Distribution Pattern of Cinnamomum tamala in Annapurna Conservation Area, Kaski, Nepal

Subedi, Mukti Ram; Timilsina, Yajna Prasad

doi:10.3126/njst.v15i2.12110

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…Several authors tested the significance of

I_{δ}

by chi‐square (Sousa & Cunha, 2018; Souza & Coimbra, 2005; Subedi & Timilsina, 2014). In this test, after calculating

χ_{I_{δ}}^{2} = I_{δ} ((), n - 1) + N - n

, the distribution is considered random when

χ_{(1 - α / 2; N - 1)}^{2} < χ_{I_{δ}}^{2} < χ_{(α / 2; N - 1)}^{2}

; uniform when

χ_{I_{δ}}^{2} < χ_{(1 - α / 2; N - 1)}^{2}

; or aggregate when

χ_{I_{δ}}^{2} > χ_{(α / 2; N - 1)}^{2}

; where

χ_{(1 - α / 2; N - 1)}^{2}

and

χ_{(α / 2; N - 1)}^{2}

are the critical chi‐square values with N –1 degrees of freedom and

α

significance.…”

Section: Morisita's Index and Its Standardizationmentioning

confidence: 99%

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Why do traditional dispersion indices used for analysis of spatial distribution of plants tend to become obsolete?

Alves

Santana

2021

Population Ecology

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It is common to characterize the spatial distribution of plant patterns as random, aggregate, or uniform. In this context, a major challenge for the researcher is the choice of the method to identify the spatial pattern correctly as well as the factors related to it. The vast literature on the subject is not recent, especially regarding the dispersion indices. The aim of this review was to conduct a critical and temporal analysis of these dispersion indices and test their effectiveness in determining the spatial distribution of Paepalanthus chiquitensis Herzog (Eriocaulaceae). This species is a meaningful model due to its occurrence in specific sites. The Lexis, Charlier, dispersion, relative variance, aggregation, Green, inverse of k of the negative binomial, Morisita, and standardized Morisita indices were limited to indicating that the individuals of the species are aggregate and did not provide information on neither spatial dimension (scale) where the aggregation occurs, nor the factors related to this aggregation. Although they have distinct magnitudes, the algebraic expressions of dispersion, relative variance, aggregation, Green, inverse of k, Morisita, and standardized Morisita indices exhibited a close relationship with each other and little progress from their precursors Lexis and Charlier. By disregarding the possibility of spatial dependence, these indices make it impossible to generate important hypotheses for the investigation of factors related to spatial structure. Therefore, they became obsolete and are falling into disuse. It should be noted that these measurements accomplished their role and contributed to science in times of limited technologies for spatial data.

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“…Several authors tested the significance of

I_{δ}

by chi‐square (Sousa & Cunha, 2018; Souza & Coimbra, 2005; Subedi & Timilsina, 2014). In this test, after calculating

χ_{I_{δ}}^{2} = I_{δ} ((), n - 1) + N - n

, the distribution is considered random when

χ_{(1 - α / 2; N - 1)}^{2} < χ_{I_{δ}}^{2} < χ_{(α / 2; N - 1)}^{2}

; uniform when

χ_{I_{δ}}^{2} < χ_{(1 - α / 2; N - 1)}^{2}

; or aggregate when

χ_{I_{δ}}^{2} > χ_{(α / 2; N - 1)}^{2}

; where

χ_{(1 - α / 2; N - 1)}^{2}

and

χ_{(α / 2; N - 1)}^{2}

are the critical chi‐square values with N –1 degrees of freedom and

α

significance.…”

Section: Morisita's Index and Its Standardizationmentioning

confidence: 99%

“…Several authors tested the significance of I δ by chisquare (Sousa & Cunha, 2018;Souza & Coimbra, 2005;Subedi & Timilsina, 2014). In this test, after calculating…”

Section: Significance Tests For Morisita's Indexmentioning

confidence: 99%

Why do traditional dispersion indices used for analysis of spatial distribution of plants tend to become obsolete?

Alves

Santana

2021

Population Ecology

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“…We collected the data through the inventory of natural stands of C. tamala, which includes 30 destructively sampled trees covering a wider range of H and D. Trees were selected for felling covering all the site conditions and tree size (H and D) distributions across the study sites in and outside of sampling plots. Nondestructive sample data came from a phytosociological survey of same forests [27]. We used systematic sampling plots with a size of 10 m × 10 m. Each plot has 5 m × 5 m subplots at northeast and southwest corners and 1 m × 1 m subplots at four corners.…”

Section: Data Collection and Processingmentioning

confidence: 99%

“…Since bark is a highly traded product, H-D relationship model is an essential decision-making tool both for management institutions and government for estimating forest volume and above-ground biomass, growth and yield modeling, and modeling ecophysiological processbased models of forests. However, research in this direction is limited within the distribution range of C. tamala and has mostly focused on conservation, ethnopharmacological properties, dispersion pattern, cultivation, and harvesting practices [25][26][27][28][29]. In Nepal, Tree Improvement and Silviculture Component, TISC [30], and Poudel et al [31] developed bark biomass production models in farm-grown trees and natural forests [32].…”

Section: Introductionmentioning

confidence: 99%

“…An IVI value was calculated by summing relative density, relative frequency, and relative dominance. The most frequent common associate species, with their IVI values, are Schima wallichii (77.89), Castanopsis indica (26.35, Madhuca latifolia (5.29), and Macaranga postulata (4.30)[27]. The study site falls under the humid subtropical monsoon climatic zone with hot and wet summers and relatively cold winters.…”

mentioning

confidence: 99%

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Height-Diameter Modeling ofCinnamomum tamalaGrown in Natural Forest in Mid-Hill of Nepal

Subedi

Oli²,

Shrestha

et al. 2018

International Journal of Forestry Research

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Tree height (H) and diameter at breast height (D) are key variables to calculate tree volume and biomass. We developed a height-diameter (H-D) model forCinnamomum tamalaby evaluating 18 nonlinear models. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), mean bias, Mean Absolute Error (MAE), graphical appearance, and biological logic were the criteria used to evaluate the predictive performance of the models. Gompertz model (M14) performed the best for predicting the total height ofC. tamalatrees with the least RMSE (1.742 m), mean bias (0.012 m), and MAE (1.342 m) and satisfied model assumptions and biological logic. Validation data ranked the Gompertz model as the best model with RMSE (1.546 m), mean bias (-0.106 m), and MAE (1.149 m). Despite the consistent performance of the Gompertz model, it tended to underestimate the height prediction for taller (dominant crown class) and larger trees. Further work on refitting and validation of the proposed model with data from a larger geographic area, wider-ranging sites, and stand conditions is recommended.

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Spatial Distribution and Sequential Sampling of the Banana Root Borer

et al. 2016

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The banana root borer, Cosmopolites sordidus Germar (Coleoptera: Curculionidae), is a major pest in many banana (Musa spp.)‐producing regions of the world, causing yield losses of up to 100%. The aims of this study were to define the spatial distribution pattern of C. sordidus in banana plantations, to determine the probability distribution model that best describes the sampling data, and to develop a sequential sampling plan for this pest. Aggregation was investigated using aggregation indices and theoretical frequency distributions. To quantify the number of insects, 80 traps were distributed in a commercial banana plantation with an area of 4 ha and inspections were performed every 2 wk from 2005 to 2008. The sequential sampling plan was based on Wald's sequential probability ratio test. The calculated indices indicated moderate aggregation, with a common k parameter of the negative binomial distribution equal to 3.29. The best model for predicting the spatial distribution of the insects was the negative binomial distribution, which fit 93% of the inspections, followed by the Neyman Type A distribution, which fit 90% of the surveys, and Poisson's distribution, which fit only one survey. A sequential sampling plan was generated with Type I and II error rates of 5%, for which 19 was the maximum expected number of samples required to reach a decision for an infestation mean of approximately 3.7 insects per trap. The validation showed that the constructed plan is reliable and provides correct recommendations for decision‐making in pest control. The banana root borer has an aggregated spatial distribution in the field. Negative binomial distribution is the best model to explain species spatial pattern. A sequential sampling plan was developed and validated. The sequential sampling plan showed reliable decision rates. The sequential sampling plan showed a reasonable number of required sample units.

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Distribution Pattern of Cinnamomum tamala in Annapurna Conservation Area, Kaski, Nepal

Cited by 4 publications

References 39 publications

Why do traditional dispersion indices used for analysis of spatial distribution of plants tend to become obsolete?

Why do traditional dispersion indices used for analysis of spatial distribution of plants tend to become obsolete?

Height-Diameter Modeling ofCinnamomum tamalaGrown in Natural Forest in Mid-Hill of Nepal

Spatial Distribution and Sequential Sampling of the Banana Root Borer

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