Optimum Nonparametric Estimation of Population Density Based on Ordered Distances

Patil, S. A.; Kovner, J. L.; Burnham, Kenneth P.

doi:10.2307/2530307

Cited by 19 publications

(29 citation statements)

References 5 publications

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“…It is beyond the scope of this paper to derive these formulae. The latter paper revises their earlier work and the estimates, which we state without proof, come from the suggested formulae in Patil et al (1982).…”

Section: A Non-parametric Estimatementioning

confidence: 66%

“…In contrast, the use of non-parametric statistics to develop a density estimate would require no assumption about the underlying distribution of organisms. Patil et al (1979) and Patil et al (1982) developed a distance-based, non-parametric estimate of plant density. It is beyond the scope of this paper to derive these formulae.…”

Section: A Non-parametric Estimatementioning

confidence: 99%

“…In the discussion section of Patil et al (1982), the authors note that the price for a robust density estimate "is the considerable increase in variance as compared to a parametric estimator which assumes a specific spatial distribution of plants. "…”

Section: An Estimate Of the Variance Is Given Bymentioning

confidence: 99%

“…Using these formulae with the modifications in Patil et al (1982) leads to the following. Let w be the upper limit for the radius beyond which one does not search.…”

Section: Truncated Samplingmentioning

confidence: 99%

“…Script to Estimate Density Non-parametrically Appendix C described a distance-based, non-parametric estimate of plant density developed by Patil et al (1982). The data for this method consist of the distances of the nearest individual to each sample point.…”

Section: ## [1] 153559 299691mentioning

confidence: 99%

See 4 more Smart Citations

Reproductive success of Field Sparrows (Spizella Pusilla) in response to invasive Morrow’s Honeysuckle: does Morrow’s Honeysuckle promote population sinks?

McChesney¹,

Anderson²

2015

The Wilson Journal of Ornithology

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This document is an introduction to the use of the point-centered quarter method. It briefly outlines its history, its methodology, and some of the practical issues (and modifications) that inevitably arise with its use in the field. Additionally this paper shows how data collected using point-centered quarter method sampling may be used to determine importance values of different species of trees and describes and derives several methods of estimating plant density and corresponding confidence intervals. New to this revision is an appendix of R functions to carry out these calculations.

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Section: A Non-parametric Estimatementioning

confidence: 66%

Section: A Non-parametric Estimatementioning

confidence: 99%

Section: An Estimate Of the Variance Is Given Bymentioning

confidence: 99%

“…Using these formulae with the modifications in Patil et al (1982) leads to the following. Let w be the upper limit for the radius beyond which one does not search.…”

Section: Truncated Samplingmentioning

confidence: 99%

Section: ## [1] 153559 299691mentioning

confidence: 99%

See 3 more Smart Citations

Reproductive success of Field Sparrows (Spizella Pusilla) in response to invasive Morrow’s Honeysuckle: does Morrow’s Honeysuckle promote population sinks?

McChesney¹,

Anderson²

2015

The Wilson Journal of Ornithology

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On tree intensity estimation for forest inventories: Some statistical issues

2011

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Registro de acceso restringido Este recurso no está disponible en acceso abierto por política de la editorial. No obstante, se puede acceder al texto completo desde la Universitat Jaume I o si el usuario cuenta con suscripción. Registre d'accés restringit Aquest recurs no està disponible en accés obert per política de l'editorial. No obstant això, es pot accedir al text complet des de la Universitat Jaume I o si l'usuari compta amb subscripció. Restricted access item This item isn't open access because of publisher's policy. The full--text version is only available from Jaume I University or if the user has a running suscription to the publisher's contents.

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Distance‐based methods for estimating density of nonrandomly distributed populations

Shen

Wang

2020

Ecology

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Population density is the most basic ecological parameter for understanding population dynamics and biological conservation. Distance-based methods (or plotless methods) are considered as a more efficient but less robust approach than quadrat-based counting methods in estimating plant population density. The low robustness of distance-based methods mainly arises from the oversimplistic assumption of completely spatially random (CSR) distribution of a population in the conventional distance-based methods for estimating density of non-CSR populations in natural communities. In this study we derived two methods to improve on density estimation for plant populations of non-CSR distribution. The first method modified an existing composite estimator to correct for the long-recognized bias associated with that estimator. The second method was derived from the negative binomial distribution (NBD) that directly deals with aggregation in the distribution of a species. The performance of these estimators was tested and compared against various distance-based estimators by both simulation and empirical data of three large-scale stem-mapped forests. Results showed that the NBD point-to-tree distance estimator has the best and most consistent performance across populations with vastly different spatial distributions. This estimator offers a simple, efficient and robust method for estimating density for empirical populations of plant species.

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Optimum Nonparametric Estimation of Population Density Based on Ordered Distances

Cited by 19 publications

References 5 publications

Reproductive success of Field Sparrows (Spizella Pusilla) in response to invasive Morrow’s Honeysuckle: does Morrow’s Honeysuckle promote population sinks?

Reproductive success of Field Sparrows (Spizella Pusilla) in response to invasive Morrow’s Honeysuckle: does Morrow’s Honeysuckle promote population sinks?

On tree intensity estimation for forest inventories: Some statistical issues

Distance‐based methods for estimating density of nonrandomly distributed populations

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