Predicting species distributions based on incomplete survey data: the trade‐off between precision and scale

By Veronika Braunisch, R. Suchant. 2010

101 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/fd7186e0d8f65bc1877098e5ae69991650dc08bc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. Matt D. White
2. G. Newell
3. Miguel B. Araújo
4. Joaquín Hortal
5. Rune Halvorsen
6. Canran Liu
7. Thiago Fernando Rangel
8. José Carlos Brito

In Ecology

1. Matt D. White
2. G. Newell
3. Rune Halvorsen
4. Miguel B. Araújo
5. Canran Liu
6. Joaquín Hortal
7. Thiago Fernando Rangel
8. José Carlos Brito

In Nature and Landscape Conservation

1. Matt D. White
2. G. Newell
3. Miguel B. Araújo
4. Joaquín Hortal
5. Canran Liu
6. Thiago Fernando Rangel
7. José Carlos Brito
8. Rune Halvorsen

Citations in the corpus

• 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): “Moreover, less precise data from large representative regions provide more robust models than systematically sampled data from restricted regions (Braunisch & Suchant, 2010).”
• Selecting thresholds for the prediction of species occurrence with presence‐only data (2013, methodology): “Braunisch & Suchant (2010) used two threshold selection methods in their study using presence-only data.”
• Uncertainty associated with survey design in Species Distribution Models (2014, background): “In fact, the design of the surveys can have a large impact on SDM performance (Hirzel & Guisan, 2002; Edwards et al., 2006; Albert et al., 2010; Braunisch & Suchant, 2010).”
• Emerging issues and challenges in conservation of biodiversity in the rangelands of Tanzania (2013, background): “Despite considerable advances in the theory and practice of optimal spatial sampling (e.g. Braunisch and Suchant 2010, Lin et al. 2008), trade-offs between optimal spatial and optimal temporal sampling are not yet completely resolved.”
• Two alternative evaluation metrics to replace the true skill statistic in the assessment of species distribution models (2019, background): “This is because commission errors can be relatively obscure and should be given less weight than omission errors in model evaluation (Braunisch and Suchant 2010; Liu et al. 2013).”
• Feeding habitat of the whale shark Rhincodon typus in the northern Gulf of Mexico determined using species distribution modelling (2012, background): “This typically results in higher AUC scores in MaxEnt models when compared to ENFA and less area predicted as highly suitable (Sérgio et al. 2007, Benito et al. 2009, Tittensor et al. 2009, Braunisch & Suchant 2010).”
• A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, methodology): “Evaluability by predictive performance on empirical data is an important difference between SPM and ERM (Araújo & Guisan 2006, Jiménez-Valverde et al. 2008, Braunisch & Suchant 2010, Warren & Seifert 2011, Halvorsen 2012).”