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Invasive alien species threaten tropical grasslands and native biodiversity across the globe, including in the natural mosaic of native grasslands and forests in the Shola Sky Islands of the Western Ghats. Here, grasslands have been lost to exotic tree invasion (Acacias, Eucalyptus, and Pines) since the 1950s, but differing invasion intensities between these species and intermixing with native species constitutes a major challenge for remotely sensed assessments. In this study, we assess the accuracy of three satellite and airborne remote sensing sensors (Sentinel-1 radar data, Sentinel-2 multispectral data and AVIRIS-NG hyperspectral data) and three machine learning classification algorithms to identify the spatial extent of native habitats and invasive tree species. We used the support vector machine (SVM), classification and regression trees (CART), and random forest (RF) algorithms implemented on the Google Earth Engine platform. Results indicate that AVIRIS-NG data in combination with SVM produced the highest classification accuracy (98.7%). Fused Sentinel-1 and Sentinel-2 produce 91% accuracy, while Sentinel-2 alone yielded 91% accuracy; but only with higher coverage of ground control points. The hyperspectral data (AVIRIS-NG) was the only sensor that permitted distinguishing recent invasions (young trees) with high precision. We suspect that large areas will have to be mapped and assessed in the coming years by conservation managers, NGOs to plan restoration or to assess the success of restoration activities, for which a choice of sensors may have to be made based on the age of invasion being mapped, and the quantum of ground control data available.

The objective of this analysis was to identify topographic and bioclimatic factors that predict occurrence of forest and grassland patches within tropical montane forest-grassland mosaics. We further investigated whether interactions between topography and bioclimate are important in determining vegetation pattern, and assessed the role of spatial scale in determining the relative importance of specific topographic features. Finally, we assessed the role of elevation in determining the relative importance of diverse explanatory factors. The study area consists of the central and southern regions of the Western Ghats of Southern India, a global biodiversity hotspot. Random forests were used to assess prediction accuracy and predictor importance. Conditional inference classification trees were used to interpret predictor effects and examine potential interactions between predictors. GLMs were used to confirm predictor importance and assess the strength of interaction terms. Overall, topographic and bioclimatic predictors classified vegetation pattern with approximately 70% accuracy. Prediction accuracy was higher for grassland than forest, and for mosaics at higher elevations. Elevation was the most important predictor, with mosaics above 2000 m dominated largely by grassland. Relative topographic position measured at a local scale (within a 300 m neighbourhood) was another important predictor of vegetation pattern. In high elevation mosaics, northness and concave land surface curvature were important predictors of forest occurrence. Important bioclimatic predictors were: dry quarter precipitation, annual temperature range and the interaction between the two. The results indicate complex interactions between topography and bioclimate and among topographic variables. Elevation and topography have a strong influence on vegetation pattern in these mosaics. There were marked regional differences in the roles of various topographic and bioclimatic predictors across the range of study mosaics, indicating that the same pattern of grass and forest seems to be generated by different sets of mechanisms across the region, depending on spatial scale and elevation.

Tropical montane habitats, grasslands, in particular, merit urgent conservation attention owing to the disproportionate levels of endemic biodiversity they harbour, the ecosystem services they provide, and the fact that they are among the most threatened habitats globally. The Shola Sky Islands in the Western Ghats host a matrix of native forest-grassland matrix that has been planted over the last century, with exotic timber plantations. The popular discourse on the landscape change is that mainly forests have been lost to the timber plantations and recent court directives are to restore Shola forest trees. In this study, we examine spatiotemporal patterns of landscape change over the last 40 years in the Palani Hills, a significant part of the montane habitat in the Western Ghats. Using satellite imagery and field surveys, we find that 66% of native grasslands and 31% of native forests have been lost over the last 40 years. Grasslands have gone from being the dominant, most contiguous land cover to one of the rarest and most fragmented. They have been replaced by timber plantations and, to a lesser extent, expanding agriculture. We find that the spatial pattern of grassland loss to plantations differs from the loss to agriculture, likely driven by the invasion of plantation species into grasslands. We identify remnant grasslands that should be prioritised for conservation and make specific recommendations for conservation and restoration of grasslands in light of current management policy in the Palani Hills, which favours large-scale removal of plantations and emphasises the restoration of native forests.

Trees have emerged as a key focus of global environmental policy. Several programs promote planting of trees outside forests (ToF), in places such as farms and grazing lands, due to the potential of trees to provide a wide variety of benefits to people and nature. Yet, our knowledge of human well-being outcomes of ToFs is limited, especially in South Asia. In this systematic literature review, we examine multidimensional human wellbeing outcomes of a wide range of ToF practices in rural landscapes of South Asian countries; including Bangladesh, India, Nepal, Pakistan and Sri Lanka. Relying on six databases, we uncover a large body of research in 325 articles considered for this review. Articles from Bangladesh and India dominate our review, with 71% of the studies. Further, two ToF systems, tree and forest gardens and multipurpose trees on farms, were the most commonly studied, accounting for approximately 43% of the dataset. About 62% of publications reported increases in incomes, representing economic wellbeing, 34% and 36% of publications reported an increase in material wellbeing (access to biomass and fuelwood respectively), and 10% in dietary diversity. ToF systems also created opportunities for vocational training. Trade-offs include negative and mixed outcomes on people’s sense of agency, political voice, and social equity in particular with afforestation and monoculture plantation projects in which governmental agencies took leadership or influential roles. Some research designs were weak and it was unclear whether the studied tree-based systems reflect the actual distribution of tree-based systems in South Asia. This review offers useful insights to guide ongoing and future tree-based natural-climate solutions projects in the region and worldwide to ensure ecological security and human wellbeing are well considered. It also points to areas where future research is needed.

The objective of this analysis was to identify topographic and bioclimatic factors that predict occurrence of forest and grassland patches within tropical montane forest-grassland mosaics. We further investigated whether interactions between topography and bioclimate are important in determining vegetation pattern, and assessed the role of spatial scale in determining the relative importance of specific topographic features. Finally, we assessed the role of elevation in determining the relative importance of diverse explanatory factors. The study area consists of the central and southern regions of the Western Ghats of Southern India, a global biodiversity hotspot. Random forests were used to assess prediction accuracy and predictor importance. Conditional inference classification trees were used to interpret predictor effects and examine potential interactions between predictors. GLMs were used to confirm predictor importance and assess the strength of interaction terms. Overall, topographic and bioclimatic predictors classified vegetation pattern with approximately 70% accuracy. Prediction accuracy was higher for grassland than forest, and for mosaics at higher elevations. Elevation was the most important predictor, with mosaics above 2000m dominated largely by grassland. Relative topographic position measured at a local scale (within a 300m neighbourhood) was another important predictor of vegetation pattern. In high elevation mosaics, northness and concave land surface curvature were important predictors of forest occurrence. Important bioclimatic predictors were: dry quarter precipitation, annual temperature range and the interaction between the two. The results indicate complex interactions between topography and bioclimate and among topographic variables. Elevation and topography have a strong influence on vegetation pattern in these mosaics. There were marked regional differences in the roles of various topographic and bioclimatic predictors across the range of study mosaics, indicating that the same pattern of grass and forest seems to be generated by different sets of mechanisms across the region, depending on spatial scale and elevation.

We examined the immediate effects of laurel wilt on redbay (Persea borbonia [L.] Spreng.) and stand-level characteristics in a mixed evergreen-deciduous forest at Etoniah Creek State Forest in Florida. Percent mortality of redbay in the overstory, sapling, and seedling layers were 100%, 30.2%, and 1.8%, respectively, in the year after the first signs of infection were observed. The diameter distribution of redbay shifted from a reverse \"J\" pattern to a distribution where the only remaining living stems were <4-in. dbh. Mortality of redbay also resulted in significant reductions in overstory redbay importance values and stand-level density and basal area. Our results suggest that (1) laurel wilt has a more pronounced effect on overstory redbays than smaller stems and (2) redbay mortality caused by laurel wilt can result in modest but significant changes in stand structure.

Tropical montane forests in the Western Ghats in southern India consist of dense, insular fragments in a matrix of grasslands separated by an abrupt, natural, edge. I studied edge effects in the shola-grassland ecosystem mosaic across nine fragments in three study sites in the Western Ghats. I measured microenvironment and soil variables and overstory species in 10×5 m plots along an edge-interior gradient at 5 m intervals. Understory density and richness was recorded from 5×5 m sub plots. Conventional distance to one-edge models indicated edge-interior gradients in relative humidity (p = 0.018), magnesium (p = 0.027) and potassium (p = 0.008) in large fragments. In small fragments, gradients in air temperature (p = 0.03), light transmittance (p = 0.007) and soil moisture (p = 0.0002) were observed as a function of distance to multiple edges. We recorded 111 species (77 overstory; 83 understory) across nine fragments but did not observe any edge-interior trends in overstory density or dominance. Similarly, no edge-interior gradients were observed in understory density and structure. Non-metric multidimensional scaling techniques for overstory and understory revealed greater variation among fragments than could be attributed to edge-related within fragment variation. Overstory composition in mid-elevation fragments differed significantly from high-elevation fragments while understory vegetation varied as a function of fragment size. Our data indicate that mid-elevation fragments should not be considered with high elevation fragments in future studies. We also used a multiple logistic regression model to predict the presence of shola fragments across two study sites in the Western Ghats. Elevation, slope, aspect (expressed as eastness and northness), slope curvature and wetness index were used to predict the presence of shola fragments. We observed that shola fragments were more likely to occur on northern and western aspects than southern and eastern aspects. Shola fragments were also most likely to occur on wet, steep slopes. The stability of the shola-grassland edge appears to be driven by fire rather than frost while exposure to wind might be a driving factor also. The shola-grassland ecosystem mosaic offers insights into fragmentation related patterns in small patches and recommendations are made for future investigations.

Abstract Invasive alien species (IAS) threaten tropical grasslands and native biodiversity and impact ecosystem service delivery, ecosystem function, and associated human livelihoods. Tropical grasslands have been dramatically and disproportionately lost to invasion by trees. The invasion continues to move rapidly into the remaining fragmented grasslands impacting various native grassland-dependent species and water streamflow in tropical montane habitats. The Shola Sky Islands of the Western Ghats host a mosaic of native grasslands and forests; of which the grasslands have been lost to exotic tree invasion (Acacias, Eucalyptus and Pines) since the 1950s. The invasion intensities, however, differ between these species wherein Acacia mearnsii and Pinus patula are highly invasive in contrast to Eucalyptus globulus. These disparities necessitate distinguishing these species for effective grassland restoration. Further, these invasive alien trees are highly intermixed with native species, thus requiring high discrimination abilities to native species apart from the non-native species. Here we assess the accuracy of various satellite and airborne remote sensing sensors and machine learning classification algorithms to identify the spatial extent of native habitats and invasive trees. Specifically, we test Sentinel-1 SAR and Sentinel-2 multispectral data and assess high spatial and spectral resolution AVIRIS-NG imagery identifying invasive species across this landscape. Sensor combinations thus include hyperspectral, multispectral and radar data and present tradeoffs in associated costs and ease of procurement. Classification methods tested include Support Vector Machine (SVM), Classification and Regression Trees (CART) and Random Forest (RF) algorithms implemented on the Google Earth Engine platform. Results indicate that AVIRIS-NG data in combination with SVM recover the highest classification skill (Overall −98%, Kappa-0.98); while CART and RF yielded < 90% accuracy. Fused Sentinel-1 and Sentinel-2 produce 91% accuracy, while Sentinel-2 alone yielded 91% accuracy with RF and SVM classification; but only with higher coverage of ground control points. AVIRIS-NG imagery was able to accurately (97%) demarcate the Acacia invasion front while Sentinel-1 and Sentinel-2 data failed. Our results suggest that Sentinel-2 images could be useful for detecting the native and non-native forests with more ground truth points, but hyperspectral data (AVIRIS-NG) permits distinguishing, native and non-native tree species and recent invasions with high precision using limited ground truth points. We suspect that large areas will have to be mapped and assessed in the coming years by conservation managers, NGOs to plan restoration, or to assess the success of restoration activities, and several data procurement and analysis steps may have to be simplified. Competing Interest Statement The authors have declared no competing interest.