Comparing Selections of Environmental Variables for Ecological Studies: A Focus on Terrain Attributes

Abstract

Selecting appropriate environmental variables is a key step in ecology. Terrain attributes (e.g. slope, rugosity) are routinely used as abiotic surrogates of species distribution and to produce habitat maps that can be used in decision-making for conservation or management. Selecting appropriate terrain attributes for ecological studies may be a challenging process that can lead users to select a subjective, potentially sub-optimal combination of attributes for their applications. The objective of this paper is to assess the impacts of subjectively selecting terrain attributes for ecological applications by comparing the performance of different combinations of terrain attributes in the production of habitat maps and species distribution models. Seven different selections of terrain attributes, alone or in combination with other environmental variables, were used to map benthic habitats of German Bank (off Nova Scotia, Canada). 29 maps of potential habitats based on unsupervised classifications of biophysical characteristics of German Bank were produced, and 29 species distribution models of sea scallops were generated using MaxEnt. The performances of the 58 maps were quantified and compared to evaluate the effectiveness of the various combinations of environmental variables. One of the combinations of terrain attributes-recommended in a related study and that includes a measure of relative position, slope, two measures of orientation, topographic mean and a measure of rugosity-yielded better results than the other selections for both methodologies, confirming that they together best describe terrain properties. Important differences in performance (up to 47% in accuracy measurement) and spatial outputs (up to 58% in spatial distribution of habitats) highlighted the importance of carefully selecting variables for ecological applications. This paper demonstrates that making a subjective choice of variables may reduce map accuracy and produce maps that do not adequately represent habitats and species distributions, thus having important implications when these maps are used for decision-making.

Fig 1. German Bank study area with some of the input variables used in this study: the ground-truth data for the bottom types, the sea scallops observations, the bathymetry, the three backscatter derivatives and the six terrain attributes from Selection 1.

Fig 2

Map accuracies measured with (A) a kappa coefficient of agreement and (B) the overall accuracy.

Fig 3. Comparison of the discrimination ability of the computed classifications with that of the classification computed using only bathymetry and the backscatter derivatives, based on the number of bottom types (maximum possible of 5) that were better discriminated.

Fig 4. Performance and robustness of the 29 MaxEnt models.

Models in the top-left corner of the graph performed better and are more robust. Colour legend: Selection 1 (black), Selection 2 (blue), Selection 3 (red), Selection 4 (green), Selection 5 (purple), Selection 6 (orange), Selection 7 (white).

Fig 5. Generalizability of the 29 MaxEnt models.

Models closer to the top-left corner are more generalizable as they performed well on the training data and replicated well to the validation data. See Fig 4 for colour legend.

Fig 6. Percentage of variable contribution for the 29 MaxEnt models.

Only contributions greater than 5% are labeled.