Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Life Histories01:29

Life Histories

Constrained by limited energy and resources, organisms must compromise between offspring quantity and parental investment. This trade-off is represented by two primary reproductive strategies; K-strategists produce few offspring but provide substantial parental support, whereas r-strategists produce much progeny that receives little care. These strategies are related to an organism’s survival likelihood across its lifespan, which is represented by a survivorship curve. Three general types of...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Ecological Succession02:17

Ecological Succession

Ecological succession is influenced by the processes of facilitation, inhibition, and toleration. Facilitation occurs when early successional species create more favorable ecological conditions for subsequent species, such as enhanced nutrient, water, or light availability. In contrast, inhibition happens when early successional species create unfavorable ecological conditions for potential successive species, such as limiting resource availability. In some cases, later successional species...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Petersen estimator, Chapman adjustment, list effects, and heterogeneity.

Biometrics·2016
Same author

On population size estimators in the Poisson mixture model.

Biometrics·2013
Same author

On the nonidentifiability of population sizes.

Biometrics·2008
Same author

Computing an NPMLE for a mixing distribution in two closed heterogeneous population size models.

Biometrical journal. Biometrische Zeitschrift·2008
Same author

On comparison of mixture models for closed population capture-recapture studies.

Biometrics·2008
Same author

An empirical bayesian method for detecting differentially expressed genes using EST data.

International journal of plant genomics·2008
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
Same journal

A Bayesian phase I/II platform design with data augmentation accounting for delayed outcomes.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Jun 25, 2026

Quantifying Corticolous Arthropods Using Sticky Traps
05:28

Quantifying Corticolous Arthropods Using Sticky Traps

Published on: January 19, 2020

Comparing species assemblages via species accumulation curves.

Chang Xuan Mao1, Jun Li

  • 1Department of Statistics, University of California, Riverside, Riverside, California 92521, USA. cmao@stat.ucr.edu

Biometrics
|February 13, 2009
PubMed
Summary
This summary is machine-generated.

Comparing species assemblages using incidence data is challenging. New eigenvalue-adjusted chi-squared (Eva-chi(2)) and Eva-bootstrap tests offer robust statistical methods for ecological comparisons.

More Related Videos

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Related Experiment Videos

Last Updated: Jun 25, 2026

Quantifying Corticolous Arthropods Using Sticky Traps
05:28

Quantifying Corticolous Arthropods Using Sticky Traps

Published on: January 19, 2020

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Area of Science:

  • Ecology
  • Ecological Statistics

Background:

  • Comparing species assemblages with incidence data is crucial in ecology.
  • Current methods rely on visual inspection or ad hoc confidence bands of species accumulation curves, which can be unreliable.

Purpose of the Study:

  • To develop and evaluate robust statistical tests for comparing species assemblages based on incidence data.
  • To address the challenges associated with traditional comparison methods.

Main Methods:

  • A chi-squared (chi(2)) test was developed for comparing species assemblages.
  • Eigenvalue decomposition was used to adjust the chi(2) test, overcoming computational issues.
  • The bootstrap method was employed to approximate the distribution of the test statistic, leading to the Eva-bootstrap test.

Main Results:

  • The eigenvalue adjusted (Eva) chi(2) test and the Eva-bootstrap test were assessed through simulation studies.
  • Both proposed tests demonstrated effectiveness in comparing species assemblages.

Conclusions:

  • The Eva-chi(2) and Eva-bootstrap tests provide statistically sound and computationally feasible approaches for comparing species assemblages.
  • These methods were successfully applied to a case study involving two woody seedling species assemblages.