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Modelling Species Distributions & Niches (EVSC90026)

Graduate courseworkPoints: 12.5On Campus (Parkville) and Online

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Year of offer2018
Subject levelGraduate coursework
Subject codeEVSC90026
Semester 2 - On Campus
Semester 2 - Online
FeesSubject EFTSL, Level, Discipline & Census Date

This subject focuses on statistical models of the distribution of species and ecophysiological models of species niches. These two areas of environmental modelling have grown substantially in the last decade or two, and have become core parts of ecology. They are closely related, but they differ philosophically and practically. They are both used for understanding and predicting the distributions of species. The statistical models (also known as habitat suitability models, bioclimatic envelopes or ecological niche models) use observed geographical distributions to characterise relationships between a species and its environment and can be considered ‘top-down’ in approach. Ecophysiological (or mechanistic) models take a ‘bottom-up’ approach by characterising the physiological processes influencing a species’ distribution and integrate models of microclimates, energy balance, heat balance, and water balance.

You will learn about both approaches from lecturers who are world experts in these topics. The subject will help you to understand the merits and drawbacks of the two approaches to species modelling and equip you with important skills that are in high demand in ecology and conservation. The subject includes the following topics: compilation, processing and management of data, fitting models by statistical estimation and empirical measurement, spatial prediction of distributions (mapping), and model evaluation.

Intended learning outcomes

On successful completion of this subject, students should be able to:

  1. Understand theory about niches and distributions, and how this links to statistical and mechanistic modelling methods;
  2. Select a modelling method appropriate for a given question and dataset;
  3. Source appropriate data and prepare it for fitting models;
  4. Fit statistical models with traditional regression methods and machine learning methods;
  5. Develop mechanistic models using biophysical techniques for microclimates and organisms;
  6. Use both models to predict spatial distributions;
  7. Evaluate the models and predictions;
  8. Gain experience using the free statistical program, R, for modelling and for working with spatial data.

Generic skills

  • Analytic skills – the course will improve students analytical abilities because they will deal with data and models and program in R;
  • Problem-solving skills – both through lectures and practical work the students will learn to think about the aim of modelling and the available data and choosing the correct way to analyse it;
  • Written communication – assignments and feedback from them will improve written communication;
  • Skills in planning a work flow – the two assignments require work flow planning; the pracs will teach the necessary skills.

Last updated: 16 June 2018