CMRnet: An r package to derive networks of social interactions and movement from mark–recapture data

Abstract

Social structure and population dynamics are closely intertwined (Shizuka & Johnson, 2019). The social structure of populations is critical in shaping key ecological processes, such as the spread of information and infections (Allen et al., 2013;Aplin et al., 2015;White et al., 2017), and in driving patterns of evolutionary change (Fisher & McAdam, 2017). On the one hand, social relationships will be influenced by demographic changes; for example, individuals may interact more with others at higher population densities (O'Brien et al., 2018). On the other hand, social relationships may influence key demographic processes, such as reproductive rates (McDonald, 2007), dispersal (Blumstein et al., 2009) or survival (Ellis et al., 2017). Hence, the interplay between demography and social structure is of immediate relevance to tackling numerous important questions in behavioural and evolutionary ecology, and yet longterm demographic data are rarely associated with detailed social analyses.Many long-term studies of wild animals involve the collection of data based on capture-mark-recapture (CMR) approaches, where individuals are given a unique identifier when first captured, allowing identification if subsequently recaptured. If CMR data are spatiotemporally referenced (i.e. the location, time and date of each capture are recorded), then under the assumption that co-located

A model for leveraging animal movement to understand spatio‐temporal disease dynamics

“…The increasing ubiquity of movement data in livestock and wildlife systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk (Dougherty et al, 2018; Jacoby & Freeman, 2016). The data range from high‐resolution GPS locations recording animal movements (Hooten et al, 2017), proximity loggers, camera traps or acoustic monitors that detect hosts in the vicinity of other hosts or at specific locations (Burton et al, 2015; Lavelle et al, 2016; Stehlé et al, 2011b), and spatially explicit capture‐recapture data that provide time‐series of host movements between habitat patches or point locations (Cayuela et al, 2017; Royle et al, 2014; Silk et al, 2021). While most of these data sources can and have served as a basis for construction of spatially explicit contact networks, discrete treatment of observations limits inference on contacts that are in reality occurring in continuous time.…”

Animal social networks: Towards an integrative framework embedding social interactions, space and time

“…With these considerations in mind, new methods continue to be presented, allowing researchers to address novel challenges in ASNA such as accurate estimates of social trait heritability (Radersma, 2020), social drivers of animal movement (Milner et al, 2020) and to apply techniques developed for data collected using mark‐release‐recapture data (Silk et al, 2020) and biologging methods (Gilbertson et al, 2020; Gomes et al, 2020). The miniaturisation of biologging devices now enable the study of a wider variety of organisms, from insects to cetaceans (Börger et al., 2020).…”

Extracting spatial networks from capture–recapture data reveals individual site fidelity patterns within a marine mammal’s spatial range

“…Photo identification is a long‐recognized method to “capture” individuals with distinct markings (hereafter photo‐ID data) (Urian et al, 2015), and digital photography along with high‐resolution video and machine learning models to identify individuals has led to large capture–recapture datasets (Schneider et al, 2019). Novel statistical and computational methods applied to these capture–recapture datasets have enhanced the potential for quantifying within‐population structures through the use of social network analysis (Perryman et al, 2019; Schilds et al, 2019; Silk et al, 2021).…”