Natural forest models

The main aim is to teach the basic principles regulating the growth and development of natural forests and the data analysis methodologies required to be able to construct models to simulate forest ecosystems development.

The degree course plan includes the following topics:

Matrix and size-class models. Individual tree level distance-dependent and distance-independent models. Modeling the stand structure and competition. Mortality, regeneration and recruitment models.

These contents will be developed according to the following program:

Theoretical program

1. Forest structure
2. Competition indexes
3. Size-class models
4. Individual tree models
5. Mortality models
6. Seeding, regeneration and recruitment models

Practice program

The practice program will be focused to develop different models using the computer language R to practice the concepts discussed in the theoretical program. The architecture of different models currently used will be also analyzed and compared. The work will focus on two main groups of models:

- Empirical growth and yield models for multispecies and uneven-aged stands.
- Whole ecosystem models including ecological, ecophysiological and environmental processes.

Each of the 6 topics proposed will have an approximate dedication of 4 hours of face-to-face teaching, including the theoretical and practical parts. In this course there will be no temporal separation between theory and practice because theoretical concepts are immediately applied using the computational language R.

Basic bibliography

Clutter, J.L.; Fortson, J.C.; Pienaar, L.V.; Brister, G.H.; Bailey, R.L. 1983. Timber management: a quantitative approach. Krieger Publishing Company, Malabar (Florida) (reprint ed. 1992) (ref. BUSC Intercentros: CASI 596).
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.
Landsberg, J; Sands, P (2011). Physiological ecology of forest production. Principles, processes and models (Series: Terrestrial Ecology Series, num. 4, ser. eds. Ehleringer, J R; MacMahon, J; Turner, M. G.). Elsevier, Amsterdam.
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.

Complementary bibliography

Kiviste, A., Álvarez González, J.G., Rojo Alboreca, A., Ruiz González, A.D., 2002. Funciones de crecimiento de aplicación en el ámbito forestal. Monografías INIA: Forestal Nº. 4., Madrid, 190 p.
Oliver, C.D., Larson, B.C., 1996. Forest Stand Dynamics, Updated Edition. John Wiley and Sons, New York, 544 p.
Pretzsch, H (2009). Forest dynamics, growth and yield. Springer Verlag, Berlin.
Vanclay, J.K., 1994. Modelling forest growth and yield: application to mixed tropical forests. CAB International, UK.
van Laar, A., Akça, A., 1997. Forest mensuration. Cuvillier, Göttingen, 418 p.
Weiskittel, A.R., Hann, D.W., Kershaw, J.A., Vanclay, J.K., 2011. Forest Growth and Yield Modeling. John Wiley and Sons.

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

Basic, general and transversal 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.

CB8 - Students should be able to integrate knowledge and handle complexity and formulate judgments based on incomplete or limited information, include conclusions on social and ethical responsibilities linked to the application of their knowledge and judgments.

CB9 - The students should be able to communicate their conclusions, the knowledge and the reasons of the conclusions to specialist and non-specialist audiences clearly and unambiguously.

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

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

CG7 - Ability to develop forest policies.

CT1 - Capacity for analysis and synthesis.

CT3 - Ability to work individually with self-criticism.

CT5 - Ability to obtain adequate, diverse and updated information.

CT6 - Ability to develop and present an organized and understandable text.

CT10 - Use of bibliographic information and the Internet.

CT11 - Use information in a foreign language.

CT12 - Ability to solve problems through the integrated application of their knowledge.

Specific competences

CE46: Ability to construct models to simulate the growth and development of natural forests. Ability to simulate natural and human perturbations affecting the stand density variations

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, CB8, CB9, CB10, CT1, CT3, CT5, CT10, CT11, CT12, and CE46). 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.

The 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 (only part of the support material will be in english).

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. (CT3, CT5, CT10, CT11, CT12 and CE46).

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, CB8, CB9, CB10, CG2, CG7, CT1, CT3, CT5, CT6, CT10, CT111, CT12, and CE46).

Written exam (50%): CB6, CB10, CE46.
Delivered work (50%): CB6, CB7, CB8, CB9, CB10, CG2, CG7, CT1, CT3, CT5, CT6, CT10, CT111, CT12, and CE46.

The proposed activities are compulsory to pass the subject. The written exam will be used to assess the knowledge and expertise of the modeling approaches explained. 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 with R language is acquired within the subject Modelling forest development and validation, therefore, the review of the knowledge about this subject is recommended.