Spatially varying population indices

preprint OA: closed
Full text JSON View at publisher

Abstract

Large scale monitoring is fundamental for reliably tracking the fate of animal populations under changing environments and land-use practices. A common application of large scale population monitoring data is to produce indices of temporal change in species abundances, which are used in environmental policy assessments of species and biodiversity statuses. For index estimation, spatio-temporal models can be used to take advantage of the spatial component of large scale data in order to better capture and understand spatial variation in population change. This paper presents a generalized approach to estimating indices of relative population change across different spatial and temporal scales from fits of spatio-temporal models to population monitoring data. Using flexible specifications of baselines for indices, the approach can be used for a range of different comparisons of abundance across space and time, aggregated at small as well as large spatial and short as well as long term temporal scales. This is illustrated in an application to Swedish monitoring data of the common cuckoo, for which we estimate a range of national, county-wise and fine scale indices. An R-package, spotr, that aids computation of indices from fitted models accompanies the paper.
Full text 1,915 characters · extracted from oa-doi-fallback · 2 sections · click to expand

Abstract

Large scale monitoring is fundamental for reliably tracking the fate of animal populations under changing environments and land-use practices. A common application of large scale population monitoring data is to produce indices of temporal change in species abundances, which are used in environmental policy assessments of species and biodiversity statuses. For index estimation, spatio-temporal models can be used to take advantage of the spatial component of large scale data in order to better capture and understand spatial variation in population change. This paper presents a generalized approach to estimating indices of relative population change across different spatial and temporal scales from fits of spatio-temporal models to population monitoring data. Using flexible specifications of baselines for indices, the approach can be used for a range of different comparisons of abundance across space and time, aggregated at small as well as large spatial and short as well as long term temporal scales. This is illustrated in an application to Swedish monitoring data of the common cuckoo, for which we estimate a range of national, county-wise and fine scale indices. An R-package, spotr, that aids computation of indices from fitted models accompanies the paper. DOI https://doi.org/10.32942/X2GH04 Subjects Biodiversity, Ecology and Evolutionary Biology, Environmental Indicators and Impact Assessment, Environmental Monitoring, Population Biology

Keywords

population index; abundance; model-based; spatio-temporal; population monitoring; biodiversity index Dates Published: 2025-04-11 08:18 Last Updated: 2025-04-24 21:19 Older Versions License CC-By Attribution-NonCommercial-NoDerivatives 4.0 International Additional Metadata Data and Code Availability Statement: Data and code are publicly available, https://github.com/jknape/spotr 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