Within-population genetic structure in beech (Fagus sylvatica L.) stands characterized by different disturbance histories: does forest management simplify population substructure?
Piotti, A. ; Leonardi, S. ; Heuertz, M. ; Buiteveld, J. ; Geburek, T. ; Gerber, S. ; Kramer, K. ; Vettori, C. ; Vendramin, G.G. - \ 2013
PLoS ONE 8 (2013)9. - ISSN 1932-6203 - 9 p.
european beech - populus-trichocarpa - natural-populations - plant-populations - pollen dispersal - estimating seed - f-statistics - null alleles - douglas-fir - white-pine
The fine-scale assessment of both spatially and non-spatially distributed genetic variation is crucial to preserve forest genetic resources through appropriate forest management. Cryptic within-population genetic structure may be more common than previously thought in forest tree populations, which has strong implications for the potential of forests to adapt to environmental change. The present study was aimed at comparing within-population genetic structure in European beech (Fagus sylvatica L.) plots experiencing different disturbance levels. Five plot pairs made up by disturbed and undisturbed plots having the same biogeographic history were sampled throughout Europe. Overall, 1298 individuals were analyzed using four highly polymorphic nuclear microsatellite markers (SSRs). Bayesian clustering within plots identified 3 to 11 genetic clusters (within-plot hST ranged from 0.025 to 0.124). The proportion of within-population genetic variation due to genetic substructuring (FCluPlot = 0.067) was higher than the differentiation among the 10 plots (FPlotTot = 0.045). Focusing on the comparison between managed and unmanaged plots, disturbance mostly explains differences in the complexity of within-population genetic structure, determining a reduction of the number of genetic clusters present in a standardized area. Our results show that: i) genetic substructuring needs to be investigated when studying the within-population genetic structure in forest tree populations, and ii) indices describing subtle characteristics of the within-population genetic structure are good candidates for providing early signals of the consequences of forest management, and of disturbance events in general.
Distorted-distance models for directional dispersal: a general framework with application to a wind-dispersed tree
Putten, B. van; Visser, M.D. ; Muller-Landau, H.C. ; Jansen, P.A. - \ 2012
Methods in Ecology and Evolution 3 (2012)4. - ISSN 2041-210X - p. 642 - 652.
seed dispersal - pollen dispersal - recruitment limitation - anisotropic dispersal - mechanistic models - patterns - environments - forests - identification - consequences
1. Seed and pollen dispersal is often directionally biased, because of the inherent directionality of wind and many other dispersal vectors. Nevertheless, the vast majority of studies of seed and pollen dispersal fit isotropic dispersal kernels to data, implicitly assuming that dispersal is equally likely in all directions. 2. Here, we offer a flexible method for stochastic modelling of directional dispersal data. We show how anisotropic models can be constructed by combining standard dispersal functions with ‘distorted- distance functions’ that transform the circular contour lines of any isotropic dispersal kernel into non-circular shapes. Many existing anisotropic phenomenological models of seed and pollen dispersal are special cases of our framework. 3. We present functional forms for the specific case of elliptic distorted-distance functions, under which contour lines of the seed shadow become non-concentric, nested ellipses, and show how models using these functions can be constructed and parameterized. R-code is provided. 4. We applied the elliptic anisotropic models to characterize seed dispersal in the wind-dispersed Neotropical tree Luehea seemannii (Malvaceae) on Barro Colorado Island, Panama. We used inverse modelling to fit alternative models to data of seed rain into seed traps, the locations of seed traps and adult trees, and tree size. 5. Our anisotropic model performed considerably better than commonly applied isotropic models, revealing that seed dispersal of L. seemannii was strongly directional. The best-fitting model combined a 3-parameter elliptic distorted-distance function that captured the strong directional biases with a 1-parameter exponential dispersal kernel, a 1-parameter negative binomial probability distribution describing the clumping of seed rain and a 1-parameter function relating tree fecundity to tree diameter. 6. The framework presented in this paper enables more flexible and accurate modelling of directional dispersal data. It is applicable not only to studies of seed dispersal, but also to a wide range of other problems in which large numbers of particles disperse fromone or more point sources.