Reproductive character displacement generates reproductive isolation among conspecific populations: an artificial neural network study is a research paper published in Proceedings of the Royal Society B: Biological Sciences (2006). On theSindex it has a DataRank of 2.1. It has been cited 62 times, with 43 citing works in its 1-hop citation network. Its calibrated FAIR score is 61/100.
When interactions with heterospecifics prevent females from identifying conspecific mates, natural selection can promote the evolution of mating behaviours that minimize such interactions. Consequently, mating behaviours may diverge among conspecific populations in sympatry and in allopatry with heterospecifics. This divergence in conspecific mating behaviours—reproductive character displacement—can initiate speciation if mating behaviours become so divergent as to generate reproductive isolation between sympatric and allopatric conspecifics. We tested these ideas by using artificial neural networks to simulate the evolution of conspecific mate recognition in populations sympatric and allopatric with different heterospecifics. We found that advertisement calls diverged among the different conspecific populations. Consequently, networks strongly preferred calls from their own population to those from foreign conspecific populations. Thus, reproductive character displacement may promote reproductive isolation and, ultimately, speciation among conspecific populations.
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Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.621
From this paper's citation signal
Citation Network Contribution
1.4
From 41 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 30% comes from its base citations and 70% from the citation network (41 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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