Predictive models of fish microhabitat selection in multiple sites accounting for abundance overdispersion (original) (raw)
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Freshwater Biology, 2001
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Modeling the impact of landscape types on the distribution of stream fish species
Canadian Journal of Fisheries and Aquatic Sciences, 2009
Modifications of the landscape adjoining streams perturb their local habitat and their biological diversity, but little quantitative information is available on land cover classes that influence the fish species individually. Data collected from 191 sites in the Adour–Garonne Basin (France) were analyzed to assess the effects of land cover on the distribution of fish species. A multimodel approach was carried out to predict fish species using land cover classes and to define the most important classes applying a hierarchical filtering based on artificial neural network method and sensitivity analysis. Firstly, using three single-class models, a selection of the land cover subclasses contributing the most was carried out for each fish species and each class. Secondly, multiclass models were built with all the previously selected subclasses to predict each species (n-selected subclass model). Finally, the percentages of contribution for artificial, agricultural, and forest areas obtai...
2004
Models for predicting abundance of fishes in rivers are desired by fishery managers and the public to facilitate protection and management of stream resources, and are also used to gauge our scientific understanding of systems. Movement toward ecosystem management has stressed the need for models to predict fish assemblage structure in rivers, but such models are rare. Since fish assemblages are essentially collections of individual populations, we explored development of species-specific, predictive models for 68 commonly-occurring fishes in rivers of Michigan's Lower Peninsula using multiple linear regression techniques. We developed models for each species from All Sites (AS models) and from Sites Of its Occurrence (SOO models) in the database. We incorporated data describing site-, reach-, catchment-, and drainage networkscale aspects of habitat, species distribution ranges, and abundances of co-occurring fishes at sites to produce best predictive models. We developed two sets of significant regression models for the 68 species. Most commonly occurring variables were similar in both sets of models and included catchment area, July mean temperature, channel gradient, total phosphorus, substrate, and variables indicating connections to specific upstream and downstream aquatic habitats. Variables characterizing anthropogenic land use change and habitat connectivity were often significant for fishes in models. Landscape-scale habitat variables were slightly more common in AS models, while local-scale habitat variables occurred in higher proportions in SOO models. Strong effects of piscivores on fish abundance were not apparent in either set of models. The SOO models generally had fewer variables, explained more variance, and had lower estimation error than the AS models. Preliminary success in applying the SOO models to a river in which the list of occurring species is available and their generally good fit suggest that these models (in combination with some simple, species-specific tests to identify likely occurring fishes) show promise for predicting fish assemblage structure in Lower Michigan streams.
Predicting estuarine use patterns of juvenile fish with Generalized Linear Models
Estuarine, Coastal and Shelf Science, 2013
Statistical models are key for estimating fish distributions based on environmental variables, and validation is generally advocated as indispensable but seldom applied. Generalized Linear Models were applied to distributions of juvenile Solea solea, Solea senegalensis, Platichthys flesus and Dicentrarchus labrax in response to environmental variables throughout Portuguese estuaries. Species-specific Delta models with two sub-models were used: Binomial (presence/absence); Gamma (density when present). Models were fitted and tested on separate data sets to estimate the accuracy and robustness of predictions. Temperature, salinity and mud content in sediment were included in most models for presence/ absence; salinity and depth in most models for density (when present). In Binomial models (presence/ absence), goodness-of-fit, accuracy and robustness varied concurrently among species, and fair to high accuracy and robustness were attained for all species, in models with poor to high goodness-of-fit. But in Gamma models (density when present), goodness-of-fit was not indicative of accuracy and robustness. Only for Platichthys flesus were Gamma and also coupled Delta models (density) accurate and robust, despite some moderate bias and inconsistency in predicted density. The accuracy and robustness of final density estimations were defined by the accuracy and robustness of the estimations of presence/absence and density (when present) provided by the sub-models. The mismatches between goodness-of-fit, accuracy and robustness of positive density models, as well as the difference in performance of presence/absence and density models demonstrated the importance of validation procedures in the evaluation of the value of habitat suitability models as predictive tools.
Multiple factors and thresholds explaining fish species distributions in lowland streams
Global Ecology and Conservation, 2015
Appropriate restoration and conservation measures require a good understanding of the factors limiting the distribution of species, the presence of steep changes in the distribution along environmental gradients and the effect of environmental interactions on species distribution. We used 12 environmental variables describing connectivity, hydrology, climate and stream morphology, to model the distributions of 17 fish species from 2005 Swedish stream sites that were sampled between 2000 and 2011. Modeling was performed using boosted regression trees and random forest, two machine learning techniques to assess the relationship between species distributions and their environment. Temperature, width and connectivity (minimum distance to lake or the sea and water discharge), were the most important variables explaining changes in species distribution at large spatial scales. Response curves of fitted occurrence probabilities along predictors often showed abrupt changes, however, clear threshold effects were difficult to detect. Our results show also differences across species and even in the outcomes of the two algorithms, implying that a simultaneous assessment of multiple species may provide a better signal of ecosystem change than the use of surrogate species.
Canadian Journal of Fisheries and Aquatic Sciences, 2013
We evaluated the effects of temporal variation of fish density estimates on the explanatory power of habitat use models. Fish density estimates were obtained using visual surveys (10 visits) in eighteen 100 m reaches over a 7-week period. Physical attributes of reaches were estimated. Field data were used to develop a simulation domain (10 000 reaches) that reflected the spatio-temporal variability of fish density estimates and physical attributes. Simulations indicated that for a sampling effort of approximately 200 surveys, the number of reaches surveyed (25 to 200) and the number of surveys per reach (1 to 8) affected the adjusted R 2 of models by 5% to 42%. The established practice of sampling a maximized number of reaches once did not appear necessarily optimal for developing habitat use models. Analysis of temporal coefficients of variation suggests that species within the same family may require a similar survey design. Hence, for salmonids, it may be more appropriate to sample more reaches once, and for cyprinids, it may be more optimal to repeatedly sample fewer reaches.
2018
Despite its theoretical relationship, the effect of body size on the performance of species distribution models (SDM) has only been assessed in a few studies of terrestrial taxa. We aim to assess the effect of body size on the performance of SDM in river fish. We study seven Chilean freshwater fish, using models trained with three different sets of predictor variables: ecological (Eco), anthropogenic (Antr) and both (Eco+Antr). Our results indicate that the performance of the Eco+Antr models improves with fish size. These results highlight the importance of two novel predictive layers: the source of river flow and the overproduction of biotopes by anthropogenic activities. We compare our work with previous studies that modeled river fish, and observe a similar relationship in most cases. We discuss the current challenges of the modeling of riverine species, and how our work helps suggest possible solutions.
Canadian Journal of Fisheries and Aquatic Sciences, 2008
We quantified fish abundance and environmental variables at 170 sites distributed among 11 tributaries of the Ottawa River, Quebec, Canada, to assess the relative importance of among-and within-tributary variation in riverine fish assemblages. Additionally, we determined (i) which environmental variables were most strongly associated with each type of variation and (ii) whether ecomorphological traits in fish assemblages were predictably related to environmental gradients. Partitioning of variation by means of partial ordination indicated that assemblages were less variable among (38.7% of the total variation) than within (61.3%) tributaries. Water transparency singly accounted for 33.3% of the variation among tributaries, whereas macrophyte cover and river width jointly accounted for 8.3% of the variation within tributaries. These results suggests that differences in habitat features among tributaries may account for a substantial fraction of the predictable variation in assemblage structure at the watershed scale, an aspect not emphasized in previous studies of riverine fish assemblages. Mixed regression analyses relating ecomorphological traits to environmental variables showed that the environmental variables most strongly associated with assemblage structure were significantly related to traits associated with predator avoidance or foraging efficiency.