Which is the optimal sampling strategy for habitat suitability modelling

By A. Hirzel, Antoine Guisan. 2002

577 citations recorded by the source corpus.

Subfields: Ecological Modeling, Nature and Landscape Conservation, Social Psychology

View Paper: https://www.semanticscholar.org/paper/c0dbdb7c77feadf34a77a9344a994dd74f3584f3

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Citations in the corpus

Predicting the Potential Distribution of Paeonia veitchii (Paeoniaceae) in China by Incorporating Climate Change into a Maxent Model (2019, unknown): “At present, the most frequently used ecological models in the prediction of the distribution of various species are bioclimatic modeling (BIOCLIM) [10], domain environmental envelope (DOMAIN) [11], ecological niche factor analysis (ENFA) [12], Generalized Additive Model (GAM) [13], genetic algorithm”
Comparative interpretation of count, presence–absence and point methods for species distribution models (2012, methodology): “…statistical methods, each trying to improve our understanding of the complex link between a species and its environment (Buckland & Elston 1993; Hirzel & Guisan 2002; Fauchald & Tveraa 2003; Lehmann, Overton, & Leathwick 2003; Matthiopoulos 2003a; Drake, Randin, & Guisan 2006; Elith et al.…”
Influence of habitat heterogeneity on the distribution of larval Pacific lamprey (Lampetra tridentata) at two spatial scales (2004, methodology): “…modelling with computer simulations, demonstrating that improvements in sampling design can be made by: (i) increasing sample size (i.e. number of samples), (ii) using systematic as opposed to random sampling, and (iii) including environmental information in the design (Hirzel & Guisan, 2002).”
Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches (2011, methodology): “In the Diablerets, 912 4 m2 vegetation plots were inventoried according to a random-stratified sampling design (Hirzel & Guisan, 2002) based on elevation, slope and aspect.”
Prospective sampling based on model ensembles improves the detection of rare species (2010, result): “In comparison with traditional approaches, such as random or regular sampling (Hirzel and Guisan 2002), model-based sampling (MBS) is expected to support an increased rate of new population detection, therefore contributing to reduce sampling costs.”
Updating bird species distribution at large spatial scales: applications of habitat modelling to data from long‐term monitoring programs (2007, methodology): “Hirzel & Guisan (2002), using a similar approach on a single species, concluded that sample size was the major single factor behind predictive accuracy of predictive habitat models and that sampling strategy was in Updating bird distribution using long-term monitoring programs © 2007 The Authors…”
Measuring the relative effect of factors affecting species distribution model predictions (2014, methodology): “…GLM; McCullagh & Nelder 1989) fitted to real data (as in Wisz & Guisan 2009; Albert et al. 2010), defining theoretical response functions (e.g. Hirzel & Guisan 2002), delimiting theoretical ranges along environmen- tal gradients (e.g. Saupe et al. 2012) or using more dynamic approaches (e.g.…”
An ED-based Protocol for Optimal Sampling of Biodiversity (2005, background): “Methods of allocating sampling points in a given territory have been directed either to select points randomly in space, to cover variation regularly in space (Southwood and Henderson 2000; Hirzel and Guisan 2002), to obtain truly statistically-independent samples (Davis and Goetz 1990; Pereira and ”
Five (or so) challenges for species distribution modelling (2006, methodology): “The sub-sampling of existing data should be performed using a robust design (e.g. providing the best performing model for a target species; Hirzel & Guisan, 2002) and this operation repeated several times to assess the sensitivity of model outcomes to the sample data.”
Modelling ecological niches with support vector machines (2006, methodology): “A stratified random design, restricted to open non-woody vegetation (grasslands, rock and scree vegetation) and considering an equal number of replicates per stratum (Hirzel & Guisan 2002), was applied to the entire study area.”
Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models (2010, background): “Even if systematic sampling of the whole area of interest is not possible, future sampling should be designed to cover all of the main environmental gradients present (Hirzel and Guisan, 2002; Wintle et al., 2005).”
Uncertainty associated with survey design in Species Distribution Models (2014, background): “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).”
Community‐level plant palatability increases with elevation as insect herbivore abundance declines (2017, methodology): “…sample vegetation data along elevation gradients, a total of 912 plots of 4 m2 were selected between 400 and 3210 m using a random stratified sampling design with regard to elevation, slope and orientation (Hirzel & Guisan 2002), which were inventoried between May and September 2002–2010 (see Fig.”
Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data (2009, background): “Consequently, a growing number of studies have used virtual species in real or artificial landscapes to study the behavior of different implementations of predictive distribution models [23-27].”