Northeastern Naturalist Vol. 25, No. 4 D.M. Weaver, S.K. Ratten, S.M. Coghlan Jr., G.D. Sherwood, and J.D. Zydlewski 2018 599 2018 NORTHEASTERN NATURALIST 25(4):599–610 Age and Growth of a Native, Lightly Exploited Population of Coregonus clupeaformis (Lake Whitefish) in a Small Natural Lake in Maine Daniel M. Weaver1,*, Silas K. Ratten1, Stephen M. Coghlan Jr.1, Graham D. Sherwood2, and Joseph D. Zydlewski3,1 Abstract - We assessed annual growth of Coregonus clupeaformis (Lake Whitefish) from a natural, lightly exploited population in a small lake in northern Maine using observed and back-calculated length-at-age data. We sampled Lake Whitefish from Clear Lake, ME, with gill nets and extracted otoliths from 57 fish. We incorporated age-at-length data into a von Bertalanffy growth function, which we employed to model growth trajectories from individual fish. We used these estimates to evaluate length-at-age variability within this population. Ages for Lake Whitefish varied from 8 y to 30 y. Among all fish, we characterized incremental growth by an average-growth coefficient of K = 0.156 and an estimated L∞ of 484 mm. The oldest individuals demonstrated the slowest incremental growth (K = 0.106) when compared to younger cohorts (K = 0.218). We observed an inverse relationship between L∞ and K and the estimated age-at-capture (R2 = 0.178 and 0.723, respectively), which suggests relatively slow growth and a smaller maximum size for the longest living members of the population. Our estimated parameters serve as a reference to inform management of populations of Lake Whitefish. Introduction Fish growth rates and trajectories are important correlates of survival, size, age at maturity, and longevity, and may indicate surplus energy allocated towards somatic growth or reproduction (Beverton and Holt 1959, Charnov 1993, Ware 1980). Furthermore, age and growth data are critical components used to inform management and conservation planning for monitoring populations and shaping harvest strategies for commercial or recreational fisheries (Hilborn and Walters 1992, Isely and Grabowski 2007). Many of the world’s fisheries are overexploited (FAO 2016), but describing a species’ growth parameters in the absence of strong fishery pressure allows for greater predictive power to estimate changes in population dynamics from management strategies, recruitment success, and effects from environmental factors. In North America, Coregonus clupeaformis (Lake Whitefish) is distributed from northern Maine to the Great Lakes’ region, northwest into interior Canada and Alaska, and eastward into Labrador (Evans et al. 1988). Lake Whitefish support 1Department of Wildlife, Fisheries, and Conservation Biology, University of Maine, Orono, ME 04469. 2Gulf of Maine Research Institute, Portland, ME 04101. 3US Geological Survey, Maine Cooperative Fish and Wildlife Research Unit, Orono, ME 04469. *Corresponding author - daniel.weaver@maine.edu. Manuscript Editor: Jay Stauffer Northeastern Naturalist 600 D.M. Weaver, S.K. Ratten, S.M. Coghlan Jr., G.D. Sherwood, and J.D. Zydlewski 2018 Vol. 25, No. 4 commercial, recreational, and subsistence fisheries, and are a valuable economic resource in the Great Lakes (Fleischer 1992, Spangler 1970) and throughout much of Canada (Scott and Crossman 1973). Overexploitation (Mohr and Ebener 2005, Rennie et al. 2009), the introduction of nonnative species (DeBruyne et al. 2008, Herbst et al. 2013, Hoyle 2005), and habitat destruction (Bronte et al. 2003) have caused declines in many populations, although some recent evidence hints at local recovery (e.g., in the Chaumont Bay area of Lake Ontario; McKenna and Johnson 2009). A thorough understanding of growth and age structure of Lake Whitefish is necessary to guide conservation and management of the fishery . In contrast to populations in the Great Lakes’ region, Lake Whitefish in Maine are found in small natural lakes and are not subject to commercial harvest. These populations experience minimal impact from recreational fisheries, although historically, their exploitation for subsistence has waxed and waned with human settlement and establishment of logging camps (Basley 2001, Wood 2016). Despite presumably low levels of fishing mortality, populations of Lake Whitefish in Maine have still suffered declines and extirpation, perhaps due to habitat degradation and interactions with invasive Osmerus mordax (Mitchill) (Rainbow Smelt) and landlocked Salmo salar L. (Atlantic Salmon) (Basley 2001, Gorsky and Zydlewski 2013). The characterization of unexploited populations of Lake Whitefish in these small lakes, however, may reveal novel dynamics that provide fisheries managers with benchmark data to inform existing management strategies (Hilborn and Walters 1992). We determined the age and size structure of a natural, lightly exploited population of Lake Whitefish in a small lake in Maine. Field-Site Description We studied a population of Lake Whitefish in Clear Lake (253 ha) located in the unorganized township T10 R11 WELS, Piscataquis County, ME (46◦31'16.02''N, 69◦7'33.97''W; Fig. 1). Clear Lake is an oligotrophic lake with a mean depth of 8.8 m and a maximum depth of 26.2 m (Lake Stewards of Maine 2011). The fish assemblage of Clear Lake consists of 13 game and nongame species (Table 1). The assemblage is characteristic of other natural lakes in the region, though Rainbow Smelt are a recent nonnative addition. Methods We sampled fish during the summer of 2011with 3 identical 122-m experimental gill nets (3.8–8.9-cm mesh size). We euthanized all fish with buffered tricaine methanesulfonate (Institutional Animal Care and Use Committee protocol number A2011-06-02). For all captured Lake Whitefish, we measured total length to the nearest mm and mass to the nearest 0.1 g, determined sex, and removed sagittal otoliths. We included a total of 57 otoliths for age and growth analysis. Otolith removal and preparation We employed sagittal otoliths to examine fish age (Herbst and Marsden 2011). We wiped clean, air dried, and stored all otoliths, after using foceps to remove Northeastern Naturalist Vol. 25, No. 4 D.M. Weaver, S.K. Ratten, S.M. Coghlan Jr., G.D. Sherwood, and J.D. Zydlewski 2018 601 them from the fish. To facilitate sectioning, we embedded the otoliths in Epothin epoxy resin (Electron Microscopy Sciences, Hatfield, PA) and sectioned them with an IsoMet low-speed saw (Buehler, Lake Bluff, IL). We cut 1-mm–thick sections transversely through the otolith core, positioned sections on glass microscope Figure 1. Map of Maine. Inset indicates location of Clear Lake. Table 1. Fish species present in Clear Lake, ME. Common name Scientific name Blacknose Dace Rhinichthys atratulus (Hermann) Brook Trout Salvelinus fontinalis (Mitchill) Brown Bullhead Ameiurus nebulosus (Lesueur) Burbot Lota lota (L.) Creek Chub Semotilus atromaculatus (Mitchill) Lake Chub Couesius plumbeus (Agassiz) Lake Trout Salvelinus namaycush (Walbaum in Artedi) Lake Whitefish Coregonus clupeaformis (Mitchill) Northern Red Belly Dace Phoxinus eos (Cope) Rainbow Smelt Osmerus mordax (Mitchill) Slimy Sculpin Cottus cognatus Richardson Three Spine Stickleback Gasterosteus aculeatus L. White Sucker Catostomus commersonii (Lacepède) Northeastern Naturalist 602 D.M. Weaver, S.K. Ratten, S.M. Coghlan Jr., G.D. Sherwood, and J.D. Zydlewski 2018 Vol. 25, No. 4 slides with Crystalbond adhesive (SPI Supplies, West Chester, PA), and sanded and polished them lightly to improve visual clarity. Otolith analysis We imaged otoliths with a Nikon Digital Sight DS-5M digital camera (Nikon Inc., Melville, NY) interfaced with a dissecting microscope (Nikon SMZ800). Images were captured using incident (fluorescent) lighting and analyzed with Alicona– TEX–Basic imaging software (version 1.4.2; Alicona Corporation, Bartlett, IL). We used as a training data-set otoliths from known-age hatchery-stocked Lake Whitefish from St. Froid Lake, ME, and followed the methods reported by Mills and Chalanchuk (2004). Staff from the Michigan Department of Natural Resources, Charlevoix Research Station (Charlevoix, MI) externally validated a subset of aged otoliths from Clear Lake. We employed Image J software (version 1.8.0; Research Services Branch, National Institute of Health, Bethesda, MD; Abramoff et al. 2004) to obtain measurements of annular increments. We created growth transects at a ~45-degree angle towards the dorsal surface of the otolith. Each pair of light (hyaline) and dark (opaque) growth zones visible with transmitted light constituted 1 y of fish growth. We measured the distance between opaque zones (annulus to annulus) as an indicator of annual growth in body length. We followed Fraser–Lee methods (including a standard intercept-correction factor) to back-calculate lengths-at-age for individual fish, and provide individual growth trajectories throughout the lifetime of the fish (Isely and Grabowski 2007). Growth model We performed retrospective increment-analysis on otoliths to reconstruct growth histories of individual Lake Whitefish and used the von Bertalanffy growth function (VBGF; von Bertalanffy 1938) to describe patterns in lifetime growth: Lt = L∞(1 - e-K[t - t0]), where Lt is the mean length of fish at time t (years), L∞ is the theoretical maximum mean asymptotic length at age, K is the Brody growth coefficient that describes the decline in the growth rate as an individual approaches L∞, and t0 is the theoretical age at which body length is zero (Isley and Grabowski 2007). We incorporated back-calculated lengths at age of individual fish into a VBGF. We used nonlinear least squares to estimate parameters for individual fish. We initialized starting values of the parameters of the model by designating L∞ as the maximum total length in the observed data, K = 0.2, and t0 = 0. We averaged parameter values across all fish to arrive at a model describing the average growth pattern. To better account for the variation in growth trajectories and compare VBGF parameters among old and young individuals and also to reduce the effect of individual variability described above, we grouped fish into 3 arbitrary age categories (8–10 y, 11–15 y, and >15 y) to depict young, middle-aged, and older fish. We also based categories on sufficient sample size for each age category to obtain a precise mean value sufficient for comparison. We conducted a 1-way analysis of variance Northeastern Naturalist Vol. 25, No. 4 D.M. Weaver, S.K. Ratten, S.M. Coghlan Jr., G.D. Sherwood, and J.D. Zydlewski 2018 603 (ANOVA) to assess differences in mean growth parameters L∞ and K among the 3 age categories. We also analyzed the relationship between those 2 growth parameters and the estimated age at capture of all fish with least squares linear regression. We conducted all parameter estimations and statistical tests in the statistical package RStudio (version 1.1.447, RStudio, Boston, MA). For all tests, we set P < 0.05 as the threshold for statistical significance. We conducted a Tukey post hoc test with adjusted family-wise error rates to further examine ANOVA tests with a significant age-category effect. Results Fish capture Lake Whitefish varied in age from 8 y to 30 y and in total length at capture from 370 mm to 514 mm (Fig. 2, Table 2). All ages <8 y and several intermediate age classes were not represented in the sample. Few (6) old-age fish (>20 y old) were captured, and this age class comprised a small component of the wild population we studied. Back-calculation methods provided 822 lengths-at-ages for analysis (Fig. 2). Individual growth trajectory Our inspection of growth trajectories of individual fish within each sample revealed substantial variation among curvature (K) and/or asymptotic length (L∞) Figure 2. Number sampled (n), estimated capture age, and back-calculated size-at-age of Lake Whitefish from Clear Lake, ME. Bolded values indicate the mean measured captured length (mm) of fish (n) sampled for estimated capture age. Northeastern Naturalist 604 D.M. Weaver, S.K. Ratten, S.M. Coghlan Jr., G.D. Sherwood, and J.D. Zydlewski 2018 Vol. 25, No. 4 values among young and old fish (Fig. 3A). We observed that back-calculated lengths-at-age varied nearly 100 mm in age-1 total length. Variability in lengthat- age increased with increasing age. Back-calculated lengths-at-age demonstrated that older fish were also the slowest growing individuals, exhibiting smaller lengths-at-age in comparison to younger cohorts. This finding may result in an underestimate of mean K and/or an overestimate of mean length-at-age (Lt), which are otherwise useful in describing the average growth pattern of a population. Table 2. Mean total length and mass, numbers of males and females, and mean (± SD) L∞ and K growth parameters of 3 age groups among Lake Whitefish sampled in Clear Lake, ME. Superscripted letters identify significantly different L∞ and K parameters among age groups from a Tukey post-hoc test. Average total Average # of # of Age group length (mm) mass (g) males females L∞ ± SD (mm) K ± SD 8–10 400.2 678.8 11 9 457 ± 36A 0.218 ± 0.038A 11–15 453.0 1048.5 5 10 496 ± 36B 0.146 ± 0.019B >15 446.5 988.7 14 8 501 ± 27B 0.106 ± 0.032C All individuals 433.6 905.5 30 27 484 ± 38 0.156 ± 0.058 Figure 3. Sampled Lake Whitefish from Clear Lake, M E , d e p i c t i n g (A) length-at-age growth trajectories using Fraser–Lee back calculation and (B) back-calculated lengths-atage and von Bertalanffy growth curve describing the average growth (all sampled fish) with associated parameters. Northeastern Naturalist Vol. 25, No. 4 D.M. Weaver, S.K. Ratten, S.M. Coghlan Jr., G.D. Sherwood, and J.D. Zydlewski 2018 605 Growth model The predicted total length (mm) was described by the VBGF as Lt = 484(1 - e-0.156[t - 1.98]). When we plotted back-calculated lengths-at-age along with the VBGF curves, the influence of older and slower-growing fish was evident (Fig. 3B). Mean L∞ and K parameters differed across the 3 age categories (P < 0.05; Table 2). Tukey post hoc comparisons of the 3 categories indicated that the 8–10 y-old group (L∞ = 457 ± 36 mm) had a lower asymptotic length when compared to the 11–15-y-old group (L∞ = 496 ± 36 mm) and the >15-y-old group (L∞ = 501 ± 29 mm). Tukey post hoc comparisons indicated that all age groups differed from one another in growth coefficients (Table 2). We also observed a strong negative relationship between K and estimated age-at-capture (n = 57, R2 = 0.723, P < 0.05; Fig. 4). Conversely, we observed a modest positive relationship between L∞ and estimated age-at-capture (n = 57, R2 = 0.178, P < 0.05; Fig. 4). Discussion Few studies have examined unexploited or lightly exploited populations of Lake Whitefish in their native range (see Healey 1975, Johnson 1976, Mills et al. 2004 as examples). We examined the age and growth of Lake Whitefish in a small oligotrophic lake in northern Maine by estimating growth parameters for a population experiencing relatively little exploitation and perturbation. Our results can inform management agencies regarding the growth dynamics of Lake Whitefish to aid in Figure 4. Linear regressions with associated equations and R2 comparing L∞ and K von Bertalanffy growth parameters with the estimated age-at-capture for all sampled Lake Whitefish in Clear Lake, ME. P < 0.05 for both regression analyses, which indicated a nonzero correlation. Northeastern Naturalist 606 D.M. Weaver, S.K. Ratten, S.M. Coghlan Jr., G.D. Sherwood, and J.D. Zydlewski 2018 Vol. 25, No. 4 developing fisheries-management plans by serving as a reference for commercially exploited stocks. Our VBGM demonstrated a general pattern seen in many fish populations, whereby rapid growth in early life stages resulted in earlier maturity, faster decline in growth rate (high K), and decreased life span compared to slower-growing individuals (Alm 1959, Beverton and Holt 1959, Hutchings 1993). Our estimates of average L∞ and K values (484 mm and 0.156 respectively) were consistent with reported values for a nonnative unexploited population of Lake Whitefish (L∞ = ~500 mm; K = 0.14–0.15; Hosack and Hansen 2014) and observed growth patterns for native unexploited populations (Healey 1975, Mills et al. 2004) as well as exploited populations or stocks that are commercially fished (L∞ = ~500–800 mm, K = 0.12–0.84; Bronte et al. 2003, Chu and Koops 2007, Cook et al. 2005, Zhu et al. 2016). In theory, estimates of K among exploited populations may be higher than for unexploited populations because larger, older fish are generally harvested first. We observed a maximum age of 30 y, which is consistent with other observations of longevity in this species (Barnes and Power 1984, Herbst et al. 2011, Mills et al. 2004). In contrast, studies of commercially exploited populations in the Great Lakes generally estimated lower maximum ages varying from 5 y to 10 y (Cook et al. 2005, DeBruyne et al. 2008, Healey 1978); however, older fish (17–20 y old) are occasionally harvested (Bronte et al. 2003, Schorfhaar and Schneeberger 1997). Harvest has the potential to shape the size structure and sustainability of a fishery (Post et al. 2002). Lake Whitefish from Clear Lake may mature later, live longer, and defer growth at older ages, compared to exploited populations that may grow quickly and reach sexual maturity earlier at a smaller size (se nsu Healey 1975). We observed substantial variation in length-at-age among individual fish, demonstrated by a wide age-distribution and growth trajectories of individuals that diverged with increasing time at large (Table 2, Fig. 3A). Based on our VBGF trajectories, size estimates for early age classes (i.e., 1–5 y) derived from older fish at capture were markedly smaller (and had a lower growth coefficient; K) than those derived from fish captured at a younger age. Although this finding suggests differences in the growth trajectories of fish based on longevity, it is important to consider the possibility of this difference being an artifact of our sampling. Similar observations a have been attributed to Lee’s phenomenon, a pattern in which backcalculated lengths are smaller than actual lengths, caused by increased error in older fish age (Duncan 1980, Schirripa 2002). Thus, our estimated growth trajectories for older fish may need to be interpreted with caution. Cautions withstanding, our regression analyses revealed a marked decrease in K with age-at-capture and an increase in L∞. Such observations were congruent with the hypothesis that longerlived individuals grew slower and may have an increase in maximum size (Alm 1959, Beverton and Holt 1959, Hutchings 1993). Invasive species may compete with native species. Research from Lake Erie suggests Rainbow Smelt reduced abundance and growth of Lake Whitefish through predation and competition for resources (Oldenburg et al. 2007). In Europe, species of native Coregonus lavaretus (L.) (European Whitefish) have depressed growth and reduced abundance due to the introduction of other congener species such as Northeastern Naturalist Vol. 25, No. 4 D.M. Weaver, S.K. Ratten, S.M. Coghlan Jr., G.D. Sherwood, and J.D. Zydlewski 2018 607 Rutilus rutilus (L.) (Roach; Raitaniemi et al. 1999) and Coregonus albula (L.) (Vendace; Bhat et al. 2014). Clear Lake has an established population of Rainbow Smelt; however, the effects that population may have on recruitment and growth of Lake Whitefish is unknown. Other work has demonstrated positive relationships among prey density, growth, and survival of larval and adult Lake Whitefish (Brown and Taylor 1992, Lumb et al. 2007), which may be reduced in the presence of competing invasive species. Size and age structure are critical components used to make informed decisions regarding fisheries management (Pope et al. 2010). Our study described the age and growth of a lightly exploited population of Lake Whitefish in a small Maine lake. Our work complements existing studies that examined lightly exploited or unexploited populations (e.g., Healey 1975, Hosack and Hansen 2014, Johnson 1976, Mills et al. 2004). In addition, our work directly allows comparisons to populations that are exploited commercially (e.g., the Great Lakes; DeBruyne et al. 2008, Rennie et al. 2009, Wang et al. 2008), or in the process of recovery (Herbst et al. 2011). Quantifying age and growth parameters for Lake Whitefish can aid with the devel - opment of informed strategies for the conservation and management of populations and stocks. Acknowledgments This research was supported in part by the Maine Department of Inland Fisheries and Wildlife and US Department of Agriculture National Institute of Food and Agriculture, Hatch project number ME0-8367-0H through the Maine Agriculture and Forest Experiment Station (Publication Number 3627). Mike Brown, John Boland, David Basley, Frank Frost, Jeremiah Wood, and Derrick Cote, from the Maine Department of Inland Fisheries and Wildlife, provided technical and logistical support. We also thank Greg LaBonte and Ian Kiraly for field assistance. Logistical support was provided by US Geological Survey, Maine Cooperative Fish and Wildlife Research Unit, and the Department of Wildlife, Fisheries and Conservation Biology, University of Maine. 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