Publications

Read More: http://www.esajournals.org/doi/abs/10.1890/120111

Considerable uncertainty surrounds the fate of Amazon rainforests in response to climate change. Here, carbon (C) flux predictions of five terrestrial biosphere models (Community Land Model version 3.5 (CLM3.5), Ecosystem Demography model version 2.1 (ED2), Integrated BIosphere Simulator version 2.6.4 (IBIS), Joint UK Land Environment Simulator version 2.1 (JULES) and Simple Biosphere model version 3 (SiB3)) and a hydrodynamic terrestrial ecosystem model (the Soil-Plant-Atmosphere (SPA) model) were evaluated against measurements from two large-scale Amazon drought experiments. Model predictions agreed with the observed C fluxes in the control plots of both experiments, but poorly replicated the responses to the drought treatments. Most notably, with the exception of ED2, the models predicted negligible reductions in aboveground biomass in response to the drought treatments, which was in contrast to an observed c. 20% reduction at both sites. For ED2, the timing of the decline in aboveground biomass was accurate, but the magnitude was too high for one site and too low for the other. Three key findings indicate critical areas for future research and model development. First, the models predicted declines in autotrophic respiration under prolonged drought in contrast to measured increases at one of the sites. Secondly, models lacking a phenological response to droughtintroduced bias in the sensitivity of canopy productivity and respiration to drought. Thirdly, the phenomenological water-stress functions used by the terrestrial biosphere models to represent the effects of soil moisture on stomatal conductance yielded unrealistic diurnal and seasonal responses to drought.

The assessment of disturbance effects on wildlife and resulting mitigation efforts are founded on edge-effect theory. According to the classical view, the abundance of animals affected by human disturbance should increase monotonically with distance from disturbed areas to reach a maximum at remote locations. Here we show that distance-dependent movement taxis can skew abundance distributions toward disturbed areas. We develop an advection-diffusion model based on basic movement behavior commonly observed in animal populations and parameterize the model from observations on radio-collared caribou in a boreal ecosystem. The model predicts maximum abundance at 3.7 km from cutovers and roads. Consistently, aerial surveys conducted over 161,920 km(2) showed that the relative probability of caribou occurrence displays nonmonotonic changes with the distance to anthropogenic features, with a peak occurring at 4.5 km away from these features. This aggregation near disturbed areas thus provides the predators of this top-down-controlled, threatened herbivore species with specific locations to concentrate their search. The edge-effect theory developed here thus predicts that human activities should alter animal distribution and food web properties differently than anticipated from the current paradigm. Consideration of such nonmonotonic response to habitat edges may become essential to successful wildlife conservation.

The coming of age of global positioning system telemetry, in conjunction with recent theoretical innovations for formulating quantitative descriptions of how different ecological forces and behavioral mechanisms shape patterns of animal space use, has led to renewed interest and insight into animal home-range patterns. This renaissance is likely to continue as a result of ongoing synergies between these empirical and theoretical advances. In this article I review key developments that have occurred over the past decade that are furthering our understanding of the ecology of animal home ranges. I then outline what I perceive as important future directions for furthering our ability to understand and predict mammalian home-range patterns. Interesting directions for future research include improved insights into the environmental and social context of animal movement decisions and resulting patterns of space use; quantifying the role of memory in animal movement decisions; and examining the relevance of these advances in our understanding of animal movement behavior and space use to questions concerning the demography and abundance of animal populations.

Terrestrial biosphere models are important tools for diagnosing both the current state of the terrestrial carbon cycle and forecasting terrestrial ecosystem responses to global change. While there are a number of ongoing assessments of the short-term predictive capabilities of terrestrial biosphere models using flux-tower measurements, to date there have been relatively few assessments of their ability to predict longer term, decadal-scale biomass dynamics. Here, we present the results of a regional-scale evaluation of the Ecosystem Demography version 2 (ED2)-structured terrestrial biosphere model, evaluating the model's predictions against forest inventory measurements for the northeast USA and Quebec from 1985 to 1995. Simulations were conducted using a default parametrization, which used parameter values from the literature, and a constrained model parametrization, which had been developed by constraining the model's predictions against 2 years of measurements from a single site, Harvard Forest (42.5 degrees N, 72.1 degrees W). The analysis shows that the constrained model parametrization offered marked improvements over the default model formulation, capturing large-scale variation in patterns of biomass dynamics despite marked differences in climate forcing, land-use history and species-composition across the region. These results imply that data-constrained parametrizations of structured biosphere models such as ED2 can be successfully used for regional-scale ecosystem prediction and forecasting. We also assess the model's ability to capture sub-grid scale heterogeneity in the dynamics of biomass growth and mortality of different sizes and types of trees, and then discuss the implications of these analyses for further reducing the remaining biases in the model's predictions.

Satellite-based observations indicate that seasonal patterns in canopy greenness and productivity in the Amazon are negatively correlated with precipitation, with increased greenness occurring during the dry months. Flux tower measurements indicate that the canopy greening that occurs during the dry season is associated with increases in net ecosystem productivity (NEP) and evapotranspiration (ET). Land surface and terrestrial biosphere model simulations for the region have predicted the opposite of these observed patterns, with significant declines in greenness, NEP, and ET during the dry season. In this study, we address this issue mainly by developing an empirically constrained, light-controlled phenology submodel within the Ecosystem Demography model version 2 (ED2). The constrained ED2 model with a suite of field observations shows markedly improved predictions of seasonal ecosystem dynamics, more accurately capturing the observed patterns of seasonality in water, carbon, and litter fluxes seen at the Tapajos National Forest, Brazil (2.86 degrees S, 54.96 degrees W). Long-term simulations indicate that this light-controlled phenology increases the resilience of Amazon forest NEP to interannual variability in climate forcing.

The response of tropical forests to global climate variability and change remains poorly understood. Results from long-term studies of permanent forest plots have reported different, and in some cases opposing trends in tropical forest dynamics. In this study, we examined changes in tree growth rates at four long-term permanent tropical forest research plots in relation to variation in solar radiation, temperature and precipitation. Temporal variation in the stand-level growth rates measured at five-year intervals was found to be positively correlated with variation in incoming solar radiation and negatively related to temporal variation in night-time temperatures. Taken alone, neither solar radiation variability nor the effects of night-time temperatures can account for the observed temporal variation in tree growth rates across sites, but when considered together, these two climate variables account for most of the observed temporal variability in tree growth rates. Further analysis indicates that the stand-level response is primarily driven by the responses of smaller-sized trees (less than 20 cm in diameter). The combined temperature and radiation responses identified in this study provide a potential explanation for the conflicting patterns in tree growth rates found in previous studies.

The introduction of nonnative pathogens is altering the scale, magnitude, and persistence of forest disturbance regimes in the western United States. In the high-altitude whitebark pine (Pinus albicaulis) forests of the Greater Yellowstone Ecosystem (GYE), white pine blister rust (Cronartium ribicola) is an introduced fungal pathogen that is now the principal cause of tree mortality in many locations. Although blister rust eradication has failed in the past, there is nonetheless substantial interest in monitoring the disease and its rate of progression in order to predict the future impact of forest disturbances within this critical ecosystem.This study integrates data from five different field-monitoring campaigns from 1968 to 2008 to create a blister rust infection model for sites located throughout the GYE. Our model parameterizes the past rates of blister rust spread in order to project its future impact on high-altitude whitebark pine forests. Because the process of blister rust infection and mortality of individuals occurs over the time frame of many years, the model in this paper operates on a yearly time step and defines a series of whitebark pine infection classes: susceptible, slightly infected, moderately infected, and dead. In our analysis, we evaluate four different infection models that compare local vs. global density dependence on the dynamics of blister rust infection. We compare models in which blister rust infection is: (1) independent of the density of infected trees, (2) locally density-dependent, (3) locally density-dependent with a static global infection rate among all sites, and (4) both locally and globally density-dependent. Model evaluation through the predictive loss criterion for Bayesian analysis supports the model that is both locally and globally density-dependent. Using this best-fit model, we predicted the average residence times for the four stages of blister rust infection in our model, and we found that, on average, whitebark pine trees within the GYE remain susceptible for 6.7 years, take 10.9 years to transition from slightly infected to moderately infected, and take 9.4 years to transition from moderately infected to dead. Using our best-fit model, we project the future levels of blister rust infestation in the GYE at critical sites over the next 20 years.

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.