Drivers of Forest Structural Complexity in Mountain Forests of Nepal
Bibliographic Data
| ID | 13907761 |
|---|---|
| Authors | Prakash Basnet (0009-0008-9979-6964, University of Göttingen, corresponding author), Smita Das (0000-0002-4834-6436, Tropical Silviculture and Forest Ecology, University of Göttingen, Büsgenweg 1, 37077 Göttingen, Germany), Dirk Hölscher (0000-0002-7097-3102, Tropical Silviculture and Forest Ecology, University of Göttingen, Büsgenweg 1, 37077 Göttingen, Germany), Kerstin Pierick (0000-0002-1313-4601, University of Göttingen), Dominik Seidel (0000-0003-4131-9424, University of Göttingen) |
| Year | 2025 |
| Volume | 45 |
| Issue | 1 |
| Publication date | 2025-02-13 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Mountain Research and Development (JOURNAL) |
| Journal identifiers | ISSN: 0276-4741 • E-ISSN: 1994-7151 |
| Publisher | International Mountain Society (PUBLISHER • CH) |
| DOI | 10.1659/mrd.2024.00009 |
| OpenAlex | W4407556589 |
| Language | EN |
| Citations received | 2 |
| References cited | 1 |
Forests in the Himalayan region are crucial for maintaining the region's ecological balance, conserving biodiversity, and supporting the livelihoods of local people. However, because of limited accessibility and an adverse climate, scientific studies on how forest functions in this region depend on ecological drivers are rare. We used a handheld mobile laser scanner to assess the forest structural complexity (FSC) in the Annapurna Conservation Area of Nepal and related this to its potential drivers, including forest disturbances. Based on stratified sampling, we selected 69 plots across a gradient of elevations and precipitations. Other factors that might influence FSC were obtained from forest inventory data, climatic databases, the Google Earth platform, and digital elevation models. Using simple linear regression and multiple regression analysis, we tested for the dependency of FSC, measured using the box dimension (Db), on influential predictor variables. Overall, explanatory variables strongly influenced FSC (adjusted R2 = 0.60, P < 0.001), with Db being affected by the number of trees, the maximum height of the forests, species diversity, north-facing aspect, soil pH, and forest disturbance. Surprisingly, climatic variables, precipitation, and temperature did not show any effect on FSC. The LiDAR-based approach to FSC used in our study enabled rapid assessment in hard-to-access regions. It can be used to inform effective management and conservation, for example, in monitoring development over time or for benchmarking
Agroforestry · Biology · Geography · Physical geography · Structural complexity · African Botany and Ecology Studies · Ecology and Vegetation Dynamics Studies · Environmental Science · Forest ecology and management · Ecology · Forestry
| Citations per year | 2 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |