skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


This content will become publicly available on May 1, 2026

Title: All the light we cannot see: Climate manipulations leave short and long‐term imprints in spectral reflectance of trees
Abstract Anthropogenic climate change, particularly changes in temperature and precipitation, affects plants in multiple ways. Because plants respond dynamically to stress and acclimate to changes in growing conditions, diagnosing quantitative plant‐environment relationships is a major challenge. One approach to this problem is to quantify leaf responses using spectral reflectance, which provides rapid, inexpensive, and nondestructive measurements that capture a wealth of information about genotype as well as phenotypic responses to the environment. However, it is unclear how warming and drought affect spectra. To address this gap, we used an open‐air field experiment that manipulates temperature and rainfall in 36 plots at two sites in the boreal‐temperate ecotone of northern Minnesota, USA. We collected leaf spectral reflectance (400–2400 nm) at the peak of the growing season for three consecutive years on juveniles (two to six years old) of five tree species planted within the experiment. We hypothesized that these mid‐season measurements of spectral reflectance capture a snapshot of the leaf phenotype encompassing a suite of physiological, structural, and biochemical responses to both long‐ and short‐time scale environmental conditions. We show that the imprint of environmental conditions experienced by plants hours to weeks before spectral measurements is linked to regions in the spectrum associated with stress, namely the water absorption regions of the near‐infrared and short‐wave infrared. In contrast, the environmental conditions plants experience during leaf development leave lasting imprints on the spectral profiles of leaves, attributable to leaf structure and chemistry (e.g., pigment content and associated ratios). Our analyses show that after accounting for baseline species spectral differences, spectral responses to the environment do not differ among the species. This suggests that building a general framework for understanding forest responses to climate change through spectral metrics may be possible, likely having broader implications if the common responses among species detected here represent a widespread phenomenon. Consequently, these results demonstrate that examining the entire spectrum of leaf reflectance for environmental imprints in contrast to single features (e.g., indices and traits) improves inferences about plant‐environment relationships, which is particularly important in times of unprecedented climate change.  more » « less
Award ID(s):
2021898
PAR ID:
10632285
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Publisher / Repository:
Wiley
Date Published:
Journal Name:
Ecology
Volume:
106
Issue:
5
ISSN:
0012-9658
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Plants respond to rapid environmental change in ways that depend on both their genetic identity and their phenotypic plasticity, impacting their survival as well as associated ecosystems. However, genetic and environmental effects on phenotype are difficult to quantify across large spatial scales and through time. Leaf hyperspectral reflectance offers a potentially robust approach to map these effects from local to landscape levels. Using a handheld field spectrometer, we analyzed leaf‐level hyperspectral reflectance of the foundation tree species Populus fremontii in wild populations and in three 6‐year‐old experimental common gardens spanning a steep climatic gradient. First, we show that genetic variation among populations and among clonal genotypes is detectable with leaf spectra, using both multivariate and univariate approaches. Spectra predicted population identity with 100% accuracy among trees in the wild, 87%–98% accuracy within a common garden, and 86% accuracy across different environments. Multiple spectral indices of plant health had significant heritability, with genotype accounting for 10%–23% of spectral variation within populations and 14%–48% of the variation across all populations. Second, we found gene by environment interactions leading to population‐specific shifts in the spectral phenotype across common garden environments. Spectral indices indicate that genetically divergent populations made unique adjustments to their chlorophyll and water content in response to the same environmental stresses, so that detecting genetic identity is critical to predicting tree response to change. Third, spectral indicators of greenness and photosynthetic efficiency decreased when populations were transferred to growing environments with higher mean annual maximum temperatures relative to home conditions. This result suggests altered physiological strategies further from the conditions to which plants are locally adapted. Transfers to cooler environments had fewer negative effects, demonstrating that plant spectra show directionality in plant performance adjustments. Thus, leaf reflectance data can detect both local adaptation and plastic shifts in plant physiology, informing strategic restoration and conservation decisions by enabling high resolution tracking of genetic and phenotypic changes in response to climate change. 
    more » « less
  2. Plants respond to rapid environmental change in ways that depend on both their genetic identity and their phenotypic plasticity, impacting their survival as well as associated ecosystems. However, genetic and environmental effects on phenotype are difficult to quantify across large spatial scales and through time. Leaf hyperspectral reflectance offers a potentially robust approach to map these effects from local to landscape levels. Using a handheld field spectrometer, we analyzed leaf‐level hyperspectral reflectance of the foundation tree species Populus fremontii in wild populations and in three 6‐year‐old experi- mental common gardens spanning a steep climatic gradient. First, we show that genetic variation among populations and among clonal genotypes is detectable with leaf spectra, using both multivariate and univariate approaches. Spectra predicted population identity with 100% accuracy among trees in the wild, 87%–98% accuracy within a common garden, and 86% accuracy across different environments. Multiple spectral indices of plant health had significant heritability, with genotype accounting for 10%–23% of spectral variation within populations and 14%–48% of the variation across all populations. Second, we found gene by environment interactions leading to population‐specific shifts in the spectral phenotype across common garden environments. Spectral indices indicate that genetically divergent populations made unique adjustments to their chlorophyll and water content in response to the same environmental stresses, so that detecting genetic identity is critical to predicting tree response to change. Third, spectral indicators of greenness and photosynthetic efficiency decreased when populations were transferred to growing environments with higher mean annual maximum temperatures relative to home conditions. This result suggests altered physiological strategies further from the conditions to which plants are locally adapted. Transfers to cooler environments had fewer negative effects, demonstrating that plant spectra show directionality in plant performance adjust- ments. Thus, leaf reflectance data can detect both local adaptation and plastic shifts in plant physiology, informing strategic restoration and conservation decisions by enabling high resolution tracking of genetic and phenotypic changes in response to climate change. 
    more » « less
  3. Abstract To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C3or C4), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjustedR2 = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjustedR2 = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjustedR2 = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies (R2 = 0.65 to <0.01), butR2was >0.80 for certain lineages and sites. The relationship between Vcmax, a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients. 
    more » « less
  4. Summary Predictive relationships between plant traits and environmental factors can be derived at global and regional scales, informing efforts to reorient ecological models around functional traits. However, in a changing climate, the environmental variables used as predictors in such relationships are far from stationary. This could yield errors in trait–environment model predictions if timescale is not accounted for.Here, the timescale dependence of trait–environment relationships is investigated by regressingin situtrait measurements of specific leaf area, leaf nitrogen content, and wood density on local climate characteristics summarized across several increasingly long timescales.We identify contrasting responses of leaf and wood traits to climate timescale. Leaf traits are best predicted by recent climate timescales, while wood density is a longer term memory trait. The use of sub‐optimal climate timescales reduces the accuracy of the resulting trait–environment relationships.This study concludes that plant traits respond to climate conditions on the timescale of tissue lifespans rather than long‐term climate normals, even at large spatial scales where multiple ecological and physiological mechanisms drive trait change. Thus, determining trait–environment relationships with temporally relevant climate variables may be critical for predicting trait change in a nonstationary climate system. 
    more » « less
  5. Efforts to maintain the function of critical ecosystems under climate change often begin with foundation species. In the southwestern US, cottonwood trees support diverse communities in riparian ecosystems that are threatened by rising temperatures. Genetic variation within cottonwoods shapes communities and ecosystems, but these effects may be modified by phenotypic plasticity, where genotype traits change in response to environmental conditions. Here, we investigated plasticity in Fremont cottonwood (Populus fremontii) leaf litter traits as well as the consequences of plasticity for riparian ecosystems. We used three common gardens each planted with genotypes from six genetically divergent populations spanning a 12oC temperature gradient, and a decomposition experiment in a common stream environment. We found that leaf litter area, specific leaf area, and carbon to nitrogen ratio (C:N) were determined by interactions between genetics and growing environment, as was the subsequent rate of litter decomposition. Most of the genetic variation in leaf litter traits appeared among rather than within source populations with distinct climate histories. Source populations from hotter climates generally produced litter that decomposed more quickly, but plasticity varied the magnitude of this effect. We also found that hotter growing conditions reduced the variation in litter traits produced across genotypes, homogenizing the litter inputs to riparian ecosystems. All genotypes in the hottest garden produced comparatively small leaves that decomposed quickly and supported lower abundances of aquatic invertebrates, whereas the same genotypes in the coldest garden produced litter with distinct morphologies and decomposition rates. Our results suggest that plastic responses to climate stress may constrict the expression of genetic variation in predictable ways that impact communities and ecosystems. Understanding these interactions between genetic and environmental variation is critical to our ability to plan for the role of foundation species when managing and restoring riparian ecosystems in a warming world. 
    more » « less