Kyle D. Maurer

Sustained carbon uptake and storage following moderate disturbance in a Great Lakes forest

Carbon (C) uptake rates in many forests are sustained, or decline only briefly, following disturbances that partially defoliate the canopy. The mechanisms supporting such functional resistance to moderate forest disturbance are largely unknown. We used a large-scale experiment, in which > 6700 Populus (aspen) and Betula (birch) trees were stem-girdled within a 39-ha area, to identify mechanisms sustaining C uptake through partial canopy defoliation. The Forest Accelerated Succession Experiment in northern Michigan, USA, employs a suite of C-cycling measurements within paired treatment and control meteorological flux tower footprints. We found that enhancement of canopy light-use efficiency and maintenance of light absorption maintained net ecosystem production (NEP) and aboveground wood net primary production (NPP) when leaf-area index (LAI) of the treatment forest temporarily declined by nearly half its maximum value. In the year following peak defoliation, redistribution of nitrogen (N) in the treatment forest from senescent early successional aspen and birch to non-girdled later successional species facilitated the recovery of total LAI to pre-disturbance levels. Sustained canopy physiological competency following disturbance coincided with a downward shift in maximum canopy height, indicating that compensatory photosynthetic C uptake by undisturbed, later successional subdominant and subcanopy vegetation supported C-uptake resistance to disturbance. These findings have implications for ecosystem management and modeling, demonstrating that forests may tolerate considerable leaf-area losses without diminishing rates of C uptake. We conclude that the resistance of C uptake to moderate disturbance depends not only on replacement of lost leaf area, but also on rapid compensatory photosynthetic C uptake during defoliation by emerging later successional species.

The timing of abscission affects dispersal distance in a wind‐dispersed tropical tree

Summary 1. Seed dispersal is a short-term phenomenon with long-term consequences for population survival and spread. Physiological mechanisms that target the release of seeds to particular sets of environmental conditions that maximize the probability of long-distance dispersal should evolve if long dispersal distance enhances fitness. 2. In this study, we use high-frequency censuses of seeds actually dispersed, high-frequency within-canopy meteorological observations and long-term measurements of above-canopy wind to investigate the environmental conditions that control the timing of seed abscission at different time-scales for a wind-dispersed tropical tree, Luehea seemannii. 3. We show that seed abscission follows a typical seasonal pattern, is rare at night and is most prevalent during periods of prolonged updrafts, higher temperature, with negative feedback when the heat canopy flux is relatively high. 4. We use phenomenological (super-WALD) and mechanistic (coupled Eulerian–Lagrangian closure) models to estimate the relative effects of the timing of seed release at different subannual temporal scales (seconds–hours) on the resulting long-term (season–decade) dispersal kernels. We find that periods of high wind speed increase the probability of long-distance dispersal between 100–1000 m, but decrease the probability at distances further than 1000 m relative to unbiased environmental conditions. We also find abscission during updrafts to increase the probability of long-distance dispersal at distances greater than 100 m. 5. Synthesis: We observe preferential abscission during updrafts in a tropical wind-dispersed tree. We use mechanistic models and long-term wind statistics to estimate the dispersal consequences of preferential seed release in specific environmental conditions. We find that the timing of the dispersal season may be influenced by wind conditions that maximize long-distance dispersal; however, there are likely other environmental factors essential for their determination. Our approach provides a method to bridge between small turbulence scales and large ecosystem scales to predict dispersal kernels. These findings shed light on the evolutionary processes that drive optimization of the timing of seed abscission and may be incorporated into plant population movement models to increase their accuracy and predictive power.

Estimating plot-level tree structure in a deciduous forest by combining allometric equations, spatial wavelet analysis and airborne LiDAR

Object-oriented classification methods are increasingly used to derive plant-level structural information from high-resolution remotely sensed data from plant canopies. However, many automated, object-based classification approaches perform poorly in deciduous forests compared with coniferous forests. Here, we test the performance of the automated spatial wavelet analysis (SWA) algorithm for estimating plot-level canopy structure characteristics from a light detection and ranging (LiDAR) data set obtained from a northern mixed deciduous forest. Plot-level SWA-derived and co-located ground-based measurements of tree diameter at breast height (DBH) were linearly correlated when canopy cover was low (correlation coefficient (r) = 0.80) or moderate (r = 0.68), but were statistically unrelated when canopy cover was high. SWA-estimated crown diameters were not significantly correlated with allometrically based estimates of crown diameter. Our results show that, when combined with allometric equations, SWA can be useful for estimating deciduous forest structure information from LiDAR in forests with low to moderate (<175% projected canopy area/ground area) levels of canopy cover.

Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest

Ecological multivariate systems offer a suitable data set on which to apply recent advances in information theory and causality detection. These systems are driven by the interplay of various environmental factors: meteorological and hydrological forcing, which are often correlated with each other at different time lags; and biological factors, primary producers and decomposers with both autonomous and coupled dynamics. Here, using conditional spectral Granger causality, we quantify directional causalities in a complex atmosphere-plant-soil system involving the carbon cycle. Granger causality is a statistical approach, originating in econometrics, used to identify the presence of linear causal interactions between time series of data, based on prediction theory. We first test to see if there was a significant difference in the causal structure among two treatments where carbon allocation to roots was interrupted by girdling. We then expanded the analysis, introducing radiation and soil moisture. The results showed a complex pattern of multilevel interactions, with some of these interactions depending upon the number of variables in the system. However, no significant differences emerged in the causal structure of above and below ground carbon cycle among the two treatments.

Modeling forest carbon cycle response to tree mortality: Effects of plant functional type and disturbance intensity

Natural and anthropogenic disturbances influence ecological succession and impact the carbon cycle. Understanding disturbance effects and ecosystem recovery is essential to carbon modeling. We hypothesized that (1) species-specific disturbances impact the carbon cycle differently from nonspecific disturbances. In particular, disturbances that target early-successional species will lead to higher carbon uptake by the postrecovery, middle- and late-successional community and (2) disturbances that affect the midsuccessional deciduous species have more intense and long-lasting impacts on carbon uptake than disturbances of similar intensity that only affect the early-successional species. To test these hypotheses, we employed a series of simulations conducted with the Ecosystem Demography model version 2 to evaluate the sensitivity of a temperate mixed-deciduous forest to disturbance intensity and type. Our simulation scenarios included a control (undisturbed) case, a uniform disturbance case where we removed 30% of all trees regardless of their successional status, five cases where only early-successional deciduous trees were removed with increasing disturbance intensity (30%, 70%, 85%, and 100%), and four cases of midsuccessional disturbances with increasing intensity (70%, 85%, and 100%). Our results indicate that disturbances affecting the midsuccessional deciduous trees led to larger decreases in carbon uptake as well as longer recovery times when compared to disturbances that exclusively targeted the early-successional deciduous trees at comparable intensities. Moreover, disturbances affecting 30% to 100% of early-successional deciduous trees resulted in an increased carbon uptake, beginning 6 years after the disturbance and sustained through the end of the 100 year simulation.