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As part of the Gaborone Declaration for Sustainability in Africa, Liberia has pledged to include the value of nature in national decision making through natural capital accounting. Surveying species of concern, such as the western chimpanzee (Pan troglodytes verus), which was recently reclassified as “critically endangered” by the International Union for Conservation of Nature, and identifying protection priority areas are critical first steps towards achieving Liberia's goal to conserve 30% of its remaining forests and supporting the wave of conservation projects taking place in the country.

Liberia, Africa.

We modelled western chimpanzee habitat suitability, focusing on determining relevant environmental predictors and the most appropriate scale for modelling species–habitat relationships. We built models at six resolutions (30–960 m) to identify scale domains where relationships remain constant. We include several habitat variables that have not been included in prior modelling efforts. We then used the suitability map as the conductance input into a connectivity analysis using Circuitscape.

The amount of forest within 1–3 km was the most important predictor of chimpanzee occurrence. Variable ranks and importance shifted considerably between modelling scales, supporting the need for multiscale investigations, but scale domains were present. Several important corridors for chimpanzee habitat and movement overlap considerably with existing timber and palm oil concessions and overlap mining and rubber concessions to a lesser degree.

The proportion of primary forest within 1–3 km is critically important for chimpanzee habitat. Ongoing conservation projects and efforts taking place in Liberia including the Good Growth Partnership and the Gaborone Declaration for Sustainability in Africa can utilize the spatial findings on connectivity provided by this study to inform future conservation decisions, particularly expanding exiting protected areas.

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.

Contiguous USA.

2007–2016.

Birds and trees.

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.

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.

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.

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.

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.

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.