气候变化领域集成服务门户

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 degrees 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 degrees 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 0.04ha nested quadrat. Cumulative analysis showed a maximum of 53 invasive species at 1 degrees 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 other predictor variables (i.e. edaphic and anthropogenic) could be an inclusive input in a future perspective.