Data for: Patterns of diversity of Brazilian reef fish fauna

Reef fish communities were assessed using visual census techniques across 100 reefs spanning over 2.000 km of coastline. Each reef was considered a sample unit and species densities per 100 m² was used for calculating true diversity measures.

Diversity patterns were investigated using “true diversity” measures (sensu Jost 2006), considering the effective number of species equal to Hill numbers (qD). Values in dataset correspond to number of effective species per reef (α diversity, Dα), for q = 0, 1 and 2, using sampled area as weights. Each line represents a reef, labeled by a Code, followed by Brazilian State, Location name, Latitude, Longitude and Source of the species data. Predictors used for adjusting GLMs are presented next: Reef morphotype (categorical predictor), with five levels corresponding to Brazilian reef morphological structures (cemented terraces, fringing, patch, bank and pinnacle reefs, see Appendix S1 for a detailed description). Depth, as the average depth of the reef in meters, measured in situ, isolation, as the linear distance between the reef to the nearest point in mainland, are also presented.

采用视觉普查技术对横跨2,000公里海岸线的100个珊瑚礁的鱼群社区进行了评估。每个珊瑚礁被视为一个样本单元，并使用每100平方米的物种密度来计算真实多样性指标。通过采用“真实多样性”指标（参照Jost 2006年的定义），考虑物种的实际数量等于希尔数（qD），研究了多样性模式。数据集中的数值对应于每个珊瑚礁的有效物种数量（α多样性，Dα），其中q分别取0、1和2，采样面积作为权重。每行代表一个珊瑚礁，以代码标记，随后是巴西州名、位置名称、纬度、经度和物种数据来源。用于调整广义线性模型（GLM）的预测因子包括：珊瑚礁形态类型（分类预测因子），包含五个级别，对应于巴西珊瑚礁的五种形态结构（凝固台地、边缘礁、斑块、礁滩和尖顶礁，详见附录S1的详细描述）。此外，还包括珊瑚礁的平均深度（以米为单位，现场测量）、孤立度（珊瑚礁与最近大陆点之间的线性距离）。