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Threats to Biodiversity01:50

Threats to Biodiversity

There have been five major extinction events throughout geological history, resulting in the elimination of biodiversity, followed by a rebound of species that adapted to the new conditions. In the current geological epoch, the Holocene, there is a sixth extinction event in progress. This mass extinction has been attributed to human activities and is thus provisionally called the Anthropocene. In 2019 the human population reached 7.7 billion people and is projected to comprise 10 billion by...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic 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)...
Conservation of Small Populations02:04

Conservation of Small Populations

Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less likely to...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic 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...
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Multiple Allele Traits

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Related Experiment Video

Updated: Jun 13, 2026

Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid
09:09

Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid

Published on: August 8, 2017

Mapping brucellosis increases relative to elk density using hierarchical Bayesian models.

Paul C Cross1, Dennis M Heisey, Brandon M Scurlock

  • 1Northern Rocky Mountain Science Center, United States Geological Survey, Bozeman, Montana, United States of America. pcross@usgs.gov

Plos One
|April 30, 2010
PubMed
Summary
This summary is machine-generated.

Higher elk densities correlate with increased brucellosis transmission. Management strategies targeting broad elk population reductions are weakly supported, necessitating research into disease dynamics within large herds.

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Jun 13, 2026

Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid
09:09

Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid

Published on: August 8, 2017

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Wildlife disease ecology
  • Epidemiology of infectious diseases in large mammals
  • Conservation biology

Background:

  • Host density is critical for disease management, yet empirical data in large mammals are scarce.
  • Brucellosis (Cervus elaphus) poses a significant challenge to wildlife and livestock health.
  • Understanding host-parasite dynamics is essential for effective disease control.

Purpose of the Study:

  • To empirically estimate the relationship between host density and brucellosis transmission in elk (Cervus elaphus).
  • To assess the impact of management practices and environmental factors on brucellosis seroprevalence.
  • To inform disease management strategies for brucellosis in elk populations.

Main Methods:

  • Utilized a 19-year dataset of over 6400 brucellosis tests from adult female elk in Wyoming.
  • Applied hierarchical Bayesian methods to analyze seroprevalence data, accounting for site and year effects.
  • Correlated brucellosis seroprevalence with elk densities at the elk management unit scale.

Main Results:

  • Management-captured elk were over two times more likely to be seropositive than hunted elk.
  • Initial higher seroprevalence near feeding grounds diminished over time, with distant areas showing comparable rates by 2009.
  • Increased brucellosis seroprevalence correlated with elk densities, though the relationship's linearity could not be determined.
  • Brucellosis is expanding into new elk populations, complicating eradication efforts.

Conclusions:

  • Broad-scale elk density reduction strategies for brucellosis control are weakly supported.
  • Further research is needed on disease dynamics driven by large, aggregated elk groups.
  • Host population dynamics significantly influence disease reservoir potential and management outcomes.