Species and Environmental Diversity of Fish of the Northern Dvina River Basin

The general characteristics of ichthyofauna from the Northern Dvina River are obtained. Based on the results of ichthyological surveys in 1984–2012, as well as bibliographical and archival data, the present composition of the fish community is presented with an accounting of introduced and self-colonized species. The taxonomic status of fish and their belonging to various faunal complexes is considered. In the ecological aspect, the belonging of fish to different ecological groups differing in the feeding and natural reproduction (spawning substrate, methods of spawning, and spawning periods) is distinguished.     

Framing of Geoengineering Affects Support for Climate Change Mitigation

The growing recognition that climate change mitigation alone will be inadequate has led scientists and policymakers to discuss climate geoengineering. An experiment with a US sample found, contrary to previous research, that reading about geoengineering did not reduce conservatives’ skepticism about the existence of anthropogenic climate change. Moreover, depending on how it is framed, geoengineering can reduce support for mitigation among both conservatives and non-conservatives. When geoengineering is framed as a major solution, people worry less about climate change, leading to reduced mitigation support. When framed as disastrous, people perceived geoengineering as riskier, also leading to a decrease in mitigation support. A more moderate framing of geoengineering as a partial solution is less susceptible to moral hazard effects. Overall, results suggest that geoengineering will not lessen political polarization over anthropogenic climate change, and could undercut support for mitigation efforts. Instead, framing geoengineering as a small piece to solving a big puzzle seems to be the best strategy to weaken this potential moral hazard.     

Ambrosia artemisiifolia in an Anthropogenic Landscape of Gomel Polesye

Russian Journal of Ecology -     

Isoprene emissions and climate

Biogenic volatile organic compounds (BVOCs) play an important role in atmospheric chemistry and the carbon cycle. Isoprene is quantitatively the most important of the non-methane BVOCs (NMBVOCs), with an annual emission of about 400–600 TgC; about 90% of this is emitted by terrestrial plants. Incorporating a mechanistic treatment of isoprene emissions within land-surface schemes has recently become a focus for the modelling community, the aim being to quantify the potential magnitude of associated climate feedbacks. However, these efforts are hampered by major uncertainties about why plants emit isoprene and the relative importance of different environmental controls on isoprene emission. The availability and reliability of observations of isoprene fluxes from different types of vegetation is limited, and this also imposes constraints on model development. Nevertheless, progress is being made towards the development of mechanistic models of isoprene emission which, in conjunction with atmospheric chemistry models, will ultimately allow improved quantification of the feedbacks between the terrestrial biosphere and climate under past and future climate states.     

Wintertime size distribution of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in the urban environment: Street- vs rooftop-level measurements

The size distribution of ambient air particles and associated organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and organochlorine pesticides (OCPs) including hexachlorocyclohexanes (HCHs), DDT and metabolites, etc., was investigated at a traffic-impacted site of Thessaloniki, Greece. Investigation took place during wintertime of 2006 at two heights above ground: at the street level (1.5 m) and at the rooftop level (15 m). Size-resolved samples (<0.95 μm, 0.95–1.5 μm, 1.5–3 μm, 3–7.5 μm and >7.5 μm) were concurrently collected from the two height levels using five-stage high volume cascade impactors. At both heights, particle mass exhibited bimodal distribution with peaks in the 0.95–1.5 μm and the 3–7.5 μm size fractions, whereas most organic pollutants exhibited one peak at 0.95–1.5 μm. Apart from the 0.95–1.5 μm fraction, particle concentrations of all size ranges were significantly higher at the street level than at the rooftop as a result of more intensive vehicular emissions and road dust resuspension. On the contrary, the concentrations of most organic pollutants did not differentiate significantly between the two elevations.     

Estimating carbon in savanna ecosystems: rational distribution of effort

Minimising the cost of repeatedly estimating C (C) stocks is crucial to the financial viability of projects that seek to sell C credits. Depending on the price of C, this may imply less or more sampling effort than would be applied for science objectives. In systems with heterogeneous C pools, such as savannas, this translates into a variable-effort sampling strategy that maximises the marginal additional C that can be claimed per incremental unit of effort expended. Analysis of a savanna in north-eastern South Africa indicates relatively modest returns per hectare due to the small C quantities and low sequestration rates. Under these conditions, areas in excess of 1,000 ha and infrequent sampling frequencies of 5–10 years are required to make such projects financially viable. For such projects the sample variance, number of samples, cost per sample and establishment costs have negligible impacts on financial viability. It was also found that the soil-C pool contributes up to three times the net returns of the aboveground C pool and provides a strong argument to monitor soil C for certification and market trading. The financial viability estimates, however, do not include the management or opportunity costs incurred in changing the land use. The economies of scale identified in this study combined with the massive area covered by savannas indicate that these additional costs can be covered. Further research is recommended to quantify these costs and interrogate the feasibility of large scale (in excess of 10,000 ha) C-sink projects in savanna systems.     

Carbon accumulation through ecosystem recovery

A market has emerged for carbon sequestered through reforestation. The opportunity to restore ecosystems through this market rather than establish plantations is demonstrated by an Australian case study. In the state of Queensland there are vast areas that have been cleared relatively recently and could be restored to ecosystems with high resilience and important biodiversity values with appropriate management. In order to foster opportunities for carbon accumulation through ecosystem recovery spatially explicit information on sequestration rates, management recommendations, and clear definitions of ancillary biodiversity benefits need to be defined.     

矿山强酸性多金属污染土壤修复及麻疯树植物复垦条件研究

采用温室盆栽实验,研究了在不同剂量(质量分数分别为0、0.10%、0.25%、0.5%和1.0%)石灰石改良条件下,大宝山矿强酸性多金属不同污染程度土壤中麻疯树的生长状况和吸收金属特征,并探讨了麻疯树在酸性土壤中生长的抑制因素和石灰石改良适宜剂量.研究表明,在低污染酸性土壤中,Cu和Pb的高活性可能是抑制麻疯树生长的主要因素;而在高污染酸性土壤中,Cd、Cu、Zn等金属的高活性及由强酸引起的Al毒也可能是抑制麻疯树生长的主要因素;石灰石通过提高土壤pH值和降低多金属的生物有效态含量,促进了麻疯树在低污和高污土壤中的生长,其最佳剂量分别为0.25%和0.5%;石灰石可以不同程度地降低麻疯树地上部和地下舔的Cd、Cu、Pb、Zn和Al含量,同时随石灰石用量的增加,其金属含量基本呈降低趋势;麻疯树地下部金属含量高于地上部,且石灰石对麻疯树地下部金属(除Cd外)含量降低幅度较地上部大.因此,种植麻疯树与石灰石改良是联合修复大宝山矿酸性多金属污染土壤的有效措施之一.     

Modelling plant species richness using functional groups

Conservation biologists increasingly rely on spatial predictive models of biodiversity to support decision-making. Therefore, highly accurate and ecologically meaningful models are required at relatively broad spatial scales. While statistical techniques have been optimized to improve model accuracy, less focus has been given to the question: How does the autecology of a single species affect model quality? We compare a direct modelling approach versus a cumulative modelling approach for predicting plant species richness, where the latter gives more weight to the ecology of functional species groups. In the direct modelling approach, species richness is predicted by a single model calibrated for all species. In the cumulative modelling approach, the species were partitioned into functional groups, with each group calibrated separately and species richness of each group was cumulated to predict total species richness. We hypothesized that model accuracy depends on the ecology of individual species and that the cumulative modelling approach would predict species richness more accurately. The predictors explained plant species richness by ca. 25%. However, depending on the functional group the deviance explained varied from 3 to 67%. While both modelling approaches performed equally well, the models of the different functional groups highly varied in their quality and their spatial richness pattern. This variability helps to improve our understanding on how plant functional groups respond to ecological gradients.     

Modelling with uncertainty: Introducing a probabilistic framework to predict animal population dynamics

Predictive population models designed to assist managers and policy makers require an explicit treatment of inherent uncertainty and variability. These are particular concerns when modelling non-native and reintroduced species, when data have been collected within one geographical or ecological context but predictions are required for another, or when extending models to predict the consequences of environmental change (e.g., climate or land-use). We present an aspatial, probabilistic framework of hierarchical process models for predicting population growth even when data are sparse or of poor quality. Insight into the factors affecting population dynamics in real landscapes can be provided and Kullback–Leibler distances are used to compare the relative output of models. This flexible yet robust framework gives easily interpretable results, allowing managers as well as modellers to invalidate anomalous models and apply others to real-life scenarios.We illustrate the framework’s power with a meta-analysis of European wild boar (Sus scrofa) data. We test hypotheses about the effect of geographic region, hunting and mast years on wild boar population growth, to build models of wild boar dynamics for the UK. The framework quantifies the importance of hunting pressure as a driver of population growth, and confirms that reproductive success is greatly decreased in poor mast years, suggesting that the key to predicting wild boar dynamics is to ascertain local hunting pressure and to better understand changing food availability. Geography had no significant effect, indicating that it is not a good proxy for modelling the impact of change in climate or land-use on wild boar populations at the European scale. We use the framework to predict population abundance 9 years after an isolated population of wild boar established in the UK; in a comparison with the only field data and two independent modelling exercises, our framework provides the most robust and informative results.