SER: An R package to characterize environmental regimes
Environmental regimes (or environmental legacy or historical legacy) are the dynamics of environmental characteristics over a given (either long or short) time period, such as frequency of mean or extreme events and rate of change, which might be absent by using only contemporary variables. We prese...
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| Published in | Ecology and evolution Vol. 13; no. 3; pp. e9882 - n/a |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
John Wiley & Sons, Inc
01.03.2023
John Wiley and Sons Inc Wiley |
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| Online Access | Get full text |
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| Abstract | Environmental regimes (or environmental legacy or historical legacy) are the dynamics of environmental characteristics over a given (either long or short) time period, such as frequency of mean or extreme events and rate of change, which might be absent by using only contemporary variables. We present SER, an R package for estimating environmental regimes for different environmental variables. Using the data included in the package, several examples are shown. SER is suitable for any type of environmental or biotic variables, including nutrient concentration, light, and dissolved oxygen. In addition, by changing the argument “days_bf,” it is possible to compute environmental regimes over any time period, such as days, months, or years. Our case study showed that the inclusion of environmental regimes increased the explained variation of temporal β‐diversity and its components. Environmental regimes are expected to advance the “environment–community” relationships in ecological studies. They can further be implemented in other subjects such as social science, socioeconomics, and epidemiology.
We present a new R package to compute environmental regimes over a certain period, which has important implications for basic and applied ecology, as well as other subjects. |
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| AbstractList | Environmental regimes (or environmental legacy or historical legacy) are the dynamics of environmental characteristics over a given (either long or short) time period, such as frequency of mean or extreme events and rate of change, which might be absent by using only contemporary variables. We present SER, an R package for estimating environmental regimes for different environmental variables. Using the data included in the package, several examples are shown. SER is suitable for any type of environmental or biotic variables, including nutrient concentration, light, and dissolved oxygen. In addition, by changing the argument “days_bf,” it is possible to compute environmental regimes over any time period, such as days, months, or years. Our case study showed that the inclusion of environmental regimes increased the explained variation of temporal β-diversity and its components. Environmental regimes are expected to advance the “environment–community” relationships in ecological studies. They can further be implemented in other subjects such as social science, socioeconomics, and epidemiology. Environmental regimes (or environmental legacy or historical legacy) are the dynamics of environmental characteristics over a given (either long or short) time period, such as frequency of mean or extreme events and rate of change, which might be absent by using only contemporary variables. We present SER, an R package for estimating environmental regimes for different environmental variables. Using the data included in the package, several examples are shown. SER is suitable for any type of environmental or biotic variables, including nutrient concentration, light, and dissolved oxygen. In addition, by changing the argument "days_bf," it is possible to compute environmental regimes over any time period, such as days, months, or years. Our case study showed that the inclusion of environmental regimes increased the explained variation of temporal β-diversity and its components. Environmental regimes are expected to advance the "environment-community" relationships in ecological studies. They can further be implemented in other subjects such as social science, socioeconomics, and epidemiology.Environmental regimes (or environmental legacy or historical legacy) are the dynamics of environmental characteristics over a given (either long or short) time period, such as frequency of mean or extreme events and rate of change, which might be absent by using only contemporary variables. We present SER, an R package for estimating environmental regimes for different environmental variables. Using the data included in the package, several examples are shown. SER is suitable for any type of environmental or biotic variables, including nutrient concentration, light, and dissolved oxygen. In addition, by changing the argument "days_bf," it is possible to compute environmental regimes over any time period, such as days, months, or years. Our case study showed that the inclusion of environmental regimes increased the explained variation of temporal β-diversity and its components. Environmental regimes are expected to advance the "environment-community" relationships in ecological studies. They can further be implemented in other subjects such as social science, socioeconomics, and epidemiology. Abstract Environmental regimes (or environmental legacy or historical legacy) are the dynamics of environmental characteristics over a given (either long or short) time period, such as frequency of mean or extreme events and rate of change, which might be absent by using only contemporary variables. We present SER, an R package for estimating environmental regimes for different environmental variables. Using the data included in the package, several examples are shown. SER is suitable for any type of environmental or biotic variables, including nutrient concentration, light, and dissolved oxygen. In addition, by changing the argument “days_bf,” it is possible to compute environmental regimes over any time period, such as days, months, or years. Our case study showed that the inclusion of environmental regimes increased the explained variation of temporal β‐diversity and its components. Environmental regimes are expected to advance the “environment–community” relationships in ecological studies. They can further be implemented in other subjects such as social science, socioeconomics, and epidemiology. Environmental regimes (or environmental legacy or historical legacy) are the dynamics of environmental characteristics over a given (either long or short) time period, such as frequency of mean or extreme events and rate of change, which might be absent by using only contemporary variables. We present SER, an R package for estimating environmental regimes for different environmental variables. Using the data included in the package, several examples are shown. SER is suitable for any type of environmental or biotic variables, including nutrient concentration, light, and dissolved oxygen. In addition, by changing the argument “days_bf,” it is possible to compute environmental regimes over any time period, such as days, months, or years. Our case study showed that the inclusion of environmental regimes increased the explained variation of temporal β‐diversity and its components. Environmental regimes are expected to advance the “environment–community” relationships in ecological studies. They can further be implemented in other subjects such as social science, socioeconomics, and epidemiology. We present a new R package to compute environmental regimes over a certain period, which has important implications for basic and applied ecology, as well as other subjects. |
| Author | He, Fengzhi Zou, Yi Guo, Kun Wu, Naicheng Riis, Tenna |
| AuthorAffiliation | 1 Department of Geography and Spatial Information Techniques Ningbo University Ningbo China 5 Department of Biology Aarhus University Aarhus C Denmark 3 Department of Health and Environmental Sciences Xi'an Jiaotong‐Liverpool University Suzhou China 4 Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin Germany 2 School of Ecological and Environmental Sciences East China Normal University Shanghai China |
| AuthorAffiliation_xml | – name: 1 Department of Geography and Spatial Information Techniques Ningbo University Ningbo China – name: 3 Department of Health and Environmental Sciences Xi'an Jiaotong‐Liverpool University Suzhou China – name: 5 Department of Biology Aarhus University Aarhus C Denmark – name: 4 Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin Germany – name: 2 School of Ecological and Environmental Sciences East China Normal University Shanghai China |
| Author_xml | – sequence: 1 givenname: Naicheng orcidid: 0000-0002-5652-3631 surname: Wu fullname: Wu, Naicheng email: naichengwu88@gmail.com organization: Ningbo University – sequence: 2 givenname: Kun orcidid: 0000-0001-9597-2977 surname: Guo fullname: Guo, Kun organization: East China Normal University – sequence: 3 givenname: Yi surname: Zou fullname: Zou, Yi organization: Xi'an Jiaotong‐Liverpool University – sequence: 4 givenname: Fengzhi surname: He fullname: He, Fengzhi organization: Leibniz Institute of Freshwater Ecology and Inland Fisheries – sequence: 5 givenname: Tenna surname: Riis fullname: Riis, Tenna organization: Aarhus University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36919015$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3389/fenvs.2020.00059 10.1016/j.scitotenv.2020.144675 10.1016/j.scitotenv.2020.140720 10.1002/joc.5086 10.1111/nph.17803 10.1002/rra.700 10.1111/geb.13455 10.1007/s10452-020-09771-z 10.1111/1365-2745.13859 10.1007/s10452-015-9556-x 10.1016/j.scitotenv.2016.11.060 10.1016/j.scitotenv.2021.149864 10.1016/j.ecolind.2018.12.049 10.1002/ecs2.3669 10.3390/w13233443 10.1016/j.ecoenv.2021.113036 10.1002/ece3.3903 10.1038/s41591-021-01303-y 10.1016/j.scitotenv.2021.149169 10.1038/s41559-017-0379-0 10.1016/j.xinn.2022.100225 10.1371/journal.pone.0217148 10.5194/bg-16-785-2019 10.1139/er-2015-0030 10.1016/j.scitotenv.2018.09.210 |
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| Copyright | 2023 The Authors. published by John Wiley & Sons Ltd. 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Snippet | Environmental regimes (or environmental legacy or historical legacy) are the dynamics of environmental characteristics over a given (either long or short) time... Abstract Environmental regimes (or environmental legacy or historical legacy) are the dynamics of environmental characteristics over a given (either long or... |
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| SubjectTerms | Applied Ecology Biodiversity Ecology Community Ecology Dissolved oxygen Ecological studies Ecology environmental legacy environmental variables Epidemiology historical legacy Nutrient concentrations Precipitation R package regime SER Skewness Social sciences Stream flow Variables Viewpoint |
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| Title | SER: An R package to characterize environmental regimes |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fece3.9882 https://www.ncbi.nlm.nih.gov/pubmed/36919015 https://www.proquest.com/docview/2791954683 https://www.proquest.com/docview/2787211668 https://pubmed.ncbi.nlm.nih.gov/PMC10008288 https://doaj.org/article/4f5ed06d2e324af08a95ceeb62e093cb |
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