Comparing species distribution models constructed with different subsets of environmental predictors

By David N. Bucklin, M. Basille, Allison M. Benscoter, L. Brandt, F. Mazzotti, Stephanie S. Romañach, Carolina Speroterra, James I. Watling. 2015

179 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/5eebecd274d8ef80e57e73b03044054f9d759b62

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. Maria João Santos
2. Victoria Nolan
3. Adam B. Smith
4. Emad Kaky
5. Francis Gilbert
6. Abdulaziz S. Alatawi

In Ecology

1. Maria João Santos
2. Victoria Nolan
3. Adam B. Smith
4. Emad Kaky
5. Francis Gilbert
6. Abdulaziz S. Alatawi

In Nature and Landscape Conservation

1. Maria João Santos
2. Victoria Nolan
3. Adam B. Smith
4. Emad Kaky
5. Francis Gilbert
6. Abdulaziz S. Alatawi

Citations in the corpus

• Testing the ability of species distribution models to infer variable importance (2019, methodology): “This is alarming but supports findings that using variables with no actual relationship to a species’ occurrence can yield models as accurate as models trained using “real” variables (Buklin et al. 2015; Fourcade et al. 2018).”
• A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants (2020, background): “Many studies have compared SDM performance for both single and multiple species (Beaumont et al., 2016; Bucklin et al., 2015; Elith et al., 2006; González-Irusta et al., 2015), but there is still a lack of consensus on the best choice of model (Norberg et al., 2019).”