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Wang Z, Martin A, Brunton D, Grueter CC, Qu J, He JS, Ji W, Nan Z. The effects of grassland degradation on the genetic structure of a small mammal. Integr Zool 2024. [PMID: 38704846 DOI: 10.1111/1749-4877.12836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Grassland degradation is challenging the health of grassland ecosystems globally and causing biodiversity decline. Previous studies have demonstrated the impact of grassland degradation on the abundance and behavior of small mammals. Little is known about how it affects the genetic structure of gregarious mammals in the wild. This study explores the effects of grassland degradation on the genetic structure of a small burrowing mammal, plateau pika (Ochotona curzoniae). We used nine microsatellite loci to analyze the genetic diversity and genetic differentiation between colonies and genetic relatedness between individuals within the colony. We found that pikas in severely degraded grasslands had a significantly higher genetic diversity within colonies, a higher level of gene flow between colonies, and a lower genetic differentiation between colonies compared to pikas in less degraded grasslands. Individuals within colonies had a significantly lower genetic relatedness in severely degraded grasslands than in less degraded grasslands. This study has provided potential evidence of a significant impact of grassland degradation on the genetic structure of pikas, which has caused a breakdown of their kin-selected colony structure.
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Affiliation(s)
- Zaiwei Wang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
| | - Amy Martin
- Manaaki Whenua-Landcare Research, Lincoln, New Zealand
| | - Dianne Brunton
- School of Natural Sciences (SNS), Massey University, Auckland, New Zealand
| | - Cyril C Grueter
- Department of Anatomy, Physiology and Human Biology, School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
- International Centre of Biodiversity and Primate Conservation, Dali University, Dali, China
- Centre for Evolutionary Biology, School of Biological Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Jiapeng Qu
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
- Qinghai Province Key Laboratory of Animal Ecological Genomics, Xining, China
| | - Jin-Sheng He
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
- Institute of Ecology, College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing, China
| | - Weihong Ji
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
- School of Natural Sciences (SNS), Massey University, Auckland, New Zealand
| | - Zhibiao Nan
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
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Wang Z, Yan J, Martin A, Brunton DH, Qu J, He JS, Ji W, Nan Z. Alpine grassland degradation intensifies the burrowing behavior of small mammals: evidence for a negative feedback loop. Integr Zool 2024; 19:240-252. [PMID: 37243518 DOI: 10.1111/1749-4877.12730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Globally, grassland degradation is an acute ecological problem. In alpine grassland on the Tibetan Plateau, increased densities of various small mammals in degraded grassland are assumed to intensify the degradation process and these mammals are subject to lethal control. However, whether the negative impact of small mammals is solely a result of population size or also a result of activity and behavior has not been tested. In this study, we use plateau pika as a model to compare population size, core area of colony, and the number of burrow entrances and latrines between lightly and severely degraded grassland. We test whether the alleged contribution of pika to grassland degradation is a result of increased population size or increased burrowing activities of individuals in response to lower food abundance. We found that grassland degradation resulted in lower plant species richness, plant height, and biomass. Furthermore, the overall population size of pika was not significantly affected by location in lightly and severely degraded grassland. However, pika core areas in severely grassland degradation were significantly larger and had significantly higher densities of burrows and latrines. Our study provides convincing evidence that habitat-induced changes in the behavior of small, burrowing mammals, such as pika, can exacerbate grassland degradation. This finding has significant implications for managing small mammals and restoring degraded grassland ecosystems.
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Affiliation(s)
- Zaiwei Wang
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
| | - Jiawen Yan
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
| | - Amy Martin
- Manaaki Whenua-Landcare Research, Lincoln, New Zealand
| | - Dianne H Brunton
- School of Natural Sciences, Massey University (Albany Campus), Auckland, New Zealand
| | - Jiapeng Qu
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
- Qinghai Haibei National Field Research Station of Alpine Grassland Ecosystem, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
| | - Jin-Sheng He
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
- Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing, China
| | - Weihong Ji
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
- School of Natural Sciences, Massey University (Albany Campus), Auckland, New Zealand
| | - Zhibiao Nan
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China
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Mu W, Zhu X, Ma W, Han Y, Huang H, Huang X. Impact assessment of urbanization on vegetation net primary productivity: A case study of the core development area in central plains urban agglomeration, China. ENVIRONMENTAL RESEARCH 2023; 229:115995. [PMID: 37105286 DOI: 10.1016/j.envres.2023.115995] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 04/18/2023] [Accepted: 04/24/2023] [Indexed: 05/07/2023]
Abstract
Rapid urbanization process has a negative or positive impact on vegetation growth. Net primary productivity (NPP) is an effective indicator to characterize vegetation growth status. Taking the core development area of the Central Plains urban agglomeration as the study area, we estimated the NPP and its change trend in the past four decades using the Carnegie-Ames-Stanford Approach (CASA) model and statistical analysis based on meteorological and multi-source remote sensing data. Meanwhile, combined with the urbanization impact framework, we further analyzed urbanization's direct and indirect impact on NPP. The results showed that the urban area increased by 2688 km2 during a high-speed urbanization process from 1983 to 2019. As a result of the intense urbanization process, a continuous NPP decrease (direct impact) can be seen, which aggravated along with the acceleration of the urban expansion, and the mean value of direct impact was 130.84 g C·m-2·a-1. Meanwhile, urbanization also had a positive impact on NPP (indirect impact). The indirect impact showed an increasing trend during urbanization with a mean value of 10.91 g C·m-2·a-1. The indirect impact was mainly related to temperature in climatic factors. The indirect impact has a seasonal heterogeneity, and high-temperature environments of urban areas are more effective in promoting vegetation growth in autumn and winter than in summer. Among different cities, high-speed development cities have higher indirect impact values than medium's and low's because of better ecological construction. This study is of great significance for understanding the impact of urbanization on vegetation growth in the Central Plains urban agglomeration area, supporting urban greening plans, and building sustainable and resilient urban agglomerations.
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Affiliation(s)
- Wenbin Mu
- North China University of Water Resources and Electric Power, Zhengzhou, 450045, China; Henan Key Laboratory of Water Resources Conservation and Intensive Utilization in the Yellow River Basin, Zhengzhou, 450045, China
| | - Xingyuan Zhu
- North China University of Water Resources and Electric Power, Zhengzhou, 450045, China.
| | - Weixi Ma
- North China University of Water Resources and Electric Power, Zhengzhou, 450045, China
| | - Yuping Han
- North China University of Water Resources and Electric Power, Zhengzhou, 450045, China; Henan Key Laboratory of Water Resources Conservation and Intensive Utilization in the Yellow River Basin, Zhengzhou, 450045, China
| | - Huiping Huang
- North China University of Water Resources and Electric Power, Zhengzhou, 450045, China; Henan Key Laboratory of Water Resources Conservation and Intensive Utilization in the Yellow River Basin, Zhengzhou, 450045, China
| | - Xiaodong Huang
- North China University of Water Resources and Electric Power, Zhengzhou, 450045, China
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Geographic detector-based quantitative assessment enhances attribution analysis of climate and topography factors to vegetation variation for spatial heterogeneity and coupling. Glob Ecol Conserv 2023. [DOI: 10.1016/j.gecco.2023.e02398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
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Lu C, Zhang J, Min X, Chen J, Huang Y, Zhao H, Yan T, Liu X, Wang H, Liu H. Contrasting responses of early‐ and late‐season plant phenophases to altered precipitation. OIKOS 2023. [DOI: 10.1111/oik.09829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Chunyan Lu
- Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal Univ. Shanghai China
- Inst. of Eco‐Chongming (IEC), East China Normal Univ. Shanghai China
| | - Juanjuan Zhang
- State Key Laboratory of Grassland Agro‐Ecosystems, and College of Ecology, Lanzhou Univ. Lanzhou China
| | - Xueting Min
- Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal Univ. Shanghai China
- Inst. of Eco‐Chongming (IEC), East China Normal Univ. Shanghai China
| | - Jianghui Chen
- Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal Univ. Shanghai China
- Inst. of Eco‐Chongming (IEC), East China Normal Univ. Shanghai China
| | - Yixuan Huang
- Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal Univ. Shanghai China
- Inst. of Eco‐Chongming (IEC), East China Normal Univ. Shanghai China
| | - Hongfang Zhao
- School of Geographic Sciences, East China Normal Univ. Shanghai China
| | - Tao Yan
- State Key Laboratory of Grassland Agro‐Ecosystems, and College of Ecology, Lanzhou Univ. Lanzhou China
| | - Xiang Liu
- State Key Laboratory of Grassland Agro‐Ecosystems, and College of Ecology, Lanzhou Univ. Lanzhou China
| | - Hao Wang
- State Key Laboratory of Grassland Agro‐Ecosystems, and College of Ecology, Lanzhou Univ. Lanzhou China
| | - Huiying Liu
- Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal Univ. Shanghai China
- Inst. of Eco‐Chongming (IEC), East China Normal Univ. Shanghai China
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Pei J, Wang L, Huang H, Wang L, Li W, Wang X, Yang H, Cao J, Fang H, Niu Z. Characterization and attribution of vegetation dynamics in the ecologically fragile South China Karst: Evidence from three decadal Landsat observations. FRONTIERS IN PLANT SCIENCE 2022; 13:1043389. [PMID: 36388591 PMCID: PMC9648820 DOI: 10.3389/fpls.2022.1043389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
Plant growth and its changes over space and time are effective indicators for signifying ecosystem health. However, large uncertainties remain in characterizing and attributing vegetation changes in the ecologically fragile South China Karst region, since most existing studies were conducted at a coarse spatial resolution or covered limited time spans. Considering the highly fragmented landscapes in the region, this hinders their capability in detecting fine information of vegetation dynamics taking place at local scales and comprehending the influence of climate change usually over relatively long temporal ranges. Here, we explored the spatiotemporal variations in vegetation greenness for the entire South China Karst region (1.9 million km2) at a resolution of 30m for the notably increased time span (1987-2018) using three decadal Landsat images and the cloud-based Google Earth Engine. Moreover, we spatially attributed the vegetation changes and quantified the relative contribution of driving factors. Our results revealed a widespread vegetation recovery in the South China Karst (74.80%) during the past three decades. Notably, the area of vegetation recovery tripled following the implementation of ecological engineering compared with the reference period (1987-1999). Meanwhile, the vegetation restoration trend was strongly sustainable beyond 2018 as demonstrated by the Hurst exponent. Furthermore, climate change contributed only one-fifth to vegetation restoration, whereas major vegetation recovery was highly attributable to afforestation projects, implying that anthropogenic influences accelerated vegetation greenness gains in karst areas since the start of the new millennium during which ecological engineering was continually established. Our study provides additional insights into ecological restoration and conservation in the highly heterogeneous karst landscapes and other similar ecologically fragile areas worldwide.
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Affiliation(s)
- Jie Pei
- School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai, China
- Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Zhuhai, China
| | - Li Wang
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Huabing Huang
- School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai, China
- Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Zhuhai, China
| | - Lei Wang
- International Research Center of Big Data for Sustainable Development Goals, Beijing, China
| | - Wang Li
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Xiaoyue Wang
- The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Hui Yang
- Institute of Karst Geology, Chinese Academy of Geological Sciences (CAGS), Karst Dynamics Laboratory, Ministry of Natural Resources (MNR) & Guangxi, Guilin, China
- International Research Centre on Karst, Under the Auspices of United Nations Educational, Scientific and Cultural Organization (UNESCO), Guilin, China
| | - Jianhua Cao
- Institute of Karst Geology, Chinese Academy of Geological Sciences (CAGS), Karst Dynamics Laboratory, Ministry of Natural Resources (MNR) & Guangxi, Guilin, China
- International Research Centre on Karst, Under the Auspices of United Nations Educational, Scientific and Cultural Organization (UNESCO), Guilin, China
| | - Huajun Fang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- The Zhongke-Ji’an Institute for Eco-Environmental Sciences, Ji’an, China
| | - Zheng Niu
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
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Bonannella C, Hengl T, Heisig J, Parente L, Wright MN, Herold M, de Bruin S. Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal machine learning. PeerJ 2022; 10:e13728. [PMID: 35910765 PMCID: PMC9332400 DOI: 10.7717/peerj.13728] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/22/2022] [Indexed: 01/17/2023] Open
Abstract
This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species (Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of three million of points was used to train different algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 305 coarse and high resolution covariates representing spectral reflectance, different biophysical conditions and biotic competition was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to tune and train an ensemble model based on stacking with a logistic regressor as a meta-learner. An ensemble model was trained for each species: probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of six distribution maps per species, while for potential distributions only one map per species was produced. Results of spatial cross validation show that the ensemble model consistently outperformed or performed as good as the best individual model in both potential and realized distribution tasks, with potential distribution models achieving higher predictive performances (TSS = 0.898, R2 logloss = 0.857) than realized distribution ones on average (TSS = 0.874, R2 logloss = 0.839). Ensemble models for Q. suber achieved the best performances in both potential (TSS = 0.968, R2 logloss = 0.952) and realized (TSS = 0.959, R2 logloss = 0.949) distribution, while P. sylvestris (TSS = 0.731, 0.785, R2 logloss = 0.585, 0.670, respectively, for potential and realized distribution) and P. nigra (TSS = 0.658, 0.686, R2 logloss = 0.623, 0.664) achieved the worst. Importance of predictor variables differed across species and models, with the green band for summer and the Normalized Difference Vegetation Index (NDVI) for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter (BIO17) being the most frequent and important for potential distribution. On average, fine-resolution models outperformed coarse resolution models (250 m) for realized distribution (TSS = +6.5%, R2 logloss = +7.5%). The framework shows how combining continuous and consistent Earth Observation time series data with state of the art machine learning can be used to derive dynamic distribution maps. The produced predictions can be used to quantify temporal trends of potential forest degradation and species composition change.
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Affiliation(s)
- Carmelo Bonannella
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands
- OpenGeoHub, Wageningen, The Netherlands
| | | | - Johannes Heisig
- Institute for Geoinformatics, University of Münster, Münster, Germany
| | | | - Marvin N. Wright
- Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany
- University of Bremen, Bremen, Germany
| | - Martin Herold
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands
- Section 1.4 Remote Sensing and Geoinformatics, GFZ German Research Centre for Geosciences, Potsdam, Germany
| | - Sytze de Bruin
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands
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Accurate Measurement and Assessment of Typhoon-Related Damage to Roadside Trees and Urban Forests Using the Unmanned Aerial Vehicle. REMOTE SENSING 2022. [DOI: 10.3390/rs14092093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
With drastic changes to the environment arising from global warming, there has been an increase in both the frequency and intensity of typhoons in recent years. Super typhoons have caused large-scale damage to the natural ecological environment in coastal cities. The accurate assessment and monitoring of urban vegetation damage after typhoons is important, as they contribute to post-disaster recovery and resilience efforts. Hence, this study examined the application of the easy-to-use and cost-effective Unmanned Aerial Vehicle (UAV) oblique photography technology and proposed an improved detection and diagnostic measure for the assessment of street-level damage to urban vegetation caused by the super typhoon Mangkhut in Shenzhen, China. The results showed that: (1) roadside trees and artificially landscaped forests were severely damaged; however, the naturally occurring urban forest was less affected by the typhoon. (2) The vegetation height of roadside trees decreased by 20–30 m in most areas, and that of artificially landscaped forests decreased by 5–15 m; however, vegetation height in natural forest areas did not change significantly. (3) The real damage to vegetation caused by the typhoon is better reflected by measuring the change in vegetation height. Our study validates the use of UAV remote sensing to accurately measure and assess the damage caused by typhoons to roadside trees and urban forests. These findings will help city planners to design more robust urban landscapes that have greater disaster coping capabilities.
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Driving Climatic Factors at Critical Plant Developmental Stages for Qinghai–Tibet Plateau Alpine Grassland Productivity. REMOTE SENSING 2022. [DOI: 10.3390/rs14071564] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Determining the driving climatic factors at critical periods and potential legacy effects is crucial for grassland productivity predictions on the Qinghai–Tibet Plateau (QTP). However, studies with limited and ex situ ground samples from highly heterogeneous alpine meadows brought great uncertainties. This study determined the key climatic factors at critical plant developmental stages and the impact of previous plant growth status for interannual aboveground net primary productivity (ANPP) variations in different QTP grassland types. We hypothesize that the impact of climatic factors on grassland productivity varies in different periods and different vegetation types, while its legacy effects are not great. Pixel-based partial least squares regression was used to associate interannual ANPP with precipitation and air temperature at different developmental stages and prior-year ANPP from 2000 to 2019 using remote sensing techniques. Results indicated different findings from previous studies. Precipitation at the reproductive stage (July–August) was the most prominent controlling factor for ANPP which was also significantly affected by precipitation and temperature at the withering (September–October) and dormant stage (November–February), respectively. The influence of precipitation was more significant in alpine meadows than in alpine steppes, while the differentiated responses to climatic factors were attributed to differences in water consumption at different developmental stages induced by leaf area changes, bud sprouting, growth, and protection from frost damage. The prior-year ANPP showed a non-significant impact on ANPP of current year, except for alpine steppes, and this impact was much less than that of current-year climatic factors, which may be attributed to the reduced annual ANPP variations related to the inter-annual carbon circulation of alpine perennial herbaceous plants and diverse root/shoot ratios in different vegetation types. These findings can assist in improving the interannual ANPP predictions on the QTP under global climate change.
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