Bridging mechanistic conceptual models and statistical species distribution models of riverine fish

Bogdan Caradima,Andreas Scheidegger,Jakob Brodersen,Nele Schuwirth

doi:10.1016/j.ecolmodel.2021.109680

Bogdan Caradima, Andreas Scheidegger + Show 2 more

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https://doi.org/10.1016/j.ecolmodel.2021.109680

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Abstract

Statistical species distribution models (SDMs) are widely used to quantify how taxa respond to environmental conditions and to predict their distribution. However, the application of SDMs to freshwater fish taxa is complicated by the active dispersal of fish taxa through river networks, and the species- and habitat-dependent observation process (i.e., the sampling method and effort) required to accurately sample their distributions. Many studies have applied presence-absence models (PAMs) to fish taxa, while more recent studies have proposed zero-inflated models (ZIMs) to account for count observations with many zeroes. However, relatively few studies have incorporated the observation process into the model structure, which would facilitate the combination of data from various monitoring programs that differ in their observation process. In this study, we use conceptual models to identify potentially dominant natural and anthropogenic environmental conditions with a direct, mechanistic effect on the distributions of freshwater fish taxa in Switzerland, a region with a large range of environmental conditions, from alpine streams that are mainly affected by hydromorphological alterations to lowland streams in densely populated areas with intensive agricultural land use. Moreover, numerous barriers impede fish migration along the entire river network. Using combined data from two fish monitoring programs in Switzerland, we applied an exhaustive cross-validation procedure to select a set of environmental variables with the highest (out-of-sample) predictive performance for the PAM and ZIM for fish density (individuals/m2) of the seven most prevalent fish taxa (Salmo spp., Cottus spp., Squalius spp., Barbatula spp., Barbus spp., Phoxinus spp., Gobio spp.). We used these variables to develop a PAM and ZIM for each taxon that accounts for differences in sampling methods and sampling effort. We quantified the quality of fit during calibration using all samples and predictive performance during 5-fold cross-validation of each model.Results show that stream temperature and stream morphology within the accessible habitat commonly appear among the best predictive presence-absence models for multiple taxa. Spatial variables that account for migration barriers and quantify morphological conditions within the accessible habitat were selected for 6 out of 7 taxa. The selected PAMs performed well for all taxa with an intermediate prevalence (10–40%), with an explanatory power (D2) of between 0.32 - 0.37 during calibration using all samples and only minor decreases in explanatory power during cross-validation (D2= 0.34 – 0.44). As expected, the PAM for the highly prevalent Salmo spp. (91%) failed to predict the few absence data points. By contrast, the ZIM model performed best for Salmo spp., with a standardized likelihood ratio of 1.56. For all other taxa besides Barbus spp. the ZIM models also had likelihood ratios above one, indicating a better predictive performance than the null model. We hope this study stimulates the development and application of fish species distribution models based on prior knowledge of causally linked environmental variables and incorporating observation errors to improve their predictive performance. This can facilitate learning from biomonitoring data to support management.

Highlights

Freshwater ecosystems such as lakes and rivers are rich in biodi versity, over-exploitation places freshwater ecosystems at greater risk of habitat destruction and degradation than their terrestrial and marine counterparts (Dudgeon et al, 2006; Vorosmarty et al, 2010)
We propose two distinct statistical models to predict the occurrence of different fish taxa, namely a presence-absence model (PAM), and a zero-inflated model (ZIM) to predict fish density, respectively
The ZIM models of several other fish taxa include a positive response of the predicted counts to the accessible habitat area and/or a negative response to overhead shelter (HidingSpots)

Summary

Introduction

Freshwater ecosystems such as lakes and rivers are rich in biodi versity, over-exploitation places freshwater ecosystems at greater risk of habitat destruction and degradation than their terrestrial and marine counterparts (Dudgeon et al, 2006; Vorosmarty et al, 2010). Studies have emphasized the need for additional biomonitoring efforts to more accurately characterize the geographic distribution and population trends of freshwater fish species to (1) improve future risk assessments, (2) identify underlying environmental drivers of species’ distributions, and (3) better inform stream management (Gozlan et al, 2019; Vorosmarty et al, 2010). Additional sampling efforts may increase the proportion of fish caught (i.e., reducing observation error), including the placement of nets at the start and end points of the fished area to prevent individual fish from escaping prior to sampling, and performing multiple electrofishing “passes” or “rounds” to accurately quantify the number of individuals in a reach. Multiple fishing rounds can provide useful data for fisheries management, including population estimates that can be derived based on the number of fish captured in successive fishing rounds and on prior knowledge of the probability of capturing an individual of a given species (e.g., Carle and Strub, 1978)

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