Fish fauna of small-order streams of savannah and forest fragments landscape in the lower Tapajós River basin, Amazonia

Abstract: To better understand the fish fauna of Amazonian streams, we assessed small streams in a landscape of forest fragments and savannah in the region of the lower Tapajós River, Brazil. We sampled the fish fauna using a well-tested active capture method during two dry seasons (October 2006 and 2018) and one rainy season (March to May 2018). Species richness was calculated using an abundance matrix and first-order jackknife estimator. Using qualitative and quantitative data, we present a cluster analysis in which each stream corresponds to a sampling unit. We collected 6,094 individuals of 43 species distributed in six orders. The sampling effort represents 73% of the estimated richness (58.69 ± 7.65). The most abundant species were Copella nattereri, Iguanodectes variatus and Laimosemion dibaphus that together represent almost half of the total sample (48.7%). The most frequent species were Aequidens epae, Helogenes marmoratus and Laimosemion dibaphus, which were collected in 11 of the 13 sampled streams. This is the first fish fauna list for small-order streams of savannah and forest fragments landscape in Amazonian Brazil. The richness of fish and the presence of many rare species underscore the contribution of small streams to the regional fish fauna composition, even in dynamic and spatially restricted landscapes.

摘要：为深入了解亚马孙溪流的鱼类区系，我们在巴西塔帕若斯河下游区域的森林碎片与稀树草原景观中对小型溪流开展了调查。我们于两个旱季（2006年10月、2018年10月）及一个雨季（2018年3月至5月），采用经过充分验证的主动捕获法对鱼类区系进行采样。通过丰度矩阵（abundance matrix）与一阶刀切法估计量（first-order jackknife estimator）计算物种丰富度。结合定性与定量数据，我们开展了聚类分析，以每条溪流作为独立采样单元。本次研究共采集到6094尾个体，隶属于43个物种，分属6个目。实际采集到的物种数占估计总物种丰富度的73%，估计总物种丰富度为58.69 ± 7.65。丰度最高的物种为Copella nattereri、Iguanodectes variatus及Laimosemion dibaphus，三者合计占总采样个体数的近一半（48.7%）。出现频率最高的物种为Aequidens epae、Helogenes marmoratus与Laimosemion dibaphus，在13条被采样溪流中的11条中均有采集记录。本研究是巴西亚马孙地区稀树草原与森林碎片景观中小型溪流的首份鱼类区系名录。鱼类物种丰富度以及诸多稀有物种的存在，凸显了小型溪流即便处于动态且空间受限的景观中，对区域鱼类区系组成的重要贡献。