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Related Experiment Video

Updated: Jun 13, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

On predicting abundance from occupancy.

Andrew R Solow¹, Woollcott K Smith

¹Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, USA. asolow@whoi.edu

The American Naturalist

|May 15, 2010

Summary

This summary is machine-generated.

Predicting species abundance from grid cell occupancy is challenging. A new method using unoccupied and single-individual cells improves abundance prediction for negative binomial distributions.

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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

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Area of Science:

Ecology
Population Biology
Spatial Statistics

Background:

Estimating species abundance is crucial for ecological monitoring.
Predicting abundance from spatial occupancy data is an active research area.
Standard methods often struggle with specific abundance distributions like the negative binomial.

Purpose of the Study:

To develop a method for predicting species abundance from grid-based occupancy data.
To address limitations in predicting abundance when cell counts follow a negative binomial distribution.
To evaluate the performance of the proposed prediction method.

Main Methods:

Utilized a uniform grid overlaid on a region to analyze species occupancy.
Developed a prediction model incorporating the number of unoccupied cells.
Integrated the count of cells with a single individual into the prediction model.
Validated the method using both simulated and real-world ecological data.

Main Results:

Prediction of species abundance solely from unoccupied cells is generally not feasible for negative binomial distributions.
The proposed method, using unoccupied and single-individual cell counts, demonstrated effective abundance prediction.
The method performed well on both simulated datasets and empirical field data.

Conclusions:

A novel and effective method for predicting species abundance from occupancy data has been developed.
This approach enhances ecological monitoring capabilities by providing reliable abundance estimates.
The findings are applicable to situations where species abundance follows a negative binomial distribution.