Semi-Parametric Geographically Weighted Regression (S-GWR): a Case Study on Invasive Plant Species Distribution in Subtropical Nepal

Geographically weighted regression (GWR) is a spatial statistical methodology to explore the impact of non-stationarity on the interaction between spatially measured dependent and independent variables. In this paper we use a semi-parametric geographically weighted regression (SGWR) and demonstrate the effectiveness of the method on a case study on socio-ecological factors on forest vulnerability. The case study is based on community forests in and around the buffer zone of Chitwan National Park, Nepal, a biodiversity hotspot that is being rapidly degraded by exotic invasive plant species. This research integrated heterogeneous data sources such as observational ecological surveys, household interviews, and remotely sensed imagery. These data were utilized to extract and represent invasive plant species coverage, human activity intensity, topographical parameters and vegetation greenness indices. Research findings both demonstrate the S-GWR method and offer possible interventions that could slow the catastrophic spread of invasive plant species in Chitwan, Nepal

Elevated soil nitrogen pools after conversion of turfgrass to water-efficient residential landscapes

As a result of uncertain resource availability and growing populations, city managers are implementing conservation plans that aim to provide services for people while reducing household resource use. For example, in the US, municipalities are incentivizing homeowners to replace their water-intensive turfgrass lawns with water-efficient landscapes consisting of interspersed drought-tolerant shrubs and trees with rock or mulch groundcover (e.g. xeriscapes, rain gardens, water-wise landscapes). While these strategies are likely to reduce water demand, the consequences for other ecosystem services are unclear. Previous studies in controlled, experimental landscapes have shown that conversion from turfgrass to shrubs may lead to high rates of nutrient leaching from soils. However, little is known about the long-term biogeochemical consequences of this increasingly common land cover change across diverse homeowner management practices. We explored the fate of soil nitrogen (N) across a chronosequence of land cover change from turfgrass to water-efficient landscapes in privately owned yards in metropolitan Phoenix, Arizona, in the arid US Southwest. Soil nitrate ( ${{{\rm{NO}}}_{3}}^{-}$ –N) pools were four times larger in water-efficient landscapes (25 ± 4 kg ${{{\rm{NO}}}_{3}}^{-}$ –N/ha; 0–45 cm depth) compared to turfgrass lawns (6 ± 7 kg ${{{\rm{NO}}}_{3}}^{-}$ –N/ha). Soil ${{{\rm{NO}}}_{3}}^{-}$ –N also varied significantly with time since landscape conversion; the largest pools occurred at 9–13 years after turfgrass removal and declined to levels comparable to turfgrass thereafter. Variation in soil ${{{\rm{NO}}}_{3}}^{-}$ –N with landscape age was strongly influenced by management practices related to soil water availability, including shrub cover, sub-surface plastic sheeting, and irrigation frequency. Our findings show that transitioning from turfgrass to water-efficient residential landscaping can lead to an accumulation of ${{{\rm{NO}}}_{3}}^{-}$ –N that may be lost from the plant rooting zone over time following irrigation or rainfall. These results have implications for best management practices to optimize the benefits of water-conserving landscapes while protecting water quality

Valuing the ecosystem services of low-input, high-diversity prairie as a biofuel feedstock in southern Minnesota

Biofuels may help to address the United States\u27 dependence on fossil fuels by providing a renewable fuel source (Hill 2006, Tilman 2009). The largest biofuel industry in the United States is currently corn-based ethanol, but the negative environmental and economic impacts of corn agriculture have prompted research into other feedstocks, such as low-input, high-diversity (LIHD) prairie (Tilman 2006). We argue that incorporating the ecosystem service value of LIHD prairie grown on marginal lands in Southern Minnesota would make it an economically competitive biofuel feedstock. Using a spatially explicit model (InVEST) we found that a targeted land-use change of corn to prairie on marginal lands produced a value of $198.89/ha in ecosystem services,$163.34 higher than an all-prairie scenario and $511.28 higher than an all-corn scenario. An economic analysis incorporating the value of ecosystem services found that prairie is only competitive with corn as a feedstock when the prices of carbon and prairie feedstock are high and the price of corn is low. However, improvements in modeling could better quantify prairie\u27s ecosystem service value, making it more competitive with corn. Our results demonstrate the importance of taking ecosystem service value into account when making decisions regarding biofuel policies

Semi-parametric Geographically Weighted Regression (S-GWR): a Case Study on Invasive Plant Species Distribution in Subtropical Nepal

Geographically weighted regression (GWR) is a spatial statistical methodology to explore the impact of non-stationarity on the interaction between spatially measured dependent and independent variables. In this paper we use a semi-parametric geographically weighted regression (SGWR) and demonstrate the effectiveness of the method on a case study on socio-ecological factors on forest vulnerability. The case study is based on community forests in and around the buffer zone of Chitwan National Park, Nepal, a biodiversity hotspot that is being rapidly degraded by exotic invasive plant species. This research integrated heterogeneous data sources such as observational ecological surveys, household interviews, and remotely sensed imagery. These data were utilized to extract and represent invasive plant species coverage, human activity intensity, topographical parameters and vegetation greenness indices. Research findings both demonstrate the S-GWR method and offer possible interventions that could slow the catastrophic spread of invasive plant species in Chitwan, Nepal