We provide a step-by-step guide for combining measurements of leaf reflectance and leaf traits to build statistical models that estimate traits from reflectance, enabling rapid collection of a ...diverse range of leaf properties.
Abstract
Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. We provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.
From the Arctic to the tropics Serbin, Shawn P.; Wu, Jin; Ely, Kim S. ...
The New phytologist,
December 2019, Letnik:
224, Številka:
4
Journal Article
Recenzirano
Odprti dostop
• Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has ...been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth’s biomes, an efficient, globally generalizable approach to predict LMA is still lacking.
• We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 gm–2. Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments.
• Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R² = 0.89; root mean square error (RMSE) = 15.45 gm–2).
• Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.
Terrestrial biosphere models (TBMs) are highly sensitive to model representation of photosynthesis, in particular the parameters maximum carboxylation rate and maximum electron transport rate at 25°C ...(V
c,max.25 and J
max.25, respectively). Many TBMs do not include representation of Arctic plants, and those that do rely on understanding and parameterization from temperate species.
We measured photosynthetic CO2 response curves and leaf nitrogen (N) content in species representing the dominant vascular plant functional types found on the coastal tundra near Barrow, Alaska.
The activation energies associated with the temperature response functions of V
c,max and J
max were 17% lower than commonly used values. When scaled to 25°C, V
c,max.25 and J
max.25 were two- to five-fold higher than the values used to parameterize current TBMs. This high photosynthetic capacity was attributable to a high leaf N content and the high fraction of N invested in Rubisco. Leaf-level modeling demonstrated that current parameterization of TBMs resulted in a two-fold underestimation of the capacity for leaf-level CO2 assimilation in Arctic vegetation.
This study highlights the poor representation of Arctic photosynthesis in TBMs, and provides the critical data necessary to improve our ability to project the response of the Arctic to global environmental change.
Drought stress responses, including a drought signalling plant hormone, can be measured using light reflected from leaves before drought is visible to the naked eye.
Abstract
Drought is the most ...important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49–0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.
Cognitive function and the performance of a secondary, dual task may affect certain aspects of gait, but the relationships between cognitive function and gait are not well understood. To better ...understand the motor control of gait and the relationship between cognitive function and gait, we studied cognitive function and the effects of different types of dual tasking on the gait of patients with Parkinson's disease (PD) and controls, contrasting measures of gait automaticity and rhythmicity with other features. Patients with idiopathic PD (n = 30; mean age 71.8 year) with moderate disease severity (Hoehn and Yahr Stage 2–3) were compared to age and gender‐matched healthy controls (n = 28). Memory and executive function were also assessed. In both groups, gait speed decreased in response to dual tasking, in a parallel fashion. For the PD group only, gait variability increased compared to usual walking. Executive function was significantly worse in the PD group, while memory was not different in the two groups. Executive function measures were significantly correlated with gait variability during dual tasking, but not during usual walking. These findings demonstrate that regulation of gait variability and rhythmicity is apparently an automatic process that does not demand attention in healthy adults. In patients with PD, however, this ability becomes attention‐demanding and worsens when subjects perform secondary tasks. Moreover, the associations between executive function and gait variability suggest that a decline in executive function in PD may exacerbate the effects of dual tasking on gait, potentially increasing fall risk.
Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO.sub.2 absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to ...estimate the CO.sub.2 absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO.sub.2 exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO.sub.2 fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration (R.sub.dark25 ), the maximum rate of carboxylation by the enzyme Rubisco (V.sub.cmax25 ), the maximum rate of electron transport (J.sub.max25 ), and the triose phosphate utilization rate (T.sub.p25 ), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for V.sub.cmax25, J.sub.max25, T.sub.p25 and R.sub.dark25 (RMSE of 13, 20, 1.5 and 0.3 mumol m.sup.-2 s.sup.-1, and R.sup.2 of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.
Walking is generally viewed as an automated, over-learned, rhythmic motor task and may even be considered the lower-limb analog of rhythmic finger tapping, another automated motor task. Thus, one ...might hypothesize that walking would be associated with a simple rhythmic task like tapping rather than with a complex motor task like catching. Surprisingly, however, we find that among older adults, routine walking has more in common with complex motor tasks, like catching a moving object, than it does with tapping. Tapping performance, including both the average tapping interval and the variability of tapping interval, was not significantly associated with any gait parameter (gait speed, average stride time and stride time variability). In contrast, catch game performance was significantly associated with measures of walking, suggesting that walking is more like catching than it is like tapping. For example, participants with a higher gait speed tended to have lower times to first move when catching, better catching accuracy, and less catching errors. Stride time variability was significantly associated with each of the measures of catching. Participants with a lower stride time variability (a more steady gait) had better catching accuracy, lower time to first move, fewer direction changes when moving the cursor to catch the falling object, and less catching errors. To understand this association, we compared walking performance to performance on the Stroop test, a classic measure of executive function, and tests of memory. Walking was associated with higher-level cognitive resources, specifically, executive function, but not with memory or cognitive function in general. For example, a lower (better) stride time variability was significantly associated with higher (better) scores on the Stroop test, but not with tests of memory. Similarly, when participants were stratified based on their performance on the Stroop test and tests of memory, stride time variability was dependent on the former, but not the latter. These findings underscore the interconnectedness of gait and cognitive function, indicate that even routine walking is a complex cognitive task that is associated with higher-level cognitive function, and suggest an alternative approach to the treatment of gait and fall risk in the elderly.
The algae Ulva lactuca and Gracilaria parvispora are abundant in the Gulf of California, rich in nutrients, and may be used as a source of protein in balanced diets for shrimp. This study tests ...whether their meal, as a partial inclusion in diets for juvenile Litopenaeus vannamei, is feasible. Percentages of inclusion were 5, 10, and 15 %. Results showed that final weight, weight gain, and specific growth rate varied significantly among diets (P < 0.05). There were significant differences in growth among the trials of amount of inclusion of meal when using U. lactuca (P < 0.05), and no significant differences among the trials when using G. parvispora (P > 0.05). In general, better results were obtained when using G. parvispora compared with U. lactuca. When compared to the control diet (without inclusion), diets that included 10 and 15 % U. lactuca meal yielded a significantly lower growth (P < 0.05), but no significant differences were detected when using U. lactuca 5 % meal (P > 0.05), suggesting the feasibility of inclusion to this limited percentage. No significant differences were detected between the control and the three treatments with G. parvispora, suggesting the possibility of using higher percentages of inclusion. We conclude that both seaweeds may be used as a component in preparing feed for juvenile L. vannamei.
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