## Introduction

Inverse-Distance Weighted Model is a geographical model that uses the location of known occurrences and predicts that the likelihood of finding a species in an area depends on the distance of that area to a known occurrence point. It is different from the Geographic Distance model, in that it explicitly implements the assumption that points that are close to one another are more alike than those that are farther apart. The probability of species occurrence for an unknown location is calculated as the average of some number of surrounding known locations weighted by their inverse distance from the unknown location. The values of known locations closest to the unknown location have more influence on the predicted value than values of locations farther away. Thus, the values of nearby known locations have greater weights, and the weights decrease as a function of distance, hence the name ‘inverse-distance weighted’.

This model does not use the input of environmental variables to predict the distribution of a species.

## Advantages

Simple and easy to interpret (but less so than other geographic models)

## Limitations

Does not use environmental variables to predict species occurrence

## Assumptions

N/A

## Requires absence data

Yes

## Configuration options

EcoCommons allows the user to set model arguments as specified below.

random_seed | Seed used for generating random values. Using the same seed value, i.e. 123, ensures that running the same model, with the same data and settings generates the same result, despite stochastic processes such as machine learning or cross-validation. |

Tails (tails) | The "tails” argument can be used to ignore the left or right tail of the percentile distribution for a variable. I If supplied, tails should be a character vector with a length equal to the number of variables used in the model. Valid values are "both", "low" and "high". |

References

Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge University Press.

Hijmans RJ, Elith J (2015) Species distribution modeling with R.