Substantial uncertainty surrounds how forest ecosystems will respond to the simultaneous impacts of multiple global change drivers. Long-term forest dynamics are sensitive to changes in tree mortality rates; however, we lack an understanding of the relative importance of the factors that affect tree mortality across different spatial and temporal scales. We used the US Forest Service Forest Inventory and Analysis database to evaluate the drivers of tree mortality for eastern temperate forest at the individual-level across spatial scales from tree to landscape to region. We investigated 13 covariates in four categories: climate, air pollutants, topography, and stand characteristics. Overall, we found that tree mortality was most sensitive to stand characteristics and air pollutants. Different functional groups also varied considerably in their sensitivity to environmental drivers. This research highlights the importance of considering the interactions among multiple global change agents in shaping forest ecosystems.
Insights into vegetation and aboveground biomass dynamics within terrestrial ecosystems have come almost exclusively from ground-based forest inventories that are limited in their spatial extent. Lidar and synthetic-aperture Radar are promising remote-sensing-based techniques for obtaining comprehensive measurements of forest structure at regional to global scales. In this study we investigate how Lidar-derived forest heights and Radar-derived aboveground biomass can be used to constrain the dynamics of the ED2 terrestrial biosphere model. Four-year simulations initialized with Lidar and Radar structure variables were compared against simulations initialized from forest-inventory data and output from a long-term potential-vegtation simulation. Both height and biomass initializations from Lidar and Radar measurements significantly improved the representation of forest structure within the model, eliminating the bias of too many large trees that arose in the potential-vegtation-initialized simulation. The Lidar and Radar initializations decreased the proportion of larger trees estimated by the potential vegetation by approximately 20-30%, matching the forest inventory. This resulted in improved predictions of ecosystem-scale carbon fluxes and structural dynamics compared to predictions from the potential-vegtation simulation. The Radar initialization produced biomass values that were 75% closer to the forest inventory, with Lidar initializations producing canopy height values closest to the forest inventory. Net primary production values for the Radar and Lidar initializations were around 6-8% closer to the forest inventory. Correcting the Lidar and Radar initializations for forest composition resulted in improved biomass and basal-area dynamics as well as leaf-area index. Correcting the Lidar and Radar initializations for forest composition and fine-scale structure by combining the remote-sensing measurements with ground-based inventory data further improved predictions, suggesting that further improvements of structural and carbon-flux metrics will also depend on obtaining reliable estimates of forest composition and accurate representation of the fine-scale vertical and horizontal structure of plant canopies.
While the mechanistic links between animal movement and population dynamics are ecologically obvious, it is much less clear when knowledge of animal movement is a prerequisite for understanding and predicting population dynamics. GPS and other technologies enable detailed tracking of animal location concurrently with acquisition of landscape data and information on individual physiology. These tools can be used to refine our understanding of the mechanistic links between behaviour and individual condition through 'spatially informed' movement models where time allocation to different behaviours affects individual survival and reproduction. For some species, socially informed models that address the movements and average fitness of differently sized groups and how they are affected by fission-fusion processes at relevant temporal scales are required. Furthermore, as most animals revisit some places and avoid others based on their previous experiences, we foresee the incorporation of long-term memory and intention in movement models. The way animals move has important consequences for the degree of mixing that we expect to find both within a population and between individuals of different species. The mixing rate dictates the level of detail required by models to capture the influence of heterogeneity and the dynamics of intra-and interspecific interaction.
Observations of regional net ecosystem exchange (NEE) of CO(2) for 1997-2007 were analyzed for climatic controls on interannual variability (IAV). Quantifying IAV of regional (10(4)-10(6) km(2)) NEE over long time periods is key to understanding potential feedbacks between climate and the carbon cycle. Four independent techniques estimated monthly regional NEE for 10(4) km(2) in a spatially heterogeneous temperate-boreal transition region of the north central United States, centered on the Park Falls, Wisconsin, United States, National Oceanic and Atmospheric Administration tall tower site. These techniques included two bottom-up methods, based on flux tower upscaling and forest inventory based demographic modeling, respectively, and two top-down methods, based on tall tower equilibrium boundary layer budgets and tracer-transport inversion, respectively. While all four methods revealed a moderate carbon sink, they diverged significantly in magnitude. Coherence of relative magnitude and variability of NEE anomalies was strong across the methods. The strongest coherence was a trend of declining carbon sink since 2002. Most climatic controls were not strongly correlated with IAV. Significant controls on IAV were those related to hydrology, such as water table depth, and atmospheric CO(2). Weaker relationships were found with phenological controls such as autumn soil temperature. Hydrologic relationships were strongest with a 1 year lag, potentially highlighting a previously unrecognized predictor of IAV in this region. These results highlight a need for continued development of techniques to estimate regional IAV and incorporation of hydrologic cycling into couple carbon-climate models.
In a previous paper, we developed an analytical clumped two-stream model (ACTS) of canopy radiative transfer from an analytical geometric-optical and radiative transfer (GORT) scheme (Ni-Meister et al., 2010). The ACTS model accounts for clumping of foliage and the influence of trunks in vegetation canopies for modeling of photosynthesis, radiative fluxes and surface albedo in dynamic global vegetation models (DGVMs), and particularly for the Ent Dynamic Global Terrestrial Ecosystem Model (DGTEM). This study evaluates the gap probability and transmittance estimates from the ACTS model by comparing the modeled results with ground-based data, as well as with the original full GORT model and a layered Beer's law scheme. The ground data used in this study include vertical profile measurements of incident photosynthetically active radiation (PAR) in (1) mixed deciduous forests in Morgan-Monroe State Forest, IN, USA, (2) coniferous forests in central Canada, (3) mixed deciduous forests in Harvard Forest, MA, and (4) ground lidar measurements of the canopy gap fraction in woodland in Australia.The model comparisons with these measurements demonstrate that the ACTS model achieves better or similar performance compared to the full GORT and the layered Beer's law schemes with regard to agreements with field measurements and computational cost. The ACTS model has excellent accuracy and flexibility to model the canopy gap probability and transmittance for various forest scenarios. Also, it has advantages relative to the currently widely used two-stream scheme through better radiation estimation for photosynthesis by accounting for the impact of both vertical and horizontal structure heterogeneity of complex vegetation on radiative transfer. Currently the ACTS is being implemented in Ent and will be further tested for how it improves surface energy balance and carbon flux estimates. (C) 2010 Elsevier B.V. All rights reserved.
Recent advances in animal tracking and telemetry technology have allowed the collection of location data at an ever-increasing rate and accuracy, and these advances have been accompanied by the development of new methods of data analysis for portraying space use, home ranges and utilization distributions. New statistical approaches include data-intensive techniques such as kriging and nonlinear generalized regression models for habitat use. In addition, mechanistic home-range models, derived from models of animal movement behaviour, promise to offer new insights into how home ranges emerge as the result of specific patterns of movements by individuals in response to their environment. Traditional methods such as kernel density estimators are likely to remain popular because of their ease of use. Large datasets make it possible to apply these methods over relatively short periods of time such as weeks or months, and these estimates may be analysed using mixed effects models, offering another approach to studying temporal variation in space-use patterns. Although new technologies open new avenues in ecological research, our knowledge of why animals use space in the ways we observe will only advance by researchers using these new technologies and asking new and innovative questions about the empirical patterns they observe.
Pathogen and pest outbreaks are recognized as key processes in the dynamics of Western forest ecosystems, yet the spatial patterns of stress and mortality are often complex and difficult to describe in an explicit spatial context, especially when considering the concurrent effects of multiple agents. Blister rust, a fungal pathogen, and mountain pine beetle, an insect pest, are two dominant sources of stress and mortality to high-altitude whitebark pine within the Greater Yellowstone Ecosystem (GYE). In whitebark pine populations infested with blister rust or mountain pine beetle, the shift from green to red needles at the outer-most branches is an early sign of stress and infestation. In this analysis, we investigated a method that combines field surveys with a remote sensing classification and spatial analysis to differentiate the effects of these two agents of stress and mortality within whitebark pine. Hyperspectral remotely sensed images from the airborne HyMap sensor were classifled to determine the locations of stress and mortality in whitebark pine crowns through sub-pixel mixture-tuned matched-filter analysis in three areas of the GYE in September 2000 and July 2006. Differences in the spatial pattern of blister rust and mountain pine beetle infestation allowed us to separate areas dominated by mountain pine beetle versus blister rust by examining changes in the spatial scale of significant stress and mortality clusters computed by the Ripley's K algorithm. At two field sites the distance between clusters of whitebark pine stress and mortality decreased from 2000 to 2006, indicating domination by the patchy spatial pattern of blister rust infestation. At another site, the distance between significant stress and mortality clusters increased from 2000 to 2006, indicating that the contiguous pattern of mountain pine beetle infestation was the primary source of disturbance. Analysis of these spatial stress and mortality patterns derived from remote sensing yields insight to the relative importance of blister rust and mountain pine beetle dynamics in the landscape. (C) 2009 Elsevier Inc. All rights reserved.
For more than a century, scientists have recognized the importance of vegetation structure in understanding forest dynamics. Now future satellite missions such as Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI) hold the potential to provide unprecedented global data on vegetation structure needed to reduce uncertainties in terrestrial carbon dynamics. Here, we briefly review the uses of data on vegetation structure in ecosystem models, develop and analyze theoretical models to quantify model-data requirements, and describe recent progress using a mechanistic modeling approach utilizing a formal scaling method and data on vegetation structure to improve model predictions. Generally, both limited sampling and coarse resolution averaging lead to model initialization error, which in turn is propagated in subsequent model prediction uncertainty and error. In cases with representative sampling, sufficient resolution, and linear dynamics, errors in initialization tend to compensate at larger spatial scales. However, with inadequate sampling, overly coarse resolution data or models, and nonlinear dynamics, errors in initialization lead to prediction error. A robust model-data framework will require both models and data on vegetation structure sufficient to resolve important environmental gradients and tree-level heterogeneity in forest structure globally.
The hemlock woolly adelgid (HWA Adelges tsugae Annand) is ail introduced insect pest that threatens to decimate eastern hemlock (Tsuga canadensis (L.) Carriere) populations. In this study, we used the ecosystem demography model in conjunction with a stochastic model of HWA spread to predict the impact of HWA infestation oil the current and future forest composition, Structure, and carbon (C) dynamics in the eastern United States. The spread model predicted that oil average the hemlock stands south and east of the Great Lakes would be infested by 2015, southern Michigan would be reached by 2020, and northeastern Minnesota by 2030. For the period 2000-2040, the ecosystem demography model predicted a mean reduction of 0.011 Pg C.year(-1) (Pg C = 10(15) g C), in 8% decrease, in the uptake of carbon from eastern United States forests as a result of HWA-caused mortality, followed by an increased uptake of 0.015 Pg C.year(-1) (a 12% increase) in the period 2040-2100, as the area recovers from the loss of hemlock. Overall. we conclude that while locally severe, HWA infestation is unlikely to have a significant impact oil the regional patterns of carbon fluxes, given that eastern hemlock represents a limited friction of the standing biomass of eastern forests and that it has relatively low productivity compared with the tree species that are likely to replace it.
We assess the significance of high-frequency variability of environmental parameters (sunlight, precipitation, temperature) for the structure and function of terrestrial ecosystems under current and future climate. We examine the influence of hourly, daily, and monthly variance using the Ecosystem Demography model version 2 in conjunction with the long-term record of carbon fluxes measured at Harvard Forest. We find that fluctuations of sunlight and precipitation are strongly and nonlinearly coupled to ecosystem function, with effects that accumulate through annual and decadal timescales. Increasing variability in sunlight and precipitation leads to lower rates of carbon sequestration and favors broad-leaved deciduous trees over conifers. Temperature variability has only minor impacts by comparison. We also find that projected changes in sunlight and precipitation variability have important implications for carbon storage and ecosystem structure and composition. Based on Intergovernmental Panel on Climate Change model estimates for changes in high-frequency meteorological variability over the next 100 years, we expect that terrestrial ecosystems will be affected by changes in variability almost as much as by changes in mean climate. We conclude that terrestrial ecosystems are highly sensitive to high-frequency meteorological variability, and that accurate knowledge of the statistics of this variability is essential for realistic predictions of ecosystem structure and functioning.
Boreal forests are under strong influences from climate change, and alterations in forest dynamics will have significant impacts on global climate-biosphere feedback as well as local to regional conservation and resource management. To understand the mechanisms of forest dynamics and to assess the fate of boreal forests, simulation studies should be based on plant ecophysiological responses onto environmental conditions. In central Canadian boreal forests, local geomorphology created by past glacial activities often generates a mosaic of very distinctive forest types. On sandy hilltop of a glacial till, due to limitations in moisture availability and short fire return intervals, drought-tolerant and fire-adapted jack pine usually becomes the dominant species. On mesic and nutrient-rich slopes, fast-growing and resource-demanding trembling aspen forms mixed forests with coniferous species. In bottomland, black spruce, slowly growing but tolerant species, is often the only species that can survive to the adult stage. These three very distinctive forest types often occur within a scale of 10 m. Simulation models of boreal forests should be able to reproduce this heterogeneity in forest structure and composition as an emergent property of plant ecophysiological responses to varying environmental properties. In this study, a process-based forest dynamics model, ecosystem demography model version 1.0, is used to mechanically reproduce the landscape heterogeneity due to edaphic variations. First, boreal tree species of northern Manitoba, Canada, are parameterized according to field observations, and, to explicitly capture interactions among tree saplings, allometric equations based on diameter at height of 0.15 m, instead of the conventional breast height of 1.37 m, is parameterized. Then, soil moisture regime and nutrient concentrations are statistically incorporated from a dataset. The resultant simulation successfully reproduces the distinctive forest dynamics influenced by the edaphic heterogeneity. The sequences of succession and the trajectories of forest development are generally consistent with the field observations. The differences in resource availability are the essential control on equilibrium values of total forest leaf area index. Next, to show the effect of anthropogenic atmospheric changes, changes in temperature and CO2 concentrations are studied by a set of factorial experiments. The magnitude of CO2 fertilization is largely affected by soil fertility. The temperature rise will increase the length of growing season, but can have a negative impact on forest growth by increasing aridity and autotrophic respiration. Overall, the boreal forest responses to climate change are complex due to the inherent edaphic variations and ecophysiological responses.
Modern animal movement modelling derives from two traditions. Lagrangian models, based on random walk behaviour, are useful for multi-step trajectories of single animals. Continuous Eulerian models describe expected behaviour, averaged over stochastic realizations, and are usefully applied to ensembles of individuals. We illustrate three modern research arenas. (i) Models of homerange formation describe the process of an animal 'settling down', accomplished by including one or more focal points that attract the animal's movements. (ii) Memory-based models are used to predict how accumulated experience translates into biased movement choices, employing reinforced random walk behaviour, with previous visitation increasing or decreasing the probability of repetition. (iii) Levy movement involves a step-length distribution that is over-dispersed, relative to standard probability distributions, and adaptive in exploring new environments or searching for rare targets. Each of these modelling arenas implies more detail in the movement pattern than general models of movement can accommodate, but realistic empiric evaluation of their predictions requires dense locational data, both in time and space, only available with modern GPS telemetry.
Insights into how terrestrial ecosystems affect the Earth's response to changes in climate and rising atmospheric CO(2) levels rely heavily on the predictions of terrestrial biosphere models (TBMs). These models contain detailed mechanistic representations of biological processes affecting terrestrial ecosystems; however, their ability to simultaneously predict field-based measurements of terrestrial vegetation dynamics and carbon fluxes has remained largely untested. In this study, we address this issue by developing a constrained implementation of a new structured TBM, the Ecosystem Demography model version 2 (ED2), which explicitly tracks the dynamics of fine-scale ecosystem structure and function. Carbon and water flux measurements from an eddy-flux tower are used in conjunction with forest inventory measurements of tree growth and mortality at Harvard Forest (42.5 degrees N, 72.1 degrees W) to estimate a number of important but weakly constrained model parameters. Evaluation against a decade of tower flux and forest dynamics measurements shows that the constrained ED2 model yields greatly improved predictions of annual net ecosystem productivity, carbon partitioning, and growth and mortality dynamics of both hardwood and conifer trees. The generality of the model formulation is then evaluated by comparing the model's predictions against measurements from two other eddy-flux towers and forest inventories of the northeastern United States and Quebec. Despite the markedly different composition throughout this region, the optimized model realistically predicts observed patterns of carbon fluxes and tree growth. These results demonstrate how TBMs parameterized with field-based measurements can provide quantitative insight into the underlying biological processes governing ecosystem composition, structure, and function at larger scales.
Mechanistic home range models are important tools in modeling animal dynamics in spatially complex environments. We introduce a class of stochastic models for animal movement in a habitat of varying preference. Such models interpolate between spatially implicit resource selection analysis (RSA) and advection-diffusion models, possessing these two models as limiting cases. We find a closed-form solution for the steady-state (equilibrium) probability distribution u* using a factorization of the redistribution operator into symmetric and diagonal parts. How space use is controlled by the habitat preference function w depends on the characteristic width of the animals' redistribution kernel: when the redistribution kernel is wide relative to variation in w, u*. w, whereas when it is narrow relative to variation in w, u* alpha w(2). In addition, we analyze the behavior at discontinuities in w which occur at habitat type boundaries, and simulate the dynamics of space use given two-dimensional prey-availability data, exploring the effect of the redistribution kernel width. Our factorization allows such numerical simulations to be done extremely fast; we expect this to aid the computationally intensive task of model parameter fitting and inverse modeling.
Historically, northern peatlands have functioned as a carbon sink, sequestering large amounts of soil organic carbon, mainly due to low decomposition in cold, largely waterlogged soils(1,2). The water table, an essential determinant of soil-organic-carbon dynamics(3-10), interacts with soil organic carbon. Because of the high water-holding capacity of peat and its low hydraulic conductivity, accumulation of soil organic carbon raises the water table, which lowers decomposition rates of soil organic carbon in a positive feedback loop. This two-way interaction between hydrology and biogeochemistry has been noted(3,5-8), but is not reproduced in process-based simulations(9). Here we present simulations with a coupled physical-biogeochemical soil model with peat depths that are continuously updated from the dynamic balance of soil organic carbon. Our model reproduces dynamics of shallow and deep peatlands in northern Manitoba, Canada, on both short and longer timescales. We find that the feedback between the water table and peat depth increases the sensitivity of peat decomposition to temperature, and intensifies the loss of soil organic carbon in a changing climate. In our long-term simulation, an experimental warming of 4 degrees C causes a 40% loss of soil organic carbon from the shallow peat and 86% from the deep peat. We conclude that peatlands will quickly respond to the expected warming in this century by losing labile soil organic carbon during dry periods.
In the three decades since its introduction, resource selection analysis (RSA) has become a widespread method for analyzing spatial patterns of animal relocations obtained from telemetry studies. Recently, mechanistic home range models have been proposed as an alternative framework for studying patterns of animal space-use. In contrast to RSA models, mechanistic home range models are derived from underlying mechanistic descriptions of individual movement behavior and yield spatially explicit predictions for patterns of animal space-use. In addition, their mechanistic underpinning means that, unlike RSA, mechanistic home range models can also be used to predict changes in space-use following perturbation. In this paper, we develop a formal reconciliation between these two methods of home range analysis, showing how differences in the habitat preferences of individuals give rise to spatially explicit patterns of space-use. The resulting unified framework combines the simplicity of resource selection analysis with the spatially explicit and predictive capabilities of mechanistic home range models.
Growth rings of a tree are simultaneously affected by various environmental constraints, including regional factors such as climate fluctuations and also local, gap-scale dynamics such as competition and stochastic mortality of neighbor trees. Although these local effects are often discarded by dendroclimatologists as random variation, the dendroecological trends may provide valuable information on past forest dynamics. Since dendroecological trends arising from local stand dynamics often have medium-term frequencies with persistence of several years to a few decades, it is Usually difficult to separate local, gap-scale forcings from regional, medium-frequency forcings such as El Nino Southern Oscillation or North Atlantic Oscillation. Moreover, conventional dendroecological practices have failed to analyze the continuously changing medium frequency trends. In this study, a continuous index of medium-frequency dendrochronological trends was developed, by generalizing previous analytical methods that evaluate relative changes using moving averages. This method was then tested against a tree ring dataset from a site with a known history of release and suppression due to a hurricane disturbance. To quantify the effects of local gap dynamics against the regional, often climatic effects, increments cores of black spruce (Picea mariana) were sampled from boreal forests in Saskatchewan, Canada, using a stratified sampling design. Assuming that regional forcings affect trees in the given stand homogeneously, the relative effect of stochastic heterogeneity within stand was quantified. The results closely agreed with conventional dendrochronological observations. In closed-canopy stands, stochastic local effects explained 12.9-35.4% of the variation in tree ring widths, because interactions between neighbor trees were likely to be intense. In open-canopy stands, on the other hand, the proportion of explained variance was 1.4-10.2%, reflecting the less-intense local tree interactions in low-density stands. These advancements in statistical analysis and study design will help ecologists and paleoclimatologists to objectively evaluate the effects of climate fluctuations, relative to the effects of local, ecological interactions. Moreover, forest managers can apply concepts of filtering medium-frequency trends to assess release and suppression caused by forest management practices, such as selective cutting and forest thinning. (C) 2008 Elsevier B.V. All rights reserved.