Molecular Ecology Resources最新文献

{"title":"MixedUpParents: A New and Improved Method for Genetic Parentage Assignment in Admixed Populations","authors":"Jared A. Grummer,&nbsp;Eric C. Anderson,&nbsp;Gordon Luikart","doi":"10.1111/1755-0998.70194","DOIUrl":"10.1111/1755-0998.70194","url":null,"abstract":"<p>Hybridization is ubiquitous throughout the tree of life. Its presence in genetic datasets often violates model assumptions or leads to erroneous inferences. Nonetheless, few parentage assignment methods have been developed that account for hybridization or admixture, particularly in non-model systems. Here, we describe a novel method of parentage analysis, <i>MixedUpParents</i>, that accounts for hybridization by using species-diagnostic single nucleotide polymorphisms while accounting for mixed ancestry within discrete genomic segments. We test <i>MixedUpParents</i> alongside other parentage assignment programs through simulations to assess assignment accuracy in scenarios with and without hybridization. Our simulations included a range of parameter values, including missing genotypes, migration (hybridization) rates and proportions of un-sampled individuals. Simulation results showed that in all scenarios with admixture, <i>MixedUpParents</i> outperformed the other parentage methods tested and performed well for parent–offspring assignment even when only one parent is sampled. In the best cases, <i>MixedUpParents</i> achieved <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>≥</mo>\u0000 </mrow>\u0000 <annotation>$$ ge $$</annotation>\u0000 </semantics></math> 95% true-positive and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>∼</mo>\u0000 </mrow>\u0000 <annotation>$$ sim $$</annotation>\u0000 </semantics></math>5% false-positive rates (i.e., assigning the wrong parent). Accuracy of all parentage methods increased when a higher proportion of the population was sampled, but decreased with higher levels of missing marker data. The most challenging cases for all methods were those with intermediate migration rates, and when full-siblings were included in the parent sample (representing age-agnostic field sampling). Overall, we show that ‘traditional’ methods can yield high false-positive rates when hybridization is present, emphasizing the importance of using appropriate methods in admixed systems. Our results and freely available software should advance population management and research on measures of reproductive success (fitness) in wild and captive populations, and parentage and pedigree reconstruction broadly.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":"26 6","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148816830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Throughput DNA Barcoding Reveals Multi-Trophic Networks of Plants, Leafminers and Parasitoids in a Tropical Dry Forest","authors":"Ariel H. Muñoz-Sánchez,&nbsp;Gonzalo Contreras-Negrete,&nbsp;Alejandro Zaldívar-Riverón,&nbsp;Rafael Torres Colín,&nbsp;Evgeny V. Zakharov,&nbsp;Carlos López-Vaamonde,&nbsp;Antonio Hernández-López","doi":"10.1111/1755-0998.70183","DOIUrl":"10.1111/1755-0998.70183","url":null,"abstract":"<p>Tropical dry forests host highly diverse insect communities and complex trophic interactions, yet these networks remain difficult to resolve using conventional molecular or rearing-based approaches. Here, we integrate long-read DNA barcoding, non-target sequence co-amplification, and host plant taxonomy to reconstruct tri-trophic interactions among leaf-mining insects, their host plants, and associated parasitoids in a Mexican tropical dry forest. Using Single Molecule, Real-Time (SMRT) sequencing on the PacBio Sequel IIe platform, we generated high-fidelity barcodes from individual leaf-miner larvae and from parasitoid larvae and pupae recovered from leaf mines. From 253 specimens, we obtained 214 sequences, and identified 83 operational taxonomic units including leaf-mining Lepidoptera, Diptera, Coleoptera, and their hymenopteran parasitoids, plus 31 non-target sequences. Of the 214 sequences, 69 were parasitoid wasps (32.2%). Of these, 40 were obtained from parasitoids dissected directly from leaf mines, while 29 were co-amplified from leaf-miner samples, demonstrating that SMRT-based co-amplification allows detection of parasitism without rearing. Our analysis revealed 156 trophic interaction types and 247 interaction events. Network analyses showed a highly modular tripartite network, with module structure primarily driven by host plant identity and a reduced set of influential leaf-miner and parasitoid taxa. Bipartite networks supported the hypothesis that interactions are more strongly compartmentalized between leafminers and host plants than at higher trophic levels. These results demonstrate that long-read DNA barcoding with co-amplification detection provides a scalable framework for reconstructing multi-trophic interactions from individual specimens, overcoming key limitations of rearing-based methods and enabling robust biodiversity and ecological network assessments in species-rich ecosystems.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":"26 6","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13509077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148816897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Updating the RZooRoH Package for the Analysis of Inbreeding, Identity-By-Descent and Relatedness From Genomic Data","authors":"Natalia S. Forneris,&nbsp;Pierre Faux,&nbsp;Mathieu Gautier,&nbsp;Tom Druet","doi":"10.1111/1755-0998.70186","DOIUrl":"10.1111/1755-0998.70186","url":null,"abstract":"<p>The RZooRoH R package was implemented to characterize individual inbreeding levels. It identifies DNA segments inherited twice from a common ancestor through different paths, which are known as homozygous-by-descent (HBD) segments. The package accepts different data formats and provides multiple outputs: HBD segments, inbreeding rates and genome-wide and locus-specific HBD probabilities. In addition, it partitions HBD levels into multiple HBD classes. The length distribution varies between these classes, which therefore correspond to distinct groups of ancestors that can be traced back to different generations in the past. This provides information about mating structure and recent demographic history. The computational performance of the package has been substantially improved, enabling, for example, computing times to be reduced when working with whole-genome sequence data and more HBD classes to be fitted. It is now possible to fit one class per past generation, which facilitates interpretation of the results. Since we have previously demonstrated that the ZooRoH model can be used to characterize identity-by-descent (IBD) between haploid individuals or phased haplotypes, this option has been included in the new package version. Estimating kinship by characterizing IBD levels between the four possible pairs of haplotypes from two individuals is another feature we added to the package. Finally, new options allow models to be refined, for instance by defining HBD classes as intervals or constant inbreeding rates for neighbouring classes. Overall, the new version of the package offers improved computational efficiency and interpretability when characterizing inbreeding, IBD and relatedness levels.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":"26 6","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13509087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148816733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Quantitative Monitoring of Diverse Fish Communities at a Large Scale Combining eDNA Metabarcoding and qPCR”","authors":"","doi":"10.1111/1755-0998.70193","DOIUrl":"10.1111/1755-0998.70193","url":null,"abstract":"<p>Pont, D., P. Meulenbroek, V. Bammer, et al. 2023. “Quantitative Monitoring of Diverse Fish Communities on a Large Scale Combining eDNA Metabarcoding and qPCR.” <i>Molecular Ecology Resources</i> 23: 396–409. https://doi.org/10.1111/1755-0998.13715.</p><p>Due to an error in the calculation of teleo-eDNA concentrations (real time qPCR), all figures expressing the number of teleo-eDNA copies per litre or per sample must be divided by 100.</p><p>Figures 4, 5 and 7 are corrected (modifications of the axis numerical values and axis labels when necessary). Figures 1, 2, 3 and 6 are unchanged.</p><p>We apologize for this error.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":"26 6","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148811622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unravelling Bulk Ichthyoplankton Diversity in Vietnam: Metabarcoding Validation With Controlled Mock Samples","authors":"Cam Hong Van,&nbsp;Long Van Nguyen,&nbsp;Oanh Thi Truong,&nbsp;Sang Quang Tran,&nbsp;Huy Quoc Pham,&nbsp;Binh Thuy Dang","doi":"10.1111/1755-0998.70192","DOIUrl":"https://doi.org/10.1111/1755-0998.70192","url":null,"abstract":"<p>The sustainability of Southeast Asian fisheries hinges on high-throughput tools for monitoring early life-stage fish biodiversity. However, applying DNA metabarcoding to hyper-diverse tropical ichthyoplankton requires rigorous calibration to ensure quantitative reliability. We systematically evaluated the metabarcoding workflow using controlled mock communities, revealing that taxonomic recovery is governed by a stochastic limit of detection at a normalised proxy biomass threshold of ≤ 0.05. Quantitative analysis confirmed a significant linear relationship between specimen size and read abundance (<i>R</i>² up to 0.817), demonstrating that biomass-driven template competition induces frequent false negatives for low-biomass taxa, a phenomenon exacerbated by increasing community complexity (ANOVA: <i>p</i> &lt; 0.001). To mitigate these systemic biases, we applied a size-stratified specimen-balancing strategy intended to increase the representation of small-bodied components in natural bulk samples. Applying this optimised workflow to field samples from Khanh Hoa, Vietnam, we identified 139 species and unmasked a North–South biogeographic dichotomy (PERMANOVA: <i>R</i>² = 53%, <i>p</i> = 0.001) driven by transect-scale environmental gradients and local hydrography. Notably, we identified diversity hotspots requiring &gt; 200,000 reads for saturation, suggesting these sites act as critical larval retention zones. The contrast between functional management zones was highly significant (<i>p</i> = 0.002), with the conservation area (Zone B) exhibiting higher alpha richness and a nine-fold increase in unique indicator species compared to high-activity areas (18 vs. 2). Our work demonstrates that comprehensive validation is vital for accurate metabarcoding, offering a robust framework to understand how ecological gradients and localised human pressures shape Vietnam's critical marine spawning sites and nursery grounds.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":"26 6","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1755-0998.70192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable and Predictive Models Based on High-Dimensional Data in Ecology and Evolution","authors":"Joshua P. Jahner,&nbsp;C. Alex Buerkle,&nbsp;Dustin G. Gannon,&nbsp;Eliza M. Grames,&nbsp;S. Eryn McFarlane,&nbsp;Andrew Siefert,&nbsp;Katherine L. Bell,&nbsp;Victoria L. DeLeo,&nbsp;Matthew L. Forister,&nbsp;Joshua G. Harrison,&nbsp;Daniel C. Laughlin,&nbsp;Amy C. Patterson,&nbsp;Breanna F. Powers,&nbsp;Chhaya M. Werner,&nbsp;Isabella A. Oleksy","doi":"10.1111/1755-0998.70191","DOIUrl":"https://doi.org/10.1111/1755-0998.70191","url":null,"abstract":"<p>The proliferation of high-dimensional data in ecology and evolutionary biology raises the promise of statistical and machine learning models that are highly predictive and interpretable. However, high-dimensional data are commonly burdened with an inherent trade-off: in-sample prediction of outcomes will improve as additional variables are included in the model, but this may come at the cost of poor predictive accuracy and limited generalizability for future or unsampled observations (out-of-sample prediction). To confront this problem of overfitting, sparse models can focus on key variables by correctly placing low weight on unimportant variables. We compared nine methods to quantify their performance in variable selection and prediction using simulated data with different sample sizes, numbers of variables, and strengths of effects. Overfitting was typical for many methods and simulation scenarios. Despite this, in-sample and out-of-sample prediction converged on the true predictive target for simulations with more observations, larger causal effects, and fewer variables. Accurate variable selection to support process-based understanding will be unattainable for many realistic sampling schemes in ecology and evolution. We use our analyses to characterize data attributes for which statistical learning is possible, and illustrate how some sparse methods can achieve predictive accuracy while mitigating and learning the extent of overfitting.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":"26 6","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1755-0998.70191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate Identification of Key Groups of Microeukaryotes Using Multimodal Deep Learning: An Integrated Classification Model Combining Morphological and Molecular Data","authors":"Yumeng Song,&nbsp;Lin Zheng,&nbsp;Alan Warren,&nbsp;Mingzhuang Zhu,&nbsp;Bailin Li,&nbsp;Weidong Ji,&nbsp;Xuming Pan","doi":"10.1111/1755-0998.70190","DOIUrl":"10.1111/1755-0998.70190","url":null,"abstract":"<p>Traditional classification of flagellates (i.e., flagellated protists) relies on morphological traits or a single molecular marker, which suffer from subjectivity and limited data sources. This study proposes a multimodal deep learning model, Residual Multi-Feature Attention-50 (ResMFA50), that integrates photomicrographs and small subunit ribosomal RNA (SSU rRNA) gene sequences of flagellates. The dual-branch architecture (DNA sequence and image branches) extracts local and global features, while the Multi-Feature Attention (MFA) mechanism dynamically fuses heterogeneous data. Experiments were conducted on a dataset comprising 296 SSU rRNA gene sequences and 308 standardized photomicrographs, evaluated using 10-fold cross-validation. The results demonstrate that ResMFA50 achieves an accuracy of 92.5% in classifying flagellates at a batch size of eight, which is significantly higher than the accuracies achieved by SVM (82.4%), Random Forest (84.2%), EfficientNet (83.6%), ResNet50 (87.4%), and MMNet (91.3%). Moreover, ablation experiments comparing early, intermediate, and late fusion strategies demonstrate that the proposed late fusion scheme consistently outperforms other fusion timings, achieving improvements of 3.2%–3.8% over early fusion across different batch sizes. This study establishes a methodological foundation for modelling multimodal biological data in complex systems, advancing deep learning applications in integrative taxonomy. This advantage is attributed to the dual-channel global pooling mechanism (Global Average/Max Pooling fusion), which balances the variance-bias trade-off through complementary strategies of spatial statistical smoothing and local salient feature detection, enhancing robustness to data scale expansion.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":"26 6","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1755-0998.70190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking Full-Length ITS Metabarcoding Across Illumina 2 × 500, PacBio, and Oxford Nanopore Sequencing Using Mock and Soil Communities","authors":"Leho Tedersoo,&nbsp;Marko Prous,&nbsp;Meirong Chen,&nbsp;Sten Anslan,&nbsp;Irja Saar,&nbsp;Benjamin Dubois,&nbsp;Vladimir Mikryukov","doi":"10.1111/1755-0998.70189","DOIUrl":"10.1111/1755-0998.70189","url":null,"abstract":"<p>Metabarcoding is a powerful tool for biodiversity comparisons, where standard-size DNA barcodes (&gt; 500 bases) offer better taxonomic resolution than shorter ones. Still, the choice of sequencing platforms and bioinformatics pipelines may strongly affect inferred diversity due to various technical biases. We assessed the relative performance of Illumina MiSeq i100 (2 × 500 paired-end), PacBio Revio and Oxford Nanopore MinION sequencing and bioinformatics pipelines, using full-length ITS amplicon sequencing datasets from a 103-species mock community and 45 composite soil samples. Despite numerous low-quality reads, PacBio yielded the lowest overall error rate and highest number of taxa. Illumina revealed the highest proportion of chimeric and index-switched reads, along with a strong bias towards shorter amplicons. MinION data analysed using PRONAME and Minovar—a bioinformatics pipeline presented here—had the largest proportion of low-quality data, and rare taxa were lost during data filtering and read polishing steps. Although Minovar enabled amplicon sequence variant (ASV) level precision for common taxa, we recommend clustering ASVs into OTUs. For PacBio, standard filtering approaches outperformed the ASV approach because they retained rare taxa. For Illumina, a stringent ASV approach or removal of rare OTUs would limit artefacts. Across all platforms, excess PCR cycles promoted chimeric and low-quality reads and lost quantitativity in biodiversity assessments. With moderate differences in effect sizes, all analytical approaches supported the conclusion that sampling design determines how we see soil biodiversity responses to land use. For biodiversity surveys based on the full-length ITS metabarcoding, we recommend using PacBio sequencing with standard, non-ASV pipelines.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":"26 6","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148688211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FishChip: Development of a High-Throughput qPCR Assay for Freshwater Fish eDNA Quantification and Its Comparison With Droplet Digital PCR","authors":"Shoutao Cheng,&nbsp;Jiasheng Zhang,&nbsp;Zhuming Liang,&nbsp;Hao Xue,&nbsp;Lingsong Zhang,&nbsp;Yeyao Wang,&nbsp;Fansheng Meng","doi":"10.1111/1755-0998.70187","DOIUrl":"10.1111/1755-0998.70187","url":null,"abstract":"<p>Quantitative monitoring of fish using environmental DNA (eDNA) typically relies on quantitative real-time PCR (qPCR) and droplet digital PCR (ddPCR), yet both methods are constrained in throughput, making it difficult to meet the demands of basin-scale surveys. High-throughput qPCR (HT-qPCR) has emerged as a platform capable of simultaneously processing large numbers of targets and samples, but its performance for fish eDNA quantification and its comparability with ddPCR remain poorly characterized. Here, we selected nine widely distributed freshwater fishes and designed 19 species-specific primer pairs (2–3 per species). All assays performed well on a high-throughput chip-based qPCR (HT-qPCR) platform, with amplification efficiencies ranging from 91.07% to 105.83% and 78.9% of assays falling within the optimal 95%–105% interval. Applying HT-qPCR to quantify fish eDNA across six aquatic regions, <i>Cyprinus carpio</i> (<i>C. carpio</i>) achieved a 100% detection rate. For the same fish species, the two primer sets exhibited measurable differences in detection rate and quantitative estimates. HT-qPCR and ddPCR estimates were strongly correlated (<i>r</i> = 0.858), and Deming regression indicated near-equivalence (HT-qPCR = 0.52 + 0.99 × ddPCR). Bland–Altman analysis revealed a consistent ~3-fold overestimation by HT-qPCR relative to ddPCR, which is generally considered the more accurate method, suggesting that a correction factor could harmonize data across platforms. These results validate the utility of HT-qPCR for catchment-scale quantification of fish eDNA concentrations and provide methodological innovation and technical support for aquatic ecosystem monitoring and assessment.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":"26 6","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148676326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Malaria Parasite Gene Flow Inference Under Sparse Spatial Sampling","authors":"Yao Li,&nbsp;Bing Guo,&nbsp;Timothy D. O'Connor,&nbsp;Shannon Takala-Harrison,&nbsp;Kathleen Stewart","doi":"10.1111/1755-0998.70182","DOIUrl":"10.1111/1755-0998.70182","url":null,"abstract":"<p>Estimating malaria parasite migration is crucial for informing elimination strategies, particularly by identifying regions with higher parasite migration that may serve as transmission sources and intervention targets. Methods that infer spatial variations in gene flow from georeferenced genetic data, including Estimated Effective Migration Surfaces (EEMS) and related approaches such as estimating Migration And Population-size Surfaces (MAPS), have become widely used to visualize barriers and corridors of organism movement. However, when sampling locations are sparse or unevenly distributed, spatial gene-flow maps can contain artefacts that are difficult to interpret, and methods such as EEMS provide posterior probability summaries that indicate whether inferred migration rates are statistically supported by the genomic data. However, these summaries do not directly capture whether a spatially inferred feature, such as a migration barrier or corridor, lies in a region with sufficient sampling locations to be geographically reliable. A high posterior probability in a sparsely sampled region reflects model confidence given available data, not spatial sampling adequacy. In this study, we developed a sample location–aware (SLA) filtering workflow that applies topological skeletons and kernel density estimation to assess sample density for each contour and filter out areas with lower sample density. This workflow can be applied to any gene-flow inference approach that produces spatially explicit migration features (e.g., contours, surfaces, or high/low-migration regions) from genotype data and sample coordinates. Using simulated genomic data and different barrier configurations under systematic site subsampling scenarios, we show that posterior-only filtering can retain spurious barrier features, whereas SLA filtering consistently increases the precision of inferred low-migration regions across sample sites. In an empirical case study of <i>Plasmodium falciparum</i> from Cambodia, SLA filtering produced more stable estimates of parasite migration patterns under location subsampling than posterior probability filtering alone. These results demonstrate that incorporating an explicit measure of geographic sampling density around each inferred migration feature improves the reliability and interpretability of spatial gene-flow migration patterns for malaria parasites and provides a general diagnostic and filtering approach for a broad class of georeferenced population-genetic migration mapping methods.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":"26 6","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13436596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148672819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}