A review and classification of the existing models of cyanobacteria

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling of cyanobacteria in freshwaters is an important tool for understanding their population dynamics and predicting bloom occurrence in lakes and rivers. In this paper existing key models of cyanobacteria are reviewed, evaluated and classified. Two major groups emerge: deterministic mathematical and artificial neural network models. Mathematical models can be further subcategorized into those models concerned with impounded water bodies and those concerned with rivers. Most existing models focus on a single aspect such as the growth of transport mechanisms, but there are a few models which couple both.

Correlating Bayesian date estimates with climatic events and domestication using a bovine case study

The tribe Bovini contains a number of commercially and culturally important species, such as cattle. Understanding their evolutionary time scale is important for distinguishing between post-glacial and domestication-associated population expansions, but estimates of bovine divergence times have been hindered by a lack of reliable calibration points. We present a Bayesian phylogenetic analysis of 481 mitochondrial D-loop sequences, including 228 radiocarbon-dated ancient DNA sequences, using a multi-demographic coalescent model. By employing the radiocarbon dates as internal calibrations, we co-estimate the bovine phylogeny and divergence times in a relaxed-clock framework. The analysis yields evidence for significant population expansions in both taurine and zebu cattle, European aurochs and yak clades. The divergence age estimates support domestication-associated expansion times (less than 12 kyr) for the major haplogroups of cattle. We compare the molecular and palaeontological estimates for the Bison-Bos divergence.

Changes in the extent of glaciers in the Eastern Terskey Alatoo, the Central Tien-Shan, in response to climatic fluctuations in between the end of the 19th and the beginning of the 21st century

Changes in the extent of glaciers and rates of glacier termini retreat in the eastern Terskey-Alatoo Range, the Tien Shan Mountains, Central Asia have been evaluated using the remote sensing techniques. Changes in the extent of 335 glaciers between the end of the Little Ice Age (LIA; mid-19th century), 1990 and 2003 have been estimated through the delineation of glacier outlines and the LIA moraine positions on the Landsat TM and ASTER imagery for 1990 and 2003 respectively. By 2003, the glacier surface area had decreased by 19% of the LIA value, which constitutes a 76 km(2) reduction in glacier surface area. Mapping of 109 glaciers using the 1965 1:25,000 maps revealed that glacier surface area decreased by 12.6% of the 1965 value between 1965 and 2003. Detailed mapping of 10 glaciers using historical maps and aerial photographs from the 1943-1977 period, has enabled glacier extent variations over the 20th century to be identified with a higher temporal resolution. Glacial retreat was slow in the early 20th century but increased considerably between 1943 and 1956 and then again after 1977. The post-1990 period has been marked by the most rapid glacier retreat since the end of the LIA. The observed changes in the extent of glaciers are in line with the observed climatic warming. The regional weather stations have revealed a strong climatic warming during the ablation season since the 1950s at a rate of 0.02-0.03 degrees Ca-1. At the higher elevations in the study area represented by the Tien Shan meteorological station, the summer warming was accompanied by negative anomalies in annual precipitation in the 1990s enhancing glacier retreat. However, trends in precipitation in the post-1997 period cannot be evaluated due to the change in observational practices at this station. Neither station in the study area exhibits significant long-term trends in precipitation. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

Glacier shrinkage and climatic change in the Russian Altai from the mid-20th century: An assessment using remote sensing and PRECIS regional climate model

This paper examines changes in the surface area of glaciers in the North and South Chuya Ridges, Altai Mountains in 1952-2004 and their links with regional climatic variations. The glacier surface areas for 2004 were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Data from the World Glacier Inventory (WGI)dating to 1952 and aerial photographs from 1952 were used to estimate the changes. 256 glaciers with a combined area of 253±5.1 km2 have been identified in the region in 2004. Estimation of changes in extent of 126 glaciers with the individual areas not less than 0.5 km2 in 1952 revealed a 19.7±5.8% reduction. The observed glacier retreat is primarily driven by an increase in summer temperatures since the 1980s when air temperatures were increasing at a rate of 0.10 - 0.13oC a-1 at the glacier tongue elevation. The regional climate projections for A2 and B2 CO2 emission scenarios developed using PRECIS regional climate model indicate that summer temperatures will increase in the Altai in 2071-2100 by 6-7oC and 3-5oC respectively in comparison with 1961-1990 while annual precipitation will increase by 15% and 5%. The length of the ablation season will extend from June-August to the late April – early October. The projected increases in precipitation will not compensate for the projected warming and glaciers will continue to retreat in the 21st century under both B2 and A2 scenarios.

Soft topographic map for clustering and classification of bacteria

In this work a new method for clustering and building a topographic representation of a bacteria taxonomy is presented. The method is based on the analysis of stable parts of the genome, the so-called “housekeeping genes”. The proposed method generates topographic maps of the bacteria taxonomy, where relations among different type strains can be visually inspected and verified. Two well known DNA alignement algorithms are applied to the genomic sequences. Topographic maps are optimized to represent the similarity among the sequences according to their evolutionary distances. The experimental analysis is carried out on 147 type strains of the Gammaprotebacteria class by means of the 16S rRNA housekeeping gene. Complete sequences of the gene have been retrieved from the NCBI public database. In the experimental tests the maps show clusters of homologous type strains and present some singular cases potentially due to incorrect classification or erroneous annotations in the database.

The climatic effects of the direct injection of water vapour into the stratosphere by large volcanic eruptions

We describe a novel mechanism that can significantly lower the amplitude of the climatic response to certain large volcanic eruptions and examine its impact with a coupled ocean-atmosphere climate model. If sufficiently large amounts of water vapour enter the stratosphere, a climatically significant amount of water vapour can be left over in the lower stratosphere after the eruption, even after sulphate aerosol formation. This excess stratospheric humidity warms the tropospheric climate, and acts to balance the climatic cooling induced by the volcanic aerosol, especially because the humidity anomaly lasts for a period that is longer than the residence time of aerosol in the stratosphere. In particular, northern hemisphere high latitude cooling is reduced in magnitude. We discuss this mechanism in the context of the discrepancy between the observed and modelled cooling following the Krakatau eruption in 1883. We hypothesize that moist coignimbrite plumes caused by pyroclastic flows travelling over ocean rather than land, resulting from an eruption close enough to the ocean, might provide the additional source of stratospheric water vapour.

Emergence of Chenopodium album and Stellaria media of different origins under different climatic conditions

The emergence behaviour of weed species in relation to cultural and meteorological events was studied. Dissimilarities between populations in dormancy and germination ecology, between-year maturation conditions and seed quality and burial site climate all contribute to potentially unpredictable variability. Therefore, a weed emergence data set was produced for weed seeds of Stellaria media and Chenopodium album matured and collected from three populations (Italy, Sweden and UK). The seeds were collected in two consecutive seasons (1999 and 2000) and subsequently buried in the autumn of the same year of maturation in eight contrasting climatic locations throughout Europe and the USA. The experiment sought to explore and explain differences between the three populations in their emergence behaviour. Evidence was demonstrated of synchrony in the timing of the emergence of different populations of a species at a given burial site. The relative magnitudes of emergence from the three populations at a given burial site in a given year were generally similar across all the burial sites in the study. The resulting data set was also used to construct a simple weed emergence model, which was tested for its application to the range of different burial environments and populations. The study demonstrated the possibility of using a simple thermal time-based model to describe part of the emergence behaviour across different burial sites, seed populations and seasons, and a simple winter chilling relationship to adjust for the magnitude of the flush of emergence at a given burial site. This study demonstrates the possibility of developing robust generic models for simple predictions of emergence timing across populations.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Taxonomical vs. functional responses of bee communities to fire in two contrasting climatic regions

Valuable insights into mechanisms of community responses to environmental change can be gained by analysing in tandem the variation in functional and taxonomic composition along environmental gradients. We assess the changes in species and functional trait composition (i.e. dominant traits and functional diversity) of diverse bee communities in contrasting fire-driven systems in two climatic regions: Mediterranean (scrub habitats in Israel) and temperate (chestnut forests in southern Switzerland). In both climatic regions, there were shifts in species diversity and composition related to post-fire age. In the temperate region, functional composition responded markedly to fire; however, in the Mediterranean, the taxonomic response to fire was not matched by functional replacement. These results suggest that greater functional stability to fire in the Mediterranean is achieved by replacement of functionally similar species (i.e. functional redundancy) which dominate under different environmental conditions in the heterogeneous landscapes of the region. In contrast, the greater functional response in the temperate region was attributed to a more rapid post-fire vegetation recovery and shorter time-window when favourable habitat was available relative to the Mediterranean. Bee traits can be used to predict the functional responses of bee communities to environmental changes in habitats of conservation importance in different regions with distinct disturbance regimes. However, predictions cannot be generalized from one climatic region to another where distinct habitat configurations occur.