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Population dynamics of Paeonia officinalis in relation to forest closure: From model predictions to practical conservation management

Abstract : In Europe, the abandonment of agricultural activities and traditional forest usages have led to forest spread, which eventually affects the population ecology of open habitat species. This is particularly apparent in the Mediterranean hinterland, which hosts many endemic and/or rare species. In this study we explored the effects of forest spread on the population dynamics of Paeonia officinalis, a long-lived perennial species protected in France. We also simulated the effects of different forest management regimes, by projecting the dynamics of populations under different sylvicultural cycle lengths. From 2003 to 2009, we monitored individual plants in four sites in Southern France, where each population occurs in a woodland and in an adjacent open habitat. We found that forest encroachment negatively impacted population dynamics, manifested by reduced reproduction and germination and a marked stasis of vegetative demographic stages. Using matrix models with demographic and environmental stochasticity, we showed that for two sites, populations were only viable in the open habitat but not in the woodland. In one site, populations were viable both habitats, whereas in one site, populations were non-viable in both habitats. Tree cuttings positively affected the four populations either by increasing population stochastic growth rate or by decreasing extinction rate. However, the effectiveness of forest management depended on local ecological conditions, such as the identity of the dominant tree species (evergreen vs deciduous) and sylvicultural system (coppice vs exotic plantation). Our study highlights the need for of tailor-made rather general management recommendations in perennial plant conservation.
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Emilie Andrieu, Aurélien Besnard, Hélène Fréville, Valentine Vaudey, Perrine Gauthier, et al.. Population dynamics of Paeonia officinalis in relation to forest closure: From model predictions to practical conservation management. Biological Conservation, Elsevier, 2017, 215, pp.51-60. ⟨10.1016/j.biocon.2017.08.010⟩. ⟨hal-02620460⟩



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