Brain size, brain architecture, and eye size vary extensively in vertebrates. However, the extent to which the evolution of these components is intricately connected remains unclear. Trinidadian killifish,
Convergent mosaic brain evolution is associated with the evolution of novel electrosensory systems in teleost fishes
Larger animals tend to have larger brains and smaller animals tend to have smaller ones. However, some species do not fit the pattern that would be expected based on their body size. This variation between species can also apply to individual brain regions. This may be due to evolutionary forces shaping the brain when favouring particular behaviours. However, it is difficult to directly link changes in species behaviour and variations in brain structure. One way to understand the impact of evolutionary adaptations is to study species that have developed new behaviours and compare them to related ones that lack such a behaviour. An opportunity to do this lies in the ability of several species of fish to produce and sense electric fields in water. While this system is not found in most fish, it has evolved multiple times independently in distantly-related lineages. Schumacher and Carlson examined whether differences in the size of brains and individual regions between species were associated with the evolution of electric field generation and sensing. Micro-computed tomography, or μCT, scans of the brains of multiple fish species revealed that the species that can produce electricity – also known as ‘electrogenic’ species’ – have more similar brain structures to each other than to their close relatives that lack this ability. The brain regions involved in producing and detecting electrical charges were larger in these electrogenic fish. This similarity was apparent despite variations in how total brain size has evolved with body size across species. These results demonstrate how evolutionary forces acting on particular behaviours can lead to predictable changes in brain structure. Understanding how and why brains evolve will allow researchers to better predict how species’ brains and behaviours may adapt as human activities alter their environments.
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- Award ID(s):
- 1755071
- NSF-PAR ID:
- 10420475
- Date Published:
- Journal Name:
- eLife
- Volume:
- 11
- ISSN:
- 2050-084X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Anablepsoides hartii , are found in sites that differ in the presence and absence of large predatory fish. Decreased rates of predation are associated with evolutionary shifts in brain size; males from sites without predators have evolved a relatively larger brain and eye size than males from sites with predators. Here, we evaluated the extent to which the evolution of brain size, brain structure, and eye size covary in male killifish. We utilized wild‐caught and common garden‐reared specimens to determine whether specific components of the brain have evolved in response to differences in predation and to determine if there is covariation between the evolution of brain size, brain structure, and eye size. We observed consistent shifts in brain architecture in second generation common garden reared, but not wild caught preserved fish. Male killifish from sites that lack predators exhibited a significantly larger telencephalon, optic tectum, cerebellum, and dorsal medulla when compared with fish from sites with predators. We also found positive connections between the evolution of brain structure and eye size but not between overall brain size and eye size. These results provide evidence for evolutionary covariation between the components of the brain and eye size. Such results suggest that selection, directly or indirectly, acts upon specific regions of the brain, rather than overall brain size, to enhance visual capabilities. -
Body size is an important species trait, correlating with life span, fecundity, and other ecological factors. Over Earth’s geological history, climate shifts have occurred, potentially shaping body size evolution in many clades. General rules attempting to summarize body size evolution include Bergmann’s rule, which states that species reach larger sizes in cooler environments and smaller sizes in warmer environments, and Cope’s rule, which poses that lineages tend to increase in size over evolutionary time. Tetraodontiform fishes (including pufferfishes, boxfishes, and ocean sunfishes) provide an extraordinary clade to test these rules in ectotherms owing to their exemplary fossil record and the great disparity in body size observed among extant and fossil species. We examined Bergmann’s and Cope’s rules in this group by combining phylogenomic data (1,103 exon loci from 185 extant species) with 210 anatomical characters coded from both fossil and extant species. We aggregated data layers on paleoclimate and body size from the species examined, and inferred a set of time-calibrated phylogenies using tip-dating approaches for downstream comparative analyses of body size evolution by implementing models that incorporate paleoclimatic information. We found strong support for a temperature-driven model in which increasing body size over time is correlated with decreasing oceanic temperatures. On average, extant tetraodontiforms are two to three times larger than their fossil counterparts, which otherwise evolved during periods of warmer ocean temperatures. These results provide strong support for both Bergmann’s and Cope’s rules, trends that are less studied in marine fishes compared to terrestrial vertebrates and marine invertebrates.more » « less
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INTRODUCTION A brainwide, synaptic-resolution connectivity map—a connectome—is essential for understanding how the brain generates behavior. However because of technological constraints imaging entire brains with electron microscopy (EM) and reconstructing circuits from such datasets has been challenging. To date, complete connectomes have been mapped for only three organisms, each with several hundred brain neurons: the nematode C. elegans , the larva of the sea squirt Ciona intestinalis , and of the marine annelid Platynereis dumerilii . Synapse-resolution circuit diagrams of larger brains, such as insects, fish, and mammals, have been approached by considering select subregions in isolation. However, neural computations span spatially dispersed but interconnected brain regions, and understanding any one computation requires the complete brain connectome with all its inputs and outputs. RATIONALE We therefore generated a connectome of an entire brain of a small insect, the larva of the fruit fly, Drosophila melanogaster. This animal displays a rich behavioral repertoire, including learning, value computation, and action selection, and shares homologous brain structures with adult Drosophila and larger insects. Powerful genetic tools are available for selective manipulation or recording of individual neuron types. In this tractable model system, hypotheses about the functional roles of specific neurons and circuit motifs revealed by the connectome can therefore be readily tested. RESULTS The complete synaptic-resolution connectome of the Drosophila larval brain comprises 3016 neurons and 548,000 synapses. We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs. Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning. We used graph spectral embedding to hierarchically cluster neurons based on synaptic connectivity into 93 neuron types, which were internally consistent based on other features, such as morphology and function. We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions. We found extensive multisensory integration throughout the brain and multiple interconnected pathways of varying depths from sensory neurons to output neurons forming a distributed processing network. The brain had a highly recurrent architecture, with 41% of neurons receiving long-range recurrent input. However, recurrence was not evenly distributed and was especially high in areas implicated in learning and action selection. Dopaminergic neurons that drive learning are amongst the most recurrent neurons in the brain. Many contralateral neurons, which projected across brain hemispheres, were in-out hubs and synapsed onto each other, facilitating extensive interhemispheric communication. We also analyzed interactions between the brain and nerve cord. We found that descending neurons targeted a small fraction of premotor elements that could play important roles in switching between locomotor states. A subset of descending neurons targeted low-order post-sensory interneurons likely modulating sensory processing. CONCLUSION The complete brain connectome of the Drosophila larva will be a lasting reference study, providing a basis for a multitude of theoretical and experimental studies of brain function. The approach and computational tools generated in this study will facilitate the analysis of future connectomes. Although the details of brain organization differ across the animal kingdom, many circuit architectures are conserved. As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms. Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations. Such features could therefore increase the brain’s computational capacity, overcoming physiological constraints on the number of neurons. Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures. The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function.more » « less
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Abstract Surprisingly, little is known about body‐size evolution within the most diverse amphibian order, anurans (frogs and toads), despite known effects of body size on the physiological, ecological and life‐history traits of animals more generally. Here, we examined anuran body‐size evolution among 2,434 species with over 200 million years of shared evolutionary history. We found clade‐specific evolutionary shifts to new body‐size optima along with numerous independent transitions to gigantic and miniature body sizes, despite the upper limits of anuran body size remaining quite consistent throughout the fossil record. We found a weak, positive correlation between a species’ body size and maximum latitude and elevation, including a dearth of small species at higher elevations and broader latitudinal and elevational ranges in larger anurans. Although we found modest differences in mean anuran body size among microhabitats, there was extensive overlap in the range of body sizes across microhabitats. Finally, we found that larger anurans are more likely to consume vertebrate prey than smaller anurans are and that species with a free‐swimming larval phase during development are larger on average than those in which development into a froglet occurs within the egg. Overall, anuran body size does not conform to geographic and ecological patterns observed in other tetrapods but is perhaps more notable for variation in body size within geographic regions, ecologies and life histories. Here, we document this variation and propose target clades for detailed studies aimed at disentangling how and why variation in body size was generated and is maintained in anurans.
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null (Ed.)The cognitive-buffer hypothesis proposes that more harsh and unpredictable environments favour animals with larger brains and resulting greater cognitive skills. Comparisons across taxa have supported the hypothesis, but it has rarely been tested within a species. We measured brain size, as inferred from head dimensions, for 1141 cliff swallow specimens collected in western Nebraska, 1982–2018. Cliff swallows starving to death during unusual late-spring cold snaps had significantly smaller brains than those dying from other causes, suggesting that brain size in this species can affect foraging success and that greater cognitive ability may confer advantages when conditions exceed normal environmental extremes. Brain size declined significantly with the size of the breeding colony from which a specimen came. Larger brains may be favoured in smaller colonies that represent more unpredictable and more challenging social environments where there is less public information on food sources and less collective vigilance against predators, even in relatively normal conditions. Our results provide intraspecific support for the cognitive-buffer hypothesis and emphasize the potential evolutionary impact of rare climatic events.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7554/eLife.74159
