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Predicting freshwater fish distributions using landscape-level variables

机译:使用景观水平变量预测淡水鱼的分布

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摘要

Management and conservation of aquatic systems requires the ability to identify species' historical, current, and potential distributions. We explored how a geographic information system can be used in conjunction with a few broad landscape variables to provide watershed-scale information useful for identifying diverse aquatic areas and predicting potential fish habitat. We developed species habitat profiles for all fish species that are known to occur in Pennsylvania. Five landscape variables were used to characterize a species' habitat profile to predict its statewide distribution: presence in a major drainage basin, presence in a physiographic region, median watershed slope, level of watershed disturbance, and watershed-stream size. Each of these variables was referenced to a small watershed boundary. Using these variables, we predicted a species potential habitat range. Distribution maps that we generated were then compared to known distributions with an average accuracy of 73%. While many collections have been made in Pennsylvania over the last 50 years, we determined that many areas still remain unexplored as potential sampling locations. Among those fishes whose predicted distribution was less than the actual sampled distribution, four receive special protection in Pennsylvania and one is federally endangered. Moreover, we determined that small watersheds (1:24,000 scale) in the Allegheny River drainage, in the Pittsburgh Low Plateau Section, of small size (3-4 order), with moderate slope (2-4%), and moderate watershed disturbance (25-75%) have the highest fish species richness. Our results should facilitate the conservation of fish species and our technique should be easily repeatable in other geographic areas.
机译:水生系统的管理和保护要求具有识别物种的历史,当前和潜在分布的能力。我们探索了如何将地理信息系统与一些广泛的景观变量结合使用,以提供分水岭规模的信息,这些信息可用于识别各种水生区域并预测潜在的鱼类栖息地。我们开发了宾夕法尼亚州所有已知鱼类的物种栖息地概况。使用五个景观变量来表征物种的栖息地轮廓,以预测其在全州范围内的分布:主要流域中的存在,自然地理区域中的存在,分水岭的中值坡度,分水岭扰动的程度以及分水岭的大小。这些变量中的每一个均以较小的分水岭边界为参考。使用这些变量,我们预测了物种潜在的栖息地范围。然后将我们生成的分布图与已知分布进行比较,平均准确度为73%。在过去的50年中,尽管在宾夕法尼亚州收集了许多收藏品,但我们确定许多地区仍未被挖掘为潜在的采样地点。在那些预测分布少于实际采样分布的鱼类中,有四种在宾夕法尼亚州受到特别保护,其中一种受到联邦濒临灭绝。此外,我们确定匹兹堡低高原段的Allegheny河排水中的小流域(比例为1:24,000),面积小(3-4阶),坡度中等(2-4%),流域扰动中等(25-75%)的鱼类物种丰富度最高。我们的研究结果应有助于保护鱼类,我们的技术应在其他地理区域易于重复。

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