Brady S. Hardiman

The role of canopy structural complexity in wood net primary production of a maturing northern deciduous forest.

The even-aged northern hardwood forests of the Upper Great Lakes Region are undergoing an ecological transition during which structural and biotic complexity is increasing. Early-successional aspen (Populus spp.) and birch (Betula papyrifera) are senescing at an accelerating rate and are being replaced by middle-successional species including northern red oak (Quercus rubra), red maple (Acer rubrum), and white pine (Pinus strobus). Canopy structural complexity may increase due to forest age, canopy disturbances, and changing species diversity. More structurally complex canopies may enhance carbon (C) sequestration in old forests. We hypothesize that these biotic and structural alterations will result in increased structural complexity of the maturing canopy with implications for forest C uptake. At the University of Michigan Biological Station (UMBS), we combined a decade of observations of net primary productivity (NPP), leaf area index (LAI), site index, canopy tree-species diversity, and stand age with canopy structure measurements made with portable canopy lidar (PCL) in 30 forested plots. We then evaluated the relative impact of stand characteristics on productivity through succession using data collected over a nine-year period. We found that effects of canopy structural complexity on wood NPP (NPPw) were similar in magnitude to the effects of total leaf area and site quality. Furthermore, our results suggest that the effect of stand age on NPPw is mediated primarily through its effect on canopy structural complexity. Stand-level diversity of canopy-tree species was not significantly related to either canopy structure or NPPw. We conclude that increasing canopy structural complexity provides a mechanism for the potential maintenance of productivity in aging forests.

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.

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.

Changes in soil nitrogen cycling in a northern temperate forest ecosystem during succession

Nitrogen (N) transformations in forest soils are fundamentally important to plant and microbial N nutrition and the N balance of forest ecosystems, but changes in the patterns and rates of N transformations during forest succession are poorly understood. In order to better understand how soil N cycling changes during ecosystem succession, we analyzed four years of soil N cycling measurements in a 90-year-old secondary forest undergoing dieback of early-successional, dominant canopy trees. We expected that tree mortality would decrease root biomass, leading to increased soil NH4+ availability, and that these changes would prompt fundamental shifts in the N cycle such as the initiation of significant nitrification and increased cycling of oxidized N compounds in gas phase and soil solution. As expected, indices of soil NH4+ and NO3− availability increased with successional stage (defined as the proportion of dead trees), and were negatively correlated with the amount of fine root biomass. However, the standing amount of fine root biomass was not affected by tree mortality; increased soil NH4+ and NO3− availability therefore more likely resulted from successional increases in N-mineralization than decreases in root N uptake. Nitrification (as indicated by NO efflux as a proxy) increased due to elevated substrate (NH4+) availability, and the soil solution NO3− concentration increased as a result. Soil N2O efflux was not affected by succession, nor was it related to other N cycling parameters. Collectively, these results indicate that recent successional advancement has accelerated soil N cycling and shifted the N economy of this ecosystem towards greater importance of NO3−.

Model‐data assimilation of multiple phenological observations to constrain and predict leaf area index

Our limited ability to accurately simulate leaf phenology is a leading source of uncertainty in models of ecosystem carbon cycling. We evaluate if continuously updating canopy state variables with observations is beneficial for predicting phenological events. We employed ensemble adjustment Kalman filter (EAKF) to update predictions of leaf area index (LAI) and leaf extension using tower-based photosynthetically active radiation (PAR) and moderate resolution imaging spectrometer (MODIS) data for 2002-2005 at Willow Creek, Wisconsin, USA, a mature, even-aged, northern hardwood, deciduous forest. The ecosystem demography model version 2 (ED2) was used as the prediction model, forced by offline climate data. EAKF successfully incorporated information from both the observations and model predictions weighted by their respective uncertainties. The resulting. estimate reproduced the observed leaf phenological cycle in the spring and the fall better than a parametric model prediction. These results indicate that during spring the observations contribute most in determining the correct bud-burst date, after which the model performs well, but accurately modeling fall leaf senesce requires continuous model updating from observations. While the predicted net ecosystem exchange (NEE) of CO2 precedes tower observations and unassimilated model predictions in the spring, overall the prediction follows observed NEE better than the model alone. Our results show state data assimilation successfully simulates the evolution of plant leaf phenology and improves model predictions of forest NEE.

Coupling Fine-Scale Root and Canopy Structure Using Ground-Based Remote Sensing

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy LiDAR (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence (i.e., correlation in the frequency of spatial variation) at multiple spatial scales ≤10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more becoming even vertically as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5–4 m, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Our study highlights the potential, and limitations, for fusing LiDAR and radar technologies to quantitatively couple above- and belowground ecosystem structure.

Characterizing forest structure variations across an intact tropical peat dome using field samplings and airborne LiDAR

Tropical peat swamp forests (PSF) are one of the most carbon dense ecosystems on the globe and are experiencing substantial natural and anthropogenic disturbances. In this study, we combined direct field sampling and airborne LiDAR to empirically quantify forest structure and aboveground live biomass (AGB) across a large, intact tropical peat dome in Northwestern Borneo. Moving up a 4 m elevational gradient, we observed increasing stem density but decreasing canopy height, crown area, and crown roughness. These findings were consistent with hypotheses that nutrient and hydrological dynamics co-influence forest structure and stature of the canopy individuals, leading to reduced productivity towards the dome interior. Gap frequency as a function of gap size followed a power law distribution with a shape factor (λ) of 1.76 ± 0.06. Ground-based and dome-wide estimates of AGB were 217.7 ± 28.3 Mg C/ha and 222.4 ± 24.4 Mg C/ha, respectively, which were higher than previously reported AGB for PSF and tropical forests in general. However, dome-wide AGB estimates were based on height statistics, and we found the coefficient of variation on canopy height was only 0.08, three times less than stem diameter measurements, suggesting LiDAR height metrics may not be a robust predictor of AGB in tall tropical forests with dense canopies. Our structural characterization of this ecosystem advances the understanding of the ecology of intact tropical peat domes and factors that influence biomass density and landscape-scale spatial variation. This ecological understanding is essential to improve estimates of forest carbon density and its spatial distribution in PSF and to effectively model the effects of disturbance and deforestation in these carbon dense ecosystems.

Anthropogenic and biogenic CO2 fluxes in the Boston urban region

Significance Cities are taking a leading role in US efforts to reduce greenhouse gas emissions, and require traceable methods to assess the efficacy of their efforts. In this study, we developed an inverse model framework that quantified emissions in the Boston urban region over 16 months and is capable of detecting changes in emissions of greater than 18%. We show that a detailed representation of urban biological fluxes and knowledge of the spatial and temporal distribution of emissions are essential for accurate modeling of annual CO2 emissions. Across the globe, it is possible to quantifiably assess the efficacy of mitigation efforts by developing frameworks similar to the one we present here for Boston. With the pending withdrawal of the United States from the Paris Climate Accord, cities are now leading US actions toward reducing greenhouse gas emissions. Implementing effective mitigation strategies requires the ability to measure and track emissions over time and at various scales. We report CO2 emissions in the Boston, MA, urban region from September 2013 to December 2014 based on atmospheric observations in an inverse model framework. Continuous atmospheric measurements of CO2 from five sites in and around Boston were combined with a high-resolution bottom-up CO2 emission inventory and a Lagrangian particle dispersion model to determine regional emissions. Our model−measurement framework incorporates emissions estimates from submodels for both anthropogenic and biological CO2 fluxes, and development of a CO2 concentration curtain at the boundary of the study region based on a combination of tower measurements and modeled vertical concentration gradients. We demonstrate that an emission inventory with high spatial and temporal resolution and the inclusion of urban biological fluxes are both essential to accurately modeling annual CO2 fluxes using surface measurement networks. We calculated annual average emissions in the Boston region of 0.92 kg C·m−2·y−1 (95% confidence interval: 0.79 to 1.06), which is 14% higher than the Anthropogenic Carbon Emissions System inventory. Based on the capability of the model−measurement approach demonstrated here, our framework should be able to detect changes in CO2 emissions of greater than 18%, providing stakeholders with critical information to assess mitigation efforts in Boston and surrounding areas.