@article {Bierbach432336, author = {David Bierbach and Stefan Krause and Pawel Romanczuk and Juliane Lukas and Lenin Arias-Rodriguez and Jens Krause}, title = {Social networks in the presence and absence of visual cues}, elocation-id = {432336}, year = {2018}, doi = {10.1101/432336}, publisher = {Cold Spring Harbor Laboratory}, abstract = {We compared the social dynamics of two populations of the live-bearing Atlantic molly (Poecilia mexicana) that live in adjacent habitats with very different predator regimes: cave mollies that inhabit a low-predation environment inside a sulfidic cave with a low density of predatory water bugs (Belostoma sp.), and mollies that live directly outside the cave (henceforth called {\textquotedblleft}surface{\textquotedblright} mollies) in a high-predation environment with a high density of fish-eating birds. We filmed the social interactions of marked fish in both environments and analysed their social network dynamics using a Markov model under two different fish densities of 12 and 6 fish per 0.36 m2. As expected, surface mollies spent overall much more time social than cave mollies. This difference in overall social time was a result of surface mollies being less likely to discontinue social contact (once they had a social partner) and being more likely to resume social contact (once alone) than cave mollies. Interestingly surface mollies were also less likely to leave a current social partner than cave mollies. At low density, mollies (in both environments) were expected to show reduced social encounters which should dramatically change their social dynamics. Surface mollies, however, displayed an ability to maintain their social dynamics at low density (primarily by reducing the convex polygon spanned by the group) which was not observed in cave mollies. Despite the fact that we only compared two populations, our data provide a mechanistic explanation for density compensations of social dynamics that have also been observed in other fish species and give an example of how comparisons between the social dynamics of different populations can be made that go beyond conventional network analyses.}, URL = {https://www.biorxiv.org/content/early/2018/10/01/432336}, eprint = {https://www.biorxiv.org/content/early/2018/10/01/432336.full.pdf}, journal = {bioRxiv} }