Data from: Adaptive data collection strategies for spatial capture-recapture monitoring: Linking monitoring approaches to seasonal variation in density and space use
Data files
Apr 15, 2026 version files 495.52 KB
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2018_CH.csv
621 B
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2018_Effort.csv
61.47 KB
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2018_Sex.csv
186 B
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2018_Traps.csv
23.44 KB
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2021_CH.csv
724 B
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2021_Effort.csv
52.63 KB
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2021_Efforted.csv
52.63 KB
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2021_Sex.csv
170 B
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2021_Traps.csv
25.25 KB
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2022_CH.csv
561 B
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2022_Effort.csv
31.68 KB
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2022_Sex.csv
128 B
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2022_Traps.csv
12.65 KB
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e2dist.R
340 B
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Habitat.csv
179.66 KB
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NNP_Survey_M2.R
9.42 KB
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README.md
1.78 KB
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scrDataWS.R
2.90 KB
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SCRi.fn.par1-lionVer1003.R
39.28 KB
Abstract
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.
Citation: Chege et al. (2026) Ecological Solutions and Evidence
We provide the following R scripts and input files in the interest of transparency and reproducibility of our results.
Please note that NNP_Survey_M2.R is the main R script model to be executed, with the three R scripts listed below running in the background:-
- SCRi.fn.par1-lionVer1003.R - This is the analysis engine, and is scripted to parallelise the analysis.
- e2dist.R – This is a utility function necessary for certain computations.
- scrData.R – This is a data formatting function
Input file information:-
File name: All input files to be used for analysis were named using the format YYYY_datatype.csv, where YYYY is the year the survey was conducted and datatype describes the type of data.
e.g. 2018_CH.csv contains lion capture-histories for the year 2018.
Data type:-
- CH - This is the capture history of each individual lion (Animal_ID) per pixel (LOC_ID) per sampling occasion (SO).
- Effort - logarithm of the distance travelled (in kilometres) per pixel (LOC_ID) per sampling occasion (n = length of each survey). No effort = -20
- Traps - The pixel (trap) operations matrix. This is a binary file with information on whether a pixel (LOC_ID) was sampled on a
particular sampling occasion (n = length of each survey) where sampled = 1 and not sampled = 0
- Sex - Sex identification file for individual animals where 0 = female and 1 = male
- Habitat - This is the state space that was used for all sampling sessions where 1 = suitable habitat and 0 = unsuitable habitat
- Coordinates are in UTM
