One leaf for all: Chemical traits of single leaves measured at the leaf surface using near‐infrared reflectance spectroscopy

Rapid quantification of biological nitrogen fixation using optical spectroscopy

Biological nitrogen fixation (BNF) provides a globally important input of nitrogen (N); its quantification is critical but technically challenging. Leaf reflectance spectroscopy offers a more rapid approach than traditional techniques to measure plant N concentration ([N]) and isotopes (δ15N). Here we present a novel method for rapidly and inexpensively quantifying BNF using optical spectroscopy. We measured plant [N], δ15N, and the amount of N derived from atmospheric fixation (Ndfa) following the standard traditional methodology using isotope ratio mass spectrometry (IRMS) from tissues grown under controlled conditions and taken from field experiments. Using the same tissues, we predicted the same three parameters using optical spectroscopy. By comparing the optical spectroscopy-derived results with traditional measurements (i.e. IRMS), the amount of Ndfa predicted by optical spectroscopy was highly comparable to IRMS-based quantification, with R2 being 0.90 (slope=0.90) and 0.94 (slope=1.02) (root mean square error for predicting legume δ15N was 0.38 and 0.43) for legumes grown in glasshouse and field, respectively. This novel application of optical spectroscopy facilitates BNF studies because it is rapid, scalable, low cost, and complementary to existing technologies. Moreover, the proposed method successfully captures the dynamic response of BNF to climate changes such as warming and drought.

Small rodent population cycles and plants - after 70 years, where do we go?

Small rodent population cycles characterise northern ecosystems, and the cause of these cycles has been a long-lasting central topic in ecology, with trophic interactions currently considered the most plausible cause. While some researchers have rejected plant-herbivore interactions as a cause of rodent cycles, others have continued to research their potential roles. Here, we present an overview of whether plants can cause rodent population cycles, dividing this idea into four different hypotheses with different pathways of plant impacts and related assumptions. Our systematic review of the existing literature identified 238 studies from 150 publications. This evidence base covered studies from the temperate biome to the tundra, but the studies were scattered across study systems and only a few specific topics were addressed in a replicated manner. Quantitative effects of rodents on vegetation was the best studied topic, and our evidence base suggests such that such effects may be most pronounced in winter. However, the regrowth of vegetation appears to take place too rapidly to maintain low rodent population densities over several years. The lack of studies prevented assessment of time lags in the qualitative responses of vegetation to rodent herbivory. We conclude that the literature is currently insufficient to discard with confidence any of the four potential hypotheses for plant-rodent cycles discussed herein. While new methods allow analyses of plant quality across more herbivore-relevant spatial scales than previously possible, we argue that the best way forward to rejecting any of the rodent-plant hypotheses is testing specific predictions of dietary variation. Indeed, all identified hypotheses make explicit assumptions on how rodent diet taxonomic composition and quality will change across the cycle. Passing this bottleneck could help pinpoint where, when, and how plant-herbivore interactions have - or do not have - plausible effects on rodent population dynamics.

Active and adaptive plasticity in a changing climate

Better understanding of the mechanistic basis of plant plasticity will enhance efforts to breed crops resilient to predicted climate change. However, complexity in plasticity's conceptualisation and measurement may hinder fruitful crossover of concepts between disciplines that would enable such advances. We argue active adaptive plasticity is particularly important in shaping the fitness of wild plants, representing the first line of a plant's defence to environmental change. Here, we define how this concept may be applied to crop breeding, suggest appropriate approaches to measure it in crops, and propose a refocussing on active adaptive plasticity to enhance crop resilience. We also discuss how the same concept may have wider utility, such as in ex situ plant conservation and reintroductions.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.

Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy

Drought is a climatic event that considerably impacts plant growth, reproduction and productivity. Toona sinensis is a tree species with high economic, edible and medicinal value, and has drought resistance. Thus, the objective of this study was to dynamically monitor the physiological indicators of T. sinensis in real time to ensure the selection of drought-resistant varieties of T. sinensis. In this study, we used near-infrared spectroscopy as a high-throughput method along with five preprocessing methods combined with four variable selection approaches to establish a cross-validated partial least squares regression model to establish the relationship between the near infrared reflectance spectroscopy (NIRS) spectrum and physiological characteristics (i.e., chlorophyll content and nitrogen content) of T. sinensis leaves. We also tested optimal model prediction for the dynamic changes in T. sinensis chlorophyll and nitrogen content under five separate watering regimes to mimic non-destructive and dynamic detection of plant leaf physiological changes. Among them, the accuracy of the chlorophyll content prediction model was as high as 72%, with root mean square error (RMSE) of 0.25, and the RPD index above 2.26. Ideal nitrogen content prediction model should have R ² of 0.63, with RMSE of 0.87, and the RPD index of 1.12. The results showed that the PLSR model has a good prediction effect. Overall, under diverse drought stress treatments, the chlorophyll content of T. sinensis leaves showed a decreasing trend over time. Furthermore, the chlorophyll content was the most stable under the 75% field capacity treatment. However, the nitrogen content of the plant leaves was found to have a different and variable trend, with the greatest drop in content under the 10% field capacity treatment. This study showed that NIRS has great potential for analyzing chlorophyll nitrogen and other elements in plant leaf tissues in non-destructive dynamic monitoring.