Estimating the robustness and uncertainty of animal social networks using different observational methods

Davis, Grace H.; Crofoot, Margaret C.; Farine, Damien R.

doi:10.1016/j.anbehav.2018.04.012

Cited by 116 publications

(154 citation statements)

References 68 publications

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“…Finally, spatsoc can be used to compare networks generated with different grouping methods across a range of spatial and temporal thresholds. Davis et al () compared association networks generated from wild baboon Papio anubis telemetry data using spatial thresholds with the chain rule (as in group_pts), spatial thresholds without the chain rule (as in edge_dist), and nearest neighbours (as in edge_nn). For example, Castles et al () compared proximity networks of chacma baboons Papio ursinus built with the chain rule (as in group pts) and without (as in edge_dist) and using nearest neighbours with a maximal distance (as in edge_nn).…”

Section: Selecting and Evaluating Spatial And Temporal Thresholdsmentioning

confidence: 99%

“…Despite this, there are no hard and fast rules for selecting thresholds for spatiotemporal grouping (but see below for recommendations). Evaluating candidate thresholds is recommended and has been shown to provide valuable insights for selecting temporal (Psorakis et al, 2015) and spatial (Davis, Crofoot, & Farine, 2018) thresholds.…”

Section: S Elec Ting and E Valuating S Patial And Temp Or Al Thre Smentioning

confidence: 99%

“…Finally, spatsoc can be used to compare networks generated with different grouping methods across a range of spatial and temporal thresholds. Davis et al (2018) compared association networks generated from wild baboon Papio anubis telemetry data using spatial thresholds with the chain rule (as in group _ pts), spatial thresholds without the chain rule (as in edge _ dist), and nearest neighbours (as in edge _ nn). For example, Castles et al…”

Section: S Elec Ting and E Valuating S Patial And Temp Or Al Thre Smentioning

confidence: 99%

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Conducting social network analysis with animal telemetry data: Applications and methods using spatsoc

2019

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We present spatsoc, an r package for conducting social network analysis with animal telemetry data. Animal social network analysis is a method for measuring relationships between individuals to describe social structure. Proximity‐based social networks are generated from animal telemetry data by grouping relocations temporally and spatially, using thresholds that are informed by the characteristics of the species and study system. spatsoc fills a gap in r packages by providing flexible functions, explicitly for animal telemetry data, to generate edge lists and gambit‐of‐the‐group data, perform data‐stream randomization, and generate group by individual matrices. The implications of spatsoc are that current users of animal telemetry or otherwise georeferenced data for movement or spatial analyses will have access to efficient and intuitive functions to generate social networks.

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Section: Selecting and Evaluating Spatial And Temporal Thresholdsmentioning

confidence: 99%

Section: S Elec Ting and E Valuating S Patial And Temp Or Al Thre Smentioning

confidence: 99%

Section: S Elec Ting and E Valuating S Patial And Temp Or Al Thre Smentioning

confidence: 99%

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Conducting social network analysis with animal telemetry data: Applications and methods using spatsoc

2019

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“…The links in bold are the ones present in both top ten rankings. aggregation window of one month is usually advocated [22,48]. In the case of data 360 collected through wearable sensors on the other hand, a large amount of contacts is 361 already recorded after a few hours.…”

Section: Rank Interaction Network (Observations) Contacts Network (Sementioning

confidence: 99%

Measuring social networks in primates: wearable sensors vs. direct observations

Gelardi

Godard

Paleressompoulle

et al. 2020

Preprint

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Network analysis represents a valuable and flexible framework to understand the structure of individual interactions at the population level in animal societies. The versatility of network representations is moreover suited to different types of datasets describing these interactions. However, depending on the data collection method, different pictures of the social bonds between individuals could a priori emerge. Understanding how the data collection method influences the description of the social structure of a group is thus essential to assess the reliability of social studies based on different types of data. This is however rarely feasible, especially for animal groups, where data collection is often challenging. Here, we address this issue by comparing datasets of interactions between primates collected through two different methods: behavioral observations and wearable proximity sensors. We show that, although many directly observed interactions are not detected by the sensors, the global pictures obtained when aggregating the data to build interaction networks turn out to be remarkably similar. Sensors data yield moreover a reliable social network already over short timescales and can be used for long term campaigns, showing their important potential for detailed studies of the evolution of animal social groups. Introduction 1 Interactions between individuals are the foundation of complex social structures in 2 human and other animal societies. Network analysis represents a valuable framework to 3 understand the structure and evolution of these interactions, as it encodes a whole 4 hierarchy of patterns, from individual-level interactions to complex population-level 5 social structures. [1-3, 3-8].6 With the increasing deployment of digital devices, new ways of collecting data, 7 combined with new network analysis tools, have made possible the development of 8 quantitative measures of these relationships and patterns in modern human societies, 9 January 17, 2020 1/20leading to the emergence of computational social science in the last decades [9]. For 10 instance, social relationships have been inferred and studied using various data sources 11 ranging from phone calls [10], e-mails [11,12], online interactions [13], to face-to-face 12 interactions measured by wearable sensors [14][15][16][17][18][19][20]. 13The availability of large volumes of data with high temporal resolution has thus 14 contributed to the rapid expansion of data-driven computational studies of human 15 relationships and human social networks. On the contrary, the data collection remains 16 more challenging in the field of animal studies, because the data on animal interactions 17 are still largely obtained from direct observations [6,21]. Data resulting from such 18 observations are extremely valuable as they often include detailed information about the 19 nature, duration and location of the interactions between individuals. They thus allow 20 researchers to grasp and investigate complex social patterns in animal groups. 21 Unfortunat...

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“…Previous studies quantifying the data required to construct 98 meaningful networks have suggested that a good rule-of-thumb is that an average of 15 potential co-observations between all pairs of individuals are needed (i.e. of potential 100 associations or interactions, which could be both individuals together or just one individual in 101 the absence of the other; Farine & Strandburg-Peshkin, 2015; Davis et al, 2018). However, 102 these estimates may not translate well to shared space use networks because estimating space 103 use could require much less data than estimating relationship strengths, and because 104 connections (edges) do not aim to characterise relationship strengths but rather a likelihood of 105 encounter.…”

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confidence: 99%

Characterising shared space use networks using animal trapping data

Wanelik

Farine

2019

Preprint

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26Studying the social behaviour of small or cryptic species often relies on constructing space-27 sharing networks from sparse point-based observations of individuals. Such an approach 28 assumes that individuals that have greater shared space use will also interact more. However, 29there is very little guidance on how much data are required to construct meaningful space-30 sharing networks, or on how to interpret the relationships generated from such networks. In 31 this study, we quantify the robustness of space-sharing networks to different sampling 32 regimes, providing much needed guidance for informing the choice of sampling regime when 33 designing studies to accurately quantify space sharing. We then describe the characteristics of 34 space use in a wild population of field voles (Microtus agrestis), and use this empirical 35 dataset to develop a new method for generating shared space use networks which are 36 generally more strongly correlated with the real network, differ less from the real network 37 and are more powerful to detect effects present in the real network. Our method pools data 38 among individuals to estimate a general home range profile for a given set of individuals. 39Combining these profiles with the individual-level observation data then allows us to better 40 estimate their overlap in space and requires less data. Our new method provides the potential 41 to generate meaningful space-sharing networks, and in doing so, to address a range of key 42 questions in ecology and evolution, even when point-based observations of individuals are 43 sparse. 44 45 46 47 48 (2014). An immunological marker of tolerance to infection in wild rodents. PLoS

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Estimating the robustness and uncertainty of animal social networks using different observational methods

Cited by 116 publications

References 68 publications

Conducting social network analysis with animal telemetry data: Applications and methods using spatsoc

Conducting social network analysis with animal telemetry data: Applications and methods using spatsoc

Measuring social networks in primates: wearable sensors vs. direct observations

Characterising shared space use networks using animal trapping data

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