Experiments and Simulations on Day-to-Day Route Choice-Behaviour

The paper reports laboratory experiments on a day-to-day route choice game with two routes. Subjects had to choose between a main road M and a side road S. The capacity was greater for the main road. 18 subjects participated in each session. In equilibrium the number of subjects is 12 on M and 6 on S. Two treatments with 6 sessions each were run at the Laboratory of Experimental Economics at Bonn University using RatImage. Feedback was given in treatment I only about own travel time and in treatment II on travel time for M and S. Money payoffs increase with decreasing time. The main results are as follows. 1. Mean numbers on M and S are very near to the equilibrium. 2. Fluctuations persist until the end of the sessions in both treatments. 3. Fluctuations are smaller under treatment II .The effect is small but significant. 4. The total number of changes is significantly greater in treatment I. 5. Subjects’ road changes and payoffs are negatively correlated in all sessions. 6. A direct response mode reacts with more changes for bad payoffs whereas a contrary response mode shows opposite reactions. Both response modes can be observed. 7. The simulation of an extended payoff sum learning model closely fits the main results of the statistical evaluation of the data.travel behaviour research, information in intelligent transportation systems, day-to-day route choice, laboratory experiments, payoff sum model

Testing (beliefs about) social preferences: evidence from an experimental coordination game

We report experimental results on a simple coordination game in which two players can coordinate either on an equal distribution of payoffs or on a Pareto superior but unequal distribution of payoffs. We find that the higher the difference in individual payoffs, the less likely is a successful coordination on the Pareto superior distribution. While this is well in line with the recent models of inequity aversion, our results are best explained not by a preference for equality per se but rather by the belief that the opponent has such a preference

Commuters route choice behaviour

The paper reports laboratory experiments with a two route choice scenario. In each session 18 subjects had to choose between a main road M and a side road S. The capacity of M was larger. Feedback was given in treatment I only on the subjects' own travel time and in treatment II on travel time for M and S. The main results are as follows: • Mean numbers on M and S are near to pure equilibrium. • Fluctuations persist until the end of the sessions. • The total number of changes is significantly greater in treatment I. • Subjects' road changes and payoffs are negatively correlated. • A direct response mode results in more changes for bad payoffs whereas a contrary response mode shows opposite reactions. • Simulations of an extended payoff sum learning model fits the main results of the statistical evaluation of the data

The relationship between successional vascular plant assemblages and associated microbial communities on coal mine spoil heaps

The aim of the study was to investigate the relationships between the vascular plant species and the associated soil microbial properties at various stages of vegetation development on unclaimed hard coal mine spoil heaps in Upper Silesia (south Poland). The spontaneous vegetation, soil chemistry as well as the activity and structure of microbial communities were recorded on this specific habitat. The colliery heaps were divided into four age classes and the plant species composition and cover abundance were recorded on established plots (2 m × 2 m). The soil microbial activity under the vegetation patches was assessed using fluorescein diacetate hydrolytic activity (FDHA) and the soil microbial biomass and community composition were determined by phospholipid fatty acid (PLFA) biomarkers. Total microbial biomass in soils from the older vegetation plots was significantly higher than those in soils from the younger plots. In all studied samples, microbial communities consisted primarily of bacteria with the dominance of Gram negative bacteria over Gram positive and aerobic microorganisms were more dominant than anaerobic ones. Statistical analysis revealed a correlation between the type of vegetation and microbial community structure

How birds cope physiologically and behaviourally with extreme climatic events

As global climate change progresses, the occurrence of potentially disruptiveclimatic events such as storms are increasing in frequency, duration and inten-sity resulting in higher mortality and reduced reproductive success. Whatconstitutes an extreme climatic event? First we point out that extreme climaticevents in biological contexts can occur in any environment. Focusing on fieldand laboratory data on wild birds we propose a mechanistic approach to defin-ing and investigating what extreme climatic events are and how animals copewith them at physiological and behavioural levels. The life cycle of birds ismade up of life-history stages such as migration, breeding and moult thatevolved to match a range of environmental conditions an individual mightexpect during the year. When environmental conditions deteriorate anddeviate from the expected range then the individual must trigger copingmechanisms (emergency life-history stage) that will disrupt the temporal pro-gression of life-history stages, but enhance survival. Using the framework ofallostasis, we argue that an extreme climatic event in biological contexts canbe defined as when the cumulative resources available to an individual areexceeded by the sum of its energetic costs—a state called allostatic overload.This allostatic overload triggers the emergency life-history stage that tempor-arily allows the individual to cease regular activities in an attempt to surviveextreme conditions. We propose that glucocorticoid hormones play a majorrole in orchestrating coping mechanisms and are critical for enduring extremeclimatic events.This article is part of the themed issue ‘Behavioural, ecological andevolutionary responses to extreme climatic events’

Late-season snowfall is associated with decreased offspring survival in two migratory arctic-breeding songbird species

While the effect of weather on reproduction has been studied for many years in avian taxa, the rapid pace of climate change in arctic regions has added urgency to this question by changing the weather conditions species experience during breeding. Given this, it is important to understand how factors such as temperature, rain, snowfall, and wind affect reproduction both directly and indirectly (e.g. through their effects on food availability). In this study, we ask how weather factors and food availability influence daily survival rates of clutches in two arctic-breeding migratory songbirds: the Lapland longspur Calcarius lapponicus , a circumpolar breeder, and Gambel’s white-crowned sparrow Zonotrichia leucophrys gambelii , which breeds in shrubby habitats across tundra, boreal and continental climates. To do this, we monitored clutch survival in these two species from egg-lay through fledge at field sites located near Toolik Field Station (North Slope, Alaska) across 5 yr (2012–2016). Our results indicate that snowfall and cold temperatures decreased offspring survival rates in both species; although Lapland longspurs were more susceptible to snowfall. Food availability, quantified by pitfall sampling and sweep-net sampling methods, had minimal effects on offspring survival. Some climate models predict increased precipitation for the Arctic with global warming, and in the Toolik region, total snow accumulation may be increasing. Placed in this context, our results suggest that changes in snow storms with climate change could have substantial consequences for reproduction in migratory songbirds breeding in the North American Arctic

Eavesdropping on the Arctic: Automated bioacoustics reveal dynamics in songbird breeding phenology

Bioacoustic networks could vastly expand the coverage of wildlife monitoring to complement satellite observations of climate and vegetation. This approach would enable global-scale understanding of how climate change influences phenomena such as migratory timing of avian species. The enormous data sets that autonomous recorders typically generate demand automated analyses that remain largely undeveloped. We devised automated signal processing and machine learning approaches to estimate dates on which songbird communities arrived at arctic breeding grounds. Acoustically estimated dates agreed well with those determined via traditional surveys and were strongly related to the landscape’s snow-free dates. We found that environmental conditions heavily influenced daily variation in songbird vocal activity, especially before egg laying. Our novel approaches demonstrate that variation in avian migratory arrival can be detected autonomously. Large-scale deployment of this innovation in wildlife monitoring would enable the coverage necessary to assess and forecast changes in bird migration in the face of climate change

Using network theory to identify the causes of disease outbreaks of unknown origin.

The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal nature of informal reporting and uncertainty of pathogen identity in the early stages of emergence. We developed theory to connect disease outbreaks of known aetiology in a network using an array of properties including symptoms, seasonality and case-fatality ratio. We tested the method with 125 reports of outbreaks of 10 known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks. The approach correctly identified unknown disease outbreaks with an average sensitivity of 76 per cent and specificity of 88 per cent. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, positive predictive values = 80%), whereas others (e.g. Chandipura encephalitis) were more difficult to distinguish. These results suggest that unknown outbreaks in resource-poor settings could be evaluated in real time, potentially leading to more rapid responses and reducing the risk of an outbreak becoming a pandemic

Plant water potential improves prediction of empirical stomatal models

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.Funding for this research was provided by NSF DEB EF-1340270 and the Climate Mitigation Initiative at the Princeton Environmental Institute, Princeton University. SL acknowledges financial support from the China Scholarship Council (CSC). VRD acknowledges funding from Ramón y Cajal fellowship (RYC-2012-10970). BTW was supported by the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. DJC acknowledges funding from the National Science Centre, Poland (NN309 713340). WRLA was supported in part by NSF DEB 1714972