partial least squares regression (PLSR) (Salicaceae)

Knowledge graph centered on partial least squares regression (PLSR) (Salicaceae) with 71 nodes and 182 connections. Top connected: Rocky Mountain Biological Laboratory, Gothic, Crested Butte, Engelmann Spruce, Salix lucida subsp. lasiandra.

Method synopsis

PLSR modeling approach using leaf spectral reflectance measurements to extrapolate functional trait values from subset of direct measurements to larger sample sizes. Ten-fold cross-validation used for model calibration.

Synthesized from method descriptions across 5 papers using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Relaxation of the leaf economics spectrum within and across quaking aspen Populus tremuloides genotypes (2025), Oikos. Implementations in other papers (listed below) may differ.

1. Collect spectral reflectance measurements
   Leaf spectra were collected using a hand-held spectroradiometer (ASD FieldSpec HandHeld 2) with a 325-1075 nm spectral range and 1.5 nm resolution. The spectrometer was fitted with a leaf clip with a built-in light source. Three reflectance measurements per leaf were collected and averaged for eight leaves per tree on same leaves used for SLA and LDMC measurements.
   Quantities: 3 reflectance measurements per leaf, 8 leaves per tree, n=1376 totalDuration: Not specifiedConditions: Optimized every 15 min to ensure measurement quality
   Equipment: ASD FieldSpec HandHeld 2 spectroradiometer, leaf clip with built-in light source

Typical Equipment

• ASD FieldSpec HandHeld 2 spectroradiometer
• leaf clip

Output Measurements

• predicted LNC
• predicted Amax
• predicted Rd

Papers Using This Protocol (5)

Species Studied With This Protocol

Places Where Used