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

By Duccio Rocchini, Joaquín Hortal, Szabolcs Lengyel, Jorge M. Lobo, A. Jiménez‐Valverde, Carlo Ricotta, Giovanni Bacaro, Alessandro Chiarucci. 2011

413 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/8933e99e44a9af7ac018e062a1908c9f6e268812

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In Ecological Modeling

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Citations in the corpus

Multidimensional biases, gaps and uncertainties in global plant occurrence information. (2016, methodology): “This uncertainty hampers applications built on linking occurrences with high-resolution environmental data in species distribution models (Feeley & Silman 2010; Rocchini et al. 2011).”
An evaluation of the robustness of global amphibian range maps (2014, methodology): “…are therefore expected to be more accurately detected in regions with high sampling intensity, rather than regions where species knowledge is poor (one of the facets of the Wallacean shortfall, i.e. the uneven knowledge on species distribution across space) (Lomolino, 2004; Rocchini et al., 2011).”
Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, background): “The geographical and environmental coverage of such information is typically limited, a lack of completeness that results in a number of biases and flaws (Rocchini et al., 2011).”
Accounting for spatially biased sampling effort in presence‐only species distribution modelling (2015, methodology): “This technique offers a potential alternative to dealing with sample selection bias and thereby accounting for one of several sources of uncertainty in species distribution modelling (Beale & Lennon, 2011; Rocchini et al., 2011).”
Stacking species distribution models and adjusting bias by linking them to macroecological models (2014, background): “…of data is inherently much better than range maps or atlas data, because environmental conditions can be quantified without the loss of subscale variability than is inevitable with grid-based analyses (see Rocchini et al., 2011, and Beck et al., 2012, for recent discussions of data quality issues).”
Large‐scale DNA‐based survey of frogs in Amazonia suggests a vast underestimation of species richness and endemism (2020, background): “…climate change (Foden et al., 2013), it would be necessary to reassess these modelling results based on more solid species occurrence data (Ficetola, Rondinini, Bonardi, Baisero, & Padoa-Schioppa, 2015; Herkt, Skidmore, & Fahr, 2017; Holt et al., 2013; Hurlbert & Jetz, 2007; Rocchini et al., 2011).”
On the brink of extinction? How climate change may affect global chelonian species richness and distribution (2012, methodology): “Herein, we used the default MAXENT settings despite not allowing extrapolation beyond environmental training ranges to reduce uncertainties due to projections onto nonanalogous climates (Fitzpatrick & Hargrove, 2009; Rocchini et al., 2011).”
Discrimination capacity in species distribution models depends on the representativeness of the environmental domain (2013, background): “As pointed out by some authors, data quality is probably the most important factor influencing general model performance, an aspect to which much more effort and resources should be devoted (Lobo, 2008; Jiménez-Valverde et al., 2010; Feeley & Silman, 2011; Rocchini et al., 2011).”
Patterns of beta diversity in Europe: the role of climate, land cover and distance across scales (2012, background): “, 2008), but also for attempts to predict current distributions of species in poorly surveyed areas (Rocchini et al., 2011).”
Forecasting fish distribution along stream networks: brown trout (Salmo trutta) in Europe (2013, background): “This approach reduced the likelihood of ‘false absences’ in the unit of analyses (Rocchini et al., 2011).”
A standard protocol for reporting species distribution models (2020, background): “…such that maps of potential species distributions are accompanied by equivalent ‘maps of ignorance’ that convey how and where reliable predictions are (i.e. magnitude and extent of prediction uncertainty), thereby supporting their correct and honest interpretation (Rocchini et al. 2011).”