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opencc-zeroNov 2023View details →

Code and data for Bayesian joint species distribution model selection for community-level prediction

Code and data for reproducing the analysis in the manuscript "Bayesian joint species distribution model selection for community-level prediction." Provided data include percent cover observations for 39 modeled vascular plant species within boreal forest understory communities and environmental model covariates. R code is provided to generate model inputs, apply alternative models, generate out-of-sample predictions, and calculate associated community and species log scores and alternative model evaluation metrics. Further, R source code is provided to implement the multinomial joint species distribution model defined in the manuscript. Details on the data, its processing, and the alternative model definitions and structure can be found in the main text of the manuscript. Provided data are currently being used in ongoing analyses and coordination with authors may be warranted to avoid duplicate publication. Potential users are encouraged to consider collaboration with authors when useful and appropriate. Misinterpretation of data may occur if used outside the context of the original analysis. All data are made available in their current state. While significant efforts have been made to ensure data accuracy, complete accuracy cannot be guaranteed. Data may be updated periodically. It is the responsibility of the data user to check for updated versions of the data.

opencc-by-4.0Nov 2023View details →

III PhasAGE International Conference - PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins - Lecture

The III PhasAGE International Conference "Multiscale understanding of protein aggregation and biomolecular condensates in aging and disease" brought together members of the PhasAGE consortium as well as outstanding international speakers from multidisciplinary fields dedicated to unraveling the intricacies of protein aggregation and biomolecular condensates in the context of aging and disease. For details on the conference program please see https://phasage.eu/iii-phasage-international-conference/.

opencc-zeroNov 2023View details →

Data from: Wildlife fecal microbiota exhibit community stability across a semi-controlled longitudinal non-invasive sampling experiment

Wildlife microbiome studies are being used to assess microbial links with animal health and habitat. The gold standard of sampling microbiomes directly from captured animals is ideal for limiting potential abiotic influences on microbiome composition, yet fails to leverage the many benefits of non-invasive sampling. Application of microbiome-based monitoring for rare, endangered, or elusive species creates a need to non-invasively collect scat samples shed into the environment. Since controlling sample age is not always possible, the potential influence of time-associated abiotic factors was assessed. To accomplish this, we analyzed partial 16S rRNA genes of fecal metagenomic DNA sampled non-invasively from Rocky Mountain elk (Cervus canadensis) near Yellowstone National Park. We sampled pellet piles from four different elk, then aged them in a natural forest plot for 1, 3, 7, and 14 days, with triplicate samples at each time point (i.e., a blocked, repeat measures (longitudinal) study design). We compared microbiomes of each elk through time with point estimates of diversity, bootstrapped hierarchical clustering of samples, and a version of ANOVA–simultaneous components analysis (ASCA) with PCA (LiMM-PCA) to assess the variance contributions of time, individual and sample replication. Our results showed community stability through days 0, 1, 3 and 7, with a modest but detectable change in abundance in only 2 genera (Bacteroides and Sporobacter) at day 14. The total variance explained by time in our LiMM-PCA model across the entire 2-week period was not statistically significant (p>0.195) and the overall effect size was small (<10% variance) compared to the variance explained by the individual animal (p<0.0005; 21% var.). We conclude that non-invasive sampling of elk scat collected within one week during winter/early spring provides a reliable approach to characterize microbiome composition in a 16S rDNA survey and that sampled individuals can be directly compared across unknown time points with minimal bias. Further, point estimates of microbiome diversity were not mechanistically affected by sample age. Our assessment of samples using bootstrap hierarchical clustering produced clustering by animal (branches) but not by sample age (nodes). These results support greater use of non-invasive microbiome sampling to assess ecological patterns in animal systems.

opencc-by-4.0Dec 2023View details →

Data from: Supervised classification of plant communities with artificial neural networks

This dataset was used to test the performance of artificial neural networks for supervised classification of plant communities, published in:Černá L. & Chytrý M. (2005) Supervised classification of plant communities with artificial neural networks. Journal of Vegetation Science 16, 407-414. https://doi.org/10.1111/j.1654-1103.2005.tb02380.xThe meaning of the individual columns (separated by semicolons) in the file is as follows (for details see the above-mentioned article):<ul><li>Plot no - unique number of the vegetation plot</li><li>Group expert - plot membership in classes 1-11 of the expert  classification</li><li>Subset expert random B - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for the expert classification</li><li>Subset expert dg species B - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by  diagnostic species, for the expert classification</li><li>Assignment expert random - a class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification</li><li>Assignment expert dg-sp - a class assignment  of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification</li><li>Group cluster - plot membership in classes 1-11 of the numerical classification</li><li>Subset cluster random - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for numerical classification</li><li>Subset cluster dg species - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for expert classification, for numerical classification</li><li>Assignment cluster random - class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification, for numerical classification</li><li>Assignment cluster dg-sp - class assignment  of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification, for numerical classification </li><li>598 species, with cover/abundance estimates on an ordinal scale of 1-9</li></ul>

openbsd-3-clauseDec 2023View details →

Southern California Earthquake Center (SCEC) Community Geodetic Model (CGM)

OverviewMeasuring accurately the relative movement of the surface of the Earth is a critical constraint on the slow and broad tectonic loading and unloading to which faults respond, and is one of the few observations of the solid Earth that may be made directly without inference. High-precision geodetic observations, such as from Global Navigation Satellite Systems (GNSS), which includes the Global Positioning System (GPS), and interferometric synthetic aperture radar (InSAR), allow measurement of fault motions between, during and in the aftermath of earthquakes and other related tectonic phenomena, densely in both space and time.The Community Geodetic Model (CGM) provides velocities and time series of observed points on the Earth's surface over Southern California using data from a number of contributing researchers, institutions and analysis centers. The GNSS products provide high temporal resolution (nominally daily measurement points for continuous stations) in three dimensions at specific observation sites and the InSAR products provide high spatial resolution (approximately one point per tens of m on the ground, depending on exact specifications of data and processing). Combined, they provide the ability to study crustal deformation over a wide range of distances and periods.The CGM differs from other<a href="https://www.scec.org/research/cxm"> SCEC Community Models</a> in that it is constantly extending with time as new data are acquired daily, so it is not static.The CGM version 1 (2016; <a href="https://doi.org/10.5281/zenodo.4926528">doi:10.5281/zenodo.4926528</a>) was a collection of time-independent (velocity-only) geodetic products gathered from published papers. The GNSS velocities were then combined and modeled by a Working Group researching methods and contributing interpolated deformation fields. The main goal of the CGM version 2 is to add time-dependent (time series) products to both the GNSS and InSAR products. For the GNSS, this is done by ingesting survey and (mostly) continuous time series from five analysis centers in the U.S.: the Geodetic Facility for the Advancement of Geoscience (GAGE); the Nevada Geodetic Laboratory (NGL) at the University of Nevada, Reno (UNR); the NASA Jet Propulsion Laboratory (JPL) and Scripps Orbital and Permanent Array Center (SOPAC) contributions to the MEaSUREs ESESES project; and the U.S. Geological Survey (USGS). Like the various contributions to the CGMv1 GNSS velocities, these time series are rigorously adjusted to be self-consistent, before a weighted mean is calculated to produce the consensus products. Much of the InSAR contribution is a consensus from research by the SCEC community within the CGM (InSAR) Working Group, whose individual contributions are listed below and in more detail in the README.txt file in the top directory of the archive. The CGMv2 is therefore a "union" or "superset" of survey and continuous GNSS and InSAR time series.Please see<a href="https://www.scec.org/research/cgm"> https://www.scec.org/research/cgm</a> for more information.Version: CGMv2.0.0This is the second major release of the CGM (version 2.0.0) and is distributed as a zip-file. See below and in the README.txt file for information about the directory structure and contents of the entire zipped archive. Much of the SCEC5 activity was focused on the assembly of GNSS and InSAR time series for measuring temporally variable motions, expanding the CGMv1 with the time dimension. The CGMv2.0.0 is a time-dependent set of products, consisting of time series and velocities of the Earth's surface measured by GNSS and InSAR.Directory Structure and Contentsdata/gnss/pos/ The CGMv2.0.0 GNSS time series in <a href="https://www.unavco.org/data/gps-gnss/derived-products/docs/NOTICE-TO-DATA-PRODUCT-USERS-GPS-2013-03-15.pdf">"pos" format</a> (plain text), relative to various reference frames described below. Header lines in each file provide information about the nominal reference coordinates and data columns. Files named "*.wmrss_*" are the continuous stations (weighted mean with rescaled sigma) and files named "*.final_" are the survey sites.data/gnss/pos/igb14/ The International GNSS Service's (IGS's) <a href="https://lists.igs.org/pipermail/igsmail/2020/007917.html">revised realization of ITRF2014</a>data/gnss/pos/nam14/ North America defined by <a href="https://doi.org/10.1093/gji/ggx136">Altamimi et al.'s (2017)</a> ITRF2014 plate motion modeldata/gnss/pos/pcf14/ The Pacific defined by <a href="https://doi.org/10.1093/gji/ggx136">Altamimi et al.'s (2017)</a> ITRF2014 plate motion modeldata/gnss/pos/nam17/ North America defined by <a href="https://doi.org/10.1029/2017JB015257">Kreemer et al. (2018)</a>data/gnss/vel/ The CGMv2.0.0 GNSS velocities in a CSV file similar to <a href="https://www.unavco.org/data/gps-gnss/derived-products/docs/NOTICE-TO-DATA-PRODUCT-USERS-GPS-2013-03-15.pdf">GAGE's "vel" format</a> (plain text), relative to the same reference frames described above. Header lines in each file provide information about the data columns.data/insar/ The CGMv2.0.0 InSAR line-of-sight consensus time series and velocities for four ESA Sentinel-1 tracks (ascending tracks 64 and 166, and descending tracks 71 and 173) over Southern California, in an <a href="https://github.com/kmaterna/InSAR_CGM_readers_writers#cgm-insar-hdf5-structure">HDF5 format designed for the CGM</a>. A description of and reader for the HDF5 files may be found <a href="https://github.com/kmaterna/InSAR_CGM_readers_writers">here</a>.data/insar/contrib/ Individual contributions to the InSAR time series and velocity products, as described below and in more detail in the top-level README.txt file.ContributorsThe GNSS time series are a weighted mean, after restoration of global scale if processed using Gipsy (JPL, NGL/UNR and USGS) and self-consistent alignment of reference frame, of the following GNSS analysis centers, whose products are publicly available at the embedded hyperlinks:<ul><li>The <a href="https://www.unavco.org/data/gps-gnss/derived-products/derived-products.html">Geodetic Facility for the Advancement of Geoscience (GAGE)</a> (<a href="https://doi.org/10.1002/2016RG000529">Herring et al., 2016</a>)</li><li>The <a href="http://geodesy.unr.edu/">Nevada Geodetic Laboratory</a> at the University of Nevada, Reno (<a href="https://doi.org/10.1029/2018EO104623">Blewitt et al., 2018</a>)</li><li>NASA's <a href="http://garner.ucsd.edu/pub/solutions/gipsy">Jet Propulsion Laboratory contribution</a> to the <a href="http://sopac-csrc.ucsd.edu/index.php/measures-2/">MEaSUREs ESESES Project</a></li><li><a href="http://sopac-csrc.ucsd.edu/">SOPAC</a>'s <a href="http://garner.ucsd.edu/pub/measuresESESES_products/Timeseries/">contribution</a> to the <a href="http://sopac-csrc.ucsd.edu/index.php/measures-2/">MEaSUREs ESESES Project</a></li><li>The <a href="https://earthquake.usgs.gov/monitoring/gps">United States Geological Survey</a> (<a href="https://doi.org/10.1785/0220160204">Murray and Svarc, 2017</a>)</li><li><a href="https://www.scec.org/user/zshen">Zheng-Kang Shen's (UCLA)</a> <a href="http://scec.ess.ucla.edu/~zshen/cgm/">survey time series</a></li></ul>Z.-K. Shen processed the raw data from the <a href="https://service.scedc.caltech.edu/gps/">SCEC survey-mode GPS data archive</a> to provide the corresponding time series and velocities. A. Gonzalez Ortega provided processed time series from <a href="https://regnom.cicese.mx/">CICESE's REGNOM network of continuous GNSS stations</a>. M. Floyd and T. Herring designed the download, alignment and combination of the publicly available continuous GNSS archives, listed above, in various reference frames.Contributions from individuals and institutions within the SCEC community to the CGM (InSAR) products are:<ul><li>K. Wang contributed time series and velocity solutions</li><li>K. Guns and X. Xu contributed time series and velocity solutions</li><li>Z. Liu contributed time series and velocity solutions</li><li>S. Sangha, M. Govorcin and D. Bekaert contributed time series and velocity solutions</li><li>G. Funning contributed time series and velocity solutions</li><li>E. Tymofyeyeva calculated the combination of contributed solutions to generate the consensus product</li><li>K. Materna contributed time series and velocity solutions, and wrote the translation tools for converting to and from HDF5 format, as designed by all InSAR contributors listed immediately above plus M. Floyd</li></ul>Three groups (K. Guns and X. Xu; Z. Liu; and S. Sangha, M. Govorcin and D. Bekaert) independently processed interferograms from common raw datasets using different processing approaches.E. Tymofyeyeva coordinated and led the InSAR Working Group.M. Floyd coordinated and led the wider CGM Working Group.All contributed to the design of the HDF5 format in which the InSAR products are distributed.

opencc-zeroDec 2023View details →

Functional traits—not nativeness—shape the effects of large mammalian herbivores on plant communities

Large mammalian herbivores (megafauna) have experienced extinctions and declines since prehistory. Introduced megafauna have partly counteracted these losses yet are thought to have unusually negative effects compared to native megafauna. Using a meta-analysis of 3,995 plot-scale plant abundance and diversity responses from 221 studies, we found no evidence that megafauna impacts were shaped by nativeness, 'invasiveness', 'feralness', coevolutionary history, or functional and phylogenetic novelty. Nor was there evidence that introduced megafauna facilitate introduced plants more than native megafauna. Instead, we found strong evidence that functional traits shaped megafauna impacts, with larger-bodied and bulk-feeding megafauna promoting plant diversity. Our work suggests that trait-based ecology provides better insight into interactions between megafauna and plants than concepts of nativeness.

opencc-zeroDec 2023View details →

Costa Rica mosquito community species occurrence and site environmental data, July - August 2017

Land use change is an important driver of both biodiversity loss and zoonotic disease transmission in tropical countryside landscapes. Developing solutions for protecting biodiversity, public health, and livelihoods in working landscapes requires understanding the spatial scales at which habitat characteristics such as land cover shape biodiversity, especially for arthropods that transmit pathogens. A growing body of evidence shows that species richness for many taxa correlates with tree cover at small spatial scales of <100 m, indicating that local tree cover management is a promising conservation tool. To investigate whether mosquito species richness, community composition, and presence of specific disease vector species respond to tree cover—and if so, whether at spatial scales similar to other taxa—we surveyed mosquito communities along a tree cover gradient and across agricultural, residential, and forested land uses in rural southern Costa Rica. We found that tree cover was both positively correlated with mosquito species richness and negatively correlated with the presence of the common invasive dengue vector Aedes albopictus, particularly at small spatial scales of 80 – 200m. Beyond tree cover, land use type predicted community composition and Ae. albopictus presence, but not species richness. The results suggest that preservation and expansion of tree cover at local scales can protect biodiversity for a wide range of taxa and also confer protection against disease vector occurrence.

opencc-by-4.0Dec 2023View details →

Replication package: Dataset and stata-do-file for analysis in "Intragroup communication in social dilemmas: An artefactual public good field experiment in small-scale communities"

This dataset was used for the analysis in "Intragroup communication in social dilemmas: An artefactual public good field experiment in small-scale communities". The data was collected in Namibia in 2017 as part of the SASSCAL research project by Nils Christian Hoenow and Adrian Pourviseh as members of the Chair for Development and Cooperative Economics at the University of Marburg. Funded by the Southern African Science Service Center for Climate Change and Adaptive Land-UseManagement (SASSCAL) through the German Federal Ministry for Education and Research (Grant No. 01LG1201B).   Article Title: Intragroup communication in social dilemmas: An artefactual public good field experiment in small-scale communities  Authors: Nils Christian Hoenow* and Adrian Pourviseh**   *RWI – Leibniz Institute for Economic Research, Essen, Germany and & School of Business and Economics, University of Marburg, Marburg, Germany **School of Business and Economics, University of Marburg, Marburg, Germany Abstract:  Communication is well-known to increase cooperation rates in social dilemma situations, but the exact mechanisms behind this remain largely unclear. This study examines the impact of communication on public good provisioning in an artefactual field experiment conducted with 216 villagers from small, rural communities in northern Namibia. In line with previous experimental findings, we observe a strong increase in cooperation when face-to-face communication is allowed before decision-making. We additionally introduce a condition in which participants cannot discuss the dilemma but talk to their group members about an unrelated topic prior to learning about the public good game. It turns out that this condition already leads to higher cooperation rates, albeit not as high as in the condition in which discussions about the social dilemma are possible. The setting in small communities also allows investigating the effects of pre-existing social relationships between group members and their interaction with communication.We find that both types of communication are primarily effective among socially more distant group members, which suggests that communication and social ties work as substitutes in increasing cooperation. Further analyses rule out better comprehension of the game and increased mutual expectations of one’s group members’ contributions as drivers for the communication effect. Finally, we discuss the role of personal and injunctive norms to keep commitments made during discussions.

opencc-zeroDec 2023View details →

Regional differences in thermoregulation between two European butterfly communities

Understanding how different organisms cope with changing temperatures is vital for predicting future species' distributions and highlighting those at risk from climate change. As ectotherms, butterflies are sensitive to temperature changes, but the factors affecting butterfly thermoregulation are not fully understood. We investigated which factors influence thermoregulatory ability in a subset of a Mediterranean butterfly community. We measured adult thoracic temperature and environmental temperature (787 butterflies; 23 species) and compared buffering ability (defined as the ability to maintain a consistent body temperature across a range of air temperatures) and buffering mechanisms to previously published results from Great Britain. Finally, we tested whether thermoregulatory ability could explain species' demographic trends in Catalonia. The sampled sites in each region differ climatically, with higher temperatures and solar radiation but lower wind speeds in the Catalan sites. Both butterfly communities show nonlinear responses to temperature, suggesting a change in behaviour, from heat-seeking to heat avoidance, at approximately 22 °C. However, the communities differ in the use of buffering mechanisms, with British populations depending more on microclimates for thermoregulation compared to Catalan populations. Contrary to the results from British populations, we did not find a relationship between region-wide demographic trends and butterfly thermoregulation, which may be due to the interplay between thermoregulation and the habitat changes occurring in each region. Thus, although Catalan butterfly populations seem to be able to thermoregulate successfully at present, evidence of heat avoidance suggests this situation may change in the future.

Figure 30 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 30. Phylogeny of the Sphaerodoridae familybasedon Bayesiananalysisof the COI, 16S and 18S gene fragments. Numbersadjacent to nodes indicate posterior probabilities, and taxa for which sequences have been contributed by the present study are indicated in bold.

Figure 28 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 28. Osedax byronbayensis sp. nov. holotype AMW.53707. (A) Ethanol-preserved holotype showing majority of tube, scale bar is 1 mm; (B) detail of palp inside tube (arrowed), scale bar is 500 µm.

Figure 26 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 26 (facing page). Osedax waadjum sp. nov. (A) Living female specimen inside tube (NHMUK ANEA 2022.403), scale is 3 mm; (B) anterior of living specimen outside of tube (NHMUK ANEA 2022.403), scale is 1 mm; (C) posterior of preserved specimen showing boundary between palps and trunk (arrowed), NHMUKANEA 2022.402, scale is 1 mm; (D) posterior of preserved holotype specimen, AM W.53706, showing short oviduct emerging from top of trunk, scale is 500 µm; (E) posterior of specimen AM W.53706 showing alterative side of trunk where a small crinkled lobe is present, scale is 500 µm; (F) male specimen from tube of NHMUKANEA 2022.401, with inset showing detail of hooked chaetae (arrowed). Scale is 50 µm in main image and 25 µm in inset.

Figure 29 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 29. Sphaerodoropsis sp. (A) AM W.52205 Whole specimen scale bar 1 mm; (B) parapodia with digiform acicular lobe, scale bar is 50 µm; (C) parapodia with digiform acicular lobe and compound chaetae, scale bar is 20 µm; (D) compound chaetae with blades, scalebaris 20 µm.

Figure 24 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 24. Protodrilus cf. puniceus. (A) anterior end, scalebaris 100 µm; (B) anterior end, scalebaris 200 µm; (C) whole animal, scalebaris 200 µm.

Figure 23 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 23.?Pseudomystides sp., specimen NHMUKANEA 2022.409–411. (A) Photoofanethanol-preservedspecimen, scalebaris 200 µm; (B) light micrograph of prostomium and tentacular cirri of the first segment, scale bar is 75 µm; (C) light micrograph of compound spinigers, scale bar is 25 µm; (D) light micrograph of pygidium with anal cirri and papilla (arrow), scale bar is 100 µm.

Figure 22 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 22. Phylogeny of the genus Eumida (Phyllodocidae) based on Bayesian analysis of the COI gene only. Numbers adjacent to nodes indicate posterior probabilities, and taxa for which sequences have been contributed by the present study are indicated in bold.

Figure 21 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 21. Eumida cf. longicirrata. (A) Photo of a live specimen (NHMUK ANEA 2022.406), scale bar is 1 mm; (B) ventral aspect of the anterior end showing the prostomium and tentacular cirri (NHMUK ANEA 2022.407–408), scale bar is 250 µm; (C) fully everted proboscis (NHMUK ANEA 2022.404), scale bar is 750 µm; (D) light micrograph of mid-body parapodium (NHMUK ANEA 2022.404), scale bar is 200 µm; (E) light micrograph of heterogomph spinigers (NHMUK ANEA 2022.404), scale bar is 50 µm; (F) light micrograph of pygidium missing one anal cirrus (NHMUK ANEA 2022.406), scale bar is 250 µm.

Figure 19 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 19. Phylogeny of the Orbiniidae family based on Bayesian analysis of a combined dataset of the genes COI, 16S and 18S. Numbers adjacent to nodes indicate posterior probabilities, and taxa for which sequences have been contributed by the present study are indicated in bold.

Figure 20 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 20. Orbiniella sp. specimen NHMUK.2022.431. (A) Preserved specimen in lateral view, scale bar is 1 mm; (B) branchiae from posterior segments, scale bar is 250 µm; (C) example of crenulated capillaries, scale bar is 25 µm; (D) example of spines, scale bar is 25 µm; (E) juveniles (NHMUKANEA 2022.421–430), scalebaris 500 µm.

Figure 18 in The Annelid Community of a Natural Deep-sea Whale Fall off Eastern Australia

Figure 18. Orbiniellajamesi sp. nov. (A) Live specimen (holotype AMW.53705), scale is 1 mm; (B) preserved specimen (holotype AM W.53705) in ventro-lateral view; (C) prostomium in dorsal view, NHMUKANEA 2023.1201; (D) anterior parapodiumwith postchaetal lobe (holotype AMW.53705), scale bar is 100 µm; (E) mid-body neuropodial postchaetal lobe, specimen NHMUKANEA 2023.1201, scale bar is 25 µm; (F) small ovoid branchiae, specimen NHMUKANEA 2023.1201, scale bar is 100 µm; (G) elongated strap-like branchiae, specimen NHMUKANEA 2023.1201, scale bar is 100 µm; (H) chaetal types (crenulated capillaries and short acicular spines) of anterior parapodia, scale is 50 µm. Abbreviations: as, acicular spines; cc, crenulated capillaries.