| Literature DB >> 29991997 |
Isidore O Amahowe1, Orou G Gaoue1,2,3, Armand K Natta1, Camille Piponiot4, Irié C Zobi5, Bruno Hérault4,5.
Abstract
Understanding how trees mediate the effects of chronic anthropogenic disturbance is fundamental to developing forest sustainable management strategies. The role that intraspecific functional diversity plays in such process is poorly understood. Several tree species are repeatedly defoliated at large scale by cattle breeders in Africa to feed livestock. In addition, these tree species are also debarked for medicinal purposes. These human-induced disturbances lead to biomass loss and subsequent decline in the tree growth. The main objective of this work is to investigate how functional traits mediate tree response to chronic anthropogenic disturbance. We used a unique data set of functional traits and growth rate of 503 individual tree of Afzelia africana. We collected data on leaf mass per area (LMA), wood density (WD) and growth rate, and recorded history of human disturbances (debarking, pruning) on individual tree from 12 populations of A. africana distributed in two ecological zones in Benin (West Africa). We tested the effect of disturbances on absolute growth rate across ontogenetic stages, assessed the role of intraspecific trait variability on growth and tested the role of tree functional strategy on the tree growth response to debarking and pruning. We found that debarking did not affect stem growth, suggesting a fast compensatory regrowth of bark wounded. Moreover, tree response to debarking was independent of the functional strategy. By contrast, we found that pruning reduced tree absolute growth; however, trees with low WD were more strongly affected by pruning than trees with high WD. Our results emphasize the importance for plant functioning of the interplay between the availability of leaves for resource acquisition and a resilience strategy by mobilizing stored resources in stem wood to be reinvested for growth under severe disturbances.Entities:
Keywords: Debarking; growth performance; pruning; resilience strategy; wood density
Year: 2018 PMID: 29991997 PMCID: PMC6019090 DOI: 10.1093/aobpla/ply036
Source DB: PubMed Journal: AoB Plants Impact factor: 3.276
Figure 1.Study area and location of the 12 sampled populations of Afzelia africana studied. Number 1 indicates the drier Sudanian zone and ‘2’ indicates the wetter Sudano-Guinean ecological zone.
Figure 2.Size-dependent growth rate of Afzelia africana. The highest growth rates are obtained at intermediate DBH justifying the use of a hump-shaped growth trajectory to model the effect of stress on tree performance. White rectangles span from the first to the third quartile. A segment inside the rectangle shows the median and black lines above and below the box show the locations of the minimum and maximum. Black dots refer to outliers.
Figure 3.Effect of stress disturbance (debarking and pruning) on the absolute growth rate of Afzelia africana (Model 1). Model predictions (lines) are represented for not-pruned (yellow), pruned (red) and debarked (orange) growth trajectories with envelops showing the prediction credibility intervals.
Posterior values for the three growth models parameterized in a Bayesian framework. Model 0: the model without any effect of disturbance or functional strategy; Model 1: testing the effect of disturbance (debarking, pruning) on growth performance; Model 2: role for intraspecific trait variability on individual performance; Model 3: importance of the individual functional strategy on the growth response to disturbance. Gmax and Dopt are, respectively, to the maximum value of and the DBH value at which this maximum value is reached. is the value of Gmax where there is no disturbance (Debark and Prun equal to 0), and and the model parameters. is the value of Gmax for an average (i.e. mean values of WD and LMA) tree, and and the model parameters. is the value of Gmax for an average (i.e. mean values of WD and LMA) tree with no disturbance (Debark and Prun equal to 0) and the model parameter.
| Parameter | Value at maximum likelihood | 95 % credibility intervals | |
|---|---|---|---|
| Model 0 |
| 0.36 | [0.29; 0.43] |
|
| 8.76 | [6.77; 11.54] | |
|
| 0.51 | [0.48; 0.55] | |
| Model 1 |
| 0.44 | [0.35; 0.52] |
|
| 0.12 | [−0.44; 0.69] | |
|
| −0.24 | [−0.44; −0.03] | |
| Model 2 |
| 0.34 | [0.24; 0.48] |
|
| −0.07 | [−0.16; 0.06] | |
|
| −0.02 | [−0.13; 0.09] | |
| Model 3 |
| 0.41 | [0.27; 0.57] |
|
| −0.18 | [−0.37; 0.02] | |
|
| −0.10 | [−0.22; 0.03] | |
|
| 0.12 | [−0.09; 0.32] |
Figure 4.Weak role of intraspecific WD variability in observed growth of Afzelia africana (Model 2). Data are binned into low (WD values < quantile 0.33), high (WD values > quantile 0.66) and medium (in between) categories.
Figure 5.Weak role of intraspecific LMA variability in observed growth of Afzelia africana (Model 2). Data are binned into low (LMA values < quantile 0.33), high (LMA values > quantile 0.66) and medium (in between) categories.
Figure 6.Model predictions showing how the individual WD value mediates the tree response to pruning (Model 3). The cost of being pruned is calculated using the difference in predicted growth between a pruned and a not-pruned tree taking into account both WD and the ontogenetic stage.