Biology subjects

Bonnell, T.

Publications and source records attributed to Bonnell, T..

4 recordsLinked to original sources

bioRxiv2024

The problem of unmeasured variables in animal social network analysis: can edge-based multilevel models provide a reliable solution?

Recent approaches to analysing social networks suggest that modeling the edges of the network and using multilevel models will produce more informative estimates. These recent methods have been proposed as a way to better handle the dependency structures of social networks, account for biases in data collection, and retain uncertainty when making inferences about social network structures. We find that they have the potential to also effectively handle unmeasured variables that act as statistical confounds in social network analysis. Using simulated data, we highlight that static social network analyses can be used to identify patterns in social networks, but generally cannot be used to identify the underlying mechanisms behind the patterns. To identify mechanisms, we show that taking a dynamic approach and using edge-based models with additive and multiplicative random effects provides a means to identify mechanisms even when statistical confounds are present. Additive and multiplicative random effects also provide information about social structures not captured by the predictors, facilitating exploratory analysis. We suggest that a keep-it-maximal approach for random effects structures is beneficial for edge-based multilevel models of social networks, and that such approaches can be particularly effective when there are unmeasured variables that are not captured by model predictors.

animal behavior and cognition↗

bioRxiv2021

Extracting socio-spatial networks from photo-ID data using multilevel multinomial models

O_LIEstimating the impacts of anthropogenic disturbances requires an understanding of the habitat use patterns of individuals within a population. This is especially the case when disturbances are localized within a populations spatial range, as variation in habitat-use within a population can drastically alter the distribution of impacts. C_LIO_LIHere, we illustrate the potential for multilevel multinomial models to generate spatial networks from capture-recapture data, a common data source use in wildlife studies to monitor population dynamics and habitat use. These spatial networks capture which regions of a populations spatial distribution share similar/dissimilar individual usage patterns, and can be especially useful for detecting structured habitat use within the populations spatial range. C_LIO_LIUsing simulations and 18 years of capture-recapture data from St. Lawrence Estuary (SLE) beluga, we show that this approach can successfully estimate the magnitude of similarities/dissimilarities in individual usage patterns across sectors, and identify sectors that share similar individual usage patterns that differ from other sectors, i.e., structured habitat use. In the case of SLE beluga, this method identified multiple clusters of individuals, each preferentially using restricted areas within their summer range of the SLE. C_LIO_LISynthesis and applications. Multilevel multinomial models can be effective at estimating spatial structure in habitat use within wildlife populations sampled by capture-recapture of individuals. Our finding of a structured habitat use within the SLE beluga summer range has direct implications for estimating individual exposures to localized stressors, such as underwater noise from shipping or other activities. C_LI

ecology↗

bioRxiv2019

Ranking the Ranking Methodologies: Determining Temporal Stability in Dominance Hierarchies

The importance of social hierarchies has led to the development of many techniques for inferring social ranks, leaving researchers with an overwhelming array of options to choose from. Many of our research questions involve longitudinal analyses, so we were interested in a method that would provide reliable ranks across time. But how does one determine which method performs best?\n\nWe attempt to answer this question by using a training-testing procedure to compare 13 different approaches for calculating dominance hierarchies (seven methods, plus 6 analytical variants of these). We assess each methods performance, its efficiency, and the extent to which the calculated ranks obtained from the training dataset accurately predict the outcome of observed aggression in the testing dataset.\n\nWe found that all methods tested performed well, despite some differences in inferred rank order. With respect to the need for a \"burn-in\" period to enable reliable ranks to be calculated, again, all methods were efficient and able to infer reliable ranks from the very start of the study period (i.e., with little to no burn-in period). Using a common 6-month burn-in period to aid comparison, we found that all methods could predict aggressive outcomes accurately for the subsequent 10 months. Beyond this 10-month threshold, accuracy in prediction decreased as the testing dataset increased in length. The decay was rather shallow, however, indicating overall rank stability during this period.\n\nIn general, a training-testing approach allows researchers to determine the most appropriate method for their dataset, given sampling effort, the frequency of agonistic interactions, the steepness of the hierarchy, and the nature of the research question being asked. Put simply, we did not find a single best method, but our approach offers researchers a valuable tool for identifying the method that will work best for them.\n\nHighlightsO_LIAll ranking methods tested performed well at predicting future aggressive outcomes, despite some differences in inferred rank order.\nC_LIO_LIAll ranking methods appear to be efficient in inferring reliable ranks from the very start (i.e., with little to no burn-in period), but all showed improvement as the burn-in period increased.\nC_LIO_LIUsing a common 6-month burn-in period, we found that all methods could predict aggressive outcomes accurately for the subsequent 10 months. Beyond this threshold, accuracy in prediction decreased as the testing dataset increased in length.\nC_LIO_LISwitching to a data-driven approach to assign k-values, via the training/validation/testing procedure, resulted in a marked improvement in performance in the modified Elo-rating method.\nC_LI

animal behavior and cognition↗

bioRxiv2018

Functional Social Structure in Baboons: Modelling Interactions Between Social and Environmental Structure in Group-Level Foraging

In mobile social groups, cohesion is thought to be driven by patterns of attraction at both the individual and group level. In long-lived species with high group stability and repeated interactions, such as baboons, individual-to-individual attractions have the potential to play a large role in group cohesion and overall movement patterns. In previous work, we used GPS mapping of a group of baboons in De Hoop, Western Cape, South Africa, to demonstrate the influence of such attractions on movement patterns. We also demonstrated the existence of emergent group-level structures, which arose as a consequence of individual social influence. Specifically, we found a core-periphery structure, in which a subset of influential animals exerted an influence on each other and those animals in the periphery, while those in the periphery were influenced by the core but did not exert any influence over others. Here, we use agent-based modelling of baboon groups to investigate whether this group-level structure has any functional consequences for foraging behaviour. By varying individual level attractions, we produced baboon groups that contained influence structures that varied from more to less centralized. Our results suggest that varying centrality affects both the ability of the group to detect resource structure in the environment, as well as the ability of the group to exploit these resources. Our models predict that foraging groups with more centralized social structures will show a reduction in detection and an increase in exploitation of resources in their environment, and will produce more extreme foraging outcomes. More generally, our results highlight the link between social and environmental structure on functional outcomes for mobile social groups of animals.

animal behavior and cognition↗