scholarly journals VectorNet Data Series 3: Culicoides Abundance Distribution Models for Europe and Surrounding Regions

2020 ◽  
Vol 7 ◽  
Author(s):  
Thomas Balenghien ◽  
Neil Alexander ◽  
Auður Lilja Arnþórsdóttir ◽  
Marina Bisia ◽  
Alison Blackwell ◽  
...  
Keyword(s):  
Data Series ◽  
Abundance Distribution ◽  
Distribution Models

Species abundance distribution models of Toona ciliata communities in Hubei Province, China

2019 ◽  
Vol 32 (1) ◽  
pp. 103-117
Author(s):  
Yang Wang ◽  
Huoming Zhou ◽  
Jingyong Cai ◽  
Congwen Song ◽  
Linzhao Shi
Keyword(s):  
Species Abundance ◽  
Hubei Province ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Distribution Models ◽  
Toona Ciliata

scholarly journals Best Fitted Distribution For Meteorological Data In Kuala Krai

2021 ◽  
Vol 3 (1) ◽  
pp. 16-25
Author(s):  
Siti Mariam Norrulashikin ◽  
Fadhilah Yusof ◽  
Siti Rohani Mohd Nor ◽  
Nur Arina Bazilah Kamisan
Keyword(s):  
Climate Change ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Meteorological Data ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Data Series ◽  
Distribution Models ◽  
Goodness Of Fit Tests

Modeling meteorological variables is a vital aspect of climate change studies. Awareness of the frequency and magnitude of climate change is a critical concern for mitigating the risks associated with climate change. Probability distribution models are valuable tools for a frequency study of climate variables since it measures how the probability distribution able to fit well in the data series. Monthly meteorological data including average temperature, wind speed, and rainfall were analyzed in order to determine the most suited probability distribution model for Kuala Krai district. The probability distributions that were used in the analysis were Beta, Burr, Gamma, Lognormal, and Weibull distributions. To estimate the parameters for each distribution, the maximum likelihood estimate (MLE) was employed. Goodness-of-fit tests such as the Kolmogorov-Smirnov, and Anderson-Darling tests were conducted to assess the best suited model, and the test's reliability. Results from statistical studies indicate that Burr distributions better characterize the meteorological data of our research. The graph of probability density function, cumulative distribution function as well as Q-Q plot are presented.

scholarly journals Combining occurrence and abundance distribution models for the conservation of the Great Bustard

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4160 ◽  
Author(s):  
Chunrong Mi ◽  
Falk Huettmann ◽  
Rui Sun ◽  
Yumin Guo
Keyword(s):  
Species Abundance ◽  
Distribution Patterns ◽  
Bohai Bay ◽  
Residential Areas ◽  
Abundance Distribution ◽  
Great Bustard ◽  
Distribution Models ◽  
South Central ◽  
A Priority ◽  
Protection Index

Species distribution models (SDMs) have become important and essential tools in conservation and management. However, SDMs built with count data, referred to as species abundance models (SAMs), are still less commonly used to date, but increasingly receiving attention. Species occurrence and abundance do not frequently display similar patterns, and often they are not even well correlated. Therefore, only using information based on SDMs or SAMs leads to an insufficient or misleading conservation efforts. How to combine information from SDMs and SAMs and how to apply the combined information to achieve unified conservation remains a challenge. In this study, we introduce and propose a priority protection index (PI). The PI combines the prediction results of the occurrence and abundance models. As a case study, we used the best-available presence and count records for an endangered farmland species, the Great Bustard (Otis tarda dybowskii), in Bohai Bay, China. We then applied the Random Forest algorithm (Salford Systems Ltd. Implementation) with eleven predictor variables to forecast the spatial occurrence as well as the abundance distribution. The results show that the occurrence model had a decent performance (ROC: 0.77) and the abundance model had a RMSE of 26.54. It is noteworthy that environmental variables influenced bustard occurrence and abundance differently. The area of farmland, and the distance to residential areas were the top important variables influencing bustard occurrence. While the distance to national roads and to expressways were the most important influencing abundance. In addition, the occurrence and abundance models displayed different spatial distribution patterns. The regions with a high index of occurrence were concentrated in the south-central part of the study area; and the abundance distribution showed high populations occurrence in the central and northwestern parts of the study area. However, combining occurrence and abundance indices to produce a priority protection index (PI) to be used for conservation could guide the protection of the areas with high occurrence and high abundance (e.g., in Strategic Conservation Planning). Due to the widespread use of SDMs and the easy subsequent employment of SAMs, these findings have a wide relevance and applicability than just those only based on SDMs or SAMs. We promote and strongly encourage researchers to further test, apply and update the priority protection index (PI) elsewhere to explore the generality of these findings and methods that are now readily available.

scholarly journals An extensive comparison of species-abundance distribution models

2015 ◽  
Author(s):  
Elita Baldridge ◽  
David J. Harris ◽  
Xiao Xiao ◽  
Ethan P. White
Keyword(s):  
Statistical Methods ◽  
Negative Binomial ◽  
Species Abundance ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Distribution Models ◽  
Zipf Distribution ◽  
Taxonomic Groups ◽  
Best Fit

AbstractA number of different models have been proposed as descriptions of the species-abundance distribution (SAD). Most evaluations of these models use only one or two models, focus only a single ecosystem or taxonomic group, or fail to use appropriate statistical methods. We use likelihood and AIC to compare the fit of four of the most widely used models to data on over 16,000 communities from a diverse array of taxonomic groups and ecosystems. Across all datasets combined the log-series, Poisson lognormal, and negative binomial all yield similar overall fits to the data. Therefore, when correcting for differences in the number of parameters the log-series generally provides the best fit to data. Within individual datasets some other distributions performed nearly as well as the log-series even after correcting for the number of parameters. The Zipf distribution is generally a poor characterization of the SAD.

Testing species abundance distribution models in tropical forest successions: Implications for fine-scale passive restoration

2019 ◽  
Vol 135 ◽  
pp. 28-35 ◽  
Author(s):  
Pedro Manuel Villa ◽  
Sebastião Venâncio Martins ◽  
Alice Cristina Rodrigues ◽  
Nathália Vieira Hissa Safar ◽  
Michael Alejandro Castro Bonilla ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Species Abundance ◽  
Fine Scale ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Distribution Models ◽  
Passive Restoration

scholarly journals An extensive comparison of species-abundance distribution models

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2823 ◽  
Author(s):  
Elita Baldridge ◽  
David J. Harris ◽  
Xiao Xiao ◽  
Ethan P. White
Keyword(s):  
Statistical Methods ◽  
Negative Binomial ◽  
Species Abundance ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Distribution Models ◽  
Zipf Distribution ◽  
Taxonomic Groups ◽  
Best Fit

A number of different models have been proposed as descriptions of the species-abundance distribution (SAD). Most evaluations of these models use only one or two models, focus on only a single ecosystem or taxonomic group, or fail to use appropriate statistical methods. We use likelihood and AIC to compare the fit of four of the most widely used models to data on over 16,000 communities from a diverse array of taxonomic groups and ecosystems. Across all datasets combined the log-series, Poisson lognormal, and negative binomial all yield similar overall fits to the data. Therefore, when correcting for differences in the number of parameters the log-series generally provides the best fit to data. Within individual datasets some other distributions performed nearly as well as the log-series even after correcting for the number of parameters. The Zipf distribution is generally a poor characterization of the SAD.

scholarly journals Towards combining occurrence and abundance distribution models of Great Bustard for conservation: A global research template from Bohai Bay?

2017 ◽  
Author(s):  
Chunrong Mi ◽  
Falk Huettmann ◽  
Rui Sun ◽  
Yumin Guo
Keyword(s):  
Species Abundance ◽  
Distribution Patterns ◽  
Bohai Bay ◽  
Abundance Distribution ◽  
Great Bustard ◽  
Distribution Models ◽  
A Priority ◽  
Species Abundance Models ◽  
Combine Information ◽  
Protection Index

Species distribution models (SDMs) have become important and essential tools in conservation and management. However, SDMs built with count data, commonly referred to as species abundance models (SAMs), are still less used so far. SDMs are increasingly used now in conservation decisions, whereas SAMs are still not widely employed. Species occurrence and abundance do not frequently display similar patterns, often they are not even well correlated. This leads to an insufficient or misleading conservation. How to combine information from SDMs and SAMs all together for unified conservation remains a challenge. In this study, we put forward for the first time a priority protection index (PI). The PI combines the prediction results of occurrence and abundance models. We used the best-available presence and count records for an endangered farmland species, Great Bustard (Otis tarda dybowskii) in Bohai Bay, China, as a case study. We then applied the advanced Random Forest algorithm (Salford Systems Ltd. implementation), a powerful machine learning method, with eleven predictor variables to forecast the spatial occurrence as well as the abundance distribution. The results show that the occurrence model had a decent performance (ROC: 0.77) and the abundance model had a RMSE 26.54. It is of note that environmental variables influenced bustard occurrence and abundance differently. We found that occurrence and abundance models display different spatial distribution patterns. Still, combining occurrence and abundance indices to produce a priority protection index (PI) used for conservation could guide the protection of the areas with high occurrence and high abundance (e.g. in Strategic Conservation Planning). Due to the widespread use of SDMs and the rel. easy subsequent employment of SAMs these findings have a wide relevance and applicability, worldwide. We promote and strongly encourage to further test, apply and update the priority protection index (PI) elsewhere in order to explore the generality of these findings and methods readily available now for researchers.

Diversity, abundance and distribution of grasshopper species (Orthoptera: Acrididea) in three different types of vegetation with different levels of anthropogenic disturbances in the Littoral Region of Cameroon

2020 ◽  
Vol 14 (1) ◽  
pp. 16-33 ◽  
Author(s):  
YETCHOM-FONDJO JEANNE AGRIPPINE ◽  
KEKEUNOU - SÉVILOR ◽  
KENNE - MARTIN ◽  
MISSOUP ALAIN DIDIER ◽  
SHENG-QUAN XU
Keyword(s):  
Species Richness ◽  
Species Abundance ◽  
Anthropogenic Disturbances ◽  
Species Abundance Distribution ◽  
Abundance Distribution ◽  
Distribution Models ◽  
Eyprepocnemis Plorans ◽  
Grasshopper Species ◽  
Littoral Region ◽  
Abundance And Distribution

Grasshoppers have been identified as excellent monitors of landscape use. Despite their importance, their composition and distribution in the highly disturbed Littoral Cameroon is still unknown. The aim of this study was to determine the effect of human activities on diversity, abundance and distribution of grasshopper species in the Littoral region of Cameron. We investigated three types of vegetation differing remarkably on the level of anthropogenic impact (farmlands, fallows and forests), using sweep netting. The eight non-parametric estimators for specific richness, abundance, α and β diversity indices and species abundance distribution models, were used to compare the structure of communities among vegetation. Overall, 38 species belonging to three families and ten subfamilies were recorded. The Acrididae was the most diverse family. The species richness, abundance and diversity were higher in farmlands than in fallows and in forests. Five species occurred exclusively in farmlands, one in fallows and four in forests. Eyprepocnemis plorans, Coryphosima stenoptera, Serpusia opacula were overall the most abundant species respectively in cultivated farms, fallows and forests. Species abundance distribution fitted the Motomura model in all sites. Serpusia opacula is considered as a useful indicator since its presence and abundance significantly depend on the rate of forest naturalness. The farmlands were characterized by short vegetation while the fallows and forests were dominated by tall grasses and tall trees respectively. Anthropogenic disturbances promote the species richness, diversity and abundance of open meadow species, while it is detrimental to forest species which are sensitive, specialized and have limited dispersal abilities. Key words: Grasshopper, diversity, abundance, distribution, bioindicator

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