164
Interpreting results correctly may pose some challenges, however. Who guarantees
that the high rates of evolution in a particular spectral band really means that a certain trait is evolving at a fast pace? A potentially better approach is to infer traits
from spectra first using either statistical (e.g., partial least squares regression) or
RTM inversions (e.g., PROSPECT) and then study the evolution of those traits (see
Serbin and Townsend, Chap. 3).
We can test hypotheses about how evolution affects leaf spectra because we can
calculate the likelihood of spectral data being generated by different models of evolution, which can be compared to each other using a goodness of fit metric such as
Akaike information criterion (AIC; Burnham and Anderson 2002). We foresee
numerous interesting hypotheses being tested using this type of approach, especially related to evolutionary rates and convergent evolution.
Here is a hypothetical but realistic example: We could hypothesize that plant
lineages that shift from sunny to shade habitats see an increase in their leaf chlorophyll content from 20 to 60 ug/cm
2
, that is, they have a new chlorophyll content
optimum, and that should be reflected in their spectra (Fig. 7.6). We used the predictive approach established in the previous subsection to simulate leaf spectra under
that evolutionary scenario, which highlights the disparity in reflectance in the visible spectrum between sun and understory plants. We can then fit various models of
evolution to the spectra (including one- and two-rate Brownian motion as well as a
one-optimum Ornstein–Uhlenbeck and a two-optimum Ornstein–Uhlenbeck, the
model under which the data were simulated), calculate their AIC, and compare
models using AIC weights (Burnham and Anderson 2002), as shown in Fig. 7.6. In
this simulated scenario, we find that indeed the best fit comes from having two different optima in the spectra correlating with chlorophyll content. However, in real
data, we might find that there is a difference, but only in bands correlating with
lignin content in leaves (which could reflect different herbivore or structural pressure); that there is a difference in optimum but that understory plants are much more
constrained toward their optimum than plants from sunnier habitats; or that there is
a change but it happens over longer time periods than we expect.
7.4.3 Leaf Spectra, Biodiversity Detection, and Evolution
As other chapters discuss, one approach to assessing biodiversity from plant spectra is to use spectral indices that correlate with species richness (Gamon et al.,
Chap. 16). Another approach is to use classification models (Clark et al. 2005;
Asner and Martin 2011; Serbin and Townsend, Chap. 3). Using an empirical example within a single lineage (the oaks, genus Quercus), there is enough information
in the spectra of leaves to significantly differentiate populations within a single
species (Quercus oleoides) and assign them to the correct population most of the
time. Different species can be correctly classified with even greater accuracy, and
the four major oak clades in the example can be identified with very high accuracy
(Cavender-Bares et al. 2016).
J. E. Meireles et al.
Interpreting results correctly may pose some challenges, however. Who guarantees
that the high rates of evolution in a particular spectral band really means that a certain trait is evolving at a fast pace? A potentially better approach is to infer traits
from spectra first using either statistical (e.g., partial least squares regression) or
RTM inversions (e.g., PROSPECT) and then study the evolution of those traits (see
Serbin and Townsend, Chap. 3).
We can test hypotheses about how evolution affects leaf spectra because we can
calculate the likelihood of spectral data being generated by different models of evolution, which can be compared to each other using a goodness of fit metric such as
Akaike information criterion (AIC; Burnham and Anderson 2002). We foresee
numerous interesting hypotheses being tested using this type of approach, especially related to evolutionary rates and convergent evolution.
Here is a hypothetical but realistic example: We could hypothesize that plant
lineages that shift from sunny to shade habitats see an increase in their leaf chlorophyll content from 20 to 60 ug/cm
2
, that is, they have a new chlorophyll content
optimum, and that should be reflected in their spectra (Fig. 7.6). We used the predictive approach established in the previous subsection to simulate leaf spectra under
that evolutionary scenario, which highlights the disparity in reflectance in the visible spectrum between sun and understory plants. We can then fit various models of
evolution to the spectra (including one- and two-rate Brownian motion as well as a
one-optimum Ornstein–Uhlenbeck and a two-optimum Ornstein–Uhlenbeck, the
model under which the data were simulated), calculate their AIC, and compare
models using AIC weights (Burnham and Anderson 2002), as shown in Fig. 7.6. In
this simulated scenario, we find that indeed the best fit comes from having two different optima in the spectra correlating with chlorophyll content. However, in real
data, we might find that there is a difference, but only in bands correlating with
lignin content in leaves (which could reflect different herbivore or structural pressure); that there is a difference in optimum but that understory plants are much more
constrained toward their optimum than plants from sunnier habitats; or that there is
a change but it happens over longer time periods than we expect.
7.4.3 Leaf Spectra, Biodiversity Detection, and Evolution
As other chapters discuss, one approach to assessing biodiversity from plant spectra is to use spectral indices that correlate with species richness (Gamon et al.,
Chap. 16). Another approach is to use classification models (Clark et al. 2005;
Asner and Martin 2011; Serbin and Townsend, Chap. 3). Using an empirical example within a single lineage (the oaks, genus Quercus), there is enough information
in the spectra of leaves to significantly differentiate populations within a single
species (Quercus oleoides) and assign them to the correct population most of the
time. Different species can be correctly classified with even greater accuracy, and
the four major oak clades in the example can be identified with very high accuracy
(Cavender-Bares et al. 2016).
J. E. Meireles et al.