Evidence map›Paper›PMID 38746543›Full record

ArticleEcology and evolution2024

Estimating distribution and abundance of wide-ranging species with integrated spatial models: Opportunities revealed by the first wolf assessment in south-central Italy.

Vincenzo Gervasi, Paola Aragno, Valeria Salvatori, Romolo Caniglia, Daniele De Angelis, Elena Fabbri, Valentina La Morgia, Francesca Marucco, Edoardo Velli, Piero Genovesi

Abstract read
In one paragraph

Article in Ecology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Non-Invasive Monitoring of Hazel Dormouse (Ecology and evolution · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. One health (Amsterdam, Netherlands) · 2024
    Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Vincenzo GervasiIstituto Superiore per la Protezione e la Ricerca Ambientale Roma Italy.ORCID https://orcid.org/0000-0002-6773-9227
Paola AragnoIstituto Superiore per la Protezione e la Ricerca Ambientale Roma Italy.
Valeria SalvatoriIstituto di Ecologia Applicata Roma Italy.
Romolo CanigliaDepartment for the Monitoring and Protection of the Environment and for Biodiversity Conservation, Unit for Conservation Genetics (BIO-CGE) Istituto Superiore per la Protezione e la Ricerca Ambientale Ozzano dell'Emilia Italy.
Daniele De AngelisIstituto Superiore per la Protezione e la Ricerca Ambientale Roma Italy.
Elena FabbriDepartment for the Monitoring and Protection of the Environment and for Biodiversity Conservation, Unit for Conservation Genetics (BIO-CGE) Istituto Superiore per la Protezione e la Ricerca Ambientale Ozzano dell'Emilia Italy.
Valentina La MorgiaIstituto Superiore per la Protezione e la Ricerca Ambientale Roma Italy.ORCID https://orcid.org/0000-0001-9915-3194
Francesca MaruccoDepartment of Life Sciences and Systems Biology University of Torino Torino Italy.
Edoardo VelliDepartment for the Monitoring and Protection of the Environment and for Biodiversity Conservation, Unit for Conservation Genetics (BIO-CGE) Istituto Superiore per la Protezione e la Ricerca Ambientale Ozzano dell'Emilia Italy.
Piero GenovesiIstituto Superiore per la Protezione e la Ricerca Ambientale Roma Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Estimating demographic parameters for wide-ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south-central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7-month sampling campaign in 2020-2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non-invasive genetic samples (NGS). The model comprised (i) a single-season, multiple data-source, multi-event occupancy model and (ii) a spatially explicit capture-recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation-based assessment, to estimate the best conditions for optimizing sub-sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south-central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km

Indexed as

genetic samplinglarge carnivoresmonitoringpopulation modellingspatial capture‐recapturetransboundary wildlife

Identifiers

PMID38746543
PMCPMC11091487

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.