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Genomic divergence in sympatry indicates strong reproductive barriers and cryptic species within Eucalyptus salubris
Genetic studies are increasingly detecting cryptic taxa that likely represent a significant component of global biodiversity. However, cryptic taxa are often criticized because they are typically detected serendipitously and may not receive the follow-up study required to verify their geographic or evolutionary limits. Here, we follow-up a study of Eucalyptus salubris that unexpectedly detected two divergent lineages but was not sampled sufficiently to make clear interpretations. We undertook comprehensive sampling for an independent genomic analysis (3,605 SNPs) to investigate whether the two purported lineages remain discrete genetic entities or if they intergrade throughout the species' range. We also assessed morphological and ecological traits, and sequenced chloroplast DNA. SNP results showed strong genome-wide divergence (FST=0.252) between two discrete lineages: one dominated the north and one the southern regions of the species' range. Within lineages gene flow was high, with low differentiation (mean FST=0.056) spanning hundreds of kilometres. In the central region, the lineages were interspersed but maintained their genomic distinctiveness: an indirect demonstration of reproductive isolation. Populations of the southern lineage exhibited significantly lower specific leaf area and occurred on soils with lower phosphorus relative to the northern lineage. Finally, two major chloroplast haplotypes were associated with each lineage but were shared between lineages in the central distribution. Together, these results suggest that these lineages have non-contemporary origins and that ecotypic adaptive processes strengthened their divergence more recently. We conclude that these lineages warrant taxonomic recognition as separate species and provide fascinating insight to eucalypt speciation.
Process map and time estimation per indication
<p>Process map and time estimation per indication</p>
CYP1A expression in freshwater fish of western New York as an indicator of pollution levels
Various species of freshwater fish regulate the expression of certain proteins in response to environmental contamination. Previous research has shown that CYP1A expression increases in response to contaminant levels and can result in increased tumor formation. Fish in contaminated environments would thus benefit by downregulating the expression of CYP1A to reduce tumor prevalence as an adaptive strategy. Alternatively, monitoring of the CYP1A protein in fish can serve as a bioindicator of the pollution level of an environment. This study evaluated CYP1A expression in twelve different species of freshwater fish from seven bodies of water throughout western NY including Cuba Lake, Genesee River, Hanging Bog, Love Canal, Moss Lake, Rushford Lake and Tifft Nature Preserve. Western blot analysis was used to measure CYP1A expression as a marker of site pollution and potential fish population adaptation. It was hypothesized that low CYP1A expression at a site with known contamination would suggest signs of adaptation to pollution levels present. Furthermore, if at least one sample from a species showed CYP1A expression, then the CYP1A antibody (Caymen Chemical, USA; 173132) had compatibility with that species, eliminating falsely suspected adaptation. The results from this study suggest possible adaptation of fish may be occurring in the polluted Tifft Nature Preserve and Genesee River. --
Supplementary material 8 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 8
Supplementary material 2 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 2
Supplementary material 9 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 9.
Supplementary material 5 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 5
Supplementary material 6 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 6
Supplementary material 3 from: Hong Y, He Y, Lin Z, Du Y, Chen S, Han L, Zhang Q, Gu S, Tu W, Hu S, Yuan Z, Liu X (2022) Complex origins indicate a potential bridgehead introduction of an emerging amphibian invader (Eleutherodactylus planirostris) in China. NeoBiota 77: 23-37. https://doi.org/10.3897/neobiota.77.83205
Table S3
Supplementary material 2 from: Hong Y, He Y, Lin Z, Du Y, Chen S, Han L, Zhang Q, Gu S, Tu W, Hu S, Yuan Z, Liu X (2022) Complex origins indicate a potential bridgehead introduction of an emerging amphibian invader (Eleutherodactylus planirostris) in China. NeoBiota 77: 23-37. https://doi.org/10.3897/neobiota.77.83205
Table S2
Supplementary material 1 from: Hong Y, He Y, Lin Z, Du Y, Chen S, Han L, Zhang Q, Gu S, Tu W, Hu S, Yuan Z, Liu X (2022) Complex origins indicate a potential bridgehead introduction of an emerging amphibian invader (Eleutherodactylus planirostris) in China. NeoBiota 77: 23-37. https://doi.org/10.3897/neobiota.77.83205
Table S1
Supplementary material 3 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 3
Supplementary material 1 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 1.
Supplementary material 4 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 4
Lupi et al data for Disentangling water quality indices to enhance the valuation of divergent ecosystem services PNAS
<p>State dataset for PNAS article, in press, Lupi, F., J.A. Herriges, R.J. Stevenson, and H. Kim. <em>In Press</em>, Disentangling water quality indices to enhance the valuation of divergent ecosystem services. <em>Proceedings of the National Academy of Science.</em></p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Greece
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: Ministry of Rural Development and Food</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2020/2021 – Czechia
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/172. The quantitative minimum inhibitory concentration (MIC) data from dilution methods, as well as the voluntary reporting of and Whole Genome Sequencing (WGS) data are included.</p> <p>Reporting authorities contributing to 2021 AMR data collection: State Veterinary Administration</p>
Assessment of acoustic indices for monitoring phylogenetic and temporal patterns of biodiversity in tropical forests
<b>Description: </b><p>Acostic recordings of bird calls at SAFE in 2014. Four solar-powered Wildlife Song Meter SM3 bioacoustic recorders (Wildlife Acoustics Inc., Concord,<br>MA, USA) were deployed with omni-directional microphones (sensitivity: 20Hz - 20kHz). No exact locations recorded.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/175"><b>Continuous bio-acoustic monitoring (2020 extension)</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7740620">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Trigg_safedata.xlsx, CLIPS.zip</p><p><b>Trigg_safedata.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Data</b> (described in worksheet Data)</p><p>Description: excel sheet with clips listed and birds identified</p><p>Number of fields: 7</p><p>Number of data rows: 837</p><p>Fields: </p><ul><li><b>FileNumber</b>: The audio files, which are labelled 1 to 120 (Field type: id)</li><li><b>StartTime</b>: approximate time when the species call began. (Field type: numeric)</li><li><b>EndTime</b>: approximate end time of the call. (Field type: numeric)</li><li><b>original_taxon_names</b>: species that can be heard in the recording. Original version as entered in to worksheet (Field type: comments)</li><li><b>Species</b>: species that can be heard in the recording. Corrected name, spelling mistakes and excess spaces removed etc. (Field type: taxa)</li><li><b>CallType</b>: Type of sound being identified (Field type: categorical)</li><li><b>IdCertainty</b>: Degree of certainty (Field type: categorical)</li></ul></li></ol><p><b>CLIPS.zip</b></p><p>Description: Zip file containing original audio clips used to generate these data</p><p><b>Date range: </b>2015-04-01 to 2015-06-30</p><p><b>Latitudinal extent: </b>4.6000 to 4.8000</p><p><b>Longitudinal extent: </b>117.5000 to 117.7000</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Primates <br> -  -  -  -  -  Hylobatidae <br> -  -  -  -  -  -  <i>Hylobates</i> <br> -  -  -  -  -  -  -  <i>Hylobates funereus</i> <br> -  -  -  Aves <br> -  -  -  -  Galliformes <br> -  -  -  -  -  Phasianidae <br> -  -  -  -  -  -  <i>Argusianus</i> <br> -  -  -  -  -  -  -  <i>Argusianus argus</i> <br> -  -  -  -  Bucerotiformes <br> -  -  -  -  -  Bucerotidae <br> -  -  -  -  -  -  <i>Anorrhinus</i> <br> -  -  -  -  -  -  -  <i>Anorrhinus galeritus</i> <br> -  -  -  -  -  -  <i>Buceros</i> <br> -  -  -  -  -  -  -  <i>Buceros rhinoceros</i> <br> -  -  -  -  -  -  <i>Rhinoplax</i> <br> -  -  -  -  -  -  -  <i>Rhinoplax vigil</i> <br> -  -  -  -  Psittaciformes <br> -  -  -  -  -  Psittacidae <br> -  -  -  -  -  -  <i>Loriculus</i> <br> -  -  -  -  -  -  -  <i>Loriculus galgulus</i> <br> -  -  -  -  Passeriformes <br> -  -  -  -  -  Cisticolidae <br> -  -  -  -  -  -  <i>Orthotomus</i> <br> -  -  -  -  -  -  -  <i>Orthotomus ruficeps</i> <br> -  -  -  -  -  -  -  <i>Orthotomus atrogularis</i> <br> -  -  -  -  -  -  -  <i>Orthotomus sericeus</i> <br> -  -  -  -  -  -  <i>Prinia</i> <br> -  -  -  -  -  -  -  <i>Prinia flaviventris</i> <br> -  -  -  -  -  Chloropseidae <br> -  -  -  -  -  -  <i>Chloropsis</i> <br> -  -  -  -  -  -  -  <i>Chloropsis cyanopogon</i> <br> -  -  -  -  -  Aegithinidae <br> -  -  -  -  -  -  <i>Aegithina</i> <br> -  -  -  -  -  -  -  <i>Aegithina viridissima</i> <br> -  -  -  -  -  Pycnonotidae <br> -  -  -  -  -  -  <i>Pycnonotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus atriceps</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus simplex</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus erythropthalmos</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus goiavier</i> <br> -  -  -  -  -  -  <i>Alophoixus</i> <br> -  -  -  -  -  -  -  <i>Alophoixus bres</i> <br> -  -  -  -  -  -  <i>Iole</i> <br> -  -  -  -  -  -  -  <i>Iole olivacea</i> <br> -  -  -  -  -  Dicaeidae <br> -  -  -  -  -  -  <i>Prionochilus</i> <br> -  -  -  -  -  -  -  <i>Prionochilus xanthopygius</i> <br> -  -  -  -  -  -  <i>Dicaeum</i> <br> -  -  -  -  -  -  -  <i>Dicaeum trigonostigma</i> <br> -  -  -  -  -  Monarchidae <br> -  -  -  -  -  -  <i>Hypothymis</i> <br> -  -  -  -  -  -  -  <i>Hypothymis azurea</i> <br> -  -  -  -  -  -  <i>Rhipidura</i> <br> -  -  -  -  -  -  -  <i>Rhipidura javanica</i> <br> -  -  -  -  -  Irenidae <br> -  -  -  -  -  -  <i>Irena</i> <br> -  -  -  -  -  -  -  <i>Irena puella</i> <br> -  -  -  -  -  Muscicapidae <br> -  -  -  -  -  -  <i>Trichixos</i> <br> -  -  -  -  -  -  -  <i>Trichixos pyrropygus</i> <br> -  -  -  -  -  Tephrodornithidae <br> -  -  -  -  -  -  <i>Hemipus</i> <br> -  -  -  -  -  -  -  <i>Hemipus picatus</i> <br> -  -  -  -  -  -  -  <i>Hemipus hirundinaceus</i> <br> -  -  -  -  -  Pellorneidae <br> -  -  -  -  -  -  <i>Kenopia</i> <br> -  -  -  -  -  -  -  <i>Kenopia striata</i> <br> -  -  -  -  -  -  <i>Pellorneum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum capistratum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum bicolor</i> <br> -  -  -  -  -  -  <i>Malacopteron</i> <br> -  -  -  -  -  -  -  <i>Malacopteron affine</i> <br> -  -  -  -  -  -  <i>Malacocincla</i> <br> -  -  -  -  -  -  -  <i>Malacocincla malaccensis</i> <br> -  -  -  -  -  -  <i>Alcippe</i> <br> -  -  -  -  -  -  -  <i>Alcippe brunneicauda</i> <br> -  -  -  -  -  Corvidae <br> -  -  -  -  -  -  <i>Corvus</i> <br> -  -  -  -  -  -  -  <i>Corvus enca</i> <br> -  -  -  -  -  Acanthizidae <br> -  -  -  -  -  -  <i>Gerygone</i> <br> -  -  -  -  -  -  -  <i>Gerygone sulphurea</i> <br> -  -  -  -  -  Timaliidae <br> -  -  -  -  -  -  <i>Cyanoderma</i> <br> -  -  -  -  -  -  -  <i>Cyanoderma erythropterum</i> <br> -  -  -  -  -  -  -  <i>Cyanoderma rufifrons</i> <br> -  -  -  -  -  -  <i>Mixornis</i> <br> -  -  -  -  -  -  -  <i>Mixornis bornensis</i> <br> -  -  -  -  -  -  <i>Macronus</i> <br> -  -  -  -  -  -  -  <i>Macronus ptilosus</i> <br> -  -  -  -  -  -  <i>Stachyris</i> <br> -  -  -  -  -  -  -  <i>Stachyris maculata</i> <br> -  -  -  -  -  -  -  <i>Stachyris poliocephala</i> <br> -  -  -  -  -  -  <i>Pomatorhinus</i> <br> -  -  -  -  -  -  -  <i>Pomatorhinus montanus</i> <br> -  -  -  -  -  Nectariniidae <br> -  -  -  -  -  -  <i>Anthreptes</i> <br> -  -  -  -  -  -  -  <i>Anthreptes simplex</i> <br> -  -  -  -  -  -  <i>Arachnothera</i> <br> -  -  -  -  -  -  -  <i>Arachnothera longirostra</i> <br> -  -  -  -  -  -  -  <i>Arachnothera affinis</i> <br> -  -  -  -  -  Eurylaimidae <br> -  -  -  -  -  -  <i>Eurylaimus</i> <br> -  -  -  -  -  -  -  <i>Eurylaimus ochromalus</i> <br> -  -  -  -  Piciformes <br> -  -  -  -  -  Ramphastidae <br> -  -  -  -  -  -  <i>Megalaima</i> <br> -  -  -  -  -  -  -  <i>Megalaima australis</i> <br> -  -  -  -  -  -  -  -  <i>Megalaima australis duvaucelii</i> (as homotypic_synonym: <i>Psilopogon duvaucelii</i>)<br> -  -  -  -  -  Picidae <br> -  -  -  -  -  -  <i>Meiglyptes</i> <br> -  -  -  -  -  -  -  <i>Meiglyptes tukki</i> <br> -  -  -  -  -  -  <i>Dinopium</i> <br> -  -  -  -  -  -  -  <i>Dinopium rafflesii</i> <br> -  -  -  -  -  -  <i>Blythipicus</i> <br> -  -  -  -  -  -  -  <i>Blythipicus rubiginosus</i> <br> -  -  -  -  -  -  <i>Mulleripicus</i> <br> -  -  -  -  -  -  -  <i>Mulleripicus pulverulentus</i> <br> -  -  -  -  Cuculiformes <br> -  -  -  -  -  Cuculidae <br> -  -  -  -  -  -  <i>Cacomantis</i> <br> -  -  -  -  -  -  -  <i>Cacomantis sonneratii</i> <br> -  -  -  -  -  -  -  <i>Cacomantis merulinus</i> <br> -  -  -  -  -  -  <i>Rhinortha</i> <br> -  -  -  -  -  -  -  <i>Rhinortha chlorophaea</i> <br> -  -  -  -  -  -  <i>Cuculus</i> <br> -  -  -  -  -  -  -  <i>Cuculus vagans</i> <br> -  -  -  -  -  -  <i>Centropus</i> <br> -  -  -  -  -  -  -  <i>Centropus sinensis</i> <br> -  -  -  -  Columbiformes <br> -  -  -  -  -  Columbidae <br> -  -  -  -  -  -  <i>Chalcophaps</i> <br> -  -  -  -  -  -  -  <i>Chalcophaps indica</i> <br></div><p></p>
FIG. 3. Graph indicating the appropriate K in Reconstructing the genomic diversity of a widespread Sub-Saharan bat (Pteropodidae: Eidolon helvum) using archival museum collections
FIG. 3. Graph indicating the appropriate K value where CV error is lowest from Admixture
Data from Meta-IBD Project, metabarcoding for the diatom trophic indices evaluation, South West France
<p>These data were generated within the framework of the Meta-IBD project, led by the Laboratoires des Pyrénées et des Landes and financially supported by the Adour Garonne Water Agency, including two scientific partners, Scimabio Interface and Artemis.</p> <p>The objective of the project was to compare the evaluation of diatom trophic indices obtained by the reference method (microscopy) versus the metabarcoding method, at the local scale. This comparison is based on biofilm sampling in southwest France, in the Adour, Nives and Gaves regions, in 2021 and 2022. The project ran from May 2021 to September 2023.</p> <p>The directory contains the following files:</p> <p>756 (378 x 2 replicates) samples fastq files (file intituled MetaIBD_Biofilm2021et22_LPL.rar) - contains the 756 fastq files from the Laboratoires des Pyrénées et des Landes sequencing.</p> <p>Metadonnees_MetaIBD_20212022.csv - contains all the data available for each sample corresponding to the names of the sequencing fastq files.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.