Development of models and validation

The main aim is to teach the basic approaches used for develop forest growth models that will be used for the rest of the subjects of the module for construction or analysis of specific models. This main objective is divided into two specific objectives.

Firstly, the students will learn the use of different equations fitting techniques to analyse the field data and to get relationships between tree and stand variables.

Secondly, the students learn to use a computer language that allows (a) to carry out the statistical techniques studied in the theoretical lessons and (b) to develop complex models including ecological and yield processes represented by multiple equations and data structures.

The degree course plan includes the following topics:
Restrictions and considerations of forest modelling. Classification of forest models. Data collection. Data processing. Multivariate analysis. Fitting approaches. Implementation and use.
These contents will be developed according to the following program:

Theoretical program:

1. R language. Overview and bases
2. Data and data structure in R
3. Construction and use of basic functions in R
4. Construction of complex functions and models in R
5. Graphics in R
6. Models and data
7. Linear models fitting approaches
8. Non-linear models fitting approaches
9. Logistic regression
10. Multivariate regression
11. Model validation

Each one of the 11 proposed topics will take approximately between 2 and 3 hours of theoretical teaching.

Practice program:

The practical program is the same as the theoretical because in this subject the concepts explained in the theoretical part will be implemented immediately. The students will use computers with the R language installed, allowing them to work in a practical way the theoretical concepts. Accordingly, this subject will not have time separation between practice and theory.

Knowledge of computer programming is not necessary. R language has the advantage of being an interpreted language, allowing work interactively.

BASIC BIBLIOGRAPHY

Draper, N.R., Smith, H., 1981. Applied Regression Analysis (Second ed.). John Wiley and Sons, New York, 709 pp.

Kangas, A., Maltamo, M., 2006. Forest Inventory Methodology and Applications. Series: Managing Forest Ecosystems, Vol. 10, Springer Verlag, 362p. ISBN 978-90-481-3164-8.

Robinson, A P; Hamman, J. D., 2010. Forest analysis with R. An introduction (Series: Use R!, ser. eds. Gentleman, R; Hornik, K; Parmigiani, G). Springer, New York.

Zuur, A. F.; Ieno, E. N.; Meesters, E. H. W. G., 2009. A beginners's guide to R (Series: Use R!, ser. eds. Gentleman, R; Hornik, K; Parmigiani, G). Springer, New York.

COMPLEMENTARY BIBLIOGRAPHY

Gadow, K.v., Hui, G., 1999. Modelling Forest Development, Kluwer Academic Publishers, 213 p.

Gadow, K.v., Real, P., Álvarez-González, J.G., 2001. Modelización del crecimiento y evolución de los bosques. IUFRO World Series Vol. 12, 242 p.

Murrell, P. 2006. R graphics (Series: Computer Science and Data Analysis, ser. eds. Lafferty, J; Madigan, D; Murthag, F; Smyth, P). Chapman & Hall/CRC, London.

Weiskittel, A.R., Hann, D.W., Kershaw, J.A., Vanclay, J.K., 2011. Forest Growth and Yield Modeling. John Wiley and Sons. ISBN: 978-0-470-66500-8, 430 p.

The following skills and competences will be developed within the framework of the degree course.

BASIC COMPETENCES

CB6: Knowledge and understanding that provide a basis or opportunity for originality in developing and / or applying ideas, often in a research context.

CB7: Capacity to apply their knowledge and their ability to solve problems in new environments within broader (or multidisciplinary) contexts related to their area of study.

CB10: Capacity to reach learning skills that enable the students to continue studying in a self-directed or autonomous.

GENERAL COMPETENCES

CG2: Capacity to design, to manage, to develop and to implement and interpret projects and comprehensive action plans in the wildland.

CG7: Capacity for developing forestry policies.

TRANSVERSAL COMPETENCES

CT1: Capacity for analysis and synthesis.

CT6: Ability to elaborate and present an organized and understandable text.

CT12: Ability for problem solving through the integrated application of their knowledge.

SPECIFIC COMPETENCES

CE42: Ability to select methodologies and use appropriate tools for the development of models that simulate the growth and evolution of forest ecosystems.
CE43: Ability to validate models and adapt existing ones to different conditions than their construction.

Lectures in the computer room will be used for theoretical explanations as well as for developing practical exercises to illustrate the theory and enable the students to use the theoretical techniques and concepts. (CB6, CB7, CB10, CT1, CT12, CE42, CE43).

In addition, individual or small group tutorials will be held to provide the students with the opportunity to discuss specific aspects of the course with the lecturer.

Virtual Campus will be used as a tool to support teaching. The materials needed to perform the exercises and coursework and support materials for the theoretical lessons will be available in this tool.

STUDENT WORK

Solving exercises similar to those used in the practical lectures, in order to the students work individually to improve their knowledge and correct their difficulties. (CT1, CT12, CE42, CE43).

Case studies. The lecturer will provide data from various studies in forestry for students to independently analyze them using the adequate statistical techniques. Each student will present a report including the results obtained, the discussion of these results and the conclusions. This report will be taking into account to assess the subject. (CB6, CB7, CB10, CG2, CG7, CT1, CT6, CT12, CE42, CE43).

Written exam (50%): CB6, CB10, CE42, CE43.
Delivered work (50%): CB6, CB7, CB10, CG2, CG7, CT1, CT6, CT12, CE42, CE43.

The proposed activities are compulsory to pass the subject. The written exam will be used to assess the knowledge of R language. The delivered work will consists on the presentation and discussion of the results of the analysis of a data set assigned by the teachers. In this analysis the student will put into practice the knowledge acquired in the lectures and labs.

The written exam dates will be established by the School Administration. Final exam dates cannot be changed. The deadline of the delivered work will be one week before the examination date.
The evaluation criteria will be the same for all students, both in the ordinary and in the extraordinary exams. Students with an attendance waiver will have to carry out the proposed exercises, deliver the work requested as part of the evaluation, and take the written test, just like the other students.

In case of fraudulent completion of exercises or tests, the provisions of the "Regulations for the evaluation of the academic performance of students and review of grades" will apply.

In addition to attendance at lectures, students are recommended to spend about 2 hours on private study of the subject per hour of lecture. Students are encouraged to do the R language exercises to strengthen the theoretical knowledge.

Previous experience in computer programming is not needed but the review of the knowledge about statistic and matricial algebra previously acquired is recommended.