We are excited to announce that our January Issue, including the Animal Social Networks Special Feature, is now online! All the articles in this issue are free to access – find out more about them below.
Joint with the Journal of Animal Ecology, we held a successful open call for papers, soliciting original research capturing novel methodological developments or applications of social network theory to new empirical questions.
Read all about the Special Feature in the editorial Animal social networks: Towards an integrative framework embedding social interactions, space and time by editors Sebastian Sosa, David Jacoby, Mathieu Lihoreau and Cédric Sueur.
Special Feature Articles
Network measures in animal social network analysis *Free Access* Sosa et al. provide an overview of the most commonly-used social network measures in animal research for static networks or time‐aggregated networks. For each of these measures, they provide clear explanations as to what they measure, describe their respective variants, underline the necessity to consider these variants according to the research question addressed, and indicate considerations that have not been taken so far.
Exploiting the Full Potential of Bayesian Networks *Open Access* Ecological models used to make predictions from underlying covariates are typically unable to make predictions when the value of one or more covariates is missing during the testing. Missing values can be estimated, but methods are often unreliable and can result in poor accuracy. Bayesian networks can handle these issues, but are rarely used to their full potential. Here, Ramazi et al. provide an approach to learn a Bayesian network fully from observed data, without relying on experts, and show how to appropriately interpret the resulting network, both to identify how the variables are interrelated, and to answer probabilistic queries.
Mathematical and simulation methods for deriving extinction thresholds *Open access* In ecology, one of the most fundamental questions relates to the persistence of populations, or conversely to the probability of their extinction. Deriving extinction thresholds and characterising other critical phenomena in spatial and stochastic models is highly challenging, with few mathematically rigorous results being available for discrete‐space models such as the contact process. Here, Ovaskainen et al. present both mathematical and simulation‐based methods for deriving extinction thresholds and other critical phenomena in a broad class of agent‐based models called spatiotemporal point processes.
Null models for animal social network analysis *Open access* In social network analysis, pre‐network and node network permutation approaches have predominated in creating null models for hypothesis testing. The pre‐network permutation approach has recently been adapted to data on interactions and the focal sampling method, but its performance in different scenarios has not been thoroughly explored. Here, Puga-Gonzalez et al. show that the pre‐network permutation is sensitive to false positives in scenarios with or without observation bias, and the node network permutation approach produces fewer false positives and negatives than the pre‐network approach, but only in scenarios without observation bias.
Calculating effect sizes *Open access* Because of the nature of social interaction or association data, when testing hypotheses using social network data it is common for network studies to rely on permutations to control for confounding variables, and to not also control for them in the fitted statistical model. Here, Franks et al. implement two network simulation examples and analyse an empirical dataset to demonstrate how relying solely on permutations to control for confounding variables can result in highly biased effect size estimates of animal social preferences that are uninformative when quantifying differences in behaviour.
Estimating heritability of social phenotypes *Open access* Recently, estimating heritability with quantitative genetic models has moved to using node‐specific statistics from social networks as social phenotypes. However, parameter estimation can be problematic because social phenotypes are not independent observations and standard models tend to ignore the uncertainties around their estimates. Here Reinder Radersma presents a framework using latent variable modelling to account for these dependencies and uncertainties. The method is illustrated in Stan, a flexible Bayesian inference library, using a publicly available dataset on bottlenose dolphin networks.
Modelling and inference for the movement of interacting animals *Open access* Models of movement data are typically developed for analysing the tracks of individual animals, losing sight of the impact animals have on each other’s movement. Here, Milner et al. develop a model with a flexible social framework that captures interacting animals. The approach is based on the concept of social hierarchies, embedded in a multivariate diffusion process which models the movement of a group of animals.
Trade‐offs with telemetry‐derived contact networks *Free access* Network analysis of infectious diseases in wildlife can reveal traits or individuals critical to pathogen transmission, helping inform disease management strategies. Researchers commonly use telemetry technologies to identify animal associations, but such data may have different sampling intervals and often captures a small subset of the population. Here, Gilbertson et al. characterise trade‐offs important for using wildlife telemetry data beyond ecological studies of individual movement, finding that careful use of telemetry data has the potential to inform network models.
Network structure and the optimization of proximity‐based association criteria *Free access* Identifying associations between animals based on proximity requires deciding on quantitative criteria such as the maximum distance or the time interval between visits of different individuals. Here, Gomes et al. propose a procedure for optimising proximity‐based association criteria in animal social networks, whereby different spatial and temporal criteria are screened to determine which combination detects more network structure.
Analysis of temporal patterns in animal movement networks *Free access* Network analyses are increasingly used to capture properties of complex animal trajectories in simple graphical metrics. Here, Pasquaretta et al. introduce a method that incorporates time into movement network analyses based on temporal sequences of network motifs. The method is then illustrated using four example trajectories (bumblebee, black kite, roe deer, wolf) collected with different technologies (harmonic radar, platform terminal transmitter, global positioning system).
Constructing and analysing time‐aggregated networks *Free access* Various statistical methods exist for estimating animal social network changes through time. A time‐aggregated networks approach takes repeated snapshots of interactions within windows of time to generate a time series of networks, but analytical hurdles remain. To ameliorate this, Bonnell & Vilette introduce netTS, an R package that focuses on three steps for analysing time‐aggregated networks.
CMRnet *Open access* Long‐term capture–mark–recapture data provide valuable information on the movements of individuals between locations, and the contemporary and/or co‐located captures of individuals can be used to approximate the social structure of populations. Here, Silk et al. introduce CMRnet, an R package that generates social and movement networks from spatially explicit capture–mark–recapture data. It also provides functions for network and datastream permutations for these networks.
Other Featured Articles in our January Issue
DNA Sonication Inverse PCR *Open access* There are few available tools to comprehensively and economically identify uncharacterised flanking regions. Here, Alquezar‐Planas describe SIP, a sonication‐based inverse PCR high‐throughput sequencing strategy to investigate uncharacterised flanking region sequences, including those flanking mobile DNA. SIP combines unbiased fragmentation by sonication and target enrichment by coupling outward facing PCR priming with long‐read sequencing technologies.
Motion sensor activated suction trap to study vector–host interactions *Free access* Researchers elucidating vectors of zoonotic diseases encounter problems with inefficient surveillance techniques leading to underestimation of the importance of some species, and the overestimation of the importance of others. Carbon dioxide‐baited light traps are the most widely used traps for sampling vector groups. However, aspirating directly from the hosts is the most accurate method to incriminate vectors. Here, Sloyer & Burkett‐Cadena develop a novel and effective vector trapping system consisting of a suction trap, activated by a motion sensor, and controlled by a microcontroller, which activates automatically when host animals approach.
The Lion on the Cover
This month’s cover shows a residential male African lion (Panthera leo) taken in the Serengeti National Park, Tanzania. Lions have been studied using both VHF and GPS telemetry data, which can be used to derive contact networks for studies of infectious disease spread in wildlife. In their article, Gilbertson et al. investigated the consequences of telemetry sampling limitations–the frequency with which locations are recorded, and the proportion of the population tracked–on subsequent wildlife contact networks. Local network metrics of connectivity were fairly robust to telemetry sampling, but global metrics were less so. These results can be used to strategically plan telemetry sampling of wildlife based on trade‐offs between network metrics, sampling limitations, and the target population’s spatial configuration.