New York State’s Classifications- Ambient Air Quality Objectives System

The air pollution potential of any area is directly related to its population and economic development. In New York State, this pollution potential ranges from that existing in the sparsely inhabited recreational zones to that prevailing in the densely populated and/or heavily industrialized areas. No one set of air quality standards or objectives can be developed which can reasonably be applied on a statewide basis. A classifications-air quality objectives system has been adopted by the State Air Pollution Control Board for application in New York State. In accordance with this system, specific areas can be classified in one of 16 categories. Air quality objectives, in keeping with each classification, are designed to protect health and to promote the maximum comfort and enjoyment and use of property consistent with the needs of the area concerned. The system was developed by the staff with the assistance of a council of technical advisors. Units of measurement related to the most important effect of specific contaminants are utilized. Methods of sampling and analysis are specified. The details of the classifications-objectives system, the place of the system in the state’s air resource management plan, the manner in which each segment of the state after study will be classified, and how attainment will be evaluated are described.     

Measurement, Collaborative Learning and Research for Sustainable Use of Ecosystem Services: Landscape Concepts and Europe as Laboratory

Policies at multiple levels pronounce the need to encompass both social and ecological systems in governance and management of natural capital in terms of resources and ecosystems. One approach to knowledge production and learning about landscapes as social–ecological systems is to compare multiple case studies consisting of large spaces and places. We first review the landscape concepts’ biophysical, anthropogenic, and intangible dimensions. Second, we exemplify how the different landscape concepts can be used to derive measurable variables for different sustainability indicators. Third, we review gradients in the three dimensions of the term landscape on the European continent, and propose to use them for the stratification of multiple case studies of social–ecological systems. We stress the benefits of the landscape concepts to measure sustainability, and how this can improve collaborative learning about development toward sustainability in social–ecological systems. Finally, analyses of multiple landscapes improve the understanding of context for governance and management.     

On the elasticity of range limits during periods of expansion

Dispersal is known to play a crucial role in the formation of species' ranges. Recent studies demonstrate that dispersiveness increases rapidly during the range expansion of species due to a fitness increase for dispersers at the expanding front. R. D. Holt concluded, however, that emigration should decline after the period of invasion and hence predicted some range contraction following the initial expansion phase. In this study, we evaluate this hypothesis using a spatially explicit individual-based model of populations distributed along environmental gradients. In our experiments we allow the species to spread along a gradient of declining conditions. Results show that range contraction did emerge in a gradient of dispersal mortality, caused by the rapid increase in emigration probability during invasion and selection disfavoring dispersal, once a stable range is formed. However, gradients in growth rate, local extinction rate, and patch capacity did not lead to a noticeable contraction of the range. We conclude, that the phenomenon of range contraction may emerge, but only under conditions that select for a reduction in dispersal at the range edge in comparison to the core region once the expansion period is over.     

Information processing in models for density-dependent emigration: A comparison

Density-dependent emigration has been recognized as a fitness enhancing strategy. Yet, especially in the modelling literature there is no consensus about how density-dependent emigration should quantitatively be incorporated into metapopulation models. In this paper we compare the performance of five different dispersal strategies (defined by the functional link between density and emigration probability). Four of these strategies are based on published functional relationships between local population density and emigration probability, one assumes density-independent dispersal. We use individual-based simulations of time-discrete metapopulation dynamics and conduct evolution experiments for a broad range of values for dispersal mortality and environmental stochasticity. For each set of these conditions we analyze the evolution of emigration rates in ‘monoculture experiments’ (with only one type of dispersal strategy used by all individuals in the metapopulation) as well as in selection experiments that allow a pair-wise comparison of the performance of each functional type. We find that a single-parameter ‘asymptotic threshold’ strategy - derived from the marginal value theorem - with a decelerating increase of emigration rate with increasing population density, out-competes any other strategy, i.e. density-independent emigration, a ‘linear threshold’ strategy and a flexible three-parameter strategy. Only when environmental conditions select for extremely high emigration probabilities (close to one), strategies may perform approximately equally. A simple threshold strategy derived for the case of continuous population growth performs even worse than the density-independent strategy. As the functional type of the dispersal function implemented in metapopulation models may severely affect predictions concerning the survival of populations, range expansion, or community changes we clearly recommend to carefully select adequate functions to model density-dependent dispersal.     

New approaches to modelling fish-habitat relationships

Ecologists often develop models that describe the relationship between faunal communities and their habitat. Coral reef fishes have been the focus of numerous such studies, which have used a wide range of statistical tools to answer an equally wide range of questions. Here, we apply a series of both conventional statistical techniques (linear and generalized additive regression models) and novel machine-learning techniques (the support vector machine and three ensemble techniques used with regression trees) to predict fish species richness, biomass, and diversity from a range of habitat variables. We compare the techniques in terms of their predictive performance, and we compare a subset of the models in terms of the influence each habitat variable has for the predictions. Prediction errors are estimated by cross-validation, and variable importance is assessed using permutations of individual variable values. For predictions of species richness and diversity the tree-based models generally and the random forest model specifically are superior (produce the lowest errors). These model types are all able to model both nonlinear and interaction effects. The linear model, unable to model either effect type, performs the worst (produces the highest errors). For predictions of biomass, the generalized additive model is superior, and the support vector machine performs the worst. Depth range, the difference between maximum and minimum water depth at a given site, is identified as the most important variable in the majority of models predicting the three fish community variables. However, variable importance is highly dependent upon model type, which leads to questions regarding the interpretation of variable importance and its proper use as an indicator of causality. The representation of ecological relationships by tree-based ensemble learners will improve predictive performance, and provide a new avenue for exploring ecological relationships, both statistical and causal.     

Stoichiometric controls of nitrogen and phosphorus cycling in decomposing beech leaf litter

Resource stoichiometry (C:N:P) is an important determinant of litter decomposition. However, the effect of elemental stoichiometry on the gross rates of microbial N and P cycling processes during litter decomposition is unknown. In a mesocosm experiment, beech (Fagus sylvatica L.) litter with natural differences in elemental stoichiometry (C:N:P) was incubated under constant environmental conditions. After three and six months, we measured various aspects of nitrogen and phosphorus cycling. We found that gross protein depolymerization, N mineralization (ammonification), and nitrification rates were negatively related to litter C:N. Rates of P mineralization were negatively correlated with litter C:P. The negative correlations with litter C:N were stronger for inorganic N cycling processes than for gross protein depolymerization, indicating that the effect of resource stoichiometry on intracellular processes was stronger than on processes catalyzed by extracellular enzymes. Consistent with this, extracellular protein depolymerization was mainly limited by substrate availability and less so by the amount of protease. Strong positive correlations between the interconnected N and P pools and the respective production and consumption processes pointed to feed-forward control of microbial litter N and P cycling. A negative relationship between litter C:N and phosphatase activity (and between litter C:P and protease activity) demonstrated that microbes tended to allocate carbon and nutrients in ample supply into the production of extracellular enzymes to mine for the nutrient that is more limiting. Overall, the study demonstrated a strong effect of litter stoichiometry (C:N:P) on gross processes of microbial N and P cycling in decomposing litter; mineralization of N and P were tightly coupled to assist in maintaining cellular homeostasis of litter microbial communities.     

Sex-specific plasticity in brain morphology depends on social environment of the guppy, Poecilia reticulata

The vertebrate brain is a remarkably plastic organ, which responds quickly to environmental changes. However, to date, studies investigating plasticity in brain morphology have focused mostly on the physical properties of the surrounding environment, and little is known about brain plasticity in response to the social environment. Moreover, sex differences in brain plasticity remain virtually unexplored. Here, we tested how the social environment influenced brain morphology in adult males and females using experimental manipulation of the sex composition of social pairs (same sex vs. mixed sex) in the guppy (Poecilia reticulata). We detected substantial sex-specific plasticity in both the overall brain size (controlling for body size) and separate brain structures. The brain size was larger in males that interacted with females, and female optic tectum was larger in female-only groups. Overall, females had larger olfactory bulbs and cerebellum in comparison to males. While net sexual dimorphism in the brain structure can be explained in light of the known differences in boldness and foraging behaviour between the sexes, our results also support that cognitive demands associated with courtship behaviour can lead to plastic changes in the brain size. Our findings demonstrate that not only social environment can generate rapid, plastic responses in the vertebrate brain but also that such responses can depend strongly on sex.     

The simulation of condensation removal of a heavy metal from exhaust gases onto sorbent particles

A numerical model BAEROSOL for solving the general dynamic equation (GDE) of aerosols is presented. The goal was to model the capture of volatilized metals by sorbents under incinerator-like conditions. The model is based on algorithms presented by Jacobson and Turco [Aerosol Science and Technology 22 (1995) 73]. A hybrid size bin was used to model growth and formation of particles from the continuum phase and the coagulation of existing particles. Condensation and evaporation growth were calculated in a moving size bin approach, where coagulation and nucleation was modeled in the fixed size bin model of the hybrid grid. To account for the thermodynamic equilibrium in the gas phase, a thermodynamic equilibrium code CET89 was implemented. The particle size distribution (PSD) calculated with the model was then compared to analytical solutions provided for growth, coagulation and both combined. Finally, experimental findings by Rodriguez and Hall [Waste Management 21 (2001) 589-607] were compared to the PSD predicted by the developed model and the applicability of the model under incineration conditions is discussed.