Modelling of stream fishes in the Great Plains, USA

• Published in: Ecology of freshwater fish 2005-12, Vol.14 (4), p.361-374
• Main Authors: Oakes, R. M., Gido, K. B., Falke, J. A., Olden, J. D., Brock, B. L.
• Format: Article
• Language: English
• Subjects: artificial neural networks; classification trees; Freshwater; geographical information system; Great Plains; linear discriminant function analysis; Notropis topeka; Pisces; predictive modelling
• ISSN: 0906-6691; 1600-0633
• DOI: 10.1111/j.1600-0633.2005.00110.x

–  Predicting species distributions has important implications for the conservation and management of freshwater fishes, particularly in areas such as the Great Plains, USA where human impacts have resulted in extirpations and declines for numerous native species. There are a number of statistical approaches for constructing distributional models; the accuracy of each is likely dependent on the nature of the environmental gradients, species responses to those gradients and the spatial extent of the modelling. Thus, it is important to compare multiple approaches across species and habitats to identify the most effective modelling approach. Using geographical information system (GIS) derived characteristics of stream segments as predictors, we tested the model performance of three methodologies – linear discriminant function analysis, classification trees and artificial neural networks (ANN) – for predicting the occurrence of 38 fish species in a Great Plains river basin. Results showed that all approaches predicted species occurrences with relatively high success. ANN generally were the best models, in that they generated the most significant models (35 of 38 species) and most accurately predicted species presence for the greatest number of species (average correct classification = 81.1%). The importance of GIS variables for predicting stream fish occurrences varied among species and modelling techniques, but were generally strong predictors of species distributions, including the federally endangered Topeka shiner Notropis topeka. In summary, predictive models should be viewed as both competitive and complementary methodologies for establishing quantitative linkages between fish species and their environment. Our study demonstrates the potential utility of such an approach for guiding conservation efforts for stream fishes of the Great Plains, USA. Resumen 1. Predecir la distribución de las especies tiene importantes implicaciones para la conservación y gestión de los peces de agua dulce, particularmente en regiones tales como el Great Plains de los Estados Unidos, donde los impactos humanos han resultado en el declive o en la extirpación de numerosas especies nativas. 2. Hay varias aproximaciones estadísticas para construir modelos de distribución; la precisión de cada una probablemente dependa de la naturaleza de los gradientes ambientales, de las respuestas de las especies a esos gradientes y de la extensión espacial de los modelos. Por ello, es importa