Behavioral flexibility, the ability to adapt behavior to new circumstances, is thought to play an important role in a species' ability to successfully adapt to new environments and expand its geographic range. However, flexibility is rarely directly tested in species in a way that would allow us to determine how flexibility works and predictions a species' ability to adapt their behavior to new environments. We use great-tailed grackles (a bird species) as a model to investigate this question because they have rapidly expanded their range into North America over the past 140 years. We attempted to manipulate grackle flexibility using colored tube reversal learning to determine whether flexibility is generalizable across contexts (touchscreen reversal learning and multi-access box), whether it is repeatable within individuals and across contexts, and what learning strategies grackles employ. We found that we were able to manipulate flexibility: birds in the manipulated group took fewer trials to pass criterion with increasing reversal number, and they reversed a color preference in fewer trials by the end of their serial reversals compared to control birds who had only one reversal. Flexibility was repeatable within individuals (reversal), but not across contexts (from reversal to multi-access box). The touchscreen reversal experiment did not appear to measure what was measured in the reversal learning experiment with the tubes, and we speculate as to why. One third of the grackles in the manipulated reversal learning group switched from one learning strategy (epsilon-decreasing where they have a long exploration period) to a different strategy (epsilon-first where they quickly shift their preference). A separate analysis showed that the grackles did not use a particular strategy earlier or later in their serial reversals. Posthoc analyses using a model that breaks down performance on the reversal learning task into different components showed that learning to be attracted to an option (phi) more consistently correlated with reversal performance than the rate of deviating from learned attractions that were rewarded (lambda). This result held in simulations and in the data from the grackles: learning rates in the manipulated grackles doubled by the end of the manipulation compared to control grackles, while the rate of deviation slightly decreased. Grackles with intermediate rates of deviation in their last reversal, independently of whether they had gone through the serial reversal manipulation, solved fewer loci on the plastic and wooden multi-access boxes, and those with intermediate learning rates in their last reversal were faster to attempt a new locus on both multi-access boxes. This investigation allowed us to make causal conclusions rather than relying only on correlations: we manipulated reversal learning, which caused changes in a different flexibility measure (multi-access box switch times) and in an innovativeness measure (multi-access box loci solved), as well as validating that the manipulation had an effect on the cognitive ability we think of as flexibility. Understanding how behavioral flexibility causally relates to other traits will allow researchers to develop robust theory about what behavioral flexibility is and when to invoke it as a primary driver in a given context, such as a rapid geographic range expansion. Given our results, flexibility manipulations could be useful in training threatened and endangered species in how to be more flexible. If such a flexibility manipulation was successful, it could then change their behavior in this and other domains, giving them a better chance of succeeding in human modified environments.
Arithmetic Framework to Optimize Packet Forwarding among End Devices in Generic Edge Computing Environments
