FROM NECTAR TO CARRION: A REVIEW EXPLORING THE DIVERSE FEEDING ECOLOGY OF BUTTERFLIES

Vanshita M. Silamwar , R. J. Andrew — Sun, 14 Jun 2026 00:00:00 +0000

Butterflies exhibit a remarkable diversity of feeding behaviours that extend far beyond simple nectar feeding. While floral nectar remains the principal food source for most adult butterflies, numerous species also exploit alternative nutrient resources such as tree sap, rotting fruits, carrion, dung, sweat, tears, and mineral-rich mud. These feeding strategies provide essential carbohydrates, amino acids, sodium, and other minerals necessary for survival, flight, reproduction, and ecological adaptation. The present review synthesizes current knowledge on butterfly feeding behaviour, puddling ecology, and the structure and function of siphoning mouthparts. Special emphasis is placed on mud-puddling behaviour, nutrient acquisition, feeding specialization, and environmental influences on feeding frequency.

The review also examines ecological and evolutionary aspects of butterfly nutrition, including reproductive advantages associated with sodium acquisition and the adaptive significance of feeding on unconventional substrates. Understanding butterfly feeding ecology is important not only for evolutionary biology and insect physiology but also for biodiversity conservation and ecosystem management. The review highlights the complexity of butterfly nutritional ecology and underscores the need for further studies on feeding adaptations under changing environmental conditions.

AI-DRIVEN BEHAVIORAL ECONOMICS IN EMERGING MARKETS: MODELING INVESTOR BIASES AND MARKET ANOMALIES IN NEPAL'S STOCK EXCHANGE (NEPSE)

Rajesh Shahi — Tue, 30 Jun 2026 00:00:00 +0000

Artificial Intelligence (AI) is having a notable impact on global capital markets, influencing everything from transactions to investor behavior.The convergence of AI and behavioral economics is becoming a defining force in global capital markets, impacting both transaction dynamics and investor behavior. However, the emerging markets like the Stock Exchange of Nepal (NEPSE) are still relatively under-researched in this joint space where informational asymmetries exist, there is not enough institutional investor participation and regulation is fragmented and subject to cognitive biases. This study involves the application of machine learning, natural language processing (NLP), and structural equation modeling (SEM/PLS- SEM) to primary investor survey data as well as secondary NEPSE market indices to detect, measure, and mitigate biases of investors associated with anomalies in the NEPSE market using AI driven analytical models. A sequential explanatory mixed method was used. The questionnaire, consisting of a 5-point Likert scale questionnaire and semi-structured interviews with 22 active NEPSE institutional experts, were used to collect the quantitative data from 547 active investors in NEPSE. The secondary data consisted of day-to-day returns of NEPSE index from 2019-2025. Results confirm that overconfidence bias (beta = 0.431, p < 0.001), herding behavior (beta = 0.389, p < 0.001), loss aversion (beta = 0.312, p < 0.001), and anchoring bias (beta = 0.278, p < 0.01) significantly predict suboptimal investment decisions. The accuracy of investor sentiment classification achieved by the AI-based sentiment analysis with the BERT model is 91.7%. There is a moderation effect between herding and investment decisions when introducing the use of AI and a partial mediation of the relation of herding and loss aversion effect when introducing financial risk propensity.The use of AI in herding and the use of financial risk propensity partially mediate the relationship between herding and loss aversion. NEPSE shows non-random distribution and time varying inefficiency which is consistent with the premise of the Adaptive Market Hypothesis (AMH). It is a study that combines the AI model of behavioral econometrics with direct psychology data of investors from the primary market to Nepal, and proposes a Behavioral Anomaly Detection Framework (BADF) combining the elements of Prospect Theory, Adaptive Market Hypothesis, and Human centered AI Design Principles.

AGPE THE ROYAL GONDWANA RESEARCH JOURNAL OF HISTORY, SCIENCE, ECONOMIC, POLITICAL AND SOCIAL SCIENCE

FROM NECTAR TO CARRION: A REVIEW EXPLORING THE DIVERSE FEEDING ECOLOGY OF BUTTERFLIES

AI-DRIVEN BEHAVIORAL ECONOMICS IN EMERGING MARKETS: MODELING INVESTOR BIASES AND MARKET ANOMALIES IN NEPAL'S STOCK EXCHANGE (NEPSE)