GLOBIO-Species model

GLOBIO-Species is the most recent extension of the GLOBIO model family. It is designed to assess the impacts of human pressures on the distribution and population size of individual vertebrate species (for example, mammals and freshwater fishes).

human pressuresvertebrate species

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Initial contribute: 2019-07-17

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Application-focused categoriesHuman-perspectiveDevelopment activities
Application-focused categoriesIntegrated-perspectiveGlobal scale

Detailed Description

English {{currentDetailLanguage}} English

Quoted from: https://www.globio.info/what-is-globio

GLOBIO-Species is the most recent extension of the GLOBIO model family. It is designed to assess the impacts of human pressures on the distribution and population size of individual vertebrate species (for example, mammals and freshwater fishes). This allows for calculating multi-species indicators based on species distribution and abundance, notably the Red List Index (RLI) and the Living Planet Index (LPI). The RLI measures changes in extinction risk and the LPI measures relative changes in population abundance, in both cases aggregated across multiple species. Both indicators are commonly used to track progress to achieving (global) biodiversity targets.

Pressures currently included for terrestrial vertebrates are land use, habitat fragmentation and hunting. For freshwater fish species, GLOBIO-Species accounts for the impacts of climate change (via altered streamflow and water temperature) and habitat fragmentation by dams and barriers.

Once the species’ distribution has been delineated, the population size of the species is estimated using a population density model. Next, changes in distribution and abundance between different time steps are used to estimate RLI or LPI.

Application of GLOBIO-Species to tropical mammals

Application of GLOBIO-Species to freshwater fishes

模型元数据

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GLOBIO modeling team (2019). GLOBIO-Species model, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/c30b0331-5a74-4159-89ac-e6b959696bff
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Initial contribute : 2019-07-17

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