Estimating species commonness and prevalence through unsupervised methods

TL;DR

This paper introduces an unsupervised method to estimate how common species are in an area, improving ecological niche models using data from biodiversity databases.

Contribution

A novel, data-driven, unsupervised multi-species methodology for estimating species prevalence in ecological niche models.

Findings

A deep-learning model achieved the highest accuracy (~81–90%) in classifying species prevalence.

The methodology is scalable and reproducible, using clustering and statistical analysis for prevalence estimation.

The approach was validated in a case study of 161 species in the Massaciuccoli Lake basin.

Abstract

The prevalence of a species in a given area is crucial for estimating the environmental conditions associated with its subsistence within ecological niche models (ENMs). Prevalence is defined as the proportion of presences relative to the total number of sampled sites, reflecting prior expectation on species commonness or rarity. However, reliable estimation often faces challenges due to limited or biased occurrence data, particularly for rare or poorly monitored species. This work presents a data-driven, multi-species methodology to estimate species prevalence for use in ENMs. It leverages species occurrence records from the Global Biodiversity Information Facility and is entirely unsupervised. It utilises two clustering methods, one deep-learning model, and an ensemble model, plus statistical analysis to classify species commonness and transform classifications into prevalence probabilities. A case study is presented for 161 species living in the Massaciuccoli Lake basin (Tuscany, Italy), a wetland of high biodiversity value and ecological sensitivity. The models classified the species’ prevalence based on observations from other Italian wetland sites, and were evaluated against expert-based assessments. All models achieved high accuracy, with the deep-learning model achieving the highest (~ 81–90%). The proposed methodology is scalable and reproducible and can inform ENMs with objective, robust prevalence estimates.

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