Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Monitoring data from small mammal trapping in forest habitats in Kenya, 2021-2022

Domaine:

environment and energy
Créateur:
BulBeiBroGre
Éditeur:
BroUK CenNER
Éditeur:
NER
Hôte:avatar
This dataset contains information on the trap-setting operations and the number and species of animals captured during systematic sampling surveys in natural habitats of Kenya between 2021 and 2022. The trapping data include dates of setting, checking and removal at multiple locations. Trapping at each location typically involved 3-5 nights per trap line, but this could vary. The trap lines typically comprised 12 pairs of traps each set 10 metres apart along a transect line, with geographical coordinates for the centre of a pair of traps along the trap line. Animals were primarily captured for tissue collection for genetic analysis (not included in this dataset) to investigate the effectiveness of habitat corridors for promoting movement. Animals were captured during standardized trapping surveys with Sherman traps, and Tomahawk traps in systematic arrays. Trapping targeted small-medium sized rodents, carnivores and opossums. Captured animals were identified to species, marked individually, biometrics were recorded (ear size, hind foot and tail length), and tissue samples were collected, generally from the ear. The animals were then released alive. Trap and capture data are linked via a primary key field. At each trap check, data from captured animals were recorded in the field on data sheets, and then later computerized by entering into spreadsheets. Data were checked against original field records for quality assurance and validation.

Visit

doi.orgcatalogue.ceh.ac.uk

Tags

BiodiversityEnvironmental surveycarnivoresforestanimal corridorhabitat corridorsmustelidrodentSherman trapswoodland ecosystem+1

Licenses

Open Government Licence v3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Similaires

Data from: Context-dependent effects of large wildlife declines on small mammal communities in central KenyaMammal diversity survey in Dakatcha Woodland, Kenya: Results from a four-year camera trap survey (2019-2022)Data from: Seed-dispersal networks respond differently to resource effects in open and forest habitatsFrom Global to Local: Tailoring Forest Monitoring in Madagascar with Global Forest Watch DataAn Evaluation of 15 Years of Community-Based Monitoring in Forest Stewardship Council Certified Forest Areas in Southern Tanzania: Insights from Mammal and Indicator Bird SpeciesData from: Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities

Data from: Context-dependent effects of large wildlife declines on small mammal communities in central Kenya

Many species of large wildlife have declined drastically worldwide. These reductions often lead to

Mammal diversity survey in Dakatcha Woodland, Kenya: Results from a four-year camera trap survey (2019-2022)

The Dakatcha Woodland is one of the largest formally unprotected coastal forests in the Northern Swa

Data from: Seed-dispersal networks respond differently to resource effects in open and forest habitats

While patterns in species diversity have been well studied across large-scale environmental gradien

From Global to Local: Tailoring Forest Monitoring in Madagascar with Global Forest Watch Data

An Evaluation of 15 Years of Community-Based Monitoring in Forest Stewardship Council Certified Forest Areas in Southern Tanzania: Insights from Mammal and Indicator Bird Species

Certified community-managed forests are rare in East Africa, and the use of community-based biomonit

Data from: Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities

Camera trap surveys exclusively targeting features of the landscape that increase the probability of