Identifying Translocation Sites for a Climate Relict Population of Finescale Dace. Issue 2 (14th February 2022) - Booher, Evan C. J.; Walters, Annika W.
Abstract: Translocation is a management strategy that seeks to address threats to fish and wildlife populations by establishing new populations in ecologically suitable areas. Populations of Finescale Dace Chrosomus neogaeus in the Great Plains may benefit from translocation, as they exhibit a climate relict natural history that has led to a disjunct distribution and minimal dispersal opportunities. We assessed the translocation suitability of sites for Finescale Dace in the Belle Fourche River basin, Wyoming–South Dakota, using a ranking approach for output from multiple analyses. We used multivariate analysis to evaluate dissimilarity in fish occurrence and habitat between sites with and without Finescale Dace in contemporary surveys (2018–2019; n  = 68). We further evaluated the capacity for sites to support Finescale Dace under base case and future climate change scenarios using the predicted probability of occurrence ( P ) from species distribution models (SDMs) fitted with basinwide fish occurrence data from surveys conducted in 2008–2019 ( n  = 124) and spatially continuous environmental variables, including forecasted stream temperature scenarios. Sites with Finescale Dace tended to occur close to standing waterbodies, contained emergent vegetation cover, and did not exhibit large overlap in species‐space with either native or nonnative species. Predicted P of Finescale Dace exhibited nonlinear relationships with mean August stream temperature, channel slope, and base flow index. The amount of suitable habitat (i.e., high predicted P ) declined with forecasted stream warming scenarios in the SDMs. This study highlights the utility of using field observations, historical data, and forecasted climate change scenarios to assess translocation site suitability and inform management of at‐risk native fish populations, and the results may be transferable to other populations with limited data or restricted distributions.