Improving methods in gap ecology: revisiting size and shape distributions using a model selection approach
Article first published online: 1 OCT 2012
© 2012 International Association for Vegetation Science
Journal of Vegetation Science
Volume 24, Issue 3, pages 484–495, May 2013
How to Cite
de Lima, R. A. F., Prado, P. I., Martini, A. M. Z., Fonseca, L. J., Gandolfi, S., Rodrigues, R. R. (2013), Improving methods in gap ecology: revisiting size and shape distributions using a model selection approach. Journal of Vegetation Science, 24: 484–495. doi: 10.1111/j.1654-1103.2012.01483.x
- Issue published online: 2 APR 2013
- Article first published online: 1 OCT 2012
- Manuscript Accepted: 29 AUG 2012
- Manuscript Received: 22 DEC 2010
- Diversidade, dinâmica e conservação em florestas do Estado de São Paulo: 40 ha de parcelas permanentes. Grant Number: 99/09635-0
- CNPq. Grant Number: 132.938/2005-7
- FAPESP. Grant Number: 04/09554-0
- CNPq. Grant Number: 303878/2008-8
- Atlantic forest;
- Canopy gaps;
- Disturbance regime;
- Log-normal distribution;
- Maximum likelihood
We assess gap size and shape distributions, two important descriptors of the forest disturbance regime, by asking: which statistical model best describes gap size distribution; can simple geometric forms adequately describe gap shape; does gap size or shape vary with forest type, gap age or the method used for gap delimitation; and how similar are the studied forests and other tropical and temperate forests?
Southeastern Atlantic Forest, Brazil.
Analysing over 150 gaps in two distinct forest types (seasonal and rain forests), a model selection framework was used to select appropriate probability distributions and functions to describe gap size and gap shape. The first was described using univariate probability distributions, whereas the latter was assessed based on the gap area–perimeter relationship. Comparisons of gap size and shape between sites, as well as size and age classes were then made based on the likelihood of models having different assumptions for the values of their parameters.
The log-normal distribution was the best descriptor of gap size distribution, independently of the forest type or gap delimitation method. Because gaps became more irregular as they increased in size, all geometric forms (triangle, rectangle and ellipse) were poor descriptors of gap shape. Only when small and large gaps (> 100 or 400 m2 depending on the delimitation method) were treated separately did the rectangle and isosceles triangle become accurate predictors of gap shape. Ellipsoidal shapes were poor descriptors. At both sites, gaps were at least 50% longer than they were wide, a finding with important implications for gap microclimate (e.g. light entrance regime) and, consequently, for gap regeneration.
In addition to more appropriate descriptions of gap size and shape, the model selection framework used here efficiently provided a means by which to compare the patterns of two different types of forest. With this framework we were able to recommend the log-normal parameters μ and σ for future comparisons of gap size distribution, and to propose possible mechanisms related to random rates of gap expansion and closure. We also showed that gap shape varied highly and that no single geometric form was able to predict the shape of all gaps, the ellipse in particular should no longer be used as a standard gap shape.