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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Johannes U Mayer; Kerry L Hilligan; Jodie S Chandler; David A Eccles; +21 Authors

    The signals driving the adaptation of type 2 dendritic cells (DC2s) to diverse peripheral environments remain mostly undefined. We show that differentiation of CD11blo migratory DC2s, a DC2 population unique to the dermis, required IL-13 signaling dependent on the transcription factors STAT6 and KLF4, whereas DC2s in lung and small intestine were STAT6-independent. Analysis of public datasets indicated that human DC2s in skin expressed an IL-4 and IL-13 gene signature which was not found in blood, spleen and lung DCs. In mice, IL-13 was secreted homeostatically by dermal innate lymphoid cells and was independent of microbiota, TSLP or IL-33. In the absence of IL-13 signaling, dermal DC2s were stable in number but remained CD11bhi and showed defective activation in response to allergens, with diminished ability to support the development of IL-4+GATA3+ helper T cells (TH), whereas anti-fungal IL-17+RORgt+ TH cells were increased. Therefore, homeostatic IL-13 fosters a non-inflammatory skin environment that supports allergic sensitization. This Zenodo deposit contains source code and raw data required to generate multiple figures from this publication. Figure 1f, 1g, 1h, Figure 8a Supplemental figure 2f, 2h Bulk RNA-Sequencing Data scRNA-Sequencing Data This work was funded by an Independent Research Organization grant from the Health Research Council of New Zealand (HRC) to the Malaghan Institute (18-1003), an HRC Project grant to FR (18-510), and the Marjorie Barclay Trust. KLH was supported by a Malaghan Institute Postdoctoral Fellowship and the Intramural Research Program of the NIAID, NIH, USA. MRH was supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 105644/Z/14/Z), and a Lister Institute of Preventative Medicine Prize. RGD was supported by an EMBO long-term fellowship (ALTF 1209-2019).

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2022
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2022
    Data sources: ZENODO
    ZENODO
    Software . 2021
    Data sources: ZENODO
    ZENODO
    Software . 2021
    Data sources: ZENODO
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      ZENODO
      Software . 2022
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2022
      Data sources: ZENODO
      ZENODO
      Software . 2021
      Data sources: ZENODO
      ZENODO
      Software . 2021
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Munack, Henry; Codilean, Alexandru T.;

    This repository includes information pertaining to the relational database behind OCTOPUS v.2. OCTOPUS v.2 is an Open Geospatial Consortium (OGC) compliant web-enabled database that allows users to visualise, query, and download cosmogenic 10Be and 26Al, luminescence, and radiocarbon ages and denudation rates associated with erosional landscapes, Quaternary depositional landforms and archaeological records, along with associated geospatial (vector and raster) data layers. The database follows the FAIR data principles and is based on open-source software deployed on Google Cloud Platform. Data stored in the database can be visualised, queried, and downloaded via a custom-built web interface and via desktop GIS applications that support OGC data access protocols. OCTOPUS can export data to various formats directly, including GML, JSON, Google Earth KLM and KMZ, and ESRI shapefile. OCTOPUS-v2_Relational_Database_Schema-v.1.pdf: diagrammatic representation of the OCTOPUS v.2 relational database schema. OCTOPUS-v2_SchemaSpy_Documentation.zip: queryable relational database documentation generated using SchemaSpy. To start, run: SchemaSpy/index.html.

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Other ORP type . 2021
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Other ORP type . 2022
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Other ORP type . 2021
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Other ORP type . 2022
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Codilean, Alexandru T.; Munack, Henry; Saktura, Wanchese M.;

    This document includes field description tables for data hosted in the OCTOPUS v.2 database. OCTOPUS v.2 is an Open Geospatial Consortium (OGC) compliant web-enabled database that allows users to visualise, query, and download cosmogenic 10Be and 26Al, luminescence, and radiocarbon ages and denudation rates associated with erosional landscapes, Quaternary depositional landforms and archaeological records, along with associated geospatial (vector and raster) data layers. The database follows the FAIR data principles and is based on open-source software deployed on Google Cloud Platform. Data stored in the database can be visualised, queried, and downloaded via a custom-built web interface and via desktop GIS applications that support OGC data access protocols. OCTOPUS can export data to various formats directly, including GML, JSON, Google Earth KLM and KMZ, and ESRI shapefile.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Other ORP type . 2021
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Other ORP type . 2021
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cássia-Silva, Cibele; Oliveira, Rafael Silva; Sales, Lílian P.; Freitas, Cíntia G.; +4 Authors

    Cocoseae_Data.xlsx contains metadata for each of the following datasheets: Table_S1: Data on the growth form and habitat type for all Cocoseae species (i.e. n= 334) obtained from Kissling et al. 2019 and Cassia-Silva et al. 2019 datasets, respectively. Table_S2: Key identification of occurrences records downloaded from the Global Biodiversity Information Facility – GBIF (https://www.gbif.org). Table_S3: Species' occurrence records individually checked. Table S3 contains the number of records of each species (Records) and metrics widely used to assess model accuracy in the Ecological niche modeling procedure: deviance, COR, and true skill statistic (TSS). The area under the receiver operating characteristic curve (AUC) and initial distribution size (iniDist). Cocoseae_tree_MCC.txt contains the maximum credibility clade (MCC) tree of the Cocoseae palm tribe. Cocoseae_tree_100.txt contains 50 phylogenetic trees randomly sampled from the posterior distribution of an all-evidence species-level supertree of palms (Faurby et al. 2016/https://doi.org/10.1016/j.ympev.2016.03.002). sdm_DRYAD.r contains the script used to run the ecological niche models. HiSSE.r contains the script used to run all 24 models to the Cocoseae phylogeny that differed on how their parameters were constrained. Two models were BiSSE-like models (i.e. without hidden states), where turnover (τ = l + m) parameters were free to 100 vary, and extinction fraction (ε) or transitions rates (q) were constrained between growth forms states, depending on the model. Four models corresponded to different CID models, which assumed that rate differences are associated with two (CID-2) or four (CID-4) hidden states and extinctions or 105 transitions were constrained between states. The remaining HiSSE models were trait-dependent diversification models with hidden states, different combinations of parameter constraints, and two different types of transition matrices,one restricted (default) and the other allowing all transitions between character states (Beaulieu and O'Meara 2016). MuHiSSE.r contains the script used to run all nine MuHiSSE (Multistate Speciation and Extinction with hidden traits) models. One of these was a 'null model' where no diversification rate variation is caused by character states. One model was a character-dependent diversification without (i.e. MuSSE-like model) a hidden state and the last seven models included two to eight hidden states (MuHiSSE 2-8 models). Mapas_global_extent.rar contains all maps generated for the Ecological niche modeling procedure. Funding provided by: Conselho Nacional de Desenvolvimento Cientifico e Tecnológico*Crossref Funder Registry ID: Award Number: 407310/2013Funding provided by: Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100002322Award Number: 2019/07773-1Funding provided by: São Paulo Research FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001807Award Number: 2020/09164-0Funding provided by: Rede Cerrado CNPq/PPBio*Crossref Funder Registry ID: Award Number: 457406/2012-7Funding provided by: PROCAD/CAPES*Crossref Funder Registry ID: Award Number: 88881.068425/2014-01Funding provided by: VetenskapsrådetCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004359Award Number: 2017-04980 Rainforests have been a source of lineages to open and seasonally dry habitats throughout Angiosperm evolution, especially in the Neotropics. However, the underlying mechanisms that allow such shifts remain poorly understood at large spatial scales. Here, we test whether acaulescence (an underground stem or a very short stem concealed in the ground) has affected the colonization and speciation in Neotropical seasonally dry habitats by cocosoid palms (Cocoseae). Acaulescent species maintain their growth underground, which increases their chances of survival from prolonged seasonal dry season and frequent fires. We use an integrative approach based on trait‐dependent diversification models, phylogenetic comparative methods, and ecological niche models. We found that shifts towards acaulescent growth form were accompanied by evolutionary transitions to seasonally dry habitats. Acaulescent lineages had higher speciation rates than non-acaulescent ones. However, the interaction between acaulescence and seasonally dry habitats had no significant effect on Cocoseae speciation rates. Acaulescent palms are primarily distributed in Neotropical seasonally dry habitats and non-acaulescent palms are concentrated in Amazonian rainforests. Our results suggest that an underground stem, with high carbohydrate and water storage capacity, is a preadaptation by which rainforest lineages were able to colonize and diversify in new fire-prone, increasingly seasonal and drier adaptive zones. The projected global expansion of dry seasonal habitats requires an understanding of how drought-avoidance functional traits evolve and how they are linked to seasonally dry habitats. Our results are, thus, a step forward in determining plant response mechanisms to drier and seasonal conditions.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Other ORP type . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Other ORP type . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Other ORP type . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Other ORP type . 2021
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cazetta, Eliana; Fahrig, Lenore;

    Seed dispersal by frugivorous animals is important for plant mobility, regeneration, and persistence. Human-caused landscape change is thought to disrupt seed dispersal, but evidence is scarce. We performed a comprehensive meta-analysis on the effects of habitat spatial pattern on frugivory and seed dispersal. We found 233 effects from 71 studies. At a patch or local scale, altered habitat spatial pattern was measured as declining patch size, increasing patch isolation, or habitat edge (vs. interior). At a landscape scale it was measured as declining amount of habitat, increasing mean patch isolation, increasing number of patches, or increasing habitat edge in the landscape. We found overall negative effects of altered habitat spatial pattern on: (i) the quantity of frugivory or seed dispersal, (ii) the number of species involved in a plant-frugivore interaction, and (iii) seed dispersal distance. Moderator variable analysis was only possible for the first of these. It revealed negative responses of the quantity of frugivory or seed dispersal to habitat loss at both the local scale (declining patch size), and the landscape scale (declining habitat amount), but little evidence for a response to habitat edge at either scale. In addition, altered habitat spatial pattern reduced the quantity of frugivory or seed dispersal more strongly in temperate than tropical areas. Finally, the few-recorded effects of landscape-scale fragmentation per se (increasing patch density or edge density) on the quantity of frugivory or seed dispersal were mixed and weak. Our meta-analysis reinforces the notion that habitat loss is a major threat to frugivory and seed dispersal by animals, and reveals an insufficiency of studies of the effects of habitat fragmentation per se. Thus, based on the current literature, we conclude that maintaining and increasing habitat amount is vital for maintaining seed dispersal by frugivorous animals. We conducted a review of the scientific literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol (Moher et al., 2009). We included all records found through May 2020. We used the following sequence of search terms in the Web of Science search engine: "habitat fragm*"OR edge or isolation or connectivity OR "number of patches" OR "patch size" OR "forest cover" OR "forest loss" OR "deforestation" OR "habitat loss" AND "seed dispers* OR "seed removal" OR frugivor* OR "dispersal distance" OR "seed rain". The initial search resulted in 2141 published articles that were further screened to include only English-language studies that (i) evaluated the effects of habitat spatial pattern on any measure of frugivory or seed dispersal of zoochoric species; and (ii) provided sample sizes and quantitative measures of frugivory or seed dispersal, i.e. mean estimates and error measures for studies comparing treatments vs. controls, or correlation coefficients for gradient studies. After title and abstract evaluation, we excluded articles that did not meet these inclusion criteria, which left 99 articles. We completely read the methodologies of these 99 papers and excluded 28 that did not present complete information or did not evaluate the effects of habitat spatial pattern on frugivory or seed dispersal of zoochoric species. We ended up with 71 studies that measured the effects of habitat spatial pattern on frugivory or seed dispersal measure on different ways. README.xlxs contains the following datasheets: Cazetta&Fahrig_Dryad.csv: contains the information used to perform the meta-analysis. The information included: -Location: temperate or tropical -Habitat: forest, savanna, desert, semi-natural habitats -Response level: community or species - Spatial pattern: habitat amount, patch size, habitat edge, isolation, number of patches, edge density -Scale: patch or landscape -Yi= effect size -Vi = corresponding sampling variance References.xlxs: contains a list of the 71 published articles included in the meta-analyis, as supporting information. Funding provided by: Conselho Nacional de Desenvolvimento Científico e TecnológicoCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100003593Award Number: 306373/2018-1

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    ZENODO
    Other ORP type . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Other ORP type . 2021
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      ZENODO
      Other ORP type . 2021
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      ZENODO
      Other ORP type . 2021
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    Authors: Cássia-Silva, Cibele; Oliveira, Rafael Silva; Sales, Lílian P.; Freitas, Cíntia G.; +4 Authors

    Cocoseae_Data.xlsx contains metadata for each of the following datasheets: Table_S1: Data on the growth form and habitat type for all Cocoseae species (i.e. n= 334) obtained from Kissling et al. 2019 and Cassia-Silva et al. 2019 datasets, respectively. Table_S2: Key identification of occurrences records downloaded from the Global Biodiversity Information Facility – GBIF (https://www.gbif.org). Table_S3: Species' occurrence records individually checked. Table S3 contains the number of records of each species (Records) and metrics widely used to assess model accuracy in the Ecological niche modeling procedure: deviance, COR, and true skill statistic (TSS). The area under the receiver operating characteristic curve (AUC) and initial distribution size (iniDist). Cocoseae_tree_MCC.txt contains the maximum credibility clade (MCC) tree of the Cocoseae palm tribe. Cocoseae_tree_100.txt contains 50 phylogenetic trees randomly sampled from the posterior distribution of an all-evidence species-level supertree of palms (Faurby et al. 2016/https://doi.org/10.1016/j.ympev.2016.03.002). sdm_DRYAD.r contains the script used to run the ecological niche models. HiSSE.r contains the script used to run all 24 models to the Cocoseae phylogeny that differed on how their parameters were constrained. Two models were BiSSE-like models (i.e. without hidden states), where turnover (τ = l + m) parameters were free to 100 vary, and extinction fraction (ε) or transitions rates (q) were constrained between growth forms states, depending on the model. Four models corresponded to different CID models, which assumed that rate differences are associated with two (CID-2) or four (CID-4) hidden states and extinctions or 105 transitions were constrained between states. The remaining HiSSE models were trait-dependent diversification models with hidden states, different combinations of parameter constraints, and two different types of transition matrices,one restricted (default) and the other allowing all transitions between character states (Beaulieu and O'Meara 2016). MuHiSSE.r contains the script used to run all nine MuHiSSE (Multistate Speciation and Extinction with hidden traits) models. One of these was a 'null model' where no diversification rate variation is caused by character states. One model was a character-dependent diversification without (i.e. MuSSE-like model) a hidden state and the last seven models included two to eight hidden states (MuHiSSE 2-8 models). Mapas_global_extent.rar contains all maps generated for the Ecological niche modeling procedure. Funding provided by: Conselho Nacional de Desenvolvimento Cientifico e Tecnológico*Crossref Funder Registry ID: Award Number: 407310/2013Funding provided by: Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100002322Award Number: 2019/07773-1Funding provided by: São Paulo Research FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001807Award Number: 2020/09164-0Funding provided by: Rede Cerrado CNPq/PPBio*Crossref Funder Registry ID: Award Number: 457406/2012-7Funding provided by: PROCAD/CAPES*Crossref Funder Registry ID: Award Number: 88881.068425/2014-01Funding provided by: VetenskapsrådetCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004359Award Number: 2017-04980 Rainforests have been a source of lineages to open and seasonally dry habitats throughout Angiosperm evolution, especially in the Neotropics. However, the underlying mechanisms that allow such shifts remain poorly understood at large spatial scales. Here, we test whether acaulescence (an underground stem or a very short stem concealed in the ground) has affected the colonization and speciation in Neotropical seasonally dry habitats by cocosoid palms (Cocoseae). Acaulescent species maintain their growth underground, which increases their chances of survival from prolonged seasonal dry season and frequent fires. We use an integrative approach based on trait‐dependent diversification models, phylogenetic comparative methods, and ecological niche models. We found that shifts towards acaulescent growth form were accompanied by evolutionary transitions to seasonally dry habitats. Acaulescent lineages had higher speciation rates than non-acaulescent ones. However, the interaction between acaulescence and seasonally dry habitats had no significant effect on Cocoseae speciation rates. Acaulescent palms are primarily distributed in Neotropical seasonally dry habitats and non-acaulescent palms are concentrated in Amazonian rainforests. Our results suggest that an underground stem, with high carbohydrate and water storage capacity, is a preadaptation by which rainforest lineages were able to colonize and diversify in new fire-prone, increasingly seasonal and drier adaptive zones. The projected global expansion of dry seasonal habitats requires an understanding of how drought-avoidance functional traits evolve and how they are linked to seasonally dry habitats. Our results are, thus, a step forward in determining plant response mechanisms to drier and seasonal conditions.

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    Authors: Zachreson, Cameron; Shearer, Freya M.; Price, David J.; Lydeamore, Michael J.; +3 Authors

    The scripts and simulation model output data contained here accompany the paper entitled: "COVID-19 in low-tolerance border quarantine systems: impact of the Delta variant of SARS-CoV-2" by Zachreson et al. The set of files Quarantine_ABM_2021_01_01 contains source code and model output for an agent-based model of disease transmission within quarantine facilities. The set of files Branching_process_outbreak_model contains source code for simulating transmission clusters seeded by the output of the quarantine model.

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    Authors: Gruber, Monica; Santoro, Davide; Cooling, Meghan; Lester, Philip; +3 Authors

    README The Refs.ris file contains the library of citations used to generate the dataset. The raw SEICAT, EICAT and GISS records are in the SEICAT_EICAT_GISS workbook. The Definitions worksheet in the SEICAT_EICAT_GISS workbook describes all the fields in the other worksheets. The Manuscript Data workbook contains two supplementary Tables for the publication (Table S3 and Table S4). Table legends are: Table S3: Ant species ranked by Generic Impact Scoring System (GISS) (Nentwig et al. 2016), ordered by maximum GISS score. Table S4: Checklist of named ant species lists from sources referred to in the study (239 species). X indicates the species is present on the list. SEICAT = Socio‐Economic Impact Classification for Alien Taxa (Bacher et al. 2018), EICAT = Environmental Impact Classification for Alien Taxa. (Hawkins et al. 2015), GISS = the Generic Impact Scoring System (Nentwig et al. 2016); GISD = Global Invasive Species Database. Risk assessments are fundamental to invasive species management and are underpinned by comprehensive characterization of invasive species impacts. Our understanding of the impacts of invasive species is growing constantly, and several recently developed frameworks offer the opportunity to systematically categorize environmental and socio-economic impacts of invasive species. Invasive ants are among the most widespread and damaging invaders. We provide a global, comprehensive assessment on the impacts of ants and propose a priority list of risk species. We used the Socio-Economic Impact Classification for Alien Taxa (SEICAT), Environmental Impact Classification for Alien Taxa (EICAT) and Generic Impact Scoring System (GISS) to analyse 642 unique sources for 100 named species. This dataset includes the raw recrods on which our assessments were based, and some summary data. Other data can be found in the publication. The dataset was constructed from a literature research. To identify peer-reviewed information sources for our analyses, we conducted systematic and exhaustive searches of literature published in English prior to March 2019 in Web of Science, JSTOR, Google Scholar, and FORMIS, a curated database of all ant literature (Wojcik and Porter 2018). In our search terms we used "ant" with the additional key words "sting", "bite", "damage", "cost*" and "impact*". The resulting records were assessment using the Environmental Impact Classification for Alien Taxa [EICAT], Hawkins et al. 2015; the Generic Impact Scoring System [GISS], Nentwig et al. 2016; and the Socio-Economic Impact Classification for Alien Taxa [SEICAT] Bacher et al. 2018.

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    Authors: Zebedee Nicholls; Mathias Hauser; Lea Beusch;

    Coupling between MESMER and OpenSCM-Runner. If used, please also cite Beusch et al., GMD 2021 (https://doi.org/10.5194/gmd-2021-252)

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    Authors: Fontenelle, Leonardo Ferreira;

    This is the analytic code for the manuscript ���Research themes of family and community physicians in Brazil���. Please refer to its methods section to learn about versions of R and its packages. The first script combines restricted datasets found in https://doi.org/10.5281/zenodo.4459429 and https://doi.org/10.5281/zenodo.5797816, generating the anonymized dataset in https://doi.org/10.5281/zenodo.5798132. The second script analyzes the anonymized dataset, generating the figures and tables for the manuscript. Both scrips are encoded in UTF-8.

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    Authors: Johannes U Mayer; Kerry L Hilligan; Jodie S Chandler; David A Eccles; +21 Authors

    The signals driving the adaptation of type 2 dendritic cells (DC2s) to diverse peripheral environments remain mostly undefined. We show that differentiation of CD11blo migratory DC2s, a DC2 population unique to the dermis, required IL-13 signaling dependent on the transcription factors STAT6 and KLF4, whereas DC2s in lung and small intestine were STAT6-independent. Analysis of public datasets indicated that human DC2s in skin expressed an IL-4 and IL-13 gene signature which was not found in blood, spleen and lung DCs. In mice, IL-13 was secreted homeostatically by dermal innate lymphoid cells and was independent of microbiota, TSLP or IL-33. In the absence of IL-13 signaling, dermal DC2s were stable in number but remained CD11bhi and showed defective activation in response to allergens, with diminished ability to support the development of IL-4+GATA3+ helper T cells (TH), whereas anti-fungal IL-17+RORgt+ TH cells were increased. Therefore, homeostatic IL-13 fosters a non-inflammatory skin environment that supports allergic sensitization. This Zenodo deposit contains source code and raw data required to generate multiple figures from this publication. Figure 1f, 1g, 1h, Figure 8a Supplemental figure 2f, 2h Bulk RNA-Sequencing Data scRNA-Sequencing Data This work was funded by an Independent Research Organization grant from the Health Research Council of New Zealand (HRC) to the Malaghan Institute (18-1003), an HRC Project grant to FR (18-510), and the Marjorie Barclay Trust. KLH was supported by a Malaghan Institute Postdoctoral Fellowship and the Intramural Research Program of the NIAID, NIH, USA. MRH was supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 105644/Z/14/Z), and a Lister Institute of Preventative Medicine Prize. RGD was supported by an EMBO long-term fellowship (ALTF 1209-2019).

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    Authors: Munack, Henry; Codilean, Alexandru T.;

    This repository includes information pertaining to the relational database behind OCTOPUS v.2. OCTOPUS v.2 is an Open Geospatial Consortium (OGC) compliant web-enabled database that allows users to visualise, query, and download cosmogenic 10Be and 26Al, luminescence, and radiocarbon ages and denudation rates associated with erosional landscapes, Quaternary depositional landforms and archaeological records, along with associated geospatial (vector and raster) data layers. The database follows the FAIR data principles and is based on open-source software deployed on Google Cloud Platform. Data stored in the database can be visualised, queried, and downloaded via a custom-built web interface and via desktop GIS applications that support OGC data access protocols. OCTOPUS can export data to various formats directly, including GML, JSON, Google Earth KLM and KMZ, and ESRI shapefile. OCTOPUS-v2_Relational_Database_Schema-v.1.pdf: diagrammatic representation of the OCTOPUS v.2 relational database schema. OCTOPUS-v2_SchemaSpy_Documentation.zip: queryable relational database documentation generated using SchemaSpy. To start, run: SchemaSpy/index.html.

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    Authors: Codilean, Alexandru T.; Munack, Henry; Saktura, Wanchese M.;

    This document includes field description tables for data hosted in the OCTOPUS v.2 database. OCTOPUS v.2 is an Open Geospatial Consortium (OGC) compliant web-enabled database that allows users to visualise, query, and download cosmogenic 10Be and 26Al, luminescence, and radiocarbon ages and denudation rates associated with erosional landscapes, Quaternary depositional landforms and archaeological records, along with associated geospatial (vector and raster) data layers. The database follows the FAIR data principles and is based on open-source software deployed on Google Cloud Platform. Data stored in the database can be visualised, queried, and downloaded via a custom-built web interface and via desktop GIS applications that support OGC data access protocols. OCTOPUS can export data to various formats directly, including GML, JSON, Google Earth KLM and KMZ, and ESRI shapefile.

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    Authors: Cássia-Silva, Cibele; Oliveira, Rafael Silva; Sales, Lílian P.; Freitas, Cíntia G.; +4 Authors

    Cocoseae_Data.xlsx contains metadata for each of the following datasheets: Table_S1: Data on the growth form and habitat type for all Cocoseae species (i.e. n= 334) obtained from Kissling et al. 2019 and Cassia-Silva et al. 2019 datasets, respectively. Table_S2: Key identification of occurrences records downloaded from the Global Biodiversity Information Facility – GBIF (https://www.gbif.org). Table_S3: Species' occurrence records individually checked. Table S3 contains the number of records of each species (Records) and metrics widely used to assess model accuracy in the Ecological niche modeling procedure: deviance, COR, and true skill statistic (TSS). The area under the receiver operating characteristic curve (AUC) and initial distribution size (iniDist). Cocoseae_tree_MCC.txt contains the maximum credibility clade (MCC) tree of the Cocoseae palm tribe. Cocoseae_tree_100.txt contains 50 phylogenetic trees randomly sampled from the posterior distribution of an all-evidence species-level supertree of palms (Faurby et al. 2016/https://doi.org/10.1016/j.ympev.2016.03.002). sdm_DRYAD.r contains the script used to run the ecological niche models. HiSSE.r contains the script used to run all 24 models to the Cocoseae phylogeny that differed on how their parameters were constrained. Two models were BiSSE-like models (i.e. without hidden states), where turnover (τ = l + m) parameters were free to 100 vary, and extinction fraction (ε) or transitions rates (q) were constrained between growth forms states, depending on the model. Four models corresponded to different CID models, which assumed that rate differences are associated with two (CID-2) or four (CID-4) hidden states and extinctions or 105 transitions were constrained between states. The remaining HiSSE models were trait-dependent diversification models with hidden states, different combinations of parameter constraints, and two different types of transition matrices,one restricted (default) and the other allowing all transitions between character states (Beaulieu and O'Meara 2016). MuHiSSE.r contains the script used to run all nine MuHiSSE (Multistate Speciation and Extinction with hidden traits) models. One of these was a 'null model' where no diversification rate variation is caused by character states. One model was a character-dependent diversification without (i.e. MuSSE-like model) a hidden state and the last seven models included two to eight hidden states (MuHiSSE 2-8 models). Mapas_global_extent.rar contains all maps generated for the Ecological niche modeling procedure. Funding provided by: Conselho Nacional de Desenvolvimento Cientifico e Tecnológico*Crossref Funder Registry ID: Award Number: 407310/2013Funding provided by: Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100002322Award Number: 2019/07773-1Funding provided by: São Paulo Research FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001807Award Number: 2020/09164-0Funding provided by: Rede Cerrado CNPq/PPBio*Crossref Funder Registry ID: Award Number: 457406/2012-7Funding provided by: PROCAD/CAPES*Crossref Funder Registry ID: Award Number: 88881.068425/2014-01Funding provided by: VetenskapsrådetCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004359Award Number: 2017-04980 Rainforests have been a source of lineages to open and seasonally dry habitats throughout Angiosperm evolution, especially in the Neotropics. However, the underlying mechanisms that allow such shifts remain poorly understood at large spatial scales. Here, we test whether acaulescence (an underground stem or a very short stem concealed in the ground) has affected the colonization and speciation in Neotropical seasonally dry habitats by cocosoid palms (Cocoseae). Acaulescent species maintain their growth underground, which increases their chances of survival from prolonged seasonal dry season and frequent fires. We use an integrative approach based on trait‐dependent diversification models, phylogenetic comparative methods, and ecological niche models. We found that shifts towards acaulescent growth form were accompanied by evolutionary transitions to seasonally dry habitats. Acaulescent lineages had higher speciation rates than non-acaulescent ones. However, the interaction between acaulescence and seasonally dry habitats had no significant effect on Cocoseae speciation rates. Acaulescent palms are primarily distributed in Neotropical seasonally dry habitats and non-acaulescent palms are concentrated in Amazonian rainforests. Our results suggest that an underground stem, with high carbohydrate and water storage capacity, is a preadaptation by which rainforest lineages were able to colonize and diversify in new fire-prone, increasingly seasonal and drier adaptive zones. The projected global expansion of dry seasonal habitats requires an understanding of how drought-avoidance functional traits evolve and how they are linked to seasonally dry habitats. Our results are, thus, a step forward in determining plant response mechanisms to drier and seasonal conditions.

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    Authors: Cazetta, Eliana; Fahrig, Lenore;

    Seed dispersal by frugivorous animals is important for plant mobility, regeneration, and persistence. Human-caused landscape change is thought to disrupt seed dispersal, but evidence is scarce. We performed a comprehensive meta-analysis on the effects of habitat spatial pattern on frugivory and seed dispersal. We found 233 effects from 71 studies. At a patch or local scale, altered habitat spatial pattern was measured as declining patch size, increasing patch isolation, or habitat edge (vs. interior). At a landscape scale it was measured as declining amount of habitat, increasing mean patch isolation, increasing number of patches, or increasing habitat edge in the landscape. We found overall negative effects of altered habitat spatial pattern on: (i) the quantity of frugivory or seed dispersal, (ii) the number of species involved in a plant-frugivore interaction, and (iii) seed dispersal distance. Moderator variable analysis was only possible for the first of these. It revealed negative responses of the quantity of frugivory or seed dispersal to habitat loss at both the local scale (declining patch size), and the landscape scale (declining habitat amount), but little evidence for a response to habitat edge at either scale. In addition, altered habitat spatial pattern reduced the quantity of frugivory or seed dispersal more strongly in temperate than tropical areas. Finally, the few-recorded effects of landscape-scale fragmentation per se (increasing patch density or edge density) on the quantity of frugivory or seed dispersal were mixed and weak. Our meta-analysis reinforces the notion that habitat loss is a major threat to frugivory and seed dispersal by animals, and reveals an insufficiency of studies of the effects of habitat fragmentation per se. Thus, based on the current literature, we conclude that maintaining and increasing habitat amount is vital for maintaining seed dispersal by frugivorous animals. We conducted a review of the scientific literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol (Moher et al., 2009). We included all records found through May 2020. We used the following sequence of search terms in the Web of Science search engine: "habitat fragm*"OR edge or isolation or connectivity OR "number of patches" OR "patch size" OR "forest cover" OR "forest loss" OR "deforestation" OR "habitat loss" AND "seed dispers* OR "seed removal" OR frugivor* OR "dispersal distance" OR "seed rain". The initial search resulted in 2141 published articles that were further screened to include only English-language studies that (i) evaluated the effects of habitat spatial pattern on any measure of frugivory or seed dispersal of zoochoric species; and (ii) provided sample sizes and quantitative measures of frugivory or seed dispersal, i.e. mean estimates and error measures for studies comparing treatments vs. controls, or correlation coefficients for gradient studies. After title and abstract evaluation, we excluded articles that did not meet these inclusion criteria, which left 99 articles. We completely read the methodologies of these 99 papers and excluded 28 that did not present complete information or did not evaluate the effects of habitat spatial pattern on frugivory or seed dispersal of zoochoric species. We ended up with 71 studies that measured the effects of habitat spatial pattern on frugivory or seed dispersal measure on different ways. README.xlxs contains the following datasheets: Cazetta&Fahrig_Dryad.csv: contains the information used to perform the meta-analysis. The information included: -Location: temperate or tropical -Habitat: forest, savanna, desert, semi-natural habitats -Response level: community or species - Spatial pattern: habitat amount, patch size, habitat edge, isolation, number of patches, edge density -Scale: patch or landscape -Yi= effect size -Vi = corresponding sampling variance References.xlxs: contains a list of the 71 published articles included in the meta-analyis, as supporting information. Funding provided by: Conselho Nacional de Desenvolvimento Científico e TecnológicoCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100003593Award Number: 306373/2018-1

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    Authors: Cássia-Silva, Cibele; Oliveira, Rafael Silva; Sales, Lílian P.; Freitas, Cíntia G.; +