Spatial correlations between landscape patterns and net primary productivity: A case study of the Shule River Basin, China
Human activities and environmental degradation have resulted in landscape pattern changes and can eventually profoundly affect net primary productivity (NPP) at different scales worldwide. A comprehensive understanding of how the relationship between landscape patterns (composition and configuration...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/83be242cecb3442a823e7d849369b5f1 |
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| Sumario: | Human activities and environmental degradation have resulted in landscape pattern changes and can eventually profoundly affect net primary productivity (NPP) at different scales worldwide. A comprehensive understanding of how the relationship between landscape patterns (composition and configuration) and NPP changes across scales, is helpful for landscape planning and ecological protection and restoration. However, relevant research is currently understudied. Therefore, this study selected 39 landscape metrics and 5 types of land use in the Shule River Basin (SRB), and analysed their correlation under eight different scales via multiple linear regression models, aiming to determine the core landscape metrics to assess the NPP. Results indicate obvious spatial variations in the landscape metrics. At the same time, NPP in SRB was relatively small and showed obvious spatial heterogeneity. Landscape metrics and NPP showed different degrees of positive or negative correlation at different grid scales, and there were higher correlation at the 30 km scale. The increase in patch fragmentation and diversity promoted an increase in NPP. The correlation between landscape metrics and NPP was higher and more significant at the class level than at the landscape level, except in the case of unused land. Configuration metric (patch density and patch richness) explained 68% of the variation in NPP at the landscape level. At the class level, composition metrics (class area and percentage of landscape) played an important role in farmland, forestland, and grassland, while edge density (configuration metric) played an absolute role in the built-up land and unused land; overall, the effectiveness of the model was stronger at the class level than at the landscape level. The generated regression model allows us to quantitatively understand how to characterize changes in NPP through changes in landscape patterns. Appropriate landscape pattern and optimal scale should be considered in landscape planning and land use management to reduce the expected ecological impact. |
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