Anderegg WRL, Martinez-Vilalta J, Cailleret M, Camarero JJ, Ewers BE, Galbraith D, Gessler A, Grote R, Huang C-ying, Levick SR, et al. When a Tree Dies in the Forest: Scaling Climate-Driven Tree Mortality to Ecosystem Water and Carbon Fluxes. Ecosystems. 2016. art_10.1007_s10021-016-9982-1.pdf
Castanho ADA, D.Galbraith, K.Zhang, M.T.Coe, M.H.Costa, Moorcroft P. Changing Amazon biomass and the role of atmospheric CO2 concentration, climate, and land use. Global Biogeochemical Cycles. 2016;30 :18-39. castanho_et_al-2016-global_biogeochemical_cycles.pdf
Levine NM, et. al. Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change. PNAS. 2016;113 (3) :793-797. pnas-2016-levine-793-7.pdf
Schimel D, Sellers P, III BM, Chatterjee A, Baker D, Berry J, Bowman K, Ciais P, Crisp D, Crowell S, et al. Observing the Carbon-Climate System. arXiv:1604.02106. 2015. schmiel_et_al_arxiv_2015.pdf
Groeve JD, de Weghe NV, Ranc N, Neutens T, Ometto L, Rota-Stabelli O, Cagnacci F. Extracting spatio-temporal patterns in animal trajectories: an ecological application of sequence analysis methods. Methods in Ecology and Evolution. 2015;7 (3) :369-379. Publisher's Version de_groeve_et_al_2015-methods_in_ecology_and_evolution.pdf
Damiani ML, Issa H, Fotino G, Hachem F, Ranc N, Cagnacci F. MigrO: a plug-in for the analysis of individual mobility behavior based on the stay region model. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2015 :96. Publisher's Version damiani_et_al_2015.pdf
Swann ALS, Longo M, Knox RG, Lee E, Moorcroft PR. Future deforestation in the Amazon and consequences for South American climate. Agricultural and Forest Meteorology. 2015;214–215 :12-24. 1-s2.0-s0168192315002130-main.pdf
Knox RG, Longo M, Swann ALS, Zhang K, Levine NM, Moorcroft PR, Bras RL. Hydrometeorological effects of historical land-conversion in an ecosystem-atmosphere model of Northern South America. Hydrology and Earth System Sciences. 2015;19 :241-273. hess-19-241-2015.pdf
Kim Y, et. al. Variability of phenology and fluxes of water and carbon with observed and simulated soil moisture in the Ent Terrestrial Biosphere Model. Geosci. Model Dev. 2015;8 :3837–3865. gmd-8-3837-2015.pdf
Zhang K, de Castanho AAD, Galbraith DR, Moghim S, Levine NM, Bras RL, Coe MT, Costa MH, Malhi Y, Longo M, et al. The fate of Amazonian ecosystems over the comingcentury arising from changes in climate, atmospheric CO2,and land use. Global Change Biology. 2015;doi: 10.1111/gcb.12903. zhang_et_al-2015-global_change_biology.pdf
Rowland L, Harper A, Christoffersen BO, Galbraith DR, Imbuzeiro HMA, Powell TL, Doughty C, Levine NM, Malhi Y, Saleska SR, et al. Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses. Geosci. Model Dev. Discuss.,. 2015;7 :7823-7859.Abstract

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 Angs relationship when temperature changes and drought were introduced simultaneously. The inconsistencies in the Angs 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.

Antonarakis AS, Munger JW, Moorcroft PR. Imaging spectroscopy- and lidar-derived estimates of canopy composition and structure to improve predictions of forest carbon fluxes and ecosystem dynamics. Geophysical Research Letters. 2014;41 :2535-2542.Abstract

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

Knox RG, Longo M, Swann ALS, Zhang K, Levine NM, Moorcroft PR, Bras RL. Effects of land-conversion in a biosphere–atmosphere model of Northern South America – Part 1: Regional differences in hydrometeorology. Hydrology and Earth System Sciences Discussions. 2013;10 :15295-15335. hessd-10-15295-2013.pdf
Knox RG, Longo M, Swann ALS, Zhang K, Levine NM, Moorcroft PR, Bras RL. Effects of land-conversion in a biosphere–atmosphere model of Northern South America – Part 2: Case studies on the mechanisms of differential hydrometeorology. Hydrology and Earth System Sciences Discussions. 2013;10 :15337-15373. hessd-10-15337-2013.pdf
Coe MT, Marthews TR, Costa MH, Galbraith DR, Greenglass NL, Imbuzeiro HMA, Levine NM, Malhi Y, Moorcroft PR, Muza MN, et al. Deforestation and climate feedbacks threaten the ecological integrity of south -southeastern Amazonia. Phil. Trans. R. Soc. B. 2013;368 :20120155.
Coe MT, Marthews TR, Costa MH, Galbraith DR, Greenglass NL, Imbuzeiro HMA, Levine NM, Malhi Y, Moorcroft PR, Muza MN, et al. Deforestation and climate feedbacks threaten the ecological integrity of south-southeastern Amazonia. Phil. Trans. R. Soc., B. 2013;368. coe_etal_2013.pdf
Schimel DS, Asner GP, Moorcroft PR. Observing changing ecological diversity in the Anthropocene. Frontiers in Ecology and the Environment. 2013;11 :129–137. Publisher's VersionAbstract
Observing changing ecological diversity in the Anthropocene
David S Schimel1*Gregory P Asner2, and Paul Moorcroft3

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

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Powell TL, Galbraith DR, Christoffersen BO, Harper A, Imbuzeiro HMA, Rowland L, Almeida S, Brando PM, da Costa ACL, Costa MH, et al. Confronting model predictions of carbon fluxes with measurements of Amazon forests subjected to experimental drought. New Phytol. 2013;200 :350-364.Abstract

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.

Fortin D, Buono PL, Fortin A, Courbin N, Gingras CT, Moorcroft PR, Courtois R, Dussault C. Movement Responses of Caribou to Human-Induced Habitat Edges Lead to Their Aggregation near Anthropogenic Features. Am Nat. 2013;181 :827-836.Abstract

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.

Moorcroft PR. Mechanistic approaches to understanding and predicting mammalian space use: recent advances, future directions. Journal of Mammalogy. 2012;93 :903-916. Publisher's VersionAbstract

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.