Abstract: Species distribution models and consensus models allow knowing the distribution of species in large areas where there is no field data and identifying the most important drivers for those distributions. In this study, seven individual models were used to obtain a consensus model to determine the potential distribution for six freshwater fish species in several watersheds of Northern Spain. Moreover, three different methods of model evaluation were used for performance comparison. Fish data were obtained from databases provided by different organisms related to aquatic systems containing information on 759 field sites sampled between October 2002 and June 2011 using electrofishing techniques. Dependent variables were obtained after filtering field sites according to a human pressure gradient analysis, while independent variables were derived from a Synthetic River Network for the study area. The ?best? individual models were obtained using Random Forest, Generalized Boosted Models and Generalized Additive Models, but with differing results among species and evaluation methods. The different consensus models revealed a high degree of adjustment between modelled and observed data. The most important factors related to fish distributions were the width of the valley floor, mean annual flow, average catchment elevation, distance to the sea, and total catchment area. The importance and critical limits of presence-absence for these key variables differed among species. Use of these models could assist in the prioritization and selection of specific catchment and river reach actions for fish population management, restoration and/or conservation.
Fuente: Journal of Applied Ichthyology Volume 32, Issue 1 February 2016 Pages 204?216
Fecha de publicación: 01/02/2016
Nº de páginas: 13
Tipo de publicación: Artículo de Revista
Proyecto español: MARCE (Ref: CTM-2009-07447) ; Grant Number: 132/2010 ; BIA-2012-33572 ; BES-2013-065770 ; RYC-2011-08313
Url de la publicación: http://onlinelibrary.wiley.com/doi/10.1111/jai.13024/full