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    Genomic architecture of migration timing in a long-distance migratory songbird

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    Mangrove reforestation provides greater blue carbon benefit than afforestation for mitigating global climate change

    Literature search and screeningOur analysis included a systematic literature search and was conducted by following the PRISMA protocol55 (Supplementary Fig. 7). We searched through Web of Science and China National Knowledge Infrastructure (CNKI) platforms by using keywords listed in Supplementary Table 3. A total of 3299 potentially relevant articles were found (Mandarin and English). The availability of peer-reviewed datasets associated with these published articles11,15,56,57,58,59 and online databases (The Sustainable Wetlands Adaptation and Mitigation Program (SWAMP) database, https://www2.cifor.org/swamp) were also considered. We then removed a significant number of articles through title screening, leaving 551 articles for further inspection.For these remaining articles, we used a four-step critique process to screen their title, abstract, and full text. We determined that firstly, they must provide carbon density data for at least one of the four mangrove carbon pools (i.e., aboveground biomass, belowground biomass, sediment organic carbon, or total ecosystem carbon). Secondly, articles needed to state the forest age or the starting date of the restoration action. For those studies providing only age intervals (e.g., 10–25 years, >66 years), we excluded them from the analysis. Thirdly, a description of prior land use was required. From these, mangrove restoration could be divided into two categories—reforestation and afforestation—on whether mangroves previously existed in that location. For reforestation, the initial conditions for inclusion were: (1) abandoned agricultural/aquacultural sites built previously by excavating mangrove forests, (2) clear-felled mangrove lands after wars, timber harvest, and silvicultural management, and (3) mangrove forests with mortality due to spraying of defoliants and hydrological alteration caused by the construction of embankments. We compared the carbon densities of reforested mangroves among sites with different causes of degradation/deforestation, and no significant difference is found (Supplementary Fig. 9). For those reforested mangroves, we assumed they would be protected and conserved by local governments and non-government organizations, so that there will not be human-driven degradation or deforestation in the near future. However, we acknowledge that a fraction of mangrove reforestation is managed for wood production, which means logging would happen at a certain interval after reforestation at these sites. For these logging sites, we used their reported measurements after clear-cut, such as 0-, 5-, 10-, 15-, and 25-year post-harvest sites in Sundarbans, Bangladesh60. On the other hand, the future occurrence of natural-driven deforestation (e.g., cyclones) is difficult to predict, and thus not considered in our study. For afforestation, the initial condition for inclusion was the presence of non-mangrove habitat immediately before afforestation began, such as mudflats, seagrass, saltmarsh, coral reef, or denuded areas. In most cases, reforestation and afforestation were undertaken through active planting without much re-engineering4, but for reforestation, natural regeneration could have, and in many places likely did, augment recruitment61. Moreover, we only considered mangrove succession that started from near-barren land with an insignificant amount of biomass, and introductions of exotic species to degraded areas with sparse trees were not incorporated. Lastly, if the forest age or prior land use type was not given, the articles needed to specify the location of sampling plots (latitude, longitude). With the coordinates matching, prior land use type and establishment dates were sometimes identifiable through remote sensing (Supplementary Fig. 10). For those articles sharing the same restoration sites but showing different aspects of the data collection, we combined the results and considered the collective work as one source. Based on the space-for-time method, data in the control sites before mangrove restoration actions were also collected as a paired site of restoration (e.g., abandoned ponds before mangrove reforestation; mudflats before mangrove afforestation). In total, we obtained data from 379 mangrove restoration sites described by 106 articles.Data extractionWe extracted aboveground living biomass carbon (AGC), belowground living biomass carbon (BGC), sediment carbon (SCS), and total ecosystem carbon (TECS) density from the 106 original data sources. In most cases, numeric values were provided. For those data not provided numerically but graphed, we determined values from figures with the application of GetData Graph Digitizer (http://getdata-graph-digitizer.com/).Among the articles, aboveground and belowground biomass (Mg ha−1) data were obtained using either a harvesting method (empirical) or an allometric method (calculation). Aboveground biomass represented the sum of stem, leaf, and branch dry weight, and we included prop root biomass when Rhizophora spp. were present. For soil coring methods that determined belowground biomass or sediment carbon density, belowground biomass was considered the dry weight of living coarse and fine roots multiplied by the ratio of core area to land surface area62. For allometric methods, trunk diameter at breast height (DBH, ~1.3 m) and tree height were used to calculate aboveground and belowground biomass by species-specific or common allometric equations63. These equations were also used to calculate the belowground biomass when articles provided plot information (DBH, height) but not belowground biomass (Supplementary Table 4). Total biomass was calculated as the sum of aboveground and belowground biomass. Deadwood and pneumatophore biomass were not included in our analysis; these data are rarely provided and/or methods of determination are inconsistent among global studies64. Some articles provided total biomass and shoot/root biomass ratio (S/R), and in such cases, above- and belowground biomass data were obtained through calculation as follows:$${{{{{rm{Aboveground}}}}}},{{{{{rm{biomass}}}}}}={{{{{rm{Total}}}}}},{{{{{rm{biomass}}}}}}times frac{frac{S}{R}}{frac{S}{R}+1}$$
    (1)
    $${{{{{rm{Belowground}}}}}},{{{{{rm{biomass}}}}}}={{{{{rm{Total}}}}}},{{{{{rm{biomass}}}}}}times frac{1}{frac{S}{R}+1}$$
    (2)
    For those articles measuring carbon content, study-specific carbon conversion factors were used to transform biomass to biomass carbon density (Mg C ha−1). If carbon content data were not provided, we converted aboveground and belowground biomass to carbon density by applying a conversion of 0.47 and 0.39, respectively65. The aboveground biomass carbon density was divided by its corresponding age to get the average aboveground biomass carbon accumulation rate (Mg C ha−1 yr−1).For sediment carbon density (SCS, Mg C ha−1), we selected the top 1 m because this depth equated to the most commonly reported depth and could reflect the impact of root mass input in the deeper depth66, which is also consistent with recent blue carbon standing stock assessment guidance64,67. Sediment carbon stock was calculated by multiplying sediment organic carbon content (SOC, %) by bulk density (BD, g cm−3), integrated over depth (cm). For studies that reported sediment carbon stock to More

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    Family before work: task reversion in workers of the red imported fire ant, Solenopsis invicta in the presence of brood

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    What it would take to bring back the dodo

    The flightless dodo went extinct in the seventeenth century. Biotech company Colossal Biosciences plans to resurrect it.Credit: Hart, F/Bridgeman Images

    A biotech company announced an audacious effort to ‘de-extinct’ the dodo last week. The flightless birds vanished from the island of Mauritius — in the Indian Ocean — in the late seventeenth century, and became emblematic of humanity’s negative impacts on the natural world. Could the plan actually work?Colossal Biosciences, based in Dallas, Texas, has landed US$225 million in investment (including funds from the celebrity Paris Hilton) — having previously announced plans to de-extinct thylacines, an Australian marsupial, and create elephants with woolly mammoth traits. But Colossal’s plans depend on huge advances in genome editing, stem-cell biology and animal husbandry, making success far from certain.“It’s incredibly exciting that there’s that kind of money available,” says Thomas Jensen, a cell and molecular reproductive physiologist at Wells College in Aurora, New York. “I’m not sure that the end goal they’re going for is something that’s super feasible in the near future.”Iridescent pigeonsColossal’s plan starts with the dodo’s closest living relative, the iridescent-feathered Nicobar pigeon (Caloenas nicobarica). The company plans to isolate and culture specialized primordial germ cells (PGCs) — which make sperm and egg-producing cells — from developing Nicobars. Colossal’s scientists would edit DNA sequences in the PGCs to match those of dodos using tools such as CRISPR. These gene-edited PGCs would then be inserted into embryos from a surrogate bird species to generate chimeric — those with DNA from both species — animals that make dodo-like egg and sperm. These could potentially produce something resembling a dodo (Raphus cucullatus).To gene-edit Nicobar pigeon PGCs, scientists first need to identify the conditions that allow these cells to flourish in the laboratory, says Jae Yong Han, an avian-reproduction scientist at Seoul National University. Researchers have done this with chickens, but it will take time to identify the appropriate culture conditions that suit other birds’ PGCs.A greater challenge will be determining the genetic changes that could transform Nicobar pigeons into Dodos. A team including Beth Shapiro, a palaeogeneticist at the University of California, Santa Cruz, who is advising Colossal on the dodo project, has sequenced the dodo genome but has not yet published the results. Dodos and Nicobar pigeons shared a common ancestor that lived around 30 million to 50 million years ago, Shapiro’s team reported in 20161. By comparing the nuclear genomes of the two birds, the researchers hope to identify most of the DNA changes that distinguish between them.Insights from ratsTom Gilbert, an evolutionary biologist at the University of Copenhagen, who also advises Colossal, expects the dodo genome to be of high quality — it comes from a museum sample he provided to Shapiro. But he says that finding all the DNA differences between the two birds is not possible. Ancient genomes are cobbled together from short sequences of degraded DNA, and so are filled with unavoidable gaps and errors. And research he published last year comparing the genome of the extinct Christmas Island rat (Rattus macleari) with that of the Norwegian brown rat (Rattus norvegicus)2 suggests that gaps in the dodo genome could lie in the very DNA regions that have changed the most since its lineage split from that of Nicobar pigeons.Even if researchers could identify every genetic difference, introducing the thousands of changes to PGCs would not be simple. “I’m not sure it’s feasible in the near future,” says Jensen, whose team is encountering difficulties making a single genetic change to the genomes of quail.Focusing on only a subset of DNA changes, such as those that alter protein sequences, could slash the number of edits needed. But it’s still not clear that this would yield anything resembling a wild dodo, says Gilbert. “My worry is that Paris Hilton thinks she’s going to get a dodo that looks like a dodo,” he says.A further problem will be the need to find a large bird, such as an emu (Dromaius novaehollandiae), that can act as the surrogate, says Jensen. “Dodo eggs are much, much larger than Nicobar pigeon eggs, you couldn’t grow a dodo inside of a Nicobar egg.”Chicken embryos are fairly receptive to PGCs from other birds, and Jensen’s team has created chimeric chickens that can produce quail sperm — efforts to generate eggs have failed so far. But he thinks it will be far more challenging to transfer PGCs — particularly heavily gene-edited ones — from one wild bird into another.Conservation boon?Colossal chief executive Ben Lamm acknowledges these hurdles, but argues they aren’t dealbreakers. Work towards dodo de-extinction will help with conservation efforts for other birds, he adds. “It will bring a lot of new technologies to the field of bird conservation,” agrees Jensen.Vikash Tatayah, conservation director at the Mauritian Wildlife Foundation in Vacoas-Phoenix, is also enthusiastic about the attention dodo de-extinction could bring to conservation. “It’s something we would like to embrace,” he says.But he points out that the predators that threatened the dodo in the seventeeth century haven’t gone away, whereas most of its habitat has. “You do have to ask,” he says, “if we could have such money, wouldn’t it be better spent on restoring habitat on Mauritius and preventing species from going extinct?” More

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    Benthic biota of Chilean fjords and channels in 25 years of cruises of the National Oceanographic Committee

    The data were recorded under the DarwinCore standard55,56 in a matrix named “Benthic biota of CIMAR-Fiordos and Southern Ice Field Cruises”58. The occurrence dataset contains direct basic information (description, scope [temporal, geographic and taxonomic], methodology, bibliography, contacts, data description, GBIF registration and citation), project details, metrics (taxonomy and occurrences classification), activity (citations and download events) and download options. The following data fields were occupied:Column 1: “occurrenceID” (single indicator of the biological record indicating the cruise and correlative record).Column 2: “basisOfRecord” (“PreservedSpecimen” for occurrence records with catalogue number of scientific collection, “MaterialCitation” for any literature record).Column 3: “institutionCode” (The acronym in use by the institution having custody of the sample or information referred to in the record).Column 4: “collectionCode” (The name of the cruise).Column 5: “catalogNumber” (The repository number in museums or correlative number).Column 6: “type” (All records entered as “text”).Column 7: “language” (Spanish, English or both).Column 8: “institutionID” (The identifier for the institution having custody of the sample or information referred to in the record).Column 9: “collectionID” (The identifier for the collection or dataset from which the record was derived).Column 10: “datasetID” (The code “CONA-benthic-biota-database” for entire database).Column 11: “recordedBy” (Author/s who recorded the original occurrence [publication source]).Column 12: “individualCount” (Number of individuals recorded).Column 13: “associatedReferences” (Publication source [report and/or paper/s] for each record).Column 14: “samplingProtocol” (The sampling gear for each record).Column 15: “eventDate” (The date-time or interval during which the record occurred).Column 16: “eventRemarks” (Comments or notes about the event).Column 17: “continent” (Location).Column 18: “country” (Location).Column 19: “countryCode” (The standard code for the country in which the location occurs).Column 20: “stateProvince” (Location, refers to the Administrative Region of Chile).Column 21: “county” (Location, refers to the Administrative Province of Chile).Column 22: “municipality” (Location, refers to the Administrative Commune of Chile).Column 23: “locality” (The specific name of the place).Column 24: “verbatimLocality” (The original textual description of the place).Column 25: “verbatimDepth” (The original description of the depth).Column 26: “minimumDepthInMeters” (The shallowest depth of a range of depths).Column 27: “maximumDepthInMeters” (The deepest depth of a range of depths).Column 28: “locationRemarks” (The name of the sample station of the cruise).Column 29: “verbatimLatitude” (The verbatim original latitude of the location).Column 30: “verbatimLongitude” (The verbatim original longitude of the location).Column 31: “verbatimCoordinateSystem” (The coordinate format for the “verbatimLatitude” and “verbatimLongitude” or the “verbatimCoordinates” of the location).Column 32: “verbatimSRS” (The spatial reference system [SRS] upon which coordinates given in “verbatimLatitude” and “verbatimLongitude” are based)Column 33: “decimalLatitude” (The geographic latitude in decimal degrees).Column 34: “decimalLongitude” (The geographic longitude in decimal degrees).Column 35: “geodeticDatum” (The spatial reference system [SRS] upon which the geographic coordinates given in “decimalLatitude” and “decimalLongitude” was based).Column 36: “coordinateUncertaintyInMeters” (The horizontal distance from the given “decimalLatitude” and “decimalLongitude” describing the smallest circle containing the whole of the location).Column 37: “georeferenceRemarks” (Notes about the spatial description determination).Column 38: “identifiedBy” (Responsible for recording the original occurrence [publication source]).Column 39: “dateIdentified” (The date-time or interval during which the identification occurred.)Column 40: “identificationQualifier” (A taxonomic determination [e.g., “sp.”, “cf.”]).Column 41: “scientificNameID” (An identifier for the nomenclatural details of a scientific name).Column 42: “scientificName” (The name of species or taxon of the occurrence record).Column 43: “kingdom” (The scientific name of the kingdom in which the taxon is classified).Column 44: “phylum” (The scientific name of the phylum or division in which the taxon is classified).Column 45: “class” (The scientific name of the class in which the taxon is classified).Column 46: “order” (The scientific name of the order in which the taxon is classified).Column 47: “family” (The scientific name of the family in which the taxon is classified).Column 48: “genus” (The scientific name of the genus in which the taxon is classified).Column 49: “subgenus” (The scientific name of the subgenus in which the taxon is classified).Column 50: “specificEpithet” (The name of the first or species epithet of the “scientificName”).Column 51: “infraspecificEpithet” (The name of the lowest or terminal infraspecific epithet of the “scientificName”).Column 52: “taxonRank” (The taxonomic rank of the most specific name in the “scientificName”).Column 53: “scientificNameAuthorship” (The authorship information for the “scientificName” formatted according to the conventions of the applicable nomenclatural Code).Column 54: “verbatimIdentification” (A string representing the taxonomic identification as it appeared in the original record).The information sources (see Fig. 2b) provided a total of 107 publications (22 cruise reports and 85 scientific papers; see Fig. 2c). Nineteen of the 22 cruise reports reviewed provided species occurrence records8,28,29,30,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46, one provided qualitative or descriptive data, with no recorded occurrences31, and two did not provide information on benthic biota (CIMAR-9 and −23 cruises). Of all the scientific papers reviewed, 74 provided records of species occurrences (Table 2), while 11 did not provide any record, as they were data without occurrences of geographically referenced species or with descriptive or qualitative information: Foraminifera59,60,61,62, Annelida63,64,65,66, Fishes67, Mollusca68 and Echinodermata69. The phyla with the highest number of publications were the following: Annelida (present in 18 reports and 21 papers), Mollusca (in 14 and 20), Arthropoda (in 10 and 18), Echinodermata (in 10 and 9), Chordata (in 10 and 9) and Foraminifera (in 4 and 10).Table 2 Publications with >100 occurrences, indicating the main recorded taxa.Full size tableThe information registry includes data on occurrences and number of individuals for 8,854 records (files in the database), representing 1,225 species (Fig. 3). The main taxa in terms of occurrence and number of species were Annelida (mainly Polychaeta), Foraminifera, Mollusca and Arthopoda (mainly Crustacea), together accumulating ~70% of total occurrences and ~73% of the total species (Fig. 3). The large number of recorded occurrences of Myzozoa (10%) should be highlighted, which, however, only represent about 32 species. Echinodermata represented ~8% of occurrences and 7% of species.Fig. 3Occurrences and total species by taxon, considering large taxonomic groups of the benthic biota recorded in the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences and species are represented in parentheses.Full size imageThe cruises with the highest number of occurrences were CIMAR-2 (with 1,424), followed by CIMAR-8 (1,040) and CIMAR-16 (813) (Fig. 4). Three dominant taxonomic groups were recorded in most cruises, except for cruises CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 (Fig. 4). The cruises with the highest number of species recorded were CIMAR-2 (with 335), CIMAR-3 (328) and CIMAR-8 (323) (Fig. 5). Three or fewer dominant taxonomic groups were recorded only in the CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 cruises (Fig. 5).Fig. 4Total occurrences and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences per dominant taxon are represented in parentheses.Full size imageFig. 5Total species and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of species per dominant taxon are represented in parentheses.Full size imageThe latitudinal bands 42°S and 45°S are those with the highest number of occurrences (Fig. 6), while the 56°S and 46°S bands had the fewest. The highest number of species was recorded in the 52°S and 50°S latitudinal bands, while, as with the occurrences, the lowest values corresponded to the 56°S and 46°S latitudinal bands (Fig. 6).Fig. 6Occurrences and number of species recorded by latitudinal band from the CIMAR 1 to 25 and CDHS-1995 cruises. SEP: South-eastern Pacific.Full size image More

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    Genetic population structures of common scavenging species near hydrothermal vents in the Okinawa Trough

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