Adaptive Management Research Articles

Unveiling Climate–Land Use and Land Cover Interactions on the Kerch Peninsula Using Structural Equation Modeling

This paper examines the effects of climatic factors, specifically temperature and precipitation, on land use and land cover (LULC) on the Kerch Peninsula using structural equation modeling (SEM). The Normalized Difference Vegetation Index (NDVI) was used as a mediator in the model to accurately assess the impact of climate change on vegetation and subsequent LULC dynamics. The results indicate that temperature exerts a significant negative influence on LULC in the early periods, inducing stress on vegetation and leading to land degradation. However, this influence diminishes over time, possibly due to ecosystem adaptation and the implementation of resilient land management practices. In contrast, the impact of precipitation on LULC, which is initially minimal, increases significantly, highlighting the need for improved water resource management and adaptation measures to mitigate the negative effects of excessive moisture. The NDVI plays a crucial mediating role, reflecting the health and density of vegetation in response to climatic variables. An analysis of lagged effects shows that both precipitation and temperature exert delayed effects on LULC, underscoring the complexity of water dynamics and ecosystem responses to climatic conditions. These results have important practical implications for land resource management and climate adaptation strategies. Understanding the nuanced interactions between climatic factors and LULC can inform the development of resilient agricultural systems, optimized water management practices, and effective land use planning. Future research should focus on refining models to incorporate nonlinear interactions, improving data accuracy, and expanding the geographic scope to generalize findings. This study highlights the importance of continuous monitoring and adaptive management to develop sustainable land management practices that can withstand the challenges of climate change.

Machine learning-based investigation of forest evapotranspiration, net ecosystem productivity, water use efficiency and their climate controls at meteorological station level

Evapotranspiration (ET), net ecosystem productivity (NEP), and ecosystem water use efficiency (EWUE) of forests are changing due to climate change. Traditional models using coarse-scale climate reanalysis data fail to capture local meteorological and hydrological conditions accurately. This study combines in situ meteorological observations, remote sensing, and advanced datasets (forest age, rooting depth, soil moisture) to estimate ET, NEP, and EWUE at forest meteorological stations via machine learning. About 60.6 % of stations showed a decrease in NEP from 2003 to 2010 to 2011–2019, while 63.9 % showed an increase in ET, and 58.9 % showed a decrease in EWUE. NEP and EWUE significantly declined in forests older than 60 years, with younger forests exhibiting higher NEP. EWUE in different forest types is driven by varying mechanisms, with DBF sites influenced by VPD and ENF sites by RSDN. EWUE of regions with inconsistent VPD data between site and reanalysis, such as northwestern North America, showed divergences from previous reanalysis-based studies but aligned more with atmospheric inversion findings. Slight summer VPD increases boosted NEP, especially in high-latitude areas, while early spring phenology and increased spring VPD reduced summer water availability. Incorporating more site-specific observations, such as plant traits, could enhance understanding of climate-plant-ecosystem relationships. This study underscores the potential of meteorological station-level data to improve forest carbon and water flux dynamics understanding, aiding forest management for carbon neutrality and climate adaptation.

A deep learning-based model for estimating pollution fluxes from rivers into the sea and its optimization

Pollution fluxes from rivers into the sea are currently the main source of pollutants in nearshore areas. Based on the source-sink process of the basin-estuary-coastal waters system, the pollution fluxes into the sea and their spatiotemporal heterogeneity were estimated. A deep learning-based model was established to simplify the estimation of pollution fluxes into the sea, with socio-economic drivers and meteorological data as input variables. A method for estimating the contribution rate of pollution fluxes from different spatial gradient was proposed. In this study, we found that (1) the pollution fluxes into the sea of total nitrogen (TN) and total phosphorus (TP) from the Bohai Sea Rim Basin (BSRB) in 1980, 1990, 2000, 2010, and 2020 were 25.38 × 104, 26.12 × 104, 27.27 × 104, 29.82 × 104, 25.31 × 104 and 1.32 × 104, 2.14 × 104, 2.09 × 104, 1.87 × 104, 1.68 × 104 tons, respectively. (2) The proportion of rural life and livestock to the TN was the highest, accounting for 39.18 % and 21.19 %, respectively. The proportion of livestock to the TP was the highest, accounting for 39.20 %, followed by rural life, accounting for 24.72 %. The results indicated that the pollution fluxes in the BSRB were related to human economic activities and relevant environmental protection measures. (3) The deep learning-based model established to estimate runoff pollution fluxes into the sea had the accuracy of over 90 %. (4) As for contribution rate, in terms of the elevation, the range of 0–100 m had the highest proportion, accounting for 39.65 %. The range of 50–100 km from the coastline had the highest proportion, accounting for 18.11 %. In terms of the district, coastal area has the highest proportion, accounting for 38.00 %. This study revealed the changing trends and driving mechanisms of pollution fluxes into the sea over the past 40 years and established a simplified deep learning-based model for estimating pollution fluxes into the sea. Then, we identified regions with high pollution contribution rate. The results can provide scientific references for the adaptive management of the nearshore areas based on the ecosystem.

SOIL CONSERVATION STRATEGIES FOR THE REDUCTION OF BIODIVERSITY IN MOUNTAIN SOILS: EXAMPLE OF ULUDAĞ NATIONAL PARK

Uludag National Park in Turkey is famous for its rich biodiversity. The park serves as an essential habitat for a wide variety of flora and fauna and contributes significantly to the conservation of various species. This study examines biodiversity conservation strategies in Uludağ National Park. Soil samples were taken from 17 locations near the summit where endangered endemic plants grow. Analyses of the soil samples reveal the relationships between elevation and soil texture components. According to the soil analysis results, there was a weak positive correlation between elevation and silt content (r = 0.414) and a weak negative correlation between elevation and sand content (r = -0.375). These findings indicate that silt content tends to increase and sand content tends to decrease with increasing elevation. The geomorphological features and soil structure of Uludağ National Park were also an essential part of the research. The region's metamorphic mica schists, granites and marbles affect the soil's physical properties. For example, soils are generally sandy and permeable in areas where granite parent material is present. In contrast, areas where mica-schist parent material is distributed are less resistant and more susceptible to erosion. As a result, effective soil conservation strategies must be implemented to protect biodiversity in Uludağ National Park. These strategies are essential to reduce soil erosion, increase organic matter accumulation and minimise the impacts of climate change. Furthermore, community participation and adaptive management strategies must be adopted for sustainable development and resource management. This study provides essential information for biodiversity conservation in the Uludağ region and contributes to developing conservation strategies.

Phenotypic variation in 1,100 provenances of Picea abies measured over 50 years on 33 German trial sites

We present a database of Norway spruce phenotypic traits measured over a period of 51 years in provenance trials planted across western Germany. These trials are part of the ‘Inventory Provenance Test 1964/68 with Norway spruce’ (IPTNS) that tested a total of 1,100 provenances collected across Europe, both from the species natural and artificial range. Phenotypic traits (first height, later diameter at breast height, survival) were recorded at 33 trial sites of 100 provenances, each a subsample from the entire collection area that represents an approximately comparable climate space. The dataset contains 424,781 records of the same 65,518 trees ranging from age 7 to 51. Overall, it captures the considerable genetic and phenotypic intraspecific variation present in Norway spruce and should be of interest to various fields including quantitative genetics, ecology, biogeography and the adaptive management of forests.

Lived Experiences of Graduate School Students in A Philippine University: A Phenomenological Study

This phenomenological study explores the lived experiences of graduate school students at the University of Saint Anthony (USANT) in Iriga City, Camarines Sur, Philippines. The study aims to uncover how these students balance academic responsibilities with personal and professional obligations, adapt to online learning, and manage stress, particularly in the context of their motivations for career advancement as DepEd teachers. Data were collected through in-depth, semi-structured interviews with five graduate students and analyzed using a phenomenological approach. The findings reveal significant challenges in time management, technological adaptation, and stress management, highlighting the importance of supportive family, peer networks, and faculty. The study emphasizes the role of intrinsic motivation, driven by career aspirations, in sustaining students' engagement and perseverance. The results underscore the need for flexible learning environments, robust institutional support, and tailored policies to accommodate the diverse needs of graduate students. The integration of phenomenological and adult learning theories provides a comprehensive understanding of these experiences, offering valuable insights for improving graduate education programs. For schools offering graduate programs, the study suggests enhancing technological infrastructure, academic support services, and flexible scheduling to support their students better.

Unveiling emerging interdisciplinary research challenges in the highly threatened sclerophyllous forests of central Chile

BackgroundThe potential ecosystem collapse of forests in Mediterranean-type ecosystems (MTEs) by unprecedented droughts is worrisome due to the impacts on its exceptional biodiversity and human well-being. However, research integrating the impacts of global change drivers, forest resilience and the challenges facing human-nature relationships is still scarce.Methods and resultsUsing the central Chile megadrought and recently massive forest browning event as a model scenario, we identified, through a scientific literature review and an interdisciplinary scientific workshop, the research priorities and questions to address for MTEs in a context of global change. Our results highlighted knowledge gaps that need to be covered, particularly in social and environmental sciences, with an emphasis on soil science. Research priorities must focus on (1) the understanding of interactive effects of global and local anthropogenic drivers on MTEs and (2) the evaluation of the potential impacts of MTEs collapse on human well-being and ecosystem functioning.ConclusionsWe highlight the need for a collaborative approach involving scientists, landowners, managers/administrators, and policymakers to apply adaptive forest management against the current socio-environmental challenges under a global change context.

Climate Impacts on Tangible Coastal Cultural Heritage in the United States: Towards Sustainable and Adaptive Coastal Heritage Management

Climate Impacts on Tangible Coastal Cultural Heritage in the United States: Towards Sustainable and Adaptive Coastal Heritage Management

Quality-driven sustainability in Jordan’s food export supply chains

Purpose This paper aims to address the imperative of establishing adaptive sustainable supply chain management (SSCM) within the context of export food supply chains (EFSC) in Jordan, with potential global applicability. The research introduces a new conceptual framework validated through the analytic hierarchy process (AHP), prioritizing critical factors based on five expert opinions. The practical and managerial outcomes emphasize achieving sustainable supply chain performance through quality-driven sustainability (QDS), encapsulated in a practical framework (SSCQM). Employing the concept of QDS, this study aims to develop a novel decision framework for Sustainable Supply Chain Quality Management (SSCQM). Design/methodology/approach Conducted as an empirical qualitative study in Jordan, the research employs a systematic literature review to identify research gaps and establish the initial conceptual framework. A pilot case is subsequently conducted to refine the framework, followed by Case Studies of four Triads, elucidating network relationships between suppliers, manufacturers and customers. A total of 32 semi-structured interviews, complemented by observations, tours and document analysis, individually explored each case during the exploratory stage. Cross-analysis of the four cases at the explanatory stage provides empirical insights, and data triangulation, literature review and validation using NVivo enhance the robustness of the findings. Findings The amalgamation of the three perspectives, when treated individually, results in the integrated category of QDS influencing sustainability (SUST) performance, evident in economic, environmental and social dimensions. Various sustainable initiatives have emerged through the relationships within the Triad, comprising a company, supplier and customer in the supply chain and its broader environment, including society, aiming for enhanced sustainable development and SUST performance. Recognizing the urgency of Supply Chain Management (SCM) in the Middle East, where global supply chains are susceptible to disruptions leading to food shortages and insecurity, this research investigates the state of EFSC in the export food industry in Jordan. The novel decision (SSCQM) framework equips managers and policymakers with knowledge and practices applicable to focal actors in Triads within their EFSCs, particularly in developing countries, thereby contributing to enhancing the export food industry’s sustainability. Research limitations/implications This research provides empirical and contextual justifications for the supply chain-quality model (SSCQM) that advocates for business organizations how a desirable, sizeable and significant outcome/shock can be triggered via embedding quality elements in an international supply chain specifically in Jordan. The research also offers significant implications for practising supply chain experts in terms of how they can effectively utilize the supply chain quality to align other players in the supply chain, revenue potential, customer retainability and employee retention to achieve superior organizational performance. The research selectively encompasses three key perspectives of supply chain networks (SCN), total quality management (TQM) and SUST to elucidate the essence of SCM. Originality/value This research contributes towards filling the gap as a lack of empirical research.

Factors influencing fish migration in one of the world's largest inland fisheries

Fish from Cambodia's Tonle Sap Lake are economically, culturally, and nutritionally significant for people in the Lower Mekong Basin, providing income, livelihoods, and protein. Fish in this system generally migrate toward upstream Mekong River in dry season and return in early wet season. However, drivers of fish migration from Tonle Sap Lake to the Mekong River are not well-understood. In this paper, we utilized Mixed Effects Random Forest to predict the catch weight of six fish species migrating from the Tonle Sap Lake to the Mekong River using precipitation, lunar cycle, and hydrologic conditions like river stage, streamflow, flow magnitude, and timing as predictors. As a surrogate for fish migration, we used daily fish catch weight from 2002 through 2008 at the bagnet, or Dai, fisheries along Tonle Sap River, a migration corridor connecting Tonle Sap Lake to the Mekong River. We found that migration of large fish was mainly cued by streamflow and flow magnitude, while smaller fish migrate depending on the combination of streamflow and flow timing. Streamflow less than average cumulative flow was the most important driver for migration of Pangasianodon hypophthalmus, and Cirrhinus microlepis. Migration of Cyclocheilichthys enoplos and Osteochilus melanopleurus was highly dependent on the number of low- and minimum-flow days. Cumulative flows, period of high flow and water level were the main predictors of the small mud-carp Henicorhynchus entmema's migration, while individuals of Labiobarbus leptocheilus migrated out of the Tonle Sap Lake depending on the number of days after 7-, 30-, and 90-day minimum flows. These results suggest that flow characteristics can be used to aid conservation and adaptive management of Cambodia's Dai fisheries.

Managing the risk of program failure: Propositional Evaluation as a tool for risk management

Public policy and programs fail for a range of reasons, some preventable and some much harder to treat. Program administrators require methods for managing risk. This article argues that evaluation can be a useful tool for identifying and managing risk. It offers Propositional Evaluation as a cost-effective, risk-based approach to the evaluation of any policy, program, initiative, or simply, plan of action for the public good. Propositional Evaluation treats programs as propositions for action and focuses on developing sound, that is, valid and well-grounded propositions. It begins in the design phase and continues through delivery to provide a means for identifying and managing risks to achieving intended outcomes. This article situates Propositional Evaluation approach in the evaluation theory literature. It reflects on common causes of program failure and identifies those evaluators may help program administrators identify and mitigate. It provides a description of five critical and five residual risks that derive from the propositional approach. Evaluating these risks provides a structure for evaluation and adaptive management. The intended audience is program administrators, and evaluators working to support them who require prospective evaluation and adaptive management or rapid and cost-effective evaluation for program design and re-design.

Leveraging Edge Computing for Video Data Streaming in UAV-Based Emergency Response Systems.

The rapid advancement of technology has greatly expanded the capabilities of unmanned aerial vehicles (UAVs) in wireless communication and edge computing domains. The primary objective of UAVs is the seamless transfer of video data streams to emergency responders. However, live video data streaming is inherently latency dependent, wherein the value of the video frames diminishes with any delay in the stream. This becomes particularly critical during emergencies, where live video streaming provides vital information about the current conditions. Edge computing seeks to address this latency issue in live video streaming by bringing computing resources closer to users. Nonetheless, the mobile nature of UAVs necessitates additional trajectory supervision alongside the management of computation and networking resources. Consequently, efficient system optimization is required to maximize the overall effectiveness of the collaborative system with limited UAV resources. This study explores a scenario where multiple UAVs collaborate with end users and edge servers to establish an emergency response system. The proposed idea takes a comprehensive approach by considering the entire emergency response system from the incident site to video distribution at the user level. It includes an adaptive resource management strategy, leveraging deep reinforcement learning by simultaneously addressing video streaming latency, UAV and user mobility factors, and varied bandwidth resources.

Using Wearables to Study Biopsychosocial Dynamics in Couples Who Cope With a Chronic Health Condition: Ambulatory Assessment Study.

Technology has become an integral part of our everyday life, and its use to manage and study health is no exception. Romantic partners play a critical role in managing chronic health conditions as they tend to be a primary source of support. This study tests the feasibility of using commercial wearables to monitor couples' unique way of communicating and supporting each other and documents the physiological correlates of interpersonal dynamics (ie, heart rate linkage). We analyzed 617 audio recordings of 5-minute duration (384 with concurrent heart rate data) and 527 brief self-reports collected from 11 couples in which 1 partner had type II diabetes during the course of their typical daily lives. Audio data were coded by trained raters for social support. The extent to which heart rate fluctuations were linked among couples was quantified using cross-correlations. Random-intercept multilevel models explored whether cross-correlations might differ by social contexts and exchanges. Sixty percent of audio recordings captured speech between partners and partners reported personal contact with each other in 75% of self-reports. Based on the coding, social support was found in 6% of recordings, whereas at least 1 partner self-reported social support about half the time (53%). Couples, on average, showed small to moderate interconnections in their heart rate fluctuations (r=0.04-0.22). Couples also varied in the extent to which there was lagged linkage, that is, meaning that changes in one partner's heart rate tended to precede changes in the other partner's heart rate. Exploratory analyses showed that heart rate linkage was stronger (1) in rater-coded partner conversations (vs moments of no rater-coded partner conversations: rdiff=0.13; P=.03), (2) when partners self-reported interpersonal contact (vs moments of no self-reported interpersonal contact: rdiff=0.20; P<.001), and (3) when partners self-reported social support exchanges (vs moments of no self-reported social support exchange: rdiff=0.15; P=.004). Our study provides initial evidence for the utility of using wearables to collect biopsychosocial data in couples managing a chronic health condition in daily life. Specifically, heart rate linkage might play a role in fostering chronic disease management as a couple. Insights from collecting such data could inform future technology interventions to promote healthy lifestyle engagement and adaptive chronic disease management. RR2-10.2196/13685.

Learning consciousness in managing water for the environment, exemplified using Macquarie River and Marshes, Australia

Context Ongoing learning is essential for freshwater ecosystem management, but there is limited documentation of successful integration into management. Aims We aimed to increase learning-related understanding required for effective adaptive management of water for the environment, in water-stressed and contested river systems. Methods We developed a learning approach (requisite learning) for managing water for the environment, demonstrated with real-world examples from the Macquarie River and Marshes, Australia. Key results Four co-existing, interdependent learning types enable effective management of water for the environment: (1) ‘adjusting routines’, (2) ‘adaptive assessment’, (3) ‘changing practice’, and (4) ‘transforming governance’, exemplified by using management of water for the environment for the Macquarie River and Marshes. To enable and improve requisite learning, stakeholder social learning, and flexibility in governance arrangements, must develop. Conclusions Ongoing learning is essential for effective adaptive management. Understanding what requisite learning is and how capacity can be improved, will help achieve outcomes required of managing water for the environment. Implications Effective management of water for the environment is essential, transparently delivering environmental outcomes and accounting for decision-making. To do this, we need to improve explicit learning understanding by nurturing learning mandates and champions, fostering social learning, increasing flexibility in governance arrangements, and institutionalising learning.

The effect of land use and land cover on soil carbon storage in the Yellow River Delta, China: Implications for wetland restoration and adaptive management

Gaining a comprehensive understanding of the effect of land use/land cover (LULC) and soil depth on soil carbon storage, through the manipulation of external carbon input and turnover processes, is crucial for accurate predictions of regional soil carbon storage. Numerous research investigations have been conducted to examine the impact of LULC on the storage and cycling of carbon in the surface soils of coastal wetlands. Nevertheless, there remains a dearth of understanding concerning the implications of this phenomenon on subterranean soils, a crucial factor in discerning the capacity for carbon sequestration in coastal wetlands and implementing measures for their preservation. The study focused on the Yellow River Delta (YRD) in China, which serves as a representative model system. It aimed to assess the impact of LULC as well as soil depth on carbon storage. This was achieved by a combination of remote sensing interpretation and field samplings. The findings of the study indicate that there was an increase in soil organic carbon storage with both the area covered and the depth of the soil across the four different land use types, namely forest, grass, tidal flat, and cultivated land. Cultivated land was identified as the predominant LULC type, encompassing 41.73% of the entire YRD. Furthermore, it accounted for a substantial carbon storage of 76.08%. In comparison to soil layers at depths of 0–20 cm and 20–40 cm, 40–60 cm was discovered to have the maximum carbon storage, accounting for 42.29% of total carbon storage. Furthermore, one of the main factors influencing carbon storage is salinity, which shows a negative association with carbon storage. Moreover, the aforementioned findings underscore the significance of the conjoined physical and chemical properties induced by LULC in influencing the dynamics of soil carbon. This suggests that the inclusion of deep soil carbon in the estimation and restoration of soil carbon storage is necessary. This inclusion will support the realization of the United Nations' “Toward Zero Carbon” effort and facilitate the implementation of China's national carbon neutrality objectives.

Economic Factors and Performance of Selected Quoted Consumer Goods Manufacturing Companies: The Moderating Roles of Firm Agility

The problem of this study is the significant decline in the performance of consumer goods manufacturing companies, which is caused by various economic factors such as import policies, inflation, infrastructure, interest rates, and exchange rates. This study aims to evaluate the impact of economic factors on the performance of publicly traded consumer goods manufacturing companies and test how corporate agility moderates this relationship. The study employed a cross-sectional survey design with a population of 5,625 employees from 12 companies, yielding a final sample of 563. Data was collected through a questionnaire validated by confirmatory factor analysis and measured by a Likert scale. Data analysis was conducted with multiple regression using SPSS version 22.0. The results show that economic factors significantly affect firm performance, and firm agility significantly moderates this relationship. The conclusion of this study suggests that consumer goods manufacturing companies need to improve their agility to overcome the negative impact of economic factors. This study implies the need for more adaptive and flexible management strategies to enhance firm performance in the face of dynamic economic challenges.

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

Hydrological drought assessment of the Sava River basin in South-Eastern Europe

ABSTRACT Hydrological drought poses significant challenges to water resources, ecosystems, and human activities, necessitating comprehensive investigation. Monthly streamflow data from 12 monitoring stations across the Sava River basin were utilized to compute the streamflow drought index (SDI). The Mann–Kendall test evaluated trends, and the SDI's hydrological states were classified based on cumulative streamflow volumes. The study identified an alarming 83.3% of stations exhibiting statistically significant decreases in summer streamflow, indicating a widespread and concerning trend of declining water availability in the Sava River basin. Drought severity was particularly pronounced in tributaries such as the Vrbas and Bosna rivers, emphasizing the heterogeneous nature of hydrological changes. These findings underscore the urgent need for adaptive water resource management strategies in the face of escalating hydrological drought risks, especially given the far-reaching consequences on agriculture, industry, ecosystems, and social well-being. The study provides crucial insights for developing targeted resilience measures tailored to the specific challenges presented by the diverse hydrological conditions in the Sava River basin.

A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision

A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision

Investigating the lipid profile of Anopheles stephensi mosquitoes across developmental life stages

Holometabolous insects undergo a distinct transition in their development, tightly correlated with shifting feeding patterns from larval stages and some adult phases to non-feeding phases as pupae and during other adult phases. Furthermore, the intricate life cycle of mosquitoes involves a sequence of developmental stages influenced by aquatic and terrestrial factors, demanding precise energy resource orchestration. Lipids serve multifaceted roles, encompassing energy storage, membrane structure, and participation in signal transduction and molecular recognition processes. A significant gap in the current research landscape is the need for a comprehensive study exploring the lipid repertoire throughout the developmental stages of Anopheles stephensi mosquitoes. We undertook an analysis of the An. stephensi metabolome across all life stages. We hypothesized that An. stephensi mosquitoes will have unique lipid metabolite markers for each life stage. A specific extraction and LC-MS based lipidomic approach was used to test this hypothesis. Our findings demonstrated that our methods were successful, with lipids comprising 62.15 % of the analyzed metabolome. Additionally, phospholipids (PL), lysophospholipids (LPL), sphingomyelin (SM), and triglycerides (TG) were abundant and dynamic across all life stages. Interestingly, comparison between the L1 and L2 lipidome revealed a dominant pattern of specific TGs in decreased abundance between these two life stages. Lastly, 20-hydroxyecdysone (20E), was found to be present in similar abundance across all 4 larval stages. These data indicate that there may be lipid metabolome pathways serving unique roles during mosquito development that may be used to explore laboratory management of colonies, parasite resistance, and environmental adaptation.