Molecular Ecology Resources最新文献

Multiple species respond to each other in their co-existing communities. Such reciprocal phenotypic plasticity can shape the behaviour and evolution of ecological communities, but its genetic architecture remains elusive. We address this issue by developing a computational mapping model that combines community ecology and quantitative genetics into a unifying context. Culturing a pair of genotypes from two species in a socially isolated environment (monoculture) and a socialised environment (co-culture) allows for the quantitative estimation of reciprocal phenotypic plasticity, that is, the difference between trait values of one (defensive) species expressed in the monoculture and the co-culture with the other (offensive) species. Classic quantitative genetic theory is implemented to map genetic variants for reciprocal phenotypic plasticity, including defensive loci derived from the defensive species and offensive loci from the offensive species, and estimate the direct effects of the defensive loci, the indirect effects of the offensive loci and horizontal epistasis due to interactions between the two genomes. We design an ecological genetic experiment of monocultures and co-cultures using 100 pairs of genotyped Escherichia coli and Staphylococcus aureus strains, from which the model identifies the existence of defensive and offensive loci, despite a remarkable asymmetry between the two bacterial species. We find that horizontal epistasis between defensive and offensive loci plays a sizable role in mediating reciprocal phenotypic plasticity. The biological functions of these identified loci are annotated via GO analysis. Our model could produce unique results that shed light on the genetic mechanisms of interspecies interactions and their adaptation in ecological communities.

Environmental DNA (eDNA) analysis enables biodiversity monitoring by detecting organisms from trace genetic material, but high reagent costs, cold-chain logistics and computational demands limit its broader use, particularly in resource-limited settings. To address these challenges and improve accessibility, we directly compared multiple workflow components, including four DNA extraction methods, two primer sets, three Nanopore basecalling models, and two demultiplexing pipelines. Across 48 workflow combinations tested in an aquarium with 15 fish species, we mapped trade-offs between cost, sensitivity, and processing speed to assess where time and resource savings are possible without compromising detection. Workflows using the Qiagen Blood and Tissue (BT) extraction kit and amplification using the MiFish-U primer set provided the highest sensitivity, detecting ≥ 12 of 15 species by ~3–5 h and reaching the 15-OTU plateau at ~8–10 h with Oxford Nanopore's high accuracy (HAC) basecalling model. Chelex, an alternative lower-cost extraction method, showed partial recovery only (≤ 9 OTUs by 61 h) even with extended sequencing, and did not recover all 15 OTUs. DirectPCR and QuickExtract offered field-friendly extraction alternatives that achieved comparable recovery in ~10–12 h, though their cost-effectiveness varied. While the MarVer1 primer was designed to broaden vertebrate detection, it recovered the same fish species as MiFish-U, though with fewer total reads. Real-time sequencing trials (0–61 h) revealed that high-efficiency workflows (BT + HAC) reached detection plateaus rapidly, indicating sequencing time can be reduced without sacrificing accuracy. The OBITools4 bioinformatics pipeline enabled automated demultiplexing but discarded more reads than an alternative, ONTbarcoder2.3, which retained low-abundance taxa at the cost of manual curation. Rather than identifying a single ‘best’ workflow, this study provides a transparent decision framework for prioritising cost, speed, and sensitivity in eDNA applications, supporting scalable, cost-effective eDNA monitoring in resource-limited settings.

DNA metabarcoding of zooplankton biodiversity is used increasingly for monitoring global ocean ecosystems, requiring comparable data from different research laboratories and ocean regions. The MetaZooGene Intercalibration Experiment (MZG-ICE) was designed to examine1 and analyse patterns of variation of DNA sequence data resulting from multi-gene metabarcoding of 10 zooplankton samples carried out by 10 research groups affiliated with the Scientific Committee for Ocean Research (SCOR). Aliquots of DNA extracted from the 10 zooplankton samples were distributed to MZG-ICE groups for metabarcoding of four gene regions: V1-V2, V4 and V9 of nuclear 18S rRNA and mitochondrial COI. Molecular protocols and procedures were recommended; substitutions were allowed as necessary. Resulting data were uploaded to a common repository for centralised statistics and bioinformatics. Based on proportional sequence numbers for abundant phyla, overall patterns of variation were consistent across many—but not all—MZG-ICE groups. V9 showed highest similarity, followed (in order) by V4, V1-V2, and COI. Outlier data were hypothesised to result from the use of different PCR protocols and sequencing platforms, and possible contamination. MZG-ICE results indicated that DNA metabarcoding data from different laboratories and research groups can provide reliable, accurate and valid descriptions of biodiversity of zooplankton throughout the ocean. Recommendations included: pre-screening QA/QC of raw data, detailed records for laboratory protocols, reagents, and instrumentation, and centralised bioinformatics and multivariate statistics. In the absence of universal agreement on standardised protocols or best practices, intercalibration is the best way forward toward validation of DNA metabarcoding of zooplankton diversity for global ocean monitoring.

Demographic information, such as sex ratios, is essential for understanding population dynamics and informing conservation strategies. Yet obtaining sex ratios in natural populations can be challenging due to logistical, ethical and legal constraints. Environmental DNA (eDNA) has revolutionised non-invasive biodiversity monitoring, but its potential for assessing demographic parameters remains largely unexplored. Here we present an eDNA-based method to monitor sex ratios of populations by quantifying sex-specific SNP alleles. Using RADseq data from Balkan crested newts (Triturus ivanbureschi), we identified a male-specific allele that was consistently present in all males and absent in females. We then developed a Droplet Digital PCR (ddPCR) assay to quantify allele ratios and validated it on mock (DNA extract mixtures) and eDNA samples with known sex ratios. Our sex-specific SNP assay successfully distinguished male- and female-biassed ratios in mock samples and showed a strong positive relationship between the proportion of males and the male-specific allele. While resolution was lower in eDNA samples, sex ratio estimates reflected population composition, particularly when corrected for biomass. Performance was mainly influenced by inter-individual variation in male allele copy numbers, but this effect diminished as the number of males increased, reflecting natural populations better. For effective field application, maximising nuclear eDNA recovery, validating marker specificity and accounting for species-specific life history traits when sampling will be crucial. With further field validation, our eDNA-based method could support large-scale, non-invasive sex ratio monitoring, offering valuable insights into species phenology and population dynamics to guide conservation efforts.

Developments in the environmental DNA (eDNA) field have revolutionised our ability to map biodiversity by providing cost-effective and non-invasive means to survey organisms across the tree of life. In the terrestrial realm, a variety of eDNA sources have been employed, but we lack easily accessible and cosmopolitan sources of terrestrial eDNA. Here we document the value of a novel eDNA source for mapping lifeforms across the tree of life in temperate and tropical ecosystems: mosses (Bryophytes). First, we analysed eDNA from 25 moss patches collected using three sampling methods (swabbing, moss stubs, and washes of moss stubs) across three sites in Denmark. We detected 26 vertebrate species, 54 invertebrate genera, 21 vascular plant genera, and 553 bacterial and 210 fungal genera. Swabbing was sufficient to obtain eDNA, eliminating the need for destructive sampling of mosses. Subsequently, employing the swabbing approach in gallery forest and savanna ecosystems in Côte d'Ivoire we assessed its use for vertebrate detections. Metabarcoding of 29 moss swabs yielded 18 bird, 13 mammal, and two amphibian genera, confirming its applicability in the tropics. Our findings expand the current biodiversity monitoring toolkit by capitalising on a cosmopolitan and readily available terrestrial eDNA source.

Runs of homozygosity (ROH) are increasingly being analysed using whole genome sequences in non-model species as a measure of inbreeding and to assess demographic history, thus providing useful information for conservation. However, most studies have used Plink for ROH inference which performs poorly when sequencing depth is below 10×, often underestimating ROH. This can lead to erroneous status assessment and poor management decisions. We assessed the performance of ROHan, a program developed for ROH and heterozygosity estimation using lower coverage sequences that have so far only been optimised for human data. Using high coverage whole genomes from 22 caribou, a non-model species at risk presenting varying levels of inbreeding, we assessed the effects of sequencing depth (1–15×), the input parameter ‘rohmu’ that determines the heterozygosity rate that is tolerated within ROH regions, and demographic history on the ROH inference and heterozygosity. Accurate estimation of the percentage of the genome and lengths of ROH could be achieved at depths as low as 3–5×. However, the rohmu parameter and individual demographic history had a significant effect on the results. Heterozygosity was also overestimated at low depth. Using our optimised rohmu parameter, we re-analysed low coverage sequences from a small and isolated caribou population and demonstrated high inbreeding levels that had previously been missed. We provide recommendations for optimisation of the rohmu parameter and demonstrate the need for careful interpretation of outputs to enable robust ROH inference using low coverage whole genome sequences in wildlife species.

CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats—CRISPR-associated nucleases) systems allow researchers to detect, capture, and even alter parts of an organism's genome. However, while the use of CRISPR-Cas has revolutionised many fields in the life sciences, its full potential remains underutilised in ecology and biodiversity research. Here we outline the emerging applications of CRISPR-Cas in ecological contexts, focusing on three main areas: nucleic acid detection, CRISPR-enhanced sequencing, and genome editing. CRISPR-based nucleic acid detection of environmental DNA samples is already reshaping species monitoring, providing highly sensitive and non-invasive tools for both scientists and the public alike, with reduced costs and minimal experience required. Further, CRISPR-enhanced sequencing, including Cas-mediated target enrichment, enables efficient recovery of ecologically relevant loci and supports diverse applications such as amplification-free metagenomics. Finally, while genome editing on wild species remains largely theoretical in ecology, these tools are already being used in controlled settings to study adaptation and resilience in the face of ongoing global stressors. Together, the applications of CRISPR-Cas are paving the way for more affordable, accessible, and impactful applications for species conservation, and promise to improve our ability to tackle the ongoing global biodiversity crisis.

Inbreeding and inbreeding depression pose a critical challenge to the persistence of small and isolated populations, driving the need for precise assessment of genomic metrics. Genome-wide runs of homozygosity (ROH) have been widely used for evaluating contemporary inbreeding levels and tracing historical events, circumventing the limitations of methods based on pedigree records. However, the reliability of ROH detection is contingent upon the quality of both the reference genome and resequencing data. Here, we employed a simulation-based approach, generating an inbred population with individuals exhibiting varying inbreeding coefficients and 13 reference genomes with differing levels of contiguity. This framework enabled us to systematically investigate the effects of sequencing depth, read length, reference genome continuity and the phylogenetic divergence of reference genomes on detecting genome-wide ROH segments. We found that a sequencing depth of ≥ 15× and a reference genome with a contig N50 > 4 Mb enabled discrimination of both the recent and historical inbreeding events, and a reference genome of congeneric subspecies is an optimal choice for ROH detection if a species-specific reference genome is not available. Furthermore, we performed parameter optimisation for PLINK to enhance ROH detection accuracy under low-coverage sequencing data and imperfect reference genomes. Our findings established methodological guidance for improving ROH-based inbreeding assessments, providing critical insights for conservation genomics and breeding programmes where accurate characterisation of genomic homozygosity is paramount.