Analysis of the courtship behavior of the Navel Orangeworm, Amyelois transitella (Walker) (Lepidoptera: Pyralidae), with a commentary on methods for the analysis of sequences of behavioral transitions

The courtship behavior of the navel orangeworm, Amyelois transitella, was examined in a wind tunnel. Sixty nine courtship sequences were analyzed and successful sequences divided into two categories: rapid courtship sequences, which involved few breaks in contact, short or no periods of male/female chasing and lasted <10 s between initial contact and mating; and prolonged courtship sequences, which involved many breaks in contact, extended periods of male/female chasing and lasted >10 s. Fifty six (81%) courtships were successful (50.7% rapid courtship and 30.4% prolonged courtship); the remaining 13 (18.8%) sequences were failed courtships. Of failed courtships, 9 (13.0%) were due to males losing contact with females during courtship chases and 4 (5.8%) due to females flying away immediately after male contact. Of all courtship sequences involving a break in contact during a chase, 38.5% resulted in an unsuccessful mating attempt. These findings contrast with previous studies of the courtship behavior of the navel orangeworm, potentially indicating that the type of bioassay used to study courtship may have a large effect on the behavioral sequences displayed. We evaluate several diagnostic techniques for the analysis of sequences of behavioral transitions.

Utilizing insect behavior in chemical detection by a behavioral biosensor

Traditionally, biosensors have been defined as consisting of two parts; a biological part, which is used to detect chemical or physical changes in the environment, and a corresponding electronic component, which tranduces the signal into an electronically readable format. Biosensors are used for detection of volatile compounds often at a level of sensitivity unattainable by traditional analytical techniques. Classical biosensors and traditional analytical techniques do not allow an ecological context to be imparted to the volatile compound/s under investigation. Therefore, we propose the use of behavioral biosensors, in which a whole organism is utilized for the analysis of chemical stimuli. In this case, the organism detects a chemical or physical change and demonstrates this detection through modifications of its behavior; it is the organism's behavior itself that defines the biosensor. In this review, we evaluate the use and future prospects of behavioral biosensors, with a particular focus on parasitic wasps.

Enhanced effects of combined cognitive bias modification and computerised cognitive behaviour therapy on social anxiety

This study examines whether combined cognitive bias modification for interpretative biases (CBM-I) and computerised cognitive behaviour therapy (C-CBT) can produce enhanced positive effects on interpretation biases and social anxiety. Forty socially anxious students were randomly assigned into two conditions, an intervention group (positive CBM-I + C-CBT) or an active control (neutral CBM-I + C-CBT). At pre-test, participants completed measures of social anxiety, interpretative bias, cognitive distortions, and social and work adjustment. They were exposed to 6 × 30 min sessions of web-based interventions including three sessions of either positive or neutral CBM-I and three sessions of C-CBT, one session per day. At post-test and two-week follow-up, participants completed the baseline measures. A combined positive CBM-I + C-CBT produced less negative interpretations of ambiguous situations than neutral CBM-I + C-CBT. The results also showed that both positive CBM-I + C-CBT and neutral CBM-I + C-CBT reduced social anxiety and cognitive distortions as well as improving work and social adjustment. However, greater effect sizes were observed in the positive CBM-I + C-CBT condition than the control. This indicates that adding positive CBM-I to C-CBT enhanced the training effects on social anxiety, cognitive distortions, and social and work adjustment compared to the neutral CBM-I + C-CBT condition.

Is cognitive bias modification training truly beneficial for adolescents?

Background Cognitive Bias Modification (CBM) has been shown to change interpretation biases commonly associated with anxiety and depression and may help ameliorate symptoms of these disorders. However, its evidence base for adolescents is scarce. Previous results have been hard to interpret because of methodological issues. In particular, many studies have used negative bias training as the control condition. This would tend to inflate any apparent benefits of CBM compared to a neutral control. Most studies also only examined the effects of a single training session and lacked follow-up assessment or ecologically valid outcome measures. Method Seventy-four adolescents, aged 16–18 years, were randomised to two sessions of CBM training or neutral control. Interpretation bias and mood were assessed three times: at baseline, immediately post-training and 1 week post-training. A controlled experimental stressor was also used, and responses to everyday stressors were recorded for 1 week after training to assess responses to psychological challenges. Feedback for the training programme was collected. Results The CBM group reported a greater reduction in negative affect than control participants. However, other hypothesised advantages of CBM were not demonstrated. Regardless of training group, participants reported increased positive interpretations, decreased negative interpretations, reduced depressive symptoms and no change in trait anxiety. The two groups did not differ in their stress reactivity. After controlling for group differences in training performance, all the mood effects disappeared. Conclusions When tested under stringent experimental conditions the effects of CBM in healthy adolescents appear to be minimal. Future studies should concentrate on participants with elevated cognitive biases and/or mood symptoms who may be more sensitive to CBM.

Effects of a multi-session Cognitive Bias Modification program on interpretative biases and social anxiety symptoms in a sample of Iranian socially-anxious students

This study examines the effects of a multi-session Cognitive Bias Modification (CBM) program on interpretative biases and social anxiety in an Iranian sample. Thirty-six volunteers with a high score on social anxiety measures were recruited from a student population and randomly allocated into the experimental and control groups. In the experimental group, participants received 4 sessions of positive CBM for interpretative biases (CBM-I) over 2 weeks in the laboratory. Participants in the control condition completed a neutral task matched the active CBM-I intervention in format and duration but did not encourage positive disambiguation of socially ambiguous scenarios. The results indicated that after training the positive CBM-I group exhibited more positive (and less negative) interpretations of ambiguous scenarios and less social anxiety symptoms relative to the control condition at both 1 week post-test and 7 weeks follow-up. It is suggested that clinical trials are required to establish the clinical efficacy of this intervention for social anxiety.

Clinical implications of cognitive bias modification for interpretative biases in social anxiety: an integrative literature review

Cognitive theories of social anxiety indicate that negative cognitive biases play a key role in causing and maintaining social anxiety. On the basis of these cognitive theories, laboratory-based research has shown that individuals with social anxiety exhibit negative interpretation biases of ambiguous social situations. Cognitive Bias Modification for interpretative biases (CBM-I) has emerged from this basic science research to modify negative interpretative biases in social anxiety and reduce emotional vulnerability and social anxiety symptoms. However, it is not yet clear if modifying interpretation biases via CBM will have any enduring effect on social anxiety symptoms or improve social functioning. The aim of this paper is to review the relevant literature on interpretation biases in social anxiety and discuss important implications of CBM-I method for clinical practice and research.

Present bias predicts participation in payments for environmental services: evidence from a behavioral experiment in Uganda

Farmers are necessary agents in global efforts to conserve the environment now that croplands and pastures together constitute the largest terrestrial system on Earth – covering some 48% of ice-free land surface. Whereas standard economic models predict that farmers will participate in conservation programs so long as they are profitable, empirical findings from behavioral economics point to a number of normally unobservable preferences that may influence the decision-making process. This study tests, for the first time, whether heterogeneity in behavioral preferences correlates with decisions to participate in Payments for Environmental Services (PES) programs. We elicit individual trust and time preferences using economic experiments and link resulting measures to household survey data and participation decisions in a Ugandan PES program. We find that farmers who exhibit a preference for proximate gains – present-biased preferences – are 47.7% more likely to participate in the program than those who show time-consistent or future-biased preferences. This result has implications for ongoing and planned PES programs involving farmers, particularly in Africa, by highlighting a potential relationship between payment timing and participation, and further validates the use of behavioral experiments in explaining real-world decisions.

Compensation and Rewards for Environmental Services (CRES) and efficient design of contracts in developing countries. Behavioral insights from a natural field experiment

The use of economic incentives for biodiversity (mostly Compensation and Reward for Environmental Services including Payment for ES) has been widely supported in the past decades and became the main innovative policy tools for biodiversity conservation worldwide. These policy tools are often based on the insight that rational actors perfectly weigh the costs and benefits of adopting certain behaviors and well-crafted economic incentives and disincentives will lead to socially desirable development scenarios. This rationalist mode of thought has provided interesting insights and results, but it also misestimates the context by which ‘real individuals’ come to decisions, and the multitude of factors influencing development sequences. In this study, our goal is to examine how these policies can take advantage of some unintended behavioral reactions that might in return impact, either positively or negatively, general policy performances. We test the effect of income's origin (‘Low effort’ based money vs. ‘High effort’ based money) on spending decisions (Necessity vs. Superior goods) and subsequent pro social preferences (Future pro-environmental behavior) within Madagascar rural areas, using a natural field experiment. Our results show that money obtained under low effort leads to different consumption patterns than money obtained under high efforts: superior goods are more salient in the case of low effort money. In parallel, money obtained under low effort leads to subsequent higher pro social behavior. Compensation and rewards policies for ecosystem services may mobilize knowledge on behavioral biases to improve their design and foster positive spillovers on their development goals.

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

Ungulate behavioral responses to the heterogeneous road-network of a touristic protected area in Africa

Understanding how wildlife responds to road and traffic is essential for effective conservation. Yet, not many studies have evaluated how roads influence wildlife in protected areas, particularly within the large iconic African National Parks where tourism is mainly based on sightings from motorized vehicles with the consequent development and intense use of roads. To reduce this knowledge gap, we studied the behavioral response and local spatial distribution of impala Aepyceros melampus along the heterogeneous (with variation in road surface type and traffic intensity) road-network of Kruger National Park (KNP, South Africa). We surveyed different types of roads (paved and unpaved) recording the occurrence of flight responses among sighted impala and describing their local spatial distribution (in relation to the roads). We observed relatively few flight responses (19.5% of 118 observations), suggesting impalas could be partly habituated to vehicles in KNP. In addition, impala local distribution is apparently unaffected by unpaved roads, yet animals seem to avoid the close proximity of paved roads. Overall, our results suggest a negative, albeit small, effect of traffic intensity, and of presence of pavement on roads on the behavior of impala at KNP. Future studies would be necessary to understand how roads influence other species, but our results show that even within a protected area that has been well-visited for a long time, wildlife can still be affected by roads and traffic. This result has ecological (e.g., changes in spatial distribution of fauna) and management implications (e.g., challenges of facilitating wildlife sightings while minimizing disturbance) for protected areas where touristic activities are largely based on driving.

Is behavioral ecology important for understanding and predicting population dynamics?

Population ecology is a discipline that studies changes in the number and composition (age, sex) of the individuals that form a population. Many of the mechanisms that generate these changes are associated with individual behavior, for example how individuals defend their territories, find mates or disperse. Therefore, it is important to model population dynamics considering the potential influence of behavior on the modeled dynamics. This study illustrates the diversity of behaviors that influence population dynamics describing several methods that allow integrating behavior into population models and range from simpler models that only consider the number of individuals to complex individual-based models that capture great levels of detail. A series of examples shows the importance of explicitly considering behavior in population modeling to avoid reaching erroneous conclusions. This integration is particularly relevant for conservation, as incorrect predictions regarding the dynamics of populations of conservation interest can lead to inadequate assessment and management. Improved predictions can favor effective protection of species and better use of the limited financial and human conservation resources.

Insulin resistance determines a differential response to changes in dietary fat modification on metabolic syndrome risk factors: the LIPGENE study

Background: Previous data support the benefits of reducing dietary saturated fatty acids (SFAs) on insulin resistance (IR) and other metabolic risk factors. However, whether the IR status of those suffering from metabolic syndrome (MetS) affects this response is not established. OBJECTIVE: Our objective was to determine whether the degree of IR influences the effect of substituting high-saturated fatty acid (HSFA) diets by isoenergetic alterations in the quality and quantity of dietary fat on MetS risk factors. DESIGN: In this single-blind, parallel, controlled, dietary intervention study, MetS subjects (n = 472) from 8 European countries classified by different IR levels according to homeostasis model assessment of insulin resistance (HOMA-IR) were randomly assigned to 4 diets: an HSFA diet; a high-monounsaturated fatty acid (HMUFA) diet; a low-fat, high-complex carbohydrate (LFHCC) diet supplemented with long-chain n-3 polyunsaturated fatty acids (1.2 g/d); or an LFHCC diet supplemented with placebo for 12 wk (control). Anthropometric, lipid, inflammatory, and IR markers were determined. RESULTS: Insulin-resistant MetS subjects with the highest HOMA-IR improved IR, with reduced insulin and HOMA-IR concentrations after consumption of the HMUFA and LFHCC n-3 diets (P < 0.05). In contrast, subjects with lower HOMA-IR showed reduced body mass index and waist circumference after consumption of the LFHCC control and LFHCC n-3 diets and increased HDL cholesterol concentrations after consumption of the HMUFA and HSFA diets (P < 0.05). MetS subjects with a low to medium HOMA-IR exhibited reduced blood pressure, triglyceride, and LDL cholesterol levels after the LFHCC n-3 diet and increased apolipoprotein A-I concentrations after consumption of the HMUFA and HSFA diets (all P < 0.05). CONCLUSIONS: Insulin-resistant MetS subjects with more metabolic complications responded differently to dietary fat modification, being more susceptible to a health effect from the substitution of SFAs in the HMUFA and LFHCC n-3 diets. Conversely, MetS subjects without IR may be more sensitive to the detrimental effects of HSFA intake. The metabolic phenotype of subjects clearly determines response to the quantity and quality of dietary fat on MetS risk factors, which suggests that targeted and personalized dietary therapies may be of value for its different metabolic features.

Sea ice production and water mass modification in the eastern Laptev Sea

A simple polynya flux model driven by standard atmospheric forcing is used to investigate the ice formation that took place during an exceptionally strong and consistent western New Siberian (WNS) polynya event in 2004 in the Laptev Sea. Whether formation rates are high enough to erode the stratification of the water column beneath is examined by adding the brine released during the 2004 polynya event to the average winter density stratification of the water body, preconditioned by summers with a cyclonic atmospheric forcing (comparatively weakly stratified water column). Beforehand, the model performance is tested through a simulation of a well‐documented event in April 2008. Neglecting the replenishment of water masses by advection into the polynya area, we find the probability for the occurrence of density‐driven convection down to the bottom to be low. Our findings can be explained by the distinct vertical density gradient that characterizes the area of the WNS polynya and the apparent lack of extreme events in the eastern Laptev Sea. The simple approach is expected to be sufficiently rigorous, since the simulated event is exceptionally strong and consistent, the ice production and salt rejection rates are likely to be overestimated, and the amount of salt rejected is distrusted over a comparatively weakly stratified water column. We conclude that the observed erosion of the halocline and formation of vertically mixed water layers during a WNS polynya event is therefore predominantly related to wind‐ and tidally driven turbulent mixing processes.

Behavioral economics, wearable devices, and cooperative games: results from a population-based intervention to increase physical activity

Background: Health care literature supports the development of accessible interventions that integrate behavioral economics, wearable devices, principles of evidence-based behavior change, and community support. However, there are limited real-world examples of large scale, population-based, member-driven reward platforms. Subsequently, a paucity of outcome data exists and health economic effects remain largely theoretical. To complicate matters, an emerging area of research is defining the role of Superusers, the small percentage of unusually engaged digital health participants who may influence other members. Objective: The objective of this preliminary study is to analyze descriptive data from GOODcoins, a self-guided, free-to-consumer engagement and rewards platform incentivizing walking, running and cycling. Registered members accessed the GOODcoins platform through PCs, tablets or mobile devices, and had the opportunity to sync wearables to track activity. Following registration, members were encouraged to join gamified group challenges and compare their progress with that of others. As members met challenge targets, they were rewarded with GOODcoins, which could be redeemed for planet- or people-friendly products. Methods: Outcome data were obtained from the GOODcoins custom SQL database. The reporting period was December 1, 2014 to May 1, 2015. Descriptive self-report data were analyzed using MySQL and MS Excel. Results: The study period includes data from 1298 users who were connected to an exercise tracking device. Females consisted of 52.6% (n=683) of the study population, 33.7% (n=438) were between the ages of 20-29, and 24.8% (n=322) were between the ages of 30-39. 77.5% (n=1006) of connected and active members met daily-recommended physical activity guidelines of 30 minutes, with a total daily average activity of 107 minutes (95% CI 90, 124). Of all connected and active users, 96.1% (n=1248) listed walking as their primary activity. For members who exchanged GOODcoins, the mean balance was 4,000 (95% CI 3850, 4150) at time of redemption, and 50.4% (n=61) of exchanges were for fitness or outdoor products, while 4.1% (n=5) were for food-related items. Participants were most likely to complete challenges when rewards were between 201-300 GOODcoins. Conclusions: The purpose of this study is to form a baseline for future research. Overall, results indicate that challenges and incentives may be effective for connected and active members, and may play a role in achieving daily-recommended activity guidelines. Registrants were typically younger, walking was the primary activity, and rewards were mainly exchanged for fitness or outdoor products. Remaining to be determined is whether members were already physically active at time of registration and are representative of healthy adherers, or were previously inactive and were incentivized to change their behavior. As challenges are gamified, there is an opportunity to investigate the role of superusers and healthy adherers, impacts on behavioral norms, and how cooperative games and incentives can be leveraged across stratified populations. Study limitations and future research agendas are discussed.