Analyzing the spatial heterogeneity of number of plant individuals in grassland community by using power law model

Guan, Qingqing; Wei, Zhicheng; Wang, Yuxia; Shiyomi, Masae; Yang, Yun‐Gui

doi:10.1016/j.ecolmodel.2015.10.019

Cited by 13 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An aggregated pattern was prevalent in the four recovery phases of the grassland, consistent with multiple findings in arid or semiarid natural grasslands in China and elsewhere (Song et al, 2005 ; Tsuki et al, 2005 ; Chen et al, 2007a ; Guan et al, 2016 ). Condit et al ( 2000 ) noted that wind-dispersed species were not as well dispersed as the animal-dispersed species and tended to have aggregated distributions.…”

Section: Discussionsupporting

confidence: 85%

“…However, dramatic shifts in species abundance and composition can often occur during secondary succession (Chabrerie et al, 2003 ). Species-diversity patterns and spatial heterogeneity are important properties of plant communities and have been identified as indispensable parameters for evaluating the quality and sustainability of threatened and managed plant communities (Chen et al, 2007a ; Guan et al, 2016 ). Species diversity data can provide information on the susceptibility to invasion, trophic structure, and ecosystem resilience (Nichols and Nichols, 2003 ), while spatial heterogeneity is the degree of aggregation and patchiness of co-occurring species in a community (Tsuki et al, 2005 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Changes in Species Diversity Patterns and Spatial Heterogeneity during the Secondary Succession of Grassland Vegetation on the Loess Plateau, China

et al. 2017

View full text Add to dashboard Cite

Analyzing the dynamic patterns of species diversity and spatial heterogeneity of vegetation in grasslands during secondary succession could help with the maintenance and management of these ecosystems. Here, we evaluated the influence of secondary succession on grassland plant diversity and spatial heterogeneity of abandoned croplands on the Loess Plateau (China) during four phases of recovery: 1–5, 5–10, 10–20, and 20–30 years. The species composition and dominance of the grassland vegetation changed markedly during secondary succession and formed a clear successional series, with the species assemblage dominated by Artemisia capillaris→ Heteropappus altaicus→ A. sacrorum. The diversity pattern was one of low–high–low, with diversity peaking in the 10–20 year phase, thus corresponding to a hump-backed model in which maximum diversity occurring at the intermediate stages. A spatially aggregated pattern prevailed throughout the entire period of grassland recovery; this was likely linked to the dispersal properties of herbaceous plants and to high habitat heterogeneity. We conclude that natural succession was conducive to the successful recovery of native vegetation. From a management perspective, native pioneer tree species should be introduced about 20 years after abandoning croplands to accelerate the natural succession of grassland vegetation.

show abstract

Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Changes in Species Diversity Patterns and Spatial Heterogeneity during the Secondary Succession of Grassland Vegetation on the Loess Plateau, China

et al. 2017

View full text Add to dashboard Cite

show abstract

“…A well‐known example of emergent scaling in biology is the power law ( f ( x ) = ax b ; a = constant, b = exponent) based “Kleiber's law,” which states that a plant's metabolic rate increases with the body mass with an exponent of 0.75 (Savage et al, ). Power law relationships can represent a unique “scale invariance” or “scale‐free” attribute; that is, the functional relations do not change when the magnitude (scale) of the driving variable is altered (Farrior et al, ; Guan et al, ; Serran et al, ; Serran & Creed, ). Enquist et al () leveraged Kleiber's law to develop a generalized empirical model of nightly respiration fluxes as a function of inverse temperature for various terrestrial ecosystems across the United States and Europe.…”

Section: Introductionmentioning

confidence: 99%

Environmental Controls, Emergent Scaling, and Predictions of Greenhouse Gas (GHG) Fluxes in Coastal Salt Marshes

et al. 2018

View full text Add to dashboard Cite

Coastal salt marshes play an important role in mitigating global warming by removing atmospheric carbon at a high rate. We investigated the environmental controls and emergent scaling of major greenhouse gas (GHG) fluxes such as carbon dioxide (CO 2 ) and methane (CH 4 ) in coastal salt marshes by conducting data analytics and empirical modeling. The underlying hypothesis is that the salt marsh GHG fluxes follow emergent scaling relationships with their environmental drivers, leading to parsimonious predictive models. CO 2 and CH 4 fluxes, photosynthetically active radiation (PAR), air and soil temperatures, well water level, soil moisture, and porewater pH and salinity were measured during May-October 2013 from four marshes in Waquoit Bay and adjacent estuaries, MA, USA. The salt marshes exhibited high CO 2 uptake and low CH 4 emission, which did not significantly vary with the nitrogen loading gradient (5-126 kg · ha À1 · year À1 ) among the salt marshes. Soil temperature was the strongest driver of both fluxes, representing 2 and 4-5 times higher influence than PAR and salinity, respectively. Well water level, soil moisture, and pH did not have a predictive control on the GHG fluxes, although both fluxes were significantly higher during high tides than low tides. The results were leveraged to develop emergent power law-based parsimonious scaling models to accurately predict the salt marsh GHG fluxes from PAR, soil temperature, and salinity (Nash-Sutcliffe Efficiency = 0.80-0.91). The scaling models are available as a user-friendly Excel spreadsheet named Coastal Wetland GHG Model to explore scenarios of GHG fluxes in tidal marshes under a changing climate and environment.

show abstract

“…Reed and Hobbs () and Pertoldi, Bach, and Loeschcke () used modified forms of

β (t)

to explore extinction risk in small‐sized populations. The residuals in temporal TPL have been proposed as a measure of stability in crop yields (Döring et al., ), and similarly, the spatial heterogeneity of plants has also been described using residuals from spatial TPL regressions (Guan et al., ).…”

Section: Introductionmentioning

confidence: 99%

“…The residuals in temporal TPL have been proposed as a measure of stability in crop yields (Döring et al, 2015), and similarly, the spatial heterogeneity of plants has also been described using residuals from spatial TPL regressions (Guan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Taylor's power law captures the effects of environmental variability on community structure: An example from fishes in the North Sea

Cobain

Brede

Trueman

2018

Journal of Animal Ecology

View full text Add to dashboard Cite

Taylor's power law (TPL) describes the relationship between the mean and variance in abundance of populations, with the power law exponent considered a measure of aggregation. However, the usefulness of TPL exponents as an ecological metric has been questioned, largely due to its apparent ubiquity in various complex systems. The aim of this study was to test whether TPL exponents vary systematically with potential drivers of animal aggregation in time and space and therefore capture useful ecological information of the system of interest. We derived community TPL exponents from a long‐term, standardised and spatially dense data series of abundance and body size data for a strongly size‐structured fish community in the North Sea. We then compared TPL exponents between regions of contrasting environmental characteristics. We find that, in general, TPL exponents vary more than expected under random conditions in the North Sea for size‐based populations compared to communities considered by species. Further, size‐based temporal TPL exponents are systematically higher (implying more temporally aggregated distributions) along hydrographic boundaries. Time series of size‐based spatial TPL exponents also differ between hydrographically distinct basins. These findings support the notion that TPL exponents contain ecological information, capturing community spatio‐temporal dynamics as influenced by external drivers.

show abstract

Analyzing the spatial heterogeneity of number of plant individuals in grassland community by using power law model

Cited by 13 publications

References 35 publications

Changes in Species Diversity Patterns and Spatial Heterogeneity during the Secondary Succession of Grassland Vegetation on the Loess Plateau, China

Changes in Species Diversity Patterns and Spatial Heterogeneity during the Secondary Succession of Grassland Vegetation on the Loess Plateau, China

Environmental Controls, Emergent Scaling, and Predictions of Greenhouse Gas (GHG) Fluxes in Coastal Salt Marshes

Taylor's power law captures the effects of environmental variability on community structure: An example from fishes in the North Sea

Contact Info

Product

Resources

About