## What is species distribution modelling?

Species distribution modelling, alternatively known as environmental niche modelling, (ecological) niche modelling, predictive habitat distribution modelling, or climate envelope modelling refers to the process of using computer algorithms to predict the distribution of species in geographic space on the basis of a mathematical representation of their known distribution in environmental space (= realized ecological niche). The environment is in most cases represented by climate data (such as temperature, and precipitation), but other variables such as soil type, water depth, and land cover can also be used. These models allow for interpolating between a limited number of species occurrence and they are used in several research areas in conservation biology, ecology and evolution. (https://en.wikipedia.org/wiki/Environmental_niche_modelling)

Species distribution models can:

• help to identify areas that should be prioritised for conservation, for example for endangered species that are vulnerable to extinction.
• be of value in evaluating the potential of an invasive species to settle in particular areas.
• help determine potential routes of infections and diseases, which makes them important for animal husbandry as well as public health and safety.
• be combined with future projections of changes of the natural environment, to predict how biodiversity will be affected by impacts such as climate change or by changes in land use.

## How do we predict species distributions?

Developing a species distribution model begins with observations of species occurrences: these are places where we know a species has been found. These occurrences are mostly point-based and come from sources such as museum records and observations of experts in the field.

However, if you look up a distribution map of a species, it often shows a range rather than dots on a map. So, how do we go from specific places where individuals of a species have been observed to producing a map that gives an estimate of the distribution of that species? This is where species distribution models come into play.

There are two approaches that can be undertaken to estimate the distribution of a species:

• mechanistic: this approach specifically incorporates known species’ tolerances to environmental conditions, such as the maximum temperature in which a species can survive. This requires detailed data on the physiological response of species to environmental factors, but this data is often not available.
• correlative: this approach is used when we don’t have the detailed information about species’ tolerances to particular environmental variables. It is based on the assumption that the current distribution of a species is a good indicator of its ecological requirements. This approach is mostly used in species distribution models, and is the focus of this article.

## Calibrating and mapping models

To calibrate a correlative species distribution model we need two types of input data: species occurrences, and measurements of a suite of environmental variables, such as temperature and rainfall. These two types of data are then put into an algorithm to find associations between the known occurrences of a species and the environmental conditions at those sites, so we can identify the environmental conditions that are suitable for a species to survive. In other words, they describe relationships between species distributions and environmental variables. So we know something about where species occur and something about the environmental conditions of those places. The algorithm uses these two types of information to estimate the probability of a species occurring in a place as some function of the environmental conditions of that place.

Once the model is built, it can then project the predicted species distribution geographically on a map. For every point in the landscape, the model estimates the probability of a species occurring there. This can either be displayed as a binary outcome, that means as a presence/absence map, or as a probability on a scale from 0 to 1, with for example darker coloured areas representing a higher likelihood that a species can occur in that place.It is important to note that these maps do not show actual occurrences of a species, but highlight areas that have similar environmental conditions to areas where we have already found the species, and thus it is an estimation of where a species can occur. This does not necessarily mean that a species actually exists in the area.

## References

• Beaumont LJ, Graham E, Duursma DE, et al. 2016 Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges? Ecological Modelling, 342, 135-146.
• Elith J, Graham CH, Anderson RP, et al. (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129-151.
• Elith J & Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40(1), 677.
• Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge University Press.
• Guisan A & Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecological modelling, 135(2), 147-186.
• Guisan A & Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecology letters, 8(9), 993-1009.
• Pearson RG (2010) Species’ distribution modeling for conservation educators and practitioners. Lessons in conservation, 3, 54-89.

You can also view this information in Module 1 of our Online Open Course in SDM: https://app.bccvl.org.au/training

## Acknowledgements

Lead partners: Griffith University, James Cook University

Thanks to: University of New South Wales, Macquarie University, University of Canberra

Funded by: National eResearch Tools and Resources Project (NeCTAR)

The BCCVL is supported by the National eResearch Tools and Resources Project (NeCTAR), an initiative of the Commonwealth being conducted as part of the Super Science Initiative and financed from the Education Investment Fund, Department of Education. The University of Melbourne is the lead agent for the delivery of the NeCTAR project and Griffith University is the sub-contractor.