489
18.2.2 A Space-Time-Biology Cube
The data structure of an EBV can be described as a hypercube and has analogy to
a multidimensional data array in computer programming. The first two dimensions
of the hypercube are space (latitude and longitude) and time, while the third dimension represents biological entities (Fig. 18.1). The latter dimension can, for
instance, describe taxonomy in a species-centered EBV (see below), and values
will inform the presence/absence or population abundance (e.g., Kissling et al.
2018a). Unlike the Essential Climate Variables (ECVs), the biological dimension
of the EBVs makes them especially challenging in terms of developing the conceptual framework and producing the EBV data products. For ecosystem-level EBVs,
this dimension can also inform ecosystem structure metrics (e.g., extent of different habitat types) or functions (e.g., primary productivity) in the time-space coordinates. The hypercube thus provides an intuitive representation of the EBV
concept and at the same time has a direct translation in data computing language
that suits implementation. Other EBVs are also more challenging to represent with
three dimensions, even more so when considering that their value is likely to
change depending on the spatial scale and extent, as is the case for the community
spatial turnover.
18.2.3 Six EBV Classes
Each EBV measures a particular attribute (property) of a given entity (object).
EBVs are grouped into six broad classes based on similarities and differences in the
attributes and entities they address (Fig. 18.2). These classes are sets of variables
Intergovernmental Panel on Climate Change, but the concept has since been
expanded to go beyond climate science, including with the Essential
Biodiversity Variables (EBVs) and the Essential Ocean Variables (EOVs,
Miloslavich et al. 2018). While there is value in increasingly expanding the
concept to other domains, a coordinated approach within disciplines to define
and prioritize the EVs and avoid the duplication of efforts is currently being
discussed within the Group on Earth Observations (GEO). One example is the
joint effort by the Marine Biodiversity Observation Network of the Group on
Earth Observations Biodiversity Observation Network (GEO BON) and the
Global Ocean Observing System (GOOS) of the Intergovernmental
Oceanographic Commission (IOC) to streamline the marine observations that
underpin the EBVs and EOVs (Muller-Karger et al. 2018b). The discussion
on EVs is also permeating other domains, such as agriculture, health, and
disaster risk reduction (Reyers et al. 2017).
18 Essential Biodiversity Variables: Integrating In-Situ Observations and Remote…
18.2.2 A Space-Time-Biology Cube
The data structure of an EBV can be described as a hypercube and has analogy to
a multidimensional data array in computer programming. The first two dimensions
of the hypercube are space (latitude and longitude) and time, while the third dimension represents biological entities (Fig. 18.1). The latter dimension can, for
instance, describe taxonomy in a species-centered EBV (see below), and values
will inform the presence/absence or population abundance (e.g., Kissling et al.
2018a). Unlike the Essential Climate Variables (ECVs), the biological dimension
of the EBVs makes them especially challenging in terms of developing the conceptual framework and producing the EBV data products. For ecosystem-level EBVs,
this dimension can also inform ecosystem structure metrics (e.g., extent of different habitat types) or functions (e.g., primary productivity) in the time-space coordinates. The hypercube thus provides an intuitive representation of the EBV
concept and at the same time has a direct translation in data computing language
that suits implementation. Other EBVs are also more challenging to represent with
three dimensions, even more so when considering that their value is likely to
change depending on the spatial scale and extent, as is the case for the community
spatial turnover.
18.2.3 Six EBV Classes
Each EBV measures a particular attribute (property) of a given entity (object).
EBVs are grouped into six broad classes based on similarities and differences in the
attributes and entities they address (Fig. 18.2). These classes are sets of variables
Intergovernmental Panel on Climate Change, but the concept has since been
expanded to go beyond climate science, including with the Essential
Biodiversity Variables (EBVs) and the Essential Ocean Variables (EOVs,
Miloslavich et al. 2018). While there is value in increasingly expanding the
concept to other domains, a coordinated approach within disciplines to define
and prioritize the EVs and avoid the duplication of efforts is currently being
discussed within the Group on Earth Observations (GEO). One example is the
joint effort by the Marine Biodiversity Observation Network of the Group on
Earth Observations Biodiversity Observation Network (GEO BON) and the
Global Ocean Observing System (GOOS) of the Intergovernmental
Oceanographic Commission (IOC) to streamline the marine observations that
underpin the EBVs and EOVs (Muller-Karger et al. 2018b). The discussion
on EVs is also permeating other domains, such as agriculture, health, and
disaster risk reduction (Reyers et al. 2017).
18 Essential Biodiversity Variables: Integrating In-Situ Observations and Remote…