KOPS - Das Institutionelle Repositorium der Universität Konstanz

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

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

Zitieren

Dateien zu dieser Ressource

Dateien Größe Format Anzeige

Zu diesem Dokument gibt es keine Dateien.

DAVIS, Grace H., Margaret C. CROFOOT, Damien R. FARINE, 2018. Estimating the robustness and uncertainty of animal social networks using different observational methods. In: Animal Behaviour. 141, pp. 29-44. ISSN 0003-3472. eISSN 1095-8282. Available under: doi: 10.1016/j.anbehav.2018.04.012

@article{Davis2018Estim-42834, title={Estimating the robustness and uncertainty of animal social networks using different observational methods}, year={2018}, doi={10.1016/j.anbehav.2018.04.012}, volume={141}, issn={0003-3472}, journal={Animal Behaviour}, pages={29--44}, author={Davis, Grace H. and Crofoot, Margaret C. and Farine, Damien R.} }

Davis, Grace H. Farine, Damien R. eng Estimating the robustness and uncertainty of animal social networks using different observational methods 2018 Crofoot, Margaret C. Social network analysis is quickly becoming an estab lished framework to study the structure of animal social systems. To explore the social network of a population, observers must capture data on the in- teractions or associations between individuals. Sampling decisio ns significantly impact the outcome of data collection, notably the amount of data available from which to construct social networks. However, little is known about how different sampling methods, and more generally the extent of sampling effort, impact the robustness of social network analyses. Here, we generate proximity networks from data obtained via nearly continuous GPS tracking of members of a wild baboon troop (Papio anubis). These data allow us to produce networks based on complete observations of interindividual distances between group members. We then mimic several widely used focal animal sampling and group scanning methods by subsampling the complete data set to simulate observational data comparable to that produced by human observers. We explore how sampling effort, sampling methods, network definitions and levels and types of sampling error affect the correlation between the estimated and complete networks. Our results suggest that for some scenarios, even low levels of sampling effort (5 e 10 samples/individual) can provide the same information as high sampling effort (>64 samples/individual). However, we find that insufficient data collected across all potentially interacting individuals, certain network definitions (how edge weights and distance thresholds are calculated) and misidentifications of individuals in the network can generate spurious network structure with little or no correlation to the underlying or ‘real’ social structure. Our results suggest that data collection methods should be designed to maximize the number of potential interactions (edges) recorded for each observation. We discuss the relative trade-offs be- tween maximizing the amount of data collected across as many individuals as possible and the potential for erroneous observations. Crofoot, Margaret C. 2018-07-11T08:53:52Z 2018-07-11T08:53:52Z Farine, Damien R. Davis, Grace H.

Das Dokument erscheint in:

KOPS Suche


Stöbern

Mein Benutzerkonto