Breeding habitat prediction and nest-site characteristics of the fairy pitta (Pitta nympha) in Geoje-si, South Korea: Insights from a species distribution model

The fairy pitta ( Pitta nympha ), listed as Vulnerable by the IUCN, breeds in forested valleys across East Asia, yet its nesting habitat in South Korea remains poorly characterized. This study aimed to identify key environmental drivers of nest-site selection and predict suitable breeding habitats in Geoje-si, a core breeding region in southern Korea. Field surveys conducted from 2019 to 2023 confirmed 47 nests, primarily located on rocky substrates or in the forks of large-diameter trees (≥60 cm DBH). Using presence-only data, we developed a MaxEnt species distribution model incorporating ten ecologically relevant variables. Model optimization identified the LQH feature class with a regularization multiplier of 4 as the best-performing configuration, yielding strong predictive accuracy (AUC = 0.881 ± 0.026). The most influential predictors were the Topographic Wetness Index (TWI), dominant tree species, and slope aspect, suggesting a preference for moist, broadleaf-dominated forests on southwest-facing slopes. Habitat suitability was concentrated in low-disturbance, structurally complex valleys, yet only 2.25 km² (23.8%) of the predicted high-suitability area overlapped with legally protected zones. These findings underscore a spatial mismatch between ecological value and conservation coverage, highlighting the need to expand protection to climatically buffered breeding habitats outside current protected areas.

Authors

Institutions

Publication Details

Journal
Global Ecology and Conservation
Published
2025-10-30
DOI
https://doi.org/10.1016/j.gecco.2025.e03939
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Breeding habitat prediction and nest-site characteristics of the fairy pitta (Pitta nympha) in Geoje-si, South Korea: Insights from a species distribution model

Chung-Hyeon Oh, Jared D. Wolfe, Soo-Dong Lee, Bong-Gyo Cho et al.
Global Ecology and Conservation
Species Distribution and Climate Change
article

Breeding habitat prediction and nest-site characteristics of the fairy pitta (Pitta nympha) in Geoje-si, South Korea: Insights from a species distribution model

Chung-Hyeon Oh, Jared D. Wolfe, Soo-Dong Lee, Bong-Gyo Cho, Byeong-Hyeok Yu, Jeongho Lee, SunGu Lee
article en

Abstract

The fairy pitta ( Pitta nympha ), listed as Vulnerable by the IUCN, breeds in forested valleys across East Asia, yet its nesting habitat in South Korea remains poorly characterized. This study aimed to identify key environmental drivers of nest-site selection and predict suitable breeding habitats in Geoje-si, a core breeding region in southern Korea. Field surveys conducted from 2019 to 2023 confirmed 47 nests, primarily located on rocky substrates or in the forks of large-diameter trees (≥60 cm DBH). Using presence-only data, we developed a MaxEnt species distribution model incorporating ten ecologically relevant variables. Model optimization identified the LQH feature class with a regularization multiplier of 4 as the best-performing configuration, yielding strong predictive accuracy (AUC = 0.881 ± 0.026). The most influential predictors were the Topographic Wetness Index (TWI), dominant tree species, and slope aspect, suggesting a preference for moist, broadleaf-dominated forests on southwest-facing slopes. Habitat suitability was concentrated in low-disturbance, structurally complex valleys, yet only 2.25 km² (23.8%) of the predicted high-suitability area overlapped with legally protected zones. These findings underscore a spatial mismatch between ecological value and conservation coverage, highlighting the need to expand protection to climatically buffered breeding habitats outside current protected areas.

Global Ecology and ConservationVol. 64
Michigan Technological University (US), University of Seoul (KR), Gyeongsang National University (KR), Korea Aerospace Research Institute (KR)
Openalex Percentile: Top 83%
Species Distribution and Climate Change
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.