Aim of study: Designing adequate silvicultural systems for natural regeneration of a forest species requires sound knowledge of the underlying ecological subprocesses: flowering and fruiting, seed dispersal and predation, seed germination, seedling emergence and seedling survival. The main objective of the present work is to carry out a review on the current knowledge about the different subprocesses governing the regeneration process for the main Iberian Pinus species, in order to propose scientifically based management schedules.Area of study: The review focuses on the five main native Pinus species within their most representative areas in the Iberian Peninsula: Pinus nigra in Cuenca mountains, Pinus sylvestris in Sierra de Guadarrama, Pinus pinaster and Pinus pinea in the Northern Plateau and Pinus halepensis in Catalonia.Material and methods: Firstly, currently available information on spatiotemporal dynamics and influential factors is introduced for each subprocess and species. Secondly, current regeneration strategies are characterized and the main bottlenecks are identified. Finally, alternative silvicultural practices proposed on the light of the previous information are presented.Main results: Different climate-mediated bottlenecks have been identified to limit natural regeneration of the Iberian pine species, with seed predation and initial seedling survival among the most influential. New approaches focusing on more gradual regeneration fellings, extended rotation periods, prevent big gaps and program fellings on mast years are presented.Research highlights: Natural regeneration of the studied species exhibit an intermittent temporal pattern, which should be aggravated under drier scenarios. More flexible management schedules should fulfil these limitations.Additional keywords: seed production; seed dispersal; seed predation; germination; regeneration fellings.Correspondence should be addressed to Rafael Calama: rcalama@inia.es
Natural regeneration-based silviculture has been increasingly regarded as a reliable option in sustainable forest management. However, successful natural regeneration is not always easy to achieve. Recently, new concerns have arisen because of changing future climate. To date, regeneration models have proved helpful in decision-making concerning natural regeneration. The implementation of such models into optimization routines is a promising approach in providing forest managers with accurate tools for forest planning. In the present study, we present a stochastic multistage regeneration model for Pinus pinea L. managed woodlands in Central Spain, where regeneration has been historically unsuccessful. The model is able to quantify recruitment under different silviculture alternatives and varying climatic scenarios, with further application to optimize management scheduling. The regeneration process in the species showed high between-year variation, with all subprocesses (seed production, dispersal, germination, predation, and seedling survival) having the potential to become bottlenecks. However, model simulations demonstrate that current intensive management is responsible for regeneration failure in the long term. Specifically, stand densities at rotation age are too low to guarantee adequate dispersal, the optimal density of seed-producing trees being around 150 stems·ha −1 . In addition, rotation length needs to be extended up to 120 years to benefit from the higher seed production of older trees. Stochastic optimization confirms these results. Regeneration does not appear to worsen under climate change conditions; the species exhibiting resilience worthy of broader consideration in Mediterranean silviculture.Key words: multistage models, transition probability, stochastic spatial optimization, stochastic simulation, climatic change.Résumé : La sylviculture fondée sur la régénération naturelle est de plus en plus considérée comme une option fiable pour l'aménagement forestier durable. Cependant, il n'est pas toujours facile de réussir à établir une régénération naturelle. Récem-ment, de nouvelles préoccupations ont été soulevées en raison des futurs changements climatiques. Jusqu'à présent, les modèles de régénération se sont avérés utiles pour la prise de décision concernant la régénération naturelle. L'application de ces modèles dans des routines d'optimisation est une approche prometteuse pour fournir aux aménagistes forestiers des outils précis pour la planification forestière. Dans la présente étude, nous présentons un modèle de régénération stochastique multistage pour les forêts aménagées de Pinus pinea L. du centre de l'Espagne où la régénération a historiquement été infructueuse. Le modèle est en mesure de quantifier le recrutement en fonction de différentes méthodes sylvicoles et de divers scénarios climatiques, en plus d'optimiser le calendrier d'aménagement. Le processus de régénération de cette espèce a montré une forte variation interannuelle et tous les sous-processus (production, di...
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. The direct application of existing models for seed germination may often be inadequate in the context of ecology and forestry germination experiments. This is because basic model assumptions are violated and variables available to forest managers are rarely used. In this paper, we present a method which addresses the aforementioned shortcomings. The approach is illustrated through a case study of Pinus pinea L. Our findings will also shed light on the role of germination in the general failure of natural regeneration in managed forests of this species. The presented technique consists of a mixed regression model based on survival analysis. Climate and stand covariates were tested. Data for fitting the model were gathered from a 5-year germination experiment in a mature, managed P. pinea stand in the Northern Plateau of Spain in which two different stand densities can be found. The model predictions proved to be unbiased and highly accurate when compared with the training data. Germination in P. pinea was controlled through thermal variables at stand level. At microsite level, low densities negatively affected the probability of germination. A time-lag in the response was also detected. Overall, the proposed technique provides a reliable alternative to germination modelling in ecology/forestry studies by using accessible/suitable variables. The P. pinea case study highlights the importance of producing unbiased predictions. In this species, the occurrence and timing of germination suggest a very different regeneration strategy from that understood by forest managers until now, which may explain the high failure rate of natural regeneration in managed stands. In addition, these findings provide valuable information for the management of P. pinea under climate-change conditions.
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. Natural regeneration in stone pine (Pinus pinea L.) managed forests in the Spanish Northern Plateau is not achieved successfully under current silviculture practices, constituting a main concern for forest managers. We modelled spatio-temporal features of primary dispersal to test whether (a) present low stand densities constrain natural regeneration success and (b) seed release is a climate-controlled process. The present study is based on data collected from a 6 years seed trap experiment considering different regeneration felling intensities. From a spatial perspective, we attempted alternate established kernels under different data distribution assumptions to fit a spatial model able to predict P. pinea seed rain. Due to P. pinea umbrella-like crown, models were adapted to account for crown effect through correction of distances between potential seed arrival locations and seed sources. In addition, individual tree fecundity was assessed independently from existing models, improving parameter estimation stability. Seed rain simulation enabled to calculate seed dispersal indexes for diverse silvicultural regeneration treatments. The selected spatial model of best fit (Weibull, Poisson assumption) predicted a highly clumped dispersal pattern that resulted in a proportion of gaps where no seed arrival is expected (dispersal limitation) between 0.25 and 0.30 for intermediate intensity regeneration fellings and over 0.50 for intense fellings.To describe the temporal pattern, the proportion of seeds released during monthly intervals was modelled as a function of climate variables -rainfall events -through a linear model that considered temporal autocorrelation, whereas cone opening took place over a temperature threshold. Our findings suggest the application of less intensive regeneration fellings, to be carried out after years of successful seedling establishment and, seasonally, subsequent to the main rainfall period (late fall). This schedule would avoid dispersal limitation and would allow for a complete seed release. These modifications in present silviculture practices would produce a more efficient seed shadow in managed stands.
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