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Shen C, Wu R. Analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across China using a locally weighted regression approach. Heliyon 2023; 9:e16694. [PMID: 37292263 PMCID: PMC10245264 DOI: 10.1016/j.heliyon.2023.e16694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/10/2023] Open
Abstract
Nonlinear contributions from climate change and anthropogenic activity to the Normalized Difference Vegetation Index (NDVI) are analyzed to better understand the mechanisms underlying the nonlinear response of vegetation growth. In this study, it was hypothesized that NDVI dynamics on a nonlinear trajectory could track fluctuations of climate change and anthropogenic activity. Contributions from climate change and anthropogenic activity to NDVI were quantified using a locally weighted regression approach based on monthly timescale datasets. The findings showed that: 1) Vegetation cover fluctuated and increased in 81% of regions in China from 2000 to 2019. 2) The average predicted nonlinear contribution (APNC) of anthropogenic activity to NDVI was positive in China. The temperature APNC was positive in most of China but negative in Yunnan, where high temperatures and asynchronous temporal changes in temperature and NDVI were observed. The precipitation APNC was positive in the north of the Yangtze River, where precipitation is insufficient; but negative in South China, where precipitation is plentiful. Anthropogenic activity had the highest magnitude among the three nonlinear contributions, followed by temperature and precipitation. 3) The regions with contribution rates of anthropogenic activity greater than 80% were mainly distributed in the central Loess Plateau, North China Plain, and South China, while the areas with contribution rates of climate change greater than 80% were mainly concentrated in the northeastern QTP, Yunnan, and Northeast China. 4) The high temperature, drought, and asynchronous temporal changes in temperature, precipitation, and NDVI caused the negative average of changing trends in the predicted nonlinear contribution (PNC) of climate change to NDVI. Deforestation, land cover change, and grazing/fencing led to the negative average of changing trends in PNC from anthropogenic activity. These findings deepen our understanding of the mechanisms underlying the nonlinear responses of vegetation growth to climate change and anthropogenic activity.
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Affiliation(s)
- Chenhua Shen
- College of Geographical Science, Nanjing Normal University, Nanjing, 210046, China
- Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing, 210046, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource, Nanjing, 210046, China
| | - Rui Wu
- College of Geographical Science, Nanjing Normal University, Nanjing, 210046, China
- Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing, 210046, China
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Wu J, Li M, Zhang X, Fiedler S, Gao Q, Zhou Y, Cao W, Hassan W, Mărgărint MC, Tarolli P, Tietjen B. Disentangling climatic and anthropogenic contributions to nonlinear dynamics of alpine grassland productivity on the Qinghai-Tibetan Plateau. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 281:111875. [PMID: 33378737 DOI: 10.1016/j.jenvman.2020.111875] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 12/06/2020] [Accepted: 12/19/2020] [Indexed: 06/12/2023]
Abstract
Alpine grasslands on the Qinghai-Tibetan Plateau are sensitive and vulnerable to climate change and human activities. Climate warming and overgrazing have already caused degradation in a large fraction of alpine grasslands on this plateau. However, it remains unclear how human activities (mainly livestock grazing) regulates vegetation dynamics under climate change. Here, alpine grassland productivity (substituted with the normalized difference vegetation index, NDVI) is hypothesized to vary in a nonlinear trajectory to follow climate fluctuations and human disturbances. With generalized additive mixed modelling (GAMM) and residual-trend (RESTREND) analysis together, both magnitude and direction of climatic (in terms of temperature, precipitation, and radiation) and anthropogenic impacts on NDVI variation were examined across alpine meadows, steppes, and desert-steppes on the Qinghai-Tibetan Plateau. The results revealed that accelerating warming and greening, respectively, took place in 76.2% and 78.8% of alpine grasslands on the Qinghai-Tibetan Plateau. The relative importance of temperature, precipitation, and radiation impacts was comparable, between 20.4% and 24.8%, and combined to explain 66.2% of NDVI variance at the pixel scale. The human influence was strengthening and weakening, respectively, in 15.5% and 14.3% of grassland pixels, being slightly larger than any sole climatic variable across the entire plateau. Anthropogenic and climatic factors can be in opposite ways to affect alpine grasslands, even within the same grassland type, likely regulated by plant community assembly and species functional traits. Therefore, the underlying mechanisms of how plant functional diversity regulates nonlinear ecosystem response to climatic and anthropogenic stresses should be carefully explored in the future.
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Affiliation(s)
- Jianshuang Wu
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, 100081, Beijing, China; Freie Universität Berlin, Institute of Biology, Theoretical Ecology, 14195, Berlin, Germany.
| | - Meng Li
- Lhasa National Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modelling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China; School of Geographic Sciences, Nantong University, 226007, Nantong, Jiangsu Province, China
| | - Xianzhou Zhang
- Lhasa National Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modelling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101, Beijing, China
| | - Sebastian Fiedler
- Freie Universität Berlin, Institute of Biology, Theoretical Ecology, 14195, Berlin, Germany; University Bayreuth, Department of Ecological Modelling, 95448, Bayreuth, Germany; Berlin Brandenburg Institute of Advanced Biodiversity Research, 14195, Berlin, Germany
| | - Qingzhu Gao
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, 100081, Beijing, China
| | - Yuting Zhou
- Department of Geography, Oklahoma State University, OK, 74078, Stillwater, USA
| | - Wenfang Cao
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, 100081, Beijing, China; Department of Land, Environment, Agriculture and Forestry, University of Padova, 35020, Legnaro (PD), Italy
| | - Waseem Hassan
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, 100081, Beijing, China
| | - Mihai Ciprian Mărgărint
- Department of Geography, Geography and Geology Faculty, Alexandru Ioan Cuza University of Iaşi, 700505, RO, Iaşi, Romania
| | - Paolo Tarolli
- Department of Land, Environment, Agriculture and Forestry, University of Padova, 35020, Legnaro (PD), Italy
| | - Britta Tietjen
- Freie Universität Berlin, Institute of Biology, Theoretical Ecology, 14195, Berlin, Germany; Berlin Brandenburg Institute of Advanced Biodiversity Research, 14195, Berlin, Germany
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Wang C, Wang S, Jiao X, Yang B, Liang S, Luo Z, Mao L. Periodic density as an endpoint of customized plankton community responses to petroleum hydrocarbons: A level of toxic effect should be matched with a suitable time scale. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2020; 201:110723. [PMID: 32485490 DOI: 10.1016/j.ecoenv.2020.110723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 04/28/2020] [Accepted: 05/03/2020] [Indexed: 06/11/2023]
Abstract
As an endpoint of community response to contaminants, average periodic density of populations (APDP) has been introduced to model species interactions in a community with 4 planktonic species. An ecological model for the community was developed by means of interspecific relationship including competition and predation to calculate the APDP. As a case study, we reported here the ecotoxicological effects of petroleum hydrocarbons (PHC) collected from Bohai oil field on densities of two algae, Platymonas subcordiformis and Isochrysis galbana, a rotifer, Brachionus plicatilis, and of a cladocera, Penilia avirostris, in single species and a microcosm experiment. Time scales expressing toxic effect increased with increasing levels of toxic effect from molecule to community. Remarkable periodic changes in densities were found during the tests in microcosm experiment, revealing a strong species reaction. The minimum time scale characterizing toxic effect at a community level should be the common cycle of population densities of the microcosm. In addition, the cycles of plankton densities shortened in general with increasing PHC, showing an evident toxic effect on the microcosm. Using APDP as the endpoint, a threshold concentration for the modeled microcosm was calculated to be 0.404 mg-PHC L-1. The APDP was found to be more sensitive and reliable than the standing crops of populations as the endpoint. This indicated that the APDP, an endpoint at the community level, could be quantitatively related to the endpoints at the population level, and led to the quantitative concentration-toxic effect relationship at the community level.
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Affiliation(s)
- Changyou Wang
- School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
| | - Siwen Wang
- School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Xinming Jiao
- Jiangsu Environmental Monitoring Center, Nanjing, 210036, China
| | - Bin Yang
- Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf, Beibu Gulf University, Qinzhou, 535011, China
| | - Shengkang Liang
- Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao, 266100, China
| | - Zhuhua Luo
- School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Longjiang Mao
- School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China
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Mahaut L, Fort F, Violle C, Freschet GT. Multiple facets of diversity effects on plant productivity: Species richness, functional diversity, species identity and intraspecific competition. Funct Ecol 2019. [DOI: 10.1111/1365-2435.13473] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Lucie Mahaut
- Centre d'Ecologie Fonctionnelle et Evolutive Univ Montpellier CNRS EPHE, IRD Univ Paul Valéry Montpellier 3 Montpellier France
| | - Florian Fort
- Centre d'Ecologie Fonctionnelle et Evolutive Montpellier SupAgro CNRS Univ Montpellier Univ Paul Valéry Montpellier 3 EPHE IRD Montpellier France
| | - Cyrille Violle
- Centre d'Ecologie Fonctionnelle et Evolutive CNRS EPHE, IRD Univ Montpellier Univ Paul Valéry Montpellier 3 Montpellier France
| | - Grégoire T. Freschet
- Centre d'Ecologie Fonctionnelle et Evolutive CNRS EPHE, IRD Univ Montpellier Univ Paul Valéry Montpellier 3 Montpellier France
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Loreau M, Hector A. Not even wrong: Comment by Loreau and Hector. Ecology 2019; 100:e02794. [PMID: 31228870 DOI: 10.1002/ecy.2794] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 06/10/2019] [Indexed: 11/08/2022]
Affiliation(s)
- Michel Loreau
- Centre for Biodiversity Theory and Modelling, Theoretical and Experimental Ecology Station, CNRS, 2 route du CNRS, 09200, Moulis, France
| | - Andy Hector
- Department of Plant Sciences, University of Oxford, Oxford, OX1 3RB, UK
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Wu J, Li M, Fiedler S, Ma W, Wang X, Zhang X, Tietjen B. Impacts of grazing exclusion on productivity partitioning along regional plant diversity and climatic gradients in Tibetan alpine grasslands. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019; 231:635-645. [PMID: 30390448 DOI: 10.1016/j.jenvman.2018.10.097] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 09/20/2018] [Accepted: 10/26/2018] [Indexed: 06/08/2023]
Abstract
The biodiversity-productivity relationship is critical for better predicting ecosystem responses to climate change and human disturbance. However, it remains unclear about the effects of climate change, land use shifts, plant diversity, and their interactions on productivity partitioning above- and below-ground components in alpine grasslands on the Tibetan Plateau. To answer this question, we conducted field surveys at 33 grazed vs. fenced paired sites that are distributed across the alpine meadow, steppe, and desert-steppe zones on the northern Tibetan Plateau in early August of 2010-2013. Generalized additive models (GAMs) showed that aboveground net primary productivity (ANPP) linearly increased with growing season precipitation (GSP) while belowground net primary productivity (BNPP) decreased with growing season temperature (GST). Compared to grazed sites, short-term fencing did not alter the patterns of ANPP along climatic gradients but tended to decrease BNPP at moderate precipitation levels of 200 mm < GSP <450 mm. We also found that ANPP and BNPP linearly increased with species richness, ANPP decreased with Shannon diversity index, and BNPP did not correlate with the Shannon diversity index. Fencing did not alter the relationships between productivity components and plant diversity indices. Generalized additive mixed models furtherly confirmed that the interaction of localized plant diversity and climatic condition nonlinearly regulated productivity partitioning of alpine grasslands in this area. Finally, structural equation models (SEMs) revealed the direction and strength of causal links between biotic and abiotic variables within alpine grassland ecosystems. ANPP was controlled directly by GSP (0.53) and indirectly via species richness (0.41) and Shannon index (-0.12). In contrast, BNPP was influenced directly by GST (-0.43) and indirectly by GSP via species richness (0.05) and Shannon index (-0.02). Therefore, we recommend using a joint approach of GAMs and SEMs for better understanding mechanisms behind the relationship between biodiversity and ecosystem function under climate change and human disturbance.
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Affiliation(s)
- Jianshuang Wu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Lhasa National Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modelling, 100101 Beijing, China; Freie Universität Berlin, Institute of Biology, Biodiversity/Theoretical Ecology, 14195 Berlin, Germany.
| | - Meng Li
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Lhasa National Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modelling, 100101 Beijing, China
| | - Sebastian Fiedler
- Freie Universität Berlin, Institute of Biology, Biodiversity/Theoretical Ecology, 14195 Berlin, Germany
| | - Weiling Ma
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Lhasa National Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modelling, 100101 Beijing, China
| | - Xiangtao Wang
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Lhasa National Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modelling, 100101 Beijing, China; Xizang Agriculture and Animal Husbandry College, Department of Animal Sciences, 860000 Linzhi, China
| | - Xianzhou Zhang
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Lhasa National Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modelling, 100101 Beijing, China
| | - Britta Tietjen
- Freie Universität Berlin, Institute of Biology, Biodiversity/Theoretical Ecology, 14195 Berlin, Germany; Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany
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De Laender F. Community- and ecosystem-level effects of multiple environmental change drivers: Beyond null model testing. GLOBAL CHANGE BIOLOGY 2018; 24:5021-5030. [PMID: 29959825 DOI: 10.1111/gcb.14382] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 06/05/2018] [Accepted: 06/21/2018] [Indexed: 06/08/2023]
Abstract
Understanding the joint effect of multiple drivers of environmental change is a key scientific challenge. The dominant approach today is to compare observed joint effects with predictions from various types of null models. Drivers are said to combine synergistically (antagonistically) when their observed joint effect is larger (smaller) than that predicted by the null model. Here, I argue that this approach does not promote understanding of effects on important community- and ecosystem-level variables such as biodiversity and ecosystem function. I use ecological theory to show that different mechanisms can lead to the same deviation from a null model's prediction. Inversely, I show that the same mechanism can lead to different deviations from a null model's prediction. These examples illustrate that it is not possible to make strong mechanistic inferences from null models. Next, I present an alternative framework to study such effects. This framework makes a clear distinction between two different kinds of drivers (resource ratio shifts and multiple stressors) and integrates both by incorporating stressor effects into resource uptake theory. I show that this framework can advance understanding because of three reasons. First, it forces formalization of "multiple stressors," using factors that describe the number and kind of stressors, their selectivity and dynamic behaviour, and the initial trait diversity and tolerance among species. Second, it produces testable predictions on how these factors affect biodiversity and ecosystem function, alone and in combination with resource ratio shifts. Third, it can fail in informative ways. That is, its assumptions are clear, so that different kinds of deviations between predictions and observed effects can guide new experiments and theory improvement. I conclude that this framework will more effectively progress understanding of global change effects on communities and ecosystems than does the current practice of null model testing.
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Affiliation(s)
- Frederik De Laender
- Research Unit in Environmental and Evolutionary Biology, Namur Institute of Complex Systems, and the Institute of Life, Earth, and Environment, University of Namur, Namur, Belgium
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