Evidence map›Paper›PMID 42668897›Full record

ArticleBiodiversity data journal2026

Assessing patterns of extinction risk amongst mammal species in Nigeria: A comparative analysis of human impact.

Bernard Asanbe

Abstract read
In one paragraph

Article in Biodiversity data journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Bernard AsanbeUniversity of Gothenburg, Göteborg, Sweden University of Gothenburg Göteborg Sweden https://ror.org/01tm6cn81.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to evaluate how biological traits influence extinction risk amongst mammal species in Nigeria and how these traits interact with specific anthropogenic threats, such as agriculture, urbanisation and climate change. Focusing on mammal species in Nigeria, we used phylogenetic logistic regression to test the influence of five biological traits: body mass, brain mass, generation time, current geographic range and historical range contraction, on extinction risk across nine IUCN threat categories. Standardised models were used to compare trait sensitivity across threats. Brain mass emerged as the most consistent and influential predictor of extinction risk across threat categories, including agriculture, biological resource use, urban development and, notably, climate change, where it was the strongest predictor of all models. Species with larger brains, often primates and carnivores, were highly vulnerable. Geographic range size was a strong negative predictor of risk across most models, with range-restricted species more susceptible to habitat loss and fragmentation. Generation time was positively associated with risk under direct human pressures, but inversely linked under climate threats. Body mass showed weak and inconsistent effects overall, though it was significantly and negatively associated with extinction risk under agriculture and aquaculture. The number of species affected was highest under direct human pressures, compared to indirect anthropogenic threats, such as climate change and pollution. Extinction risk in Nigerian mammals is shaped by intrinsic traits that interact predictably with human pressures. Species with large brains, small ranges and slow reproduction are at greatest risk. Trait-based models can improve conservation planning by identifying vulnerable species before population declines become critical, especially in regions facing intensive land-use change.

Indexed as

brain massextinction riskhuman impactNigerian mammals

Identifiers

PMID42668897
PMCPMC13525407

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.