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Related Concept Videos

Distribution and Dispersion00:54

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To understand intra-specific interactions in populations, scientists measure the spatial arrangement of species individuals. This geographic arrangement is known as the species distribution or dispersion. Highly territorial species exhibit a uniform distribution pattern, in which individuals are spaced at relatively equal distances from one another. Species that are highly tied to particular resources, such as food or shelter, tend to concentrate around those resources, and thus exhibit a...
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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.
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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Related Experiment Video

Updated: Jul 24, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Joint species distribution models with imperfect detection for high-dimensional spatial data.

Jeffrey W Doser1,2, Andrew O Finley2,3, Sudipto Banerjee4

  • 1Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA.

Ecology
|July 10, 2023
PubMed
Summary
This summary is machine-generated.

A new spatial factor multi-species occupancy model effectively estimates species distributions by accounting for species correlations, imperfect detection, and spatial autocorrelation. This approach improves ecological predictions compared to models ignoring these factors.

Keywords:
BayesianNearest Neighbor Gaussian Processlatent factoroccupancy model

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Area of Science:

  • Ecology
  • Conservation Biology
  • Computational Biology

Background:

  • Estimating species distributions and biodiversity is crucial for ecology and conservation.
  • Joint species distribution models (JSDMs) analyze multi-species detection-nondetection data but face challenges like species correlations, imperfect detection, and spatial autocorrelation.
  • Few methods simultaneously address all three complexities in JSDMs.

Purpose of the Study:

  • To develop a spatial factor multi-species occupancy model that simultaneously accounts for species correlations, imperfect detection, and spatial autocorrelation.
  • To ensure computational efficiency for large datasets with many species and spatial locations.
  • To provide a user-friendly tool for analyzing complex ecological data.

Main Methods:

  • Developed a spatial factor multi-species occupancy model using spatial factor dimension reduction and Nearest Neighbor Gaussian Processes.
  • Compared the proposed model's performance against five alternative models that addressed subsets of the complexities.
  • Implemented the models in the open-source R package spOccupancy.

Main Results:

  • Simulations showed that ignoring species correlations, imperfect detection, or spatial autocorrelation leads to inferior predictive performance.
  • The proposed spatial factor multi-species occupancy model demonstrated superior predictive performance in a case study of 98 bird species across the continental US.
  • The impact of ignoring complexities varies depending on study objectives.

Conclusions:

  • The developed spatial factor multi-species occupancy model provides a robust framework for understanding spatial variation in species distributions and biodiversity.
  • The spOccupancy R package offers an accessible tool for ecologists to apply advanced occupancy modeling techniques.
  • Addressing multiple data complexities simultaneously is essential for accurate ecological inference and conservation planning.