Genomic Intelligence: How Whole-Genome Sequencing and AI Are Transforming Wildlife Forensic Science
Introduction
Wildlife trafficking is now one of the fastest-growing forms of transnational crime, netting billions of dollars a year, and driving many endangered species into a point of irreversible decline. Traditional forensic approaches—such as morphological identification, mitochondrial barcoding, and limited STR profiling—have all played their part in addressing wildlife trafficking, but they often have limitations pertaining to compromised samples, processed wildlife parts, closely-related species, and complicated trafficking routes (Smart et al., 2021). It is precisely these limitations that have created the space and demand for higher resolution tools capable of providing deeper biological insight into evidence of wildlife crime.
Forensic genomics, propelled by whole-genome sequencing (WGS), has emerged in recent years as a disruptive response to these limitations. Rather than testing small fragments of genetic material like the traditional techniques, WGS tests the entire genome, allowing investigators to accurately identify species, characterize population structure, identify geographic origins of the animal, detect hybridization, and reconstruct pedigrees (Adikari et al., 2023; Kanthaswamy et al., 2024). Species such as ivory, pangolin scales, big-cat bones, exotic birds, and shark fins are important specifically because of their status as valuable commodities that come heavily processed from traffickers, where ultimately DNA is the only viable forensic evidence.
The effectiveness of WGS has been increased because of advancements in artificial intelligence (AI), and machine learning (ML), which allow the rapid analysis of vast amounts of SNP data to decipher subtle genetic patterns. For example, GeoGenIE (Tibshirani et al., 2025) successfully predicts, with unmatched accuracy, the geographic origin of an offending wildlife product, from which poaching hotspots can also be analyzed and networks mapped. This represents a new development in the synthesis of sequencing and computation with real-time analytics, creating a new dynamic in what we term genomic intelligence, to support what wildlife law enforcement does globally.
In summary, these developments represent a substantial transition from conventional species identification towards a dynamic forensic framework, driven by intelligence, and which can respond proactively to trafficking networks.
Real-Time and High-Resolution DNA Sequencing Technologies
The introduction of portable and high-resolution sequencing platforms has been one of the most transformative forces in wildlife forensics. The Oxford Nanopore MinION is a portable device that enables investigators to conduct field-based DNA analyses directly at the border, airports, or remote crime scenes. Its reliability in the field was established by Pomerantz et al. (2018), who successfully sequenced tissues from wildlife at an unprecedented level of accuracy in the Amazon rainforest, lending credence to the idea of obtaining reliable biological data from the field, outside of a controlled laboratory setting (GigaScience). Johnson et al. (2023) also established that wildlife samples that have degraded to the point that they are typically encountered in forensic casework can be processed using the MinION technology (Molecular Ecology Resources).
These portable sequencing technologies are complemented by existing high accuracy platforms, e.g., Illumina sequencing, which will remain critical for deep-coverage genomic profiling. In addition, developing hybrid marker systems such as the FOGS SNPSTR database (Mozer et al., 2025), which integrates both STRs and SNPs for more powerful discriminatory reliability, provides the field a hybrid space for genomic wildlife forensics.
These sequencing innovations represent the intersection between portability and precision needed for modern wildlife forensic genomics to provide rapid field detection, followed by high-resolution laboratory confirmation.
AI-Enhanced Interpretation: Geographic Assignment, Hybrid Detection, and Admixture Analysis
With the increase in quantity and complexity of whole-genome datasets, AI and ML tools have begun to provide other valuable means of interfacing with these datasets. The algorithms of Random Forest, support vector machines (SVMs), deep neural networks, and convolutional AI models are able to perform characterization of species, hybridization detection, and lineage structure discovery beyond the scope of classical approaches.
One example is the GeoGenIE deep-learning model developed by Tibshirani et al. (2025) which predicts the geographic origin of wildlife samples based on genome-wide SNP patterns (Bioinformatics Advances). This builds on the original work of Ogden & Linacre (2015) and allows investigators to identify poaching hotspots with more precise spatial resolution.
Additionally, AI has improved novel detection of hybrids and admixture analysis. Kanthaswamy et al. (2024) demonstrate that WGS-based models could characterize multigenerational hybridization among congeneric captive big cats, which is not detected through traditional STR markers (FSIG). Adikari et al. (2023) further demonstrated that genome-wide SNPs can reveal meaningful patterns in lineage reconstruction that are valuable for exposing illegal breeding of threatened species (Genes).
Thus, wildlife forensic genomics is therefore advancing from a means of static identification to one of predictive inference with the potential to reveal criminal networks and population-level exploitation.
Environmental DNA, Cloud Pipelines, and Automated Bioinformatics
Besides the analysis of direct specimens, the utilization of environmental DNA (eDNA) holds the potential to revolutionize wildlife crime monitoring or surveying. For instance, Deiner et al. (2017) described eDNA metabarcoding as a "promising" method for assessing biodiversity (Nature Ecology & Evolution), and Harper et al. (2019) expanded the research on eDNA metabarcoding into the area of detecting illegal wildlife traded species at high-risk markets and ports (Biological Conservation). Most recently, Andivia et al. (2024) investigated the effect of artificial intelligence to enhance classifiers that improve detection sensitivity of complex eDNA mixtures (PLOS One).
Automated bioinformatics pipelines are equally established to support genetic data (eDNA, WGS, and SNP panels). Tools such as NVIDIA Parabricks for GPU-accelerated variant calling, DeepVariant (Poplin et al., 2018; Nature Biotechnology), and the Galaxy Cloud (Galaxy Community, 2023) can assist forensic laboratories with rapid, standardized, and reproducible genomic analysis.
In sum, all of these advancements allow wildlife forensics to develop from a reactive discipline to a proactive wildlife crime monitoring system that can track ecosystems, supply chains, and trafficking hotspots.
Securing Genomic Evidence: Blockchain, Traceability, and CRISPR-Based Diagnostics
As genomic evidence increasingly plays a key role in prosecuting wildlife crimes, and chain-of-custody will be key to a case's success, blockchain-based forensic frameworks—the central focus of the Singhal et al. (2023) study (Journal of Forensic Sciences) and subsequently pursued in projects with WWF–IBM—will allow for tamper-proof documentation of DNA profiles, sequencing logs, and metadata as evidence is held securely for court admissibility.
In the meantime, accelerated innovation is taking place in ultra-rapid diagnostics. CRISPR-based biosensors are proof of concept for near instantaneous DNA detection without the need for sequencing (Kellner et al., 2019; Nature Biotechnology; Fozouni et al., 2021; Cell). Coupled with mobile AI-based diagnostic platforms like SpeciesID and WWF’s Wildlife Guardian, these innovations might provide a compelling case for a very soon-to-be realized future of real-time forensic intelligence from wildlife campaigns, delivered out in the field.
Real-World Case Applications of WGS and AI in Wildlife Trafficking
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Elephant Ivory Trafficking: Mapping Poaching Hotspots with Genomic Intelligence
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Pangolin Scale Trafficking: Revealing Multinational Criminal Supply Chains
Pangolins, which are the most trafficked mammals in the world, also showcase the potential of WGS. Kanthaswamy et al. (2024) demonstrated that genome-wide SNP analysis could distinguish all eight pangolin species and assign them to regional lineages, paving the way for forensic identification (FSIG). nash et al. (2018) analyzed 8+ tons of confiscated pangolin scales and demonstrated that the seized pangolin scales reflected multiple species taken from a large geographic area in West and Central Africa, demonstrating a co-ordinated and multinational effort to traffic pangolins (Conservation Genetics). These results show the potential for WGS and AI to identify supply chains that traditional methods could not.
While it contains some challenges like— Non-deterrent aspects may hinder progress for wildlife forensic genomics, despite significant advancements. Most of the trafficked species are still lacking sufficient and comprehensive genomic reference databases, which lowers the accuracy of assignment of origin (Smart et al., 2021). The costs associated with sequencing and bioinformatics are also a limitation for most biodiversity-rich, wildlife crime-affected nations, resulting in a high level of global inequity.
Ethical issues are also raised regarding data sovereignty. Ogden & Linacre (2015) noted that data-sharing frameworks must be developed to respect and protect the sovereignty of other countries and Indigenous communities (FSIG). Opaque AI algorithm reliance also carries risk of misinterpretation and implications in court. Kanthaswamy et al. (2024) indicate the need for transparent, validated, and standardized genomic workflows.
Wildlife forensic science will become increasingly defined by genomics, AI and global digital networks. Ultra-portable sequencers, such as MinION, are expected to integrate on-board AI to facilitate real-time species identification and geographic assignment at border checkpoints (Johnson et al., 2023). International collaborations are working towards global SNP reference databases for elephants, pangolins, tigers, sharks, parrots and other heavily trafficked taxa (Mozer et al., 2025).
Next-gen AI systems—such as GeoGenIE—will likely integrate environmental, socio-economic & shipping data to generate predictive risk forecasts of potential trafficking routes (Tibshirani et al., 2025). CRISPR-based biosensors may evolve into handheld, court-admissible, diagnostic devices, while blockchain may standardize chain-of-custody documentation (Singhal et al., 2023).
These trends are headed towards a future where wildlife forensics shifts from reactive investigation to a predictive, global network of genomic intelligence to disrupt wildlife crime at its source.
