Alternative methods for predicting species distribution: an illustration with Himalayan river birds

Alternative methods for predicting species distribution: an illustration with Himalayan river birds 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Ecology Wiley

Alternative methods for predicting species distribution: an illustration with Himalayan river birds

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Publisher
Wiley
Copyright
Copyright © 1999 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0021-8901
eISSN
1365-2664
D.O.I.
10.1046/j.1365-2664.1999.00440.x
Publisher site
See Article on Publisher Site

Abstract

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

Journal of Applied EcologyWiley

Published: Oct 1, 1999

References

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