Relationships between Plant Species Richness and Terrain in Middle Sub-Tropical Eastern China

Song, Changchun; Cao, Mingchang

doi:10.3390/f8090344

Cited by 16 publications

(10 citation statements)

References 33 publications

(33 reference statements)

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“…GAMs are generalized models with smoothers and link functions based on an exponential relationship between the response variable and the predictor variables (Zuur et al, 2014b). GAMs have previously been used to model relationships between environmental variables and species richness (Robert et al, 2015;Song and Cao, 2017) and to identify ecological response thresholds (Foley et al, 2015;Large et al, 2015). GAMs were chosen because they can accommodate non-linear relationships and produce ecologically intuitive outputs by identifying the shape and strength of the relationship between the response and predictor variables (Zuur et al, 2014a).…”

Section: Discussionmentioning

confidence: 99%

Determining Coral Density Thresholds for Identifying Structurally Complex Vulnerable Marine Ecosystems in the Deep Sea

et al. 2020

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Vulnerable marine ecosystems (VMEs) are at risk from the impacts of deep-sea trawling.Identifying the presence of VMEs in high seas fisheries management areas has to date relied mainly on presence records, or on habitat suitability models of VME indicator taxa (e.g., the stony coral species Solenosmilia variabilis Duncan, 1873) as proxies for the occurrence of VMEs (e.g., cold-water coral reefs). However, the presence or predicted presence of indicator taxa does not necessarily equate to the occurrence of a VME. There have been very few attempts to determine density thresholds of VME indicator taxa that relate to a "significant concentration" which supports a "high diversity" of associated taxa, as per the current criterion for identifying structurally complex VMEs (FAO, 2009). Without knowing such thresholds, identifications of VMEs will continue to be subjective, impeding efforts to design effective spatial management measures for VMEs. To address this issue, we used seafloor video and still image data from the Louisville Seamount Chain off New Zealand to model relationships between the densities of live Solenosmilia variabilis coral heads, as well as percent cover of live and dead coral matrix, and the number of other epifauna taxa present. Analyses were conducted at three spatial scales; 50 and 25 m 2 for video, and 2 m 2 for stills. Model curves exhibited initial steep positive responses reaching thresholds for the number of live coral heads at 0.11 m −2 (50 m 2 ), 0.14 m −2 (25 m 2 ), and 0.85 m −2 (2 m 2 ). Both live and dead coral cover were positively correlated with the number of associated taxa up to about 30% cover, for all spatial scales (24.5-28%). We discuss the results in the context of past and future efforts to develop criteria for identifying VMEs.

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Section: Discussionmentioning

confidence: 99%

Determining Coral Density Thresholds for Identifying Structurally Complex Vulnerable Marine Ecosystems in the Deep Sea

et al. 2020

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“…Pixels with higher TWI values have higher capacity of water accumulation (Besnard et al, 2013) or, in other words, being "wetter". The index is highly correlated with several soil attributes such as horizon depth, silt percentage, organic matter content, and phosphorus (Moore et al, 1993), can be used to simulate the status of soil moisture, which also has an influence on soil pH (Song, Cao, 2017). In our case, increasing values of TWI in relation to projected habitat suitability show a steady downward trend (Figure 3), meaning habitats that are "too wet" do not favour the species.…”

Section: Topographic Variablesmentioning

confidence: 99%

Associations between habitat quality and body size in the Carpathian land snail Vestia turgida: species distribution model selection and assessment of performance.

Tytar¹

2020

Preprint

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Associations between habitat quality and body size in the Carpathian land snail Vestia turgida: species distribution model selection and assessment of performance. Tytar V., Baidashnikov O. – Species distribution models (SDMs) are generally thought to be good indicators of habitat suitability, and thus of species’ performance, consequently SDMs can be validated by checking whether the areas projected to have the greatest habitat quality are occupied by individuals or populations with higher than average fitness. We hypothesized a positive and statistically significant relationship between observed in the field body size of the snail V. turgida and modelled habitat suitability, tested this relationship with linear mixed models, and found that indeed, larger individuals tend to occupy high-quality areas, as predicted by the SDMs. However, by testing several SDM algorithms, we found varied levels of performance in terms of expounding this relationship. Marginal R2 , expressing the variance explained by the fixed terms in the regression models, was adopted as a measure of functional accuracy, and used to rank the SDMs accordingly. In this respect, the Bayesian additive regression trees (BART) algorithm (Carlson, 2020) gave the best result, despite the low AUC and TSS. By restricting our analysis to the BART algorithm only, a variety of sets of environmental variables commonly or less used in the construction of SDMs were explored and tested according to their functional accuracy. In this respect, the SDM produced using the ENVIREM data set (Title, Bemmels, 2018) gave the best result.

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“…(8,10,34) Song and Cao reported that elevation and wetness significantly correlate with tree species richness. (35) Gonzalez and Mata reported that tree species richness and diversity correlate with elevation and slope, which are important factors for predicting tree species richness and diversity. (36) On the other hand, slope is expressed as one of the eight directions, and cannot be digitalized; thus, in this study, calculations were performed using the northern slope, which can be digitalized.…”

Section: Topographic and Geographic Factorsmentioning

confidence: 99%

Spatial Distribution Characteristics of Species Diversity Using Geographically Weighted Regression Model

Jeongmook¹,

Choi²,

Lee³

2019

Sensors and Materials

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The objective of this study is to evaluate the spatial distribution patterns of species diversity at different spatial scales, focusing on the Baekdudaegan Protected Area, which is a biodiversity hotspot in the Republic of Korea. The tree species diversity index (Shannon-Weaver index; H′) was calculated using tree species data from a 1:5k forest-type map, and the spatial analysis was performed with a 1 × 1 km 2 grid. Ten factors were selected to estimate the impact of topographic (elevation, slope, northern slope, curvature, wetness, and relief) and geographic (distances from water, road, forest road, and urban area) factors on H′ using the ordinary least squares (OLS) and geographically weighted regression (GWR) models. H′ increased with the spatial scale. Also, the coefficient of determination (R²) of the GWR and OLS models increased proportionally and the R² of the GWR model was higher than that of the OLS model. Corrected Akaike information criterion (AICc) was lower in the GWR model than in the OLS model, which indicates that the GWR model fits the calculated H′ better than the OLS model. Thus, the GWR model is considered to be more practical than the OLS model for understanding the effects of topographic and geographic factors on H′ at different scales.

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Relationships between Plant Species Richness and Terrain in Middle Sub-Tropical Eastern China

Cited by 16 publications

References 33 publications

Determining Coral Density Thresholds for Identifying Structurally Complex Vulnerable Marine Ecosystems in the Deep Sea

Determining Coral Density Thresholds for Identifying Structurally Complex Vulnerable Marine Ecosystems in the Deep Sea

Associations between habitat quality and body size in the Carpathian land snail Vestia turgida: species distribution model selection and assessment of performance.

Spatial Distribution Characteristics of Species Diversity Using Geographically Weighted Regression Model

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