Geographical patterns in prediction errors of species distribution models

By J. Hanspach, I. Kühn, Oliver Schweiger, Sven Pompe, S. Klotz. 2011

75 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/6ea9c00720243b7d705b835eca1a74fa0a968453

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. O. Bykova
2. Guy F. Midgley
3. X. Morin
4. Steven Higgins
5. R. O’Hara
6. Michael R. Kearney
7. Matthew J. Smith
8. O. D. Hierro

In Ecology, Evolution, Behavior and Systematics

1. Guy F. Midgley
2. O. Bykova
3. Steven Higgins
4. R. O’Hara
5. Michael R. Kearney
6. David A. Coomes
7. Thomas Hickler
8. M. Cramer

In Nature and Landscape Conservation

1. Guy F. Midgley
2. Steven Higgins
3. X. Morin
4. O. Bykova
5. Michael R. Kearney
6. O. D. Hierro
7. Matthew J. Smith
8. R. O’Hara

Citations in the corpus

• The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models (2013, background): “Our analyses of the spatial variation in the predictive performance indicate clear differences between datasets, highlighting the value of this approach [58].”
• A physiological analogy of the niche for projecting the potential distribution of plants (2012, background): “Hanspach et al. (2011), in a study of species distribution models for 1000 European plant species, reported false positive and false negative rates of 0.22 and 0.16.”
• A framework for species distribution modelling with improved pseudo-absence generation (2015, methodology): “For instance, Hanspach et al. (2011)366 excluded species with less than 50 records to allow reliable modelling.”