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AIM: Climate is widely recognized as a major predictor of species richness patterns along large‐scale environmental gradients. Nevertheless, the mechanisms by which climate influences species richness are still a matter of debate. We disentangle whether climate influences species richness of birds directly via physiological limitations or indirectly via vegetation structure or the availability of food resources. LOCATION: Mount Kilimanjaro, Tanzania. METHODS: We recorded bird species richness along an elevational gradient from 870 to 4550 m a.s.l. We quantified local climatic conditions, vegetation structure and the availability of food resources, and applied path analysis to disentangle their direct and indirect effects on species richness of all birds, frugivores and insectivores. RESULTS: Overall, we recorded 2945 individuals from 114 bird species. Species richness of all birds was closely correlated with temperature, vegetation structure and invertebrate biomass and both direct and indirect (via vegetation structure and availability of food resources) climatic effects were important for the diversity of the whole, trophically heterogeneous bird community. The species richness of insectivorous birds was linked to vegetation structure and invertebrate biomass, while the richness of frugivores was strongly associated with fruit abundance. Climatic factors influenced bird species richness of both avian feeding guilds exclusively indirectly via vegetation structure and availability of food resources. MAIN CONCLUSIONS: We reveal the importance of trophic interactions for generating species richness patterns along large‐scale environmental gradients. Our results challenge the general assumption that temperature and water availability influence species richness mostly directly, and underscore the importance of vegetation structure and the availability of food resources as principal mediators of climatic effects on species richness patterns on macroecological scales.

ContextDeciphering how the complex environmental dynamics of highland temperate forests drive local patterns of genetic variation is key to understanding species diversity and distribution.ObjectivesEvaluate how the environmental complexity of La Malinche volcano influences patterns of genomic variation in Peromyscus melanotis, across two mountain slopes and global and regional geographic scales.MethodsUsing reduced representation genomic sequencing we estimated population genetic diversity, subdivisions and migration rates. Remote sensing data and drone image processing were used to characterize landscape variables. We evaluated their effect on connectivity, based on a landscape analyses framework, using resistance surfaces, circuit theory and omnidirectional connectivity.ResultsIn our global analysis assessing multiple elevation levels on two mountain slopes, we identified three genetic clusters differentiating two elevation levels of the north mountain slope (ECLM; 3150 m, 3300 m) from the rest of sampled areas. Within the northeast mountain slope (CVM), we found reduced genetic variation, limited connectivity, fewer migrants and isolation in higher elevation populations. ECLM populations showed higher genetic diversity and lowest connectivity at intermediate levels. NDVI and tree height were the main factors promoting connectivity in CVM, while tree height and litter cover were most important in ECLM.ConclusionsOur findings showed how the forest environmental complexity across different geographic scales drives dispersal, genomic structure and connectivity patterns in this rodent, where dirt roads and disturbed areas limit its connectivity, while exhibiting higher connectivity at the highest elevations where the forest is less disturbed. Notably, that our 3D variable of tree height was significant demonstrates the utility in incorporating 3D vegetation structure variables into landscape genetic analyses.

The spatial arrangement and vertical structure of vegetation in urban green spaces are key factors in determining the types of benefits that urban parks provide to people. This includes opportunities for recreation, spiritual fulfilment and biodiversity conservation. However, there has been little consideration of how the fine-scale spatial and vertical structure of vegetation is distributed in urban parks, primarily due to limitations in methods for doing so. We addressed this gap by developing a method using Light Detection and Ranging (LiDAR) data to map, at a fine resolution, tree cover, vegetation spatial arrangement, and vegetation vertical structure. We then applied this method to urban parks in Brisbane, Australia. We found that parks varied mainly in their amount of tree cover and its spatial arrangement, but also in vegetation vertical structure. Interestingly, the vertical structure of vegetation was largely independent of its cover and spatial arrangement. This suggests that vertical structure may be being managed independently to tree cover to provide different benefits across urban parks with different levels of tree cover. Finally, we were able to classify parks into three distinct classes that explicitly account for both the spatial and vertical structure of tree cover. Our approach for mapping the three-dimensional vegetation structure of urban green space provides a much more nuanced and functional description of urban parks than has previously been possible. Future research is now needed to quantify the relationships between vegetation structure and the actual benefits people derive from urban green space.

Aim Tall and structurally complex forests can provide ample habitat and niche space for climbing plants, supporting high liana species richness. We test to what extent canopy height (as a proxy of 3‐D habitat structure), climate and soil interact to determine species richness in the largest clade of Neotropical lianas. We expect that the effect of canopy height on species richness is higher for lianas from closed tropical rain forests compared to riparian and savanna habitats. Location Neotropics. Time period Present. Major taxa studied Tribe Bignonieae (Bignoniaceae). Methods We used structural equation models to evaluate direct and indirect effects of canopy height, climate (temperature, precipitation and precipitation seasonality), and soil (cation exchange capacity and soil types) on overall Bignonieae species richness (339 liana species), as well as on species richness of lianas from forest, riparian and savanna habitats, respectively. We further performed multiple regression models with Moran's eigenvector maps to account for spatial autocorrelation. Results Canopy height was a key driver of liana species richness, in addition to climate and soil. Species richness of forest lianas showed a strong positive relationship with canopy height whereas the relationship was less pronounced for riparian species. Richness of savanna species decreased with increasing canopy height. Climate also explained a substantial proportion of variation in liana species richness whereas soil variables showed little explanatory power. Main conclusions The relationship between canopy height and liana species richness differs among habitats. While forest and riparian lianas benefit from tall and complex habitats that provide physical support to reach the canopy to escape low light availability in the understorey, high light availability in open habitats and an increased risk of embolism of conductive vessels for lianas with long stems living in areas with high seasonality might explain the inverse relationship between species richness and canopy height in savannas.

The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure.

Question: What are the relative influences of environment and space in structuring the plant composition in a peatland complex? Location: Lakkasuo, southern boreal zone, Finland. Method: We used principal coordinates of neighbour matrices (PCNM) to model spatial structures in the plant composition of a peatland complex comprising ombrotrophic and minerotrophic, open and forested areas. We used redundancy analyses (RDA) and variation partitioning to assess the relative influences of chemical variables (peat and water characteristics), physical variables (hydrology, soil properties, shade), as well as broad-scale (>350 m) and medium-scale (100–350 m) spatial structures on vegetation assemblages. Results: We identified five different significant spatial patterns circumscribing (1) the minerotrophic–ombrotrophic gradient; (2) dry ombrotrophic and wet minerotrophic areas; (3) open and shaded areas; (4) dry open/shaded and wet patches within the ombrotrophic areas; and (5) dry open patches and dry forested patches. With spatial structures and environmental variables, we were able to model 30% of the variability in plant composition in the peatland complex, 13% of which was attributable to spatial structures alone. Conclusions: We demonstrated that in the peatland complex, the spatial dependence processes were more important at the broadest scale, and found that patterns at a medium scale might reflect finer-scale patterns that were not investigated here. Spatial autocorrelation in vegetation composition in the peatland complex appeared to be driven by Sphagnum species. Our results emphasize that spatial modelling should be routinely implemented in studies looking at species composition, since they significantly increase the explained proportion of variance.

A vegetation classification approach is needed that can describe the diversity of terrestrial ecosystems and their transformations over large time frames, span the full range of spatial and geographic scales across the globe, and provide knowledge of reference conditions and current states of ecosystems required to make decisions about conservation and resource management. We summarize the scientific basis for EcoVeg, a physiognomic-floristic-ecological classification approach that applies to existing vegetation, both cultural (planted and dominated by human processes) and natural (spontaneously formed and dominated by nonhuman ecological processes). The classification is based on a set of vegetation criteria, including physiognomy (growth forms, structure) and floristics (compositional similarity and characteristic species combinations), in conjunction with ecological characteristics, including site factors, disturbance, bioclimate, and biogeography. For natural vegetation, the rationale for the upper levels (formation types) is based on the relation between global-scale vegetation patterns and macroclimate, hydrology, and substrate. The rationale for the middle levels is based on scaling from regional formations (divisions) to regional floristic-physiognomic types (macrogroup and group) that respond to meso-scale biogeographic, climatic, disturbance, and site factors. Finally, the lower levels (alliance and association) are defined by detailed floristic composition that responds to local to regional topo-edaphic and disturbance gradients. For cultural vegetation, the rationale is similar, but types are based on distinctive vegetation physiognomy and floristics that reflect human activities. The hierarchy provides a structure that organizes regional/continental vegetation patterns in the context of global patterns. A formal nomenclature is provided, along with a descriptive template that provides the differentiating criteria for each type at all levels of the hierarchy. Formation types have been described for the globe; divisions and macrogroups for North America, Latin America and Africa; groups, alliances and associations for the United States, parts of Canada, Latin America and, in partnership with other classifications that share these levels, many other parts of the globe.

Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs. Significance Models on vegetation dynamics are indispensable for our understanding of climate change impacts. These models contain variables describing vegetation attributes, so-called traits. However, the direct impacts of trait variation on global vegetation distribution are unknown. We derived global trait maps based on information on environmental drivers. Subsequently, we characterized nine globally representative vegetation types based on their trait combinations and could make valid predictions of their global occurrence probabilities based on trait maps. This study provides a proof of concept for the link between plant traits and vegetation types, stimulating enhanced application of trait-based approaches in vegetation modeling. We envision that our approach, our observation-driven trait maps, and vegetation maps may inspire a new generation of powerful traits-based vegetation models.

The leaf economics spectrum[1,2] and the global spectrum of plant forms and functions[3] revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species[2]. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities[4]. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability[4,5]. Here we derive a set of ecosystem functions[6] from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems[7,8]. © 2021. The Author(s).

The Universal Soil Loss Equation model is often used to improve soil resource conservation by monitoring and forecasting soil erosion. This study tested a novel method to determine the cover and management factor (C) of this model by coupling the leaf area index (LAI) and soil basal respiration (SBR) to more accurately estimate a soil erosion map for a typical region with red soil in Hetian, Fujian Province, China. The spatial distribution of the LAI was obtained using the normalized difference vegetation index and was consistent with the LAI observed in the field (R2 = 0.66). The spatial distribution of the SBR was obtained using the Carnegie–Ames–Stanford Approach model and verified by soil respiration field observations (R2 = 0.51). Correlation analyses and regression models suggested that the LAI and SBR could reasonably reflect the structure of the forest canopy and understory vegetation, respectively. Finally, the C-factor was reconstructed using the proposed forest vegetation structure factor (Cs), which considers the effect of the forest canopy and shrub and litter layers on reducing rainfall erosion. The feasibility of this new method was thoroughly verified using runoff plots (R2 = 0.55). The results demonstrated that Cs may help local governments understand the vital role of the structure of the vegetation layer in limiting soil erosion and provide a more accurate large-scale quantification of the C-factor for soil erosion.