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Tropical forests are global centres of biodiversity and carbon storage. Many tropical countries aspire to protect forest to fulfil biodiversity and climate mitigation policy targets, but the conservation strategies needed to achieve these two functions depend critically on the tropical forest tree diversity-carbon storage relationship. Assessing this relationship is challenging due to the scarcity of inventories where carbon stocks in aboveground biomass and species identifications have been simultaneously and robustly quantified. Here, we compile a unique pan-tropical dataset of 360 plots located in structurally intact old-growth closed-canopy forest, surveyed using standardised methods, allowing a multi-scale evaluation of diversity-carbon relationships in tropical forests. Diversity-carbon relationships among all plots at 1 ha scale across the tropics are absent, and within continents are either weak (Asia) or absent (Amazonia, Africa). A weak positive relationship is detectable within 1 ha plots, indicating that diversity effects in tropical forests may be scale dependent. The absence of clear diversity-carbon relationships at scales relevant to conservation planning means that carbon-centred conservation strategies will inevitably miss many high diversity ecosystems. As tropical forests can have any combination of tree diversity and carbon stocks both require explicit consideration when optimising policies to manage tropical carbon and biodiversity.

Effects of agricultural intensification (AI) on biodiversity are often assessed on the plot scale, although processes determining diversity also operate on larger spatial scales. Here, we analyzed the diversity of vascular plants, carabid beetles, and birds in agricultural landscapes in cereal crop fields at the field ( n == 1350), farm ( n == 270), and European-region ( n == 9) scale. We partitioned diversity into its additive components αα, ββ, and γγ, and assessed the relative contribution of ββ diversity to total species richness at each spatial scale. AI was determined using pesticide and fertilizer inputs, as well as tillage operations and categorized into low, medium, and high levels. As AI was not significantly related to landscape complexity, we could disentangle potential AI effects on local vs. landscape community homogenization. AI negatively affected the species richness of plants and birds, but not carabid beetles, at all spatial scales. Hence, local AI was closely correlated to ββ diversity on larger scales up to the farm and region level, and thereby was an indicator of farm- and region-wide biodiversity losses. At the scale of farms (12.83-–20.52%%) and regions (68.34-–80.18%%), ββ diversity accounted for the major part of the total species richness for all three taxa, indicating great dissimilarity in environmental conditions on larger spatial scales. For plants, relative importance of αα diversity decreased with AI, while relative importance of ββ diversity on the farm scale increased with AI for carabids and birds. Hence, and in contrast to our expectations, AI does not necessarily homogenize local communities, presumably due to the heterogeneity of farming practices. In conclusion, a more detailed understanding of AI effects on diversity patterns of various taxa and at multiple spatial scales would contribute to more efficient agri-environmental schemes in agroecosystems.

Aim Species abundance data is commonly used to study biodiversity patterns. In this context, comparing α‐ and β‐diversity across incomplete samples can lead to biases. Therefore, it is essential to employ methods that enable standardised and accurate comparisons of α‐ and β‐diversity across varying sample sizes. In addition, biodiversity studies also often require robust estimates of the total number of species within a community and the number of species shared by two communities. Innovation Rarefaction methods are commonly used to calculate α‐diversity for standardised sample sizes, and they can also serve as the basis for calculating β‐diversity. In this application note, we present rarestR, a new R package designed for calculating abundance‐based α‐ and β‐diversity measures for inconsistent samples using rarefaction‐based metrics. The package also includes parametric extrapolation techniques to estimate the total expected number of species within a community, as well as the total number of species shared between two communities. Additionally, rarestR provides visualisation tools for curve‐fitting associated with these estimators. Main Conclusions Overall, the rarestR package is a valuable tool for comparing α‐ and β‐diversity values among incomplete samples, such as those involving highly mobile or species‐rich taxa. In addition, our species estimators offer a complementary approach to non‐parametric methods, including the Chao series of estimators.

Assessing the level of diversity in plant communities from field‐based data is difficult for a number of practical reasons: (1) establishing the number of sampling units to be investigated can be difficult; (2) the choice of sample design can impact on results; and (3) defining the population of concern can be challenging. Satellite remote sensing (SRS) is one of the most cost‐effective approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field‐based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, SRS provides repeated measures, thus making it possible to study temporal changes in biodiversity. Here, we provide a concise review of the potential of satellites to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of satellite imagery to predict species diversity. Our work shows that, while the assessment of alpha‐diversity is relatively straightforward, calculation of beta‐diversity (variation in species composition between adjacent locations) is challenging, making it difficult to reliably estimate gamma‐diversity (total diversity at the landscape or regional level). We conclude that an increased collaboration between the remote sensing and biodiversity communities is needed in order to properly address future challenges and developments. Assessing the level of diversity in plant communities from field‐based data is difficult for a number of practical reasons: (1) establishing the number of sampling units to be investigated can be difficult; (2) the choice of sample design can affect results; and (3) defining the population of concern can be challenging. Satellite remote sensing (SRS) is one of the most cost‐effective approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field‐based methods, it allows for complete spatial coverage of the Earth's surface under study over a short period of time. Furthermore, SRS provides repeated measures, thus making it possible to study temporal changes in biodiversity. Here we provide a concise review of the potential of remote sensing to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of SRS to predict species diversity. Figure reproduced from: Feret, J.‐B., and G. P. Asner. 2014. Mapping tropical forest canopy diversity using high‐delity imaging spectroscopy. Ecol. Appl. 24:1289–1296. ‐ with kind permission from the Ecological Society of America.

Aim Global change, especially landscape simplification, is a main driver of species loss that can alter ecological interaction networks, with potentially severe consequences to ecosystem functions. Therefore, understanding how landscape simplification affects the rate of loss of plant–pollinator interaction diversity (i.e., number of unique interactions) compared to species diversity alone, and the role of persisting abundant pollinators, is key to assess the consequences of landscape simplification on network stability and pollination services. Location France, Germany, and Switzerland. Methods We analysed 24 landscape‐scale plant–pollinator networks from standardised transect walks along landscape simplification gradients in three countries. We compared the rates of species and interaction diversity loss along the landscape simplification gradient and then stepwise excluded the top 1%–20% most abundant pollinators from the data set to evaluate their effect on interaction diversity, network robustness to secondary loss of species, and flower visitation frequencies in simplified landscapes. Results Interaction diversity was not more vulnerable than species diversity to landscape simplification, with pollinator and interaction diversity showing similar rates of erosion with landscape simplification. We found that 20% of both species and interactions are lost with an increase of arable crop cover from 30% to 80% in a landscape. The decrease in interaction diversity was partially buffered by persistent abundant generalist pollinators in simplified landscapes, which were nested subsets of pollinator communities in complex landscapes, while plants showed a high turnover in interactions across landscapes. The top 5% most abundant pollinator species also contributed to network robustness against secondary species loss but could not prevent flowers from a loss of visits in simplified landscapes. Main Conclusions Although persistent abundant pollinators buffered the decrease in interaction diversity in simplified landscapes and stabilised network robustness, flower visitation frequency was reduced, emphasising potentially severe consequences of further ongoing land‐use change for pollination services.

Knowledge of global patterns of biodiversity and regulating variables is indispensable to develop predictive models. The present study used predictive modelling approaches to investigate hypotheses that explain the variation in fish species richness between estuaries over a worldwide spatial extent. Ultimately, such models will allow assessment of future changes in ecosystem structure and function as a result of environmental changes. A comprehensive worldwide data base was compiled of the fish assemblage composition and environmental characteristics of estuaries. Generalized Linear Models were used to quantify how variation in species richness among estuaries is related to historical events, energy dynamics and ecosystem characteristics, while controlling for sampling effects. At the global extent, species richness differed among marine biogeographic realms and continents and increased with mean sea surface temperature, terrestrial net primary productivity and the stability of connectivity with a marine ecosystem (open vs. temporarily open estuaries). At a smaller extent (within a marine biogeographic realm or continent), other characteristics were also important in predicting variation in species richness, with species richness increasing with estuary area and continental shelf width. The results suggest that species richness in an estuary is defined by predictors that are spatially hierarchical. Over the largest spatial extents, species richness is influenced by the broader distributions and habitat use patterns of marine and freshwater species that can colonize estuaries, which are in turn governed by history contingency, energy dynamics and productivity variables. Species richness is also influenced by more regional and local parameters that can further affect the process of community colonization in an estuary including the connectivity of the estuary with the adjacent marine habitat, and, over smaller spatial extents, the size of these habitats. In summary, patterns of species richness in estuaries across large spatial extents seem to reflect from global to local processes acting on community colonization. The importance of considering spatial extent, sampling effects and of combining history and contemporary environmental characteristics when exploring biodiversity is highlighted.

We studied the relationships between functional alpha and beta diversities of fleas and their small mammalian hosts in 4 biogeographic realms (the Afrotropics, the Nearctic, the Neotropics and the Palearctic), considering 3 components of alpha diversity (functional richness, divergence and regularity). We asked whether (a) flea alpha and beta diversities are driven by host alpha and beta diversities; (b) the variation in the off-host environment affects variation in flea alpha and beta diversities; and (c) the pattern of the relationship between flea and host alpha or beta diversities differs between geographic realms. We analysed alpha diversity using modified phylogenetic generalized least squares and beta diversity using modified phylogenetic generalized dissimilarity modelling. In all realms, flea functional richness and regularity increased with an increase in host functional richness and regularity, respectively, whereas flea functional divergence correlated positively with host functional divergence in the Nearctic only. Environmental effects on the components of flea alpha diversity were found only in the Holarctic realms. Host functional beta diversity was invariantly the best predictor of flea functional beta diversity in all realms, whereas the effects of environmental variables on flea functional beta diversity were much weaker and differed between realms. We conclude that flea functional diversity is mostly driven by host functional diversity, whereas the environmental effects on flea functional diversity vary (a) geographically and (b) between components of functional alpha diversity.

Question How does grazing intensity affect plant community functional traits and the spatial components of functional diversity in subtropical grasslands? Location Long‐term cattle grazing management experiment in subtropical Campos grassland, southern Brazil. Methods Fourteen experimental units (paddocks) maintained under seven grazing intensity treatments for 26 years. In each paddock, we recorded plant species cover and species functional traits in nine systematically located plots of 1 m2. Nineteen functional traits were used in the ordination of species to identify main axes of trait variation. Functional diversity measured by Rao entropy was partitioned into alpha, beta and gamma components. We tested, by linear models, for the effects of grazing intensity on community‐weighted mean traits and functional diversity components, using as traits the species scores on the PCA axes. Results The two main axes of trait variation suggest a separation in species by their functional strategies (acquisition–conservation and tolerance–avoidance trade‐offs). Acquisitive and tolerant species increased while conservative and avoidant species decreased with grazing intensity. Rao quadratic entropy, considering the three spatial components, decreased with grazing intensity, but this trend was more accentuated with beta‐diversity. Conclusions The long‐term, strictly‐managed grazing experiment allowed us to reveal the effect of not only grazing disturbance per se, but also of different grazing intensities. Under high grazing intensity, frequent and severe defoliation allows only the persistence of species similarly adapted to regrowth. Under low grazing intensity, the lack of frequent defoliation enables the development of species with high investment in strong and long‐lived leaves. The partitioning of functional diversity revealed that the increase in functional diversity in areas with low grazing intensity is mostly due to an increase in heterogeneity among patches (beta‐component). The double stratum vegetation structure: tussocks, which escape grazing control, and short‐grazed patches often overgrazed, is maintained by grazer selectivity. We explore the relationship between grazing intensity and plant community functional aspects. Using functional strategy axes instead of single traits for assessing community weighted means and functional diversity, we studied paddocks of native grassland submitted to a long‐term experiment of grazing pressure in Southern Brazil. Functional diversity partition allowed us to observe that the most prominent effect of high grazing intensity was decreasing patch heterogeneity.

Understanding the resilience of moist tropical forests to treefall disturbance events is important for understanding the mechanisms that underlie species coexistence and for predicting the future composition of these ecosystems. Here, we test whether variation in the functional composition of Amazonian forests determines their resilience to disturbance. We studied the legacy of natural treefall disturbance events in four forests across Amazonia that differ substantially in functional composition. We compared the composition and diversity of all free‐standing woody stems 2–10 cm diameter in previously disturbed and undisturbed 20 × 20 m subplots within 55, one‐hectare, long‐term forest inventory plots. Overall, stem number increased following disturbance, and species and functional composition shifted to favour light‐wooded, small‐seeded taxa. Alpha‐diversity increased, but beta‐diversity was unaffected by disturbance, in all four forests. Changes in response to disturbance in both functional composition and alpha‐diversity were, however, small (2 – 4% depending on the parameter) and similar among forests. Synthesis. This study demonstrates that variation in the functional composition of Amazonian forests does not lead to large differences in the response of these forests to treefall disturbances, and overall, these events have a minor role in maintaining the diversity of these ecosystems.

QUESTIONS: Patterns of functional and phylogenetic homogenization of plant diversity at the scale of entire cities are well established. However, fewer studies have investigated how shifts in the composition of urban species pools may alter spatial variation in community assembly across patches within a city. We compared plant diversity both within and between vacant lots in a single urban neighborhood and asked: (1) how do heterogeneous human legacies of land use within vacant lots structure plant community diversity and composition and (2) what is the importance of human legacies for structuring community composition, relative to spatial and environmental variation? LOCATION: Urban residential neighbourhood in Baltimore, MD, US (39°17′ N, 76°38′ W). METHODS: We surveyed herbaceous plant species identity and abundance in 24 unmanaged vacant lots. We constructed functional and phylogenetic diversity metrics from databases and the literature and compared taxonomic, functional and phylogenetic diversity between sections of the lot where a residential building was once located (building footprint), and the area that was originally the garden or backyard (remnant garden). We term these different ‘human legacy’ groups. We partitioned variation in plant community composition between human legacy groups as well as across measured environmental and spatial gradients. RESULTS: We report significant plant community compositional divergence between human legacy groups. Beta‐diversity, in particular, was significantly higher in remnant garden sections compared to building footprint sections for all metrics of biodiversity. Plant community compositional variation was primarily explained by differences in human legacies. We found no measurable effects of selected metrics of local environmental variation (abiotic soil characteristics) or environmental context (lot area, proximity to other vacant lots and tree canopy) on compositional variation. Geographic distance between sites, however, did interact with human legacy variation to structure phylogenetic composition. CONCLUSIONS: Our findings suggest that human land‐use legacies have complex but measurable effects on both the patterns and the processes by which species co‐existence is maintained across the urban landscape. Within a single habitat type (here, residential vacant lots), variation in legacies of land use may have a stronger plant community structuring effect than contemporary environmental variation.