170
band of a spectrum can be analyzed as a trait in an evolutionary model. This does
not necessarily mean that spectra themselves are traits nor that they themselves
evolve. For example, there is no reason for evolution to favor lower reflectance at
660 nm. However, there may be biological reasons for natural selection to favor
higher amounts of chlorophyll a in a leaf, which happens to absorb light at 660 nm.
Terminology such as “evolution of spectra” or “spectral niches” may be efficient
communication shortcuts but can also cause confusion. They may make it all too
easy to lose sight of the biological mechanisms behind the observed phenomena.
Advances in analyzing spectral data in light of evolution will require keeping
mechanisms in mind. That said, phylogenetic inference on spectra can be used as a
discovery tool. Consistently finding high rates evolution in a spectral region not associated with a known function should trigger further investigation. Moreover, mechanistic thinking may end up proving us wrong and show that spectra in fact evolve (at
least some regions). For example, increased leaf reflectance that prevents leaf overheating could be favored by evolution. In such a situation, high reflectance would
result from “real” traits—such as bright hairs, cuticles, and waxes—but one can
argue that there is biological meaning in the evolution of reflectance itself in this case.
7.5.4 Ignore Phylogeny at Your Peril
Phylogeny adds complexity to an analysis but has benefits in new insights (estimating ancestral leaf spectra, helping to go from observations to traits, and more).
However, it can be tempting to analyze data on multiple species without accounting
for shared evolutionary history. The problem with methods that ignore the phylogeny, such as partial least squares regression, is that they assume that species are
independent data points. They are not! There is thus the risk of “overcounting” some
parts of the tree of life: for example, if one wants to develop a model for all plants,
and one has five oak species, a ginkgo, a pine, and a magnolia, the final model will
essentially be an oak model with some deviations. However, the five oaks have
shared much of their evolutionary history and so do not represent five independent
instances of evolution. Phylogenies can be included into such analyses, and their
importance appropriately scaled (in some cases, they will not affect results, but this
is only knowable once the tree is used), and make results far more robust.
7.6 Moving Forward
The integration of leaf spectra and phylogenies using evolutionary models is still in
its infancy. Phylogenetic models have the potential to unlock what drives evolution
of the traits leading to different spectra. Spectra may have the potential, combined
with phylogenies, to help identify species from afar, and even contain phylogenetic
information themselves.
J. E. Meireles et al.
band of a spectrum can be analyzed as a trait in an evolutionary model. This does
not necessarily mean that spectra themselves are traits nor that they themselves
evolve. For example, there is no reason for evolution to favor lower reflectance at
660 nm. However, there may be biological reasons for natural selection to favor
higher amounts of chlorophyll a in a leaf, which happens to absorb light at 660 nm.
Terminology such as “evolution of spectra” or “spectral niches” may be efficient
communication shortcuts but can also cause confusion. They may make it all too
easy to lose sight of the biological mechanisms behind the observed phenomena.
Advances in analyzing spectral data in light of evolution will require keeping
mechanisms in mind. That said, phylogenetic inference on spectra can be used as a
discovery tool. Consistently finding high rates evolution in a spectral region not associated with a known function should trigger further investigation. Moreover, mechanistic thinking may end up proving us wrong and show that spectra in fact evolve (at
least some regions). For example, increased leaf reflectance that prevents leaf overheating could be favored by evolution. In such a situation, high reflectance would
result from “real” traits—such as bright hairs, cuticles, and waxes—but one can
argue that there is biological meaning in the evolution of reflectance itself in this case.
7.5.4 Ignore Phylogeny at Your Peril
Phylogeny adds complexity to an analysis but has benefits in new insights (estimating ancestral leaf spectra, helping to go from observations to traits, and more).
However, it can be tempting to analyze data on multiple species without accounting
for shared evolutionary history. The problem with methods that ignore the phylogeny, such as partial least squares regression, is that they assume that species are
independent data points. They are not! There is thus the risk of “overcounting” some
parts of the tree of life: for example, if one wants to develop a model for all plants,
and one has five oak species, a ginkgo, a pine, and a magnolia, the final model will
essentially be an oak model with some deviations. However, the five oaks have
shared much of their evolutionary history and so do not represent five independent
instances of evolution. Phylogenies can be included into such analyses, and their
importance appropriately scaled (in some cases, they will not affect results, but this
is only knowable once the tree is used), and make results far more robust.
7.6 Moving Forward
The integration of leaf spectra and phylogenies using evolutionary models is still in
its infancy. Phylogenetic models have the potential to unlock what drives evolution
of the traits leading to different spectra. Spectra may have the potential, combined
with phylogenies, to help identify species from afar, and even contain phylogenetic
information themselves.
J. E. Meireles et al.