Examining the expansion of Spartina alterniflora in coastal wetlands using an MCE-CA-Markov model

Yan, Dandan; Li, Jingtai; Xie, Siying; Liu, Yao; Sheng, Yufeng; Luan, Zhaoqing

doi:10.3389/fmars.2022.964172

Cited by 9 publications

(6 citation statements)

References 40 publications

(57 reference statements)

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“…Our study confirmed a negative correlation between macrophyte diversity indicators (except for the proportion of emergent vegetation) with agricultural use and urbanised areas. Increasing agricultural intensity and urbanisation in lake watersheds leads to increased nutrient delivery to the lake littoral [ 48 , 49 ]. This leads to eutrophication, which ultimately reduces lake macrophyte diversity [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Response of Macrophyte Diversity in Coastal Lakes to Watershed Land Use and Salinity Gradient

Grzybowski

Burandt

Glińska-Lewczuk

et al. 2022

IJERPH

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Coastal lakes are subject to multiple stressors, among which land use, hydrological connectivity, and salinity have the greatest effect on their biodiversity. We studied the effects that various land cover types (CORINE) of coastal lake watersheds had on macrophyte diversity in ten coastal lakes along the southern Baltic coast as characterised by twelve phytocenotic indices: these being a number of communities, Shannon–Wiener diversity, evenness, and indices of taxonomic distinctiveness of plant communities: vegetation coverage; colonisation index; share of the phytolittoral area in the total lake area, as well as shares of nympheides, pondweeds, charophytes, marine, emerged and submerged communities in the total lake area. The effects were checked for three groups of lakes distinguished by differences in salinity–freshwater (F, 5), transitional (T, 4), and brackish (B, 1)—in which a total of 48 macrophyte communities were identified. The most abundant in aquatic phytocoenoses were lakes of T type. A partial least squares regression model (PLS-R) showed a stronger impact of land-use types in immediate vicinities and entire watersheds than the impact of physico-chemical properties of water on phytocenotic indices in the lakes. Macrophyte diversity was relatively low in urban and agricultural catchments and relatively high in forest and wetland areas. Agriculture had a negative impact on the number of macrophyte communities in F lakes and, in T lakes, on the number of macrophyte communities, biodiversity, evenness, and proportion of emerged, submerged, and marine communities. Urban areas contributed to lower values of evenness, vegetation coverage, and share of marine communities in F, but, in T, to lower the number of macrophyte communities, evenness, and proportion of submerged and marine communities. Our results confirm the significant impact of land use on macrophyte diversity in coastal aquatic ecosystems. Combined analysis of anthropogenic and natural descriptors is a prerequisite for analysing human threats to biodiversity in coastal lakes. Macrophyte community-based measures of biodiversity are sensitive indicators of anthropogenic impact on the ecological condition of coastal ecosystems.

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Section: Discussionmentioning

confidence: 99%

Response of Macrophyte Diversity in Coastal Lakes to Watershed Land Use and Salinity Gradient

Grzybowski

Burandt

Glińska-Lewczuk

et al. 2022

IJERPH

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“…The Markov model is a spatial transformation model based on raster data and is used to predict event probabilities [51]. The state and development trend of incidents were predicted by a transition probability matrix between different time states [52].…”

Section: Landscape Pattern Prediction Modelsmentioning

confidence: 99%

“…The CA-Markov model combines the ability of CA models to simulate complex spatial variations with the strengths of Markov models in temporal prediction [54], and it has both the ability of CA models to simulate the spatial variations of complex systems and the numerical analysis capability of Markov models to predict the long-term dynamics [51]. Therefore, in this study, the CA-Markov model in IDRISI Selva software was used to simulate and predict the distribution of landscape patterns in 2030 from the land use data of each period.…”

Section: Landscape Pattern Prediction Modelsmentioning

confidence: 99%

Exploring and Predicting Landscape Changes and Their Driving Forces within the Mulan River Basin in China from the Perspective of Production–Living–Ecological Space

Zhou,

Wen,

Wang

et al. 2024

Sustainability

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With rapid economic development and urban expansion, China faces a serious imbalance between production, living, and ecological land use, in which the erosion of water ecological space by urban expansion is especially notable. In order to alleviate or solve this imbalance, this study constructs the water ecological space in the Mulan River Basin based on national land spatial planning using remote sensing statistics and the 2000–2020 statistical yearbooks for the Mulan River Basin. A landscape index is applied to explore this landscape in terms of its production–living–ecological space (PLES) patterns and evolutionary characteristics. Factors affecting the drivers of PLES changes are analyzed through Geo-Detector, and predictions are made using the cellular automata Markov (CA-Markov) model. It was found that (1) PLES distribution patterns in the Mulan River Basin from 2000 to 2020 are dominated by non-watershed ecological spaces, with a significant expansion of living space. Its ecological space is shrinking, and there is significant spatial variation between its near-river and fringe areas. (2) Of the PLES conversions, the most dramatic conversions are those of production space and living space, with 81.14 km2 of production space being transferred into living space. Non-water ecological space and water ecological space are also mainly transferred into production space. (3) As shown by the results of the landscape index calculation, non-water ecological space in the Mulan River Basin is the dominant landscape, the values of the Shannon diversity index (SHDI) and Shannon homogeneity index (SHEI) are small, the overall level of landscape diversity is low, the aggregation index (AI) is high, and the degree of aggregation is obvious. (4) The progressive PLES changes in the Mulan River Basin are influenced by a combination of natural geographic and socioeconomic factors, with the mean population density and mean elevation being the most important factors affecting PLES changes among social and natural factors, respectively. (5) The Kappa coefficient of the CA-Markov model simulation is 0.8187, showing a good simulation accuracy, and it is predicted that the area of water ecological space in the Mulan River Basin will increase by 3.66 km2 by 2030, the area of production space and non-water ecological space will further decrease, and the area of construction land will increase by 260.67 km2. Overall, the aquatic ecological space in the Mulan River Basin has made progress in terms of landscape ecological protection, though it still faces serious erosion. Therefore, attaching importance to the restoration of the water ecological space in the Mulan River Basin, integrating multiple elements of mountains, water, forests, fields, and lakes, optimizing the spatial structure of its PLES dynamics, and formulating a reasonable spatial planning policy are effective means of guaranteeing its ecological and economic sustainable development. This study offers recommendations for and scientific defenses of the logical design of PLES spatial functions in the Mulan River Basin.

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“…We coupled CA with Markov models, and utilized the matrix of transition probabilities to model an alteration in the pattern of land cover over time, by employing the CA-Markov model, which is part of the IDRISI software package [29]. Firstly, we acquired the transition probability matrix of ground-cover type changes in the research region from 2000 to 2010, 2010 to 2015, and 2010 to 2020, by making use of the Markov module, which is included in the IDRISI program.…”

Section: Mce Ca-markovmentioning

confidence: 99%

Spatio-Temporal Distribution Characteristics of Glacial Lakes in the Altai Mountains with Climate Change from 2000 to 2020

et al. 2023

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The evolution of a glacial lake is a true reflection of glacial and climatic change. Currently, the study of glacial lakes in the Altai Mountains is mainly concerned with the application of high-resolution remote sensing images to monitor and evaluate the potential hazards of glacial lakes. At present, there is no rapid and large-scale method to monitor the dynamical variation in glacial lakes in the Altai Mountains, and there is little research on predicting its future tendency. Based on the supervised classification results obtained by Google Earth Engine (GEE), combined with an analysis of meteorological data, we analyzed the spatial and temporal variations in glacial lakes in the Altai Mountains between 2000 and 2020, and used the MCE-CA-Markov model to predict their changes in the future. According to the results, as of 2020, there are 3824 glacial lakes in the Altai Mountains, with an area of 682.38 km2. Over the entire period, the glacial lake quantity growth rates and area were 47.82% and 17.07%, respectively. The distribution of glacial lakes in this region showed a larger concentration in the north than in the south. Most glacial lakes had areas smaller than 0.1 km2, and there was minimal change observed in glacial lakes larger than 0.2 km2. Analyzing the regional elevation in 100 m intervals, the study found that glacial lakes were predominantly distributed at elevations from 2000 m to 3000 m. Interannual rainfall and temperature fluctuations in the Altai Mountains have slowed since 2014, and the trends for the area and number of glacial lakes have stabilized. The growth of glacial lakes in both number and surface area is expected to continue through 2025 and 2030, although the pace of change will slow. In the context of small increases in precipitation and large increases in temperature, in the future, glacial lakes with faster surface area growth rates will be located primarily in the southern Altai Mountains.

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Examining the expansion of Spartina alterniflora in coastal wetlands using an MCE-CA-Markov model

Cited by 9 publications

References 40 publications

Response of Macrophyte Diversity in Coastal Lakes to Watershed Land Use and Salinity Gradient

Response of Macrophyte Diversity in Coastal Lakes to Watershed Land Use and Salinity Gradient

Exploring and Predicting Landscape Changes and Their Driving Forces within the Mulan River Basin in China from the Perspective of Production–Living–Ecological Space

Spatio-Temporal Distribution Characteristics of Glacial Lakes in the Altai Mountains with Climate Change from 2000 to 2020

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