442
and 16.6), much in the way that semivariograms are used to express landscape structure (Curran 1988). These patterns could be compared to the driving variables,
including the underling patterns of “geodiversity,” (Record et al., Chap. 10, this
volume). Such a system would reveal ideal spatial scales for sampling alpha and
beta diversity and would help reveal the causes of biodiversity patterns at multiple
scales, as illustrated in Figs. 16.3 and 16.6.
Such a scale-aware global biodiversity monitoring system could incorporate and
integrate many aspects of existing networks such as NEON (Hopkin 2006), Forest
GEO (Anderson-Teixeira et al. 2014), and many others (see Geller et al., Chap. 20),
but would provide many benefits currently not provided by existing biodiversity
monitoring efforts. The system would require global imaging spectrometry with
repeat coverage, revealing global patterns in time and space and providing essential
context for more detailed studies at higher spatial resolution (see Schimel et al.
Chap. 19). More detailed resolution could be achieved by a fleet of regional aircraft
carrying imaging spectrometers at a spatial resolution matching the crown sizes of
many shrub and tree species (e.g., Kampe et al. 2010). For even more detailed sampling resolution needed for smaller statured vegetation or for resolving individual
leaf traits, UAVs, robotic, or tower-mounted imaging spectrometers could be
deployed at key sites. These methods would be coupled to systematic ground
sampling of species composition (alpha and beta diversity) using traditional field
sampling methods, along with leaf and canopy optical properties (using field spectrometry) for a detailed assessment of plant traits. Radiative transfer models and
statistical scaling methods (Serbin and Townsend, Chap. 3) could provide a framework for integrating and analyzing data across scales.
A systematic global evaluation of optical diversity across multiple scales could
readily detect dynamics in biodiversity, identify causes biodiversity changes,
adapt to these changes, and help to identify monitoring and conservation priorities. By integrating diversity metrics with measures of ecosystem function, our
understanding of the ecosystem impacts of biodiversity would be enhanced, allowing better management for resilience. On our rapidly changing planet, such a system would enable the monitoring required for sustainable management of
ecosystems globally.
Acknowledgments The authors wish to acknowledge funding from a NSF and NASA Dimensions
grant to JCB (DEB 1342872), PT (DEB 1342778), and JG (DEB 1342823), as well as the Cedar
Creek NSF Long-Term Ecological Research grant (DEB 1234162) for supporting many of the
examples cited here. We also thank NIMBioS and the Keck Institute of Space Studies (KISS) for
sponsoring meetings that helped develop some of the ideas in this chapter. We also acknowledge
additional funding support from NSERC, CFI, and from Rangeland Research Institute (U. Alberta)
grants to JG. Thanks to Evan Delancey for spectral data (Fig. 16.1), Greg Crutsinger for access to
the Parrot Drone data (Fig. 16.5), and to Rick Perk and Brian Leavitt (CALMIT, University of
Nebraska-Lincoln) for technical assistance in airborne data collection and processing (Figs. 16.3
and 16.6).
J. Gamon et al.
and 16.6), much in the way that semivariograms are used to express landscape structure (Curran 1988). These patterns could be compared to the driving variables,
including the underling patterns of “geodiversity,” (Record et al., Chap. 10, this
volume). Such a system would reveal ideal spatial scales for sampling alpha and
beta diversity and would help reveal the causes of biodiversity patterns at multiple
scales, as illustrated in Figs. 16.3 and 16.6.
Such a scale-aware global biodiversity monitoring system could incorporate and
integrate many aspects of existing networks such as NEON (Hopkin 2006), Forest
GEO (Anderson-Teixeira et al. 2014), and many others (see Geller et al., Chap. 20),
but would provide many benefits currently not provided by existing biodiversity
monitoring efforts. The system would require global imaging spectrometry with
repeat coverage, revealing global patterns in time and space and providing essential
context for more detailed studies at higher spatial resolution (see Schimel et al.
Chap. 19). More detailed resolution could be achieved by a fleet of regional aircraft
carrying imaging spectrometers at a spatial resolution matching the crown sizes of
many shrub and tree species (e.g., Kampe et al. 2010). For even more detailed sampling resolution needed for smaller statured vegetation or for resolving individual
leaf traits, UAVs, robotic, or tower-mounted imaging spectrometers could be
deployed at key sites. These methods would be coupled to systematic ground
sampling of species composition (alpha and beta diversity) using traditional field
sampling methods, along with leaf and canopy optical properties (using field spectrometry) for a detailed assessment of plant traits. Radiative transfer models and
statistical scaling methods (Serbin and Townsend, Chap. 3) could provide a framework for integrating and analyzing data across scales.
A systematic global evaluation of optical diversity across multiple scales could
readily detect dynamics in biodiversity, identify causes biodiversity changes,
adapt to these changes, and help to identify monitoring and conservation priorities. By integrating diversity metrics with measures of ecosystem function, our
understanding of the ecosystem impacts of biodiversity would be enhanced, allowing better management for resilience. On our rapidly changing planet, such a system would enable the monitoring required for sustainable management of
ecosystems globally.
Acknowledgments The authors wish to acknowledge funding from a NSF and NASA Dimensions
grant to JCB (DEB 1342872), PT (DEB 1342778), and JG (DEB 1342823), as well as the Cedar
Creek NSF Long-Term Ecological Research grant (DEB 1234162) for supporting many of the
examples cited here. We also thank NIMBioS and the Keck Institute of Space Studies (KISS) for
sponsoring meetings that helped develop some of the ideas in this chapter. We also acknowledge
additional funding support from NSERC, CFI, and from Rangeland Research Institute (U. Alberta)
grants to JG. Thanks to Evan Delancey for spectral data (Fig. 16.1), Greg Crutsinger for access to
the Parrot Drone data (Fig. 16.5), and to Rick Perk and Brian Leavitt (CALMIT, University of
Nebraska-Lincoln) for technical assistance in airborne data collection and processing (Figs. 16.3
and 16.6).
J. Gamon et al.