How Did Different Species of Telescopefishes Evolve in the Barrier-Free Deep Sea?

The deep ocean is a realm of darkness, cold temperatures, and immense pressure, yet it harbors some of the most mysterious organisms on Earth. Compared with terrestrial ecosystems, scientists still know remarkably little about how deep-sea species evolve or how new species arise in what appears to be a vast, uninterrupted body of water. Without obvious geographic barriers such as mountains or rivers, how can populations diverge into distinct species? To explore this question, researchers examined two closely related telescopefishes of the genus Gigantura: Gigantura chuni and Gigantura indica. These fishes inhabit depths ranging from roughly 200 to 3,000 meters below the ocean surface and are highly specialized for life in dimly lit waters. Their tubular eyes project forward like tiny periscopes, enabling them to detect faint traces of light in the deep ocean, while their elongated bodies and narrow, tooth-lined jaws reflect a lifestyle built around hunting prey in darkness.

Head of Gigantura indica（Image source：David Johnson， CC BY-NC-SA 3.0 ）

Investigating speciation in the deep sea presents major difficulties. In many classical examples of speciation, geographic isolation plays a central role. Populations separated by mountains, rivers, or other barriers lose the ability to exchange genes, gradually diverging into distinct species over time. In the open ocean, however, such obvious physical barriers rarely exist. Earlier research on another group of deep-sea fishes, the bristlemouth genus Cyclothone, revealed that populations collected from different regions of the ocean still showed genetic differences even though the species appeared to be widely distributed. This pattern suggests that the ocean may contain invisible boundaries—subtle environmental gradients rather than solid geographic barriers. Differences in temperature, salinity, nutrient levels, or other environmental factors could influence gene flow between populations, functioning as hidden boundaries that ultimately promote speciation.

Illustration of Gigantura chuni（Image source：A. Brauer，CC0 1.0 ）

To determine whether similar processes affect telescopefishes, researchers applied a method known as species distribution modeling. This approach integrates ecological theory with computational analysis to estimate where a species is most likely to occur based on environmental conditions. The underlying idea comes from ecological niche theory: each species occupies a particular set of environmental conditions defined by variables such as temperature, salinity, oxygen concentration, and nutrient availability. By understanding those requirements, scientists can reconstruct the environmental “envelope” within which a species can survive and reproduce. When mapped across the globe, that envelope reveals the potential geographic distribution of suitable habitats.

Occurrence records for the two telescopefish species were obtained from the Global Biodiversity Information Facility database. Initially, the dataset contained 454 records for Gigantura indica and 191 for Gigantura chuni. After removing duplicates, inaccurate coordinates, and other unreliable entries, researchers retained 185 high-quality records for G. indica and 88 for G. chuni. Environmental information was then incorporated from the Bio-Oracle marine database, which includes numerous global ocean variables. Eighteen environmental factors were initially considered, including sea surface temperature, chlorophyll concentration, nitrate levels, silicate concentrations, dissolved oxygen, and ocean current velocity. To ensure statistical stability, variables that were strongly correlated with each other were removed, leaving five key environmental variables for the final models.

The modeling itself was performed using a machine-learning method known as the Maximum Entropy algorithm, commonly referred to as Maxent. The core principle of maximum entropy is to estimate the most uniform probability distribution that still satisfies the environmental constraints observed in the known occurrence data. In other words, the model avoids making unnecessary assumptions while still identifying patterns linking species occurrences to environmental conditions. Because deep-sea species are often represented by relatively few samples, Maxent is especially suitable for analyzing presence-only data and predicting distributions in poorly sampled environments.

Once the models were constructed, they produced maps predicting where suitable habitat for each species is likely to occur across the global ocean. When the predictions from the two species models were compared, an interesting pattern emerged. Approximately 73.4% of the known occurrence points of G. indica fell within the habitat predicted by the G. chuni model, while the G. indica model correctly predicted the presence of G. chuni about 69.3% of the time. This high level of overlap indicates that the two species share very similar environmental requirements. In ecological terms, this pattern reflects niche conservatism: even after diverging into separate species, both lineages retain environmental preferences similar to those of their common ancestor.

Among all the environmental variables examined, one factor emerged as particularly important for both species: chlorophyll concentration at the ocean surface. At first glance, this might seem surprising, since telescopefishes live far below the surface in deep water. However, chlorophyll serves as an indicator of phytoplankton abundance, which forms the base of the marine food web. High levels of surface productivity ultimately support greater numbers of midwater organisms that feed on plankton. Previous studies of telescopefish diets show that they prey on midwater fishes, shrimp, and squid—organisms that themselves depend on planktonic food sources. As a result, productivity in the sunlit surface layer indirectly determines the availability of prey in the deep ocean. Nutrients and organic matter sink from the upper ocean to deeper layers, creating a vertical ecological connection that influences where deep-sea predators can thrive.

The models also revealed that both species are widely distributed across the world's oceans, but their most suitable habitats are concentrated in tropical and subtropical regions. Gigantura chuni shows particularly strong habitat suitability in the South Atlantic and Indian Oceans, whereas G. indica occurs broadly across the Atlantic, Indian, and Pacific Oceans. In both cases, suitable environments are largely confined to latitudes within roughly 35 degrees north or south of the equator. These limits suggest that large-scale environmental gradients such as temperature and nutrient availability play a significant role in shaping the distribution of these deep-sea fishes.

If the ecological niches of these species remain so similar, their divergence likely occurred without dramatic shifts in environmental preference. This raises an intriguing possibility: instead of evolving through adaptation to different ecological conditions, the two species may have been separated by subtle environmental structures within the ocean. Layers such as thermoclines, haloclines, or oxygen gradients can create sharp transitions in water properties at particular depths. These invisible boundaries may restrict movement and gene flow between populations inhabiting different water masses. Over long periods, such barriers could isolate populations sufficiently for genetic divergence to occur. In this sense, the deep ocean may contain hidden forms of geographic isolation that are far less obvious than mountains or land barriers but equally capable of driving evolutionary change.

Thermocline, halocline, and pycnocline（Image source：Hannahvdzande， CC BY-SA 4.0 ）

The deep sea therefore challenges traditional views of how species form. Rather than relying on visible barriers, speciation may arise through subtle environmental gradients and vertical ecological connections within the water column. Temperature, nutrients, and productivity gradients shape the distribution of organisms and may quietly separate populations over evolutionary time. As researchers combine environmental data, ecological modeling, and evolutionary analysis, the mechanisms underlying deep-sea biodiversity are gradually coming into focus. Even in the vast and seemingly uniform expanse of the open ocean, unseen boundaries can guide the emergence of new forms of life.

Author: Shui-Ye You

Reference:

Richarte DR. (2022). Species Distribution Modeling of Telescopefishes (Actinopterygii: Giganturidae). Harvard University.

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