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Statistics and Environmental Modelling

Code: 42923 ECTS Credits: 12
Degree Type Year Semester
4313774 Land Ecology and Biodiversity Management OB 0 A
The proposed teaching and assessment methodology that appear in the guide may be subject to changes as a result of the restrictions to face-to-face class attendance imposed by the health authorities.


Miquel Riba Rovira

Use of Languages

Principal working language:
spanish (spa)


Josep Piņol Pascual
Javier Retana Alumbreros
Miquel Riba Rovira



Objectives and Contextualisation

The main objective is to provide methodological advanced skills to perform quantitative analyses that can be applied in studies associated with the areas of ecology, biodiversity and environmental management.An important part of the module is focused on the development of advanced numerical and computer programming skills that are nowadays essential and very useful in ecology as well as many other  technical and professional jobs.

The structure of the module is therefore is made of two complementary parts, the first dealing with advanced statistical methods and the second one with producing numerical models that can be applied n ecology and environmental management.


  • Communicate and justify conclusions clearly and unambiguously to both specialist and non-specialist audiences.
  • Integrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.
  • Use advanced modelling and statistical tools in the field of terrestrial ecology and conservation of biodiversity.

Learning Outcomes

  1. Design a quantitative model and apply it to solve a particular problem in environmental biology.
  2. Design a statistical sampling plan.
  3. Interpret and integrate statistical evidence in the evaluation of environmental problems.
  4. Know and correctly apply the main statistical techniques used in environmental biology.
  5. Know the fundamentals of programming and its potential for solving problems in the field of environmental biology, both basic and applied.
  6. Present and defend the results of an original study before an expert audience.


Statistical methods *. The most important estatistical methods used in environmental biology are introduced and discussed, from basic statistics to more elaborate experimental and mutivariate approaches. The mein objective is to provide students with a general understanding and a wide view of  complementary tools that allow them to understand their own problems and databases using statistical analyses. Among others, the statistical methods covered are:

                1. Experimental design

                2. Basic statistics (Student's t test, ANOVA, simple regression, Chi-aquared test)

                3. Log-linear models

                4. Multiple regression analyses

                5. Path analysis and SEM

                6. General and generalized linear models

                7. Ordination methods (multivariate analysis)

                8. Classification methods (multivariate analysis)


Modelling *. This part introduces students into the basic conceptual modelling approaches in general, but with strong emphasis in the area of environmental biology. The aim is to provide students with the basic methods  to develop independent and autonomous skills towards problem solving. In-class lectures will provide examples of the most common modelling methods. Later on, through personal tutorized work, students are asked to apply one of these methods to a particular situation or problem of their own choice. The contents include:

             1. Introduction to modelling in ecology and environmental sciences.

             2. Introduction to programming in R language. Variables. Conditional statements. Loops. Functions.

                3. Models based on differential equations. Population exponential and logistic growth. Competition and predation models.Modelos de competencia y de depredación. Flow and compartment models.

                4. Matrix models. Structured matrix population models. Community succession models.

                5. Cellular automata models. The game of life. Epidemiological models: SI and SIR.

                6. Calibration and validation of quatitative models. Model efficiency. Methods.


* Unless the requirements enforced by the health authorities demand a prioritization or reduction of these contents


Lectures, seminars, computer lab, deeolpment of a simulation model, personal study and assignments*.


*The proposed teaching methodology may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.


Title Hours ECTS Learning Outcomes
Type: Directed      
In-Class Theoretical Lectures 20 0.8 4, 2, 3
Seminars 48 1.92 6, 1
Type: Autonomous      
Deelopment of a simulation model 80 3.2 5, 1
Study and assignments 143 5.72 4, 5, 6, 1, 2, 3


The final mark of the module (F) will be the weighted average of the four evaluation activities according to the weights given in the previous table.

To pass the module will require both F being iqual or greater than 5 and the mark on the two parts of the module (Statistics and Modelling) greater than 4.

Only A3 and A4 evaluation activities can be reassessed at the end of the module.

The schedule of the evaluation tasks and the reassessment exams will be given in the programming schedule provided by the coordination of the Master or otherwise established by the teacher in charge.

Students will be graded as "Not Assessable" when the number of evaluation activities fulfilled is less than 50% of all the total programmed activities.

* Student’s assessment may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.

Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
A1. Exam on computer programming 17% 1 0.04 5
A2. Personal work and public defense of a simulation model. 25% 4 0.16 5, 6, 1
A3. Exam on multiariate statistics 29% 2 0.08 4, 6, 2, 3
A4. Exam on linear models and experimental design. 29% 2 0.08 4, 6, 2, 3


Beven K (2009) Environmental modelling: an uncertain future? Routledge, London

Braun WJ, Murdoch DJ (2007) A first course in statistical programming with R. Cambridge University Press, Cambridge

Case TJ (2000) An illustrated guide to theoretical ecology. Oxford University Press, Oxford.

Crawley M. J. (2005). Statistical Computing: An Introduction to Data Analysis Using S-Plus. Wiley & Sosns Inc.

Faraway J.J. (2005). Linear Models with R. Chapman & Hall.

Faraway J.J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Second Edition. Chapman & Hall.

Harte J. (1985.) Consider a spherical cow. A course in environmental problem solving. William Kaufmann, Los Altos, CA (USA).

Hector A. (2015). The New Statistics with R: An Introduction for Biologists. Oxford University Press.

Hilborn R & Mangel M (1997) The ecological detective. Confronting models with data. Princeton University Press, Princeton, NJ (USA).

Otto SP & Day T (2007) A Biologist Guide to Mathematical Modelling in Ecology and Evolution. Princeton University Press, Princeton.

Piñol J & Martínez-Vilalta J (2006) Ecología con números. Problemas y ejercicios de simulación. Lynx, Bellaterra (Barcelona).

Roff D.A (2006). Introduction to Computer-Intensive Methods of Data Analysis in Biology. Cambridge.

Starfield AM, Smith KA & Bleloch AL (1990) How to model it: problem solving for the computer age. McGraw-Hill, New York.

Stevens MHH (2009) A primer of ecology with R, Springer, Dordrecht.