238
10.4 Remote Sensing of Biodiversity
Approaches for using RS to track biodiversity are reviewed in several chapters in
this book (Fernandez and Pereira, Chap. 18; Serbin and Townsend, Chap. 4; Meireles
et al., Chap. 7). Biodiversity has many forms—including taxonomic, functional,
genetic, and phylogenetic diversity (Serbin and Townsend, Chap. 4; Meireles et al.,
Chap. 7). Each form may exhibit different relationships with both geophysical and
biological drivers, owing to a variety of mechanisms (Gaston 2000; Lomolino et al.
2010). For example, reorganization of organisms in response to changing environments leads to species assemblages becoming more or less similar through biotic
homogenization or differentiation (Baiser et al. 2012). Such biotic homogenization/
differentiation is usually characterized taxonomically (Olden and Rooney 2006).
However, functional traits (i.e., traits representing the interface between species and
their environment) possessed by species are often more important to ecosystem
functions valued by society (Baiser and Lockwood 2011) and may be more appropriate to use in assessing biodiversity-ecosystem function relationships (Flynn et al.
2011). Many functional traits may also exhibit a phylogenetic signal (Srivastava
et al. 2012), so it is important to consider multiple measures of diversity (i.e., taxonomic, functional, and phylogenetic) when assessing patterns of biodiversity
(Serbin and Townsend, Chap. 4; Meireles et al., Chap. 7; Lausch et al. 2016; Lausch
et al. 2018).
One caveat to measures of biodiversity generated from high-resolution RS data
is that as the spatial resolution of data increases, the spatial extent typically decreases
(Turner 2014; Gamon et al., Chap. 16). This limitation hinders our ability to understand how biodiversity relates to different drivers (e.g., geodiversity) at different
spatial scales to better inform conservation decisions. There have been recent calls
from scientists for new satellite missions and data integration efforts to address this
issue (Schimel et al., Chap. 19). For instance, Jetz et al. (2016) call for a Global
Biodiversity Observatory to generate worldwide remotely sensed data on several
plant functional traits. Petorelli et al. (2016) and Fernández and Pereira (Chap. 18)
identify satellite RS data that, given technological and algorithmic developments in
the near future, could be capable of meeting the criteria of EBVs for conservation
outlined by the international Group on Earth Observations—Biodiversity
Observation Network (GEO BON) at a global spatial extent.
Until finer resolution, remotely sensed biodiversity data exist at large spatial
extents, data available from in-situ measurements of organisms can inform the relationships between biodiversity and geodiversity. Publically available biodiversity
data with geographic locations include expert range maps of individual species from
IUCN (IUCN 2017), occurrence data [e.g., Global Biodiversity Information Facility
(GBIF, GBIF 2016); Botanical Information and Ecology Network (BIEN, Enquist
et al. 2016)], citizen science networks [e.g., Invasive Plant Atlas of New England,
IPANE, Bois et al. 2011], and national [e.g., US Forest Service Forest Inventory and
Analysis (FIA), Bechtold and Paterson 2005)] and international inventory networks
[e.g., the Amazon Forest Inventory Network (RAINFOR), Peacock et al. 2007].
S. Record et al.
10.4 Remote Sensing of Biodiversity
Approaches for using RS to track biodiversity are reviewed in several chapters in
this book (Fernandez and Pereira, Chap. 18; Serbin and Townsend, Chap. 4; Meireles
et al., Chap. 7). Biodiversity has many forms—including taxonomic, functional,
genetic, and phylogenetic diversity (Serbin and Townsend, Chap. 4; Meireles et al.,
Chap. 7). Each form may exhibit different relationships with both geophysical and
biological drivers, owing to a variety of mechanisms (Gaston 2000; Lomolino et al.
2010). For example, reorganization of organisms in response to changing environments leads to species assemblages becoming more or less similar through biotic
homogenization or differentiation (Baiser et al. 2012). Such biotic homogenization/
differentiation is usually characterized taxonomically (Olden and Rooney 2006).
However, functional traits (i.e., traits representing the interface between species and
their environment) possessed by species are often more important to ecosystem
functions valued by society (Baiser and Lockwood 2011) and may be more appropriate to use in assessing biodiversity-ecosystem function relationships (Flynn et al.
2011). Many functional traits may also exhibit a phylogenetic signal (Srivastava
et al. 2012), so it is important to consider multiple measures of diversity (i.e., taxonomic, functional, and phylogenetic) when assessing patterns of biodiversity
(Serbin and Townsend, Chap. 4; Meireles et al., Chap. 7; Lausch et al. 2016; Lausch
et al. 2018).
One caveat to measures of biodiversity generated from high-resolution RS data
is that as the spatial resolution of data increases, the spatial extent typically decreases
(Turner 2014; Gamon et al., Chap. 16). This limitation hinders our ability to understand how biodiversity relates to different drivers (e.g., geodiversity) at different
spatial scales to better inform conservation decisions. There have been recent calls
from scientists for new satellite missions and data integration efforts to address this
issue (Schimel et al., Chap. 19). For instance, Jetz et al. (2016) call for a Global
Biodiversity Observatory to generate worldwide remotely sensed data on several
plant functional traits. Petorelli et al. (2016) and Fernández and Pereira (Chap. 18)
identify satellite RS data that, given technological and algorithmic developments in
the near future, could be capable of meeting the criteria of EBVs for conservation
outlined by the international Group on Earth Observations—Biodiversity
Observation Network (GEO BON) at a global spatial extent.
Until finer resolution, remotely sensed biodiversity data exist at large spatial
extents, data available from in-situ measurements of organisms can inform the relationships between biodiversity and geodiversity. Publically available biodiversity
data with geographic locations include expert range maps of individual species from
IUCN (IUCN 2017), occurrence data [e.g., Global Biodiversity Information Facility
(GBIF, GBIF 2016); Botanical Information and Ecology Network (BIEN, Enquist
et al. 2016)], citizen science networks [e.g., Invasive Plant Atlas of New England,
IPANE, Bois et al. 2011], and national [e.g., US Forest Service Forest Inventory and
Analysis (FIA), Bechtold and Paterson 2005)] and international inventory networks
[e.g., the Amazon Forest Inventory Network (RAINFOR), Peacock et al. 2007].
S. Record et al.