Last updated: 2021-05-19

Checks: 2 0

Knit directory: booksn_ppm/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Repository version: 09971e1

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 09971e1. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/.DS_Store
    Ignored:    data/data_raw/

Untracked files:
    Untracked:  glmulti.analysis.modgen.back
    Untracked:  glmulti.analysis.mods.back

Unstaged changes:
    Modified:   analysis/computeDisparity.Rmd
    Modified:   analysis/computeMannKendall.Rmd
    Modified:   analysis/evolucionTemporal.Rmd
    Modified:   data/coplas2019sn.csv
    Modified:   output/comparaD_especies.pdf
    Modified:   output/patron_nao_ppm.pdf
    Modified:   output/pearson_NAO_especies.pdf

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	a9b238a	Antonio J Perez-Luque	2021-05-19	Build site.
Rmd	8b7563d	Antonio J Perez-Luque	2021-05-19	update index
html	b9f3a00	Antonio J Perez-Luque	2021-05-18	Build site.
Rmd	2e8acb8	Antonio J Perez-Luque	2021-05-18	update index and add new analysis, diparity
html	95422ea	Antonio J Perez-Luque	2021-05-18	Build site.
Rmd	e061f4e	Antonio J Perez-Luque	2021-05-18	update index
html	afdfe93	Antonio J Perez-Luque	2021-05-18	Build site.
Rmd	54923d4	Antonio J Perez-Luque	2021-05-18	include link to temporal pattern
html	3c695d9	Antonio J Perez-Luque	2021-05-17	Build site.
html	6e7b510	Antonio J Perez-Luque	2021-05-17	Build site.
Rmd	b29af0a	Antonio J Perez-Luque	2021-05-17	fix links
html	09743fc	Antonio J Perez-Luque	2021-05-17	Build site.
Rmd	f6f2948	Antonio J Perez-Luque	2021-05-17	select data for SN
html	c73db62	Antonio J Perez-Luque	2021-05-17	Build site.
Rmd	fd92f89	Antonio J Perez-Luque	2021-05-17	add preparacion de datos
html	dbfdfa7	Antonio J Perez-Luque	2021-05-17	Build site.
Rmd	a022266	Antonio J Perez-Luque	2021-05-17	Start workflowr project.

Preparación de datos

En este script partimos de los datos originales de COPLAS desde 1992-2019 y generamos un data.frame “limpio” (corrección de errores), con la elevación y la especie de pino. Este dataset se guarda en /data/coplas2019.csv.
Para el análisis de los datos de Sierra Nevada, vamos a generar un buffer de diferentes distancias para ver que parcelas elegimos. El proceso puede verse aquí. Hemos cogido un buffer de 20 km y hemos añadido algunas parcelas (de Motril), y hemos eliminado algunas de Sierra Alhamilla. Los datos están en: /data/coplas2019sn.csv.

Exploración del patrón temporal

Vamos a explorar el patrón temporal de los niveles de infestación medios por año, agrupados por grupo de elevación y por especie.
Nos quedamos con la agrupación por especie. Seleccionamos 4 especies (P. nigra, P. halpensis, P. sylvestris, P. pinaster)
Los resultados pueden verse aquí

Análisis de Mann-Kendall

Analizmos las tendencias temporales para cada parcela utilizando el test no paramétrico de Mann-Kendall.

Análisis de Disparidad de las series de datos

Disparidad entre los valores de infestación entre años para cada parcela
Variabilidad para cada serie de datos (cada parcela)
Resultados aquí
Mas información sobre estos índices: (Fernández‐Martínez et al. 2018; Fernández‐Martínez and Peñuelas 2021; Heath and Borowski 2013)

Relación nivel de infestación con la NAO

Re-visitamos el trabajo de Hódar, Zamora, and Cayuela (2012), y volvemos a explorar la relación entre la NAO y el % de parcelas con nivel de infestacion >= 3.
Análisis exploratorio aquí

Referencias

Fernández‐Martínez, Marcos, and Josep Peñuelas. 2021. “Measuring Temporal Patterns in Ecology: The Case of Mast Seeding.” Ecology and Evolution, March, ece3.7291. https://doi.org/10.1002/ece3.7291.

Fernández‐Martínez, Marcos, Sara Vicca, Ivan A. Janssens, Jofre Carnicer, Javier Martín‐Vide, and Josep Peñuelas. 2018. “The Consecutive Disparity Index, D : A Measure of Temporal Variability in Ecological Studies.” Ecosphere 9 (12): e02527. https://doi.org/10.1002/ecs2.2527.

Heath, Joel P., and Peter Borowski. 2013. “Quantifying Proportional Variability.” Edited by Shu-Dong Zhang. PLoS ONE 8 (12): e84074. https://doi.org/10.1371/journal.pone.0084074.