Abstract

MSA

Author(s)

Kees Klein Goldewijk, Utrecht University

Production date

2013-6-24

Variable(s)

MSA (Mean Species Abundance)

Keywords

MSA, biodiversity, land use change, cropland , pasture expansion

Time period

1500 -2000

Geographical coverage

Worldwide

Methodologies used for data collection and processing

This dataset is based on the GLOBIO3 approach, represented by the Mean Species Abundance (MSA) indicator. Due to historical data availability only a selective set of pressures (cropland and grazing) is included here. This dataset therefore gives an overestimation of remaining biodiversity or naturalness, as compared to other studies in which the GLOBIO approach was used for the more recent time periods, e.g. Environmental Data Compendium ( HYPERLINK "http://www.compendiumvoordeleefomgeving.nl/" http://www.compendiumvoordeleefomgeving.nl/ ) and the Global Biodiversity Outlook4 ( HYPERLINK "https://www.cbd.int/gbo4/" https://www.cbd.int/gbo4/ . GLOBIO3 is built on a set of equations linking environmental drivers and biodiversity impact (cause–effect relationships). Cause–effect relationships are derived from available literature using meta-analyses. GLOBIO3 describes biodiversity as the remaining mean species abundance (MSA) of original species, relative to their abundance in pristine or primary vegetation, which are assumed to be not disturbed by human activities for a prolonged period. MSA is similar to the Biodiversity Integrity Index (Majer and Beeston 1996) and the Biodiversity Intactness Index (Scholes and Biggs 2005) and can be considered as a proxy for the CBD indicator on trends in species abundance (UNEP 2004). The main difference between MSA and BII is that every hectare is given equal weight in MSA, whereas BII gives more weight to species rich areas. MSA is also similar to the Living Planet Index (Loh and others 2005), which compares changes in populations to a 1970 baseline, rather than to primary vegetation. It should be emphasized that MSA does not completely cover the complex biodiversity concept, and complementary indicators should be included, when used in extensive biodiversity assessments (Faith and others 2008). The output of GLOBIO is expressed here as MSA, an indicator of naturalness or biodiversity intactness. It is defined as the mean abundance of original species relative to their abundance in undisturbed ecosystems. An area with an MSA of 100% means a biodiversity that is similar to the natural situation. An MSA of 0% means a completely destructed ecosystem, with no original species remaining. Global environmental drivers of biodiversity change are input for GLOBIO3. In this particular case, a simplified method is used since not all required drivers are available for the historical period. Therefore, only historical land use changes are the main driver here. Long term historical expansion of cropland, pasture (land used for grazing livestock, intensive and extensive) and built-up area (urban sprawl, growth of cities and towns) are taken from the HYDE 3.1 database (Klein Goldewijk et al. 2011). GLOBIO3 calculates the overall MSAi value by substracting the individual MSAX maps from the potential maximum available grid cell land area (and dividing with it so a fraction is obtained): MSAi,t = (Gareai – 0.7* Croplandi,t – 0.3 *Pasturei,t – 0.95 * Built-upi,t)/Gareai where i is a grid cell, t is (historical) time step, MSAi is the overall value for grid cell i, Gareai is the total available land area of grid cell i. Cropland, Pasture and Built-up are the corresponding historical land use areas at time step t. The multipliers are derived from expert judgment, indicating a very high negative impact on biodiversity (0.95), a severe impact (0.7) and a modest impact (0.3)

Period of collection

Data collectors

HYDE database , GLOBIO project


General references

HYPERLINK "http://www.globio.info/" http://www.globio.info/

Alkemade R, van Oorschot M, Miles L, Nelleman C, Bakkenes M, ten Brink

(2009) GLOBIO3: A framework to investigate options for reducing global

terrestrial biodiverity loss, Ecosystems 12: 374-390.

http:// HYPERLINK "http://www.pbl.nl/hyde" www.pbl.nl/hyde

Klein Goldewijk, K. , A. Beusen, M. de Vos and G. van Drecht, 2011. The

HYDE 3.1 spatially explicit database of human induced land use change

over the past 12,000 years, Global Ecology and Biogeography 20(1):

73-86.  HYPERLINK

"http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2010.00587.x/abs

tract" DOI: 10.1111/j.1466-8238.2010.00587.x.

Caribbean

Anguilla[No Data]

Antigua and Barbuda1500 (5)-2013 (21)

Aruba[No Data]

Bahamas1500 (5)-2013 (23)

Barbados1500 (5)-2013 (27)

Bonaire, Sint Eustatius and Saba[No Data]

British Virgin Islands[No Data]

Cayman Islands[No Data]

Cuba1500 (8)-2012 (34)

Curaçao[No Data]

Dominica1500 (5)-2013 (20)

Dominican Republic1500 (6)-2013 (40)

Grenada1500 (5)-2013 (21)

Guadeloupe[No Data]

Haiti1500 (6)-2013 (36)

Jamaica1500 (6)-2013 (38)

Martinique[No Data]

Montserrat[No Data]

In 2010, the Netherlands Organisation for Scientific Research (NWO) awarded a subsidy to the Clio Infra project, of which Jan Luiten van Zanden was the main applicant and which is hosted by the International Institute of Social History (IISH). Clio Infra has set up a number of interconnected databases containing worldwide data on social, economic, and institutional indicators for the past five centuries, with special attention to the past 200 years. These indicators allow research into long-term development of worldwide economic growth and inequality.

Global inequality is one of the key problems of the contemporary world. Some countries have (recently) become wealthy, other countries have remained poor. New theoretical developments in economics - such as new institutional economics, new economic geography, and new growth theory - and the rise of global economic and social history require such processes to be studied on a worldwide scale. Clio Infra provides datasets for the most important indicators. Economic and social historians from around the world have been working together in thematic collaboratories, in order to collect and share their knowledge concerning the relevant indicators of economic performance and its causes. The collected data have been standardized, harmonized, and stored for future use. New indicators to study inequality have been developed. The datasets are accessible through the Clio Infra portal which also offers possibilities for visualization of the data. Clio Infra offers the opportunity to greatly enhance our understanding of the origins, causes and character of the process of global inequality.