486
there is no consensus on adopting priority metrics to quantify biodiversity change.
While strengthening efforts to reduce the multiple biases present in biodiversity
data remains critical (including spatial, temporal, and taxonomic biases, among others; Meyer et al. 2016; Proença et al. 2017), parallel efforts are needed to consolidate data from in-situ and remote sensing (RS) EO so as to increase their usability
and information value.
The concept of essential biodiversity variables (EBVs) was proposed in 2013 as
a framework to prioritize, integrate, and consolidate biodiversity observations and
monitoring programs worldwide (Pereira et al. 2013). Since then, EBVs have gained
acceptance among scientists, along with the interest and endorsement of the policymaking community, including the Convention on Biological Diversity (e.g.,
Decision XI/3 in UNEP/CBD/COP/11/35) and the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services (IPBES). By providing an
integrative framework for quantifying biodiversity change in time, EBVs also hold
great potential for advancing research on biodiversity and responses to pressures
and conservation actions. However, the concept is still evolving, and divergent
viewpoints have emerged on what actually constitutes an EBV. Here we discuss
recent progress in defining an operational EBV framework and the importance of
this framework for biodiversity data integration. We start with discussing key attributes of EBVs. We then describe recent conceptual developments in support of their
implementation. Finally, we illustrate the role of biodiversity models as a cornerstone for integrating data obtained from in-situ and satellite RS EO to support global
assessments of biodiversity change and as a critical component of a global biodiversity monitoring system (Geller et al., Chap. 20).
18.2 The EBV Framework
18.2.1 Definition of Essential Biodiversity Variables
EBVs are defined as a minimum set of complementary measurements needed to
detect and document biodiversity change across all levels of biodiversity, from
genes to species and ecosystems (Pereira et al. 2013). EBVs are part of a larger family of Essential Variables (EVs) that was first conceptualized by the climate community with the Essential Climate Variables (Box 18.1).
Like all EVs, EBVs must meet the criteria of feasibility, cost-effectiveness, and
scientific and policy relevance. Additional characteristics that might be specific to
the EBVs are generalization (to the best extent possible) across terrestrial, marine,
and freshwater realms and scalability. Importantly, the EBVs evolved from initially
covering multiple aspects of the Driver-Pressure-State-Impact-Response (DPSIR)
framework to focusing exclusively on biological state variables (i.e., EBVs describe
the condition or the status of a particular biological entity). This is not to say that
nonbiological variables are irrelevant for EBVs. On the contrary, some of these
N. Fernández et al.
there is no consensus on adopting priority metrics to quantify biodiversity change.
While strengthening efforts to reduce the multiple biases present in biodiversity
data remains critical (including spatial, temporal, and taxonomic biases, among others; Meyer et al. 2016; Proença et al. 2017), parallel efforts are needed to consolidate data from in-situ and remote sensing (RS) EO so as to increase their usability
and information value.
The concept of essential biodiversity variables (EBVs) was proposed in 2013 as
a framework to prioritize, integrate, and consolidate biodiversity observations and
monitoring programs worldwide (Pereira et al. 2013). Since then, EBVs have gained
acceptance among scientists, along with the interest and endorsement of the policymaking community, including the Convention on Biological Diversity (e.g.,
Decision XI/3 in UNEP/CBD/COP/11/35) and the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services (IPBES). By providing an
integrative framework for quantifying biodiversity change in time, EBVs also hold
great potential for advancing research on biodiversity and responses to pressures
and conservation actions. However, the concept is still evolving, and divergent
viewpoints have emerged on what actually constitutes an EBV. Here we discuss
recent progress in defining an operational EBV framework and the importance of
this framework for biodiversity data integration. We start with discussing key attributes of EBVs. We then describe recent conceptual developments in support of their
implementation. Finally, we illustrate the role of biodiversity models as a cornerstone for integrating data obtained from in-situ and satellite RS EO to support global
assessments of biodiversity change and as a critical component of a global biodiversity monitoring system (Geller et al., Chap. 20).
18.2 The EBV Framework
18.2.1 Definition of Essential Biodiversity Variables
EBVs are defined as a minimum set of complementary measurements needed to
detect and document biodiversity change across all levels of biodiversity, from
genes to species and ecosystems (Pereira et al. 2013). EBVs are part of a larger family of Essential Variables (EVs) that was first conceptualized by the climate community with the Essential Climate Variables (Box 18.1).
Like all EVs, EBVs must meet the criteria of feasibility, cost-effectiveness, and
scientific and policy relevance. Additional characteristics that might be specific to
the EBVs are generalization (to the best extent possible) across terrestrial, marine,
and freshwater realms and scalability. Importantly, the EBVs evolved from initially
covering multiple aspects of the Driver-Pressure-State-Impact-Response (DPSIR)
framework to focusing exclusively on biological state variables (i.e., EBVs describe
the condition or the status of a particular biological entity). This is not to say that
nonbiological variables are irrelevant for EBVs. On the contrary, some of these
N. Fernández et al.