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

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 d..., , # Data from: Adaptive data collection strategies for spatial capture-recapture monitoring: Linking monitoring approaches to seasonal variation in density and space use 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:- 1. SCRi.fn.par1-lionVer1003.R - This is the analysis engine, and is scripted to parallelise the analysis. 2. e2dist.R – This is a utility function necessary for certain computations. 3. 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..., ,对野生动物种群及其随时间的动态变化开展有效监测，是指导保护策略制定的核心前提。要让监测发挥这一核心作用，需要投入大规模、长期的资源，但此类资源投入往往十分匮乏。因此，针对关键物种的实地监测往往频次零散、标准不一且与野生动物管理机构脱节，削弱了监测在适应性管理中的作用。 为破解上述困境，肯尼亚野生动物管理与研究机构协调并开展了三项调查，旨在在现实监测约束条件下，评估适用于狮子（Panthera leo）的空间捕获-重捕（spatial capture-recapture, SECR）框架下的适应性搜索偶遇数据收集策略。三项调查均在肯尼亚首都的内罗毕国家公园内开展：第一项调查由多机构野外生物学家团队于2018年湿季完成；第二、三项调查则于d...开展。 # 数据集来源：《空间捕获-重捕监测的适应性数据收集策略：将监测方法与种群密度和空间利用的季节变异相挂钩》 引用：Chege等人（2026），《生态解决方案与证据》（Ecological Solutions and Evidence） 为确保研究结果的透明度与可复现性，我们公开了以下R脚本与输入文件： --- 请注意，NNP_Survey_M2.R为需执行的主R脚本模型，其余三款R脚本将在其后台运行： 1. SCRi.fn.par1-lionVer1003.R：本脚本为分析引擎，已设置为并行化分析模式。 2. e2dist.R：本脚本为部分特定计算所需的工具函数。 3. scrData.R：本脚本为数据格式化函数。 输入文件说明：所有用于分析的输入文件均采用`YYYY_datatype.csv`格式命名，其中YYYY为调查开展年份，datatype用于描述数据类型。例如，2018_CH.csv包含狮子捕获历史数据等内容。