Molecular Ecology Resources

Understanding evolutionary processes is greatly facilitated by high-quality data on genetic variation. We report the development of a genomic toolkit for a recently bottlenecked, long-term studied species, the Seychelles warbler (Ptimerl dezil; Acrocephalus sechellensis). This toolkit comprises a reference genome assembled into 31 chromosomes, together with functional annotations and reference-panel-free imputation of whole-genome sequences from 1,935 individuals. The genomic data have been used to assign the sequenced individuals into a genetic pedigree. Individual genomic data are associated with a suite of phenotypic metadata, amassed from three decades of fieldwork in this closed, long-term monitored population. We compared sex and parentage assigned using the genomic data with the previously recorded sex and parentage metadata to identify and correct 41 sample DNA samples labelled with the wrong identity. This population resource enables a wide range of analyses, that include, but are not limited to phylogenetics, metabarcoding, recombination rates, linkage patterns, adaptation, heritability, demographic history, selection, and inbreeding estimates. We wish to encourage interest from researchers seeking to collaborate on future analyses and data collection. Overall, our methods demonstrate the potential of next generation sequencing and statistical tools to provide dense genomic datasets at large sample sizes for wild populations.

Airborne environmental DNA (eDNA) is a promising tool for detecting a wide range of taxa including threatened and invasive species, yet its application in management is constrained by a limited understanding of its temporal persistence, particularly in nature. We investigated the temporal persistence of airborne eDNA in a natural outdoor setting, using Bennetts wallaby Notamacropus rufogriseus as a case study. We captured airborne eDNA from a single Bennetts wallaby carcass, deployed in an area where wallabies are otherwise not present. A total of 180 samples were collected, spanning the period before deploying the carcass, the 11 days it was on site, and for 32 days after its removal, at distances of 1, 10, and 100 metres using both active (fan-assisted) and passive (no fan) collection methods. Although overall detection rates were low, wallaby DNA was detectable up to 100 metres shortly after the wallaby was introduced to the site and for up to three days after its removal. These findings indicate that airborne eDNA persists only briefly. Actively sampling air using battery-powered fans significantly improved detection rates relative to passive sampling. We demonstrate that airborne eDNA can detect individual organisms in outdoor environments, but reliable detection requires robust sampling and replication to capture rare, transient signals. By revealing how these signals persist over time, our findings provide a framework for optimizing field deployment and for distinguishing remnant DNA from new incursions.

Cryptic species diversity, overlooked due to extreme morphological similarity, is a common phenomenon among ants. The honeypot ant genus Myrmecocystus (Wesmael, 1838; Formicinae: Lasiini) likely features multiple cryptic species, as previously suggested by phylogenetic studies based on ultraconserved elements (UCEs). Here, this work is expanded upon by examining 140 specimens and 2,508 UCE loci, with a particular focus on the M. mendax species complex from the southwestern USA and northern Mexico. Phylogenomic and population genomic analyses revealed five distinct M. mendax-like lineages and identified two potential cases of cryptic species diversity, one within samples matching the morphology of M. mendax and another within samples conforming to M. placodops. Most specimens morphologically identified as M. mendax formed a well-supported monophyletic group sister to M. melliger assigned individuals, with evidence for ongoing hybridization between both species in the Madrean Sky Islands along the USA-Mexico border. Patterns in the main M. mendax clade also suggest adaptive divergence across ecological gradients, warranting further investigation. Overall, these findings highlight the power of UCE-based genomic data in phylogenetic reconstructions and population genetic analyses to better resolve cryptic species diversity, and clarify complex evolutionary histories shaped by introgression and incomplete lineage sorting.

Museum collections of Coleoptera contain genetic material of potential interest to biotechnology, and non-destructive DNA extraction enables the preservation of important specimens with concomitant release of mitochondrial and genomic DNA. Mini-barcoding of regions of the mitochondrial cytochrome oxidase subunit I (MT-COI) gene helps identify and eliminate known species from further investigation. Here we identify a novel luciferase gene, using Consensus Degenerate Hybrid Oligonucleotide (CODEHOP) primers targeting the region of the luciferase gene spanning the fourth exon, intron, and fifth exon to detect luciferase gene content and eliminate samples containing known luciferase sequences. Biotinylated luciferase gene probes from the firefly Photinus pyralis enabled the enrichment of potential luciferase gene fragments for next-generation sequencing. A bioinformatic analysis suite was then used to identify a luciferase gene sequence from a previously unidentified firefly originally collected in Costa Rica in 2012. We demonstrate that this newly discovered luciferase, termed CRLuc, catalyses a bioluminescent reaction and we determined its emission spectra, Km for the substrates ATP and D-luciferin, and pH stability.

Metataxonomic analysis targeting the V4 region of the 18S rDNA gene, combined with molecular phylogenetic inference, was applied to detect nematode DNA of public health relevance in environmental matrices. A total of 25 mOTUs corresponding to six nematode taxa were detected in environmental samples from the Andean region of Colombia. Analysis of 12 water and sludge samples from wastewater treatment plants, 5 artisanal agricultural bioinputs, and 3 food samples revealed multiple species of public health significance: Trichuris trichiura, Enterobius vermicularis, Ascaris spp., and Necator americanus. We also confirmed zoonotic species, including Angiostrongylus cantonensis and Trichinella spp. These findings demonstrate that combining metataxonomics with molecular phylogeny provides a scalable molecular framework for the environmental surveillance of parasitic nematodes, overcoming the limitations of traditional morphological identification methods. This approach offers a replicable model for strengthening control and monitoring programs for parasitism in human populations.

Morphological trait datasets and phylogenies are routinely paired to investigate macroevolutionary patterns during disparity analyses. However, incomplete fossil sampling can distort disparity estimates, obscuring true evolutionary signals. Ancestral state estimation can be used for both continuous and discrete traits to extend these analyses beyond incomplete fossil data, such as investigations into disparity through time. However, when ancestral state estimation occur in the disparity pipeline, and the inevitable uncertainty in these estimates, complicate their integration. Determining the most robust workflow for integrating ancestral state estimation in disparity analyses remains a critical methodological challenge. Using simulations to attain a ground-truth disparity value, we evaluated different approaches to performing ancestral state estimation and incorporating uncertainty across varying continuous and discrete trait models, fossil sampling densities and disparity metrics. Ancestral state estimation generally improved recovery of true disparity relative to tip-only analyses, though the optimal approach depended on the interaction between trait model and fossil sampling density. For continuous traits, probabilistic approaches were most accurate, but were sensitive to model misspecification under low fossil sampling density. For discrete traits, pre-ordination methods were most reliable and probabilistic approaches outperformed point estimates under low sampling, while point estimates became increasingly accurate as sampling density increased. Fossil sampling density was a stronger predictor of disparity accuracy than estimation method choice, underscoring that methodologies are only as powerful as the data provided. Our findings offer a practical decision framework for selecting the most appropriate workflow given the sampling density and trait characteristics of a dataset.

Ancient DNA (aDNA) research has advanced rapidly with the development of high-throughput sequencing, which now enables genome-wide analyses of large collections of prehistoric specimens. However, analysing palaeontological and archaeological material with highly degraded DNA constitutes a major bioinformatic challenge. DNA from such samples is characterised by short fragment lengths, low endogenous content, post-mortem damage, and considerable cross-species contamination, which can increase spurious mapping and reference bias, affecting downstream population genetic inferences. Here we present DNAharvester, a modular and reproducible pipeline designed specifically for the processing of highly degraded DNA from ancient and historical specimens. DNAharvester integrates metagenomic filtering before mapping, competitive mapping, adaptive aligner selection (incorporating algorithms such as BWA-aln, BWA-mem, and Bowtie2), and systematic evaluation of reference bias and spurious mapping. By incorporating flexible mapping and filtering strategies, the pipeline can be adapted to varying sample preservation, with a distinct focus on maximising authentic data recovery from highly degraded material. Furthermore, DNAharvester features comprehensive subworkflows for iterative assembly of mitogenomes, identification of genomic repeats and CpG sites, taxonomic classification, microbial/pathogen screening of unmapped reads, genetic sex determination, and variant calling for downstream analyses. To accommodate datasets with varying sequencing depths, the pipeline incorporates multiple variant calling strategies, including diploid variant calling, genotype likelihood estimation, and pseudo-haploid random allele calling. Implemented in Nextflow, DNAharvester provides a highly scalable, containerised framework that enhances reproducibility, portability, and robustness in aDNA analyses. We validated the pipeline across a gradient of simulated scenarios and empirical datasets, demonstrating its ability to systematically mitigate complex background contamination while preserving authentic genomic signals even in the most challenging of circumstances. By streamlining complex bioinformatic tasks through simple configuration files, DNAharvester establishes a standardised approach for the rigorous analysis of highly degraded DNA datasets and makes genomic analyses of ancient remains accessible to the broader research community.

Hypotheses concerning the ecology and evolution of bacteria commonly relate to the presence and abundance of species in various settings and conditions. Shotgun metagenomics may address these hypotheses, which previously relied on PCR or culture. However, the problem of determining the presence/absence of a given species of interest is not trivial, particularly when closely related species are present in the reference database or metagenomic sample. Reference-based methods to detect species-level taxa mostly rely on thresholding of aligned reads or mapped k-mers or derivative metrics like genomic coverage, and create a trade-off between recall/completeness and precision/purity. New methods for species-level profiling (YACHT, metapresence and sylph) have recently been published. Here we test the performance of these methods, along with Kraken2/bracken and MetaPhlAn4, to detect related species of interest using simulated metagenomic samples from genomes in the families Mycobacteriaceae and Neisseriaceae, which contain closely related genomes. Among methods tested, metapresence, when used with an alignment quality filter, and sylph offer the best overall performance. Sylph maintains high precision but requires a depth of coverage greater than approximately 0.1x to reliably detect a genomes presence. Metapresence has a lower limit of detection of hundreds of reads but this is balanced against relatively lower precision. Both methods are relatively robust to the presence of reads from genomes outside the groups of interest. We demonstrate the application of these methods in two real-world datasets: a mycobacterial community in a drinking water system and the community of Neisseriaceae present in the human oral cavity. ImportanceDetecting which bacterial species of interest are present in a given sample is fundamental to studies of microbial ecology and evolution, and to applied microbiology (e.g. clinical diagnostics). Culture-dependent and independent (e.g. PCR) approaches are increasingly complemented by metagenomic approaches, but methods to accurately identify specific low-abundance species-level genomes in a shotgun metagenomic sample are still being refined. Here we comprehensively test YACHT, Kraken2/bracken, metapresence, MetaPhlAn4, and sylph using two simulated datasets of bacterial families, Mycobacteriaceae and Neisseriaceae that contain closely related species. Our simulations exploit natural genomic diversity to create a challenging benchmark. We demonstrate that metapresence and sylph perform best, with the former being well-suited to low-biomass host-associated datasets, and the latter with environmental metagenomic samples. This study is the first extensive benchmark of these methods for this use case, and demonstrates these methods can accurately identify closely related species of interest.

1. Diptera represent a diverse insect order, including vectors of human and animal pathogens. Their accurate species identification remains a major bottleneck in ecological and epidemiological studies. Morphological identification requires taxonomic expertise, while molecular methods are costly and not universally reliable. Wing geometric morphometrics offers an alternative, but manual landmark annotation is time-consuming and introduces observer bias. 2. We developed ITHILDIN, an automated pipeline for landmark and semilandmark annotation of Diptera wings, combining UNet++ segmentation and an Hourglass landmark prediction model. Using mosquitoes as the primary model system, we extended an existing repository with 5,793 additional images. Models were trained on 5991 annotations of landmarks and segmentations and then evaluated on 12,522 images across 34 taxa. We assessed landmark prediction accuracy against human observers and ML-morph, evaluated species identification using Linear Discriminant Analysis on 17 homologous landmarks and 52 semilandmarks, and tested out-of-distribution generalisation by reproducing an independent study. Transferability was demonstrated by adapting the pipeline to the Dipteran families Drosophilidae and Glossinidae. 3. The Hourglass model achieved a mean landmark error of 4.5 pixels (95% CI: 4.3-4.6), within human observer variability (4.7 pixels, 95% CI: 4.4-5.0) and substantially outperforming ML-Morph (12.7 pixels, 95% CI: 11.1-14.2). The semilandmark-based approach for species identification achieved 91% balanced accuracy across 34 taxa, comparable to CNN performance (94%). On out-of-distribution data, the landmark pipeline generalised substantially better than the CNN and a soft-voting ensemble of the landmark and CNN classifiers achieved 88% balanced accuracy on a replicated study. 4. Combining geometric morphometrics with deep learning provides a reproducible, interpretable, and generalisable alternative to black-box CNN classifiers for Diptera wing analysis. By acting as a consistent single observer comparable to human annotation, the system eliminates inter-observer bias, enabling large-scale and cross-study morphometric analyses of Dipteran wings. The system is publicly available at www.ithildin.bnitm.de and transferable to other Diptera families with moderate retraining effort. Data availabilityImages used in this study are accessible under CC BY 4.0 license at https://doi.org/10.6019/S-BIAD1478. Downloadable and installable docker application can be accessed on the applications git page: https://anonymous.4open.science/r/ITHILDIN-4313/

Biodiversity patterns in tropical freshwater ecosystems remain unevenly understood, particularly in high-integrity regions such as Indigenous territories. In this study, we assessed taxonomic and functional beta diversity of Ephemeroptera, Plecoptera, and Trichoptera (EPT) in Amazonian streams located within the Panara Indigenous Territory, Brazil. We evaluated the relative contributions of local environmental variables, spatial processes, and landscape context to beta-diversity patterns. We disentangled the roles of replacement and richness differences across taxonomic and functional dimensions. EPT larvae were sampled in 31 streams during the dry season. Beta diversity was quantified using Sorensen-based dissimilarity indices, and functional dissimilarity was calculated from seven ecological traits using Gower distances. Taxonomic beta diversity was dominated by genus replacement and was jointly structured by local habitat variables and spatial components, indicating the combined influence of environmental filtering and dispersal limitation. In contrast, functional beta diversity was higher than taxonomic beta diversity and was predominantly structured by richness differences, with significant effects of local environmental variables but no detectable influence of spatial processes. This pattern indicates a decoupling between taxonomic and functional dimensions, suggesting high levels of functional redundancy among EPT genera across streams. Our findings demonstrate that Amazonian streams within Indigenous territories provide key systems for understanding community assembly processes under low levels of direct anthropogenic disturbance. By revealing contrasting mechanisms underlying taxonomic and functional beta diversity, this study underscores the importance of integrating multiple facets of biodiversity and reinforces the role of Indigenous territories as strategic landscapes for safeguarding Amazonian freshwater biodiversity.

Several recent studies have quantified signed linkage disequilibrium (LD) among mutations in genomic datasets, often reporting positive LD, particularly among mutations presumed to be less deleterious, such as synonymous variants. In this article, we investigate two potential sources of this positive LD: the focus on rare alleles, as adopted in several previous studies, and errors arising in the mapping of short-read sequences onto a reference genome. Using coalescent simulations, we extend previous theoretical results of the effect of focusing on rare alleles, and show that derived alleles present at similar frequencies tend to be in positive LD. Reanalyzing datasets from Capsella grandiflora and Drosophila melanogaster, we show that LD among synonymous derived alleles vanishes in the absence of any conditioning on frequency, while LD between mutations categorized as potentially deleterious by the SIFT4G program stays positive. However, we show that in both cases, this positive LD may be at least partly caused by the potential mismapping of a small fraction of sequences in some individuals, which could be a consequence of structural variants that are absent from the reference genome. Overall, these results show that average signed LD among mutations can be strongly affected by technical artifacts even if these concern only a minority of variants. Finally, we discuss other possible sources of positive LD among deleterious mutations.

Sheep husbandry played an important role in the agrarian and textile economies of Late Iron Age Denmark, yet the genetic structure of sheep populations from this period remains poorly understood. In this study, we analyse complete mitochondrial genomes from five Late Iron Age sheep recovered from four Danish archaeological sites dating to the fifth to tenth centuries AD. All sequenced sheep belong to mitochondrial haplogroup B and fall within the B1a lineage, the predominant maternal lineage in European sheep. Each individual represents a distinct haplotype, resulting in high haplotype diversity despite the limited sample size. Population differentiation between Danish ancient sheep and modern reference populations from Fennoscandia and northwest Europe is very low, indicating limited maternal genetic differentiation at the regional scale. Median-joining network analysis further shows that Danish haplotypes are distributed across the broader northern European haplogroup B lineage rather than forming a geographically distinct cluster. These results suggest that Danish sheep populations during the Late Iron Age maintained multiple maternal lineages and were embedded within wider northern European genetic networks. The observed mitochondrial diversity is consistent with sheep husbandry systems that were not restricted to narrow maternal breeding stocks during a period associated with expanding textile production. HighlightsO_LIComplete mitogenomes from Danish Late Iron Age sheep C_LIO_LIHigh maternal diversity within haplogroup B C_LIO_LIGenetic links between Late Iron Age and modern sheep C_LI

Invasive insect species pose serious threats to agriculture and ecosystems, with their spread increasingly accelerated by global trade and climate change. To support prevention and mitigation efforts, it is essential to map the regions where these pests can survive and thrive. Here, we apply MaxEnt, a leading species distribution modeling framework, to estimate current (2020) and future (2040-2060) suitable habitats for five major invasive insects across the contiguous United States: brown marmorated stink bug, corn earworm, spongy moth, root weevil, and spotted lanternfly. To account for an uncertain climatic future, these projections are generated under four shared socioeconomic pathways, which reflect a range of plausible climate change scenarios. Beyond forecasting distributions, we examine several key modeling decisions, especially those often overlooked in practice. In particular, we find that background sampling strategies play a critical role in model calibration and that a hybrid sampling approach with a moderate buffer bias provides better predictive accuracy. We also show that permutation importance scores, commonly used to rank environmental variables, are highly sensitive to small changes in the background data and should be interpreted with caution. Finally, to bridge the gap between ecological modeling and applied machine learning, we provide a self-contained, math-focused background to MaxEnt aimed at practitioners outside of traditional ecological fields. Overall, this work delivers reproducible modeling workflows and critical insights into building robust, transparent, and ecologically meaningful MaxEnt models for climate-informed species distribution analysis.

The detection and monitoring of bat species using non-invasive sampling and molecular techniques has become increasingly popular in recent years. In Europe, these approaches have been applied to identify horseshoe bats of the genus Rhinolophus, which includes five species: R. hipposideros, R. ferrumequinum, R. euryale, R. mehelyi and R. blasii. While species-specific real-time PCR assays exist for R. ferrumequinum and R. hipposideros, no unified panel of real-time PCR assays currently enables the identification of all five European Rhinolophus species from non-invasively collected samples. Here, we developed five species-specific real-time PCR assays, each targeting interspecies nucleotide variation within the mitochondrial cytochrome b gene. To enhance single-base discrimination, RNase H-dependent PCR (rhPCR) primers were employed, incorporating cleavable blocked primers that require perfect complementarity for extension. The assays were applied to droppings non-invasively collected from 18 caves and one church in Serbia and Romania. Of the 149 samples analysed, 131 (88%) yielded successful amplification of Rhinolophus DNA. Detection probabilities for the three species identified in the field ranged from 0.49 to 0.82. Occupancy estimates varied, with R. euryale showing the highest (0.86; UI: 0.69-0.97) and R. mehelyi the lowest (0.23; UI: 0.08-0.43). The assays were capable of detecting up to three species concurrently within a single pooled sample (approximately 15 droppings). These assays are especially valuable for detecting R. mehelyi, given its rarity and uncertain distribution, and offer a robust tool for monitoring Rhinolophus populations across Europe.

Widow spiders of the genus Latrodectus are important animals for biomedical, pest and conservation research. Here, we present the assembled genomes of two closely related Latrodectus species: the Australian L. hasselti and the New Zealand endemic L. katipo. The genome of L. katipo consists of 13 scaffolds likely corresponding to chromosomes (90% of the total length) and 1267 short scaffolds (10%). It has a total length of 1.5 Gbp and BUSCO of 94.9%. The genome of L. hasselti consists of 379 scaffolds and has a total length of 1.7 Gbp and a BUSCO score of 95.4%. The repeat content is very similar in both genomes with a total proportion of 37.2% for L. katipo and 39.9% for L. hasselti. Genome annotation predicted 12706 and 15111 genes for L. katipo and L. hasselti respectively. An ortholog analysis shows large overlap between orthogroups suggesting either duplication events in L. hasselti or loss of genes in L. katipo.

Species distribution models (SDMs) are widely used to support risk assessment for invasive non-native plant species (INNPS), but their performance is constrained by the coverage of occurrence data. Combining occurrences from citizen science (CS) platforms with data from structured state agency (StAg) monitoring provides unique advantages, yet they are rarely integrated. Here, we systematically compare how CS, StAg, and combined (COM) occurrence data influence the inferred environmental niches, predictive performance, and spatial applicability of SDMs for three widespread INNPS (A. altissima, H. mantegazzianum, I. glandulifera) in central Germany. We quantified niche overlap between datasets using PCA and Schoeners D and applied a hierarchical SDM utilizing boosted regression trees, while the Area of Applicability (AOA) was assessed to identify monitoring gaps. CS data were strongly biased toward lower-elevation, urbanized environments, whereas StAg data captured higher-elevation, remote habitats, particularly along watercourses. Niche overlap reflected both invasion stage and habitat preferences: A. altissima, a species that is spreading, showed the lowest overlap. H. mantegazzianum, associated with linear habitats like watercourses and infrastructure, exhibited intermediate overlap, while I. glandulifera, a widespread species, displayed the highest overlap. Overall, combined models achieved the highest predictive performance (AUC: 0.85, TSS: 0.58), reduced uncertainty along environmental gradients and produced more ecologically plausible suitability patterns. AOA analysis revealed high applicability ([≥]59%) across data sources and species, with COM models consistently reducing extrapolation uncertainty. Our findings highlight that integrating CS and StAg data reduces spatial biases and enhances SDM robustness, which is vital to improve INNPS risk assessments and management. HighlightsO_LICitizen science and state agency data capture distinct environmental spaces. C_LIO_LIOverlap between data sources is related to invasion stage and habitat preference. C_LIO_LICombined data improves invasive species niche representation and model accuracy. C_LIO_LIAOA analysis reveals monitoring gaps, especially in remote and high-elevation areas. C_LI

O_LIEcological communities are complex and exhibit considerable spatial variability, presenting challenges in accurately understanding these systems. A primary obstacle in ecological research is the existence of missing links between species: inevitable unobserved interactions that limit our comprehension of ecological networks and their response to change. While link prediction methods have been developed to address this challenge, most approaches overlook the intrinsic spatial variability of ecological systems. C_LIO_LIWe introduce a flexible, spatially explicit framework based on matrix decomposition that leverages latent structural patterns to predict missing interactions and their strength, without requiring species traits or environmental data. The framework integrates information from paired auxiliary and target networks (locations) using thresholded SVD for link prediction. We applied it to plant-pollinator networks across the Canary Islands, performing pairwise predictions between locations, comparing them to within-location predictions (as a control), and quantifying how spatial variability influences predictive performance. C_LIO_LIPredictions revealed that latent network structure contains substantial predictive information, with F0.5 scores consistently exceeding a random baseline (mean F0.5 = 0.67 {+/-} 0.02 SD), while being less sensitive to interaction strength. The method enabled identifying plausible gaps in the data and producing ecologically coherent predictions. Incorporating information from auxiliary locations enhanced predictive accuracy in certain cases, but success depended on spatial context: predictions were most reliable when derived from nearby, ecologically similar locations, and declined with increasing geographic and ecological distance, consistent with a distance-decay effect. C_LIO_LIWe conclude that the predictability of missing links is spatially variable, reflecting both network and species-level heterogeneity. These patterns provide insights into network structure and the ecological processes shaping it, complementing trait-based approaches. While network structure offers rich predictive information, spatial context is essential for applying it effectively: ignoring spatial variability can obscure ecological signals and inflate predictive error. Our framework is computationally efficient, transferable, and readily applicable to any system with spatial or temporal replication. It can be used for a variety of ecological contexts, including island systems, fragmented landscapes, and environmental gradients, making it a practical and scalable tool for advancing link prediction in ecology. C_LI

Indo-Pacific humpback dolphin (Sousa chinensis) and finless porpoise (Neophocaena phocaenoides) are increasingly threatened across their native range, yet the relative influence of multiple stressors in shaping their population dynamics remains unclear. Current conservation strategies for both species are limited by incomplete data and limited assessment of affecting factors. Here, we integrated eDNA metabarcoding with Joint Species Distribution Modeling (JSDM) to assess how environmental gradients, pollution, and trophic associations interactively influence cetacean distributions in Hong Kong waters. We show that degraded water quality and intensified human activity negatively associated with cetacean occurrence, with clear species-specific responses to different stressors. S. chinensis covaried most strongly with Secchi disc depth, and presence of vessels, while N. phocaenoides was negatively associated with nitrate nitrogen and microbial pollution (sewage). The JSDM variance partitioning analysis highlighted that the occurrence of S. chinensis was primarily associated with anthropogenic disturbances (30.04%), followed by water physical properties (26.63%), whereas N. phocaenoides was more strongly associated with physical (40.9%) and anthropogenic disturbances (35.2%). By integrating eDNA and JSDM, our approach provides fine-scale diagnostics of species-specific vulnerabilities, supporting adaptive conservation strategies and guiding the realignment of protected areas to mitigate biodiversity loss in urbanized marine ecosystems. Environmental ImplicationOur study demonstrates that hazardous water pollutants, including microbial contamination, nutrient enrichment, and chemical stressors, vessel pressure, show strong, species-specific impacts on resident cetaceans in Hong Kong. By integrating eDNA metabarcoding with joint species distribution models, we provide a diagnostic framework that directly links pollutant profiles to ecological risk. These findings highlight that conventional conservation strategies overlooking pollution drivers are insufficient for marine megafauna persistence. Our approach offers an early-warning system for monitoring hazardous pollutants in coastal ecosystems and supports adaptive management strategies to mitigate biodiversity loss in urbanized seascapes.

Scyphozoan jellyfish have a complex life cycle that includes a characteristic transition known as strobilation. Retinoid signaling has been suggested to be involved in jellyfish metamorphosis and development. However, the genomic basis of signaling pathways associated with metamorphosis has not been sufficiently compared at the class level. Experimental studies have reported that indole compounds can induce metamorphosis in some jellyfish species. Indole- and tryptophan-derived metabolites are known to function as ligands for the aryl hydrocarbon receptor (AhR) in other organisms. However, the potential role of AhR signaling in jellyfish metamorphosis has not been previously explored. We compared the distribution of retinoid- and AhR-associated gene families across multiple scyphozoan genomes. This analysis aimed to characterize their distribution patterns in relation to signaling pathways associated with development and environmental responses. A standard gene prediction and annotation pipeline was applied to 20 species from 21 publicly available scyphozoan reference genome assemblies retrieved from the NCBI database. The distribution and copy number of these gene families were compared across species. Retinoid-associated gene families were detected across almost all Scyphozoa genomes, and core components of AhR signaling (AhR, ARNT) were identified in most species. These results suggest that scyphozoan genomes contain genetic components of retinoid- and AhR-related signals. This study presents the distribution of gene families related to developmental signaling across Scyphozoa using a comparative genomic approach. It does not imply direct functional involvement of retinoid or AhR signaling, but instead focuses on potential signaling pathways at the genome level. It also provides an overview of currently available scyphozoan genomic data. These findings provide a basis for future hypothesis generation and functional validation in jellyfish metamorphosis research.

BackgroundTransposable elements (TEs) are repetitive genetic elements that can jump to new loci causing genome expansions, structural rearrangements, and can, ultimately, propel the evolution of genomes. Despite their significance, the role of TEs in the evolution of genomes and phylogenetic groups remains largely understudied in early diverging lineages. Further, the extent to which TE content varies across species is still an open question. Medusozoa, a group within Cnidaria encompassing jellyfish and hydroids, exhibits an exceptional diversity of life history strategies, body plans, and physiological capabilities. These characteristics, along with its early-diverging phylogenetic position, establish Medusozoa as an ideal system for investigating the composition and evolutionary history of TEs within the group. ResultsWe generated a custom repeat library built from annotations of 25 Medusozoan genomes and used it to characterize TEs, aiming to identify lineage-specific TE content and activity that may correlate with the diversity observed within the group. We found that repetitive element percentage and genome size varied considerably, with Hydrozoa exhibiting the most variation among classes in both respects. DNA transposons were the most prevalent TE classification in all but two genomes, averaging 28% of all genomes. Intra-genus comparisons revealed a surprising degree of differences in TE content. In the genus Aurelia, the expansion of a single DNA transposon superfamily accounted for much of the difference in repetitive element percentage between two species, whereas in the genus Turritopsis, a similar divergence resulted from the proliferation of multiple superfamilies. Interestingly, most genomes showed evidence of recent TE expansions, suggesting ongoing activity in many medusozoan species. ConclusionWe present the first comparative analysis of TEs across all medusozoan classes. Our results reveal class-specific TE dynamics and highlight cases of TE proliferations as lineages diverge. This research provides data on TE activity and diversity that can be used as a resource for future study and fills important gaps in our understanding of TEs in early diverging animal lineages.