Mountains). We show that annually averaged, satellite‐based observations of SIF (retrieved from GOME‐2) were significantly correlated with annual GPP observed at eddy covariance towers over several years. Further, SIF responded quantitatively to drought‐induced mortality, suggesting that SIF may be capable of detecting ecosystem disturbance in coniferous forests

Solar-Induced Fluorescence Detects Interannual Variation in Gross Primary Production of Coniferous Forests in the Western United States

Quantifying gross primary production (GPP), the largest flux of the terrestrial carbon cycle, remains difficult at the landscape scale. Evergreen needleleaf (coniferous) forests in the western United States constitute an important carbon reservoir whose annual GPP varies from year‐to‐year due to drought, mortality, and other ecosystem disturbances. Evergreen forest productivity is challenging to determine via traditional remote sensing indices (i.e., NDVI and EVI), because detecting environmental stress conditions is difficult. We investigated the utility of solar‐induced chlorophyll fluorescence (SIF) to detect year‐to‐year variation in GPP in four coniferous forests varying in species composition in the western United States (Sierra Nevada, Cascade, and Rocky Mountains). We show that annually averaged, satellite‐based observations of SIF (retrieved from GOME‐2) were significantly correlated with annual GPP observed at eddy covariance towers over several years. Further, SIF responded quantitatively to drought‐induced mortality, suggesting that SIF may be capable of detecting ecosystem disturbance in coniferous forests

Seasonal variation in the canopy color of temperate evergreen conifer forests

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO₂ flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices

Learning needs analysis to guide teaching evidence-based medicine: knowledge and beliefs amongst trainees from various specialities

<p>Abstract</p> <p>Background</p> <p>We undertook a needs assessment exercise using questionnaire survey of junior doctors' knowledge and beliefs concerning evidence-based medicine (EBM) and critical literature appraisal, as this is a core competence in postgraduate medical education.</p> <p>Methods</p> <p>We surveyed 317 junior doctors in various specialities in the UK West Midlands Deanery. Using validated questionnaires we compared the needs of different trainee groups. Results overall were internally consistent (Cronbach's alpha 0.929).</p> <p>Results</p> <p>Respondents' generally felt that they had poor training in EBM (Mean score 2.2, possible range 1 – 6) and that they needed more education (Mean score 5.3, possible range 1–6). Male trainees felt more confident at evaluating statistical tests than females (p = 0.002). Female trainees considered patient choice above the evidence more often than males (p = 0.038). Trainees from surgical speciality felt more confident at assessing research evidence (p = 0.009) whereas those from medical speciality felt more confident at evaluating statistical tests (p = 0.038) than other specialities. However, non-surgical specialities tended to believe that EBM had little impact on practice (p = 0.029). Respondents who had been qualified for 11 years or over felt overall more confident in their knowledge relating to EBM than those who had been qualified less than 10 years. In particular, they felt more confident at being able to assess study designs (p = < 0.001) and the general worth of research papers (p = < 0.001). Trainees with prior research experience were less likely to find original work confusing (p = 0.003) and felt more confident that they can assess research evidence (p = < 0.001) compared to those without previous research experience. Trainees without previous research experience felt that clinical judgement was more important than evidence (p = < 0.001).</p> <p>Conclusion</p> <p>There is a perceived deficit in postgraduate doctors' EBM knowledge and critical appraisal skills. Learning needs vary according to gender, place of basic medical qualification, time since graduation, prior research experience and speciality. EBM training curricular development should take into account the findings of our needs assessment study.</p

Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest

Photosynthesis by terrestrial plants represents the majority of CO2 uptake on Earth, yet it is difficult to measure directly from space. Estimation of gross primary production (GPP) from remote sensing indices represents a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, these have mostly been investigated with intermittent field campaigns or with narrow-band spectrometers in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400&ndash;900 nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components co-varied with carotenoid content and GPP, supporting the interpretation of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI). Although the entire 400&ndash;900 nm range showed additional spectral changes near the red edge, it did not provide significant improvements in GPP predictions. We found little seasonal variation in both normalized difference vegetation index (NDVI) and the near-infrared vegetation index (NIRv) in this ecosystem. In addition, we quantitatively determined needle-scale chlorophyll-to-carotenoid ratios as well as anthocyanin contents using full-spectrum inversions, both of which were tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using partial least-squares regression (PLSR) explained approximately 87 % of the variability in observed GPP. Our results linked the seasonal variation in reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400&ndash;900 nm associated with GPP seasonality in evergreen forests.</p

Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence

Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO_2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R^2 = 0.62–0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution

Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence

Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO_2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R^2 = 0.62–0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution

Mapping oral health related quality of life to generic health state values

BACKGROUND: A summary utility index is useful for deriving quality-adjusted life years (QALY) for cost analyses or disability weights for burden of disease studies. However, many quality of life instruments provide descriptive profiles rather than a single utility index. Transforming quality of life instruments to a utility index could extend the use of quality of life instruments to costs analyses and burden of disease studies. The aims of the study were to map a specific oral health measure, the Oral Health Impact Profile to a generic health state measure, the EuroQol, in order to enable the estimation of health state values based on OHIP data. METHODS: Data were collected from patients treated by a random sample of South Australian dentists in 2001–02 using mailed self-complete questionnaires. Dentists recorded the diagnosis of dental conditions and provided patients with self-complete questionnaires to record the nature, severity and duration of symptoms using the EuroQol (EQ-5D) and 14-item version of the Oral Health Impact Profile (OHIP-14) instruments. Data were available from 375 patients (response rate = 72%). A random two-thirds sample of patients was used in tobit regressions of EQ-5D health state values estimated using OHIP-14 in a model with categories of OHIP responses as indicator variables and in a model with OHIP responses as continuous variables. Age and sex were included as covariates in both models. The remaining one-third sample of patients was used to test the models. RESULTS: The OHIP item 'painful aching in mouth' was significantly related to health state values in both models while 'life less satisfying' was also significant in the continuous model. Mean forecast errors relative to the mean observed health state value were higher when fitted to the categorical model (17.4%) compared to the continuous model (15.2%) (P < 0.05). CONCLUSION: The findings enable health state values to be derived from OHIP-14 scores for populations where utility has not or cannot be measured directly