Modelling Support

Modelling Support 2018-12-08T06:21:58+00:00

Modelling Support

The CCSG modelling support activity aims to provide resources, review and advice to aid modelling undertaken by Specialist Groups (SGs), Red List Authorities (RLAs), and Task Forces (TFs). The ability to develop, run and interpret models has been identified as a limiting factor in integrating the threat of climate change in IUCN SSC work. Models for assessing species vulnerability to climate change vary in their underlying approach (e.g., correlative versus mechanistic) and what they aim to predict (e.g., Species Distribution Models predict range shifts, and Population Viability Analyses predict changes in abundance). The models have known limitations that lead to uncertainties, making interpretation of outputs difficult; however, there is a pressing need to incorporate climate change in SSC activities, notably Red List assessments, and the goal of this activity is to help ensure models are used appropriately.

The modelling support activity has three main functions:

  1. Provide an online list of resources that can be used to develop and run a range of different models (e.g., links to software tools, R libraries, data sources, and training materials)
  2. Provide advice on the use of models for assessing impacts of climate change
  3. Provide feedback on interpretation of results

The modelling support group does not carry out modelling, but provides advice and support to SGs, RLAs, TFs, and other activities within the CCSG. The group also provides expert advice to the IUCN (e.g., the Standards and Petitions Sub-Committee) on whether models have been appropriately applied and interpreted.



Richard Pearson (University College London)

Yvonne Buckley (Trinity College Dublin)

Robert Hijmans (University of California, Davis)

Tara Martin (University of British Columbia)

Damaris Zurell (Swiss Federal Research Institute WSL)



The resources here focus primarily on modelling approaches that are described in section 12.1 of the Guidelines for Using the IUCN Red List Categories and Criteria (available in English and Français), in particular species distribution models (sections 12.1.9 and 12.1.12) and demographic models (section 12.1.11). These resources are recommended, but the list is by no means exhaustive. Many of the resources are available online free of charge.

Species Distribution Modelling

  • Videos of training course:

Species Distribution Modelling training course: A set of talks that introduce the field of Species Distribution Modelling (also called Ecological Niche Modelling or Bioclimatic Modelling).

  • Introduction to the basics of distribution modelling:

Pearson, R.G. 2007. Species distribution modeling for conservation educators and practitioners. Lessons in Conservation 3:54-89.

  • Introduction to the use of distribution modelling for conservation decision-making:

Guisan et al. 2013. Predicting species distributions for conservation decisions. Ecology Letters 16: 1424–1435 (doi: 10.1111/ele.12189, open access).

  • Books:

Guisan, A. et al. 2017. Habitat Suitability and Distribution Models: With Applications in R. Cambridge University Press. (Note that a website at contains the codes and supporting material required to run examples.)

Franklin, J. 2010. Mapping species distributions: Spatial inference and prediction. Cambridge University Press.

Peterson, A.T., et al. 2011. Ecological Niches and Geographic Distributions. Monographs in Population Biology, Princeton University Press.

  • Software/code:

Several tools are available for running species distribution models, including the widely-used Maxent software. The guidelines for using the IUCN Red List categories and criteria state that multiple model algorithms should be compared when projecting responses to climate change (section 12.1.12) so we recommend the following R packages, both of which enable multiple algorithms to be run and facilitate data handling (with raster package):

dismo: Training and documentation are available here.

biomod: Further information and references are available here.

Several other methods are available and we emphasize that this list is not exhaustive. The CCSG modelling support group very much supports the development and application of different approaches.

  • Datasets:

Species’ occurrence records are available from a wide variety of sources, many of which are specific to a particular region and/or taxonomic group (such as national and regional surveys). At a global extent, a vast number of records are collated by the Global Biodiversity Information Facility (GBIF) and Map of Life.

Climate data, including future scenarios, are also available from a variety of sources. The worldclim dataset provides current (representative of 1960-1990) and projected future (CMIP5 scenarios used in the IPCC’s 5th Assessment Report) conditions globally at a resolution of ~1km2. The bioclimatic variables provided by worlclim are commonly used for species distribution modelling. Alternatively, the CHELSA climatologies take into account topoclimate in downscaling algorithms (e.g. orographic rainfall and wind fields). CHELSA provides similar data to worldclim (monthly temperature and precipitation for period 1979-2013, bioclimatic variables, and CMIP5 scenarios).

Land cover data are also available from many sources, but recommended global datasets include the Global Land Cover Facility’s MODIS product, the European Space Agency’s GlobCover, and the 1-km consensus land-cover product derived by Tuanmu and Jetz (2014).


Demographic modelling

  • Software:

Several methods are available to estimate extinction risk with coupled habitat and metapopulation models (guidelines section 12.1.11; for a review of methods see Lurgi et al. 2015 Methods in Ecology and Evolution doi: 10.1111/2041-210X.12315, and for a model comparison see Zurell et al. 2016 Global Change Biology doi: 10.1111/gcb.13251). Recommended tools include:

RAMAS: Proprietary software applied in several studies that coupled habitat and metapopulation models to estimate extinction risk (e.g., Pearson et al. 2014 Nature Climate Change doi: 10.1038/nclimate2113; Fordham et al. 2013 Nature Climate Change doi: 10.1038/nclimate1954).

demoniche: R package for running spatially-explicit demographic models.

  • Datasets:

COMADRE and COMPADRE: the COMPADRE plant matrix database and COMADRE animal matrix database contain matrix population models of hundreds of plant and animal species.


Enquiries about the modelling support activity can be addressed to Richard Pearson (