Changes
On February 20, 2023 at 10:18:28 AM UTC, Administrator:
-
Set author of Drivers of the microbial metabolic quotient across global grasslands to [{"affiliation": "Swiss Federal Institute for Forest, Snow and Landscape Research WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": ["collection", "validation", "curation", "publication"], "email": "anita.risch@wsl.ch", "given_name": "Anita C.", "identifier": "", "name": "Risch"}, {"affiliation": "Swiss Federal Institute for Forest, Snow and Landscape Research WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": ["collection", "validation"], "email": "stephan.zimmermann@wsl.ch", "given_name": "Stephan", "identifier": "0000-0002-7085-0284", "name": "Zimmermann"}, {"affiliation": "WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": "curation", "email": "martin.schuetz@wsl.ch", "given_name": "Martin", "identifier": "", "name": "Sch\u00fctz"}, {"affiliation": "USDA-ARS Grassland, Soil, and Water Research Laboratory, Temple, TX, 76502, USA", "affiliation_02": "", "affiliation_03": "", "data_credit": "collection", "email": "philip.fay@usda.gov", "given_name": "Philip A.", "identifier": "0000-0003-2259-5853", "name": "Fay"}, {"affiliation": "Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN 55108", "affiliation_02": "", "affiliation_03": "", "data_credit": "collection", "email": "borer@umn.edu", "given_name": "Elizabeth T.", "identifier": "0000-0002-8438-7163", "name": "Borer"}, {"affiliation": "Department of Earth and Environmental Sciences, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK", "affiliation_02": "", "affiliation_03": "", "autocomplete": "", "data_credit": "curation", "email": "arthur.broadbent@manchester.ac.uk", "given_name": "Arthur A. D.", "identifier": "0000-0002-8438-7163", "name": "Broadbent"}] (previously [{"affiliation": "Swiss Federal Institute for Forest, Snow and Landscape Research WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": ["collection", "validation", "curation", "publication"], "email": "anita.risch@wsl.ch", "given_name": "Anita C.", "identifier": "", "name": "Risch"}, {"affiliation": "Swiss Federal Institute for Forest, Snow and Landscape Research WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": ["collection", "validation"], "email": "stephan.zimmermann@wsl.ch", "given_name": "Stephan", "identifier": "0000-0002-7085-0284", "name": "Zimmermann"}, {"affiliation": "WSL", "affiliation_02": "", "affiliation_03": "", "data_credit": "curation", "email": "martin.schuetz@wsl.ch", "given_name": "Martin", "identifier": "", "name": "Sch\u00fctz"}, {"affiliation": "USDA-ARS Grassland, Soil, and Water Research Laboratory, Temple, TX, 76502, USA", "affiliation_02": "", "affiliation_03": "", "data_credit": "collection", "email": "philip.fay@usda.gov", "given_name": "Philip A.", "identifier": "0000-0003-2259-5853", "name": "Fay"}, {"affiliation": "Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN 55108", "affiliation_02": "", "affiliation_03": "", "data_credit": "collection", "email": "borer@umn.edu", "given_name": "Elizabeth T.", "identifier": "0000-0002-8438-7163", "name": "Borer"}])
f | 1 | { | f | 1 | { |
2 | "author": "[{\"affiliation\": \"Swiss Federal Institute for Forest, | 2 | "author": "[{\"affiliation\": \"Swiss Federal Institute for Forest, | ||
3 | Snow and Landscape Research WSL\", \"affiliation_02\": \"\", | 3 | Snow and Landscape Research WSL\", \"affiliation_02\": \"\", | ||
4 | \"affiliation_03\": \"\", \"data_credit\": [\"collection\", | 4 | \"affiliation_03\": \"\", \"data_credit\": [\"collection\", | ||
5 | \"validation\", \"curation\", \"publication\"], \"email\": | 5 | \"validation\", \"curation\", \"publication\"], \"email\": | ||
6 | \"anita.risch@wsl.ch\", \"given_name\": \"Anita C.\", \"identifier\": | 6 | \"anita.risch@wsl.ch\", \"given_name\": \"Anita C.\", \"identifier\": | ||
7 | \"\", \"name\": \"Risch\"}, {\"affiliation\": \"Swiss Federal | 7 | \"\", \"name\": \"Risch\"}, {\"affiliation\": \"Swiss Federal | ||
8 | Institute for Forest, Snow and Landscape Research WSL\", | 8 | Institute for Forest, Snow and Landscape Research WSL\", | ||
9 | \"affiliation_02\": \"\", \"affiliation_03\": \"\", \"data_credit\": | 9 | \"affiliation_02\": \"\", \"affiliation_03\": \"\", \"data_credit\": | ||
10 | [\"collection\", \"validation\"], \"email\": | 10 | [\"collection\", \"validation\"], \"email\": | ||
11 | \"stephan.zimmermann@wsl.ch\", \"given_name\": \"Stephan\", | 11 | \"stephan.zimmermann@wsl.ch\", \"given_name\": \"Stephan\", | ||
12 | \"identifier\": \"0000-0002-7085-0284\", \"name\": \"Zimmermann\"}, | 12 | \"identifier\": \"0000-0002-7085-0284\", \"name\": \"Zimmermann\"}, | ||
13 | {\"affiliation\": \"WSL\", \"affiliation_02\": \"\", | 13 | {\"affiliation\": \"WSL\", \"affiliation_02\": \"\", | ||
14 | \"affiliation_03\": \"\", \"data_credit\": \"curation\", \"email\": | 14 | \"affiliation_03\": \"\", \"data_credit\": \"curation\", \"email\": | ||
15 | \"martin.schuetz@wsl.ch\", \"given_name\": \"Martin\", \"identifier\": | 15 | \"martin.schuetz@wsl.ch\", \"given_name\": \"Martin\", \"identifier\": | ||
16 | \"\", \"name\": \"Sch\\u00fctz\"}, {\"affiliation\": \"USDA-ARS | 16 | \"\", \"name\": \"Sch\\u00fctz\"}, {\"affiliation\": \"USDA-ARS | ||
17 | Grassland, Soil, and Water Research Laboratory, Temple, TX, 76502, | 17 | Grassland, Soil, and Water Research Laboratory, Temple, TX, 76502, | ||
18 | USA\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", | 18 | USA\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", | ||
19 | \"data_credit\": \"collection\", \"email\": \"philip.fay@usda.gov\", | 19 | \"data_credit\": \"collection\", \"email\": \"philip.fay@usda.gov\", | ||
20 | \"given_name\": \"Philip A.\", \"identifier\": | 20 | \"given_name\": \"Philip A.\", \"identifier\": | ||
21 | \"0000-0003-2259-5853\", \"name\": \"Fay\"}, {\"affiliation\": | 21 | \"0000-0003-2259-5853\", \"name\": \"Fay\"}, {\"affiliation\": | ||
22 | \"Department of Ecology, Evolution, and Behavior, University of | 22 | \"Department of Ecology, Evolution, and Behavior, University of | ||
23 | Minnesota, St. Paul, MN 55108\", \"affiliation_02\": \"\", | 23 | Minnesota, St. Paul, MN 55108\", \"affiliation_02\": \"\", | ||
24 | \"affiliation_03\": \"\", \"data_credit\": \"collection\", \"email\": | 24 | \"affiliation_03\": \"\", \"data_credit\": \"collection\", \"email\": | ||
25 | \"borer@umn.edu\", \"given_name\": \"Elizabeth T.\", \"identifier\": | 25 | \"borer@umn.edu\", \"given_name\": \"Elizabeth T.\", \"identifier\": | ||
n | 26 | \"0000-0002-8438-7163\", \"name\": \"Borer\"}]", | n | 26 | \"0000-0002-8438-7163\", \"name\": \"Borer\"}, {\"affiliation\": |
27 | \"Department of Earth and Environmental Sciences, The University of | ||||
28 | Manchester, Oxford Road, Manchester, M13 9PT, UK\", | ||||
29 | \"affiliation_02\": \"\", \"affiliation_03\": \"\", \"autocomplete\": | ||||
30 | \"\", \"data_credit\": \"curation\", \"email\": | ||||
31 | \"arthur.broadbent@manchester.ac.uk\", \"given_name\": \"Arthur A. | ||||
32 | D.\", \"identifier\": \"0000-0002-8438-7163\", \"name\": | ||||
33 | \"Broadbent\"}]", | ||||
27 | "author_email": null, | 34 | "author_email": null, | ||
28 | "creator_user_id": "d30dde41-6b11-44de-9191-23cdd5bda0e9", | 35 | "creator_user_id": "d30dde41-6b11-44de-9191-23cdd5bda0e9", | ||
29 | "date": "[{\"date\": \"2015-04-01\", \"date_type\": \"collected\", | 36 | "date": "[{\"date\": \"2015-04-01\", \"date_type\": \"collected\", | ||
30 | \"end_date\": \"2016-12-31\"}]", | 37 | \"end_date\": \"2016-12-31\"}]", | ||
31 | "doi": "10.16904/envidat.379", | 38 | "doi": "10.16904/envidat.379", | ||
32 | "funding": "[{\"grant_number\": \"\", \"institution\": \"WSL | 39 | "funding": "[{\"grant_number\": \"\", \"institution\": \"WSL | ||
33 | internal competitive grant\", \"institution_url\": \"\"}, | 40 | internal competitive grant\", \"institution_url\": \"\"}, | ||
34 | {\"grant_number\": \"NSF-DEB-1042132\", \"institution\": \"National | 41 | {\"grant_number\": \"NSF-DEB-1042132\", \"institution\": \"National | ||
35 | Science Foundation Research Coordination Network\", | 42 | Science Foundation Research Coordination Network\", | ||
36 | \"institution_url\": \"\"}, {\"grant_number\": \"NSF-DEB-1234162 to | 43 | \"institution_url\": \"\"}, {\"grant_number\": \"NSF-DEB-1234162 to | ||
37 | Cedar Creek LTER\", \"institution\": \"Long Term Ecological Research | 44 | Cedar Creek LTER\", \"institution\": \"Long Term Ecological Research | ||
38 | \", \"institution_url\": \"\"}, {\"grant_number\": \"DG-0001-13\", | 45 | \", \"institution_url\": \"\"}, {\"grant_number\": \"DG-0001-13\", | ||
39 | \"institution\": \"Institute on the Environment\", | 46 | \"institution\": \"Institute on the Environment\", | ||
40 | \"institution_url\": \"\"}, {\"grant_number\": \"UID/AGR/00239/2019\", | 47 | \"institution_url\": \"\"}, {\"grant_number\": \"UID/AGR/00239/2019\", | ||
41 | \"institution\": \"CEF, a research unit funded by FCT, Portugal\", | 48 | \"institution\": \"CEF, a research unit funded by FCT, Portugal\", | ||
42 | \"institution_url\": \"\"}, {\"grant_number\": \"FZT 118, 202548816\", | 49 | \"institution_url\": \"\"}, {\"grant_number\": \"FZT 118, 202548816\", | ||
43 | \"institution\": \"German Research Foundation\", \"institution_url\": | 50 | \"institution\": \"German Research Foundation\", \"institution_url\": | ||
44 | \"\"}]", | 51 | \"\"}]", | ||
45 | "groups": [], | 52 | "groups": [], | ||
46 | "id": "679bdff7-9fb3-4704-be93-8add5cb206ba", | 53 | "id": "679bdff7-9fb3-4704-be93-8add5cb206ba", | ||
47 | "isopen": true, | 54 | "isopen": true, | ||
48 | "language": "en", | 55 | "language": "en", | ||
49 | "license_id": "odc-odbl", | 56 | "license_id": "odc-odbl", | ||
50 | "license_title": "ODbL with Database Contents License (DbCL)", | 57 | "license_title": "ODbL with Database Contents License (DbCL)", | ||
51 | "license_url": "https://opendefinition.org/licenses/odc-odbl", | 58 | "license_url": "https://opendefinition.org/licenses/odc-odbl", | ||
52 | "maintainer": "{\"affiliation\": \"Swiss Federal Institute for | 59 | "maintainer": "{\"affiliation\": \"Swiss Federal Institute for | ||
53 | Forest, Snow and Landscape Research WSL\", \"email\": | 60 | Forest, Snow and Landscape Research WSL\", \"email\": | ||
54 | \"anita.risch@wsl.ch\", \"given_name\": \"Anita C\", \"identifier\": | 61 | \"anita.risch@wsl.ch\", \"given_name\": \"Anita C\", \"identifier\": | ||
55 | \"\", \"name\": \"Risch\"}", | 62 | \"\", \"name\": \"Risch\"}", | ||
56 | "maintainer_email": null, | 63 | "maintainer_email": null, | ||
57 | "metadata_created": "2023-02-18T03:38:29.406811", | 64 | "metadata_created": "2023-02-18T03:38:29.406811", | ||
t | 58 | "metadata_modified": "2023-02-20T10:16:15.862259", | t | 65 | "metadata_modified": "2023-02-20T10:18:27.980101", |
59 | "name": | 66 | "name": | ||
60 | drivers-of-the-microbial-metabolic-quotient-across-global-grasslands", | 67 | drivers-of-the-microbial-metabolic-quotient-across-global-grasslands", | ||
61 | "notes": "This dataset contains all data on which the following | 68 | "notes": "This dataset contains all data on which the following | ||
62 | publication below is based.\r\n\r\nPaper Citation:\r\n\r\nRisch Anita | 69 | publication below is based.\r\n\r\nPaper Citation:\r\n\r\nRisch Anita | ||
63 | C., Zimmermann, Stefan, Sch\u00fctz, Martin, Borer, Elizabeth T., | 70 | C., Zimmermann, Stefan, Sch\u00fctz, Martin, Borer, Elizabeth T., | ||
64 | Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., | 71 | Broadbent, Arthur A.D., Caldeira, Maria C., Davies, Kendi F., | ||
65 | Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, | 72 | Eisenhauer, Nico, Eskelinen, Anu, Fay, Philip A., Hagedorn, Frank, | ||
66 | Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., | 73 | Knops, Johannes M.H., Lembrechts, Jonas, J., MacDougall, Andrew S., | ||
67 | McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, | 74 | McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, | ||
68 | Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, | 75 | Sally A., Seabloom, Eric W., Silveira, Maria L., Virtanen, Risto, | ||
69 | Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the | 76 | Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Drivers of the | ||
70 | microbial metabolic quotient across global grasslands. Global Ecology | 77 | microbial metabolic quotient across global grasslands. Global Ecology | ||
71 | and Biogeography\r\n\r\nPlease cite this paper together with the | 78 | and Biogeography\r\n\r\nPlease cite this paper together with the | ||
72 | citation for the datafile.\r\n\r\nThe microbial metabolic quotient | 79 | citation for the datafile.\r\n\r\nThe microbial metabolic quotient | ||
73 | (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 | 80 | (MMQ; mg CO2-C mg MBC-1 h-1), defined as the amount of microbial CO2 | ||
74 | respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C | 81 | respired (MR; mg CO2-C kg soil-1 h-1) per unit of microbial biomass C | ||
75 | (MBC; mg C kg soil-1), is a key parameter for understanding the | 82 | (MBC; mg C kg soil-1), is a key parameter for understanding the | ||
76 | microbial regulation of the carbon (C) cycle, including soil C | 83 | microbial regulation of the carbon (C) cycle, including soil C | ||
77 | sequestration. Here, we experimentally tested hypotheses about the | 84 | sequestration. Here, we experimentally tested hypotheses about the | ||
78 | individual and interactive effects of multiple nutrient addition | 85 | individual and interactive effects of multiple nutrient addition | ||
79 | (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ | 86 | (NPK+micronutrients) and herbivore exclusion on MR, MBC, and MMQ | ||
80 | across 23 sites (5 continents). Our sites encompassed a wide range of | 87 | across 23 sites (5 continents). Our sites encompassed a wide range of | ||
81 | edaphoclimatic conditions, thus we assessed which edaphoclimatic | 88 | edaphoclimatic conditions, thus we assessed which edaphoclimatic | ||
82 | variables affected MMQ the most and how they interacted with our | 89 | variables affected MMQ the most and how they interacted with our | ||
83 | treatments. Soils were collected in plots with established | 90 | treatments. Soils were collected in plots with established | ||
84 | experimental treatments. MR was assessed in a five-week laboratory | 91 | experimental treatments. MR was assessed in a five-week laboratory | ||
85 | incubation without glucose addition, MBC via substrate-induced | 92 | incubation without glucose addition, MBC via substrate-induced | ||
86 | respiration. MMQ was calculated as MR/MBC and corrected for soil | 93 | respiration. MMQ was calculated as MR/MBC and corrected for soil | ||
87 | temperatures (MMQsoil). Using LMMs and SEMs, we analysed how | 94 | temperatures (MMQsoil). Using LMMs and SEMs, we analysed how | ||
88 | edaphoclimatic characteristics and treatments interactively affected | 95 | edaphoclimatic characteristics and treatments interactively affected | ||
89 | MMQsoil. MMQsoil was higher in locations with higher mean annual | 96 | MMQsoil. MMQsoil was higher in locations with higher mean annual | ||
90 | temperature, lower water holding capacity, and soil organic C | 97 | temperature, lower water holding capacity, and soil organic C | ||
91 | concentration, but did not respond to our treatments across sites as | 98 | concentration, but did not respond to our treatments across sites as | ||
92 | neither MR nor MBC changed. We attributed this relative homeostasis to | 99 | neither MR nor MBC changed. We attributed this relative homeostasis to | ||
93 | our treatments to the modulating influence of edaphoclimatic | 100 | our treatments to the modulating influence of edaphoclimatic | ||
94 | variables. For example, herbivore exclusion, regardless of | 101 | variables. For example, herbivore exclusion, regardless of | ||
95 | fertilization, led to greater MMQsoil only at sites with lower soil | 102 | fertilization, led to greater MMQsoil only at sites with lower soil | ||
96 | organic C (<1.7%). Our results pinpoint the main variables related to | 103 | organic C (<1.7%). Our results pinpoint the main variables related to | ||
97 | MMQsoil across grasslands and emphasize the importance of the local | 104 | MMQsoil across grasslands and emphasize the importance of the local | ||
98 | edaphoclimatic conditions in controlling the response of the C cycle | 105 | edaphoclimatic conditions in controlling the response of the C cycle | ||
99 | to anthropogenic stressors. By testing hypotheses about MMQsoil across | 106 | to anthropogenic stressors. By testing hypotheses about MMQsoil across | ||
100 | global edaphoclimatic gradients, this work also helps to align the | 107 | global edaphoclimatic gradients, this work also helps to align the | ||
101 | conflicting results of prior studies. \r\n", | 108 | conflicting results of prior studies. \r\n", | ||
102 | "num_resources": 1, | 109 | "num_resources": 1, | ||
103 | "num_tags": 7, | 110 | "num_tags": 7, | ||
104 | "organization": { | 111 | "organization": { | ||
105 | "approval_status": "approved", | 112 | "approval_status": "approved", | ||
106 | "created": "2018-04-20T09:51:26.756810", | 113 | "created": "2018-04-20T09:51:26.756810", | ||
107 | "description": "We are studying the distribution of and | 114 | "description": "We are studying the distribution of and | ||
108 | interactions among producers, consumers as well as decomposers and | 115 | interactions among producers, consumers as well as decomposers and | ||
109 | between these communities and their environment. We focus on food webs | 116 | between these communities and their environment. We focus on food webs | ||
110 | in real world ecosystems and thus mainly sample our data during | 117 | in real world ecosystems and thus mainly sample our data during | ||
111 | experimental field campaigns. Data collection under controlled | 118 | experimental field campaigns. Data collection under controlled | ||
112 | conditions in the greenhouse or experimental garden are, however, | 119 | conditions in the greenhouse or experimental garden are, however, | ||
113 | common add-ons hereby. We are mainly interested in the functioning of | 120 | common add-ons hereby. We are mainly interested in the functioning of | ||
114 | natural ecosystems and often conduct research in National Parks around | 121 | natural ecosystems and often conduct research in National Parks around | ||
115 | the world. Our main study area is, however, the Swiss National Park. | 122 | the world. Our main study area is, however, the Swiss National Park. | ||
116 | While working on basic research questions, we regularly consider | 123 | While working on basic research questions, we regularly consider | ||
117 | applied aspects that are related to protecting or conserving | 124 | applied aspects that are related to protecting or conserving | ||
118 | endangered ecosystems.\r\n\r\nExamples of research question of our | 125 | endangered ecosystems.\r\n\r\nExamples of research question of our | ||
119 | research group assesses are:\r\n\r\nHow is species loss related to | 126 | research group assesses are:\r\n\r\nHow is species loss related to | ||
120 | ecosystem processes and functions? Which species or species groups are | 127 | ecosystem processes and functions? Which species or species groups are | ||
121 | particularly relevant for ecosystem functioning? Which effects are | 128 | particularly relevant for ecosystem functioning? Which effects are | ||
122 | expected with the loss of such important species groups? How are | 129 | expected with the loss of such important species groups? How are | ||
123 | aboveground organisms interacting with belowground organisms? Which | 130 | aboveground organisms interacting with belowground organisms? Which | ||
124 | abiotic and biotic conditions favor diverse ecosystems? Is global | 131 | abiotic and biotic conditions favor diverse ecosystems? Is global | ||
125 | change (for example eutrophication, habitat fragmentation, climate) a | 132 | change (for example eutrophication, habitat fragmentation, climate) a | ||
126 | thread for diverse ecosystems? How can diverse ecosystems be | 133 | thread for diverse ecosystems? How can diverse ecosystems be | ||
127 | protected?", | 134 | protected?", | ||
128 | "id": "60e92a46-5f9b-4a06-a32e-6a5e04869486", | 135 | "id": "60e92a46-5f9b-4a06-a32e-6a5e04869486", | ||
129 | "image_url": "2018-07-10-090227.680797LogoWSL.svg", | 136 | "image_url": "2018-07-10-090227.680797LogoWSL.svg", | ||
130 | "is_organization": true, | 137 | "is_organization": true, | ||
131 | "name": "plant-animal-interactions", | 138 | "name": "plant-animal-interactions", | ||
132 | "state": "active", | 139 | "state": "active", | ||
133 | "title": "Plant-Animal Interactions", | 140 | "title": "Plant-Animal Interactions", | ||
134 | "type": "organization" | 141 | "type": "organization" | ||
135 | }, | 142 | }, | ||
136 | "owner_org": "60e92a46-5f9b-4a06-a32e-6a5e04869486", | 143 | "owner_org": "60e92a46-5f9b-4a06-a32e-6a5e04869486", | ||
137 | "private": false, | 144 | "private": false, | ||
138 | "publication": "{\"publication_year\": \"2023\", \"publisher\": | 145 | "publication": "{\"publication_year\": \"2023\", \"publisher\": | ||
139 | \"EnviDat\"}", | 146 | \"EnviDat\"}", | ||
140 | "publication_state": "pub_pending", | 147 | "publication_state": "pub_pending", | ||
141 | "related_datasets": "", | 148 | "related_datasets": "", | ||
142 | "related_publications": "Risch Anita C., Zimmermann, Stefan, | 149 | "related_publications": "Risch Anita C., Zimmermann, Stefan, | ||
143 | Sch\u00fctz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., | 150 | Sch\u00fctz, Martin, Borer, Elizabeth T., Broadbent, Arthur A.D., | ||
144 | Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, | 151 | Caldeira, Maria C., Davies, Kendi F., Eisenhauer, Nico, Eskelinen, | ||
145 | Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., | 152 | Anu, Fay, Philip A., Hagedorn, Frank, Knops, Johannes M.H., | ||
146 | Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., | 153 | Lembrechts, Jonas, J., MacDougall, Andrew S., McCulley, Rebecca L., | ||
147 | Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric | 154 | Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Seabloom, Eric | ||
148 | W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, | 155 | W., Silveira, Maria L., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, | ||
149 | Raul (accepted). Drivers of the microbial metabolic quotient across | 156 | Raul (accepted). Drivers of the microbial metabolic quotient across | ||
150 | global grasslands. Global Ecology and Biogeography", | 157 | global grasslands. Global Ecology and Biogeography", | ||
151 | "relationships_as_object": [], | 158 | "relationships_as_object": [], | ||
152 | "relationships_as_subject": [], | 159 | "relationships_as_subject": [], | ||
153 | "resource_type": "datapaper", | 160 | "resource_type": "datapaper", | ||
154 | "resource_type_general": "datapaper", | 161 | "resource_type_general": "datapaper", | ||
155 | "resources": [ | 162 | "resources": [ | ||
156 | { | 163 | { | ||
157 | "cache_last_updated": null, | 164 | "cache_last_updated": null, | ||
158 | "cache_url": null, | 165 | "cache_url": null, | ||
159 | "created": "2023-02-18T03:43:03.643074", | 166 | "created": "2023-02-18T03:43:03.643074", | ||
160 | "description": "Study sites and experimental design\r\nWe | 167 | "description": "Study sites and experimental design\r\nWe | ||
161 | collected data from 23 sites that are part of the Nutrient Network | 168 | collected data from 23 sites that are part of the Nutrient Network | ||
162 | Global Research Cooperative (NutNet, https://nutnet.umn.edu/). The | 169 | Global Research Cooperative (NutNet, https://nutnet.umn.edu/). The | ||
163 | mean annual air temperature (MAT) across these sites ranged from -4 to | 170 | mean annual air temperature (MAT) across these sites ranged from -4 to | ||
164 | 22\u00b0C, mean annual precipitation (MAP) from 252 to 1592 mm, and | 171 | 22\u00b0C, mean annual precipitation (MAP) from 252 to 1592 mm, and | ||
165 | elevations from 6 to 4261 m above sea level (Fig 1a, Supplementary | 172 | elevations from 6 to 4261 m above sea level (Fig 1a, Supplementary | ||
166 | Table S1), hence cover a wide range of climatic conditions under which | 173 | Table S1), hence cover a wide range of climatic conditions under which | ||
167 | grasslands occur (Fig 1b). Soil organic C concentrations ranged | 174 | grasslands occur (Fig 1b). Soil organic C concentrations ranged | ||
168 | between 0.8 to 7.8%, soil total N concentrations between 0.1 and 0.6%, | 175 | between 0.8 to 7.8%, soil total N concentrations between 0.1 and 0.6%, | ||
169 | and the soil C:N ratio between 9.1 and 21.5. Soil clay content spanned | 176 | and the soil C:N ratio between 9.1 and 21.5. Soil clay content spanned | ||
170 | from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2). | 177 | from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2). | ||
171 | \r\nAt each site, the effects of nutrient addition and herbivore | 178 | \r\nAt each site, the effects of nutrient addition and herbivore | ||
172 | exclusion were tested via a randomized-block design (Borer et al., | 179 | exclusion were tested via a randomized-block design (Borer et al., | ||
173 | 2014). Three blocks with 10 treatment plots each were established at | 180 | 2014). Three blocks with 10 treatment plots each were established at | ||
174 | each site, except for the site at bldr.us (only two blocks). Each of | 181 | each site, except for the site at bldr.us (only two blocks). Each of | ||
175 | these 10 plots was randomly assigned to a nutrient or fencing | 182 | these 10 plots was randomly assigned to a nutrient or fencing | ||
176 | treatment. An individual plot was 5 x 5 m, divided into four 2.5 x 2.5 | 183 | treatment. An individual plot was 5 x 5 m, divided into four 2.5 x 2.5 | ||
177 | m subplots. Each subplot was further divided into four 1 x 1 m square | 184 | m subplots. Each subplot was further divided into four 1 x 1 m square | ||
178 | sampling plots, one of which was set aside for soil sampling (Borer et | 185 | sampling plots, one of which was set aside for soil sampling (Borer et | ||
179 | al., 2014). Plots were separated by at least 1 m wide walkways. We | 186 | al., 2014). Plots were separated by at least 1 m wide walkways. We | ||
180 | collected soil samples from four different treatments for this study: | 187 | collected soil samples from four different treatments for this study: | ||
181 | (i) untreated control plots (Control), (ii) herbivore exclusion plots | 188 | (i) untreated control plots (Control), (ii) herbivore exclusion plots | ||
182 | (Fence), (iii) plots fertilized with N, P, K, plus nine essential | 189 | (Fence), (iii) plots fertilized with N, P, K, plus nine essential | ||
183 | macro and micronutrients (NPK), and (iv) plots with simultaneous | 190 | macro and micronutrients (NPK), and (iv) plots with simultaneous | ||
184 | fertilizer addition and herbivore exclusion (NPK+Fence). The | 191 | fertilizer addition and herbivore exclusion (NPK+Fence). The | ||
185 | experiments were established at different times in the past, with | 192 | experiments were established at different times in the past, with | ||
186 | years of treatment different among sites (2 \u2013 9 years since start | 193 | years of treatment different among sites (2 \u2013 9 years since start | ||
187 | of treatment; Supplementary Table S1). For the nutrient additions, all | 194 | of treatment; Supplementary Table S1). For the nutrient additions, all | ||
188 | sites applied 10 g N m-2 each year as time-release urea; 10 g P m-2 | 195 | sites applied 10 g N m-2 each year as time-release urea; 10 g P m-2 | ||
189 | yr-1 as triple-super phosphate; 10 g K m-2 yr-1 as potassium sulfate. | 196 | yr-1 as triple-super phosphate; 10 g K m-2 yr-1 as potassium sulfate. | ||
190 | A micro-nutrient mix (Fe, S, Mg, Mn, Cu, Zn, B, Mo, Ca) was applied at | 197 | A micro-nutrient mix (Fe, S, Mg, Mn, Cu, Zn, B, Mo, Ca) was applied at | ||
191 | 100 g m-2 together with K in the first year of treatments but not | 198 | 100 g m-2 together with K in the first year of treatments but not | ||
192 | thereafter. \r\nWe excluded large vertebrate herbivores (Fence) by | 199 | thereafter. \r\nWe excluded large vertebrate herbivores (Fence) by | ||
193 | fencing two plots, one with and one without NPK additions, within each | 200 | fencing two plots, one with and one without NPK additions, within each | ||
194 | block. The fences excluded all aboveground mammalian herbivores with a | 201 | block. The fences excluded all aboveground mammalian herbivores with a | ||
195 | body mass of over 50 g (Borer et al., 2014). At most sites, the fences | 202 | body mass of over 50 g (Borer et al., 2014). At most sites, the fences | ||
196 | were 180 cm high, and the fence contained a wire mesh (1 cm holes) for | 203 | were 180 cm high, and the fence contained a wire mesh (1 cm holes) for | ||
197 | the bottom 90 cm with a 30 cm outward-facing flange stapled to the | 204 | the bottom 90 cm with a 30 cm outward-facing flange stapled to the | ||
198 | ground to exclude burrowing animals. Climbing and subterranean animals | 205 | ground to exclude burrowing animals. Climbing and subterranean animals | ||
199 | may potentially still access these plots (Borer et al., 2014). For | 206 | may potentially still access these plots (Borer et al., 2014). For | ||
200 | slight modifications in fence design at a few sites see Supplementary | 207 | slight modifications in fence design at a few sites see Supplementary | ||
201 | Table S3. Most sites only had wild herbivores, although four sites | 208 | Table S3. Most sites only had wild herbivores, although four sites | ||
202 | were also grazed by domestic animals (Supplementary Table | 209 | were also grazed by domestic animals (Supplementary Table | ||
203 | S1).\r\n\r\nCollection of soil samples, soil microbial respiration, | 210 | S1).\r\n\r\nCollection of soil samples, soil microbial respiration, | ||
204 | microbial biomass, and other soil properties\r\nEach of the 23 sites | 211 | microbial biomass, and other soil properties\r\nEach of the 23 sites | ||
205 | received a package containing identical material from the Swiss | 212 | received a package containing identical material from the Swiss | ||
206 | Federal Institute for Forest, Snow and Landscape Research WSL, | 213 | Federal Institute for Forest, Snow and Landscape Research WSL, | ||
207 | Switzerland to be used for sampling (Risch et al., 2015; Risch et al., | 214 | Switzerland to be used for sampling (Risch et al., 2015; Risch et al., | ||
208 | 2019). We collected two soil cores of 5 cm diameter and 12 cm depth in | 215 | 2019). We collected two soil cores of 5 cm diameter and 12 cm depth in | ||
209 | each sampling plot and composited them to measure MR, MBC, and soil | 216 | each sampling plot and composited them to measure MR, MBC, and soil | ||
210 | chemical properties (see below). An additional sample (5 x 12 cm) was | 217 | chemical properties (see below). An additional sample (5 x 12 cm) was | ||
211 | collected to assess soil physical properties. This sample remained | 218 | collected to assess soil physical properties. This sample remained | ||
212 | within a steel sampling core after collection and both ends were | 219 | within a steel sampling core after collection and both ends were | ||
213 | tightly closed with plastic caps to avoid disturbance. All soils were | 220 | tightly closed with plastic caps to avoid disturbance. All soils were | ||
214 | shipped cooled to the laboratory at (Location will be disclosed after | 221 | shipped cooled to the laboratory at (Location will be disclosed after | ||
215 | manuscript acceptance) within a few days after collection. Soils were | 222 | manuscript acceptance) within a few days after collection. Soils were | ||
216 | sampled roughly 6 weeks prior to peak biomass at each site during 2015 | 223 | sampled roughly 6 weeks prior to peak biomass at each site during 2015 | ||
217 | and 2016.\r\nTo assess MR (CO2 production) in a laboratory incubation | 224 | and 2016.\r\nTo assess MR (CO2 production) in a laboratory incubation | ||
218 | experiment we weighed duplicate soil samples (8 g dry soil equivalent) | 225 | experiment we weighed duplicate soil samples (8 g dry soil equivalent) | ||
219 | into 50-ml Falcon tubes. No additional substrate (glucose, sugar) was | 226 | into 50-ml Falcon tubes. No additional substrate (glucose, sugar) was | ||
220 | added to these samples. We adjusted the soil moisture of each sample | 227 | added to these samples. We adjusted the soil moisture of each sample | ||
221 | to 60% field capacity. We then placed a 15 ml plastic test tube | 228 | to 60% field capacity. We then placed a 15 ml plastic test tube | ||
222 | (Semadeni 1701A) containing 7.25 ml 0.05 M NaOH over each soil sample. | 229 | (Semadeni 1701A) containing 7.25 ml 0.05 M NaOH over each soil sample. | ||
223 | The test tube was fixed with a plastic rod so that it was not in | 230 | The test tube was fixed with a plastic rod so that it was not in | ||
224 | contact with the soil sample. The Falcon tubes were then sealed with a | 231 | contact with the soil sample. The Falcon tubes were then sealed with a | ||
225 | screw cap and placed in an incubator under completely dark conditions | 232 | screw cap and placed in an incubator under completely dark conditions | ||
226 | at 20\u00b0C. The CO2 produced by microbial respiration was absorbed | 233 | at 20\u00b0C. The CO2 produced by microbial respiration was absorbed | ||
227 | by the 0.05 M NaOH. For five weeks we measured the decrease in | 234 | by the 0.05 M NaOH. For five weeks we measured the decrease in | ||
228 | conductivity within the 0.05 M NaOH solution on a weekly basis with a | 235 | conductivity within the 0.05 M NaOH solution on a weekly basis with a | ||
229 | Multimeter WTW Multi 3410 (WTW GmbH, Germany) and replaced the 0.05 M | 236 | Multimeter WTW Multi 3410 (WTW GmbH, Germany) and replaced the 0.05 M | ||
230 | NaOH with fresh solution. We included Falcon tubes without soil | 237 | NaOH with fresh solution. We included Falcon tubes without soil | ||
231 | samples in each incubation run as blanks to test if tubes were tight | 238 | samples in each incubation run as blanks to test if tubes were tight | ||
232 | and no CO2 could enter or escape. We calibrated the relationship | 239 | and no CO2 could enter or escape. We calibrated the relationship | ||
233 | between conductivity reduction and NaOH absorbed as follows: 400 ml | 240 | between conductivity reduction and NaOH absorbed as follows: 400 ml | ||
234 | 0.05 M NaOH was placed in a beaker and its conductivity was measured | 241 | 0.05 M NaOH was placed in a beaker and its conductivity was measured | ||
235 | with the multimeter. While stirring, air containing CO2 was blown into | 242 | with the multimeter. While stirring, air containing CO2 was blown into | ||
236 | the solution for approximately one minute, which reacts with NaOH to | 243 | the solution for approximately one minute, which reacts with NaOH to | ||
237 | form Na2CO3. After this process, conductivity was measured again. We | 244 | form Na2CO3. After this process, conductivity was measured again. We | ||
238 | then transferred 7.25 ml of the solution into a smaller beaker and | 245 | then transferred 7.25 ml of the solution into a smaller beaker and | ||
239 | added 1 ml of 0.1 M BaCl2 to precipitate Na2CO3 and then titrated the | 246 | added 1 ml of 0.1 M BaCl2 to precipitate Na2CO3 and then titrated the | ||
240 | solution with 0.05 M HCl to determine the remaining NaOH. We then | 247 | solution with 0.05 M HCl to determine the remaining NaOH. We then | ||
241 | repeated these steps with the remaining solution a total of nine times | 248 | repeated these steps with the remaining solution a total of nine times | ||
242 | and plotted the conductivities (y-axis) against the NaOH consumed | 249 | and plotted the conductivities (y-axis) against the NaOH consumed | ||
243 | (x-axis, Supplementary Fig S1). This regression line was used to infer | 250 | (x-axis, Supplementary Fig S1). This regression line was used to infer | ||
244 | the consumption of NaOH from the conductivity reduction in the | 251 | the consumption of NaOH from the conductivity reduction in the | ||
245 | incubation experiments and to calculate CO2 evolution during | 252 | incubation experiments and to calculate CO2 evolution during | ||
246 | incubation. In addition, we determined the optimum concentration for | 253 | incubation. In addition, we determined the optimum concentration for | ||
247 | the NaOH solution in series of preliminary experiments, so that the | 254 | the NaOH solution in series of preliminary experiments, so that the | ||
248 | concentration was not too high to become insensitive, but also not too | 255 | concentration was not too high to become insensitive, but also not too | ||
249 | low so that not all NaOH reacts during incubation. We then calculated | 256 | low so that not all NaOH reacts during incubation. We then calculated | ||
250 | MR (mg CO2-C kg dry soil-1 h-1) as total amount of CO2 released over | 257 | MR (mg CO2-C kg dry soil-1 h-1) as total amount of CO2 released over | ||
251 | the 5 weeks divided by the duration of the entire incubation in | 258 | the 5 weeks divided by the duration of the entire incubation in | ||
252 | hrs.\r\nSoil microbial biomass carbon (MBC; mg C kg soil-1 ) was | 259 | hrs.\r\nSoil microbial biomass carbon (MBC; mg C kg soil-1 ) was | ||
253 | measured at the beginning of the experiment by measuring the maximal | 260 | measured at the beginning of the experiment by measuring the maximal | ||
254 | respiratory response to the addition of glucose solution (4 mg glucose | 261 | respiratory response to the addition of glucose solution (4 mg glucose | ||
255 | per g soil dry weight dissolved in distilled water; substrate-induced | 262 | per g soil dry weight dissolved in distilled water; substrate-induced | ||
256 | respiration method) on approximately 5.5 g of soil (J. P. E. Anderson | 263 | respiration method) on approximately 5.5 g of soil (J. P. E. Anderson | ||
257 | & Domsch, 1978; Nico Eisenhauer et al., 2018; Scheu, 1992). For this | 264 | & Domsch, 1978; Nico Eisenhauer et al., 2018; Scheu, 1992). For this | ||
258 | purpose we used an O2-micro-compensation apparatus (Scheu, 1992). More | 265 | purpose we used an O2-micro-compensation apparatus (Scheu, 1992). More | ||
259 | specifically, substrate-induced respiration was calculated from the | 266 | specifically, substrate-induced respiration was calculated from the | ||
260 | respiratory response to D-glucose for 10 hr at 20\u00b0C. Glucose was | 267 | respiratory response to D-glucose for 10 hr at 20\u00b0C. Glucose was | ||
261 | added according to preliminary studies to saturate the catabolic | 268 | added according to preliminary studies to saturate the catabolic | ||
262 | enzymes of microorganisms (4 mg g soil-1 dissolved in 400 ml deionized | 269 | enzymes of microorganisms (4 mg g soil-1 dissolved in 400 ml deionized | ||
263 | water). The mean of the lowest three readings within the first 10 hrs | 270 | water). The mean of the lowest three readings within the first 10 hrs | ||
264 | (between the initial peak caused by disturbing the soil and the peak | 271 | (between the initial peak caused by disturbing the soil and the peak | ||
265 | caused by microbial growth) was taken as maximum initial respiratory | 272 | caused by microbial growth) was taken as maximum initial respiratory | ||
266 | response (MIRR; ml O2 kg soil-1 h-1) and microbial biomass (mg C kg | 273 | response (MIRR; ml O2 kg soil-1 h-1) and microbial biomass (mg C kg | ||
267 | soil-1) was calculated as 38 x MIRR (Beck et al., 1997; Cesarz et al., | 274 | soil-1) was calculated as 38 x MIRR (Beck et al., 1997; Cesarz et al., | ||
268 | 2022; Thakur et al., 2015).\r\nThe rest of the composited sample was | 275 | 2022; Thakur et al., 2015).\r\nThe rest of the composited sample was | ||
269 | dried at 65\u00b0C for 48 h, ground and sieved (2 mm mesh) to assess | 276 | dried at 65\u00b0C for 48 h, ground and sieved (2 mm mesh) to assess | ||
270 | the soil pH, mineral soil total C and N and C:N ratio, and mineral | 277 | the soil pH, mineral soil total C and N and C:N ratio, and mineral | ||
271 | soil organic C (Risch et al., 2019). The undisturbed sample was used | 278 | soil organic C (Risch et al., 2019). The undisturbed sample was used | ||
272 | to assess water holding capacity (WHC), bulk density (BD), and soil | 279 | to assess water holding capacity (WHC), bulk density (BD), and soil | ||
273 | texture [sand, silt, clay; methods in (Risch et al., 2019)]. We used | 280 | texture [sand, silt, clay; methods in (Risch et al., 2019)]. We used | ||
274 | the percentage of sand and clay as an indicator of soil texture in | 281 | the percentage of sand and clay as an indicator of soil texture in | ||
275 | this study. MAT (\u00b0C), MAP (mm) and temperature of the wettest | 282 | this study. MAT (\u00b0C), MAP (mm) and temperature of the wettest | ||
276 | quarter (\u00b0C) were obtained from www.worldclim.com (Fick & | 283 | quarter (\u00b0C) were obtained from www.worldclim.com (Fick & | ||
277 | Hijmans, 2017; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005). These | 284 | Hijmans, 2017; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005). These | ||
278 | variables were selected as they were found to be drivers of soil | 285 | variables were selected as they were found to be drivers of soil | ||
279 | nutrient processes across these sites in earlier studies (Risch et | 286 | nutrient processes across these sites in earlier studies (Risch et | ||
280 | al., 2020; Risch et al., 2019). Mean annual soil temperatures (MAST; | 287 | al., 2020; Risch et al., 2019). Mean annual soil temperatures (MAST; | ||
281 | \u00b0C) for the 0 to 5 cm soil layer were obtained for each site from | 288 | \u00b0C) for the 0 to 5 cm soil layer were obtained for each site from | ||
282 | the SoilTemp maps (J. Lembrechts et al., 2021; J. J. Lembrechts et | 289 | the SoilTemp maps (J. Lembrechts et al., 2021; J. J. Lembrechts et | ||
283 | al., 2022), global gridded modelled products of soil bioclimatic | 290 | al., 2022), global gridded modelled products of soil bioclimatic | ||
284 | variables for the 1979-2013 period at a 1-km\u00b2 resolution, based | 291 | variables for the 1979-2013 period at a 1-km\u00b2 resolution, based | ||
285 | on CHELSA, ERA5 and in-situ soil temperature | 292 | on CHELSA, ERA5 and in-situ soil temperature | ||
286 | measurements.\r\nNumerical calculations and statistical analyses\r\nWe | 293 | measurements.\r\nNumerical calculations and statistical analyses\r\nWe | ||
287 | calculated MMQ as MR/MBC. We corrected this measure using the average | 294 | calculated MMQ as MR/MBC. We corrected this measure using the average | ||
288 | soil temperature of each site (MMQsoil). This temperature correction | 295 | soil temperature of each site (MMQsoil). This temperature correction | ||
289 | is necessary as incubation temperatures are usually much higher than | 296 | is necessary as incubation temperatures are usually much higher than | ||
290 | site mean annual soil temperatures (see Xu et al. 2017). MMQsoil = MMQ | 297 | site mean annual soil temperatures (see Xu et al. 2017). MMQsoil = MMQ | ||
291 | x Q10(MAST \u2013 20)/10, where Q10 was assumed to be 2 (Xu et al. | 298 | x Q10(MAST \u2013 20)/10, where Q10 was assumed to be 2 (Xu et al. | ||
292 | 2017). See Supplementary Fig S2 for comparison of air and soil | 299 | 2017). See Supplementary Fig S2 for comparison of air and soil | ||
293 | temperatures across the 23 sites as well as the incubation | 300 | temperatures across the 23 sites as well as the incubation | ||
294 | temperature. \r\nSome of the explanatory variables (clay, soil organic | 301 | temperature. \r\nSome of the explanatory variables (clay, soil organic | ||
295 | C, C:N ratio) were skewed and were thus log-transformed prior to | 302 | C, C:N ratio) were skewed and were thus log-transformed prior to | ||
296 | analyses. All continuous explanatory variables were centred and scaled | 303 | analyses. All continuous explanatory variables were centred and scaled | ||
297 | to have a mean of zero and variance of one. To avoid collinearity | 304 | to have a mean of zero and variance of one. To avoid collinearity | ||
298 | between them we filtered them using correlation analysis | 305 | between them we filtered them using correlation analysis | ||
299 | (Supplementary Fig S3). From the variables that were strongly | 306 | (Supplementary Fig S3). From the variables that were strongly | ||
300 | correlated (Pearson\u2019s |r| > 0.70) (Dormann et al., 2013), we | 307 | correlated (Pearson\u2019s |r| > 0.70) (Dormann et al., 2013), we | ||
301 | selected the ones that allowed us to minimize the number of variables | 308 | selected the ones that allowed us to minimize the number of variables | ||
302 | (Supplementary Fig S3). Specifically, soil total N concentration, soil | 309 | (Supplementary Fig S3). Specifically, soil total N concentration, soil | ||
303 | total C concentration, soil sand content and soil bulk density were | 310 | total C concentration, soil sand content and soil bulk density were | ||
304 | dropped from the dataset. We then assessed how these edaphoclimatic | 311 | dropped from the dataset. We then assessed how these edaphoclimatic | ||
305 | variables are related to MMQ across our global grasslands.\r\nFor | 312 | variables are related to MMQ across our global grasslands.\r\nFor | ||
306 | this, we used linear mixed effects models (LMMs) fitted by maximum | 313 | this, we used linear mixed effects models (LMMs) fitted by maximum | ||
307 | likelihood with the lme function in the nlme package (version 3.1-153) | 314 | likelihood with the lme function in the nlme package (version 3.1-153) | ||
308 | (Pinheiro, Bates, DebRoy, & Sarkar, 2021) in R version 3.6.3. (R Core | 315 | (Pinheiro, Bates, DebRoy, & Sarkar, 2021) in R version 3.6.3. (R Core | ||
309 | Team, 2019). We used treatment as a fixed effect and plot nested in | 316 | Team, 2019). We used treatment as a fixed effect and plot nested in | ||
310 | site as random effects to assess treatment differences in MMQsoil, as | 317 | site as random effects to assess treatment differences in MMQsoil, as | ||
311 | well as MR, and MBC. The number of years since the treatment started | 318 | well as MR, and MBC. The number of years since the treatment started | ||
312 | was included as a fixed effect in all the initial models but was not | 319 | was included as a fixed effect in all the initial models but was not | ||
313 | significant and therefore not retained in the models. To assess how | 320 | significant and therefore not retained in the models. To assess how | ||
314 | differences in MMQsoil were affected by environmental factors (soil, | 321 | differences in MMQsoil were affected by environmental factors (soil, | ||
315 | climatic properties) we again used LMMs. Soil and climatic properties | 322 | climatic properties) we again used LMMs. Soil and climatic properties | ||
316 | were included as fixed effects and plot nested in site as random | 323 | were included as fixed effects and plot nested in site as random | ||
317 | effects. We did not include interactions between environmental | 324 | effects. We did not include interactions between environmental | ||
318 | variables. We then used the MuMin package (Barton, 2018) (version | 325 | variables. We then used the MuMin package (Barton, 2018) (version | ||
319 | 1.42.1) to select the best models that explained the most variation | 326 | 1.42.1) to select the best models that explained the most variation | ||
320 | based on Akaike\u2019s information criterion (AIC; model.avg | 327 | based on Akaike\u2019s information criterion (AIC; model.avg | ||
321 | function). We used the corrected AIC (AICc) to account for our small | 328 | function). We used the corrected AIC (AICc) to account for our small | ||
322 | sample size and selected the top models that fell within 4 AICc units | 329 | sample size and selected the top models that fell within 4 AICc units | ||
323 | (delta AICc < 4) (Burnham & Anderson, 2002; Johnson & Omland, 2004). | 330 | (delta AICc < 4) (Burnham & Anderson, 2002; Johnson & Omland, 2004). | ||
324 | We present all our top models rather than model averages. Conditional | 331 | We present all our top models rather than model averages. Conditional | ||
325 | averages are provided in the Supplementary material. \r\nBased on | 332 | averages are provided in the Supplementary material. \r\nBased on | ||
326 | findings from analyses described above and the literature, we | 333 | findings from analyses described above and the literature, we | ||
327 | developed a conceptual model of direct and indirect relationships | 334 | developed a conceptual model of direct and indirect relationships | ||
328 | between both edaphoclimatic variables and experimental treatments | 335 | between both edaphoclimatic variables and experimental treatments | ||
329 | (Supplement Figure S4) to obtain a more holistic approach in | 336 | (Supplement Figure S4) to obtain a more holistic approach in | ||
330 | understanding how these properties affect MMQsoil. We had data from 23 | 337 | understanding how these properties affect MMQsoil. We had data from 23 | ||
331 | sites with 272 observations. We tested this model using structural | 338 | sites with 272 observations. We tested this model using structural | ||
332 | equation modelling based on a d-sep approach (Lefcheck, 2016; Shipley, | 339 | equation modelling based on a d-sep approach (Lefcheck, 2016; Shipley, | ||
333 | 2009). We considered those environmental drivers that were included in | 340 | 2009). We considered those environmental drivers that were included in | ||
334 | our top LMMs, namely temperature of the wettest quarter (T.q.wet), | 341 | our top LMMs, namely temperature of the wettest quarter (T.q.wet), | ||
335 | soil pH, water holding capacity (WHC) and soil organic C (organic C; | 342 | soil pH, water holding capacity (WHC) and soil organic C (organic C; | ||
336 | Supplementary Figure S4). These factors were allowed to directly | 343 | Supplementary Figure S4). These factors were allowed to directly | ||
337 | affect MMQsoil, and via their interactions with treatments. In | 344 | affect MMQsoil, and via their interactions with treatments. In | ||
338 | addition, treatments were allowed to directly affect MMQsoil. | 345 | addition, treatments were allowed to directly affect MMQsoil. | ||
339 | Treatments were included as dummy variables in the model. We tested | 346 | Treatments were included as dummy variables in the model. We tested | ||
340 | our conceptual model (Supplementary Fig S4) using the piecewiseSEM | 347 | our conceptual model (Supplementary Fig S4) using the piecewiseSEM | ||
341 | package (version 2.0.2; Lefcheck, 2016) in R 3.4.0, in which a | 348 | package (version 2.0.2; Lefcheck, 2016) in R 3.4.0, in which a | ||
342 | structured set of linear models are fitted individually. This approach | 349 | structured set of linear models are fitted individually. This approach | ||
343 | allowed us to account for the nested experimental design, and overcome | 350 | allowed us to account for the nested experimental design, and overcome | ||
344 | some of the limitations of standard structural equation models, such | 351 | some of the limitations of standard structural equation models, such | ||
345 | as small sample sizes (Lefcheck, 2016; Shipley, 2009). We used the lme | 352 | as small sample sizes (Lefcheck, 2016; Shipley, 2009). We used the lme | ||
346 | function of the nlme package to model response variables, including | 353 | function of the nlme package to model response variables, including | ||
347 | site as a random factor. Good fit of the SEM was assumed when | 354 | site as a random factor. Good fit of the SEM was assumed when | ||
348 | Fisher\u2019s C values were non-significant (p > 0.05). For all | 355 | Fisher\u2019s C values were non-significant (p > 0.05). For all | ||
349 | significant interactions between soil or climate variables and | 356 | significant interactions between soil or climate variables and | ||
350 | treatments detected in the SEMs, we calculated treatment effect sizes, | 357 | treatments detected in the SEMs, we calculated treatment effect sizes, | ||
351 | i.e., the differences in MMQsoil between Control and treatments as log | 358 | i.e., the differences in MMQsoil between Control and treatments as log | ||
352 | response ratios (LRR) and plotted these values against the climate or | 359 | response ratios (LRR) and plotted these values against the climate or | ||
353 | soil factors. The LRR were defined as log(Control/Treatment), where | 360 | soil factors. The LRR were defined as log(Control/Treatment), where | ||
354 | treatment was either Fence, NPK or NPK+Fence. To assess which of the | 361 | treatment was either Fence, NPK or NPK+Fence. To assess which of the | ||
355 | LRR-climate or soil property relationships were significant we again | 362 | LRR-climate or soil property relationships were significant we again | ||
356 | used LMMs, in which soil and climatic properties were included as | 363 | used LMMs, in which soil and climatic properties were included as | ||
357 | fixed effects and plot nested in site as random effects. \r\n", | 364 | fixed effects and plot nested in site as random effects. \r\n", | ||
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