A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.

BACKGROUND: 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. METHODS: 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 ∼ 85 % of the individuals have been directly monitored. RESULTS: 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. CONCLUSIONS: 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.

Climate change is affecting mortality of weasels due to camouflage mismatch.

Direct phenological mismatch caused by climate change can occur in mammals that moult seasonally. Two colour morphs of the weasel Mustela nivalis (M. n.) occur sympatrically in Bialowieza Forest (NE Poland) and differ in their winter pelage colour: white in M. n. nivalis and brown in M. n. vulgaris. Due to their small body size, weasels are vulnerable to attacks by a range of different predators; thus cryptic coat colour may increase their winter survival. By analysing trapping data, we found that the share of white subspecies in the weasel population inhabiting Bialowieza Forest decreases with decreasing numbers of days with snow cover. This led us to hypothesise that selective predation pressure should favour one of the two phenotypes, according to the prevailing weather conditions in winter. A simple field experiment with weasel models (white and brown), exposed against different background colours, revealed that contrasting models faced significantly higher detection by predators. Our observations also confirmed earlier findings that the plasticity of moult in M. n. nivalis is very limited. This means that climate change will strongly influence the mortality of the nivalis-type due to prolonged camouflage mismatch, which will directly affect the abundance and geographical distribution of this subspecies.