Modeling lamprey distribution using flow, geomorphology, and elevation in a terminal lake system.

Abstract

Objective: Lampreys are an ecologically important group of fishes; however, several species are imperiled and lack key distribution and habitat data, particularly those in arid and semi-arid aquatic ecosystems. The terminal Goose Lake Basin, U.S.A. is home to two such species, the Goose Lake Lamprey, Entosphenus sp. (formally undescribed), and the Pit-Klamath Brook Lamprey, E. lethophagus. Species distribution models (SDMs) are useful for identifying key habitats; however, SDMs are subject to accuracy impairments caused by spatial autocorrelation and scale mismatches-both exacerbated by the hierarchical structure of dendritic stream networks. Our objective was to examine factors influencing lamprey distribution for both species in the Goose Lake Basin across multiple scales.

Methods: We integrated fish count and presence-absence data from five previously collected surveys and relevant habitat variables sourced from publicly available, geospatial datasets to build logistic regression models. To account for potential mismatches of scale, we compared three sample grains for slope and sinuosity (i.e., stream segment lengths: 250, 500, and 1,000 m), and two scales of elevation (site and watershed). We accounted for spatial autocorrelation by incorporating network-based and Euclidean spatial dependencies using a spatial stream network (SSN) modeling approach. Using the best-fit spatial and non-spatial models, we predicted basin-wide lamprey distribution.

Result: Flow, slope, and sinuosity at our largest sample grain (1,000 m), and area-weighted elevation at the watershed scale were associated with lamprey presence. The non-spatial model generally predicted lamprey presence among sinuous, low-gradient streams, whereas the spatial model, which identified Euclidean and flow-connected spatial relationships, predicted contiguous patches with a high probability of occurrence near areas with previously observed presences.

Conclusion: Our study revealed ecological relationships and produced an accurate, basin-wide SDM. Prediction and inference were both improved after accounting for spatial relationships across multiple scales. Developing accurate and efficient modeling strategies that incorporate the hierarchical structure inherent to stream ecosystems aids in the management and conservation of native fishes such as lampreys.

Impact statement: Lampreys are ecologically important and understudied; knowledge gaps regarding habitat and spatial distribution hinder their conservation. Our riverscape-based approach used multiple datasets to produce an accurate species distribution model linking lamprey distribution to flow, slope, sinuosity, and elevation.