Johanna T. Malle | Searcharxiv

arXiv2026

Multi-scale species richness estimation with deep learning

Biodiversity assessments depend critically on the spatial scale at which species richness is measured. How species richness accumulates with sampling area is influenced by natural and anthropogenic processes whose effects vary across spatial scales. These accumulation dynamics, described by the species-area relationship (SAR), are challenging to assess because most biodiversity surveys cover sampling areas far smaller than the scales at which these processes operate. Here, we combine sampling theory with deep learning to estimate species richness at arbitrary spatial scales across geographic space from existing ecological surveys. We apply our model, named MuScaRi, to ~350k vegetation surveys across Europe. Validated against independent regional plant inventories, MuScaRi reduces root mean squared error of vascular plant richness estimates by 61% relative to conventional estimators, yields substantially less biased predictions, and produces multi-scale richness maps alongside spatially explicit estimates of the species accumulation rate, a key indicator for biodiversity conservation. By encompassing the full spectrum of ecologically relevant spatial scales within a single unified framework, MuScaRi provides an essential tool for robust biodiversity assessments and forecasts under global change.

q-bio.PE↗

arXiv2025

When and where higher-resolution climate data improve impact model performance

Climate impact assessments increasingly rely on high-resolution climate and forcing datasets, under the premise that finer detail enhances both the accuracy and policy relevance of projections. Yet systematic evaluations of when and where higher resolution actually improves impact model outcomes remain limited, and it is unclear whether increasing spatial resolution consistently enhances performance across sectors, regions, and forcing variables. Here we show that gains in climate input accuracy and impact model performance are largest when moving from coarse (60 km) to intermediate (10 km) resolution, while further refinement to 3 km and 1 km yields more modest and inconsistent benefits. Using cross-sectoral simulations from the Inter-Sectoral Impact Model Intercomparison Project, we find that higher resolution substantially improves model skill in temperature-sensitive impact models and topographically complex regions, whereas precipitation-driven and low-relief systems show weaker and less systematic improvements. For temperature, both climate inputs and model outputs improve most strongly at the 60 km to 10 km transition, with diminishing gains at finer scales; for precipitation, some models even exhibit reduced performance beyond 10 km. These results highlight that optimal resolution depends on sectoral and regional context, and point to the need for improving model process representation and downscaling techniques so that added spatial detail translates into meaningful skill gains. For data providers, this implies prioritizing resolutions that maximize improvements where they matter most, while for modelling groups and users it underscores the need for explicit benchmarking of resolution choices in climate impact assessments.

physics.ao-ph↗