On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

Abstract

Abstract. Understanding the causes of variation in functional plant traits is a central issue in ecology, particularly in the context of global change. Spectroscopy is increasingly used for rapid and non-destructive estimation of foliar traits, but few studies have evaluated its accuracy when assessing phenotypic variation in multiple traits. Working with 24 chemical and physical leaf traits of six European tree species growing on strongly contrasting soil types (i.e. deep alluvium versus nearby shallow chalk), we asked (i) whether variability in leaf traits is greater between tree species or soil type, and (ii) whether field spectroscopy is effective at predicting intraspecific variation in leaf traits as well as interspecific differences. Analysis of variance showed that interspecific differences in traits were generally much stronger than intraspecific differences related to soil type, accounting for 25 % versus 5 % of total trait variation, respectively. Structural traits, phenolic defences and pigments were barely affected by soil type. In contrast, foliar concentrations of rock-derived nutrients did vary: P and K concentrations were lower on chalk than alluvial soils, while Ca, Mg, B, Mn and Zn concentrations were all higher, consistent with the findings of previous ecological studies. Foliar traits were predicted from 400 to 2500 nm reflectance spectra collected by field spectroscopy using partial least square regression, a method that is commonly employed in chemometrics. Pigments were best modelled using reflectance data from the visible region (400-700 nm), while all other traits were best modelled using reflectance data from the shortwave infrared region (1100-2500 nm). Spectroscopy delivered accurate predictions of species-level variation in traits. However, it was ineffective at detecting intraspecific variation in rock-derived nutrients (with the notable exception of P). The explanation for this failure is that rock-derived elements do not have absorption features in the 400-2500 nm region, and their estimation is indirect, relying on elemental concentrations covarying with structural traits that do have absorption features in that spectral region ("constellation effects"). Since the structural traits did not vary with soil type, it was impossible for our regression models to predict intraspecific variation in rock-derived nutrients via constellation effects. This study demonstrates the value of spectroscopy for rapid, non-destructive estimation of foliar traits across species, but highlights problems with predicting intraspecific variation indirectly. We discuss the implications of these findings for mapping functional traits by airborne imaging spectroscopy.

Resilience of Spanish forests to recent droughts and climate change

“…For instance, fire occurrence data can be tracked from satellite (Justice et al., 2002; Turco, Herrera, Tourigny, Chuvieco, & Provenzale, 2019), and pathogen damage can be detected from hyperspectral data, lidar data or both (Lin, Huang, Wang, Huang, & Liu, 2019; Stereńczak et al., 2019). Furthermore, species composition and stand development information are becoming more and more accessible thanks to hyperspectral imagery and repeat lidar scans (Coomes et al 2017; Jucker et al., 2018; Nunes, Davey, & Coomes, 2017; Simonson, Allen, & Coomes, 2012; Simonson, Ruiz‐Benito, Valladares, & Coomes, 2016). Future work could also explore whether stand height, age, soil type and species composition influence resilience; and include field data at the same resolution to gain a better understanding of the linkages between remotely sensed greenness and forest change.…”

Imaging spectroscopy reveals the effects of topography and logging on the leaf chemistry of tropical forest canopy trees

“…The advantage of remote sensing lies in the massive number of measurements made, providing opportunities to map entire landscapes and increase statistical power. However, the spectranomic estimates of functional traits will never be as accurate as those made on the ground due to their indirect nature (Nunes et al, ). Indirect measurements, such as leaf traits measured through imaging spectrometry, contain measurement error arising as the residual error of statistical models used for trait prediction.…”

“…The advantage of remote sensing lies in the massive number of measurements made, providing opportunities to map entire landscapes and increase statistical power. However, the spectranomic estimates of functional traits will never be as accurate as those made on the ground due to their indirect nature (Nunes et al, 2017).…”

Changes in leaf functional traits of rainforest canopy trees associated with an El Niño event in Borneo

“…To avoid overfitting the calibration models, the number of latent variables was chosen according to the 'one standard error' rule that selects the least complex model with the average crossvalidated accuracy within one standard error from that in the optimal model (Breiman et al 1984). We determined the contribution of individual ASD wavebands within the visible (VIS; 400-750 nm), near infrared (NIR; 751-1300) and shortwave infrared (SWIR; 1301-2400 nm) to the model performance (Serbin et al 2014, Nunes et al 2017. We also combined two or more spectral regions to evaluate performance.…”

“…We also combined two or more spectral regions to evaluate performance. In general, previous research indicates that the SWIR contains absorption features for most traits (Curran 1989, Kokaly et al 2009, Nunes et al 2017, with the visible being useful for pigments (Curran et al 1991, Sims andGamon 2002), and the NIR for leaf mass per area (LMA; Asner et al 2011). Performance of the final models was evaluated using an 80/20 split of the data for calibration/validation, respectively, over 100 randomised permutations of the dataset.…”