Plant invasion correlation with climate anomaly: an Indian retrospect

Abstract

Plant invasion is highly responsive to rising temperature, altered precipitation and various anthropogenic disturbances. Therefore, climate anomalies might provide opportunities to identify the relationship of past climate in deriving the distribution of invasive species and to detect their probable future distribution. In this work, we studied the correlation of climate anomaly i.e. temperature and precipitation with an indicative map of plant invasive species (1° grid) of India. The indicative map was generated through the plant data available from the project 'Biodiversity Characterization at Landscape Level'. Climate anomaly was calculated and represented by average temperature and precipitation using 'Climate Research Unit' data for the period of 1901 to 2000. A comparison of local geographically weighted regression (GWR) model and a global ordinary least square regression (OLS) model was carried out for statistical analysis to depict the correlation at 1° spatial grids. Overall, 20,501 records with a total of 9112 unique plots and 161 unique invasive species were recorded in the database that shows a maximum of 15 invasive species in a 0.04 ha nested quadrat. Cumulative analysis showed a maximum of 53 invasive species at 1° grid. Individually, GWR could reveal a significant correlation with invasive species distribution for temperature anomaly (r 2 = 0.73, AIC= 2206) and precipitation anomaly (r 2 = 0.74, AIC= 2221), while OLS model did not offer a good correlation (r 2 < 0.001, AIC > 2400) compared to GWR. Combination of temperature and precipitation anomaly (shared model) showed an improved spatial correlation (r 2 > 0.75) using GWR. Variation partitioning revealed the dominant influence (> 0.40 of variation) of temperature anomaly over Deccan Peninsula, Himalaya and North East zone. Influence of precipitation anomaly was more prominent over arid and semi-arid zone explaining > 0.35 of variation. Results revealed the strength of GWR to see the interaction of invasive plant species w.r.t. climate anomalies that explain the influence of spatial variation due to heterogeneity at varying neighbour distances. The significant correlation of invasive species with both the anomalies revealed the affinity of invasive species towards warmer, drier and wet places. This gives an indication that the distribution of invasive species could be triggered by climate anomaly. The use of Communicated by M.

Soil Nutrients Can Influence Exotic Species Richness in Urban Areas: A Case Study from the City of Kolkata

“…For example, the richness of naturalized invasive species was found to be positively related to states receiving higher amounts of precipitation [23] while the probable distribution of Lantana camara and Cassia tora towards northern and north-eastern directions has been demonstrated to be due to changes in moisture availability [24]. The affinity of invasive species towards warmer, drier and wet places was reported in a study by Tripathi et al [25], where a significant correlation of invasive species with anomalies in both temperature as well as precipitation was observed.…”

Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modelling: A case study of Northwest Yunnan, China

“…In addition, GWR model is a local regression model, which can profoundly explain the spatial non-stationarity relationship between response variables and explanatory variables by decomposing global parameters into local parameters (Tripathi et al, 2019a). The regression equation can be developed as (Han et al, 2016;Tripathi et al, 2019b):…”

“…However, the relationship between them often has spatial non-stationarity (i.e., Relationship between independent and dependent variables will change with geographical location) (Gouveia et al, 2013). Geographically weighted regression (GWR) model, which is an extension of traditional regression model (e.g., Ordinary least squares, OLS) (S , tefȃnescu et al, 2017;Tripathi et al, 2019a;Tripathi et al, 2019b;Xue et al, 2020), has become one of the crucial spatial heterogeneity modeling tools (Lu et al, 2020). In recent years, many domestic and foreign scholars have carried out in-depth and extensive research in various fields by using GWR model, including social environmental factors and regional economy, regional house prices and pollution (McCord et al, 2018;Xu et al, 2019), the impacts of environmental heterogeneity and land use change on wild animal distribution (Liu et al, 2019;Wang et al, 2020;Xue et al, 2020), vegetation activity and climate change (Gao et al, 2019).…”

Recent advances in biodiversity and climate change studies in India

“…Climate anomalies may therefore provide opportunities to identify the relationship of past climate in explaining the distribution of invasive species and to detect their probable future distribution. Using a geographically weighted regression (GWR) model, Tripathi et al (2019) reported a spatial correlation of invasive species distribution with temperature (r2 = 0.73, AIC = 2206) and precipitation anomalies (r2 = 0.74, AIC = 2221), and a better spatial correlation (r2 > 0.75) when temperature and precipitation anomalies were considered. The significant correlation of plant invasion and climate anomaly revealed an affinity of invasive species towards warmer, drier, and wet places-with consequent management implications.…”