why do assessments of demersal stocks largely ignore habitat?

Why do assessments of demersal stocks largely ignore habitat?

John F. Caddy*Via Cervialto 3, Aprilia 04011, Latina, Italy

*Corresponding Author: tel: +39 069256538; e-mail: jfcaddy1@yahoo.co.uk

Caddy, J. F. Why do assessments of demersal stocks largely ignore habitat? – ICES Journal of Marine Science, doi:10.1093/icesjms/fss199.

Received 1 October 2012; accepted 6 December 2012.

The divergence between most stock assessments and studies in marine ecology is characterized by the low priority usually given toassessing the holding capacity of marine habitats. Habitats of high structural complexity are relatively uncommon in shelf waters, arecontagiously distributed, and are damaged incidentally by bottom-towed gear. Structurally complex habitats are used by many de-mersal fish and crustaceans for predation abatement and as a site for feeding forays. Successive life-history stages typically migratethrough several structurally complex habitats which recent studies show often to have fractal properties. One consequence offractal structures as cover is a rapid reduction of protection from predators with growth in size: migration is the only response possiblewhen further growth of the recruiting age class renders individuals in that habitat vulnerable to predation. A common feature ofstructurally complex habitats with high vulnerability at size is the occurrence of population bottlenecks. It is suggested that identifyingand rectifying shortages of structured habitat, and eliminating habitat bottlenecks, will be effective in stock enhancement. This willrequire placing strict spatial constraints on the operation of bottom gear. This paper reviews new methods of estimating juvenile pre-dation mortality, including mortality-at-age and mortality-at-life-history stage, which depend on the fractal characteristics of structur-ally complex habitats.

Keywords: habitat, structural complexity, cover, life history stages, migration, fractals, bottlenecks, natural mortalities.

IntroductionThe 1926 study of cod and herring life histories by Johan Hjort(1926) is one of the first contributions to understanding the lifehistory of marine fishes. To this author it was significant,because the conclusions he reached on what was then a new phe-nomenon are now built into the current paradigm we share asfishery biologists. It is helpful then to re-examine statementswhich are now truisms such as: “the size of the recruiting yearclass needs to be taken into account before predicting potentialyields”, and his conclusion that the new year class is not necessarilya simple function of the size of the spawning population; state-ments since confirmed by many subsequent studies. This papertries to follow up on one ancient belief he quoted, namely thatchanges in holding capacity and productivity of a habitat can in-fluence recruitment variation. To do so, I distinguish between“Environment”, the qualities of the water mass of the aquatic en-vironment, and “Habitat”, the structural aspects of where fish liveand depend on for vital life-history functions. These two charac-teristics are distinct for demersal/benthic resources, but muchless so for pelagic resources (Figure 1). It is also clear that theimpacts of fisheries on cover, and of cover on demersal/benthic

recruitment, have usually been ignored until quite recently inthe literature on fisheries assessment.

Allowing for the impacts of environmental change on fish re-cruitment is a fairly recent management strategy, even though

Hjort made this possibility explicit in his 1926 study of cod re-

cruitment. In the 1960–1970s, for example, approaches to produc-

tion modelling in support of fishery management sought to

eliminate environmental effects by assuming an “equilibrium”

between surplus production of resources and fishing effort. This

was achieved by an “equilibrium adjustment procedure”

(Gulland, 1971) but, later, time-series analyses of several import-

ant fisheries (e.g. Caddy and Gulland, 1983) showed that fisheries

could be classified into categories that clearly diverged from any

sort of equilibrium. Although the driving functions behind

annual yield fluctuations rarely distinguished between environ-

ment and habitat, there was an incentive to develop empirical

dynamic production models which fitted better the real data on

catch and effort, but still provided no information on the mechan-

isms behind recruitment variation.The wide variability of annual recruitment also poses a problem

for so-called analytical approaches, which use stock–recruit

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relationships without explaining the wide scatter of points theytypically incorporate. Since there have been few experimentalstudies on marine fishery impacts with suitable controls, the con-sequences of changes in the fishery are usually modelled in what isassumed to be an invariant and uniform environment and habitat.

Effects of habitats on recruitmentOne aspect that has diverted attention from our understanding ofthe post-spawning period has been the widespread use of stock–recruitment relationships (SRRs) to “forecast” future year-classstrength. SSRs rarely show year-class strength of recruits as aclear function of parental population size. When investigatingcod reproduction, Hjort (1926) noted: “it is difficult to avoidthe conclusion that the actual quantity of eggs spawned is not afactor in itself sufficient to determine the numerical value of ayear class”. Nor does the SRR gives any explanation for the typic-ally wide annual scatter of points it contains. This relationshiponly gives us a glimpse into the highly variable but usuallyunknown events between hatching and when the juveniles reacha potentially exploitable size. In many cases, these events areamong the more influential of their life histories. Knowledge ofthem could suggest specific actions to improve habitat quality,enhance stock size, or reduce negative impacts of fishing. In con-clusion, critical events from reproduction to recruitment are not“explained” by the SRR, but it seems likely that the physical struc-ture of the habitat plays a role in the success or failure of recruit-ment for marine fish, as we know it does for freshwater fish (e.g.Cowx and Welcomme, 1998).

For terrestrial fauna, for example, a threshold of habitat lossof between 30% and 50% is believed to be critical for survival offorest animals (Andren, 1994), irrespective of their state of exploit-ation. It is not obvious then why habitat fragmentation should not

also be critical in the marine environment. Robbins and Bell (1994)documented the fragmentation of sea grass beds, which isoften caused by towed gear over a landscape scale, in theMediterranean. The “gap structure” of cover units that resultsfrom dragging gear over vegetated bottoms could be critical, espe-cially for species that migrate from structurally complex inshorenurseries across open sand or mud bottoms to offshore reefs orshelf edges.

Another commonly applied idea assumes that a “dynamicpool” exists which spreads the effect of fishing evenly over thewhole stock, i.e. the stock range is “homogenous”, and interven-tions necessarily occur at this level. However, underwater observa-tions show that some habitats and stock components are morevital than others for the survival of economically importantspecies. Fish are usually contagiously distributed, and fromdetailed studies it seems that key events/locations change in thelife history, and must be identified on a GIS landscape scale,rather than on the scale of the unit stock.

Among the phenomena usually missing from assessments ofmarine fish stocks is how habitat requirements change during theearly life history, and how habitat quality declines with destructivefishing. Such information has generally come from direct under-water observations. The renewal of a fish stock in Hjort’s words,“is dependent on many factors, all necessary, and all more or lessvariable”. As an example, spiny lobster juveniles may pass a criticalstage of their early life history in large sponges (Herrnkind et al.,1997); hence, the loss of sponges, due to disease or dragged gear,impacts recruitment through loss of critical habitat. At a stillearlier stage, spiny lobsters pass a period where survival is likelyto be a function of the health and extent of sea grass meadows(e.g. Bell et al., 2001). Nonetheless, in enhancement work aimedat reducing bottlenecks in life histories, we should consider thehigh vulnerability of population stages when concentrated insmall areas of a habitat critical to a life-history stage.

The extensive use of “casetas” (artificial habitats) on theYucatan and Cuban shelves undoubtedly increased cover forspiny lobsters in an area of predominantly sand and sea grassbeds (Arce et al., 1997) with many lobster predators and littlenatural cover other than sea grass beds, and led to increasedlobster production. Similarly, favourable survival during juvenilecod recruitment is associated with restricted and structurallycomplex seaweed and cobble bottoms on the Nova Scotian shelf(Keats et al., 1987; Tupper and Boutillier, 1995). Nordeide(1993) also found that stocking fjords with cod juveniles was inef-fective in increasing adult populations because suitable juvenilehabitat was limiting. Much earlier, the Danish biologist GunnarThorson (1957) commented on the shortage of “structuredhabitat” in the sea. It is therefore reasonable to suppose that thisshortage affects recruitment and subsequent survival of recruits.

The “open seabed” is a high-risk environment, and has pro-vided an evolutionary incentive to develop anti-predator beha-viours and anatomies. Among these are the radical anatomicaladaptations of flatfish and rays, allowing them to hide in bottomsediments, of conchs which develop thick anti-predator shells,the schooling behaviour of juvenile fish in shallow water, andthe “queuing” demonstrated by groups of spiny lobsters whencrossing sandy bottoms without cover. These examples illustratethe high risk encountered in life on the open seabed without cover.

For bottom-dwelling organisms, recruitment and survival tospawning depend on life-history stages overcoming sequentialrisks. Survival may be dependent on stage-specific habitat, and

Figure 1. Illustrating the difference between pelagic and demersal/benthic resources where potential impacts of habitat/cover onsurvival to recruitment are concerned. (Pelagic fisheries impact thestock, but demersal/benthic fisheries potentially also damage thehabitat on which the resources are dependent, as do on occasionsthe resources themselves.)

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its absence may create a “bottleneck” in recruitment supply alongthe “early recruit � spawner” trajectory. That there are several ju-venile stages of many species marked by different food and habitatpreferences has been well documented but rarely used in stock as-sessment. The favoured habitat often changes during develop-ment, and Gillanders et al. (2003) documented numeroushabitat changes by marine organisms in their life histories.These emphasize the need for habitat restoration, and the restor-ation of spatial linkages between habitats favoured by successivelife-history stages (e.g. Lipcius et al., 2008).

Experiments with controls are rare in marine fishery studies, andinvestigations tend to rely heavily on analysis of fishery time series,often ignoring ongoing changes in habitats. An investigation incorp-orating habitat preferences requires in situ studies, perhaps employ-ing artificial structures installed on the seabed, or spatial comparisonsbetween areas subject to different treatments, as in agriculture re-search. These effort-intensive approaches are in danger of disruptionby towed gear, and, even if this disruption is only a possibility, a lackof control over fishing activities discourages field experiments of longduration. Although a growing number of marine protected areas(MPAs) permit such comparisons, the shortage of quantitativestudies on marine habitat and, more to the point, their lack of incorp-oration into stock assessment procedures, limits the realism ofcurrent fishery models.

Effectively there are two separate schools of study of livingmarine resources: population models based on fishery data ortrawl surveys of biomass and age composition; and more detailedin situ studies by marine biologists of marine fauna in relation totheir habitat. One may speculate that promoting habitat consid-erations in stock assessment methodologies will require furtherexchanges between these two fields. In fact, a “habitat complexity”paradigm seems highly desirable as a next step in fishery science,and it could lead directly to bioengineering and habitat interven-tions. The conclusion of conservationists working on terrestrialspecies conservation can be recalled—that critical habitat and itscontinuity in space, and not just species conservation, is the keyissue. The current situation of biologists working on fishery con-servation resembles that of ornithologists studying bird behaviourwithout paying attention to the clear-cutting of forests!

Egg – spawner relationshipsThe SRR in its simplest form assumes equilibrium conditions(Figure 2a). Individual yearly points of stock size are plotted vs. thenumber of recruits they produce; often incorporating more than adecade of observations. What may have happened to the habitat,or what other environmental changes occurred in the environmentduring this period, are ignored. Monitoring survival in the larvaland post-larval stages needs a dedicated research programmein which habitat requirements and availability, habitat-specific sur-vival rates, and associated migrations are documented. Only for afew species do we have a figure similar to hypothetical Figure 2bwhich documents habitat-specific survival prior to recruitment tothe fishery.

Common marine habitats of the continentalshelvesAs a broad generalization, on heavily fished continental shelves,most epifauna are sparse but contagiously distributed, withpatches separated by wide areas of smooth sediments supporting

limited epifauna. This configuration contrasts with the high prior-ity for conservation of cover needed by demersal juveniles toprotect them from predation during feeding or migration.

If fishing adversely affects habitats, how will this reduceproductivity?It would not be surprising if the smoothing effect of bottom gearleaves large areas of sedimentary bottom without significant epi-fauna. Puig et al. (2012) recently demonstrated large-scale modifi-cations to shelf and slope environments by trawling. On a smallerscale, Walters and Juanes (1993) illustrated the protective role ofcover for small fish when they are not foraging, and explainedits trophic significance. That is, motile organisms must remainwithin a specified distance from cover while feeding, in order tobe able to return there when predators appear. The first impactof the absence of cover over extensive areas of flat bottom(perhaps due to past trawl operations) is the additional predationrisk this generates for foraging recruits. An additional factor is thatit restricts feeding access to only a small proportion of the availablefood resources in open bottom areas, since anti-predation behav-iour requires remaining close to cover while feeding, i.e. the loss ofcomplex habitat has a trophic consequence, namely that “availablefood” is significantly less available than the total biomass of foodorganisms present. The likelihood of obtaining large recruitingyear classes without access to cover, and hence food, is correspond-ingly reduced.

Acadjas and other complexities that improvehabitatsKnowledge of the role of habitat in increasing fish production (e.g.Caddy and Defeo, 2003) seems to point to habitat modificationsthat could increase fishery productivity. One methodology used inWest Africa illustrates the potential of modifying habitat to offerprotection and increase food resources. This functions by “coral-ling” juvenile fish in an area planted densely with stakes (calledlocally the acadja; Welcomme, 1972). Juvenile fish in acadjas mayeven be protected from predators by netting, forming an“acadja-enclos” (Figure 3; Hem and Avit, 1994). One consequenceof the acadja methodology is that it leads to early maturity andstunting, given that the quasi-fractal configuration of the plantedbrush stakes only protects juveniles, not adults. Nonetheless, highdensities and productivities of small fish result, without the needfor supplementary feeding—they eat the epifauna/flora growingon the stakes. One example from Hem and Avit (1994) illustrateshow efficient this methodology is, resulting in yields per hectaremuch higher than open lagoon habitats. The owners of acadjas incoastal lagoons were accused by conventional “free fishers” of de-pleting adjacent fishing grounds by aggregating free-living fish inacadjas. The netting enclosure of “acadjas-enclos” was in fact firstinstalled to exclude these fish from entering, in order to satisfythe “free fishers”. Only subsequently was it evident that the“acadja-enclos” operators receive their recruits directly throughthe enclosing mesh, which also excludes escapement of older fish:hence the yield per area increases far in excess of the average prod-uctivity of lagoons!

This example illustrates two important aspects of favourablefish habitat, namely the role of structural complexity in increasingcover (e.g. Gotceitas and Colgan, 1989) and how biological prod-uctivity is increased by the epifauna/flora that grow on the stakes.Since epifauna/flora biomass increases with habitat complexity,

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juvenile food resources are readily available in acadjas withoutsupplementary feeding. Calculating what configuration anddensity of structural habitat elements would maximize safe for-aging area is a geometric problem which could be tackled byanyone who is interested in applying this mechanism in stock res-toration or enhancement.

One mechanism that has been developed to dissuade users oftowed bottom gear from working in conserved areas is the “sleep-ing policeman”; namely, a cement block with metal hooksdesigned to catch towed gear, installed in sea grass beds orsimilar structurally complex habitats (Guillen et al., 1994).Marking such structures with warning floats seems an appropriatesafety measure.

My definition of habitat follows Peters and Cross (1992): “Thestructural component of the environment that attracts organismsand serves as a centre for biological activity”. Among these struc-tural components is a concept well known to terrestrial ecologists

Figure 3. Diagrammatic representation of an “acadja-enclos” used inWest African lagoons for fish accumulation—using either brushparks or bamboo stakes (from Caddy, 2007).

Figure 2. Comparison of the spawner–recruit relationship (a) where survival to spawning is a simple function of fishing and natural mortality,and recruitment is an inevitable consequence of egg production, and (b) where spawner abundance is vital, but spawning also depends on anadult refugium, and recruitment success of the juveniles is subject to sequential risk/bottlenecks during migration between stage-specifichabitats (from Caddy, 2008).

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but less so in fish stock assessment—the concept of “cover”. Thisencompasses all characteristics of a habitat, including thefollowing:

(i) The absolute abundance of structural components.

(ii) The relative abundance of different structural components.

(iii) The scale used to measure elements of habitat (Lipcius et al.,1998).

Note that pelagic larvae often settle in complex fractal habitats,but fractal habitats offer progressively less cover with growth insize of the protected organism. Migration will eventually berequired to a different habitat type, and migration itself involvesa higher risk of predation. Life-history stages (larvae to maturity)may each show different behaviours and habitat requirements, butif stage-specific habitats are limited, this could lead to a “fractalbottleneck” for pre-recruits.

In conclusion:

† Fine sediments dominate benthic habitats. These are low instructural complexity on a macroscale and offer little cover.

† Structural elements are scarce in the sea, are easily damaged byhuman activities, but may be essential to life-history completion.

† Restoring natural structural elements or adding artificial onesmay help restore depleted populations.

Since shortages of specific habitats-at-stage may create bottle-necks, this could reduce recruitment and nullify a stock recoverystrategy. For early life-history stages, these “critical habitats” maycurrently be relatively small areas where habitat componentscould be protected or restored, or artificial habitats installed,and where foolproof local bans on the use of towed bottom gearcan be assured. Examples of soft bottom epifaunal species indeep water, the sea pen and cold-water corals, have both beendescribed as nurseries for red fish (Baillon et al., 2012). Clearlythis type of epifauna is very vulnerable to bottom trawling, withpredictable consequences.

Habitat complexities in the early life-history stagesof demersal and benthic speciesThe preferred habitats for juvenile fish and invertebrates tend tophysical complexity. Red algae, bryozoans, coral reefs, andperforated or crevice-ridden rock surfaces have all been docu-mented as showing fractal distributions (e.g. Burrough, 1981;Williamson and Lawton, 1991). This is characterized by gradationfrom a few large internal spaces to many more small ones, i.e. in-versely, the number of protective structures declines rapidly withgrowth in size. A simple simulation using the function suggestedby Morse et al. (1985) for the number of crevices with size in ter-restrial vegetation is revealing. When displayed on a fractal surface(i.e. a perforated surface with a fractal coefficient of: 2 , D + 1 ≤ 3),Caddy and Stamatopoulos (1990) showed that with growth in size,crevice dwelling in fractal habitats leads to predation mortality andhence emigration or stunting. The growth potential of a juvenileforces it into increased vulnerability to predation as larger protect-ive structures in the habitat effectively diminish in number withfish growth, and, in particular, larger cover units are subject tohigher competition. This appears to have been the case for thelarge crevices on the Pedro Bank reefs, as suggested by Caddy(2011) when analysing the data in Munro (1983) with fractaltheory in mind. Migration is therefore, in part, structure driven.

A high-risk migration to other structured surfaces with larger cre-vices becomes necessary, unless stunting allows the species toremain in its original habitat and mature and breed at a smallersize.

Structurally complex habitats often havequasi-fractal characteristicsMany structurally complex habitats in nature have been shown tohave fractal characteristics. The fractal coefficient of a habitat isoften measured along a line or transect (e.g. Burrough, 1981;Bradbury et al., 1984) and values are in the range 1 , D , 2.However when considering an irregular surface with embeddedcrevices, the effective dimension must be considered to be in therange: 2 , D + 1 ≤ 3, as for the coral reefs described by Purkisand Kohler (2008). My proposition is that an ideal habitatfor many demersal fish is intermediate dimensionally betweena surface and a volume. The term “quasi-fractal” used here isintended to include surfaces where the fractal coefficient mayvary spatially, while retaining the general property of fractalhabitats that the numbers of spaces between structural elementsfall off rapidly with increasing scale.

In quasi-fractal habitats, large crevices are in shortsupply and this leads to migration or predationFor shelter-dependent animals, competition for the progressivelyfewer large interstices in a fractal habitat structure inevitably willincrease as predicted by fractal theory. Hence, the longer residencetimes in a given size of crevice for older fish with slower growth (aspredicted by a von Bertalanffy curve; Caddy and Stamatopoulos,1990) further increases competition for the few large habitatunits available. This in turn can promote migration and/or arise in predation. It is reasonable to define such a shortage ofhabitat when encountered in the growth history as a “fractalbottleneck”. We may expect bottlenecks which occur on fractalsurfaces to affect the survival of larger fish subject to predation(Caddy, 2011). Other types of bottleneck will occur when thereis an abrupt decline in suitable “predator-resistant” habitat withfish size, even if the habitat is not fractal; alternatively, thismay occur at the boundary between two types of habitat. It issurprising, therefore, that fish stock modellers make little use ofthis concept.

Using a fractal array to explore survivalof cover-dependent recruitsA number of investigators (e.g. Hacker and Steneck, 1990;Gunnarsson, 1992; Gee and Warwick, 1994) commented on howthe fractal nature of many structurally complex marine habitatsis particularly relevant for small organisms and juvenile stages oflarger, resident species. A study by Morse et al. (1985) on how ter-restrial vegetation supports insect abundance-at-size had sug-gested a relationship between the number of cover units on afractal surface and the number of organisms at size NL whichinhabit it. He formulated this relationship as follows:

NL = k/LD+1 (1)

where k is a constant, L is the crevice size (and the limiting dimen-sion of the organism inhabiting it), and D is the fractal coefficient.

I simulated how fractal arrays of hole sizes affect survivalof inhabitants, using the unpublished “Holes for Windows”

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software written by C. Stamatopoulos in 1991, which createsperforated surfaces using Equation (1) to disperse predictednumbers of holes-at-size randomly over a surface. Thesearrays help visualize how the fractal dimension of a habitatdetermines its holding capacity at size, expressed as a functionof the growth rate of its inhabitants. Hole numbers fall offmore rapidly with size as the fractal coefficient increases: anarray with a coefficient approaching 3 (visualized as a convo-luted surface with crevices leading into interior volumes, asin a coral reef), is in fact dominated by small holes(Figure 4). A cohort of small fishes obliged to grow on sucha surface rapidly exceed the numbers of suitably sized vacantcrevices as they increase in size. We could also expect compe-tition between small adults and the juveniles of largercrevice-dwelling species within a coral fish assemblage. It maybe argued that crevice dwelling is not often practised bymany demersal fishes. It is common, however, to observehow “free-living” fish aggregate around or in marine vegetationor other complex surfaces; hence, the above argument seems tohave some general validity, though, as for all generalizations,many exceptions can be found. It is suggested to use themethod described in the following to analyse size frequenciesfor the rate of decline in frequency-at-size, which can be an in-dicator of habitat availability and its use as cover.

Low fractal surfaces are more “recruitmentfriendly”A surface with a low fractal coefficient, or a natural surface sup-plemented artificially with larger holes, will facilitate a muchless restrictive regime for growth and survival of larger cover-dependent organisms than those with a high fractal coefficientD′ which are ideal habitats for post-larvae but promote subse-quent migration. It seems possible that migration will beinitiated by a predator attack on an individual of close to themaximum size for which the present cover is an effective anti-predator device.

Supplementing a habitat with large intersticesshould be useful for enhancing spawning stocksCompetition for large crevices in artificial reefs, according toPolovina (1991a, b), effectively concentrates larger bottom fish,making them vulnerable to overfishing. This is another way ofsaying that for larger fish, suitable shelter from predators in adja-cent benthic environments is relatively uncommon (Bohnsack,1989). Also relevant here are observations by Beck (1995)showing that larger boulders are used by stone crabs to hideunder while moulting, and their absence in some bays stunts thegrowth of local crab populations. Auster et al. (1996), Caddy(2007, 2011), and Brown et al. (2010) argued that structurallycomplex shelter is rendered scarce in most flat sedimentary envir-onments by swept-bottom fishing gear. Langton et al. (1996,1999), and later, Kaiser et al. (2003), raised the need for fisherymanagement to recognize the importance of conserving essentialfish habitat, and Cross et al. (1997) began considering how toclassify it.

Geometrically speaking, there is more “space” available forsmall than large organisms in fractal cover (Morse et al., 1985),and a method was proposed in Caddy (2011) for identifying bot-tlenecks from plots of log-size frequencies of resident fish. If ahabitat used as cover displays fractal characteristics (see, forexample, Burrough, 1981; Bradbury et al., 1984; Bell et al., 1991;Li, 2000), it is postulated that the size frequency data of the inha-biting organisms may be used to determine the value of a scalingquotient of the habitat, Q. This measures the mean rate of deple-tion in numbers-at-size over a segment in a logarithmic size fre-quency. Consider two crevice sizes that can just accommodatefish of lengths L1 and L2, respectively: the limiting ratio of indivi-duals resident there [Equation (2)] is postulated to be related totheir size by:

NL2 = NL1 × (L Q1 /L Q

2 ) (2)

This relationship can be fitted over close to linear segments of aspecies log-size frequency to find values of Q for that segment(Figure 5). Expressed as ln (NL2/NL1) ¼ ln (L1

Q/L2Q), Equation

(2) is equivalent to an instantaneous depletion rate per centimetreincrease in fish size. In some restricted circumstances (in the

Figure 4. Illustrating how the numbers of “cover units” fororganisms of a range of sizes decline with size on surfacescorresponding to two estimates of the fractal coefficient D’ (fromCaddy, 2007).

Figure 5. A combined log frequency plot for 15 reef species fromMunro (1983), with fittings of the values of Q to three lengthsegments.

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absence of fishing or migration), it could also provide a roughmeasure of the fractal coefficient of the surface, as well asbroadly reflecting the apparent mortality rate of resident fish.

Effects of migration, predation, or fishing cannotnecessarily be separated based on size frequencydeclines aloneFor unfished resident stocks, the value of Q may reflect habitatavailability for populations without cover from predation.However, if fishing mortality or migration are added to thehabitat constraint, size frequency analyses will yield Q values po-tentially much higher than the maximum spatial dimension 3:hence Q no longer has a dimensional significance. Where fractalprocesses are the main constraint on abundance-at-size, and pre-dation mainly occurs outside cover but neither migration norfishing mortality apply, values of Q ≤ 3 could reflect the fractal di-mension of the habitat surface available for cover over that sizeinterval.

In a study of data from Munro (1983) for an unfished popula-tion of Jamaican reef fish, Caddy (2011) found evidence for 12species from fish trap samplings where the numbers of fish atsize [body height calculated from Fischer (1978) as the limitingbody dimension] declined log-linearly at Q ≤ 3 until a meanbody height of 12.5+ 3.5 cm was reached. Above this size, therate of decline increased sharply to Q ≥ 8.

Taking into consideration Munro’s comments on the relativescarcity of complex reef cover and the high abundance of sharkson Pedro Bank, this supports the idea that predation risessteeply once reef fish have problems finding cavities largerthan �12 cm or so in height on the reef. Of course this is an ad-venturous deduction without data on reef crevice sizes, butseems a reasonable one, and is suggested by Munro’s excellentdataset which is one of the few available for an unfished marinepopulation. In fact, the combined log plot for 15 species(Figure 5) supports the deduction that all species occupying acommon habitat were subject to the same constraints in the faceof high predation pressure.

Reanalysing size frequency data from Munro’s virgin popula-tion, Caddy (2011) found individual scaling coefficients Q forquasi-linear segments of log-size frequency plots. For smallerreef fish subject to predation, trends in log numbers-at-size wereclose to linear, with Q ≤ 3, suggesting a fractal expectation forthe reef surface/volume their size group inhabited. For larger spe-cimens, steeper declines in numbers-at-size suggest that for them,cover was limited, and predation or migration was occurring wellbefore the maximum size for the species was reached.

I conclude this section by briefly considering a species (Rajaclavata) that is not dependent on complex cover (Figure 6). Avery low value for juvenile Q confirms this; although I would bespeculating to suggest why Q reaches a very high value for theseanimals once over 90 cm (migration or fishing mortality?).

Thus, a shortage of structurally complex elements in the naturalenvironment may expose larger fish to predation (see Walters andJuanes, 1993). This may occur when feeding and predation aremore intensive beyond the boundary between structurallycomplex habitats which provide shelter, and the surroundingflat, sedimentary habitats where foraging and predation oftentake place. This conclusion stems from numerous field observa-tions (e.g. Sale, 1978; Shulman, 1985; Hixon and Beets, 1989;Holbrook and Schmidt, 2002; Bologna and Heck, 2002).

A typical demersal/benthic life-historydisplacementAn idealized “epifaunal unit” has progressively finer branchingswhere “hiding places” and surface areas for growth of food organ-isms both increase with structural complexity as a function offractal geometry (Mandelbrot, 1977). A fairly typical life-historytrajectory is a migration from a nearshore finely branchednursery or a complex quasi-fractal habitat, passing across opensediment surfaces, to an offshore habitat where “live bottom” orboulders/caves/outcrops offer larger shelters for adult organisms(Figure 7).

Testing for habitat availability in natureThe “Fractal sampler” proposed by Caddy and Stamatopoulos(1990) is a structure with known numbers of crevices of differentdiameters, whose individual occupancy rates should be controlledat intervals by divers. A high occupancy rate at size implies thatholes for this size of organism are scarce in the surroundingarea. A similar structure was used by Beck (1995) to explain sizedistributions of stone crabs in Florida bays; size composition oflocal populations was found to be a function of the size of crevicesunder the range of boulder sizes locally available.

Sixteen reef fish species were sampled by Munro (1983) beforecommercial fishing began on Pedro Bank. These showed similarsteep trajectories in the rate of decline of log numbers with size,despite wide species variations in the von Bertalanffy growth coef-ficient K and natural mortality rate M (Figure 5). Together withthe analyses just reported, this suggests that the demography of un-exploited reef fish is largely a function of their common habitatrather than simply an expression of different species growth andmortality rates as implied by simplistic parameter analysis.

Different modelling approaches to early life historymortalityA new paradigm comes from working in situations where the oldone clearly no longer applies. In the 1980s I was assessingMediterranean fine-mesh trawl fisheries where the “constant M”approach was evidently inappropriate, since harvesting beginswith 0+ to 1+ age groups for which M ¼ 0.2 was improbable!From the limited literature elsewhere, M-at-age for these earlyage groups is of the order of 0.8–1.5+ annually, and a steepdecline with age nullifies the conventional assumption ofBeverton and Holt (1957) of “constant M”. Their assumption

Figure 6. Size frequency plot for Raja clavata in the North Sea, fromShepherd (1987), with Q values calculated for two segments.

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was a reasonable one in the North Sea immediately before andafter the Second World War, at a time when larger fish were tar-geted. However, when juveniles and smaller species are theobject of fisheries, as they are in the Mediterranean and many trop-ical areas, this assumption is inappropriate.

Three approaches to generating rapidly declining juvenileM-at-age were investigated which all show similar trajectories forM-at-age.

An empirical fit to data on M-at-ageThe size of small fishes in the stomachs of predators within theICES area implied a high natural mortality of pre-recruits due topredation (Figure 8). By the time maturity approached, M haddeclined rapidly to a plateau: (i.e. the “constant M” hypothesismay still be a reasonable approximation for adults that are notshelter dependent).

The function: Mt ¼ A + B/t (Caddy 1991) gave a satisfactoryfit to data on M-at-age from MSVPA (multispecies VPA) studiesin the North Sea (Figure 8).

When used in yield/recruit calculations, it was evident that this“reciprocal M” trajectory reflects the loss of many juveniles due topredation, possibly including an indirect mortality componentafter passing through the trawl (see Caddy and Seijo, 2011).Whatever the causes of mortality, few of these recruits survive tocontribute eventually to yield as maturity approaches, which iswhat the “constant M” yield/recruit model assumes. That is, theadvantages of a mesh size increase are much less than expectedwith the “reciprocal M” assumption than with constant M-at-age.

Conserving the few individuals which reach maturity was evi-dently the top priority in the Mediterranean, but where are theadults? Certainly mature specimens of large species such as hakeare uncommon in the inshore trawl fishery. This question led to

Figure 7. Diagrammatic representation of inshore� offshore life-history displacement in search of suitable cover resources of the appropriatesize (from Caddy, 2007).

Figure 8. Comparing M vectors with age from the North SeaMSVPA experiment for cod, whiting, and haddock (data fromSparholt, 1990) with an overall fit to the data by the equation Mt ¼A + B/t (squares).

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the “refugium” hypothesis, which is supported by the early work ofnaturalists such as Doumenge (1966), who assumed that the fewsurvivors to maturity are in the deeper, rocky, and untrawlableareas of the shelf edge.

A fractal model of M-at-size (Caddy, 1986, 1991)Structural complexity allows more “spaces” for small than for largeorganisms in fractal habitats, as described earlier. Habitats withhigh fractal coefficients make good nurseries for small post-larvae,but low fractal coefficients favour larger organisms (Figure 4).Both types of habitat are present in the marine environment.

A parallel between the increasing spatial scales of fractal theoryand the increasing time intervals needed to complete successivelife-history stages led to me postulating a “gnomonic” strategy oftime division (Caddy, 1996). Successive gnomonic intervals aredefined by multiplying a given “seed” interval t1 ¼ D1 (perhapsthe duration of the first life-history stage?), by a constant a,giving D2 ¼ a.t1 and t2 ¼ t1 + D2, and so on, repeating the proced-ure to obtain a series of progressively longer intervals, until a givenmaximum age is reached. This strategy was first used to fit inter-moult intervals of crustaceans (Caddy, 2003) and then to observeddurations of successive life-history stages of different species.

Fractal surfaces imply self-similarity at different scales; thus, fortwo apertures on a fractal surface of dimensions Li and Li+1, Morseet al. (1985) suggested how holding capacity Ni declines with thesize of organisms capable of entering the niches:

Ni+1/Ni = L D+1i /L D+1

i+1 (3)

If animals “under cover” are the only ones that can avoid preda-tors, and the population is unexploited and does not migrate,this equation also becomes an expression for the natural mortalityrate of the inhabitants of a fractal surface (Figure 9). The M-at-sizethat Equation (3) predicts would be relevant to stock assessment ofunexploited juveniles dependent on effective cover.

M-at-stage, knowing stage durationsMost fishery theory is expressed in sizes or ages—but is there a rolefor “life-history stages? Motile organisms with planktonic larvaego through successive stages in ontogeny, each with its physicalor behavioural adaptations, and such stages may continue after re-cruitment. Their duration generally increases in ontogeny, but themortality rate due to natural causes usually declines with size. Itseems reasonable to assume that the total risk of death due tonatural causes remains roughly the same for successive stagesdespite their different durations, but bottlenecks may occur,perhaps due to a stage-specific habitat shortage.

The gnomonic strategy (Caddy, 1996) essentially asks: into whattime intervals can a lifespan be divided such that the same risk ofdeath applies in each stage? This reflects the idea that no single life-history stage is favoured by selection over another, and was origin-ally assumed to result in an arbitrary series of intervals of increasingduration. However, this method was used by the following authorsto estimate M-at-stages for different species, e.g. jumbo squid(Martınez-Aguilar et al., 2010); pink shrimp (Ramirez-Rodriguezand Arreguin-Sanchez, 2003), red grouper (Gimenez-Hurtadoet al., 2009), and venus clams (Arreguın-Sanchez et al., 2012). Bycomparing gnomonic time durations with development stagedurations for a range of organisms, the above-cited authorsfound that the predicted intervals from Equation (2) for a given

life history-stage fitted quite well to the life-history stage durationsdocumented in the literature. Assuming then that the product ofstage duration (Ds) and M-at-stage (Ms) is roughly constant for dif-ferent stages, (i.e. Ms.Ds ¼ const), we can arrive at approximationsfor M-at-stage (Figure 10) if we have an estimate of M for one stage,or if we are willing to simulate from mean fecundity to the survivalof one female recruit in an unfished population, following the ideasof Charnov (1993).

Figure 9. Predicted M-at-size for a life history passed in a fractalenvironment, if only those in crevices survive.

Figure 10. Gnomonic vector of M-at-stage, generated from a “seed”interval of 0.05 year, a ¼ 1.13, and a common M.Dt ¼ 1.5. (M1 ¼2.75; M10 ¼ 0.28). (As the time interval Dt becomes longer followingthe gnomonic expectation, the mortality rate M declines, but M.Dtremains constant.)

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Why should successive life-history stages show roughly thesame risk of mortality per gnomonic interval? If each stage hasits own habitat preferences, this would suggest that genotypefitness is converged on independently by each life-history stage.If so, survival to spawning would not necessarily be optimizedby just optimizing habitat for any one stage. If habitat protectionof successive life-history stages is a reality, then overall survival tospawning is equally affected by low survival through any earlierstage. Thus, if the survival rate of any stage is anomalously low,it affects population replacement just as surely as a shortage ofspawners. The “survival-at-stage” issue and survival “bottlenecks”are therefore relevant to the theme of habitat and cover, and infor-mation on preferences of life history-stages should help define anyoptimal fisheries management strategy.

BottlenecksHabitat bottlenecks have been described for a number of species,e.g. stone crabs (Beck, 1995), the crab Cyrtograpsus angulatus(Casariego et al., 2004), the Caribbean spiny lobster (Egglestonet al., 1990), the spiny lobster Panulirus marginatu (Parrish andPolovina, 1994), American lobsters (Wahle and Steneck 1991),juvenile fish (Gorham and Alevizon, 1989), coral reef fish assem-blages (Hixon and Beets, 1993), and damsel fishes (Holbrookand Schmitt, 2002). Many more examples could be extractedfrom the literature. One may comment on the high proportionof crustaceans and reef fish in the above list, but this is probablya function of ease of investigation: even temperate zone demersalfish which are “free living” as adults are described as spending aproportion of their time close to rock outcrops, drilling rigs, ora “live bottom” of epifaunal species.

Assuming that the survivors in Figure 11 decline following theM-at-age trajectory discussed earlier, adding one or more bottle-necks, each causing a significant die-off of pre-recruits, willreduce the number of survivors. This will create “unused”habitat and food resources for the few older/larger animals thatsurvive the bottleneck. Then, if the bottleneck reduces density of

later stages on more extensive favoured habitats, trophic orhabitat constraints will be less severe for the survivors. Habitat res-toration as a means of stock enhancement could be undertaken atthe bottleneck size without causing density-dependent foodshortages (see, for example, Butler and Hernkind, 1997).

DiscussionSeasonal or life-history displacements are common for motileorganisms. Some 30+ different habitat types in the marine envir-onment were distinguished by Gillanders et al. (2003). Juvenilesmoving between them are presumably subject to a higher risk ofmortality than when under cover. Habitat structure plays a keyrole in management of freshwater fish (Cowx and Welcomme,1998), and this potentially applies to motile demersal marineresources. We may assume that both foraging and migration arehigh-risk activities when cover is unavailable.

A focus on “cover” is emerging from direct observationalstudies on “landscape scales” in marine ecology, but the constraintimposed by a shortage of structural habitat is rarely discussed infish stock assessments. The approach followed here is suited towork on pre-recruit survival on the landscape scales describedby GIS techniques, and not exclusively over the area of a unitstock as usually discussed in stock assessment.

For motile marine organisms, at least two rates of natural mor-tality must apply: one “under cover” and a higher rate “in theopen” between stage-specific habitats (Walters and Juanes,1993), as experienced during “open bottom” feeding or migration.Adult population size in a habitat will be determined by that stage-specific juvenile habitat with the lowest carrying capacity, but onoccasions this can be improved on by habitat restoration. Thus,the “bottleneck” is not always on gamete production as usuallysuggested, or on the trophic supplies available to adults, but pri-marily on the suitable cover available for different life-historystages.

For an exploited stock, the shortage of cover may be the funda-mental constraint that leads to local food shortages. Other relatedissues are as follows:

† A stock recovery strategy may be effective if it restores vegeta-tion or epifauna, or adds extensive artificial structures.

† For demersal organisms, many life-history interactions occurnear the boundary of cover and open bottom.

† Bottlenecks in critical habitat availability may occur forjuveniles and nullify earlier spawning success.

† Human activities may also fragment cover and createbottlenecks.

† Mature individuals may require unfished spawning refugiawhere habitat protection is essential to stock replenishment.

† By analogy with terrestrial conservation considerations, covercontinuity could also be important: “vegetated corridors”between stage-specific habitats would improve survival of mi-grating stages (e.g. Micheli and Peterson, 1999).

† Remediation of damaged critical habitat has the potential to actas a form of stock enhancement.

† Critical habitat conservation measures cannot coexist with theuncontrolled spatial action of bottom gear.

Figure 11. A bottleneck for juveniles may lead to unused adultholding capacity in terms of food and space. In this hypotheticalexample, three successive bottlenecks cause a dramatic reduction insurviving recruits.

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† If we wish our research to result in practical solutions when dis-cussing alternative paradigms, we should ask: do they lead tointeresting questions, new avenues for research, or useful appli-cations? I believe the approach described here does so, and,while speculative in part, merits further experimental study.

Closing areas with high relief and abundant epifauna to fishingwith towed gear may increase egg production. Installing artificialreefs within MPAs, with their crevice sizes adjusted to the dimen-sions of mature fish, could create spawning refugia which wouldsupply recruits to adjacent fished areas. Installing acadjas or otherjuvenile protection devices in reserves could improve survival andfeeding of juveniles. Ensuring that migration between stage-specifichabitats are to some extent protected by the availability of cover enroute seems to be another potential stock enhancement measure(Bell et al., 2001; Caddy and Defeo, 2003; Lipcius et al., 2008).Certainly, taking the time to consider the habitat question inmore detail for key species will undoubtedly result in a variety ofuseful results.

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