Crayfish plague is a water mold, an oomycete pathogen native to southeastern North America, where local crayfish species tolerate it thanks to millions of years of coevolution. When North American crayfish were shipped to Europe for aquaculture and stocking, they brought the fungus with them as an invisible passenger. The pathogen reproduces asexually by releasing zoospores, microscopic swimming spores that home in on new hosts using chemical cues. For European native crayfish, which never evolved defenses against it, infection is usually catastrophic: the fungus invades the cuticle, melanization responses fail, and mass mortality follows within days to weeks.

The consequences for Europe’s freshwater ecosystems have been devastating. Native crayfish are keystone species and ecosystem engineers, large omnivores that structure food webs and shape streambeds. Crayfish plague swept across Europe in two major waves, the first beginning in northern Italy in 1859 and the second in the late 1980s, driven by introduced signal crayfish (Pacifastacus leniusculus) and spinycheek crayfish (Faxonius limosus). Populations of the white-clawed crayfish have declined by an estimated 50 to 80 percent since the 2000s. Five major genotype groups of A. astaci (A through E) have been described, with group A representing the oldest European lineage and the others associated with different North American carriers, each differing in virulence and transmission efficiency.

Croatia’s history with the pathogen stretches back to the late nineteenth century, when the first plague-related mass mortalities were recorded, with another outbreak documented in the late 1960s. Molecular confirmation came only in 2016, and that initial survey covered a limited number of localities using earlier qPCR assays with known specificity issues. To fill the gap, the new study turned to a remarkable resource: archived crayfish specimens preserved in 96 percent ethanol, collected over 27 years from both the Black Sea and Adriatic Sea drainages. The researchers analyzed 238 specimens representing all seven crayfish species present in the country, four native and three invasive.

The technical approach was meticulous. Tissue pools of eyes, abdominal cuticle, and appendage setae were dissected under bleach-decontaminated conditions to prevent cross-contamination. DNA was extracted with a modified insect kit protocol, tripling reagent volumes to fully submerge the tissue. Detection relied on a TaqMan qPCR assay targeting a 131-base-pair fragment of the pathogen’s internal transcribed spacer region, with synthetic gBlocks gene fragments serving as positive controls and contamination sentinels. Every presumptive positive was confirmed by Sanger sequencing against the GenBank database, and an exogenous internal positive control flagged any PCR inhibition from the decades-old ethanol-preserved samples.

The headline result: 91 of 238 samples, 38.2 percent, tested positive for A. astaci. The pathogen was detected in both major drainages, in all seven host species, and throughout the entire sampling period. Positives ranged from 33.8 percent in stone crayfish (Austropotamobius torrentium) to 42.3 percent in narrow-clawed crayfish (Pontastacus leptodactylus) among native species, and reached 50 percent in signal crayfish among invasives. Metadata accompanying the archived material also revealed four suspected plague outbreaks: Kotluša Stream in 2019 and the Krupa River in 2020, both involving stone crayfish; the Bregana River in 2025, involving stone crayfish again; and the Otuča River in 2019, involving noble crayfish (Astacus astacus).

Genotyping proved harder than detection, as expected. The researchers integrated three methods: a pentaplex qPCR assay distinguishing genotype groups A through E using five fluorescently labeled probes, mitochondrial haplotyping of the rnnS and rnnL ribosomal genes, and microsatellite genotyping. Only 11 of the 91 positive samples yielded interpretable consensus genotype profiles, because genotyping requires substantially higher pathogen loads than species-level detection, and archived specimens were mostly collected from apparently healthy populations where pathogen loads were low. DNA degradation from years in ethanol further reduced amplification success.

Those 11 profiles told a striking story. Genotype group A, the old European lineage, appeared in six samples across three native species and both drainages, including two outbreak localities, with the earliest record extending back to 2007. Group A is generally considered less virulent, but the study shows it can still trigger mass mortalities when hosts are stressed by pollution or elevated water temperatures. Genotype group B, typically carried by signal crayfish, was confirmed in two native species, noble crayfish and stone crayfish, including a 2025 outbreak in the Bregana River, a locality outside the signal crayfish’s known invasion range. Two previously unreported profiles also emerged: an SSR-UP-like genotype in a signal crayfish from the Drava River, the first such record in an invasive host, and a putatively novel genotype in a narrow-clawed crayfish from the Danube River that yielded discordant results across methods.

The detection of group B in native crayfish far from any known signal crayfish population is particularly alarming. Similar patterns, where NICS-associated genotypes appear in native populations years before their carriers are identified, have been reported in Switzerland, the Czech Republic, and Ireland. The researchers suggest two explanations: spillover from undetected invasive crayfish upstream, or transfer via contaminated fishing gear. Either way, the finding implies that A. astaci strains can reach highly susceptible native populations through pathways that current monitoring does not capture. The marbled crayfish (Procambarus virginalis), present in Croatia since 2013, tested positive in two samples from Šoderica Lake, but their genotypes could not be resolved, leaving open the question of whether genotype group D lurks undetected. Similarly, several positive spinycheek crayfish samples could not be genotyped, so genotype group E may also be present but invisible to current methods.

The study’s authors argue that Croatia needs a coordinated, genotype-resolved national monitoring framework, modeled on systems in Norway, Switzerland, and Ireland. Although crayfish plague has been legally notifiable in Croatia since 1997, practical reporting of wild crayfish mortality events remains limited, hampered by field observers who are not trained to recognize outbreaks and by diagnostic pathways that depend on authorized veterinarians visiting sites before carcasses degrade. The researchers call for targeted training of field personnel, integration of samples from protected-area staff and researchers into the official diagnostic pathway, continuous biosafety education for fishermen, and the adoption of environmental DNA screening as a non-invasive early-warning tool. Priority zones should include invasion fronts and contact zones between native and invasive crayfish, such as the Drava–Mura system and the Korana–Mrežnica area. As this study demonstrates, the pathogen’s true distribution may still be broader than documented, and only sustained, genotype-informed surveillance can reveal where it will strike next.

Subject of Research: Time capsules of infection: Updating the distribution of Aphanomyces astaci in Croatia using archived crayfish specimens

Article Title: Time capsules of infection: Updating the distribution of Aphanomyces astaci in Croatia using archived crayfish specimens

Article References: Škugor, T., Baudry, T., Maguire, I., Hudina, S., Jemmi, E., Delaunay, C., Pisano, S. R. R., Temunović, M., Schmidt-Posthaus, H., & Bielen, A. (2026). Time capsules of infection: Updating the distribution of Aphanomyces astaci in Croatia using archived crayfish specimens. NeoBiota, 109, 247-270. https://doi.org/10.3897/neobiota.109.200821

Image Credits: AI Generated

DOI: 10.3897/neobiota.109.200821

Keywords: Time, capsules, infection, Updating, distribution, Aphanomyces, astaci, Croatia, archived, crayfish, specimens, scientific research

The authors, Gabriel Chavira Juárez, Eder Jahir Gonzalez Bravo, Guadalupe Esmeralda Rivera García, Javier A. Arcos Espinosa, and Salvador W. Nava-Díaz, from Universidad Autónoma de Tamaulipas and Tecnológico Nacional de México, propose a substrate-aware view of controlled knowledge updating. Their argument is deceptively simple: when new evidence arrives, an agent should alter the least invasive substrate sufficient for the claim’s scope, expected persistence, and evidentiary strength. Sometimes that means editing model parameters. Sometimes it means writing to external memory. Sometimes it means doing nothing at all.

The substrate concept is the framework’s technical core. A substrate is any component of the agentic system whose content, state, or configuration can be changed to shape subsequent behavior. The authors list transient context, external memory, model parameters, activation states, and reusable tool or workflow definitions as comparable intervention targets. Each differs in scope, persistence, reversibility, and auditability. Model editing changes parameters directly, while forgetting can remove or attenuate information across several substrates at once. The invasiveness of a change combines its blast radius, the difficulty of reversal, the opacity of its audit trail, and its computational cost.

Three principles make this perspective operational. Substrate proportionality means that update persistence and blast radius should match the scope of the claim, the confidence of the evidence, and the expected half-life of the information. Temporal defeasibility means that persistent knowledge remains revisable: each update should carry validity conditions and a path for revision, attenuation, or rollback. Auditable continuity means that future behavior remains traceable to update decisions, sources, and affected substrates. Together, these principles translate the least invasive substrate into concrete decision variables: scope, evidence confidence, expected duration, reversibility requirement, interference tolerance, and audit requirement.

The authors describe a control loop that begins with an update candidate: a user correction, environmental feedback, a new document, a contradiction, a failed action, or an instruction. The agent first classifies the evidence and intended scope. Uncertain or transient claims may remain in retrieval or context injection. User-specific or task-specific claims may move to external memory. Stable global corrections may justify a curated global update, such as an external knowledge-base change or, under stricter evidence and regression tests, model editing. Behavioral modulation may be better handled through steering vectors, changes in how the agent acts may belong in tools or workflows, and obsolete, harmful, or unauthorized information may call for forgetting or attenuation.

A concrete example illustrates the framework. Consider an assistant serving the same user across sessions. A one-off request to keep answers short classifies as transient evidence with no persistent effect. The same preference confirmed across several unrelated sessions moves to external memory, since evidence has accumulated and the scope is user-level. A later request for full detail on a single report remains a same-turn override, leaving the stored preference untouched. An unverified instruction embedded in a retrieved document is logged and refused rather than written to any substrate. Structurally similar inputs are routed to different substrates once scope, evidence accumulation, and persistence are taken into account.

Forgetting becomes part of the same control problem. Rather than a memory failure alone, it is a control action that limits the future influence of obsolete, contradicted, unsafe, or low-value state. The authors note that forgetting can mean deletion, decay, masking, reduced retrieval priority, provenance-preserving archival, or replacement by a superseding memory. These choices have different privacy, reliability, and audit consequences. A system that remembers everything can still be unreliable if it cannot attenuate the influence of what should no longer guide behavior.

Hard cases make the control problem visible. Supersession occurs when a once-valid memory is replaced by newer evidence. Contradiction occurs when two retained items cannot both guide behavior. Personalization drift occurs when a local preference is overgeneralized across contexts. Privacy revocation occurs when previously authorized information should no longer be used. Adversarial memory injection occurs when an external actor tries to persist a malicious instruction or false fact through an integrated application or poisoned retrieval corpus. Failure to separate these cases reflects weak governance of persistent state.

Evaluation as longitudinal control is the framework’s second major contribution. A correct response does not establish that the underlying update was justified. An agent may answer correctly after storing information that should have remained temporary. The authors outline criteria for assessing whether an agent controls knowledge updates rather than merely accumulating state: scope fit, evidence quality, temporal validity, interference, reversibility, auditability, efficiency, and robustness. Each criterion has an operational test and a failure signal. A local preference becoming a global behavioral change, or a stable correction remaining trapped in transient context, signals a scope fit error. Persisting rumors, adversarial instructions, or single-instance noise signals an evidence quality error.

The authors propose a minimal benchmark unit with four phases. First, an update event presents new evidence together with uncertainty, source, scope, and expected duration. Second, the agent must choose a substrate or refuse to update, with a short justification. Third, the system is immediately probed for factual correctness and interference. Fourth, delayed probes test temporal consistency, supersession, abstention, and audit trace. Selection can be scored against a reference policy or expert labels, while execution and maintenance can be scored by behavioral probes. Scoring can then separate selection error, execution error, and maintenance error rather than reducing all failures to a single end-task score.

Since substrate selection involves policy judgment, evaluation should allow a set of admissible choices rather than a single gold label. A benchmark instance can be annotated by multiple experts who assign an update event to one or more acceptable substrates and record the rationale in terms of scope, evidence strength, expected duration, reversibility, and audit needs. Disagreement should be reported rather than hidden: high-agreement cases test basic competence, while low-agreement cases identify ambiguous update policies and can be scored with partial credit or preference rankings. The primary unit of analysis should be the update event, with scores aggregated over trajectories to capture accumulation, supersession, and interference across time.

The research agenda that follows has four priorities. The first is update triage: agents need policies that classify evidence, scope, persistence, and risk before changing state. These policies could be rule-based, learned, or hybrid, but they should be explicit enough to audit. The second is memory design, with provenance and validity tracked across other substrates as well. Memory items should retain their source, time, confidence, validity conditions, and supersession status. Corresponding records should link parameter edits, steering configurations, and workflow revisions to their evidence and justification.

Cross-substrate benchmarking is the third priority, building on the selection criteria established by triage policies and the update traces provided by provenance records. Benchmarks of long-term memory and agent task performance could be extended with controlled update trajectories that assess selection, execution, and maintenance under delayed contradiction or supersession. Finally, forgetting should be an explicit design and evaluation target within longitudinal maintenance. The relevant question is not only whether information disappears from recall, but which mechanism limits its future influence and whether that mechanism preserves the required audit trail.

The practical value of controlled knowledge updating, the authors conclude, is restraint. A well-designed agent avoids parametric edits for local or uncertain information, persistent memory for transient context, reliance on superseded evidence as if it were current, and irreversible deletion when traceability is required. The long-term aim is not agents that remember more, but agents that know what deserves to persist, what should remain provisional, and what must be revised, attenuated, or forgotten.

Subject of Research: Artificial Intelligence

Article Title: Controlled knowledge updating in memory-enabled AI agents: beyond model editing and recall

Article References: Chavira Juárez, G., Gonzalez Bravo, E. J., Rivera García, G. E., Arcos Espinosa, J. A., & Nava-Díaz, S. W. (2026). Controlled knowledge updating in memory-enabled AI agents: beyond model editing and recall. Frontiers in Big Data, 9, Article 1938137. https://doi.org/10.3389/fdata.2026.1938137

Image Credits: AI Generated

DOI: 10.3389/fdata.2026.1938137

Keywords: AI agents, LLM, memory, knowledge editing, machine learning, benchmarking, Controlled, knowledge, updating, memory-enabled, agents, beyond