Analysis of the pattern of potential woody cover in Texas savanna

Abstract

While woody plant encroachment has been observed worldwide in savannas and adversely affected the ecosystem structure and function, a thorough understanding of the nature of this phenomenon is urgently required for savanna management and restoration. Among others, potential woody cover (the maximum realizable woody cover that a given site can support), especially its variation over environment has huge implication on the encroachment management in particular, and on tree-grass interactions in general. This project was designed to explore the pattern of potential woody cover in Texas savanna, an ecosystem with a large rainfall gradient in west-east direction. Substantial random pixels were sampled across the study area from MODIS Vegetation Continuous Fields (VCF) tree cover layer (250m). Since potential woody cover is suggested to be limited by water availability, a nonlinear 99 th quantile regression was performed between the observed woody cover and mean annual precipitation (MAP) to model the pattern of potential woody cover. Research result suggests a segmented relationship between potential woody cover and MAP at MODIS scale. Potential biases as well as the practical and theoretical implications were discussed. Through this study, the hypothesis about the primary role of water availability in determining savanna woody cover was further confirmed in a relatively understudied US-located savanna.

An Assessment of Multiple Drivers Determining Woody Species Composition and Structure: A Case Study from the Kalahari, Botswana

“…Our results generally agree with the observation that the rainfall gradient of the Kalahari is associated with an increase in woody vegetation [16][17][18][19][20]. Interestingly, the highest species richness was recorded at Kuke (Figure 1-Site 7), where the annual precipitation is 450 mm (in the middle of the rainfall gradient).…”

“…Rainfall affects water availability, and this factor has been described as the most important determinant describing woody vegetation communities, particularly as it limits the amount of primary productivity within an area [16][17][18][19][20]. For example, in a continental study of African savannas, Sankaran et al [16] identified that woody cover increased linearly with mean annual precipitation (MAP) above 150 mm until maximum woody cover was reached at 650 mm.…”

“…Joubert et al [21] also found that at least two successive seasons of favorable rainfall was required for seed recruitment in Senegalia mellifera. While precipitation intensities [22], season lengths [23], and interactions with other factors (e.g., grazing [24]) all influence woody vegetation cover, the consensus is that MAP is the primary factor contributing to woody vegetation cover [10,[16][17][18][19][20]].…”

Estimating Savanna Clumping Index Using Hemispherical Photographs Integrated with High Resolution Remote Sensing Images

“…Hence, they are anticipated to be among the ecosystems most sensitive to future land use and climate changes [9,10], and it is important to gain a mechanistic understanding of 2 of 17 their vegetation-atmosphere exchange. While the woody cover in savannas, e.g., tree cover, adversely affect ecosystem structures and functions, an intensive understanding of woody cover is required for savanna conservation and management [11]. Woody cover growth is related to available water, climate, human activities [12], and herbivory level [11] in the form of porosity, leaf area index (LAI), and clumping index (CI).…”

“…While the woody cover in savannas, e.g., tree cover, adversely affect ecosystem structures and functions, an intensive understanding of woody cover is required for savanna conservation and management [11]. Woody cover growth is related to available water, climate, human activities [12], and herbivory level [11] in the form of porosity, leaf area index (LAI), and clumping index (CI). Savannas are spatially heterogeneous, open ecosystems, and the canopy openness presents a challenge for evaluating canopy LAI and other biophysical parameters, since most remotely sensed methods were developed for ideal and closed canopies [13,14].…”