With so many different species of fish with often very minor differences, how is the precise species identified authoritatively?

With so many different species of fish with often very minor differences, how is the precise species identified authoritatively?

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While it seems to me that common mammal species are usually distinct enough that there isn't much trouble in accurately defining and identifying a specific species, for example of a deer, I see that with fishes there are often a whole lot of very similar species with close to none differences between them.

How can an individual specimen be authoritatively assigned to a specific species, is there an authoritative catalogue of features that one can look up to be able to say with certainty is an examined specimen of a certain species or not?

For example in the family Scaridae there are species Chlorurus bleekeri, Chlorurus bowersi, Chlorurus gibbus, Chlorurus frontalis, Chlorurus gibbus, Chlorurus japanensis, Chlorurus perspicillatus, Chlorurus strongylocephalus, Chlorurus troschelii which are all kinds of parrotfish very minor differences if I look at photos on FishBase.

How did anyone even decide these are distinct species of fish, instead of varieties of the same species, and then is there a central authority where newly defined species like this are described in enough detail for future occurrences to match with the same species name?

What are the relative risks of mortality and injury for fish during downstream passage at hydroelectric dams in temperate regions? A systematic review

Fish injury and mortality resulting from entrainment and/or impingement during downstream passage over/through hydropower infrastructure has the potential to cause negative effects on fish populations. The primary goal of this systematic review was to address two research questions: (1) What are the consequences of hydroelectric dam fish entrainment and impingement on freshwater fish productivity in temperate regions? (2) To what extent do various factors like site type, intervention type, and life history characteristics influence the consequences of fish entrainment and impingement?


The review was conducted using guidelines provided by the Collaboration for Environmental Evidence and examined commercially published and grey literature. All articles found using a systematic search were screened using a priori eligibility criteria at two stages (title and abstract, and full-text, respectively), with consistency checks being performed at each stage. The validity of studies was appraised and data were extracted using tools explicitly designed for this review. A narrative synthesis encompassed all relevant studies and a quantitative synthesis (meta-analysis) was conducted where appropriate.

Review findings

A total of 264 studies from 87 articles were included for critical appraisal and narrative synthesis. Studies were primarily conducted in the United States (93%) on genera in the Salmonidae family (86%). The evidence base did not allow for an evaluation of the consequences of entrainment/impingement on fish productivity per se therefore, we evaluated the risk of freshwater fish injury and mortality owing to downstream passage through common hydropower infrastructure. Our quantitative synthesis suggested an overall increased risk of injury and immediate mortality from passage through/over hydropower infrastructure. Injury and immediate mortality risk varied among infrastructure types. Bypasses resulted in decreased injury risk relative to controls, whereas turbines and spillways were associated with the highest injury risks relative to controls. Within turbine studies, those conducted in a lab setting were associated with higher injury risk than field-based studies, and studies with longer assessment time periods (≥ 24–48 h) were associated with higher risk than shorter duration assessment periods (< 24 h). Turbines and sluiceways were associated with the highest immediate mortality risk relative to controls. Within turbine studies, lab-based studies had higher mortality risk ratios than field-based studies. Within field studies, Francis turbines resulted in a higher immediate mortality risk than Kaplan turbines relative to controls, and wild sourced fish had a higher immediate mortality risk than hatchery sourced fish in Kaplan turbines. No other associations between effect size and moderators were identified. Taxonomic analyses revealed a significant increased injury and immediate mortality risk relative to controls for genera Alosa (river herring) and Oncorhynchus (Pacific salmonids), and delayed mortality risk for Anguilla (freshwater eels).


Our synthesis suggests that hydropower infrastructure in temperate regions increased the overall risk of freshwater fish injury and immediate mortality relative to controls. The evidence base confirmed that turbines and spillways increase the risk of injury and/or mortality for downstream passing fish compared to controls. Differences in lab- and field-based studies were evident, highlighting the need for further studies to understand the sources of variation among lab- and field-based studies. We were unable to examine delayed mortality, likely due to the lack of consistency in monitoring for post-passage delayed injury and mortality. Our synthesis suggests that bypasses are the most “fish friendly” passage option in terms of reducing fish injury and mortality. To address knowledge gaps, studies are needed that focus on systems outside of North America, on non-salmonid or non-sportfish target species, and on population-level consequences of fish entrainment/impingement.

Fish Physiology in a Warmer Future

For ectothermic animals, it has been known for over a century that an increase in body temperature usually means an exponential increase in resting metabolic rate, typically rising two- to threefold with each 10°C increase in temperature (6, 12, 21). The reason is, of course, that metabolism is based on chemical reactions that are temperature dependent (13). A detailed discussion of the factors determining how temperature affects metabolic processes is given by Schulte (61). To some extent, the rise in metabolic rate with an increment in temperature can be compensated for by thermal acclimation (57), but, acclimation included, animals will still have thermal optima in their physiological performance, including growth, reproduction, and ability to take up oxygen.

Compared with terrestrial ectothermic vertebrates, aquatic ectotherms such as fish have in most cases no physiological means of regulating their body temperature, particularly since their gills are highly efficient heat exchangers between the blood and the water, effectively making the vast majority of fish species poikilothermic (i.e., having a body temperature that closely follows the ambient temperature). The very few exceptions to this rule include tuna, billfishes, and some sharks, which use internal heat exchangers to warm particular tissues (14). A central role for oxygen uptake in determining thermal performance of fish was originally proposed by Fry (28, 29). More recently, the connection between the ability to take up oxygen and temperature tolerance has been highlighted by Pörtner and coworkers (53, 54), who proposed the concept of “oxygen- and capacity-limited thermal tolerance” (OCLTT). The OCLTT hypothesis suggests that it is a reduction in the capacity for oxygen delivery to tissues, the aerobic scope, that sets the lower and higher thermal limits for an animal, and determines the performance between those limits. The aerobic scope is defined as the difference between the oxygen uptake (ṀO2) needed to sustain basal metabolic functions and the maximal rate of oxygen uptake (ṀO2max) of the same animal. Experimentally, the first value can be estimated by respirometry on resting and unfed animals, and is often referred to as resting oxygen consumption (ṀO2rest). In fish, ṀO2max is commonly measured in either a swim-respirometer, where the water current is increased until it brings the fish to the limit of its aerobic swimming capacity, or by measuring oxygen uptake immediately after having chased the fish to exhaustion (8). Extrapolating swim-respirometry data back to zero water speed can also be used to estimate ṀO2rest. The aerobic scope can either be expressed as absolute aerobic scope (AAS), which is obtained by subtracting ṀO2rest from ṀO2max, or as factorial aerobic scope (FAS), which is obtained by dividing ṀO2max with ṀO2rest.

Mechanistically, the OCLTT model makes intuitive sense to many physiologists, since it suggests that it is the capacity of the circulatory and respiratory systems to deliver oxygen to the tissues that limits performance. In other words, performance starts falling above an optimal temperature when oxygen delivery cannot keep pace with the exponential rise in resting oxygen needs (i.e., when the ṀO2max no longer keeps up with the rise in ṀO2rest). Performance will also fall at temperatures below the optimal temperature, although this is of less interest in the context of global warming. Graphically, this model is often presented as a bell-shaped curve (e.g., Ref. 54) describing the relationship between aerobic scope and temperature (FIGURE 1A). At the high end of this curve, ṀO2max levels off or starts falling, and a critical temperature is reached when ṀO2max equals ṀO2rest and aerobic scope becomes zero. At higher temperatures, any increase in resting metabolism needs to be supported by anaerobic glycolysis, leading to an unsustainable build-up of lactic acid. Such a temperature will soon be lethal for the individual animal.

FIGURE 1.Aerobic scope as a function of temperature

A: absolute aerobic scope (AAS) is the difference (vertical arrow) between the basal oxygen consumption of a resting animal (ṀO2rest) and its maximal capacity for oxygen uptake (ṀO2max), and is, according to the OCLTT model, expected to be described by a bell-shaped curve (e.g., Ref. 54). At temperatures above the highest (optimal) point on the aerobic scope curve, the amount of energy that can be devoted to processes like growth and reproduction will decline. At even higher temperatures, the animal will finally lose all its aerobic scope and has then reached its upper lethal temperature, since it can no longer sustain its oxygen demands. This is often the case, but not always, as exemplified in the experimentally determined aerobic scope curves for four fishes (B). Fish from temperate climates that experience wider annual temperature changes, like the rainbow trout (blue) and common killifish (black), have broad response curves, whereas that of the tropical four-striped damselfish (orange) is very narrow, like the temperature range in its coral reef habitat. Finally, there are examples, like the barramundi, that reach its upper lethal temperature while its aerobic scope is at its highest level, suggesting that other processes are failing. To allow comparison, the aerobic scopes of these fishes are shown in percent of the highest measured scope for each species. Data are from Refs. 3, 32, 50, 60. Broken lines for two species indicate an acute temperature challenge, with acclimation temperature indicated by @. The two other species had been allowed to acclimate to each temperature. Approximate lethal temperature is indicated by † for each species. Illustration of barramundi by Dieter Tracey, and four-striped damselfish, common killifish, and rainbow trout by Tracey Saxby [Integration and Application Network, University of Maryland Center for Environmental Science (].

However, to sustain a population where long-term survival and fitness are what counts, and factors like growth and reproduction become important, the situation will start getting worse much before lethal temperatures are reached. Certainly, according to the OCLTT hypothesis, the decline in fitness should already start happening at temperatures just above the optimal temperature since the aerobic scope will start falling off and less energy can be devoted to growth and reproduction. Importantly, this hypothesis suggests that determining the temperature dependence of the aerobic scope for a species would allow us to predict how it would be affected by rising temperatures, thus providing a physiological variable that can be used in modeling the future fate of not only species but also ecosystems.

A relatively large number of studies have now been carried out on fish within the mindset of the OCLTT model (see Ref. 41 for review). Some of the first studies were done on tropical coral-reef fish (see Ref. 46 for review), which are adapted to a warm and stable environment, and are likely to live near their thermal optimum. Indeed, the studies on reef fish yielded aerobic scope curves that are generally supportive of the OCLTT hypothesis (FIGURE 1B). Similar support was obtained by studies on the thermal performance of salmonid fish, like sockeye salmon (Oncorhynchus nerka) populations in the Fraser River in British Columbia (22).

It is of course conceivable that factors other than oxygen delivery will affect physiological performance as the environment gets warmer (61), and the general applicability of the OCLTT model has been questioned, rising a somewhat hectic debate (e.g., Refs. 8, 25, 55). Actually, a whole day session, entitled “Oxygen- and Capacity-Limited Thermal Tolerance: A Universal Concept?” was organized at the Society for Experimental Biology Annual Main Meeting in 2015 to provide a forum for this debate. It has been noted that the temperature for optimal growth and reproduction, as well as the behaviorally preferred temperature, do not necessarily coincide with the temperature for optimal aerobic scope, arguing against the universality of the OCLTT model (e.g., Refs. 8, 30, 50). It was pointed out, for example, by Clark et al. (8) that adult pink salmon (Oncorhynchus gorbuscha) has an optimal aerobic scope at 21°C (7), even though such a high temperature would cause mortality within days (37) and the optimal temperature for spawning in these fishes is typically below 14°C (59). Moreover, the optimal temperature for aerobic scope may not coincide with important ecological factors like food availability or competition from other species, factors that may guide the temperature preference of a species. In juvenile barramundi (Lates calcarifer), Norin et al. (50) found that the behaviorally preferred temperature was 31°C, whereas aerobic scope continued to increase up to at least 38°C, close to the lethal temperature of this species (FIGURE 1B). Such data suggest that the function of other organ systems can start degrading although cardio-respiratory functions determining aerobic scope are still on the rise. A failure of, for instance, neural function may determine the lethal temperature in some animals (e.g., Ref. 23). Moreover, in an experimental study where European sea bass (Dicentrarchus labrax) were made anemic, causing a 50% reduction in blood oxygen-carrying capacity, the effect of this treatment on upper critical temperature was very minor (a reduction from 35.8 to 35.1°C), a result that is difficult to reconcile with the OCLTT model guiding lethal temperature in this species (65).

The generality of the OCLTT model has now been examined by Lefevre in a comprehensive meta-study covering ectothermic marine vertebrates and invertebrates (41). This survey shows that for some 50% of the 53 species studied so far, the temperature response of aerobic scope shows an “increase-optimum-decrease”-type reaction norm in line with the OCLTT model. The other half of the species displays an aerobic scope that increases over most of the tolerated temperature range, indicating that factors other than oxygen supply to tissues become limiting when temperature rises. These numbers were similar for fishes and invertebrates. This suggests that the OCLTT model for explaining the temperature response of marine ectotherms may be useful as a mechanistic model for at most half of the species but only if the optimal temperature of aerobic scope coincides with that for optimal fitness, which may not always be the case (e.g., Ref. 30). Furthermore, since there was no clear phylogenetic signal in the meta-data (41), the conclusion is that every species of interest has to be examined with regard to the temperature dependence of factors other than oxygen uptake that are likely to be fitness related. Such factors include growth, gonad size, preferred temperature, and ideally fecundity and offspring survival. So there is clearly a lot of experimental work to be done before we can attempt to predict how different animals and their ecosystems are going to react to a warmer future. Thus it can be argued that it is unfortunate that the OCLTT model was given such a strong weight in the last IPCC report (56) since this may bias the direction of future research and suppress alternative approaches.

Finally, in some cases, the lethal temperature could be more important for future survival than reductions in aerobic scope. This would apply to populations that find themselves in an environment with an increased likelihood for short but extreme temperature peaks that exceed lethal limits. Such extreme climatic events, which can have immediate consequences for animal populations, are expected to be more common in the future (27).

Nevertheless, although the OCLTT model is not as generally applicable as first hoped, it has attracted numerous physiologists to this area of research and has aided in bringing physiology into the center of biological climate change research.

How Muscle Structure and Composition Influence Meat and Flesh Quality

Skeletal muscle consists of several tissues, such as muscle fibers and connective and adipose tissues. This review aims to describe the features of these various muscle components and their relationships with the technological, nutritional, and sensory properties of meat/flesh from different livestock and fish species. Thus, the contractile and metabolic types, size and number of muscle fibers, the content, composition and distribution of the connective tissue, and the content and lipid composition of intramuscular fat play a role in the determination of meat/flesh appearance, color, tenderness, juiciness, flavor, and technological value. Interestingly, the biochemical and structural characteristics of muscle fibers, intramuscular connective tissue, and intramuscular fat appear to play independent role, which suggests that the properties of these various muscle components can be independently modulated by genetics or environmental factors to achieve production efficiency and improve meat/flesh quality.

1. Introduction

The muscle mass of livestock and fish species used to produce human food represents 35 to 60% of their body weight. The striated skeletal muscles attached to the backbone are involved in voluntary movements and facilitate the locomotion and posture. Skeletal muscles exhibit a wide diversity of shapes, sizes, anatomical locations, and physiological functions. They are characterized by a composite appearance because in addition to muscle fibers, they contain connective, adipose, vascular, and nervous tissues. Muscle fibers, intramuscular connective tissue, and intramuscular fat play key roles in the determination of meat and fish flesh quality. Concerning meat and aquatic products, the different stakeholders, that is, producers, slaughterers, processors, distributors, and consumers, exhibit varied and specific requirements about quality that depend on their use of the products. Quality is generally described by 4 terms: security (hygienic quality), healthiness (nutritional quality), satisfaction (organoleptic quality), and serviceability (ease of use, ability to be processed, and prices). Satisfaction is driven by the qualities perceived by consumers. They include color, texture, and juiciness as well as flavor, which is associated with the aromas released in the mouth when the product is consumed. Satisfaction is also driven by technological qualities that reflect the ability of the product to be processed. They are mostly associated with a decrease in technological yield because of a decrease in water-holding capacity during cold storage (exudations) and cooking or because of damage that occurs after slicing. Better technological qualities are associated with low losses. The nutritional qualities depend primarily on the nutritional value of the fats, carbohydrates, and proteins that make up the food. A meat that is rich in proteins with a high proportion of essential amino acids and polyunsaturated fatty acids is considered to exhibit good nutritional quality. Finally, hygienic qualities reflect the product’s capacity to be safely consumed. They are primarily related to the bacterial load of the product and the presence of chemical residues such as herbicides or pesticides and other environmental pollutants in the product. Among the cited qualities, critical points concerning the quality of beef for consumers are primarily tenderness, color, and healthiness. However, the primary cause of the consumer failure to repurchase beef is variability in tenderness [1]. In fish, the best quality is firm, cohesive flesh with a good water-holding capacity [2]. In meat and fish flesh, these qualities are influenced by many in vivo and postmortem (pm) factors such as species, genotypes, nutritional and environmental factors, slaughtering conditions, and pm processing. Because these factors also influence the structure and composition of skeletal muscle, their effect on meat quality could largely involve direct relationships between intramuscular biological properties and meat quality traits. However, such relationships are not always clear among species. Thus, the aim of this paper is to provide an overview of the structure and composition (muscle fibers, intramuscular connective tissue, and intramuscular fat) of muscle in livestock and fish and their relationships with the different qualities. Recent genomic studies on various rearing species to identify new biomarkers of meat quality have been previously reviewed [3] and when necessary will be briefly addressed in this paper.

2. Muscle Structure

2.1. Macroscopic Scale

Skeletal muscle consists of approximately 90% muscle fibers and 10% of connective and fat tissues. The connective tissue in skeletal muscle is divided into the endomysium, which surrounds each muscle fiber, the perimysium, which surrounds bundles of muscle fibers, and the epimysium, which surrounds the muscle as a whole [4, 5].

When meat pieces consist of a unique muscle, the epimysium is removed. However, when a meat piece includes several muscles, only the external epimysium is absent (Figure 1). Skeletal muscle also contains fat tissue and to a lesser extent vascular and nervous tissues. In fish, the edible part, the fillets, consists of several muscles (myomeres), which are fitted into one another and separated by connective tissue sheaths of a few millimeters thickness, known as myosepta. The myosepta exhibit structural continuity from the vertebral axis to the skin. Their role is to ensure the transmission of the fiber-contraction forces of one myomere to another and to the skeleton and skin. This particular structure, with alternating muscle and connective sheaths, is termed a metameric organization. In a “round” fish of commercial size, the shape of the myomeres of a fillet resembles a W (Figure 2). However, this organization is more complex in cross section (i.e., a cutlet) (Figure 3). The myosepta can be considered to be the epimysia of terrestrial livestock species muscle. The other intramuscular connective tissues of fish exhibit a similar organization to that found in terrestrial animals. A unique characteristic of fish muscle is an anatomical separation at the macroscopic scale of the three main types of muscle: a major white muscle, a superficial red muscle (along the skin), and an intermediate pink muscle. These muscles are present in each myomere (Figure 3). The fish fillet also contains intramuscular adipose tissue located within a myomere between the myofibers and in the perimysium, but mainly in the myosepta separating myomeres.

2.2. Microscopic Scale

Muscle fibers are elongated, multinucleated, and spindle-shaped cells of approximately 10 to 100 micrometers diameter and a length that ranges from a few millimeters in fish to several centimeters in terrestrial animals. In all species, the fiber size increases with animal age and is an important parameter of postnatal muscle growth. Muscle fiber plasma membrane is known as the sarcolemma. The cross-sectional area (CSA) of fibers depends on their metabolic and contractile types (see Section 3.1 for the types of muscle fiber). In fish, the fiber size distribution varies according to the importance of the hypertrophic (increase in cell size due to an increase in volume) and the hyperplasic growth stages (an increase of muscle volume due to an increase in cell number). The simultaneous presence of small and large fibers results in the so-called “mosaic” structure typically encountered in fish (Figure 4).

Regardless of the species, the myofibrils lined up in bundles occupy nearly the entire intracellular volume of muscle fibers. Myofibrils have a diameter of approximately 1 μm and consist of small subunits: the myofilaments (Figure 1). Longitudinal cross sections of myofibrils observed by electron microscopy exhibit alternating dark (A bands) and light areas (I bands). Each I band is divided into two portions by a Z line. The repeating unit found between two Z lines is the sarcomere, which is the contractile functional unit of the myofibril (Figure 5). Thin myofilaments primarily consist of actin, the troponins T, I, and C (which regulate muscle contraction) and tropomyosin arranged end to end along the actin filament. Thick myofilaments primarily consist of an assembly of myosin molecules whose ATPase activity catalyzes the breakdown of adenosine triphosphate (ATP) into adenosine diphosphate (ADP) and provide the chemical energy required for muscle contraction. Sarcoplasm, that is, the cytoplasm of muscle fibers, contains many soluble proteins, including enzymes of the glycolytic pathway and myoglobin, which carries oxygen to the mitochondria and stains cells red. It also contains glycogen granules, which represent the primary local energy reserve of muscle cells, in addition to lipid droplets.

3. Muscle Biochemical Composition

Skeletal muscles contain approximately 75% water, 20% protein, 1–10% fat, and 1% glycogen. The biochemical properties of the major muscle components (i.e., myofibers, connective tissue, and adipose tissue) are described in the following.

3.1. Muscle Fibers

Muscle fibers are generally characterized by their contractile and metabolic properties [6, 7]. The contractile properties primarily depend on myosin heavy-chain isoforms (MyHCs) present within the thick filaments. In most mature mammalian skeletal striated muscles, four types of MyHC are expressed: I, IIa, IIx, and IIb. The ATPase activity of these MyHCs is related to the speed of contraction: slow (type I) and fast (types IIa, IIx, and IIb). Type I fibers exhibit low-intensity contractions but are resistant to fatigue. They predominate in postural and respiratory muscles. Muscle contraction requires energy from ATP, whose requirements differ widely among the muscle fiber types [8].

Two major pathways of ATP regeneration are used in the muscle: the oxidative (aerobic) pathway through which pyruvate is oxidized by the mitochondria, and the glycolytic (anaerobic) pathway wherein pyruvate is converted into lactic acid in the sarcoplasm. The relative importance of these two pathways determines the metabolic fiber type: oxidative (red rich in myoglobin which is the oxygen carrier and pigment responsible for the red color), or glycolytic (white nearly devoid of myoglobin because oxygen requirements are highly limited). Generally, oxidative red fibers exhibit a smaller CSA than glycolytic white fibers. However, the differential size between fiber types can vary depending on the muscle and within the same muscle. For example, oxidative fiber CSA is greater than glycolytic fiber CSA in the red part of the semitendinosus muscle in pigs [10]. Similarly in the Rectus abdominis muscle of cattle, the oxidative red fiber CSA is larger than white glycolytic fiber CSA [11]. Finally, muscle fibers are dynamic structures that can switch from one type to another one according to the following pathway: I

IIA IIX IIB [12]. A summary of the different fiber type properties in mature mammalian skeletal muscle is shown in Table 1. Despite the obvious presence of their genes, none of the three isoforms of adult fast MyHC are present in the mature muscles of all mammalian species. In fact, the IIb MyHC is not expressed in sheep and horses and has been found only in certain cattle muscles with strong differences between breeds [13]. In contrast, strong expression of IIb MyHC is observed in skeletal muscles of conventional pig breeds selected for leanness and high growth performance [14]. Regardless of the species, the most important factor that determines muscle fiber composition is muscle type, likely in relation to its specific physiological function. For a given muscle, the fiber composition varies depending on the species. Thus, pig Longissimus muscle contains approximately 10% type I fibers, 10% IIA, 25% IIX, and 55% IIB, whereas bovine Longissimus contains on average 30% type I fibers, 18% IIA, and 52% IIX. The composition of muscle fibers is also influenced by breed, gender, age, physical activity, environmental temperature, and feeding practices. As in mammals, the muscle fibers of birds can be classified based on their contractile and metabolic activities. However, additional classes, for example, the multitonic innervated slow fibers of types IIIa and IIIb, which are specific to avian muscles, have been described [15]. In birds, it is difficult to match an isoform of MyHC with a fiber type due to the simultaneous presence of adult and developmental types of MyHC in mature fibers. Fish also exhibit different types of muscle fiber characterized by their contractile and metabolic properties. However, in contrast to mammals or birds, an anatomical separation between the two main fiber types can be observed in fish. For example, in trout, fast fibers (similar to mammalian IIB fibers) are found in the center in a cross-sectional body area, and slow fibers (similar to the mammalian type I) are found at the periphery along a longitudinal line under the skin [16]. In addition to these two main fiber types, minor types, such as the intermediate type (e.g., the pink fiber type, comparable to the type IIA) can be found in certain species or at certain stages of development. The two main types of white and red fiber have been associated with the expression of fast and slow MyHC, respectively [17]. However, it can be difficult to systematically match a MyHC isoform with a fiber type due to the simultaneous presence of several MyHCs within the same fiber in fish, particularly in the small muscle fibers.

3.2. Intramuscular Connective Tissue

The connective tissue that surrounds muscle fibers and fiber bundles is a loose connective tissue. It consists of cells and an extracellular matrix (ECM) that primarily consists of a composite network of collagen fibers wrapped in a matrix of proteoglycans (PGs) [4, 18, 19]. This paper focuses on the molecules that have been demonstrated or suspected to play a role in the determination of meat sensory quality. The collagens are a family of fibrous proteins. Regardless of the collagen type, the basic structural unit of collagen (tropocollagen) is a helical structure that consists of three polypeptide chains coiled around one another to form a spiral. Tropocollagen molecules are stabilized by interchain bonds to form fibrils of 50 nm diameter. These fibrils are stabilized by intramolecular bonds (disulphide or hydrogen bridges) or intermolecular bonds (including pyridinoline and deoxypyridinoline), known as cross-links (CLs). Various types of collagen are found in skeletal muscle. Fibrillar collagens I and III are the major ones that appear in mammals [19]. In fish, collagen types I and V predominate [20]. The other main components of connective tissue are the PGs [21]. The PGs are complex multifunctional molecules that consist of a core protein of molecular weight that ranges from 40 to 350 kDa, linked by covalent bonds to several dozen glycosaminoglycan chains (GAGs). PGs form large complexes by binding to other PGs and to fibrous proteins (such as collagen). They bind cations (e.g., sodium, potassium, and calcium) and water [22]. The proportion and the degree of intramuscular collagen cross-linking depend on muscle type, species, genotype, age, sex, and level of physical exercise [23]. The total collagen content varies from 1 to 15% of the muscle dry weight in adult cattle [19], whereas it varies between 1.3 (Psoas major) and 3.3% (Latissimus dorsi) of muscle dry weight in Large White pigs at the commercial slaughter stage [24]. In poultry, the collagen represents 0.75 to 2% of the muscle dry weight [25]. In fish, variable contents have been reported according to species (quantities vary from 1 to 10% between sardines and congers [26]), within species and between the front and caudal parts (richer) of the fillet [27]. PGs represent a small proportion of the muscle dry weight (0.05% to 0.5% in cattle according to muscles) [28].

3.3. Intramuscular Fat

In mammals, reserve fat is located in several external and internal anatomical locations such as around and within the muscle for the intermuscular and intramuscular (IMF) fats. In this paper, we focus essentially on IMF because intermuscular fat is trimmed during cutting and thus has less impact on pork and beef meat. In fish, fat are located subcutaneously and within the perimysium and myosepta, and mainly the latter contribute to flesh quality and is considered in this paper. IMF mostly consists of structural lipids, phospholipids, and storage lipids (the triglycerides). The latter are primarily (approximately 80%) stored in the muscle adipocytes found between fibers and fiber bundles, and a minor proportion (5–20%) is stored as lipid droplets within myofibers in the cytoplasm (intracellular lipids) [29]. Between muscle types, the phospholipid content is relatively constant (i.e., ranging from 0.5 to 1% of fresh muscle in pigs), whereas the muscle triglyceride content is highly variable whatever the species [30, 31]. The IMF content strongly depends on the size and number of intramuscular adipocytes. In pigs [32, 33] and cattle [30, 34], the interindividual variation in IMF content of a given muscle between animals of similar genetic background has been associated with variation in the number of intramuscular adipocytes. In contrast, variation in the IMF content of a given muscle between animals of the same genetic origin and subjected to different dietary energy intakes has been demonstrated to be associated with variation in adipocyte size [33]. In fish, the increase in myosepta width is likely related to an increase in the number and size of adipocytes [35]. The IMF content varies according to anatomical muscle origin, age, breed, genotype, diet, and the rearing conditions of livestock [30, 36–39]. For example, Chinese and American pigs (e.g., Meishan and Duroc, resp.) or European local pig breeds (e.g., Iberian and Basque) have higher levels of IMF than do European conventional genotypes, such as Large White, Landrace, or Pietrain [40]. The IMF content varies from 1 to approximately 6% of the fresh Longissimus muscle weight in conventional genotypes of pigs at the commercial slaughter stage, with values up to 10% in certain breeds [38]. In cattle, the IMF content of Longissimus muscle varies from 0.6% in Belgian Blue to 23.3% in Black Japanese at slaughter at 24 months of age [41]. In French cattle breeds, it has been demonstrated that selection on muscle mass has been associated with a decrease in IMF and collagen contents. For example, the main meat breeds Charolaise, Limousine, and Blonde d’Aquitaine have less IMF than hardy breeds, such as Aubrac and Salers, all exhibiting lower IMF levels than dairy breeds [42] or American or Asian breeds reared under the same conditions [36, 43]. In fish, the IMF content also varies between species from less than 3% in “lean” species such as cod to more than 10% in “fatty” species, such as Atlantic salmon [37], but also within species. For example, in salmon flesh, fat content may vary between 8 and 24% [44].

4. Relations between the Different Muscle Components

Studies based on comparisons between muscle types indicate that IMF content is typically positively correlated with the percentage of oxidative fibers and negatively with the glycolytic fibers [45]. Although oxidative fibers, particularly slow fibers, exhibit a higher intramyocellular lipid content than fast glycolytic fibers do [46] and although the IMF content has often been found to be higher in oxidative than in glycolytic pig muscles (i.e., Semispinalis versus Longissimus muscles) [47], many studies also indicate no strict relationship between total IMF content and muscle fiber composition [6]. In extreme cases, the IMF content can be three times higher in the white glycolytic than in the red oxidative part of the Semitendinosus muscle in the pig [34] (Figure 6). A negative correlation between IMF content and the oxidative metabolism was also found in the pig Longissimus muscle in a functional genomic approach [48]. However, positive genetic and phenotypic correlations were observed between IMF content and muscle fiber CSA in pig Longissimus muscle [49]. In fish, in which white and red muscles are anatomically separated, it is assumed that red muscles exhibit more elevated fat content than white muscles due to higher numbers of fat cells in the perimysium and higher numbers of lipid droplets within muscle fibers. In Atlantic salmon, a negative genetic correlation (rg = −0.85) has been reported between the total number of fibers and the IMF content, which suggests that, at a similar weight, selection for low IMF would result in an increase in the number of fibers [50]. Additionally, a negative correlation between collagen content and IMF (rg = −0.8) has been observed, which indicates that an increase in IMF would cause a relative decrease in muscle collagen content likely due to its “dilution” within muscle tissue [51]. No systematic relationship between the biochemical characteristics of the connective tissue and muscle fiber type has been found in meat-producing animals. In contrast, in fish, collagen content is higher in red than in white muscles [52].

5. Mechanisms of Muscle pm Changes and Quality of Meat and Flesh: Modulation by Muscle Properties

After slaughter, the meat is typically stored in a cold room at 4°C for 2 to 30 days depending on species, subsequent processing methods, and packaging. The longest storage periods are used for beef (one to two weeks for carcasses to one month for meat pieces stored under vacuum) to facilitate a natural tenderizing (aging) process. The reduction of muscle fiber CSA observed during the refrigeration results from a lateral shrinkage of myofibrils whose amplitude depends on the slaughter stress of animals and of the stunning technology (Figure 7) [53]. The aging phase is characterized by various ultrastructural changes and results in the fragmentation of muscle fibers. The action of different proteolytic systems results in characteristic myofibrillar ruptures along the Z lines (Figure 7). Mitochondria are deformed and their membranes altered [18, 54]. As a consequence of the degradation of costameres, that is, the junction of cytoskeletal proteins to the sarcolemma, the sarcolemma separates from peripheral myofibrils [55]. According to Ouali et al. [54], the enzymatic process starts as soon as bleeding occurs, with an activation of caspases, which are responsible for damage to cellular components during apoptosis. Other proteolytic systems (e.g., calpain, proteasome, and cathepsins) take over to continue the protein degradation of cells and muscle tissue [56].

Connective tissue also undergoes morphological changes during meat-aging [19, 21], which are detectable as early as 12 h pm in chickens [25] but only after 2 weeks pm in cattle [57]. This degradation facilitates the solubilization of collagen during cooking, thus improving the tenderness of cooked meat. An indirect effect of PGs on the tenderness of cooked meat has also been suggested. In fact, during aging, reduction of the perimysium resistance is associated with decreasing amounts of PGs along with an increase in collagen solubility due to the increased activity of certain enzymes. One hypothesis is that PGs may be degraded (spontaneously or enzymatically) during maturation and no longer protect collagen from enzymatic attacks [21]. In fish, flesh tenderization is associated with a gradual breakdown of the endomysium [58] and a detachment of the fibers from one another due to the rupture of ties with the endomysium and with the myosepta [59]. Soft-flesh fish demonstrate more endomysium (collagen, PGs) breakdown [60]. Fish myofibrils exhibit weak ultrastructural changes of the actomyosin complex, unlike bovine muscle [61]. Thus, in sea bream (Sparus aurata), I and Z bands are only partially degraded after 12 days of refrigerated storage [62].

6. Relations between Muscle Properties and Meat Quality

Among the various components of meat quality, the technological, nutritional, and sensory dimensions are considered. The nutritional quality component is primarily determined by the chemical composition of muscle tissue at slaughter, whereas the technological and sensorial components result from complex interactions among the chemical composition and metabolic properties of the muscle at slaughter and pm biochemical changes that lead to its conversion into meat [56, 63]. The structure and muscle composition, the kinetics of pm changes, and the additional meat use and processing methods that are applied (e.g., mincing, cooking) vary according to species and cuts, which results in major intrinsic differences in meat qualities between animal species and cuts. Therefore, the hierarchy between the most desired qualitative components varies between species. Prominent examples include tenderness in cattle, firmness in fish flesh, and water-holding capacity in pigs and chickens.

6.1. Technological Quality

After slaughter, depending on the species and the markets, the carcasses are stored in a cold room and then cut into pieces or muscles. During storage, the internal structure of muscles changes. The muscle fibers shrink laterally while expelling intracellular water to extracellular spaces, whose size increases. Subsequently, this water is expelled at the cut ends of muscles [53]. Regarding processing into cooked products, the technological quality is related to the water-holding capacity of meat, that is, its ability to retain its intrinsic water. The water-holding capacity is strongly influenced by the rate and extent of decrease in the pm pH. A high rate combined with a high muscle temperature (e.g., from stress or intensive physical activity directly prior to slaughter) causes denaturation of muscle proteins, reduced water-holding capacity and increase exudation, and cooking loss of meat in pigs and poultry. A large extent of pH decrease (i.e., acid meat) reduces the net electric charge of proteins, which also reduces the water-holding capacity [64, 65]. Measuring pH within one hour after slaughter and then on the following day to assess the rate and extent of pH decline, the determination of color and water loss during cold storage are the main indicators of the technological quality of meat. Muscle fiber composition influences the technological quality of meat, such as the water-holding capacity, which depends on the evolution of muscle pm pH kinetics and temperature. The pm pH decrease generally occurs faster in glycolytic muscles than in oxidative ones [66] although this relationship is not systematic. In fact, the pH at 45 min pm is much lower in pork Psoas major muscle (27% fiber I) than in Longissimus muscle (10% I fibers) [6], which could be explained by the lower buffering capacity of type I fiber (Table 1) or differences in the kinetics of pm temperature decline according to the anatomical location of muscles. In addition, stimulation of muscle glycolytic metabolism in the hour following slaughter increases the rate of pH decrease, which when combined with a high muscle temperature may result in protein denaturation and pale, soft, and exudative (PSE) syndrome in white muscles, particularly in pigs and chickens. In contrast, the extent of pm pH drop (ultimate pH typically determined 24 h pm) is consistently greater in white glycolytic than in red oxidative muscles due to a higher muscle glycogen content in vivo and during slaughter in the fast-twitch white glycolytic fibers. In Large White pig Longissimus muscle, the increase in rate and extent of pm pH decrease are associated with a paler color and higher luminance and exudation [49, 67]. In pigs, two major genes that substantially influence the kinetics of pm pH decrease and water-holding capacity have been identified. Mutation in the RYR1 gene (also known as the halothane gene), which encodes a ryanodine receptor that is part of the calcium release channel of the sarcoplasmic reticulum, is responsible for a rapid decrease in pm pH and the development of PSE meat [68]. Another pork quality defect is due to a mutation in the PRKAG3 gene that encodes a subunit of the AMP-activated protein kinase (AMPK) [69]. This mutation results in a very high muscle glycogen level at slaughter (+70%), particularly in the glycolytic muscles, which is responsible to a significant extent for the pm pH decrease and “acid meat” with low water-holding capacity. Interestingly, the Longissimus muscle of mutated PRKAG3 pigs contains more oxidative fibers [47] and a lower buffering capacity [70] which contributes to the low ultimate pH in addition to the greater lactate production from glycogen. A recent proteomic study in cattle revealed some correlations between metabolic, antioxidant and proteolytic enzymes with pH decline. These data allow a better understanding of the early pm biological mechanisms involved in pH decline [71].

6.2. Nutritional Quality

Meat and flesh are an important source of proteins, essential amino acids (AAs), essential fatty acids (FAs), minerals, and vitamins (A, E, and B), which determine nutritional quality. The AA profile is relatively constant between muscles or between species [72]. However, collagen-rich muscles have a lower nutritional value because of their high glycine content, a nonessential AA [19]. Compared with white muscles, red muscles have larger myoglobin content and thereby provide higher amounts of heme iron, which is easily assimilated by the body. Although IMF constitutes a small fraction of muscle mass, it is involved in human FA intake because the content and nature (i.e., the profile) of meat FA varies according to species, the anatomical origin of a given muscle, and animal diet [30, 73]. Dietary strategies have been intensively studied and optimized to decrease saturated fatty acid intakes and increase cis-monounsaturated and polyunsaturated fatty acids or other bioactive lipids in animal-derived products for human consumption [30, 73]. In addition, because n-3 fatty acids with more than 20 carbons are primarily incorporated into phospholipids rather than into triglycerides, it is possible to enrich meat content in these polyunsaturated fatty acids without increasing IMF. For example, regarding bioactive lipids, the peculiarity of meat from ruminants is the presence of fatty acids that directly or indirectly result from ruminal biohydrogenation and that are proposed to be bioactive fatty acids, such as rumenic acid, which is the main natural isomer of the conjugated linoleic acids [30] and known to prevent certain forms of cancer in animal models. However, during pm aging and meat storage, lipids undergo alterations (e.g., peroxidation), whose importance depends on the FA composition of the meat. These alterations may impair the sensory (e.g., color, flavor) and nutritional qualities of the meat [63, 74].

6.3. Sensory Quality
6.3.1. Color and Appearance

The composition of muscle fibers influences meat color via the amount and the chemical state of myoglobin. The high myoglobin content of type I and type IIA fibers results in a positive relationship between the proportion of these fibers and red color intensity. In deep muscles and meat stored under vacuum, myoglobin is in a reduced state and exhibits purple red color. When exposed to oxygen, myoglobin is oxygenated into oxymyoglobin, which gives the meat an attractive bright red color. During meat storage, myoglobin can be oxidized into metmyoglobin, which produces a brown, unattractive color that is negatively perceived by consumers [75, 76]. Many ante- and pm factors, such as animal species, sex, age, the anatomical location and physiological function of muscles, physical activity, the kinetics of pm pH decrease, the carcass chilling rate, and meat packaging, influence the concentration and chemical state of pigments and consequently meat color [77]. Muscles from cattle, sheep, horses, and migratory birds (e.g., geese, ducks) that contain high proportions of type I fibers rich in myoglobin are thus prone to metmyoglobin formation and decreased color stability. In contrast, a high proportion of glycolytic fibers results in the production of white meat, as found in chickens and pigs. Double-muscled cattle (mutation in the myostatin gene) present muscles with a high proportion of fast glycolytic fibers and consequently pale meat [3].

Meat color also depends on diet. For example, the feeding of calves with cow’s milk that is free of iron limits myoglobin biosynthesis, which results in pale meat as a result of iron deficiency.

In fish, only the superficial lateral red muscle, which is rich in myoglobin, exhibits intense (generally brown) color, whereas the white muscle is rather translucent. In the case of salmonids, the orange-red color of the flesh is due to the presence of food-supplied carotenoid pigments, such as astaxanthin, in the muscle fibers. Differences in lipid levels can result in variations in the thickness of myosepta (i.e., the "white stripes" trait), which can be detected by a trained sensory panel in fish that exhibit the contrasted muscle yields associated with different lipid contents [78]. On a given fish slice (cross section), red muscles can also be observed on the edge of white muscle, which represents approximately 90% of the muscle. Consumer perception of the red muscle, which oxidizes quickly pm to brown and then to black, is generally negative, and this red muscle is occasionally removed for premium products (e.g., smoked fillets). In addition to color, the quantity and distribution of marbling within a muscle slice affect appearance and thus can affect the acceptance of meat and meat products by consumers (cf. Section 6.3.3). In fish, another major defect of flesh (fillet) appearance is the so-called “gaping” defect, which results from the partial disruption of the myosepta or the fiber/myosepta interface. The biological and/or technological origin of this quality defect remains unclear.

6.3.2. Tenderness

Tenderness and its variability are the most important sensory characteristic for beef consumers. Beef meat has a much higher basic toughness (determined by the proportion, distribution, and nature of the intramuscular connective tissue) and lower pm tenderization process than those of pork or poultry [63]. Thus, the pm aging duration is essential for beef tenderness [79]. In pigs and poultry, the pm acidification kinetics of muscles, which is faster than in cattle [79], strongly influences the texture (i.e., juiciness, tenderness) and the technological properties of meat (e.g., water-holding capacity) [63]. In cattle, the relationships between fiber characteristics and tenderness are complex and vary according to muscle, sex, age, and breed [80]. For example, among bulls, Longissimus thoracis tenderness is often associated with a decrease in fiber CSA and an increase in oxidative metabolism, whereas in the Vastus lateralis and semitendinosus muscles, the higher that the glycolytic activity is, the tenderer the meat is [81]. However, a negative correlation between the intensity of the oxidative metabolism and tenderness has also been observed in the Longissimus muscle of cattle [82]. Using biomarkers of beef tenderness Picard et al. [83] demonstrated that in breeds characterized by a muscle metabolism more fast glycolytic, such as the French beef breeds, the most tender Longissimus thoracis are the most oxidative. On the contrary, in breeds whose muscle metabolism is more oxidative, such as Aberdeen Angus, the most glycolytic Longissimus thoracis are the tenderest. This is in accordance with the fact that in breeds that exhibit oxidative muscles, such as Angus or dairy breeds, rib steaks with low red color intensity are tenderer. In contrast, among the main French beef breeds that exhibit more glycolytic muscles, the reddest the muscle is, the tenderer the meat is [83]. A higher proportion of glycolytic fibers could improve the tenderness of certain muscles by accelerating pm aging due to the presence of a higher calpain/calpastatin ratio (two proteins involved in proteolysis) [84] in the meat of animal species with slow meat-aging, such as cattle and sheep [82]. However, for other authors, the improvement in meat tenderness associated with the increase in the type I fiber proportion is explained by the higher protein turnover and associated proteolytic activity in the oxidative fibers [85]. Among bulls, except for rib steak, meat tenderness does not seem to be associated with fiber CSA but with the metabolic properties of muscle fibers.

In pigs, a functional genomic study has reported a negative impact of the abundance of fast fibers and of high glycolytic metabolism on meat tenderness [48]. This study also demonstrates that reduced expressions of protein synthesis genes (e.g., antiapoptotic heat shock-proteins genes and the calpastatin gene) and an increase in the expression level of genes involved in protein degradation (particularly proteasomes) are associated with a lower shear force (i.e., improved tenderness) at 1 day pm. A negative relationship between average fast glycolytic fiber CSA and tenderness has been demonstrated in pigs [86]. Therefore, a strategy aimed at increasing the total number of fibers combined with moderate fiber CSA and an increase in the percentage of slow-twitch oxidative fibers could be a promising means to increase muscle quantity while preserving the sensory quality of pork [6]. In contrast, in chickens, an increase in fiber CSA in the Pectoralis muscle is associated with a decrease in muscle glycogen content, higher ultimate pH and water-holding capacity, and improved tenderness [87]. However, contradictory data for chickens also report negative effects of fiber CSA on meat water-holding capacity and tenderness [88]. In fish, comparisons between species have observed a negative correlation between the mean diameter of muscle fibers and flesh firmness. However, this relationship seems more controversial within species: similar results have been found for smoked Atlantic salmon and the raw flesh of brown and rainbow trout, whereas other studies did not demonstrate a relationship between fiber size and the texture of salmon or cod flesh. Altogether, as in pigs, it appears that hyperplasic rather than hypertrophic muscle growth is better for the quality of fish products.

Connective tissue influences meat tenderness by its composition and structure [4], particularly in cattle, whereby collagen is generally considered to be the major determinant of the shear force. However, there are substantial differences between raw and cooked meat. The shear force of raw meat is highly correlated with its collagen content [21, 89]. In cooked meat, the level of correlation between the content, thermal solubility, or cross-linking level of collagen and meat shear force is unclear and varies according to muscle type and cooking conditions [90, 91]. During heating, the collagen fibers shrink and pressurize muscle fibers with a magnitude that depends on the degree of collagen cross-linking and the organization of the endomysium and the perimysium. The level of interaction between collagen and muscle fibers modulates the thermal denaturation of collagen (i.e., its gelatinization) and therefore the development of meat tenderness during cooking [89]. In pigs and chickens, it is generally considered that collagen has a limited impact on meat sensory quality. The reason is that the animals are slaughtered at a relatively early physiological stage, at which intramuscular collagen is not significantly cross-linked [19].

In addition to its composition, the structure of connective tissue, in particular its organization and the size of the perimysium bundles (which determine the grain of the meat, particularly in beef), also plays a role in the development of meat texture [92]. According to Purslow [23], the relationships between the grain of meat and texture indicate that tenderness is positively correlated with the proportion of small diameter bundles (termed primary bundles) but that this parameter does not accurately predict tenderness. Ellies-Oury et al. [80] demonstrated no significant relationship between grain of meat and tenderness evaluated by a trained sensory panel, shear force, or collagen content and solubility. Additionally, the shear force of the muscle increases with the thickness of the secondary perimysium bundles in cattle [93] and pigs [94]. Larger bundles (e.g., tertiary, quaternary) occur but are rarely considered in studies that address meat tenderness. Thus, their influence on the structure of muscle connective tissue and meat tenderness remains unclear.

In fish, comparisons among species have demonstrated a positive relationship between the firmness of raw flesh and its collagen content. However, this relationship was not observed within species. Regarding the influence of collagen cross-linking on the firmness of raw flesh, only a low relationship (

= 0.25) between the content in hydroxylysyl pyridinoline (CLs) and the mechanical strength of the fillet has been observed in salmon [95]. Because of its low thermal stability compared with that of mammals, muscle fish collagen does not maintain its structural properties during cooking. Thus, the texture of the cooked flesh mostly depends on the myofibrillar proteins. Comparisons between species have noted positive correlations between muscle collagen content and the tenderness and elasticity of the cooked flesh [26]. However, none of these results were found within fish species. Fish species with firm flesh exhibit a highly dense network of collagen fibers in the endomysium, whereas this network is much looser in the less firm flesh species [96].

6.3.3. Juiciness and Flavor

In cattle and lambs, an increased proportion of type I fibers is associated with improved meat juiciness and flavor [85, 97]. This favorable effect on flavor is probably explained by the high phospholipid content of type I fibers, the phospholipids being a major determinant of the flavor of cooked meat [98]. However, the high content of polyunsaturated FAs in phospholipids increases the risk of a rancid taste. In pigs, a high percentage of fast oxidoglycolytic fibers impairs the water-holding capacity and juiciness of the meat [85, 99]. IMF is often recognized as playing a key role in the determination of sensory qualities of meat or flesh in different animal species by positively influencing juiciness, flavor, and tenderness, although its influence on sensory traits varies among species [37]. It is generally accepted that very low levels of IMF result in dry meat with low taste. However, a high correlation between IMF and sensory quality ratings assigned by a trained panel may be observed only when important variations and high maximal levels of IMF occur (i.e., in pigs) [100]. In fact, other factors can modulate this relationship, such as the ultimate pH of meat in pigs, or the content and the type of intramolecular CLs of collagen in cattle [37]. For example, beef with similar levels of IMF (approximately 3.2%) but issued from four different breeds (Angus, Simmental, Charolais, and Limousine) exhibited similar flavor but higher juiciness in the Limousine and lower juiciness in the Angus breeds [101]. Regarding the assessment of fresh meat and meat products by consumers, the influence of IMF seems contradictory. Before consumption, consumers prefer less marbled pork, whereas at the time of consumption, the most marbled meats are considered to be juicier, tenderer, and tastier [100, 102, 103]. Although fats are a key factor in the development of flavor during meat cooking and in meat juiciness, consumers are often resistant to meat that exhibits visible IMF. Thus, several studies have demonstrated that the level of overall acceptability of pork increases with IMF content up to 2.5–3.5% [102, 104]. However, other studies observe that a significant number of consumers prefer less marbled pork (1 to 1.5% IMF) [100, 105]. A distinction between consumer groups based on the preference for moderately or slightly marbled beef has also been noted and associated with taste or nutritional expectations, respectively [106]. Thus, the assessment of relationships between IMF content and the sensory attributes of meat depends on the dietary habits and cultures of the consumers and on the considered products. For example, the tenderness, juiciness, and acceptability of dry ham have been demonstrated to increase with IMF content [107]. However, the reverse has been observed for cooked ham, whose acceptability decreases with an increase in IMF from 2 to 4% in the Semimembranosus muscle [108]. Similarly, a variation from 2.9 to 10.7% in IMF differently influences the acceptability of salmon fillets depending on the particular product. A decreased IMF content is more favorable for the baked fillet, whereas the opposite is true for smoked fillets [109].

7. Conclusion

The three main components of muscle (i.e., muscle fibers, connective tissue, and adipose tissue) are involved in the determination of various meat quality dimensions but to varying degrees depending on species, muscle type, and postslaughter meat-processing techniques. The relative independence among the characteristics of these three major muscle constituents suggests that it is possible to independently manipulate these characteristics by genetic, nutritional, and environmental in order to control the quality of products and thus better fulfill the expectations of producers, meat processors, and consumers. Therefore, precise knowledge regarding the structural and biochemical characteristics of each muscle component and their relationships with growth performance and meat quality dimensions is a prerequisite to understanding and controlling the biological basis of the quantity and quality of animal products. Future research should focus on the modulation of muscle properties that determine the major components of meat quality in the different species: tenderness in cattle, water-holding capacity and tenderness in pigs and poultry, and flesh texture in fish.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


The authors thank those who participated in the various projects that led to these results and all those who provided funding support for this research. This paper is based on a French-language article by Listrat et al. [110].


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Copyright © 2016 Anne Listrat et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Some solutions and some additional difficulties

To overcome the problem that measurements of RMR do not adequately capture the total daily rates of energy expenditure and to explore the links between daily energy demands and lifespan, I compiled data on the daily energy demands of mammals and birds measured using the doubly-labelled water (DLW) method. The DLW method is an isotope-based method for the measurement of whole body CO2 production and hence energy expenditure (Nagy, 1994, 2005 Speakman, 1997). The main advantage of the technique, over traditional methods of quantifying energy demands, is that it can be employed without the need to restrict subjects inside a calorimetry chamber (either direct, to measure its heat flow, or indirect, to measure its gas exchange). As such, the method has become the gold standard technique for measurement of free-living energy demands. The method depends on the observation that oxygen isotopes in body water are in complete and rapid exchange equilibrium with the oxygen in dissolved CO2. The main consequence of this exchange is that an isotope of oxygen introduced into body water will be eliminated by both the flux of water through the body and the uptake of unlabelled inspired oxygen combined with the elimination of labelled expired CO2. Since a simultaneously introduced isotopic label of hydrogen would only be washed out by the flux of water, a measure of CO2 production and hence energy expenditure is made possible by the differential elimination of the two labels. The real power of the method, however, is that the elimination rates of the isotopes can be reconstructed by taking one sample shortly after the isotopes are eliminated and a second sample some time later. Between these samples the subject can engage in its normal daily activities unencumbered by the traditional apparatus that is used routinely to measure energy metabolism.

This simple description of the method belies some subtleties in its use. One important aspect that has emerged over the past decade is that the calculation method that provides the best fit of experimental DLW data to simultaneous validation data using conventional methods differs for animals in different size ranges. For animals weighing less than about 5 kg the best method involves multiplying the isotope turnover constants by a single estimate of the pool size. For larger animals and humans, however, the better equation includes both hydrogen and oxygen pool sizes applied to their own turnovers (reviewed extensively in Speakman, 1997). The calculation techniques differ by between 3 and 20%, depending on the actual isotope divergences. Unfortunately this fact has gone largely unrecognised, or studies were performed before the problem was recognised, and almost all the applications of the method to animals weighing greater than 5 kg have utilised the wrong calculation. This leads to a systematic bias in measurements of larger animals that makes estimates of their energy demands, and thus derivation of scaling exponents, subject to substantial error. Yet, the necessary baseline data to recalculate the original estimates using the more appropriate equations are generally not available in the original papers.

To overcome the above calculation problem I restricted the data collection to include only animals weighing less than 4 kg. This database included 73 species of small mammal and 90 species of bird. I then searched the literature for estimates of the maximal lifespans of these animals, utilising as a key reference and starting point the compiled database from Carey and Judge(2000). This generated 249 estimates of maximal lifespan for small mammals and 163 estimates for small birds. In combination, there were estimates of both maximal lifespan and daily energy demands for 48 of the 73 mammals and 44 of the 90 birds where estimates of daily energy expenditures were also available.

The patterns of variation in lifespan as a function of body size in these data conformed to the previously observed patterns. The daily energy metabolism was positively related to body mass in both groups(Fig. 4A,B). In mammals, the least-squares fit regression was loge(daily energy expenditure in kJ day -1 )=2.05+0.621 ×logeMb(in g) [F=420.5, P<0.001, r 2 =0.856, N=73, reduced major axis (RMA) gradient=0.671]. In birds the regression was loge(daily expenditure of energy in kJ day -1 )=2.31+0.692 ×logeMb (in g) (F=1087.25, P<0.001, r 2 =0.925, N=90, RMA gradient=0.719). In the combined data set the difference in slopes was only marginally significant (GLM: F=3.95, P=0.048), but the difference in intercepts was highly significant(GLM: F=74.68, P<0.001). Independent of body size, birds expend on average more energy per day than the mammals. Larger mammals and larger birds both lived longer than their smaller equivalents(Fig. 1A,D), and the birds on average lived longer than equivalent sized mammals. The gradients of the scaling relationships were both shallow. In mammals the least-squares fit regression was loge(lifespan) (y)=0.851+0.209 ×logeMb (g), F=157.7, P<0.001, r 2 =0.390, RMA gradient=0.334, N=249. In birds the relationship was loge(lifespan)=1.514+0.216×logeMb, F=134.12, P<0.001, r 2 =0.458, RMA gradient=0.319, N=163. In the combined data set (N=412) the difference in slopes was again marginally significant [general linear model (GLM): F=4.77, P=0.03], and the difference in intercepts was much larger (GLM: F=62.07, P<0.001). Neither of these results differs from the patterns that were established when comparisons were made using resting metabolic rate (RMR).

Loge(daily energy expenditure in kJ day -1 ) measured using the doubly-labelled water (DLW) technique for free-living animals as a function of logeMb (in g) for (A) 73 small mammals and (B) 90 birds. See text for statistics.

Loge(daily energy expenditure in kJ day -1 ) measured using the doubly-labelled water (DLW) technique for free-living animals as a function of logeMb (in g) for (A) 73 small mammals and (B) 90 birds. See text for statistics.

When the daily energy expenditure and lifespans were combined to yield the lifetime expenditure of energy, however, a novel pattern emerged. In the mammals there was a strong negative relationship between lifetime expenditure of energy per gram and body mass the least-squares fit regression was loge(lifespan expenditure of energy per gram) (in kJ g -1 life -1 )=9.04-0.208 ×logeMb (in g) (F=27.82, P<0.001, N=49, r 2 =0.377). In birds the relationship also had a negative trend but in this case failed to reach significance: loge(lifespan expenditure of energy per gram) (in kJ g -1 life -1 )=9.693-0.0696 ×logeMb (in g)(F=2.5, P<0.121, N=44, r 2 =0.056). In the combined data set the difference in slopes was significant at P<0.05 but P>0.01 (GLM: F=5.65, P<0.020). Excluding this minor slope effect the intercept effect was highly significant (F=139.65, P<0.001). Combining the data, and excluding the marginally significant interaction, the effects of body mass and class were both highly significant, and the best-fit pooled regression equation was loge(lifetime expenditure of energy per gram) (kJ g -1 life -1 )= 8.75-0.145 ×logeMb(in g) (t=-4.85, P<0.001)+1.26 ×Class(t=12.22, P<0.001, where Class is a dummy variable for mammals for birds, (overall regression F=92.78, P<0.001). This equation indicates that independent of body mass, a gram of tissue in a bird expends about 3.5 × the amount of energy over a lifespan as a gram of tissue in a mammal of the same body mass. In both the birds and mammals there was substantial inter-individual variation in the lifetime expenditure per gram (Fig. 5). I explored the nature of this variation in the mammals and found that residual variation in the relationship was related to the average temperature where the measurements of daily energy expenditure had been made(Fig. 6). Thus, independent of the body size effect, animals living in colder habitats tended on average to have greater lifetime expenditures of energy per gram of body tissue. In combination, body mass and ambient temperature explained 45% of the variability in the lifetime energy expenditure per gram of the mammals.

Lifetime expenditure of energy per gram of body tissue plotted against logeMb for (A) mammals and (B) birds. See text for statistics.

Lifetime expenditure of energy per gram of body tissue plotted against logeMb for (A) mammals and (B) birds. See text for statistics.

Residual loge(lifetime expenditure of energy per gram of body tissue) plotted against ambient temperature at which the measurement was made for small mammals.

Residual loge(lifetime expenditure of energy per gram of body tissue) plotted against ambient temperature at which the measurement was made for small mammals.

I removed the shared effects of body mass on both lifespan and daily energy expenditure and sought associations between the residuals. In the mammals there was a significant negative association between residual lifespan and residual daily energy expenditure. Mammals that had high rates of expenditure for their body masses died sooner (F=18.47, P<0.001, r 2 =0.139: Fig. 7). In birds, however, the association was not significant(F=1.85, P=0.181).

Residual logelifespan (years) plotted against residual loge(daily energy expenditure in kJ day -1 ) measured using the doubly-labelled water method for (A) mammals and (B) birds. See text for statistics.

Residual logelifespan (years) plotted against residual loge(daily energy expenditure in kJ day -1 ) measured using the doubly-labelled water method for (A) mammals and (B) birds. See text for statistics.

In part, therefore, replacing the estimate of RMR with daily energy expenditure confirms several of the basic arguments that have been used to undermine the rate of living theory - namely that birds expend more energy per gram of tissue over their lifespans than mammals of equivalent sizes, and within each class there is an enormous variation in the lifetime expenditure of energy. However, these analyses have revealed some evidence in support of the rate of living theory and some additional evidence against it. In support of the theory, the residual lifespans were negatively associated with the residual daily energy expenditures - at any given size mammals that expend more energy appear to expire sooner, but in birds this did not hold. However,the independence of the lifetime expenditure of energy per gram of tissue as a function of size (Fig. 1C,F)was not confirmed when RMR was replaced by FMR. The amount of energy a gram of tissue expends over a lifetime does appear to differ as a function of body size (particularly in mammals) - such that this level is about 62% lower in a mammal weighing 2000 g (lifetime expenditure per gram averaging 1737 kJ g -1 life -1 ) compared with one weighing 20 g (4518 kJ g -1 life -1 ). In fact this negative effect of mass was evident (and highly significant) in the original data based on RMR(Fig. 1C,F - see statistics in the legend), but the effect becomes magnified when considering daily energy expenditure rather than RMR.

There are, however, some aspects of this analysis that require further consideration. Firstly, it is generally the case that the estimates of energy metabolism utilised in this analysis (i.e. daily energy expenditures measured on animals living in their natural environment) have not been made under the same conditions that pertain for the measurements of maximum longevity, which generally refer to animals kept in captivity. It seems likely that the energy demand levels of captive animals will not be as high as their wild counterparts because the foraging requirements are reduced. If the rate of living theory postulates that energy metabolism rates and lifespans are causally related, then it is clearly necessary to compare data for lifespans with data for energy metabolism derived under the same conditions. So while it is certain that measurements of RMR are not an appropriate measure to evaluate the rate of living theory, we may simply have replaced one inappropriate index of energy metabolism with another. This effect is probably more significant in birds than mammals because in captivity the metabolic rates of the former are probably more reduced than in small mammals because of the restriction in their flight behaviour. This may explain why significant effects were more evident in mammals than birds. Unfortunately, there are very few measures of daily energy demands in captive conditions made under the same conditions as lifespan estimates.

Moreover, there are probably systematic changes in activity (e.g. Minois et al., 2001 Carey, 2003) and RMR (e.g. Hughes et al., 1998 Van Pelt et al., 2001 Speakman et al., 2003) as a function of age, that call into question the validity of characterising the energy demand levels for a species over its entire lifespan with estimates derived from a cohort of individuals that may be unrepresentatively sampled. The interesting patterns in lifetime expenditure of energy that emerged as a function of both body mass and temperature may be artefacts of combining inappropriate measures (but it is also true that the absence of these effects in the original data based on RMR may also be an artefact).

A deeper problem exists, however, which suggests that such suitable information may never emerge from this type of analysis as a test of the rate of living theory. As pointed out by Ramsey et al.(2000), in the context of responses of animals to caloric restriction, and more generally by Speakman et al. (2002), the rate of living theory posits only that metabolic rate is related to the rate of ageing. A fair test of the theory is therefore only feasible if one modulates the rate of metabolism while holding other variables that might have an influence constant. Obviously when comparisons are made between different species this assumption that everything else is held constant is violated because different species differ enormously in their capacities for oxidative defence and repair (Cleaver et al.,1995 Portero-Otin et al.,2001 Pamplona et al.,2002) and also in the precise stoichometry of the production of radical oxygen species during oxidative phosphorylation (Barja, 1998, 1999, 2002). The much cited comparison of birds and mammals as a refutation of the ROL theory(Holmes and Austad, 1995a Austad, 2000 Holmes et al.,2001) is consequently not very strong evidence, because these groups differ in many other aspects, as well as in their metabolic rates and lifespans. It rather points to a lack of generality in the concept when applied to larger taxonomic groupings.

One conclusion that can be drawn from this discussion is that because differences between species may reflect adaptive differences in the stoichiometry of free-radical production in relation to oxidative phosphorylation, or differences in the capacity of oxidative defence and repair mechanisms, many of which may have a genetic basis, tests of the rate of living theory may be better performed by considering the associations between energy metabolism and ageing within species.

Global and Regional Patterns in Riverine Fish Species Richness: A Review

We integrate the respective role of global and regional factors driving riverine fish species richness patterns, to develop a synthetic model of potential mechanisms and processes generating these patterns. This framework allows species richness to be broken down into different components specific to each spatial extent and to establish links between these components and the processes involved. This framework should help to answer the questions that are currently being asked by society, including the effects of species invasions, habitat loss, or fragmentation and climate change on freshwater biodiversity.

1. Introduction

The diversity of life, usually referred to as “biodiversity”, is not evenly distributed throughout the globe. A considerable proportion is to be found in the tropics, while the poles are only home to a small fraction, and between the two extremes there is a whole diversity gradient. Ecologists, bio-geographers, and paleontologists have studied the reasons for these differences, but the question remains open despite the dozens of hypotheses that have been put forward on the subject [1–5]. The present analysis is limited to one important aspect of biodiversity, species richness, which is defined as the number of species present at a given time in a given place. Species richness gradients can be examined across a variety of spatial extents (extent is the geographic separation between the furthest points) and grains (grain is the area of the sampling unit) [6]. But ecologists, who up to the 90s preferred experimental approaches, mainly focussed on the factors and processes that influence species richness at fine grain sizes and spatial extents (based on published papers in Ecology between 1980 and 1986, cited by May [7]). However, it is now recognized that species richness patterns are directly influenced by processes working at much larger scales that is, regional or even continental [8–12]. This gave birth to macroecology [13, 14], whose aim is to highlight the statistical properties that emerge from complex ecosystems, in order to identify general patterns at different space-time scales of observation, and particularly at the macroscopic scale. If we follow Brown's ([13, page 6]) definition of macroecology: “it is a non-experimental, statistical investigation of the relationship between the dynamics and interactions of species populations that have typically been studied on small scales by ecologists and the processes of speciation, extinction, and expansion and contraction of ranges that have been investigated on much larger scales by biogeographers, paleontologists, and macroevolutionists. It is an effort to introduce simultaneously a geographical and a historical perspective in order to understand more completely the local abundance, distribution, and diversity of species, and to apply an ecological perspective in order to gain insights into the history and composition of regional and continental biotas.” In fact, determining which factors and processes are responsible for the variation in species richness patterns is a crucial issue for conservation planning in the face of current and future global and regional anthropogenic impacts [15].

Here, we review patterns and predictors of riverine fish species richness at the drainage basin grain and at global and regional extents. The “freshwater fish” model is particularly well adapted to this type of study since drainage basins are separated from one another by barriers (oceans, or land) that are—for all practical purposes—insurmountable for strictly freshwater fishes, and thus form a kind of insular habitats. Like remote islands, drainage basins are not under equilibrium conditions, as they receive new colonists so rarely that immigration and speciation often occur on similar timescales. This absence of migration between river basins over large temporal scales implies that extinction and speciation processes are specific of each drainage basin [16]. Thus, river basins are, to some extent, independent entities that could be used in comparative analysis to explore the factors that shape overall fish community richness between them. Incidentally, a considerable amount of exploitable data is now available that enables the use of comparative approaches to test the main ecological hypotheses currently under consideration. In this chapter we will use this natural experiment framework to review and discuss the relative role of regional and continental features in determining river drainage basin diversity patterns.

Unless otherwise specified, the term “river drainage basin” will refer to rivers flowing into the ocean (including all their tributaries). For rivers that are part of a bigger drainage basin, the term “tributary” will be used. In this paper we will focus on two grains sizes (i.e., river drainage basin or tributary drainage basin) at two different extents (i.e., global to regional). The term species richness (or species diversity) describes here the total number of species encountered within a river basin or within a tributary.

2. Global Approach to Riverine Fish Species Richness

At the intercontinental scale, three major hypotheses that sum up the majority of different hypotheses proposed (see [3] for a review) have already been tested to explain the variability of riverine fish species richness.

The first, the area hypothesis [17, 18] refers to the existence of a positive relationship between the number of species present in a given area and the size of this area. This relationship has been described by a power function in the form

(where is the number of species, is the (surface) area, and and are constants to be fitted) [19, 20]. It suggests that size (the surface of a river drainage basin in the case of riverine fishes) limits the number of species an area can harbor, and, due to its universal application, almost serves as a law in community ecology [21]. Several nonexclusive explanations have been put forward to explain this species-area relationship (Schoener 2010) but three of them are most often invoked: (1) the size-dependent extinction rate [17, 18], (2) the size-dependent speciation rate [22], and (3) the diversity of the habitat [18]. According to the first explanation the probability of extinction of a species increases with a reduction in the size of the “island”, due to a decrease in its population size. The second explanation suggests a positive effect of area on speciation rate by exposing species to greater ecological heterogeneity and/or geographical barriers [5]. The third explanation suggests that the heterogeneity of the habitat and the diversity of available food resources increase with the size of the “island” thus offering a large number of available niches and consequently favouring the coexistence of a large number of species [23].

The second hypothesis, the species-energy hypothesis [24, 25] predicts a positive correlation between species richness and the energy available within the system. This hypothesis has received empirical support from a large number of studies carried out on different communities of animals and plants [24, 26–36]. This being said, there is still a certain ambiguity even in the way the hypothesis is expressed. In fact, energy can influence richness by means of two rather different processes. Wright [24] considers energy to be a factor that determines resources available for a given biological community and thus as a productivity factor per se, whereas Turner et al. [33] and Currie [27], for example, consider energy to be a factor that determines the physiological limits of the species. In the former, one would expect a variable such as net primary production to be an important predictor of species richness whereas in the later, variables linked with temperature or available solar energy would predominate [29].

Finally, the third hypothesis, the historical hypothesis [37], attempts to explain differences in richness gradients by the potential for recolonisation of systems and thus by the degree of maturity achieved since the last major climate change or by the degree of stability in past climatic conditions [38, 39]. This last hypothesis, which combines past environmental conditions with geographic contingencies regulating dispersal possibilities, has been relatively neglected compared to the others. Two main reasons can explain this gap: (i) in essence, past conditions are much more difficult to evaluate and accurately measure than present conditions and (ii) current and past conditions are globally highly correlated.

2.1. The Roles of Area and Energy

In the first global studies conducted in this topic Oberdorff et al. [40] and Guégan et al. [41] used data obtained for 292 drainage basins on 5 different continents to identify the factors responsible for variations in riverine fish species richness within the framework of the three above-mentioned hypotheses. The models resulting from these exploratory analyses tend to show that, at this spatial extent, the factors associated with the first two hypotheses (i.e., the area hypothesis and the species-energy hypothesis) predominate. Only taking into consideration three summary factors, that is, the total surface area of the river drainage basin, the mean flow at the river mouth and the net terrestrial primary productivity within the basin, those models explain between 78 and 93% (depending on the statistical model) of the natural variability of the river basin species richness, the mean annual river discharge explaining the greater part of the variance in species richness.

Based on a comprehensive species richness dataset (Figure 1) recently compiled (926 river basins analyzed, see [42, 43] and the Supplementary Appendix available online at doi: 10.1155/2011/967631 for further details on the database), we performed a spatial autoregressive model (SAR, see [44]) accounting for the spatial configuration of drainage basins. The final model explains 77.1% of the total variation in species richness. Results of this new analysis confirm previous findings concerning the effects of area-related and climate-related variables, but also reveal a significant influence of past climatic changes and geographic isolation of drainage basins on species richness patterns (see Table 1 and Figure 2). These historical effects have also been revealed in previous regional analyses (see [45–48] although on a different spatial grain) but, regarding freshwater fish, this is the first time that the effect of past climatic variability (from glacial periods of the Pleistocene to present day) on species richness patterns is detected at the global scale (but see [39] for an effect of climatic variability on beta diversity).

statistical package [49] and spdep library [50] (see the Supplementary Appendix for further explanations). The spatial structure was implemented by a neighbourhood matrix of the drainage basins (see [46] and the Supplementary Appendix for further explanations) and assuming that the autoregressive process occurs in the error term (i.e., the “spatial error model” described by Dormann et al. [44]). Further methodological details on species richness, environmental variables computing, and modelling procedure are available in the Supplementary Appendix. Habitat heterogeneity was estimated by applying Shannon’s diversity index to proportions of biomes (i.e., vegetation types associated with regional variations in climate) within drainage basins. Temperature anomaly represents the Quaternary climate variability measured as the change in mean annual temperature between the present and the Last Glacial Maximum (LGM, circa 21 thousand years ago). Following Oberdorff et al. [51] we also considered whether or not a drainage basin was on a land mass, a peninsula, or an island (LPI continental mass = 0 peninsula = 1 island = 2). All other variables are fully explained in the Supplementary Appendix. The Moran’s I value represents the remaining autocorrelation on the residuals of the model for the first distance class, that is, neighbour drainages (the values for the remaining distance classes are also nonsignificant).

(j) Fish species richness for each river basin as a function of drainage area (a), habitat heterogeneity (b), altitudinal range (c), runoff (d), temperature anomaly (e), LPI (Land-Peninsula-Island) (f), actual evapotranspiration (g), precipitation (h), temperature (i), and precipitation seasonality (j). See Table 1 for variables description.

With respect to the area hypothesis, these results confirm those of several previous studies carried out at the regional scale that identified the size of the river drainage basin and/or the mean flow at the river mouth as important predictors of river basin species richness [46, 52–56]. Furthermore, according to our SAR model, habitat diversity still plays a significant role in explaining richness gradients after accounting for drainage area (Table 1). However these results do not fully answer the questions following from the area hypothesis, that is, are species richness patterns due to area-dependant rates of extinction and/or speciation, or to an increase in habitat diversity, or both?

With respect to the species-energy hypothesis, the results obtained by Oberdorff et al. [40] and Guégan et al. [41] tend to favour the hypothesis of an effect of energy on richness through an increase in available resources for the species. (Net Primary Productivity is an important predictors of species richness.) However, a difficulty in discussing further this last result is that these authors used estimates of terrestrial primary productivity from Lieth's models [57] instead of real aquatic primary productivity (data not available). Even if considering that terrestrial productivity gives a correct estimation of aquatic productivity (as food webs supporting fish are largely based on allochthonous inputs), using estimates of terrestrial primary productivity probably under-estimates true aquatic productivity (see [58] for a review). However, our SAR model also gives support to an indirect effect of energy through species physiological limits (positive effect of variables linked with temperature in the model, see Table 1).

The species-energy theory as developed by Wright et al. [25] posits a positive link between species richness and energy availability [59]. However, in plant and animal communities, a variety of patterns in species richness have been observed over productivity gradients, including positive, negative, and unimodal relationships [60–63]. It is not clear yet why richness shows these (apparently) contradictory relationships with productivity even if some explanations have already been proposed. For example, it has been suggested that all these noted relationships may just be incomplete segments of an overall hump-shaped, unimodal relationship over a broader range of productivity. Nevertheless, evidence for this possibility is currently limited at best [60, 63]. Results from Oberdorff et al. [40], Guégan et al. [41], and our SAR model support the view of a monotonically increase of riverine fish species richness with increasing productivity at the global scale (Figure 2).

At this spatial scale, the only direct historical factor significantly acting on species richness was past climatic variability (see Figure 2 and results of the SAR model in Table 1). It is thus tempting to conclude that history is a minor driver of diversity at the global scale. However we should keep in mind that all the variables used in the SAR model are interrelated to some extent and difficult to separate. This can be visualized in Figure 3, where the explained variance of a linear regression has been partitioned into three different groups of factors related to the area, energy and historical hypotheses. Currie [27], referring to land animals, put forward an explanation for the absence of influence of history on contemporary diversity patterns: that historical factors only influence species richness over relatively short periods, that is, less than the period of time since the last glacial maximum. Nevertheless this explanation seems inappropriate for riverine fishes. In their case, community saturation should be more difficult to reach than for land animals in the sense that their colonization depends on potential connections between river drainage basins. It is thus logical to expect that the influence of historical events should still be detectable in riverine fish communities at the global scale and that the weak influence of this driver most often noticed comes preliminary from difficulties in defining the appropriate variables.

Variance partitioning in explaining species richness gradients between area-related, climate-related, and historical variables. The analysis was performed using the “varpart” function from the vegan R package [64] and grouping variables as in Table 1.
2.2. The Role of History

It is not always simple to separate effects linked to history from those linked to current environmental factors, but comparisons between similar environments in different regions could address variation in speciation and extinction caused by different history [66]. In order to highlight the potential influence of historical factors on species richness, Oberdorff et al. [65] studied rivers on two different continents, North America and Western Europe, which have comparable climatic and environmental characteristics but a rather different history (Figure 4). After having initially identified the main ecological factors responsible for variations in species richness on the two continents (i.e., factors related to river size, productivity, and climate), the second phase of the study integrated in the model factors presumed to reflect historical events (i.e., distance from the larger refugial area and surface area of drainage basin covered by the ice sheet during the last Pleistocene glaciation) in order to examine their relative contribution in explaining species richness gradients.

Plot of fish species richness as a function of the surface area of drainage basins for West European and North American rivers. Variables expressed in logarithmic values (Ln): redrawn from Oberdorff et al. [65].

Results showed that ecological factors (particularly the size of the river and to a lesser extent available energy) explain a large part of variations in species richness between the two continents, while one historical factor (distance from the larger refugial area) appears to be more marginal though significant, while the other (surface area of drainage basin covered by the ice sheet during the last Pleistocene glaciation) is invariably rejected. These results thus seem to agree with conclusions reached at the global scale, suggesting a marginal role of history in driving contemporary diversity patterns. This is rather surprising if one accepts the low dispersion capacity generally attributed to fish communities. A preliminary explanation is that the most northern regions of Western Europe and North America are mainly populated with euryhaline species that could have rapidly recolonised rivers via coastal fringes. At the same time, the fact that a “continental” effect is highly significant in the final model leads to think that other historical factors not taken into account in the study are perhaps involved in differences between rivers on the two continents, like, for example, differences in the process of speciation which seem to occur more often in North American refuge zones [67]. In fact, some North American genera such as Notropis have radiated at a rate not encountered in any of the European genus [67]. If speciation rate is assumed to be inversely related to body size [68], a low speciation rate is also suggested by body size distribution of European fish with dominance of medium and large species, conversely to North America were small fish predominate [67, 69, 70]. A complex array of factors is probably involved in this pattern, but speciation events seem to have occurred more frequently in North American refugial areas than in West European ones [67]. Moreover, the data analysed by Oberdorff et al. [65] also show that after river size and net primary productivity have been factored out, North American rivers are still 1.7 times as rich as European ones.

Consistent with this, other recent studies trying to evaluate the role of history in shaping riverine fish diversity patterns at regional and intercontinental scales found a significant influence of history in forging riverine species richness patterns [45–48]. For example, Tedesco et al. [46] have analyzed the effect of rain forest refuges at the last glacial maximum (LGM) on tropical freshwater fish diversity patterns in three different regions, that is, Tropical South America, Central America, and West Africa. At the end of the most recent glacial period (Last Glacial Maximum, LGM 18,000 years BP), while ice sheets in the Northern Hemisphere extended from the Arctic southward to cover most of North America and central Asia to approximately 45°N latitude, African, and Amazonian rain forests contracted in response to glacial aridity [71]. Following this scenario, in the Northern Hemisphere, high fish species extinctions should have occurred in the rivers totally or partially glaciated, while few extinctions should have occurred in the few refuge zones representing remnants of preglaciation habitats. At the same time, in the tropical zone of the Southern Hemisphere, overall reduced precipitation should have led to high extinction rates in river basins affected by the drought (through a decrease in river basins discharge and active surface area), and few or no extinctions in river basins having kept their characteristic natural (e.g., precipitation patterns and vegetation conditions). Indeed, Tedesco et al. [46] found that both river drainage area and contact (or absence of contact) with LGM rain forest refuges explained the greatest proportions of variance in the geographical pattern of riverine species richness. In the three examined regions, highest richness was found in drainages that were connected to the rain forest refuges (Figure 5). However, they also found that, at the continental scale, South American rivers were more species rich than their African and Central American counterparts, respectively. Therefore, a historical signal seems to persist even when the regional historical effect (climate at the LGM) has already been accounted for. These results suggest that from the LGM to the present day (a time scale of 18,000 years), extinction processes should have played a predominant role in shaping the current diversity pattern. By contrast, the continental effects could reflect historical contingencies explained by differences in speciation and extinction rates between continents at larger time scales (millions of years). Despite these few studies, the role of historical processes in shaping present-day distribution patterns of diversity is still the subject of considerable debate, stressing the difficulties of testing historical processes based on current species distributions. More refined tests of historical factors involving intercontinental comparisons are needed to better assess the relative importance of ecological and historical processes in shaping contemporary diversity patterns. In this context, endemic species have always been fascinating because they should reflect the roles of speciation, extinction, and dispersal ultimately responsible for their restricted geographic distribution. They are then good candidates for analysing the role of historical processes in present-day distribution patterns of diversity.


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With so many different species of fish with often very minor differences, how is the precise species identified authoritatively? - Biology


Fish and fisheries are an integral part of most societies and make important contributions to economic and social health and well-being in many countries and areas. It has been estimated that approximately 12.5 million people are employed in fishery-related activities, and in recent years global production from capture fisheries has tended to vary between approximately 85 and 90 million tonnes. The products from these fisheries are used in a wide variety of ways, ranging from subsistence use to international trade as highly sought-after and highly-valued items. The value of fish traded internationally is approximately US$40 billion per year.

Despite this enormous importance and value, or more correctly, because of these attributes, the world’s fish resources are suffering the combined effects of heavy exploitation and, in some cases, environmental degradation. The FAO (2000) estimated that, in 1999, 47% of the 441 stocks for which some information on status was available were fully exploited, 18% overexploited, 9% depleted and 1% recovering. This pattern is broadly consistent with similar statistics available from other regions. For example, the National Marine Fisheries Service of the United States of America estimated in 1998 that 30% of the stocks in the waters of that country for which information was available were overfished. In the waters of the European Community, it was estimated that in 1990, 57% of the stocks were ‘heavily exploited’. Fish stocks throughout the rest of the globe are likely to be in a similar condition to those in these regions.

There are many reasons for this unacceptable state of affairs, but the primary reasons all come down to a failure in fisheries governance in most countries. The responsibility for declining stocks and falling economic returns and employment opportunities in fisheries must be shared amongst fishers, fisheries management authorities, fishery scientists and those involved in environmental degradation. Not all of the underlying problems lie within the realm of fisheries management, but the fisheries manager is the person who is most often in the best position to observe and record what is happening in the fisheries under his or her jurisdiction, to establish the underlying cause or causes of any problems, to rectify those within their jurisdiction, and to bring the others to the attention of both the interested parties in fisheries and those with a responsibility covering the external causes. However, all too often the fisheries manager remains either unaware of the state of the resources, or fails to act sufficiently as the fisheries slip further and further into decay and crisis, or both. This is rarely, if ever, a deliberate choice and more often comes down to a lack of available information, an incomplete understanding of the nature of the task of fisheries management, and inadequate resources, structures and support to address the problems and utilise the resources in a planned and efficient manner.

The FAO Code of Conduct for Responsible Fisheries was produced in response to global concern over the clear signs of over-exploitation of fish stocks throughout the world and to recommend new approaches to fisheries management which included conservation, environmental, social and economic considerations. It was developed by and through FAO and was accepted as a voluntary instrument by the 28th Session of the FAO Conference in October 1995. In addition to five introductory Articles and one on General Principles, the Code contains six thematic articles on Fisheries Management, Fishing Operations, Aquaculture Development, Integration of Fisheries into Coastal Area Management, Post-Harvest Practices and Trade, and Fisheries Research. Overall it incorporates the key considerations in responsible fisheries and provides guidance on how these should be addressed in order to ensure sustainable and responsible fisheries. Subsequently, FAO has produced a number of Technical Guidelines on different aspects of the Code, including the FAO Technical Guidelines for Responsible Fisheries No. 4: Fisheries Management, which specifically addresses Article 7: Fisheries Management of the Code. The following Technical Guidelines had been produced at the time of printing this Guidebook (late 2001):


There is no clear and generally accepted definition of fisheries management. We do not wish to get embroiled in a debate about exactly what fisheries management is and isn’t, but use here the working definition used in the Technical Guidelines to provide a summary of the task of fisheries management:

There has been a lot of interest in recent years in moving from fisheries management focused essentially on single-species or single fisheries, to management with an ecosystem orientation. This expanded approach has been termed ecosystem-based fisheries management (EBFM) and was recently discussed at “The Reykjavik Conference on Responsible Fisheries in the Marine Ecosystem” (1-4 October 2001), which was organised jointly by FAO and the Governments of Iceland and Norway. The Conference agreed on the Reykjavik Declaration[1] which included an affirmation “that incorporation of ecosystem considerations implies more effective conservation of the ecosystem and sustainable use” and also a reaffirmation of the principles of the FAO Code of Conduct for Responsible Fisheries.

In writing this Guidebook, the authors have implicitly accepted EBFM as a principle inherent in fisheries management and, while the term is not explicitly referred to in the Guidebook, its principles and requirements in fisheries management are incorporated and discussed throughout the volume.

Figure 1 Diagrammatic representation of the functions and responsibilities of a fisheries management authority in relation to fishing, and the inter-relationships between the functions.


The above description presents a complex and possibly confusing picture of all the tasks which need to be considered by the fisheries manager. Some of this complexity can be reduced by attempting to highlight the underlying key issues. There are both benefits and risks in attempting to simplify a subject and over-simplification can lead to neglect of important details. However, simplification can facilitate understanding important principles and highlighting the broad areas which need attention. Arising from the considerations discussed above, a number of key principles can be identified which may serve to focus attention on the starting points for effective fisheries management (Table 1).

Table 1 Suggested fundamental principles of fisheries management (modified from Cochrane, 2000).

Fish stocks and communities are finite and biological production constrains the potential yield from a fishery.

The potential yield needs to be estimated and the biological constraints identified.

i) Biological production of a stock is a function of the size of the stock andii) it is also a function of the ecological environment. It is influenced by natural or human-induced changes in the environment.

i) Target reference points need to be established through data collection and fisheries assessment and

ii) environmental impacts should be identified and monitored, and the management strategy adjusted in response as necessary.

Human consumptive demands on fish resources are fundamentally in conflict with the constraint of maintaining a suitably low risk to the resource. Further, modern technology provides humans with the means, and demand for its benefits provides the motivation, to extract fish biomass at rates much higher than it can be produced.

Realistic goals and objectives must be set.

Achieving the objectives will inevitably require controls on fishing effort and capacity.

In a multispecies fishery, which description encompasses almost all fisheries, it is impossible to maximise or optimise the yield from all fisheries simultaneously.

Realistic goals and objectives must be established across ecosystems, so as to manage for species and fisheries interactions.

Uncertainty pervades fisheries management and hinders informed decision-making. The greater the uncertainty, the more conservative should be the approach (i.e. as uncertainty increases, realised yield as a proportion of estimated maximum average yield should be decreased).

Risk assessment and management must be done in development and implementation of management plans, measures and strategies.

The short-term dependency of society on a fishery will determine the relative priority of the social and/or economic goals in relation to sustainable utilisation.

Fisheries cannot be managed in isolation and must be integrated into coastal zone and fisheries policy and planning and national policies.

A sense of ownership and a long-term stake in the resource for those (individuals, communities or groups) with access are most conducive to maintaining responsible fisheries.

A system of effective and appropriate access rights must be established and enforced.

Genuine participation in the management process by fully-informed users is consistent with the democratic principle, facilitates identification of acceptable management systems and encourages compliance with laws and regulations

Communication, consultation and co-management should underlie all stages of management


In keeping with the integrated nature of fisheries ecosystems, these principles cannot be considered in isolation in considering how best to manage fisheries: their implications and consequences overlap, complement and confound each other which is what makes fisheries management so demanding and challenging. Nevertheless, the consequences of the principles for fisheries give rise to the fundamental nature and tasks of fisheries management, and hence to the general structure of this Guidebook (Table 1).


The Technical Guidelines (FAO, 1997) suggest that fisheries management institutions have two major components: the fisheries management authority and the interested parties. The fishers and fishing companies would usually be the major participants amongst the interested parties. The fisheries management authority is that entity which has been given the mandate by the State (or States in the case of an international authority) to perform specific management functions. In many countries that authority would be a Department of Fisheries or, within a broader Department, a Division of Fisheries. However, a fisheries management authority does not have to fall directly within central government, and could be, for example, provincial, local, parastatal or private. Any one of these arrangements can function effectively, given an adequate legal framework in which to operate and the resources necessary to fulfil their function.

Who, then, within this authority is the fisheries manager and to whom is this Guidebook addressed? In fact, despite the fact that we have deliberately used the term in the title, we suggest that in modern fisheries management, there is rarely a single individual who fulfils the functions of “fisheries manager”. The head of the authority, for example, a Director of Fisheries, may have overall responsibility for implementing fisheries management and, as well as being accountable and responsible for the advice passed on from his or her Department to the political decision-maker, may act in an overall coordinating role. However, this individual is unlikely to, and generally should not, have sole responsibility for receiving information, formulating advice and making and implementing decisions. Fisheries management is a complex and multi-faceted discipline and requires input from a range of perspectives. It is therefore inappropriate to expect any individual to fulfil this function on their own. In addition, as discussed above and reflected in Paragraph 7.1.2 of the Code of Conduct, fisheries management should involve the legitimate interested parties in the management process.

Perhaps the closest we can come to a “fisheries manager” is the management authority as a whole, including technical experts, monitoring, control and surveillance (MCS) units, administrative units, the executive body of the formal authority, the consultative mechanisms, the advisory body where one exists, and the responsible political head who is often a Minister. Each member of these functional bodies is, to some extent, a fishery manager and this Guidebook is aimed at all of them. It is not designed to go into great technical and operational detail on each function or task as this would require a set of Guidebooks. Instead, it is intended to give a holistic picture of how the different functions should interact in a fisheries management authority in order to develop appropriate objectives, management strategies and plans, and how to encourage all those participating in a fishery to collaborate in and adhere to the agreed strategy.


The responsibility for fisheries management rests with the designated fisheries arrangement or organization which, in this Guidebook, we have referred to without distinction as the fisheries management authority. Following the practice used in the Technical Guidelines on Fisheries Management (FAO, 1997) the term is used broadly here to describe that legal entity which has been designated by the State as having the mandate to perform specified fisheries management functions. In practice, it may be a national or provincial ministry, a department within a ministry, or an agency and could be governmental, parastatal or private. In the case of shared resources it should be international.

The area of competence, geographical area, fish resources and fisheries for which a given management authority is responsible must be precisely specified in each case in the appropriate legislation. The task of an authority is diverse and complex and as a result, fisheries management authorities are normally divided into institutional support structures: the fisheries management institutions. The institutions need to encompass the basic tasks and functions of fisheries management as described in Section 2 and Figure 1 of this Chapter. The actual institutional structure and mechanisms may differ from authority to authority and it would be inappropriate for us in this Guidebook to attempt to prescribe any specific set of characteristics as representing the ‘best’ institutional structure and processes. What is best in each case will depend in large part on the specific circumstances and context. What is universal, however, is that it is essential for the different institutions concerned with management of any fishery or fisheries to be able to interact effectively, requiring good channels of communication and feedback. The institutions must also be seen by the different interested parties as being legitimate.

The need for collaboration between the authority and the interested parties is as important as collaboration between the institutions within the authority and is discussed at length in Chapter 7. That chapter also examines the pre-requisites for effective partnerships between the management authority and the interested parties and the different types of partnership which can be considered.

It is common and frequently desirable for the national government to devolve all or some fisheries management functions to local government or to smaller groups such as fishing communities. In such cases, it is essential to specify precisely the responsibilities and functions, including the geographical area, falling under this local authority or smaller group. The institutions within the local authority must follow the same principles as those for a national authority as discussed here.

The Code of Conduct requires that fisheries management should be concerned with the whole stock over its entire area of distribution (Code of Conduct, Paragraph 7.3.1) and therefore that States should cooperate in the management of transboundary, straddling, highly migratory and high seas fish stocks exploited by two or more states (Paragraph 7.1.3). General rules for cooperation towards conservation of such fish stocks are foreseen in the United Nations Convention on the Law of the Sea of 10 December 1982, and in the 1995 UN Fish Stocks Agreement (see Table 2). The responsibilities, functions and structure of international or regional fisheries authorities will usually not differ substantively from those of national authorities.


The over-riding goal of fisheries management is the long-term sustainable use of the fisheries resources (Code of Conduct, Paragraph 7.2.1). Achieving this requires a proactive approach and should involve actively seeking ways to optimise the benefits derived from the resources available. This rarely happens, though, and fisheries management is still most commonly practised as a reactive activity, where decisions are made and actions taken largely in response to problems or crises. The resulting crisis decisions are then normally attempts merely to solve the immediate problems without properly considering the broader perspective and the longer-term objectives. Such an approach may succeed in maintaining dissatisfaction sufficiently low to avoid major conflict, but it is extremely unlikely to result in the best use of the marine resources being exploited by the fishery.

The first step in proactive fisheries management is to decide what is meant by optimising the benefits for each fishery - what can the State or the collection of legitimate interested parties agree on as being optimal benefits? This may be described in general terms in the national fisheries policy which must be the starting point for determining the specific objectives for each fishery. The broad goals stated in the fisheries policy may need to be tailored for a specific fishery, but the goals for each fishery should be consistent with the policy.

In general terms, the goals in fisheries management can be divided into four subsets: biological ecological economic and social, where social includes political and cultural goals. The biological and ecological goals may be more correctly thought of as constraints in achieving desired economic and social benefits but for simplicity and consistency with the terminology most commonly used in fisheries management, we will include them as goals in this Guidebook. Examples of goals under each of these categories include:

    to maintain the target species at or above the levels necessary to ensure their continued productivity (biological)

Identifying such goals is important in clarifying how the fish resources are to be used to benefit society, and they should be agreed upon and recorded, both at the policy level and for each fishery. Without such goals, there is no guidance on how the fishery should be operated, which results in a high probability of ad hoc decisions and sub-optimal use of the resources (resulting in lost benefits), and increases the probability of serious conflicts as different interest groups jostle for greater shares of the benefits. This is often seen in practice and one of the important causes of failures in fisheries management has been identified as the frequent absence of clear and precise objectives.

While setting goals is an essential first step, the goals stated above have two obvious limitations. Firstly, they have clear conflicts in intention as it is impossible, for example, to minimise impacts of the fishery on the ecosystem and simultaneously to maximise net incomes. Similarly, it is very probable that management strategies that aim to maximise net incomes will not also maximise employment opportunities. Some compromise between these goals has to be achieved before an effective management strategy can be devised. The second limitation of the goals is that they are too vague to be of much benefit to the manager. For example, the impacts of fishing can only be “minimised” by having no fishing at all, which is unlikely to have been the intention of those who stated the goal. Maximising employment opportunities could mean allowing as many fishers as possible to participate, regardless of whether or not they could make a living from the fishery, or it could mean maximising the number which could still earn some acceptable income, or many other such targets. Too much is left to the discretion of the manager with these examples of goals.

It is therefore necessary to refine the goals further and to develop operational objectives for each fishery (Figure 2). Operational objectives are very precise and are formulated in such a way that they should be simultaneously achievable in that fishery. In other words, the trade-offs between the biological, ecological, economic and social goals must have been agreed upon and the conflicts and contradictions resolved. The development of operational objectives is discussed in Chapter 5 but, to illustrate the difference between goals and operational objectives, two examples of objectives taken from Chapter 5 are:

    to maintain the stock at all times above 50% of its mean unexploited level (biological)

With operational objectives such as these, it is possible for any observer, including the manager, to establish whether or not they are being achieved and hence whether or not the management strategy is appropriate and being successfully implemented. These operational objectives can also easily be used as the foundation for reference points, which are essentially the operational objectives expressed in a way which can be estimated or simulated in a fisheries assessment (Figure 2). Once operational objectives have been agreed upon, a management strategy can be developed, made up of a suite of different management measures, to achieve those objectives.

All of this may sound complex, but in reality is no more than most people do in order to develop a budget for their personal finances. Most of us have realistic but imprecisely expressed hopes and needs for our lifestyle as well as a knowledge of the nature of the resource (in this case our net income). These hopes and needs are the goals of our budget but they will all compete for the same resource, our net income, so there will probably be conflicts which need to be resolved. Therefore we have to modify our goals and express them more precisely: we develop operational objectives in which we specify what we can realistically achieve in terms of food, housing, education etc. Thereafter, we need to decide on our budget strategy: how can we meet those objectives: what type and quantities of food and clothing should we be buying what type of housing can we consider can we consider an annual holiday, etc.

Clearly, our operational objectives must be consistent with the yield we can expect from the resource (our income). Normally the process of developing realistic objectives will require trade-offs and most of us find, for example, that we cannot allocate as much for entertainment or holidays as we would like and at the same time make our rental or mortgage payments. Therefore priorities are established and compromises made until eventually we arrive at realistic objectives that balance our desires with our income, and that provide a good guide on how to manage our finances from month to month and in the longer-term. At the end of this, we should have a feasible financial management strategy that, barring totally unexpected events, will have a predictable outcome. If we have done our calculations correctly and responsibly, the strategy should mean we enjoy a reasonable lifestyle without being sued for bankruptcy. This is little different from the basic task, and overall hope, of the fisheries manager!


There is a lot of terminology floating around in fisheries management that, unless clearly understood, can cause further confusion in an already confusing environment. In addition to the words ‘goals’ and ‘operational objectives’, the terms management plans, management measures and management strategies will crop up on many occasions in this Guidebook and we need to clarify what we mean by each of them and how they differ.

The Technical Guidelines on Fisheries Management (FAO, 1997) describe a management plan as “a formal or informal arrangement between a fisheries management authority and interested parties which identifies the partners in the fishery and their respective roles, details the agreed objectives for the fishery and specifies the management rules and regulations which apply to it and provides other details about the fishery which are relevant to the task of the management authority.” A well formulated management plan should be prepared for every fishery and the Code of Conduct (Paragraph 7.3.3) states that: “Long-term management objectives should be translated into management actions, formulated as a fishery management plan or other management framework.” Given the importance of management plans in fisheries, the final chapter in the Guidebook, Chapter 9, is devoted to their development.

As discussed in the previous section, fisheries policy is translated into goals and the goals into objectives that indicate precisely what is expected to be achieved from the fishery. The objectives are achieved through the implementation of a management strategy which will also be a central element of a management plan. The management strategy is the sum of all the management measures selected to achieve the biological, ecological, economic and social objectives of the fishery. It is possible that in a single species fishery a management strategy could consist of a single management measure, such as a specified total allowable catch (TAC), but in practice the great majority of management strategies consist of a number of management measures, encompassing technical, input and output controls and a system of user rights. An effective management strategy, however, should not contain so many management measures that compliance and enforcement become so difficult as to be practically impossible.

A management measure is the smallest unit of the fishery manager’s tool kit and consists of any type of control implemented to contribute to achieving the objectives. Management measures are classified as technical measures (Chapters 2 and 3), input (effort) and output (catch) controls (Chapter 4), and any access rights designed around input and output controls (Chapter 6). Technical measures can be sub-divided into regulations on gear-type or gear design (Chapter 2) and closed areas and closed seasons (Chapter 3). A minimum legal mesh size, a seasonal closure of the fishery, a total allowable catch (TAC), a limit on the total number of vessels in a fishery, and a licensing scheme to achieve the limit are all examples of management measures. A substantial part of the Guidebook is intended to assist managers in considering and selecting different management measures for a given fishery.


If marine living resources were infinite and indestructible, we could leave people to use and abuse them at will. However, this is not the case and we therefore need to manage fisheries to ensure that the resources are utilised in a sustainable and responsible way, and that the potential benefits are not inefficiently dissipated and possibly totally lost. Fisheries production and yield are constrained by a number of factors which can be classified as biological, ecological and environmental, technological, social and cultural, and economic considerations. There are frequently also considerations imposed by other users of the fishing grounds and neighbouring areas. These considerations are discussed in considerable detail in the Technical Guidelines on Fisheries Management (FAO, 1997). Some of the key points are discussed immediately below, but the reader is referred to that publication for more detail. Several of the themes are also dealt with in subsequent chapters of this Guidebook.

8.1 Biological Considerations

As living populations or communities, aquatic living resources are capable of on-going renewal through the processes of growth in size and mass of individuals and additions to the population or community through reproduction (leading to what in fisheries is often called ‘recruitment’). In a population at equilibrium, the additive processes of growth and reproduction on average equal the loss process of total mortality. In an unexploited population, total mortality consists only of natural mortality, made up of processes such as predation, disease, and death through drastic changes in the environment. In a fished population, total mortality consists of natural mortality plus fishing mortality, and a primary task of fisheries management is to ensure that fishing mortality does not exceed the amount which the population can withstand, in addition to natural mortality, without undue harm or damage to the sustainability and productivity of the population. This requires not only that the total population is maintained above a certain abundance or biomass, but also that the age structure of the population is maintained in a state in which it is able to maintain the level of reproduction, and hence recruitment, necessary to replenish the losses through mortality. Further, fishing over a long period on selected portions of a stock, for example large individuals or individuals spawning at a specific time or locality within a wider spawning season or range, can reduce the frequency of the particular genetic characteristics giving rise to that feature or behaviour. This has the effect of reducing the overall genetic diversity of the stock. With reduced genetic diversity, the production potential of the population can be adversely affected and it may also become less resilient to environmental variability and change. Fisheries management needs to be aware of this danger and avoid maintaining such selective pressures over a prolonged period.

Achieving an appropriate level and pattern of fishing mortality is hindered substantially by difficulties in estimating population abundance and population dynamics rates and the variability in these rates. The fisheries manager must, however, have sufficient knowledge to make good decisions. The Code of Conduct specifies (Paragraph 7.2.1): “. States. should, inter alia , adopt appropriate measures, based on the best scientific evidence available, which are designed to maintain or restore stocks at levels capable of producing maximum sustainable yield, as qualified by relevant environmental and economic factors. ” and further (Paragraph 7.5.2, referring to the Precautionary Approach) “In implementing the precautionary approach States should take into account, inter alia , uncertainties relating to the size and productivity of the stocks, reference points, stock condition in relation to reference points, . ”. These issues are discussed in Chapter 5.

Fishery managers must also respect the stock structure of the resources. Fish populations are frequently made up of a number of different stocks, each of which is genetically largely isolated from the others through behavioural or distributional differences. The different stocks also reflect genetic diversity and if a particular stock is fished to extinction or to very low levels, this genetic diversity may be lost. The stock will not readily be replenished from other stocks, because of the genetic isolation, and therefore the production it was generating will also be lost, leading to a permanent or at least long-term loss of benefits. Fisheries management should therefore attempt to address each stock separately and to ensure sustainable use of each stock and not just of the population as a whole. In this regard, the Code of Conduct states (Paragraph 7.3.1): “To be effective, fisheries management should be concerned with the whole stock unit over its entire area of distribution and take into account previously agreed management measures established and applied in the same region, all removals and the biological unity and other biological characteristics of the stock.”

8.2 Ecological and Environmental Considerations

The abundance and dynamics of a population place an important constraint on fisheries but aquatic populations do not live in isolation. They exist as components of a frequently complex ecosystem, consisting of biological components which may feed on, be fed on by, or compete with a given stock or population. Even those populations which are not directly linked through the food web may indirectly affect each other through their direct interactions with predators, prey or competitors of the other. The physical component of the ecosystem, the water itself, the substrate, inflows of freshwater or nutrients and other non-biological processes may also be very important. Different substrates may be essential for the production of food organisms, for shelter, or as spawning or nursery grounds.

The environment of fish is very rarely static and conditions, particularly of the aquatic environment, can vary substantially over time, from hourly variability, such as the tides, to seasonal variability in, for example, water temperature and currents, to decadal variability as in the occurrence of El Niño events and regime shifts. These changes frequently affect the population dynamics of fish populations, resulting in variability in growth rates, recruitment, natural mortality rates or any combination of these. Such variability can also affect the availability of fish resources to fishing gear, not only affecting the success of the fishing industry, but also the way in which the fishery scientist must interpret catch and catch rate information from the fishery.

Changes in any of the biological, chemical, geological or physical components of the ecosystem can have impacts on the resource population and community. Some of these changes may be beyond human control, such as upwelling processes enriching some coastal ecosystems or large scale temperature anomalies, but they still need to be considered in the management of the resource. Others, such as the destruction of coastal habitats for development, or the direct impact of fishing on the substrate or on other species impacting the resources, are due to human action. In these cases, fisheries management should both take into account their impacts on the resource and, in consultation with other relevant agencies and parties, take steps to minimize their impacts on the fishery ecosystem.

The manager also needs to consider the impact of the fishery on the ecosystem as a whole (Code of Conduct, Paragraph 7.2.2 g and 7.6.9). There are four types of impact of fisheries on the ecosystem: direct impact on the target species direct impacts on the bycatch species (including discards and by-mortality - Chapter 2) indirect impacts on other organisms transmitted through the food chain (i.e. by changing the abundance of predators, prey or competitors of a population) and direct impact of fishing on the physical or chemical environment. The manager needs to be aware of these potential impacts and to use management measures that minimise negative impacts.

The potential to address the ecosystem considerations will vary depending on whether they are caused by or independent of human action, but in both cases the constraints imposed on the resources and the fishery by biological and non-biological ecosystem factors need to be recognised. At the most fundamental level, these factors in combination with the biology of the species determine the maximum abundance, or carrying capacity, and productivity of the resources. Changes in the ecosystem can affect both and, where they are occurring, need to be considered by the fisheries manager.

Again, these aspects are dealt with by the Code of Conduct. Amongst several references, Paragraph 7.2.3 specifies “States should assess the impacts of environmental factors on target stocks and species belonging to the same ecosystem or associated with or dependent upon the target stocks, and assess the relationship among the populations in the ecosystem.” and Paragraph 7.6.9 affirms “States should take appropriate measures to minimize waste, discards, catch by lost or abandoned gear, catch of non-target species, both fish and non-fish species, and negative impacts on associated or dependent species, in particular endangered species.

8.3 Technological Considerations

The fishery manager has very little, if any, ability to influence directly the dynamics of the fish populations or communities which support a fishery. In some case, particularly inland waters, there may be opportunities and a desire to undertake stock and habitat enhancement and in some coastal fisheries, habitat destruction may have had an impact on fish production. In the latter case, restoration or stabilization may well be an issue the fisheries manager needs to consider (Code of Conduct, Paragraph 7.2.2 f) and Article 10). However, in most fisheries, the only mechanism the fishery manager has to ensure sustainable utilization of the resources is by regulating the quantity of fish caught, when and where they are caught and the size at which they are caught. This can be done through directly regulating the catch taken, by regulating the amount of effort allowed in the fishery, by specifying closed seasons and closed areas and by regulating the type of gear and fishing methods used. However, there are constraints on how precise the manager can be in setting such regulations. Catch controls are often difficult to monitor and therefore to implement. It is difficult to estimate fishing effort precisely, and normally improving technology and developing skills result in on-going increases in the efficiency of fishing operations, leading to continuing increases in effective effort, unless steps are actively taken to counter these improvements or their consequences. Fishing gear is rarely strongly selective and bycatch of non-target species or unwanted sizes of target species is frequently a problem. The uncertainties in fisheries management are not just at the level of predicting the status and dynamics of the resources, and uncertainties in the real consequences of implementing fishery measures is also a significant problem to the manager. The properties of these measures and when and how to use them is dealt with in considerable detail in subsequent chapters, especially Chapters 2 to 4.

A fundamental problem in many fisheries is the existence of too much effort. The presence of excess effort will frequently result in on-going pressure on the fisheries manager to exceed the sustainable fishing mortality on a resource. The social and political pressure to provide employment and opportunities for all those with a stake in the fishery is often hard to resist and readily leads to over-exploitation. The Code of Conduct requires that States take measures to prevent or eliminate excess fishing capacity (Code of Conduct, Paragraph 7.1.8) and such is the global level of concern that the FAO members have agreed on an International Plan of Action (IPOA) for the Management of Fishing Capacity[2].

8.4 Social and Cultural Considerations

Human populations and societies are as dynamic as other biological populations, and social changes take place continuously and on different scales, affected by changes in weather, employment, political circumstances, supply of and demand for fisheries products and other factors. Such changes can affect the appropriateness and effectiveness of management strategies, and therefore need to be considered and accommodated by them. However, again as with biological and technological factors, it can be difficult to identify and quantify the key social and cultural factors influencing fisheries management, generating additional uncertainties for the manager.

A major social constraint in fisheries management is that human societies and behaviour are not easily transformed and fishing families and communities may not be willing to move into other occupations, or away from their normal homes when there is surplus capacity in a fishery, even when their quality of life may be suffering as a result of depleted fish resources. The problem is much worse when there are no other opportunities outside of fisheries in which they could earn a basic living. Under such circumstances, the political decision to reduce capacity in the fishery is an extremely unattractive option, as the short-term costs of excluding dependent people from the fishery will be much more visible and hence unpopular than a “hands-off” approach which allows the resource and fishery to dwindle in magnitude and quality under sustained excess fishing mortality. Nevertheless, the ecological, economic and social consequences of the latter choice are far more serious in the longer term. This reluctance or inability to take decisions with serious, immediate social consequences for some has been one of the constraints most responsible for over-fishing around the world.

A key requirement for ensuring that social and cultural considerations are properly considered is to involve the interested parties in fisheries management, keeping them well-informed on the management aspects of the fishery and providing them with the opportunity to express their needs and concerns. This is discussed in Chapter 7 of the Guidebook. The Code of Conduct (Paragraph 7.2.2) suggests that “the interests of fishers, including those engaged in subsistence, small-scale and artisanal fisheries are taken into account” and (Paragraph 7.1.2) “Within areas under national jurisdiction, States should seek to identify relevant domestic parties having a legitimate interest in the use and management of fisheries resources and establish arrangements for consulting them to gain their collaboration in achieving responsible fisheries”.

The relative balance between social and economic considerations in a fishery will depend on the priority given by the appropriate authority to social objectives and economic objectives. Social and economic objectives can conflict: for example it is unlikely that maximising economic efficiency and maximising employment could be simultaneously pursued within a given fishery, and attempting to do so will result in conflict. A common example of such conflicts is that between a commercial fleet pursuing essentially economic objectives and an artisanal fleet fulfilling primarily social objectives, with both having an impact on the same stock, and possibly also interfering with each other’s fishing operations. It is important for the management authority to have identified such potential conflicts and to have resolved them, identifying and specifying compromise objectives that achieve general support.

8.5 Economic Considerations

In a fishery for which sustainable economic efficiency had been specified as the sole benefit to be extracted and in which optimum circumstances prevailed, market forces could be anticipated to lead to the desired objective of economic efficiency. However, in reality such optimum conditions are rarely if ever found and uncertainties and externalities distort the natural selection of market forces. Uncertainties include unpredictable variability in resources and other sources of imperfect information, and externalities can include the impacts of other fisheries on the target resources (e.g. taking them as bycatch), subsidies, trade regulations, fiscal regulations and variability in markets and demand. All of these introduce complexity and additional uncertainty into a fishery and, without proper management, will lead to sub-optimum economic performance. It is important for the management authority to consider the broad economic context of a fishery, including relevant macroeconomic factors. As with social considerations, this requires close consultation with the legitimate users who will be the ones most affected by and sensitive to these issues.

At one extreme, although still very common in fisheries especially in many developing countries, are the problems of open access fisheries, in which anyone is allowed entry into a fishery. Under these circumstances, people will continue to enter the fishery until the benefits from fishing are so low as to be unattractive to prospective new entrants (Section 2, Chapter 6). How low this is will depend largely on the availability of other options and in many countries, especially developing countries, such alternatives may be extremely scarce. Even where there are reasonable alternatives, the inevitable result of open access fisheries is dissipation of rent leading to very poor economic efficiency and, unless strong and effective management measures are in place and enforced, to over-exploitation of resources. Such circumstances prevail in many fisheries around the world.

Recognizing this most elementary lesson in fisheries management, the Code of Conduct calls for the adoption of “measures to ensure that no vessel (by which should also be understood no shore-based fisher) be allowed to fish unless so authorised..” (Paragraph 7.6.2), that “States should ensure that the level of fishing is commensurate with the state of fisheries resources.” (Paragraph 7.6.1), and going further than that, that “Where excess fishing capacity exists, mechanisms should be established to reduce capacity to levels commensurate with the sustainable use of fisheries resources so as to ensure that fisheries operate under economic conditions that promote responsible fisheries.” (Paragraph 7.6.3), where underlining and bracketed comments on Code text are additions by the Chapter author. Taken together, these three paragraphs specify that responsible fisheries require limited and authorised access by fishers, where actual and potential effort is appropriate to the productivity of the resource or resources being exploited.

8.6 Considerations Imposed by Other Parties

Some offshore fisheries operate in effective isolation from any other users and the regional fisheries organisations charged with their management may be able to manage the fisheries without needing to consider conflicts with or interference from non-fishery users. However, the bulk of global fishery landings come from coastal waters and for many if not most of the fisheries producing these landings, other users are an important consideration and frequently a constraint. Other users of the fishing grounds can include, for example, tourism, conservation, oil and gas extraction, offshore mining and shipping, while use of the intertidal and coastal area can include tourism again, aquaculture and mariculture, coastal zone development for housing, business or industry, and agriculture. All of these can impose significant constraints on fishing activities and may be impacted by fishing activities. The manager therefore needs to be aware of such activities and of real or potential impacts in both directions. When developing management strategies and formulating management measures, potential conflicts with other users need to be identified and addressed, and the potential impacts of other users on the efficacy of the management strategy and measures need to be considered. The strategy must be adapted so as to account for and be robust to these impacts.

An unavoidable implication of overlapping interests is that the fishery manager, through the management authority, must ensure that suitable structures and mechanisms are put into place for effective communication and decision-making with representatives of the other users. In addition to reference in Paragraphs 6.8 and 6.9, this is dealt with mainly in Article 10 of the Code of Conduct: Integration of Fisheries into Coastal Area Management, which includes the requirement that (Paragraph 10.4.1): “States should establish mechanisms for cooperation and coordination among national authorities involved in planning, development, conservation and management of coastal areas.”


It should not need to be stated that it is essential for the fisheries manager to be thoroughly conversant with the laws and regulations which control the fisheries within his or her jurisdiction. These laws and regulations constitute the legal regime under which the fishery should be operated and managed, and include the national legislation and any relevant international legal instruments (Table 2). The term legislation is used here to include all types of national laws, local laws, regulations and customs.

9.1 National Legislation

The scope of the national legislation varies substantially between countries, depending on, for example, whether a particular country has a common law system, a civil law system or any other system. However, typically the primary legislation is broad, prescribing the principles and policy relating to fisheries and is usually approved by the Legislature of that country, which may be the national Congress or Parliament. It may also specify details on the implementation of aspects of the policy considered to be particularly important or sensitive and should include reference to establishing fishery management plans and the procedures for the planning process. This primary legislation would usually be described in a Fisheries Act or equivalent legislation. As passage through the legislature is usually a slow process, such primary legislation should normally not need to be changed frequently. Therefore, for example, control measures such as the amount of effort allowed in a given fishery, or the annual TAC should not be included in the primary legislation.

The primary legislation would typically provide the legal basis for development of detailed procedures and regulations for its implementation by a designated law-making authority. The delegated powers should define and empower the designated institutional components responsible for fisheries management, including specification of who is responsible for administration and control of fisheries management. The second-tier laws, or so-called subsidiary legislation, produced by the delegated regulatory authority are often referred to as regulations, orders, proclamations etc. They would include specifying the control measures which require frequent, typically annual, revision such as licences, gear restrictions, closed areas and seasons and input and output controls (Chapters 2 to 4).

9.2 International legislation and instruments

The modern fisheries manager is required to be familiar not only with the national legislation governing fisheries, but also with the bewildering diversity of international legislation and voluntary instruments dealing directly with or impinging on fisheries. There has been a proliferation of such instruments in recent decades and a few of the more important examples and types are listed in Table 2.

Chief amongst the international instruments is the United Nations Convention on the Law of the Sea of 10 December 1982 (LOS Convention), which entered into force in 1994 (Table 2). This convention sets the legal context for all subsequent international arrangements and agreements relating to the use of the oceans and seas. Arising directly from the LOS Convention and designed to strengthen its provisions relating to high seas fisheries and transboundary stocks, are the UN Fish Stocks Agreement and the FAO Compliance Agreement.

There is also a host of other global agreements, both binding and voluntary. To date the Convention for International Trade in Endangered Species of Fauna and Flora (CITES) has had little impact on marine fisheries management, but concern about some marine species subjected to international trade is growing. Given this growing attention, there is a high likelihood that more species of fisheries interest will be listed through CITES in the future. For example, sturgeon species (Acipenseriformes spp.) are currently listed on Appendix II, under which international trade is carefully monitored and controlled, and the basking shark was placed on Appendix III of CITES by the United Kingdom in 2001. Some other global instruments of more immediate relevance are also shown in Table II, including the Convention on Biological Diversity.

Most countries involved in fisheries are or will become members of one or more regional bodies involved in utilization, management and conservation of marine living resources. These include bodies such as the various tuna commissions (e.g. the International Convention on the Conservation of Atlantic Tuna (ICCAT) and the Convention on Indian Ocean Tuna (IOTC)), the Convention on the Conservation of Antarctic Living Marine Resources (CCAMLR), various FAO regional fishery bodies such as the Fishery Committee for the Eastern Central Atlantic (CECAF) and the Asia-Pacific Fishery Commission (APFIC), and many others. The manager must be aware of those in which his or her country is involved, and the implications and obligations of membership.


Berkes, F, R. Mahon, P. McConney, R. Pollnac & R. Pomeroy. 2001. Managing Small-scale Fisheries. Alternative Directions and Methods . IDRC, Canada. 320 pp.

Caddy, J.F. and Griffiths, R.C. 1995. Living marine resources and their sustainable development. Some environmental and institutional perspectives. FAO Fisheries Technical Paper , 353. 167 pp .

Charles, A. T. 2001. Sustainable Fishery Systems . Blackwell Science, London. 384 pp.

Cochrane, K.L. 2000. Reconciling sustainability, economic efficiency and equity in fisheries: the one that got away? Fish and Fisheries , 1 : 3-21.

FAO. 1995. Code of Conduct for Responsible Fisheries. FAO, Rome. 41pp.

FAO. 1997. FAO Technical Guidelines for Responsible Fisheries No. 4 : Fisheries Management. FAO, Rome. 82pp.

FAO. 2000. The State of World Fisheries and Aquaculture. 2000. FAO, Rome.

United Nations. 1998. International Fisheries Instruments with Index . Division for Ocean Affairs and the Law of the Sea, Office of Legal Affairs. United Nations, New York. 110 pp.

Table 2. Some key legislation and agreements which make up the legal regime of fisheries management.

Fish abundance and species richness across an estuarine–freshwater ecosystem in the Neotropics

We investigated the distributional patterns of shallow-water fish and their environmental correlates along a broad spatial scale encompassing estuarine and freshwater ecosystems. Marine-vagrant species were restricted to the sites under the influence of salinity intrusion, whereas estuarine-related and freshwater guilds were found along the entire estuarine–freshwater gradient. Primary- and secondary-freshwater fish guilds had the most widespread spatial distribution and comprised a major fraction of the total abundance and species richness. Abiotic factors correlated with fish abundance and composition along two main environmental axes, one related with variation in salinity, water transparency, and sediment granulometry and the other with the slope gradient. Species richness was significantly higher at the natural channel linking the estuarine- and freshwater-ecosystem, which probably was due to: (a) a steeper slope that favored the confluence of fish from the littoral (<2 m) and limnetic (>2 m) zones and (b) the sporadic inflow of saltwater that carried into this region several marine-related species. Although estuarine–freshwater ecotones are known to support few species, mainly salinity tolerant, our results suggest that habitat features and seasonal fish movement associated with salinity intrusion could lead to more diverse fish assemblages in this transitional zone.

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Goats (Capra hircus), one of the most important livestock animals, were domesticated from a single wild ancestor 10,000 years ago [42]. Since then, due to their ability to adapt to various territories, goats have accompanied humans in all activities and have spread all over the world [43]. During domestication, some specialized goat breeds with high performance in many characteristics were generated which now provide important resources for humans. Nevertheless, genetic research on goat traits is lacking, and the limited information we currently have is mostly derived from sheep studies.

With the goal of contributing to our knowledge of goats, we performed a study in Capra hircus about the genomic organization of the TRB and TRG loci, which revealed a surprising example of gene family expansion in ruminant species studied so far (i.e., sheep and cattle).

Given the complexity of the TR loci, the recent release of the highly contiguous reference goat genome generated by a combination of methods [44] improving the previous whole genome shotgun assembly [45] was a further stimulus for our analysis of the goat TR loci.

As expected, the general genomic organization of goat TRB is similar to that of the other artiodactyl species [11,12,13,14, 16, 17], with three in-tandem D-J-C clusters located downstream of an array of TRBV genes and upstream of a single TRBV gene, which is positioned in an inverted transcriptional orientation (Fig. 1). Analysis of a publicly available pool of goat cDNAs has shown that the presence of three D-J-C clusters offers to this species, as in the other artiodactyls, a biological advantage from the improved combinatorial and junctional diversity of the CDR3 domain, involved in the antigen binding site, that results from the increased number of the TRBD and TRBJ genes (Fig. 4). Moreover, expression analysis has reported that, beside the intra-cluster rearrangements, TRBD/TRBJ inter-cluster rearrangements occur during TRB recombination, thus increasing once more the functional diversity of the TR β repertoire.

In contrast to the great diversity in the combinatory region, the three TRBC genes are highly similar to each other and to the other TRBC genes of artiodactyl species. This conservation may reflect a strong functional constraint linked to the role of the TR constant domain in signal transduction or in interactions with other molecules on the cell surface [46, 47].

The structure of the TRBV region appears, however, to be unique within the goat TRB locus. Ninety-one goat TRBV genes, grouped into 27 distinct subgroups, lie in a region of approximately 434 kb. Of these, 182 kb are occupied by a massive expansion of the TRBV5 and TRBV6 gene subgroups, with 30 and 29 members, respectively. The 6 gene members of the TRBV21 subgroup represent the other multimember subgroup. As shown in the locus map (Fig. 1), the TRBV5 and TRBV6 genes are alternated and intermingled within the genomic region, indicating that shared duplicative events contributed to their extensive expansion, unlike the TRBV21 genes that are clustered together due to tandem duplications occurring from a single ancestral gene. Dot-plot analyses of the duplicate TRBV region reinforce the conclusion that the TRBV5 and TRBV6 genomic organization arose through a series of complex tandem duplication events, which rarely involved either the TRBV5 or the TRBV6 gene, but, more frequently, involved genes from both subgroups generating duplication units of different sizes (Additional file 5-6: Figure S2A-B).

The same extensive gene duplications occur in the sheep and bovine TRB loci [11, 13]. In sheep, 94 TRBV genes have been identified and grouped into 26 subgroups. Among these genes are 33 TRBV5 and 30 TRBV6 genes, which are intermingled as observed in the sheep locus. Also in this species, six clustered genes form the TRBV21 subgroup. In this regard, our phylogenetic study (Fig. 3) demonstrates that the gene duplications occurred during the shared evolutionary history of sheep and goat. In fact, most of the sheep TRBV5, TRBV6 and TRBV21 genes correspond to the orthologous goat genes, indicating that the duplication events occurred in a common ancestor of the two species.

In cattle, although the TRB sequence in the third bovine assembly seems incomplete [13], 134 TRBV genes, distributed among 24 subgroups have been found. In this case, the major germline repertoire is also attributable to the expansion of two TRBV subgroups whose genes are alternated at the 5′ of the V-cluster. One subgroup is the TRBV6, as in goat and sheep, which consists of 40 members the other subgroup, with 35 members, is classified as TRBV9 and likely corresponds to the goat TRBV5. As a matter of a fact, the same authors mention that the identity between the nucleotide sequences of the TRBV9 and TRBV5 genes is often > 75%. In addition, dot-plot analyses of the bovine TRB locus (Fig. 3 in [13]) show the same duplication scheme observed in the goat TRBV5 and TRBV6 genomic region (Additional file 5-6: Figure S2A-B). Furthermore, the bovine TRBV21 subgroup contain 16 members which, unlike in sheep and goat, appears to have been generated by duplications that also involved the bovine TRBV18, TRBV19 and TRBV20 genes [13].

If ancient gene duplications within the TRB locus led to the generation of the different TRBV subgroups shared among mammals [48], in ruminants the framework of the TRBV germline repertoire evolved with a more recent expansion of two main TRBV subgroups rather than with the emergence of diverse TRBV subgroups. Overall, this extensive gene expansion resulted in ruminant species (goat, sheep and cattle) possessing a germline TRBV repertoire with the highest number of genes among all the mammalian species studied so far [7, 11], including other artiodactyl species such as pigs and camels [14,15,16,17].

For comparison, it is interesting to note that TRBV5, TRBV6 and TRBV21 are also multimember subgroups in rabbits [8, 11], which possess 17 TRBV5, 14 TRBV6 and 7 TRBV21 genes. Humans and rhesus monkey possess major expansion in TRBV5 and the TRBV6 [11], though the gene number of each subgroup is never as high as in ruminants. The human TRB locus contains 8 TRBV5 and 9 TRBV6 genes, whereas 10 TRBV5 and 8 TRBV6 genes are present in rhesus monkey.

However, the ruminant functional TRB repertoire is strongly conditioned by the proportion of no- functional germline TRBV5 and TRBV6 genes. In fact, the percentage of no-functional goat TRBV5 genes is 40% (12/30), while 62% (18/29) of the TRBV6 subgroup genes are no-functional. The percentage of no-functional genes for the two subgroups is similarly high in sheep and cattle: in sheep, 57.5 and 66.6% of TRBV5 and TRBV6 genes, respectively, are no-functional, whereas in cattle, 50 and 34.2% of TRBV6 and TRBV9 genes are no-functional. Therefore, it appears that the gene expansion of these subgroups might be related not to specific functional needs but rather to the scheme of gene duplications. In contrast, it has been reported [19, 22] that the sheep and cattle TRD repertoire is clearly determined by the high percentage of functional germline genes belonging to the multimember TRDV1 subgroup, where the percentage of non-functional genes is very low (22% (6/27) in sheep and 14.2% (8/56) in cattle).

The organization of the TRG genes into two distinct and separate genomic regions was already known in sheep [26, 27] and cattle [24, 25]. However, in both species, the genomic structures of the two paralogous TRG loci was archived by analysis of BAC clones, and the structural relationship between them had not yet been determined. The deduced genomic organization of the TRG loci from the goat genomic assembly allowed us to establish the precise chromosomal position of the two loci, their distance and their reciprocal transcriptional orientation. Moreover, in all mammalian species with a single TRG locus, the AMPH and STARD3NL genes represent the IMGT 5′ and IMGT 3′ borne, respectively, since they are located upstream of the first and downstream of the last TRG gene (IMGT®, In goat, and likely other ruminants, the synteny has been broken as a consequence of the evolutionary TRG split, with the AMPH located at the 5′ end of the TRG1 locus and the STARD3NL gene at the 3′ end of the TRG2 locus (Fig. 5). Taking into account that an intrachromosomal transposition seems to have moved the TRG2 genes to the current 4q15–22 position [27, 41], this implies that the split also involved the STARD3NL gene. Therefore, in goat and likely all ruminant species, two more gene boundaries should be defined: the IMGT 3′ borne of the TRG1 and the IMGT 5′ borne of the TRG2 locus. We propose the LSM8 gene located 4.5 kb from the TRGC4 gene as the 3′ borne of the TRG1 locus, whereas no gene was found in the vicinity of the TRGV5–1 gene to be proposed as the 5′ borne of the TRG2 locus.

The molecular characterization of the goat TRG loci showed that the TRG1 locus is very similar to the corresponding sheep locus in terms of gene content and genomic organization (Fig. 5 and Additional file 14-15: Figure S6A-B). The comparison between the goat and sheep TRG1 sequences (Additional file 18: Figure S8A), however, revealed, between TRGV2 and TRGV9 genes, homology traces with the J-C regions, likely due to an additional cassette, which is still present in the same position in the bovine TRG1 locus (TRGC7 cassette in

An additional functional V-J-J-C cassette is, however, present in the goat TRG2 locus compared to that of sheep and cattle, as result of a recent duplication event involved the ancestral TRGC2 cassette giving rise to TRGC2A and TRGC2B. As a matter of a fact, the two TRGC2 cassettes show the highest nucleotide similarity between them (> 97%) even if the presence of a complete deletion of EX2 in the TRGC2A gene probably makes this cassette not functional.

In line with the evolutionary scenario proposed by Vaccarelli et al., [26] for the formation of the sheep TRG loci, we hypothesize that similar reiterated in-tandem duplications of V-J-J-C units may also have generated the goat TRG loci. Briefly, after the duplication of a minimum ancestral cassette consisting of one V, three J and one C gene, the ancestral TRG locus consisted of two cassettes, that were likely the forerunners of the TRGC5 and TRGC6 cassettes and were bordered by the AMPH and STARD3NL genes at their 5′ and 3′ ends, respectively. Subsequently, the TRGC5 cassette formed the TRGCC3 cassette, which in turn duplicated to generate the TRGC4 cassette. A duplication of the TRGC4 cassette produced the TRGC2 cassette, which in turn generated the TRGC1 cassette. At this point, in the goat TRG2 locus, a further duplicative event involving the TRGC2 cassette may have generated the fourth cassette. However, given the high identity between the TRGC2A, TRGC2B and TRGC1 cassettes, it is also possible that the additional cassette resulted from an unequal crossing-over event between the ancestral TRGC1 and TRGC2 cassette, and the reworked TRGC2A pseudogene may represent the outcome of this event. Unequal crossing-over events have previously been evoked in artiodactyls as the origin of the third TRBD-J-C cluster [12, 13, 49].

Author information


Division of Endocrinology and Diabetology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany

Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine University, Düsseldorf, Germany

German Center for Diabetes Research, Partner Düsseldorf, Düsseldorf, Germany

Departments of Internal Medicine and Cellular and Molecular Physiology, Yale Diabetes Research Center, Yale School of Medicine, New Haven, CT, USA