Lantana invasion: An overview,

We review the key features of Lantana ( Lantana camara L.), an invasive plant species considered to be among the world's 10 worst weeds. Lantana occurs in diverse habitats and on a variety of soil types, and its spread is encouraged by animal activities and by human disturbances, such as cultivation, road construction, and changes in fire regimes. Lantana is morphologically distinct in the different regions of its invasive range compared to those regions in its native range. The biological attributes contributing to the success of Lantana as an invader species include: fitness homeostasis, phenotypic plasticity, dispersal benefits from destructive foraging activities, widespread geographic range, vegetative reproduction, fire tolerance, better competitive ability compared to native flora, and allelopathy. Mechanical, chemical and biological options for the eradication and control of Lantana are available. It is emphasized that ecosystem-level consequences of Lantana invasion, particularly on the biodiversity of native flora, are little understood and studies are needed to fulfill this knowledge gap

Lantana invasion: an overview

We review the key features of Lantana (Lantana camara L.), an invasive plant species considered to be among the world's 10 worst weeds. Lantana occurs in diverse habitats and on a variety of soil types, and its spread is encouraged by animal activities and by human disturbances, such as cultivation, road construction, and changes in fire regimes. Lantana is morphologically distinct in the different regions of its invasive range compared to those regions in its native range. The biological attributes contributing to the success of Lantana as an invader species include: fitness homeostasis, phenotypic plasticity, dispersal benefits from destructive foraging activities, widespread geographic range, vegetative reproduction, fire tolerance, better competitive ability compared to native flora, and allelopathy. Mechanical, chemical and biological options for the eradication and control of Lantana are available. It is emphasized that ecosystem-level consequences of Lantana invasion, particularly on the biodiversity of native flora, are little understood and studies are needed to fulfill this knowledge gap

DataSheet_1_Variations in the plasticity of functional traits indicate the differential impacts of abiotic and biotic factors on the structure and growth of trees in tropical dry forest fragments.docx

Abiotic and biotic factors have considerable impact on the plasticity of plant functional traits, which influences forest structure and productivity; however, their inter-relationships have not been quantified for fragmented tropical dry forest (TDF) ecosystems. We asked the following questions: (1) what are the variations in the plasticity of functional traits due to soil moisture availability in TDF fragments? (2) what are the roles of soil nutrients and forest disturbances in influencing variations in the plasticity of functional traits in the TDF fragments? and (3) how do the variations in the plasticity of functional traits influence the structure and productivity of TDF fragments? Based on linear mixed-effects results, we observed significant variations among tree species for soil moisture content (SMC) under the canopy and selected functional traits across forest fragments. We categorized tree species across fragments by principal component analysis (PCA) and hierarchical clustering on principal components (HCPC) analyses into three functional types, viz., low wood density high deciduous (LWHD), high wood density medium deciduous (HWMD), and high wood density low deciduous (HWLD). Assemblage of functional traits suggested that the LWHD functional type exhibits a drought-avoiding strategy, whereas HWMD and HWLD adopt a drought-tolerant strategy. Our study showed that the variations in functional trait plasticity and the structural attributes of trees in the three functional types exhibit contrasting affinity with SMC, soil nutrients, and disturbances, although the LWHD functional type was comparatively more influenced by soil resources and disturbances compared to HWMD and HWLD along the declining SMC and edge distance gradients. Plasticity in functional traits for the LWHD functional type exhibited greater variations in traits associated with the conservation of water and resources, whereas for HWMD and HWLD, the traits exhibiting greater plasticity were linked with higher productivity and water transport. The cumulative influence of SMC, disturbances, and functional trait variations was also visible in the relative abundance of functional types in large and small sized fragments. Our analysis further revealed the critical differences in the responses of functional trait plasticity of the coexisting tree species in TDF, which suggests that important deciduous endemic species with drought-avoiding strategies might be prone to strategic exclusion under expected rises in anthropogenic disturbances, habitat fragmentation, and resource limitations.</p

Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years

Runoff is a crucial water cycle component that contributes to the water resources to sustain human life. Historical trends in runoff, when examining climate change scenarios, provide vital information about past variability and support the design of adaptation measures. However, hydrological models based on climate data, such as the Budyko model, can be biased in estimating annual runoff due to input data uncertainty. Therefore, it is vital to utilize advanced machine learning-based computing models to reduce uncertainty and reconstruct climate variables over a long period of time and sufficiently large spatial coverage, preferably at a continental scale. We propose and test a novel machine learning-based framework called Hybrid Ensemble Multi-Model Framework (HEMMF) to reconstruct the gridded runoff of Europe over a 500-year historical period (1500 to 1999). The HEMMF combines non-parametric extended data pattern recognition and data-driven methods. The extended data patterns are computed using Moran’s spatial autocorrelation (SPA) index of the climate variable fields and the Budyko models output, whereas the data-driven methods contain nine different machine learning (ML) algorithms and four ensembles of ML. The extended data patterns are jointly ingested with climate-reconstructed data (precipitation, temperature, Palmer’s drought severity index) as predictor variables, which serve as input for the data-driven methods. To assess the impact and contribution of SPA, the runoff is simulated based on three different input training datasets in the HEMMF: (1) a dataset containing only precipitation, temperature, Palmer’s drought severity index, and four different estimates of runoff from the Budyko model, (2) a dataset containing only SPA of the first input datasets, and (3) a dataset created by merging the first and second datasets. The HEMMF offers the best reconstruction performance when using the third input dataset. This reconstructed runoff helps to explain the runoff trend, drought propagation, and runoff’s link with the climate variables. The proposed methodology has the potential to be applied to past hydroclimatic data and related analyses across different temporal periods, climate scenarios, and geographical scales.Web of Science97art. no. 10180