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Record W7110033465 · doi:10.5539/jas.v18n1p93

The Nesting of Grauer’s Gorillas (Gorilla beringei graueri) and the Management of Wildlife Tourism at High Altitude in Kahuzi-Biega National Park (DRC): The Case of the Sylverblack Chimanuka Family

2025· article· W7110033465 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Agricultural Science · 2025
Typearticle
Language
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsnot available
FundersMinistry of Environment and Sustainable DevelopmentMinistry of Environment
KeywordsGorillaNational parkWildlifeNest (protein structural motif)HabitatWildlife managementVisitor patternClimbingWildlife conservation

Abstract

fetched live from OpenAlex

Kahuzi-Biega National Park, in the eastern Democratic Republic of the Congo, currently hosts only 3,815 eastern lowland gorillas (Grauer’s gorillas) according to a recent survey conducted by the Wildlife Conservation Society (WCS) led by Dr. Andrew Plumptre. At higher altitudes, there are approximately 170 individuals of Grauer’s gorilla (Gorilla beringei graueri, Hominidae, Primates), including 143 regularly monitored as follows: 10 adult males, 58 adult females, 9 blackbacks, 20 sub-adults, 17 juveniles, and 29 infants, grouped into 9 semi-habituated families and 2 habituated families (the Chimanuka family and the Banonane family). The Chimanuka family consists of 1 dominant silverback male, adult females, blackbacks, sub-adults, juveniles, and infants. Visitor satisfaction also results from observing fresh nests built by the gorillas before their visit. Grauer’s gorillas build nests every night on the site. Our study aims to: (i) identify fresh nests built by Grauer’s gorillas in the different sectors of high altitude areas; (ii) characterize the dominant vegetative materials by layer and by season that show preference in the construction of nest types; (iii) determine the part of vegetative material most used in nest construction; (iv) assess the regenerative aspect of fruit trees at terrestrial nest sites by season; (v) identify the heights used in constructing different types of aerial nests and their pattern of habitat use by season. Using the method of reconnaissance walks, direct observation, and measuring the height of nests built on trees with a measuring tape by climbing the trees with a rope to assess the vertical distance between the base and the nests. EXCEL software was used to represent diagrams of nest types and the averages of the strata. The study was able to identify the areas visited by the Chimanuka family gorillas by season, with more areas visited during the rainy season than in the dry season. The categories of terrestrial and aerial nests showed that there were more terrestrial nests in the rainy season than in the dry season. Herbaceous strata were more visited during the rainy season, while in the dry season, gorillas more frequently slept in the shrub strata. Terrestrial nests are built in several categories; during the rainy season, they are mostly constructed from grasses (59%), and in the dry season, they are mostly made from shrub foliage (50%). 55% of ground nests are built by a dominant species, Mimulopsis solmsii, during the rainy season, and in the dry season, it is the species Chassalia subochreata (60%). Air nests are also built during both seasons: In the rainy season, gorillas construct nests at heights of 6 to 10 m, accounting for 55% of nests, and 40% of nests are built at heights of 1 to 5 m. The species Dombeya torrida is the most used tree for constructing aerial nests, followed by the shrub Galiniera coffeoides. In the dry season, 55% of nests are built at heights of 4 to 6 m and 40% at heights of 1 to 3 m. The shrub species Xymalos monospora is the most used, followed by Chassalia subochreata and the tree Trichilia volkensis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.290
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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