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1. Leaf spectroscopy of resistance to Ceratocystis wilt of ‘Ōhi’a

   https://doi.org/10.1371/journal.pone.0287144  

   Seeley, Megan M; Martin, Roberta E; Giardina, Christian; Luiz, Blaine; Francisco, Kainana; Cook, Zachary; Hughes, Marc A; Asner, Gregory P (June 2023, PLOS ONE)

   Sarrocco, Sabrina (Ed.)

   Plant pathogens are increasingly compromising forest health, with impacts to the ecological, economic, and cultural goods and services these global forests provide. One response to these threats is the identification of disease resistance in host trees, which with conventional methods can take years or even decades to achieve. Remote sensing methods have accelerated host resistance identification in agricultural crops and for a select few forest tree species, but applications are rare. Ceratocystis wilt of ʻōhiʻa, caused by the fungal pathogenCeratocystis lukuohiahas been killing large numbers of the native Hawaiian tree,Metrosideros polymorphaor ʻŌhiʻa, Hawaii’s most common native tree and a biocultural keystone species. Here, we assessed whether resistance toC.lukuohiais detectable in leaf-level reflectance spectra (400–2500 nm) and used chemometric conversion equations to understand changes in leaf chemical traits of the plants as indicators of wilt symptom progression. We collected leaf reflectance data prior to artificially inoculating 2–3-year-oldM.polymorphaclones with C.lukuohia. Plants were rated 3x a week for foliar wilt symptom development and leaf spectra data collected at 2 to 4-day intervals for 120 days following inoculation. We applied principal component analysis (PCA) to the pre-inoculation spectra, with plants grouped according to site of origin and subtaxon, and two-way analysis of variance to assess whether each principal component separated individuals based on their disease severity ratings. We identified seven leaf traits that changed in susceptible plants following inoculation (tannins, chlorophyll a+b, NSC, total C, leaf water, phenols, and cellulose) and leaf chemistries that differed between resistant and early-stage susceptible plants, most notably chlorophyll a+b and cellulose. Further, disease resistance was found to be detectable in the reflectance data, indicating that remote sensing work could expedite Ceratocystis wilt of ʻōhiʻa resistance screenings. 

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2. Large-Scale Controls on the Leaf Economic Spectrum of the Overstory Tree Species Metrosideros polymorpha

   https://doi.org/10.3390/rs15194707  

   Seeley, Megan M; Asner, Gregory P (October 2023, Remote Sensing)

   The role of intraspecific trait variation in functional ecology has gained traction in recent years as many papers have observed its importance in driving community diversity and ecology. Yet much of the work in this field relies on field-based trait surveys. Here, we used continuous canopy trait information derived from remote sensing data of a highly polymorphic tree species, Metrosideros polymorpha, to quantify environmental controls on intraspecific trait variation. M. polymorpha, an endemic, keystone tree species in Hawai’i, varies morphologically, chemically, and genetically across broad elevation and soil substrate age gradients, making it an ideal model organism to explore large-scale environmental drivers of intraspecific trait variation. M. polymorpha canopy reflectance (visible to shortwave infrared; 380–2510 nm) and light detection and ranging (LiDAR) data collected by the Global Airborne Observatory were modeled to canopy trait estimates of leaf mass per area, chlorophyll a and b, carotenoids, total carbon, nitrogen, phosphorus, phenols, cellulose, and top of canopy height using previously developed leaf chemometric equations. We explored how these derived traits varied across environmental gradients by extracting elevation, slope, aspect, precipitation, and soil substrate age data at canopy locations. We then obtained the feature importance values of the environmental factors in predicting each leaf trait by training random forest models to predict leaf traits individually. Of these environmental factors, elevation was the most important predictor for all canopy traits. Elevation not only affected canopy traits directly but also indirectly by influencing the relationships between soil substrate age and canopy traits as well as between nitrogen and other traits, as indicated by the change in slope between the variables at different elevation ranges. In conclusion, intraspecific variation in M. polymorpha traits derived from remote sensing adheres to known leaf economic spectrum (LES) patterns as well as interspecific LES traits previously mapped using imaging spectroscopy. 

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3. Quantifying the Variation in Reflectance Spectra of Metrosideros polymorpha Canopies across Environmental Gradients

   https://doi.org/10.3390/rs15061614  

   Seeley, Megan M; Martin, Roberta E; Vaughn, Nicholas R; Thompson, David R; Dai, Jie; Asner, Gregory P (March 2023, Remote Sensing)

   Imaging spectroscopy is a burgeoning tool for understanding ecosystem functioning on large spatial scales, yet the application of this technology to assess intra-specific trait variation across environmental gradients has been poorly tested. Selection of specific genotypes via environmental filtering plays an important role in driving trait variation and thus functional diversity across space and time, but the relative contributions of intra-specific trait variation and species turnover are still unclear. To address this issue, we quantified the variation in reflectance spectra within and between six uniform stands of Metrosideros polymorpha across elevation and soil substrate age gradients on Hawai‘i Island. Airborne imaging spectroscopy and light detection and ranging (LiDAR) data were merged to capture and isolate sunlit portions of canopies at the six M. polymorpha-dominated sites. Both intra-site and inter-site spectral variations were quantified using several analyses. A support vector machine (SVM) model revealed that each site was spectrally distinct, while Euclidean distances between site centroids in principal components (PC) space indicated that elevation and soil substrate age drive the separation of canopy spectra between sites. Coefficients of variation among spectra, as well as the intrinsic spectral dimensionality of the data, demonstrated the hierarchical effect of soil substrate age, followed by elevation, in determining intra-site variation. Assessments based on leaf trait data estimated from canopy reflectance resulted in similar patterns of separation among sites in the PC space and distinction among sites in the SVM model. Using a highly polymorphic species, we demonstrated that canopy reflectance follows known ecological principles of community turnover and thus how spectral remote sensing addresses forest community assembly on large spatial scales. 

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4. The acquisitive–conservative axis of leaf trait variation emerges even in homogeneous environments

   https://doi.org/10.1093/aob/mcaa198  

   Gorné, Lucas D; Díaz, Sandra; Minden, Vanessa; Onoda, Yusuke; Kramer, Koen; Muir, Christopher; Michaletz, Sean T; Lavorel, Sandra; Sharpe, Joanne; Jansen, Steven; et al (November 2020, Annals of Botany)

   Abstract Background and Aims The acquisitive–conservative axis of plant ecological strategies results in a pattern of leaf trait covariation that captures the balance between leaf construction costs and plant growth potential. Studies evaluating trait covariation within species are scarcer, and have mostly dealt with variation in response to environmental gradients. Little work has been published on intraspecific patterns of leaf trait covariation in the absence of strong environmental variation. Methods We analysed covariation of four leaf functional traits [specific leaf area (SLA) leaf dry matter content (LDMC), force to tear (Ft) and leaf nitrogen content (Nm)] in six Poaceae and four Fabaceae species common in the dry Chaco forest of Central Argentina, growing in the field and in a common garden. We compared intraspecific covariation patterns (slopes, correlation and effect size) of leaf functional traits with global interspecific covariation patterns. Additionally, we checked for possible climatic and edaphic factors that could affect the intraspecific covariation pattern. Key Results We found negative correlations for the LDMC–SLA, Ft–SLA, LDMC–Nm and Ft–Nm trait pairs. This intraspecific covariation pattern found both in the field and in the common garden and not explained by climatic or edaphic variation in the field follows the expected acquisitive–conservative axis. At the same time, we found quantitative differences in slopes among different species, and between these intraspecific patterns and the interspecific ones. Many of these differences seem to be idiosyncratic, but some appear consistent among species (e.g. all the intraspecific LDMC–SLA and LDMC–Nm slopes tend to be shallower than the global pattern). Conclusions Our study indicates that the acquisitive–conservative leaf functional trait covariation pattern occurs at the intraspecific level even in the absence of relevant environmental variation in the field. This suggests a high degree of variation–covariation in leaf functional traits not driven by environmental variables. 

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5. Reading light: leaf spectra capture fine‐scale diversity of closely related, hybridizing arctic shrubs

   https://doi.org/10.1111/nph.17731  

   Stasinski, Lance; White, Dawson M.; Nelson, Peter R.; Ree, Richard H.; Meireles, José Eduardo (October 2021, New Phytologist)

   Summary Leaf reflectance spectroscopy is emerging as an effective tool for assessing plant diversity and function. However, the ability of leaf spectra to detect fine‐scale plant evolutionary diversity in complicated biological scenarios is not well understood.We test if reflectance spectra (400–2400 nm) can distinguish species and detect fine‐scale population structure and phylogenetic divergence – estimated from genomic data – in two co‐occurring, hybridizing, ecotypically differentiated species ofDryas. We also analyze the correlation among taxonomically diagnostic leaf traits to understand the challenges hybrids pose to classification models based on leaf spectra.Classification models based on leaf spectra identified two species ofDryaswith 99.7% overall accuracy and genetic populations with 98.9% overall accuracy. All regions of the spectrum carried significant phylogenetic signal. Hybrids were classified with an average overall accuracy of 80%, and our morphological analysis revealed weak trait correlations within hybrids compared to parent species.Reflectance spectra captured genetic variation and accurately distinguished fine‐scale population structure and hybrids of morphologically similar, closely related species growing in their home environment. Our findings suggest that fine‐scale evolutionary diversity is captured by reflectance spectra and should be considered as spectrally‐based biodiversity assessments become more prevalent. 

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