{"_links":{"self":{"href":"/api/v2/search?q=carbon"},"first":{"href":"/api/v2/search?q=carbon"},"last":{"href":"/api/v2/search?page=145\u0026q=carbon"},"next":{"href":"/api/v2/search?page=2\u0026q=carbon"}},"count":20,"total":2900,"_embedded":{"stash:datasets":[{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.ns1rn8ptc"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.ns1rn8ptc/versions"},"stash:version":{"href":"/api/v2/versions/136695"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.ns1rn8ptc/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.ns1rn8ptc","id":72436,"storageSize":238134,"relatedPublicationISSN":"2045-7758","title":"Data from: An inventory of the foliar, soil, and dung arthropod communities in pastures of the Southeastern United States","authors":[{"firstName":"Ryan","lastName":"Schmid","email":"ryan.schmid@ecdysis.bio","affiliation":"Ecdysis Foundation","affiliations":[{"name":"Ecdysis Foundation"}],"orcid":"0000-0002-7638-5619"},{"firstName":"Kelton","lastName":"Welch","email":"","affiliation":"Ecdysis Foundation","affiliations":[{"name":"Ecdysis Foundation"}]},{"firstName":"Jonathan","lastName":"Lundgren","email":"","affiliation":"Ecdysis Foundation","affiliations":[{"name":"Ecdysis Foundation"}]}],"abstract":"\u003cp\u003eGrassland systems constitute a significant portion of the land area in the U.S. and as a result harbors significant arthropod biodiversity. During this time of biodiversity loss around the world, bioinventories of ecologically important habitats serve as important indicators for the effectiveness of conservation efforts. We conducted a bioinventory of the foliar, soil, and dung arthropod communities in 10 cattle pastures located in the southeastern U.S. during the 2018 grazing season. In sum, 126,251 arthropod specimens were collected. From the foliar community, 13 arthropod orders were observed, with the greatest species richness found in Hymenoptera, Diptera, and Hemiptera. The soil-dwelling arthropod community contained 18 orders. The three orders comprising the highest species richness were Coleoptera, Diptera, and Hymenoptera. Lastly, 12 arthropod orders were collected from cattle dung, with the greatest species richness found in Coleoptera, Diptera, and Hymenoptera. Herbivores were the most abundant functional guild found in the foliar community, and predators were most abundant in the soil and dung communities. Arthropod pests constituted a small portion of the pasture arthropod communities, with 1.01%, 0.34%, and 0.46% pests found in the foliar, soil, and dung communities, respectively. While bioinventories demand considerable time, energy, and resources to accomplish, the information from these inventories has many uses for conservation efforts, land management recommendations, and the direction of climate change science.\u003c/p\u003e\r\n","funders":[{"organization":"Carbon Nation Foundation*","identifier":"","awardNumber":""},{"organization":"Carbon Nation Foundation","identifier":""}],"fieldOfScience":"Natural sciences","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1002/ece3.7941"},{"relationship":"preprint","identifierType":"DOI","identifier":"https://doi.org/10.22541/au.161285608.87270046/v1"}],"versionNumber":4,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2021-08-26","lastModificationDate":"2021-08-26","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.ns1rn8ptc","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":207,"downloads":16,"citations":2}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.k6djh9w7k"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.k6djh9w7k/versions"},"stash:version":{"href":"/api/v2/versions/153774"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.k6djh9w7k/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.k6djh9w7k","id":79960,"storageSize":331692,"relatedPublicationISSN":"1758-3012","title":"Carbon sequestration of a forested wetland receiving nutrient inputs - soil, tree and greenhouse gas data","authors":[{"firstName":"Robert","lastName":"Lane","email":"rlane@comiteres.com","affiliation":"Comite Resources","affiliations":[{"name":"Comite Resources"}],"orcid":"0000-0002-3374-903X"}],"abstract":"\u003cp style=\"text-align:start;text-indent:0px;\"\u003e\u003cspan\u003e\u003cspan style=\"font-style:normal;\"\u003e\u003cspan\u003e\u003cspan style=\"font-weight:normal;\"\u003e\u003cspan style=\"letter-spacing:normal;\"\u003e\u003cspan\u003e\u003cspan\u003e\u003cspan style=\"white-space:normal;\"\u003e\u003cspan\u003e\u003cspan\u003e\u003cspan\u003eHere we describe a pilot wetland carbon project located 30 km west of New Orleans where measurements were taken in 2013 and 2018, and applied to the carbon offset methodology, “Restoration of Degraded Deltaic Wetlands of the Mississippi Delta” (“the ACR Methodology”) published by the American Carbon Registry (ACR). Baseline emissions were modeled using values derived from scientific literature. Results indicate net sequestration rate of 619,727 tons carbon dioxide equivalent (CO\u003csub\u003e2\u003c/sub\u003ee) over the 40 year project duration, which equates to 16,527 t CO2-e/yr, if wetland greenhouse gases (GHGs) are included, and 200,143 t CO\u003csub\u003e2\u003c/sub\u003ee over 40 years, or 5,003 t CO2-e/yr, if wetland greenhouse gasses were conservatively omitted. A kriging exercise was carried out that modeled the tree and soil pools, which resulted in net sequestration of 723,375 t CO2-e over 40 years (annual mean 18,084 t CO2-e/yr) with greenhouse gases, and 262,472 t CO2-e over 40 years (annual mean rate 6,560 t CO2-e/yr) if greenhouse gases were omitted. Unfortunately, the project was withdrawn, prohibiting the issuance and eventual transaction of carbon credits, due to very large uncertainty estimates mostly associated with GHG emissions and the kriging approach as in situ sampling could not be conducted as required by the methodology.\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\r\n","funders":[{"organization":"Entergy*","identifierType":"crossref_funder_id","identifier":"","awardNumber":""}],"keywords":["carbon accounting","Forested Wetlands","Wetland Assimilation"],"fieldOfScience":"Earth and related environmental sciences","methods":"\u003cp\u003eMethods are detailed in 'Lane RR, Mack SK, Day JW, et al. Carbon sequestration at a forested wetland receiving treated municipal effluent. Wetlands 2017;37(5):861-873.'\u003c/p\u003e\r\n\r\n\u003cp\u003eDatasets include tree biomass, soil organic carbon and greenhouse gasses\u003c/p\u003e\r\n","usageNotes":"\u003cp\u003ePlease refer to 'Lane RR, Mack SK, Day JW, et al. Carbon sequestration at a forested wetland receiving treated municipal effluent. Wetlands 2017;37(5):861-873.'\u003c/p\u003e\r\n","relatedWorks":[{"relationship":"article","identifierType":"DOI","identifier":"https://doi.org/10.1007/s13157-017-0920-6"},{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1080/17583004.2022.2112292"}],"versionNumber":6,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2021-12-09","lastModificationDate":"2021-12-09","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.k6djh9w7k","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":229,"downloads":38,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.fj6q573v2"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.fj6q573v2/versions"},"stash:version":{"href":"/api/v2/versions/144374"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.fj6q573v2/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.fj6q573v2","id":68697,"storageSize":8698798,"title":"Code in support of: Physical and chemical mechanisms that influence the electrical conductivity of lignin-derived biochar","authors":[{"firstName":"Seth","lastName":"Kane","email":"sethkane@montana.edu","affiliation":"Montana State University","affiliationROR":"https://ror.org/02w0trx84","affiliations":[{"name":"Montana State University","ror_id":"https://ror.org/02w0trx84"}],"orcid":"0000-0002-6940-1369"},{"firstName":"Rachel","lastName":"Ulrich","email":"rachel.ulrich@montana.edu","affiliation":"Montana State University","affiliationROR":"https://ror.org/02w0trx84","affiliations":[{"name":"Montana State University","ror_id":"https://ror.org/02w0trx84"}]},{"firstName":"Abigail","lastName":"Harrington","email":"abigail.harrington14@gmail.com","affiliation":"Montana State University","affiliationROR":"https://ror.org/02w0trx84","affiliations":[{"name":"Montana State University","ror_id":"https://ror.org/02w0trx84"}]},{"firstName":"Nicholas P.","lastName":"Stadie","email":"nstadie@montana.edu","affiliation":"Montana State University","affiliationROR":"https://ror.org/02w0trx84","affiliations":[{"name":"Montana State University","ror_id":"https://ror.org/02w0trx84"}],"orcid":"0000-0002-1139-7846"},{"firstName":"Cecily","lastName":"Ryan","email":"cecily.ryan@montana.edu","affiliation":"Montana State University","affiliationROR":"https://ror.org/02w0trx84","affiliations":[{"name":"Montana State University","ror_id":"https://ror.org/02w0trx84"}],"orcid":"0000-0001-8335-2287"}],"abstract":"\u003cp\u003eLignin-derived biochar is a promising, sustainable alternative to petroleum-based carbon powders (e.g., carbon black) for electrode and energy storage applications. Prior studies of these biochars demonstrate that high electrical conductivity and good capacitive behavior are achievable. These studies also show high variability in electrical conductivity between biochars (~10^-2-10^2 S/cm). The underlying mechanisms that lead to desirable electrical properties in these lignin-derived biochars are poorly understood. In this work, we examine the causes of the variation in conductivity of lignin-derived biochar to optimize the electrical conductivity of lignin-derived biochars. To this end, we produced biochar from three different lignins, a whole biomass source (wheat stem), and cellulose at two pyrolysis temperatures (900 C, 1100  C). These biochars have a similar range of conductivities (0.002 to 18.51 S/cm) to what has been reported in the literature. Results from examining the relationship between chemical and physical biochar properties and electrical conductivity indicate that decreases in oxygen content and changes in particle size are associated with increases in electrical conductivity. Lignin isolated with an acidification process yielded biochar with higher electrical conductivity than lignin isolated with sulfate processes. These findings indicate how lignin composition and processing may be further selected and optimized to target specific energy-related applications.\u003c/p\u003e\r\n","funders":[{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"DMS-1748883"},{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"ECCS-1542210"}],"fieldOfScience":"Materials engineering","methods":"\u003cp\u003eThis code was produced by Rachel Ulrich for the statistical analysis presented in the article. Please see the methods and Supplemental Methods for a complete description of the statistical model applied in this code. \u003ca href=\"https://doi.org/10.1016/j.cartre.2021.100088\" rel=\"noreferrer noopener\" target=\"_blank\" title=\"Persistent link using digital object identifier\"\u003ehttps://doi.org/10.1016/j.cartre.2021.100088\u003c/a\u003e\u003c/p\u003e\r\n","usageNotes":"\u003cp\u003eThis code is written in R and is designed to run in RStudio Version 1.4.1106\u003cbr\u003e\r\n\u003cbr\u003e\r\nemmeans, lmerTest, lme4, and Matrix packages are required to run this code, (see \u003ca href=\"https://doi.org/10.1016/j.cartre.2021.100088\" rel=\"noreferrer noopener\" target=\"_blank\" title=\"Persistent link using digital object identifier\"\u003ehttps://doi.org/10.1016/j.cartre.2021.100088\u003c/a\u003e for full citations of these packages).\u003c/p\u003e\r\n","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1016/j.cartre.2021.100088"}],"versionNumber":2,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"files_changed","publicationDate":"2021-10-12","lastModificationDate":"2021-10-12","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.fj6q573v2","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":166,"downloads":5,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.8pk0p2ntf"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.8pk0p2ntf/versions"},"stash:version":{"href":"/api/v2/versions/377529"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.8pk0p2ntf/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.8pk0p2ntf","id":112955,"storageSize":6947625,"relatedPublicationISSN":"0160-4120","title":"Carbon monoxide exposure inside UK road vehicles: a pilot study","authors":[{"firstName":"Sophie","lastName":"Duggan","email":"drsophieduggan@airsafe.london","affiliation":"Carbon Monoxide Research Trust","affiliations":[{"name":"Carbon Monoxide Research Trust"}],"orcid":"0009-0006-9383-0012"}],"abstract":"\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eObjective\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eTo test the following hypotheses:\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003e \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e(i) \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003eCO is present inside the passenger cabins of road vehicles driven by members of the general public in the UK;\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoListParagraphCxSpMiddle\"\u003e\u003cspan lang=\"EN-US\"\u003e \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e(ii) \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003ein-cabin CO is due, at least in part, to internal exhaust leakage; and\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoListParagraphCxSpMiddle\"\u003e\u003cspan lang=\"EN-US\"\u003e(iii) the use of handheld dataloggers by laypeople can generate data relevant to the above two objectives.\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eDesign                       \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003ePilot study.\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eSetting\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e                       \u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eTwo centres: privately-owned cars in Chesham and Amersham, and cars used by engineers at Southern Gas Networks in Epsom.\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eParticipants             \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003e28 participants entered the study, through online local recruitment (first centre, n=21) and through line management (second centre, n=7), driving 33 vehicles. The study excluded vehicles carrying smokers.\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ePrimary outcome measure\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eParts per million CO, logged continuously during journeys. Mean journey per cent ppm CO was calculated and peak journey ppm CO noted.\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eMethods                    \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eMeasurement of CO using mobile-compatible dataloggers.\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eResults                      \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003e33 vehicles returned 230 journey logs in all. 20 (61%) of cars logged non-zero CO. Mean all-journey average was 6.629ppm\u003c/span\u003e \u003cspan lang=\"EN-US\"\u003eCO. 10 journeys measured \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e≥\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e10ppm CO at least once. Peak single-journey average CO was 192.174\u003c/span\u003e \u003cspan lang=\"EN-US\"\u003eppm. Some patterns of CO ingress were suggestive of internal exhaust leakage. \u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eConclusions              \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eThis is the first public-engagement UK-based study of CO levels within vehicles. It shows that in-cabin average CO levels are non-zero overall, and in some cases markedly raised. \u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eChronic low-level CO exposure has a range of harmful effects. In addition to causing hypoxic stress, it contributes to cardiovascular disease, generation of reactive oxygen species, and demyelination of white matter. Pregnant women, the unborn and children are especially vulnerable to its effects, which include gestational-specific harms.\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eAdding in-car air quality measurement to the MOT would benefit public health, alerting vehicle owners, who may be asymptomatic, to raised in-vehicle exhaust levels. Routine MOT air quality testing would have the additional benefit of capturing wider data on the problem, as would larger studies of similar design.\u003c/span\u003e\u003c/p\u003e","funders":[{"organization":"Carbon Monoxide Research Trust","identifierType":"crossref_funder_id","identifier":"","awardNumber":"GST/2018/001","awardDescription":"","awardTitle":""}],"keywords":["automobiles","cars","vehicles","carboxyhemoglobin","demyelination ","atherosclerosis ","Carbon monoxide","Cardiovascular diseases","Vehicle emissions","patient and public involvement"],"fieldOfScience":"Medical and health sciences","methods":"\u003cp\u003eTesters measured in-cabin COppm, \u003cspan lang=\"EN-US\"\u003eusing the Kane COA1 Carbon Monoxide Detector Adapter. Although the maximum range of the sensor was 999 COppm, the app supporting the datalogger generated some graphs with readings in excess of this level. For calculation purposes, values have been capped at 999ppm. \u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eData is left in arithmetic form, with right-skewing unaltered.\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eData analysis was performed with custom scripts written in Lua.\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\"\u003eReadings from one datalog (see Supplemental Material) were set aside due to likely artefactual interference on the sensor. \u003c/span\u003e\u003c/p\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1016/j.envint.2024.109070"}],"versionNumber":9,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2025-07-21","lastModificationDate":"2025-07-21","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.8pk0p2ntf","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":39,"downloads":15,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.p2ngf1w06"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.p2ngf1w06/versions"},"stash:version":{"href":"/api/v2/versions/309625"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.p2ngf1w06/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.p2ngf1w06","id":134180,"storageSize":376337,"title":"Data from: The importance of accounting method and sampling depth to estimate changes in soil carbon stocks","authors":[{"firstName":"Anna","lastName":"Raffeld","email":"araffeld@edf.org","affiliation":"Environmental Defense Fund","affiliationROR":"https://ror.org/02tj7r959","affiliations":[{"name":"Environmental Defense Fund","ror_id":"https://ror.org/02tj7r959"}],"orcid":"0000-0002-5036-6460"},{"firstName":"Randall","lastName":"Jackson","email":"rdjackson@wisc.edu","affiliation":"University of Wisconsin–Madison","affiliationROR":"https://ror.org/01y2jtd41","affiliations":[{"name":"University of Wisconsin–Madison","ror_id":"https://ror.org/01y2jtd41"}],"order":1},{"firstName":"Gregg","lastName":"Sanford","email":"gsanford@wisc.edu","affiliation":"University of Wisconsin–Madison","affiliationROR":"https://ror.org/01y2jtd41","affiliations":[{"name":"University of Wisconsin–Madison","ror_id":"https://ror.org/01y2jtd41"}],"order":2},{"firstName":"Daniel","lastName":"Rath","email":"drath@nrdc.org","affiliation":"Natural Resources Defense Council","affiliationROR":"https://ror.org/05tff2467","affiliations":[{"name":"Natural Resources Defense Council","ror_id":"https://ror.org/05tff2467"}],"order":3},{"firstName":"Nicole","lastName":"Tautges","email":"ntautges@michaelfields.org","affiliation":"Michael Fields Agricultural Institute","affiliations":[{"name":"Michael Fields Agricultural Institute"}],"order":4}],"abstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs interest in the voluntary soil carbon market surges, carbon registries have been developing new soil carbon measurement, reporting, and verification (MRV) protocols. These protocols are inconsistent in their approaches to measuring soil organic carbon (SOC). Two areas of concern include the type of SOC stock accounting method (fixed-depth (FD) vs. equivalent soil mass (ESM)) and sampling depth requirement. Despite evidence that fixed-depth measurements can result in error because of changes in soil bulk density and that sampling to 30 cm neglects a significant portion of the soil profile’s SOC stock, most MRV protocols do not specify which sampling method to use and only require sampling to 30 cm. Using data from UC Davis’s Century Experiment (“Century”) and UW Madison’s Wisconsin Integrated Cropping Systems Trial (WICST), we quantify differences in SOC stock changes estimated by FD and ESM over 20 years, investigate how sampling at-depth (\u0026gt; 30 cm) affects SOC stock change estimates, and estimate how crediting outcomes taking an empirical sampling-only crediting approach differ when stocks are calculated using ESM or FD at different depths.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe find that FD and ESM estimates of stock change can differ by over 100 percent and that, as expected, much of this difference is associated with changes in bulk density in surface soils (e.g., \u003cem\u003er\u003c/em\u003e = 0.90 for Century maize treatments). This led to substantial differences in crediting outcomes between ESM and FD-based stocks, although many treatments did not receive credits due to declines in SOC stocks over time. While increased variability of soils at depth makes it challenging to accurately quantify stocks across the profile, sampling to 60 cm can capture changes in bulk density, potential SOC redistribution, and a larger proportion of the overall SOC stock.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eESM accounting and sampling to 60 cm (using multiple depth increments) should be considered best practice when quantifying change in SOC stocks in annual, row crop agroecosystems. For carbon markets, the cost of achieving an accurate estimate of SOC stocks that reflect management impacts on soils at-depth should be reflected in the price of carbon credits.\u003c/p\u003e","funders":[{"organization":"Bezos Earth Fund*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":0},{"organization":"King Philanthropies*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":1},{"organization":"Arcadia Fund","identifierType":"ror","identifier":"https://ror.org/051z6e826","awardNumber":"","awardDescription":"","order":2},{"organization":"National Institute of Food and Agriculture","identifierType":"ror","identifier":"https://ror.org/05qx3fv49","awardNumber":"","awardDescription":"","order":3},{"organization":"UW-Madison Office of Vice Chancellor for Research \u0026 Graduate Education Bridge Fund*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":4}],"keywords":["soil carbon concentration","equivalent soil mass","simpleESM","bulk density","Soil carbon","Soil science","deep soil sampling"],"fieldOfScience":"Agricultural sciences","methods":"\u003cp\u003eThis data is comprised of two different datasets. The first dataset comes from University of California, Davis’ Century Experiment at the Russel Ranch Sustainable Agriculture Facility (38°32′24″N, 121°52′12″W). The second dataset comes from UW Madison’s Wisconsin Integrated Cropping Systems Trial (WICST) at the UW Madison Agricultural Research Station in Arlington, WI (43°18″N, 89°20″W). Data were previously published in Sanford et al. (2012) (\u003ca href=\"https://doi.org/10.1016/j.agee.2012.08.011)\"\u003ehttps://doi.org/10.1016/j.agee.2012.08.011)\u003c/a\u003e and Tautges et al. (2019) (\u003ca href=\"https://doi.org/10.1111/gcb.14762)\"\u003ehttps://doi.org/10.1111/gcb.14762)\u003c/a\u003e. To explore management impacts on soil carbon accrual over time. See these publications for details on data collection.\u003c/p\u003e\n\u003cp\u003eAll datasets were cleaned to remove any missing values and organized for input into the SimpleESM R function (\u003ca href=\"https://github.com/fabienferchaud/SimpleESM)\"\u003ehttps://github.com/fabienferchaud/SimpleESM)\u003c/a\u003e. SimpleESM requires an excel input document with bulk density (g cm\u003csup\u003e-3\u003c/sup\u003e) and carbon concentration (g kg\u003csup\u003e-1\u003c/sup\u003e) data. Bulk density (\"BD\")and carbon concentration (\"Concentrations\") are recorded on separate sheets. Each sheet contains a campaign variable (year in which the sample was taken), treatment (ID of experimental treatment), block (ID of experimental block),  plot (ID of sampled plot), point (ID of sampled point), and the lower depth and upper depth (in centimeters) of the sample.\u003c/p\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1186/s13021-024-00249-1"}],"versionNumber":4,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"files_changed","publicationDate":"2024-08-07","lastModificationDate":"2024-08-07","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.p2ngf1w06","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":47,"downloads":14,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2547d7x2d"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2547d7x2d/versions"},"stash:version":{"href":"/api/v2/versions/351240"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2547d7x2d/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.2547d7x2d","id":148138,"storageSize":1659998,"relatedPublicationISSN":"2375-2548","title":"The potential of wastewater treatment on carbon storage through ocean alkalinity enhancement","authors":[{"firstName":"Li-wen","lastName":"Zheng","email":"zhengliwen516@sdu.edu.cn","affiliation":"Shandong University","affiliationROR":"https://ror.org/0207yh398","affiliations":[{"name":"Shandong University","ror_id":"https://ror.org/0207yh398"}],"orcid":"0000-0002-6535-4171","order":0},{"firstName":"Yubin","lastName":"Hu","email":"yubinhu@sdu.edu.cn","affiliation":"Shandong University","affiliationROR":"https://ror.org/0207yh398","affiliations":[{"name":"Shandong University","ror_id":"https://ror.org/0207yh398"}],"order":1},{"firstName":"Bei","lastName":"Su","email":"201999900096@sdu.edu.cn","affiliation":"Shandong University","affiliationROR":"https://ror.org/0207yh398","affiliations":[{"name":"Shandong University","ror_id":"https://ror.org/0207yh398"}],"orcid":"0000-0002-1663-6295","order":2},{"firstName":"Qian-ying","lastName":"Chen","email":"cikpistachiooo@outlook.com","affiliation":"Shandong University","affiliationROR":"https://ror.org/0207yh398","affiliations":[{"name":"Shandong University","ror_id":"https://ror.org/0207yh398"}],"order":3},{"firstName":"Jihua","lastName":"Liu","email":"liujihua1982@foxmail.com","affiliation":"Shandong University","affiliationROR":"https://ror.org/0207yh398","affiliations":[{"name":"Shandong University","ror_id":"https://ror.org/0207yh398"}],"orcid":"0000-0001-7391-6085","order":4}],"abstract":"\u003cp\u003eOcean alkalinity enhancement (OAE) implemented through wastewater treatment plants increases the alkalinity of the effluents and discharges them into the ocean, referred to as wastewater-based OAE. However, the alkalization capability and its carbon storage stability when adding alkaline minerals to wastewater treatment are uncertain. In this study, total alkalinity was enhanced to over 10 mmol kg\u003csup\u003e−1 \u003c/sup\u003eand phosphate removal was improved when we added olivine to wastewater in a laboratory setting. The alkalization rate by olivine dissolution in aerobically treated wastewater was 20 times higher than in seawater. We estimated the potential of carbon sequestration through wastewater-based OAE to be 18.8 ± 6.0 Tg CO\u003csub\u003e2\u003c/sub\u003e per year globally, with notable potential in the 20°N to 60°N region.\u003c/p\u003e","funders":[{"organization":"Global Ocean Negative Carbon Emissions (ONCE) program","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":""}],"keywords":["coastal wastewater treatment","potential carbon sequestration","Ocean Alkalinity Enhancement"],"fieldOfScience":"Earth and related environmental sciences","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1126/sciadv.ads0313"}],"versionNumber":6,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2025-03-12","lastModificationDate":"2025-03-12","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.2547d7x2d","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":61,"downloads":24,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.qv9s4mwn7"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.qv9s4mwn7/versions"},"stash:version":{"href":"/api/v2/versions/273615"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.qv9s4mwn7/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.qv9s4mwn7","id":123014,"storageSize":835909,"relatedPublicationISSN":"1537-2537","title":"N2O and CO2 fluxes and related environmental conditions in response to manure and synthetic fertilization treatments, with and without a urease inhibitor","authors":[{"firstName":"Sarah","lastName":"Brickman","email":"sbrickman@ucdavis.edu","affiliation":"University of California, Davis","affiliationROR":"https://ror.org/05rrcem69","affiliations":[{"name":"University of California, Davis","ror_id":"https://ror.org/05rrcem69"}],"orcid":"0000-0001-5000-0461"},{"firstName":"Heather","lastName":"Darby","email":"","affiliation":"University of Vermont","affiliationROR":"https://ror.org/0155zta11","affiliations":[{"name":"University of Vermont","ror_id":"https://ror.org/0155zta11"}],"order":1},{"firstName":"Lindsey","lastName":"Ruhl","email":"","affiliation":"University of Vermont","affiliationROR":"https://ror.org/0155zta11","affiliations":[{"name":"University of Vermont","ror_id":"https://ror.org/0155zta11"}],"order":2},{"firstName":"Carol","lastName":"Adair","email":"","affiliation":"University of Vermont","affiliationROR":"https://ror.org/0155zta11","affiliations":[{"name":"University of Vermont","ror_id":"https://ror.org/0155zta11"}],"order":3}],"abstract":"\u003cp\u003eAgricultural best management practices (BMPs) intended to solve one environmental challenge may have unintended climate impacts. For example, manure injection is often promoted for its potential to reduce runoff and N loss as NH\u003csub\u003e3\u003c/sub\u003e, but the practice has been shown to increase N\u003csub\u003e2\u003c/sub\u003eO, a powerful GHG, compared to surface application. Urease inhibitor application with N fertilizer is another BMP that can enhance N retention by reducing NH\u003csub\u003e3\u003c/sub\u003e emissions, but its impact on N\u003csub\u003e2\u003c/sub\u003eO emissions is mixed. Thus, we measured N\u003csub\u003e2\u003c/sub\u003eO, CO\u003csub\u003e2\u003c/sub\u003e, soil mineral N availability, soil moisture, soil temperature, and yield in a two-year perennial hayfield trial with four fertilization treatments (manure injection, manure broadcast, synthetic urea, and control) applied with or without a urease inhibitor in Alburgh, VT (n=4 treatment replicates; 32 total subplots). This dataset includes the daily N\u003csub\u003e2\u003c/sub\u003eO and CO\u003csub\u003e2\u003c/sub\u003e fluxes in every subplot on each sampling day in our trial, along with cumulative emissions for 2020 and 2021. We also include the other variables that we measured (listed above) and relevant weather data (precipitation and air temperature). Note that during the second treatment application in 2021, the incorrect treatment was applied to one synthetic urea subplot. We therefore excluded this subplot from our analysis of daily fluxes after 30 July 2021 (n=3 synthetic urea replicates). Details about our sampling methods are available in the corresponding manuscript.\u003c/p\u003e","funders":[{"organization":"Ben \u0026 Jerry's Carbon Fund*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":0},{"organization":"Gund Institute for Environment","identifierType":"crossref_funder_id","identifier":"http://dx.doi.org/10.13039/100016485","awardNumber":"","awardDescription":"","order":1}],"keywords":["Greenhouse gases","Carbon dioxide","nitrous oxide","manure injection","urease inhibitor","hayfield","Vermont","manure","Urea","Denitrification"],"fieldOfScience":"Agricultural sciences","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1002/jeq2.20536"}],"versionNumber":4,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2024-01-23","lastModificationDate":"2024-01-23","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.qv9s4mwn7","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":41,"downloads":18,"citations":0}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.z8w9ghxg4"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.z8w9ghxg4/versions"},"stash:version":{"href":"/api/v2/versions/217790"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.z8w9ghxg4/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.z8w9ghxg4","id":96815,"storageSize":138220,"relatedPublicationISSN":"1297-966X","title":"Black spruce seed availability and viability after fire, Northwest Territories, Canada","authors":[{"firstName":"Kirsten A.","lastName":"Reid","email":"kirsten.reid@mun.ca","affiliation":"Memorial University of Newfoundland","affiliationROR":"https://ror.org/04haebc03","affiliations":[{"name":"Memorial University of Newfoundland","ror_id":"https://ror.org/04haebc03"}],"orcid":"0000-0002-8373-336X","order":0},{"firstName":"Nicola J.","lastName":"Day","email":"","affiliation":"Victoria University of Wellington","affiliationROR":"https://ror.org/0040r6f76","affiliations":[{"name":"Victoria University of Wellington","ror_id":"https://ror.org/0040r6f76"}],"order":1},{"firstName":"Raquel","lastName":"Alfaro Sánchez","email":"r.alfarosanchez@gmail.com","affiliation":"Wilfrid Laurier University","affiliationROR":"https://ror.org/00fn7gb05","affiliations":[{"name":"Wilfrid Laurier University","ror_id":"https://ror.org/00fn7gb05"}],"orcid":"0000-0001-7357-3027","order":2},{"firstName":"Jill F.","lastName":"Johnstone","email":"","affiliation":"University of Saskatchewan","affiliationROR":"https://ror.org/010x8gc63","affiliations":[{"name":"University of Saskatchewan","ror_id":"https://ror.org/010x8gc63"}],"order":3},{"firstName":"Steven G.","lastName":"Cumming","email":"","affiliation":"Université Laval","affiliationROR":"https://ror.org/04sjchr03","affiliations":[{"name":"Université Laval","ror_id":"https://ror.org/04sjchr03"}],"order":4},{"firstName":"Michelle C.","lastName":"Mack","email":"","affiliation":"Northern Arizona University","affiliationROR":"https://ror.org/0272j5188","affiliations":[{"name":"Northern Arizona University","ror_id":"https://ror.org/0272j5188"}],"order":5},{"firstName":"Merritt R.","lastName":"Turetsky","email":"","affiliation":"University of Colorado Boulder","affiliationROR":"https://ror.org/02ttsq026","affiliations":[{"name":"University of Colorado Boulder","ror_id":"https://ror.org/02ttsq026"}],"order":6},{"firstName":"Xanthe J.","lastName":"Walker","email":"","affiliation":"Northern Arizona University","affiliationROR":"https://ror.org/0272j5188","affiliations":[{"name":"Northern Arizona University","ror_id":"https://ror.org/0272j5188"}],"order":7},{"firstName":"Jennifer L.","lastName":"Baltzer","email":"jbaltzer@wlu.ca","affiliation":"Wilfrid Laurier University","affiliationROR":"https://ror.org/00fn7gb05","affiliations":[{"name":"Wilfrid Laurier University","ror_id":"https://ror.org/00fn7gb05"}],"order":8}],"abstract":"\u003cp\u003eContext : Black spruce ( Picea mariana ) is an important conifer in boreal North American that develops a semi-serotinous, aerial seedbank and releases a pulse of seeds after fire. Variation in post-fire seed rain has important consequences for black spruce regeneration and stand composition.\u003c/p\u003e\n\u003cp\u003eAims : We explore the possible effects of changes in fire regime on the abundance and viability of black spruce seeds following a very large wildfire season in the Northwest Territories, Canada (NWT).\u003c/p\u003e\n\u003cp\u003eMethods: We measured post-fire seed rain over two years at 25 black sprucedominated sites and evaluated drivers of stand characteristics and environmental conditions on total black spruce seed rain and viability.\u003c/p\u003e\n\u003cp\u003eResults : We found a positive relationship between black spruce basal area and total seed rain. However, at high basal areas this increasing rate of seed rain was not maintained. Viable seed rain was greater in stands that were older, closer to unburned edges, and where canopy combustion was less severe. Finally, we demonstrated positive relationships between seed rain and seedling establishment, confirming our measures of seed rain were key drivers of post-fire forest regeneration.\u003c/p\u003e\n\u003cp\u003eConclusion: These results suggest that black spruce recruitment after fire may be reduced with projected increases in fire activity.\u003c/p\u003e","funders":[{"organization":"Government of the Northwest Territories*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"Project 170"},{"organization":"Natural Sciences and Engineering Research Council","identifierType":"ror","identifier":"https://ror.org/01h531d29","awardNumber":""},{"organization":"Northern Scientific Training Program*","identifierType":"crossref_funder_id","identifier":"","awardNumber":""},{"organization":"NSF DEB RAPID*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"1542150"},{"organization":"NASA Arctic Boreal and Vulnerability Experiment (ABoVE)*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"Legacy Carbon grant: NNX15AT71A"},{"organization":"CFREF Global Water Futures*","identifierType":"crossref_funder_id","identifier":"","awardNumber":""}],"keywords":["seed rain","Picea mariana","Fire return interval","Combustion severity","Fire size ","post-fire regeneration"],"fieldOfScience":"Natural sciences","methods":"\u003cp\u003eMethodology is available in :\u003c/p\u003e\n\u003cp\u003eKirsten A. Reid, Nicola J. Day, Raquel Alfaro-Sánchez, Jill F. Johnstone, Steven G. Cumming, Michelle C. Mack, Merritt R. Turetsky, Xanthe J. Walker, Jennifer L. Baltzer (2023) Black spruce (Picea mariana) seed availability and viability in boreal forests after large wildfires. Annals of Forest Science. DOI: 10.1186/s13595-022-01166-4.\u003c/p\u003e","usageNotes":"\u003cp\u003eThis dataset can be opened in excel.\u003c/p\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1186/s13595-022-01166-4"}],"versionNumber":5,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2023-01-30","lastModificationDate":"2023-01-30","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.z8w9ghxg4","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":180,"downloads":13,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.prr4xgxqx"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.prr4xgxqx/versions"},"stash:version":{"href":"/api/v2/versions/211181"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.prr4xgxqx/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.prr4xgxqx","id":100695,"storageSize":406347,"relatedPublicationISSN":"1757-1693","title":"Dataset for: Exploring the legacy effect of biochar application on soil nitrous oxide emissions","authors":[{"firstName":"Shumin","lastName":"Guo","email":"","affiliation":"Nanjing Agricultural University","affiliationROR":"https://ror.org/05td3s095","affiliations":[{"name":"Nanjing Agricultural University","ror_id":"https://ror.org/05td3s095"}],"order":0},{"firstName":"Jie","lastName":"Wu","email":"","affiliation":"Nanjing Agricultural University","affiliationROR":"https://ror.org/05td3s095","affiliations":[{"name":"Nanjing Agricultural University","ror_id":"https://ror.org/05td3s095"}],"order":1},{"firstName":"Zhaoqiang","lastName":"Han","email":"","affiliation":"Nanjing Agricultural University","affiliationROR":"https://ror.org/05td3s095","affiliations":[{"name":"Nanjing Agricultural University","ror_id":"https://ror.org/05td3s095"}],"order":2},{"firstName":"Zhutao","lastName":"Li","email":"","affiliation":"Nanjing Agricultural University","affiliationROR":"https://ror.org/05td3s095","affiliations":[{"name":"Nanjing Agricultural University","ror_id":"https://ror.org/05td3s095"}],"order":3},{"firstName":"Pinshang","lastName":"Xu","email":"","affiliation":"Nanjing Agricultural University","affiliationROR":"https://ror.org/05td3s095","affiliations":[{"name":"Nanjing Agricultural University","ror_id":"https://ror.org/05td3s095"}],"order":4},{"firstName":"Shuwei","lastName":"Liu","email":"","affiliation":"Nanjing Agricultural University","affiliationROR":"https://ror.org/05td3s095","affiliations":[{"name":"Nanjing Agricultural University","ror_id":"https://ror.org/05td3s095"}],"order":5},{"firstName":"Jinyang","lastName":"Wang","email":"jywang@njau.edu.cn","affiliation":"Nanjing Agricultural University","affiliationROR":"https://ror.org/05td3s095","affiliations":[{"name":"Nanjing Agricultural University","ror_id":"https://ror.org/05td3s095"}],"orcid":"0000-0003-0668-336X","order":6},{"firstName":"Jianwen","lastName":"Zou","email":"","affiliation":"Nanjing Agricultural University","affiliationROR":"https://ror.org/05td3s095","affiliations":[{"name":"Nanjing Agricultural University","ror_id":"https://ror.org/05td3s095"}],"order":7}],"abstract":"\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThis dataset contains data for exploring the legacy effects of biochar addition on soil nitrous oxide emissions and their effects on functional gene abundance associated with soil nitrogen cycling. The dataset contains two data files, one is a data table on the impact of biochar application on soil nitrous oxide emissions, which contains the coordinates of the study site, climate, basic physical and chemical properties of the soil, biochar characteristics and study duration, crop type and management. The other is the data table on the effect of biochar application on the abundance of functional genes related to soil nitrogen cycling, which contains information such as test site coordinates, climate, biochar characteristics, crop type and management. This data can be referenced and reused without any legal or ethical considerations\u003c/span\u003e.\u003c/p\u003e","funders":[{"organization":"National Natural Science Foundation of China","identifierType":"ror","identifier":"https://ror.org/01h0zpd94","awardNumber":"42007072"},{"organization":"National Natural Science Foundation of China","identifierType":"ror","identifier":"https://ror.org/01h0zpd94","awardNumber":"42177285"},{"organization":"Ministry of Science and Technology of the People's Republic of China","identifierType":"ror","identifier":"https://ror.org/027s68j25","awardNumber":"2022YFD2300300"},{"organization":"Jiangsu Provincial Special Project for Carbon Peak Carbon Neutrality Science and Technology Innovation*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"BE2022308"},{"organization":"Jiangsu Provincial Special Project for Carbon Peak Carbon Neutrality Science and Technology Innovation*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"BE2022423"}],"keywords":["N2O","N-cycling gene","Legacy effect","biochar"],"fieldOfScience":"Agricultural sciences","methods":"\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eWe compiled and updated the datasets of several existing meta-analysis studies.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e The literature collection cut-off date is March 2022. Our database contained only field and pot studies and excluded laboratory incubation experiments because of its underrepresentation of in situ environments. In addition, we referred to the search terms and screening criteria used in previous meta-analyses when updating the literature. In total, we obtained 747 pairs of observations from 96 articles, of which 398 were for N\u003csub\u003e2\u003c/sub\u003eO emissions, 166 were for genes related to ammonia oxidation processes, and 183 were for genes related to denitrification processes.\u003c/span\u003e\u003c/p\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1111/gcbb.13022"}],"versionNumber":6,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"files_changed","publicationDate":"2022-12-14","lastModificationDate":"2022-12-15","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.prr4xgxqx","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":155,"downloads":24,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.9w0vt4bm6"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.9w0vt4bm6/versions"},"stash:version":{"href":"/api/v2/versions/228750"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.9w0vt4bm6/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.9w0vt4bm6","id":106994,"storageSize":521218,"relatedPublicationISSN":"0378-1127","title":"Effects of nurse shrubs and biochar on planted conifer seedling survival and growth in a high-severity burn patch in New Mexico, USA","authors":[{"firstName":"Christopher","lastName":"Marsh","email":"chmarsh@unm.edu","affiliation":"University of New Mexico","affiliationROR":"https://ror.org/05fs6jp91","affiliations":[{"name":"University of New Mexico","ror_id":"https://ror.org/05fs6jp91"}],"orcid":"0000-0001-7500-2025"},{"firstName":"Joseph C.","lastName":"Blankinship","email":"jcrockett@unm.edu","affiliation":"University of Arizona","affiliationROR":"https://ror.org/03m2x1q45","affiliations":[{"name":"University of Arizona","ror_id":"https://ror.org/03m2x1q45"}],"orcid":"0000-0001-6204-5069","order":1},{"firstName":"Matthew D.","lastName":"Hurteau","email":"mhurteau@unm.edu","affiliation":"University of New Mexico","affiliationROR":"https://ror.org/05fs6jp91","affiliations":[{"name":"University of New Mexico","ror_id":"https://ror.org/05fs6jp91"}],"orcid":"0000-0001-8457-8974","order":2}],"abstract":"\u003cp\u003eThe synergistic effects of widespread high-severity wildfire and anthropogenic climate change are driving large-scale vegetation conversion. In the southwestern United States, areas that were once dominated by conifer forests are now shrub- or grasslands after high-severity wildfire, an ecosystem conversion that could be permanent without human intervention. Yet, the reforestation of these landscapes is rarely successful, with a mean planted seedling survival of just 25 %. Given these low rates, we carried out a planting experiment to quantify the impacts of biochar as a soil amendment and shrubs as nurse plants on planted conifer seedling survival and growth following high-severity wildfire. We planted 1200 seedlings of three species (\u003cem\u003ePinus\u003c/em\u003e \u003cem\u003eponderosa\u003c/em\u003e, \u003cem\u003eP. strobiformis\u003c/em\u003e, and \u003cem\u003ePseudotsuga\u003c/em\u003e \u003cem\u003emenziesii\u003c/em\u003e) in a 2-ha area within the footprint of the Las Conchas fire in New Mexico, USA. We used four treatments: under shrubs, or in the open and with or without biochar in a full-factorial design. We found that planting tree seedlings underneath shrubs increased tree seedling survival by 46 % after 3 years, with some marginal evidence that shrubs inhibited seedling diameter growth (mean \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.08). The addition of biochar increased seedling survival by 11 % but had no effect on seedling growth. Our study suggests that planted seedling survival in post-wildfire areas can be increased by planting under shrubs in soil amended with biochar. The widespread adoption of these methods may improve the success rates of post-wildfire reforestation efforts in semi-arid areas, regaining some of the ecosystem services lost to high-severity wildfire.\u003c/p\u003e","funders":[{"organization":"Interagency Carbon Cycle Science program*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"2017-67004-26486  1012226"},{"organization":"United States Department of Agriculture","identifierType":"ror","identifier":"https://ror.org/01na82s61","awardNumber":"2021-67034-35106  1026366"}],"keywords":["Planting experiment","biochar","Treatment","reforestation","Nurse plants"],"fieldOfScience":"Earth and related environmental sciences","methods":"\u003cp style=\"text-align:left;\" data-html-name=\"para\" data-name=\"OPT_ID_174\" data-attr-id=\"p0045\" data-attr-view=\"all\" data-para=\"true\"\u003eIn 2011, the Las Conchas fire burned approximately 63,130 ha in the Jemez Mountains of New Mexico, USA, ∼30 % of which was high-severity as defined by Eidenshink and colleagues, (2007). The Las Conchas fire burned over footprints from five previous wildfires (​Coop et al, 2016​). This repeated high-severity burning has led to a large-scale vegetation shift, from ponderosa pine-dominated forest to a Gambel oak (\u003cem\u003eQuercus gambelii\u003c/em\u003e) and New Mexico locust (\u003cem\u003eRobinia\u003c/em\u003e \u003cem\u003eneomexicna\u003c/em\u003e) shrub-dominated landscape (​Coop et al., 2016; Kaufmann et al., 2016​). \u003cbr\u003e\u003cbr\u003eWe selected a 2 ha area within the footprint of the Las Conchas fire for our planting experiment (lat: 35.785, long: -106.415), due to its location within an area classified as having experienced high-severity wildfire, its elevation, 2520 m above sea level, (close to the mean elevation of the Las Conchas fire footprint, 2541 m) and proximity to a road to enable planting efforts. We planted all 1200 seedlings during July 15–17 of 2019, between 2516 m and 2524 m in elevation, with the maximum distance of 174 m between seedlings to ensure climatological and meteorological influences on seedlings were similar. 400 seedlings of each species (ponderosa pine, southwestern white pine, and Douglas-fir) were planted equally across four treatment types; in the open, in the open and with biochar amended to the soil, under shrub canopy, under shrub canopy and with biochar amended to the soil, so that 100 seedlings of each species were in each treatment type. Seedlings planted in the open treatments were ≥ 2 m from the nearest shrub, and planted in grids of nine seedlings, three individuals of each species planted 30 cm apart. We co-located the grids of individuals of each treatment (open, open with biochar) to minimize potential microtopographical variance which may impact experiment results (​Marsh et al., 2022b​). Seedlings planted beneath shrubs (shrub, shrub with biochar) were planted in groups of six (one individual of each species and treatment combination), each 30 cm apart, so that they were subject to similar conditions (Supplementary Fig. 1). All seedlings in the “under shrub canopy” were planted beneath Gambel oak shrubs and were located far enough from the edge to remain in shade. An equal number of seedlings were planted by three researchers using dibble bars to reduce the potential influence of different planting techniques (​Pinto et al., 2011a​).\u003cbr\u003e\u003cbr\u003eThe soil at this site is classified as “very paragravelly sandy loam” to two inches in depth, “very paragravelly sandy clay loam” from 2 to 5 in. in depth and “very gravelly sandy clay loam” from 5 to 13 in. of depth (SSS, 2023), with low nitrate-nitrogen levels, possibly due to soil run-off after wildfire (​Ebel et al., 2018​). In high-severity burn areas, carbon was measured as 10.37 ± 3.77 % and nitrogen as 0.80 ± 0.35 %, with pH values 5.01 ± 0.83 in ponderosa pine sites in 2011, three months after the Las Conchas fire (​Weber et al., 2014​). The climate is characterized by a bimodal precipitation distribution, with 41 % of the 496 mm mean annual precipitation falling as summer rain and 59 % falling as winter snow (​Guiterman et al., 2018​). July is historically the warmest month, with a mean temperature of 28 °C, and January is the coldest month, with a mean temperature of -1.6 °C, as recorded by the Los Alamos weather station roughly 10 km northeast of the study site (recorded between 1911 and 1988, ​Bowen, 1990​). Historically, summer precipitation is highest across July (80.7 mm), August (99.8 mm), and September (41 mm), with the onset of the North American monsoon (​Bowen, 1990​). Planting during summer is increasingly becoming common practice in the region because planting can be timed with the onset of the monsoon, whereas fall planting carries the risk of subsequent winter snow drought.\u003cbr\u003e\u003cbr\u003eOver the three-year period of the experiment, 2019 was the most climatically similar to historical maximum, mean, and minimum air temperatures (mean difference; 0.26 °C / -0.7 °C/ -0.45 °C, respectively) and was considerably wetter in July, the hottest month (101.8 mm rainfall) than the historical rainfall average (80.7 mm). In contrast, 2020 was hotter than historical means with a particularly hot and dry June, with maximum, mean, and minimum air temperatures reaching 4.7 °C, 3.7 °C, and 4.9 °C, higher than mean historical records, respectively. Coupled with 100 mm less rainfall than historical averages for the year, 2020 was likely climatically unfavorable for planted seedlings. Following this relatively hot and dry year, 2021 was uncharacteristically cool and wet until the end of the experiment, with maximum, mean, and minimum air temperatures cooler than historical averages (-7.58 °C / -1°C / -0.82 °C) and an average of 65 mm more rainfall per month than the historical mean (Supplemental Materials Fig. 2).\u003cbr\u003e\u003cbr\u003eThree weeks prior to planting (June 24–27), we amended soils with pecan biochar. The biochar had a mean pH of 9.44 (n = 3), electrical conductivity of 909.4 μS cm\u003csup\u003e-1\u003c/sup\u003e, nitrogen concentration of 0.59 %, carbon concentration of 84.1 %, and a bulk density of 0.29 g cm\u003csup\u003e-3\u003c/sup\u003e. This high electrical conductivity, carbon-to-nitrogen ratio, and low bulk density can aid in water retention and increase soil pore space (​Novak et al., 2009; Busscher et al., 2010, Richard et al., 2018​). When the biochar was applied at the study site, we excavated soil at each seedling planting location in a 20 cm diameter circle down to a 15 cm depth, or ∼ 4.7 L of soil. The biochar was applied at a rate of 4 % by mass to the soil when it was excavated, then backfilled into excavated holes and patted flat, so each seedling was treated with 188 g of biochar, equivalent to 80 metric tons/ha if biochar was applied evenly throughout the study site. This application rate was based on the results of Omondi and colleagues’ study (2016) and Edeh and colleagues’ meta-analysis (2020), the results of which found increased water capacity in coarse-textured soils at biochar applications of 30–70 MT ha\u003csup\u003e-1\u003c/sup\u003e. We chose to exceed this upper limit as seedling mortality had been high in previous planting efforts (​Marsh et al., 2022b​).\u003cbr\u003e\u003cbr\u003eSeedlings were cultivated at the New Mexico State University John T. Harrington Forestry Research Center as one growing season-old container stock, grown in greenhouses in 7 × 14 “cone-tainer” trays. Each seedling was initially grown in SC10R “cone-tainers” with a diameter of 1.5”, a depth of 8”, and a volume of 164 ml or 10 cu. in. Seed sourcing followed USFS seed transfer guidelines, in that the nearest available seed sources of each planted species were selected and approval was given by the regional USFS geneticist. Ponderosa pine and Douglas-fir seed were from populations in the Jemez Mountains, and southwestern white pine seed was sourced from the Lincoln National Forest. At the time of planting, the mean ponderosa pine seedling diameter was 3.3 ± 0.69 mm, and height was 17.9 ± 3.1 cm. For southwestern white pine, mean seedling diameter was 3.3 mm ± 0.57 mm, and height was 15.5 ± 2.49 cm. For Douglas-fir seedling, mean stem diameter was 2.4 ± 0.49 mm and height was 20.5 ± 3.21 cm. Each seedling was tagged and protected with a Vexar tube to reduce the risk of herbivory and remained covered for the duration of the experiment. At the beginning (April) and end (October) of each growing season for three years, 2019, 2020, and 2021. We monitored seedling survival as the presence of green needles and measured growth. We measured height using a tape measure from the ground to the topmost woody part of the plant (in cm) and basal diameter (in mm) using calipers. Because of herbivory and partial stem death of some individuals, the change in some growth metrics was negative (n = 150).\u003cbr\u003e\u003cbr\u003eFor some seedlings (n = 162) in the shrub treatments, we measured shrub height above the seedlings by creating a canopy height model using data from an Unpiloted Aerial System (UAS) 3-dimensional structure-from-motion workflow that had been collected as part of ​Krofcheck et al. (2019)​. These data were collected for a portion of the experimental planting area between July 2–18 in 2018 using a custom‐built hexacopter equipped with a Sony a6000 RGB camera (\u003ca href=\"https://www.sony.com/\" rel=\"noreferrer\" data-name=\"OPT_ID_205\" data-html-name=\"link\" data-actor=\"org\" data-type=\"web\" data-standard-html-version=\"1.0\" data-attr-xlink_type=\"simple\" data-attr-id=\"ir005\" data-non-partial-selection=\"true\"\u003ehttps://www.sony.com/\u003c/a\u003e) and a 19 mm prime lens. The camera shutter activated with an EMLID Reach global navigation satellite system (GNSS) receiver on the UAS, so each picture taken simultaneously recorded location data relative to a second EMLID Reach GNSS receiver, positioned as a base station and running concurrently during the UAS operation. Flight planning occurred in Mission Planner (version 1.3 [\u003ca href=\"https://ardupilot.org/\" rel=\"noreferrer\" data-name=\"OPT_ID_208\" data-html-name=\"link\" data-actor=\"org\" data-type=\"web\" data-standard-html-version=\"1.0\" data-attr-xlink_type=\"simple\" data-attr-id=\"ir010\" data-non-partial-selection=\"true\"\u003ehttps://ardupilot.org\u003c/a\u003e]) and all flights were within visual line of sight and at 80–100 m above ground level, resulting in a ground sample distance of 1.4–3.5 cm per pixel. A front image overlap of 85 % and side image overlap of 80 % were designated for the flight plan. The RAW format images were converted to 16‐bit linear TIFF files in Python 3.6 and imported into Agisoft Metashape (version 1.7.0, Agisoft 2017) for structure-from-motion processing.\u003cbr\u003e\u003cbr\u003eImagery was post-processed using RTKLib and nearby CORS stations, resulting in a total geolocation accuracy of +/- 5 cm. In-situ measurement accuracy of the imagery was characterized using distributed targets of known dimensions and determined an RMSE in image-derived measurement uncertainty of +/- 3 cm in the × and y dimensions, for targets on level ground. We then followed a procedure similar to Cunliffe et al. (2016), to convert imagery into raster layers of a digital surface model (DSM) and digital elevation model (DEM) by manually removing vegetation points from the DSM point cloud. Using tools in QGIS (version 3.14, QGIS Development Team, 2020), we then measured the amount of depth of shrub ‘canopy’ above each seedling and the distance from seedling to shrub edge.\u003cbr\u003e\u003cbr\u003eWe computed survival curves using non-parametric Kaplan-Meier methods, which estimate the true survival function of populations by using a tabulation of the number of seedlings at risk, and the number of deaths at each measurement point. Seedling survival probability was estimated for each species and treatment using the ‘survival’ R package (Therneau et al., 2013), with results plotted using the ‘survminer’ R package (​Kassambara et al., 2017​) to provide a survival probability for each species and treatment through time. We evaluated the effects of different treatments on seedling growth by comparing height and stem width growth between groups using ANOVAs, followed by the post-hoc Tukey’s honest significant difference method, a multiple comparison test of means allowing the comparison between more than two groups (​Abdi and Williams, 2010​). We visualized growth results by plotting cumulative diameter and height growth through time with Local Polynomial Regression (loess) curves. For 162 seedlings planted in shrub treatments, we explored the effects of distance from shrub edge (we treated the shrub edge as ‘zero’ and values increased toward the shrub center), and the amount of shrub above each seedling on height and diameter growth at the time of the last measurement using simple linear regression models. For linear models, each seedling was treated as an individual data point, grouped by species.\u003c/p\u003e","relatedWorks":[{"relationship":"software","identifierType":"DOI","identifier":"https://doi.org/10.5281/zenodo.7796194"},{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1016/j.foreco.2023.120971"}],"versionNumber":5,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2023-04-06","lastModificationDate":"2023-04-06","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.9w0vt4bm6","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":190,"downloads":17,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.z08kprrjp"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.z08kprrjp/versions"},"stash:version":{"href":"/api/v2/versions/265221"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.z08kprrjp/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.z08kprrjp","id":113542,"storageSize":248104,"relatedPublicationISSN":"0016-7061","title":"Testing the feasibility of quantifying change in agricultural soil carbon stocks through empirical sampling","authors":[{"firstName":"Mark","lastName":"Bradford","email":"mark.bradford@yale.edu","affiliation":"Yale University","affiliationROR":"https://ror.org/03v76x132","affiliations":[{"name":"Yale University","ror_id":"https://ror.org/03v76x132"}],"orcid":"0000-0002-2022-8331"},{"firstName":"Eash","lastName":"Lisa","email":"lisa.eash@yale.edu","affiliation":"Yale University","affiliationROR":"https://ror.org/03v76x132","affiliations":[{"name":"Yale University","ror_id":"https://ror.org/03v76x132"}],"order":1},{"firstName":"Polussa","lastName":"Alexander","email":"alexander.polussa@yale.edu","affiliation":"Yale University","affiliationROR":"https://ror.org/03v76x132","affiliations":[{"name":"Yale University","ror_id":"https://ror.org/03v76x132"}],"orcid":"0000-0002-5559-9984","order":2},{"firstName":"Fiona","lastName":"Jevon","email":"fiona.jevon@yale.edu","affiliation":"Yale University","affiliationROR":"https://ror.org/03v76x132","affiliations":[{"name":"Yale University","ror_id":"https://ror.org/03v76x132"}],"order":3},{"firstName":"Sara","lastName":"Kuebbing","email":"sara.kuebbing@yale.edu","affiliation":"Yale University","affiliationROR":"https://ror.org/03v76x132","affiliations":[{"name":"Yale University","ror_id":"https://ror.org/03v76x132"}],"orcid":"0000-0002-0834-8189","order":4},{"firstName":"Ashley","lastName":"Hammac","email":"AHammac@ecosystemservicesmarket.org","affiliation":"Ecosystem Services Market Consortium","affiliations":[{"name":"Ecosystem Services Market Consortium"}],"order":5},{"firstName":"Steven","lastName":"Rosenzweig","email":"Steven.Rosenzweig@genmills.com","affiliation":"General Mills (United States)","affiliationROR":"https://ror.org/03kgyg741","affiliations":[{"name":"General Mills (United States)","ror_id":"https://ror.org/03kgyg741"}],"order":6},{"firstName":"Emily","lastName":"Oldfield","email":"eoldfield@edf.org","affiliation":"Environmental Defense Fund","affiliationROR":"https://ror.org/02tj7r959","affiliations":[{"name":"Environmental Defense Fund","ror_id":"https://ror.org/02tj7r959"}],"orcid":"0000-0002-6181-1267","order":7}],"abstract":"\u003cp class=\"MsoNormal\"\u003eThere is disagreement about the potential for regenerative management practices to sequester sufficient soil organic carbon (SOC) to help mitigate climate change. Measuring change in SOC stocks following practice adoption at the grain of farm fields, within the extent of regional agriculture, could help resolve this disagreement. Yet sampling demands to quantify change are considered infeasible primarily because within-field variation in stock sizes is thought to obscure accurate quantification of management effects on incremental SOC accrual. We evaluate this ‘infeasibility assumption’ using high-density, within-field, sampling data from 45 cropland fields inventoried for SOC. We explore how within-field sampling density, field numbers, and magnitude of simulated change in SOC stocks impacts the ability to accurately quantify management effects on SOC change. We find that (1) stock change estimates for individual fields are inaccurate and variable, where marked losses and gains in SOC stocks are frequently estimated even when no change has occurred. Higher sampling densities narrow the range of estimated stock changes but inaccuracies remain large. (2) The accuracy of stock change estimates at the project level (i.e., multiple fields) were similarly sensitive to sampling density. In contrast to individual fields, however, higher sampling densities (e.g., 1.2 ha sample\u003csup\u003e-1\u003c/sup\u003e), as well as a greater number of fields (e.g., 30), generated robust and accurate, mean project-level estimates of carbon accrual, with ~80% of the estimates falling within 20% of the simulated stock change. Yet such monitoring designs do not account for dynamic baselines, which necessitates measurement of stock changes in control, non-regenerative fields. We find (3) that higher sampling densities, field numbers, and magnitudes of simulated SOC stock change are then collectively required to make accurate estimates of management effects on stock change at the project level. The simulated effect sizes that could be consistently detected included rates of SOC accrual considered achievable and meaningful for climate mitigation (e.g., 3 Mg C ha\u003csup\u003e-1\u003c/sup\u003e 10 y\u003csup\u003e-1\u003c/sup\u003e), using field numbers and sampling densities that are reasonable given current sampling methods. Our findings reveal the potential to use empirical approaches to accurately quantify, at project scales, SOC stock responses to practice change. We provide recommendations for data that government, farmer and corporate entities should measure and share to build confidence in the effects of regenerative practices, freeing the SOC debate from overreliance on theory and data collected at scales mismatched with agricultural management.\u003c/p\u003e","funders":[{"organization":"General Mills (United States)","identifierType":"ror","identifier":"https://ror.org/03kgyg741","awardNumber":"","awardDescription":"","order":0},{"organization":"The Earth Fund*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":1},{"organization":"King Philanthropies*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":2},{"organization":"Arcadia Fund","identifierType":"ror","identifier":"https://ror.org/051z6e826","awardNumber":"","awardDescription":"","order":3},{"organization":"Yale Center for Natural Carbon Capture*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":4}],"keywords":["carbon credits","carbon markets","causal inference","Conservation agriculture","population sampling","regression to the mean","soil carbon stocks","soil monitoring","Sustainable agriculture","natural experiments"],"fieldOfScience":"Agricultural sciences","usageNotes":"\u003cp\u003eR code is open-source statistical software and can be converted as a text file. Other file types are text for metadata and csv for data.\u003c/p\u003e","relatedWorks":[{"relationship":"software","identifierType":"DOI","identifier":"https://doi.org/10.5281/zenodo.10081118"},{"relationship":"supplemental_information","identifierType":"DOI","identifier":"https://doi.org/10.5281/zenodo.10081120"},{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1016/j.geoderma.2023.116719"}],"versionNumber":5,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2023-11-17","lastModificationDate":"2023-11-17","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.z08kprrjp","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":210,"downloads":56,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.9cnp5hqvk"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.9cnp5hqvk/versions"},"stash:version":{"href":"/api/v2/versions/409052"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.9cnp5hqvk/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.9cnp5hqvk","id":145740,"storageSize":54032,"relatedPublicationISSN":"1748-9326","title":"Data from: Brucite-inspired ocean alkalinity enhancement alters the biogeochemistry and composition of a phytoplankton community: A Santa Barbara channel case report","authors":[{"firstName":"Zoe","lastName":"Welch","email":"zoe.welch@lifesci.ucsb.edu","affiliation":"University of California, Santa Barbara","affiliationROR":"https://ror.org/02t274463","affiliations":[{"name":"University of California, Santa Barbara","ror_id":"https://ror.org/02t274463"}],"orcid":"0009-0008-3023-3047"},{"firstName":"Sylvia","lastName":"Kim","email":"sylvia.kim@ucsb.edu","affiliation":"University of California, Santa Barbara","affiliationROR":"https://ror.org/02t274463","affiliations":[{"name":"University of California, Santa Barbara","ror_id":"https://ror.org/02t274463"}],"order":1},{"firstName":"Michael","lastName":"Liu","email":"maliu@ucsb.edu","affiliation":"University of California, Santa Barbara","affiliationROR":"https://ror.org/02t274463","affiliations":[{"name":"University of California, Santa Barbara","ror_id":"https://ror.org/02t274463"}],"order":2},{"firstName":"An","lastName":"Bui","email":"an_bui@ucsb.edu","affiliation":"University of California, Santa Barbara","affiliationROR":"https://ror.org/02t274463","affiliations":[{"name":"University of California, Santa Barbara","ror_id":"https://ror.org/02t274463"}],"orcid":"0000-0002-9548-7776","order":3},{"firstName":"Jesse","lastName":"Grigolite","email":"jessegrigolite@ucsb.edu","affiliation":"University of California, Santa Barbara","affiliationROR":"https://ror.org/02t274463","affiliations":[{"name":"University of California, Santa Barbara","ror_id":"https://ror.org/02t274463"}],"order":4},{"firstName":"Janice","lastName":"Jones","email":"janice.jones@lifesci.ucsb.edu","affiliation":"University of California, Santa Barbara","affiliationROR":"https://ror.org/02t274463","affiliations":[{"name":"University of California, Santa Barbara","ror_id":"https://ror.org/02t274463"}],"order":5},{"firstName":"Debora","lastName":"Iglesias-Rodriguez","email":"iglesias@ucsb.edu","affiliation":"University of California, Santa Barbara","affiliationROR":"https://ror.org/02t274463","affiliations":[{"name":"University of California, Santa Barbara","ror_id":"https://ror.org/02t274463"}],"order":6}],"abstract":"\u003cp\u003eThe dramatic impacts of global climate change have driven marine carbon dioxide removal (mCDR) innovation, including ocean alkalinity enhancement (OAE), in an attempt to keep global warming under 2 °C. We experimented to assess the impacts of brucite-inspired alkalinity addition (BIAA) as an OAE approach on the carbonate chemistry, biogeochemistry, and composition of the Santa Barbara Channel phytoplankton community sourced from a spring upwelling event. The BIAA treatment used MgCl\u003csub\u003e2\u003c/sub\u003e * 6H\u003csub\u003e2\u003c/sub\u003eO and NaOH to yield a total alkalinity (TA) concentration of 3000 µmol/kg, in contrast with the untreated seawater controls (TA = 2300 µmol/kg). Our results suggest that BIAA altered the phytoplankton community composition, including reduced contribution of diatoms and enhanced numbers of Prymnesiophyceae (coccolithophores and \u003cem\u003ePhaeocystis\u003c/em\u003e sp.). These results are in agreement with observations that biogenic silica content was lower under BIAA treatment. While the concentration of particulate inorganic carbon was consistently higher compared to controls, these differences were not statistically significant. Results revealed no differences between control and BIAA treatment in particulate organic carbon and particulate organic nitrogen concentrations. The proxy for cellular photosynthetic health, F\u003csub\u003ev\u003c/sub\u003e/F\u003csub\u003em,\u003c/sub\u003e revealed that cells were photosynthetically healthy for both control and BIAA treatments, but values were lower in the BIAA treatment at the beginning of the exponential phase. While statistical power limitations of laboratory results might restrict applicability to other systems, our overall results suggest that BIAA has a differential impact on phytoplankton functional groups and their biogeochemical performance. \u003c/p\u003e\n","funders":[{"organization":"University of California, Santa Barbara","identifierType":"ror","identifier":"https://ror.org/02t274463","awardNumber":"CF-202204-02452","awardDescription":"Coastal Fund","order":0},{"organization":"University of California, Santa Barbara","identifierType":"ror","identifier":"https://ror.org/02t274463","awardNumber":"CF-202311-08493","awardDescription":"Coastal Fund","order":1},{"organization":"Carbon To The Sea","identifierType":"ror","identifier":"","awardNumber":"","awardDescription":"","awardTitle":"","order":2},{"organization":"United States Department of Energy","identifierType":"ror","identifier":"https://ror.org/01bj3aw27","awardNumber":"A24-2263-S001","awardDescription":"Office of Fossil Energy and Carbon Management","order":3}],"keywords":["Ocean Alkalinity Enhancement","Phytoplankton","Climate change","marine calcification","Silicification","marine biogeochemistry","Diatoms","Coccolithophores","brucite","magnesium hydroxide","Dinoflagellates","ocean carbon dioxide removal","Biological oceanography","carbonate chemistry","total alkalinity","pH","biogenic silica","Phytoplankton community","Santa Barbara Channel","California Current","Phaeocystis","particulate organic nitrogen","particulate organic carbon","particulate inorganic carbon","phytoplankton physiology","community abundance","alkalinity addition","laboratory experiment","California Current Large Marine Ecosystem","CO2Sys","relative community abundance","Community composition  "],"fieldOfScience":"Earth and related environmental sciences","methods":"\u003cp\u003eThis dataset comprises data from analyzed seawater samples sourced from a 12-day, laboratory-based experiment during a seasonal upwelling event in spring 2023 and examines the impacts of magnesium-based alkalinity addition upon the carbonate chemistry, biogeochemistry, and phytoplankton community of the Santa Barbara Channel (SBC). Whole water was sourced from the SBC in a single-day sourcing event, and was filtered in-field to exclude zooplankton and then immediately transported back to the lab for additional filtration and preparation for a same-day experiment start. We utilized a \"brucite-inspired\" alkalinity addition (BIAA) approach, which consisted of adding MgCl\u003csub\u003e2\u003c/sub\u003e * 6H\u003csub\u003e2\u003c/sub\u003eO and NaOH to yield a total alkalinity (TA) concentration of 3000 µmol/kg, in contrast with the untreated seawater controls (TA = 2300 µmol/kg). We used a full-factorial experimental design consisting of 4 treatments: abiotic no alkalinity added (Control-A), biotic no alkalinity added (Control-B), abiotic with BIAA (BIAA-A), biotic with BIAA (BIAA-B). Sampling occurred every third day of the experiment, with 3 independent replicate bottles being sacrificed from every treatment on every sampling day. Samples were analyzed for pH, Total Alkalinity, salinity, dissolved inorganic phosphate, dissolved inorganic silicate, and temperature to then calculate the remaining parameters of the seawater carbonate chemistry system using the program CO2Sys. Samples were also analyzed for the following biological and chemical indicators that have relevance to biogeochemistry and planktonic physiology: particulate organic carbon (POC); particulate organic nitrogen (PON); particulate inorganic carbon (PIC) a.k.a. CaCO\u003csub\u003e3\u003c/sub\u003e; dissolved Ca, Mg, and Na; dissolved inorganic nutrients (silicate, phosphate, nitrite+nitrate); biogenic silica (BSi); photosynthetic properties (F\u003csub\u003ev\u003c/sub\u003e/F\u003csub\u003em\u003c/sub\u003e and FixArea). Samples were also processed via light microscopy to generate phytoplankton community composition and growth data (cell densities over time with genus-based cell identifications). \u003c/p\u003e\n","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1088/1748-9326/ae1752"},{"relationship":"software","identifierType":"DOI","identifier":"https://doi.org/10.5281/zenodo.14235181"},{"relationship":"supplemental_information","identifierType":"DOI","identifier":"https://doi.org/10.5281/zenodo.14235184"}],"versionNumber":6,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"files_changed","publicationDate":"2025-11-20","lastModificationDate":"2025-11-20","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.9cnp5hqvk","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":9,"downloads":3,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.q573n5tv7"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.q573n5tv7/versions"},"stash:version":{"href":"/api/v2/versions/363997"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.q573n5tv7/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.q573n5tv7","id":155842,"storageSize":7921,"relatedPublicationISSN":"2045-7758","title":"Data from: Legacy effects of flooding duration on growth and reproductive traits of Carex cinerascens Kük. in the Poyang Lake wetland","authors":[{"firstName":"Wenlan","lastName":"Feng","email":"fengwenjuan12@mails.ucas.ac.cn","affiliation":"Nanjing Institute of Geography and Limnology","affiliationROR":"https://ror.org/03k6r8t20","affiliations":[{"name":"Nanjing Institute of Geography and Limnology","ror_id":"https://ror.org/03k6r8t20"}],"order":0},{"firstName":"Pierre","lastName":"Mariotte","email":"pierre.mariotte@hotmail.com","affiliation":"Agroscope","affiliationROR":"https://ror.org/04d8ztx87","affiliations":[{"name":"Agroscope","ror_id":"https://ror.org/04d8ztx87"}],"orcid":"0000-0001-8570-8742","order":1},{"firstName":"Ligang","lastName":"Xu","email":"lgxu@niglas.ac.cn","affiliation":"University of Chinese Academy of Sciences","affiliationROR":"https://ror.org/05qbk4x57","affiliations":[{"name":"University of Chinese Academy of Sciences","ror_id":"https://ror.org/05qbk4x57"}],"order":2},{"firstName":"Luca","lastName":"Bragazza","email":"luca.bragazza@agroscope.admin.ch","affiliation":"Agroscope","affiliationROR":"https://ror.org/04d8ztx87","affiliations":[{"name":"Agroscope","ror_id":"https://ror.org/04d8ztx87"}],"order":3},{"firstName":"Alexandre","lastName":"Buttler","email":"alexandre.buttler@epfl.ch","affiliation":"École Polytechnique Fédérale de Lausanne","affiliationROR":"https://ror.org/02s376052","affiliations":[{"name":"École Polytechnique Fédérale de Lausanne","ror_id":"https://ror.org/02s376052"}],"order":4},{"firstName":"Junxiang","lastName":"Cheng","email":"","affiliation":"Nanjing Institute of Geography and Limnology","affiliationROR":"https://ror.org/03k6r8t20","affiliations":[{"name":"Nanjing Institute of Geography and Limnology","ror_id":"https://ror.org/03k6r8t20"}],"order":5},{"firstName":"Mathieu","lastName":"Santonja","email":"mathieu.santonja@gmail.com","affiliation":"Institut Méditerranéen de Biodiversité et d'Ecologie Marine et Continentale","affiliationROR":"https://ror.org/0409c3995","affiliations":[{"name":"Institut Méditerranéen de Biodiversité et d'Ecologie Marine et Continentale","ror_id":"https://ror.org/0409c3995"}],"orcid":"0000-0002-6322-6352","order":6}],"abstract":"\u003cp\u003eAlteration of flooding regimes due to global change may have cascading effects on plant community composition and associated ecosystem services. Here, we experimentally investigated the effects of six flooding regimes with contrasting combinations of flooding duration (5.5, 6, and 6.5 months) and submergence rate (from 3.3 to 17.5 cm/day) on the growth and reproductive traits of \u003cem\u003eCarex cinerascens\u003c/em\u003e, a dominant plant species of the Poyang Lake wetland in southern China. The time span of this study included a summer flooding event and the following growing seasons (autumn of first year and spring of following year) before the return of the next flooding event. The six flooding treatments affected plant traits during the flooding and the following growing seasons, but the different submergence rates under the same flooding duration did generally not show significant influence on plant traits. The 6.5-month flooding treatments had many fewer old (0.4 on average) and new stems (1 on average) than the 5.5-month treatments (8.3 and 29 stems, respectively) at the end of the flooding. The treatments with 5.5 months of flooding had 23% more stems than the other treatments and 26% more community biomass than the 6-month flooding treatments during the autumn growing season. The effects of summer flooding persisted in spring of the following year, but with an opposite trend of \u003cem\u003eC. cinerascens\u003c/em\u003e growth traits response to flooding treatments compared to autumn. In addition, the 6-month flooding treatments induced a higher number of inflorescences (39) than the 5.5-month (22) and 6.5-month floods (3). Altogether, our findings highlighted the important legacy effects of summer flooding with some trade-offs between growth recovery (autumn) and resilience (following spring) and between resource allocation to biomass production in autumn and resource allocation to sexual reproduction in the following spring, that were both mediated by flooding duration.\u003c/p\u003e","funders":[{"organization":"National Key Research and Development Program","identifierType":"crossref_funder_id","identifier":"","awardNumber":"2024YFE0106400","awardDescription":"","order":0},{"organization":"National Natural Science Foundation of China","identifierType":"ror","identifier":"https://ror.org/01h0zpd94","awardNumber":"U2240224","awardDescription":"","order":2},{"organization":"Jiangsu Carbon Peak Carbon Neutralization Science and Technology Innovation Special Fund Project","identifierType":"crossref_funder_id","identifier":"","awardNumber":"BK20220042","awardDescription":"","order":3},{"organization":"Jiangxi Science and Technology Program Project","identifierType":"crossref_funder_id","identifier":"","awardNumber":"20224BAB213035","awardDescription":"","order":4},{"organization":"China Scholarship Council","identifierType":"ror","identifier":"https://ror.org/04atp4p48","awardNumber":"","awardDescription":"PhD grant","order":6},{"organization":"Jiangxi Science and Technology Program Project","identifierType":"crossref_funder_id","identifier":"","awardNumber":"20223BBG74003","awardDescription":"","order":5},{"organization":"National Key Research and Development Program","identifierType":"crossref_funder_id","identifier":"","awardNumber":"2023YFF0807204","awardDescription":"","order":1}],"keywords":["flood regime","Hydrodynamics","Sexual reproduction","Wetlands","Plant ecology","Carex cinerascens"],"fieldOfScience":"Earth and related environmental sciences","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1002/ece3.71395"}],"versionNumber":5,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2025-05-12","lastModificationDate":"2025-05-12","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.q573n5tv7","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":6,"downloads":2,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.ht76hdrgb"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.ht76hdrgb/versions"},"stash:version":{"href":"/api/v2/versions/132018"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.ht76hdrgb/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.ht76hdrgb","id":73094,"storageSize":276570785,"relatedPublicationISSN":"2045-2322","title":"Data from: Novel and disappearing climates in the global surface ocean from 1800 to 2100","authors":[{"firstName":"Katie","lastName":"Lotterhos","email":"k.lotterhos@northeastern.edu","affiliation":"Northeastern University","affiliationROR":"https://ror.org/04t5xt781","affiliations":[{"name":"Northeastern University","ror_id":"https://ror.org/04t5xt781"}],"orcid":"0000-0001-7529-2771"}],"abstract":"\u003cp\u003eMarine ecosystems are experiencing unprecedented warming and acidification caused by anthropogenic carbon dioxide. For the global sea surface, we quantified the degree that present climates are disappearing and novel climates (without recent analogs) are emerging, spanning from 1800 through different emission scenarios to 2100. We quantified the sea surface environment based on model estimates of carbonate chemistry and temperature. Between 1800 and 2000, no gridpoints on the ocean surface were estimated to have experienced an extreme degree of global disappearance or novelty. In other words, the majority of environmental shifts since 1800 were not novel, which is consistent with evidence that marine species have been able to track shifting environments via dispersal. However, between 2000 and 2100 under Representative Concentrations Pathway (RCP) 4.5 and 8.5 projections, 10–82% of the surface ocean is estimated to experience an extreme degree of global novelty. Additionally, 35–95% of the surface ocean is estimated to experience an extreme degree of global disappearance. These upward estimates of climate novelty and disappearance are larger than those predicted for terrestrial systems. Without mitigation, many species will face rapidly disappearing or novel climates that cannot be outpaced by dispersal and may require evolutionary adaptation to keep pace.\u003c/p\u003e\r\n","funders":[{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"16,354,231,655,701"},{"organization":"National Science Foundation, Ocean Acidification PI Workshop, Ocean Carbon and Biogeochemistry Program","identifier":"","awardNumber":"1558412"},{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"1842-1210"}],"relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1038/s41598-021-94872-4"}],"versionNumber":2,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"files_changed","publicationDate":"2021-07-28","lastModificationDate":"2021-07-28","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.ht76hdrgb","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":433,"downloads":42,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.nk98sf7wr"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.nk98sf7wr/versions"},"stash:version":{"href":"/api/v2/versions/201095"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.nk98sf7wr/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.nk98sf7wr","id":96900,"storageSize":6636066883,"relatedPublicationISSN":"0962-8436","title":"A practice-led assessment of landscape restoration potential in a biodiversity hotspot","authors":[{"firstName":"Abigail","lastName":"Wills","email":"wills.abigail@gmail.com","affiliation":"University of York","affiliationROR":"https://ror.org/04m01e293","affiliations":[{"name":"University of York","ror_id":"https://ror.org/04m01e293"}],"orcid":"0000-0003-3370-156X","order":0},{"firstName":"Andrew","lastName":"Marshall","email":"amarsha1@usc.edu.au","affiliation":"University of the Sunshine Coast","affiliationROR":"https://ror.org/016gb9e15","affiliations":[{"name":"University of the Sunshine Coast","ror_id":"https://ror.org/016gb9e15"}],"order":1},{"firstName":"Deo","lastName":"Shirima","email":"","affiliation":"Reforest Africa","affiliations":[{"name":"Reforest Africa"}],"order":2},{"firstName":"Olivier","lastName":"Villemaire-Côté","email":"","affiliation":"Université Laval","affiliationROR":"https://ror.org/04sjchr03","affiliations":[{"name":"Université Laval","ror_id":"https://ror.org/04sjchr03"}],"order":3},{"firstName":"Philip","lastName":"Platts","email":"","affiliation":"University of York","affiliationROR":"https://ror.org/04m01e293","affiliations":[{"name":"University of York","ror_id":"https://ror.org/04m01e293"}],"order":4},{"firstName":"Sarah","lastName":"Knight","email":"","affiliation":"University of York","affiliationROR":"https://ror.org/04m01e293","affiliations":[{"name":"University of York","ror_id":"https://ror.org/04m01e293"}],"order":5},{"firstName":"Robin","lastName":"Loveridge","email":"","affiliation":"University of York","affiliationROR":"https://ror.org/04m01e293","affiliations":[{"name":"University of York","ror_id":"https://ror.org/04m01e293"}],"order":6},{"firstName":"Hamidu","lastName":"Seki","email":"","affiliation":"University of York","affiliationROR":"https://ror.org/04m01e293","affiliations":[{"name":"University of York","ror_id":"https://ror.org/04m01e293"}],"order":7},{"firstName":"Catherine","lastName":"Waite","email":"","affiliation":"University of the Sunshine Coast","affiliationROR":"https://ror.org/016gb9e15","affiliations":[{"name":"University of the Sunshine Coast","ror_id":"https://ror.org/016gb9e15"}],"order":8},{"firstName":"Pantaleo","lastName":"Munishi","email":"","affiliation":"Sokoine University of Agriculture","affiliationROR":"https://ror.org/00jdryp44","affiliations":[{"name":"Sokoine University of Agriculture","ror_id":"https://ror.org/00jdryp44"}],"order":9},{"firstName":"Herman","lastName":"Lyatuu","email":"","affiliation":"Reforest Africa","affiliations":[{"name":"Reforest Africa"}],"order":10},{"firstName":"Blanca","lastName":"Bernal","email":"","affiliation":"GreenCollar US","affiliations":[{"name":"GreenCollar US"}],"order":11},{"firstName":"Marion","lastName":"Pfeifer","email":"","affiliation":"Newcastle University","affiliationROR":"https://ror.org/01kj2bm70","affiliations":[{"name":"Newcastle University","ror_id":"https://ror.org/01kj2bm70"}],"order":12}],"abstract":"\u003cp\u003eEffective restoration planning tools are needed to mitigate global carbon and biodiversity crises. Published spatial assessments of restoration potential are often at large scales or coarse resolutions inappropriate for local action. Using a Tanzanian case study, we introduce a systematic approach to inform landscape restoration planning, estimating spatial variation in cost-effectiveness, based on restoration method, logistics, biomass modelling and uncertainty mapping. We found potential for biomass recovery across 77.7% of a 53,000 km\u003csup\u003e2\u003c/sup\u003e region, but with some natural spatial discontinuity in moist forest biomass, that was previously assigned to human causes. Most areas with biomass deficit (80.5%) were restorable through passive or assisted natural regeneration. However, cumulative biomass gains from planting outweighed initially high implementation costs meaning that, where applicable, this method yielded greater long-term returns on investment. Accounting for ecological, funding and other uncertainty, the top 25% consistently cost-effective sites were within protected areas and/or moderately degraded moist forest and savanna. Agro-ecological mosaics had high biomass deficit but little cost-effective restoration potential. Socio-economic research will be needed to inform action towards environmental and human development goals in these areas. Our results highlight value in long-term landscape restoration investments and separate treatment of savannas and forests. Furthermore, they contradict previously asserted low restoration potential in East Africa, emphasising the importance of our regional approach for identifying restoration opportunities across the tropics.\u003c/p\u003e","funders":[{"organization":"IUCN Sustain*","identifierType":"crossref_funder_id","identifier":"","awardNumber":""},{"organization":"African Wildlife Foundation","identifierType":"ror","identifier":"https://ror.org/023esdc37","awardNumber":""},{"organization":"United Bank of Carbon","identifierType":"ror","identifier":"https://ror.org/0103we012","awardNumber":""},{"organization":"Australian Research Council","identifierType":"ror","identifier":"https://ror.org/05mmh0f86","awardNumber":"FT170100279"},{"organization":"Rainforest Trust","identifierType":"ror","identifier":"https://ror.org/01tkekz66","awardNumber":""},{"organization":"Flamingo Land Limited*","identifierType":"crossref_funder_id","identifier":"","awardNumber":""}],"keywords":["Above-ground biomass ","assisted natural regeneration","Biodiversity conservation","climate change mitigation","forest landscape restoration","tree planting"],"fieldOfScience":"Earth and related environmental sciences","methods":"\u003cp class=\"MsoNormal\"\u003eHere, we develop and apply a systematic approach to inform spatially-explicit forest landscape restoration planning. Our approach prioritizes cost-effective ecosystem recovery for timely achievement of global and regional restoration targets, accounting for biomass accumulation (and thus carbon sequestration and storage) objectives in a strategic region in Tanzania. The approach can be applied to any landscape-scale restoration project, using spatial prioritisation methods for more detailed planning than is possible with existing restoration decision support tools. It is based on direct financial implementation costs of the most appropriate methods for restoring native vegetation and associated biomass, biodiversity, ecological function and livelihood options under different scenarios and investment timeframes. In achieving this, unlike previous studies, we account for direct implementation and community engagement costs, logistics, expected vegetation growth, and estimated uncertainty resulting from incomplete ecological knowledge. The findings are intended to be useful for advancing the science of restoration planning and for inspiring donors, through development of metrics directly useful for attracting and prioritising grant funding.\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003eOur approach comprised four steps to determine the cost-effectiveness of ecological landscape restoration per hectare, using a 5.3 million hectare region in Tanzania as a case study. First, we estimated landscape above-ground biomass (AGB) deficit (and thus restoration potential) from current and maximum potential AGB, predicted by up-scaling AGB measurements in 195 plots using spectral-reflectance and climatic predictors, respectively. Secondly, we assigned the most appropriate silvicultural method for restoring AGB, i.e. passive regeneration, assisted natural regeneration (ANR), or planting native vegetation, to each hectare pixel with restoration potential, basing this on key landscape variables, including: vegetation type, elevation, severity of degradation (using AGB deficit as a proxy), and distance from proximal intact habitat. Thirdly, we modelled the expected rate of AGB gain through application of these methods and number of years to full-recovery of the AGB deficit. This was achieved using a regional dataset comprising cumulative modelled annual estimates of above-ground carbon (AGC, from zero to maximum) in naturally-regenerating African forests, generated based on vegetation plot AGB measurement over time (Bernal et al. 2018). Finally, we estimated the financial implementation costs of restoration, and therein cost-effectiveness (AGB gain per dollar spent), per hectare, accounting for land procurement, labour equipment and transport, community engagement, project management and administrative costs.\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003eWe incorporated pessimistic, realistic, and optimistic scenarios into all four stages and estimated AGB gains, implementation costs and cost-effectiveness over two investment timeframes: (1) five years, to represent a typical upper limit of donor investment; and (2) expected time to full AGB recovery. A combination of expert knowledge, pilot data and literature review were used to determine: (a) environmental degradation thresholds for selecting methods; and (b) comprehensive implementation costs.\u003c/p\u003e\n\u003cp class=\"MsoNormal\"\u003eSpatial variations in restoration potential, AGB gain, cost and cost-effectiveness were evaluated retrospectively in terms of technical implementation and landscape features of use to practitioners, namely: (a) restoration method; (b) landcover class; and (c) governance (protected versus unprotected areas). Means with standard deviations were used to summarise estimates of landscape AGB, which followed a broadly normal distribution, whereas costs and cost-effectiveness were described using medians and inter-quartile ranges. Cost-effectiveness of different methods, landcover and governance types was compared using Kruskal-Wallis tests with Dunn posthoc tests and Holm-adjusted P-values, Padj (Dunn, 1964). We also identified the top 25% most cost-effective sites for restoration across all scenarios (pessimistic, realistic and optimistic) and investment timeframes (five years and to full recovery of AGB deficit) combined. We did this both overall within the landscape and specifically outside protected areas, to identify locations with potential for community restoration schemes. To account for uncertainty in our estimates, we reported restoration potential before and after discounting areas with high “ecological uncertainty” from climate modelling (defined using envelope uncertainty maps, EUMs, following Platts et al. 2008).\u003c/p\u003e","usageNotes":"\u003cp class=\"MsoNormal\"\u003eAll statistical and spatial analyses were conducted using R version 4.0.1 (R Core Team 2020), besides the distance matrices and maps, which were produced in ArcGIS Pro version 2.7.1 (ESRI Inc. 2020). The \u003cem\u003ecaret \u003c/em\u003epackage was used for modelling (Kuhn 2008) and the \u003cem\u003eraster \u003c/em\u003epackage for spatial up-scaling (Hijmans and van Etten 2012). Statistical analyses were performed using the R \u003cem\u003ebase \u003c/em\u003epackage. Both our R script (\u003ca href=\"https://bit.ly/3KO5Hgz\"\u003ehttps://bit.ly/3KO5Hgz\u003c/a\u003e) and all input and output maps (\u003ca href=\"https://bit.ly/3QkIrrI\"\u003ehttps://bit.ly/3QkIrrI\u003c/a\u003e) produced during our stepwise method are available online.\u003c/p\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1098/rstb.2021.0070"},{"relationship":"software","identifierType":"URL","identifier":"https://bit.ly/3KO5Hgz"},{"relationship":"supplemental_information","identifierType":"URL","identifier":"https://bit.ly/3QkIrrI"},{"relationship":"article","identifierType":"DOI","identifier":"https://doi.org/10.1098/rstb.2022.0472"}],"versionNumber":3,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"files_changed","publicationDate":"2022-10-16","lastModificationDate":"2022-10-16","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.nk98sf7wr","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":193,"downloads":11,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.7wm37pw0j"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.7wm37pw0j/versions"},"stash:version":{"href":"/api/v2/versions/270530"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.7wm37pw0j/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.7wm37pw0j","id":121693,"storageSize":144393494,"relatedPublicationISSN":"1365-2486","title":"Net greenhouse gas balance in U.S. croplands: How can soils be a part of the climate solution?","authors":[{"firstName":"Yongfa","lastName":"You","email":"yongfa.you@bc.edu","affiliation":"Boston College","affiliationROR":"https://ror.org/02n2fzt79","affiliations":[{"name":"Boston College","ror_id":"https://ror.org/02n2fzt79"}],"orcid":"0000-0002-8916-2940"},{"firstName":"Hanqin","lastName":"Tian","email":"hanqin.tian@bc.edu","affiliation":"Boston College","affiliationROR":"https://ror.org/02n2fzt79","affiliations":[{"name":"Boston College","ror_id":"https://ror.org/02n2fzt79"}],"orcid":"0000-0002-1806-4091","order":1},{"firstName":"Shufen","lastName":"Pan","email":"","affiliation":"Boston College","affiliationROR":"https://ror.org/02n2fzt79","affiliations":[{"name":"Boston College","ror_id":"https://ror.org/02n2fzt79"}],"order":2},{"firstName":"Hao","lastName":"Shi","email":"","affiliation":"Research Center for Eco-Environmental Sciences","affiliationROR":"https://ror.org/03rpsvy57","affiliations":[{"name":"Research Center for Eco-Environmental Sciences","ror_id":"https://ror.org/03rpsvy57"}],"order":3},{"firstName":"Chaoqun","lastName":"Lu","email":"","affiliation":"Iowa State University","affiliationROR":"https://ror.org/04rswrd78","affiliations":[{"name":"Iowa State University","ror_id":"https://ror.org/04rswrd78"}],"order":4},{"firstName":"William","lastName":"Batchelor","email":"","affiliation":"Auburn University","affiliationROR":"https://ror.org/02v80fc35","affiliations":[{"name":"Auburn University","ror_id":"https://ror.org/02v80fc35"}],"order":5},{"firstName":"Bo","lastName":"Cheng","email":"","affiliation":"Auburn University","affiliationROR":"https://ror.org/02v80fc35","affiliations":[{"name":"Auburn University","ror_id":"https://ror.org/02v80fc35"}],"order":6},{"firstName":"Dafeng","lastName":"Hui","email":"","affiliation":"Tennessee State University","affiliationROR":"https://ror.org/01fpczx89","affiliations":[{"name":"Tennessee State University","ror_id":"https://ror.org/01fpczx89"}],"order":7},{"firstName":"David","lastName":"Kicklighter","email":"","affiliation":"Marine Biological Laboratory","affiliationROR":"https://ror.org/046dg4z72","affiliations":[{"name":"Marine Biological Laboratory","ror_id":"https://ror.org/046dg4z72"}],"order":8},{"firstName":"Xin-Zhong","lastName":"Liang","email":"","affiliation":"University of Maryland Center for Environmental Science","affiliationROR":"https://ror.org/04dqdxm60","affiliations":[{"name":"University of Maryland Center for Environmental Science","ror_id":"https://ror.org/04dqdxm60"}],"order":9},{"firstName":"Xiaoyong","lastName":"Li","email":"","affiliation":"Research Center for Eco-Environmental Sciences","affiliationROR":"https://ror.org/03rpsvy57","affiliations":[{"name":"Research Center for Eco-Environmental Sciences","ror_id":"https://ror.org/03rpsvy57"}],"order":10},{"firstName":"Jerry","lastName":"Melillo","email":"","affiliation":"Marine Biological Laboratory","affiliationROR":"https://ror.org/046dg4z72","affiliations":[{"name":"Marine Biological Laboratory","ror_id":"https://ror.org/046dg4z72"}],"order":11},{"firstName":"Naiqing","lastName":"Pan","email":"","affiliation":"Boston College","affiliationROR":"https://ror.org/02n2fzt79","affiliations":[{"name":"Boston College","ror_id":"https://ror.org/02n2fzt79"}],"order":12},{"firstName":"Stephen","lastName":"Prior","email":"","affiliation":"Edward T. Schafer Agricultural Research Center","affiliationROR":"https://ror.org/04x68p008","affiliations":[{"name":"Edward T. Schafer Agricultural Research Center","ror_id":"https://ror.org/04x68p008"}],"order":13},{"firstName":"John","lastName":"Reilly","email":"","affiliation":"Massachusetts Institute of Technology","affiliationROR":"https://ror.org/042nb2s44","affiliations":[{"name":"Massachusetts Institute of Technology","ror_id":"https://ror.org/042nb2s44"}],"order":14}],"abstract":"\u003cp\u003eAgricultural soils play a dual role in regulating the Earth’s climate by releasing or sequestering carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) in soil organic carbon (SOC) and emitting non-CO\u003csub\u003e2\u003c/sub\u003e greenhouse gases (GHGs) such as nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO) and methane (CH\u003csub\u003e4\u003c/sub\u003e). To understand how agricultural soils can play a role in climate solutions requires a comprehensive assessment of net soil GHG balance (i.e., sum of SOC-sequestered CO\u003csub\u003e2\u003c/sub\u003e and non-CO\u003csub\u003e2\u003c/sub\u003e GHG emissions) and the underlying controls. Herein, we used a model-data integration approach to understand and quantify how natural and anthropogenic factors have affected the magnitude and spatiotemporal variations of the net soil GHG balance in U.S. croplands during 1960-2018. Specifically, we used the Dynamic Land Ecosystem Model (DLEM) for regional simulations and used field observations of SOC sequestration rates and N\u003csub\u003e2\u003c/sub\u003eO and CH\u003csub\u003e4\u003c/sub\u003e emissions to calibrate, validate, and corroborate model simulations. Results show that U.S. agricultural soils sequestered 13.2±1.16 Tg CO\u003csub\u003e2\u003c/sub\u003e-C yr\u003csup\u003e-1\u003c/sup\u003e in SOC (at a depth of 3.5 m) during 1960-2018 and emitted 0.39±0.02  Tg N\u003csub\u003e2\u003c/sub\u003eO-N yr\u003csup\u003e-1\u003c/sup\u003e and 0.21±0.01  Tg CH\u003csub\u003e4\u003c/sub\u003e-C yr\u003csup\u003e-1\u003c/sup\u003e, respectively. Based on the GWP100 metric (global warming potential on a 100-year time horizon), the estimated national net GHG emission rate from agricultural soils was 122.3±11.46  Tg CO\u003csub\u003e2\u003c/sub\u003e-eq yr\u003csup\u003e-1\u003c/sup\u003e, thus contributing to climate warming. The sequestered SOC offset ~28% of the climate-warming effects resulting from non-CO\u003csub\u003e2\u003c/sub\u003e GHG emissions, and this offsetting effect increased over time. Increased nitrogen fertilizer use was the dominant factor contributing to the increase in net GHG emissions during 1960-2018, explaining ~47% of total changes. In contrast, reduced cropland area, the adoption of agricultural conservation practices (e.g., reduced tillage), and rising atmospheric CO\u003csub\u003e2\u003c/sub\u003e levels attenuated net GHG emissions from U.S. croplands. Our study highlights the importance of concurrently quantifying SOC-sequestered CO\u003csub\u003e2\u003c/sub\u003e and non-CO\u003csub\u003e2\u003c/sub\u003e GHG emissions for developing effective agricultural climate change mitigation measures.\u003c/p\u003e","funders":[{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"1903722","awardDescription":"","order":0},{"organization":"NASA Carbon System Monitoring Program*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"NNX12AP84G","awardDescription":"","order":2},{"organization":"NASA Interdisciplinary Science Program*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"NNX10AU06G","awardDescription":"","order":4},{"organization":"United States Department of Agriculture","identifierType":"ror","identifier":"https://ror.org/01na82s61","awardNumber":"2022-38821-37341","awardDescription":"","order":5},{"organization":"USDA CBG project*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"TENX12899","awardDescription":"","order":7},{"organization":"United States Department of the Treasury","identifierType":"ror","identifier":"https://ror.org/028t43p77","awardNumber":"DISL-MESC-ALCOE-06","awardDescription":"","order":8},{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"1922687","awardDescription":"","order":1},{"organization":"NASA Carbon System Monitoring Program*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"NNX14AO73G","awardDescription":"","order":3},{"organization":"United States Department of Agriculture","identifierType":"ror","identifier":"https://ror.org/01na82s61","awardNumber":"20206801231674","awardDescription":"","order":6}],"keywords":["Agriculture","Greenhouse gases","Soil carbon"],"fieldOfScience":"Earth and related environmental sciences","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1111/gcb.17109"}],"versionNumber":6,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"files_changed","publicationDate":"2023-12-22","lastModificationDate":"2023-12-22","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.7wm37pw0j","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":62,"downloads":19,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.931zcrjq4"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.931zcrjq4/versions"},"stash:version":{"href":"/api/v2/versions/235528"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.931zcrjq4/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.931zcrjq4","id":102772,"storageSize":14983568,"relatedPublicationISSN":"1365-2486","title":"Supporting data for: Topographic information improves simulated patterns of post-fire conifer regeneration in the Southwest U.S.","authors":[{"firstName":"Chang Gyo","lastName":"Jung","email":"cg.jung86@gmail.com","affiliation":"University of New Mexico","affiliationROR":"https://ror.org/05fs6jp91","affiliations":[{"name":"University of New Mexico","ror_id":"https://ror.org/05fs6jp91"}],"orcid":"0000-0002-9845-7732","order":0},{"firstName":"Alisa","lastName":"Keyser","email":"arkeyser@gmail.com","affiliation":"Colorado State University","affiliationROR":"https://ror.org/03k1gpj17","affiliations":[{"name":"Colorado State University","ror_id":"https://ror.org/03k1gpj17"}],"order":1},{"firstName":"Cecile","lastName":"Remy","email":"cecile.remy2@gmail.com","affiliation":"Augsburg University","affiliationROR":"https://ror.org/057ewhh68","affiliations":[{"name":"Augsburg University","ror_id":"https://ror.org/057ewhh68"}],"order":2},{"firstName":"Daniel","lastName":"Krofcheck","email":"krofcheck@gmail.com","affiliation":"University of New Mexico","affiliationROR":"https://ror.org/05fs6jp91","affiliations":[{"name":"University of New Mexico","ror_id":"https://ror.org/05fs6jp91"}],"order":3},{"firstName":"Craig","lastName":"Allen","email":"craigdallen@unm.edu","affiliation":"University of New Mexico","affiliationROR":"https://ror.org/05fs6jp91","affiliations":[{"name":"University of New Mexico","ror_id":"https://ror.org/05fs6jp91"}],"orcid":"0000-0002-8777-5989","order":4},{"firstName":"Matthew","lastName":"Hurteau","email":"mhurteau@unm.edu","affiliation":"University of New Mexico","affiliationROR":"https://ror.org/05fs6jp91","affiliations":[{"name":"University of New Mexico","ror_id":"https://ror.org/05fs6jp91"}],"orcid":"0000-0001-8457-8974","order":5}],"abstract":"\u003cp\u003eThe western U.S. is projected to experience more frequent and severe wildfires in the future due to drier and hotter climate conditions, exacerbating destructive wildfire impacts on forest ecosystems such as tree mortality and unsuccessful post-fire regeneration. While empirical studies have revealed strong relationships between topographical information and plant regeneration, ecological processes in ecosystem models have either not fully addressed topography-mediated effects on the probability of plant regeneration, or the probability is only controlled by climate-related factors, e.g., water and light stresses. In this study, we incorporated seedling survival data based on a planting experiment in the footprint of the 2011 Las Conchas Fire into the PnET extension of the LANDIS-II model by adding topographic and an additional climatic variable to the probability of regeneration. The modified algorithm included topographic parameters such as heat load index (HLI) and ground slope and spring precipitation. We ran simulations on the Las Conchas Fire landscape for 2012–2099 using observed and projected climate data (i.e., RCP 4.5 and 8.5). Our modification significantly reduced the number of regeneration events of three common southwestern conifer tree species (piñon, ponderosa pine and Douglas-fir), leading to decreases in aboveground biomass, regardless of climate scenario. The modified algorithm decreased regeneration at higher elevations and increased regeneration at lower elevations relative to the original algorithm. Regenerations of three species also decreased on eastern aspects. Our findings suggest that ecosystem models may overestimate post-fire regeneration events in the southwest U.S. To better represent regeneration processes following wildfire, ecosystem models need refinement to better account for the range of factors that influence tree seedling establishment. This will improve model utility for projecting the combined effects of climate and wildfire on tree species distributions.\u003c/p\u003e","funders":[{"organization":"Interagency Carbon Cycle Science program*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"2017-67004-26486"},{"organization":"National Institute of Food and Agriculture","identifierType":"ror","identifier":"https://ror.org/05qx3fv49","awardNumber":"1012226"},{"organization":"Joint Fire Science Program","identifierType":"ror","identifier":"https://ror.org/03ccbtk93","awardNumber":"JFSP 16-1-05-8"}],"keywords":["post-fire regeneration","southwestern U.S.","Topography","conifer","LANDIS-II","PnET","DFFS","HPC"],"fieldOfScience":"Earth and related environmental sciences","methods":"\u003cp\u003eThese outputs are from a set of simulations using the LANDIS-II (v7.0) model with a modified PnET Succession extension (based on v.4.1) and the Dynamic Fuels and Fire System (v3.0) extension. Our simulated landscape is the footprint of the 2011 Las Conchas fire in the Jemez Mountains, New Mexico. Observed climate data (Daymet) and downscaled climate projection data (access1.0.1, canesm2.1, cesm1-bgc.1 and hadgem2-es.1) forced with two emission scenarios (RCP4.5 and 8.5) from the CMIP5 models were used. We used contemporary fire conditions for simulating fire events (Keyser et al., 2020; Krofcheck et al., 2019). \u003c/p\u003e\n\u003cp\u003eWe ran 10 replicate simulations from 2012 to 2099. We summarized the data to calculate cumulative post-fire regenerations and biomass distribution as well as the relationship of regeneration with topographical variables such as elevation and aspect. \u003c/p\u003e","usageNotes":"\u003cp\u003eThe archived file includes an R script for creating figures with a Rdata file. \u003c/p\u003e\n\u003cp\u003eThe modified PnET extension scripts (C#), executable setup files for LANDIS-II (v7.0) with the modified PnET extension and Dynamic Fuels and Fire System (v3.0) extension setup files and one set of examples for HPC application (SLURM) are archived at Zenodo (\u003ca href=\"https://doi.org/10.5281/zenodo.7535673\"\u003ehttps://doi.org/10.5281/zenodo.7535673\u003c/a\u003e). Docker images for the HPC application are available in the Docker Hub (Original algorithm: \u003ca href=\"https://hub.docker.com/r/ecochang/landis_pnet_dffs_org_v2\"\u003ehttps://hub.docker.com/r/ecochang/landis_pnet_dffs_org_v2\u003c/a\u003e; Modified algorithm: \u003ca href=\"https://hub.docker.com/r/ecochang/landis_pnet_dffs_mod_v2)\"\u003ehttps://hub.docker.com/r/ecochang/landis_pnet_dffs_mod_v2)\u003c/a\u003e.\u003c/p\u003e","relatedWorks":[{"relationship":"software","identifierType":"DOI","identifier":"https://doi.org/10.5281/zenodo.7535673"},{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1111/gcb.16764"}],"versionNumber":8,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"files_changed","publicationDate":"2023-05-17","lastModificationDate":"2023-05-17","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.931zcrjq4","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":105,"downloads":6,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.pk0p2ngxn"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.pk0p2ngxn/versions"},"stash:version":{"href":"/api/v2/versions/314577"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.pk0p2ngxn/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.pk0p2ngxn","id":136089,"storageSize":2879322,"relatedPublicationISSN":"1553-7374","title":"Data from: Increased susceptibility of WHIM mice to Papillomavirus-induced disease is dependent upon immune cell dysfunction","authors":[{"firstName":"Wei","lastName":"Wang","email":"wwang93@wisc.edu","affiliation":"University of Wisconsin–Madison","affiliationROR":"https://ror.org/01y2jtd41","affiliations":[{"name":"University of Wisconsin–Madison","ror_id":"https://ror.org/01y2jtd41"}],"orcid":"0000-0001-9575-489X","order":0},{"firstName":"Ali","lastName":"Pope","email":"ajpope2@wisc.edu","affiliation":"University of Wisconsin–Madison","affiliationROR":"https://ror.org/01y2jtd41","affiliations":[{"name":"University of Wisconsin–Madison","ror_id":"https://ror.org/01y2jtd41"}],"order":1},{"firstName":"Ella","lastName":"Ward-Shaw","email":"etward@wisc.edu","affiliation":"University of Wisconsin–Madison","affiliationROR":"https://ror.org/01y2jtd41","affiliations":[{"name":"University of Wisconsin–Madison","ror_id":"https://ror.org/01y2jtd41"}],"order":2},{"firstName":"Darya","lastName":"Buehler","email":"buehler2@wisc.edu","affiliation":"University of Wisconsin–Madison","affiliationROR":"https://ror.org/01y2jtd41","affiliations":[{"name":"University of Wisconsin–Madison","ror_id":"https://ror.org/01y2jtd41"}],"order":3},{"firstName":"Francoise","lastName":"Bachelerie","email":"francoise.bachelerie@universite-paris-saclay.fr","affiliation":"Inserm","affiliationROR":"https://ror.org/02vjkv261","affiliations":[{"name":"Inserm","ror_id":"https://ror.org/02vjkv261"}],"order":4},{"firstName":"Paul","lastName":"Lambert","email":"plambert@wisc.edu","affiliation":"University of Wisconsin–Madison","affiliationROR":"https://ror.org/01y2jtd41","affiliations":[{"name":"University of Wisconsin–Madison","ror_id":"https://ror.org/01y2jtd41"}],"order":5}],"abstract":"\u003cp\u003eWarts, Hypogammaglobulinemia, Infections, and Myelokathexis (WHIM) syndrome is a rare primary immunodeficiency disease in humans caused by a gain of function in CXCR4, mostly due to inherited heterozygous mutations in \u003cem\u003eCXCR4\u003c/em\u003e. One major clinical symptom of WHIM patients is their high susceptibility to human papillomavirus (HPV) induced disease, such as warts. Persistent high risk HPV infections cause 5% of all human cancers, including cervical, anogenital, head and neck and some skin cancers. WHIM mice bearing the same mutation identified in WHIM patients were created to study the underlying causes for the symptoms manifest in patients suffering from the WHIM syndrome. Using murine papillomavirus (MmuPV1) as an infection model in mice for HPV-induced disease, we demonstrate that WHIM mice are more susceptible to MmuPV1-induced warts (papillomas) compared to wild type mice. Namely, the incidence of papillomas is higher in WHIM mice compared to wild type mice when mice are exposed to low doses of MmuPV1. MmuPV1 infection facilitated both myeloid and lymphoid cell mobilization in the blood of wild type mice but not in WHIM mice. Higher incidence and larger size of papillomas in WHIM mice correlated with lower abundance of infiltrating T cells within the papillomas. Finally, we demonstrate that transplantation of bone marrow from wild type mice into WHIM mice normalized the incidence and size of papillomas, consistent with the WHIM mutation in hematopoietic cells contributing to higher susceptibility of WHIM mice to MmuPV1-induced disease. Our results provide evidence that MmuPV1 infection in WHIM mice is a powerful preclinical infectious model to investigate treatment options for alleviating papillomavirus infections in WHIM syndrome.\u003c/p\u003e","funders":[{"organization":"National Institutes of Health","identifierType":"ror","identifier":"https://ror.org/01cwqze88","awardNumber":"","awardDescription":"","order":0},{"organization":"American Association For Cancer Research","identifierType":"ror","identifier":"https://ror.org/02fnpv153","awardNumber":"","awardDescription":"","order":1},{"organization":"University of Wisconsin Carbone Cancer Center","identifierType":"ror","identifier":"https://ror.org/01e4byj08","awardNumber":"","awardDescription":"","order":2},{"organization":"Fondation pour la Recherche Médicale","identifierType":"ror","identifier":"https://ror.org/04w6kn183","awardNumber":"","awardDescription":"","order":3}],"keywords":["Papillomaviruses","WHIM syndrome","Immune dysfunction","infectious disease","Warts","Mouse models"],"fieldOfScience":"Biological sciences","relatedWorks":[{"relationship":"preprint","identifierType":"DOI","identifier":"https://doi.org/10.1101/2023.11.15.567210"},{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1371/journal.ppat.1012472"}],"versionNumber":4,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"files_changed","publicationDate":"2024-09-04","lastModificationDate":"2024-09-04","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.pk0p2ngxn","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":47,"downloads":14,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.m905qfv85"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.m905qfv85/versions"},"stash:version":{"href":"/api/v2/versions/429025"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.m905qfv85/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.m905qfv85","id":125473,"storageSize":83940421941,"relatedPublicationISSN":"0012-9658","title":"Ecology of community reassembly: Movements and diets of megafauna during a decade of restoration in Mozambique","authors":[{"firstName":"Matthew","lastName":"Hutchinson","email":"mhutchinson6@ucmerced.edu","affiliation":"University of California, Merced","affiliationROR":"https://ror.org/00d9ah105","affiliations":[{"name":"University of California, Merced","ror_id":"https://ror.org/00d9ah105"}],"orcid":"0000-0002-2423-4026","order":0},{"firstName":"Hallie","lastName":"Walker","email":"","affiliation":"Princeton University","affiliationROR":"https://ror.org/00hx57361","affiliations":[{"name":"Princeton University","ror_id":"https://ror.org/00hx57361"}],"order":1},{"firstName":"Joel","lastName":"Abraham","email":"","affiliation":"Princeton University","affiliationROR":"https://ror.org/00hx57361","affiliations":[{"name":"Princeton University","ror_id":"https://ror.org/00hx57361"}],"order":2},{"firstName":"Justine","lastName":"Becker","email":"","affiliation":"Montana State University","affiliationROR":"https://ror.org/02w0trx84","affiliations":[{"name":"Montana State University","ror_id":"https://ror.org/02w0trx84"}],"order":3},{"firstName":"Dominique","lastName":"Gonçalves","email":"","affiliation":"University of Kent","affiliationROR":"https://ror.org/00xkeyj56","affiliations":[{"name":"University of Kent","ror_id":"https://ror.org/00xkeyj56"}],"order":4},{"firstName":"Ciara","lastName":"Nutter","email":"","affiliation":"Princeton University","affiliationROR":"https://ror.org/00hx57361","affiliations":[{"name":"Princeton University","ror_id":"https://ror.org/00hx57361"}],"order":5},{"firstName":"Johan","lastName":"Pansu","email":"","affiliation":"Université Claude Bernard Lyon 1","affiliationROR":"https://ror.org/029brtt94","affiliations":[{"name":"Université Claude Bernard Lyon 1","ror_id":"https://ror.org/029brtt94"}],"order":6},{"firstName":"Erin","lastName":"Phillips","email":"","affiliation":"Princeton University","affiliationROR":"https://ror.org/00hx57361","affiliations":[{"name":"Princeton University","ror_id":"https://ror.org/00hx57361"}],"order":7},{"firstName":"Arjun","lastName":"Potter","email":"","affiliation":"Wake Forest University","affiliationROR":"https://ror.org/0207ad724","affiliations":[{"name":"Wake Forest University","ror_id":"https://ror.org/0207ad724"}],"order":8},{"firstName":"Beto","lastName":"Tenente","email":"","affiliation":"Gorongosa National Park","affiliationROR":"https://ror.org/00byf8747","affiliations":[{"name":"Gorongosa National Park","ror_id":"https://ror.org/00byf8747"}],"order":9},{"firstName":"Marc","lastName":"Stalmans","email":"","affiliation":"Gorongosa National Park","affiliationROR":"https://ror.org/00byf8747","affiliations":[{"name":"Gorongosa National Park","ror_id":"https://ror.org/00byf8747"}],"order":10},{"firstName":"Ryan","lastName":"Long","email":"","affiliation":"University of Idaho","affiliationROR":"https://ror.org/03hbp5t65","affiliations":[{"name":"University of Idaho","ror_id":"https://ror.org/03hbp5t65"}],"order":11},{"firstName":"Robert","lastName":"Pringle","email":"","affiliation":"Princeton University","affiliationROR":"https://ror.org/00hx57361","affiliations":[{"name":"Princeton University","ror_id":"https://ror.org/00hx57361"}],"order":12}],"abstract":"\u003cp\u003eThis dataset documents large-herbivore movement, morphology, condition, fate, and diet during community reassembly in Gorongosa National Park, Mozambique (2013–2023), following megafaunal declines during civil war and subsequent restoration, including apex predator reintroductions beginning in 2018. The study period spans substantial interannual climatic variability, including extreme wet and dry years.\u003c/p\u003e\n\u003cp\u003eThe movement dataset comprises GPS telemetry records from 277 individuals across seven herbivore species (Cape bushbuck, nyala, greater kudu, common eland, waterbuck, plains zebra, and African elephant), with median monitoring durations ranging from 13 to 707 days and median location counts per individual ranging from 1,171 to 33,122 fixes. For 302 immobilized individuals, we provide morphological measurements (chest girth, body length, hind-foot length, body mass), reproductive and nutritional condition metrics (ultrasound measurements, palpation scores), and fate data (mortality date and cause, when known).\u003c/p\u003e\n\u003cp\u003eThe diet dataset includes DNA metabarcoding results from 3,785 fecal samples collected from 27 mammal species (11 families, 7 orders), identifying 516 food-plant taxa representing at least 87 families and 39 orders. Sample sizes per species range from 1 to 499 (median = 92), with larger sample sizes for dominant large herbivores (median = 216).\u003c/p\u003e\n\u003cp\u003eAll datasets include associated field metadata (date, time, location, sex, age), laboratory notes, and plant taxonomic information. In addition, filtering scripts and raw data are provided, facilitating alternative filtering approaches. These data support analyses of movement ecology, trophic interactions, nutritional ecology, and large-mammal community reassembly under restoration and climatic variability.\u003c/p\u003e\n","funders":[{"organization":"U.S. National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"1355122","awardDescription":"Division of Environmental Biology","order":0},{"organization":"U.S. National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"1501306","awardDescription":"Division of Environmental Biology","order":1},{"organization":"U.S. National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"1656527","awardDescription":"Division of Integrative Organismal Systems","order":2},{"organization":"U.S. National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"2225088","awardDescription":"Division of Environmental Biology","order":3},{"organization":"U.S. National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"1656642","awardDescription":"Division of Integrative Organismal Systems","order":4},{"organization":"U.S. National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"1656466","awardDescription":"Division of Graduate Education","order":5},{"organization":"Princeton University","identifierType":"ror","identifier":"https://ror.org/00hx57361","awardNumber":"","awardDescription":"Innovation Fund for New Ideas in Natural Sciences","order":6},{"organization":"Princeton University","identifierType":"ror","identifier":"https://ror.org/00hx57361","awardNumber":"","awardDescription":"Institute for African Studies","order":7},{"organization":"Princeton University","identifierType":"ror","identifier":"https://ror.org/00hx57361","awardNumber":"","awardDescription":"Institute for International and Regional Studies","order":8},{"organization":"Princeton University","identifierType":"ror","identifier":"https://ror.org/00hx57361","awardNumber":"","awardDescription":"High Meadows Environmental Institute","order":9},{"organization":"Cameron Schrier Foundation","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":10},{"organization":"Greg Carr Foundation","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":11},{"organization":"Carbon Mitigation Initiative","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":12},{"organization":"Rufford Foundation","identifierType":"ror","identifier":"https://ror.org/02bxrrf91","awardNumber":"","awardDescription":"","order":13},{"organization":"Campizondo Foundation","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":14},{"organization":"Artiopart","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":15},{"organization":"Save the Elephants","identifierType":"ror","identifier":"https://ror.org/019ae2j05","awardNumber":"","awardDescription":"","order":16},{"organization":"Sherwood Family Foundation","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":17},{"organization":"Porter Family Fund","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":18},{"organization":"National Geographic Society","identifierType":"ror","identifier":"https://ror.org/04bqh5m06","awardNumber":"9459-14","awardDescription":"","order":19},{"organization":"National Geographic Society","identifierType":"ror","identifier":"https://ror.org/04bqh5m06","awardNumber":"EC-412R-18","awardDescription":"","order":20},{"organization":"National Geographic Society","identifierType":"ror","identifier":"https://ror.org/04bqh5m06","awardNumber":"9291-13","awardDescription":"","order":21},{"organization":"National Geographic Society","identifierType":"ror","identifier":"https://ror.org/04bqh5m06","awardNumber":"WW-070ER-17","awardDescription":"","order":22},{"organization":"National Geographic Society","identifierType":"ror","identifier":"https://ror.org/04bqh5m06","awardNumber":"WW-268C-17","awardDescription":"","order":23},{"organization":"Gorongosa Restoration Project","identifierType":"crossref_funder_id","identifier":"","awardNumber":"","awardDescription":"","order":24}],"keywords":["African savannas","Animal movement ecology","community reassembly","diet selection","ecological networks","ecosystem restoration","Environmental DNA","eDNA","metabarcoding","foraging behavior","Space use","Species interactions","trophic rewilding","ungulates"],"fieldOfScience":"Biological sciences","versionNumber":10,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"files_changed","publicationDate":"2026-03-04","lastModificationDate":"2026-03-04","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.m905qfv85","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":0,"downloads":0,"citations":0}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.np5hqbzvf"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.np5hqbzvf/versions"},"stash:version":{"href":"/api/v2/versions/160267"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.np5hqbzvf/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.np5hqbzvf","id":82677,"storageSize":54476,"relatedPublicationISSN":"0021-8901","title":"Feasible carbon-trade model for low-carbon density ecosystem","authors":[{"firstName":"Xueyan","lastName":"Zhang","email":"xyzhang@igsnrr.ac.cn","affiliation":"Chinese Academy of Sciences","affiliationROR":"https://ror.org/034t30j35","affiliations":[{"name":"Chinese Academy of Sciences","ror_id":"https://ror.org/034t30j35"}]},{"firstName":"Xin","lastName":"Ma","email":"maxin02@caas.cn","affiliation":"Chinese Academy of Agricultural Sciences","affiliationROR":"https://ror.org/0313jb750","affiliations":[{"name":"Chinese Academy of Agricultural Sciences","ror_id":"https://ror.org/0313jb750"}]}],"abstract":"\u003cp style=\"text-align:justify;\"\u003eChina has set a carbon-neutrality target for 2060; carbon sinks are vital tools to meet this target. China is leading the effort in greening the world through the restoration of low-carbon density ecosystems (LCDEs). The potential carbon sinks of LCDEs provide opportunities for carbon trading projects that make cash benefits accessible to the owners, thereby incentivizing ecosystem restoration. Unfortunately, carbon trading in LCDEs has, to date, been unsuccessful in China. Therefore, it is important to identify the barriers in the development of carbon trading projects in LCDEs.\u003c/p\u003e\r\n\r\n\u003cp style=\"text-align:justify;\"\u003eThis study aimed at creating a feasible model for carbon trading in LCDEs in China. We first accounted for the carbon sink of LCDEs based on field sampling of 169 quadrants and 3,471 plants. Thereafter, we investigated the trade-off between the cost and efficiency of carbon projects in LCDEs. Finally, we explored the feasibility of the corresponding carbon sink potential by considering carbon price fluctuations and public–private partnership models.\u003c/p\u003e\r\n\r\n\u003cp style=\"text-align:justify;\"\u003eThe main findings were as follows: (i) Carbon trading in LCDEs is not economically feasible at the current market price of carbon. In the pilot case, the LCDE carbon trading could only recover 41.72% of the project cost. This partially explains the scarcity of carbon trading for LCDEs in the current emission trading scheme; (ii) A benefit transfer model is essential, wherein the costs of ecosystem restoration are paid by the central government, and the benefits of carbon trade are transferred to the owners of LCDEs, providing sufficient incentives for the owners to participate in carbon trading.\u003c/p\u003e\r\n\r\n\u003cp style=\"text-align:justify;\"\u003ePolicy implications. Given the scarcity of large-scale organisations and expertise, carbon trading in low-carbon density ecosystems (LCDEs) should not be treated as a purely commercial project. A public–private partnership network is a suitable model for engaging stakeholders to complete the carbon trading process in LCDEs. For the success of carbon trading in LCDEs, viable carbon prices and transfer of benefits from public investments are policy issues that need to be further explored. Our findings provide a policy basis for the Chinese government to mobilise more LCDE owners to enter the carbon market and achieve carbon-neutrality.\u003c/p\u003e\r\n","funders":[{"organization":"National Natural Science Foundation of China","identifierType":"ror","identifier":"https://ror.org/01h0zpd94","awardNumber":"No. 32171561"},{"organization":"Chinese Academy of Tropical Agricultural Sciences","identifierType":"ror","identifier":"https://ror.org/003qeh975","awardNumber":"No. Y2020PT05"},{"organization":"Strategic Priority Research Program of Chinese Academy of Sciences*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"XDA20010302"},{"organization":"Youth Fund Project of Humanities and Social Sciences Research of the Ministry of Education*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"No. 18YJC630216"},{"organization":"National Key R\u0026D Program of China*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"No. 2018YFC1508805"},{"organization":"National Key R\u0026D Program of China*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"No. 2019YFA0607403"},{"organization":"National Key R\u0026D Program of China*","identifierType":"crossref_funder_id","identifier":"","awardNumber":"No. 2016YFC0500508"},{"organization":"Chinese Academy of Tropical Agricultural Sciences","identifierType":"ror","identifier":"https://ror.org/003qeh975","awardNumber":"No. BSRF201901"},{"organization":"National Key R\u0026D Program of China**","identifierType":"crossref_funder_id","identifier":"","awardNumber":"No.2021xjkk0903"}],"fieldOfScience":"Agriculture, forestry, and fisheries","methods":"\u003cp style=\"margin-bottom:11px;\"\u003eThe Bureau of Forestry of local government provided restoration project documentation and official statistics on grassland restoration. Shrubs (Caragana) were planted as degraded grassland restoration. The area contained a total of 10,467.73 ha of Caragana, and shrubs were planted on 923 plots of land in Siziwang Banner from 2005 to 2013. Some interviews were conducted with the grassland owners. Some face-to-face interviews were conducted with local government officials about the current status of grassland, the role of local governments, degraded grassland restoration investments, and awareness of carbon trade.\u003c/p\u003e\r\n","usageNotes":"\u003cp class=\"MDPI13authornames\" style=\"margin-bottom:8px;\"\u003e\u003cspan style=\"font-weight:bold;\"\u003e\u003cspan lang=\"EN-GB\" style=\"font-weight:normal;\"\u003eA README file is provided to aid use of the dataset.\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\r\n","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1111/1365-2664.14119"}],"versionNumber":2,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2022-01-24","lastModificationDate":"2022-01-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.np5hqbzvf","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":111,"downloads":9,"citations":1}}]}}