Tristramella simonis (Gunther, 1864)
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publication ID |
https://doi.org/10.1515/9783111677811 |
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DOI |
https://doi.org/10.5281/zenodo.17821635 |
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persistent identifier |
https://treatment.plazi.org/id/C85F87D2-FC86-FCCC-28AB-FCCEFC8AF8F1 |
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treatment provided by |
Felipe |
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scientific name |
Tristramella simonis |
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Tristramella simonis View in CoL
Common name. Short jaw Tristramella .
Diagnosis. Distinguished from other species of Cichlidae in West Asia by: ● lower jaw slightly projecting / ○ outer teeth bicuspid, inner teeth tricuspid / ○ chest never red / ○ nuptial male pale-grey or pinkish white without white spots or vermiculation on flank / ○ 10–12 gill rakers on lower part of first branchial arch / ○ chest, belly, and isthmus in front of pelvic covered by small scales / ○ 3 anal spines / ○ scales cycloid / ○ no ocelli on anal. Size up to 250 mm SL.
Distribution. Jordan drainage: Lakes Muzayrib ( Syria), Hula and Kinneret (Tiberias) (both in Israel), Dara’a reservoir and a canal at Al Asha’ari ( Syria), Damascus basin, and Lake Hula. Non-native in Orontes and Nahr al Kabir ( Syria).
Habitat. Lakes, reservoirs, springs, and spring-fed streams and canals.
Biology. Spawns March–August. Maternal mouthbrooder or both partners participate in brooding. Feeds on phytoplankton and invertebrates.
Conservation status. VU; extirpated from Hula due to drainage of this wetland and extirpated from Damascus basin due to drying up of lakes it inhabited. Apparently stable in Kinneret. Probably declining outside Lake Kinneret, but inhabiting reservoirs and invasive in Syria.
Further reading. Goren 1974 (distribution); Borkenhagen & Freyhof 2009 (distribution).
Brackish rivers in southern Iran are the habitat of Iranocichla species.
The promise and paradox of using artificial intelligence in ichthyology. In a time when artificial intelligence ( AI) is reshaping almost every field, ichthyology cannot remain untouched. As in all other disciplines, it presents a double-edged sword, offering powerful new tools while raising complex challenges. AI’s capabilities in pattern recognition and data analysis can totally change how we identify species, automate literature reviews, improve ecological modelling, and even help us discover hidden biodiversity by detecting patterns in genes and morphology. Deep learning models can process thousands of specimens with remarkable speed, spot tiny morphological differences invisible to the human eye, and predict species distributions under climate change scenarios. AI can also speed up phylogenetic reconstructions and help to reveal hidden biodiversity in large, under-explored datasets. But, as with anything new, there are risks. Over-reliance on AI-generated identifications without expert validation may introduce and propagate systematic errors. Many algorithms operate as opaque “black boxes,” which makes it hard to understand and doesn't meet the standards of scientific rigour. AI models trained on biased or incomplete datasets can end up making existing taxonomic gaps worse, and this can have a bigger effect on rare or understudied species. Importantly, blind trust in AI threatens to deskill young taxonomists and diminish the value of foundational expertise in morphology, ecology, and evolution—a paradox where technological advancement could weaken the scientific foundation it aims to strengthen. So, recognising this risk calls for a balanced perspective. Further points for consideration could include the following: the potential for the development of standards with which to evaluate and verify AI-driven decisions in the context of taxonomy and conservation. The question of responsibility in the curation of training data and the crediting of AI-assisted contributions is a critical issue. Of equal concern is the potential for AI to produce entire research publications, which could overwhelm scientific literature with unverified content and erode the standards of peer review. The future of ichthyology will not be defined by algorithms alone, but by the wisdom with which we choose to use them.
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