Predicted areas of potential distributions of alpine wetlands under different scenarios in the Qinghai-Tibetan Plateau, China

Xue, Zhenshan; Zhang, Zhongsheng; Lü, Xianguo; Zou, Yuanchun; Lu, Yonglei; Jiang, Ming; Tong, Shuo; Zhang, Kun

doi:10.1016/j.gloplacha.2014.10.012

Cited by 31 publications

(18 citation statements)

References 62 publications

(58 reference statements)

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“…TWI was computed using the multiple flow direction algorithm (Quinn, Beven, Chevallier, & Planchon, 1991), as recommended by Kopecký and Čížková (2010) and the SAGA GIS software (Conrad et al, 2015). TWI has been used in several vegetation studies to represent topographic control on hydrological process and soil moisture (Ågren, Lidberg, Strömgren, Ogilvie, & Arp, 2014;Allié et al, 2015;McFadden et al, 2018;Méndez-Toribio, Meave, Zermeño-Hernández, & Ibarra-Manríquez, 2016;Xue et al, 2014) and is related to other soil features including soil depth, pH, and nutrient availability (Gessler et al, 1995;Moore, Grayson, & Ladson, 1991;Sørensen et al, 2006). Other factors potentially affecting water availability (the curvature, the slope and the distance to the closest river) were also considered, but finally removed from the analyses because of high co-variation with TWI (Appendix S1).…”

Section: Environmental Variablesmentioning

confidence: 99%

Regional rainfall and local topography jointly drive tree community assembly in lowland tropical forests of New Caledonia

Blanchard

Munoz

Ibanez

et al. 2019

J Vegetation Science

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Aim To understand how variations in precipitation and topographic wetness influence tree community assembly at both regional and landscape scales in tropical forests. Location New Caledonia (SW Pacific). Methods We sampled 40 tree communities in 0.04‐ha plots laid along topographic gradients within two landscapes with contrasting precipitation. Within a dry (<1,400 mm/year) and a wet (>2,500 mm/year) landscape we used the Topographic Wetness Index (TWI) to sample communities in topographic position with low (e.g., ridges) and high (e.g., valleys) water accumulation. For each sampled species, we measured five functional traits involved in drought resistance and resource acquisition (wood density, leaf area, leaf specific area, leaf dry matter content, and leaf thickness). We first examined trait covariation across species. We then analysed how the functional composition of communities varied between landscapes (according to precipitation) and within landscapes (according to TWI), using trait‐based statistics and null models. Results We identified two ecological trade‐offs driving trait variation across species: (i) one opposing high hydraulic efficiency to drought resistance, related to a wood economic spectrum; and (ii) the other opposing resource acquisition to resource conservation, related to a leaf economic spectrum. Across landscapes, species with drought resistance strategies were favoured at lower precipitation. Within landscapes, drought resistant species were selected under low TWI in the dry landscape, while low TWI increased the abundance of species with conservative strategies in the wet landscape. Conclusions Precipitation and topography jointly shape the functional composition of tree communities. At low precipitation, hydric constraints prevailed on ridges and upslopes by filtering drought resistant strategies along the wood economic spectrum. Contrastingly, higher precipitation relaxed the hydric constraints and resource availability became a primary driver of changing strategies along the leaf economic spectrum. Thus, the landscape scale influence of topography on processes driving community assembly and functional composition critically depends on the regional climatic context.

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Section: Environmental Variablesmentioning

confidence: 99%

Regional rainfall and local topography jointly drive tree community assembly in lowland tropical forests of New Caledonia

Blanchard

Munoz

Ibanez

et al. 2019

J Vegetation Science

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“…Maxent (v.3.4.1; http://biodiversityinformatics.amnh.org/open_source/maxent/) is a machine learning software package attempting to simulate and predict how the distribution of a selected species will be modified in response to given environment changes (mostly climate and land use changes) [45] with a maximum entropy approach for species distribution simulations. It has proven useful and is widely used in habitats (e.g., wetlands and agriculture) distribution modeling [46][47][48][49][50]. In this study, the model was developed by running 10 replicates with randomly splitting the distribution data into two subsets: 75% for calibrating and training the models and the reminder for testing and evaluating the model performance [51].…”

Section: Quantitatively Distinguishing the Impacts Of Anthropogenic Amentioning

confidence: 99%

“…Please refer to [59] and [60] for the detailed explanation of WorldClim database. According to some previous studies [47,49,50], for prediction of wetlands distributions, eight climatic variables, including growing season temperature (GST), growing season precipitation (GSP), annual biological temperature (ABT) [61], coldness index (CI) [62,63], warmness index (WI) [62,63], dryness index (DI) [37], humidity index (HI) [64] and potential evapotranspiration ratio (PER) [61], were calculated based on the corresponding meteorological dataset. It should be noted that the growing season is defined as the period from May to September [50].…”

Section: Environmental Data For the Maxent Modelingmentioning

confidence: 99%

“…According to some previous studies [47,49,50], for prediction of wetlands distributions, eight climatic variables, including growing season temperature (GST), growing season precipitation (GSP), annual biological temperature (ABT) [61], coldness index (CI) [62,63], warmness index (WI) [62,63], dryness index (DI) [37], humidity index (HI) [64] and potential evapotranspiration ratio (PER) [61], were calculated based on the corresponding meteorological dataset. It should be noted that the growing season is defined as the period from May to September [50]. Additionally, wetlands vary in their source and degree of wetness, and as a first-order control on spatial variation of hydrological conditions, topography affects the spatial distribution of soil moisture and groundwater flow and thus influences distribution of wetlands [65].…”

Section: Environmental Data For the Maxent Modelingmentioning

confidence: 99%

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Climate Change and Anthropogenic Impacts on Wetland and Agriculture in the Songnen and Sanjiang Plain, Northeast China

et al. 2018

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Abstract:Influences of the increasing pressure of climate change and anthropogenic activities on wetlands ecosystems and agriculture are significant around the world. This paper assessed the spatiotemporal land use and land cover changes (LULCC), especially for conversion from marshland to other LULC types (e.g., croplands) over the Songnen and Sanjiang Plain (SNP and SJP), northeast China, during the past 35 years . The relative role of human activities and climatic changes in terms of their impacts on wetlands and agriculture dynamics were quantitatively distinguished and evaluated in different periods based on a seven-stage LULC dataset. Our results indicated that human activities, such as population expansion and socioeconomic development, and institutional policies related to wetlands and agriculture were the main driving forces for LULCC of the SJP and SNP during the past decades, while increasing contributions of climatic changes were also found. Furthermore, as few studies have identified which geographic regions are most at risk, how the future climate changes will spatially and temporally impact wetlands and agriculture, i.e., the suitability of wetlands and agriculture distributions under different future climate change scenarios, were predicted and analyzed using a habitat distribution model (Maxent) at the pixel-scale. The present findings can provide valuable references for policy makers on regional sustainability for food security, water resource rational management, agricultural planning and wetland protection as well as restoration of the region.

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“…However, in the long term, there remains large uncertainty, as the wetland extent is quite uncertain. For the TP, future continuous loss of glacier mass would reduce water supply to wetlands, which would reverse the wetland expansion to wetland dry up [Xue et al, 2014], though the exact timing is quite uncertain [Lutz et al, 2014;Su et al, 2016]. On the other hand, precipitation is expected to increase under a warmer climate [Su et al, 2013], which would partially compensate the decrease in glacier-melt-induced water supply.…”

Section: Dynamics Of Ch 4 Emissionsmentioning

confidence: 99%

CH₄ exchanges of the natural ecosystems in China during the past three decades: The role of wetland extent and its dynamics

Wei

2016

JGR Biogeosciences

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CH4 is the second largest contributor to human‐induced global warming. However, large uncertainties still exist regarding the magnitude and temporal variation of CH4 exchanges in China's natural ecosystems, especially under climate changes. In this study, we assessed its uncertainty and temporal variation during 1979–2012, by integrating a biogeochemical model, extensive in situ measurements, and various sources of wetland maps. Uncertainty analyses suggested that previous studies might have underestimated CH4 emissions, primarily due to bias in wetland extents in NE China. After that, 1 km resolution wetland maps were used to drive the model, together with a 0.1° resolution climate data set. The model showed that China's natural wetlands emitted 4.56 ± 1.24 Tg CH4 yr−1 during the 1980s, which decreased to 3.86 ± 1.09 Tg CH4 yr−1 in the 2000s, mainly due to wetland drainage in NE China. However, recent glacier‐melt‐induced wetland expansion has enhanced CH4 emissions by 28% on the Tibetan Plateau since the 1980s. The magnitude of CH4 uptake by the natural ecosystems has remained relatively stable, e.g., −2.57 ± 0.18 and −2.70 ± 0.19 Tg CH4 yr−1 in the 1980s and 2000s, respectively. In summary, the net CH4 balance of China's natural ecosystems has shown a decreasing pattern, i.e., 1.99 ± 1.42 and 1.16 ± 1.28 Tg CH4 yr−1 in the 1980s and 2000s, respectively, despite distinct regional differences between NE China and the Tibetan Plateau. Furthermore, this study emphasizes the correct representation of wetland extent and its dynamics, i.e., wetland drainage in populated regions and wetland expansion in glacier‐fed regions, in driving the decadal CH4 exchange magnitude.

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Predicted areas of potential distributions of alpine wetlands under different scenarios in the Qinghai-Tibetan Plateau, China

Cited by 31 publications

References 62 publications

Regional rainfall and local topography jointly drive tree community assembly in lowland tropical forests of New Caledonia

Regional rainfall and local topography jointly drive tree community assembly in lowland tropical forests of New Caledonia

Climate Change and Anthropogenic Impacts on Wetland and Agriculture in the Songnen and Sanjiang Plain, Northeast China

CH₄ exchanges of the natural ecosystems in China during the past three decades: The role of wetland extent and its dynamics

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Predicted areas of potential distributions of alpine wetlands under different scenarios in the Qinghai-Tibetan Plateau, China

Cited by 31 publications

References 62 publications

Regional rainfall and local topography jointly drive tree community assembly in lowland tropical forests of New Caledonia

Regional rainfall and local topography jointly drive tree community assembly in lowland tropical forests of New Caledonia

Climate Change and Anthropogenic Impacts on Wetland and Agriculture in the Songnen and Sanjiang Plain, Northeast China

CH4 exchanges of the natural ecosystems in China during the past three decades: The role of wetland extent and its dynamics

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CH₄ exchanges of the natural ecosystems in China during the past three decades: The role of wetland extent and its dynamics