jueves, 19 de noviembre de 2009

Ecología

Agenda

Libros

Literatura seleccionada:

* Hairston, N.G. (1989). Ecological Experiments: Purpose, Design and Execution. Cambridge Studies in Ecology, Cambridge Univ. Press, Cambridge.
* Underwood, A.J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge Univ. Press, Cambridge.
* Scheiner, S.M.; Gurevitch, J. (2001). Design and analysis of ecological experiments. Chapman & Hall, New York.
* Quinn, G.P.; Keough, M.J. (2002). Experimental design and data analysis for biologists. Cambridge Univ. Press, Cambridge.
* Keppel, G. (1991). Design and analysis: a researcher’s handbook. Prentice Hall, New Jersey. Nueva versión.
* Breiman, L. (1984). Classification and Regression Trees. Chapman & Hall
* Crawley, M.J. (1998). GLIM for Ecologists. Blackwell Science.
* Hastie, T.J.; Tibshirani, R.J. (1997). Generalized Additive Models. Chapman & may.
* Maxwell, S.E.; Delaney, H.D. (1990). Designing Experiments and Analyzing Data. A model comparison perspective. Wadsworth Publishing Company, Belmont, CA. Nueva versión.
* Burnham, K.P.; Anderson, D. (2003). Model Selection and Multi-Model Inference. Springer.
* Valencia, J.L.; Díaz-Llanos, F.J. (2004). Métodos de Predicción en Situaciones Límite. Ed. La Muralla, Madrid.
* Davison, A.C.; Hinkley, D.V. (2007). Bootstrap methods and their application. Cambridge Univ. Press. [link]

Curiosidades

Científicos

Novedades

Planteamientos rigurosos con modales atinados


fuente: http://www.facebook.com/notes/eduard-punset/la-delicadeza-de-darwin/158490496777

Dice Charles Darwin en su carta:
«Aunque soy un fuerte defensor de la libertad de pensamiento en todos los ámbitos, soy de la opinión, sin embargo –equivocadamente o no–, que los argumentos esgrimidos directamente contra el cristianismo y la existencia de Dios apenas tienen impacto en la gente; es mejor promover la libertad de pensamiento mediante la iluminación paulatina de la mentalidad popular que se desprende de los adelantos científicos. Es por ello que siempre me he fijado como objetivo evitar escribir sobre la religión limitándome a la ciencia».

martes, 3 de noviembre de 2009

Más libros gratis






Mathematical Modeling of Biological Systems Vol.2 Epidemiology Evolution and Ecology

Theoretical Ecology: Principles and Applications 3rdEd

Allan Berryman: Population Analysis System (PAS)

The Population Analysis System (PAS) consists of a series of microcomputer programs designed to help you construct models and analyze the dynamics of populations of organisms inhabiting variable environments.


One-species Analysis
Pressing [2] in the PAS Main Menu will access programs for modeling and analysis of populations consisting of a single species. All other species are relegated to the environment of the subject population. The programs are:
  • P1a - Analyzes and models population census (time series) data.
  • P1b - Builds logistic population models and simulates dynamics.

Two-species Analysis
Pressing [3] in the PAS Main Menu will access programs for modeling and analysis of the interaction between two covarying populations such as predators and their prey, competitors or cooperators. The programs are:
  • P2a - Analyzes census data collected at equal time intervals on two coexisting species.
  • P2b - Models and simulates the dynamics of two interacting populations.

Applications
Pressing [4] in the PAS Main Menu will access programs that use models created by other PAS routines. They are mainly self-explanatory and no user manuals are provided. Clicking on the programs below show examples of them in action:
  • P1i - Interprets the biological meaning of model parameters in P1a or P1b.
  • P1s - Evaluates the sensitivity and stability of single-species models.
  • P2p - Forecasts future population densities and probabilities of outbreak or extinction.
  • P2m - Plays management games and tests harvesting or pest control strategies.

Lessons
Pressing [5] in the Main Menu will access programs for learning the theory of population dynamics and how it can be applied to real-life data. They are mainly self-explanatory and no user manuals are provided. Clicking on the programs below show examples of them in action:
  • PL1 - Exponential growth and the limits to growth.
  • PL2 - Predator-prey interactions in time and space.
  • PL3 - Competition between species.
  • PL4 - Interaction between plants and herbivores and pest management.
  • PL5 - Predator-prey cycles and pest management.

Games
Pressing [6] in the Main Menu will access programs for studying simple artificial life systems, like cellular automata. They are mainly self-explanatory and no user manuals are provided. Clicking on the programs below show examples of them in action:
  • PG1 - The Game of Life.
  • PG2 - Logistic growth of cellular automata.
  • PG3 - Predator-prey automata.

System


Ingresar y preparar datos con R

Ingresar/Preparar los datos
  • Lectura
    • read.table
    • Datos:
      • data types are integer, numeric (real numbers), logical (TRUE or FALSE), and character (alphanumeric strings)
      • data frame is a table of data that combines vectors (columns) of different types (e.g.
        character, factor, and numeric data). hybrid of two simpler data structures: lists, which can mix arbitrary types of data but have no other structure, and matrices, which have rows and columns but usually contain only one data type (typically numeric).
  • Organización o forma
    • stack and unstack are simple but basic functions — stack converts from wide to long format and unstack from long to wide; they aren’t
    • reshape is very flexible and preserves more information than stack/unstack,
      but its syntax is tricky: if long and wide are variables holding the
      data in the examples above, then
    • library(reshape): melt, cast, and recast functions, which are similar to reshape but sometimes easier to use
  • Chequeo
    • ˆ Is there the right number of observations overall? Is there the right number of observations in each level for factors?
    • Do the summaries of the numeric variables — mean, median, etc. — look reasonable? Are the minimum and maximum values about what you expected?
    • Are there reasonable numbers of NAs in each column? If not (especially if you have extra mostly-NA columns), you may want to go back a few steps and look at using count.fields or ill=FALSE to identify rows with extra fields . . .
      • str: tells you about the structure of an R variable
      • class: prints out the class (numeric, factor, Date, logical,etc.) of a variable.
      • head: prints out the beginning of a data frame;
      • table: command for cross-tabulation
      • NAs: identificarlos
Análisis exploratorio de los datos