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 of Classification models based on leaf spectra identified two species of 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.
Imaging spectroscopy has the potential to map closely related plant taxa at landscape scales. Although spectral investigations at the leaf and canopy levels have revealed relationships between phylogeny and reflectance, understanding how spectra differ across, and are inherited from, genotypes of a single species has received less attention. We used a common-garden population of four varieties of the keystone canopy tree,
- Award ID(s):
- 2218932
- NSF-PAR ID:
- 10413067
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Oecologia
- ISSN:
- 0029-8549
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary Dryas . We also analyze the correlation among taxonomically diagnostic leaf traits to understand the challenges hybrids pose to classification models based on leaf spectra.Dryas with 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. -
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1. Host plant phenotypic traits affect the structure of the associated consumer community and mediate species interactions. Intraspecific variation in host traits is well documented, although a functional understanding of variable traits that drive herbivore community response is lacking. We address this gap by modelling the trait‐environment relationship using insect traits and host plant traits in a multilevel model.
2. We compare herbivore assemblages from the canopy of the phenotypically variable tree
Metrosideros polymorpha on Hawai‘i Island. Multiple distinct varieties ofM. polymorpha frequently co‐occur, with variation in morphological traits. Using this system, we identify host and insect traits that underlie patterns of herbivore abundance and quantify the strength of host‐insect trait interactions.3. This work examines plant‐insect interactions at a community scale, across 36 herbivore species in three orders. We find that co‐occurring trees of varying phenotype support distinct communities. Leaf traits, including specific leaf area, trichome presence, and leaf nutrients, explain 46% of variation in insect communities. We find that feeding guild and nymphal life history are correlated with host plant traits, and we show that model predictions are improved by including the host and insect trait interaction.
4. This study demonstrates how insect herbivores traits influence community response to morphologically variable hosts. Environmental heterogeneity indirectly affected herbivore community structure via intraspecific variation in host plants, providing an important source of variation for maintaining diversity in the broader community.
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Abstract The coordination of traits from individual organs to whole plants is under strong selection because of environmental constraints on resource acquisition and use. However, the tight coordination of traits may provide underlying mechanisms of how locally adapted plant populations can become maladapted because of climate change.
To better understand local adaptation in intraspecific trait coordination, we studied trait variability in the widely distributed foundation tree species,
Populus fremontii using a common garden near the mid‐elevational point of this species distribution. We examined 28 traits encompassing four spectra: phenology, leaf economic spectrum (LES), whole‐tree architecture (Corner's Rule) and wood economic spectrum (WES).Based on adaptive syndrome theory, we hypothesized that trait expression would be coordinated among and within trait spectra, reflecting local adaptation to either exposure to freeze‐thaw conditions in genotypes sourced from high‐elevation populations or exposure to extreme thermal stress in genotypes sourced from low‐elevation populations.
High‐elevation genotypes expressed traits within the phenology and WES that limit frost exposure and tissue damage. Specifically, genotypes sourced from high elevations had later mean budburst, earlier mean budset, higher wood densities, higher bark fractions and smaller xylem vessels than their low‐elevation counterparts. Conversely, genotypes sourced from low elevations expressed traits within the LES that prioritized hydraulic efficiency and canopy thermal regulation to cope with extreme heat exposure, including 40% smaller leaf areas, 67% higher stomatal densities and 34% higher mean theoretical maximum stomatal conductance. Low‐elevation genotypes also expressed a lower stomatal control over leaf water potentials that subsequently dropped to pressures that could induce hydraulic failure.
Synthesis . Our results suggest thatPopulus fremontii expresses a high degree of coordination across multiple trait spectra to adapt to local climate constraints on photosynthetic gas exchange, growth and survival. These results, therefore, increase our mechanistic understanding of local adaptation and the potential effects of climate change that in turn, improves our capacity to identify genotypes that are best suited for future restoration efforts. -
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Many correlations exist between spectral reflectance or transmission with various phenotypic responses from plants. Of interest to us are metabolic characteristics, namely, how the various polarimetric components of plants may correlate to underlying environmental, metabolic, and genotypic differences among different varieties within a given species, as conducted during large field experimental trials. In this paper, we overview a portable Mueller matrix imaging spectropolarimeter, optimized for field use, by combining a temporal and spatial modulation scheme. Key aspects of the design include minimizing the measurement time while maximizing the signal-to-noise ratio by mitigating systematic error. This was achieved while maintaining an imaging capability across multiple measurement wavelengths, spanning the blue to near-infrared spectral region (405–730 nm). To this end, we present our optimization procedure, simulations, and calibration methods. Validation results, which were taken in redundant and non-redundant measurement configurations, indicated that the polarimeter provides average absolute errors of (5.3±2.2)×10−3and (7.1±3.1)×10−3, respectively. Finally, we provide preliminary field data (depolarization, retardance, and diattenuation) to establish baselines of barren and non-barren
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Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).
Location Eastern USA.
Time period 2017–2018.
Taxa studied Trees.
Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.
Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.
Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.
