The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models

By W. Bean, R. Stafford, J. Brashares. 2012

317 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/46a0716ee7368f364b2b6e68cd78465e7dfe58e8

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern (2014, background): “Second, predictions of presence and absence can be misleading for species with low prevalence, small sample size and/or gradual responses to environmental gradients (Bean et al., 2011; Meynard & Kaplan, 2011), and SSC tend to have low prevalence and small sample sizes.”
Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara–Sahel transition zone (2014, background): “The influence of data sparseness in threshold selection was also observed in regional models, despite the fact that all thresholds used tended to fit presence data reasonably well, which further supports that an optimal threshold is more difficult to discern with limited data (Bean et al., 2012).”
Using species distribution models to inform IUCN Red List assessments (2014, background): “However, this can lead to predicted presences occurring over a much wider geographic range of localities than occur in reality (Bean et al. 2012) – an unhelpful overprediction when IUCN Red List assessments should follow the precautionary principle and assign the most threatened category plausible…”
Selecting thresholds for the prediction of species occurrence with presence‐only data (2013, methodology): “Therefore, the small-sample-size effect needs to be further investigated, as in Bean et al. (2012).”
Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes (2013, background): “First, choosing an appropriate threshold becomes ifficult with limited occurrence records (Bean et al., 2012).”
On the selection of thresholds for predicting species occurrence with presence‐only data (2015, methodology): “With this method, sensitivity achieves its maximum value 1 with the evaluation data, but it is extremely sensitive to low sample sizes (Bean et al. 2012), and sometimes it predicts species’ presence everywhere in the study area (Phillips et al.”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, background): “Oversampling occurs when some occupied regions of environmental space are sampled at higher intensities than others (Bean et al. 2012, Gelfand et al. 2012), thereby causing model coefficients to become more representative of areas that have been heavily sampled than of a species’ entire range…”