How linear features alter predator movement and the functional response

In areas of oil and gas exploration, seismic lines have been reported to alter the movement patterns of wolves (Canis lupus). We developed a mechanistic first passage time model, based on an anisotropic elliptic partial differential equation, and used this to explore how wolf movement responses to seismic lines influence the encounter rate of the wolves with their prey. The model was parametrized using 5 min GPS location data. These data showed that wolves travelled faster on seismic lines and had a higher probability of staying on a seismic line once they were on it. We simulated wolf movement on a range of seismic line densities and drew implications for the rate of predator–prey interactions as described by the functional response. The functional response exhibited a more than linear increase with respect to prey density (type III) as well as interactions with seismic line density. Encounter rates were significantly higher in landscapes with high seismic line density and were most pronounced at low prey densities. This suggests that prey at low population densities are at higher risk in environments with a high seismic line density unless they learn to avoid them

Towards quantifying uncertainty in predictions of Amazon 'dieback'.

This is the final version of the article. It first appeared from The Royal Society via http://dx.doi.org/10.1098/rstb.2007.0028Simulations with the Hadley Centre general circulation model (HadCM3), including carbon cycle model and forced by a 'business-as-usual' emissions scenario, predict a rapid loss of Amazonian rainforest from the middle of this century onwards. The robustness of this projection to both uncertainty in physical climate drivers and the formulation of the land surface scheme is investigated. We analyse how the modelled vegetation cover in Amazonia responds to (i) uncertainty in the parameters specified in the atmosphere component of HadCM3 and their associated influence on predicted surface climate. We then enhance the land surface description and (ii) implement a multilayer canopy light interception model and compare with the simple 'big-leaf' approach used in the original simulations. Finally, (iii) we investigate the effect of changing the method of simulating vegetation dynamics from an area-based model (TRIFFID) to a more complex size- and age-structured approximation of an individual-based model (ecosystem demography). We find that the loss of Amazonian rainforest is robust across the climate uncertainty explored by perturbed physics simulations covering a wide range of global climate sensitivity. The introduction of the refined light interception model leads to an increase in simulated gross plant carbon uptake for the present day, but, with altered respiration, the net effect is a decrease in net primary productivity. However, this does not significantly affect the carbon loss from vegetation and soil as a consequence of future simulated depletion in soil moisture; the Amazon forest is still lost. The introduction of the more sophisticated dynamic vegetation model reduces but does not halt the rate of forest dieback. The potential for human-induced climate change to trigger the loss of Amazon rainforest appears robust within the context of the uncertainties explored in this paper. Some further uncertainties should be explored, particularly with respect to the representation of rooting depth

Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses

Accurately predicting the response of Amazonia to climate change is important for predicting climate change across the globe. Changes in multiple climatic factors simultaneously result in complex non-linear ecosystem responses, which are difficult to predict using vegetation models. Using leaf- and canopy-scale observations, this study evaluated the capability of five vegetation models (Community Land Model version 3.5 coupled to the Dynamic Global Vegetation model – CLM3.5–DGVM; Ecosystem Demography model version 2 – ED2; the Joint UK Land Environment Simulator version 2.1 – JULES; Simple Biosphere model version 3 – SiB3; and the soil–plant–atmosphere model – SPA) to simulate the responses of leaf- and canopy-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, but all the models were consistent with the prediction that GPP would be higher if tropical forests were 5 °C cooler than current ambient temperatures. There was greater model–data consistency in the response of net ecosystem exchange (NEE) to changes in temperature than in the response to temperature by net photosynthesis (An), stomatal conductance (gs) and leaf area index (LAI). Modelled canopy-scale fluxes are calculated by scaling leaf-scale fluxes using LAI. At the leaf-scale, the models did not agree on the temperature or magnitude of the optimum points of An, Vcmax or gs, and model variation in these parameters was compensated for by variations in the absolute magnitude of simulated LAI and how it altered with temperature. Across the models, there was, however, consistency in two leaf-scale responses: (1) change in An with temperature was more closely linked to stomatal behaviour than biochemical processes; and (2) intrinsic water use efficiency (IWUE) increased with temperature, especially when combined with drought. These results suggest that even up to fairly extreme temperature increases from ambient levels (+6 °C), simulated photosynthesis becomes increasingly sensitive to gs and remains less sensitive to biochemical changes. To improve the reliability of simulations of the response of Amazonian rainforest to climate change, the mechanistic underpinnings of vegetation models need to be validated at both leaf- and canopy-scales to improve accuracy and consistency in the quantification of processes within and across an ecosystem.This research was enabled by a grant from the Andes–Amazon Initiative of The Gordon and Betty Moore Foundation. L. Rowland gratefully acknowledges financial support from the Natural Environment Research Council (UK) for a NERC PhD studentship, and NERC grant NE/J011002/1; PM also acknowledges support from ARC FT110100457

Exploring High Aspect Ratio Gold Nanotubes as Cytosolic Agents: Structural Engineering and Uptake into Mesothelioma Cells.

The generation of effective and safe nanoagents for biological applications requires their physicochemical characteristics to be tunable, and their cellular interactions to be well characterized. Here, the controlled synthesis is developed for preparing high-aspect ratio gold nanotubes (AuNTs) with tailorable wall thickness, microstructure, composition, and optical characteristics. The modulation of optical properties generates AuNTs with strong near infrared absorption. Surface modification enhances dispersibility of AuNTs in aqueous media and results in low cytotoxicity. The uptake and trafficking of these AuNTs by primary mesothelioma cells demonstrate their accumulation in a perinuclear distribution where they are confined initially in membrane-bound vesicles from which they ultimately escape to the cytosol. This represents the first study of the cellular interactions of high-aspect ratio 1D metal nanomaterials and will facilitate the rational design of plasmonic nanoconstructs as cytosolic nanoagents for potential diagnosis and therapeutic applications.BLF-Papworth Fellowship from the British Lung Foundation and the Victor Dahdaleh Foundation