Data from: Repeated parallel differentiation of social learning differences in benthic and limnetic threespine stickleback fish

Individuals can reduce sampling costs and increase foraging efficiency by using information provided by others. One simple form of social information use is delayed local enhancement, or increased interest in a location because of the past presence of others. We tested for delayed local enhancement in two ecomorphs of stickleback fish, benthic and limnetic, from three different lakes with putative independent evolutionary origins. Two of these lakes have reproductively isolated ecomorphs (‘species-pairs’), whereas in the third a previously intact species-pair recently collapsed into a hybrid swarm. Benthic fish in both intact species-pair lakes were more likely to exhibit delayed local enhancement despite being more solitary than limnetic fish. Their behaviour and morphology suggest their current perceived risk and past evolutionary pressure from predation did not drive this difference. In the hybrid swarm lake, we found a reversal in patterns of social information use, with limnetic-looking fish showing delayed local enhancement rather than benthic-looking fish. Together, our results strongly support parallel differentiation of social learning differences in recently evolved fish species, although hybridization can apparently erode and possibly even reverse these differences.Data files are csv format. We used R v 4.0.2 for analysis. Necessary packages are listed in the code: ggplot2, car, MASS, AER.个体可通过利用其他个体提供的信息降低取样成本并提升觅食效率。一类简单的社会信息利用形式为延迟局部增强（delayed local enhancement），即因某一区域曾有其他个体停留而对该区域产生更高的关注度。我们针对来自三个推测独立演化起源的不同湖泊的棘鱼（stickleback fish）的两种生态形态——底栖型（benthic）与浮游型（limnetic）——开展了延迟局部增强效应的检测。其中两个湖泊存在生殖隔离的生态形态（即“物种对”），而第三个湖泊原本完整的物种对近期已崩溃为杂交集群。尽管底栖型棘鱼较浮游型更为独居，但在两个存在完整物种对的湖泊中，底栖个体更易表现出延迟局部增强效应。它们的行为与形态特征表明，当前感知到的捕食风险以及过往演化经历的捕食压力并未驱动这一差异的产生。在该杂交集群湖泊中，社会信息利用的模式出现反转：表现出浮游型形态的个体而非底栖型个体呈现出延迟局部增强效应。综上，本研究结果强力支持近期演化出的鱼类类群间社会学习差异的平行分化，尽管杂交作用显然可削弱甚至逆转这类差异。本研究所用数据文件格式为逗号分隔值（CSV）。我们采用R语言4.0.2版本进行数据分析，所需R包已在代码中列明：ggplot2、car、MASS、AER。