Integration of habitat models to predict fish distributions in several watersheds of Northern Spain

Abstract

Summary 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.

Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia

“…This huge difference between the models' predictions can affect the monitoring of invasive species. According to González-Ferreras et al (2016), models with AUC values below 0.7 and above 0.9 are considered as "very poor" and "highly accurate," respectively. Besides, models with TSS and kappa values below 0.4 and above 0.8 are considered as "poor" and "excellent," respectively.…”

Mapping the temporary and perennial character of whole river networks

“…While the use of UAVs for hydrological processes and modeling is still experimental and in continuous development, some studies have already shown satisfactory results [ Spence and Mengistu , ]. Finally, our training data set included a balanced training data set (47.5% perennial and 52.5% temporary), which has been shown to be very relevant for increasing the accuracy of RF models [ González‐Ferreras et al ., ]. Conversely, extremely imbalanced classes result in poor accuracy for the minority class because RF tends to focus more on the prediction accuracy of the majority class [ Chen et al ., ].…”

Spread of proliferative kidney disease in fish along stream networks: A spatial metacommunity framework

“…With this regard, the use of habitat models of fish distribution (e.g. González‐Ferreras, Barquín, and Peñas ()) is beneficial. Moreover, innovative techniques in environmental monitoring like environmental DNA offer new opportunities.…”