Strong and recurring seasonality revealed within stream diatom assemblages.

Improving stream water quality in agricultural landscapes is an ecological priority and a legislative duty for many governments. Ecosystem health can be effectively characterised by organisms sensitive to water quality changes such as diatoms, single-celled algae that are a ubiquitous component of stream benthos. Diatoms respond within daily timescales to variables including light, temperature, nutrient availability and flow conditions that result from weather and land use characteristics. However, little consideration has been given to the ecological dynamics of diatoms through repeated seasonal cycles when assessing trajectories of stream function, even in catchments actively managed to reduce human pressures. Here, six years of monthly diatom samples from three independent streams, each receiving differing levels of diffuse agricultural pollution, reveal robust and repeated seasonal variation. Predicted seasonal changes in climate-related variables and anticipated ecological impacts must be fully captured in future ecological and water quality assessments, if the apparent resistance of stream ecosystems to pollution mitigation measures is to be better understood.

Major agricultural changes required to mitigate phosphorus losses under climate change.

Phosphorus losses from land to water will be impacted by climate change and land management for food production, with detrimental impacts on aquatic ecosystems. Here we use a unique combination of methods to evaluate the impact of projected climate change on future phosphorus transfers, and to assess what scale of agricultural change would be needed to mitigate these transfers. We combine novel high-frequency phosphorus flux data from three representative catchments across the UK, a new high-spatial resolution climate model, uncertainty estimates from an ensemble of future climate simulations, two phosphorus transfer models of contrasting complexity and a simplified representation of the potential intensification of agriculture based on expert elicitation from land managers. We show that the effect of climate change on average winter phosphorus loads (predicted increase up to 30% by 2050s) will be limited only by large-scale agricultural changes (e.g., 20-80% reduction in phosphorus inputs).The impact of climate change on phosphorus (P) loss from land to water is unclear. Here, the authors use P flux data, climate simulations and P transfer models to show that only large scale agricultural change will limit the effect of climate change on average winter P loads in three catchments across the UK.

Long-term responses of rainforest erosional systems at different spatial scales to selective logging and climatic change.

Long-term (21-30 years) erosional responses of rainforest terrain in the Upper Segama catchment, Sabah, to selective logging are assessed at slope, small and large catchment scales. In the 0.44 km(2) Baru catchment, slope erosion measurements over 1990-2010 and sediment fingerprinting indicate that sediment sources 21 years after logging in 1989 are mainly road-linked, including fresh landslips and gullying of scars and toe deposits of 1994-1996 landslides. Analysis and modelling of 5-15 min stream-suspended sediment and discharge data demonstrate a reduction in storm-sediment response between 1996 and 2009, but not yet to pre-logging levels. An unmixing model using bed-sediment geochemical data indicates that 49 per cent of the 216 t km(-2) a(-1) 2009 sediment yield comes from 10 per cent of its area affected by road-linked landslides. Fallout (210)Pb and (137)Cs values from a lateral bench core indicate that sedimentation rates in the 721 km(2) Upper Segama catchment less than doubled with initially highly selective, low-slope logging in the 1980s, but rose 7-13 times when steep terrain was logged in 1992-1993 and 1999-2000. The need to keep steeplands under forest is emphasized if landsliding associated with current and predicted rises in extreme rainstorm magnitude-frequency is to be reduced in scale.

Quantitative assessment of soil parameter (KD and TC) estimation using DGT measurements and the 2D DIFS model.

Diffusive gradients in thin films (DGT) is a dynamic, in situ measuring technique that can be used to supply diverse information on concentrations and behaviour of solutes. When deployed in soils and sediments, quantitative interpretation of DGT measurements requires the use of a numerical model. An improved version of the DGT induced fluxes in soils and sediments model (DIFS), working in two dimensions (2D DIFS), was used to investigate the accuracy with which DGT measurements can be used to estimate the distribution coefficient for labile metal (KD) and the response time of the soil to depletion (TC). The 2D DIFS model was used to obtain values of KD and TC for Cd, Zn and Ni in three different soils, which were compared to values determined previously using 1D DIFS for these cases. While the 1D model was shown to provide reasonable estimates of KD, the 2D model refined the estimates of the kinetic parameters. Desorption rate constants were shown to be similar for all three metals and lower than previously thought. Calculation of an error function as KD and TC are systematically varied showed the spread of KD and TC values that fit the experimental data equally well. These automatically generated error maps reflected the quality of the data and provided an appraisal of the accuracy of parameter estimation. They showed that in some cases parameter accuracy could be improved by fitting the model to a sub-set of data.

Theoretical comparison of how soil processes affect uptake of metals by diffusive gradients in thinfilms and plants.

The theoretical basis for using measurements of metal uptake by the technique of diffusive gradients in thinfilms (DGT) to mimic processes in soils that affect uptake of metals by plants is examined. The uptake of metals by plants and DGT were compared conceptually and quantitatively by using the classic Barber model of plant uptake and the DIFS (DGT-induced fluxes in soils) model of uptake by DGT. For most metals and plants considered, uptake fluxes were similar to those induced by DGT using the most common gel layer thicknesses of 0.2 to 2 mm. Consequently DGT perturbs the chemical equilibrium of metals in the soil solution and between soil solution and solid phase, to a similar extent to plants, and therefore induces a similar balance in supply by diffusion and by release from the solid phase. DIFS was used to show that desorption kinetics, which are not considered by the plant uptake model, are likely important for uptake when the capacity of the soil solid phase is large. Model calculations showed that mass flow into a plant root would only contribute appreciably to the total flux of metal under circumstances when the solid phase reservoir of metal was very low. Generally, however, DGT is likely to emulate supply processes from the soil that govern uptake of metal by plants. Exceptions are likely to be found in poorly buffered soils (typically sandy and/or low pH), and at very high concentrations of metals in soil solution, such that the soil solution concentration at the plant root interface is higher than the Michaelis-Menten constant (Km).

Mathematical modelling of the reaction latency. Part III: A model of avoidance reaction latency and avoidance learning.

The linear-dynamic-stochastic model of a reaction latency as applied to avoidance experiment is presented. Reactions are classified on the basis of the model into following classes: escape, avoidance, late avoidance, three types of inter-trial responses and "no reaction". The experimental latency distribution is split into latency distributions of the escape, avoidance and late avoidance responses, providing a new insight into latency distribution. The results of fitting the model to latency measurements obtained in the avoidance conditioning experiment are presented. The same processes of the parameter changes as in the escape conditioning are discovered, one causing a latency to decrease and the other causing a latency to increase during learning. The first process affects a latency stronger than the second and, consequently, the latency decreases during learning. The second process is responsible for a decay of inter trial-responses during experiment. The value of the correlation coefficient between the threshold of avoidance reaction and the threshold of escape reaction was also estimated. Negative values of this coefficient were obtained, therefore, on the average, the greater the avoidance reaction threshold the smaller the escape one. In the course of learning the correlation coefficient tends to be equal to - 1, i.e., as a result of training, both thresholds became dependent in a functional (non-random) way. This result may provide an objective index of the "state of learning". The model provides a new tool for analysis of results of latency-based experiments.