Mapa Renee, Choy Stefan, Lapko Mia, Kimmel Alli, Carino-Bazan Isabel, Kowalko Johanna E
Department of Biological Sciences, Lehigh University, Bethlehem, PA.
bioRxiv. 2025 Jul 10:2025.07.07.663556. doi: 10.1101/2025.07.07.663556.
Across the animal kingdom, social behaviors such as aggression are critical for survival and reproductive success. While there is significant variation in social behaviors within and between species, the genetic mechanisms underlying natural variation in social behaviors are poorly understood. A central challenge to investigating the mechanisms contributing to the evolution of social behaviors is that these behaviors are typically complex, making them a challenge to quantify. The Mexican tetra, is a powerful model for investigating the evolution of traits, as it is a single species that exists as populations of eyed, river-dwelling surface fish and blind cave-dwelling fish. The blind cavefish have evolved morphological and behavioral differences compared to surface fish, including reduced aggression. Here, we developed and validated an automated machine learning pipeline that integrates pose-estimation and supervised behavioral classification to track and quantify aggression-associated behaviors-striking, following, and circling. Using this pipeline, we established that these behaviors are quantitatively different between surface and cave fish during juvenile stages in , similar to what was observed previously in adults. Moreover, assessment of these aggressive behaviors in surface-cave F2 hybrid fish revealed that striking and following are strongly positively correlated, while striking and circling are negatively correlated, suggesting that these behaviors evolved through some shared genetic mechanisms. These findings demonstrate the power of automated tracking and behavioral phenotyping in multiple fish in and establish a foundation for future studies investigating the genetic basis of evolution of social behaviors.
在整个动物界，诸如攻击行为等社会行为对于生存和繁殖成功至关重要。虽然物种内部和物种之间的社会行为存在显著差异，但对社会行为自然变异背后的遗传机制却知之甚少。研究促成社会行为进化的机制面临的一个核心挑战是，这些行为通常很复杂，难以进行量化。墨西哥丽脂鲤是研究性状进化的有力模型，因为它是一个单一物种，存在有眼睛的、生活在河流中的表层鱼种群和盲眼的洞穴鱼种群。与表层鱼相比，盲眼洞穴鱼已经进化出形态和行为上的差异，包括攻击性降低。在这里，我们开发并验证了一种自动化机器学习流程，该流程整合了姿态估计和监督行为分类，以跟踪和量化与攻击相关的行为——攻击、跟随和环绕。使用这个流程，我们确定在幼鱼阶段，表层鱼和洞穴鱼之间的这些行为在数量上是不同的，这与之前在成鱼中观察到的情况类似。此外，对表层 - 洞穴F2杂交鱼的这些攻击行为的评估表明，攻击和跟随呈强烈正相关，而攻击和环绕呈负相关，这表明这些行为是通过一些共同的遗传机制进化而来的。这些发现证明了在多条鱼中进行自动跟踪和行为表型分析的能力，并为未来研究社会行为进化的遗传基础奠定了基础。
