Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands

Abstract

Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies to model both spatial turnover in species composition and abundances (β diversity) and species diversity at aggregate spatial scales (γ diversity). Shannon indices of γ and β diversity were calculated from field measurements of the number and relative abundances of plant species at each of two spatial grains (0.45 m2 and 35.2 m2) in mesic grasslands in central Texas, USA. Spectral signatures of reflected radiation at each grain were measured from ground-level or an unmanned aerial vehicle (UAV). Partial least squares regression (PLSR) models explained 59–85% of variance in γ diversity and 68–79% of variance in β diversity using spatial heterogeneity in canopy optical properties. Variation in both γ and β diversity were associated most strongly with heterogeneity in reflectance in blue (350–370 nm), red (660–770 nm), and near infrared (810–1050 nm) wavebands. Modeled diversity was more sensitive by a factor of three to a given level of spectral heterogeneity when derived from data collected at the small than larger spatial grain. As estimated from calibrated PLSR models, β diversity was greater, but γ diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Both γ and β diversity of grassland can be modeled by using spatial heterogeneity in vegetation optical properties provided that the grain of reflectance measurements is conserved.

Temporal stability of grassland metacommunities is regulated more by community functional traits than species diversity

“…We modeled α D of each 2‐patch community as a function of the between‐patch CV in reflectance at each waveband measured, as described by Polley et al (2019 b ). The PLSR model of diversity was developed by using the CV in reflectance between patch pairs that were randomly selected (with replacement) from the total of 32 patches for which species diversity was measured ( n = 48 patch pairs).…”

“…Techniques also are being developed to estimate community attributes that may explain local and spatial variation in ecosystem stability. Remote measurements of vegetation have, for example, been used to estimate communityweighted values of plant traits linked to ecosystem function (Chadwick and Asner 2016, Ali et al 2017, Polley et al 2020) and components of species diversity (Dalmayne et al 2013, Möckel et al 2016, Polley et al 2019b.…”

“…Community LDMC is a functional attribute (trait) that mechanistically links variation in plant species abundances and ecosystem functioning (Duru et al 2013). Species dissimilarity between communities, α D , and the abundance-weighted LDMC of grassland were derived from hyperspectral measurements of canopy reflectance (Polley et al 2019b(Polley et al , 2020. Precipitation variability is the primary driver of interannual variability in ANPP in the grassland studied (Polley et al 2020).…”

“…For example, ANPP stability of communities of perennial species depended on both diversity and a community attribute (weighted SLA; Polley and Wilsey 2018). Diversity and community attributes, in turn, can depend on soil properties (Polley et al 2019b(Polley et al , 2020.…”

Spectrally derived values of community leaf dry matter content link shifts in grassland composition with change in biomass production

“…LDMC was measured on fully expanded single leaves (leaf samples) or groups of leaves (3-7 leaves; canopy samples) in the upper canopy of each species using methods outlined by Cornelissen et al (2003). Leaves were collected from species within LTBE stands and from adjacent woodlands and experimental grassland plots (Wilsey et al 2011;Polley et al 2019) to capture the range of LDMC values represented in the local area. We collected canopy samples from five functional groups of species, including C 4 perennial grasses, C 3 perennial grasses, C 3 annual grasses, C 3 annual forbs and C 3 perennial forbs.…”