Categories Of Behavior Research Articles

An Inconvenient Truth: A Comprehensive Examination of the Added Value (or Lack Thereof) of Leadership Measures

AbstractThe leadership literature encompasses a bewildering array of leadership styles, with most studies focussing on the nature and consequences of a single leadership style in isolation. This isolationist approach has led researchers to mostly ignore the similarities between supposedly different leadership styles, and few studies have examined these overlaps empirically. To understand the extent of this problem, we use bifactor exploratory structural equation modelling to examine whether 12 dominant leadership measures capture shared variance and whether any variance unique to a particular style is related to theoretically and empirically established covariates. Moreover, we explore what the shared variance of these leadership measures may represent. Across seven samples, five countries, multiple organizational contexts, and 4000 respondents, the 12 leadership measures shared significant amounts of variance and did not systematically capture unique leadership‐related variance. Further analyses indicated this shared variance mainly represented the affective quality of the leader–follower relationship. The results reveal an inconvenient truth for leadership researchers who wish to differentiate styles, as the styles have much more in common than differences. Contrasting with previous recommendations to refine styles, we argue that a taxonomic leadership behaviour categories approach to leadership research is the most parsimonious way forward.

Driver behavior recognition based on dual-branch and deformable convolutional network method

Aiming at the task of driver behavior recognition in the car cockpit, this paper proposes a recognition method based on a dual-branch neural network. The main branch of the network model uses ResNet50 as the backbone network for feature extraction, and uses deformable convolution to adapt the model to the shape and position changes of the driver in the image. The auxiliary branch assists in updating the parameters of the backbone network during the gradient backpropagation process, so that the backbone network can better extract features that are conducive to driver behavior recognition, thereby improving the recognition performance of the model. The ablation experiment and comparative experiment results of the network model on the State Farm public dataset show that the recognition accuracy of the proposed network model can reach 96.23%, and the recognition effect is better for easily confused behavior categories. The research results are of great significance for understanding driver behavior in the car cockpit and ensuring driving safety.

EthoWatcher OS: improving the reproducibility and quality of categorical and morphologic/kinematic data from behavioral recordings in laboratory animals.

Behavioral recordings annotated by human observers (HOs) from video recordings are a fundamental component of preclinical animal behavioral models of neurobiological diseases. These models are often criticized for their vulnerability to reproducibility issues. Here, we present the EthoWatcher-Open Source (EW-OS), with tools and procedures for the use of blind-to-condition categorical transcriptions that are simultaneous with tracking, for the assessment of HOs intra- and interobserver reliability during training and data collection, for producing video clips of samples of behavioral categories that are useful for observer training. The use of these tools can inform and optimize the performance of observers, thus favoring the reproducibility of the data obtained. Categorical and machine vision-derived outputs are presented in an open data format for increased interoperability with other applications, where behavioral categories are associated frame-by-frame with tracking, morphological and kinematic attributes of an animal's image. The center of mass (X and Y pixel coordinates), the animal's area in square millimeters, the length and width in millimeters, and the angle in degrees were recorded. It also assesses the variation in each morphological descriptor to produce kinematic descriptors. While the initial measurements are in pixels, they are later converted to millimeters using the scale calibrated by the user via the graphical user interfaces. This process enables the creation of databases suitable for machine learning processing and behavioral pharmacology studies. EW-OS is constructed for continued collaborative development, available through an open-source platform, to support initiatives toward the adoption of good scientific practices in behavioral analysis, including tools for evaluating the quality of the data that can alleviate problems associated with low reproducibility in the behavioral sciences.

Predictors of emergency medical transport refusal following opioid overdose in Washington, DC.

Patient initiated transport refusal during Emergency Medical Service (EMS) opioid overdose encounters has become an endemic problem. This study aimed to quantify circumstantial and environmental factors which predict refusal of further care. In this cross-sectional analysis, a case definition for opioid overdose was applied retrospectively to EMS encounters. Selected cases had sociodemographic and situational/incident variables extracted using patient information and free text searches of case narratives. 50 unique binary variables were used to build a logistic model. Prehospital EMS overdose encounters in Washington, DC, USA, from July 2017 to July 2023. Of EMS encounters in the study timeframe, 14 587 cases were selected as opioid overdoses. Predicted probability for covariates was the outcome variable. Model performance was assessed using Stratified K-Fold Cross-Validation and scored with positive predictive value, sensitivity and F1. Prediction accuracy and McFadden's pseudo-R squared are also determined. The model achieved a predictive accuracy of 78% with a high positive predictive value (0.83) and moderate sensitivity (0.68). Bystander type influenced the refusal outcome, with decreased refusal probability associated with family (nondescript) (-28%) and parents (-16%), while presence of a girlfriend increased it (+28%). Negative situational factors like noted physical trauma (-62%), poor weather (-14%) and lack of housing (-14%) decreased refusal probability. Characteristics of the emergency response team, like a prior crew member encounter (+20%) or crew experience <1year (-36%), had a variable association with transport. Refusal of emergency transport for opioid overdose cases in Washington, DC, USA, has expanded by 43.8% since 2017. Several social, environmental and systematic factors can predict this refusal. Logistic regression models can be used to quantify broad categories of behavior in surveillance medical research.

The Impact of Machine Learning-based Student Behaviour Detection on Teaching in Teaching Classroom Scenarios

The purpose of this study is to address the issue of low efficiency and time-consuming monitoring of student classroom behaviour in teaching classroom scenarios, as well as how to apply deep learning to student classroom behaviour detection. Classroom teaching is the main way for students to acquire and master knowledge, and teachers need to pay attention to students’ behaviour in the classroom and make timely corrections. In this study, the teaching classroom of undergraduate students was used as an example for application and analysis. Based on the You Only Look Once v5s network, a student behaviour detection algorithm was designed by combining the ghosting module and optimised feature fusion structure, and a student behaviour association technology suitable for continuous teaching classrooms was designed. Through the visualisation results, it can be seen that the research method can accurately detect the behaviour categories of students, and the effect is better in target dense scenes. Through quantitative analysis using correlation technology, it can be concluded that there are very few students with the lowest level of seriousness, while the majority of students have a level of seriousness above the qualified level. The overall situation of the teaching classroom is good. These student behaviour analyses can energy the level of seriousness of students, form effective and objective classroom feedback, provide teachers with objective and accurate reference value, and promote the construction of smart classrooms.

Thermographic inspections of solar photovoltaic plants in India using Unmanned Aerial Vehicles: Analysing the gap between theory and practice

Aerial inspection of solar PV plants using Unmanned Aerial Vehicles (UAVs) is gaining traction due to benefits such as no downtime and cost-effectiveness. This technology is proven to be the low-cost alternative to conventional approaches involving visual inspection and I-V curve tracing to identify physical damages and underperforming strings, respectively. Though the use of UAVs for thermographic solar PV inspection is a popular alternative in developed countries, its use in developing economies experience various challenges. Studies emphasizing these challenges especially in the context of rapid evolution of drones are limited. To overcome this limitation, literature scoping, a one-on-one survey, focus group discussion, and a flight campaign using a UAV with a thermal payload is conducted in India to identify the limitations. These are further categorized into Technical, Behavioural, Implementation, Pre-deployment, Deployment, and Post-deployment categories. The relevance and significance of each challenge are analysed using a hybrid multi-criteria framework developed in this study. Findings of this study highlight the importance of drone regulations, technology readiness, and workshops for drone pilots, industry professionals, and solar developers in India. This study aid developing economies in devising strategies that can promote the use of UAVs for solar PV plant commissioning activities.

Understanding Entrepreneurial Commitment: A Test of Side-Bet Theory

This paper contributes to the burgeoning literature on entrepreneurial commitment by providing a first empirical investigation of Howard Becker’s side-bet theory in the context of nascent entrepreneurship. We investigate how different forms of commitment relate to both entrepreneurial behavior and various side-bet categories: satisfying conditions, non-work concerns, others’ expectations, impersonal bureaucratic arrangements, perceived lack of alternatives, individual adjustments to social positions, and self-presentation concerns. We collected data from a representative sample (n = 242) of individuals granted with the French national student-entrepreneur status (NSES). Results froms hierarchical linear regressions reveal different patterns of side bets associated with entrepreneurial commitment, depending on its nature (value-based or exchange-based) and on its focus (the project or the profession). Our study (1) extends side-bet theory to the field of entrepreneurship and (2) improves understanding of factors associated with commitment during the volitional phase of the entrepreneurial process.

Predictive Model of Risk Behaviour in the University Student Community

Aim. The study aimed to attempt to explain the probable causality of undertaken risk behaviours in a group of university students, from the perspective of various predictors (which included low individual and social resources, as well as other risk behaviours). The choice of predictors was dictated, among other things, by the assumptions of risk behaviour theory and gateway theory. Methods. The research was conducted among university students from many cities in Poland. Empirical data were collected using the survey method. The tool was a survey questionnaire, constructed from the author's scale and the Resilience Scale by Gail Wagnild in the Polish adaptation by Janusz Surzykiewicz and Karol Konaszewski (2019). The data were statistically analysed. The target procedure became path analysis. Results. The study revealed several plausible predictive factors for specific risk behaviours of students. Analyses included behavioural categories such as the use of psychoactive substances and behaviour associated with them, risky sexual contact, low physical and psychosocial health care, dangerous behaviour in daily life, and disclosure of images and private data on the Internet. Each form of risky behaviour was situated in the model. The predictive factors in the path structure were identified as, among others, low social and cultural resources and low levels of resilience. It also appeared that some risk behaviours may be predictors for others. Conclusion. The study reveals that not only syndromes of risk behaviours, but even entire risk pathways creating the possibility to infer probable causality, are noticeable in the group of university students.

Investigation of Superstitious Behavior Tendencies of Professional Football Players and Coaches

This study aims to elucidate the superstitious behavior tendencies of professional football players and club coaches. The research group consists of 22 football players and 9 football coaches selected through the "Criterion Sampling" method. The research is considered as basic qualitative research. These are qualitative research studies in which data are collected through documents, interviews, or observation analyses. The research design employed in this study is phenomenology (phenomenological research design) which is a qualitative research design. In this research, “Superstitious Belief” has been selected as the phenomenon and the meanings attributed to this phenomenon by football players and coaches are attempted to be revealed. Semi-structured interview forms have been prepared to identify the superstitious behavior tendencies of players and coaches. In this context, open-ended questions are asked to the participants to determine their superstitious behavior tendencies. The data obtained have been analyzed using the content analysis method. According to the findings, the Superstitious Behavior Tendency comprises a theme, a sub-theme, and six categories. Under the sub-theme of superstitious behavior tendencies among professional football players and coaches are the categories of Clothing-Appearance, Lucky Objects, Pre-match Ritual, In-match Behavior, Team Ritual, Faith and Prayer. The opinions of players and coaches regarding the questions prepared for this theme are presented. Word clouds have been created illustrating prominent codes under the identified categories based on data obtained from interviews with participants. In conclusion, beyond effective preparation and training phases for performance enhancement, it is discerned that some superstitious behavior tendencies exist among players, contributing to elevating their motivation and self-confidence to higher levels.

PSIII-15 Feeding patterns of yearling Angus bulls based on feed efficiency metrics

Abstract The study objective was to determine how feeding behavior relates to feed efficiency in yearling Angus bulls at the Cal Poly Bull test. Bulls (n = 102) were housed in four pens sorted according to initial body weight (BW; 460 ± 35, 407 ± 15, 374 ± 20, 323 ± 30; mean kg ± SD), resulting in 24 to 28 bulls/pen. Feeding behavior was monitored using an electronic feeding system (SmartFeed, C-Lock, Inc.) during an 85-d feed efficiency test. Four feeders in each pen were filled twice daily at approximately 0630 h and 1700 h. The number of visits to the feed bunk, visit duration, total feeding time, and hourly feed intake were determined from data collected from the feeding system. These data were summarized by hour in a 24-h period, across the 85-d period. Bulls were retrospectively classified into a high feed efficiency group (HIFE) and low feed efficiency group (LOFE) based on an assessment of their summed residual feed intake rank and their feed:gain ratio rank. Within each pen, 5 HIFE and 5 LOFE bulls were selected, resulting in 20 bulls/treatment used for the final analyses. Data were analyzed using the MIXED procedure in SAS 9.4. The model included the fixed effects of feed efficiency group (HIFE vs LOFE), hour (0 to 24), and the feed efficiency group × hour interaction. Bull within pen was considered a random effect and hour a repeated measure. LOFE bulls visited the bunk more frequently throughout the day during the entire testing period (P = 0.04). Differences between feed efficiency groups depended on hour and most differences were observed within 3 h of fresh feed delivery (P ≤ 0.02). A feed efficiency group × hour interaction (P &amp;lt; 0.001) indicated that LOFE bulls had longer feed bunk visit durations predominantly between fresh feed deliveries (P ≤ 0.048). LOFE bulls spent more time feeding throughout the entire day (P = 0.003) when compared with HIFE bulls. Feeding duration per hour was longer for LOFE bulls during the post-morning feeding period (7-h duration) and post-evening feeding period (4-h duration; P ≤ 0.045). More feed was consumed by LOFE bulls throughout the day (P &amp;lt; 0.001), particularly during the post-morning feeding period (P ≤ 0.05). Overall, bulls exhibiting decreased feed efficiency had different feeding behavior patterns in all behavioral categories than their more feed efficient counterparts, especially around feed delivery. Thus, more active bulls at the feed bunk were less feed efficient, and this behavioral pattern may be an important consideration in bull selection.

Ethograms reveal a fear conditioned visual cue to organize diverse behaviors.

Recognizing and responding to threat cues is essential to survival. In rats, freezing is the most common behavior measured. Previously we demonstrated a threat cue can organize diverse behaviors (Chu et al., 2024). However, the experimental design of Chu et al. (2024) was complex and the findings descriptive. Here, we gave female and male Long Evans rats simple paired or unpaired presentations of a light and foot shock (8 total) in a conditioned suppression setting, using a range of shock intensities (0.15, 0.25, 0.35 or 0.5 mA). We found that conditioned suppression was only observed at higher foot shock intensities (0.35 mA and 0.5 mA). We constructed comprehensive, temporal ethograms by scoring 22,272 frames of behavior for 12 mutually exclusive behavior categories in 200 ms intervals around cue presentation. A 0.5 mA and 0.35 mA shock-paired visual cue suppressed reward seeking, rearing and scaling, as well as light-directed rearing and light-directed scaling. The shock-paired visual paired cue further elicited locomotion and freezing. Linear discriminant analyses showed that ethogram data could accurately classify rats into paired and unpaired groups. Considering the complete ethogram data produced superior classification than considering subsets of behaviors. The results demonstrate diverse threat-elicited behaviors - in a simple Pavlovian fear conditioning design - containing sufficient information to distinguish the fear learning status of individual rats.

Behavioral mirroring in Wistar rats investigated through temporal pattern analysis

The study of social interactions lies at the core of several disciplines such as psychiatry, psychology and ethology, just to name a few. In this context, understanding the temporal patterns underlying interactive behaviors is of crucial importance. Here, we employed T-pattern detection and analysis to study social interactions in ten pairs of Wistar rats tested in an Open-Field environment. We found four different categories of interactive behaviors. One of them was of particular interest to us because it consisted of behavioral events that, taken individually, should not underlie an interaction of any kind; however, they were included in T-patterns, which is suggestive of a dyadic temporal coordination in the behavioral expression of two individuals. Within this category, we described for the first time a new subcategory of apparent interaction patterns characterized by events that one of the two rats repeats only if previously produced by the partner (i.e., behavioral mirroring). These findings are discussed in functional terms for rodents and in light of our current understanding of social interactions in humans.

The Positive Deviance Concept: Pro and Con

ABSTRACT Multiple sociologists use the term, “positive deviance,” to refer to a sociologically meaningful category of behavior. Earlier, the author questioned the concept’s utility on the basis of its grab-bag, miscellaneous character – and hence, the inability of any known sociological theory to explain or account for it – as well as the fact that its components, “positive” and “deviance” are based on qualities lodged in distinctly separate dimensions. Still, curious about how sociologists of deviance regard the concept, the author contacted more than 200 instructors of the course and asked them how they feel about its coherence and utility; out of the 50 replies, only a quarter (N = 12) of the respondents rejected the meaningfulness of the concept out of hand, while two-thirds (N = 33) accepted it; the remaining one out of ten (N = 5) weren’t sure or reacted non-judgmentally. Though not rigorous or consistent, the concept seems to have achieved a secure place in the sociology of deviance literature; perhaps, on that basis, the nay-sayers should re-think their opposition to the concept.

Experiential Factors as a Determinant for Climate Change Mitigation Behavior: A Case of Tourist Hotel Managers in Naivasha Sub-County, Kenya

Climate change is typically abstract, large scale, slow and often unrelated to the welfare of people’s usual activities. There are however moments when the consequences of climate change are readily apparent, such as through experiencing extreme weather events. This study examined the association between personal experiences with extreme weather events together with emotions and taking actions that mitigate the consequences of climate change, a largely under researched topic. This relationship was tested among 182 randomly selected tourist hotel managers in Naivasha Sub-County in Kenya using paper and pencil survey techniques. An instrument made up of questions on the managers’ extent of their personal involvement with extreme climate risks and events was utilized. Another set of questions that rated the managers’ level of emotions towards climate change was also included. The study identified two categories of climate change mitigation behaviors (CCMB) using principle component analysis that are adopted by managers namely efficiency and curtailment practices. A beta regression model that accounts for the non-normality of the data was used to examine the relationship between experiential factors and CCMB. Findings indicate a moderate engagement in CCMB among the surveyed managers. Experiencing disasters was negatively and significantly associated with both curtailment and efficiency CCMB. Emotions were positively associated with efficiency CCMB but had a negative association with curtailment CCMB. The findings suggest that public education would benefit from strategies that focus on connecting experiencing weather related disasters and emotions with the reality of climate change.

Motion focus global–local network: Combining attention mechanism with micro action features for cow behavior recognition

The use of machine vision technology for recognizing cow behavior plays a crucial role in daily management, health monitoring, and breeding and reproduction in dairy farming, making it an essential component of modern smart agriculture. This paper presents a novel dual-stream network model, the Motion Focus Global-Local Network (MFGN), for analyzing cow video data. The dual-stream network consists of a global spatiotemporal feature stream and a fine motion feature stream. The global spatiotemporal feature stream extracts key frames to remove redundant information and utilizes a Transformer network for global spatio-temporal feature extraction, reflecting the dynamic changes in cow videos and the temporal relationships between video frames. The fine motion feature stream, based on frame differencing of cow videos, uses focal convolution to capture subtle movements of cows, enhancing the focus on minor behavioral changes. To evaluate the performance of the proposed model, video data samples were collected from eight cows marked on their bodies and heads at an Australian farm site (CSIRO Armidale), including a total of 1715 video sequences across three behavior categories. The model achieved recognition accuracies of 98.1% for drinking, 95.5% for grazing, and 49.3% for other behaviors, with an overall average recognition accuracy of 79.4%, representing a 7.4% improvement over the classic TSN model. Overall, the MFGN network effectively extracts and integrates global spatiotemporal features with fine motion features from cow video data, modeling both the overall sequence characteristics and focusing on local motion details, achieving precise behavioral recognition. This research not only enhances the accuracy of cow behavior recognition but also provides new technological means for precise management in modern smart agriculture, with broad industry application potential to improve farm efficiency, profitability, and disease control and prevention.

Correlation of Parenting Styles and Oral Health Behavior of Children with Down Syndrome in Banjarmasin

Background: Parental, Down syndrome is a trisomy chromosomal genetic disorder that leads newborn child having more than one chromosome so that could interfere the normal growth of the body due to an excess of protein level. Children with Down syndrome have different oral health problems compared to the general population. They need assistance to maintain their oral health because of mental and physical deficiencies as well as limitations in optimally cleaning their teeth. Objective: To analyze the relationship between parenting styles and oral health behavior of children with Down syndrome in Banjarmasin Special Needs School, Indonesia. Materials and Method: This is an observational analytic study with cross-sectional approach. Sample was taken using simple random sampling technique and a cross-sectional sample formula obtained for 32 respondents. The instrument used a parenting style questionnaire and a behavioral modification questionnaire for children's oral health. Results: Democratic (87.5%) compared switch order parenting styles. It was found that there were more children with Down syndrome in the category of moderate behavior (59%) than children with good behavior (38%) and poor behavior (3%). The results of the Somers'd test data analysis obtained a significance value of 0.034 (p&lt;0.05) and the correlation value obtained was 0.571, which means that the correlation strength is moderate with a positive correlation direction (unidirectional). Conclusion: The more democratic the parents use as parenting style the better the oral health behavior of children with Down syndrome will be.

Public Behavior and Emotion Correlation Mining Driven by Aspect From News Corpus.

Emotion motivates behavior. Investigating the correlation between behavior and emotion, an often overlooked perspective, plays a significant role in uncovering the underlying motives behind behaviors and the intrinsic cause-effects of social events. This article proposes a methodology for mining the correlation between public behavior and emotion using daily news data. Initially, aspect-emotion-reaction (A-E-R) triplets are extracted and generalized, encompassing both explicit and implicit patterns. Then, a knowledge representation model based on hypothetical context (KRHC) with a self-reflection mechanism is proposed to uncover implicit relationships between emotion and behavior through attention mechanisms. By combining rule-based methods for explicit relationships and deep learning for implicit ones, an understanding of emotion-behavior patterns is achieved. In this study, the behaviors are divided into three categories of prosocial, antisocial, and normal behaviors with ten secondary types. Seven categories of emotions are adopted. The proposed deep learning model KRHC is validated on A-E-R datasets and public KINSHIP datasets. The experiment results are concluded; for example, when "fear", "sad", and "surprise" emotions appear, it drives behavior "panic" with most probability. These findings could provide insights for both human-computer interaction and public safety management applications.

Investigating the influence of current trends and behaviours on household structures and housing consumption patterns

As a major contributor to overall carbon emissions and energy consumption, the housing sector has great potential to reduce energy consumption, whether by reducing the number of appliances, heating temperature or floor space. Consumption patterns encompass how people choose and consume products that satisfy their needs and wants. However, wants, and to some extent needs, are influenced by various factors and existing material and non-material (infra)structures, especially in the housing sector. Focusing on the floor area, this article aims to identify potentials towards lower consumption lifestyles by applying the Avoid-Shift-Improve framework in the residential sector. Through a conceptual review, the article explores what shapes current patterns of space use and outlines potential future pathways. Starting from the macro level, the article examines existing and emerging (societal) trends with (potential) impacts on housing consumption. It then looks at the structural development of households affected by the studied trends. At the micro level, the article provides an overview of the potential impact of individual behaviour on space use patterns within different categories of housing behaviour. The article identifies the potential for social and technical change in the housing sector and concludes that promoting non-materialistic narratives (avoid), offering alternative and innovative solutions to satisfy people’s spatial needs (shift) and designing flexible buildings (improve) appear to be effective ways for fostering behavioural change towards more efficient use of space.

Community Nursing Care: Unhealthy Lifestyle as A Risk Factor of Hypertension in Older Adults in Rural Areas

Introduction: Adults-elderly habits of unhealthy lifestyles increase the risk of hypertension. The high incidence of hypertension causes indications for the diagnosis of community health deficit. Hypertension can be associated with heredity, an unhealthy lifestyle such as smoking, less consumption of vegetables and fruits, lack of physical activity, alcohol consumption and stress. Objective: to determinan the picture of community nursing care, unhealthy lifestyles health deficit problems in older adults with behavioral promotion interventions for health efforts in RW 02 Baratan Village. Method: The method use in this study was quantitative descriptive and quota sampling technique to determine the sample. Results: The research results show that implementig promotion interventions for health efforts are able to increase knowledge, atttudes, and behavior towards healthy lifestyles through education programs, exercise, and demonstrations. The Sig(2-tailed) value obtained in the knowledge category was P=0.025; attitude category P=0.000; behavior category P=0.000 where a&lt;0.05 means there is a difference between before and after implementation. Conclusion: There has been increase in health program participation, health standars compliance, and monitoring of communty health standards.

Landscape structure influences the spatial distribution of urban bird attractiveness

ContextLandscape change affects biological diversity and the distribution of species traits related to spiritual, educational, and recreational benefits people derive from nature. These traits are associated with color, song and behavioral characteristics that influence people's perceptions of how attractive an assemblage is. However, the environmental variables that affect the spatial distribution of traits related to the attractiveness of biological diversity remain unexplored.ObjectivesWe tested how landscape structure influences patterns of perceived bird attractiveness (trait diversity associated with colorfulness, behavioral and song categories) across an urbanization gradient.MethodsWe used data from standardized surveys of birds and landscapes within 42 landscape units of 1km2 across the city of Brisbane in eastern Australia. We used structural equation modeling to test effects of landscape composition (built infrastructure, percentage of tree cover) and landscape configuration (fragmentation of tree cover) on mean bird community attractiveness. Relationships between individual traits and landscape structure were analyzed using multinomial logistic regression models.ResultsOur analysis across 82 bird species shows that the relative amount of built infrastructure in a landscape interacts with fragmentation to reduce the overall attractiveness of the landscape’s bird assemblage. However, built areas can exhibit high overall bird attractiveness where there is (1) reduced fragmentation and (2) increased diversity of vegetation structure that provides key habitats for many colorful species with a high diversity of calls. Relationships between bird attractiveness and landscape structure change when they are analyzed at the guild level (insectivores vs frugivores/nectarivores). In addition, body size moderates the effects of landscape structure on song complexity, personality, and color.ConclusionsSmall bodied, colorful and melodious species are negatively affected by built infrastructure and fragmentation. By learning how habitat loss and fragmentation affect the distribution of species-rich, attractive bird assemblages, our findings can inform how urban landscapes might be structured to increase people’s connection with nature.