Related Experiment Video
Updated: May 17, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Experimentally testing the accuracy of an extinction estimator: Solow's optimal linear estimation model
Christopher F Clements1, Nicholas T Worsfold, Philip H Warren
1Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK.
The Journal of Animal Ecology
|October 17, 2012
Summary
Optimal linear estimation, a method for predicting species extinction times from sighting data, was tested using microcosm experiments. Results show accuracy depends on search effort and species identity, highlighting the need to account for these factors.
Area of Science:
- Ecology
- Conservation Biology
- Mathematical Biology
Background:
- Mathematical models, such as optimal linear estimation (Weibull model), are used to predict species extinction times from sighting data.
- Previous assessments of optimal linear estimation's accuracy are limited, often relying on field data with imprecise extinction dates and poor sighting records.
- Microcosm experiments offer a controlled environment to test extinction prediction accuracy against known extinction dates under varied conditions.
Purpose of the Study:
- To rigorously evaluate the accuracy of the optimal linear estimation method for inferring extinction times.
- To assess the influence of search effort, search regimes, sighting frequencies, and extinction rates on estimation accuracy.
- To determine the impact of observer-controlled and inherent system variables on the reliability of extinction time predictions.
Main Methods:
- Utilized experimental microcosm data with known extinction dates to test the optimal linear estimation technique.
- Varied environmental conditions, species identity, and species richness to create diverse extinction rates.
- Analyzed the impact of different search efforts, search regimes, and sighting frequencies on estimation accuracy.
Main Results:
- Optimal linear estimation generally provides accurate and precise extinction time estimates.
- Estimation accuracy is significantly influenced by observer-controlled factors (e.g., search effort) and inherent system features (e.g., species identity).
- The method is susceptible to both overestimation and underestimation of the actual extinction date.
Conclusions:
- Microcosm experiments provide a robust framework for validating extinction prediction models.
- Search effort, search regularity, and species identity are critical variables that must be considered when applying and evaluating extinction predictors.
- Future research should incorporate these factors to improve the reliability of extinction time estimations.
Related Concept Videos
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
01:13Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:09Censoring Survival Data
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
01:20Calibration Curves: Linear Least Squares
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
For data that follow a straight line, the standard method for fitting is the linear...