Overlap Analysis

Overlap Analysis is one of the simplest forms of modelling species’ distributions. It assesses the ranges of values of the given predictor variables at the sites where a species has been recorded present, and predicts where that species should be able to occur based on those presence data (e.g. Brito et al. 1999, Arntzen & Teixeira 2006).

OA, now included in the modEvA package (Barbosa et al. 2014), can also be useful when extrapolating models outside their original scope (geographical area, time period or spatial resolution), as it can identify which localities are within the model’s domain — i.e., within the analysed ranges of values of the variables, outside which the model may not be reliable (e.g. Barbosa et al. 2009). In this case, the sp.cols should contain not species’ presence/absence, but rather indicate (with value 1) all observations that have been included in the model. Cf. modEvA function MESS, which is more informative but also more computationally intensive.

OA <-
function(data, sp.cols, var.cols) {
  # version 2.1 (13 May 2014)
 
  if (length(sp.cols) > 1) stop ("Sorry, OA is currently implemented for only one response variable at a time, so 'sp.cols' must indicate only one column")
 
  input.data <- data
 
  na.rm = TRUE
  if(na.rm) {
    mod.data <- data[ , c(sp.cols, var.cols)]
    data <- data[complete.cases(mod.data), ]
  }
 
  predictors <- data[ , var.cols]
  nvar <- ncol(predictors)
 
  response <- data[ , sp.cols]
  predictors.presence <- predictors[response > 0, ]
  var.presmin <- var.presmax <- vector("numeric", nvar)
  predictors.overlap <- matrix(data = NA, nrow = nrow(predictors), ncol = nvar)
  for (v in 1:nvar) {
    var.presmin[v] <- min(predictors.presence[ , v])
    var.presmax[v] <- max(predictors.presence[ , v])
    predictors.overlap[ , v] <- ifelse((predictors[ , v] >= var.presmin[v] & predictors[ , v] <= var.presmax[v]), 1, 0)
  }  # end for v
  overlap.noNA <- as.integer(rowSums(predictors.overlap) == nvar)
  return(overlap.noNA)
}

[presented with Pretty R]

NOTE that the input data format for this function has changed in mid May 2014, with former parameters response and predictors now replaced with data, sp.cols and var.cols (for coherence with multGLM and other functions in modEvA). Input data for the OA function are now a matrix or data frame containing the response variable (presences vs. absences of a species if we want to model its occurrence, or modelled vs. non-modelled sites if we want to know which non-modelled sites are within the modelled range), and the predictor variables to consider. The output is a binary vector whith 1 where the values of all predictors lie within the ranges observed for the presence records, and 0 otherwise. For example, if you have a table called mydata with your species’ presence data in the first column and your predictor variables in columns 2 to 12, paste in R the OA function followed by this command:

OA(data = mydata, sp.cols = 1, var.cols = 2:12)

References

Arntzen JW, Teixeira J. 2006. History and new developments in the mapping and modelling of the distribution of the golden-striped salamander, Chioglossa lusitanica. Zeitschrift für Feldherpetologie, Supplement: 1-14.

Barbosa A.M., Brown J.A. & Real R. (2014) modEvA – an R package for model evaluation and analysis. R package, version 0.1.

Barbosa, A.M., Real, R. & Vargas, J.M. (2009) Transferability of environmental favourability models in geographic space: the case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain. Ecological Modelling 220: 747–754.

Brito JC, Crespo EG, Paulo OS 1999. Modelling wildlife distributions: Logistic Multiple Regression vs Overlap Analysis. Ecography 22: 251-260.

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