Components of change in the evolution of learning and unlearned preference

Dunlap, Aimee S.; Stephens, David W.

doi:10.1098/rspb.2009.0602

Cited by 115 publications

(80 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another potential example of reversible plasticity is cognitive ability, particularly given its role in enabling behavioral flexibility (26). Consistent with our predictions, the evolution of cognitive enhancement appears to be driven in many systems by the exposure to intense, short-term, and only moderately predictable environmental variation (27)(28)(29). In contrast, most well-documented examples of developmental (i.e., irreversible) plasticity occur when environmental features remain constant during a lifetime but vary among individuals.…”

Section: Discussionsupporting

confidence: 61%

Evolutionary tipping points in the capacity to adapt to environmental change

Botero

Weissing

Wright

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

393

523

View full text Add to dashboard Cite

In an era of rapid climate change, there is a pressing need to understand how organisms will cope with faster and less predictable variation in environmental conditions. Here we develop a unifying model that predicts evolutionary responses to environmentally driven fluctuating selection and use this theoretical framework to explore the potential consequences of altered environmental cycles. We first show that the parameter space determined by different combinations of predictability and timescale of environmental variation is partitioned into distinct regions where a single mode of response (reversible phenotypic plasticity, irreversible phenotypic plasticity, bet-hedging, or adaptive tracking) has a clear selective advantage over all others. We then demonstrate that, although significant environmental changes within these regions can be accommodated by evolution, most changes that involve transitions between regions result in rapid population collapse and often extinction. Thus, the boundaries between response mode regions in our model correspond to evolutionary tipping points, where even minor changes in environmental parameters can have dramatic and disproportionate consequences on population viability. Finally, we discuss how different life histories and genetic architectures may influence the location of tipping points in parameter space and the likelihood of extinction during such transitions. These insights can help identify and address some of the cryptic threats to natural populations that are likely to result from any natural or human-induced change in environmental conditions. They also demonstrate the potential value of evolutionary thinking in the study of global climate change.fluctuating selection | global change | phenotypic plasticity | bet-hedging | adaptive tracking

show abstract

Section: Discussionsupporting

confidence: 61%

Evolutionary tipping points in the capacity to adapt to environmental change

Botero

Weissing

Wright

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

393

523

View full text Add to dashboard Cite

show abstract

“…Environmental variation has been proposed as one of the major factors driving the evolution of variation in learning and memory retention [7,[16][17][18]. More variable environments promote short lasting memories, which can be updated continuously.…”

Section: Effects Of Environmental Variation and Spatial Foraging Behamentioning

confidence: 99%

The complexity of learning, memory and neural processes in an evolutionary ecological context

Smid

Vet

2016

Current Opinion in Insect Science

View full text Add to dashboard Cite

The ability to learn and form memories is widespread among insects, but there exists considerable natural variation between species and populations in these traits. Variation manifests itself in the way information is stored in different memory forms. This review focuses on ecological factors such as environmental information, spatial aspects of foraging behavior and resource distribution that drive the evolution of this natural variation and discusses the role of different genes and neural networks. We conclude that at the level of individual, population or species, insect learning and memory cannot be described as good or bad. Rather, we argue that insects evolve tailor-made learning and memory types; they gate learned information into memories with high or low persistence. This way, they are prepared to learn and form memory to optimally deal with the specific ecologies of their foraging environments. Highlights:• Environmental variation, spatial foraging behavior and the distribution of resources are factors that drive the evolution of differences in prepared learning • Considerable heritable variation exists in numerous learning and memory traits • Insects have evolved tailor-made memory types, adapted to the specific ecology of their foraging environment • The foraging gene is an important gene underlying natural variation in learning and memory • Dopaminergic signaling may drive the adaptive gating of information into different memory formsThe use of previous experience to optimize behavior in an adaptive manner is obviously of great benefit to all animals, including insects [1,2]. However, this does not mean that insects should learn and remember information from all possible experiences they encounter. Studies on diverse insect species have revealed the daunting complexity of different forms of memory each with different stabilities, including short-, mid-, anesthesia resistant-and long term memory (STM, MTM, ARM and LTM), e.g. [3][4][5][6]. In Fig. 1 we provide a basic description of these memory forms and their abbreviations. Why does learning not always result in the formation of LTM? To ensure the reliability of learned information, most animals require multiple, spaced experiences before information is stored as LTM. Nevertheless, some animals form LTM after a single experience suggesting that they may have evolved to rely on the Accepted in Current Opinion in Insect Science, CC-BY-NC-ND

show abstract

“…(b) The constraints on learning to choose among strategies in a game Learning is often considered an adaptation to environmental change, but it also requires some level of environmental predictability [8,9,40]. Without it, recent experience cannot reinforce the behaviours that are likely to be adaptive in the future.…”

Section: Discussionmentioning

confidence: 99%

“…It is well recognized that learning may be beneficial when it allows animals to refine their behaviour in relation to a changing environment [8,9] and ecological or phenotypic conditions that might not have been important in their genetic evolution [10]. In these cases, learning enables prediction of the behaviours that are likely to be associated with higher payoffs.…”

Section: Introductionmentioning

confidence: 99%

Evolution of learned strategy choice in a frequency-dependent game

et al. 2011

View full text Add to dashboard Cite

In frequency-dependent games, strategy choice may be innate or learned. While experimental evidence in the producer -scrounger game suggests that learned strategy choice may be common, a recent theoretical analysis demonstrated that learning by only some individuals prevents learning from evolving in others. Here, however, we model learning explicitly, and demonstrate that learning can easily evolve in the whole population. We used an agent-based evolutionary simulation of the producer -scrounger game to test the success of two general learning rules for strategy choice. We found that learning was eventually acquired by all individuals under a sufficient degree of environmental fluctuation, and when players were phenotypically asymmetric. In the absence of sufficient environmental change or phenotypic asymmetries, the correct target for learning seems to be confounded by game dynamics, and innate strategy choice is likely to be fixed in the population. The results demonstrate that under biologically plausible conditions, learning can easily evolve in the whole population and that phenotypic asymmetry is important for the evolution of learned strategy choice, especially in a stable or mildly changing environment.

show abstract

Components of change in the evolution of learning and unlearned preference

Cited by 115 publications

References 21 publications

Evolutionary tipping points in the capacity to adapt to environmental change

Evolutionary tipping points in the capacity to adapt to environmental change

The complexity of learning, memory and neural processes in an evolutionary ecological context

Evolution of learned strategy choice in a frequency-dependent game

Contact Info

Product

Resources

About