On Age and Species Richness of Higher Taxa
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Many studies have tried to identify factors that explain differences in numbers of species between clades against the background assumption that older clades contain more species because they have had more time for diversity to accumulate. The finding in several recent studies that species richness of clades is decoupled from stem age has been interpreted as evidence for ecological limits to species richness. Here we demonstrate that the absence of a positive age-diversity relationship, or even a negative relationship, may also occur when taxa are defined based on time or some correlate of time such as genetic distance or perhaps morphological distinctness. Thus, inferring underlying processes from distributions of species across higher taxa requires caution concerning the way in which higher taxa are defined. When this definition is unclear, crown age is superior to stem age as a measure of clade age.Nonnative plant species are considered one of the greatest threats to biodiversity, yet we still are unable to predict how production and diversity of a community will change once a species has invaded. Ponderosa pine woodlands in the Front Range of Colorado are ideal for studying the impacts of nonnative plants on production and species richness. We selected 5 sites along the northern Front Range with varying proportions of nonnative and native species and compared understory production and species richness along the gradient of nonnative species dominance. Total species production was positively and significantly related to total species richness, and total species production increased significantly with increasing nonnative species richness. There was a negative relationship between native and nonnative species production, and there was no relationship between nonnative species richness and native species richness. This study demonstrates the complex nature of species richness and productivity relationships and should serve as a starting point for future research in which a variety of other variables are considered.
Dominance (genetics)
Understory
Native plant
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BEF studies aim to understand how ecosystems respond to a gradient of species diversity. Diversity-Interactions (DI) models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The number of species in the community (richness) is also implicitly modelled through this approach. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden. In this paper, we introduce the DImodels R package for implementing DI models. We also compare DI models to traditional modelling approaches to highlight the advantages of using a multi-dimensional definition of species diversity. We show that using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity. Predicting from a DI model is not limited to the study design points, the model can extrapolate to predict for any species composition and proportions (assuming there is sufficient coverage of this space in the study design). Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends. However, collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. DI modelling provides a framework to test the multiple components of species diversity in the BEF relationship. It facilitates uncovering a deeper ecological understanding of the BEF relationship and can lead to enhanced inference.
Global biodiversity
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Rank abundance curve
Gamma diversity
Global biodiversity
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There exists limited information on biodiversity including avifaunal diversity and habitat condition in community forests (CF) of Nepal; thus we aimed to fulfill such gaps in Tibrekot CF of Kaski district. We used the point count method for assessing bird diversity and laid out a circular plot size of radius 5-m within 15-m distance from each point count station for recording the biophysical habitat characteristics. Bird species’ diversity, richness and evenness were calculated using popular indexes and General Linear Model (GLM) was used to test the respective effect of various biophysical factors associated with the richness of bird species. In total, 166 (summer 122, winter 125) bird species were recorded in 46 sample plots. The Shannon-Wiener diversity index was calculated as 3.99 and 4.09, Margalef’s richness index as 16.84 and 17.53 and Pielou’s evenness index as 0.83 and 0.84 for summer and winter, respectively. The influencing factors for richness of bird species were season (χ21, 90= 112.21; P= 0.016) with higher richness in the summer season and low vegetation cover (χ21, 89= 113.88; P= 0.0064) with higher richness in lower percentage cover. Thus, community managed forest should be protected as it has a significant role in increasing bird diversity, which has potential for attracting avifaunal tourism for the benefit of the local communities.
Diversity index
Gamma diversity
Global biodiversity
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Species richness (number of species) of the butterfly community in the Gwangneung forest, Korea, was estimated using data of the long-term butterfly monitoring, which had been carried out 291 times in the Korea National Arboretum or forest area of Gwangneung from 1998 to 2008. Abundance of each butterfly species was monitored using the line-transact method. In the present study, 13,333 butterflies belonging to 112 species were observed. Species accumulation curve and species richness was obtained using a software, EstimateS. The species accumulation curve shows an increase tendency even at 291 survey times, implying the possibility of the presence of a few unfound species. However, values of species richness estimated by the seven estimators were stabilized around 240-250 survey times. Species richness estimated by the estimators ranged from 120 species to 141 species with 128 species in average. However, the figure estimated by the previous studies since 1958 was 148 species. We estimated the reasonable scale of species richness on the base of recent analysis on the change of butterfly species. Species richness of the Korea National Arboretum was higher than that of natural forest and of plantation. However, species richness of butterfly was not different between natural forest and plantation. It is likely that increase of grasslands and habitat diversity in arboretum led to the increase of species richness of butterfly community.
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Species richness and distribution patterns of wood-inhabiting fungi and mycetozoans (slime moulds) were investigated in the canopy of a Central European temperate mixed deciduous forest. Species richness was described with diversity indices and species-accumulation curves. Nonmetrical multidimensional scaling was used to assess fungal species composition on different tree species. Different species richness estimators were used to extrapolate species richness beyond our own data. The reliability of the abundance-based coverage estimator, Chao, Jackknife and other estimators of species richness was evaluated for mycological surveys. While the species-accumulation curve of mycetozoans came close to saturation, that of wood-inhabiting fungi was continuously rising. The Chao 2 richness estimator was considered most appropriate to predict the number of species at the investigation site if sampling were continued. Gray's predictor of species richness should be used if statements of the number of species in larger areas are required. Multivariate analysis revealed the importance of different tree species for the conservation and maintenance of fungal diversity within forests, because each tree species possessed a characteristic fungal community. The described mathematical approaches of estimating species richness possess great potential to address fungal diversity on a regional, national, and global scale.
Rank abundance curve
Jackknife resampling
Relative abundance distribution
Species distribution
Rarefaction (ecology)
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Recurrent outbreaks of senecavirus A (SVA)-associated vesicular disease have led to a large number of infected pigs being culled and has caused considerable economic losses to the swine industry. Although SVA was discovered 2 decades ago, knowledge about the evolutionary and transmission histories of SVA remains unclear. Herein, we performed an integrated analysis of the recombination, phylogeny, selection, and spatiotemporal dynamics of SVA. Phylogenetic analysis demonstrated that SVA diverged into two main branches, clade I (pre-2007 strains) and clade II (post-2007 strains). Importantly, analysis of selective strength showed that clade II was evolving under relaxed selection compared with clade I. Positive selection analysis identified 27 positive selective sites, most of which are located on the outer surface of capsid protomer or on the important functional domains of nonstructure proteins. Bayesian phylodynamics suggested that the estimated time to the most recent common ancestor of SVA was around 1986, and the estimated substitution rate of SVA was 3.3522 × 10-3 nucleotide substitutions/site/year. Demographic history analysis revealed that the effective population size of SVA has experienced a gradually increasing trend with slight fluctuation until 2017 followed by a sharp decline. Notably, Bayesian phylogeographic analysis inferred that Brazil might be the source of SVA's global transmission since 2015. In summary, these data illustrated that the ongoing evolution of SVA drove the lineage-specific innovation and potentially phenotypically important variation. Our study sheds new light on the fundamental understanding of SVA evolution and spread history. IMPORTANCE Recurrent outbreaks and global epidemics of senecavirus A-associated vesicular disease have caused heavy economic losses and have threatened the development of the pig industry. However, the question of where the virus comes from has been one of the biggest puzzles due to the stealthy nature of the virus. Consequently, tracing the source, evolution, and transmission pattern of SVA is a very challenging task. Based on the most comprehensive analysis, we revealed the origin time, rapid evolution, epidemic dynamics, and selection of SVA. We observed two main genetic branches, clade I (pre-2007 strains) and clade II (post-2007 strains), and described the epidemiological patterns of SVA in different countries. We also first identified Brazil as the source of SVA's global transmission since 2015. Findings in this study provide important implications for the control and prevention of the virus.
Viral phylodynamics
Lineage (genetic)
Evolutionary Dynamics
Negative selection
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Gamma diversity
Dominance (genetics)
Rank abundance curve
Diversity index
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Abstract Biodiversity and ecosystem function (BEF) studies aim to understand how ecosystems respond to a gradient of species diversity. Generalised Diversity‐Interactions (DI) models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The number of species in the community (richness) is included implicitly in a DI model. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is broader than richness alone, and important patterns in the BEF relationship may remain hidden. Here, we introduce the DImodels R package for implementing DI models. We show how richness is mathematically equivalent to a simplified DI model under certain conditions, and illustrate how using the DI multidimensional definition of species diversity can provide deeper insight to the BEF relationship compared to traditional approaches. Using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity. Predicting from a DI model is not limited to the study design points, the model can interpolate or extrapolate to predict for any species composition and proportions (assuming there is sufficient coverage of this space in the study design). Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends. However, collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. DI modelling provides a framework to test the multiple components of species diversity in the BEF relationship. It facilitates uncovering a deeper ecological understanding of the BEF relationship and can lead to enhanced inference. The open‐source DImodels R package provides a user‐friendly way to implement this modelling approach.
Global biodiversity
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Abstract The land snail community of Idanre hills was studied using a combination of direct search and leaf litter‐sieving techniques. In total, 36 species and 2192 individuals in nine molluscan families were collected from 19 plots of 400 m 2 each. Species richness varied from 8 to 23 and the number of individuals from 21 to 566 per plot. Species richness was dominated by the carnivorous Streptaxidae, while numerical abundance was dominated by the Subulinidae, Streptaxidae and Urocyclidae, contributing to more than 95% of the total number of individuals. The single most abundant species was the urocyclid Trochozonites talcosus , contributing to almost 20% of the total number of individuals. The species richness and high abundance of land snails make Idanre hills a unique site for molluscan conservation in Nigeria.
Land snail
Global biodiversity
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