Spatial Autocorrelation and its Relationships with Community Dynamics, Soil and Site quality factors: A study from Arid desert with Different Magnitudes of Resource Pulses
Mathur Manish*,**, Sundaramoorthy S.
Plant Ecology Laboratory, Jai Narayan Vyas University, Jodhpur, India
*Present Address: Central Arid Zone Research Institute, Jodhpur, India
**Corresponding author E-mail: firstname.lastname@example.org; email@example.com
Spatial autocorrelation (SAc) is a useful descriptor for horizontal assemblage of plant. Most of the spatial autocorrelation studies have focused on the measurement of spatial dependency, however majority of them did not addressed the three major attributes, a. how spatial autocorrelation changes with different temporal events, b. how the spatial autocorrelation information can be use to predict other community variables and c. what are the underlying factors associated with patterns of autocorrelation. Objectives of the present study encompasses temporal changes in spatial autocorrelation under different pulse events (pulse, rain; inter-pulse, winter; and non-pulse, summer) in arid plant communities and factors associated with spatial autocorrelation. During different temporal events both Moran's I and Geary's C indices showed positive and significant spatial autocorrelation however, the strength of autocorrelation was subsequently decreased from pulse to nonpulse events. Regression analysis revealed that community, soil and site quality factors influenced the patterns and strength of spatial autocorrelation. Among the soil factors, organic carbon, electric conductivity and the available phosphorus were identified as the significant controlling factors for SAc. Community indices like diversity, richness, evenness, dominance and Community Maturity Index were also associated with spatial autocorrelation. Within such specific set of environmental heterogeneity (arid region), species richness form 6 to 9 were identified as threshold limit for positive autocorrelation. Among site quality parameters percent bare surface area and species composition of climax species can be use as predictor's for changes in spatial autocorrelation specifically in arid region.