Accounting for uncertainty when mapping species distributions: The need for maps of ignorance

Abstract

Accurate mapping of species distributions is a fundamental goal of modern biogeography, both for basic and applied purposes. This is commonly done by plotting known species occurrences, expert-drawn range maps or geographical estimations derived from species distribution models. However, all three kinds of maps are implicitly subject to uncertainty, due to the quality and bias of raw distributional data, the process of map building, and the dynamic nature of species distributions themselves. Here we review the main sources of uncertainty suggesting a code of good practices in order to minimize their effects. Specifically, we claim that uncertainty should be always explicitly taken into account and we propose the creation of maps of ignorance to provide information on where the mapped distributions are reliable and where they are uncertain

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

“…While the concepts associated with error and uncertainty propagation have been studied in the geospatial literature (Fisher and Tate, 2006;Wilson, 2012), the attempts to raise end-users' awareness in fields like ecology and environmental modeling have failed (Brown and Heuvelink, 2007;. With a few exceptions from the terrestrial literature (e.g., van Niel and Austin, 2007;Livne and Svoray, 2011), and despite repeated calls for the appropriate consideration of error and uncertainty propagation in environmental modeling and mapping (Rocchini et al, 2011;Beale and Lennon, 2012;Lechner et al, 2012), these concepts have yet to be better implemented, especially in a marine context .…”

“…However, subjectivity would be removed if complete metadata including quality information would be associated with each dataset, enabling a robust data quality assessment. Rocchini et al (2011) and Diesing et al (2016), among other authors, have highlighted the need for maps of ignorance, i.e., maps that spatially display uncertainty and errors associated with the primary maps and can assist decision-makers in assessing the reliability of predictions. While some tools have been provided to assess individual components of data quality [e.g., Combined Uncertainty Bathymetric Estimator (Calder and Mayer, 2003); Data Uncertainty Engine (Brown and Heuvelink, FIGURE 3 | Example of how using data of different quality can produce different outcomes.…”

“…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 …”

Population signatures of large-scale, long-term disjunction and small-scale, short-term habitat fragmentation in an Afromontane forest bird

“…Non-analogous climatic conditions within projection areas exceeding those that a SDM was trained for, may reduce the reliability of predictions (Fitzpatrick and Table 1.CH, Chyulu Hills; TH, Taita Hills. Rocchini et al, 2011). This potential source of uncertainty was quantified in a spatially explicit way in each scenario by using multivariate environmental similarity surfaces (Elith et al, 2010).…”

Uncertainty visualization of remote sensing crop maps enriched at parcel scale: a contribution for a more conscious GIS dataset usage

“…above 50%) and/or those with a more regular/compact distribution of the inner patches. Therefore, map usage can incorporate quantitative information about the spatial distribution of the uncertainty and even the knowledge of the uncertainty at parcel level (Rocchini et al, 2011).…”