Akaike Information Criterion(AIC) Is Measure of the Relative Quality of Models for a Specific

Glossary

Akaike Information Criterion(AIC)is measure of the relative quality of models for a specific set of data. It is used to choose the best model from a collection of models built using the same data. It is a relative measure, i.e. can rank the models but only as they relate to each other; thus it is not a goodness of fit test.

Alpha-Hull model is a statistical method for determining area based on a set of spatial distribution points.

Area of Occupancy (AoO) is the area over which a species actually occurs. It is by definition smaller than the EoO.

Area Under the Receiver Operating Characteristic Curve(AUC)is a commonly used score that assesses the performance of a binary classifier (Hirzel et al. 2006). An AUC value of 0.5 indicates a model that is no better than chance and a value of 1.0 indicates a perfect prediction.

Boyce test assesses how much a model’s predictions depart from random expectation. It varies from -1 to 1. Positive values indicate the model’s consistency with the evaluation dataset; negative values indicate an incorrect model, that predicts low suitability areas where are many presences; and values close to 0 indicate the predictions are no different than a random model (Hirzel et al. 2006).

Cost-Weighted Distance(CWD)is the sum of all resistance layer cell values a least-cost path crosses in a SDM.

Cross validation(CV)is a technique for model validation. The general framework involves splitting the dataset into training and testing complementary subsets, using the former to train and the latter to validate the model. Multiple rounds are performed using different training and testing subsets of the same dataset and the results are averaged over the rounds.

Extent of Occurence (EoO) is a commonly used metric of the overall geographic area within which the species occurs.

Extrapolation is the process of predicting (or estimating) beyond the original observation range. If a model was built P(y) = f(x), with x (the observed variable) in the range Rx = (0, 10), extrapolating would mean predicting the values of y outside Rx, e.g. predicting y for x = 12. Extrapolating is prone to producing meaningless values and highest errors.

False absence is a case when an observation (or prediction by a model) indicates absence, when in reality the truth is presence. For example, when eDNA sampling indicates GCN absence from a pond, but in reality there is GCN in the pond.

Getis-Ord Hot-Spot Analysis is an ArcGIS tool for identifying where areas of high occupancy cluster together.

Integral Index of Connectivity (IIC) is an index of how well connected occupied sites are within a landscape.

Least Cost Path (LCP) is the path by which newts move most easily between ponds (= path of least resistance) as determined by a SDM.

Probability of Connectivity (PC) is an index of how well connected occupied sites are within a landscape.

Rondinini et al. (2011) prediction performance measure is the ratio of correctly predicted presences divided by the ratio of cells in the map that the species is predicted to be present. In our case the best fitting GLM predicted correctly 407 of the 453 presences, while the ratio of “presence” cells was 282,595/55,2677. The Rondinini et al. (2011) statistic has been recently used to evaluate SDMs for amphibians (Ficetola et al. 2014).

True Skill Statistic= . It ranges from1 (perfect agreement) to -1, with values less than zero indicating a no better than random performance.

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