User:Owenpetchey/sandbox

Biodiversity is the variety of life across levels of organisation from genes to ecosystems. Genetic diversity, species richness, functional diversity and landscape diversity are each a type of biodiversity, and when measured are each represented in a biodiversity variable. Furthermore, there are many methods for sampling and measuring biodiversity, as well as many indices for quantifying biodiversity. As a result, there are myriad biodiversity variables.

Essential Biodiversity Variables aim to be the minimum set of broadly agreed upon necessary and sufficient biodiversity variables for at least national to global monitoring, researching, and forecasting biodiversity. As such, the initiative to create the EBVs aims for a harmonised global biodiversity monitoring system. EBVs would be used to inform biodiversity change indicators, such as the CBD Biodiversity Indicators for the Aichi Targets.

The development and success of the EBVs requires consensus.

The concept of Essential Variables is used in the Essential Climate Variables.

EBV Classes / Categories
The current candidate EBVs occupy six classes of Essential Biodiversity Variable: genetic composition, species populations, species traits, community composition, ecosystem structure, and ecosystem function. Within each class are a few to several variables.

Associated projects and organisations
GlobDiversity, GLOBIS-B, GEO BON.

2012
EBVs discussed at the Friscati Workshop, hosted by ESA and part sponsored by GEO BON.

2013
CBD SBSTTA meets and reports on: the need for EBVs, what are EBVs, the EBV framework, their use to derive high-level indicators, characteristics of EBVs, the six classes of EBVs, and measuring EBVs in the real world. These issues are summarised in a publication in the journal Science "Essential Biodiversity Variables".

2014
No entries

2015
A ConnectinGEO Workshop "Towards a sustainable process for GEOSS Essential Variables (EVs)" (including EBVs) covered issues including: what are EVs, the value of EVs, the definition of EVs, the current status of EVs across multiple societal benefit and thematic areas, including biodiversity, and recommendation / the way forward.

The challenge and feasibility of remote sensing of EBVs was discussed at the RS-EBV Workshop and the GEO BON RS4EBV project was initiated.

Ten variables to be tracked from space were proposed, including species occurrence, plant traits, land cover, vegetation height, and primary productivity.

GLOBIS-B global cooperation project, funded by the EU Horizon 2020 programme starts, aimed to advance the challenge of practical implementation of EBVs, via supporting interoperability and cooperation activities among diverse biodiversity infrastructures.

2016
ConnectinGEO SDGs-EVs analysis.

A comparison of the requirements of the United Nation's Strategic Plan for Biodiversity 2011-2020 with its implementation revealed information gaps in the EBV classes Genetic Composition and Species Populations. Furthermore, the potential for using EBVs as a tool for identifying mismatch in available biodiversity information and that required for reporting was discussed.

2017
GlobDiversity project of GEO BON, led by University of Zurich, starts, focusing on specification and engineering of three RS-enabled EBVs.

Publication of GEO BON strategy for development of EBVs.

GEO BON / GlobDiversity / NextGEOSS / ITC Workshop for Prioritising Essential Biodiversity Variables Derived from Earth Observation.

Against the possibility of a large number of potential / candidate EBVs, clearer and more precise definition of what an EBV should be focuses on them being biological state variables, distinct to environmental variables. For example, "disturbance regime" would not be an EBV; rather it is (or at least may be) a driver of change in a biodiversity variable.

Apparently diverse interpretation of the definition of an EBV may have resulted from its rather broad original definition. One clarification of the definition focuses on the necessary biological nature of EBVs (similar as in ), adding that they sit between raw observational data and biodiversity indicators. Analogy is draw between EBVs / Indicators and stocks / stock market indices, in an attempt to clarify.

GLOBIS-B EBVs Workshop on Species Traits discussed issues such as societal importance of traits, redefinition / restructuring of the species traits EBV class, trait data collection and annotation standards, and current access to trait datasets.

A paper focusing on operationalising species distribution and abundance related EBVs, which resulted from the first two GLOBIS-B Workshops , covers definition of the species distribution EBV and population abundance EBV, operationalisation of the EBV framework, data and tools for building EBV data products, workflow for building EBV data products, metadata and data sharing standards.

The term "EBV data products" is increasingly used. They are the end product in the EBV information supply chain, from raw observations, to EBV-useable data, to EBV-ready data, to EBV data products. Each of these three types of EBV datasets could be used to produce Indicators.

Data sources for EBVs are categorised into four types: extensive and intensive monitoring schemes, ecological field studies, and remote sensing. Each have their own often complementary properties, implying that data integration will be important for creation of representative EBVs, as well as identifying and filling data gaps.

EBVs tailored / prioritised for national level policy and management may be seen as desirable, even necessary and important, but need to be also developed to allow comparisons across broader scales.

There is re-emphasis on EBVs being theory-driven, rather than data-driven. Concrete, well explained examples of exactly what this means, or how it is implemented, remain scarce.

EBVs can assist in setting priorities for biodiversity assessment and monitoring activities.

2018
Integration of abiotic variables (e.g. those emphasised in the Ecosystem Integrity framework) with biotic variables (emphasised in the EBV framework), i.e. merging the two frameworks, is proposed to achieve comprehensive ecosystem monitoring.