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

Abstract

Remote sensing techniques are currently the main methods providing elevation data used to produce Digital Terrain Models (DTM). Terrain attributes (e.g. slope, orientation, rugosity) derived from DTMs are commonly used as surrogates of species or habitat distribution in ecological studies. While DTMs' errors are known to propagate to terrain attributes, their impact on ecological analyses is however rarely documented. This study assessed the impact of data acquisition artefacts on habitat maps and species distribution models. DTMs of German Bank (off Nova Scotia, Canada) at five different spatial scales were altered to artificially introduce different levels of common data acquisition artefacts. These data were used in 615 unsupervised classifications to map potential habitat types based on biophysical characteristics of the area, and in 615 supervised classifications (MaxEnt) to predict sea scallop distribution across the area. Differences between maps and models built from altered data and reference maps and models were assessed. Roll artefacts decreased map accuracy (up to 14% lower) and artificially increased models' performances. Impacts from other types of artefacts were not consistent, either decreasing or increasing accuracy and performance measures. The spatial distribution of habitats and spatial predictions of sea scallop distributions were always affected by data quality (i.e. artefacts), spatial scale of the data, and the selection of variables used in the classifications. This research demonstrates the importance of these three factors in building a study design, and highlights the need for error quantification protocols that can assist when maps and models are used in decision-making, for instance in conservation and management.

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

“…However, errors and uncertainty can result in inaccurate representation of the environments, and thus inaccurate maps (Wintle et al, 2005, Figure 3). While looking at the impact of artifacts in bathymetric data on the habitat mapping workflow, Lecours et al (2017a) found that poor data quality affects the outcome of the mapping process, although in an often unpredictable way: the presence of artifacts can sometimes artificially increase the measured map accuracy and model predictive power while in other cases it can decrease them. A lack of awareness and understanding of the concepts associated with data quality can in turn lead to misinformed conservation and management decisions (Rondinini et al, 2006;Beale and Lennon, 2012;Katsanevakis et al, 2012) with dire results for biodiversity (Regan et al, 2005).…”

“…The reference map is the same as in Figure 2. The altered map used bathymetric data impacted by artifacts caused by a 0.25 s mis-synchronization in the surveying system (see Lecours et al, 2017a). The presence of artifacts made the classification vary of 2.1%.…”

“…While this may seem obvious, data content is a matter that needs to be carefully approached: it has been reported that data for marine characterization are not always carefully selected, with data being selected primarily based on availability rather than ecological meaning and fitness-for-use (Peterson et al, 2011;Tulloch et al, 2016;Lecours et al, 2017a). It is evident that selecting different variables, for instance broad-scale currents instead of fine-scale substrate types, is likely to produce different maps and influence models differently.…”

“…Data quality Van Oort and Bregt, 2005Devillers and Goodchild, 2010Li et al, 2012Robertson et al, 2010Lechner et al, 2012Rocchini et al, 2011Gallo and Goodchild, 2012Moudrý and Šímová, 2012Cros et al, 2014Lecours et al, 2017a Scale Woodcock, 1987Quattrochi and Goodchild, 1997Marceau and Hay, 1999Duncan et al, 2002Goodchild, 2011Zhang et al, 2014Wiens, 1989Borcard et al, 2004Rahbek, 2005Seo et al, 2009Austin and Van Niel, 2011Moudrý and Šímová, 2012Bradter et al, 2013Wilson et al, 2007Anderson et al, 2008Brown et al, 2011Harris and Baker, 2012bRengstorf et al, 2012 …”

Multiscale and Hierarchical Classification for Benthic Habitat Mapping

“…Numerous habitat mapping studies have tested the importance of environmental predictor scale when creating habitat maps [2,3,56]. There have also been a number of studies, primarily terrestrial, that have examined the effects of hierarchical classifications on habitat prediction [33,35,36].…”