Data Quality Research Articles

The Immune Epitope Database (IEDB): 2024 update.

Over the past 20 years, the Immune Epitope Database (IEDB, iedb.org) has established itself as the foremost resource for immune epitope data. The IEDB catalogs published epitopes and their contextual experimental data in a freely searchable public resource. The IEDB team manually curates data from the literature into a structured format and spans infectious, allergic, autoimmune, and transplant diseases. Here, we describe the enhancements made since our 2018 paper, capturing user-directed updates to the search interface, advanced data exports, increases in data quality, and improved interoperability across related resources. As we look forward to the next 20 years, we are confident in our ability to meet the needs of our users and to contribute to the broader field of data standardization.

Effect of different pooled qc samples on data quality during an inter-batch experiment in untargeted UHPLC-HRMS analysis on two different MS platforms.

Quality control (QC) samples are commonly used in metabolomics approaches for three main reasons: (i) the initial conditioning of the column; (ii) the correction of analytical drift especially between batches; and (iii) the evaluation of measurement precision. In practice, there are several ways to prepare and conserve QC samples. The most common in untargeted metabolomics is to pool samples after or before extraction, in order to obtain pooled QC samples accounting, respectively, for analytical variance or for both analytical and sample preparation variances. In this study, focusing on untargeted analysis of tea (Camellia sinensis) leaves, we compared three ways of preparing pooled QC samples (two usual and one unusual QC sample preparations) and their efficiency to improve data quality in terms of inter-batch correction, measurement precision, and VIP candidates selection on datasets obtained using two mass spectrometry (MS) technologies (Orbitrap and time of flight (QToF)). We also investigated the effect of data processing modalities, based on the different QC preparations, on data loss and on the global structure of the datasets. Generally, our results show that usual QC sample preparation leads to comparable datasets quality in terms of precision and dispersion on both MS instruments. They also show that QC preparation is crucial for VIP selection; in fact, up to 54% of biomarkers candidates were specific of the QC preparation type used for data processing.

Data Quality in the Fitting of Approximate Models: A Computational Chemistry Perspective.

Empirical parametrization underpins many scientific methodologies including certain quantum-chemistry protocols [e.g., density functional theory (DFT), machine-learning (ML) models]. In some cases, the fitting requires a large amount of data, necessitating the use of data obtained using low-cost, and thus low-quality, means. Here we examine the effect of using low-quality data on the resulting method in the context of DFT methods. We use multiple G2/97 data sets of different qualities to fit the DFT-type methods. Encouragingly, this fitting can tolerate a relatively large proportion of low-quality fitting data, which may be attributed to the physical foundations of the DFT models and the use of a modest number of parameters. Further examination using "ML-quality" data shows that adding a large amount of low-quality data to a small number of high-quality ones may not offer tangible benefits. On the other hand, when the high-quality data is limited in scope, diversification by a modest amount of low-quality data improves the performance. Quantitatively, for parametrizing DFT (and perhaps also quantum-chemistry ML models), caution should be taken when more than 50% of the fitting set contains questionable data, and that the average error of the full set is more than 20 kJ mol-1. One may also follow the recently proposed transferability principles to ensure diversity in the fitting set.

Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook.

The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. However, the accuracy of ML predictions is strongly dependent on the underlying data, while the data of lithium battery materials faces many challenges, such as the multi-sources, heterogeneity, high-dimensionality, and small-sample size. Through the systematic review of the existing literatures, several effective strategies are proposed for data processing as follows: classification and extraction, screening and exploration, dimensionality reduction and generation, modeling and evaluation, and incorporation of domain knowledge, with the aim to enhance the data quality, model reliability, and interpretability. Furthermore, other possible strategies for addressing data quality such as database management techniques and data analysis methodologies are also emphasized. At last, an outlook of ML development for data processing methods is presented. These methodologies are not only applicable to the data of lithium battery materials, but also endow important reference significance to electrocatalysis, electrochemical corrosion, high-entropy alloys, and other fields with similar data challenges.

Data-driven prediction framework of surrounding rock pressure in a fully mechanized coal face with temporal-spatial correlation.

Surrounding rock pressure prediction for fully mechanized coal face (FMCF) roof management and control is of great significance. The key challenge is to effectively combine various factors to evolve the surrounding rock pressure trend with high temporal-spatial correlation and heterogeneity. The hydraulic support load is an important indicator that reflects the change in the surrounding rock pressure. Inspired by the research on 'support-surrounding rock' and the massive data collected by sensors, this paper proposes a data-driven prediction framework for surrounding rock pressure in FMCFs. The framework includes the multidimensional working condition matrix (MWCM) building module, the hydraulic support group temporal-spatial feature fusion (HTSFF) network, and the adaptive deployment strategy (ADS). The MWCM generating methods of hydraulic support in combination with the mining process and the data quality and quantity requirements of the conventional supervised learning method are weakened by the treatment of missing data adapting to the working conditions. The HTSFF network focuses on capturing the spatial correlation of the surrounding rock pressure along the mining direction and the temporal periodicity along the advancing direction of the FMCF and fully considers the dependencies between them. ADS aims to solve the problem of missing data in model deployment and reduce the interference of abnormal data in the model prediction process by adaptively generating MWCM. The rationality and practicability of various designs of the proposed framework were verified on our dataset. Quantitative experiments show that the average error of the surrounding rock pressure prediction is 1.2406MPa, and the accuracy, precision, and recall rate of periodic pressure prediction are all above 95%. This method can effectively assist FMCF roof management and control. Although the method is effective, it may require further fine-tuning of the model to adapt to different geological conditions and varying sensor quality, ensuring broader applicability.

Classification of lumbar spine disorders using large language models and MRI segmentation.

MRI is critical for diagnosing lumbar spine disorders but its complexity challenges diagnostic accuracy. This study proposes a BERT-based large language model (LLM) to enhance precision in classifying lumbar spine disorders through the integration of MRI data, textual reports, and numerical measurements. The segmentation quality of MRI data is evaluated using dice coefficients (cut-off: 0.92) and intersection over union (IoU) metrics (cut-off: 0.88) to ensure precise anatomical feature extraction. The CNN extracts key lumbar spine features, such as lumbar lordotic angle (LLA) and disc heights, which are tokenized as direct scalar values representing positional relationships. A data source of 28,065 patients with various disorders, including degenerative disc disease, spinal stenosis, and spondylolisthesis, is used to establish diagnostic standards. These standards are refined through post-CNN processing of MRI texture features. The BERT-based spinal LLM model integrates these CNN-extracted MRI features and numerical values through early fusion layers. Segmentation analysis illustrate various lumbar spine disorders and their anatomical changes. The model achieved high performance, with all key metrics nearing 0.9, demonstrating its effectiveness in classifying conditions like spondylolisthesis, herniated disc, and spinal stenosis. External validation further confirmed the model's generalizability across different populations. External validation on 514 expert-validated MRI cases further confirms the model's clinical relevance and generalizability. The BERT-based model classifies 61 combinations of lumbar spine disorders. The BERT-based spinal LLM significantly improves the precision of lumbar spine disorder classification, supporting accurate diagnosis and treatment planning.

Gastric Cancer at a Nigerian Tertiary Referral Center: Experiences With Establishing an Institutional Cancer Registry.

In Nigeria, gastric cancer is the 10th most common and 9th most deadly malignancy. The limited availability of robust data makes further characterizing it challenging. The objective of this study was to assess the presentation, and management of gastric cancer in Nigeria using an institutional cancer registry. We reviewed a prospective database of patients diagnosed with any gastric cancer at a single tertiary referral center over 15 years (2007-2022). Patients with suspected gastric cancer were surveyed for sociodemographics and then added to the institutional gastric cancer registry. Thereafter, periodic chart review and phone call was used to obtain investigation results, and survival data, respectively. Only patients with complete histopathology were included in analysis. 138 patients met inclusion criteria (mean age 55.3 years, 68.8% male). Patients typically presented with weight loss (119, 86.2%) and anorexia (92, 66.7%). Blood work (132, 95.7%) and ultrasound (80, 57.9%) were the most common investigations. Most fully staged patients presented with metastatic disease (39, 90.2%). Patients underwent at least one treatment modality (109, 79.0%), and most 54 (49.5%) underwent both chemotherapy and surgery. Patients undergoing surgery usually had resection of their tumor (58, 67.4%). The median time of follow-up was 45.6 months, and 51.4% (71) of patients were dead at that time point. Our gastric cancer database identified that most patients present with advanced disease and are undergoing at least one treatment modality. The next steps include initiatives to strengthen the quality of registry data, identify high-risk patients, and provide timely treatment.

A Scoping Review of Episodes of Lucidity in People Living With Dementia Near the End of Life: The Potential Role of Speech-Language Pathology in Research and Practice

Purpose: Emerging evidence suggests that people with dementia may experience episodes of lucidity (ELs) near the end of life, characterized by a temporary recovery of function, communication abilities, and/or awareness of surroundings. This scoping review aims to identify the available evidence on EL, clarify its key concepts and characteristics, identify knowledge gaps, describe the disciplines involved in EL research, and provide recommendations for speech-language pathologists (SLPs) working with people with dementia at the end of life. The ultimate goal is to guide future research and develop clinical strategies for SLPs involved in end-of-life care for this population. Method: A scoping review was conducted with a systematic literature search across five major databases using relevant terms such as “dementia,” “end-of-life,” and “lucidity,” covering all records up to May 2023. The review included quantitative, qualitative, and mixed-methods studies focused on lucid episodes at the end of life. Results: Of 5,780 articles screened by two independent reviewers, 10 met the criteria and were synthesized using narrative methods. Data extraction and quality assessment were independently performed by these reviewers. Among the selected studies, three were case reports, while the remaining seven employed qualitative assessments based on caregiver or informant reports. Most studies involved disciplines such as nursing, medicine, and psychology. All definitions and reports of lucidity involved aspects of verbal and nonverbal communication by individuals living with dementia. Conclusions: The scoping review provides a valuable synthesis of evidence on lucid episodes in people with severe dementia near the end of life, highlighting an area of growing research interest. Exploring the neurobiological and behavioral aspects of lucidity in dementia can lead to enhanced care for older adults. Given the fundamental role of communication in defining lucidity, SLPs are well positioned to contribute to this research and care continuum.

COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study.

According to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), scientific research data should be findable, accessible, interoperable, and reusable. The COVID-19 pandemic has led to massive research activities and an unprecedented number of topical publications in a short time. However, no evaluation has assessed whether this COVID-19-related research data has complied with FAIR principles (or FAIRness). Our objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness. We conducted a comprehensive search and retrieved all open-access articles related to COVID-19 from journals indexed in PubMed, available in the Europe PubMed Central database, published from January 2020 through June 2023, using the metareadr package. Using rtransparent, a validated automated tool, we identified articles with links to their raw data hosted in a public repository. We then screened the link and included those repositories that included data specifically for their pertaining paper. Subsequently, we automatically assessed the adherence of the repositories to the FAIR principles using FAIRsFAIR Research Data Object Assessment Service (F-UJI) and rfuji package. The FAIR scores ranged from 1-22 and had four components. We reported descriptive analysis for each article type, journal category, and repository. We used linear regression models to find the most influential factors on the FAIRness of data. 5,700 URLs were included in the final analysis, sharing their data in a general-purpose repository. The mean (standard deviation, SD) level of compliance with FAIR metrics was 9.4 (4.88). The percentages of moderate or advanced compliance were as follows: Findability: 100.0%, Accessibility: 21.5%, Interoperability: 46.7%, and Reusability: 61.3%. The overall and component-wise monthly trends were consistent over the follow-up. Reviews (9.80, SD = 5.06, n = 160), articles in dental journals (13.67, SD = 3.51, n = 3) and Harvard Dataverse (15.79, SD = 3.65, n = 244) had the highest mean FAIRness scores, whereas letters (7.83, SD = 4.30, n = 55), articles in neuroscience journals (8.16, SD = 3.73, n = 63), and those deposited in GitHub (4.50, SD = 0.13, n = 2,152) showed the lowest scores. Regression models showed that the repository was the most influential factor on FAIRness scores (R2 = 0.809). This paper underscored the potential for improvement across all facets of FAIR principles, specifically emphasizing Interoperability and Reusability in the data shared within general repositories during the COVID-19 pandemic.

Enhancing Patient-Centered Care Through Process Improvement: A Systematic Review of patient Satisfaction Outcomes

Patient-centered care has become a cornerstone in modern healthcare, focusing on personalized, respectful, and effective treatment that meets patients' unique needs and preferences. Process improvement methodologies, such as Lean, Six Sigma, and workflow optimization, have been increasingly adopted to streamline healthcare operations, reduce inefficiencies, and improve the quality of care. However, the specific impact of these methodologies on patient satisfaction within patient-centered care frameworks remains to be systematically evaluated. This systematic review aims to examine the effects of Lean, Six Sigma, and workflow optimization on patient satisfaction outcomes in patient-centered care settings, offering evidence-based insights for healthcare providers and policymakers.A comprehensive search was conducted across multiple databases, including PubMed, CINAHL, and Cochrane Library, to identify studies published from 2016 onward that evaluated patient satisfaction outcomes related to Lean, Six Sigma, and workflow optimization within patient-centered care models. Eligible studies were screened, assessed for quality, and analyzed based on patient satisfaction outcomes. Data extraction and quality assessments followed PRISMA guidelines.Findings from the selected studies indicate that process improvement methodologies positively impact patient satisfaction, with notable improvements in wait times, care coordination, and communication quality. Lean and workflow optimization showed a significant reduction in bottlenecks, while Six Sigma was associated with error reduction and standardized practices, contributing to higher patient satisfaction scores.Lean, Six Sigma, and workflow optimization methodologies offer promising avenues to enhance patient-centered care by improving patient satisfaction metrics. These methodologies provide healthcare organizations with structured approaches to meet patient needs more effectively and consistently, reinforcing the critical role of process improvement in achieving high-quality, patient-centered healthcare.

No differences between remote and laboratory-based testing of cardiac interoceptive accuracy using the phase adjustment task

AbstractIn recent years, there has been an increased interest in remote testing methods for quantifying individual differences in interoception, the perception of the body’s internal state. Hampering the adoption of remote methods are concerns as to the quality of data obtained remotely. Using data from several studies, we sought to compare the performance of individuals who completed the Phase Adjustment Task—a new measure of cardiac interoceptive accuracy that can be administered via a smartphone application—supervised in a laboratory against those who completed the task remotely. Across a total sample of 205 individuals (119 remote and 86 laboratory), we observed no significant differences in task performance between the two groups. These results held when matching groups on demographic variables (e.g., age) and considering only individuals who had successfully completed a screener task. Overall, these data attest to the suitability of the Phase Adjustment Task for remote testing, providing an opportunity to collect larger and more diverse samples for future interoception research.

Standardization of radiation therapy quality control system through mutual quality control based on failure mode and effects analysis.

The advancement of irradiation technology has increased the demand for quality control of radiation therapy equipment. Consequently, the number of quality control items and required personnel have also increased. However, differences in the proportion of qualified personnel to irradiation techniques have caused bias in quality control systems among institutions. To standardize the quality across institutions, researchers should conduct mutual quality control by analyzing the quality control data of one institution at another institution and comparing the results with those of their own institutions. This study uses failure mode and effects analysis (FMEA) to identify potential risks in 12 radiation therapy institutions, compares the results before and after implementation of mutual quality control, and examines the utility of mutual quality control in risk reduction. Furthermore, a cost-effectiveness factor is introduced into FMEA to evaluate the utility of mutual quality control.

AT-DBN: A Novel Deep Learning Approach for Accurate Flue Gas Pressure Forecasting in Power Plants

Accurately predicting and controlling the flue gas pressure at the induced draft fan inlet is crucial for environmental protection and boiler efficiency. This study designed a model of Attention-enhanced Deep Belief Network (AT-DBN) to predict the flue gas pressure parameters at the induced draft fan inlet, based on key improvements to the Deep Belief Network (DBN). The main improvement involves introducing an attention mechanism to enhance the prediction accuracy of the model for the flue gas pressure parameters at the induced draft fan inlet. The study begins with meticulous preprocessing of historical operational data, including identifying and removing outliers to ensure data quality for subsequent analysis. Next, Pearson correlation analysis is employed to systematically evaluate the correlation between various variables and the flue gas pressure, thereby identifying key variables that significantly affect prediction performance. Finally, an attention mechanism is integrated into the traditional DBN architecture, aiming to enhance the network’s ability to perceive important information within the input features, thereby improving prediction accuracy and model generalization. The results demonstrate that the proposed AT-DBN model can accurately predict the flue gas pressure at the induced draft fan inlet across multiple test sets, providing valuable guidance for power plant production operations.

The Role of AI in Data Engineering and Integration in Cloud Computing

This article presents a comprehensive analysis of the transformative role of Artificial Intelligence (AI) in revolutionizing data engineering and integration processes within cloud computing environments. The article examines the implementation of AI-driven solutions across multiple dimensions, including automated pipeline generation, intelligent schema matching, anomaly detection, and real-time data integration. Through a mixed-methods approach incorporating both quantitative and qualitative analyses, the article demonstrates significant improvements in data processing efficiency, with organizations achieving up to 67% reduction in processing time and 89% enhancement in accuracy. The article encompasses case studies from financial services, healthcare, and e-commerce sectors, providing concrete evidence of practical applications and scalability. Key findings reveal that AI-powered systems substantially outperform traditional approaches in cost efficiency, scalability, and data quality management, while simultaneously reducing operational overhead. The article also addresses implementation challenges, including legacy system integration and initial deployment complexities, offering strategic insights for organizations pursuing AI integration in their data engineering workflows. These article contribute to the broader understanding of how AI technologies can be effectively leveraged to address the growing challenges of data management in cloud computing environments, while providing a framework for future developments in this rapidly evolving field.

Artificial intelligence in environmental monitoring: in-depth analysis

AbstractThis study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations since 2010, with China, the United States, and India emerging as leading contributors. Key areas of research include air and water quality monitoring, climate change modeling, biodiversity assessment, and disaster management. The integration of AI with emerging technologies, such as the Internet of Things (IoT) and remote sensing, has significantly expanded real-time environmental monitoring capabilities and data-driven decision-making. In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. Challenges such as the “black-box” nature of AI models, the need for high-quality data in resource-constrained regions, and the complexity of real-time disaster management are also addressed. The study highlights ongoing efforts to develop explainable AI (XAI) models, which aim to improve model transparency and trust in critical environmental applications. Future research directions emphasize improving data quality and availability, fostering interdisciplinary collaborations across environmental and computer sciences, and addressing ethical considerations in AI-driven environmental management. These findings underscore the transformative potential of AI and ML technologies for sustainable environmental management, offering valuable insights for researchers and policymakers in addressing global environmental challenges.

Transforming Tax Compliance with Machine Learning: Reducing Fraud and Enhancing Revenue Collection

The integration of machine learning (ML) in tax administration has the potential to revolutionize tax compliance, enhancing fraud detection and optimizing revenue collection. This literature review explores the application of ML in tax systems, emphasizing its transformative role in addressing the limitations of traditional, labor-intensive compliance methods. Justification for adopting a literature review approach is rooted in the need to consolidate diverse perspectives, address research gaps, and provide an informed synthesis of existing findings. The study highlights the criteria used for selecting case studies and research papers, ensuring a robust analysis of ML’s ability to automate detection processes, improve risk assessment, and enable predictive analytics for efficient tax administration. Despite its potential, ML adoption is challenged by data quality issues, privacy concerns, technical infrastructure demands, and ethical considerations, which must be systematically addressed. This paper also identifies literature gaps, particularly the lack of balanced discourse, and provides recommendations for overcoming barriers, including enhancing data management practices, adopting ethical frameworks, and fostering cross-border collaboration. By addressing these challenges, ML can equip tax authorities with tools for creating efficient, adaptive, and fair systems. This research underscores ML’s growing importance in transforming global tax compliance, setting the stage for a future of more responsive and effective revenue administration.

ILDIM-MFAM: interstitial lung disease identification model with multi-modal fusion attention mechanism

This study aims to address the potential and challenges of multimodal medical information in the diagnosis of interstitial lung disease (ILD) by developing an ILD identification model (ILDIM) based on the multimodal fusion attention mechanism (MFAM) to improve the accuracy and reliability of ILD. Large-scale multimodal medical information data, including chest CT image slices, physiological indicator time series data, and patient history text information were collected. These data are professionally cleaned and normalized to ensure data quality and consistency. Convolutional Neural Network (CNN) is used to extract CT image features, Bidirectional Long Short-Term Memory Network (Bi-LSTM) model is used to learn temporal physiological metrics data under long-term dependency, and Self-Attention Mechanism is used to encode textual semantic information in patient’s self-reporting and medical prescriptions. In addition, the multimodal perception mechanism uses a Transformer-based model to improve the diagnostic performance of ILD by learning the importance weights of each modality’s data to optimally fuse the different modalities. Finally, the ablation test and comparison results show that the model performs well in terms of comprehensive performance. By combining multimodal data sources, the model not only improved the Precision, Recall and F1 score, but also significantly increased the AUC value. This suggests that the combined use of different modal information can provide a more comprehensive assessment of a patient’s health status, thereby improving the diagnostic comprehensiveness and accuracy of ILD. This study also considered the computational complexity of the model, and the results show that ILDIM-MFAM has a relatively low number of model parameters and computational complexity, which is very favorable for practical deployment and operational efficiency.

Efficient Imputation Methods for Estimating Population Proportion in Diagonal Systematic Sampling

Accurate estimation of proportions is essential for effective decision-making, resource management, and ensuring the reliability of statistical analyses. It underpins many critical processes across various domains, making it a foundational aspect of data analysis and interpretation. In this paper, we have proposed a regression-type and exponential type imputation methods which are free of unknown parameters for estimating missing values or non-responses while estimating population proportion under diagonal systematic sampling design. Imputation methods are crucial in data analysis and machine learning because they address the issue of missing data. Some key significance includes improved data quality, preservation of sample size, bias reduction and enhanced predictive power. The estimators of the proposed imputation methods were derived. The properties (biases and MSEs) of the class of estimators of the proposed imputation methods were derived up to first order approximation. Results of numerical illustration using simulated data revealed that the proposed estimators are more efficient and practicable than exiting estimators considered in the study.

Five years after the Brumadinho dam collapse: Evaluation of water quality based on combined analysis of land use and environmental data.

The collapse of the dam in the Paraopeba River watershed in 2019 triggered significant concerns regarding water quality in the region. This study aimed to assess, five years after the disaster, the effects on water quality and understand the underlying factors of environmental pressure contributing to the observed changes. To perform the evaluation, the study utilized surface water quality data pre-disaster (2012-2018) and post-disaster (2019-2023), environmental data regarding the identification of high-polluting potential industries operating in the region of interest, and land use for the watershed as a combined evaluation. Nonparametric statistical tests Kruskal-Wallis, complemented by Dunn's, were employed to assess the significance of changes in water quality parameters post-collapse. The results indicate a relatively stable baseline scenario of land use dominated by agriculture and pasture, with minor changes observed in forest cover and urban development. However, post-collapse assessments showed significant variations in water quality parameters, with turbidity exceeding conformity levels by up to 68 % (over 100 NTU), dissolved iron (Fe) by up to 70 % (over 0.3 mg/L), manganese (Mn) by up to 91 % (over 0.1 mg/L), dissolved aluminum (Al) by up to 83 % (over 0.01 mg/L), and lead (Pb) by up to 26 % (over 0.01 mg/L). Statistical tests suggested possible effects of the dam collapse on turbidity, pH, dissolved Fe, Mn, and dissolved Al. Temporal analysis showed constant effects on water quality, with notable increases in dissolved Fe and Mn concentrations observed upstream post-disaster and persistent impacts downstream. New mining activities licensed after 2019 may have contributed to the deterioration of water quality, highlighting the relevant relationship of anthropogenic activities and the environmental disaster in the Paraopeba River watershed.

Habitat Assessment of Bocachico (Prochilodus magdalenae) in Ciénaga de Betancí, Colombia, Using a Habitat Suitability Index Model

This study evaluates the habitat of the Bocachico fish (Prochilodus magdalenae) in the Ciénaga de Betancí, Colombia, using a habitat suitability index (HSI) model. Wetlands like the Ciénaga de Betancí are under significant pressure from anthropogenic activities, affecting biodiversity and ecosystem health. The Bocachico, a species of immense cultural and economic importance, faces habitat degradation and fragmentation. Using hydrodynamic and water quality data, a numerical model (EFDC+ Explorer 11.5), and field data collected from multiple sampling campaigns, we assessed habitat suitability based on five key parameters: water temperature, dissolved oxygen, ammonia nitrogen, velocity, and depth. The model results indicated that environmental conditions in the wetland remained relatively stable during the dry season, with an average HSI score of 0.67, where 9% of the wetland area displayed acceptable conditions, and the remaining 91% displayed medium conditions. The wet season, on the other hand, had an average HSI score of 0.64, with 7.2% of the area in the acceptable suitability range, and the remaining 92.8% in the medium category. Variations in HSI were primarily driven by ammonia nitrogen levels, water velocity, and depth. Despite limited fluctuations in the HSI, areas of low suitability were identified, particularly in regions impacted by human activities. These findings have practical implications for conservation strategies, providing valuable insights for the sustainable management and conservation of the Ciénaga de Betancí, informing strategies for improving habitat conditions for the Bocachico, and supporting wetland restoration efforts.