Received: 14 August 2019 | Revised: 17 December 2019 | Accepted: 7 January 2020 DOI: 10.1111/jai.14014

TECHNICAL CONTRIBUTION

The why and how of determining length-weight relationships of from preserved museum specimens

Amanda Hay1 | Weiwei Xian2 | Nicolas Bailly3,4 | Cui Liang2 | Daniel Pauly4

1Australian Museum Research Institute, Australian Museum, Sydney, NSW, Abstract 2Institute of Oceanology, Chinese Academy The rationale and a strategy for the estimation of length-weight relationships (LWR) of Sciences, Qingdao, China using preserved specimens of less common fish species in museums is presented, 3Beaty Biodiversity Museum, University of British Columbia, Vancouver, BC, Canada along with preliminary results pertaining to 56 specimens and 31 species of fish from 4Sea Around Us, Institute for the Oceans the Australian Museum, Sydney, Australia, the Marine Biological Specimen Museum and , The University of British of Chinese Academy of Sciences in Qingdao, China, and the Beaty Biodiversity Columbia, Vancouver, BC, Canada Museum of the University of British Columbia, Vancouver, Canada. These results are Correspondence discussed in the context of the effects of seasonality (in fresh specimens) and preser- Amanda Hay, Australian Museum Research Institute, Sydney, NSW, Australia. vation (in museum specimens) on the estimation of LWR parameters. Email: [email protected] KEYWORDS data gaps, FishBase, Ichthyology, museum collections

1 | INTRODUCTION Equation (1) is not the only method available for estimating the a and b parameters of Equation 2 (Binohlan & Pauly, 2000); for exam- Length-weight relationships (LWR) are required for a wide variety ple, they can be estimated by non-linear regression (Saila, Recksiek, of studies on fish, notably to estimate standing biomass based on & Prager, 1988). However, these methods are all of limited use when visual census as performed by SCUBA (e.g., Edgar, Ward, & Stuart- dealing with non-commercial, rarer species that are not straightfor- Smith, 2018) for shallow-water species, by remote observation ve- ward to collect, e.g., deep-sea species, sufficient specimens may not hicles (ROV) for deep-water species (e.g., Stierhoff, Butler, Mau, be available for Equation (1) to be applied (see, e.g., Paxton, 1989). & Murfin, 2013) or more commonly Baited Remote Underwater In such cases, one can obviously use LWR available from a similar Video Stations (BRUVS) for both shallow and deep-water species, as done by Stierhoff et al. (2013) in a few cases. Here we (e.g., Cappo, Harvey, & Shortis, 2007; Letessier, Bouchet, & propose another approach, i.e., using preserved museum specimens Meeuwig, 2017). of fish to estimate LWR. We are aware of the biases that are con- Commercially exploited fish are usually available in large num- nected with this, both due to few specimens being used for each bers over a wide range of sizes such that LWR can be established LWR (in some cases, only one specimen; Figure 1b) and to pre- using linear regression of log weight on log length of the form. served fish not having the same weight as their fresh counterparts (Buchheister & Wilson, 2005; Hay, 1982; Hjort, 1977; Hunter, 1985; =α+ log10 W b log10 L (1) Kristoffersen & Salvanes, 1998; Leslie & Moore, 1986; Macdonald, Williamson, Patterson, & Herunter, 1997; Parker, 1963). whose parameters provide estimates of the exponent and multiplica- tive term in the relationship. = ⋅ b (2) W a L 2 | MATERIALS AND METHODS where a = antilog(α) (Figure 1a; see also Froese, 2006). Currently (August 2019), FishBase (Froese & Pauly, 2019) includes LWR parameters for nearly 5,500 species, which still implies that *For re-submission to the Journal of Applied Ichthyology; FishBase lacks LWR for over 29,000 species. For this proof-of-concept

J Appl Ichthyol. 2020;00:1–7. wileyonlinelibrary.com/journal/jai © 2020 Blackwell Verlag GmbH | 1 2 | HAY et al.

FIGURE 1 Different types of length- weight relationships (LWR). (a) Common LWR as obtained by measuring a large number of fish over a given period (but see Figure 2). (a): This typical LWR, whose parameters and confidence interval (CI) were estimated based on Equation 1, with a = 0.00375 (cm; TL) and b = 3.082 (from Dorel, 1986); (b): LWR based on one L/W data pair, and the assumption that b = 3. (c): The same as (b), but based on 3 L/W data pairs; an approximate CI could be added to the graph; (d): An absolute error of measurement will be magnified when it pertains to small specimens; hence, it is better to measure large specimens of a given species for methods (a) and (b)

TABLE 1 Standard (SL) and total length (TL) of 18 preserved fish specimens in 10 species held at the Australian Museum, Sydney (AMS) with the parameter a of length-weight relationships for which b = 3; the ‘a’ value pertain to SL or TL in cm (see text)

Species AMS number SL (mm) TL (mm) Weight (g) a (SL; cm) a (TL; cm)

Lepidogalaxias I.28905-001 29.5 35.3 0.11 0.0043 0.0025 salamandroides I.28905-001 33.5 42.7 0.25 0.0066 0.0032 salamandroides Lepidogalaxias I.28905-001 47 54 0.44 0.0042 0.0028 salamandroides Cyema atrum I.18570-001 108 - 1.3 0.0010 - Gigantura indica I.36460-002 139 260 3.55 0.0013 0.00020 Rondeletia loricata I.42718-001 99 108 25 0.026 0.020 munda I.43183-002 25 29.7 0.08 0.0051 0.0031 Galaxiella munda I.43183-002 24.6 28.5 0.08 0.0054 0.0035 Galaxiella munda I.43183-002 28.5 32.8 0.14 0.0060 0.0040 Howella brodiei I.21365-002 64 79 5.2 0.020 0.011 Howella brodiei I.21365-002 68 82 6.5 0.021 0.012 Howella brodiei I.21365-002 65 78 5.55 0.020 0.012 Toxotes chatareus I.20457-001 160 205 150 0.037 0.017 Toxotes chatareus I.20457-001 195 235 240 0.032 0.018 Heterodontus I.39888-001 - 430 540 - 0.0068 portusjacksoni Heterodontus I.3767 - 420 460 - 0.0062 portusjacksoni Orectolobus ornatus I.43621-003 - 860 3440 - 0.0054 Pseudotriakis microdon I.41474-001 - 1325 4580 - 0.0020

contribution, 18 preserved specimens of 10 species without LWR in Museum of the University of British Columbia in Vancouver, Canada FishBase were weighed and measured at the Australian Museum, (BBM; Table 3). Sydney, Australia (AMS; Table 1), 14 specimens in eight species were At all three institutions, any liquid preservative adhering to spec- weighted and measured in the Marine Biological Specimen Museum imens was removed with paper towels; the specimens were then of Chinese Academy of Sciences in Qingdao, China (Table 2), while gently straightened up, if necessary, measured to the nearest milli- 24 preserved specimens representing 13 species without LWR in meter (standard, fork and total length where feasible) and weighed FishBase were measured and weighed at the Beaty Biodiversity to the nearest 10th of a gram with a precision balance. HAY et al. | 3

TABLE 2 Standard (SL) and total length (TL) of 14 preserved fish specimens in 8 species held at the Marine Biological Specimen Museum (MBSM), CAS, Qingdao, China, with the parameter a of length-weight relationships for which b = 3; the ‘a’ value pertain to SL or TL in cm (see text)

Species MBSM number SL (mm) TL (mm) Weight (g) a (SL; cm) a (TL; cm)

Stegostoma fasciatum 3509 286 528 404 0.017 0.0027 Stegostoma fasciatum 3509 281 519 396 0.018 0.0028 Chiloscyllium plagiosum 3506 196 243 54.4 0.0072 0.0038 Narcine maculata 2152 371 395 716 0.014 0.0120 Narcine maculata 2152 365 390 702 0.014 0.0120 Chimaera phantasma 3609 380 560 323 0.0059 0.0018 Lethenteron camtschaticum 3650 - 436 136 - 0.0016 Lethenteron camtschaticum 3650 - 432 134 - 0.0017 batavianus 3699 254 318 1145 0.070 0.0360 Platax batavianus 3699 251 314 1139 0.072 0.0370 Halichoeres bicolor 5456 88 97 16.0 0.023 0.0180 Halichoeres bicolor 5456 85 94 15.0 0.024 0.0180 Halichoeres bicolor 5456 91 100 21.0 0.028 0.0210 Gymnothorax melanospilus 0228 - 232 20.4 - 0.0016

TABLE 3 Standard (SL) and total length (TL) of 24 preserved fish specimens in 13 species held at the Beaty Biodiversity Museum (BBM), UBC, with the parameter a of length-weight relationships for which b = 3; the a value pertain to SL or TL in cm (see text)

Species BBM number SL (mm) TL (mm) Weight (g) a (SL; cm) a (TL; cm)

Argyropelecus affinis 1962-0169-01 34 40 0.8 0.0204 0.0125 Argyropelecus affinis 1962-0169-01 48 58 2.3 0.0208 0.0118 Argyropelecus sladeni 2011-0633-01 43 - 1.4 0.0176 - Gila intermedia 1956-0078-01 43 50 1.4 0.0176 0.0112 Gila intermedia 1956-0078-01 44 53 1.5 0.0176 0.0101 Gila intermedia 1956-0078-01 53 63 2.7 0.0181 0.0108 Gila copei 2006-0085-01 83 97 8.9 0.0156 0.0098 Cyclopsetta querna 1959-0686-36 225 265 195 0.0171 0.0105 Cyclopsetta querna 1959-0247-32 300 350 464 0.0172 0.0108 Etrumeus micropus 1959-0657-01 157 179 46.2 0.0119 0.0081 Etrumeus micropus 1956-0028-01 238 283 203 0.0151 0.0090 Aristostomias scintillans 1965-0622-05 174 183 39.0 0.0074 0.0064 Dussumieria hasseltii 1963-1355-21 120 162 26.5 0.0153 0.0062 Chauliodus macouni 1965-0621-09 116 130 4.9 0.0031 0.0022 Sagamichthys abei 1962-0187-01 78 - 4.0 0.0084 - Sagamichthys abei 1965-0624-11 83 - 5.6 0.0098 - Sagamichthys abei 1964-0210-02 114 - 13.0 0.0088 - Talismania bifurcata 1962-0159-01 144 186 28.1 0.0094 0.0044 Bajacalifornia burragei 1962-0155-01 98 113 6.2 0.0066 0.0043 Bajacalifornia burragei 1962-0155-01 126 144 13.1 0.0065 0.0044 Hybognathus placitus 2012-0042-01 63 78 4.8 0.0192 0.0101 Hybognathus placitus 2012-0042-01 69 86 6.7 0.0204 0.0105 Hybognathus regius 1955-0482-02 48 63 1.8 0.0163 0.0072 Hybognathus regius 1955-0482-02 75 94 6.5 0.0154 0.0078 4 | HAY et al.

TABLE 4 Some studies on the shrinkage of fish due to preservation

Length Weight Time change change Species (stages) Preservation fluid (years) (%) (%) Source

Coregonus artedi (juv. & adults) 4% formalin 0.135 2.7 - Hile (1936); p. 217 Salvelinus fontinalis (juv. & adults) 10% formalin->70% alcohol 0.619 3.4 - Shetter (1936) Salmo trutta (juv. & adults) 10% formalin->70% alcohol 0.619 3.4 - Shetter (1936) Perca flavescens (juv. & adults) 10% formalin 1.542 0.0 −9.4 Stobo (1972) vulgaris (adults) 10% formalin 0.671 4.9 −7.0 Cadwallader (1974) Coregonus artedi (juv. & adults) 10% formalin 0.008 1.3 −1.8% Engel (1974) Perca flavescens (juv. & adults) 10% formalin 0.008 0.7 −5.0 Engel (1974) Squalus suckleyi (juveniles) 5% formaldehyde FW 2%o 0.027 4.2 - Jones and Geen (1977) Squalus suckleyi (juveniles) 5% formaldehyde SW 30%o 0.027 5.6 - Jones and Geen (1977) Lepomis macrochirus (juv. & adults) 10% formalin 0.189 1.3 −13.1 Yeh and Hodson (1975) Pomoxis annularis (juv. & adults) 10% formalin 0.189 0.6 −14.9 Yeh and Hodson (1975) Oreochromis mossambicus (juveniles) 10% formalin->70% alcohol 0.192 −1.7 −22.5 Billy (1982) O. mossambicus (juv. and adults) 10% formalin->70% alcohol 0.192 −0.9 −16.1 Billy (1982) Phoxinus phoxinus (adults) 5% formalin->70% alcohol 0.667 3.9 - Puigcerver (1999) Phoxinus phoxinus (adults) 5% formalin->70% alcohol 0.667 3.4 - Puigcerver, 1999 Micropterus salmoides (juveniles) 10% formalin 0.123 3.0 −12.0 Yokogawa (2009) Micropterus salmoides (juv. & adults) 10% formalin 0.468 0.0 −3.0 Yokogawa (2009) Lepomis macrochirus (juv. & adults) 10% formalin->70% ethanol 1.000 6.0 - Gaston, Jacquemin, and Lauer (2013) Lepomis cyanellus (juveniles) 10% formalin->70% ethanol 1.000 2.0 - Gaston et al. (2013) Tlaloc labialis (juv. & adults) 10% formalin->70% ethanol 2.083 2.4 26.1 Anzueto-Calvo, Velázquez- Velazquez, Matamoros, Cruz Maza, and Nettel-Hernanz (2017)

Because a range of sizes was, in most cases, not available to esti- fish larvae). In this preliminary study, we have only measured adult mate both the a and b parameters of LWR, it was assumed that the specimens. However, we note that for future references, preserved LWR of all our relationships were isometric, i.e., that the parameter juvenile specimens may be measured and weighted where they are b = 3, which is a good approximation in the majority of cases (Froese, the only known representatives of a species. 2006). Thus, with a pair of L and W measurements from a given, preferably large specimen, the parameter a can be estimated from a = W L3 (Figure 1b). When several specimens (n) are available, an aver- 3 | RESULTS age value of a can be obtained by computing: Tables 1‒3 present the estimates of a, or ā based on the measurements n W ∑i=1 3 of preserved fish, all assuming a value of b = 3. These LWR have been a = L (3) n entered into FishBase, which has been modified to identify LWR based on preserved fish, to allow authors who routinely use LWR to choose as illustrated in Figure 1c. whether or not to include LWR based on preserved fish. Alternatively, Smaller fish are generally more difficult to handle, blot and mea- they can perform analyses based on such LWR, for example, to follow sure; thus care was taken to measure relatively large specimens, up on contributions cited above and in Table 4, which report on the as absolute measurement errors when pertaining to smaller fish changes in length and/or weight caused by preservation, as influenced have a larger effect on a LWR than when pertaining to larger fish by various factors (alcohol vs. formalin, age of the sample, etc.). (Figure 1d). Note, moreover, that this contribution does not deal with fish larvae, which tend to be strongly affected by preservation (Schnack & Rosenthal, 1978; Tucker & Chester, 1984). 4 | DISCUSSION There is nothing new about this approach, which is described as ‘Method 6’ for the case of 1 fish specimen, and ‘Method 7’ for Assuming a b value of 3 in all cases (i.e., a slope of 3 in plots such several in the FishBase manual (Binohlan & Pauly, 2000). What is as Equation (1) may appear to represent another source of bias. novel here is that it was applied to preserved specimens (but not However, we believe that this bias is negligible, at least when HAY et al. | 5

FIGURE 2 Variation of the parameters a and b of length-weight relationships (LWR). (a): Simulation of the seasonal trajectories of LWR based on monthly samples from a fish similar to Atlantic cod (Gadus morhua) exhibiting growth oscillation in both length and weight, as occur in the Northern hemisphere (adapted from Pauly, 2010); the simulation suggest that LWR that do not explicitly account for seasonal oscillations of growth in length and weight are biased. (b): The 31 published LWR for Atlantic cod estimated in different contributions (of which the extreme 2 are cited here), as included in FishBase as of August 2019. Note that log (a) inversely correlates with b, and that, therefore, the weights predicted by these 31 different LWR for average weights are not very different

compared with published LWR of fresh fish, which have values of a country/island’, clicking ‘Missing data’ will provide a list of species and b which will always depend on the season when the specimens lacking LWR. were sampled. This work highlights a new approach to gaining valuable applied In fact, while b = 3 is the mean and the mode of a large number of biological data from Museum collections. This work also provides published compilations of LWR (e.g., Bernardes & Wongtschowski, the opportunity to photograph the specimens that are measured and 2002; Carlander, 1969; Cinco, 1982; Yamagawa, 1994), these studies weighed, and to contribute the photos to FishBase, which not only also show that strong deviations from b = 3 frequently originate in lacks LWR but also photos of less-common fishes. studies covering a small range of sizes and/or few individuals (see also Froese, 2006). ACKNOWLEDGMENTS Another issue is that b values, when based on limited sampling Amanda Hay would like to thank Kerryn Parkinson for helping periods (e.g. monthly) oscillate seasonally, along with the corre- measure specimens. Weiwei Xian and Cui Liang thank Shanshan sponding estimates of a (Figure 2a). Indeed, this may be the key rea- Zhang and Wenlong Li for helping measure specimens in the Marine son why these parameters, even when estimated from data covering Biological Specimen Museum of Chinese Academy of Sciences in the entire range of sizes, and using hundreds of specimens, differ as Qingdao. Daniel Pauly and Nicolas Bailly thank the Sea Around Us, a much as they do. This is illustrated here by Figure 2b for Atlantic cod research initiative funded by a number of philanthropic foundations, (Gadus morhua) and by the LWR in FishBase for other well-studied notably the Oak and Marisla Foundations. We also thank Ms. Evelyn fishes. Liu for drafting Figures 1 and 2. Nicolas Bailly undertook this work The authors will continue to generate LWR for uncommon spe- under the BBM internal programme “Scientific valorization of the cies based on specimens in their respective museums or other in- collections”. stitutions and submit them to FishBase. All authors also hope that this contribution will inspire the staff of other institution to estimate DATA AVAILABILITY STATEMENT LWR for species without this key information in FishBase, based on The data that support the findings of this study are available from specimens that they hold, and which will improve the results of the the corresponding author upon reasonable request. Bayesian algorithm that FishBase uses for inferring LWR for species without one (Froese, Thorson, & Reyes, 2014). For this purpose, the ORCID authors will send to anyone asking for it an updated list of species Amanda Hay https://orcid.org/0000-0002-1335-2342 for which FishBase has no LWR. Alternatively, in the FishBase land- Nicolas Bailly https://orcid.org/0000-0003-4994-0653 ing page, after a country has been selected from ‘Information by Daniel Pauly https://orcid.org/0000-0003-3756-4793 6 | HAY et al.

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