Ecography (2026)
Projected warming and drying raise concerns about the resilience of stress-adapted ecosystems, including the Brazilian Campo Rupestre, an exceptionally biodiverse mountaintop grassland mosaic on ancient, nutrient-poor substrates. Here, we combine field-based trait data and long-term remote sensing to assess the functional structure and temporal dynamics of these communities.
Using foliar trait measurements from 247 vegetation plots across five contrasting habitats, we:
1) quantify contemporary community-level functional structure,
2) evaluate how edaphic and climatic filters shape spatial variation in community-weighted foliar traits,
3) reconstruct multi-decadal trait trajectories by hindcasting from long-term Landsat reflectance (1984–2022).
Contemporary communities occupy a narrow and predominantly conservative region of the leaf-economic trait spectrum, yet habitats differ in their functional positions within CSR strategy space, indicating non-uniform trait coordination despite overall conservatism. Soil texture and acidity define the primary conservative–acquisitive axis of trait variation, while climatic water balance acts as a secondary modulator; together, these predictors explain 39% of the spatial variation in community-weighted traits. Contrary to expectations of increasing conservatism under progressive climatic stress, Landsat-based hindcasts reveal only modest temporal reorganisation. Specific leaf area and leaf area increase across habitats, while leaf dry matter content declines slightly, indicating a subtle relaxation of conservative trait expression. Temporal changes are small relative to the pronounced spatial differentiation, suggesting strong functional inertia in this OCBIL system.
Overall, Campo Rupestre communities persist within a conservative functional domain while exhibiting fine-scale, habitat-dependent differentiation structured by enduring soil and water-balance gradients.
Related Research Themes

Ecology
Testing the effectiveness of different ecological approaches for nature recovery to support biodiversity and the delivery of ecosystem services such as climate change mitigation and adaptation.

Scale and Technology
Tracking and evaluating nature recovery at both fine resolution and large spatial scales utilising state-of-the-art remote sensing, big data, and deep machine learning techniques.

