The research team, led by Linying Ye, Xuelian Cheng, Guichao Xiao and corresponding authors Chao Liu and Ling Chen, set out to address a well-known weakness in forensic genetics. Human genetic markers such as short tandem repeats are powerful tools for identifying individuals, but they perform poorly when samples are trace-sized or badly degraded, which is precisely the condition of much of the biological material collected in real casework. Microbes, by contrast, are present in enormous numbers, differ markedly from person to person, and change over time in structured ways. These three properties, high quantity, individual specificity and temporal succession, make the microbiome an attractive complement to conventional DNA profiling.

To test how the vaginal microbiome behaves once it leaves the body, the investigators exposed vaginal swabs to a controlled, constant-temperature environment for a full 90 days. Rather than sampling at a handful of arbitrary intervals, they collected data at 13 separate time points across the exposure period, giving them an unusually dense picture of how the microbial community evolved. The study was approved by the Biomedical Ethics Committee of Southern Medical University, and informed consent was obtained from all participants, an important consideration given the intimate nature of the samples involved.

The analytical engine of the study was high-throughput absolute quantitative sequencing, a technique that goes beyond the relative abundance measures used in most microbiome research. Traditional amplicon sequencing tells scientists what percentage of a community each bacterial species occupies, but it says nothing about how many microbial cells were actually present. Absolute quantification adds that missing dimension, allowing researchers to track the total microbial load of a sample as it decays. This distinction proved central to the study’s success, because the two kinds of information, community composition and absolute abundance, turned out to answer two entirely different forensic questions.

When the team examined community structure, they found something initially counterintuitive. Even after three months of exposure to the environment, the overall architecture of the vaginal microbial community remained relatively stable. The broad ecological profile of each sample did not collapse or converge toward a single decayed state. However, within that stable framework, the composition of amplicon sequence variants, the fine-grained taxonomic units that can distinguish closely related bacterial strains, showed significant individual specificity. In other words, each donor’s sample carried a microbial fingerprint that persisted through time, separate from the fingerprints of every other donor in the study.

This persistence of individual signatures opened the door to source attribution. The researchers built a Random Forest model, a machine learning algorithm that combines the votes of many decision trees trained on random subsets of the data, and used it to match exposed samples back to their individual sources. Random Forest classifiers have become a workhorse of forensic microbiology because they handle high-dimensional compositional data well and are resistant to overfitting when properly tuned. In this study, the model was able to accurately identify the individual origin of the samples, demonstrating that the microbial fingerprint survives environmental exposure long enough to be forensically useful.

The second half of the study tackled time since deposition, a parameter that can be critical for reconstructing the timeline of a crime. Here the absolute abundance data took center stage. The researchers observed that the absolute quantity of microorganisms in the exposed swabs decreased over the 90-day period in a clear, time-dependent pattern. Microbial populations do not simply vanish the moment a secretion leaves the body; they decline gradually as cells die off under environmental conditions. By modeling this decline, the team converted microbial load into a kind of molecular clock.

To read that clock, the investigators turned to a Support Vector Regression model, a machine learning approach that fits a continuous output, in this case the number of days since deposition, rather than a categorical label. The SVR model, built on the dynamics of microbial load, achieved a mean absolute error of 8.01 days with a coefficient of determination, or R-squared, of 0.859. In practical terms, the model could estimate the age of a deposited sample to within roughly eight days on average across a three-month window, while explaining about 86 percent of the variance in the true deposition times. For an investigative tool that operates on environmentally exposed trace evidence, that level of precision represents a meaningful advance.

The study fits into a rapidly growing body of forensic microbiome research. Previous work from the same group and others has explored microbial time-of-deposition markers in saliva, semen and bloodstains, and has shown that species-level taxonomic characterization can sharpen the inferential power of stain microbiota. Other laboratories have applied targeted bacterial DNA approaches to saliva, RNA markers to crime scene traces, and spectroscopic or metabolomic methods to blood, urine and semen. What distinguishes the new work is its dual strategy: using community structure for identification and absolute abundance for dating, and applying both to vaginal secretions, a sample type that is crucial in sexual assault cases but has received less TsD-focused attention than blood or saliva.

The authors conclude that their findings clarify the dynamic evolutionary characteristics of both community structure and biomass in vaginal microorganisms after environmental exposure, providing a theoretical basis and practical analytical strategies for the forensic use of vaginal secretion samples. Challenges remain before such models reach the courtroom: real crime scenes present fluctuating temperatures, humidity and substrate effects that a constant-temperature laboratory design cannot fully capture, and larger donor cohorts will be needed to confirm that individual signatures remain distinguishable across populations. Nevertheless, the combination of stable community fingerprints and a quantifiable microbial decay curve offers investigators a genuinely new pair of tools, one that could help attribute intimate trace evidence to a specific person and place it on a timeline, even when human DNA is too degraded to tell the story on its own.

Subject of Research: Forensic application of the exposed vaginal microbiome for individual identification and time since deposition estimation

Article Title: Individual identification and time since deposition estimation based on community structure and absolute abundance of the exposed vaginal microbiome

Article References: Individual identification and time since deposition estimation based on community structure and absolute abundance of the exposed vaginal microbiome. (n.d.). https://doi.org/10.1007/s00414-026-04015-5

Image Credits: AI Generated

DOI: 10.1007/s00414-026-04015-5

Keywords: forensic science, vaginal microbiome, time since deposition, individual identification, absolute quantitative sequencing, Random Forest, Support Vector Regression, microbial load, sexual assault evidence, machine learning, International Journal of Legal Medicine, microbial decay

The research team, led by Tamandra H. D’Ombrain of La Trobe University, focused on sambar deer, the most abundant introduced deer species in Victoria, where an estimated 123,061 animals roam public land. Sambar are elusive creatures, and managing them, alongside five other established deer species, has long been hampered by the difficulty of monitoring populations that are widely dispersed, wary of people and expensive to track. Traditional genetic studies have relied almost entirely on tissue samples from culling and recreational hunting, meaning data collection has been opportunistic and restricted to dead animals, precluding repeated sampling of living individuals for mark-recapture population estimates, movement tracking, or before-and-after comparisons of control programs.

To break that bottleneck, the team turned to Genotyping-in-Thousands by sequencing, or GT-seq, a method that uses custom amplicon sequencing of hundreds of targeted DNA loci via a single multiplexed PCR reaction. Unlike reduced-representation sequencing approaches such as DArTseq or RADseq, which can generate thousands of markers but demand high-quality DNA and substantial budgets, GT-seq is designed for exactly the kind of short, degraded, contaminated DNA that faecal samples contain. Species-specific primers and relatively short fragments make it well suited to non-invasive material, and it has already been applied successfully to coyote scats, polar bear faeces, tiger dung and even archival fish scales.

Building the panel required careful balancing. The researchers drew on existing DArTseq data from 739 tissue and blood samples of sambar, rusa and fallow deer across Australia, deliberately selecting markers that could be co-analysed with a decade of existing sambar genetic data. Simulations indicated that at least 200 highly variable SNPs would be needed for reliable individual identification in a species with low genetic diversity stemming from the small number of founding individuals. After iterative rounds of primer design, testing and optimisation, anchored to the closest available reference genome, the red deer mCerEla1.1 assembly, the final panel contained 371 autosomal SNP loci plus three newly designed sex markers, all amplified in a single PCR. Twenty-one loci were deliberately fixed between sambar and rusa, allowing hybridisation, a genuine management concern in southeastern Australia, to be detected.

The heart of the study, and its most striking contribution, was a degradation experiment. Colon sections containing faecal material were collected by Parks Victoria during control operations in northeast Victoria, giving the researchers paired tissue and dung from the same individuals and a uniform starting point. Pellets were distributed across three conditions: fully exposed outdoors, protected from rain and ultraviolet light beneath a 99 percent UV-blocking polycarbonate sheet, and kept indoors at room temperature. Samples were swabbed for DNA after zero, two, four, seven and fourteen days, during a period in October 2023 that included 35.6 millimetres of rainfall and typical spring temperatures. In total, 12.8 million paired-end reads were generated, with 56 percent landing squarely on target loci.

The results were unambiguous. Fresh faecal samples performed as well as, or better than, colon tissue, genotyping an average of 300 of 374 loci. Pellets kept indoors showed virtually no decline, and even fourteen days later the indoor samples still yielded more than 300 loci on average. Protected pellets likewise remained viable throughout the study, with no statistically significant loss of genotyping rate or increase in error. But fully exposed pellets told a different story. Their genotyping rate declined steeply over time, dropping to an average of just 76 loci by day fourteen, while their error rate climbed from 1.4 percent at day two to 13.1 percent at day fourteen. With rain striking on six days of the trial, the researchers concluded that rainfall, rather than sunlight, is the primary driver of DNA loss, consistent with earlier microsatellite work but contrasting with a handful of SNP-based studies conducted under more sheltered, simulated conditions.

The practical implications for field sampling are considerable. Pellets exposed to rain and UV remained usable for roughly four days on average, whereas sheltered samples survived well beyond two weeks. The team recommends sampling during dry spells, prioritising fresh-looking pellets or those protected by vegetation, and avoiding collection immediately after rain, when rehydrated pellets can appear deceptively fresh. Crucially, hard, dry pellets should not be discarded: desiccation itself preserves DNA, and a pellet that looks ancient may still genotype beautifully. The study also showed that visual appearance alone is an unreliable guide, reinforcing calls for research into whether pellet characteristics such as colour and texture can predict genotyping success.

Accuracy, not just yield, was the study’s second battleground. Without filtering, degraded samples produced genetic distances that could scramble individual identities, making different animals appear alike or the same animal appear different. But with a simple two-step filtering threshold, excluding samples with more than 50 percent missing data and then loci with more than 20 percent missing, the panel achieved 100 percent accurate individual assignment using a genetic distance cut-off of 0.1. The three new sex markers, designed from conserved regions between the X and Y chromosomes of deer and other even-toed ungulates, correctly assigned sex in every fresh, indoor and protected sample, with errors appearing only in heavily degraded exposed samples, and never in individuals whose genotyping rate exceeded 0.5.

Perhaps most importantly for managers, the panel proved compatible with the existing DArTseq datasets that underpin sambar monitoring. Discordance between faecal and tissue genotypes from the same individual was below one percent, comparable to the best published GT-seq faecal studies, and discordance with DArTseq data, though higher at around three percent, remained low enough for co-analysis. When the 200 most reliably genotyped SNPs were tested against a filtered dataset of 7,412 SNPs, both recovered the same three main population clusters across southeastern Australia, and pairs flagged as close relatives in the small dataset were almost always close in the large one. The authors caution that kinship resolution is coarser with 200 markers and that high background relatedness among sambar complicates precise relationship classification, but first-order kinship and broad population structure remain firmly within reach.

At an estimated AUD 36 per sample for consumables, with a plate of 96 samples processed from extraction to sequencing in one to two weeks, the panel offers a realistic route to large-scale, non-invasive genetic monitoring of living deer. That opens the door to genetic mark-recapture population estimates, evaluation of control programs, dispersal tracking and early detection of hybrids or new populations, all without ever handling an animal. The study is, to the authors’ knowledge, the first to quantify environmental degradation effects on GT-seq faecal genotyping, and its lessons, sample fresh, sample dry, filter stringently, extend far beyond deer, offering a blueprint for non-invasive genetic monitoring of both invasive and threatened species worldwide.

Subject of Research: Development of a GT-seq SNP amplicon panel for non-invasive genotyping of invasive sambar deer faecal pellets and the effects of environmental exposure on faecal DNA degradation and genotyping success.

Article Title: Development of a SNP Amplicon Panel for Invasive Deer Faecal Pellets and the Effects of Environmental Exposure on Genotyping Success

Article References: D'Ombrain, T. H., Harrisson, K. A., Pacioni, C., Hill, E., & Murphy, N. P. (2026). Development of a SNP Amplicon Panel for Invasive Deer Faecal Pellets and the Effects of Environmental Exposure on Genotyping Success. Ecology and Evolution, 16(9), Article e74388. https://doi.org/10.1002/ece3.74388

Image Credits: AI Generated

DOI: 10.1002/ece3.74388

Keywords: sambar deer, faecal DNA, GT-seq, SNP panel, non-invasive genetic sampling, DNA degradation, wildlife monitoring, invasive species, population genetics, genotyping error, individual identification, Australia