Does spatial heterogeneity of landscape explain the process of plant invasion? A case study of Hyptis suaveolens from Indian Western Himalaya

Abstract

Biological invasion is probably one of the most serious threats to biodiversity after climate change. Landscape distinguished by the heterogeneity of structure, forms, human interferences, and environmental settings plays an important role in the establishment and spread of invasive species. We investigated the effect of the spatial heterogeneity for a selected landscape upon the invasion process through a case study of Hyptis (Hyptis suaveolens) in the Indian Western Himalayan region. The selected study site constitutes a heterogeneous landscape of 32,300 ha in the state of Uttarakhand, placed at the lower elevation of the Indian Himalaya. The landscape has varying levels and patterns of Hyptis invasion. We quantified the spatial heterogeneity in terms of elevation; distance from the canal, river, road, and settlement; and 18 landscape metrics (at the patch and land use class level) to investigate their influence on the invasion; for this purpose, a logistic regression model was developed. The invasion of Hyptis was found to be governed by spatial heterogeneity. The highest probability of invasion was found in the areas adjacent to rivers and roads. The analysis at patch level revealed that the invasion is largely governed by the perimeter-area ratio of patches and is positively correlated. This suggests for greater invasion chances in smaller patches as compared with larger ones. The analysis for the land use class metrics indicated a higher influence of edge density expressed as total edge length of patches per unit area, followed by patch density expresses as a total number of patches per unit area. Hence, the landscapes with larger edges and more number of patches are supposed to be more prone to invasion risks. The results of the study can be used by forest managers in designing a landscape-level system to control invasion.

Keywords: Invasive alien plants; Landscape metrics; Logistic regression model; Sub-pixel classification.