Estimating Geographic Variation in Allometric Growth and Body Condition of Blue Suckers with Quantile Regression

Cade, Brian S.; Terrell, James W.; Neely, Ben C.

doi:10.1080/00028487.2011.641885

Cited by 12 publications

(40 citation statements)

References 40 publications

(97 reference statements)

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“…Linear quantile regression is a more statistically rigorous tool that can be used to compare changes in weight–length relationships before and after specific treatments or management actions, or to compare the weight–length relationships of multiple populations (e.g., Cade et al. , ; Crane et al. ; Crane and Farrell ).…”

Section: Discussionmentioning

confidence: 99%

“…Averaging quantile estimates across multiple populations—based on constructing appropriate contrasts in the linear model—is an alternative approach to having a single “reference” population (Cade et al. ).…”

Section: Discussionmentioning

confidence: 99%

“…Previous quantile regression analyses (i.e., Cade et al. , ) have shown that a single linear model can provide estimates for separate population‐level β 0 and β 1 values when a categorical factor (i.e., reference and each state population identifier) and its interaction with the continuous predictor variable (i.e., log 10 TL) are included in the same model. I used the model

Q_{{log}_{10} W} false(τ false| {log}_{10} TL, I_{j} false) = {normalβ}_{0 j} false(τ false) I_{j} + {normalβ}_{1 j} false(τ false) I_{j} {log}_{10} TL,

where β 0 j and β 1 j are the intercepts and slopes for each reference and state population, and I j is the population identifier.…”

Section: Methodsmentioning

confidence: 99%

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Quantile Regression Estimates of Body Weight at Length in Walleye

Ranney

2018

N American J Fish Manag

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Quantile regression is a method of estimating fish weight at length for alternate portions of a probability distribution, but it is an approach that has not received much attention in fisheries literature. Quantile regression can provide estimates of any quantile of weight at length without bias (including the 75th quantile, which was often the focus of standard weight [Ws] equations), and this is more advantageous than previously defined Ws equations derived from linear or quadratic regression methods. The goal of this study was to demonstrate the utility of quantile regression as a tool to assess fish weight at length at various portions of the probability distribution without bias using Walleye Sander vitreus as a case study. Quantile regression models at the 75th quantile were developed for three randomly selected Walleye populations from Georgia and South Dakota and compared with a large (N = 33,589) reference population. Bootstrap resampling procedures indicated that only one population from the state of Georgia had an intercept and slope similar to the reference population. For the one population that had similar intercept and slope to the reference population, predictions of weight at various lengths still fell below the 95% CIs for predicted weights of the reference population, suggesting that slight differences in intercepts and slopes in allometric relationships can result in predicted weights that still differ at some lengths. Predicted weights of Walleye derived from the 10th, 25th, 50th, 75th, and 90th quantiles were used to demonstrate how individuals and populations may be compared at different management targets. Overall, this study demonstrates the relative ease with which quantile regression may be used to compare fish body condition between populations without bias.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Q_{{log}_{10} W} false(τ false| {log}_{10} TL, I_{j} false) = {normalβ}_{0 j} false(τ false) I_{j} + {normalβ}_{1 j} false(τ false) I_{j} {log}_{10} TL,

where β 0 j and β 1 j are the intercepts and slopes for each reference and state population, and I j is the population identifier.…”

Section: Methodsmentioning

confidence: 99%

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Quantile Regression Estimates of Body Weight at Length in Walleye

Ranney

2018

N American J Fish Manag

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“…Quantile regression is a method for estimating the functional relations between variables for all portions of a probability distribution [38]. Instead of focusing on the mean, quantile regression gives the relationship between at least one covariate and the conditional median or other quantiles of the distribution of the response variable, which is usually between zero and one [39][40][41][42]. Because this method fits the regression line to a portion of the distribution [43], it can be used to retrieve boundary lines in two-dimensional spectral space [44,45].…”

Section: Quantile Regression Methodsmentioning

confidence: 99%

A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters

Ahmadian

Demattê

et al. 2016

Remote Sensing

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Extraction of vegetation information from remotely sensed images has remained a long-term challenge due to the influence of soil background. To reduce this effect, the slope and intercept of the soil line (SL) should be known to calculate SL-related vegetation indices (VIs). These VIs can be used to estimate the biophysical parameters of agricultural crops. However, it is a difficult task to retrieve the SL parameters under the vegetation canopy. A feasible method for retrieving these parameters involves extracting the bottom boundary line in two-dimensional spectral spaces (i.e., red and near-infrared bands). In this study, the slope and intercept of the SL was extracted from Landsat 8 OLI images of a test site in northeastern Germany. Different statistical methods, including the Red-NIR min method, quantile regression method (using a floating tau with the smallest p-value), and a new approach proposed in this paper using a fixed quantile tau known as the diffuse non-interceptance (DIFN) value, were applied to retrieve the SL parameters. The DIFN value describes the amount of light visible below the canopy that reaches the soil surface. Therefore, this value can be used as a threshold for retrieving the bottom soil line. The simulated SLs were compared with actual ones extracted from ground truth data, as recorded by a handheld spectrometer, and were also compared with the SL retrieved from bare soil pixels of the Landsat 8 image collected after harvest. Subsequently, the SL parameters were used to separately estimate the dry biomasses of winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.) at the local and field scales using different SL-related vegetation indices. The SL can be retrieved more accurately at the local scale compared with the field scale, and its simulation can be critical in the field due to significant differences from the actual SL. Moreover, the slope and intercept of the simulated SLs found using the floating and fixed quantile tau (slope ≈ 1.1 and intercept ≈ 0.05) show better agreement with the actual SL parameters (slope ≈ 1.2 and intercept ≈ 0.03) in the late growing stages (i.e., end of ripening and senescence stages) of crops. The slope and intercept of the soil line extracted from bare soil pixels of the Landsat 8 OLI data after harvest (slope = 1.3, intercept = 0.03, and R 2 = 0.94) are similar to those of the simulated SL. The correlation coefficient (R 2 ) of the simulated SLs are greater than 0.97 during different growing stage and all of the SL parameters are statistically significant (p < 0.05) at the local scale. The results also imply the need for different vegetation indices to best retrieve the crop biomass depending on the growing stage, but relatively small differences in performances were observed in this study.

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“…Instead of focusing on the mean, quantile regression fits the linear or non-linear regression on the selected quantile (0-1) of the distribution of the response variable [33][34][35]. Because quantile regression fits regression curves to part of the distribution, this method has proved its ability to extract boundary lines in two-dimensional scatter plots [36,37].…”

Section: Quantile Regressionmentioning

confidence: 99%

A Study of Soil Line Simulation from Landsat Images in Mixed Grassland

Guo

2013

Remote Sensing

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Abstract:The mixed grassland in Canada is characterized by low to medium green vegetation cover, with a large amount of canopy background, such as non-photosynthetic vegetation residuals (litter), bare soil, and ground level biological crust. It is a challenge to extract the canopy information from satellite images because of the influence of canopy background. Therefore, this study aims to extract a soil line, a representation of bare soil with litter and soil crust in the surface, from Landsat images to reduce the background effect. Field work was conducted in the West Block of Grasslands National Park (GNP) in Canada, which represents the northern mixed grassland from late June to early July 2005. Six TM images with either no or only a small amount of cloud content were collected in 2005. In this study, soil lines were extracted directly from images by quantile regression and the (R, NIR min ) method. The results show that, (1) both cloud and cloud shadow have obvious influence on simulating soil line automatically from images; (2) green up and late senescence seasons are relatively better for soil line simulation; (3) the (R, NIR min ) method is better for soil line simulation than quantile regression to extract green biomass or green cover information.

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Estimating Geographic Variation in Allometric Growth and Body Condition of Blue Suckers with Quantile Regression

Cited by 12 publications

References 40 publications

Quantile Regression Estimates of Body Weight at Length in Walleye

Quantile Regression Estimates of Body Weight at Length in Walleye

A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters

A Study of Soil Line Simulation from Landsat Images in Mixed Grassland

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