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Morphometric trait measurements of Arctic char in foothill lakes of arctic Alaska

Citation

Klobucar, Stephen (2022), Morphometric trait measurements of Arctic char in foothill lakes of arctic Alaska, Dryad, Dataset, https://doi.org/10.5061/dryad.80gb5mkqc

Abstract

Polymorphism facilitates coexistence of divergent morphs (e.g., phenotypes) of the same species by minimizing intraspecific competition, especially when resources are limiting. Arctic char (Salvelinus sp.) are a Holarctic fish often forming morphologically, and sometimes genetically, divergent morphs. In this study, we assessed the morphological and genetic diversity and divergence of 263 individuals from seven populations of arctic char with varying length-frequency distributions across two distinct groups of lakes in northern Alaska. Despite close geographic proximity, each lake group occurs on landscapes with different glacial ages and surface water connectivity, and thus were likely colonized by fishes at different times. Across lakes, a continuum of physical (e.g., lake area, maximum depth) and biological characteristics (e.g., primary productivity, fish density) exists, likely contributing to characteristics of present-day char populations. Although some lakes exhibit bimodal size distributions, using model-based clustering of morphometric traits corrected for allometry, we did not detect morphological differences within and across char populations. Genomic analyses using 15,934 SNPs obtained from genotyping-by-sequencing demonstrated differences among lake groups related to historical biogeography, but within lake groups and within individual lakes, genetic differentiation was not related to total body length. We used PERMANOVA to identify environmental and biological factors related to observed char size structure. Significant predictors included water transparency (i.e., a primary productivity proxy), char density (fish·ha-1), and lake group. Larger char occurred in lakes with greater primary production and lower char densities, suggesting less intraspecific competition and resource limitation. Thus, char populations in more productive and connected lakes may prove more stable to environmental changes, relative to food-limited and closed lakes, if lake productivity increases concomitantly. Our findings provide some of the first descriptions of genomic characteristics of char populations in arctic Alaska, and offer important consideration for the persistence of these populations for subsistence and conservation.

Methods

We sampled arctic char in 2016 (May-Sept) and 2017 (May) via gill nets and hook-and-line sampling. We used eight panel, experimental benthic gill nets (gill net mesh size range = 18 – 64 mm; Lester et al. 2009) set perpendicular to shore on the lake bottom, which extended from the littoral zone to bottom depths in open water areas, and checked nets every half hour to minimize mortalities. We conducted hook-and-line sampling alone through the ice (May) and concurrently with gill nets during open water periods. We used a mix of hook-and-line methods (e.g., lure size), in addition to experimental gill nets, to sample char across sizes classes. We sampled all lakes at similar time periods and used same sampling methods at each lake. Therefore, we are confident we sampled across all fish greater than approximately 115 mm (but see Finstad and Berg 2004).. For example, hook-and-line sampling was conducted both littoral and in open water habitats throughout the water column. For each arctic char captured, we measured, weighed, and then photo-documented the fish on a grid board for later trait measurement. We placed each fish flat, oriented head to the left, and photographed the fish from approximately 60 cm directly above the fish prior to releasing the char.

We subsequently used photos to make morphometric measurements (mm) including: snout length (SL), eye width (EW), maxilla length (ML), head depth (HD), head length (HL), body depth posterior (BDP), body depth anterior (BDA), post pelvic fin length (PPF) and caudal peduncle depth (CP), using the software program ImageJ (e.g., Skoglund et al., 2015; Figure 2). To account for allometric size differences, we first log-transformed measurements to reduce heterogeneity in variance, and then size-adjusted our measurement using an allometric growth formula (e.g., Senar et al., 1994):

 

log10Yi= log10Mi+b(log10Lm- log10Li)

 

where Yi is the size-adjusted trait value, Mi is the measured trait value, Li is the measured total length, b is the slope of the log-transformed measured trait (log10Mi) against log-transformed total length (log10Li), and Lm is the mean total length for all fish (e.g., all char for comparisons across lakes, all char within a lake for within lake comparisons).

Funding

National Science Foundation, Award: DEB 1026843

National Science Foundation, Award: DEB 1637459