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.     

Prevalence of the Crayfish Plague Pathogen Aphanomyces astaci in Invasive American Crayfishes in the Czech Republic

Abstract:  In Central Europe invasive North American crayfishes are carriers of the oomycete Aphanomyces astaci, which causes crayfish plague. This lethal disease currently represents one of the major threats to native European crayfishes. We used molecular methods—species-specific amplification and sequencing of the pathogen DNA—to investigate the prevalence of individuals latently infected with A. astaci in 28 populations of two invasive American crayfish species (6 of the signal crayfish [ Pacifastacus leniusculus ] and 22 of the spiny-cheek crayfish [ Orconectes limosus ]) in the Czech Republic. The pathogen occurred in 17 investigated populations. We recorded a high variation in positive reactions, ranging from 0% to 100%, in populations of O. limosus . In P. leniusculus, however, only one individual out of 124 tested positive for the pathogen. There was a clear relationship between the water body type and pathogen prevalence in O. limosus . Infection ratios in isolated standing waters were usually low, whereas in running waters, pathogen prevalence often exceeded 50%. Other evaluated characteristics of potential plague pathogen carriers (size, sex, and the presence of melanized spots in the cuticle) seemed to be unrelated to infection. Our data suggest that in contrast to other European countries, O. limosus seems to be the primary reservoir of crayfish plague in the Czech Republic. Although all populations of alien American crayfishes may be potential sources of infections and should be managed as such, knowledge on the prevalence of the plague pathogen at various localities may allow managers to focus conservation efforts on the most directly endangered populations of native crayfishes.     

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.     

EPA’s Clean Air Act Operating Permit Rules

Title V of the Clean Air Act requires tens of thousands of air pollution sources to obtain an operating permit incorporating all applicable requirements under the Act. EPA recently promulgated its controversial Title V regulations, which establish the minimum elements for state permit programs.

The new permit system is among the most important changes made by the 1990 Clean Air Act Amendments, and will significantly alter the way companies comply with air pollution requirements. Previously, the Act only required certain sources to obtain a new source review permit before constructing or modifying the facility (although many states established operating permit systems on their own). Now, all states must adopt operating permit programs consistent with the minimum federal requirements, and submit them to EPA by November 1993. Even though EPA has established minimum requirements, these programs are likely to vary widely from state to state.     

Deriving environmental quality standards in European surface waters: when are there too few data?

Recent technical guidance has been published by the European Commission that outlines methodologies for the derivation of Environmental Quality Standards (EQS) in European surface waters under the Water Framework Directive (WFD). The guidance allows the derivation of a long-term EQS from a small dataset. Specifically an EQS can be derived from just three acute data points, although the safety factors built into such an EQS are large (e.g. up to a factor of 1,000). Large safety factors make such EQS uncertain, and often difficult to achieve in practice. We examine dataset requirements for the derivation of EQS and specifically the minimum number of tests needed for setting EQS for long-term chemical exposures that result in reduced relative uncertainty, as assessed simply through the reduction in standard deviation of the means of the values derived. Using ecotoxicity datasets for four example chemicals, for which EQS have been derived in many jurisdictions, we show that variation in the EQS is greatest when using the minimum dataset allowable under the WFD guidance, but decreases rapidly when seven or more datapoints are available. Increasing the minimum number of ecotoxicity data in deriving an EQS results in a greater understanding of ecotoxicological effects. With this knowledge, the mitigating effects of water chemistry can be accounted for in deriving an EQS, even with relatively limited datasets. The new guidance suggests “simplistic” approaches to account for chemical availability, but does not detail how this might be undertaken. We provide examples of ways by which water chemistry effects can be included in deriving implementable EQS for metals with relatively few reliable and relevant data.     

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.     

Impacts of “metals” on human health: a comparison between nine different methodologies for Life Cycle Impact Assessment (LCIA)

This paper looks into the differences and uncertainties in determining the impact of “metals’” emissions on human health, in Life Cycle Impact Assessment (LCIA). Metals are diverse substances, with different properties and characteristics, considered important in LCIA because of their toxicity to humans and ecosystems. First, we defined a list of the most significant metals in terms of impacts on human health. This was done according to precise criteria accounting for both physical and toxic properties of the metals. Second, we performed an LCIA on different key processes using various existing LCIA methodologies and including also USEtox: the recently developed consensus-model for LCIA. Last, we compared the results in relative terms using a contribution analysis. The analysis showed poor or no agreement between the methods: the relative contribution of each metal and of the metals in total to the total impact on human health changes greatly according to the LCIA method used. These differences are due mainly to the number of metals included in each method and to the technique used to calculate the characterization factors. Results obtained with USEtox show no apparent correlation with results calculated with other methods. At present time USEtox is recommended as the best model for LCIA on human toxicity, but mainly because of the large consensus behind it, because its uncertainties regarding metals are still high. The study gives a good and simple overview regarding the way different methods address the impact assessment for metals, and helps in the interpretation of LCIA results for actual LCA studies where metal emissions are involved.     

The copper level dependence in vegetable crops at pesticide treatment

Abstract

Pesticides containing copper are used on wide range in vegetable production, especially in the open field. Copper has an excellent fungicidal effect against diseases of tomato, pepper, onion and in practical use it is essential as bactericide in the above mentioned crops.

The fungicides containing copper are freely available on the market, they are not expensive and widely used without any special knowledge. They are popular in simple home gardens as well as in hi‐tech vegetable production and are used even in all forms of “bio”; culture.

From the other side, copper can be toxic. The allowable level is 10 mg Cu/kg product. This concentration may be found after the first treatment and it increases after every further application.

In our experiments the copper absorbancy and accumulation has been investigated at different pesticide doses and under different experimental conditions.

The analytical measurements, the sample preparation, the instrument calibration have been made by the Inductively Coupled Plasma Spectrometer at the Department of Chemistry of the University of Horticulture and Food Industry.     

Treatment of hydrocarbon contamination under flow through conditions by using magnetite catalyzed chemical oxidation

Soil pollution by hydrocarbons (aromatic and aliphatic hydrocarbons) is a major environmental issue. Various treatments have been used to remove them from contaminated soils. In our previous studies, the ability of magnetite has been successfully explored to catalyze chemical oxidation for hydrocarbon remediation in batch slurry system. In the present laboratory study, column experiments were performed to evaluate the efficiency of magnetite catalyzed Fenton-like (FL) and activated persulfate (AP) oxidation for hydrocarbon degradation. Flow-through column experiments are intended to provide a better representation of field conditions. Organic extracts isolated from three different soils (an oil-contaminated soil from petrochemical industrial site and two soils polluted by polycyclic aromatic hydrocarbon (PAH) originating from coking plant sites) were spiked on sand. After solvent evaporation, spiked sand was packed in column and was subjected to oxidation using magnetite as catalyst. Oxidant solution was injected at a flow rate of 0.1 mL min^?1 under water-saturated conditions. Organic analyses were performed by GC–mass spectrometry, GC–flame ionization detector, and micro-Fourier transform infrared spectroscopy. Significant abatement of both types of hydrocarbons (60–70 %) was achieved after chemical oxidation (FL and AP) of organic extracts. No significant by-products were formed during oxidation experiment, underscoring the complete degradation of hydrocarbons. No selective degradation was observed for FL with almost similar efficiency towards all hydrocarbons. However, AP showed less reactivity towards higher molecular weight PAHs and aromatic oxygenated compounds. Results of this study demonstrated that magnetite-catalyzed chemical oxidation can effectively degrade both aromatic and aliphatic hydrocarbons (enhanced available contaminants) under flow-through conditions.