Simulation and Empirical Analysis of Novel Sibship-Based Genetic Determination of Fish Passage

Improperly designed road-stream crossings can adversely affect the viability and resilience of stream fish populations by preventing or impeding movement. Genetic methods may be more cost- and time-efficient than direct tagging-based efforts to detect passage through road crossings. Traditional population-level genetic approaches lack resolution to detect individual movement at an ecological time scale in many situations. Here, we develop and test a new approach, which we term ‘sib-split’, based on the genetic analysis of full-siblings on opposite sides of potential barriers. We used simulations of a wide array of fish passage conditions to test the accuracy of the sib-split approach and to compare the accuracy of our approach to population-level analyses. We then applied this new method to two empirical case studies involving brook trout (Salvelinus fontinalis) movement with respect to barriers that varied in strength of effect on fish passage. Simulations revealed that both methods performed well under easy-to-detect conditions (accuracies > 98%).  Sib-split (accuracy = 98%) outperformed population-level analysis (accuracy = 87%) under difficult-to-detect (and more realistic for road crossings) simulated conditions.  Sib-split also provided more reliable and easily interpretable movement detection in both easy- and difficult-to-detect empirical case studies compared to population-level analyses.