Animal Molecular Breeding 2024, Vol.14, No.1, 130-140 http://animalscipublisher.com/index.php/amb 138 the computational and sampling constraints associated with genomic tools in wild species is crucial. This involves investing in infrastructure and training to build capacity for genomic research in biodiverse regions. Educational initiatives, such as field courses that provide hands-on training in molecular biology and real-time DNA sequencing, are essential for empowering local scientists and conservationists. Third, continued development and refinement of machine learning algorithms and remote sensing techniques are necessary to enhance the accuracy and efficiency of wildlife monitoring. This includes improving the integration of acoustic analysis with camera trap images to monitor population dynamics and track endangered species. Finally, fostering collaborations between researchers, conservationists, and policymakers is vital to ensure that the insights gained from genomics and remote sensing technologies are translated into effective conservation actions. By leveraging these advanced technologies, we can make informed decisions and take targeted actions to protect and preserve wildlife and their habitats for future generations. In conclusion, the integration of genomics and remote sensing technologies holds great promise for the future of wildlife monitoring and conservation. By addressing current challenges and building on the progress made, we can enhance our ability to monitor, manage, and protect wildlife populations in an ever-changing world. Acknowledgements Author would like to express our gratitude to the two anonymous peer reviewers for their critical assessment and constructive suggestions on our manuscript. Conflict of Interest Disclosure Author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Blanchong J., Robinson S., Samuel M., and Foster J., 2016, Application of genetics and genomics to wildlife epidemiology, The Journal of Wildlife Management, 80(4): 593-608. https://doi.org/10.1002/jwmg.1064 Bourlat S., Borja Á., Gilbert J., Taylor M., Davies N., Weisberg S., Griffith J., Lettieri T., Field D., Benzie J., Glöckner F., Rodríguez‐Ezpeleta N., Faith D., Bean T., and Obst M., 2013, Genomics in marine monitoring: new opportunities for assessing marine health status, Marine Pollution Bulletin, 74(1): 19-31. https://doi.org/10.1016/j.marpolbul.2013.05.042 PMid:23806673 Carroll E., Bruford M., Dewoody J., Leroy G., Strand A., Waits L., and Wang J., 2018, Genetic and genomic monitoring with minimally invasive sampling methods, Evolutionary Applications, 11(7): 1094-1119. https://doi.org/10.1111/eva.12600 PMid:30026800 PMCid:PMC6050181 Cordier T., Alonso-Sáez L., ApotheLoz-Perret-Gentil L., Aylagas E., Bohan D., Bouchez A., Chariton A., Creer S., Frühe L., Keck F., Keeley N., Laroche O., Leese F., Pochon X., Stoeck T., Pawłowski J., and Lanzen A., 2020, Ecosystems monitoring powered by environmental genomics: a review of current strategies with an implementation roadmap, Molecular Ecology, 30(13): 2937-2958. https://doi.org/10.1111/mec.15472 PMid:32416615 PMCid:PMC8358956 Cruz F., Brennan A., Gonzalez-Voyer A., Muñoz-Fuentes V., Eaaswarkhanth M., Roques S., and Picó F., 2012, Genetics and genomics in wildlife studies: implications for ecology, evolution, and conservation biology, BioEssays, 34(3):245-246. https://doi.org/10.1002/bies.201100171 PMid:22223439 Drakshayini A., Mohan S.T, Swathi M, and Nanjundeshwara K., 2023, Leveraging machine learning and remote sensing for wildlife conservation: a comprehensive review, International Journal of Advanced Research, 11: 636-647. https://doi.org/10.21474/IJAR01/17110 Fitak R., Antonides J., Baitchman E., Bonaccorso E., Braun J., Kubiski S., Chiu E., Fagre A., Gagne R., Lee J., Malmberg J., Stenglein M., Dusek R., Forgacs D., Fountain‐Jones N., Gilbertson M., Worsley-Tonks K., Funk W., Trumbo D., Ghersi B., Grimaldi W., Heisel S., Jardine C., Kamath P., Karmacharya D., Kozakiewicz C., Kraberger S., Loisel D., McDonald C., Miller S., O'Rourke D., Ott-Conn C., Páez-Vacas M., Peel A., Turner W., VanAcker M., Vandewoude S., and Pecon-Slattery J., 2019, The expectations and challenges of wildlife disease research in the era of genomics: forecasting with a horizon scan-like exercise, The Journal of Heredity, 110(3): 261-274. https://doi.org/10.1093/jhered/esz001 PMid:31067326
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