Publications

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.

In this paper, we extend traditional methods of spatial statistics to study spatiotemporal correlations between two different point processes. After introducing the methodology, we apply this analysis to a particular case study of interest in ecology, the interaction between damage by a particular forest pest (western spruce budworm (Choristoneura occidentalis)) and forest fires. Our analysis, which covers parts of British Columbia in the 26-year period from 1970 to 1995, indicates that areas affected by budworm infestation have a significantly decreased risk of forest fire for the 7 years following the infestation. Conversely, forest fires decrease the risk of infestation for at least 6 years after the fire. These temporal correlations extend over a spatial range of at least 25 km. Our study rejects the common assumption that insect infestation necessarily results in increased fire risk. This case study illustrates the utility of point process modeling and spatial statistics to understanding ecosystem dynamics extending over both space and time.

The Ecosystem Demography (ED) model was parameterized with ecological, forest inventory, and historical land use observations in an intensively managed, wetland-rich forested landscape in the upper midwest United States. Model results were evaluated against a regional network of eddy covariance flux towers and analyzed about the roles of disturbance, forest management, and CO2 fertilization. The model captured modern regional vegetation structure with worst comparison in wetlands. Model net ecosystem exchange of CO2 ( NEE) was highly correlated on monthly (r(2) = 0.65) and annual (r(2) = 0.53) timescales to 7 years of NEE observed at a 396-m-tall eddy covariance (EC) tower and to 2 years of growing season NEE from 13 regional stand-scale EC sites of varying cover and age (r(2) = 0.64). Model summer NEE had higher than observed net uptake for the tall tower and mature hardwood sites, and correlation to growing season ecosystem respiration at these sites was poor (r(2) = 0.09). Exclusion of forestry led to overestimation of aboveground living plant biomass accumulation by 109% between two forest inventory cycles (1996-2004). On the long-term ( 200 years), forestry significantly altered ecosystem cover and age, and increased NEE by 32%. CO2 fertilization over that time period increased NEE by 93% owing to a doubling of plant density. While the model showed that harvest and afforestation had smaller impacts on NEE than CO2 increase, the former were still significant and require consideration when making future NEE predictions or scaling plot-level data to regional and global flux estimates.

Atmospheric measurements and land-based inventories imply that terrestrial ecosystems in the northern hemisphere are taking up significant amounts of anthropogenic cabon dioxide (CO2) emissions; however, there is considerable disagreement about the causes of this uptake, and its expected future trajectory. In this paper, we use the ecosystem demography (ED) model to quantify the contributions of disturbance history, CO2 fertilization and climate variability to the past, current, and future terrestrial carbon fluxes in the Eastern United States. The simulations indicate that forest regrowth following agricultural abandonment accounts for uptake of 0.11 Pg C yr(-1) in the 1980s and 0.15 Pg C yr(-1) in the 1990s, and regrowth following forest harvesting accounts for an additional 0.1 Pg C yr(-1) of uptake during both these decades. The addition of CO2 fertilization into the model simulations increases carbon uptake rates to 0.38 Pg C yr(-1) in the 1980s and 0.47 Pg C yr(-1) in the 1990s. Comparisons of predicted aboveground carbon uptake to regional-scale forest inventory measurements indicate that the model's predictions in the absence of CO2 fertilization are 14% lower than observed, while in the presence of CO2 fertilization, predicted uptake rates are 28% larger than observed. Comparable results are obtained from comparisons of predicted total Net Ecosystem Productivity to the carbon fluxes observed at the Harvard Forest flux tower site and in model simulations free-air CO2 enrichment (FACE) experiments. These results imply that disturbance history is the principal mechanism responsible for current carbon uptake in the Eastern United States, and that conventional biogeochemical formulations of plant growth overestimate the response of plants to rising CO2 levels. Model projections out to 2100 imply that the carbon uptake arising from forest regrowth will increasingly be dominated by forest regrowth following harvesting. Consequently, actual carbon storage declines to near zero by the end of the 21st century as the forest regrowth that has occurred since agricultural abandonment comes into equilibrium with the landscape's new disturbance regime. Incorporating interannual climate variability into the model simulations gives rise to large interannual variation in regional carbon fluxes, indicating that long-term measurements are necessary to detect the signature of processes that give rise to long-term uptake and storage.

Since the decomposition rate of soil organic carbon (SOC) varies as a function of environmental conditions, global climate change is expected to alter SOC decomposition dynamics, and the resulting changes in the amount of CO2 emitted from soils will feedback onto the rate at which climate change occurs. While this soil feedback is expected to be significant because the amount of SOC is substantially more than the amount of carbon in the atmosphere, the environmental dependencies of decomposition at global scales that determine the magnitude of the soil feedback have remained poorly characterized. In this study, we address this issue by fitting a mechanistic decomposition model to a global dataset of SOC, optimizing the model's temperature and moisture dependencies to best match the observed global distribution of SOC. The results of the analysis indicate that the temperature sensitivity of decomposition at global scales (Q(10)=1.37) is significantly less than is assumed by many terrestrial ecosystem models that directly apply temperature sensitivity from small-scale studies, and that the maximal rate of decomposition occurs at higher moisture values than is assumed by many models. These findings imply that the magnitude of the soil decomposition feedback onto rate of global climate change will be less sensitive to increases in temperature, and modeling of temperature and moisture dependencies of SOC decomposition in global-scale models should consider effects of scale.

In just 20 years, the field of biosphere-atmosphere interactions has gone from a nascent discipline to a central area of modern climate change research. The development of terrestrial biosphere models that predict the responses of ecosystems to climate and increasing CO2 levels has highlighted several mechanisms by which changes in ecosystem composition and function might alter regional and global climate. However, results from empirical studies suggest that ecosystem responses can differ markedly from the predictions of terrestrial biosphere models. As I discuss here, the challenge now is to connect terrestrial biosphere models to empirical ecosystem measurements. Only by systematically evaluating the predictions of terrestrial biosphere models against suites of ecosystem observations and experiments measurements will a true predictive science of the biosphere be achieved.

We examined the historical record of mountain pine beetle (Dendroctonus ponderosae Hopkins) activity within Yellowstone National Park, Wyoming, for the 25-years period leading up to the 1988 Yellowstone fires (1963-86) to determine how prior beetle activity and the resulting tree mortality affected the spatial pattern of the 1988 Yellowstone fires. To obtain accurate estimates of our model parameters, we used a Markov chain Monte Carlo method to account for the high degree of spatial autocorrelation inherent to forest fires. Our final model included three statistically significant variables: drought, aspect, and sustained mountain pine beetle activity in the period 1972-75. Of the two major mountain pine beetle outbreaks that preceded the 1988 fires, the earlier outbreak (1972-75) was significantly correlated with the burn pattern, whereas the more recent one (1980-83) was not. Although regional drought and high winds were responsible for the large scale of this event, the analysis indicates that mountain pine beetle activity in the mid-1970s increased the odds of burning in 1988 by 11% over unaffected areas. Although relatively small in magnitude, this effect, combined with the effects of aspect and spatial variation in drought, had a dramatic impact on the spatial pattern of burned and unburned areas in 1988.

Patterns of space-use by individuals are fundamental to the ecology of animal populations influencing their social organization, mating systems, demography and the spatial distribution of prey and competitors. To date, the principal method used to analyse the underlying determinants of animal home range patterns has been resource selection analysis (RSA), a spatially implicit approach that examines the relative frequencies of animal relocations in relation to landscape attributes. In this analysis, we adopt an alternative approach, using a series of mechanistic home range models to analyse observed patterns of territorial space-use by coyote packs in the heterogeneous landscape of Yellowstone National Park. Unlike RSAs, mechanistic home range models are derived from underlying correlated random walk models of individual movement behaviour, and yield spatially explicit predictions for patterns of space-use by individuals. As we show here, mechanistic home range models can be used to determine the underlying determinants of animal home range patterns, incorporating both movement responses to underlying landscape heterogeneities and the effects of behavioural interactions between individuals. Our analysis indicates that the spatial arrangement of coyote territories in Yellowstone is determined by the spatial distribution of prey resources and an avoidance response to the presence of neighbouring packs. We then show how the fitted mechanistic home range model can be used to correctly predict observed shifts in the patterns of coyote space-use in response to perturbation.

Recent investigations have shown how chance, long-range dispersal events can allow tree populations to migrate rapidly in response to changes in climate. However, this apparent solution to Reid's paradox applies solely within the context of single species models, while the rapid migration rates seen in pollen records occurred within multispecies communities. Ecologists are therefore presented with a new challenge: reconciling the macroscopic dynamics of spread seen in the pollen record with the rules and interactions governing plant community assembly. A case that highlights this issue is the rapid spread of Beech during the Holocene into a landscape already dominated by a close competitor, Hemlock. In this study, we analyse a simple model of plant community assembly incorporating competition for space and dispersal dynamics, showing how, even when a species is capable of rapid migration into an empty landscape, the presence of an ecologically similar competitor causes Reid's paradox to re-emerge because of the dramatic slowing effect of competitive interactions on a species' rate of spread. We then show how the answer to the question of how tree species dispersed rapidly into occupied landscapes may lie in secondary interactions with host-specific pathogens and parasites. Inclusion of host-specific pathogens into the simple community assembly model illustrates how tree species undergoing range expansions can temporarily outstrip specialist predators, giving rise to a transient Jansen-Connell effect, in which the invader acts as temporary 'super-species' that spreads rapidly into communities already occupied by competitors at rates consistent with those observed in the paleo-record.