Temperature-Dominated Driving Mechanisms of the Plant Diversity in Temperate Forests, Northeast China

Gu, Yue; Han, Shijie; Zhang, Junhui; Chen, Zhijie; Wang, Wenjie; Feng, Yue; Jiang, Yuwei; Geng, Shicong

doi:10.3390/f11020227

Cited by 13 publications

(14 citation statements)

References 104 publications

(152 reference statements)

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“…Both variables were strongly and positively influenced by altitude, soil nitrogen, carbon and sand contents, and mean annual rainfall; and negatively mainly by temperature and radiation variables. Past studies on forest biomass' drivers [81][82][83] found relatively comparable results implying that biomass was positively influenced by altitude and rainfall (or water availability) and negatively by temperature variables. However, [84], in their study, found that altitude had a negative impact on basal area.…”

Section: Discussionmentioning

confidence: 95%

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

González-Rodríguez,

2023

Preprint

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Understanding the spatial variations of forest features and their underlying drivers is crucial for managing and preserving forest ecosystems. Remote sensing techniques, due to their vast spatial coverage, are proving an essential tool for mapping and analyzing the complex spatial patterns of forests characteristics. In the current study, we use high-resolution Gaofen 1 satellite imagery to map features, such as biomass, biodiversity, and habitat type, of the subtropical forests in the Nanling area, a major biodiversity hot spot in the south of China; for subsequently analyzing the association between forest features and environmental drivers. We based our modeling approach on a semi-supervised fully convolutional neural network consisting of a ResNet architecture and a masking and aggregation module for computing predictions for forest survey plots of varied sizes. The fitted convolutional models yielded very high performance statistics, with test R2 values above 0.90 for basal area, aboveground biomass and Shannon diversity index regressions, slightly lower values for the regression of species richness (R2=0.83), and average test classification accuracy of forest types of 0.89, with lower performance for infrequent forest types, such as dwarf forests. The association analysis between predicted maps of forest features and environmental drivers revealed significant correlations being the strongest drivers the altitude, nitrogen content and climate variables, such as mean annual precipitation and temperature. Altitude was strongly associated with dwarf forests and high basal area and high aboveground biomass locations. Basal area and aboveground biomass were also positively influenced by annual rainfall and soil nitrogen and carbon, and negatively by temperature and radiation variables. Species richness was associated with high nitrogen and carbon contents, while the Shannon diversity index showed overall mild correlations with most drivers, with no remarkable patterns. We concluded that our semi-supervised convolutional approach using Gaofen 1 imagery proved useful for modeling the spatial distribution of the subtropical forests in Nanling; however, more research and data collection is necessary to improve the performance of species richness modeling and segmentation of infrequent forest types. Moreover, the strong associations found between climate, and predicted maps of biomass and forest types, provided key insights into the potential repercussions of climate change on Nanling forests.

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Section: Discussionmentioning

confidence: 95%

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

González-Rodríguez,

2023

Preprint

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“…Furthermore, elevation can promote diversification of species by increasing the environmental gradient in the same area. At a large scale, studies have shown that variations in plant richness patterns represent overlapping differences in dispersal range, whereas the edge of dispersal limits individuals to tolerating and occupying an environment that is influenced by macroscopic climate (Gu et al, 2020;O'Brien, 1998).…”

Section: Discussionmentioning

confidence: 99%

“…The elevation ranges from 30 to 2100 m, and the slope ranges from 0 to 45°. The vegetation types include temperate deciduous broad-leaved forest, temperate mixed coniferous and broad-leaved forest, temperate coniferous forest and cold temperate coniferous forest (Gu et al, 2020).…”

Section: Study Region and Sample Sitesmentioning

confidence: 99%

Environmental factors driving tree richness at multiple spatial scales in temperate forests, Northeast China

Han

Zhang

et al. 2022

Preprint

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Aim: Uncertainty remains in revealing the determinants of plant diversity over regional and larger scales. Both plant diversity and the effects of environmental factors on it are scale dependent. This study aims to integrate and analyse the variations in the ecological mechanisms by which environmental factors drive plant diversity. Location: Montane forests, Northeast China Taxon: Tree Methods: In this study, we used field survey data from 3,078 sample plots in temperate forest ecosystems. First, we partitioned the species–area curve into different area scales with segmented models to explain changes in the dominant factors affecting tree richness. Then, we quantified the contribution of environmental factors to tree richness with gradient boosted model (GBM) and fitted their correlations at different spatial scales. Finally, we partitioned the contribution of climate, topography and both to tree richness with redundancy analysis (RDA) and analysed how the driving mechanisms varied with spatial scale. Results: Two area scales (small scale A and large scale A ) were segmented in regional temperate forests. The correlations between environmental contributions and spatial scale at the A scale were better than those at the A scale. The effects of environmental factors driving tree richness varied spatially nonlinearly. Tree richness at the A scale was determined by both climate and topography and climate alone, while it was determined by climate alone at the A scale. Main conclusion: Spatial heterogeneity affected the effects of environmental factors on tree diversity, especially at the A scale. Environmental factors exerted a dominant control at different spatial scales. Environmental filtering determined tree diversity at the A scale, and dispersal limitation determined tree diversity at the A scale for temperate forests. The spatial heterogeneity and collinearity of climate and topography prevented us from drawing consistent conclusions.

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“…We quantified the relative contribution of 22 driving factors-19 Bioclim variables, soil type, altitude, and slope direction (mentioned above)-to study diversity with the gradient boosting model (GBM) [38] based on the Shannon index of each plot. This model continuously fits the nonlinear relationship between diversity and factors, and its flexibility, explanatory variable selection, and cross-validation approach offer an advantage in ecology studies [39,40]. Additionally, marginal plots were constructed that reflected the influence from one predictor variable when other predictor variables were fixed.…”

Section: Detection Of Driving Factors In Community Constructionmentioning

confidence: 99%

Patterns and Driving Factors of Diversity in the Shrub Community in Central and Southern China

Deng¹,

Song²,

Ma³

et al. 2022

Forests

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Climate, topography, and human activities are known to influence plant diversity. In the present study, species-abundance distribution (SAD) patterns of the shrub community were fitted, and the mechanism of contribution of 22 driving factors was assessed. The results showed that the α-diversity index exhibited no significant differences between artificial disturbance and the natural community. The Zipf and Zipf–Mandelbrot models were found to exhibit a good SAD fitting of the communities, thereby exhibiting a different diversity structure. It was observed that the SAD followed more than one rule, and the Zipf–Mandelbrot model was better than other models. The gradient boosting model indicated that precipitation in the wettest month, annual precipitation, and slope direction showed the strongest impact on plant richness. The indicator species of the artificial disturbance and natural community were identified from a multiple regression tree. Furthermore, an increase in species diversity was observed with a rise in latitude, exhibiting a single-peaked curve with increased altitude. β-diversity analysis indicated that both habitat filtering and the neutral effect influenced the establishment of the natural community, while the establishment of the artificial disturbance community was only affected by habitat filtering. Our study provides a better understanding of the ecological process of the maintenance of shrub-community diversity.

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Temperature-Dominated Driving Mechanisms of the Plant Diversity in Temperate Forests, Northeast China

Cited by 13 publications

References 104 publications

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

Environmental factors driving tree richness at multiple spatial scales in temperate forests, Northeast China

Patterns and Driving Factors of Diversity in the Shrub Community in Central and Southern China

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