Does a Time-Lagged Effect Exist Between Landscape Pattern Changes and Giant Panda Density?

Qingxia Zhao, Qifeng Zhu, Jiqin Huang, Yueduo Cui, Yutai Liu, Dong Chen () and Xuelin Jin ()
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Qingxia Zhao: Shaanxi Key Laboratory of Qinling Ecological Security, Shaanxi Institute of Zoology, Xi’an 710032, China
Qifeng Zhu: Shaanxi Key Laboratory of Qinling Ecological Security, Shaanxi Institute of Zoology, Xi’an 710032, China
Jiqin Huang: Shaanxi Key Laboratory of Qinling Ecological Security, Shaanxi Institute of Zoology, Xi’an 710032, China
Yueduo Cui: Shaanxi Key Laboratory of Qinling Ecological Security, Shaanxi Institute of Zoology, Xi’an 710032, China
Yutai Liu: Shaanxi Key Laboratory of Qinling Ecological Security, Shaanxi Institute of Zoology, Xi’an 710032, China
Dong Chen: Shaanxi Key Laboratory of Qinling Ecological Security, Shaanxi Institute of Zoology, Xi’an 710032, China
Xuelin Jin: Shaanxi Key Laboratory of Qinling Ecological Security, Shaanxi Institute of Zoology, Xi’an 710032, China

Land, 2025, vol. 14, issue 5, 1-18

Abstract: Land use and land cover change (LULCC) can influence giant panda distributions by altering landscape structure and configuration. However, the spatial impacts and potential time lag effects of landscape pattern changes on giant pandas remain underexplored. In this study, we applied a random forest classification method to analyze LULCC in 1990, 2000, and 2010, alongside calculating a set of landscape metrics to assess changes in landscape fragmentation, connectivity, and diversity. Random forest regression models were then used to evaluate the spatial relationships between landscape metrics and giant panda density, with the aim of identifying whether a time lag effect exists. The results revealed the following: (1) The random forest classification achieved high land use classification accuracy. Forests remained the dominant land cover, occupying approximately 97% of the study area throughout the period, with only minor fluctuations observed among other land use types. (2) Landscape metrics indicated increasing landscape fragmentation, connectivity, and diversity. While increased landscape fragmentation can negatively impact giant panda habitat, improvements in landscape connectivity and diversity could mitigate these effects by preserving movement corridors and enhancing habitat accessibility. (3) The strongest correlations between giant panda density and landscape metrics were observed when the time points aligned. Landscape metrics from 2010 showed the highest correlation with the 4th NGPS (around 2010), and landscape metrics from 2000 had the highest correlation with the 3rd NGPS (around 2000). The results revealed that giant panda density responded most strongly to contemporary landscape pattern changes, suggesting an immediate response. However, correlations with earlier landscape metrics also suggest that a relatively weak time lag effect may be present. All landscape metrics were derived from remote sensing data, enabling scalable and repeatable GIS-based analysis. These findings highlight the utility of spatial landscape indicators for monitoring species distribution patterns and underscore the importance of maintaining and enhancing habitat connectivity within giant panda conservation efforts.

Keywords: giant panda; land use and land cover change; landscape pattern; time lag effect (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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