Multi-scale species richness estimation with deep learning

TL;DR

This paper introduces MuScaRi, a deep learning model that accurately estimates species richness across multiple spatial scales, improving biodiversity assessments and conservation strategies.

Contribution

It combines sampling theory with deep learning to estimate species richness at arbitrary scales, addressing limitations of traditional methods.

Findings

Reduces RMSE of richness estimates by 61%

Produces less biased and more accurate multi-scale richness maps

Provides spatially explicit estimates of species accumulation rates

Abstract

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.