Comparison of Numerical Street Dispersion Models with Results from Wind Tunnel and Field Measurements

Numerical dispersion models developed and validated in different European countries were applied to data sets from wind tunnel and field measurements. The comparison includes the Danish Operational Street Pollution Model (OSPM) and the microscale flow and dispersion model MISKAM. The latter is recommended for application in built-up areas in the draft of the new German guideline VDI 3782/8. In a first step the models were applied to simplified street configurations. Different parameters as length and height of adjacent buildings and the angle of the incoming flow were varied. The results were compared to recent wind tunnel measurements. In a second step the models were applied to two extensively investigated field data sets from Jagtvej, Copenhagen and G ttinger Straße, Hannover. Intensified and more transparent and accessible validation procedures would be helpful for the thorough user.     

Projecting population trends of endangered amphibian species in the face of uncertainty: A pattern-oriented approach

Amphibian populations have been declining worldwide for the last three decades. Determining the risk of extinction is one of the major goals of amphibian conservation, yet few quantitative models have been developed for amphibian populations. Like most rare or threatened populations, there is a paucity of life history data available for most amphibian populations. Data on the critical juvenile life stage are particularly lacking. Pattern oriented modeling (POM) has been used successfully to estimate life history parameters indirectly when critical data lacking, but has not been applied to amphibian populations. We describe a spatially explicit, individual-based, stochastic simulation model developed to project population dynamics of pond-breeding amphibian populations. We parameterized the model with life history and habitat data collected for the endangered Houston toad (Bufohoustonensis), a species for which there is a high degree of uncertainty for juvenile and adult male survival. During model evaluation, we focused on explicitly reducing this uncertainty, evaluating 16 different versions of the model that represented the range of parametric uncertainty for juvenile and adult male survival. Following POM protocol, we compared simulation results to four population-level patterns observed in the field: population size, adult sex ratio, proportion of toads returning to their natal pond, and mean maximum distance moved. Based on these comparisons, we rejected 11 of the 16 model versions. Results of the remaining versions confirmed that population persistence depends heavily on juvenile survival, and further suggested that probability of juvenile survival is likely between 0.0075 and 0.015 (previous estimates ranged from 0.003 to 0.02), and that annual male survival is near 0.15 (previous estimates ranged up to 0.43).     

Impact of climate changes on pollution levels in Denmark

The impact of climate changes on the pollution levels in Denmark is the major topic of this paper. Variations of the Danish air pollution levels that are caused by climatic changes are studied together with variations caused by other factors (emissions, inter-annual variability of meteorological conditions, etc.). The Unified Danish Eulerian Model (UNI-DEM) was run on a fine, 10 km × 10 km, grid over a space domain covering all of Europe to minimize the influence of the boundary conditions on the Danish pollution levels. This study is based on four categories of scenarios: (i) traditional scenarios, (ii) climatic scenarios, (iii) scenarios with variations of the human-made (anthropogenic) emissions and (iv) scenarios in which the biogenic emissions were varied. The total number of applied scenarios was 14, and a time-period of 16 years was used. The results show clearly that although the concentrations of the major pollutants do not depend too much on the climatic changes, some quantities, in particular quantities related to high ozone levels, might be increased significantly as a result of the warming trends in the future climate. The reason for this phenomenon is explained.     

Competition, predation and coexistence in a three trophic system

Simple ecological models that mostly operate with population densities using continuous variables, explain quite well the behavior of real populations. In this work we propose and discuss the continuous dynamics of a system of three species, which belongs to the well-known family of Lotka–Volterra models. In particular, the proposed model includes direct effects such as predation and competition among species, and indirect effects such as refuge. The model is proposed to explain recent studies about a group of crustacean (amphipods of genus Hyallela) found in all the plain streams and shallow lakes of the American continent. The studied system includes three compartments: algae, a strictly herbivore amphipod and an omnivore (herbivore and carnivore) one. The analysis of the model shows that there are stable extinction equilibria throughout all the parameters’ space. There are also equilibria with stable coexistence of the three species and two interesting binary equilibria: one with stable coexistence of algae and herbivore and other with coexistence between algae and omnivore amphipods. The presence of Allee effect in the algae growth and the existence of refuge for the herbivore amphipod (prey) determine a bottom-up control.     

Structural changes in lake functioning induced from nutrient loading and climate variability

Climate variability is increasingly recognized as an important regulatory factor, capable of influencing the structural properties of aquatic ecosystems. Lakes appear to be particularly sensitive to the ecological impacts of climate variability, and several long time series have shown a close coupling between climate, lake thermal properties and individual organism physiology, population abundance, community structure, and food web dynamics. Thus, understanding the complex interplay among meteorological forcing, hydrological variability, and ecosystem functioning is essential for improving the credibility of model-based water resources/fisheries management. Our objective herein is to examine the relative importance of the ecological mechanisms underlying plankton seasonal variability in Lake Washington, Washington State (USA), over a 35-year period (1964–1998). Our analysis is founded upon an intermediate complexity plankton model that is used to reproduce the limiting nutrient (phosphate)–phytoplankton–zooplankton–detritus (particulate phosphorus) dynamics in the lake. Model parameterization is based on a Bayesian calibration scheme that offers insights into the degree of information the data contain about model inputs and allows obtaining predictions along with uncertainty bounds for modeled output variables. The model accurately reproduces the key seasonal planktonic patterns in Lake Washington and provides realistic estimates of predictive uncertainty for water quality variables of environmental management interest. A principal component analysis of the annual estimates of the underlying ecological processes highlighted the significant role of the phosphorus recycling stemming from the zooplankton excretion on the planktonic food web variability. We also identified a moderately significant signature of the local climatic conditions (air temperature) on phytoplankton growth (r = 0.41), herbivorous grazing (r = 0.38), and detritus mineralization (r = 0.39). Our study seeks linkages with the conceptual food web model proposed by Hampton et al. [Hampton, S.E., Scheuerell, M.D., Schindler, D.E., 2006b. Coalescence in the Lake Washington story: interaction strengths in a planktonic food web. Limnol. Oceanogr. 51, 2042–2051.] to emphasize the “bottom-up” control of the Lake Washington plankton phenology. The posterior predictive distributions of the plankton model are also used to assess the exceedance frequency and confidence of compliance with total phosphorus (15 μg L⁻¹) and chlorophyll a (4 μg L⁻¹) threshold levels during the summer-stratified period in Lake Washington. Finally, we conclude by underscoring the importance of explicitly acknowledging the uncertainty in ecological forecasts to the management of freshwater ecosystems under a changing global environment.     

Mathematical modelling for conservation and management of gorgonians corals: youngs and olds,could they coexist?

Gorgonian corals are long-lived, slow-growing marine species dominating Mediterranean rocky bottoms. Endowed with complex morphologies they give a structure to the whole community, moreover, being efficient suspension feeders, they play a key role in plankton-benthos energy flow and CO₂${\text{CO}}_2$ storage. Thus, the structure and the development of benthic, hard bottom communities are linked to gorgonian survival. The red coral Corallium rubrum (L. 1758) is a precious gorgonian endemic to the Mediterranean Sea. Harvested and traded world-wide since ancient times red coral is a clear example of overexploited marine resource. This species is structured into self-seeding, genetically differentiated populations, some of which, living in the shallower part of the species bathymetric distribution, was recently affected by anomalous mortality events linked to global climate change. The co-occurrence of overharvesting and mass mortality could dramatically affect such populations. Demographic population models, widely applied by conservation biologists to check population viability and to project population trends over time are fundamental to foster survival of such populations matching harvesting to population growth rates. Therefore we set out a dynamic model of a genetically differentiated red coral population living in shallow waters. This population is characterised by small/young, crowded colonies and high recruitment rate. On the basis of the size–age structure determined for this population, a static life-history table, in which survival and reproduction coefficients of the different size–age classes were reported, has been set out. Demographic data were included in a non-linear, discrete, age-structured dynamic model, based on a Leslie-Lewis transition matrix. Our field data indicate that the recruits-to-larvae ratio is actually density-dependent. Such dependence, positive for low and negative for high density values, was included into the model and the effect of colonies of different size–age classes on recruits-to-larvae ratio was considered to be proportional to the number of polyps they have. We applied such model to simulate the trends of the studied population under different increases of survival and life-span. As some populations of gorgonians actually show the dominance of sparse, big/old colonies and low recruitment rate, while others are characterised by crowded, small/young colonies and high recruitment rate, we simulated the shift from the former to the latter structure increasing survival and life-span. Our results suggest that a dramatic mortality increase of bigger–older colonies (due, in the case of red coral to overfishing) could have determined the population structure we found.     

Are network indices robust indicators of food web functioning? A Monte Carlo approach

Indices based on network theory are often used to describe food web functioning. These indices take as input food web flows that are estimated based on merging of (scarce) data with linear inverse methods (LIMs). Due to under sampling, most food webs are highly uncertain and can only be quantified within a specific uncertainty range. The linear inverse method (LIM) can estimate food web flows using a variety of techniques, e.g. the parsimonious or minimum norm (MN) solution, which selects one food web, based on a quadratic minimization technique or the Monte Carlo solution where a finitely many random solutions are generated which are then averaged. We use the Monte Carlo approach (MCA) to estimate the values of several indices from four published food webs, the Gulf of Riga for the autumn, summer and spring seasons, and the Takapoto atoll system. We first show that network indices are much better constrained than the uncertain food webs from which they are calculated. Therefore, even in the face of food web uncertainty, they are robust estimators of food web functioning. We then use the MCA-derived network indices to generate cumulative density functions for each index. These serve to compute the probabilities of the MN indices estimates being an extreme solution as compared to the median values. Our findings show that 82% of the MN solutions are smaller than the MCA solutions, and 63% of the network indices are significantly under-estimated.     

Using accelerometer,high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.     

Bridging biogeochemical and food web models for an End-to-End representation of marine ecosystem dynamics: The Venice lagoon case study

The simultaneous accounting of effects of natural and anthropogenic changes within a common framework calls for the development of new comprehensive tools capable of integrating biological processes that span a huge range of scales, from viruses to fishes, in addition to their interactions with physical–chemical environmental properties, i.e. End-to-End models.     

A Bayesian hierarchical framework for calibrating aquatic biogeochemical models

Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management.