Kaur Prabhleen, Ciuti Simone, Ossi Federico, Cagnacci Francesca, Morellet Nicolas, Loison Anne, Atmeh Kamal, McLoughlin Philip, Reinking Adele K, Beck Jeffrey L, Ortega Anna C, Kauffman Matthew, Boyce Mark S, Haigh Amy, David Anna, Griffin Laura L, Conteddu Kimberly, Faull Jane, Salter-Townshend Michael
School of Mathematics and Statistics, University College Dublin, Dublin, Ireland.
Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland.
Mov Ecol. 2024 Aug 6;12(1):55. doi: 10.1186/s40462-024-00494-6.
Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored.
We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where of the individuals have been directly monitored.
Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples.
The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.
对动物群体进行社会网络分析能让科学家检验有关社会进化、行为及动态过程的假设。然而，估计指标的准确性取决于数据特征，如样本比例、样本量和频率。尤其在监测个体数量有限时，需要一种方案来评估为动物种群估计的社会网络指标的偏差和稳健性。
我们使用了五种有蹄类动物的GPS遥测数据集，将已知的社会网络方法与新方法结合成一个全面的五步方案。为了量化从部分种群获得的网络指标中的偏差和不确定性，我们提出了特别适用于自相关数据（如遥测重定位数据）的新统计方法。该方案通过第六种物种——黇鹿进行了验证，其种群规模已知，且部分个体已被直接监测。
通过该方案，我们展示了网络前数据排列如何使研究人员能够评估种群内相互作用的非随机方面。该方案评估全局网络指标中的偏差，获得置信区间，并根据网络中的节点数量量化全局和节点级网络指标的不确定性。我们发现，即使样本量降低，密度等全局网络指标仍保持稳健，而特征向量中心性等局部网络指标对其中四种物种来说不可靠。黇鹿网络即使在较低采样比例下也显示出低不确定性和偏差，这表明全面采样种群的重要性，同时也证明了我们对较小样本评估方法的准确性。
该方案使研究人员能够分析基于GPS的无线电遥测数据或其他数据，以确定社会网络指标的可靠性。这些估计值能够对不同条件下的网络进行统计比较，例如分析网络密度的每日和季节性变化。这些方法还可以指导动物社会网络研究中的方法学决策，如采样设计，并能从现有数据中进行更准确的生态推断。R包aniSNA使研究人员能够在其数据集上实施此工作流程，生成可靠的推断并指导方法学决策。
