Measuring Fish Condition: an Evaluation of New and Old Metrics for Three Species with Contrasting Life Histories

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Measuring Fish Condition: an Evaluation of New and Old Metrics for Three Species with Contrasting Life Histories Canadian Journal of Fisheries and Aquatic Sciences Measuring fish condition: an evaluation of new and old metrics for three species with contrasting life histories Journal: Canadian Journal of Fisheries and Aquatic Sciences Manuscript ID cjfas-2018-0076.R1 Manuscript Type: Article Date Submitted by the 02-Jul-2018 Author: Complete List of Authors: Wuenschel, Mark; National Marine Fisheries Service - NOAA, McElroy, W. ; Integrated Statistics Inc. Oliveira, Kenneth; UMASS Dartmouth, Department of Biology McBride, Richard; National Marine Fisheries Service - NOAA ENERGETICS < General, REPRODUCTION < General, Keyword: PHYSIOLOGY < General, LIPIDS < General, BIOMETRICS < General Is the invited manuscript for consideration in a Not applicable (regular submission) Special Issue? : Draft https://mc06.manuscriptcentral.com/cjfas-pubs Page 1 of 64 Canadian Journal of Fisheries and Aquatic Sciences 1 Measuring fish condition: an evaluation of new and old metrics for three species with 2 contrasting life histories 3 Mark J. Wuenschel1*, W. David McElroy2, Kenneth Oliveira3, and Richard S. McBride1 4 1 Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and 5 Atmospheric Administration Woods Hole, MA 02543, USA. 2Integrated Statistics Inc. Woods 6 Hole, MA 02543, USA. Under contract to: Northeast Fisheries Science Center, National Marine 7 Fisheries Service, National Oceanic and Atmospheric Administration, Woods Hole, MA 02543, 8 USA 3Department of Biology, University of Massachusetts Dartmouth. 9 [email protected], [email protected], [email protected], 10 [email protected] 11 Corresponding author: Mark J. Wuenschel,Draft e-mail [email protected]. Northeast 12 Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric 13 Administration, 166 Water Street, Woods Hole, MA 02543, USA. Phone (508)-495-2276, Fax 14 (508)-495-2258. 15 16 Abstract 17 Measuring fish condition should link ecosystem drivers with population dynamics, if the 18 underlying physiological basis for variations in condition indices are understood. We evaluated 19 traditional (K, Kn, hepatosomatic index, gonadosomatic index, energy density and percent dry 20 weight of muscle [%DWM] and liver [%DWL]) and newer (bioelectrical impedance analysis 21 [BIA] and scaled mass index [SMI]) condition indices to track seasonal cycles in three 22 flatfishes– winter (3 stocks), yellowtail (3 stocks), and summer flounder (1 stock) –with 23 contrasting life histories: in habitat, feeding, and reproduction. The %DWM and %DWL were 1 https://mc06.manuscriptcentral.com/cjfas-pubs Canadian Journal of Fisheries and Aquatic Sciences Page 2 of 64 24 good proxies for energy density (r2 > 0.96) and more strongly related to K, Kn and SMI than to 25 BIA metrics. Principal component analysis indicated many metrics performed similarly across 26 species; some confounded by size, sex, and maturity along PC1, others effectively characterizing 27 condition along PC2. Stock differences were along PC1 in winter flounder, reflecting different 28 sizes across stocks, whereas in yellowtail flounder differences occurred along PC2 related to 29 condition. These comparisons, within and across species, highlight the broad applicability of 30 some metrics and limitations in others. 31 32 Introduction 33 The physiological health and energetic status of fishes is increasingly evaluated for a broad range 34 of purposes; as a proxy for reproductiveDraft output (Marshall et al. 1999; 2003, 2006), to assess 35 responses to biological interactions (Marshall et al. 2004; Cade et al. 2008), ecosystem change 36 (Choi et al. 2004), as prey to higher trophic levels (Renkawitz et al. 2015), to categorize life 37 history types within a population (Larsen et al. 2017), and to inform population status for 38 management (Blackwell et al. 2000; Brosset et al 2017; Morgan et al. 2018) and conservation 39 (Stevenson and Woods 2006). Depending on the intended purpose (e.g. single species energy 40 content and reproductive potential, or multi-species ecosystem indicators) the desired 41 characteristic of the metric chosen will vary. The concept of overall ‘health’ of an organism is 42 somewhat ambiguous, and considered to be an integration of many factors acting at the sub- 43 organism level that are more clearly defined and quantifiable (e.g. fat content, RNA/DNA, 44 hemoglobin concentration, etc.). The goal of defining the ‘best’ states of condition in individuals 45 and populations, and deviations from this optima, has been a pursuit of fisheries biologists for 46 many years, and approaches can generally be classified as either morpho-physiological (e.g. 2 https://mc06.manuscriptcentral.com/cjfas-pubs Page 3 of 64 Canadian Journal of Fisheries and Aquatic Sciences 47 weight at a given length, hereafter referred to as morphological) or physiological-biochemical 48 (Shulman and Love 1999). To be useful, methods should 1- characterize functional features of 49 organisms or populations, 2- encompass the range of variability in the process examined, 3- be 50 representative of the population, and 4- be easily measured under field conditions (Shulman and 51 Love 1999). While physiological-biochemical methods have generally been more informative, 52 they require more effort and are impractical for field sampling and large sample sizes. Numerous 53 methods have been proposed to relate the physiological health (state of wellbeing, condition) of 54 fishes based on physiological-morphological characteristics, with the premise that heavier or 55 fatter is better (Le Cren 1951; Ricker 1975; Hayes and Shonkwiler 2001). The morphological 56 approach offers ease of collection, but still needs to be validated to some functional feature (e.g. 57 energy content), and often fails to accuratelyDraft reflect nutritional condition of individual fishes. 58 59 The nondestructive nature of morphological indices is appealing, especially if they can be related 60 to variables such as fat or energy content that are much more difficult to measure. Simple metrics 61 such as Fulton’s K relate the weight of an individual to that predicted from the cube of their 62 length; K = W/L3. Though Fulton’s K has been in use for many years (Nash et al. 2006), 63 problems related to the assumption of weight scaling as a cube of length have been demonstrated 64 (Cone 1989). Since the exponent of the weight-length relation deviates from 3 in most species, 65 the calculated Fulton’s K is dependent on size, invalidating comparison of values from samples 66 or individuals of different lengths. The relative condition factor (Kn) does not assume a length 67 exponent, but fits a length-weight relationship to the available data (sample or population). 68 Individual Kn is then calculated as the observed weight/predicted weight based on length (Le 69 Cren 1951); thus, a value of 1 indicates ‘average’ condition. One disadvantage of Kn is that 3 https://mc06.manuscriptcentral.com/cjfas-pubs Canadian Journal of Fisheries and Aquatic Sciences Page 4 of 64 70 given its ‘relative’ nature, when additional data is added a new predictive regression is required 71 which can change prior individual Kn values (e.g. with additional sampling or data, what was 72 considered ‘average’ condition may now be shown to be above or below average). This ‘internal’ 73 (study-specific) aspect of calculating Kn also makes it difficult to compare values across studies. 74 The relative weight index (Wr; Blackwell et al. 2000) addresses this reliance on an internally 75 estimated length-weight relation by using a standard equation for each species (i.e. a ‘global 76 mean’) and allows cross study comparison. Wr has been applied more commonly for freshwater 77 species, where it may be impractical to develop length-weight relations for every pond or lake. 78 More recently, Peig and Green (2009) proposed use of a scaled mass index (SMI), which is 79 based on the central principle of scaling. Compared to other (more traditional) morphometric 80 condition indices, the SMI was shown toDraft be a better predictor of fat and energy reserves in a 81 variety of organisms (small mammals, birds and snakes), and has since been applied to a broader 82 range of animals including amphibians (MacCracken and Stebbings 2012) and fishes (Camizuli 83 et al. 2014; Maceda-Veiga et al. 2014; Morita et al. 2015; Masse et al. 2016). 84 85 In addition to advances in analytic methods for morphological condition indices, new 86 technologies have emerged and been applied to quantify physiological condition, including total 87 body electrical conductivity (TOBEC; Scott et al. 2001), bioelectrical impedance analysis (BIA; 88 Cox and Hartman 2005), and the fatmeter (Crossin and Hinch 2005; Davidson and Marshall 89 2010; Schloesser and Fabrizio 2017). Both TOBEC and BIA operate on the principle that the 90 conductivity of an organism is determined mainly by its lean tissues (Scott et al. 2001). BIA has 91 been applied successfully to estimate whole body fat and energy in fish in several studies (Cox 4 https://mc06.manuscriptcentral.com/cjfas-pubs Page 5 of 64 Canadian Journal of Fisheries and Aquatic Sciences 92 and Hartman 2005; Cox and Heintz 2009; Hafs and Hartman 2011), but less successful in other 93 studies (Pothoven et al. 2008; Garner et al. 2012; Klefoth et al. 2013; Dibble et al. 2017). 94 95 Regardless of condition index chosen, there is a need to relate any index at different stages in a 96 fish’s life to standard physiological variables (e.g. fat or energy density, Davidson and Marshall 97 2010; Schloesser and Fabrizio 2017). For example, because condition indices, whether 98 morphometric or derived from physical [bioelectrical] properties, may reflect different aspects of 99 an individual’s physiologyand may be affected differently by both the reproductive state (on an 100 annual cycle; Robards et al. 1999) and reproductive mode of a given species. This study focuses 101 on three species with different life histories, particularly in terms of reproduction, which is 102 energetically demanding and likely to affectDraft fish condition.
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