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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) and c . 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.
Explaining the distribution of a species by using local environmental features is a long-standing ecological problem. Often, available data are collected as a set of presence locations only, thus precluding the possibility of a desired presence-absence analysis. We propose that it is natural to view presence-only data as a point pattern over a region and to use local environmental features to explain the intensity driving this point pattern. We use a hierarchical model to treat the presence data as a realization of a spatial point process, whose intensity is governed by the set of environmental covariates. Spatial dependence in the intensity levels is modelled with random effects involving a zero-mean Gaussian process. We augment the model to capture highly variable and typically sparse sampling effort as well as land transformation, both of which degrade the point pattern. The Cape Floristic Region in South Africa provides an extensive class of such species data. The potential (i.e. non-degraded) presence surfaces over the entire area are of interest from a conservation and policy perspective. The region is divided into about 37 000 grid cells. To work with a Gaussian process over a very large number of cells we use a predictive spatial process approximation. Bias correction by adding a heteroscedastic error component has also been implemented. We illustrate with modelling for six different species. Also, a comparison is made with the now popular Maxent approach though it is limited with regard to inference. The resultant patterns are important on their own but also enable a comparative view, for example, to investigate whether a pair of species are potentially competing in the same area. An additional feature of our modelling is the opportunity to infer about biodiversity through species richness, i.e. the number of distinct species in an areal unit. Such an investigation immediately follows within our modelling framework.
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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