Correspondence site: http://www.respond2articles.com/MEE/
APPLICATION
NichePy: modular tools for estimating the similarity of ecological niche and species distribution models
Article first published online: 24 JAN 2012
DOI: 10.1111/j.2041-210X.2011.00184.x
© 2012 The Authors. Methods in Ecology and Evolution © 2012 British Ecological Society
Additional Information
How to Cite
Bentlage, B. and Shcheglovitova, M. (2012), NichePy: modular tools for estimating the similarity of ecological niche and species distribution models. Methods in Ecology and Evolution, 3: 484–489. doi: 10.1111/j.2041-210X.2011.00184.x
Publication History
- Issue published online: 7 JUN 2012
- Article first published online: 24 JAN 2012
- Received 27 September 2011; accepted 8 December 2011 Handling Editor: David Orme
- Abstract
- Article
- References
- Cited By
Keywords:
- ecological niche modelling;
- niche evolution;
- niche overlap;
- niche similarity;
- software;
- species distribution modelling
Summary
1. Ecological niche and species distribution models (ENMs and SDMs) are widely used in macroecological studies to investigate the potential geographic distributions of invasive species, the effects of climate change on past, present and future species distributions as well as the evolution of ecological niches. For many of these studies, estimating the similarity of different ENMs and SDMs is critical to evaluate the robustness of models.
2. We implement previously described randomization tests for evaluating the similarity of ENMs and SDMs in a package of three programs/scripts using the Python Programming Language. Our implementation is modular and flexible. It can be used with a variety of existing ENM and SDM algorithms and can be integrated into high-throughput analyses using a command-line shell (e.g. the Bash shell that is found on many Unix-like operating systems).
3. We demonstrate the application of NichePy using distributional data of two Anolis species from Hispaniola. An analysis integrating NichePy and openModeller, including a simple and straight-forward approach to ensemble modelling, is outlined.

2041-210X/asset/olbannerleft.gif?v=1&s=1e80b9367ecb31f1b5cc2830ff8e98a4f5dd6f23)
2041-210X/asset/olbannerright.gif?v=1&s=0a5762ce8b20769bd5f856575232909b64428b79)
