InVEST GLOBIO Model

The GLOBIO model provides an index of biodiversity according to mean species abundance (MSA), the average population-level response across a range of species, to different stressors, including land-use change, fragmentation, and infrastructure.

biodiversitymean species abundance
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contributed at 2019-07-14

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Stanford University
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Application-focused categoriesNatural-perspectiveLand regions

Model Description

English {{currentDetailLanguage}} English

Quoted from: https://storage.googleapis.com/releases.naturalcapitalproject.org/invest-userguide/latest/globio.html

Summary

The GLOBIO model provides an index of biodiversity according to mean species abundance (MSA), the average population-level response across a range of species, to different stressors, including land-use change, fragmentation, and infrastructure. The model can be used as a static assessment of how far below a pristine state the current environment is or to estimate how a change in any of the stressors would lead to a stress in biodiversity or ecosystem integrity, as indicated by MSA.

Introduction

The GLOBIO methodology was developed by the United Nations Environmental Programme (UNEP, Alkemade et al. 2009) to model human impacts on biodiversity, measured by mean species abundance (MSA). Mean Species Abundance is an improvement over the more traditional species-area curve approach for two reasons. First, it gives aggregate estimates of species densities, not just species presence, which is important to represent true diversity since presence alone gives limited information about population viability (Balmford et al 2012). Second, it relates more than habitat area to changes in biodiversity by including information about the impact of fragmentation and threats from infrastructure (and climate change and nitrogen deposition if the full version of GLOBIO were implemented).

The model

The GLOBIO method consists of a set of equations linking environmental drivers to biodiversity impact, tables of parameters to estimate the above equations, based on a broad literature review, and suggested methodologies for inputting and processing the spatial data required. We have extended the GLOBIO methodology to downscale their global approach to a landscape level.

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How to Cite

Natural Capital Project (2019). InVEST GLOBIO Model, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/173147ac-7a6d-4f8f-a922-d9709b25a66d
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Initial contribute: 2019-07-14

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Affiliation:  
Stanford University
Homepage:  
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