Researchers show dinos hatched eggs less efficiently than modern birds

Research using dinosaur body model suggests that – unlike modern birds – bird-like dinosaurs may have used the sun’s warmth to help hatch eggs, shedding light on the evolution of avian-style incubation.

Frontiers

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image: 

Lateral view of the clutch. The eggs were molded from casting resin. 

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Credit: Chun-Yu Su.

What do we really know about how oviraptors – bird-like but flightless dinosaurs – hatched their eggs? Did they use environmental heat, like crocodiles, or body heat from an adult, like birds? In a new Frontiers in Ecology and Evolution study, researchers in Taiwan examined the brooding behavior and hatching patterns of oviraptors. They also modelled heat transfer simulations of oviraptor clutches and compared hatching efficiency to modern birds. To do so, they experimented with a life-sized oviraptor incubator and eggs.

“We show the difference in oviraptor hatching patterns was induced by the relative position of the incubating adult to the eggs,” said senior author Dr Tzu-Ruei Yang, an associate curator of vertebrate paleontology at Taiwan’s National Museum of Natural Science.

“Moreover, we obtained an estimate of the incubation efficiency of oviraptors, which is much lower than that of modern birds,” added first author Chun-Yu Su, who attended Washington High School in Taichung when the research was conducted.

Building a dinosaur

The reconstructed oviraptor Heyuannia huangi lived between 70 and 66 million years ago in what today is China. Estimated to be around 1.5 meters long and weighing around 20kg, it built semi-open nests made up of several rings of eggs.

The incubating oviraptor’s trunk was made from polystyrene foam and wood for the skeletal frame and cotton, bubble paper, and cloth for the soft tissue. Eggs were molded from casting resin. In the two clutches used in the experiments, eggs were arranged in double-rings based on real oviraptor clutches.

“Part of the difficulty lies in reconstructing oviraptor incubation realistically,” said Su. “For example, their eggs are unlike those of any living species, so we invented the resin eggs to approximate real oviraptor eggs as best as we could.”

When the team ran experiments to find out if clutch attendance of a brooding adult or different environmental circumstances may have impacted hatching patterns, they found that in colder temperatures, where a brooding adult attended the clutch, the eggs’ temperatures in the outer ring differed by up to 6°C, which could have resulted in asynchronous hatching, a pattern where eggs in the same nest hatch at different times. In warmer conditions, the difference in egg temperatures in the outer ring was just 0.6°C, suggesting that oviraptors living in warmer conditions may have exhibited a different pattern of asynchronous hatching because they could use the sun as an additional, powerful heat source.

“It’s unlikely that large dinosaurs sat atop their clutches. Supposedly they used the heat of the sun or soil to hatch their eggs, like turtles. Since oviraptor clutches are open to the air, heat from the sun likely mattered much more than heat from the soil,” Yang explained.

Better hatchers?

The team also investigated how oviraptor incubation efficiency compares to that of modern birds. Most birds use thermoregulatory contact incubation (TCI), where adults sit directly on the eggs to transfer heat. TCI requires three prerequisites – the adult bird must be in contact with every egg, be the main heat source, and maintain all eggs within a constrained temperature range – which oviraptors didn’t fulfil. For example, their egg arrangement prevented the adult from making full contact with all eggs in the clutch.

“Oviraptors may not have been able to conduct TCI as modern birds do,” said Su. Instead, these dinosaurs and the sun may have been co-incubators – a less efficient incubation behavior than that displayed by modern birds. Yet, the combination of adult incubation and an ambient heat source – perhaps a behavioral adaptation associated with the evolution from buried to semi-open nests – isn’t necessarily worse.

“Modern birds aren’t ‘better’ at hatching eggs. Instead, birds living today and oviraptors have a very different way of incubation or, more specifically, brooding,” Yang pointed out. “Nothing is better or worse. It just depends on the environment.”

The team pointed out that their findings are specific to the reconstructed nest and are limited by the fact that today’s climate does not resemble the Late Cretaceous climate, which may have impacted the results. Oviraptors also exhibited a longer incubation period than modern birds.

Yet, the study advances our understanding of oviraptor brooding strategies through innovative approaches. It represents an important bridge between physics-based simulations and paleontological interpretations, potentially enabling paleontologists to investigate topics for which approaches were limited until now.  

“It also truly is an encouragement for all students, especially in Taiwan,” concluded Yang. “There are no dinosaur fossils in Taiwan but that does not mean that we cannot do dinosaur studies.”

Lateral view of the clutch with the incubator on top

Photograph of the generalized clutch after Experiment III.

Dorsal view of the incubator.

Credit

Chun-Yu Su.

The arrangement of thermometers in the incubation experiments. Thermometers 1 (with thicker outlines) were used in Experiment II. Thermometers 2 (with lighter outlines) were the additional thermometers used in Experiment III. The schematic presents a lateral view of the clutch and the incubator.

Credit

Su et al.,2026.

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Journal

Frontiers in Ecology and Evolution

DOI

10.3389/fevo.2026.1351288 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Heat transfer in a realistic clutch reveals a lower efficiency in incubation of oviraptorid dinosaurs than of modern birds

Article Publication Date

17-Mar-2026

 

What flocking birds can teach AI

Researchers draw from flocking bird patterns to help AI produce more reliable outputs

New York University

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Among the primary concerns surrounding artificial intelligence is its tendency to yield erroneous information when summarizing long documents. These “hallucinations” are problematic not only because they convey falsehoods, but also because they reduce efficiency—sorting through content to search for mistakes of AI outputs is time-consuming.

To help address this challenge, a team of computer scientists has created an algorithmic framework that draws from a natural phenomenon—bird flocking—by mimicking how birds efficiently self-organize. The framework serves as a preprocessing step for large language models (LLMs), helping them produce more reliable summaries of large documents.

The work is reported in the journal Frontiers in Artificial Intelligence. 

The researchers created the bird-flocking algorithm by first unpacking how AI agents make mistakes. 

These systems are built on LLMs that are designed to autonomously research, write, and summarize. But while they may write well, they do not always produce accurate or faithful summaries. 

“One contributing factor is that when input text is excessively long, noisy, or repetitive, model performance degrades, causing AI agents and LLMs to lose track of key facts, dilute critical information among irrelevant content, or drift away from the source material entirely,” explains Anasse Bari, a computer science professor at NYU’s Courant Institute School of Mathematics, Computing, and Data Science and director of the Predictive Analytics and AI Research Lab, which conducted the work.

Drawing from the cause of this shortcoming, Bari and co-author Binxu Huang, an NYU computer science researcher, turned to an orderly and time-tested method of gathering disparate parts—bird flocking—and applied it as a preprocessing step to generative AI.

Their method considered each sentence in a long document—a scientific study or a legal analysis—as a virtual bird. In yielding a simplified outcome, it evaluated the document’s sentences based on their position, thematic centrality, and topical relevance, then grouped them into clusters that mirror how birds self-organize into flocks.

This grouping reduced each cluster to its most representative sentences, with the goal of minimizing redundancy and preserving key points. The resulting curated summary was then passed to an LLM as a structured, concise, and reduced input. 

“The intention was to ground AI models more closely to the source material while reducing repetition and noise before generating a final summary,” says Bari, who previously turned to natural phenomena in devising an algorithm to improve online searches.

Here is how it works in greater detail:

Phase 1: Score Every Sentence

• Each sentence is cleaned by keeping only nouns, verbs, and adjectives, while stripping out articles, prepositions, conjunctions, and punctuation. Among other natural language processing techniques, multi-word terms are also merged (“lung cancer” becomes “lung_cancer”) so single concepts stay intact. 

• Each sentence is then converted into a numerical vector by fusing lexical, semantic, and topical features. Sentences are scored on document-wide centrality, section-level importance, and alignment with the abstract, with a numerical boost for key sections like the Introduction, Results, and Conclusion.

Phase 2: Bird Flocking for Diversity

• Taking only the top-scored sentences risks repetition—and stymies flocking. For instance, in a cancer research paper, the five highest-ranked sentences might all discuss treatment outcomes. Instead, the framework treats each sentence as a bird positioned in an imaginary space according to its meaning. Much like real birds in nature, which self-organize into flocks by following three simple rules known as cohesion (stay close to nearby birds), alignment (move in the same direction as neighbors), and separation (avoid crowding), sentences with similar meanings naturally cluster together while maintaining distinct groupings. Leaders emerge within each cluster and followers attach to their nearest leader. 

• From each final flock of similar sentences, only the highest-scoring ones are selected, so the summary covers background, methods, results, and conclusions, rather than echoing one theme—thereby reflecting a document’s diversity of content without repeating it. The chosen sentences are reordered and fed to an AI agent powered by an LLM, which synthesizes them into a fluent summary grounded in the original source content.

• The researchers evaluated the algorithm on over 9,000 documents, examining whether this approach produced better outputs compared to an AI agent powered by an LLM alone. The framework, including its bird-flocking-inspired algorithm, combined with LLMs, helped generate summaries with greater factual accuracy than did LLMs producing content without the algorithm.

“The core idea of our work is that we developed an experimental framework that serves as a preprocessing step for large texts before it is fed to an AI agent or LLM and not as a competitor to LLMs or AI agents,” Bari says. “The framework identifies the most important sentences in a document and creates a more concise representation and summary of the original text, removing repetition and noise before it reaches the AI.” 

However, the authors acknowledge that their approach is not a panacea.

“The goal is to help the AI generate summaries that stay closer to the source material,” notes Bari. “While this approach has the potential to partially address the issue of hallucination, we do not want to claim we have solved it—we have not.”

# # #

Editor’s Note: In November 2025, NYU announced the establishment of the Courant Institute School of Mathematics, Computing, and Data Science. The newly established school recognizes the storied history of the Courant Institute of Mathematical Sciences—and its strengths in both applied and pure mathematics—while encompassing NYU’s Center for Data Science and linking the computer science departments at Courant and the Tandon School of Engineering.

 

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Journal

Frontiers in Artificial Intelligence

DOI

10.3389/frai.2026.1703769 

Method of Research

Data/statistical analysis

Article Title

A Bird-Inspired Artificial Intelligence Framework for Advanced Large Text Summarization

Article Publication Date

17-Mar-2026

 

New rare bird species discovered in Japan

Uppsala University

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Tokara Leaf Warbler Nakanoshima  Tokara Leaf Warbler Phylloscopus tokaraensis (the same individual as in photo labelled A singing male Tokara Leaf Warbler) 

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Credit: Per Alström, Uppsala University

A previously unknown species of leaf warbler has been discovered in Japan. The Ijima’s Leaf Warbler has proven to be two different species, not just one. Every year, a few new bird species are identified around the world. The unusual aspect in this case is that it is not primarily the appearance that reveals that two species are involved – it is above all DNA analyses that prove it.

“This shows how important it is to use genetic methods to reveal hidden biodiversity at a time of global biodiversity crisis. These methods can help provide more complete knowledge on which to base future nature conservation efforts,” says Per Alström at Uppsala University, one of the researchers who have discovered the new species Tokara Leaf Warbler.

The Ijima’s Leaf Warbler (Phylloscopus ijimae) is a rare migratory bird only found on the Izu Islands south of Tokyo, Japan, and on the Tokara Islands approximately 1,000 km to the south-west.

The scientists had already discovered that the birds on these two groups of islands were clearly distinct from one another ten years ago, when they analysed their DNA sequences. This led to extensive studies on the islands, in museum collections and in the DNA lab. Analyses based on the entire genome showed that the birds on the Tokara Islands are very unlike those on the Izu Islands, a finding that was corroborated by careful comparisons of their songs.

The researchers, from Uppsala University, University of Gothenburg and two Japanese institutions, now describe the Tokara Leaf Warbler (Phylloscopus tokaraensis) as a new species for science. The last time a new species was discovered in Japan was in 1982, when the Okinawa Rail (Gallirallus okinawae) was first described.

“The new species is a little cryptic and tricky to define. In terms of appearance, it doesn’t differ from the Ijima’s Leaf Warbler. It is DNA analyses and differences in song that show that this is a separate species,” Alström says.

Because the groups of islands where the birds live are small, their populations are tiny. The Tokara Islands have a combined area of just over 100 square kilometres, smaller than that of Fårö (an island off the coast of Gotland), divided between twelve islands.

As both species have very low genetic diversity, they may be vulnerable to changes in nature and the environment as well as to diseases. Yet both species also show signs that they may have recovered somewhat from past population declines.

The Ijima’s Leaf Warbler is already officially listed as Vulnerable by the International Union for Conservation of Nature and is protected as a Japanese “Natural Monument”. As the Tokara Leaf Warbler is at least as rare as the Ijima’s Leaf Warbler, the scientists recommend that both species should be classified as Vulnerable and both should be monitored to detect any future population changes.

View over Nakanoshima with a mix of local bird species calling in the background. [VIDEO] 

Tokara Leaf Warbler Song [AUDIO]

Tokara Leaf Warbler Nakanoshima 11June2017-1 Per Alstrom. A singing male Tokara Leaf Warbler Phylloscopus tokaraensis on Nakanoshima, Tokara Islands, in June 2017. P

One of the Tokara Leaf Warblers Phylloscopus tokaraensis caught on Nakanoshima, Tokara Islands, in June 2017. 

 

Tokara Islands, Japan June 2017

View from Nakanoshima, Tokara Islands, Japan, June 2017. 

Credit

Per Alström, Uppsala University

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Journal

PNAS Nexus

DOI

10.1093/pnasnexus/pgag037 

Method of Research

Observational study

Subject of Research

Animals

Article Title

Discovering and protecting cryptic biodiversity: a case study of a previously undescribed, vulnerable bird species in Japan

Article Publication Date

17-Mar-2026