1 Introduction

Gene editing technologies, particularly CRISPR-Cas systems, have rapidly advanced over the past decade, enabling precise and efficient modifications across a wide range of organisms. These breakthroughs have catalyzed cross-sector applications, from agriculture and conservation to bioremediation and aquaculture, offering solutions to pressing challenges such as biodiversity loss, food security, and climate resilience (Phelps et al., 2019; Hassan and Ganai, 2023; Robinson et al., 2023; Chavhan et al., 2025). Unlike traditional genetic modification, which often involves random insertion of foreign DNA, modern gene editing allows for targeted, site-specific changes, minimizing unintended effects and enhancing the precision of genetic interventions. This precision not only accelerates the development of improved traits but also expands the potential for functional studies in non-model organisms and the manipulation of complex ecological interactions (Chen et al., 2014; Agapito-Tenfen et al., 2018; Hamdan et al., 2022).

However, the release of gene-edited organisms into natural environments raises critical questions about their potential impacts on ecological balance. These organisms may influence natural populations, disrupt food webs, or alter key ecological processes, with both beneficial and adverse outcomes possible. For example, gene editing could enhance species’ resilience to environmental stressors or facilitate bioremediation, but it also carries risks such as off-target effects, genetic introgression, and unforeseen ecological disturbances (Phelps et al., 2019; Hassan and Ganai, 2023). The complexity and context-dependence of these impacts underscore the need for robust scientific assessment and adaptive governance frameworks (Agapito-Tenfen et al., 2018; Kofler et al., 2018; Robinson et al., 2023; Li, 2024).

This study will analyze representative cases covering conservation, agriculture, aquaculture, and environmental management, assess the ecological impacts of gene-edited organisms (both positive and negative), and propose governance recommendations to balance innovation and ecological management. This study aims to provide a reference for scientific assessment practices, guide the responsible deployment of gene-editing technology, and ensure that its transformative benefits are achieved while maintaining ecological integrity.

2 Primary Ecological Intervention Scenarios of Gene-Edited Organisms

2.1 Gene editing in agricultural systems (crops and pests)

Gene editing technologies, such as CRISPR, enable precise modification of crop genomes to enhance resistance to pests, diseases, and environmental stresses. These targeted interventions can improve crop yields, reduce reliance on chemical pesticides, and contribute to sustainable agriculture. However, concerns remain regarding off-target effects and the broader ecological consequences of introducing gene-edited crops into complex ecosystems, necessitating robust risk assessment frameworks (Agapito-Tenfen et al., 2018; Myskja and Myhr, 2020).

Gene drive systems represent a transformative approach to pest management by promoting the inheritance of specific genetic traits, such as sterility or susceptibility to pesticides, throughout pest populations. This strategy aims to suppress or even eradicate pest species, thereby reducing the need for chemical interventions. While gene drives offer significant potential for ecological and agricultural benefits, they also pose risks of unintended ecological disturbance, evolutionary resistance, and potential impacts on non-target species, highlighting the need for careful governance and local stakeholder engagement (Kofler et al., 2018; Kuzma, 2021).

2.2 Gene editing in conservation and species restoration

Gene editing is increasingly explored in conservation biology for restoring lost functions in endangered or extinct species. By recovering or introducing adaptive traits, gene editing can enhance species’ resilience to environmental changes or facilitate de-extinction initiatives. These interventions hold promise for biodiversity conservation but also raise concerns about ecological balance, potential disruption of existing communities, and ethical considerations. The application of gene editing in conservation requires pioneering efforts and comprehensive ecological assessments to ensure positive outcomes (Kofler et al., 2018; Phelps et al., 2019).

Gene editing technologies, particularly CRISPR, are increasingly used to improve disease resistance and reproductive fitness in endangered species. By repairing harmful mutations, introducing beneficial genes, or enhancing genetic diversity, these interventions can help vulnerable populations withstand threats such as emerging diseases and environmental stressors. For example, gene editing has been proposed to combat diseases like chytridiomycosis in amphibians and to address inbreeding depression by reintroducing genetic diversity. These approaches support ecosystem restoration by increasing the survival and reproductive success of key species, thereby stabilizing population dynamics and promoting ecological resilience (Breed et al., 2019; Phelps et al., 2019; Segelbacher et al., 2021). However, technical challenges (e.g., limited genomic data for non-model species) and ethical concerns (e.g., altering natural evolutionary processes) remain significant considerations (Breed et al., 2019; Zahoor et al., 2025).

2.3 Ecological regulation for public health

Gene editing, especially through gene drive systems, has been applied to mosquitoes to reduce the transmission of diseases such as malaria and dengue. By introducing genes that either suppress mosquito populations or render them incapable of transmitting pathogens, these interventions aim to decrease disease incidence in human populations. CRISPR-based gene drives can rapidly spread these traits through wild mosquito populations, offering a potentially powerful tool for public health (Kofler et al., 2018).

While gene editing of vectors like mosquitoes holds promise for disease control, it also raises ecological concerns. Altering or suppressing mosquito populations can have cascading effects on food webs, potentially impacting predators, competitors, and other species that rely on mosquitoes as a food source. There is also the risk of off-target effects, evolutionary resistance, and unintended ecological disturbances, emphasizing the need for comprehensive ecological assessments and adaptive governance frameworks (Kofler et al., 2018; Breed et al., 2019; Phelps et al., 2019).

3 Theoretical Basis of Ecological Impacts of Gene Editing

3.1 Concepts and indicators of ecosystem balance

Biodiversity, ecological network stability, and ecosystem service functionality are central indicators of ecosystem balance. Biodiversity underpins the resilience and adaptability of ecosystems, while the stability of ecological networks—comprising species interactions such as predation, competition, and symbiosis—ensures the persistence of ecosystem structure and function. Ecosystem service functionality refers to the capacity of ecosystems to provide essential services, such as pollination, nutrient cycling, and disease regulation, which are vital for both environmental and human well-being (Agapito-Tenfen et al., 2018; Phelps et al., 2019).

3.2 Potential ecological impact pathways and mechanisms of gene-edited organisms

Gene-edited organisms, especially those released with gene drives or other population-altering technologies, can rapidly shift the genetic and demographic makeup of wild populations. For example, gene drives can spread engineered traits through entire populations over multiple generations, potentially leading to population suppression or replacement. Such changes may offer benefits, such as controlling invasive species or disease vectors, but also carry risks of unintended ecological effects and loss of genetic diversity (Esvelt et al., 2014; Kofler et al., 2018).

The introduction of gene-edited organisms can disrupt established species interactions within ecological networks. Altered traits may affect predation, competition, or symbiotic relationships, potentially destabilizing food webs and leading to cascading ecological consequences. For instance, suppressing a pest population could inadvertently impact predators or competitors, while engineered resistance traits in crops might influence herbivore and pollinator dynamics (Esvelt et al., 2014; Agapito-Tenfen et al., 2018; Kofler et al., 2018).

Gene-edited traits may spread beyond target populations through gene flow, especially if engineered organisms interbreed with wild relatives. This can alter genetic diversity at the population or community level, with possible consequences for evolutionary trajectories and ecosystem resilience. Off-target effects and unintended introgression of edited genes into non-target species are additional concerns that require careful monitoring and risk assessment (Esvelt et al., 2014; Agapito-Tenfen et al., 2018).

4 Case Studies: Specific Impacts of Gene Editing on Ecological Balance

4.1 Case 1: impacts of pest-resistant crops on non-target insects and predators

Gene editing in crops such as rice and maize has enabled the development of varieties resistant to stem borers and other pests, reducing the need for chemical pesticides. However, these edited traits can have indirect effects on non-target insect communities and their natural enemies. For example, changes in plant chemistry or physiology may alter the abundance or behavior of beneficial predators and parasitoids, potentially disrupting established ecological relationships. Additionally, edited crops can influence soil ecosystems through modified pollen and root exudates, affecting microbial communities and nutrient cycling processes. These feedback effects highlight the need for comprehensive ecological assessments when deploying gene-edited crops (Nascimento et al., 2023).

4.2 Case 2: disturbance risks of gene drive–modified mosquitoes in wetland food webs

Gene drive technologies have been proposed to suppress mosquito populations and reduce the transmission of diseases such as malaria and dengue. While this approach offers significant public health benefits, it also poses risks to wetland food webs. Mosquitoes serve as prey for fish, birds, and other insects; their suppression could impact these predators and alter broader ecosystem dynamics. Furthermore, gene drives are designed for rapid and widespread dissemination, raising concerns about irreversibility and the difficulty of containing their spread once released into the environment. These factors underscore the importance of robust governance and local stakeholder engagement in decision-making (Kofler et al., 2018).

4.3 Case 3: ecological adaptability concerns with releasing gene-edited endangered animals

The release of gene-edited endangered animals, engineered for enhanced disease resistance or reproductive fitness, raises questions about their adaptability and ecological integration. There is potential for these individuals to alter existing population niches, outcompete wild counterparts, or disrupt local ecological balances (Phelps et al., 2019). Risks of genetic pollution and hybridization with wild populations may threaten genetic diversity and long-term species resilience. Effective regulation and monitoring are essential to mitigate these risks and ensure that conservation goals are met without unintended ecological consequences (Phelps et al., 2019; Li, 2024).

5 Ecological Risk Assessment Framework and Indicator System

5.1 Multi-scale ecological assessment indicators

At the individual level, indicators focus on direct physiological and biochemical changes in gene-edited organisms or affected non-target species. These may include stress protein expression, enzyme activity, growth rates, and reproductive health. Such endpoints are essential for detecting early adverse effects and serve as the foundation for higher-level risk assessments (Suter, 2006).

Population-level indicators assess changes in reproductive success, population growth rates, migration patterns, and competitive interactions. These metrics help determine whether gene-edited traits influence population viability, alter migration corridors, or shift competitive balances within or between species. Population-level vulnerability and resilience are critical for understanding the persistence and spread of gene-edited traits in natural settings (Suter, 2006; Lange et al., 2010).

At the community and ecosystem level, indicators expand to include interspecies relationships (e.g., predation, competition, symbiosis), food web connectivity, biodiversity indices, and the stability of ecosystem functions such as nutrient cycling and primary productivity. These broader indicators are vital for capturing cascading effects and feedbacks that may arise from the introduction of gene-edited organisms, ensuring that risk assessments reflect the complexity and dynamic nature of ecological systems (Landis and Mclaughlin, 2000; Suter, 2006; Lange et al., 2010; Harwell et al., 2019).

5.2 Monitoring strategies for time-scale and irreversibility

Effective risk assessment requires both immediate and long-term monitoring. Short-term strategies focus on detecting rapid physiological, behavioral, or population changes following the release of gene-edited organisms. For longer-term impacts, modeling approaches—such as entropy-based analysis and time series modeling—can help identify when system parameters stabilize and provide early warnings of ecological shifts or emerging risks. Optimizing monitoring locations and durations using entropy and data worth analysis ensures that key ecological parameters are captured efficiently over time, maximizing information while minimizing uncertainty (Dai et al., 2022).

Proactive risk management involves defining thresholds for ecological indicators that, when crossed, trigger management responses. This approach relies on continuous data collection and modeling to establish baseline conditions and detect deviations. By integrating real-time and historical data, risk–response–threshold models can support adaptive management and timely intervention, especially in systems where gene editing may cause irreversible changes (Rong and Shang, 2018; Zanin, 2021; Zanin and Papo, 2021; Dai et al., 2022; Yin et al., 2023; Zanin and Papo, 2025).

5.3 Data integration and modeling tools

Advanced modeling tools are essential for predicting and visualizing the ecological impacts of gene-edited organisms. SDMs can forecast changes in species ranges, while community succession models and population dynamics simulations help anticipate shifts in community structure and ecosystem function. These models are strengthened by integrating spatial and temporal data, allowing for scenario testing and risk forecasting (Sun and Chen, 2019; Dai et al., 2022).

Multi-omics technologies (e.g., genomics, transcriptomics, proteomics) enable the detection and tracking of transgenic signatures in the environment. Integrating these datasets with ecological monitoring provides a comprehensive view of how gene-edited traits persist, spread, and influence ecosystem processes. Zahoor et al. (2025) emphasized the potential for integrating modern genomics tools in ecosystem restoration. Through precise seed selection and germplasm screening, the adaptability of species in degraded environments can be improved; meta-omics can be used to track the recovery dynamics of microorganisms and ecological networks; and gene editing technologies (such as adaptive drive and invasion suppression) provide new ideas for rebuilding native populations and controlling alien species (Figure 1). Data integration frameworks, such as deep-learning–based tools for large-scale, heterogeneous datasets, facilitate real-time analysis and adaptive risk assessment (Xiong et al., 2021).

Figure 1 New ICT based fertility management model in private dairy farm India as well as abroad

6 Governance Measures and Ethical Controversies

6.1 Risk mitigation and technical regulatory recommendations

Effective risk mitigation for gene editing requires robust, enforceable regulations and oversight mechanisms. Failures in governance—such as the case of the first gene-edited babies—highlight the dangers of relying solely on self-regulation and the need for clear, enforceable technical and ethical guidelines, rigorous ethics review processes, and valid informed consent procedures (Li et al., 2019). Recommendations include establishing high-level ethics review systems, improving coordination among legislative bodies and regulatory agencies, and ensuring that technical standards keep pace with scientific advances (Kritikos, 2018; Feeney et al., 2021; Peng et al., 2022). Regulatory prudence, as exemplified by the Asilomar conference and German Ethics Council guidelines, is advocated to address unknown and significant risks (Schweikart, 2019).

6.2 International collaboration and harmonization of regulatory standards

Gene editing’s global implications demand international collaboration and harmonization of regulatory standards. Current governance is fragmented, with significant variation in national laws and enforcement, leading to risks such as “ethics dumping” where less regulated jurisdictions become sites for controversial research . International organizations, such as the World Health Organization, have called for polycentric governance models that combine overlapping regulatory mechanisms and encourage democratic deliberation among stakeholders worldwide (Dryzek et al., 2020; Marchant, 2021; Xue and Shang, 2022). The European Union, for example, is considering inclusive frameworks to reinforce public trust and address socio-ethical challenges at a regional level (Kritikos, 2018).

6.3 Scientific transparency and public participation

Transparency and public participation are essential for legitimate and effective governance of gene editing. The lack of transparency and public engagement in controversial cases has eroded trust and highlighted the need for open, inclusive dialogue (Li et al., 2019). Recommendations include establishing public education initiatives, citizen assemblies, and advisory committees to ensure that diverse perspectives inform policy decisions. Scientific transparency—through open data, clear communication of risks and benefits, and accessible regulatory processes—supports accountability and helps align technological development with societal values (Kritikos, 2018; Dryzek et al., 2020; Feeney et al., 2021; Peng et al., 2022; Xue and Shang, 2022).

7 Concluding Remarks

Gene editing technology offers transformative potential for ecology, conservation, and sustainable agriculture, enabling precise interventions to address biodiversity loss, enhance species resilience, and improve food security. However, its application also introduces complex and sometimes unpredictable systemic effects, including risks of off-target mutations, ecological disturbance, and potential irreversibility in natural systems.

A guiding principle for the ecological deployment of gene editing should be “predictability + controllability.” This means that interventions must be designed and assessed with a clear understanding of their likely ecological outcomes and with robust mechanisms in place to monitor, manage, and, if necessary, reverse unintended effects. Achieving this requires interdisciplinary risk assessment systems that integrate ecological, genetic, ethical, and social perspectives, as well as responsive governance frameworks that can adapt to new scientific insights and emerging risks.

International collaboration and harmonization of regulatory standards are essential, as gene-edited organisms can cross borders and impact global ecosystems. Effective governance should combine local, national, and international oversight, ensuring that local communities are included in decision-making and that regulatory systems are transparent, inclusive, and adaptive. Scientific transparency and public participation are also critical for building trust, ensuring accountability, and aligning technological advances with societal values.

Acknowledgments

The author would like to thank Mrs Zhang for her invaluable guidance, insightful suggestions, and continuous support throughout the development of this study.

Conflict of Interest Disclosure

The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Agapito-Tenfen S., Okoli A., Bernstein M., Wikmark O., and Myhr A., 2018, Revisiting risk governance of GM plants: the need to consider new and emerging gene-editing techniques, Frontiers in Plant Science, 9: 1874.

https://doi.org/10.3389/fpls.2018.01874

PMid:30622546 PMCid:PMC6308909

Breed M., Harrison P., Blyth C., Byrne M., Gaget V., Gellie N., Groom S., Hodgson R., Mills J., Prowse T., Steane D., and Mohr J., 2019, The potential of genomics for restoring ecosystems and biodiversity, Nature Reviews Genetics, 20: 615-628.

https://doi.org/10.1038/s41576-019-0152-0

PMid:31300751

Chavhan R., Jaybhaye S., Hinge V., Deshmukh A., Shaikh U., Jadhav P., Kadam U., Hong J., Solanke A., and Azameti M., 2025, Emerging applications of gene editing technologies for the development of climate-resilient crops, Frontiers in Genome Editing, 7: 1524767.

https://doi.org/10.3389/fgeed.2025.1524767

PMid:40129518 PMCid:PMC11931038

Chen L., Tang L., Xiang H., Jin L., Li Q., Dong Y., Wang W., and Zhang G., 2014, Advances in genome editing technology and its promising application in evolutionary and ecological studies, GigaScience, 3: 24.

https://doi.org/10.1186/2047-217X-3-24

Dai Z., Zhang X., Chen J., Ershadnia R., Luan X., and Soltanian M., 2022, An integrated experimental design framework for optimizing solute transport monitoring locations in heterogeneous sedimentary media, Journal of Hydrology, 614: 128541.

https://doi.org/10.1016/j.jhydrol.2022.128541

Dryzek J., Nicol D., Niemeyer S., Pemberton S., Curato N., Bächtiger A., Batterham P., Bedsted B., Burall S., Burgess M., Burgio G., Castelfranchi Y., Chneiweiss H., Church G., Crossley M., De Vries J., Farooque M., Hammond M., He B., Mendonça R., Merchant J., Middleton A., Rasko J., Van Hoyweghen I., and Vergne A., 2020, Global citizen deliberation on genome editing, Science, 369: 1435-1437.

https://doi.org/10.1126/science.abb5931

Esvelt K., Smidler A., Catteruccia F., and Church G., 2014, Concerning RNA-guided gene drives for the alteration of wild populations, eLife, 3: e03401.

https://doi.org/10.1101/007203

Feeney O., Cockbain J., and Sterckx S., 2021, Ethics, patents and genome editing: a critical assessment of three options of technology governance, Frontiers in Political Science, 3: 731505.

https://doi.org/10.3389/fpos.2021.731505

Hamdan M., Karlson C., Teoh E., Lau S., and Tan B., 2022, Genome editing for sustainable crop improvement and mitigation of biotic and abiotic stresses, Plants, 11(19): 2625.

https://doi.org/10.3390/plants11192625

Harwell M., Gentile J., McKinney L., Tunnell J., Dennison W., Kelsey R., Stanzel K., Stunz G., Withers K., and Tunnell J., 2019, Conceptual framework for assessing ecosystem health, Integrated Environmental Assessment and Management, 15: 544-564.

https://doi.org/10.1002/ieam.4152

Hassan S., and Ganai B., 2023, Deciphering the recent trends in pesticide bioremediation using genome editing and multi-omics approaches: a review, World Journal of Microbiology and Biotechnology, 39: 1-20.

https://doi.org/10.1007/s11274-023-03603-6

Kritikos M., 2018, Governing gene editing in the European Union: legal and ethical considerations, Ethics and Integrity in Health and Life Sciences Research, 4: 99-114.

https://doi.org/10.1108/S2398-601820180000004007

Kofler N., Collins J., Kuzma J., Marris E., Esvelt K., Nelson M., Newhouse A., Rothschild L., Vigliotti V., Semenov M., Jacobsen R., Dahlman J., Prince S., Caccone A., Brown T., and Schmitz O., 2018, Editing nature: local roots of global governance, Science, 362: 527-529.

https://doi.org/10.1126/science.aat4612

Kuzma J., 2021, Deficits of public deliberation in U.S. oversight for gene edited organisms, The Hastings Center Report, 51(Suppl 2): S25-S33.

https://doi.org/10.1002/hast.1317

Landis W., and McLaughlin J., 2000, Design criteria and derivation of indicators for ecological position, direction, and risk, Environmental Toxicology and Chemistry, 19(4): 1059-1065.

https://doi.org/10.1002/etc.5620190436

Lange H., Sala S., Vighi M., and Faber J., 2010, Ecological vulnerability in risk assessment: a review and perspectives, The Science of the Total Environment, 408(18): 3871-3879.

https://doi.org/10.1016/j.scitotenv.2009.11.009

Li J., Walker S., Nie J., and Zhang X., 2019, Experiments that led to the first gene-edited babies: the ethical failings and the urgent need for better governance, Journal of Zhejiang University-Science B, 20: 32-38.

https://doi.org/10.1631/jzus.B1800624

Li T., 2024, A Chinese model for legal regulation of gene-edited endangered animals and plants, Gene, 911: 148350.

https://doi.org/10.1016/j.gene.2024.148350

Marchant G., 2021, Global governance of human genome editing: what are the rules?, Annual Review of Genomics and Human Genetics, 22(1): 385-405.

https://doi.org/10.1146/annurev-genom-111320-091930

Myskja B., and Myhr A., 2020, Non-safety assessments of genome-edited organisms: should they be included in regulation?, Science and Engineering Ethics, 26: 2601-2627.

https://doi.org/10.1007/s11948-020-00222-4

Nascimento F., Rocha A., Soares J., Mascarenhas M., Ferreira M., Lino L., De Souza Ramos A., Diniz L., Mendes T., Ferreira C., Santos-Serejo J., and Amorim E., 2023, Gene editing for plant resistance to abiotic factors: a systematic review, Plants, 12(2): 305.

https://doi.org/10.3390/plants12020305

Peng Y., Lv J., Ding L., Gong X., and Zhou Q., 2022, Responsible governance of human germline genome editing in China, Biology of Reproduction, 107: 261-268.

https://doi.org/10.1093/biolre/ioac114

Phelps M., Seeb L., and Seeb J., 2019, Transforming ecology and conservation biology through genome editing, Conservation Biology, 34(1): 54-65.

https://doi.org/10.1111/cobi.13292

Robinson N., Østbye T., Kettunen A., Coates A., Barrett L., Robledo D., and Dempster T., 2023, A guide to assess the use of gene editing in aquaculture, Reviews in Aquaculture, 16(2): 775-784.

https://doi.org/10.1111/raq.12866

Rong L., and Shang P., 2018, The novel multi-scale local irreversibility analysis method based on segmentation about time series, Nonlinear Dynamics, 95: 2117-2130.

https://doi.org/10.1007/s11071-018-4680-5

Schweikart S., 2019, What is prudent governance of human genome editing?, AMA Journal of Ethics, 21(12): E1042-1048.

https://doi.org/10.1001/amajethics.2019.1042

Segelbacher G., Bosse M., Burger P., Galbusera P., Godoy J., Helsen P., Hvilsom C., Iacolina L., Kahrić A., Manfrin C., Nonić M., Thizy D., Tsvetkov I., Veličković N., Vilà C., Wisely S., and Bužan E., 2021, New developments in the field of genomic technologies and their relevance to conservation management, Conservation Genetics, 23: 217-242.

https://doi.org/10.1007/s10592-021-01415-5

Sun L., and Chen X., 2019, Bayesian temporal factorization for multidimensional time series prediction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 44: 4659-4673.

https://doi.org/10.1109/TPAMI.2021.3066551

Suter G., 2006, Ecological risk assessment, Technometrics, 37: 240.

https://doi.org/10.1201/9781420012569

Xiong L., Tian K., Li Y., and Zhang Q., 2021, Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space, Nature Communications, 13(1): 6118.

https://doi.org/10.1038/s41467-022-33758-z

Xue Y., and Shang L., 2022, Governance of heritable human gene editing world-wide and beyond, International Journal of Environmental Research and Public Health, 19(11): 6739.

https://doi.org/10.3390/ijerph19116739

Yin Y., Wang X., Wang W., Li Q., and Shang P., 2023, Multiscale cross-sample entropy based on visibility graph for quantifying time series irreversibility, Communications in Nonlinear Science and Numerical Simulation, 124: 107308.

https://doi.org/10.1016/j.cnsns.2023.107308

Zahoor Z., Arif M., Haidar S., Rafique S., Ahmed U., Rauf U., Amir M., Hanif U., and Shehryar M., 2025, Biotechnological advances in wildlife conservation: genetic engineering, cloning, ecosystem restoration, and nanoparticle applications, Scholars Academic Journal of Biosciences, 4: 386-416.

https://doi.org/10.36347/sajb.2025.v13i04.005

Zanin M., 2021, Assessing time series irreversibility through micro-scale trends, Chaos, 31(10): 103118.

https://doi.org/10.1063/5.0067342

Zanin M., and Papo D., 2021, Algorithmic approaches for assessing irreversibility in time series: review and comparison, Entropy, 23(11): 1474.

https://doi.org/10.3390/e23111474

Zanin M., and Papo D., 2025, Algorithmic approaches for assessing multiscale irreversibility in time series: review and comparison, Entropy, 27(2): 126.

https://doi.org/10.3390/e27020126