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.

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

“…The fast pace at which tools and techniques are developing may commonly prevent scientists not directly involved in spatial sciences from remaining apprised of new developments in these fields. In addition, the development of easily accessible GIS tools bring hidden dangers by facilitating non-critical use by end-users who may have limited appreciation of spatial concepts (Lecours et al, 2016b). Because the tools are made to be intuitive, they often do not require end-users to fully understand the characteristics of the underlying processes and parameters implemented in the tools, and of the spatial data that form the basis for analysis.…”

“…Developments in data collection techniques in the last few decades have increased the types, amount and quality of data that can be collected for marine environmental characterization, particularly in terms of remotely sensed data (Brown et al, 2011;Kachelriess et al, 2014;Lecours et al, 2016b). Despite all the benefits associated with new data collection tools, Wells et al (2016) have warned against an over-dependence on technology, which can create a false feeling of trust in the data (see Section Data Quality) to the detriment of collecting appropriate ground-truthing data to verify the maps.…”

“…The input data were bathymetric data, backscatter data, and topographic data (i.e., slope, easterness, northerness, and relative deviation from mean value) (see Lecours et al, 2016b). The area shown is a 13.4 km by 12 km subset of the German Bank study area presented in Brown et al (2012) and Lecours et al (2016b).…”

Influence of artefacts in marine digital terrain models on habitat maps and species distribution models: a multiscale assessment

“…Six terrain attributes that together summarize topographic variability were derived from the reference and altered DBMs using the TASSE toolbox for ArcGIS (Lecours ): slope, easterness and northerness, topographic mean, rugosity and topographic position (see Lecours et al. , ). Backscatter data ( i.e .…”

“…Details on these measures can be found in Lecours et al. (). Finally, correlations between model outputs were calculated to evaluate spatial similarity of predictions.…”