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Data from: Adaptive data collection strategies for spatial capture-recapture monitoring: Linking monitoring approaches to seasonal variation in density and space use

Identification

Clé publique

radar:r-2db82ac2542dd680e25d37627d43ae3c

Auteurs

Mumbi Chege · Arjun M. Gopalaswamy · Elphas Bitok · Femke Broekhuis · Laura D. Bertola · Linus Kariuki · Bernard Kuloba · Stephen Ndambuki · Patrick Omondi · G.R. de Snoo · Nadia de Souza · Grace Waiguchu · Hans H. de Iongh · Nicholas B. Elliot

Année déclarée

2026

Langue déclarée

en

Type source

dataset

Première détection

2026-11-14T02:13:07.687523+00:00

Dernière observation des métadonnées

2026-11-14T02:13:07.687523+00:00

Dernière vérification HTTP

Non effectuée

Institutions déclarées par la source

Leiden University · Wildlife Research and Training Institute · Wageningen University & Research · National Centre for Biological Sciences · Kenya Wildlife Service · University of Antwerp · Nelson Mandela University

Pays d’affiliation déclarés

BE · IN · KE · NL · ZA

Résumé documentaire — langue originale

Effective monitoring of wildlife populations and their changes over time is essential for guiding conservation strategies. For monitoring to fulfil this role, large-scale and long-term resource commitments are required, yet are frequently lacking. Consequently, site-based monitoring of key species is often sporadic, inconsistent, and disconnected from wildlife agencies, reducing the role of monitoring in adaptive management. To overcome these challenges, Kenya’s wildlife management and research agencies coordinated and participated in three surveys that were designed to evaluate adaptable, search-encounter data collection strategies within a spatial capture-recapture (SECR) framework for lions (Panthera leo) under real-world monitoring constraints. Three surveys were conducted in Nairobi National Park, which lies in Kenya’s capital. The first survey was conducted by a multi-agency team of field biologists during the wet season (2018). The second and third surveys were conducted in the dry season (2021 and 2022) by trained wildlife enthusiasts and by field biologists participating in a SECR training workshop, respectively. The resulting estimates were used to assess their consistency with known spatiotemporal patterns of variation in lion population density. Our results were consistent with those reported in previous studies. Lion density (lions/100 km2) in the park was higher during the dry season (2021: 26.16, PSD=5.27; 2022: 28.05, PSD=5.18), compared to the wet season (2018: 16.77, PSD=4.27) when prey are dispersed outside the park. Movement for all lions (inferred by sex ratio-weighted spatial scale parameter, σw, in km) was largest during the wet season (2018: 3.38, PSD=0.7) compared to the dry season (2021: 1.38, PSD=0.2; 2022: 1.44, PSD=0.2). Lion activity centres were more clustered on the southern boundary of the park during the wet season compared to during the dry season. Our results support existing evidence that human-lion conflicts are more likely to peak during the wet season, thus providing key insights for the conservation of lions in this ecosystem. Our study demonstrates that monitoring constraints need not preclude adaptability, provided data collection evolves within a robust framework that yields comparable estimates and informs management interventions.

Identifiants

OPENALEX

https://openalex.org/W7154473942

DOI

10.5061/dryad.vt4b8gv6t

Repères documentaires

Famille documentaire

dataset

Types déclarés par les sources

dataset

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Sources

OpenAlex · Document 1

• Document · https://research.wur.nl/en/datasets/data-from-adaptive-data-collection-strategies-for-spatial-capture

Statut documentaire

Pertinence à examiner

Bibliothèque

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