Effects of defoliation, foliar nutrients and climate on deciduous tree phenology in Europe

Contributors: Stavros D Veresoglou, Matthias C Rillig, Rautio Pasi.

            Plant phenology represents an important component of plant ecology that has been largely overlooked by plant ecologists (Cleland et al. 2007). Optimization of plant phenology may be of high ecosystem significance as it has pronounced implications for the quantity and quality of seeds produced by the plant, plant coexistence patterns (through temporal complementarity in plant growth) and nutrient cycling fluxes in general (Cleland et al. 2007). The most apparent driver of interannual variability in growth onset appears to be temperature. Most temperate deciduous trees only initiate budburst after a considerable amount of thermal hours, following winter has been reached (Lechowicz 1984). On the other hand, deciduous trees in arid regions depend more on precipitation events as a means of optimizing phenology (e.g. Jolly and Running 2001). Despite some additional, less important drivers of phenology having been identified (e.g. tree height – Seiwa 1999) most phenological models currently consider only temperature and precipitation (e.g. Li and Zhou 2012).

            An alternative overlooked driver of tree phenology may be herbivory/pathogenicity experienced by the tree over the preceding growth season. Contrary to the extensive literature that addresses the implications of tree budburst and herbivore/pathogen emergence synchrony on herbivore/pathogen fitness (e.g. Fox et al. 1997; Hunter and Elkinton 2000; Tikkanen and Julkunen-Tiitto 2003), there is considerably more limited information on the way herbivore/pathogen emergence may affect plant phenology which is additionally limited to short-term, limited-scale studies. While manual defoliation of trees has been shown to result in delayed budburst (Haukioja et al. 1988; Tuomi et al. 1989), Kaitaniemi et al. (1997) were able to observe changes in birch phenology only in cases when Epirrita autumnata had completely defoliated its host plants. Existing evidence on the potential of deciduous trees altering their budburst date in response to defoliation is conflicting (e.g. Watt and MacFarlane 1991; Hunter 1992). Leather (2000) proposed that this may have been because  the vast majority of studies does not consider phenological responses within single tree individuals; heavily defoliated branches of Acer pseudoplatanus, in an experiment, demonstrated accelerated budburst the subsequent year (Leather 1996). Alternatively, any herbivore/pathogen effects on plant phenology rather than delaying budburst may affect synchronization of leaf emergence for a plant species. Synchronization of bud burst as a means of saturating herbivores has been proposed in the past (for tropical ecosystems - Coley and Barone 1996) but this has not been tested as it requires long-term data.

            We are interested in using the ICP-Forests extensively-monitored plots to address potential relationships between recorded defoliation and plant phenology. Other than nitrogen saturation, there appear to be three main causes of defoliation: herbivory, pathogenicity and drought (Veresoglou et al. 2014 New Phytologist). We hypothesize that high defoliation in the preceding year(s) will result in delays of budburst in sites that experience sufficient precipitation (herbivory/pathogenicity induced defoliation) whereas it will result in accelerated budburst in arid/semiarid sites. We also hypothesize that the response of plant phenology to defoliation in arid and sufficient moisture plots will differ. We expect that our modeling effort will fill a major gap in the existing literature.

            We request Level II data on defoliation in plots where deciduous plant phenology is monitored as well as the phenological data themselves. Tissue N and P data are also desirable so as to exclude plots for which signs of N saturation are present. Climatic data could also secure more accurate estimates of temperature and precipitation at a stand level. Data will be divided in three groups based on the existing definitions of aridness: Arid plots (mean annual precipitation below 150mm); semi-arid plots (mean annual precipitation between 150 and 300mm) and sufficient moisture plot (those with more than 300mm mean annual precipitation). Our base model will be formulated on the sufficient-moisture plots. An optimal model that will account for temperature will be built so as to account for dependencies related to the identity of plant species and spatial positions of the trees. Then we will test whether incorporation of defoliation as a new covariate improves performance of the model. We will test both whether delayed budburst occurs in trees that experienced heavy defoliation the year before and whether we note any differences in the variance of the response (effect on synchrony of budburst). We will subsequently try to fit the optimal model to the sites that are exposed to arid and semiarid conditions. Should we record a poor fit (as expected), we will try to incorporate precipitation parameters assuming a logistic response of trees to water deficiency (effect present when annual precipitation is low but absent when it is high). In our modeling exercise we will use R for data manipulations and WinBugs for Bayesian model execution.

References

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