8
a general approach for earlier, more detailed and accurate decline assessment. They
also discuss the importance of engaging land managers, practitioners, and decisionmakers in these efforts to ensure that the products developed can be utilized by
stakeholders to maximize their impact.
Meireles et al. (Chap. 7) provide a framework to explain how spectral reflectance
data from plants is tightly coupled to the tree of life and demonstrate how spectra
can reveal evolutionary processes in plants. They clarify that many spectral features
in plants are inherited and are thus very similar among close relatives—in other
words, they are highly phylogenetically conserved. Simulations reveal that spectral
information of plants appears to follow widely used evolutionary models, making it
possible to link plant spectra to the tree of life in a predictive manner. As a consequence, methods developed in evolutionary biology to understand the tree of life
can now benefit the RS community. The chapter provides evidence that evolutionary
lineages may be easier than individual species to detect through RS methods, particularly if they are combined with other approaches for estimating which species
and lineages have the potential to be present in a given location. A caveat is that
spatial resolution of satellite spectral data will limit such inferences, but leaf- and
canopy-level spectra (obtainable from proximal and airborne sensing) can contribute enormously to our understanding of these fundamental links between spectral
patterns and gene sequences.
Madritch et al. (Chap. 8) link aboveground plant biodiversity and productivity to
belowground processes. They explain the functional mechanisms—which can be
revealed by remotely sensed spectral data—that influence interactions of plant hosts
with insects and soil organisms, in turn influencing ecosystem functions, such as
decomposition and nutrient cycling. The chapter provides an example of using the
concept of surrogacy, in which the biochemical linkage of spectral measurements to
associated patterns and processes aboveground is used to provide estimates of soil
and microbial processes belowground that are not directly observable via RS.
The next three chapters focus on linking satellite-based remotely sensed data to
biodiversity prediction. Pinto-Ledezma and Cavender-Bares (Chap. 9) present an
example of how currently available satellite-based RS products can be used to generate next-generation species distribution models to predict where species and lineages
are likely occur and the habitats they may have access to and persist in under altered
climates in the future. They compare RS-based methods for generating predictive
models with widely used approaches that use meteorologically derived climate variables. They demonstrate the advantages of RS-based models in regions where meteorological data is only sparsely available. Such predictive modeling that harnesses
species occurrence data and temporal information about the biotic environment may
make spectral methods of species and evolutionary lineage detection more tractable.
Building on the availability of satellite RS data with near-global coverage to predict biodiversity, Record et al. (Chap. 10) explore how RS illuminates the relationship
between biodiversity and geodiversity—the variety of abiotic features and processes
that provide the template for the development of biodiversity. They introduce a variety
of globally available geodiversity measures and examine how they can be combined
with biodiversity data to understand how biodiversity responds to geodiversity.
The authors use the analogy of the “stage” that defines the patterns of life to some
J. Cavender-Bares et al.
a general approach for earlier, more detailed and accurate decline assessment. They
also discuss the importance of engaging land managers, practitioners, and decisionmakers in these efforts to ensure that the products developed can be utilized by
stakeholders to maximize their impact.
Meireles et al. (Chap. 7) provide a framework to explain how spectral reflectance
data from plants is tightly coupled to the tree of life and demonstrate how spectra
can reveal evolutionary processes in plants. They clarify that many spectral features
in plants are inherited and are thus very similar among close relatives—in other
words, they are highly phylogenetically conserved. Simulations reveal that spectral
information of plants appears to follow widely used evolutionary models, making it
possible to link plant spectra to the tree of life in a predictive manner. As a consequence, methods developed in evolutionary biology to understand the tree of life
can now benefit the RS community. The chapter provides evidence that evolutionary
lineages may be easier than individual species to detect through RS methods, particularly if they are combined with other approaches for estimating which species
and lineages have the potential to be present in a given location. A caveat is that
spatial resolution of satellite spectral data will limit such inferences, but leaf- and
canopy-level spectra (obtainable from proximal and airborne sensing) can contribute enormously to our understanding of these fundamental links between spectral
patterns and gene sequences.
Madritch et al. (Chap. 8) link aboveground plant biodiversity and productivity to
belowground processes. They explain the functional mechanisms—which can be
revealed by remotely sensed spectral data—that influence interactions of plant hosts
with insects and soil organisms, in turn influencing ecosystem functions, such as
decomposition and nutrient cycling. The chapter provides an example of using the
concept of surrogacy, in which the biochemical linkage of spectral measurements to
associated patterns and processes aboveground is used to provide estimates of soil
and microbial processes belowground that are not directly observable via RS.
The next three chapters focus on linking satellite-based remotely sensed data to
biodiversity prediction. Pinto-Ledezma and Cavender-Bares (Chap. 9) present an
example of how currently available satellite-based RS products can be used to generate next-generation species distribution models to predict where species and lineages
are likely occur and the habitats they may have access to and persist in under altered
climates in the future. They compare RS-based methods for generating predictive
models with widely used approaches that use meteorologically derived climate variables. They demonstrate the advantages of RS-based models in regions where meteorological data is only sparsely available. Such predictive modeling that harnesses
species occurrence data and temporal information about the biotic environment may
make spectral methods of species and evolutionary lineage detection more tractable.
Building on the availability of satellite RS data with near-global coverage to predict biodiversity, Record et al. (Chap. 10) explore how RS illuminates the relationship
between biodiversity and geodiversity—the variety of abiotic features and processes
that provide the template for the development of biodiversity. They introduce a variety
of globally available geodiversity measures and examine how they can be combined
with biodiversity data to understand how biodiversity responds to geodiversity.
The authors use the analogy of the “stage” that defines the patterns of life to some
J. Cavender-Bares et al.