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Understanding Dynamics of Information Transmission in Drosophila melanogaster Using a Statistical Modeling Framework for Longitudinal Network Data (the RSiena Package).

Pasquaretta C, Klenschi E, Pansanel J, Battesti M, Mery F, Sueur C - Front Psychol (2016)

Bottom Line: We estimated the degree to which the uninformed flies had successfully acquired the information carried by informed individuals using the proportion of eggs laid by uninformed flies on the medium their conspecifics had been trained to favor.Interestingly, we found that the degree of reciprocation, the tendency of individuals to form mutual connections between each other, strongly affected oviposition site choice in uninformed flies.This work highlights the great potential of RSiena and its utility in the studies of interaction networks among non-human animals.

View Article: PubMed Central - PubMed

Affiliation: Département Ecologie, Physiologie et Ethologie, Centre National de la Recherche ScientifiqueStrasbourg, France; Institut Pluridisciplinaire Hubert Curien, Université de StrasbourgStrasbourg, France.

ABSTRACT
Social learning - the transmission of behaviors through observation or interaction with conspecifics - can be viewed as a decision-making process driven by interactions among individuals. Animal group structures change over time and interactions among individuals occur in particular orders that may be repeated following specific patterns, change in their nature, or disappear completely. Here we used a stochastic actor-oriented model built using the RSiena package in R to estimate individual behaviors and their changes through time, by analyzing the dynamic of the interaction network of the fruit fly Drosophila melanogaster during social learning experiments. In particular, we re-analyzed an experimental dataset where uninformed flies, left free to interact with informed ones, acquired and later used information about oviposition site choice obtained by social interactions. We estimated the degree to which the uninformed flies had successfully acquired the information carried by informed individuals using the proportion of eggs laid by uninformed flies on the medium their conspecifics had been trained to favor. Regardless of the degree of information acquisition measured in uninformed individuals, they always received and started interactions more frequently than informed ones did. However, information was efficiently transmitted (i.e., uninformed flies predominantly laid eggs on the same medium informed ones had learn to prefer) only when the difference in contacts sent between the two fly types was small. Interestingly, we found that the degree of reciprocation, the tendency of individuals to form mutual connections between each other, strongly affected oviposition site choice in uninformed flies. This work highlights the great potential of RSiena and its utility in the studies of interaction networks among non-human animals.

No MeSH data available.


Related in: MedlinePlus

Rate parameters of the networks estimated from the multi-group analysis in RSiena for each of our two experimental conditions (“Followed” and “Avoided”). Rate parameters express the number of opportunities for change between successive networks for one given actor. A change is understood as the creation or the deletion of relations among actors during two successive networks. The number of observed changes is, however, always lower than what rate parameters imply; an opportunity for change can be resolved by a ‘no change’ decision, and successive changes can cancel each other out (e.g., create then dissolve a given relation during the same interval). They do not differ between conditions; in both cases actors always have opportunities for change from one network to the next, and although the number of opportunities varies over time, it evolves similarly whether information was followed or not. The success of information transmission is thus not primarily dependent on the opportunities actors get to change their connections to other actors. Best fitted lines for a non-linear model are represented for “Followed” (black) and “Avoided” (grey) conditions. Shaded areas represent the standard errors of the models.
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Figure 2: Rate parameters of the networks estimated from the multi-group analysis in RSiena for each of our two experimental conditions (“Followed” and “Avoided”). Rate parameters express the number of opportunities for change between successive networks for one given actor. A change is understood as the creation or the deletion of relations among actors during two successive networks. The number of observed changes is, however, always lower than what rate parameters imply; an opportunity for change can be resolved by a ‘no change’ decision, and successive changes can cancel each other out (e.g., create then dissolve a given relation during the same interval). They do not differ between conditions; in both cases actors always have opportunities for change from one network to the next, and although the number of opportunities varies over time, it evolves similarly whether information was followed or not. The success of information transmission is thus not primarily dependent on the opportunities actors get to change their connections to other actors. Best fitted lines for a non-linear model are represented for “Followed” (black) and “Avoided” (grey) conditions. Shaded areas represent the standard errors of the models.

Mentions: Jaccard indexes were superior to 0.2 in 675 out of 696 and 437 out of 456 10-min matrices used from our “Followed” and “Avoided” conditions respectively, ensuring a sufficient change among consecutive networks to apply our subsequent RSiena analysis. Indeed, mean rate parameters evolve over time as a sinusoidal distribution for both the “Followed” and “Avoided” conditions, meaning that the dynamics of the networks reveal similar, comparable patterns between conditions (Figure 2). Applications of the multi-group stochastic estimation procedure performed by RSiena on our experimental data (conditions “Followed” and “Avoided”) yielded models illustrating the dynamics of network measures and the influence of oviposition experience on behavior and network structure. The stepwise model selection procedure yielded a parsimonious model defined by the density, reciprocity, alter, and ego effects. For the “Followed” condition model, the square of the number of contacts sent was also retained. Whether information was followed or not, uninformed individuals received interactions from the opposite fly type (alter effect) more frequently than informed ones (Followed: t = -3.973, P < 0.001; Avoided: t = -4.103, P < 0.001) and they initiated interactions toward the opposite fly type (ego effect) significantly more than informed individuals (Followed: t = -10.036, P < 0.001; Avoided: t = -13.449, P < 0.001).


Understanding Dynamics of Information Transmission in Drosophila melanogaster Using a Statistical Modeling Framework for Longitudinal Network Data (the RSiena Package).

Pasquaretta C, Klenschi E, Pansanel J, Battesti M, Mery F, Sueur C - Front Psychol (2016)

Rate parameters of the networks estimated from the multi-group analysis in RSiena for each of our two experimental conditions (“Followed” and “Avoided”). Rate parameters express the number of opportunities for change between successive networks for one given actor. A change is understood as the creation or the deletion of relations among actors during two successive networks. The number of observed changes is, however, always lower than what rate parameters imply; an opportunity for change can be resolved by a ‘no change’ decision, and successive changes can cancel each other out (e.g., create then dissolve a given relation during the same interval). They do not differ between conditions; in both cases actors always have opportunities for change from one network to the next, and although the number of opportunities varies over time, it evolves similarly whether information was followed or not. The success of information transmission is thus not primarily dependent on the opportunities actors get to change their connections to other actors. Best fitted lines for a non-linear model are represented for “Followed” (black) and “Avoided” (grey) conditions. Shaded areas represent the standard errors of the models.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835720&req=5

Figure 2: Rate parameters of the networks estimated from the multi-group analysis in RSiena for each of our two experimental conditions (“Followed” and “Avoided”). Rate parameters express the number of opportunities for change between successive networks for one given actor. A change is understood as the creation or the deletion of relations among actors during two successive networks. The number of observed changes is, however, always lower than what rate parameters imply; an opportunity for change can be resolved by a ‘no change’ decision, and successive changes can cancel each other out (e.g., create then dissolve a given relation during the same interval). They do not differ between conditions; in both cases actors always have opportunities for change from one network to the next, and although the number of opportunities varies over time, it evolves similarly whether information was followed or not. The success of information transmission is thus not primarily dependent on the opportunities actors get to change their connections to other actors. Best fitted lines for a non-linear model are represented for “Followed” (black) and “Avoided” (grey) conditions. Shaded areas represent the standard errors of the models.
Mentions: Jaccard indexes were superior to 0.2 in 675 out of 696 and 437 out of 456 10-min matrices used from our “Followed” and “Avoided” conditions respectively, ensuring a sufficient change among consecutive networks to apply our subsequent RSiena analysis. Indeed, mean rate parameters evolve over time as a sinusoidal distribution for both the “Followed” and “Avoided” conditions, meaning that the dynamics of the networks reveal similar, comparable patterns between conditions (Figure 2). Applications of the multi-group stochastic estimation procedure performed by RSiena on our experimental data (conditions “Followed” and “Avoided”) yielded models illustrating the dynamics of network measures and the influence of oviposition experience on behavior and network structure. The stepwise model selection procedure yielded a parsimonious model defined by the density, reciprocity, alter, and ego effects. For the “Followed” condition model, the square of the number of contacts sent was also retained. Whether information was followed or not, uninformed individuals received interactions from the opposite fly type (alter effect) more frequently than informed ones (Followed: t = -3.973, P < 0.001; Avoided: t = -4.103, P < 0.001) and they initiated interactions toward the opposite fly type (ego effect) significantly more than informed individuals (Followed: t = -10.036, P < 0.001; Avoided: t = -13.449, P < 0.001).

Bottom Line: We estimated the degree to which the uninformed flies had successfully acquired the information carried by informed individuals using the proportion of eggs laid by uninformed flies on the medium their conspecifics had been trained to favor.Interestingly, we found that the degree of reciprocation, the tendency of individuals to form mutual connections between each other, strongly affected oviposition site choice in uninformed flies.This work highlights the great potential of RSiena and its utility in the studies of interaction networks among non-human animals.

View Article: PubMed Central - PubMed

Affiliation: Département Ecologie, Physiologie et Ethologie, Centre National de la Recherche ScientifiqueStrasbourg, France; Institut Pluridisciplinaire Hubert Curien, Université de StrasbourgStrasbourg, France.

ABSTRACT
Social learning - the transmission of behaviors through observation or interaction with conspecifics - can be viewed as a decision-making process driven by interactions among individuals. Animal group structures change over time and interactions among individuals occur in particular orders that may be repeated following specific patterns, change in their nature, or disappear completely. Here we used a stochastic actor-oriented model built using the RSiena package in R to estimate individual behaviors and their changes through time, by analyzing the dynamic of the interaction network of the fruit fly Drosophila melanogaster during social learning experiments. In particular, we re-analyzed an experimental dataset where uninformed flies, left free to interact with informed ones, acquired and later used information about oviposition site choice obtained by social interactions. We estimated the degree to which the uninformed flies had successfully acquired the information carried by informed individuals using the proportion of eggs laid by uninformed flies on the medium their conspecifics had been trained to favor. Regardless of the degree of information acquisition measured in uninformed individuals, they always received and started interactions more frequently than informed ones did. However, information was efficiently transmitted (i.e., uninformed flies predominantly laid eggs on the same medium informed ones had learn to prefer) only when the difference in contacts sent between the two fly types was small. Interestingly, we found that the degree of reciprocation, the tendency of individuals to form mutual connections between each other, strongly affected oviposition site choice in uninformed flies. This work highlights the great potential of RSiena and its utility in the studies of interaction networks among non-human animals.

No MeSH data available.


Related in: MedlinePlus