A fundamental theory of actual error for species population monitoring

2025 · doi:10.32942/x2591n
preprint OA: closed
Full text JSON View at publisher

Abstract

Progress towards many national and international targets to halt and reverse declines of species populations (abundances) will be measured against Multispecies Biodiversity Indicators (MSIs). Like any sample-based estimator, MSIs approximate some real-world quantity (the estimand), and the difference between the two is the ‘actual’ or realised statistical error. We propose a general estimator and its corresponding estimand, both of which apply to many high-profile MSIs. Doing so allows us to decompose the error into a within-species component reflecting the impact of missing data for relevant locations and a cross-species component reflecting the impact of non-sampled species. Building on recent developments in sampling theory, we further decompose each of the within- and cross-species errors into three contributing factors: the ‘data defect’ (akin to sampling bias), the ‘data scarcity’ (reflecting the proportion of sites and species sampled) and the ‘problem difficulty’ (variability of abundance across sites and species). Approaches to reducing the error of MSIs can be recast as approaches to minimising one or more of these three quantities: for example, sample weighting reduces the data defect, sampling previously unmonitored species and locations minimises the data scarcity and focusing on functionally similar species may reduce the problem difficulty. Our theoretical framework thus unifies existing approaches to reducing the error of MSIs, reveals alternative approaches that might be considered in future and highlights opportunities for improving the communication of uncertainty.
Full text 2,571 characters · extracted from oa-doi-fallback · click to expand
This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Progress towards national and international targets to halt and reverse declines in species’ abundances will be assessed using Multispecies Indicators (MSIs). A distinction must be drawn between two MSIs. One is the ideal, but unobserved, MSI that would have been estimated had all species and sites within the scope of the target been sampled. The other is the empirical MSI estimated from the sample in hand. The discrepancy between the two, the sampling error, determines whether the empirical MSI faithfully reflects progress towards abundance targets. We decompose the sampling error of common sample-based MSIs algebraically into a geographic component reflecting the effect of non-sampled sites and a taxonomic component reflecting the effect of non-sampled species. Building on established results from sampling theory, we further decompose each component into three contributing factors: the data defect (capturing the bias of the sampling process), the data scarcity (capturing the odds that species and sites are not sampled) and the problem difficulty (caused by variation in abundance across sites and species). Having shown that both error components are determined by the same three factors, we review approaches to mitigating them. The approaches can be categorised broadly as obtaining more data, estimating the sample-based MSI in a different way (e.g. using a model), or redefining the ‘target population’. The target population is effectively the spatial and taxonomic scope of the MSI, so redefining it changes what is being estimated and modifies the problem at hand. Hence, it can be justified only when an accurate answer to a different question (e.g. pertaining to a subset of species from the original population) is preferable to an inaccurate answer to the original one. https://doi.org/10.32942/X2591N Life Sciences Biodiversity indicator;, Data defect correlation, Essential Biodiversity Variable, missing data, species abundance Published: 2025-03-25 16:07 Last Updated: 2026-01-08 01:11 CC BY Attribution 4.0 International Conflict of interest statement: None Data and Code Availability Statement: NA Language: English

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00