Leaf age effects on the spectral predictability of leaf traits in Amazonian canopy trees

Abstract

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age-from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (C area ) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for P area and N area . Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass-and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass-based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotelysensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology.

Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests

“…However, a more likely explanation is that the spectra–trait– V c,max25 linkages (e.g. regression coefficients) vary with leaf age (Field, ; Wilson et al ., ; Chavana‐Bryant et al ., , ; Wu et al ., ) and forest sites of different soil types and fertility (Walker et al ., ; Norby et al ., ). Regardless of these potential reasons, this finding (Fig.…”

“…Because the correlation with leaf N might not be the only reason for the derived spectra– V c,max25 model, this further leads to the second hypothesis: leaf V c,max25 is correlated with multiple leaf traits and processes that determine Rubisco content and activity (e.g. leaf N content, leaf P content, leaf Chl concentrations, LMA, leaf age and many others we do not yet understand), and leaf spectra emerge from the ensemble of properties that define leaf chemical, morphological and phenological status (Asner & Martin, ; Serbin et al ., ; Chavana‐Bryant et al ., , ). As such, leaf spectra can be used to help infer leaf V c,max25 , and are indeed a better predictor of leaf V c,max25 (Serbin et al ., ) than alternative trait approaches that leverage well‐established links between V c,max25 and just one or a few individual leaf traits (Walker et al ., ).…”

Quantitative estimation of wastewater quality parameters by hyperspectral band screening using GC, VIP and SPA

“…The VIP value of a spectral variable reflects the importance of this variable in the prediction of the substance to be measured (Oussama et al, 2012; De Almeida et al, 2013). The VIP value reflects the explanatory power of independent variable over dependent variable, and represents the importance of independent variable to model fitting (Chavana-Bryant et al, 2019). If the explanatory ability of each variable to Y is the same, then the VIP value of all independent variables is 1.…”

Assessing invertebrate herbivory in human‐modified tropical forest canopies

“…Comparisons of herbivory can also be confounded by stem age and longevity. First, leaf life spans can vary between six months and five years across Amazonian species (Chavana-Bryant et al, 2019;Reich et al, 1991Reich et al, , 2004) and tend to be longer in the understorey than in the canopy (Reich et al, 2004) and shorter in pioneer species (Galia Selaya et al, 2008) when compared to old-growth species.…”