Modeling spatially explicit data provides a powerful approach to identify the effects of exogenous features associated with biological processes, including recruitment of stream fishes. However, the complex spatial and temporal dynamics of the stream and the species' reproductive and early life stage behaviors present challenges to drawing valid inference using traditional regression models. In these settings it is often difficult to ensure the spatial independence among model residuals---a key assumption that must be met to ensure valid inference. We present statistical models capable of capturing complex residual anisotropic patterns through the addition of spatial random effects within an inferential framework that acknowledges uncertainty in the data and parameters. Proposed models are used to explore the impact of environmental variables on Lake sturgeon (Acipenser fulvescens) reproduction, particularly questions about patterns in egg deposition. Our results demonstrate the need to apply valid statistical methods to identify relationships between response variables, e.g., egg counts, across locations, and environmental covariates in the presence of strong and anisotropic autocorrelation in stream systems. The models may be applied to other settings where gamete distribution or, more generally, other biotic phenomena may be associated with spatially dynamic and anisotropic processes.