Detecting sexual conflict and sexually antagonistic coevolution is a research paper published in Philosophical Transactions of the Royal Society B: Biological Sciences (2006). On theSindex it has a DataRank of 3.3. It has been cited 109 times, with 95 citing works in its 1-hop citation network. Its calibrated FAIR score is 61/100.
We begin by providing an operational definition of sexual conflict that applies to both inter- and intralocus conflict. Using this definition, we examine a series of simple coevolutionary models to elucidate fruitful approaches for detecting interlocus sexual conflict and resultant sexually antagonistic coevolution. We then use published empirical examples to illustrate the utility of these approaches. Three relevant attributes emerge. First, the dynamics of sexually antagonistic coevolution may obscure the conflict itself. Second, competing models of inter-sexual coevolution may yield similar population patterns near equilibria. Third, a variety of evolutionary forces underlying competing models may be acting simultaneously near equilibria. One main conclusion is that studies of emergent patterns in extant populations (e.g. studies of population and/or female fitness) are unlikely to allow us to distinguish among competing coevolutionary models. Instead, we need more research aimed at identifying the forces of selection acting on shared traits and sexually antagonistic traits. More specifically, we need a greater number of functional studies of female traits as well as studies of the consequences of both male and female traits for female fitness. A mix of selection and manipulative studies on these is likely the most promising route.
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Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.705
From this paper's citation signal
Citation Network Contribution
2.6
From 78 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 21% comes from its base citations and 79% from the citation network (78 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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