Summary 1. Current emphasis on species conservation requires the development of specific distribution models. Several modelling methods are available, but their performance has seldom been compared. We therefore used discriminant analysis, logistic regression and artificial neural networks with environmental data to predict the presence or absence of six river birds along 180 Himalayan streams. We applied each method to calibration sites and independent test sites. With logistic regression, we compared performance in predicting presence–absence using map‐derived predictors (river slope and altitude) as opposed to detailed data from a standardized river habitat survey (RHS). 2. Using the entire calibration data, overall success at predicting presence or absence was only slightly greater using artificial neural networks (89–100%) than either logistic regression (75–92%) or discriminant analysis (81–95%), and on this criterion all methods gave good performance. 3. When applied to independent test data, overall prediction success averaged 71–80%, with logistic regression marginally but significantly out‐performing the other methods. Encouragingly for researchers with limited data, model performance in jack‐knife tests faithfully represented performance in more rigorous validations where calibration (n = 119) and test sites (n = 61) were in separate geographical regions. 4. All three methods predicted true absences (83–92% success) better than true presences (31–44%). Results from logistic regression were the most variable across species, but positive prediction declined with increasing species rarity in each method. 5. Applications with logistic regression illustrated that significant habitat predictors varied between data sets within species. Hypotheses about causal effects by habitat structure on distribution were thus difficult to erect or test. Logistic regression also showed that detailed data from the river habitat survey substantially improved positive prediction by comparison with prediction using slope or altitude alone. 6. We conclude that discriminant analysis, logistic regression and artificial neural networks differ only marginally in performance when predicting species distributions. Model choice should therefore depend on the nature of the data, on the needs of any particular analysis, and on whether assumptions for each method are satisfied. All three methods share drawbacks due to systematic effects by species rarity on performance measures. They also share limitations due to the correlative nature of survey data often used for model development at the spatial scales required in macro‐ecology and conservation biology. Tests with independent data, using a wider range of performance measures than those used traditionally, will be important in examining models and testing hypotheses for such applications.
Journal of Applied Ecology – Wiley
Published: Oct 1, 1999
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera