Determining when and how Cisco and Lake Whitefish recruitment can be reliably indexed to support evaluation, restoration, and management

Andrew E. Honsey (USGS, ahonsey@usgs.gov), Taylor Brown (Cornell), Ralphy Tingley (USGS), Erin Dunlop (OMNRF)

Long-term, standardized early life monitoring initiatives for Cisco (Coregonus artedi) and Lake Whitefish (C. clupeaformis) are needed to answer fundamental questions regarding recruitment bottlenecks and to evaluate outcomes of restoration interventions (Bunnell et al. 2023). A previously funded project, entitled “Development of conceptual early life history models and evaluation of sampling techniques in support of long-term monitoring for Cisco and Lake Whitefish,” (PI R. Tingley) organized a multi-faceted workshop to build conceptual models of Cisco and Lake Whitefish early life history, suggest best sampling methodologies, and refine understanding of mechanisms underlying survival at each life stage. Ultimately, a key uncertainty identified by workshop participants was determining when and how recruitment can be reliably indexed during early life-stages. Following a review of existing datasets and a critical evaluation of alternative sampling strategies, participants determined that Cisco and Lake Whitefish recruitment could likely be indexed from existing surveys. Workshop outcomes recommended the aggregation and analysis of these datasets to better understand when and how early life-stages (<2 years of age) of Cisco and Lake Whitefish should be monitored. Although several existing surveys sample Cisco and Lake Whitefish early life-stages across the Great Lakes, the degree to which these surveys effectively index recruitment (e.g., to sexual maturity, to the fishery) remains uncertain. Most workshop participants agreed that recruitment bottlenecks likely occur during the first year of life, highlighting the potential utility of surveys that observe age-1 coregonines for indexing recruitment (e.g., Vinson et al. 2023). However, age-1 Cisco and Lake Whitefish are difficult to sample in most lakes and represent mixed stocks, complicating the ability to monitor stock-specific trajectories. In contrast, larvae are relatively easy to sample and could provide stock-specific forecasts a year in advance of an age-1 index (e.g., Cunningham and Dunlop, 2023). However, participants disagreed as to whether larval surveys can reliably index recruitment because recruitment bottlenecks may occur during the larval stage. Identifying the earliest life stage at which future recruitment can be reliably predicted would improve the efficacy and efficiency of future monitoring efforts. Further, workshop participants disagreed on how to best sample each life stage. Direct comparisons among existing surveys have been problematic because many surveys target different life stages, use different sampling gears, and sample different habitats. For example, there was a lack of consensus as to the least biased method(s) to sample larvae, largely due to uncertainty in gear effectiveness and habitat associations. Larvae and young juveniles undergo multiple ontogenetic habitat shifts that affect their vulnerability to sampling gears; therefore, surveys utilizing multiple gears spanning these habitat shifts might produce more reliable indices of recruitment. However, early life-stage Cisco and Lake Whitefish habitat use (e.g., bottom depth, distance from shore) may also be dependent on the physical structure of available habitats, which differs among lakes (Brown et al. 2023). It is possible that sampling design recommendations will need to be tailored for specific lakes. Partitioning the methodological and environmental sources of variability among existing surveys would provide key insight into how early life-stages of coregonines should be monitored for the purposes of indexing recruitment. Here, we propose to synthesize data from existing early life-stage monitoring programs to determine which life stages and sampling strategies provide reliable indices of Cisco and Lake Whitefish recruitment across the Great Lakes. This project will directly leverage the outcomes of the 2023 Great Lakes Cisco and Lake Whitefish Early Life Stage Workshop to fill critical knowledge gaps identified therein. In doing so, this project will improve understanding of when Cisco and Lake Whitefish recruitment can be reliably indexed, which survey characteristics (e.g., gears deployed, habitats sampled) result in highly predictive indices, and how these differ between species and among lakes. These results will inform strategies for early life monitoring programs and deepen understanding of Cisco and Lake Whitefish ecology by identifying when important recruitment bottlenecks occur between species and among lakes.