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  1. Abstract

    Both tree size and life history variation drive forest structure and dynamics, but little is known about how life history frequency changes with size. We used a scaling framework to quantify ontogenetic size variation and assessed patterns of abundance, richness, productivity and light interception across life history strategies from >114,000 trees in a primary, neotropical forest. We classified trees along two life history axes: afast–slowaxis characterized by a growth–survival trade‐off, and astature–recruitmentaxis with tall,long‐lived pioneersat one end and short,short‐lived recruitersat the other.

    Relative abundance, richness, productivity and light interception follow an approximate power law, systematically shifting over an order of magnitude with tree size.Slowsaplings dominate the understorey, butslowtrees decline to parity with rapidly growingfastandlong‐lived pioneerspecies in the canopy.

    Like the community as a whole,slowspecies are the closest to obeying the energy equivalence rule (EER)—with equal productivity per size class—but other life histories strongly increase productivity with tree size. Productivity is fuelled by resources, and the scaling of light interception corresponds to the scaling of productivity across life history strategies, withslowandallspecies near solar energy equivalence. This points towards a resource‐use corollary to the EER: the resource equivalence rule.

    Fitness trade‐offs associated with tree size and life history may promote coexistence in tropical forests by limiting niche overlap and reducing fitness differences.

    Synthesis. Tree life history strategies describe the different ways trees grow, survive and recruit in the understorey. We show that the proportion of trees with a pioneer life history strategy increases steadily with tree size, as pioneers become relatively more abundant, productive, diverse and capture more resources towards the canopy. Fitness trade‐offs associated with size and life history strategy offer a mechanism for coexistence in tropical forests.

     
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  2. Species richness of marine mammals and birds is highest in cold, temperate seas—a conspicuous exception to the general latitudinal gradient of decreasing diversity from the tropics to the poles. We compiled a comprehensive dataset for 998 species of sharks, fish, reptiles, mammals, and birds to identify and quantify inverse latitudinal gradients in diversity, and derived a theory to explain these patterns. We found that richness, phylogenetic diversity, and abundance of marine predators diverge systematically with thermoregulatory strategy and water temperature, reflecting metabolic differences between endotherms and ectotherms that drive trophic and competitive interactions. Spatial patterns of foraging support theoretical predictions, with total prey consumption by mammals increasing by a factor of 80 from the equator to the poles after controlling for productivity. 
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  3. Abstract Aim

    We may be able to buffer biodiversity against the effects of ongoing climate change by prioritizing the protection of habitat with diverse physical features (high geodiversity) associated with ecological and evolutionary mechanisms that maintain high biodiversity. Nonetheless, the relationships between biodiversity and habitat vary with spatial and biological context. In this study, we compare how well habitat geodiversity (spatial variation in abiotic processes and features) and climate explain biodiversity patterns of birds and trees. We also evaluate the consistency of biodiversity–geodiversity relationships across ecoregions.

    Location

    Contiguous USA.

    Time period

    2007–2016.

    Taxa studied

    Birds and trees.

    Methods

    We quantified geodiversity with remotely sensed data and generated biodiversity maps from the Forest Inventory and Analysis and Breeding Bird Survey datasets. We fitted multivariate regressions to alpha, beta and gamma diversity, accounting for spatial autocorrelation among Nature Conservancy ecoregions and relationships among taxonomic, phylogenetic and functional biodiversity. We fitted models including climate alone (temperature and precipitation), geodiversity alone (topography, soil and geology) and climate plus geodiversity.

    Results

    A combination of geodiversity and climate predictor variables fitted most forms of bird and tree biodiversity with < 10% relative error. Models using geodiversity and climate performed better for local (alpha) and regional (gamma) diversity than for turnover‐based (beta) diversity. Among geodiversity predictors, variability of elevation fitted biodiversity best; interestingly, topographically diverse places tended to have higher tree diversity but lower bird diversity.

    Main conclusions

    Although climatic predictors tended to have larger individual effects than geodiversity, adding geodiversity improved climate‐only models of biodiversity. Geodiversity was correlated with biodiversity more consistently than with climate across ecoregions, but models tended to have a poor fit in ecoregions held out of the training dataset. Patterns of geodiversity could help to prioritize conservation efforts within ecoregions. However, we need to understand the underlying mechanisms more fully before we can build models transferable across ecoregions.

     
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  4. Abstract Issue

    Geodiversity (i.e., the variation in Earth's abiotic processes and features) has strong effects on biodiversity patterns. However, major gaps remain in our understanding of how relationships between biodiversity and geodiversity vary over space and time. Biodiversity data are globally sparse and concentrated in particular regions. In contrast, many forms of geodiversity can be measured continuously across the globe with satellite remote sensing. Satellite remote sensing directly measures environmental variables with grain sizes as small as tens of metres and can therefore elucidate biodiversity–geodiversity relationships across scales.

    Evidence

    We show how one important geodiversity variable, elevation, relates to alpha, beta and gamma taxonomic diversity of trees across spatial scales. We use elevation from NASA's Shuttle Radar Topography Mission (SRTM) andc. 16,000 Forest Inventory and Analysis plots to quantify spatial scaling relationships between biodiversity and geodiversity with generalized linear models (for alpha and gamma diversity) and beta regression (for beta diversity) across five spatial grains ranging from 5 to 100 km. We illustrate different relationships depending on the form of diversity; beta and gamma diversity show the strongest relationship with variation in elevation.

    Conclusion

    With the onset of climate change, it is more important than ever to examine geodiversity for its potential to foster biodiversity. Widely available satellite remotely sensed geodiversity data offer an important and expanding suite of measurements for understanding and predicting changes in different forms of biodiversity across scales. Interdisciplinary research teams spanning biodiversity, geoscience and remote sensing are well poised to advance understanding of biodiversity–geodiversity relationships across scales and guide the conservation of nature.

     
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