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Updated: May 16, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection.

Elise F Zipkin¹, Evan H Campbell Grant, William F Fagan

¹USGS Patuxent Wildlife Research Center, 12100 Beech Forest Rd., Laurel, Maryland 20708, USA. ezipkin@usgs.gov

Ecological Applications : a Publication of the Ecological Society of America

|December 6, 2012

Summary

This summary is machine-generated.

Predicting amphibian wetland use is key for conservation. Wetland hydroperiod and previous year occupancy are the most important factors, improving community-level predictions.

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Last Updated: May 16, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Area of Science:

Ecology
Conservation Biology
Wildlife Management

Background:

Accurate prediction of species occurrences is vital for effective animal community management.
Understanding species-habitat relationships and environmental change impacts is essential for optimal management strategies.

Purpose of the Study:

To develop and evaluate multispecies hierarchical models for estimating amphibian wetland use.
To identify key factors influencing amphibian habitat use and predict future occurrences.

Main Methods:

Utilized five years of monitoring data from Chesapeake and Ohio Canal National Historical Park.
Developed four multispecies hierarchical models incorporating wetland characteristics, environmental trends, and previous occupancy.
Employed a Bayesian approach with receiver operating characteristic area under the curve (ROC AUC) to quantify prediction uncertainty.

Main Results:

Wetland hydroperiod and prior year occupancy were the most significant predictors of amphibian wetland use.
Habitat-only models showed good predictive accuracy, but incorporating previous year's use improved community-level predictions.
The methodology effectively quantified prediction uncertainty and accounted for detection biases.

Conclusions:

Multispecies models are valuable for understanding community-level habitat use drivers.
Previous year's occupancy is a strong predictor of current amphibian wetland use.
The enhanced AUC methodology improves the reliability of model predictions in ecological studies.