ReviewEcology and evolution2026
Hybridization and Immunology in Animals: A Review.
Review in Ecology and evolution, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathogens exert strong selective pressures on their hosts by threatening their fitness and survival, driving evolutionary innovations in many aspects of host biology. The innate immune system and adaptive immunity enable many host species to withstand such pressures; however, when pathogens exceed a host's defensive capacity, the host is forced to evolve novel adaptations to survive and reproduce. One understudied mechanism for generating this novel adaptation is hybridization, a process by which beneficial immune alleles may introgress into hybrid offspring and, via backcrossing, spread through parental populations. In this review, we synthesize evidence across diverse animal systems showcasing how hybridization shapes immunological traits. We highlight cases where hybrid offspring exhibit enhanced tolerance or resistance to pathogens relative to their parent species, and identify gaps in research needed to further examine hybridization as a pathway for species to persist in the face of infectious disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.