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Article

The Relationship between Mean Length at Maturity and Maximum Length in Coral Reef Fish

by
Elaine Chu
* and
Daniel Pauly
Sea Around Us, Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Fishes 2024, 9(4), 130; https://doi.org/10.3390/fishes9040130
Submission received: 15 March 2024 / Revised: 6 April 2024 / Accepted: 7 April 2024 / Published: 9 April 2024
(This article belongs to the Special Issue Understanding and Managing the Spatial, Population and Ecological Dynamics of Coral Reef Fish)
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Abstract

:
This article proposes a mechanism that triggers first maturation and spawning in coral reef (bony) fish, which allows for predicting their length at first maturity. Thus, mean lengths at first maturity (Lm) and the corresponding maximum lengths (Lmax) in 207 populations of 131 species of coral reef fish were assembled and used to test the hypotheses that (a) there is, in coral reef fish, a single value of a size-related parameter acting as a trigger for their maturation and eventual spawning, and (b) that this single value is statistically the same as that published previously for other bony fish. The results, based on the assembled Lm and Lmax data and on estimates of the parameter D, which link the length of fish with the relative surface of their gills, covered 44 families and Lmax values ranging from 1.8 to 181.6 cm and confirmed that the threshold in (a) exists. Also, we assessed (in b) that this threshold value, i.e., LmaxD/LmD = 1.35 (±0.02), is not statistically different from similar estimates for other groups of teleosts, notably semelparous salmonids, cichlids, sturgeons and Chinese and Turkish freshwater and marine fish. One implication is that given ocean warming and deoxygenation, coral reef fish will not only be smaller than they currently are, but also mature and spawn at smaller sizes, and thus produce fewer, smaller eggs.
Keywords:
coral reef fish; GOLT; length at maturity; maximum length
Key Contribution: The main finding of this study is that in coral reef fish, the ratio of their metabolic rate at their maximum length (Lmax) over their metabolic rate at first maturity (Lm) has a threshold value that triggers their maturation and spawning. This value is the same as in other groups of teleosts. This suggests that with ocean warming and deoxygenation, coral reef fish will mature and spawn at smaller sizes, leading to fewer and smaller eggs being produced.

1. Introduction

The age and, particularly, the size when fish mature are important parameters of their life history and are important for fisheries management [1,2]. Compared to mammals and birds, fish mature at much smaller lengths (Lm) than the maximum lengths reached in the population to which they belong (Lmax), a feature even more pronounced when one deals with weight (W), where Wm << Wmax [3,4].
This “early maturation” of fish may have been the reason why ichthyologists and fisheries biologists have believed that the “energy” that was previously used in growth is, once maturity is reached, transferred to gonad development, slowing down their growth all the way until it ceases [5,6,7]. However, this belief, which has undoubtedly been reinforced by the perception of a transition in length growth curves, from fast to slow growth following first maturity (Figure 1a), cannot be upheld when growth curves in weight are considered (Figure 1b).
The notion that it is reproduction that slows down the growth of fish, which may be referred to as the “reproductive load hypothesis”, is also refuted (i) by every lone goldfish in a bowl, whose growth ceases at some point although they have never reproduced, (ii) by the fact that in 80% of fish species, it is the female who grow to larger sizes, although they have a bigger reproductive effort, and (iii) by the fact that sterile triploid fish do not exhibit higher growth rates than their fertile and diploid conspecifics [8]. There are other reasons why the “reproductive load hypothesis” is untenable [3,4], and the time has come to consider an alternative.
Fishes 09 00130 g001
Figure 1. Two versions of the effect of reproduction on fish growth. (a) Representation of the “Reproductive Drain Hypothesis” (RDH), i.e., the notion that reaching the size at first maturity causes previously “linear” growth (line 1) to decline due to “energy” previously used for somatic growth being transferred to the elaboration of gonads, with the dotted line 2 implying a small, and line 3 a strong, transfer of “energy” (modified from Figure 2 in Lester et al. [9]). (b) When growth in weight is considered, the weight at first maturity (Wm) in most species of fish is reached at a size where growth is accelerating, i.e., well below the weight at which the maximum growth rate is attained (at Wi), as illustrated for yellowbelly threadfin bream (Nemipterus bathybius), based on data in Li et al. [10]. This is incompatible with the RDH.
Figure 1. Two versions of the effect of reproduction on fish growth. (a) Representation of the “Reproductive Drain Hypothesis” (RDH), i.e., the notion that reaching the size at first maturity causes previously “linear” growth (line 1) to decline due to “energy” previously used for somatic growth being transferred to the elaboration of gonads, with the dotted line 2 implying a small, and line 3 a strong, transfer of “energy” (modified from Figure 2 in Lester et al. [9]). (b) When growth in weight is considered, the weight at first maturity (Wm) in most species of fish is reached at a size where growth is accelerating, i.e., well below the weight at which the maximum growth rate is attained (at Wi), as illustrated for yellowbelly threadfin bream (Nemipterus bathybius), based on data in Li et al. [10]. This is incompatible with the RDH.
Fishes 09 00130 g001
The hypothesis proposed by Pauly [11], based on 34 species and 56 populations of marine bony fish, to replace the ‘reproductive load hypothesis’ has since been shown to apply to a vast number of other species [12,13,14,15,16].
Here, two hypotheses based on Pauly [11] are tested for 131 species of coral reef fish: (a) that there is, in coral reef fish, a single value of a size-related parameter acting as a trigger for their maturation and eventual spawning, and (b) that this single value is statistically the same as that published previously for bony fish.
The underlying growth model considered here was proposed by Pütter [17], and has the form
dW/dt = HWd − kW
where dW/dt is the rate of growth, HWd is the rate of protein synthesis, which is dependent on the oxygen supplied by the gills, and kW is the spontaneous denaturation rate of protein, a process requiring no oxygen, but which removes “working” proteins from the bodies of fish, and which, therefore, requires these proteins to be resynthesized [18,19]. Important here is that the parameter d in HWd is related to the gill surface area (S, and hence oxygen supply) through a relationship is of the form S ∝ Wd (or respiration ∝ Wd), with d < 1.
The parameter d < 1 implies that, as weight increases, kW will increase faster than HWd, and that, when the rate of protein synthesis equals the rate of protein denaturation, growth ceases (at Wmax). The overwhelming majority of bony fish (i.e., excluding those breathing air) have d ranging between 0.6 and 0.9 [20,21], but always less than 1 [22,23].
It is commonly accepted that fish start maturing when environmental stimuli “trigger” the hormonal cascade that leads to maturation and spawning [24]. However, this does not explain the fact that long-lived fish, despite experiencing—as juveniles—multiple spawning seasons and, thus, being exposed to the same environmental stimuli, do not actually start spawning until later in life, when a critical size is reached [23].
Therefore, a size-related internal readiness event ought to occur before any external stimuli and their triggering effect are perceived. The hypothesis proposed by Pauly [11] is that this internal readiness is established, in an individual fish, when its metabolic rate (Qm) relative to its (maintenance) metabolic rate (Qmaint) decreases below a critical level (Qm/Qmaint). It is this readiness that causes the fish to start responding to the external triggers [23].
Pauly [11] demonstrated that LmaxD vs. LmD, with D = 3(1 − d), is algebraically equivalent to Qm vs. Qmaint and, based on a variety of marine fish species, that the critical level (Qm/Qmaint) is 1.36 (95% C.I. 1.22–1.53). This estimate was confirmed by studies that produced estimates not significantly different from 1.36, pertaining to 3 species and 51 populations of semelparous freshwater salmonids [12]; 7 species and 41 populations of cichlids [13]; 96 species and 24 populations of marine and freshwater fish from Chinese waters [14]; 22 species of sturgeons [15]; and 57 species and 120 populations of marine and freshwater fish from Turkish waters [16].
The ubiquity of this ratio suggests that this is a trait that has been conserved through millions of years of evolution. Here, we test this ratio on 207 populations in 131 coral reef fish species.

2. Materials and Methods

The maximum length (Lmax; fork length; in cm) and mean length at first maturity (Lm; fork length; in cm) of coral reef fish from various geographical locations were collected from the published literature on dioecious fish, i.e., hermaphroditic species—when known as such—were excluded. Care was taken to assemble data that (i) covered most families of coral reef fish (ii) originating from the Atlantic, Indian and Pacific Oceans, and the waters of both economically developed and developing countries, and (iii) which spanned a wide range of sizes. In total, 207 pairs were assembled and used for analysis. In cases where only the asymptotic length (Linf) was available, Linf was multiplied by 0.95 to obtain an approximate value of Lmax [25].
The Lmax values were then converted into Wmax estimates using the parameters (a, b) of the length–weight relationship (LWR) obtained from FishBase (www.fishbase.org) in the form of W = a·Lb. Length–weight relationships from the same locality were used when available. In cases where several LWRs were available (e.g., in Acanthurus chirurgus) or in cases where no LWRs were available for the species in question, the Bayesian estimates of a and b from FishBase were used, which account for seasonal variations and other sources of uncertainly in the LWR [26]. Also, note that the precision of the a and b estimates of the LWR had a minimal effect on the consideration that follows.
We used the empirical equation
d = 0.674 + 0.0357·log(Wmax)
Based on estimates of d from gill surface area and respiratory studies in 27 populations of 24 species of teleost fish ranging from guppies to tuna [18,27], we estimated d values with Wmax in g; then, D was computed from D = 3(1 − d) to simplify things.
Table A1 presents the compiled life history traits and the resulting LmaxD and LmD values for the 207 coral reef cases that were assembled for this study.
The mean ratio LmaxD vs. LmD was estimated as the slope of a regression of LmaxD vs. LmD, along with its 95% confidence interval (C.I.), by running a Bayesian regression model with the intercept forced at zero using the brm function in the brms R package in R Statistical Software (v4.3.1, [28,29]).
To test for the effect of phylogeny on the estimated value, the effect of phylogenetic biases was accounted for by associating the mean LmaxD and LmD of each species with the full phylogeny tree obtained from the Fish Tree of Life through the R package fishtree [30]. A number of species (n = 131 − 11 = 120) that were not available in the Fish Tree of Life were removed from further analysis. Using the brm function [29], we re-estimated the slope with and without the phylogenetic component.
Comparing the results of the regression models with and without the phylogenetic component should allow for testing whether the inclusion of shared evolutionary history between species is an important factor to consider in the relationship between LmaxD and LmD. Although the model with the phylogenetic component requires a Bayesian framework, it is comparable to the widely used phylogenetic generalized least squares regression [29]. Furthermore, by employing Bayesian methods to estimate these models, we are provided with the advantage of generating a distribution of the slopes (i.e., a posterior distribution), which enables better comparison among slope estimates.

3. Results

In total, 207 Lmax and Lm data pairs accounting for 131 species from 44 different families were collected. Out of the 131 species in the dataset of this study, 11 species did not have resolved phylogenetic positions on the Fish Tree of Life, leaving 120 species to be further analyzed separately with and without phylogeny taken into consideration.
Considering all LmaxD vs. LmD data pairs, the resulting slope was LmaxD = 1.35·LmD·(±0.02). For species that were on the Fish Tree of Life, but without phylogeny, the result was similar, with LmaxD = 1.34·LmD·(±0.03) (Figure 2a, Table 1). When phylogeny was considered, the resulting slope was LmaxD = 1.20·LmD·(±0.11), i.e., not statistically different, but with the mean exhibiting a bias that is discussed below (Figure 2b, Table 1).
Thus, Lm in coral reef fish can be estimated from Lm = Lmax/1.351/D, with the D value estimated from D = 3(1 − d) and d from Equation (2). As for its C.I., it can be estimated by using the standard error of 1.35, i.e., ±0.02. Note, however, that the uncertainty in Lm values obtained by this relationship is likely to be an underestimate, because, while it accounts for the uncertainty in the 1.35 ratio, it does not account for the uncertainly in Lmax and D.

4. Discussion

As was the case with previous tests, this study generated results compatible with the two-part hypotheses of Pauly [11] that in coral fish (i) the same relative individual size induces a readiness to perceive environmental stimuli that trigger maturation and spawning and (ii) that this relative size is not significantly different from Lm = Lmax/1.351/D.
More precisely, the slope of the plot of LmaxD vs. LmD in Figure 2a (=1.35; 95% C.I. = 1.33–1.37) overlaps with confidence intervals reported in previous contributions dealing with other bony fish [11,12,13,14,15,16], implying that the slope estimates are not statistically different.
When phylogeny is considered (Figure 2b), the change in slope is similar to what was observed by Warren [31] for cartilaginous species, i.e., that the correlation was weak, with a wide confidence interval, which is apparently a common result when including phylogenetic signals into analyses such as ours [32,33]. While some authors have suggested that statistical analyses without phylogenetic elements are “flawed” or “biased” [32], it has also been demonstrated that “poor statistical performances” will be the result when phylogenetic methods based on incorrect assumptions are applied to regression models [34]. Our coral reef fish dataset is phylogenetically extremely diverse, which suggests that the consideration of phylogeny in our analysis may not only be superfluous, but also result in misleading results [32]. Therefore, we are focusing our remaining discussion solely on the results derived from the data without considering phylogeny, as these are more likely to provide a reliable basis for our conclusion.
The estimated critical threshold of the LmaxD vs. LmD ratio (1.35) varies slightly between populations and species because it is a heuristic [35] used by individual fish to determine when to start perceiving the external stimuli that make them start their maturation process [23]. As such, this heuristic can generate predictions (i.e., values of Lm) that are too low (thus leading to an egg production that is lower than would have been possible by allowing more growth before first maturity) or too high (thus exposing the individual to an elevated risk of being predated upon before having spawned at least once). This explains some of the differences between the lines and the dots in Figure 2a,b, the rest of these differences being mostly caused by imprecisions in the estimation of Lm and Lmax.
What this study establishes, however, is that coral reef (bony) fish, for all the specificities associated with the singular ecosystems within which they evolved, initiate their maturation and reproduction under the same respiratory constraints as other teleosts. Notably, our results add to the evidence against the “Reproductive Drain Hypothesis”, and in favor of the alternative hypothesis as presented in Pauly and Liang [4]; see also refs. [11,12,13,14,15,16]. Our results, thus, also suggest that generalizations concerning other aspects of the biology of coral reef fish, e.g., their respiratory physiology, would also benefit from being compared with the respiratory physiology of well-studied temperate fish, including freshwater species, rather than being a priori assumed to be different from other fish.
Some studies have shown that reef-associated fish have evolved a relatively high hypoxia tolerance, probably due to the fact that coral reefs go through daily cycles of oxygen levels [36,37,38]. However, the above considerations lead one to predict that the increased stress of ocean deoxygenation and increased temperatures [39] will not only lead to smaller maximum sizes in coral reef fish, but also to smaller sizes at first maturity, generally associated with fewer and smaller eggs [40] and, thus, with reduced fitness.

5. Conclusions

The Gill Oxygen Limitation Theory (GOLT) as proposed by Pauly [11] suggests that the triggering of maturation in fish occurs when the growth-induced reduction in gill surface area relative to body weight (and hence oxygen supply) reaches a critical level. This study confirms that this triggering effect also occurs in coral reef fish and that its level is the same as in other fish populations. Understanding the size and age of maturity of fish is an important aspect of effective fisheries management. The results of this study suggest that with increasing temperature and deoxygenation, coral reef fish will mature at smaller sizes and, as a result, will produce smaller eggs. These changes will influence the factors that must be considered in the management of coral reef fisheries.

Author Contributions

Conceptualization, D.P.; Data curation, E.C.; Formal analysis, E.C.; Investigation, E.C.; Methodology, E.C. and D.P.; Resources, E.C.; Software, E.C.; Supervision, D.P.; Validation, E.C. and D.P.; Visualization, E.C. and D.P.; Writing—original draft, E.C.; Writing—review and editing, D.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The research that we have done in our manuscript does not involve direct research on humans or animals. The data we used was assembled by compiling the results from various published literature sources. Therefore, the requirement for ethical committee approval does not apply to our manuscript.

Data Availability Statement

Data are contained within the article and Appendix A.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Assembled data on reef-associated species for the analysis of the relationship between length at first maturity (Lm) and maximum length (Lmax), arranged alphabetically by family and by species names. Lengths are in fork lengths. Lmax values in brackets were estimated from Linf using Lmax = 0.95×Linf. Wmax estimated from Lmax using length–weight relationship coefficients from FishBase. (F = female; M = male; U = unsexed).
Table A1. Assembled data on reef-associated species for the analysis of the relationship between length at first maturity (Lm) and maximum length (Lmax), arranged alphabetically by family and by species names. Lengths are in fork lengths. Lmax values in brackets were estimated from Linf using Lmax = 0.95×Linf. Wmax estimated from Lmax using length–weight relationship coefficients from FishBase. (F = female; M = male; U = unsexed).
No.FamilySpeciesLocationSexLmax (cm)Lm (cm)Wmax (g)DLmaxDLmDReference
1AcanthuridaeAcanthurus chirurgusPedro Bank, JamaicaM35.017.0978.30.6610.336.43[41]
2AcanthuridaeAcanthurus lineatusTutuila Isl. Amer.U28.918.0670.20.679.667.02[42]
3AcanthuridaeAcanthurus lineatusPohnpei State, MicronesiaF20.516.8250.30.728.817.63[43]
4AcanthuridaeAcanthurus nigricaudaPohnpei State, MicronesiaF22.618.4311.20.719.157.91[43]
5AcanthuridaeAcanthurus nigrofuscusCoil reef, Northern QueenslandU15.510.585.70.778.266.12[44]
6AcanthuridaeAcanthurus nigrofuscusYankee reef, N. QueenslandU17.910.5108.70.768.955.96[44]
7AcanthuridaeAcanthurus triostegusLakshadweep lagoons, IndiaU17.57.3154.30.748.364.38[45]
8AcanthuridaeNaso lituratusTerengganu, MalaysiaU38.119.91331.20.6410.366.82[46]
9AlbulidaeAlbula vulpesFlorida Keys, USF70.048.85534.20.5811.569.39[47]
10AlbulidaeAlbula vulpesFlorida Keys, USM70.241.85584.80.5811.568.57[47]
11ApogonidaeCheilodipterus artusTerengganu, MalaysiaU17.711.2112.60.768.806.21[46]
12ApogonidaeCheilodipterus macrodonTerengganu, MalaysiaU23.615.1306.80.719.466.89[46]
13ApogonidaeCheilodipterus quinquelineatusTerengganu, MalaysiaU11.98.226.80.827.695.68[46]
14ApogonidaeOstorhinchus compressusTerengganu, MalaysiaU11.17.424.20.837.345.24[46]
15ApogonidaePterapogon kauderniBanggai ArchipelagoU7.64.910.40.875.793.99[48]
16BalistidaeBalistapus undulatusKavieng, PNGF20.215.7217.60.738.897.40[49]
17BalistidaeBalistes capriscusGhanaF34.014.5679.50.6710.766.06[50,51]
18BalistidaeBalistes vetulaPedro Bank, JamaicaF39.023.51936.20.629.877.19[52]
19BalistidaeBalistes vetulaPedro Bank, JamaicaM44.026.52738.90.6110.017.35[52]
20BelonidaeTylosurus acusSuez Canal, EgyptM74.545.9727.30.6718.0013.02[53]
21BelonidaeTylosurus acusSuez Canal, EgyptF74.545.3727.30.6718.0012.91[53]
22BelonidaeTylosurus crocodilusSuez Canal, EgyptM94.450.11759.70.6317.5111.75[53]
23BelonidaeTylosurus crocodilusSuez Canal, EgyptF94.449.51759.70.6317.5111.66[53]
24CarangidaeAlepes djedabaKerala, IndiaU(26.2)16.0339.00.7110.037.08[54]
25CarangidaeAlepes kleiniiSW Coast, IndiaU(14.2)11.358.90.798.096.75[54]
26CarangidaeAtule mateKerala, IndiaU(29.0)15.4360.90.7010.686.85[54]
27CarangidaeCarangoides bajadShathleen, EgyptU56.434.82970.50.6111.478.56[55]
28CarangidaeCarangoides bajadCoast of Abu Dhabi, UAEU(38.4)24.7697.70.6711.628.64[56]
29CarangidaeCarangoides equulaNorthern South China SeaU(28.1)18.7513.00.699.887.48[57]
30CarangidaeCaranx heberiSouth AfricaU100.050.019,887.80.5210.797.54[58,59]
31CarangidaeCaranx ignobilisNorthwestern Islands, HawaiiU162.656.083,360.90.459.876.12[60]
32CarangidaeCaranx melampygusNorthwestern Islands, HawaiiU70.832.710,551.00.5510.236.71[60]
33CarangidaeCaranx melampygusShathleen, EgyptU73.944.3652.80.6818.3012.96[55]
34CarangidaeCaranx sexfasciatusSouth AfricaU80.050.09456.40.5511.198.64[58,59]
35CarangidaeDecapterus macrosomaJava Sea, IndonesiaM20.113.775.60.7810.277.61[61,62]
36CarangidaeDecapterus macrosomaJava Sea, IndonesiaF20.114.375.60.7810.277.88[61,62]
37CarangidaeDecapterus maruadsiEast China SeaU20.817.5133.10.759.748.55[63,64]
38CarangidaeDecapterus maruadsiGulf of Tonkin/Beibu GulfU24.217.1100.10.7611.388.74[65]
39CarangidaeDecapterus punctatusSouth Atlantic BightU21.011.0125.30.759.886.08[66,67]
40CarangidaeElagatis bipinnulataPernambuco, BrazilF97.064.67563.30.5613.0510.39[68]
41CarangidaeMegalaspis cordylaSW coast, IndiaU(33.6)22.5502.20.6911.228.50[54]
42CarangidaeMegalaspis cordylaEast Coast, IndiaU(35.0)22.5517.00.6911.478.46[54]
43CarangidaeMegalaspis cordylaNW Coast IndiaU(44.8)22.5837.50.6612.487.89[54]
44CarangidaeParastromateus nigerTaiwan Strait, TaiwanU30.519.11131.10.659.226.80[69]
45CarangidaeScomberoides commersonnianusWeipa region, Queensland, AustraliaM(108.3)38.511,888.70.5412.587.19[70]
46CarangidaeScomberoides commersonnianusWeipa region, Queensland, AustraliaF(122.6)63.516,788.90.5212.458.82[70]
47CarangidaeSelar crumenophthalmusCaribbean coast, ColombiaU(27.8)19.6342.30.7110.458.18[71]
48CarangidaeSelaroides leptolepisTamil Nadu/Pondicherry, IndiaU(17.0)8.969.00.789.125.53[54]
49CarangidaeSelaroides leptolepisInner Gulf of ThailandU(16.8)8.980.40.778.875.44[72]
50CarangidaeSeriola dumeriliPelagie Islands, ItalyF157.2114.343,955.90.4811.319.70[73]
51CarangidaeSeriola dumeriliPelagie Islands, ItalyM157.2118.443,009.60.4811.379.92[73]
52CarangidaeTrachinotus falcatusFlorida Keys/Tampa Bay, USM85.548.613,816.40.5310.737.94[74]
53CarangidaeTrachinotus falcatusFlorida Keys/Tampa Bay, USF91.654.716,760.50.5210.698.16[74]
54CarangidaeTrachurus lathamiSouthern region, BrazilU21.411.8118.80.7510.106.43[75]
55CentriscidaeCentriscus scutatusTerengganu, MalaysiaU15.010.04.20.9111.788.14[46]
56ChaenopsidaeAcanthemblemaria paulaCarrie Bow Cay, BelizeU2.01.30.01.122.171.31[76]
57ChaetodontidaeChaetodon aurigaLakshadweep lagoons, IndiaU14.913.086.30.778.007.20[45]
58DorosomatidaeAmblygaster sirmLagoons, New CaledoniaU21.014.671.30.7810.728.06[77]
59DorosomatidaeHerklotsichthys quadrimaculatusSeychellesU12.810.131.20.828.036.62[78]
60DorosomatidaeOpisthonema oglinumCeará, BrazilM17.011.069.30.789.126.49[79,80]
61DorosomatidaeOpisthonema oglinumCeará, BrazilF17.011.569.30.789.126.72[79,80]
62DorosomatidaeOpisthonema oglinumPernambuco, BrazilU22.412.5126.40.7510.356.69[81]
63DorosomatidaeSardinella albellaMandapam, IndiaU(10.9)7.816.70.857.565.67[82]
64EngraulidaeEncrasicholina devisiYsabel Passage, PNGU(6.2)3.61.90.955.673.40[83]
65EngraulidaeEncrasicholina devisiKarnataka, IndiaU9.66.05.30.907.645.04[84]
66EngraulidaeEncrasicholina heterolobaSingapore StraitU(8.9)5.37.40.886.924.36[85]
67EngraulidaeStolephorus insularisSingapore StraitU(10.0)5.38.10.887.564.33[85]
68FistulariidaeFistularia commersoniiMediterranean Sea, LebanonF113.065.41969.10.6219.1213.59[86]
69FistulariidaeFistularia commersoniiMediterranean Sea, LebanonM100.054.71368.00.6419.1613.01[86]
70GerreidaeGerres filamentosusManila Bay, PhilippinesM14.38.450.90.798.255.43[87]
71GerreidaeGerres filamentosusManila Bay, PhilippinesF12.77.935.60.817.845.35[87]
72GerreidaeGerres longirostrisSouthern Arabian GulfM(17.9)16.31680.60.636.185.83[88]
73GerreidaeGerres longirostrisSouthern Arabian GulfF(20.1)20.62404.10.616.346.43[88]
74GobiidaeEviota melasmaLizard Island, AustraliaM2.71.10.11.072.911.10[89]
75GobiidaeEviota melasmaLizard Island, AustraliaF2.71.20.11.072.911.16[89]
76GobiidaeEviota queenslandicaLizard Island, AustraliaM2.61.30.11.082.771.34[89]
77GobiidaeEviota queenslandicaLizard Island, AustraliaF2.61.40.11.082.771.43[89]
78GobiidaeEviota sigillataLizard Island, AustraliaM1.81.10.00361.131.941.13[89]
79GobiidaeEviota sigillataLizard Island, AustraliaF1.81.10.00361.131.941.14[89]
80GobiidaeExyrias belissimusTerengganu, MalaysiaU15.010.031.80.829.126.55[46]
81GobiidaeIstigobius decoratusTerengganu, MalaysiaU13.09.022.40.838.466.23[46]
82GobiidaeIstigobius goldmanniTerengganu, MalaysiaU6.05.02.30.945.374.53[46]
83HaemulidaeDiagramma pictumSouthern Arabian GulfM(57.6)30.71832.30.6312.728.58[90]
84HaemulidaeDiagramma pictumSouthern Arabian GulfF(60.6)31.82137.00.6212.768.55[90]
85HaemulidaeDiagramma pictumArabian Gulf, KuwaitU(69.1)52.34963.30.5811.729.97[91]
86HaemulidaeHaemulon aurolineatumPernambuco, BrazilM23.515.3178.10.7410.217.45[92]
87HaemulidaeHaemulon aurolineatumPernambuco, BrazilF23.515.0178.10.7410.217.34[92]
88HaemulidaeHaemulon plumieriiCeará State, BazilF34.316.9843.60.6610.456.53[93]
89HaemulidaeHaemulon plumieriiCeará State, BrazilM27.718.6446.90.6910.007.59[93]
90HaemulidaePomadasys stridensGulf of SuezF18.310.3104.90.769.135.90[94]
91HaemulidaePomadasys stridensGulf of SuezM18.39.1104.90.769.135.36[94]
92HemiramphidaeHemiramphus brasiliensisPernambuco, BrazilM29.918.6229.70.7211.718.31[95]
93HemiramphidaeHemiramphus brasiliensisPernambuco, BrazilF29.919.3229.70.7211.718.53[95]
94HemiramphidaeHemiramphus farBardawil lagoon, EgyptM27.621.1128.30.7512.109.87[96]
95HemiramphidaeHemiramphus farBardawil lagoon, EgyptF28.121.3127.90.7512.259.94[96]
96HolocentridaeHolocentrus adscensionisPernambuco, BrazilF17.812.1211.00.738.136.13[97,98]
97HolocentridaeHolocentrus rufusJamaicaF23.013.5206.80.739.846.67[99]
98HolocentridaeMyripristis murdjanLakshadweep lagoons, IndiaU19.215.6212.90.738.597.39[45]
99HolocentridaeSargocentron rubrumTerengganu, MalaysiaU29.118.2571.90.689.947.22[46]
100KyphosidaeKyphosus bigibbusNorthwest Kyushu, JapanF57.436.03327.50.6011.358.58[100]
101KyphosidaeKyphosus bigibbusNorthwest Kyushu, JapanM50.628.42320.00.6211.247.87[100]
102KyphosidaeKyphosus cinerascensKavieng, Papua New GuineaF34.022.6935.20.6610.217.80[49]
103KyphosidaeKyphosus cinerascensKavieng, Papua New GuineaM30.020.1647.30.689.977.60[49]
104LabridaeHalichoeres hortulanusLakshadweep lagoons, IndiaU28.912.8356.20.7010.676.02[45]
105LabridaeHalichoeres marginatusLakshadweep lagoons, IndiaU17.97.099.60.769.044.42[45]
106LethrinidaeLethrinus borbonicusSouthern Arabian GulfM28.722.1366.80.7010.578.80[101]
107LethrinidaeLethrinus borbonicusSouthern Arabian GulfF28.721.3366.80.7010.578.57[101]
108LethrinidaeLethrinus borbonicusGulf of Suez, South Sinai coastU27.619.4426.80.7010.057.88[102]
109LethrinidaeLethrinus borbonicusFoul Bay, Egypt, Red SeaU28.919.3501.90.6910.117.65[103]
110LethrinidaeLethrinus lentjanSouthern Arabian GulfM(29.2)24.6446.90.6910.369.21[104]
111LethrinidaeLethrinus lentjanSouthern Arabian GulfF(32.4)27.7604.70.6810.619.54[104]
112LethrinidaeLethrinus microdonSouthern Arabian GulfM(32.6)27.4512.80.6910.949.72[101]
113LethrinidaeLethrinus microdonSouthern Arabian GulfF(32.0)29.1487.20.6910.9010.21[101]
114LethrinidaeLethrinus nebulosusSouthern Arabian GulfM54.128.62230.20.6211.807.95[90]
115LethrinidaeLethrinus nebulosusSouthern Arabian GulfF55.727.62423.50.6111.827.68[90]
116LethrinidaeMonotaxis grandoculisPohnpei state, MicronesiaF33.027.5858.70.6610.159.00[43]
117LutjanidaeAphareus rutilansSouth China SeaU(67.2)41.75356.00.5811.368.62[105]
118LutjanidaeAprion virescensHawaii, USF102.844.915,361.50.5311.577.47[106]
119LutjanidaeApsilus dentatusJamaicaF54.040.02346.20.6211.679.70[107,108]
120LutjanidaeApsilus dentatusJamaicaM56.044.02634.20.6111.6810.08[107,108]
121LutjanidaeEtelis coruscansHawaii, USF96.966.313,830.80.5311.479.37[106]
122LutjanidaeLutjanus apodusGreat Barrier Reef, AustraliaM92.834.311,905.60.5411.576.76[109]
123LutjanidaeLutjanus apodusJamaicaF57.025.03764.40.5911.046.77[107]
124LutjanidaeLutjanus boharGreat Barrier Reef, AustraliaF67.542.95932.50.5711.178.61[110]
125LutjanidaeLutjanus buccanellaJamaicaF49.024.01741.20.6311.617.40[107,108]
126LutjanidaeLutjanus buccanellaJamaicaM49.026.01494.10.6411.937.97[107,108]
127LutjanidaeLutjanus carponotatusPalm Island, GBR, AustraliaF33.719.0558.50.6811.057.47[111]
128LutjanidaeLutjanus carponotatusLizard Island, AustraliaF35.419.0646.40.6811.157.32[111]
129LutjanidaeLutjanus ehrenbergiiSouthern Arabian GulfU(23.0)20.4199.10.739.899.06[104]
130LutjanidaeLutjanus ehrenbergiiSouthern Arabian GulfM(20.8)19.9148.00.749.599.27[104]
131LutjanidaeLutjanus erythropterusGreat Barrier Reef, AustraliaU62.448.534,639.30.497.606.72[112]
132LutjanidaeLutjanus fulviflammaSouthern Arabian GulfM(21.2)16.7254.60.728.997.58[113]
133LutjanidaeLutjanus fulviflammaSouthern Arabian GulfF(22.4)18.7301.00.719.148.04[113]
134LutjanidaeLutjanus fulviflammaOkinawa islandF34.219.6931.80.6610.257.11[114]
135LutjanidaeLutjanus fulvusYaeyama Isl., Okinawa, JapanM31.420.7495.90.6910.738.05[115]
136LutjanidaeLutjanus fulvusYaeyama Isl., Okinawa, JapanF33.222.5585.60.6810.858.32[115]
137LutjanidaeLutjanus gibbusPohnpei State, MicronesiaF33.521.5756.80.6710.477.78[43]
138LutjanidaeLutjanus lutjanusPersian Gulf and Sea of OmanU25.517.231.50.8214.0810.19[116]
139LutjanidaeLutjanus malabaricusGreat Barrier Reef, AustraliaF81.059.56923.80.5712.0110.09[117]
140LutjanidaeLutjanus sebaeGreat Barrier Reef, AustraliaF72.054.87956.90.5610.939.38[117]
141LutjanidaeLutjanus synagrisJamaicaF43.026.81288.80.6411.278.31[118]
142LutjanidaeLutjanus griseusFlorida, USF72.423.06463.80.5711.435.95[119,120]
143MegalopidaeMegalops atlanticusSanta Fe, Ceará State, BrazilM153.6120.023,369.20.5112.9711.44[121,122]
144MegalopidaeMegalops atlanticusSanta Fe, Ceará State, BrazilF181.6160.030,615.00.5013.2312.42[121,122]
145MenidaeMene maculataTaiwanU23.015.3263.80.729.497.10[123]
146MonacanthidaeAluterus monocerosVeraval, IndiaU58.948.52031.20.6212.6611.22[124]
147MugilidaeMugil curemaSergipe State, BrazilM29.625.1317.90.7111.049.82[125]
148MugilidaeMugil curemaSergipe State, BrazilF34.322.5496.60.6911.398.52[125]
149MullidaeMulloidichthys flavolineatusLakshadweep lagoons, IndiaU24.216.0200.70.7310.277.58[45]
150MullidaeMulloidichthys martinicusJamaicaF28.018.0410.80.7010.217.50[126]
151MullidaeMulloidichthys martinicusJamaicaM28.019.0332.30.7110.558.02[126]
152MullidaePseudupeneus maculatusPernambuco, BrazilU29.220.0634.30.689.827.60[127]
153MullidaePseudupeneus maculatusJamaicaF24.918.0232.50.729.978.10[126,128]
154MullidaePseudupeneus maculatusJamaicaM26.418.5344.90.719.957.83[126,128]
155MuraenidaeMuraena augustiCanary IslandsU90.055.81750.10.6317.0012.58[129]
156MuraenidaeMuraena helenaAdriatic Sea, CroatiaM121.079.03541.70.6017.5013.57[130]
157MuraenidaeMuraena helenaAdriatic Sea, CroatiaF113.176.02679.80.6117.8814.03[130]
158MuraenidaeMuraena helenaCanary IslandU134.075.15714.90.5716.6811.96[129]
159NemipteridaeNemipterus japonicusManila Bay, PhilippinesF16.29.269.20.788.795.66[87,131]
160PlatycephalidaePlatycephalus indicusHong Kong, ChinaM44.223.5624.10.6813.038.50[132]
161PlatycephalidaePlatycephalus indicusHong Kong, ChinaF62.245.71862.00.6313.3110.98[132]
162PomacanthidaePomacanthus maculosusSouthern Arabian GulfF33.321.61070.90.659.857.43[101]
163PomacentridaeAbudefduf vaigiensisLakshadweep lagoons, IndiaU16.810.7146.40.758.185.83[45]
164PomacentridaeChromis viridisLakshadweep lagoons, IndiaU9.74.921.40.836.653.78[45]
165PomacentridaeDascyllus trimaculatusTerengganu, MalaysiaU13.18.479.30.777.335.21[46]
166PomacentridaePomacentrus coelestisTerengganu, MalaysiaU8.35.611.50.866.244.39[46]
167PriacanthidaePriacanthus hamrurSaurashtra, IndiaF29.218.5409.80.7010.517.65[133]
168RachycentridaeRachycentron canadumNorthwest Coast, IndiaU(176.0)66.953,225.40.4711.407.23[134]
169SciaenidaePennahia aneusManila Bay, PhilippinesM21.113.1128.90.759.896.89[87,135]
170SciaenidaePennahia aneusManila Bay, PhilippinesF20.012.6112.70.769.676.81[87,135]
171ScombridaeScomberomorus brasiliensisMaranhâoF79.541.13804.20.5913.429.08[136]
172ScombridaeScomberomorus brasiliensisMaranhâo, BrazilM76.544.33405.30.6013.429.68[136]
173ScombridaeScomberomorus brasiliensisRio Grande do Norte, BrazilM72.731.23943.70.5912.647.66[137]
174ScombridaeScomberomorus brasiliensisRio Grande do Norte, BrazilF54.025.31675.40.6312.437.70[137]
175ScombridaeScomberomorus cavallaCeará State, BrazilF100.563.07535.80.5613.3210.25[138]
176ScombridaeScomberomorus cavallaCeará State, BrazilF113.677.010,910.20.5413.1510.64[139]
177ScombridaeScomberomorus maculatusCeará, State, BrazilF65.541.02304.00.6213.199.88[138]
178ScombridaeScomberomorus maculatusCeará State, BrazilF78.046.03878.60.5913.229.67[140]
179ScorpaenidaePterois russeliiTerengganu, MalaysiaU30.019.0249.10.7211.598.34[46]
180SiganidaeSiganus canaliculatusSouthern Arabian GulfM33.221.5731.90.6710.467.82[141]
181SiganidaeSiganus canaliculatusSouthern Arabian GulfF36.925.71004.90.6610.658.40[141]
182SillaginidaeSillago sihamaGulf of Mannar, IndiaU(26.2)12.8137.70.7511.526.73[142]
183SillaginidaeSillago sihamaPulicat Lake, IndiaU(38.0)22.1327.30.7113.138.95[143]
184SparidaeArchosargus rhomboidalisTerminos Lagoon, MexicoU24.68.5491.00.699.074.38[144]
185SparidaeRhabdosargus sarbaSouthern Arabian GulfM29.323.5513.50.6910.178.74[104]
186SparidaeRhabdosargus sarbaSouthern Arabian GulfF29.323.7513.50.6910.178.79[104]
187SparidaeRhabdosargus sarbaSouth-eastern AustraliaU(25.1)19.4325.10.719.798.17[145]
188SparidaeSparus aurataNorth Island, New ZealandU(55.9)24.03388.10.6011.136.71[146]
189SparidaeSparus aurataWestern North Island, N.Z.U(63.4)24.04818.10.5811.216.37[146]
190SparidaeSparus aurataWestern South Island, N.Z.U(66.1)24.05426.20.5811.236.26[146]
191SphyraenidaeSphyraena barracudaFlorida, USAF141.865.660,587.50.469.996.98[147]
192SynanceiidaeInimicus didactylusTerengganu, MalaysiaU25.016.0231.40.7210.287.44[46]
193SynodontidaeSaurida tumbilEast China SeaU(54.7)25.72664.80.6111.507.25[148]
194SynodontidaeSaurida tumbilManila Bay, PhilippinesM28.023.9192.50.7311.4810.22[87]
195SynodontidaeSaurida tumbilManila Bay, PhilippinesF29.224.7218.90.7311.5910.27[87]
196SynodontidaeSaurida undosquamisoff Visakhapatnam, IndiaU(34.1)20.9364.20.7011.968.48[149]
197SynodontidaeSynodus variegatusTerengganu, MalaysiaU36.815.6659.80.6811.406.40[46]
198SynodontidaeTrachinocephalus myopsMinnan-Taiwan BankU(41.0)16.5804.70.6711.866.47[150]
199TetraodontidaeCanthigaster valentiniLizard Island, AustraliaF8.85.822.30.836.144.33[151]
200TetraodontidaeCanthigaster valentiniLizard Island, AustraliaM10.76.739.50.816.774.63[151]
201TetraodontidaeLagocephalus sceleratusSuez Canal, EgyptF76.542.25076.60.5812.388.77[152]
202TetraodontidaeLagocephalus sceleratusSuez Canal, EgyptM76.541.05076.60.5812.388.63[152]
203TetraodontidaeLagocephalus sceleratusRhodes, GreeceU61.535.12646.60.6112.368.77[153]
204TetraodontidaeLagocephalus sceleratusLebanonU71.639.05439.20.5811.758.27[154]
205TetraodontidaeLagocephalus sceleratusSouthwest CyprusUI71.240.84454.70.5912.188.80[155]
206TetraodontidaeLagocephalus sceleratusSoutheast CyprusU78.047.65872.60.5712.159.15[155]
207TetraodontidaeLagocephalus sceleratusCyprusF75.019.45355.10.5812.115.54[156]

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Fishes 09 00130 g002
Figure 2. Plot of LmaxD vs. LmD for (a) all 207 cases; (b) 120 species on the Fish Tree of Life with phylogenetic affinities considered. Shaded area indicates the 95% confidence interval of slope.
Figure 2. Plot of LmaxD vs. LmD for (a) all 207 cases; (b) 120 species on the Fish Tree of Life with phylogenetic affinities considered. Shaded area indicates the 95% confidence interval of slope.
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Table 1. Comparison of estimated coefficients and their 95% confidence interval for different subsets in the relationship between length at first maturity and maximum length.
Table 1. Comparison of estimated coefficients and their 95% confidence interval for different subsets in the relationship between length at first maturity and maximum length.
DatasetNSlope (95% C.I.)
All cases2071.35 (1.33–1.37)
Species on FishTree with phylogeny considered1201.20 (1.09–1.31)
Species on FishTree without phylogeny considered1201.34 (1.31–1.37)
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Chu, E.; Pauly, D. The Relationship between Mean Length at Maturity and Maximum Length in Coral Reef Fish. Fishes 2024, 9, 130. https://doi.org/10.3390/fishes9040130

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Chu E, Pauly D. The Relationship between Mean Length at Maturity and Maximum Length in Coral Reef Fish. Fishes. 2024; 9(4):130. https://doi.org/10.3390/fishes9040130

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Chu, Elaine, and Daniel Pauly. 2024. "The Relationship between Mean Length at Maturity and Maximum Length in Coral Reef Fish" Fishes 9, no. 4: 130. https://doi.org/10.3390/fishes9040130

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Chu, E., & Pauly, D. (2024). The Relationship between Mean Length at Maturity and Maximum Length in Coral Reef Fish. Fishes, 9(4), 130. https://doi.org/10.3390/fishes9040130

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