Nonlinear tree growth dynamics predict resilience to disturbance

Abstract

Abstract. Following a disturbance, why does one tree survive while another dies? Physiological mechanisms may explain varying responses to disturbance between different tree species, but fewer studies have investigated conspecific variation in resilience to forest disturbance. We propose that a dynamic signal found in trees may provide clues to their post-disturbance fate. Specifically, linear versus nonlinear growth dynamics of a tree may be an indicator of its likelihood to survive a disturbance. Here, we investigate stands of red oak (Quercus rubra L.) that experienced disturbances in the form of drought and insect attack. Earlier work indicated that oaks dying during these disturbances had faster growth rates in their first years of life, but there was no obvious difference in canopy status, size, age, or microsite habitat between trees that survived and those that died. To investigate potential differences in growth dynamics between these trees, we quantified radial growth of individual trees and used two forecasting models to classify tree growth dynamics as linear or nonlinear. Trees were classified as healthy, declining, or dying based on crown cover, and dynamic patterns of growth were related to these health classifications. Contrary to expectations, we found healthy Q. rubra were significantly more likely to exhibit nonlinear growth dynamics relative to declining and dying trees. The drivers of this effect remain unclear, but nonlinear growth dynamics in healthy trees may represent an enhanced ability to benefit from resource pulses, in turn promoting greater resilience. Our work suggests that forecasting models offer a means of predicting tree survival during forest disturbances and thus represent an increasingly valuable tool as forest disturbances increase in frequency and severity.

Nonlinear Weather–Growth Relationships Suggest Disproportional Growth Changes of Norway Spruce in the Eastern Baltic Region

“…Spatiotemporal shifts in the linear weather-growth correlations (Figure 2 and Figures S4-S9) and the estimated response curves (Figure 3) highlighted the plasticity and nonlinearity of weather-growth relationships [14,19,25] of Norway spruce in the Eastern Baltic region. The plasticity of responses is indicative of both regional specialization [17,18,26,31,35] and adaptability to changing conditions [30,34,68]. Within the studied region, which included the southern margin of lowland distribution of spruce [36,50], phenotypical plasticity exceeded genetic specialization of weather-growth sensitivity (Table 4), implying some adaptability of populations in the medium term [4,18,30,42,68].…”

“…The ecological responses of quantitative traits across the environmental gradient are bell-shaped and, hence, nonlinear per se, though their plasticity (width and rigidity) can vary among species, populations, and genotypes [11,14,[25][26][27]. Accordingly, close-to-linear responses can be observed only in distinct parts of a gradient [14].…”

“…A temporal shift of the environmental gradient, as in the case of climatic changes, is likely to result in the nonstationarity of the local linear responses [14,23,24,28,29], implying that they are often outdated [14,30]. A generalization of responses accounting for their nonlinearity across substantial parts of the spatiotemporal climatic gradient(s) is therefore essential for more reliable predictions [12,22,26,27]. Analysis of the climatic rather than spatial gradient provides better insight into the growth responses of trees under a changing climate (shifting climatic gradient) [11,25,27,31].…”

Predicting the vessel lumen area tree-ring parameter of <i>Quercus robur</i> with linear and nonlinear machine learning algorithms

“…A similar idea is implemented in a simplified version of the VS model -the VS-Lite model (Tolwinski-Ward et al, 2011). The advantages of using nonlinear models have also been demonstrated in other studies using tree-ring data, such as predicting resilience to disturbance (Billings et al, 2015), modelling tree growth response after partial cuttings (Montoro Girona et al, 2017) and predicting the response of tree growth to climate change (Williams et al, 2010).…”

Revisiting the logistic map: A closer look at the dynamics of a classic chaotic population model with ecologically realistic spatial structure and dispersal

“…Chaos has been detected in many and diverse biological systems, such as the spread of infectious disease (Sugihara and May 1990), marine environments (Glaser et al 2014), and plant growth dynamics (Billings et al 2015). These studies focus on detection of chaos as a way to understand system stability and future behavior.…”