Survey-gap analysis in expeditionary research: where do we go from here?

By V. Funk, K. Richardson, Simon Ferrier. 2005

85 citations recorded by the source corpus.

Subfields: Ecological Modeling, Genetics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/811df0ba8791d1ae117b5120c14c4130f212ce31

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In Ecological Modeling

In Genetics

In Nature and Landscape Conservation

Citations in the corpus

Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment (2007, methodology): “We have now applied this approach in a number of regions by linking GDM to a survey-gap analysis procedure (Ferrier, 2002; Funk et al., 2005; Fig.”
Multidimensional biases, gaps and uncertainties in global plant occurrence information. (2016, background): “2013) to taxonomy, environmental bias (Funk et al. 2005) to space, and seasonal bias (ter Steege & Persaud 1991) to time.”
Atlas of Brazilian Snakes: Verified Point-Locality Maps to Mitigate the Wallacean Shortfall in a Megadiverse Snake Fauna (2019, background): “1A) and especially for remote or inaccessible regions (Funk et al., 2005; Duputié et al., 2014).”
Uncertainty associated with survey design in Species Distribution Models (2014, unknown): “Many methods to design surveys have been proposed with the objective of maximizing the amount of biodiversity captured, while incorporating time and cost limitations (Austin & Heyligers, 1989; Pereira & Itami, 1991; Hirzel & Guisan, 2002; Funk et al., 2005; Hortal & Lobo, 2005; Medina et al., 2013).”
Species Distribution Modeling in the Tropics: Problems, Potentialities, and the Role of Biological Data for Effective Species Conservation (2009, methodology): “This has led to the development of techniques aimed at overcoming the analytical challenges posed by incomplete spatial coverage.”
Keep collecting: accurate species distribution modelling requires more collections than previously thought (2011, background): “…strategy may be outweighed in many cases by simply adding additional sampling efforts in areas with marginal predictive certainty in existing models, along environmental gradients (Austin & Heyligers, 1989; Wessels et al., 1998), or as selected through survey-gap analyses (Funk et al., 2005).”
Phylogenetic diversity (PD) and biodiversity conservation: some bioinformatics challenges (2006, background): “This raises issues about the relative cost-effectiveness of sampling more places versus using spatial predictive models, drawing on available environmental/climatic data, to extrapolate biodiversity patterns to new places (Ferrier 2002, Funk et al 2005).”
The effect of prevalence and its interaction with sample size on the reliability of species distribution models (2009, background): “Thus, the lower the sample size, the greater the relevance of well-designed sampling protocols designed to select training points across the whole spatial and environmental gradient (Wessels et al. 1998, Jiménez-Valverde and Lobo 2004, Kadmon et al. 2004, Hortal and Lobo 2005, Funk et al. 2005).”
Bias in freshwater biodiversity sampling: the case of Iberian water beetles (2008, background): “As discussed above, well-sampled and undersampled cells differ significantly in a number of variables, and these differences should be borne in mind to diminish environmental bias in future surveys (Funk et al ., 2005; Hortal & Lobo, 2005).”