Accurately predicting the response of Amazonia to climate change is important for predicting changes across the globe. However, changes in multiple climatic factors simultaneously may result in complex non-linear responses, which are difficult to predict using vegetation models. Using leaf and canopy scale observations, this study evaluated the capability of five vegetation models (CLM3.5, ED2, JULES, SiB3, and SPA) to simulate the responses of canopy and leaf scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation. There was greater model–data consistency in the response of net ecosystem exchange to changes in temperature, than in the response to temperature of leaf area index (LAI), net photosynthesis (An) and stomatal conductance (gs). Modelled canopy scale fluxes are calculated by scaling leaf scale fluxes to LAI, and therefore in this study similarities in modelled ecosystem scale responses to drought and temperature were the result of inconsistent leaf scale and LAI responses among models.
Across the models, the response of An to temperature was more closely linked to stomatal behaviour than biochemical processes. Consequently all the models predicted that GPP would be higher if tropical forests were 5 °C colder, closer to the model optima for gs. There was however no model consistency in the response of the An–gs relationship when temperature changes and drought were introduced simultaneously. The inconsistencies in the An–gs relationships amongst models were caused by to non-linear model responses induced by simultaneous drought and temperature change. To improve the reliability of simulations of the response of Amazonian rainforest to climate change the mechanistic underpinnings of vegetation models need more complete validation to improve accuracy and consistency in the scaling of processes from leaf to canopy.
Citation: Rowland, L., Harper, A., Christoffersen, B. O., Galbraith, D. R., Imbuzeiro, H. M. A., Powell, T. L., Doughty, C., Levine, N. M., Malhi, Y., Saleska, S. R., Moorcroft, P. R., Meir, P., and Williams, M.: Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses, Geosci. Model Dev. Discuss., 7, 7823-7859, doi:10.5194/gmdd-7-7823-2014, 2014.
The composition and structure of vegetation are key attributes of ecosystems, affecting their current and future carbon, water, and energy fluxes. Information on these attributes has traditionally come from ground-based inventories of the plant canopy within small sample plots. Here we show how imaging spectrometry and waveform lidar can be used to provide spatially comprehensive estimates of forest canopy composition and structure that can improve the accuracy of the carbon flux predictions of a size-structured terrestrial biosphere model, reducing its root-mean-square errors from 85%-104% to 37%-57%. The improvements are qualitatively and quantitatively similar to those obtained from simulations initialized with ground measurements and approximately double the estimated rate of ecosystem carbon uptake as compared to a potential vegetation simulation. These results suggest that terrestrial biosphere model simulations can utilize modern remote-sensing data on vegetation composition and structure to improve their predictions of the current and near-term future functioning of the terrestrial biosphere.Key PointsPredictions of forest change hampered by errors in current model formulations Remote Sensing can derive fine-scale information on the current ecosystem state Regional carbon fluxes can be constrained using remote sensing derived info
Observing changing ecological diversity in the Anthropocene
David SSchimel1*, Gregory PAsner2, and PaulMoorcroft3
As the world enters the Anthropocene – a new geologic period, defined by humanity's massive impact on the planet – the Earth's rapidly changing environment is putting critical ecosystem services at risk. To understand and forecast how ecosystems will change over the coming decades, scientists will require an understanding of the sensitivity of species to environmental change. The current distribution of species and functional groups provides valuable information about the performance of various species in different environments. However, when the rate of environmental change is high, information inherent in the ranges of many species will disappear, since that information exists only under more or less steady-state conditions. The amount of information about species' relationships to climate declines as their distributions move farther from steady state. New remote-sensing technologies can map the chemical and structural traits of plant canopies and will allow for the inference of traits and, in many cases, species' ranges. Current satellite remote-sensing data can only produce relatively simple classifications, but new techniques will produce data with dramatically higher biological information content.
1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA
2Department of Global Ecology, Carnegie Institution for Science, Stanford, CA;
3Organismic and Evolutionary Biology Department, Harvard University, Cambridge, MA
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.