Author: Francis Wamonje, PhD

The battle against invasive species has found an unlikely ally in one of nature’s most organised workforces: ants. Recent research has uncovered that these industrious insects can serve as remarkably effective biological collectors of environmental DNA (eDNA). Specifically, ants are proving instrumental in detecting one of North America’s most damaging invasive pests: the spotted lanternfly (SLF). This discovery is not merely a curious scientific finding; it offers a transformative approach to early detection and control, providing real hope in a battle often defined by costly management and reactive solutions.

The Promise of Environmental DNA

Environmental DNA has already profoundly reshaped our understanding of biodiversity. Every organism, as it interacts with its surroundings, leaves behind traces of its genetic material. This eDNA can be sampled from the environment, enabling scientists to detect species without the need for direct observation.

In aquatic habitats, this technique has become invaluable. Water, by its very nature, gathers and disperses DNA effectively, making it comparatively straightforward to analyse. However, on land, eDNA monitoring faces considerable difficulties. Unlike the uniform distribution found in rivers and lakes, terrestrial landscapes scatter genetic traces unpredictably. This complexity means identifying exactly where and how to sample becomes a significant challenge.

Targeted Foraging Behaviour: Ants actively seek carbohydrate-rich resources, including honeydew from sap-sucking insects. This behaviour means they directly interact with spotted lanternfly excretions, which contain detectable DNA. Unlike passive sampling methods, ants actively aggregate eDNA through their natural foraging activities.

Extended Retention and Distribution: Worker ants can retain liquid food in their digestive systems for extended periods, later sharing it with nestmates through trophallaxis. This biological storage system effectively concentrates and preserves eDNA within the ant colony network, creating mobile reservoirs of genetic material.

Extensive Coverage Areas: Ants do not just forage randomly—they systematically cover large territories. Some species, like Camponotus ants, found in this study, forage 10-30 meters from their nests. Their polydomous nature, where colonies spread across multiple spatially separated nests, further extends this coverage area. This natural surveillance network can detect spotted lanternfly presence even in low-density populations where the insects might otherwise go unnoticed.

To ensure scientific rigour, the researchers also collected other insects, such as ladybirds and leafhoppers, which do not usually feed on lanternfly honeydew. This control group was essential to rule out the possibility that positive results arose simply from environmental contamination rather than targeted ingestion.

High Detection Rates: Ant samples from infested sites showed remarkable detection success: 60-100% using endpoint PCR and 80-100% using qPCR. This consistency across different molecular methods demonstrates the reliability of ants as eDNA samplers.

Perfect Specificity: Perhaps more importantly, zero ant samples from non-infested control sites tested positive for spotted lanternfly DNA using either detection method. This specificity eliminates concerns about false positives that could lead to unnecessary management interventions.

This pest severely impacts agriculture, particularly orchards and vineyards, by extracting large quantities of sap and depositing sticky honeydew, which fosters mould growth. Such damage significantly reduces fruit yield and quality, threatening livelihoods and regional economies dependent on horticulture. SLF infestations also weaken native trees, undermine forest health, and disrupt local ecosystems.

Beyond managing the spotted lanternfly, this research suggests broader possibilities. Many invasive insects produce detectable genetic markers through their excretions. Thus, the general principle—using insects’ natural behaviours to sample eDNA—might be adaptable to monitor other pest species effectively.

Sensitivity Testing: Future studies should evaluate detection sensitivity when spotted lanternfly populations are extremely low, determining the minimum density thresholds for reliable detection.

Automated Collection Methods: Transitioning from manual collection using aspirators to automated lure-based systems could enable continuous monitoring across large geographic areas without constant human intervention.

Understanding biodiversity in forest ecosystems is fundamental to assessing environmental health and guiding conservation strategies. Yet, for all our advances in ecological science, one part of these ecosystems has remained largely invisible: the microbial life beneath our feet. A recent study led by a German research team has opened up a bold new frontier in biodiversity monitoring by combining environmental DNA (eDNA) with satellite hyperspectral data. The result? A powerful methodology for predicting microbial diversity in forest soils at unprecedented scales.

By uniting these two innovative tools—molecular biology and remote sensing—the researchers have demonstrated a way to bridge the microscopic and the landscape-level. Their findings could reshape how we monitor biodiversity, manage forests, and respond to ecological change.

Why Soil Microbiomes Matter—And Why They’re Hard to Track

Environmental DNA (eDNA) can detect thousands of microbial species from a small soil sample, offering fine-scale biological insight. In parallel, hyperspectral imaging from satellites like the DESIS (DLR Earth Sensing Imaging Spectrometer) captures detailed spectral and spatial data about ecosystems at large scales. The hypothesis was that, together, these methods could identify microbial diversity patterns and link them to broader environmental variables—such as forest type, elevation, and soil chemistry.

Soil samples were collected from each location and subjected to high-throughput DNA sequencing. Microbial species were identified using global databases like SILVA (for bacteria) and UNITE (for fungi), and alpha diversity metrics—including the Shannon Index, functional richness, and phylogenetic diversity—were calculated.

These patterns were not only statistically robust but visually compelling. The team developed intuitive colour-coded maps that translated complex microbial data into spatially explicit, easy-to-read visualisations. These maps represent a new way of communicating biodiversity information—accessible to scientists, policymakers, and land managers alike.

Second, the biological interpretation of hyperspectral data remains an emerging field. It’s not yet fully understood why certain spectral bands are so strongly associated with microbial diversity. Are we seeing the spectral fingerprint of the microbes themselves? Or is the signal coming from correlated variables like vegetation, soil texture, or organic matter?

As technology advances and satellite resolution improves, we can expect to see even more detailed biodiversity assessments made possible from space. Meanwhile, molecular methods like eDNA continue to evolve, offering richer insights into community structure, function, and change over time.

When combined, these tools allow us to move from reactive conservation—based on limited field surveys—to proactive landscape-scale planning that incorporates biological complexity in real time.

As the twin crises of climate change and biodiversity loss escalate, tools like this offer both clarity and hope. Clarity helps us understand where biodiversity is flourishing or faltering, and we hope that we can act in time to protect it.

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What possibilities do you see in combining molecular data with remote sensing? Could these methods inform your work in conservation, agriculture, forestry, or land restoration? Let’s open the discussion.

Forests play a pivotal role in carbon sequestration, biodiversity conservation, and the provision of economic resources. However, plant pests and pathogens pose significant threats, resulting in billions of dollars in annual losses and reducing the effectiveness of forests in mitigating climate change. Traditional forest health surveillance, which relies largely on visual inspection, often detects disease only after substantial spread has occurred. This delay in detection highlights the urgent need for innovative technologies that enable sustainable and proactive forest protection. A recent study illustrates how environmental DNA (eDNA) offers groundbreaking possibilities for early pathogen detection, saving time and costs while improving eradication outcomes.

Understanding Environmental DNA (eDNA)

Environmental DNA refers to genetic material shed by organisms into their surroundings, detectable in rainwater, soil, or air samples. It provides a snapshot of biological diversity, allowing for the detection of organisms before visible signs of their presence emerge. In this study, rainwater samples collected from forest sites in Northern Ireland were analysed to detect fungal and oomycete pests, demonstrating the viability of eDNA for forest health monitoring.

Rainwater Sampling Across Northern Ireland’s Forests

The study gathered data from five forest sites: Lough Navar, Davagh, Loughgall, Hillsborough, and Mount Stewart. These sites were selected to represent a range of forest types, from dense spruce and pine monocultures to mixed recreational areas containing oak, ash, and beech. Environmental conditions, including rainfall and temperature, were also recorded to contextualise the findings.

The highest number of pest detections occurred in November, with autumn emerging as the most active season for fungal and oomycete pests. However, pest diversity peaked during both summer and autumn, reflecting the life cycles of many fungi, which tend to fruit and sporulate during these periods.

Environmental DNA metabarcoding has thus emerged as a transformative approach for forest health surveillance. Offering early, accurate, and broad insights into pest presence, it empowers authorities to act swiftly and decisively. This study demonstrates not only the feasibility but also the profound potential of rainwater eDNA monitoring in modern forest management. As methods and databases advance, eDNA could become a cornerstone of resilient, future-ready strategies for protecting forests in a changing world.

Biodiversity monitoring is a cornerstone of ecological research, guiding conservation initiatives and environmental policy. Traditionally, this research has grappled with complex challenges, especially in accurately measuring and interpreting the intricate relationships within ecosystems. However, recent advancements in technology, particularly in environmental DNA (eDNA) metabarcoding and deep learning, have introduced transformative methods for studying biodiversity. A recent publication introduces an innovation called ORDNA (ORDination via Deep Neural Algorithm), a pioneering tool leveraging artificial intelligence (AI) to redefine how we analyse and interpret eDNA metabarcoding data.

The Importance of eDNA Metabarcoding

By performing this analysis directly on raw data, ORDNA preserves delicate genetic signals often lost in standard workflows. The result is an ordination (or “map”) of samples that better reflects how species clusters align with real-world ecosystems. This efficient, more faithful representation of biodiversity is a significant step forward, as it can reveal subtle distinctions between different sites or times that older methods might miss.

Freshwater Samples from French Guiana: The first dataset looked at fish eDNA in rivers using a 12S rRNA gene fragment. By focusing on raw sequence data, ORDNA teased out a smooth biodiversity gradient from river sources to downstream regions. Traditional approaches, by contrast, sometimes produced fragmented patterns, possibly reflecting the loss of subtle details in standard data processing.

Marine Samples from Brittany, France: Over three years (2020–2022), researchers collected marine eDNA to check for shifts in species composition. After ORDNA was trained on the 2020 data, it was able to project the following years’ samples onto the same “map”, revealing changes in ecosystem structure over time. This ability to handle time-series data without re-training a model from scratch can help scientists track evolving environmental threats.

Forest Soils Across Switzerland: Forest ecosystems contain intricate webs of life, from fungi and insects to microbes. Soil eDNA was taken from both managed forests and more untouched reserves. ORDNA reliably grouped samples according to how they were used and maintained. Most managed forests were distinguishable from forest reserves, showcasing how ORDNA can highlight the impacts of human activity.

Mercury-Polluted Soils in Visp, Switzerland: This last dataset examined soils contaminated with mercury. ORDNA revealed distinct spatial patterns that correlated with pollution levels. In fact, it better separated contaminated sites from cleaner ones than PCoA, indicating it might be especially sensitive to environmental gradients like pollution levels.

Avoiding Data Loss: By skipping regular steps like denoising or alignment with reference databases, ORDNA minimises the loss of rare or delicate signals. Traditional techniques risk discarding potentially useful information in an effort to remove noise.

Non-Linear Embeddings: Methods like PCoA often rely on linear assumptions that are not well-suited to complex genetic data. ORDNA’s deep learning architecture reveals non-linear links, painting a more accurate picture of ecological patterns.

Adaptability to Different Habitats: ORDNA has already shown promise in various settings: tropical rivers, ocean samples, forest soils, and polluted sites. This flexibility means it can be used in multiple conservation and research efforts without needing major changes.

Time-Series Analysis: Once ORDNA is trained on a set of data, fresh samples can be quickly placed on the existing “map”. This feature is invaluable when tracking seasonal changes or monitoring areas over several years, as researchers do not have to start from scratch every time.

Bigger, More Varied Datasets: Using larger and more varied collections of eDNA—covering more taxa, primer sets, and sequencing platforms—could strengthen ORDNA’s overall performance. More diverse training data often leads to more robust machine learning models.

Integration with Other Analysis Tools: The embeddings generated by ORDNA might serve as inputs for other methods. For example, ecologists could use these embeddings in species distribution models or network analyses to explore relationships between species in even greater detail.

Deployment for Non-Experts: Making ORDNA easier to use for people outside data science—such as conservation workers, policymakers, and land managers—would broaden its reach. User-friendly interfaces and automatic pipelines could allow real-time decision-making in the field.

In freshwater or marine environments, where conditions can change quickly, ORDNA can spot small shifts in biodiversity from one year to the next. These shifts might be warning signs of overfishing, climate change, or invasive species. With near real-time updates, agencies could act faster to curb harmful activities or protect key habitats. Over the long term, governments and NGOs might use ORDNA as part of larger programmes that take global snapshots of biodiversity, pinpointing risk zones before it is too late.

One of the biggest challenges facing researchers, conservation groups, and governments is how to keep pace with the rapid changes battering our planet. Tools like ORDNA could be vital in mapping and monitoring these shifts at speed. As it matures, we may see a time when conservationists in the field collect soil or water samples, feed them into a user-friendly ORDNA system, and get immediate, detailed biodiversity readings. That immediacy could inspire faster, evidence-based action to protect threatened habitats and species.

Have you ever sat in an airport lounge, gazing out at the runway, and found yourself marvelling at just how quickly the world can change? That was precisely my mood yesterday at Heathrow as I prepared to depart for Kenya. My companions and I, from Kenyatta University, are about to embark on an exciting endeavour—using environmental DNA (eDNA) techniques to track mango and avocado pollinators and link their vital role to improved crop productivity.

It is a moment that reminds me of a similar journey a little over a decade ago when I was at Cambridge University. Back then, eDNA was still in its infancy, though its foundational technologies—like virus metagenomics—were already showing promise. I remember the thrill of my first real foray into genomics and the sheer power of high-throughput sequencing, even if it did feel rather like peering into a crystal ball.

Fifty-two weeks ago, I set out to share my readings on biodiversity research, initially with a focus on the Global South but gradually spanning the entire globe. Throughout this series, eDNA has taken centre stage—especially when paired with high-throughput sequencing and artificial intelligence—offering a treasure trove of insights: monitoring biodiversity at scale, tracking pests and diseases in crops and livestock, managing invasive species, verifying the authenticity of biological products, unravelling the interactions between plants and insects, and even ensuring safe drinking water. The past 52 weeks have been a constant reminder to me, and perhaps the reader of this newsletter, that the well-being of our planet and ourselves is inextricably linked.

Our hosts in Kenya were scientists at the Biosciences Eastern and Central Africa Hub (BecA-ILRI) at the International Livestock Research Institute (ILRI). I had worked at ILRI as a research assistant before joining Cambridge University. James Wainaina, then a research assistant working on aflatoxins, joined John and me for the fieldwork. He had the social capital we needed. Over time he had forged strong connections with the Kenya Agricultural and Livestock Research Organization and a host of farmers who grew beans. James was about to leave for his PhD studies in Australia. His research interest was whiteflies on beans, which created a perfect convergence of our scientific interests.

Our journey took us to Katumani and into the bean fields. However, we were two weeks early. There were hardly any aphids in the area. Usually, aphids accumulate on beans as they flower and pod. At times, they can be found in younger plants, but this was not the case at Katumani. Curiously, whiteflies were all over the place, which was great for James’ work. With not much to find, we headed for Kaiti in Makueni- a richly agricultural county in Eastern Kenya. Beans and cowpeas (Vigna unguiculata and locally called ‘kunde’) were plentiful. There were no aphids on the bean crop there, either. Instead, in our wandering through cowpea plots, we found plenty of other closely related species of aphid (the cowpea aphid), which was not what we wanted, save for scientific curiosity. It was a brutal initiation into fieldwork for John and I.

The following morning, we set out for the highlands of Kiambu to a farmer called Njiiri, where his wife received us. It was a smallholder farm in the classic sense, optimised to produce as much crop variety as possible. Sweet potato, maize, kale, tree tomato, and the occasional beehive thrived alongside bean plots. Aphids were all over the place. Though we were happy to find aphids, Njiiri’s wife was not pleased. From the level of aphid infestation, it was apparent that she was not keen to spray to kill the aphids. Some of the plants showed signs of disease. It may have been that either the pesticides were financially inaccessible or undesirable environmentally. Notably, part of her farmland was leased to other farmers who also did not use pesticides and whose cultivated plots would continue to be a source of aphids and infection. As we were about to leave, she asked us in the precise way that farmers always speak, ‘Will I harvest anything on those bean plots, or do I just uproot everything?’.

It was a poignant moment. Our next words were chosen carefully. We could tell the crop was lost to both disease and aphid infestation but had to hedge our words and actions to blunt the sharp edge of our assessment. So we said, ‘Look, we have trampled all over your shamba, mama. Surely, anything you would have harvested here is almost gone. We are more than happy to compensate you in cash for allowing us to work in your bean plots’. Her smile indicated to us that she understood what we implied. She would buy fresh seed and have another go the following season. Over that week, we found similar success in trapping aphids in smallholder farms in Oloirien and Oloolua in Kajiado. We also found despondency in farmers caused by insect and virus burdens- their efforts would not translate into bumper harvests.

Back at the Beca-ILRI hub, we prepared the samples for High Throughput Sequencing on the Illumina Platform. Like all ‘clever-thinking’ scientists, we had a fair expectation of what we would get, but nature is full of surprises. I was back in the UK when the findings came through.

Yes, insect-vectored plant viruses were detected, but plenty of sequences were annotated as ‘Aphid lethal paralysis virus’ and ‘Big Sioux River virus’. We had found dicistroviruses.

Dicistroviruses are remarkable insect-infecting viruses; they use plants as reservoirs to infect their insect hosts. They can, in some cases, kill aphids, decrease their reproduction, or cause confusion, which predisposes aphids to attacks by predators and parasitoids. Concurrently, another researcher based in the UK detected similar and related viruses in maise while searching for components for maise lethal necrosis disease, which at the time was wreaking havoc for maize farmers in East and Central Africa. This finding of dicistroviruses was the first report of these viruses in the black bean aphid globally.

I would go on to design research to explore the use of these potentially beneficial dicistroviruses for crop protection (bio-control). Funded through a Royal Society FLAIR Fellowship, I hoped to put a halt to aphid infestation and secure better yields for farmers by protecting crops without wrecking the environment. The work began in June 2019 at the International Centre of Insect Physiology and Ecology (icipe) in Nairobi, Kenya. icipe is a storied, one-of-a-kind organisation with a rich history of research in entomology to address grower challenges. I was looking to dig for the long haul, build collaborations, establish a research group, make discoveries, and translate those into solutions.

Then, suddenly, the covid pandemic struck, and the world stopped.

When we re-emerged, the world was different; the cost of sustaining the research proved a mile too far for the funding agency. John would carry on the research at Cambridge. I moved to the UK and joined Niab as a Research Leader In Entomology.

Niab is very big on applied entomology, and I quickly noticed that my skillsets could help advance the fortunes of growers, especially with pest identification and monitoring. Identification of cryptic insects using traditional methods is difficult, fault-prone for non-specialists, tedious and very difficult to scale. DNA-based tools are a modern and efficient solution. Pretty soon, this has gravitated to environmental DNA as an important tool for this work. The more I study the eDNA method, the more I am fascinated by its versatility in answering questions across different spheres, be it plant health, biodiversity, one health, pests, diseases, or plant-insect interactions.

So here we are.

In the coming weeks, I will share more about the eDNA project in Kenya as well as other exciting advancements in the 52 Science Stories space. Stay in touch for the next ‘52’.

Urban waterways often seem harmless, but hidden beneath their calm surfaces can lurk serious health hazards. Advances in environmental DNA (eDNA) metabarcoding techniques have transformed the way researchers monitor microbial pathogens in aquatic ecosystems. In a study conducted in Okinawa, Japan, the research team set out to investigate the prevalence of pathogenic Leptospira—bacteria known to cause leptospirosis—in urban water bodies that had traditionally been viewed as low-risk.

What is Leptospira?

Leptospira are bacteria responsible for leptospirosis, a disease that spreads when people come into contact with water contaminated by the urine of infected animals. Traditionally, urban areas in Okinawa were seen as low-risk zones. However, rapid urbanisation, agriculture, and climate change have reshaped the landscape, creating hidden reservoirs where pathogens can thrive unnoticed.

The One Health framework teaches us that the health of humans, animals, and ecosystems is intertwined. Leptospirosis is a prime example of why this integrated approach is critical. In acknowledging these dynamics, the study captured how urban expansion, agricultural practices, and environmental changes—exacerbated by climate change—had altered the landscape of zoonotic disease transmission.

The study pursued several objectives:

Demonstrating eDNA Utility: The study aimed to validate the effectiveness of eDNA metabarcoding as a non-invasive and efficient tool for monitoring waterborne pathogens, thereby offering a modern means to survey pathogens that were challenging to culture with traditional methods.

Assessing Prevalence: The research team aimed to determine whether pathogenic Leptospira were present in urban water bodies on Okinawa Island and rural waterways on Ishigaki Island.

Understanding Diversity: The study sought to elucidate the genetic diversity of Leptospira, differentiating between high-virulence pathogens (P1+) and low-virulence strains (P1–) along with other species in the P2 clade.

Association with Environmental and Human Factors: One of the study’s most compelling outcomes was the discovery of a significant correlation between leptospiral DNA detection and specific environmental as well as human-related factors. In Okinawa, a marked association was observed between the number of Leptospira sequences and the presence of cattle eDNA. This correlation suggested that increased livestock farming and related anthropogenic activities had contributed to elevated levels of environmental contamination. Meanwhile, in rural Ishigaki Island, the proximity of sampling sites to mountainous woodlands was associated with higher detection rates, which implicated wild mammals inhabiting these regions as natural reservoirs.

The analysis clearly demonstrated that while water-related recreational activities traditionally had been viewed as the leading risk factor for leptospirosis, human behavioural factors such as animal farming could also serve as significant sources of environmental contamination.

Species Diversity and Distribution: The eDNA metabarcoding approach allowed the researchers to distinguish among 11 operational taxonomic units (OTUs) of Leptospira. Although several OTUs were present in both urban and rural settings, some species appeared to be exclusive to Okinawa. For example, OTUs related to L. alstonii and an uncharacterised strain named Leptospira sp. unknown-P1 were found only in Okinawa. Additionally, the study’s results demonstrated that the diversity of Leptospira in Okinawa was comparable to—or even higher than—that in rural Ishigaki Island.

Informed One Health Interventions: By integrating data on pathogen presence with environmental and behavioural factors, the study offered valuable guidance for developing comprehensive One Health interventions. For example, the observed association between cattle DNA and leptospiral abundance suggested that improvements in livestock waste management near urban areas could help mitigate environmental contamination. Such insights had far-reaching implications not only for public health policy but also for urban planning, agricultural regulations, and ecosystem management.

Impacts on Urban Planning and Livestock Management: The research findings provided actionable insights for urban planners and local policymakers. The eDNA-based approach revealed that even regions with few reported cases might harbour dangerous pathogens if environmental factors changed abruptly—for instance, after flooding events. By incorporating continuous eDNA surveillance into urban water quality assessments, authorities could monitor pathogen circulation in near real-time. Concurrently, better management of livestock farming practices—such as the strategic placement of waste treatment facilities—could reduce the input of pathogenic bacteria into urban waterways, thereby lowering overall public health risks.

Value of eDNA in Public Health Research: The study highlighted several benefits of eDNA metabarcoding that had far-reaching implications:

Non-invasive Methodology: Researchers collected water samples without disturbing the natural environment or requiring direct access to animal hosts, which reduced labour and minimised potential harm.

Comprehensive Biodiversity Assessment: By simultaneously detecting pathogenic bacteria and vertebrate DNA, the method provided a single, integrated snapshot of the ecosystem, revealing not only pathogen prevalence but also possible host relationships.

Rapid and Sensitive Detection: eDNA allowed for quick processing and analysis, which was particularly useful in pinpointing emerging public health threats before they escalated into full-scale outbreaks.

Enhanced Surveillance Networks: To fully harness the power of eDNA, expanding surveillance networks to include more urban, peri‑domestic, and rural locations will provide a clearer picture of pathogen dynamics over time.

Integration with Climate Data: Future studies should integrate regional and global climate datasets to predict leptospirosis outbreaks. Understanding how heavy rains, floods, and temperature fluctuations influence Leptospira spread will allow for better risk management.

Global Data Sharing: Collaboration across regions and borders is essential. By sharing eDNA data internationally, scientists can build comprehensive models that predict infectious disease outbreaks in a rapidly changing global environment.

The study exemplifies why embracing a One Health approach is imperative. It reminds us that the health of our cities is never isolated from the health of our environment and the animals within it. As climate change and urbanisation continue to reshape our landscapes, innovative tools like eDNA will be essential in guiding policy decisions and protecting public health.

These findings are a call to action for professionals in public health, urban planning, agriculture, and policy-making. Better surveillance, integrated data analysis, and strategic interventions are needed to mitigate the risks of leptospirosis and other emerging infectious diseases.

Natural history collections have long served as invaluable archives of our planet’s biological heritage. In recent years, technological breakthroughs in environmental DNA (eDNA) analysis have opened up new pathways for exploring the interactions between plants and their animal visitors. By retrieving genetic material remaining on preserved flowers, researchers are now able to infer historical ecological relationships that help illuminate patterns of pollination, herbivory, and mutualisms across decades. This innovative approach not only deepens our understanding of past biodiversity but also offers crucial insights for contemporary conservation efforts.

Why eDNA from Herbarium Specimens Matters

With global environmental change and declining pollinator populations marking our current era, understanding historical plant–animal interactions are essential not only for retrospective research but also for guiding future biodiversity conservation and restoration strategies. Herbarium specimens, traditionally valued for their contributions to taxonomy, biogeography, and evolutionary biology, are now being used to reveal hidden interactions between plants and animals. A recent study from USA-based researchers shows how careful analyses of eDNA derived from these preserved materials can provide a historical snapshot of ecological dynamics.

The researchers designed their study to address a series of important questions:

1. Is it possible to extract and identify high-quality eDNA from animal visitors preserved on herbarium specimens?

2. How does the age of a herbarium specimen affect the recovery of usable eDNA?

3. How do the floral visitor communities detected on preserved specimens compare with those obtained from fresh samples?

By focusing on these questions, they sought to validate herbarium specimens as a novel resource for historical ecological research.

Innovative Methods: Harnessing the Power of eDNA

The cornerstone of this research lies in the innovative use of eDNA extracted from both herbarium specimens and fresh flower samples. The researchers employed a modified DNA isolation protocol specifically designed to maximise the recovery of genetic material from preserved flowers.

This process involved the careful removal of fully open, intact flowers from herbarium sheets—a practice that, although destructive, is justified by the potential scientific yield. A key element of the methodology was the inclusion of samples spanning a wide range of specimen ages—from freshly collected flowers to herbarium specimens that were up to 69 years old. By comparing eDNA read counts between these samples with controlled parameters, the researchers were able to assess the impact of specimen age on DNA recovery, an insight of great relevance for studies relying on archival materials.

Remarkably, eDNA was retrieved from specimens as old as 69 years. For example, a 51-year-old Hypericum frondosum specimen contained detectable eDNA from ruby-throated hummingbirds (Archilochus colubris), and a 69-year-old Physaria globosa specimen retained traces of thrips.

The age of herbarium specimens had a negligible impact on the quantity of target eDNA extracted. While fresh material offered up to ten times more eDNA reads on average compared to herbarium samples (52,873 vs. 5,546 reads per sample), linear regression models revealed no significant loss of usable eDNA from older herbarium specimens. This finding underscores the potential longevity and conservation value of eDNA preserved in herbaria.

Herbarium specimens provided eDNA evidence for a broad range of floral visitor taxa similar to those found in fresh samples, though certain underrepresented groups (e.g., bees) were less detectable using herbarium materials. Underrepresentation of bees (e.g., carpenter bees) may stem from their grooming behaviours, which reduce the deposition of DNA-containing material or potential primer biases in eDNA amplification protocols.

Another challenge related to sample collection is the unpredictable nature of eDNA presence on herbarium specimens. Unlike fresh samples, where conditions such as temperature, precipitation, and time of day can be optimised to maximise DNA yield, herbarium specimens do not come with such environmental metadata. As a result, some specimens may fail to produce usable eDNA entirely, necessitating the sampling of multiple flowers from different parts of the same specimen to improve the chances of recovery.

Despite these challenges, the study emphasises that advancements in both laboratory protocols and bioinformatics tools have the potential to refine eDNA metabarcoding techniques further. Future methodological improvements may mitigate issues of primer efficiency and differential shedding, thereby enhancing the comparability of data obtained from both fresh and archived specimens.

Methodological advances open a compelling new chapter in ecological research. Successfully extracting animal DNA from historical plant specimens allows researchers to explore questions previously thought impossible. How have pollinator communities evolved over decades or centuries? How have environmental changes reshaped plant-pollinator interactions? These questions are increasingly relevant in an age of rapid biodiversity loss and environmental upheaval.

Improving DNA extraction protocols and refining computational tools will help mitigate issues such as inconsistent primer efficiency—DNA markers that help identify species—and the varied ability of different organisms to shed DNA. Such developments will allow scientists to produce more accurate and comparable data from both fresh and historical samples.

Looking forward, future research must focus on expanding DNA reference databases to enhance species identification accuracy and further refine laboratory methods for improved eDNA recovery. Researchers should also integrate genetic insights with historical observations to create comprehensive ecological models capable of tracking subtle and significant shifts in species interactions.

Ultimately, the integration of eDNA technology with traditional herbarium collections represents more than scientific curiosity—it is a groundbreaking approach with real-world implications. By decoding the genetic messages locked within old plant specimens, scientists can better understand the resilience and vulnerability of ecosystems, guiding informed conservation actions to protect biodiversity today and into the future.

Forests are complex and dynamic systems supporting everything from towering trees to microscopic fungi, and providing essential benefits such as carbon storage, flood mitigation, and wildlife habitat. Under increasing pressure from climate change and development, these ecosystems require accurate, timely monitoring to guide conservation and land management. Conventional field surveys, while valuable, can be labour-intensive, expensive, and prone to missing elusive or seasonal species. Recent research illustrates how soil environmental DNA (eDNA) offers a promising alternative, albeit with improvements. By isolating traces of genetic material shed by plants into the soil, researchers can capture a broad snapshot of both current and historical biodiversity.

Why Soil eDNA Matters

Vegetation maps and species inventories underpin reforestation initiatives, habitat restoration, and biodiversity protection. Yet many surveys are constrained by their reliance on visible plant features, which can be transitory or difficult to spot. Soil eDNA addresses these gaps by detecting genetic fragments that persist long after a plant has flowered or shed its leaves. It can also capture cryptic species that appear only for short periods each year.

This capacity to reveal both present and historical traces proves especially useful in forests spanning strong environmental gradients. For instance, changes in soil calcium or water availability may only be apparent to the human observer at certain times, whereas genetic material can linger for months or even years. As such, eDNA analysis has become an appealing method for detecting subtle shifts in forest composition that might otherwise be missed.

A study conducted in Norway sought to answer three key questions to evaluate eDNA’s potential:

1. Can soil eDNA reliably capture the plant communities that occupy a given site?
2. How do eDNA-based findings compare with conventional forest mapping?
3. Do eDNA signals reveal environmental gradients reminiscent of those identified through traditional, field-based approaches?

Study Overview and Methods

Researchers chose Norway’s Hvaler archipelago—a region known for forests ranging from calcium-rich to relatively dry—to test soil eDNA’s effectiveness. They collected 31 soil samples from sites previously classified by the Nature in Norway (NiN) system, which organises forests into types based on expert knowledge and visible vegetation. After clearing surface debris, they extracted a standardised core of the organic horizon, where plant debris and roots accumulate. In the laboratory, each sample was homogenised to ensure consistency, and DNA was isolated through a two-step extraction process designed to capture both intracellular and extracellular fragments.

Two genetic markers were targeted:

• trnL (UAA) intron p6 loop – A short marker suited to degraded DNA, thereby capturing a more extended historical signal.
• ITS2 – Potentially offering finer resolution for modern-day species, though it can suffer in the face of heavy DNA fragmentation typical of older soil samples.

Soil eDNA metabarcoding is poised to reshape forest ecology by offering rapid, inclusive snapshots of plant communities over time. Although it does not replace the depth of field-based expertise, it adds a powerful, complementary dimension. The traces of genetic material preserved in soil can illuminate both the present state and the recent past, revealing shifts that conventional surveys might easily overlook.

It is a rapidly changing world. Integrating eDNA insights with traditional methods may enable forest managers, conservationists, and policymakers to spot emerging issues sooner and act more strategically. As these techniques become more refined, they promise a brighter, more adaptive future for forest monitoring—one where silent molecular evidence works hand in hand with expert observation. By embracing this innovative approach, we stand better equipped to protect and restore these vital ecosystems for generations to come.

Coral reefs are vibrant ecosystems that underpin marine life, protect coastal communities, and support local economies. Yet, rising sea temperatures and pollution have destabilised these delicate environments, triggering bleaching events and escalating disease outbreaks. Recent research has revealed an innovative method for detecting coral diseases at their earliest stages, combining environmental DNA (eDNA) analysis with portable biosensors.

Preserving Coral Reefs in Challenging Times

Often referred to as “rainforests of the sea,” coral reefs harbour rich biodiversity, promote coastal resilience, and support vital industries such as tourism and fisheries. However, prolonged environmental stress has eroded their defences. One notable threat is the pathogen Vibrio coralliilyticus, which remains benign under normal conditions but becomes virulent at higher temperatures, causing tissue damage and coral bleaching. Traditional monitoring methods, such as visual surveys and manual sampling, can be slow, labour-intensive, and risk overlooking early-stage disease. Scientists and engineers have therefore sought new ways to track pathogens before visible signs of coral decline appear.

A New Paradigm for Early Detection

This emerging solution combines eDNA-based bioindicators with a portable electrochemical biosensor that detects low concentrations of Vibrio coralliilyticus DNA in water. eDNA consists of genetic material shed by organisms through skin cells, mucus, and waste. Since it appears in the surrounding environment before corals exhibit clear signs of stress, eDNA provides an invaluable early warning. The portable biosensor then converts this genetic information into an electrochemical signal, enabling real-time monitoring with minimal effort.

Several key questions guided the research. Could a portable biosensor accurately detect Vibrio coralliilyticus at extremely low levels in seawater long before visible damage was done to corals? Might nanomaterials—specifically cobalt-iron Prussian Blue Analogue (Co-Fe PBA)—enhance the sensor’s performance? Could early detection genuinely enable timely interventions and prevent the cascading damage often observed in reef ecosystems?

Developing the Portable Electrochemical Biosensor

The newly designed biosensor results from an interdisciplinary effort at the intersection of chemistry, biology, and engineering. Its core relies on two elements: eDNA as the biological indicator and Co-Fe PBA nanomaterials embedded within the electrode. This combination ensures that the sensor can detect even minute traces of target DNA.

Further evaluations revealed a linear relationship between the sensor’s current signal and the logarithm of the target DNA concentration, which spanned from 100 fM to 100 nM. The limit of detection was 19.0 fM, demonstrating impressive sensitivity. In specificity tests, the sensor largely disregarded potential interfering species, emphasising its precise focus on Vibrio coralliilyticus. Its stability also proved remarkable; after a week at room temperature, the sensor maintained approximately 88% of its signal—an essential quality for field-based applications where regular maintenance is challenging.

Real-time data analytics represents another significant frontier. Pairing the biosensor with advanced analytical platforms could revolutionise how field data is processed, enabling immediate insights into coral health trends and facilitating proactive interventions. Engaging with communities, policymakers, and conservation organisations will be essential for integrating these innovations into existing management frameworks and maximising real-world impact.

For many countries, coastal resources are national treasures worth preserving. A portable electrochemical biosensor offers a compelling glimpse of how technology can bolster conservation. Its remarkable sensitivity, reliability, and ease of use allow environmental managers and local stakeholders to respond faster than ever before. By detecting the earliest signals of coral disease, we move closer to preserving the reefs that sustain biodiversity, livelihoods, and coastal protection.

Monitoring marine ecosystems is critical to understanding environmental changes and managing human impact. Traditional biomonitoring methods, while valuable, often struggle with limitations such as species misidentification and difficulty distinguishing closely related organisms.

Environmental DNA (eDNA) analysis has transformed marine monitoring by detecting traces of genetic material left behind by organisms in water and sediment. However, eDNA studies generate vast datasets that require advanced analytical tools to extract meaningful insights. This is where machine learning, specifically the Random Forest algorithm, offers a breakthrough.

A study demonstrated how Random Forest can process complex eDNA datasets to improve the efficiency and cost-effectiveness of marine biomonitoring. By optimising data usage, it provides accurate ecological assessments while reducing sequencing costs and computational burdens.

What is Random Forest?

Random Forest is an ensemble machine learning method that operates by constructing multiple decision trees. Each tree analyses a subset of the data and makes predictions, and the aggregated results provide a more robust and reliable outcome. This method is particularly well-suited to handling large, noisy datasets, such as those generated in eDNA studies.

Refining eDNA-Based Marine Biomonitoring

This study sought to optimise the use of eDNA data for marine biomonitoring by addressing two key questions:

1. What is the minimum amount of sequence data required to maintain accurate predictions using Random Forest?
2. Is this minimum threshold consistent across different monitoring objectives, such as assessing biotic indices, geographic origins, or aquaculture production phases?

The model was trained using thousands of decision trees to enhance predictive accuracy. To assess performance, researchers employed out-of-bag (OOB) testing, a technique where each tree predicts outcomes for data it was not trained on. This provided an unbiased estimate of how well the model would generalise to new samples.

• Predicting proximity to the fish farm: The full dataset achieved an 89% accuracy rate. Even when the sequence count was reduced to 5,000 per sample, predictions remained highly reliable.
• Classifying salmon production phases: Remarkably, reducing the dataset to as few as 50 sequences per sample still maintained an accuracy of approximately 89%. This suggests that when differences between categories are distinct, minimal sequencing data is sufficient for robust predictions.
• Assessing ecological quality and ballast water origin: The model required a higher sequence count—typically 2,500 to 5,000 per sample—to maintain performance. Predictions became less reliable when categories had overlapping or indistinct boundaries.

By refining our approach to eDNA analysis, we can enhance our ability to detect ecological changes, support conservation efforts, and improve the sustainability of marine industries. This study marks a significant advance in making marine biomonitoring more accessible, effective, and responsive to real-world environmental challenges.