Where should we meet? Mapping social network interactions of sleepy lizards shows sex-dependent social network structure

Spiegel, Orr; Sih, Andrew; Leu, Stephan T.; Bull, C. Michael

doi:10.1016/j.anbehav.2017.11.001

Cited by 39 publications

(50 citation statements)

References 48 publications

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“…; Spiegel et al . ). The decreasing costs of these technologies could soon offer opportunities to monitor entire populations, thereby shifting researchers from extrapolating risk across a population to measuring contact rates directly.…”

Section: Discussion and Future Directionsmentioning

confidence: 97%

“…1 Hz fix rates) will help researchers parse movement tracks at an even finer scale than current path segmentation methods allow (McClintock et al 2017). In doing so, proximity-based social networks could be further informed with the behavioural states of individuals, potentially clarifying the epidemiological relevance of such points of contact (Spiegel et al 2016;Sih et al 2017;Spiegel et al 2017b). The decreasing costs of these technologies could soon offer opportunities to monitor entire populations, thereby shifting researchers from extrapolating risk across a population to measuring contact rates directly.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…An extension of this approach allowed for the identification of the locations of interactions and revealed the sex‐specific patterns underlying the network structure (Spiegel et al . ). These networks have been a key part of efforts to understand how ticks are transmitted in sleepy lizards ( Tiliqua rugosa ), reptiles with an unusual life‐long pair breeding pattern that may facilitate tick transmission (Sih et al .…”

Section: Movement Affects Diseasementioning

confidence: 97%

“…For example, by randomising the order of daily movement paths within each individual, rather than between individuals (as is typical in most network randomisation methods), Spiegel et al (2016) developed a method to assess sociality separate from associations resulting from the spatial structure of the environment. An extension of this approach allowed for the identification of the locations of interactions and revealed the sex-specific patterns underlying the network structure (Spiegel et al 2017b). These networks have been a key part of efforts to understand how ticks are transmitted in sleepy lizards (Tiliqua rugosa), reptiles with an unusual life-long pair breeding pattern that may facilitate tick transmission (Sih et al 2017).…”

Section: Movement Affects Diseasementioning

confidence: 99%

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Going through the motions: incorporating movement analyses into disease research

et al. 2018

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Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviours, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesise the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions, expanding our understanding of host-pathogen dynamics and improving our predictive capacity for wildlife and even human diseases.

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Section: Discussion and Future Directionsmentioning

confidence: 97%

Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Movement Affects Diseasementioning

confidence: 97%

Section: Movement Affects Diseasementioning

confidence: 99%

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Going through the motions: incorporating movement analyses into disease research

et al. 2018

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“…As animal telemetry data have become more accessible and available at a fine scale, a number of techniques and methods have been developed to quantify various aspects of animal social structure (Webber & Vander Wal, 2018). These include dynamic interaction networks (Long, Nelson, Webb, & Gee, 2014), PBSNs (Spiegel, Sih, Leu, & Bull, 2017) and the development of traditional randomization techniques to assess non-random structure of PBSNs constructed using animal telemetry data (Spiegel et al, 2016). Despite the recent increase in the number of studies using animal telemetry data and GPS relocation data (Webber & Vander Wal, 2019), there is no comprehensive r package that generates PBSNs using animal telemetry data.…”

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confidence: 99%

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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Intrinsic traits, social context, and local environment shape home range size and fidelity of sleepy lizards

Payne

Spiegel

Sinn

et al. 2022

Ecological Monographs

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Home ranges (HRs), the regions within which animals interact with their environment, constitute a fundamental aspect of their ecology. HR sizes and locations commonly reflect costs and benefits associated with diverse social, biotic, and abiotic factors. Less is known, however, about how these factors affect intraspecific variation in HR size or fidelity (the individual's tendency to maintain the same HR location over time) or whether variation in these features emerge from consistent differences among individuals or among the sites they occupy. To address this knowledge gap, we used an extensive GPS-tracking data set of a long-lived lizard, the sleepy lizard (Tiliqua rugosa),

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Where should we meet? Mapping social network interactions of sleepy lizards shows sex-dependent social network structure

Cited by 39 publications

References 48 publications

Going through the motions: incorporating movement analyses into disease research

Going through the motions: incorporating movement analyses into disease research

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

Intrinsic traits, social context, and local environment shape home range size and fidelity of sleepy lizards

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