158
to reconstruct the phylogeny from scratch using DNA sequences and then by timecalibrating the tree using fossil information and molecular clock models. Tree
reconstruction is tricky and laborious, but there are many tools that can help (e.g.,
Antonelli et al. 2016; Pearse and Purvis 2013). Cobbling together a phylogenetic
tree by manually assembling branches is not recommended for analysis of spectra
or other traits.
Finally, as seen in the previous section, phylogenies are estimates, and systematists have means of assessing uncertainty in their topology and their branch lengths,
which are together referred to as phylogenetic uncertainty. For example, the divergence between two lineages may have a mean of 20 million years and a confidence
interval or 95% highest posterior density of 18–22 million years. That uncertainty
can (and should) be carried over to downstream statistical analyses.
7.3 The Evolution of Quantitative Traits
The study of evolution is fundamentally concerned with describing how organisms
change through time and with understanding the processes driving change.
Evolutionary change, however, can be thought about at different phylogenetic and
temporal scales. Because we are interested in understanding spectra in light of phylogenies, we will not discuss microevolutionary processes that occur at the population level such as genetic drift and natural selection. Instead, we will focus on
describing macroevolution and how traits—such as leaf structure and chemical
composition—change across entire lineages over long timescales (usually millions
of years).
7.3.1 Macroevolutionary Models of Trait Evolution
Macroevolutionary models of trait evolution describe the long-term consequences
of short timescale evolution. At any given time step, a trait value can increase or
decrease due to mechanisms like selection, drift, and migration. For example, the
reflectance in one spectral region may decrease due to selection for higher levels of
a particular pigment, while reflectance in another spectral region may decrease due
to a random change in leaf hair density. Many such changes occur over long evolutionary time in each lineage.
7.3.1.1 Brownian Motion
Most models for evolution of quantitative traits leverage the central limit theorem
from statistics, which states that the sum of many random changes leads to a normal
distribution. Because trait evolution at macroevolutionary scales integrates over
J. E. Meireles et al.
to reconstruct the phylogeny from scratch using DNA sequences and then by timecalibrating the tree using fossil information and molecular clock models. Tree
reconstruction is tricky and laborious, but there are many tools that can help (e.g.,
Antonelli et al. 2016; Pearse and Purvis 2013). Cobbling together a phylogenetic
tree by manually assembling branches is not recommended for analysis of spectra
or other traits.
Finally, as seen in the previous section, phylogenies are estimates, and systematists have means of assessing uncertainty in their topology and their branch lengths,
which are together referred to as phylogenetic uncertainty. For example, the divergence between two lineages may have a mean of 20 million years and a confidence
interval or 95% highest posterior density of 18–22 million years. That uncertainty
can (and should) be carried over to downstream statistical analyses.
7.3 The Evolution of Quantitative Traits
The study of evolution is fundamentally concerned with describing how organisms
change through time and with understanding the processes driving change.
Evolutionary change, however, can be thought about at different phylogenetic and
temporal scales. Because we are interested in understanding spectra in light of phylogenies, we will not discuss microevolutionary processes that occur at the population level such as genetic drift and natural selection. Instead, we will focus on
describing macroevolution and how traits—such as leaf structure and chemical
composition—change across entire lineages over long timescales (usually millions
of years).
7.3.1 Macroevolutionary Models of Trait Evolution
Macroevolutionary models of trait evolution describe the long-term consequences
of short timescale evolution. At any given time step, a trait value can increase or
decrease due to mechanisms like selection, drift, and migration. For example, the
reflectance in one spectral region may decrease due to selection for higher levels of
a particular pigment, while reflectance in another spectral region may decrease due
to a random change in leaf hair density. Many such changes occur over long evolutionary time in each lineage.
7.3.1.1 Brownian Motion
Most models for evolution of quantitative traits leverage the central limit theorem
from statistics, which states that the sum of many random changes leads to a normal
distribution. Because trait evolution at macroevolutionary scales integrates over
J. E. Meireles et al.