A TAXONOMY AND TREATMENT OF UNCERTAINTY FOR ECOLOGY AND CONSERVATION BIOLOGY

By Helen M. Regan, Mark Colyvan, Mark A. Burgman. 2002

832 citations recorded by the source corpus.

Uncertainty is pervasive in ecology where the difficulties of dealing with sources of uncertainty are exacerbated by variation in the system itself. Attempts at classifying uncertainty in ecology have, for the most part, focused exclusively on epistemic uncertainty. In this paper we classify uncertainty into two main categories: epistemic uncertainty (uncertainty in determinate facts) and linguistic uncertainty (uncertainty in language). We provide a classification of sources of uncertainty under the two main categories and demonstrate how each impacts on applications in ecology and conservation biology. In particular, we demonstrate the importance of recognizing the effect of linguistic uncertainty, in addition to epistemic uncertainty, in ecological applications. The significance to ecology and conservation biology of developing a clear understanding of the various types of uncertainty, how they arise and how they might best be dealt with is highlighted. Finally, we discuss the various general strategies for dealing with each type of uncertainty and offer suggestions for treating compounding uncertainty from a range of sources.

Subfields: Artificial Intelligence, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/168b10618a5168a5e2627f258ba815be4107f880

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Citations in the corpus

The IUCN Red List of Ecosystems: Motivations, Challenges, and Applications (2015, background): “Conservation Letters published by Wiley Periodicals, Inc. 3 inconsistently applied theoretical concepts (Regan et al. 2002).”
Connectivity, Probabilities and Persistence: Comparing Reserve Selection Strategies (2006, background): “Further, it is important to acknowledge and communicate uncertainties that accompany the use of observation data and modelling occurrence to users of reserve selection methods (Elith et al. 2002; Regan et al. 2002).”
NEW HORIZONS FOR MANAGING THE ENVIRONMENT: A REVIEW OF COUPLED SOCIAL‐ECOLOGICAL SYSTEMS MODELING (2012, background): “Ambiguity is exacerbated by linguistic uncertainty, which arises because much of our natural and scientific language is underspecific, vague, or context-dependent (Regan et al. [2002]).”
Defining and using 'best available science': a policy conundrum for the management of aquatic ecosystems. (2010, background): “Linguistic uncertainty arises because much of the scientific vocabulary is technical jargon, ambiguous, context-dependent or otherwise difficult for lay audiences to understand (Regan et al. 2002).”
An overview of methods to evaluate uncertainty of deterministic models in decision support (2015, positive): “Divergent, overlapping uncertainty classifications can be found in literature, the typology varying remarkably depending on the context and scope (see e.g. Regan et al., 2002; Walker et al., 2003; Refsgaard et al., 2007; Skinner et al., 2014).”
Pest Risk Maps for Invasive Alien Species: A Roadmap for Improvement (2010, background): “Parametric uncertainty may develop from measurement and systematic errors, incomplete or sparse data, natural variability in the system, or subjective judgment in the estimation of parameter values (Elith et al. 2002, Regan et al. 2002, Barry and Elith”
QUANTILE REGRESSION REVEALS HIDDEN BIAS AND UNCERTAINTY IN HABITAT MODELS (2005, background): “The philosophy embodied in our simulations reflects a view that most ecological relations have an appearance of randomness not because they are inherently random but because we are always estimating them with incomplete information (Regan et al. 2002).”
Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication (2016, background): “…and errors, including aleatory uncertainty, epistemic uncertainty, semantic or linguistic uncertainty, and ontological uncertainty (e.g. Beven and Binley 1992, McBratney 1992, Regan et al. 2002, Ascough et al. 2008, Beven 2009, Raadgever et al. 2011, Beven and Young 2013, Beven et al. 2014).”
A rapid assessment of co-benefits and trade-offs among Sustainable Development Goals (2017, background): “These steps were taken to reduce linguistic uncertainty arising from the communication between the workshop participants and the experts [18].”
Guidelines for the application of IUCN Red List of Ecosystems categories and criteria (2015, background): “This relatively large grain size is applied for four reasons: (i) ecosystem boundaries are inherently vague (Regan et al., 2002), so it is easier to determine that an ecosystem occurrence falls within a larger grid cell than a smaller one; (ii) larger cells may be required to diagnose the presence o”
Core concepts of spatial prioritisation in systematic conservation planning (2012, neutral): “Such variation, undesirably, introduces linguistic uncertainty (Regan et al., 2002; Bonn & Gaston, 2005; Carey & Burgman, 2008; Regan, Ensbey & Burgman, 2009) into discussions, negotiations, and planning.”
An operational model for mainstreaming ecosystem services for implementation (2008, background): “Scenarios can be especially effective when they capture alternative futures visually and dramatically, in such a way as to reduce stakeholder confusion by providing clarity about complex issues and vague language (67, 68).”