Fish diversity and ecology, habitats and fisheries for the un-dammed riverine axis Paraguay-Parana-Rio de la Plata (Southern South America) - Quiros, Rolando (2007)
The riverine complex Paraguay-middle Parana-Rio de la Plata extends more than 3700 km southwards from its sources in the western hills of the Brazilian Shield to its discharge into the Rio de la Plata River. The high variety of habitats is reflected in the large diversity of its fish communities, which are dominated by characiform and siluriform fish species. The potamic axis is evolutionarily and ecologically open to fish movements and there are more than 400 fish species listed for the whole system, but only 100 species are common to both upper and lower basins. However, data limitations in some portions of the system need to be addressed before creation of an ichthyogeographic classification. The river basins that make up the potamic axis are low to medium developed and environmental pressures are unevenly distributed. Chemical pollution is a concern throughout. In the Pantanal, small hydro-projects and sedimentation from agricultural activities have had adverse effects on fish habitat inducing a loss of fish diversity. Un-dammed but more regulated and developed lowland rivers, may be impacted by upstream dams that may create unsuitable habitats for fish adapted to normal main channel conditions because they increase river flows during periods that were formerly low waters or change flows at random. The fisheries are lightly to moderately exploited compared to other subtropical and tropical riverine fisheries, and retain several of their original characteristics in less developed river reaches, although changes are evident. Large potamodromous fish are usually present in the catch, but the abundance of large piscivores is lower and fish size at catch is smaller. The development of the riverine system is expected to continue throughout the basin. If it is implemented as it was executed in the past, a continual loss of fish habitats and a general decrease in ecosystem health can be predicted.