LebaMod

Laboratoire d'écologie et biologie aquatique : species distribution ensemble modelling for presence only data with automated jobs management on calculation server. (version: 0.1.13)

LebaMod

LebaMod is a tool to analyse species presence only dataset with classification models. The name come from Leba, 'Laboratoire d'écologie et biologie acquatique, (Université de Genève)', and mod, statistical modelling. It could be seen as a graphical interface to Caret package ('classification and regression training'), with automated calculation done on remote server and locally managed into a Sqlite database and R single objects for easy filtering and ensemble analysis. The focus is set on the exploration of a potentially large combinaison of models, predictors and parameterers while getting the most accurate spatial and temporal prediction data. Every parameters, method, dataset, predictors used in finals models are collected and can be downloaded for further analysis. It should be quite easy to reproduce every output produced by this application : output objects (class lebaBundle) contain a list filled with species, predictors and parameters data (class lebaData), together with a standard Caret object (class train/train.formula). The project itself is completly available in a git repository. Update are made each time new results are available.

Input data

The data used in this application is stored in a single SQLite database with two tables : species presence coordinates and predictors values at the same spatial and time extent. See in attribution part for more information.

Usage

The main form at left select and filter species, predictors and method data. Every change produce a summary in corresponding tabs : e.g. select a statistical method produce a list of method, with hyperlink to CRAN (Comprehensive R Archive Network) and set of tags, as defined by the Caret package.

Attributions

Authors

moser.frederic@gmail.com



Email for notification.

- Your email will be stored only to link the resulting models and send you a notifications when the process is done. No other uses will be attempted. However, this email is stored as is in a database, on our server. We can not guarantee a perfect secure transmission and storage. With this in mind, you could choose to use one from a disposable email service such as mailinator.com. For more informations on this topic, see http://en.wikipedia.org/wiki/Disposable_email_address

Species

- Multiple selection of species to be modelled, based on a list returned by slider 'Range of distinct species sites'. Models based on species distribution with few occurence could fail or be inaccurate. In species tab, a map is updated with every records found in database. Spatial aggregation presented in this map is done by a clustering alogorithm to give a better view of sites spatial distribution.
- Slider 'Range of distinct presence sites' filters the range of distinct presence sites across whole period, for each species. A distinct site of presence is a unique coordinate for a given year. If multiple records are found with same coordinates and same year for a particular species, they are counted as one site.

Pseudo-absence generator

- Type of pseudo absence selection : to calibrate presence only model, we need to generate absences with a selection algoritm, e.g. random site sampling. The number of pseudo absence to generate depends of multiple criteria, e.g. type of model (regression/classification) or number of presences available. More informations about selection strategy in barbet2012, lobo2011 and elith2011. In this version of lebaMod, only random spatial sampling with fixed number or multiplicator of pseudo-absence are available. nPa= number of pseudo absence = fixed number, mPa: number of pseudo absence =multiplicator * presence

Predictors

- Function 'caret::findCorrelation' can help to remove highly correlated predictors before modelisation, based on pair-wise absolute correlation of the training set. With this option set at 1, nothing will be removed, except identical predictors. From the documentation : If two variables have a high correlation, the function looks at the mean absolute correlation of each variable and removes the variable with the largest mean absolute correlation. Predictors with low correlation will be named 'prdLoCorr' and those considered as highly correlated 'prdHiCorr'. Only low correlated ones are used in model. See also in model details/tags to see if it use predictors auto removal internal procedure
- Multiple predictors selection. Predictors descriptions and spatialy summarized time series are available in predictors tab. The minumum number of predictors is 3.

Method


Group


Multi-runs

- Compute all pseudo absences selection and models multiple times.

Compute models

Map of occurences : selected species, all years.

Display all occurences, possibly with multiple occurences at the same site and same year. Duplicate will be removed during modelling process

Summary of predictors and spatial data for each presence distribution.

Description of selected methods

List of selected method with

Time series of predictors

Display spatialy aggregated time series for predictors.

Predictor description list :

Table of jobs to be computed.

Table of jobs in queue.



Filter available results

Predictor filter

Subset models available to plot and download

(based on filtered models)

Download selected models (zip) Download predictions data (rds)

Presence probabilities bivariate plot.

(based on filtered models. Could take some time, be patient)

Download plot (lattice/rds)

R session info on local host.


          

R session info on remote host.


        
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song2007
Self-organizing mapping of benthic macroinvertebrate communities implemented to community assessment and water quality evaluation
M. Y. Song and H. J. Hwang and I. S. Kwak and C. W. Ji and Y. N. Oh and B. J. Youn and T. S. Chon
ecological modelling  203  18--25  (2007)
thuiller2004
{Patterns and uncertainties of species' range shifts under climate change}
W. Thuiller
Global Change Biology  10  2020--2027  (2004)
thuiller2003a
{BIOMOD -- optimizing predictions of species distributions and projecting potential future shifts under global change}
W. Thuiller
Global Change Biology    1353--1362  (2003)
thuiller2003
{Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales}
W. Thuiller and M. B. Araujo and S. Lavorel
Journal of Vegetation Science  14  669  (2003)
thuiller2004a
{Effects of restricting environmental range of data to project current and future species distributions}
W. Thuiller and L. Brotons
Ecography  2    (2004)
tison2005
Typology of diatom communities and the influence of hydro-ecoregions: A study on the French hydrosystem scale
J. Tison and Y. S. Park and M. Coste and J. Wasson and L. Ector and F. Rimet and F. Delmas
Water research  39  3177--3188  (2005)
wehrens2007
Self-and super-organizing maps in R: the Kohonen package
R. Wehrens and L. M. C. Buydens
Journal of Statistical Software  21  19  (2007)
zuur2009
{A beginner's Guide to R}
A. F. Zuur and E. N. Ieno and E. H. H. G. Meesters
    219  (2009)
zuur2007
{Analysing ecological data}
A. F. Zuur and E. N. Ieno and G. M. Smith
    672  (2007)
zuur2009a
{Mixed Effects Models and Extensions in Ecology with R.}
A. F. Zuur and E. N. Ieno and N. J. Walker and A. A. Saveliev and G. M. Smith
    574  (2009)