The intrinsic vulnerability to fishing of coral reef fishes

Rev Fish Biol Fisheries DOI 10.1007/s11160-014-9362-x

REVIEWS

The intrinsic vulnerability to fishing of coral reef fishes and their differential recovery in fishery closures Rene A. Abesamis • Alison L. Green • Garry R. Russ • Claro Renato L. Jadloc

Received: 19 December 2013 / Accepted: 9 July 2014 Ó Springer International Publishing Switzerland 2014

Abstract Coral reef fishes differ in their intrinsic vulnerability to fishing and rates of population recovery after cessation of fishing. We reviewed life historybased predictions about the vulnerability of different groups of coral reef fish and examined the empirical evidence for different rates of population recovery inside no-take marine reserves to (1) determine if the empirical data agree with predictions about vulnerability and (2) show plausible scenarios of recovery within fully protected reserves and periodicallyharvested fishery closures. In general, larger-bodied carnivorous reef fishes are predicted to be more vulnerable to fishing while smaller-bodied species lower in the food web (e.g., some herbivores) are predicted to be less vulnerable. However, this Electronic supplementary material The online version of this article (doi:10.1007/s11160-014-9362-x) contains supplementary material, which is available to authorized users. R. A. Abesamis (&) C. R. L. Jadloc Silliman University-Angelo King Center for Research and Environmental Management (SUAKCREM), Dumaguete City, Negros Oriental, Philippines e-mail: [email protected]

prediction does not always hold true because of the considerable diversity of life history strategies in reef fishes. Long-term trends in reef fish population recovery inside no-take reserves are consistent with broad predictions about vulnerability, suggesting that moderately to highly vulnerable species will require a significantly longer time (decades) to attain local carrying capacity than less vulnerable species. We recommend: (1) expanding age-based demographic studies of economically and ecologically important reef fishes to improve estimates of vulnerability; (2) long term (20–40 years), if not permanent, protection of no-take reserves to allow full population recovery and maximum biomass export; (3) strict compliance to no-take reserves to avoid considerable delays in recovery; (4) carefully controlling the timing and intensity of harvesting periodic closures to ensure long-term fishery benefits; (5) the use of periodicallyharvested closures together with, rather than instead of, permanent no-take reserves. Keywords Coral reef fish Vulnerability to fishing Population recovery No-take marine reserves Periodically-harvested closures

A. L. Green Indo-Pacific Division, The Nature Conservancy, Brisbane, QLD, Australia

Introduction G. R. Russ School of Marine and Tropical Biology and Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, QLD, Australia

Fishing is a ubiquitous form of disturbance to marine ecosystems and its impacts antedate those of all other

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man-made disturbances by decades to thousands of years (Jackson et al. 2001). Fishing has caused enormous reductions in the biomass of major fish stocks, often resulting in collapse with little chance for recovery under current levels of fishing mortality (Hutchings 2000; Pauly et al. 2002; Myers and Worm 2005; Froese et al. 2012; Watson et al. 2013). Overfishing and by-catch fishing mortality are most frequently implicated in regional and localized losses of marine fish species (Casey and Myers 1998; Dulvy and Reynolds 2002; Bellwood et al. 2012) and are likely to remain as significant threats to the persistence of species (Musick 1999; Dulvy et al. 2003; Graham et al. 2011). A worrisome sign of overfishing is the gradual decline through time in the mean trophic level of global fish landings—a pattern caused by ‘‘fishing down food webs’’ (Pauly et al. 1998, 2002). While there is intense debate on whether this declining pattern truly reflects a shift in the trophic structure of the overall fish community (see Branch et al. 2010), at the very least it indicates that fish species that differ in terms of life history also differ in their vulnerability to fishing. The first species to be depleted are usually those with life history traits that make them inherently more sensitive to exploitation (Pauly et al. 1998, 2002; Jennings et al. 1999a; Myers and Worm 2005). For example, Myers and Worm (2003) estimated that about 80 % of the biomass of large predatory fishes (e.g., sharks, rays, tuna, billfish, cod, etc.) was typically removed from the world’s major oceans within the first 15 years of industrialized fishing. Apart from their larger body size, these predatory fishes tend to grow slower, live longer and mature later in life. Where large predatory fishes have disappeared, they were usually replaced in the fisheries catch by species that are lower in the food web and have life history characteristics that confer greater resilience to fishing (Pauly et al. 1998, 2002). Fishing is the most important human activity on coral reefs. Reef fisheries provide food and livelihoods to millions of small-scale fishers worldwide, are typically multispecies, involve multiple types of fishing gear and techniques, and encompass a broad spectrum of social and economic conditions (Munro 1983; Russ 1991; Jennings and Polunin 1996a, b; Polunin and Roberts 1996). They can be classified into four categories (modified from Ruddle 1996) on the basis of human population density, overall fishing

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intensity and whether or not the fish are used for food. First, there are overexploited reef fisheries where hundreds of fish species are harvested intensely, often unsustainably, for subsistence and commerce. This situation is common in densely populated regions of South and Southeast Asia (McManus et al. 1992) and the Caribbean (Munro 1983). Secondly, there are the less exploited reef fisheries in less populated regions, such as parts of Melanesia, Micronesia, Polynesia, and the Red Sea. Fisheries catch in these areas is generally lower due to lower market demand, but local extirpations of species that are favoured as food and are highly sensitive to exploitation can still occur (e.g., Johannes 1998; Bellwood et al. 2012). Thirdly, there are highly selective reef fisheries in developed countries such as the United States and Australia where the larger predatory reef fishes, particularly groupers (Serranidae), emperors (Lethrinidae) and snappers (Lutjanidae), are almost exclusively targeted for commerce and recreation (e.g., Mapstone et al. 2004). Lastly, there are reef fisheries that target the smaller species of reef fish to supply the global aquarium industry. About 1,000 species are exploited by aquarium reef fisheries worldwide, with most specimens originating from Southeast Asia (mainly Indonesia and the Philippines; Wood 2001). The fourth category of fishing often occurs concurrently with any of the first three. These contrasting types of fisheries indicate varying degrees of disturbance caused by fishing on the reef ecosystem across different geographic regions, societies and economic settings around the world. However, overfishing is now a problem in more than 55 % of coral reefs worldwide, making it one of the most pervasive immediate threats to coral reefs (Burke et al. 2011). Fishing affects reef fishes at the level of the population and community directly through the removal of individuals or indirectly by altering species interactions or the reef habitat (Russ and Alcala 1989; Russ 1991; Jennings and Lock 1996; McClanahan 2000). Overfishing can thus be described in four stages: growth, recruitment, ecosystem (or ecological), and ‘Malthusian’ (Pauly 1988). Growth overfishing occurs when fish are caught before they have enough time to grow, resulting in suboptimal yields and a significant reduction in the proportion of larger size classes in a population. Recruitment overfishing is when fishing reduces the size of spawning stocks so much that recruitment declines. Ecosystem


overfishing is reached when fishing alters the relative abundance of species within a fish community, or in more severe cases, alters the species composition of the community. Ecosystem overfishing usually results in shifts in the relative abundance of species or species composition of the catch and an overall decrease in catch rates (described earlier as a result of ‘‘fishing down food webs’’) (Pauly et al. 1998, 2002). Finally, Malthusian overfishing is a result of too many fishers and not enough fish for them. This extreme stage of overfishing involves fishers using highly efficient fishing methods that are destructive to both the target fish communities and their habitats in a desperate attempt to maintain catch rates and incomes (Pauly 1988). Previous reviews of the effects of fishing on reef fish stocks (Russ 1991; Jennings and Lock 1996; Jennings and Polunin 1996a; Jennings and Kaiser 1998) show a large amount of evidence for overfishing. Moreover, the exploitation of reef fisheries resources to the point of Malthusian overfishing is a serious concern especially in developing coral reef nations (Russ 1991; McManus 1997; McClanahan et al. 2008). All these examples are consistent with the notion that coral reef fishes are highly prone to overfishing (Stevenson and Marshall 1974; Munro and Williams 1985; Sadovy 2002). Some attempts have been made to evaluate the risk of extinction in reef fishes in the face of exploitation, habitat loss due to global climate change and other stressors (Hawkins et al. 2000; Wilson et al. 2006; Graham et al. 2011; Comeros-Raynal et al. 2012). However, relatively little attention has been focused towards examining vulnerability to fishing of the vast number of targeted species of coral reef fish (Jennings et al. 1999b). Understanding the responses of different species to exploitation or to the reduction or removal of fishing mortality is a prerequisite to assessing the sustainability of various fishing practices (Jennings and Polunin 1996a, b) and evaluating the effectiveness of fishery closures, particularly fully protected no-take marine reserves (i.e., areas of reef where fishing of all species had been totally banned), as a tool for reef fisheries management and biodiversity conservation (Russ 2002). Here, we summarized the body of information on key life history traits of marine fishes that predict their intrinsic vulnerability to fishing, and reviewed how these traits have been evaluated against available data on the responses of coral reef fish populations to

exploitation. We then highlighted some emergent patterns that may help reef fisheries managers predict the vulnerability of targeted coral reef fishes. We also evaluated the reverse of declines due to fishing: the recovery of reef fish populations inside no-take marine reserves. We summarized the theory behind population recovery in reserves and reviewed, in relation to life history-based predictions about vulnerability, the available evidence showing different rates and patterns of population recovery in reef fishes. This is important because, first, no-take marine reserves continue to be advocated and implemented for reef fisheries management and there is a widespread expectation that reserves will always bring back the former abundances of targeted reef fishes, at least locally. Second, there seems to be some disagreement on whether reef fish populations will recover rapidly or slowly inside reserves and whether reserve age (time elapsed since protection) has a strong influence on recovery (Halpern and Warner 2002; Russ and Alcala 2004, 2010; Russ et al. 2005; Claudet et al. 2008; Molloy et al. 2009). It is important to clarify these issues if no-take reserves will continue to be the cornerstone approach to fisheries management and biodiversity conservation on coral reefs in many parts of the world (Russ 2002; Mora et al. 2006; HoeghGuldberg et al. 2009). Stakeholders want to know how quickly changes will occur inside reserves, how stable these changes are through time (Babcock et al. 2010) or when significant export of larvae or adult biomass from reserves will start to occur (Sale et al. 2005). Finally, we briefly discuss the implications of the findings of this review for reef fisheries management using no-take marine reserves and periodically-harvested fishery closures. The latter type of fishery closure is probably the most commonly employed customary approach to increasing or maintaining reef fish catch in the Indo-Pacific region (Johannes 1978, 2002; Cinner et al. 2006; Govan 2009; Cohen and Foale 2013).

Intrinsic vulnerability of coral reef fishes to fishing Life history correlates of intrinsic vulnerability to fishing Intrinsic vulnerability to fishing (hereinafter ‘vulnerability’) is the inherent capacity of a species to

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withstand the additional mortality imposed by exploitation, determined primarily by the intrinsic rate of population growth (r) (Myers et al. 1997; Reynolds et al. 2001; Cheung et al. 2005). Reliable estimates of r, however, are difficult to obtain for any species because the relevant methods are data-intensive, requiring age-specific survival and fecundity schedules (e.g., Hutchings and Myers 1994) or a wellestablished spawner-recruit relationship (r is a function of the slope of the spawner-recruit relationship at low spawner abundance) (e.g., Myers et al. 1997). Another practical problem is that population growth rates are density-dependent (maximum r occurs at low to intermediate densities) and density-dependence is difficult to quantify in field studies (Reynolds et al. 2001). Robust estimates of population growth rates in coral reef fishes subject to exploitation are very rare (e.g., Hisano et al. 2011). Reynolds et al. (2001) also pointed out the impracticality of obtaining estimates of population growth rates for the large number of targeted coral reef fishes due to immense data requirements and the limited resources in many coral reef nations to produce such data (also see Johannes 1998). An alternative approach is to use measureable life history parameters (maximum body size, body growth rate, maximum age or life-span, age or body size at maturity and rate of natural mortality) that are correlated with r as proxy indicators for vulnerability. This is a reasonable option because life history traits ultimately underpin population dynamics and therefore largely determine the demographic response of a species to mortality caused by fishing (Reynolds et al. 2001; Dulvy et al. 2004). Early theoretical studies suggested that the life history approach was promising. For instance, Adams (1980) showed that the maximum yield that can be harvested from the growth of a cohort (yield-per-recruit) would occur at a higher level of fishing mortality and at an earlier age at first entry into the fishery for species with ‘faster’ life histories (higher r). These are the so-called r-selected species, i.e., they are smaller, have shorter life-spans, grow more rapidly, mature earlier and have higher rates of natural mortality (e.g., herring and anchovies). On the other hand, species that have the opposite life history strategy (K-selected) would be more prone to overfishing because they can only withstand low levels of fishing mortality and produce lower yield-perrecruit (Adams 1980).

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Many studies have since investigated correlations between individual life history parameters and trends in population abundance in various species of marine fish, including elasmobranchs (e.g., Smith et al. 1998; Stevens et al. 2000; Dulvy and Reynolds 2002), major fish stocks in the northern Atlantic (Myers et al. 1997; Jennings et al. 1998, 1999a; Denney et al. 2002) and coral reef fishes belonging to several commonlytargeted families (Russ and Alcala 1998; Jennings et al. 1999b). Detailed reviews of this literature are provided by Dulvy et al. (2004), Cheung et al. (2005) and Reynolds et al. (2005). Some of the most informative examples showing the usefulness of life history traits as indicators of vulnerability come from a series of papers by Simon Jennings and colleagues. Jennings et al. (1998) examined the potential relationship of several life history traits with changes in the abundance of 18 fish stocks (cod, haddock, plaice, whiting, etc.) in the northeastern Atlantic over periods ranging from 12 to 20 years. They used an approach that accounted for the phylogenetic relationships between stocks in order to avoid the potential problem of statistical non-independence in more traditional cross-species analysis. Their analysis involving 9 pairwise comparisons of closely-related stocks showed that stocks tended to decline in abundance more quickly than their closest relative when they had a larger maximum body size and later age of maturity (they were not able to detect these relationships when they analysed the data without accounting for the relatedness between stocks). Using a similar approach in a second study of temperate fish stocks, Jennings et al. (1999a) searched for life history correlates of population declines in nine pairs of closely-related demersal fish species in the North Sea. Their analysis once again confirmed that those species that declined more rapidly relative to their sister species matured later at a larger body size and grew more slowly towards a larger maximum body size. Furthermore, they showed that over the 71-year period when fishing effort (mainly trawling) in their study area increased steadily and stocks declined, the structure of the demersal fish community shifted towards one with a lower mean maximum body size, lower mean age, lower mean body size at maturity and higher mean body growth rate. Jennings et al. (1999b) then tested their approach on coral reef fish to determine if maximum body size was a good predictor of trends in population abundance in

Rev Fish Biol Fisheries Table 1 Life history parameters of reef fish that correlate with vulnerability to fishing (adapted from Reynolds et al. 2001) Life history trait or parameter

More vulnerable

Less vulnerable Smaller

Maximum body size

L? = Theoretical maximum size from the von bertalanffy equation

Larger

Body growth rate

K = Growth coefficient from the Von Bertalanffy equation

Slower

Faster

Longevity Age at maturity

Tmax = Maximum recorded age Tmat = Age at which 50 % of the population reach maturity

Higher Later

Lower Earlier

Length at maturity

Lmat = Length at which 50 % of the population reach maturity

Larger

Smaller

Natural mortality

M = Instantaneous rate of natural mortality

Lower

Higher

responses to fishing. They compared the abundance trends of groupers (2 pairs of related species), snappers (2 pairs or groups of related species) and parrotfishes (formerly family Scaridae, now Labridae, subfamily Scarinae; 5 pairs or groups of related species) at 10 traditional fishing sites in Fiji that had different levels of fishing intensity. Similar to the temperate fish stocks, they found that reef fishes that decreased in abundance due to fishing more quickly than their closest relative had greater maximum body size (this trend was not apparent when data were analysed without accounting for the relatedness among species). However, the relationship between maximum body size and rate of decline of abundance was more evident in the intensely fished large predatory fishes (snappers and groupers) than in the lightly fished parrotfishes. These results indicated that maximum body size was less effective in predicting vulnerability in less intensely-fished species. Maximum body size (length), body growth rate, longevity, age or length at sexual maturity, and rate of natural mortality are the main life history parameters that have been shown to correlate with vulnerability (reviewed by Reynolds et al. 2001, 2005; Cheung et al. 2005; Table 1). A larger maximum body size, slower body growth rate, higher maximum age (longevity), later age or larger body size at maturity, and lower rates of natural mortality would indicate higher vulnerability. The opposite of each of these traits would suggest lower vulnerability. Many studies have shown that maximum body size is a good predictor of vulnerability (Dulvy and Reynolds 2002; Dulvy et al. 2003; Cheung et al. 2005, but see Hutchings et al. 2012). A good number of studies also indicate that delayed maturation equates to higher vulnerability (reviewed by Musick 1999; Reynolds et al. 2005; Cheung et al. 2005). For instance, Myers et al. (1997) showed that in Atlantic cod (Gadus morhua) age at

maturity has an inverse relationship with intrinsic rate of population increase (r). Smith et al. (1998) found that late-maturing shark species tend to have a lower r than early-maturing species. Furthermore, Hutchings et al. (2012) found that age at maturity was strongly and negatively correlated with r in elasmobranchs and teleosts, suggesting that it may be the universal predictor of vulnerability for fish as well as mammals. Far less empirical evidence is available for longevity, body growth rate and rate of natural mortality as indicators for vulnerability (Cheung et al. 2005; Reynolds et al. 2005). However, there is usually a strong degree of correlation among many of these key life history parameters (e.g., Beverton 1963; Pauly 1980) because of fundamental trade-offs between them (see Reynolds et al. 2001 for a detailed discussion). These inter-correlations among life history parameters impede judgement of their relative importance in assessing vulnerability but also suggest that one or two of the more commonly measured parameters (e.g., maximum size, body growth rate, longevity, age or length at maturity) can be used to gauge the sensitivity of a species to exploitation (Reynolds et al. 2005). Of the parameters that are usually used to describe the life history strategies of fish, fecundity is the one that has often been found to not correlate with vulnerability in bony fishes (Jennings et al. 1998, 1999a; Hutchings 2001; Sadovy 2001; Denney et al. 2002; Hutchings et al. 2012). Reynolds et al. (2005) argued that this is not at all surprising because a large body of life history and demographic theory indicates that fecundity will have little effect on population growth rates relative to other traits such as age at maturity. Also, high fecundity in bony fishes does not necessarily confer greater resilience to fishing (Sadovy 2001; Dulvy et al. 2003; Hutchings et al. 2012). Fecundity is positively correlated with maximum

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body size (Sadovy 1996; Denney et al. 2002) and the latter parameter had been repeatedly shown to have a direct relationship with vulnerability. The many large and highly fecund species of fish that are presently threatened by extinction or have been locally extirpated are probably the clearest indication that fecundity is not a very useful predictor of vulnerability (Sadovy 2001; Dulvy et al. 2003). However, for elasmobranchs, it is reasonable to argue that low fecundity can indicate high vulnerability (Musick 1999; Cheung et al. 2005). Vulnerability may also be associated with other important life history or behavioural traits. For instance, many species of coral reef fish that are heavily targeted by fisheries such as groupers, snappers and the largest species of wrasse (Cheilinus undulatus) are known to aggregate at certain times and locations to spawn (Domeier and Colin 1997; Sadovy et al. 2003; Claydon 2004; Colin 2010). The largest species of parrotfish, Bolbometopon muricatum, is highly susceptible to spearfishing when they form resting (sleeping) aggregations at night in shallow water (Johannes 1981; Aswani and Hamilton 2004). Intense fishing of these aggregations of reef fish can rapidly result in substantial population declines (Aswani and Hamilton 2004; Sadovy and Domeier 2005; Hamilton et al. 2012). Furthermore, groupers, similar to the parrotfishes and wrasses (Labridae), are protogynous, i.e., adult females change sex to become adult males (Choat and Robertson 1975; Thresher 1984). Males are usually larger than females (Sadovy 1996). Size-selective (or effectively, sex-selective) fishing of protogynous species can thus lead to a disproportionate loss of males, which may result in the problematic situation of not having enough males to fertilize the eggs produced by females in a breeding population (Sadovy 1996; Sadovy and Domeier 2005). Many reef fishes also exhibit a highly restricted geographic distribution (Hawkins et al. 2000), which would also tend to increase their vulnerability to global extinction (Musick 1999; Cheung et al. 2005; Graham et al. 2011). However, relatively few restricted-range species of reef fish are exploited compared to the more widely distributed species (Hawkins et al. 2000). Methods to estimate vulnerability to fishing The interest to use information on key life history parameters as ‘rules of thumb’ to rapidly estimate

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vulnerability to fishing of multiple fish species or stocks has grown in the past two decades. One of the earliest studies to espouse this approach involved coral reef fishes. Kulbicki (1992) proposed using life history characteristics to categorize Pacific coral reef fishes into SIX groups of vulnerability. The life history traits that he used included maximum body length, patterns of reproduction, behaviour, body growth rate, natural mortality rate and longevity. In the late 1990’s, the American Fisheries Society (AFS) proposed a twostep system of assessing the productivity (the opposite of vulnerability) of marine fish stocks and their risk of extinction (Musick 1999). The first step is to classify the fish stock into one of four categories of productivity (high, medium, low, or very low) based on an estimate of its r. Available information on key life history traits (i.e., body growth rate, age at maturity, maximum age, and/or fecundity) can be used in lieu of r if the latter is unknown but as a precaution, the stock is classified according to the lowest productivity category for which any of its available life history parameters fits. Furthermore, information on age at maturity (if available) takes precedence over other life history parameters as a proxy for r (Musick 1999). The second step is to determine the risk of extinction of the fish stock in question. This is achieved by comparing observed population trends in response to fishing (or other threats) against decline thresholds (ranging from 70 to 99 % reduction) specific to the productivity category of the stock. If the decline threshold is exceeded, the stock is considered vulnerable to extinction (local or otherwise) and further studies on it are recommended. Other factors that increase risk of extinction such as rarity, endemism and habitat specialization may also be taken into consideration (Musick 1999). Similar to the two-step system used by the AFS, Dulvy et al. (2004) also advocated for the use of relatively simple, life history-based rules of thumb as a first step to quickly identify fish species or stocks that are potentially highly vulnerable to extinction. Species or stocks that are flagged as highly vulnerable can then be studied in a more focused and rigorous manner. The latest and probably the most sophisticated method that can be used to estimate vulnerability to fishing is the ‘‘fuzzy logic expert system’’ (hereinafter fuzzy logic system) developed by Cheung et al. (2005). This method attempts to combine all available information on key life history parameters (i.e.,


maximum body length, age at first maturity, longevity, body growth rate, rate of natural mortality and fecundity) and ecological traits (i.e., geographic range and strength of site fidelity during spawning or feeding) of a fish species in order to generate an index of vulnerability that is arbitrarily scaled from 1 (least vulnerable) to 100 (most vulnerable). This numerical output can also be expressed according to four nominal categories of vulnerability: low, moderate, high and very high. At the heart of the fuzzy logic system is a set of heuristic rules (a set of conditions each with a prediction about degree of vulnerability) that were defined according to relationships between life history traits and population trends in response to fishing described in the literature. However, these rules do not include the largely disproven idea that high fecundity equates to low vulnerability to fishing. Inferences about vulnerability generated from each rule are combined using a method that involves weightedaveraging. A unique feature of the fuzzy logic system is that it can deal with vagueness and uncertainty in a quantitative manner by allowing for expert judgement in the application of the heuristic rules (see Cheung et al. 2005 for a detailed description of methods). This feature becomes advantageous when data on life history and ecological traits are imperfect or when it is difficult to make precise definitions of how certain traits can mean higher or lower vulnerability. Data on life history and ecological traits can be obtained from the literature, which are now made more accessible by online databases, particularly FishBase (www. fishbase.org; Froese and Pauly 2012). Cheung et al. (2005) showed that estimates of vulnerability based on the fuzzy logic system correlated with observed declines in abundance due to fishing of various fish species (i.e., species threatened with extinction, temperate demersal fish and coral reef fishes) better than the AFS system and vulnerability assessments based on a single life history parameter (e.g., maximum body length or age at first maturity only). However, as would be expected in any predictive model, the ability of this method to estimate vulnerability may decrease as input data becomes scarce. For example, predicted vulnerability had a tendency to be underestimated when only information on maximum body length was available (Cheung et al. 2005, 2007). Also, the quality of the prediction may be strongly affected by the presence or absence of information on certain traits (Cheung et al. 2005).

Age-based demographic studies on fishes deserve mention here because they are the main source of information for many of the life history parameters that can be used to predict vulnerability. In teleosts, the most reliable way to determine the age of an individual fish is to count incremental structures (i.e., daily rings and annual bands) that occur in otoliths (e.g., Pannella 1971; Secor et al. 1991; Fowler 1995). Size-at-age data can be used to estimate the parameters L? and K in the Von Bertalanffy growth function (VBGF), which are indicative of the maximum body size and body growth rate of species, respectively. Data on actual age of fishes also enable the estimation of longevity and age at first maturity. A vast literature on age-based life history patterns exists for temperate fish stocks (Hilborn and Walters 1992). Age-based demography of coral reef fish, on the other hand, was hampered by the long-held notion that such fish lacked annual bands (annuli) in their otoliths due to the absence of strong seasonal variation in water temperature in the tropics (Munro 1983; Polunin et al. 1996; Morales-Nin and Panfili 2005). This perception, which almost gained paradigm status, was a strong motivation in the development of length-based (rather than age-based) stock assessment methods for tropical fish in the 1980s (Pauly and Morgan 1987; Polunin et al. 1996). In the past two decades an extensive amount of research has demonstrated unequivocally that a broad range of coral reef fish, especially those subjected to fishing, can have their ages determined reliably by counting validated annuli in otoliths (Choat and Axe 1996; Cappo et al. 2000; Choat and Robertson 2002; Choat et al. 2009). This has permitted age-based studies of demography of coral reef fishes (e.g., Choat and Axe 1996; Russ et al. 1996; Newman et al. 1996; Choat et al. 2003; Begg et al. 2005; Gust 2004; Grandcourt et al. 2005, 2006; Taylor and McIlwain 2010) and paved the way for far more reliable assessments of vulnerability to fishing of coral reef fish than were available previously. However, it is still true to say that far less age-based information about exploited coral reef fish is available compared to fish from temperate regions, due to the sheer number of coral reef fish species targeted by fisheries and the paucity of appropriate laboratory facilities that can be accessed by biologists working in the tropics (Choat and Robertson 2002). Among the major groups of targeted coral reef fishes, data on age-based growth and life history

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patterns are most extensive for snappers (reviewed by Choat and Robertson 2002). Age-based demographic data are increasing for groupers, emperors, parrotfishes and surgeonfishes (Acanthuridae). However, agebased studies are lacking for targeted reef fishes at lower latitude regions where fishing pressure on the reef ecosystem is greater (e.g., Southeast Asia). The limited, but growing, database of age-based life history information in reef fishes indicates that many targeted species of reef fish typically have a long lifespan, with maximum age often exceeding 10–12 years in groupers (Ferreira and Russ 1992, 1994; Sheaves 1995; Grandcourt et al. 2005), 20–25 years in snappers (Sheaves 1995; Newman et al. 1996), 10–15 years in emperors (Williams et al. 2003; Taylor and McIlwain 2010) and parrotfishes (Choat et al. 1996; Choat and Robertson 2002) and 30–40 years in surgeonfishes (Choat and Axe 1996; Choat and Robertson 2002). This body of information has also revealed that maximum body size has no strong direct relationship with maximum age in some major targeted reef fish families (e.g., snappers and surgeonfishes) (Choat and Robertson 2002). Thus, maximum body size alone may be a poor indicator of vulnerability to fishing in small but long-lived reef fishes. Age at maturity, on the other hand, is often correlated with maximum age (Musick 1999) and may be a more reliable indicator of vulnerability in reef fishes. Predicted vulnerability of coral reef fishes to fishing Using the fuzzy logic system, Cheung et al. (2007) estimated the vulnerability to fishing of more than 1,300 species of marine fish and categorized each species into four groups according to their degree of association to several habitats: (1) coral reef-associated, (2) estuarine, (3) seamount-associated and (4) seamount-aggregating. They predicted that seamountaggregating fish were the most vulnerable among the four groups, having a mean vulnerability index that was about the same as that of fish species threatened by extinction (Cheung et al. 2007). Coral reef fishes, on the other hand, had the lowest mean vulnerability index among the four groups. However, the mean vulnerability indices of coral reef-associated, estuarine and seamount-associated species did not differ significantly from each other.

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Although coral reef fishes were predicted to be not as vulnerable as seamount-aggregating species, they showed the strongest decline over time in the global fish catch in terms of the vulnerability index. The mean vulnerability index of coral reef fish decreased from 50 in 1950, to 40 by 2003 (Cheung et al. 2007). In contrast, the mean index of vulnerability of all exploited marine fishes declined from about 51 to 47 within the same 53-year period. Cheung et al. (2007) suggested that the marked decline observed in coral reef fishes was due to the rapid depletion of the more vulnerable species. Which coral reef fishes are more vulnerable to fishing? Which are less vulnerable? Is it possible to generalize at the family level how reef fishes differ in terms of vulnerability? How useful is trophic level (Pauly et al. 1998) as an indicator of vulnerability in reef fishes? To address these questions, we compiled estimates of vulnerability for 145 targeted species of coral reef fish belonging to 10 families (Table S1), ranging from large apex predators to smaller-bodied species lower in the food web. Estimates of vulnerability were based on the fuzzy logic system (Cheung et al. 2005) and were directly extracted from FishBase (Froese and Pauly 2012). Estimates of trophic level occupied by each species were also obtained from FishBase. This compilation of target species is by no means comprehensive but we aimed to include a wide range of genera, maximum body sizes (20–750 cm total length) and trophic groups (piscivores, invertivores, zooplanktivores, herbivores, detritivores, omnivores). We also tried to include representative target species for major coral reef regions in the Indo-Pacific and the Atlantic. However, it must be stressed that we did not calculate vulnerability indices de novo for the 145 species. We assumed that these estimates of vulnerability provided by FishBase were generated using the best available information on key life history parameters and ecological traits for each species. This assumption means that if the input parameters were incomplete or problematic, some estimates of vulnerability may be inaccurate. A query with FishBase during this study showed that none of the species had empirical data for all life history and ecological characteristics required by the fuzzy logic system. Out of the 145 species, only 65 species (mostly sharks, groupers, snappers, emperors, surgeonfishes and parrotfishes) had data on age and/or growth (e.g., VBGF parameters), which may be used to estimate other


Fig. 1 The relationship between maximum body size (total length (TL) in cm) and predicted vulnerability to fishing (Cheung et al. 2005) in 145 species of reef fishes belonging to 10 families. For clarity, two species belonging to Family Carcharhinidae (sharks), with maximum body length values of 400 and

700 cm and vulnerability indices of 64 and 88, respectively, were excluded from the plot. Selected species are identified on the graph to provide an indication of the range of body size and probable vulnerability per family. IUCN threatened categories: CR critically endangered; EN endangered; VU Vulnerable

parameters (e.g., longevity and natural mortality rate). However, all species had data on maximum body size. The potential for inaccuracy due to incomplete data is a valid concern but considering the paucity of agebased life history information in coral reef fishes, these initial, quantitative approximations of vulnerability should suffice for the purposes of this review. Three broad, non-independent patterns emerge from the information provided by our sample of target species. The first shows a positive relationship between maximum body size and vulnerability, which was not unexpected because maximum body length is an input parameter in computing the vulnerability index. This relationship is not very robust (R2 = 0.36) but species with a larger body size are often predicted to be more vulnerable than smaller species (Fig. 1). The direct relationship between body size and vulnerability to fishing is evident within most families but stronger in the groupers, jacks/scads (Carangidae), snappers, emperors, wrasses, fusiliers (Caesionidae), angelfishes (Pomacanthidae) and parrotfishes (R2 = 0.57–0.71) than in the surgeonfishes (Acanthuridae) (R2 = 0.30) (Figure S1). The relationship is almost non-existent in our relatively small sample of sharks (R2 = 0.02) (Figure S1). However, as a group,

sharks are the most vulnerable (median vulnerability index = 67.6). The vulnerability indices of the largest species within all families, except Caesionidae, ranged from ‘‘moderate to high’’ to ‘‘very high’’. Furthermore, the largest species of grouper (Epinephelus lanceolatus, E. itajara), wrasse (Cheilinus undulatus) and parrotfish (Bolbometopon muricatum), which are all threatened by extinction globally (IUCN 2012), had some of the highest vulnerability indices (Fig. 1). The positive relationship between maximum body size and vulnerability is also somewhat evident at the family level. The species that have a maximum body size longer than or equal to 100 cm most often belonged to the Carcharhinidae, Serranidae, Carangidae and Lutjanidae and were predicted to be some of the most vulnerable reef fishes (Fig. 1). On the other side of the size spectrum, species that have a maximum body size of less than or equal to 50 cm were more often members of the Caesionidae, Acanthuridae, Labridae: Scarinae and Pomacanthidae that were often predicted to be less vulnerable. Note, however, that there are clear exceptions to generalizations about vulnerability at the family level, such as Bolbometopon muricatum (Labridae: Scarinae) and Cheilinus undulatus (Labridae), which are very large

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and predicted to be highly vulnerable species. Furthermore, as mentioned earlier, these species are also highly susceptible to overfishing due to their aggregating behaviour and because they are heavily targeted by fisheries (e.g., Aswani and Hamilton 2004; Sadovy et al. 2003). A plot of the mean maximum body size versus the median vulnerability index of species grouped into families showed a moderately strong positive trend (R2 = 0.71) (Figure S2). The second pattern suggested that families composed of species occupying higher trophic levels (on average) are generally more vulnerable than those composed of species occupying lower trophic levels. This was indicated by a positive relationship between the mean trophic level and the median vulnerability index of species belonging to the 10 families (R2 = 0.46) (Fig. 2). Note that this relationship is still present, but less clear, if species-level data on trophic level and vulnerability are used (R2 = 0.21) (see Figure S3). In general, vulnerability is predicted to increase moving up the food web from the herbivores/detritivores (Labridae: Scarinae, with the exception of B. muricatum and Chlorurus microrhinus, which consume live coral) to the herbivores/ detritivores/zooplanktivores (Acanthuridae), omnivores (Pomacanthidae), invertivores/piscivores (Labridae, Lethrinidae, Lutjanidae), and the piscivores (Serranidae and Carcharhinidae). The ‘outliers’ in this apparent relationship between trophic level and vulnerability at the family level were the Carangidae (jacks and scads) and Caesionidae (fusiliers). A lower than expected vulnerability of the Carangidae can be explained by our inclusion of reef-associated scads (Selaroides, Selar, Atule spp.), which are small-bodied (typically \ 30 cm TL maximum length), highly productive planktivores that are predicted to be less vulnerable than the many larger piscivorous species of jacks (e.g., Caranx ignobilis) in the same family. The Caesionidae, on the other hand, is almost entirely composed of small-bodied, highly productive and schooling zooplanktivorous species. A likely explanation for why the Caesionidae is an outlier in Fig. 2 is that zooplanktivorous fish often have a higher than expected trophic level presumably due to consumption of fish eggs or larvae in the water column. However, the prediction that fusiliers are the least vulnerable among all families was not unexpected because they probably have short life-spans, mature early in life and experience high rates of natural mortality due to

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Fig. 2 The relationship between mean trophic level (Pauly et al. 1998) and predicted vulnerability to fishing (Cheung et al. 2005) in 145 species of reef fishes belonging to 10 families

predation by piscivores (Cabanban 1984; Carpenter 1988). The third pattern indicates that vulnerability to fishing can vary widely within families (Fig. 3). This variation is ultimately due to the high diversity of life history strategies in coral reef fishes (Choat and Robertson 2002). A characteristic feature of reef fishes is the wider range of maximum body size within many families (Fig. 1). There is also significant trophic diversity within some families (e.g., Acanthuridae, Pomacanthidae). In our sample, only two families exhibited a more limited range of potential vulnerability, namely the Lethrinidae, which consists mostly of large invertivores, and the Caesionidae, which are comprised exclusively of small planktivores (Fig. 3). Furthermore, there are clear exceptions to the ‘rule’ that maximum body size and trophic level are good predictors of vulnerability. For instance, small species of herbivorous surgeonfishes, Zebrasoma scopas and Acanthurus coeruleus, are predicted to be highly vulnerable (Fig. 1). These species, together with several other surgeonfishes of moderate vulnerability, are known to live longer than 30 years (Choat and Axe 1996; Choat and Robertson 2002) and are thus predicted to be less productive than would be expected for their small sizes. The whitetip reef shark Triaenodon obesus is another example. This species is one of the smaller species of shark (maximum size of 160 cm TL only), yet is predicted to be more vulnerable than much larger sharks (such as the tigershark, Galeocerdo cuvieri) probably because it is highly restricted to coral reefs. A general prediction that can be made from the abovementioned patterns is that large carnivorous reef

Rev Fish Biol Fisheries Fig. 3 Frequency distribution of estimated vulnerability to fishing (Cheung et al. 2005) of 145 species of reef fishes belonging to 10 families. Data ordered from top to bottom according to decreasing median vulnerability per family (i.e., from Carcharhinidae = 67.6 to Caesionidae = 28.0)

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fishes (i.e., sharks, groupers, jacks, snappers, emperors) are more vulnerable to fishing while reef fishes that belong to lower trophic guilds (smaller carnivores, omnivores, zooplanktivores, herbivores and/or detritivores) are less vulnerable. However, apart from their ‘slower’ life history, large predatory reef fishes are inherently more susceptible to fishing due to their predatory nature and the fact that they are favoured targets of most reef fisheries (Munro and Williams 1985; Russ 1991). Many studies suggest that large predatory reef fishes are usually the first to disappear or decline with exploitation (e.g., Koslow et al. 1988; Russ 1991; Jennings et al. 1995; Jennings and Polunin 1996a,b; Friedlander and DeMartini 2002; Edgar et al. 2014). For instance, Friedlander and DeMartini (2002) showed that apex predators and higher-level carnivorous reef fishes (mainly sharks and jacks) accounted for more than 54 % of the total fish biomass in the remote and lightly fished northern Hawaiian islands but\3 % of the fish biomass in the heavily fished main Hawaiian islands. In contrast, the differences in the standing stock of targeted lower-level carnivores (mainly wrasses) and herbivores (parrotfishes, surgeonfishes) and chubs (Kyphosidae) between the two groups of Hawaiian islands were far less dramatic. Jennings and Polunin (1996a) also showed that in Fiji, large predators (groupers, snappers and emperors) comprised up to 40 % of the target fish biomass in lightly-fished traditional fishing grounds (qoliqoli) but \30 % of the biomass in more intensely-fished fishing grounds. They found no significant differences in the biomass of herbivores (parrotfishes and surgeonfishes) among the fishing grounds they studied. In the Philippines, sharks and other large predatory reef fishes are virtually absent from heavily fished reefs presumably due to overfishing but many smaller targeted species of parrotfish, surgeonfish, wrasses and other less vulnerable species are still relatively common (Russ 1991; Lavides et al. 2009). This is consistent with the results of predictive modelling of the global distribution of target fish species richness done by Edgar et al. (2014), which suggested that the species richness of sharks, jacks and groupers were lower than expected in the Coral Triangle region of Southeast Asia compared to more isolated reefs in the Pacific. Edgar et al. attributed this geographic pattern to overfishing of these higher-level carnivores. Furthermore, Edgar et al. (2014) estimated that globally, about 93, 85 and 84 % of the biomass of sharks, jacks

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and groupers had been removed from tropical (and temperate) reefs by fishing. At least two studies reported abundance trends that appear to be consistent with disparate vulnerabilities of different species within the same family. Both studies dealt with parrotfishes. Clua and Legendre (2008), using the life history-based categories proposed by Kulbicki (1992), demonstrated a clear pattern of decreasing relative abundance of the larger and more vulnerable species of parrotfish along a gradient of increasing fishing pressure among five fishing areas in Tonga (South Pacific). In contrast, the smaller and less vulnerable parrotfishes increased in relative abundance (dominance) along the same gradient. Similarly, Bellwood et al. (2012) reported that large parrotfishes had virtually disappeared from the most heavily fished Indo-Pacific reefs that they studied. Along an increasing gradient of fishing pressure (using human population density as a proxy for fishing intensity), the largest and most vulnerable parrotfishes (B. muricatum and large Chlorurus) rapidly decreased in abundance or disappeared but the smaller and less vulnerable species showed either a slight increase in abundance (small Chlorurus) or no clear trends (Scarus and Hipposcarus) (Bellwood et al. 2012). The detection of clear correlations between life history and fishing impacts on a population can be made difficult by variations in the intensity of fishing and in the susceptibility of species to different types of fishing gear and techniques (Russ and Alcala 1998; Jennings et al. 1999b). For example, Russ and Alcala (1989, 1998) showed that both the moderately to highly vulnerable large predators (groupers, snappers, jacks and emperors) and the much less vulnerable (much more productive) fusiliers declined significantly in abundance inside Sumilon reserve, Philippines when the reserve was subjected to two fishing events, the first in 1984–1986 (after 9.5 years of protection) and again in 1992–1993 (after 5 years of protection). During the first fishing event, when more than 100 fishers used drive-in net muro-ami and explosives occasionally in addition to the more commonly employed traditional fishing methods (bamboo traps, hook and line, gill nets and spears), the density of large predators decreased by 82 % while that of fusiliers declined by 60 %. During the second fishing event, muro-ami and explosives were not used, but the density of large predators still decreased by


69 % while that of fusiliers declined by 44 %. The fusiliers suffered proportional losses that were comparable to the declines in the large predators because of three factors combined: (1) fusiliers, like the large predators, are highly favoured targets of fishers at Sumilon (Alcala and Russ 1990); (2) fusiliers, due to certain behavioural traits such as schooling, are highly susceptible to the types of fishing gear and techniques that were used by fishers in the reserve, particularly bamboo traps, muro-ami and fishing using explosives (Carpenter and Alcala 1977; Russ and Alcala 1989; Alcala and Russ 1990); (3) fishing was highly intensive in the reserve during both fishing events (the first event more so than the second) (Russ and Alcala 1989). Jupiter et al. (2012) also documented similar levels of depletion (70–80 % loss in biomass) among the more vulnerable large carnivorous snappers and jacks and the less vulnerable planktivorous and herbivorous surgeonfishes (Acanthurus and Naso spp.) when the Cakaulevu tabu area (traditional periodically-harvested closure) in Fiji was intensively fished for 5 weeks after more than 3 years of protection. The snappers, jacks and surgeonfishes all declined substantially because they were very susceptible to daytime spearfishing due to their high position in the water column (Jupiter et al. 2012). Furthermore, they reported dramatic declines in the abundance of large parrotfishes, including B. muricatum and Chlorurus spp., due to spearfishing at night (see also Johannes 1981; Aswani and Hamilton 2004). There was also clear evidence showing that all of the groups of reef fishes mentioned above continued to decline up to 1 year after closure of the tabu was reinstated, suggesting non-compliance by fishers to the fishing ban (Jupiter et al. 2012).

Recovery from fishing in no-take marine reserves Predicting population recovery after cessation of fishing The intrinsic rate of population growth (r) is a major driver of population recovery (i.e., increase in numbers and biomass) after cessation of fishing (Jennings 2001). Thus, one would expect that reef fish species that have life history traits which indicate higher vulnerability (lower r) will be slower to recover, while those that have life history traits suggesting lower

vulnerability (higher r) will recover more rapidly (Table 1). However, the rate of population recovery will also depend on the size of the remaining population and the degree of compensation (increase in recruits per spawner as spawner abundance decreases) or depensation (lower than expected recruitment at low spawner abundance) (Jennings 2001). As fishing reduces the abundance of a population, compensation will result in a higher per capita rate of population growth. On the other hand, if fishing results in severe depletion of the population, depensation (also known as the ‘‘Allee effect’’) may slow down or prevent any recovery. Within no-take marine reserves, the recovery of local populations of reef fishes is contingent on three other factors. First is the extent to which fishing mortality (F) is reduced after the creation of a marine reserve, which is a function of compliance to the notake regulation, reserve size and transfer rates (movement patterns) of adults between the reserve and adjacent fished areas (DeMartini 1993; Jennings 2001; Russ 2002). The potential of reserves to promote population recovery is expected to increase with reserve size but decrease with greater adult transfer rates (DeMartini 1993; Kramer and Chapman 1999). DeMartini (1993) showed that recovery potential (in terms of increased spawning stock biomass per recruit) in reserves will be reduced for reef fishes that have fundamentally higher transfer rates (e.g., highly vagile species such as jacks) but enhanced for those that are likely to have lower transfer rates (e.g., moderately vagile species such as surgeonfishes). Kramer and Chapman (1999) further pointed out that recovery in reef fishes will be facilitated inside reserves that have a greater area: edge ratio, more habitat types and better habitat quality relative to adjacent fished areas, and natural barriers to emigration. The second and third factors are recruitment variation and metapopulation structuring (Jennings 2001). Both stem from the fact that most reef fishes (teleosts) have a bipartite life history (a relatively sedentary benthic adult stage and a dispersive pelagic larval stage) and exist as many discrete local populations, each of which may largely determine its own dynamics but whose demographic patterns may also be significantly influenced by external populations through the dispersal of larvae, i.e., the definition of a metapopulation adopted by Kritzer and Sale (2004). As such, local recruitment (i.e., the addition of

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juveniles to a population) may be strongly decoupled from local reproduction. Because reserves tend to be small (e.g., Halpern 2003; Weeks et al. 2010) and are unlikely to encompass multiple breeding populations that exchange larvae, the populations that they protect can often be considered ‘open’ to a large degree (Caley et al. 1996). In open populations, recovery will be strongly driven by larval recruitment patterns, modified to some extent by post-settlement densitydependent mortality (Jones 1991; Caley et al. 1996). For example, Russ and Alcala (1998) documented a significant decline in the mean density of the shortlived and fast-growing fusiliers (Caesionidae) within Apo reserve in 1992–1993, despite continuous protection and the steady recovery of this family in the reserve since 1983. They attributed this decline to the disappearance of what was formerly a very abundant species of fusilier (Pterocaesio tessellata) at Apo and another island (Sumilon), possibly due to recruitment failure several years in a row (Russ and Alcala 1998). Conversely, Russ and Alcala (2003) detected significant recruitment pulses of juvenile groupers (Cephalopholis argus and C. sexmaculatus) at Sumilon reserve in 1990–1991 and 1994–1995, which strongly influenced rapid increases in grouper density after fishing was eliminated or restricted. In general, however, recruitment is nearly impossible to forecast and may be driven largely by the biological and physical variability of the environment (Cushing 1982). Highly variable recruitment in space and time at different scales is a hallmark of coral reef fish populations (reviewed by Doherty 1991, 2002). Nonetheless, the temporal patterns of communitywide recruitment in reef fishes are somewhat predictable at the intra-annual scale and appear to be influenced by seasonal variations in temperature, rainfall, and wind regimes (Russell et al. 1977; Robertson et al. 1999; Srinivasan and Jones 2006; Abesamis and Russ 2010). Knowledge of metapopulation structuring is clearly important in predicting population recovery of reef fishes in marine reserves (Caley et al. 1996; Jennings 2001). Crowder et al. (2000) argued that reef fishes are likely to exhibit ‘source-sink’ dynamics where some local populations may have higher rates of population growth, reproduction and survival than others, presumably because of strong underlying differences in habitat quality. They emphasized the importance of placing reserves in these highly productive source

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habitats in order to help ensure population enhancement. Furthermore, they warned that random placement of reserves (or worse, placing reserves in unproductive sink habitats) is unlikely to result in detectable increases in fish populations. This point is further illustrated in the study by Wen et al. (2013), which showed that the mean densities of adult snapper (Lutjanus carponotatus) and grouper (Plectropomus maculatus) were approximately three times higher in reserves which had areas that consistently received greater recruitment (presumably due to better larval supply and better quality of habitat for juveniles) than in reserves that did not. Wen and co-workers suggested careful placement of reserves to ensure that they include these so-called ‘recruitment hotspots’. Another way to view the ‘source-sink’ problem is in terms of larval dispersal, where sources can be thought of as local populations situated upstream of a directional current, supplying larvae to sink populations downstream. Roberts (1997) used this approach to show potential patterns of connectivity (i.e., larval exchange among local populations) among coral reefs in the Caribbean. He did this by mapping the probable larval dispersal ‘envelopes’ of reef organisms based on well-known regional current patterns. Although Roberts made the simplifying, but now largely invalid (at least for reef fishes), assumption of passive larval dispersal in that study, he made the important point that recovery after reserve protection may be slow in certain locations that are likely to have very limited sources of larvae. A logical extension of this idea is that recovery within reserves that are situated downstream from potential source populations may also be slow or even impossible, if fishing has severely depleted these source populations. In practice, however, it is difficult to determine sources and sinks because the demographic rates (e.g., growth, reproduction, mortality) within and larval connectivity patterns between subpopulations must be estimated. Furthermore, larval dispersal studies indicate that delivery of larvae from one site to another is likely to vary over time, such that a location might act as a source in 1 year, but not another (Jones et al. 2009). We are only beginning to realize the true spatial extent of demographically-relevant larval dispersal (reviewed by Jones et al. 2009) and the consistency of larval connectivity patterns through time (e.g., Berumen et al. 2012; Saenz-Agudelo et al. 2012) in reef fishes. Recent studies that have successfully


tracked the dispersal of reef fish larvae using genetic parentage analysis suggest that recovery of local populations within reserves may be facilitated both by self-recruitment as well as importation of larvae that originated from nearby (‘‘sustaining dispersal’’ within a few km) and distant (‘‘seeding dispersal’’ up to more than 100 km) populations (Planes et al. 2009; Harrison et al. 2012; Almany et al. 2013). It must be emphasised that the preceding discussion on population recovery assumes that ecological factors, such as significant changes in habitat quality or interactions with populations of predators, competitors or prey, have a weak or negligible influence on the rates and patterns of recovery after cessation of fishing. However, this situation seems unlikely in the coral reef ecosystem (e.g., McClanahan 2000; Graham et al. 2003; Mumby et al. 2006; Babcock et al. 2010). One case study that reported predator–prey interactions potentially influencing population recovery in parrotfishes will be briefly discussed below (Scarus tricolor population in Apo reserve; Babcock et al. 2010). Approaches to measure recovery from fishing inside no-take reserves There are now an overwhelming number of empirical studies demonstrating the positive impacts of no-take marine reserves on the abundance of exploited species, including reef fishes (major reviews in the past decade: Halpern and Warner 2002; Russ 2002; Gell and Roberts 2003; Micheli et al. 2004; Lester et al. 2009; Molloy et al. 2009; Babcock et al. 2010). This substantial literature indicates that there are four major approaches to gauge the recovery of populations within reserves: (1) spatial comparisons between reserve and fished sites at a single point in time; (2) meta-analyses of data from spatial comparisons at one point in time or temporal (before-after) comparisons of the same sites; (3) space-for-time substitutions that account for the duration of reserve protection in spatial comparisons; and (4) monitoring that incorporates both spatial as well as temporal (including before-after sampling) comparisons (e.g., Before-After-ControlImpact-Pairs (BACIP) design). Which of these approaches are more useful in evaluating the predicted vulnerability to fishing of coral reef fishes against their observed patterns of recovery if fishing is reduced or stopped?

Obviously, ‘‘one-off’’ spatial comparisons of reserve and fished sites are the least useful for the purposes of this review because they can show nothing more than differences in abundance between sites (reserve/fished site ratios), with limited ability to demonstrate what caused any differences observed. While results from spatial comparisons at one time may suggest that some level of recovery of any number of species occurred in the reserve, no reliable information on different rates of recovery can ever be gleaned from such data. Furthermore, the same results may be confounded by spatial differences in fish abundance that already existed prior to reserve creation (e.g., Edgar et al. 2004). This form of bias may be due to differences in habitat, history of fishing and larval supply (Russ 1985, 2002). Thus, one cannot unequivocally conclude that protection resulted in population recovery inside reserves. Another ‘‘design issue’’ with reserve/fished site ratios is that relative differences in abundance between reserve and fished sites may be caused by increased fishing mortality outside reserves, not increased abundance in the reserve (Babcock et al. 2010). Studies that employ meta-analysis of research results have become popular in the last decade or so (Mosquera et al. 2000; Coˆte´ et al. 2001; Micheli et al. 2004; Claudet et al. 2008; Lester et al. 2009; Maliao et al. 2009; Molloy et al. 2009) because they enable the synthesis of results from spatial or temporal comparisons to infer patterns of recovery across broad regions and taxonomic groups. However, many have cautioned that synthesizing across systems with potentially different dynamics can obscure the actual temporal responses of exploited species to protection, leading to variable or contradicting conclusions about rates and patterns of recovery (Edgar et al. 2004; Russ et al. 2005; Babcock et al. 2010; Russ and Alcala 2010; Edgar and Barrett 2012; see also Osenberg et al. 1999). For example, Halpern and Warner (2002), in a metaanalysis of data from 112 independent measurements of the impacts of 80 reserves, did not detect significant differences in the response ratios (essentially ratios of variables like density, biomass, body size and species richness inside versus outside a reserve) of different trophic groups of fish (carnivores, herbivores, planktivores) to different durations of protection. They did, however, acknowledge the potential for rates of recovery to vary considerably among these groups due to contrasting life history traits or degree of

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exploitation. In a meta-analysis of spatial and temporal comparisons of 19 reserves spread across a large area of the Philippines, Maliao et al. (2009) showed that different trophic groups of reef fish (piscivores, herbivores, omnivores, planktivores, corallivores and invertivores) had varying responses to protection in space (reserve vs. fished) and time (before and after reserve establishment). However, there was very little consistency in the responses of each trophic group to protection, preventing any clear conclusions about potential differences in the rates of recovery of these groups. Their results may have been confounded by a range of ecological factors (e.g., differences in habitat quality, larval recruitment history, etc.) as well as management effectiveness varying across the 19 reserves. Another inherent potential problem with meta-analyses is publication bias wherein results are skewed by the use of data that have been nonrandomly selected through the publication process (Edgar et al. 2004; Edgar and Barrett 2012). Thus, studies showing non-significant results (e.g., no difference between reserve and fished sites or before and after reserve protection) may have been filtered out more often than those with significant results, which may lead to overestimating the actual magnitude of reserve impacts. Edgar et al. (2004) and Edgar and Barrett (2012) argued that this kind of bias probably led in part to the conclusion by Halpern and Warner (2002) that strong reserve effects, across all groups of fish (and invertebrates) that were examined, occurred within a surprisingly very short period of time (1–3 years). Note, however, that rapid increases in the abundance of target species inside reserves may also occur because of immigration of individuals from areas outside reserves (e.g., Denny et al. 2004; Eggleston and Parsons 2008). Monitoring programs that employ the BACIP design and implemented at the appropriate temporal scale are probably the most useful for examining different patterns of recovery of exploited species after reserve establishment (Russ 2002). However, studies that employ BACIP are still rare and encompass relatively short time scales (e.g., Hawkins et al. 2006; Russ et al. 2008; Moland et al. 2013), 20 years after Jones et al. (1992) emphasised the need for them and 10 years after Russ (2002) reiterated the point. Furthermore, the long potential lifespan of many targeted reef fishes (Choat and Robertson 2002), the long history of fishing impacts on coral reefs (Jackson

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et al. 2001) and possible predator–prey interactions between reef fishes developing through time inside reserves (Graham et al. 2003; Mumby et al. 2006; Babcock et al. 2010) suggest that monitoring of reserves over many years, if not decades, is necessary. At present, long-term (decadal) monitoring studies that incorporate both spatial and temporal comparisons (with or without data collected before reserves were established) provide the best empirical evidence on the rates and patterns of recovery of exploited species in reserves. Long-term data can show change in population and community structure through time and point to processes that likely drive this change (Russ et al. 2005). Unfortunately, however, long-term studies of reserves are also rare. Space-for-time substitution studies are an attempt to circumvent the problem of scarcity of long-term monitoring studies. The idea behind these studies is to combine data from one-off spatial comparisons and/or temporal monitoring of reserves with different ages to create a long-term timeline of reserve effects. This approach inherently assumes that communities in younger reserves develop in the same temporal manner as in the older reserves. The rates and patterns of recovery that can be inferred from space-for-time substitution studies are useful for the purposes of this review, provided that sites are generally similar and/or potential confounding factors (e.g., history, sampling methods, habitats, levels of protection, etc.) are accounted for (Russ et al. 2005). Space-for-time substitution studies on reserves on coral reefs show compelling patterns of change in fish and benthic communities for time scales of up to 11–40 years (e.g., McClanahan 2000; Russ et al. 2005; McClanahan et al. 2007; Stockwell et al. 2009; McClanahan and Humphries 2012). Differential patterns of recovery from fishing inside no-take reserves We examined the available long-term data on rates and patterns of recovery of heavily-targeted coral reef fish species inside (and outside) reserves to see how well they agree with predictions about intrinsic vulnerability to fishing (Table 2). We focused on data from the long-term monitoring and space-for-time substitution studies done by G. Russ, A. Alcala and colleagues in the central Philippines, and by T. McClanahan and colleagues in Kenya. The time scales


covered by these studies are by far the longest for reserves on coral reefs (close to 3 decades of almost annual monitoring of 2 reserves in the Philippines; space-for-time studies of reserves up to 4 decades old in Kenya). These rare data sets also show some evidence for the density and biomass of several reef fish species or taxonomic groups becoming stable over time or attaining apparent maximum levels in reserves, suggesting full recovery of local populations (Babcock et al. 2010). It must be stressed that by using the term ‘full recovery’, we mean that a local population (of an individual species or taxonomic group) may have reached a certain level of carrying capacity in the reserve. However, it can never be known for sure if this level approximates an unfished baseline level because no data prior to any fishing was available. Furthermore, the level of carrying capacity for each individual species may shift through time depending on ecological factors, such as changes in habitat quality or abundance of predators, competitors and prey in response to reserve protection. It must also be noted that at large spatial scales in both the Philippines and Kenya, fishing pressure is very high outside reserves, fisheries catch is highly diverse (Russ and Alcala 1998; McClanahan et al. 2008), fishery laws (e.g., catch or gear restrictions) are weakly enforced and overfishing (growth, recruitment, Malthusian and local extinctions) has been suggested in some areas (Russ 1991; Green et al. 2003; McClanahan et al. 2008; Lavides et al. 2009; Hicks and McClanahan 2012). Thus, the observed and inferred trends in fish abundance through time in Philippine and Kenyan reserves may be indicative of scenarios for recovery amidst heavy exploitation, which may significantly differ from recovery patterns in reserves that are situated in less heavily fished regions or places where reef fisheries are relatively well-managed [e.g., Great Barrier Reef (GBR), Australia]. Our general hypothesis is that patterns of recovery through time will vary among species or groups that differ in their predicted vulnerability and that the rates of recovery will be slower in large carnivorous fishes (groupers, snappers, etc.) than in lower trophic groups such as planktivores or herbivores. The long-term data on biomass of the moderately to highly vulnerable large predatory reef fishes, i.e., groupers, snappers, emperors and jacks, in 2 reserves in the central Philippines indicated that recovery followed a logistic pattern over 3 decades of

monitoring (1983–2009) (Russ and Alcala 2003, 2004, 2010) (Table 2). Biomass was increasing exponentially during the first 9 and 18 years of protection at Sumilon and Apo reserves, respectively (Russ and Alcala 1996, 2004). Almost a full decade later, the rate of biomass recovery of large predators in the two reserves showed definite signs of slowing down, possibly approaching local carrying capacity (Russ and Alcala 2010). The time required for full recovery (i.e., attaining local carrying capacity) at Sumilon was estimated to be 15–20 years (Russ and Alcala 2004, 2010). However, this duration may have been overestimated (and the level of local carrying capacity may have been underestimated) because hook-and-line fishing still occurred in Sumilon reserve for some of the monitored period. Also, the true local carrying capacity (in terms of biomass per unit area) at Sumilon reserve may be much greater than what the long-term data suggested because of better habitat (therefore the potential for higher abundance) for large predatory reef fishes at that site (Russ and Alcala 2003). In contrast, the time needed for full recovery at Apo reserve was estimated to be 40 years (Russ and Alcala 2004, 2010), notwithstanding the very strict and effective protection of this reserve. It is noteworthy that after 27 years of protection, biomass recovery of large predatory reef fishes at Apo reserve had not shown any indication of reaching an asymptote (Russ and Alcala 2010). Similar to the large predatory reef fishes, the recovery of a moderately vulnerable planktivorous species of surgeonfish (Acanthuridae), Naso vlamingii, in Apo reserve occurred over decadal time scales (21 years of protection, monitored from 1983 to 2003) (Abesamis and Russ 2005) (Table 2). Long-term monitoring showed that density of N. vlamingii was increasing rapidly in the first 10 years of protection, but slowing down in the years that followed. However, unlike the large predatory reef fishes, the density of N. vlamingii in Apo reserve exhibited stability after 15–20 years of protection, suggesting that local carrying capacity was attained by this species (Abesamis and Russ 2005). In general, these decadal-scale temporal patterns of recovery and sustained levels of density are consistent with the long lifespan (up to 40 years: Choat and Axe 1996) and territorial behaviour (Abesamis and Russ 2005) of N. vlamingii. On the other hand, the space-for-time substitution study done by Stockwell et al. (2009) on 15 small

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123 Carangidae—45 to 74

Lethrinidae (6 spp.)

23–66

20–58

Herbivorous surgeonfishesc (9 spp., Acanthuridae)

Herbivorous parrotfishesc

14–31

Planktivorous fusilierse (7 spp. Caesionidae)

Logistic increase in density with a slight slowing down towards 40 year; exponential increase in biomass over the same period

46* 30?

40* 26* 32*

Wrasses (Labridae, up to 25 spp.)

Triggerfishes (Balistidae, Balistapus undulatus and 3 other spp.)g

Angelfishes (Pomacanthidae, up to 4 spp.)f

Surgeonfishes (Acanthuridae, up to 13 spp.)g

Parrotfishes (Labridae: Scarinae, up to 11 spp.)g

Ricker curve for density, logistic increase for biomass over 40 year

Logistic increase in density with a slight slowing down towards 40 year; exponential increase in biomass over the same period

Ricker curve—density and biomass peaked between 7 and 10 year, declining significantly in the following 3 decades

Ricker curve—density and biomass peaked between 15–20 year, then declined

Lutjanidae—45* Lethrinidae—41* Haemulidae—45*

Ricker curve—density and biomass peaked between 20–26 year, then declined

Rapid increase within 4 year after reserve was reinstated in Sumilon; rapid increase during the first 9 year of protection in Apo

Rapid increase in density within the first 5 year in Sumilon reserve, stable for the next 9 year; density increased after 8–12 year in Apo reserve but decreased from 13 to 26 year, possibly due to natural mortality (predation)

Ricker curve—rapid increase in biomass in first 5 year, peaking between 7–8 year, slight decrease thereafter

Exponential—slow increase in biomass in the first 7 year, rapid increase from 8–11 year

Logistic—exponential increase in density within the first 10 year, slowing down in succeeding years, reaching an asymptote

Logistic—exponential increase in biomass in the first 9 year and 18 year at Sumilon and Apo reserves, respectively, with signs of slowing down possibly approaching carrying capacity almost a decade later at each site

Rates and patterns of recovery after protection in reserves

Larger carnivoresf (‘‘Lutjanidae’’)

Kenya

28

Herbivorous parrotfishd(Scarus tricolor, Labridae: Scarinae)

(21 spp., Labridae: Scarinae)

38

Planktivorous surgeonfishb (Naso vlamingii, Acanthuridae)

Carangidae (3 spp.)

Serranidae—14 to 72

Lutjanidae—23 to 69 Lethrinidae— 29 to 46

Serranidae (19 spp.) Lutjanidae (11 spp.)

Predicted vulnerabilityh

Large predatory reef fishesa

Philippines

Family or species

Table 2 Summary of rates and patterns of population recovery of coral reef fishes inside reserves based on long-term studies

10–15 year for density; 20–25 year for biomass

Unknown ([40 year)

7–10 year

Unknown ([40 year)

15–20 year

20–26 year

*5–6 year in Sumilon reserve? \10 year in Apo?

*5 year in Sumilon reserve

7–8 year

Unknown ([11 year)

15–20 year in Apo reserve

15–20 year in Sumilon reserve; 40 year in Apo reserve

Time to full recovery


Data sources and notes: a Russ and Alcala (1996, 2003, 2004, 2010), long-term monitoring up to 27 year of protection; b Abesamis and Russ (2005) and Babcock et al. (2010), long-term monitoring up to 21 year of continuous protection; c Stockwell et al. (2009), space-for-time substitution study of 15 Philippine reserves (0.5–11 year old); d Babcock et al. (2010), long-term monitoring up to 14 year of continuous protection in Sumilon reserve, 26 years in Apo reserve; e Russ and Alcala (1998), long-term monitoring during complex management history of Sumilon and up to 11 year of continuous protection of Apo reserve; f McClanahan et al. (2007), space-for-time substitution study of 4 Kenyan reserves (max age of 14–37 years); g McClanahan et al. (2007) and Babcock et al. (2010), space-for-time substitution study of 4 Kenyan reserves (max age. of almost 17–40 years); h indices of vulnerability based on Cheung et al. (2005).* Median vulnerability of family based on species in Appendix 1. ? Predicted vulnerability of Balistapus undulatus

Unknown (more rapid?) 23* Rabbitfishes (Siganidae)f

Exponential increase in density and biomass through 37 years, but pattern may be an artefact of sampling

Predicted vulnerabilityh Family or species

Table 2 continued

Rates and patterns of recovery after protection in reserves

Time to full recovery


(0.06–0.30 km2) reserves in the central Philippines suggested that the accumulation of biomass of herbivorous surgeonfishes (9 spp.) during the first decade of protection was exponential. Recovery of surgeonfish biomass was slow from 0.5 to 7 years of protection but was considerably more rapid in succeeding years. There was no suggestion of an asymptote in biomass for the oldest reserves that were studied (8–11 years old), which indicated that a longer duration of protection is required for surgeonfish populations to fully recover. This pattern of biomass recovery inferred by Stockwell et al. (2009) is to be expected given the ‘square’ growth pattern and relatively long lifespan (16–42 years) of the surgeonfish species that they studied (Choat and Axe 1996; Choat and Robertson 2002). Recovery of one species of herbivorous parrotfish (Labridae: Scarinae), Scarus tricolor, in Sumilon reserve over a period of 14 years (1994–2008) (Babcock et al. 2010) was much faster than the recovery of large predators and surgeonfishes, which is consistent with the predicted lower vulnerability or higher productivity of parrotfishes (Choat et al. 1996) (Table 2). After a rapid increase in density within the first 5 years of continuous protection from fishing (except from hook-and-line, to which parrotfishes are not susceptible), density of S. tricolor at Sumilon reserve was stable for the next 9 years. This trend was mirrored by the inferred pattern of recovery of parrotfishes (21 spp.) in the space-for-time substitution study done by Stockwell et al. (2009). Results from that study suggested that total herbivore density (dominated by parrotfishes) and total parrotfish biomass increased rapidly within 5 years of protection, reaching maximum levels after 7–8 years (Stockwell et al. 2009). On the other hand, in Apo reserve, the density of S. tricolor responded positively to 8–12 years of protection but decreased significantly from 13 to 26 years of protection (Babcock et al. 2010). Babcock et al. (2010) argued that increased predation on juvenile parrotfish due to the build-up of large predatory reef fishes in Apo reserve may have prevented the full recovery of S. tricolor (see also Mumby et al. 2006). There is some evidence for the very rapid recovery of the highly resilient fusiliers after cessation of fishing at Sumilon and Apo reserves (Russ and Alcala 1998) (Table 2). In Sumilon reserve, the density of fusiliers increased by a factor of 2.3 between 1985 to

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1991 as a result of the reinstatement of protection in 1987 (following the first pulse fishing event in 1984–1986). It is interesting to note that mean density of fusiliers in Sumilon reserve in 1990–1991 were at levels that were only slightly lower than that in 1983 (Russ and Alcala 1998). There is compelling evidence showing that by 1983, after 9.5 years of protection, Sumilon reserve had been exporting fish biomass (dominated by fusiliers) to adjacent fished areas before protective management broke down (Alcala and Russ 1990). This presents the possibility that the density of fusiliers in 1990–1991 in Sumilon reserve was approaching local carrying capacity after just 4 years of protection. On the other hand, protection of Apo reserve resulted in a two-fold increase in the density of fusiliers from 1983 to 1991, after 9 years of continuous protection yet there was no indication that the local population was approaching carrying capacity (Russ and Alcala 1998). However, as previously mentioned, the recovery of fusiliers in Apo reserve suffered a setback in 1992–1993, possibly due to recruitment failure (Russ and Alcala 1998). The findings of the space-for-time substitution study done by McClanahan et al. (McClanahan et al. 2007, and in Babcock et al. 2010) in Kenya agree with the long-term monitoring studies of Russ, Alcala and colleagues in the Philippines that full recovery of reef fish populations within reserves will require decadal timescales of protection. McClanahan et al. (2007) described the recovery trajectories of several taxonomic groups of reef fishes using data from four Kenyan reserves (Mombasa, Watamu, Malindi, and Kisite) of varying sizes (6–28 km2 but \10 km2 of reef in each reserve) with maximum durations of protection ranging from 14 to 37 years. They found contrasting patterns of recovery among reef fish families that varied in their predicted vulnerability (Table 2). Density and biomass of the larger carnivores [‘‘Lutjanidae’’—which included snappers, emperors and grunts (Haemulidae)], wrasses, angelfishes and parrotfishes reached maximum levels at different durations of protection ranging from 7 to 26 years before declining (i.e., Ricker curve) (McClanahan et al. 2007; Babcock et al. 2010). The density of the larger carnivores peaked later (20–26 years) than the wrasses, parrotfishes and angelfishes (Table 2). The declines in parrotfishes and wrasses were attributed to decreases in the abundance of smaller individuals (10–20 cm) through time, which

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could be due to predation and/or inter-specific competition (McClanahan et al. 2007). In the surgeonfishes and triggerfishes (Balistidae), the inferred patterns of increase in density with duration of reserve protection were logistic, slowing down and approaching an asymptote towards the full 37 years encompassed by the study. However, in the same period, the surgeonfishes and triggerfishes had yet to reach their full biomass, showing an exponential pattern of increase (McClanahan et al. 2007; Babcock et al. 2010). The inferred recovery trajectories of density and biomass of the rabbitfishes (Siganidae) were also exponential over the 4 decade period. This result was somewhat unexpected given the high turnover rates of rabbitfishes (Froese and Pauly 2012) and may have been due in part to ineffective sampling of smaller, more cryptic individuals (McClanahan et al. 2007). McClanahan et al. (2007) and Stockwell et al. (2009) found broadly similar patterns of recovery of parrotfish and surgeonfish biomass in Kenya and the Philippines, respectively. Both studies showed that initial recovery of parrotfishes in reserves can occur rather quickly (\5–10 years), whereas surgeonfishes may take much longer to respond positively to protection. Samoilys et al. (2007) also detected rapid increases in parrotfish density within the first 3–4 years of protection in Philippine reserves. Overall, the results of these independent studies, all indicating more rapid recovery in the parrotfishes, are consistent with the shorter lifespans, faster growth and higher turnover rates of many species within this family (Choat et al. 1996; Choat and Robertson 2002). However, the timeframe for full biomass recovery of parrotfishes in the larger Kenyan reserves was considerably longer (20–25 years in McClanahan et al. 2007) than in the smaller Philippine reserves (7–8 years in Stockwell et al. 2009). Furthermore, it took almost four times longer for surgeonfishes in Kenyan reserves to attain biomass levels (per unit area) similar to Philippine reserves (McClanahan et al. 2007; Stockwell et al. 2009). These differences between Kenya and the Philippines indicate that rates and patterns of recovery can differ due to species composition, reserve size, general reef habitat type, local productivity and ecological interactions (McClanahan et al. 2007; Stockwell et al. 2009). In general, the data from decadal-scale monitoring and space-for-time substitution studies in the Philippines and Kenya support the prediction that the more


vulnerable large carnivorous reef fishes will recover more slowly than less vulnerable, usually smaller, reef fishes in lower trophic groups (Table 2). These studies indicate that the time required for full recovery (i.e., attaining local carrying capacity) of large carnivorous reef fishes in reserves can range from 15 to 40 years, whereas full recovery in the wrasses, angelfishes, parrotfishes, and fusiliers can be more rapid. Full recovery of surgeonfishes, on the other hand, may take longer than 2–3 decades. However, there is evidence for the rapid initial recovery of large predatory reef fishes within reserves in less heavily fished reefs in the United States and Australia. For example, Ault et al. (2006) estimated that after only 3 years of protection, the abundance of black grouper (Mycteroperca bonaci) and mutton snapper (Lutjanus analis) had increased two- and fourfold, respectively, in the Tortugas Bank No-take Marine Reserve, Florida Keys. They also noted promising indications that two endangered species of grouper (Epinephelus itajara and E. striatus) were beginning to recover in the reserve and an adjacent lightly-fished area, although sightings of these species were still quite rare. Russ et al. (2008) also reported rapid initial recovery of a large predatory reef fish (grouper) in the GBR marine park, Australia. They detected increases of 57–75 % in the density of coral trout (Plectropomus leopardus) inside newly-established no-take zones in 6 out of 8 regions of the GBR, after only 1.5–2 years of protection. For perspective, note that after 11 years of continuous protection, the mean density of groupers (3 spp.) at Apo reserve, Philippines (Russ and Alcala 1996) was still less than half of the mean densities of P. leopardus in the new no-take zones in two regions (Palm and Whitsunday) where Russ et al. (2008) detected significant increases (65–68 %) in average coral trout density through time. It is likely that the rapid rates of initial recovery exhibited by populations of large predatory reef fishes in the Tortugas Bank NTMR and the GBR were also facilitated by cooccurring fisheries management measures outside reserves, particularly legal size limits and catch quotas (e.g., Ault et al. 2006; Mapstone et al. 2004). These conventional fisheries management measures are hardly used or non-existent in multispecies reef fisheries in developing countries (Russ 1991; Jennings and Polunin 1996a; Green et al. 2003). Protected populations of vulnerable targeted reef fishes in the Tortugas Bank NTMR and the GBR will most likely

attain maximum density and biomass much sooner than those in the Philippines and Kenya.

Implications for coral reef fisheries management Here, we discuss some of the implications of different degrees of vulnerability and varying patterns of recovery for the management of reef fisheries using no-take reserves and periodically-harvested closures. Networks of no-take reserves are increasingly being implemented worldwide as a key approach to conserve marine biodiversity and at the same time sustain, or even enhance, fisheries (Sale et al. 2005; Mora et al. 2006; Hoegh-Guldberg et al. 2009; Weeks et al. 2010). Periodically-harvested closures, on the other hand, are one of the many traditional management methods that have been used for centuries by human communities in the Indo-Pacific region often to provide short term fisheries benefits (e.g., stockpiling community resources for cultural or fisheries management purposes: Johannes 1978, 2002; Govan et al. 2009; Foale and Manele 2004; Cohen and Foale 2013). Both approaches require support from local stakeholders in order to work as fisheries management tools on coral reefs. However, the ways by which stakeholders can benefit from no-take reserves and periodically-harvested closures differ fundamentally. Foale and Manele (2004) point out that using reserves is analogous to saving money in the bank indefinitely and harvesting only the ‘interest’ in the form of adult spillover or larval recruitment subsidies to fished areas outside the reserve (e.g., Russ and Alcala 2004; Harrison et al. 2012), whereas using periodically-harvested closures is akin to saving money in the bank and then, during certain periods, withdrawing a small or large part of the ‘savings’ by harvesting the fish biomass that had accumulated inside the closure (e.g., Cinner et al. 2005; Jupiter et al. 2012). Our aim here is not to suggest whether one approach will achieve fisheries management goals better than the other, or whether one or the other approach will be better suited to the needs of the stakeholders that manage them (Cinner et al. 2005; Govan 2009; Cohen and Foale 2013). Rather, we will focus on the probable outcomes or consequences of (1) protecting reef fish populations over short (several years) versus long (decades) periods within reserves or closures, (2) failure to protect the managed area during fishing bans and (3)

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intensive fishing when periodic closures are opened for harvesting. These are pressing issues considering the widespread interest to implement no-take reserves, the desire to build on customary marine tenure systems to manage fisheries more effectively (Govan 2009) and the need to define expectations from reserves and periodically-harvested closures more realistically. Protecting over short versus long periods By rescaling the data on long-term recovery of reef fishes in Philippine and Kenyan reserves (Table 2) in proportion to probable local carrying capacity (K, i.e., maximum density or biomass), we can directly compare different recovery trajectories in order to assess which groups or species of reef fish are likely to recover, and the extent of recovery, over the course of a few years to several decades of protection (Fig. 4a–c). In this approach, the first step involved plotting the recovery trajectories of selected trophic/taxonomic groups of targeted reef fishes using best estimates of the parameters of Ricker or logistic functions reported by studies listed on Table 2. We did this for biomass of large carnivores (groupers, snappers, jacks, emperors) in Sumilon reserve, Apo reserve (Russ and Alcala 2010) and Kenyan reserves (McClanahan et al. 2007), small carnivores (wrasses) in Kenyan reserves (McClanahan et al. 2007), herbivores (parrotfishes) in Philippine and Kenyan reserves (Stockwell et al. 2009 and McClanahan et al. 2007, respectively) and density of planktivorous surgeonfish (Naso vlamingii) in Apo reserve (Abesamis and Russ 2005). The second step was to plot each recovery trajectory on the same scale, by expressing predicted y (biomass or density) values for every x value (duration of protection) as a proportion of probable local carrying capacity (such that K is set at 100 %). However, rescaling the data by directly using this approach was not possible for two cases involving exponential patterns of recovery in the biomass of herbivorous surgeonfishes (in McClanahan et al. 2007 and Stockwell et al. 2009) and two cases of recovery in the density of fusiliers (Sumilon and Apo reserves, both in Russ and Alcala 1998) because there was no definite indication of local populations reaching K in these cases. For the surgeonfishes, we rescaled the data by assuming, conservatively, that the predicted highest level of biomass in both cases were 60 % of K. For the fusiliers, we rescaled the data by assuming that levels of K at Sumilon and Apo reserves were similar to the mean

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Fig. 4 Recovery trajectories of reef fishes in reserves based on long-term empirical evidence. Data from decadal-scale studies of Philippine and Kenyan reserves (Table 2) are rescaled in proportion to probable local carrying capacity (K, i.e., maximum density or biomass)

density that was recorded in Sumilon reserve in 1983 (Russ and Alcala 1998), when the reserve had been protected for 9.5 years and was likely to have been exporting fish biomass before protective management broke down in 1984 (Alcala and Russ 1990). Furthermore, we assumed that recovery of density through time in fusiliers was linear (i.e., we fitted linear regression models to the data). Finally, note that the rescaled recovery trajectories of the selected trophic/taxonomic groups have different starting points on the y-axis (initial density or biomass at duration of protection = 0 year) (Fig. 4a–c). These starting points may be indicative of the intensity of exploitation of these groups at that geographic location prior to the initiation of no-take regulations. We describe below the probable outcomes of continuous full protection of no-take reserves in heavily fished regions at four arbitrary time frames.


Our intention here is to provide an initial exploration of the potential patterns of population recovery in reef fishes through time driven mainly by life history traits. However, it is important to keep in mind that the rates and patterns of recovery may change depending on factors other than life history. For instance, the curves may stretch or shrink along the x-axis (time) depending on local productivity. Also, it is probably inevitable that changes through time in the abundance or biomass of competitors, predators or prey will alter the pattern of recovery of a species at a given site. 0–5 years: Within this relatively short period, species with low vulnerability such as fusiliers and parrotfishes are likely to display rapid initial recovery (Fig. 4b, c). In protected areas with very high potential for recovery (e.g., good larval supply and habitat quality) and/or high initial population size, these highly productive groups may be approaching carrying capacity at the end of 5 years. Moderately vulnerable small carnivores (wrasses) and moderately to highly vulnerable large carnivores (snappers and emperors) may be increasing as rapidly as the less vulnerable species but attaining local carrying capacity at the end of 5 years is highly unlikely (Fig. 4a, c). Recovery rates of surgeonfishes, on the other hand, may be difficult to predict due to species- or sitespecific conditions for recovery. For instance, recovery of long-lived planktivorous surgeonfishes may be rapid if initial population size is relatively high (Fig. 4c). On the other hand, recovery of herbivorous surgeonfishes as a group composed of species with low to high vulnerability may be very slow within the first 5 years of protection (Fig. 4b). 6–10 years: Less vulnerable species, such as fusiliers and parrotfishes, may attain local carrying capacity within 10 years if conditions are favourable for recovery (Fig. 4b, c), resulting in maximum rates of density-dependent adult spillover and larval recruitment subsidy to fished areas for these groups. Moderately to highly vulnerable species (small carnivores, large planktivorous surgeonfishes, large carnivores) may still be increasing rapidly within this period, with little indication of approaching carrying capacity (Fig. 4a, c). However, in areas with low potential for recovery (i.e., lower larval supply or habitat quality), large carnivores may show only moderate levels of recovery (Fig. 4a). Recovery of surgeonfishes may be rapid or slow within this period depending on site- and species-specific conditions for recovery (Fig. 4b).

11–20 years: Full recovery of species with low to moderate vulnerability such as parrotfishes and wrasses is likely within this period (Fig. 4a, b), which should result in the highest possible rates of adult spillover and larval recruitment subsidy to areas open to fishing for these groups. Large carnivores in protected areas with higher potential for recovery may be approaching carrying capacity after 20 years (Fig. 4a). However, within this period, large carnivores in protected areas with lower potential for recovery may still be increasing with no indication of slowing down (Fig. 4a). Surgeonfishes, on the other hand may be approaching full recovery, increasing rapidly with no signs of slowing down or increasing very slowly, depending on site- and species-specific conditions for recovery (Fig. 4c). [21 years: Highly vulnerable large carnivores (groupers, snappers, jacks, emperors) are likely to attain local carrying capacity between 20 to 40 years, depending on site-specific conditions for recovery (Fig. 4a). Full recovery of large carnivores will likely result in maximum adult or larval export benefits from the reserve to fished areas but may also have negative consequences for the abundance of their potential prey species in the reserve, such as parrotfishes, surgeonfishes and small carnivores (Fig. 4a, b). In some areas, biomass of parrotfishes may remain stable (but density may decline) while biomass of herbivorous surgeonfishes may still be recovering at a moderate rate after 30 years without any indication of slowing down (Fig. 4b). Thus, for no-take marine reserves to benefit the full range of highly targeted species, they most likely need to be in protected for the long term ([20 years), if not permanently. Non-compliance to fishing bans in no-take reserves Illegal harvesting or poaching may have a significant detrimental effect on the efficacy of reserves (e.g., Little et al. 2005; Samoilys et al. 2007; Sethi and Hilborn 2008; Bergseth et al. 2013). For instance, Sethi and Hilborn (2008) used a modelling approach to examine the consequences of non-compliance in reserves and found that harvest rates of up to 15 % within closed areas had strong negative effects on yield outside reserves and total system reproductive output and age-structure. The recovery trajectories shown in Fig. 4 can offer some insights as to how the

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recovery of species or families with different vulnerabilities will be affected by varying poaching intensities, i.e., how far recovery will be ‘pushed’ or set back in time. These insights can also indicate how much time will be required by reef fish populations within periodically-harvested closures to recover lost biomass during fishing bans in-between ‘pulse fishing’ events. The recovery trajectories in Fig. 4 suggest that fishing even at a relatively low intensity when local abundance or biomass are at, or close to, maximum levels can push back recovery by several years to more than a decade for most groups, especially moderately and highly vulnerable reef fishes. Much shorter recovery times may be needed by the least vulnerable groups in areas with high potential for recovery. For example, removal of just 10 % of the standing stock of the moderately to highly vulnerable large carnivorous reef fishes when these groups are at 95–100 % of K will conceivably push back recovery by 3–8 years in Sumilon reserve, about 5–9 years in Kenyan reserves and by 5–12 years in Apo reserve (Fig. 4a). A harvesting rate of 10 % imposed on the less vulnerable parrotfishes when biomass is at 95–100 % of K may set recovery back by 1.4–2.3 years in Philippine reserves and about 3.4–8.2 years in Kenyan reserves (Fig. 4b). If 10 % of the abundance of the moderately vulnerable planktivorous surgeonfish Naso vlamingii were removed by fishing from Apo reserve when abundance is at 95–100 % of K, recovery may be delayed by 4.5–9 years (Fig. 4c). Subject to the same harvesting rate, the fusiliers, the least vulnerable group, would probably need only about 1–1.6 years to recover (Fig. 4c). All of the above projections of course assume that compliance will be strictly observed after protection is reinstated, habitat quality in the reserve will be maintained, the timing of significant recruitment pulses will remain more or less the same, and trophic interactions between groups will not prevent recovery. Intensive harvesting of areas periodically closed to fishing The foregoing discussion also implies that intensive harvesting of periodically-harvested closures, especially when fishing is directed towards moderately to highly vulnerable reef fishes, is unlikely to be locally sustainable in the long-term, thus negating the usefulness of periodically-harvested closures as a traditional

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fisheries management approach for these groups. For example, Jupiter et al. (2012) reported reductions of about 70–80 % of the biomass of snappers, jacks and planktivorous surgeonfishes when the Cakaulevu tabu area was intensively fished for 5 weeks after more than 3 years of protection from fishing. They also reported reductions of up to 70 % of the biomass of the less vulnerable parrotfishes during the harvesting event. If we assume that the standing stock of these groups within the tabu were at 75 % of carrying capacity before harvesting (and that recovery rates of reef fishes in Fijian reefs were comparable to Philippine and Kenyan reefs), the recovery trajectories shown in Fig. 4a–c would suggest that intensive fishing of the Cakaulevu tabu area pushed back recovery by at least 5–11 years for the large carnivores, 9 years for the planktivorous surgeonfishes and 2–5 years for the parrotfishes. These are also the minimum number of years needed by each fish group in-between harvesting of the tabu (at the same intensity) in order to regain lost biomass. This example of a periodically-harvested closure in Fiji together with the first pulse fishing event that was documented in Sumilon (Russ and Alcala 1989) strongly make the point that what takes 5–20 years to fully recover can be severely depleted within a few weeks to a few months of intensive fishing. If fishing of periodically-harvested closures occurs at very high intensities and at intervals that are shorter than what is needed by reef fish populations to recover, localized declines of reef fish stocks may be inevitable (Cohen and Foale 2013). This was clearly demonstrated by Williams et al. (2006), who assessed the decadal trends in reef fish abundance within rotational closures at the Waikiki-Diamond Head Fishery Management Area (FMA) in Hawaii. They showed that between 1978 and 2002, the total biomass of reef fishes declined by around two-thirds. During this period of decline, the main groups targeted by fisheries (i.e., larger-bodied surgeonfishes, parrotfishes and goatfishes (Mullidae)) had virtually disappeared. Although fish biomass tended to recover during the 1–2 year closure periods, the level of recovery was not enough to compensate for the intensity of fishing during open periods (Williams et al. 2006). Thus, the effectiveness of periodically-harvested closures as a fisheries management approach also depends on imposing limits on fishing effort or fish catch during times when they are open to fishing.


The limited evidence suggesting that periodicallyharvested closures can be locally sustainable indicates that harvests removed no[10 % of the biomass in the closure and open periods were restricted events that lasted a few hours (with many fishers) to a single day (with a small group of fishers) although closures were harvested up to 3 times per year (e.g., Ahus Island, Papua New Guinea described by Cinner et al. 2005; North Efate region of Vanuatu described by Bartlett et al. 2009). Fishing intensity in these very few cases appears to be relatively light. Furthermore, harvesting is done for ceremonial purposes or purely for subsistence, not for commercial enterprises. Increasing human populations and thus greater demand for fish, coupled with increasing commercialisation, will most likely make the sustainability of periodically-harvested closures increasingly difficult in all but the most remote reef areas.

Conclusion In data-poor situations, life history traits can serve as initial indicators of the relative vulnerability to fishing of different species of coral reef fish. Higher vulnerability is often associated with a larger maximum body size, slower body growth rate, longer lifespan, later age or larger body size at maturity and lower rates of natural mortality. The opposite of each of these life history traits would indicate lower vulnerability. Generally, larger-bodied carnivorous coral reef fishes (e.g., sharks, groupers, jacks, snappers, emperors) are predicted to be more vulnerable to fishing while smaller-bodied carnivores, omnivores, zooplanktivores, herbivores and detritivores (e.g., wrasses, angelfishes, fusiliers, surgeonfishes, parrotfishes) are predicted to be less vulnerable. However, the considerable diversity of life history strategies and ecological traits in reef fishes prevents universal predictions about vulnerability with respect to body size, taxonomic grouping at the family level and trophic characteristics. This underscores the urgent need to strategically expand age-based demographic studies on economically and ecologically important coral reef fishes in order to provide the necessary life history parameters to rapidly estimate vulnerability. Among the life history parameters, age at maturity is probably the most important in generating reliable estimates of vulnerability in reef fishes.

The broad predictions about vulnerability in coral reef fishes are consistent with observed trends in population recovery shown by the relatively few longterm studies of no-take reserves in heavily-fished regions. Collectively, these studies indicate that highly vulnerable larger-bodied carnivores (e.g., groupers, snappers, jacks) are much slower to recover and attain local carrying capacity at a much later time than many of the smaller-bodied carnivores, zooplanktivores and herbivores (e.g., wrasses, fusiliers, parrotfishes) with low to moderate vulnerability. In overfished regions, full recovery of moderately to highly vulnerable targeted reef fish (e.g., large predators, wrasses, surgeonfishes) within no-take reserves will likely be measured in decades (20–40 years). In contrast, full recovery of the more intrinsically resilient, smallerbodied zooplanktivores (fusiliers) and herbivores (smaller species of parrotfish) may be more rapid (within 10 years). The slow rates of recovery of many important targeted reef fishes are consistent with their relatively long lifespans and indicate that decades of protection from fishing are required for no-take reserves in overfished regions to develop significant levels of adult spillover and larval export to fished areas. The relatively slow rates of recovery of many species of reef fish underscores the need for long term (at least 20–40 years), if not permanent, protection of no-take reserves in order to gain significant benefits to fisheries and biodiversity conservation (see also Edgar et al. 2014). However, in less intensely-fished regions where fisheries management measures apart from notake reserves are in place, faster recovery rates are possible even for highly vulnerable species (e.g., groupers). More detailed studies of reef fish population recovery encompassing decadal time scales are required to reveal the full range of differential recovery rates of the many targeted species of reef fish that vary widely in their life history and degree of exposure to fishing and other threats around the world. The long-term trends in population recovery inside no-take reserves further suggest that fishing even at relatively low intensities (e.g., removal of 10 % of standing stock) can push back recovery by several years in less vulnerable species (e.g., fusiliers, some parrotfishes) to more than a decade in moderately to highly vulnerable species (e.g., surgeonfishes, groupers, snappers). There is also compelling evidence from a limited number of studies showing that highly intensive fishing occurring even within relatively short

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periods (several weeks to several months) can severely deplete (reductions of [50 % of standing stock) reef fish populations that may require 5–20 years to fully recover. Declines over time of reef fish stocks within periodically-harvested closures may be unavoidable if fishing occurs at very high intensities and the gaps between fishing events are too short for target fish populations to regain lost biomass. We strongly emphasize the need to carefully control the timing and intensity of harvesting short-term or periodicallyharvested closures, paying close attention to species that are predicted to be highly vulnerable to fishing. As an added precaution, we strongly recommend that periodically-harvested closures be used together with, rather than instead of, no-take reserves. Furthermore, fisheries management measures should be implemented outside no-take reserves and periodicallyharvested closures where possible. In places where overfishing threatens reef-based livelihoods and customs, the need for strict and permanent protection of no-take reserves and careful management of periodically-harvested closures cannot be overstated if these spatial conservation measures are the only feasible approaches to effectively manage reef fisheries. Acknowledgments This paper is based on a review commissioned by The Nature Conservancy with support from the U.S. Agency for International Development of the United States Government (USAID) funded Coral Triangle Support Partnership (CTSP). CTSP is a consortium led by the Word Wildlife Fund, the Nature Conservancy and Conservation International. Funding was made possible by the generous support of the American people through USAID Project Number: GCP LWA Award # LAG-A-00-99-00048-00. The contents are the responsibility of the authors and do not necessarily reflect the views of USAID. We thank two anonymous reviewers whose excellent comments and suggestions greatly improved the paper. We are grateful to Alan White and Andrew Smith for making this work possible.

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The intrinsic vulnerability to fishing of coral reef fishes

The intrinsic vulnerability to fishing of coral reef fishes

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