Asset-Based Adaptation Project Promotes Tree and Shrub Diversity and Above-Ground Carbon Stocks in Smallholder Agroforestry Systems in Western Kenya

Agroforestry has potential to address the adverse effects of climate change through carbon sequestration, increasing biodiversity and improving adaptive capacity and resilience among smallholder farmers. However, this potential is context specific and insufficiently quantified in smallholder faming systems, partly because of inherent variability of smallholder farms. Our study aimed to determine the tree/shrub diversity and carbon stocks in different agroforestry systems within smallholder farms in two 100 km2 sites, the so-called lower and middle Nyando sites, in western Kenya. In both, context-specific agroforestry adoption had been promoted among households of four community associations through an asset-based community development (ABCD) approach. Their farms were assessed and compared with those of relevant comparison samples. Trees and shrubs were inventoried on a total of 106 farms, and their formations classified in five major agroforestry practices: hedgerows, multipurpose trees on farm (MPT), riparian buffers, woodlots, and boundary planting. To assess above-ground biomass (AGB) of individual trees/shrubs, diameter at breast height measurements were taken. Strong regional differences were considered in data analysis and presentation. Altogether, 3,353 and 6,346 trees/shrubs were inventoried in the lower and middle Nyando sites, respectively. AGB was significantly higher in middle than in lower Nyando. Woodlots had the highest amount of AGB carbon stock, while MPT had the highest diversity of tree/shrub species in all the groups. Conversely, boundary planting had the highest number of trees/shrubs inventoried and hence was the most common agroforestry practice across all the samples in both regions. Dominant AGB contributor species were Grevillea robusta (37.8%) in middle, and Eurphobia tirucalli (16.5%) in lower Nyando. This study provides empirical evidence that asset-based and community-driven selection and implementation of both tree/shrub species and agroforestry practices can contribute positively to species and practice diversity, which are associated with AGB carbon stock levels and wider agro-ecosystem diversity. This study hence provides benchmark information that is relevant for SDG goal 15 on “life on land,” and various specific targets, and can inform sustainable establishment of carbon sink facilities by supporting smallholders to uptake contextually suitable and economically sensible agroforestry practices in an overall effort to foster and support sustainable development.

Potential nursing effects of Parkia platycephala Benth. (Fabaceae) in a disturbed Brazilian Savanna area undergoing restoration

We evaluated the influence of Parkia platycephala on plant assemblages under its crown and surrounding areas in a disturbed Brazilian Savanna, as well as the effects of aqueous extracts of its leaves on the germination and development of three species, under the hypothesis that P. platycephala might work as a nurse species. Eleven areas of direct (ADIs) and indirect (AIIs) influence of P. platycephala and 11 control areas (COs) were delineated. All tree and shrub species present in these areas, including seedlings, were sampled. Richness, composition and abundance of woody taxa were determined. Laboratory studies analyzed the effects of P. platycephala aqueous leaf extracts on the germination and initial development of Dipteryx alata, Enterolobium gummiferum and Magonia pubescens, which in the field exhibited different abundance levels under its crown. Regarding plant assemblages, no differences in terms of richness and abundance of woody taxa were detected among the areas. However, we recorded a gradient of species composition and abundance ranks from ADI to CO areas. The number of seedlings also differed, with higher values near P. platycephala. Soil characteristics were similar among areas, but shading, plant height and diameter differed. The aqueous leaf extract at 25% provided the best germination of M. pubescens. The extract at 75% favored the initial development of this species in terms of root length. These results indicate that the presence of P. platycephala affects the structure of neighboring plant assemblages, possibly working as a facilitator for some species in areas undergoing restoration.

Water availability, bedrock, disturbance by herbivores, and climate determine plant diversity in South-African savanna

To identify factors that drive plant species richness in South-African savanna and explore their relative importance, we sampled plant communities across habitats differing in water availability, disturbance, and bedrock, using the Kruger National Park as a model system. We made plant inventories in 60 plots of 50 × 50 m, located in three distinct habitats: (i) at perennial rivers, (ii) at seasonal rivers with water available only during the rainy season, and (iii) on crests, at least ~ 5 km away from any water source. We predicted that large herbivores would utilise seasonal rivers’ habitats less intensely than those along perennial rivers where water is available throughout the year, including dry periods. Plots on granite harboured more herbaceous and shrub species than plots on basalt. The dry crests were poorer in herb species than both seasonal and perennial rivers. Seasonal rivers harboured the highest numbers of shrub species, in accordance with the prediction of the highest species richness at relatively low levels of disturbance and low stress from the lack of water. The crests, exposed to relatively low pressure from grazing but stressed by the lack of water, are important from the conservation perspective because they harbour typical, sometimes rare savanna species, and so are seasonal rivers whose shrub richness is stimulated and maintained by the combination of moderate disturbance imposed by herbivores and position in the middle of the water availability gradient. To capture the complexity of determinants of species richness in KNP, we complemented the analysis of the above local factors by exploring large-scale factors related to climate, vegetation productivity, the character of dominant vegetation, and landscape features. The strongest factor was temperature; areas with the highest temperatures reveal lower species richness. Our results also suggest that Colophospermum mopane, a dominant woody species in the north of KNP is not the ultimate cause of the lower plant diversity in this part of the park.

Classification of Mediterranean Shrub Species from UAV Point Clouds

Modelling fire behaviour in forest fires is based on meteorological, topographical, and vegetation data, including species’ type. To accurately parameterise these models, an inventory of the area of analysis with the maximum spatial and temporal resolution is required. This study investigated the use of UAV-based digital aerial photogrammetry (UAV-DAP) point clouds to classify tree and shrub species in Mediterranean forests, and this information is key for the correct generation of wildfire models. In July 2020, two test sites located in the Natural Park of Sierra Calderona (eastern Spain) were analysed, registering 1036 vegetation individuals as reference data, corresponding to 11 shrub and one tree species. Meanwhile, photogrammetric flights were carried out over the test sites, using a UAV DJI Inspire 2 equipped with a Micasense RedEdge multispectral camera. Geometrical, spectral, and neighbour-based features were obtained from the resulting point cloud generated. Using these features, points belonging to tree and shrub species were classified using several machine learning methods, i.e., Decision Trees, Extra Trees, Gradient Boosting, Random Forest, and MultiLayer Perceptron. The best results were obtained using Gradient Boosting, with a mean cross-validation accuracy of 81.7% and 91.5% for test sites 1 and 2, respectively. Once the best classifier was selected, classified points were clustered based on their geometry and tested with evaluation data, and overall accuracies of 81.9% and 96.4% were obtained for test sites 1 and 2, respectively. Results showed that the use of UAV-DAP allows the classification of Mediterranean tree and shrub species. This technique opens a wide range of possibilities, including the identification of species as a first step for further extraction of structure and fuel variables as input for wildfire behaviour models.

Classifying Individual Shrub Species in UAV Images—A Case Study of the Gobi Region of Northwest China

Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data create vegetation type inventories over large areas. However, high spectral similarity between shrublands and surrounding areas remains a challenge. In this study, we provide a case study that integrates object-based image analysis (OBIA) and the random forest (RF) model to classify shrubland species automatically. The Gobi region on the southern slope of the Tian Shan Mountains in Northwest China was analyzed using readily available unmanned aerial vehicle (UAV) RGB imagery (1.5 cm spatial resolution). Different spectral and texture index images were derived from UAV RGB images as variables for species classification. Principal component analysis (PCA) extracted features from different types of variable sets (original bands, original bands + spectral indices, and original bands + spectral indices + texture indices). We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. Moreover, we analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. We found that the RF model could generate higher accuracy compared with the other two models. The best results were obtained using a combination of the optimal variable set and the RF model with an 88.63% overall accuracy and 0.82 kappa coefficient. Integrating OBIA and RF in the species classification process provides a promising method for automatic mapping of individual shrub species in the Gobi region and can reduce the workload of individual shrub species classification.

Shrub richness is primarily driven by climate conditions in Southwestern European woodlands

• Key message The shrub species richness in Spanish forests is mainly linked to climatic variables and the importance of the groups of variables scarcely differs among forest types. Forest surrounding the Mediterranean Basin exhibit the highest levels of shrub richness. • Context Shrub species account for a high proportion of the plant diversity in Spanish forests and are a determinant factor in forest dynamics and ecosystem functionality. • Aims To investigate the relative importance of climatic, forest stand features, soil and topographic variables in explaining shrub richness in Spanish forests and if the relative importance of these four groups of variables reflects variations among forest types. • Methods We used the Spanish National Forest Inventory and a boosted regression trees approach to identify which climatic, soil, stand and topographic variables (N = 19 variables) are related to the richness of shrub species in Spanish woodlands. • Results The shrub species richness is mainly related to climatic variables followed by soil variables whereas stand and topographic variables play a minor role. The importance of the groups of variables scarcely differs among forest types although forests located around the Mediterranean Sea display the highest levels of shrub richness. • Conclusion Shrub richness in Spain is primarily driven by climatic and soil variables, both at country and forest-type scales. Forests surrounding the Mediterranean Basin account for the highest richness of shrub species but are also those most threatened by global change. Therefore, special attention must be paid to the monitoring and assessment of these forest ecosystems.

Plant Functional Groups Dominate Responses of Plant Adaptive Strategies to Urbanization

Urbanization causes alteration in atmospheric, soil, and hydrological factors and substantially affects a range of morphological and physiological plant traits. Correspondingly, plants might adopt different strategies to adapt to urbanization promotion or pressure. Understanding of plant traits responding to urbanization will reveal the capacity of plant adaptation and optimize the choice of plant species in urbanization green. In this study, four different functional groups (herbs, shrubs, subcanopies, and canopies, eight plant species totally) located in urban, suburban, and rural areas were selected and eight replicated plants were selected for each species at each site. Their physiological and photosynthetic properties and heavy metal concentrations were quantified to reveal plant adaptive strategies to urbanization. The herb and shrub species had significantly higher starch and soluble sugar contents in urban than in suburban areas. Urbanization decreased the maximum photosynthetic rates and total chlorophyll contents of the canopies (Engelhardtia roxburghiana and Schima superba). The herbs (Lophatherum gracile and Alpinia chinensis) and shrubs (Ardisia quinquegona and Psychotria rubra) species in urban areas had significantly lower nitrogen (N) allocated in the cell wall and leaf δ15N values but higher heavy metal concentrations than those in suburban areas. The canopy and subcanopy (Diospyros morrisiana and Cratoxylum cochinchinense) species adapt to the urbanization via reducing resource acquisition but improving defense capacity, while the herb and shrub species improve resource acquisition to adapt to the urbanization. Our current studies indicated that functional groups affected the responses of plant adaptive strategies to the urbanization.