Bioclimatic and remote sensing factors are better key indicators than local topography and soil: Vegetation composition variability in forests of Pakistan's Spin Ghar Mountain range

Abstract

The composition, structure and distribution of vegetation are influenced by diverse environmental factors. Such research inquiries provide the initial data for future conservation and management efforts. The study area of North Waziristan district, Khyber Pakhtunkhwa, Pakistan comprises diverse forests of the Spin Ghar Mountain Range (at the border areas of Pakistan and Afghanistan), and a highly remote, mountainous, and unexplored region. There was little information on the complex relationships that existed between the study area's ambient environment and vegetation. This study hypothesized that the varying environmental complexity in the study area and vegetation variety may be significantly correlated, and the ranking of leading influencing factors might enhance our ecological understanding. A total of 61 study sites comprising 183 transects (50 m each) were randomly selected to record the vegetation-environment data from January-2018 to December-2020 (3 years). Monte Carlo permutation testing, hierarchical clustering of study samples, indicator species analysis, and ordination were applied to assess the sampling data. The results indicated that there were a total of 391 vascular plant species which were further classified into seven significantly different (p < 0.05) plant assemblages, each comprising of a distinct species makeup. A variety of different environmental variables (topographic (06), bioclimatic (19), edaphic (09), remote sensing, and anthropogenic predictors (16)) were considered. Simple term effects testing results depicted the significant (p(adj) < 0.05) role of 39 variables initially, whereas, conditional term effects testing (with variance inflation factor (VIF) threshold value of < 10, and forward selecting variables that provided the most unique information) results highlighted the prominent role of eight (08) contributors. The results of the latter analysis ranked the mean temperature of the warmest quarter (Bio10) as the most influencing factor, followed by Normalized Difference Vegetation Index (NDVI), longitude, continuous heat insulation load index (CHILI), precipitation of the warmest quarter (Bio18), global human modification of the terrestrial systems (gHM), organic carbon density (OCD), and annual precipitation (Bio12). This study concluded that the vegetation variability in the study area was significantly correlated with the prevailing environment, and considered bioclimatic and remote sensing factors were better key indicators for any vegetation distribution when the study was conducted on a large spatial scale. Based on these results, the anticipated future variations in climate, particularly global warming, lengthy drought spells, and population explosion might remarkably lead to decline in local plant species richness and distribution. For the study area to guard its priceless biodiversity for future generations, careful and prompt conservation and management planning are required.