Formerly Rune H. Økland.
Modelling and predicting fungal distribution patterns using herbarium data
Article first published online: 10 SEP 2008
© 2008 University of Oslo. Journal compilation © 2008 Blackwell Publishing Ltd
Journal of Biogeography
Volume 35, Issue 12, pages 2298–2310, December 2008
How to Cite
Wollan, A. K., Bakkestuen, V., Kauserud, H., Gulden, G. and Halvorsen, R. (2008), Modelling and predicting fungal distribution patterns using herbarium data. Journal of Biogeography, 35: 2298–2310. doi: 10.1111/j.1365-2699.2008.01965.x
- Issue published online: 19 NOV 2008
- Article first published online: 10 SEP 2008
- Environmental variables;
- generalized linear models;
- geographical range;
- herbarium data;
Aim The main aims of this study are: (1) to test if temperature and related parameters are the primary determinants of the regional distribution of macrofungi (as is commonly recognized for plants); (2) to test if the success of modelling fungal distribution patterns depends on species and distribution characteristics; and (3) to explore the potential of using herbarium data for modelling and predicting fungal species’ distributions.
Location The study area, Norway, spans 58–71° N latitude and 4–32° E longitude, and embraces extensive ecological gradients in a small area.
Methods The study is based on 1020 herbarium collections of nine selected species of macrofungi and a set of 75 environmental predictor variables, all recorded in a 5 × 5-km grid covering Norway. Primarily, generalized linear model (GLM; logistic regression) analyses were used to identify the environmental variables that best accounted for the species’ recorded distributions in Norway. Second, Maxent analyses (using variables identified by GLM) were used to produce predictive potential distribution maps for these species.
Results Variables relating to temperature and radiation were most frequently included in the GLMs, and between 24.8% and 59.8% of the variation in single-species occurrence was accounted for. The fraction of variation explained by the GLMs ranged from 41.6% to 59.8% for species with restricted distributions, and from 24.8% to 39.3% for species with widespread/scattered and intermediate distributions. The two-step procedure of GLM followed by Maxent gave predictions with very high values for the area under the curve (0.927–0.997), and maps of potential distribution were generally credible.
Main conclusions We show that temperature is a key factor governing the distribution of macrofungi in Norway, indicating that fungi may respond strongly to global warming. We confirm that modelling success depends partly on species and distribution characteristics, notably on how the distribution relates to the extent of the study area. Our study demonstrates that the combination of GLM and Maxent may be a fruitful approach for biogeography. We conclude that herbarium data improve insight into factors that control the distributions of fungi, of particular value for research on fleshy fungi (mushrooms), which have largely cryptic life cycles.