Thermal Preference Research Articles

Topical menthol, a pharmacological cold mimic, induces cold sensitivity, adaptive thermogenesis and brown adipose tissue activation in mice.

Brown adipose tissue (BAT) thermogenesis has profound energy-expanding potential, which makes it an attractive target tissue to combat ever-increasing obesity and its other associated metabolic complications. Although it is fairly accepted that cold is a potent inducer of BAT activation and function, there are limited studies on the mechanisms of pharmacological cold-mimicking agents, such as the TRPM8 agonist, menthol, on BAT thermogenesis and activation. Herein, we sought to determine the effect of topical application of menthol (10% w/v [4 g/kg] cream formulation/day for 15 days) on temperature sensitivity behaviour (thermal gradient assay, nesting behaviour), adaptive thermogenesis (infrared thermography, core body temperature), BAT sympathetic innervation (tyrosine hydroxylase immunohistochemistry) and activation (18F-FDG PET-CT analysis, Uncoupling Protein 1 immunohistochemistry and BAT gene expression), whole-body energy expenditure (indirect calorimetry) and other metabolic variables in male C57BL/6N mice. We show that male C57BL/6N mice: (a) develop a warm-seeking and cold-avoiding thermal preference phenotype; (b) display increased locomotor activity and adaptive thermogenesis; (c) show augmented sympathetic innervation in BAT and its activation; (d) exhibit enhanced gluconeogenic capacity (increased glucose excursion in response to pyruvate) and insulin sensitivity; and (e) show enhanced whole-body energy expenditure and induced lipid-utilizing phenotype after topical menthol application. Taken together, our findings highlight that pharmacological cold mimicking using topical menthol application presents a potential therapeutic strategy to counter weight gain and related complications.

Leveraging camera traps and artificial intelligence to explore thermoregulation behaviour.

Behavioural thermoregulation has critical ecological and physiological consequences that profoundly influence individual fitness and species distributions, particularly in the context of climate change. However, field monitoring of this behaviour remains labour-intensive and time-consuming. With the rise of camera-based surveys and artificial intelligence (AI) approaches in computer vision, we should try to build better tools for characterizing animals' behavioural thermoregulation. In this study, we developed a deep learning framework to automate the detection and classification of thermoregulation behaviour. We used lizards, the Rough-tail rock agama (Laudakia vulgaris), as a model animal for thermoregulation. We colour-marked the lizards and curated a diverse dataset of images captured by trail cameras under semi-natural conditions. Subsequently, we trained an object-detection model to identify lizards and image classification models to determine their microclimate usage (activity in sun or shade), which may indicate thermoregulation preferences. We then evaluated the performance of each model and analysed how the classification of thermoregulating lizards performed under different solar conditions (sun or shade), times of day and marking colours. Our framework's models achieved high scores in several performance metrics. The behavioural thermoregulation classification model performed significantly better on sun-basking lizards, achieving the highest classification accuracy with white-marked lizards. Moreover, the hours of activity and the microclimate choices (sun vs shade-seeking behaviour) of lizards, generated by our framework, are closely aligned with manually annotated data. Our study underscores the potential of AI in effectively tracking behavioural thermoregulation, offering a promising new direction for camera trap studies. This approach can potentially reduce the labour and time associated with ecological data collection and analysis and help gain a deeper understanding of species' thermal preferences and risks of climate change on species behaviour.

Thermal preference prediction through infrared thermography technology: Recognizing adaptive behaviors

The recognition of thermal adaptation behaviors is a crucial and noninvasive method for accurately and swiftly predicting the thermal preferences of occupants. This is instrumental in enhancing the energy efficiency, indoor personnel comfort, and overall productivity of office buildings. This study employed deep-learning models to analyze a thermal adaptive behavior video dataset captured by Infrared thermography (IRT) cameras to predict the thermal preferences of occupants. A large-scale infrared thermography dataset was developed for recognizing thermal adaptive behaviors using 9650 video samples. Two adaptive behavior recognition models for infrared videos were developed by leveraging neural network models: Two-Stream Inflated 3D ConvNet (I3D) and SlowFast (SF) networks. These models achieved the average prediction accuracies of 83.53 % and 88.56 %, respectively. Notably, both models exhibited a recognition time of milliseconds, facilitating the real-time recognition of thermal adaptive behaviors. This study offers vital technical and theoretical underpinnings for developing decision-making solutions for smart buildings based on noninvasive thermal preference prediction.

Advancing personal thermal comfort prediction: A data-driven framework integrating environmental and occupant dynamics using machine learning

Various individual thermal preferences do not always lead to optimal thermal comfort, despite the high energy consumption of Heating, Ventilation, and Air Conditioning (HVAC) systems. Thermal comfort models often struggle to accurately predict occupants’ thermal sensations due to their inability to adapt to diverse scenarios. This research employs machine learning techniques to develop personalized data-driven thermal comfort models using a global database of 17,814 samples from the ASHRAE Global Thermal Comfort Database II. Specifically, the study implements random forest and k-nearest neighbor algorithms to analyze the influence of environmental factors (such as air temperature, outdoor temperature, relative humidity, and air velocity) and personal factors (including clothing insulation, activity level, and individual differences) on thermal comfort perception. The study systematically compares the predictive performance of different output types (thermal sensation vs. preference vote), input parameters (6 vs. 12), and machine learning methods. The optimized random forest model with 12 inputs demonstrated an accuracy of over 70% in predicting thermal preference votes, which was significantly higher than the 34% accuracy of the PMV model. The results present new optimized data-driven models and offer insights into the relative influence of parameters such as climate, building cooling strategies, and occupant age and gender. This research aims to enable personalized thermal comfort predictions to improve satisfaction and energy efficiency in various buildings.

Data-efficient comfort modeling: Active transfer learning for predicting personal thermal comfort using limited data

Personal comfort models are used to predict thermal comfort responses at the individual level rather than predicting the average thermal comfort responses for large populations. These models, data-driven in nature, need to be trained on large amounts of occupant comfort feedback and sensor data to achieve accurate predictions. However, collecting such data is often expensive and labor-intensive in reality. To address this, we proposed a data-efficient active transfer learning (ATL) framework to improve the performance of personal comfort models under limited data. To demonstrate the validity of this framework, we developed a base Convolutional Neural Network-Long Short-term Memory (CNNLSTM) model alongside two transfer learning models utilizing feature extraction (TL-CNNLSTM-FE) and fine-tuning (TL-CNNLSTM-FE) approaches, enhanced by a novel active learning strategy. Using these models, three comfort prediction tasks (i.e., thermal preference, thermal acceptability, and air movement preference) were performed by transferring the knowledge from the ASHRAE Global Thermal Comfort Database II to a limited dataset collected in the tropics. Empirical results indicate that the active transfer learning framework proposed was able to consistently outperform the base and transfer learning models using only less than 10% of the training data for all personal comfort tasks, highlighting the effectiveness of this strategy. The implications of this work are especially useful for the research community working on the practical applications of data-efficient machine learning approaches for personal thermal comfort predictions.

Greater severity of depressive symptoms is associated with changes to perceived sweating, preferred ambient temperature, and warmth-seeking behavior

ABSTRACT The thermosensory system is relevant to both the conceptualization and treatment of depression. There is evidence that depression is associated with changes in thermoregulatory functioning, and that thermosensory pathways can be recruited to influence affect and reduce depressive symptoms. In this study, we investigated the relationship between severity of depressive symptoms and changes to measures of subjective experiences associated with thermoregulatory processes as well as the relationship between severity of depressive symptoms and affective responses to warm stimuli, specifically frequency of warmth-seeking behavior. Participants (N = 529) completed measures of depressive symptoms, subjective experiences associated with thermoregulatory processes (i.e., perceived sweating and preferred ambient temperature) and frequency of warmth-seeking behavior (e.g., long hot baths, saunas, etc.). We demonstrate that, controlling for age and gender, greater severity of depressive symptoms is associated with greater perceived sweating and lower preferred ambient temperature. Furthermore, we demonstrate that greater severity of depressive symptoms is associated with more frequent warmth-seeking behavior, and that something other than thermal preference (i.e., stated preference for warmer temperature) is driving this behavior. These data highlight the importance of incorporating the thermoregulatory system in our conceptualization of the pathophysiology of depression and support the potential to recruit thermosensory pathways to target depressive symptoms.

Cooling collars incorporated with PCM packs and gels to improve indoor thermal comfort in healthy young females

The human neck is one of the most thermally sensitive body regions to nociceptive stimuli. Neck cooling has been extensively researched to improve athletic performance in the heat. However, there has been very little research into the use of conductive neck cooling for indoor thermal comfort. We thereby designed three ultralight cooling collars with cooling gels or phase change materials (GEL6, PCM15, and PCM18). Their cooling performance on thermal comfort enhancement of 14 healthy young female participants under two warm indoor temperatures (28 °C and 30 °C) was investigated. Thermophysiological and perceptual responses, including skin temperature, heart rate, sweating, thermal sensation vote (TSV), thermal comfort vote (TCV), and thermal preference vote (TPV), were extensively studied. Results showed that, in contrast to GEL6, PCM15 and PCM18 significantly decreased the neck skin temperature, with PCM15 exhibiting the greatest reduction of 4.37 °C at 28 °C. PCM15 and PCM18 significantly decreased overall and local TSVs while also increasing overall and local TCVs (all p < 0.05), thus they could maintain >80 % occupant satisfaction at 28 °C. At 30 °C indoor temperature, however, none of these three cooling collars were found to meet the 80 % occupant satisfaction criterion. When compared to GEL6, PCM15 and PCM18 were found to only significantly reduce local TSVs at the head and neck (all p < 0.05). In sum, PCM15 and PCM18 have the potential to increase acceptable HVAC summer cooling setpoint temperature by 2.5 °C, from 25.5 °C to 28.0 °C, leading to a 25 % reduction in building cooling energy consumption.

Temperature and body size affect movement of juvenile Atlantic cod (Gadus morhua) and saithe (Pollachius virens) at nearshore nurseries.

Seasonal migrations of marine fish between shallow summer feeding habitats and deep overwintering grounds are driven by fluctuations in the biotic and abiotic environment as well as by changes in the internal state. Ontogenetic shifts in physiology and metabolism affect the response to environmental drivers and may lead to changes in migration timing and propensity. In this study, we investigated the effect of temperature and body size on migration timing and depth distribution in acoustically tagged Atlantic cod, Gadus morhua, and saithe, Pollachius virens, during the period of seasonal migration from shallow summer habitats. The results from our study revealed a wide range of horizontal and vertical distribution of age 1 and 2 G. morhua within the fjord. Larger G. morhua inhabited deeper, cooler waters than smaller juveniles, likely reflecting size-dependent thermal preferences and predation pressure. Conversely, juvenile P. virens occupied primarily shallow waters close to land. The variation in depth distribution of G. morhua was mainly explained by body size and not, against our predictions, by water temperature. Conversely, the dispersal from the in-fjord habitats occurred when water temperatures were high, suggesting that seasonal temperature fluctuations can trigger the migration timing of P. virens and larger G. morhua from summer habitats. Partial migration of small juvenile G. morhua from in-fjord foraging grounds, likely influenced by individual body condition, suggested seasonal migration as a flexible strategy that individuals may use to reduce predation and energetic expenditure. Predation mortality rates of tagged juveniles were higher than previously suggested and are the first robust predation mortality rates for juvenile G. morhua and P. virens estimated based on acoustic transmitters with acidity sensors. The results have relevance for climate-informed marine spatial planning as under the scenario of increasing ocean temperatures, increasing summer temperatures may reduce the juveniles' resource utilization in the shallow summer nurseries, resulting in lower growth rates, increased predation pressure, and lower chances of juvenile winter survival.

Life Table Study of Liriomyza trifolii and Its Contribution to Thermotolerance: Responding to Long-Term Selection Pressure for Abamectin Resistance.

Liriomyza trifolii is a significant invasive pest that targets horticultural and vegetable crops, causing large-scale outbreaks characterized by pronounced thermotolerance and insecticide resistance. This study examined the impact of long-term selection for abamectin resistance during the larval stage of L. trifolii on its population dynamics and thermal tolerance. We conducted a comprehensive comparison between the abamectin-resistant strain (AB-R) and the susceptible strain (S), including age-stage, two-sex life table analysis, thermal preference (Tpref), critical thermal maximum (CTmax), heat knockdown times (HKDTs), eclosion and survival rates, and LtHsp expression under heat stress. Our results showed that while selection for abamectin resistance was detrimental to survival and reproduction, it activated self-defense mechanisms and rapid adaptive adjustments and conferred modest thermal tolerance, which suggests a dual nature of insecticide effects. The AB-R strain exhibited significantly higher thermal preference and CTmax values, along with a longer HKDT and improved survival. Additionally, there was a significant upregulation of LtHsp expression in the AB-R strain compared to the S strain. These findings indicate that the evolution of thermal adaptation was accompanied by abamectin resistance development, emphasizing the necessity of considering temperature effects when applying chemical control. Our study provides valuable insights into how physiological acclimation may help mitigate the toxic effects of insecticides and illustrate how insects respond to multiple environmental pressures.

Temperature-dependent exercise recovery is not associated with behavioral thermoregulation in a salmonid fish

The relationship between behavioral thermoregulation and physiological recovery following exhaustive exercise is not well understood. Behavioral thermoregulation could be beneficial for exercise recovery; for example, selection of cooler temperatures could reduce maintenance metabolic cost to preserve aerobic scope for recovery cost, or selection of warmer temperatures could accelerate recovery of exercise metabolites. While post-exercise behavioral thermoregulation has been observed in lizards and frogs, little is known about its importance in fish. We examined the influence of post-exercise recovery temperature on metabolic rate, thermal preference, and metabolite concentrations in juvenile brook char (Salvelinus fontinalis). Fish were acclimated to and exercised at 15 °C, then recovered at either 15 °C or 10 °C while their metabolic rate was measured via respirometry. Metabolite concentrations were measured in fish after exercise at 15 °C and recovery under one of three thermal treatments (to simulate various behavioral thermoregulation scenarios): (i) 6 h recovery at 15 °C, (ii) 6 h recovery at 10 °C, or (iii) 3 h recovery at 10 °C followed by 3 h recovery at 15 °C. Thermal preference was quantified using a static temperature preference system (15 °C vs. 10 °C). Metabolic rates returned to resting faster at 10 °C compared with 15 °C, although at 10 °C there was a tradeoff of delayed metabolite recovery. Specifically, post-exercise plasma osmolality, plasma lactate, and muscle lactate remained elevated for the entire period in fish recovering at 10 °C, whereas these parameters returned to resting levels by 6 h in fish from the other two recovery groups. Regardless, fish did not exhibit clear behavioral thermoregulation (i.e., fish overall did not consistently prefer one temperature) to prioritize either physiological recovery process. The advantage of metabolic rate recovery at cooler temperatures may balance against the advantage of metabolite recovery at warmer temperatures, lessening the usefulness of behavioral thermoregulation as a post-exercise recovery strategy in fish.

Predicting individual thermal preferences in an office: Assessing the performance of mixed-effects models

Multiple studies suggested that existing thermal comfort models inadequately predict occupants' individual thermal preferences. Personalised comfort models offer an alternative to conventional comfort models aiming to forecast individual's thermal preference. Implementation of these personalised models in occupant-centric control of heating, ventilation, and air-conditioning (HVAC) systems can enhance their performance.A promising technique for personalised comfort modelling is mixed-effects (ME) modelling. A ME model accounts for fixed effects, representing the trends in the general sample, and for random effects, representing variations of specific clusters in the data. In contrast to fixed-effects (FE) models, ME models can capture individual differences. However, its effectiveness in predicting occupants' thermal preferences based on field measurement data, as well as the influence of variations in ME models on prediction accuracy, remains to be thoroughly investigated.This study aims to assess the prediction accuracy of ME models in contrast to FE models using field measurement data, including thermal preference votes from 30 unique occupants. The prediction performance was evaluated across three testing scenarios, each representing a different application of the models. Furthermore, two random effect structures were tested for the ME model: an intercept-only model and an intercept and slope model. The results show that ME models, in comparison to FE models, achieve an improved prediction performance of 8.0 % on average and up to 28.4 % for individual occupants. Moreover, the addition of a random slope to the ME resulted in deteriorated predictions. Finally, occupants’ individual variations were determined with an uncertainty of 6 % after 20 observations.

Altered thermal preferences of infected or immune-challenged Aedes aegypti and Aedes japonicus mosquitoes

Temperature is a critical factor shaping physiology, life cycle, and behaviour of ectothermic vector insects, as well as the development and multiplication of pathogens within them. However, the influence of pathogen infections on thermal preferences (behavioural thermoregulation) is not well-understood. The present study examined the thermal preferences of mosquitoes (Aedes aegypti and Ae. japonicus) infected with either Sindbis virus (SINV) or Dirofilaria immitis over 12 days post exposure (p.e.) or injected with a non-pathogenic Sephadex bead over 24 h in a thermal gradient (15–30 °C). SINV-infected Ae. aegypti preferred 5 °C warmer temperatures than non-infected ones at day 6 p.e., probably the time of highest innate immune response. In contrast, D. immitis-infected Ae. japonicus preferred 4 °C cooler temperatures than non-infected ones at day 9 p.e., presumably a stress response during the migration of third instar larvae from their development site to the proboscis. Sephadex bead injection also induced a cold preference in the mosquitoes but to a level that did not differ from control-injections. The cold preference thus might be a strategy to escape the risk of desiccation caused by the wound created by piercing the thorax. Further research is needed to uncover the genetic and physiological mechanisms underlying these behaviours.

Thermo-physiological changes and reproductive investment in a liolaemid lizard at the extreme of the slow-fast continuum.

Gravid female lizards often experience reduced thermal preferences and impaired locomotor performance. These changes have been attributed to the physical burden of the clutch, but some authors have suggested that they may be due to physiological adjustments. We compared the thermal biology and locomotor performance of the lizard Liolaemus wiegmannii 1week before and 1week after oviposition. We found that gravid females had a thermal preference 1°C lower than that of non-gravid females. This was accompanied by a change in the thermal dependence of maximum running speed. The thermal optimum for locomotor performance was 2.6°C lower before oviposition than after. At relatively low temperatures (22 and 26°C), running speeds of females before oviposition were up to 31% higher than for females after oviposition. However, at temperatures above 26°C, females achieved similar maximum running speeds (∼1.5 m s-1) regardless of reproductive stage. The magnitude of the changes in thermal parameters and locomotor performance of L. wiegmannii females was independent of relative clutch mass (clutches weighed up to 89% of post-oviposition body mass). This suggests that the changes are not simply due to the clutch mass, but are also due to physiological adjustments. Liolaemus wiegmannii females simultaneously adjusted their own physiology in a short period in order to improve locomotor performance and allocated energy for embryonic development during late gravid stage. Our findings have implications for understanding the mechanisms underlying life histories of lizards on the fast extreme of the slow-fast continuum, where physiological exhaustion could play an important role.

Humidity and temperature preference in two Neotropical species of sand flies

BackgroundArthropods vector a multitude of human disease-causing organisms, and their geographic ranges are shifting rapidly in response to changing climatic conditions. This is, in turn, altering the landscape of disease risk for human populations that are brought into novel contact with the vectors and the diseases they carry. Sand flies in the genera Lutzomyia and Pintomyia are vectors of serious disease-causing agents such as Leishmania (the etiological agent of leishmaniasis) and may be expanding their range in the face of climate change. Understanding the climatic conditions that vector species both tolerate physiologically and prefer behaviorally is critical to predicting the direction and magnitude of range expansions and the resulting impacts on human health. Temperature and humidity are key factors that determine the geographic extent of many arthropods, including vector species.MethodsWe characterized the habitat of two species of sand flies, Lutzomyia longipalpis and Pintomyia evansi. Additionally, we studied two behavioral factors of thermal fitness–thermal and humidity preference in two species of sand flies alongside a key aspect of physiological tolerance–desiccation resistance.ResultsWe found that Lu. longipalpis is found at cooler and drier conditions than Pi. evansi. Our results also show significant interspecific differences in both behavioral traits, with Pi. evansi preferring warmer, more humid conditions than Lu. longipalpis. Finally, we found that Lu. longipalpis shows greater tolerance to extreme low humidity, and that this is especially pronounced in males of the species.ConclusionsTaken together, our results suggest that temperature and humidity conditions are key aspects of the climatic niche of Lutzomyia and Pintomyia sand flies and underscore the value of integrative studies of climatic tolerance and preference in vector biology.Graphical

Effect of street trees shade on perceived thermal comfort in a south temperate climate: The sidewalks of Montevideo (Uruguay)

The use of vegetation in cities is one of the most promising strategies for urban climate change adaptation and mitigation. Tree shade influences heat storage from surfaces reducing long wave radiation emission which directly affects people. People 's heat perception depends more on insolation and the temperature of surrounding objects than on air temperature itself. There is a need for analyzes that include the combined effects of physical and human variables on thermal comfort, as well as location-based studies to address its climatic and social conditions. In order to compare the effect of the trees on microenvironmental temperature and perceived thermal comfort, we measured physical parameters and performed structured interviews on three downtown streets of Montevideo, Uruguay, which had sections with and without trees on four dates during the summer. Generally, people surveyed under both treatments stated they did not feel fully comfortable due to summer heat, but the proportion of people who stated feeling in thermal comfort under tree shade was more than double than the unshaded sections.The seasonal ARIMA analysis supported that the tree shade reduced the microenvironmental temperature by its effect on radiant temperature. By using a statistical decision tree methodology that combines all the variables in the same analysis, we found a greater impact of physical variables than personal variables on people's thermal comfort and thermal preferences. We also identified gender as a significant variable that affects people's thermal preferences, where 46.4 % of females preferred a slightly colder environment.

Thermophysiological responses and thermal comfort of occupants in indoor spaces under different speaking and non-speaking conditions

As speaking is one of the fundamental behaviours in human activities, understanding the thermal comfort of occupants during speaking is crucial to providing a satisfactory environment. To further strengthen this understanding and identify how occupant thermal responses change during speaking activities, this study performs an experiment to examine the thermophysiological parameters and thermal comfort of occupants across three different speaking and non-speaking conditions: (1) silent (i.e., non-speaking); (2) speech (i.e., continuous speaking); and (3) conversation (i.e., intermittent speaking). During the experiment, thermophysiological parameters, including skin temperature, electrodermal activity, and heart rate, were collected using smart wristbands from 30 subjects at three different temperature settings to represent three distinct thermal environmental conditions: (1) Cold: 20 °C; (2) Neutral: 24 °C; and (3) Hot: 28 °C. Subjects were also required to report their thermal comfort perceptions in terms of thermal sensation votes, thermal preference votes, and thermal satisfaction votes. Repeated measures ANOVA test and Friedman test were used to analyse the thermophysiological parameters and thermal comfort perceptions. The null hypothesis, which suggests no significant differences in thermal responses and comfort across the three speaking conditions, will be rejected at a significance level of 0.05 (two-tail). The results revealed significant differences in all three thermophysiological parameters between speaking conditions at all temperature settings, while the thermal comfort perception did not consistently show statistically significant differences between speaking conditions. The findings contribute to our understanding of the relationship between communication and thermal responses, highlighting the importance of contextual factors in designing comfortable environments for speaking-related activities.

Impact of temperature and season on ant foraging activity of two invasive species in a Mediterranean urban area

Abstract Invasive species often have harmful effects on human activities and biodiversity. The Tapinoma nigerrimum complex includes three species that exhibit invasive traits and the ability to form supercolonies, including Tapinoma magnum and Tapinoma darioi. While these species are native to the Mediterranean basin, they have recently been reported as invasive in more northern regions of Europe. However, T. magnum and T. darioi show different invasion patterns in Europe, suggesting differences in thermal preferences between these species. We aimed to determine whether there was any difference in activity pattern depending on temperature or season between the two Tapinoma species and between Tapinoma and co‐occurring native Mediterranean ants. We measured ambient temperature and ant foraging activity around the nest entrances of T. darioi and T. magnum in urbanised sites around Montpellier, southern France, over almost 1 year. We did not detect differences in activity between the two species according to temperature, but T. magnum was more active than T. darioi in winter. Compared to the co‐occurring native species, the two Tapinoma species were more active at low temperatures and earlier in the year. These results suggest that the difference in invasion success between T. magnum and T. darioi may not be explained by temperature preference. The difference in activity between native Mediterranean species and invasive Tapinoma suggests a coexistence through niche partitioning. In addition, our results suggest that control measures against invasive Tapinoma species would be more effective if applied at the end of winter.

How to quantify the regional effects of ocean temperature rise due to climate change: implications of Octopus maya ecophysiology on food security of the Yucatan shelf artisanal fishermen

The vital survival, maturation, and reproduction rates of Octopus maya were formulated according to the thermal preferences in each stage (juvenile and adult) and the bottom temperature of the Yucatan shelf projected from different shared socioeconomic pathways (SSPs): SSP1-5, to describe the species population growth in shallow waters. The dispersion of each individual in the population and the spread of the offshore population were incorporated with an integrodifference equation. For each SSP, the food security of the artisanal fishermen in the Western, Central, and Eastern Yucatan shelf region was analyzed based on the proposed availability, access, and utilization indices of Octopus maya as food. The analysis was complemented with an average monthly protein and income poverty indicators that this species’ shallow water fishery would have the capacity to supply by the year 2100. Although the proportion of the legally O. maya catchable population may be favored with the gradual increase in temperature in the coming years, this increase may not translate into greater food security for the artisanal fishermen who catch this species in shallow waters. Moreover, this fishery alone may not have the capacity to supply the average annual intake of 10.6 kg of protein per person recommended and maintain fishermen and their families above the income poverty line by 2100.

Northeast Atlantic fish stock productivity hindcasts and forecasts from a Bayesian framework reveal pronounced climate‐induced dynamics

AbstractClimate change continues to exert pressure on ocean ecosystems. The fisheries‐related responses, such as altered body growth, recruitment and spatial distribution of the targeted stock(s), have generally been reasonably well investigated. Nevertheless, there are still important knowledge gaps in how biophysical drivers impact stock productivity and thereby sustainable harvest levels. Considering this, we investigated 26 fish stocks in the Northeast Atlantic, a region characterized by accelerated climate change effects and a diverse set of fisheries. A novel, stepwise, Bayesian framework to quantify stock productivity was established to identify shared trends and project future patterns, aiming at determining essential baselines for adaptive fishery management in the face of climate change. Despite variation among large marine ecosystems and stocks, an overall declining trend in productivity over the past four decades was observed, especially in high‐latitude areas. These hindcast results were mainly attributed to higher temperatures posing negative effects on productivity, which was dependent on the stock's thermal preference. Contrastingly, the proxy for food availability – gross secondary production – exhibited less consistent impacts. In the forecast, the applied Shared Socio‐economic Pathways (SSPs) indicated that most stocks are likely to encounter adverse effects, with the worst cases expected to occur under SSP2‐4.5 in the 2050s, and under SSP5‐8.5 in the 2090s. Thus, this study generally not only supports earlier climate vulnerability assessments (‘scorings’) of experts but also provides evidence for revised directional effects under climate change, underlining the complexity of processes affecting stock productivity.

A living lab to develop smart home services for the residential welfare of older adults

Owing to the decline in the physical and social functions of older adults, the residential environment substantially impacts the psychological and emotional stability and life satisfaction for older adults. Recently, there has been a growing interest in providing residential welfare services related to the health and independent living of the older adults. The primary objective of this research was to identify the needs and perceptions of older adults regarding smart home services and associated sensors and evaluate the indoor environment conditions of older adults through a living lab. Survey results indicate that older adults felt the burden of electricity bills in summer and winter and had relatively high safety-related smart home service needs. Focus group interviews with the participants in the living lab confirmed that older adults positively perceived the benefits and privacy of the living-lab sensors. In addition, the analysis of indoor environment data over one year from a case study household showed that approximately 66.4 % during the winter season and 13.1 % during the summer season have fallen within the neutral thermal comfort range according to the proposed approach (i.e., predicted mean vote (PMV) calculated using heart rate). This study adopted the proposed PMV method (hereafter PMV-P) that takes into account personal factors (e.g., metabolic rate of older adults that can be calculated using heart rate) and environmental factors (e.g., temperature, humidity) by using the real-time data collected from IoT (Internet of Things)-based sensors. Furthermore, this study proposed a new home energy management system (EMS) algorithm on the basis of model predictive control for reducing heating energy consumption while satisfying the thermal comfort. The new EMS was demonstrated to achieve 5–12 % heating energy savings depending on the thermal comfort preferences of occupants. Overall, the results of multidisciplinary research not only provided the preliminary evidence regarding the needs and perceptions of older adults toward IoT-based sensors but also showcased the development of IoT-based smart home service system through the living lab.