Using maps of biogeographical ignorance to reveal the uncertainty in distributional data hidden in species distribution models
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2021
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Resumo
Species distribution models (SDMs) are subject to many sources of uncertainty, limiting
their application in research and practice. One of their main limitations is the
quality of the distributional data used to calibrate them, which directly influences
the accuracy of model predictions. We propose a standardized methodology to create
maps, describing the limitations of occurrence data for covering the distribution
of a species. We develop a set of tools based on the general framework of Maps of
Biogeographical Ignorance to describe the main sources of data-driven uncertainty:
taxonomic stability, environmental similarity, geographical proximity and temporal
decay of the underlying biodiversity data. The so-derived indicators of data-driven
uncertainty account for inventory completeness, taxonomic quality, time since the
surveys and geographical (and environmental) distance to localities with information.
These indicators form the basis of ignorance maps, which can be used to visualize the
reliability of SDM projections in geographical space, to estimate the uncertainty of
these predictions and to identify target survey areas. To demonstrate the application
of our approach, we use data on fourteen Iberian species of Scarabaeidae dung beetles.
Data-driven uncertainty is widespread even for this well-surveyed group; more than
60% of the region has distributional uncertainty values higher than 0.6, and 30%
higher than 0.7. Ignorance maps can be jointly evaluated with SDM predictions to
generate spatially explicit maps of uncertainty, identifying where predictions are reliable/
unreliable. Neglecting such uncertainty can severely affect SDM effectiveness, as
it can introduce biases and inaccuracies into the measured species–environment relationships.
These errors could result in incorrect theoretical or practical applications,
including ill-advised conservation actions. We therefore advocate for the routine use of
ignorance maps or similar techniques as supporting information in SDM applications.
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Bias, Wallacean shortfall, Biodiversity data, Temporal decay, Distributional uncertainty, Ecological niche models, Predictive accuracy, Taxonomic quality, SDM performance, Spatial decay
Citação
TESSAROLO, Geiziane; LADLE, Richard J.; LOBO, Jorge M.; RANGEL, Thiago Fernando; HORTAL, Joaquín. Using maps of biogeographical ignorance to reveal the uncertainty in distributional data hidden in species distribution models. Ecography, Copenhagen, v. 44, p. 1743-1755, 2021. DOI: 10.1111/ecog.05793. Disponível em: https://onlinelibrary.wiley.com/doi/full/10.1111/ecog.05793. Acesso em: 27 mar. 2023.