Encouraging the Use of Underutilized Marine Fishes by Southeastern U.S. Anglers,

Part I: The Research

JEFFREY C. JOHNSON, DAVID C. GRIFFITH, and JAMES D. MURRAY

Introduction

Every year, millions of fishermen dis­card, release, or unnecessarily ruin and waste millions of pounds of saltwater fish that they consider poor eating or inedible. The National Marine Fisheries Service (NMFS) estimates that, from the Atlantic and Gulf coasts alone, recreational fish­ennen catch over 33 million fish belong­ing to such underutilized groups such as jacks, catfishes, tunas, and skates and rays (USDOC, 1980). Most of these fish are discarded or released in favor of more highly desired and perhaps overfished species like grouper, snapper, and king mackerel (Bell, et a!. 1982). This under­utilization of potentially valuable marine resources occurs at a time: I) When in­creased real and perceived pressure is being placed on preferred marine re­sources by both recreational and com­mercial fishennen, both of whom use in­creasingly more efficient technologies to locate and catch fish; and 2) with political conflicts between marine recreational fishermen and commercial fishermen

ABSTRACT-Ths paper is the first of a two-part series which describes and dis­cusses the integration of research and ex­tension increase 10 the use of nontradi­tional fishes among marine recreational fishermen in the southeastern United States. Recreational fishermen within this region target and use or reject fish on the basis of a variety of criteria. Many fish caught incidentally are discarded because of myth, rumor, or perceived negative characteristics that mask the species' posi­tive values. To discover the factors influ­encing the angler's evaluations concerning the desirability of fish that ultimately af­

over access to and claim over marine re­sources (Berkes, 1984). Presumably, in­creasing the share of underutilized spe­cies in the total recreational catch will aid in reducing both biological pressures and subsequent political conflicts.

In 1983, we began investigating south­eastern U.S. marine recreational fisher­men's beliefs about species of saltwater fish in an attempt to isolate the specific criteria upon which they base their decisions to use or reject a fish. This was part of a 3-year program to increase de­mand for underutilized species among marine recreational fishennen of the U. S. southeast. During the first year we empirically investigated fishermen's per­ceptions concerning fish and developed informant-based models, which fonns the core of this paper. Based on this ear­lier research, we have subsequently at­tempted to enhance the images or to "repackage" underutilized species with an educational program consisting of brochures, posters, recipes, and a slide/ tape presentation.

In this paper, Part I, we present a brief

fects their decision to accept or reject a particular species, we collected judged­similarity and belief-frame comparison data in Florida, North Carolina, and Texas, analyzing these data with the use of multidimensional scaling, hierarchical clustering, and entailment analysis. We briefly describe the use of these procedures in providing for a svstematic understanding offishermen's perceptions concerning fish and discuss the implications of our findings for the development of educational materi­als directed at enhancing the image of cer­tain underutilized species among marine recreational fishermen.

description of the research and its find­ings, focusing on the implications of this work in the development of the educa­tional program. The philosophy, dynam­ics, and achievements of the educational program are the focus of Part II (Murray et a!., 1987).

Methods

Data Collection and Analysis

In exploring the perceptions that recre­ational fishermen have of various marine species, we incorporated methods and theories from the fields of anthropology and consumer research. Two techniques we used are multidimensional scaling (MDS) (Kruskal, 1964) and hierarchical clustering (HCL) (Johnson, 1967). Gen­erally, any items that can be compared on the basis of similarity or dissimilarity can be visually represented as points spatially distributed in euclidean space (MDS) or as items grouped together hierarchically as a taxonomic structure (HCL). Both techniques display relationships among items or stimuli (e.g., different kinds of fish) based on measures of similarity/dis­similarity (a more detailed discussion is given by Romney et a!., 1972).

Jeffrey C. Johnson is Associate Scientist and David C. Griffith is Assistant Scientist with the Institute for Coastal and Marine Resources. East Carolina University. Greenville. NC 27858­4353. James D. Murray is Director. Marine Ad­visory Services. UNC Sea Grant College Pro­gram, North Carolina State University, Raleigh. NC 27695 This research was funded by the Na­tional Marine Fisheries Service. NOAA. under the Saltonstall-Kennedy Program Contract No. NA83-WC-H-OOI6. Portions of this paper were derived from Johnson and Griffith (1985) and Johnson et al (1986).

Marine Fisheries Review 122

We used these techniques to explore fishermen's judged similarities between selected saltwater fishes. To accomplish this, we asked fishermen to sort cards with pictures and names of fish on them into piles on the basis of how they per­ceived species to be similar to one an­other. We then asked them to explain their groupings. Consequently, the com­mon group memberships among species, the relationships among the groups, and the derived similarity measures between the species were determined by the man­ner in which fishermen sorted species into piles.

Two methods for deriving similarity data from the pile sorts were explored. The first is based on information theory and tends to emphasize minor distinc­tions made by subjects (Burton, 1972). The second is based on the summing of co-occurrence of items (stimuli) in a pile across all subjects (Weller, 1984). Com­parisons and tests of both techniques con­vinced us that, for our purposes, the latter provided a better measure of similarity for use with these statistical procedures.

The information derived from these methods is necessary to first identify rela­tionships among saltwater species as per­ceived by recreational fishermen, and to determine the characteristics which make saltwater species desirable or undesir­able. Discovering the relative position of underutilized species within a multidi­mensional scaling's configuration of points is analogous to the concept of "product positioning" in marketing re­search.

The concept of "product positioning" refers to the discovery of the structure of a particular product domain (e.g., differ­ent kinds of coffee) and the development and packaging of new products or old ones for new markets based on identifica­tion of yet unexploited portions of this particular domain. A good example of this is the development of a new popular brand of coffee with the use of the above methods (Stefflre, 1972).

We used one further method to identify and understand the ways recreational fishermen think about their prey. This in­volved constructing sentence frames (belief-frames) based on interviews with recreational fishermen from each study area. Recurring descriptions of both tra­

49(2), 1987

diational and nontraditional recreational species (e.g., fighting characteristics, eating characteristics, etc.) were used to produce fill-in-the-blank sentences. In subsequent interviews, subjects were asked to provide the species (from an ap­propriate list) associated with the at­tribute implied in each sentence, such as "You cannot eat because it has worms."

These species/belief-frame compari­sons were incorporated into an "item-by­use" matrix (Stefflre, 1972) organized in a species-by-attribute form for each of the study areas. This is similar to a method used in the study of food snacks and their attributes with respect to when they are eaten (Stefflre, 1972). Each spe­cies/belief-frame matrix was sorted by rows and columns so that rows that were similar to one another were near one an­other; and columns that were similar to one another were near one another. This was accomplished through a combination of techniques used by both D' Andrade et a!. (1972) and Stefflre (1972). D' Andrade et a!. (1972) computed Pear­son correlations for belief-frames across items and for items across belief-frames. These coefficients represented similarity measures and were clustered for rows and columns through the use of a hierarchical clustering scheme (Johnson, 1967).

Stefflre (1972), on the other hand, pro­duced a similarity measure based on row­row and column-column similarity in patterning. For our purposes, however, we use a computationally equivalent al­gorithm which alleviates the need for transposing row and column vectors. These similarities were then used in an iterative process based on "linear equiva­lence chains" to sort rows and columns on the basis of similarity (Stefflre, 1972).

In this analysis, row-row and column­column similarities were derived with the use of the computationally equivalent version of Stefflre's algorithm discussed above. These similarity measures for both rows and columns were subjected to HCL to obtain the sorted species/belief-frame matrices for each region.

Data from the belief-frame compari­sons can also be modeled in terms of im­plicational or logical relationships (D' Andrade, 1976; Schoeptle et a!., 1984; White et a!., 1977; White and Mc­

Cann l ). The structure of these relation­ships, or the entailment structure, is obtained through a multivariate contin­gency analysis of paired dichotomous variables similar to Guttman scaling. The logical or implicational relations are modeled in an "If A then B" (visualized in Figures 10-13 as A<--------B) form and are not symmetrical. It allows for both complete and partial orderings in which relationships are transitive. Two other forms are possible. The first is the equivalence relation, which takes the symmetrical form of "A = B" and the contrast relation of the form "If A then not B." A more detailed description can be found in D' Andrade (1976) and White et a!. (1977).

Sampling

An important consideration for the ap­plication of these techniques is the as­sumption that there are shared under­standings, beliefs, or pools of information among respondents and the cultures or subcultures of which they are a part, in the same way that a handful of English speakers can provide a complete grammar of English. For example, a re­view of studies that employed MDS in­terviewed between 10 and 50 subjects with one using as little as 5 while another used as many as 600 in a national survey (e.g., D' Andrade, 1976; Romney, et a!., 1972). Stefflre (1972:214) stated: "This kind of data stabilizes with fairly small samples of respondents (N=30-60)." These techniques are not as reliant upon random selection or sample size for gain­ing statistical significance as would be found among other statistical procedures. Rather, it is more important in these pro­cedures to sample subjects who have a shared understanding of the domain under study.

Like most anthropologists, we assume that members of human societies share beliefs and ways of behaving. These shared understandings and actions are what constitute "culture." In every human society, culturally coherent pools

IWhite, D. R., and H. G. McCann. 1981. Mate­rial and probablistic entailment analysis: Multi­variate analysis of "If ... then" statements in cultural systems. Manuscr. on file at School Soc. Sci., Univ. Calif., Irvine.

123

of information and knowledge are trans­mitted from individual to individual through processes of enculturation or so­cialization. In this research, our interests lie in describing the social behavior of recreational fishermen that may be di­rectly attributable to the ways they cate­gorize or rate fish.

All beliefs and perceptions will be af­fected by the degree to which subjects have been socialized into a particular sys­tem. In other words, an II-year-old's understanding of their kinship system is less robust than, for example, his or her 30-year-old father's. We assume, of course, distinct parameters defining the nature and extent of knowledge about a particular domain. This knowledge is shared to varying extents among all members of the system-from a norma­tive standpoint-and is passed on to new members through a socialization process. In this case, an individual who is new to recreational fishing will generally be so­cialized as a "recreational fisherman" through his or her experiences and subse­quent discussions with more integrated members of the recreational "subculture" (e.g., at parties, bars, at home, on boats, on piers, etc.)2 These assumptions guided our sample selection, in that we were interested in locating relatively ex­perienced fishermen.

For the most part, fishermen in this

ZThis is not to say that the population of U.S. marine recreational fishermen is homogeneous. consisting of a single language or ethnic group whose attitudes toward fish are uniform. In fact, there are segments of the total recreational fish­ing population to which our findings may not apply. For example, it could be argued that be­cause the fishermen in our sample are over­whelmingly white males, drawn from fishing clubs, our findings cannot be extended to black, Hispanic, Korean, Vietnamese or other minority recreational fishermen in the United States. The basis for this argument lies in the findings of linguists and other social scientists, who argue that distance differences in linguistic behavior, socialization, and ethnicity between whites and these other ethnic groups result in different meanings, perceptions, and beliefs about the things of this world. It is important to note, how­ever, that is has been shown more recently by Romney, et al. (1979) that ethnic enclaves in the United States may show more in common cogni­tively with the mainstream of American culture than is evident from casual observation. This seems to point to the importance of length of exposure to American popular culture (e.g., tele­vision, radio. etc.) and interaction with other social groups in the United States.

study belonged to non-species-specific fishing clubs. Four such clubs were iden­tified for data collection. These were lo­cated in and drew their members from east Florida, west Florida, Texas, and North Carolina. About 30 fishermen from each area were interviewed. A fifth sample of nonfishing club members was taken from the piers and other fishing spots in east Florida for comparative pur­poses (N = 10)3. Some selected character­istics of the fishing club members and their fishing and fish preparation behav­iors are included in Table 1.

Results

Hierarchical Clustering Analysis (HCL)

Table 2, a summary of the results of the HCL for the four regions4 , shows that the same general categories, presented along the left-hand side of the chart, were found in all regions. These categories re­flect the general ways that marine recre­ational fishermen in each of the areas group species of saltwater fish. The boxes with the names of the fish repre­sent the clusters of species that fishermen put together most often in the sorting tasks. As Table 2 shows, the general criteria that fishermen used to categorize species were:

I) Sportfish, or species that are fun or exciting to catch. In east and west Flor­ida, fishermen differentiated between good-eating and poor-eating sportfish, while neither Texas nor North Carolina fishermen made these finer distinctions.

2) Meatfish, or fish that are good to eat. In addition to the meatfish designa­tion, fishermen in all four regions sepa­rated their meatfish in terms of the ranges or habitats of the species. In all regions, the groupers and snappers were placed together and often described as "good­eating reef fish," while trout, bluefish,

3To address the question of how similar fishing club members are to nonclub members, we com­pared responses from club and non-club mem­bers in east Florida with Pearson (0.78) and Spearman (0.63) correlation coefficients. These were significantly similar at the 0.000\ level for both tests. 4Clusterings were produced using the SAS aver­age linkage procedure.

red drum,. etc. were described as "fish you can eat that you catch in the surf or from a pier."

3) A third category of lower quality or less well-known fish, also divided on the basis of range, begins the categories that contain species many fishermen reject. Texas fishermen lumped these species in with their "trashfish," while some fisher­men in the other three areas acknowl­edged the utility of some of these species as fish they would use for bait. Most of these species, however, were perceived to possess one or two negative qualities, as will be seen below in the item-by-use matrices. These qualities made them less desirable than the fish in the second cate­gory.

4) Trashfish. Fishermen saw these spe­cies as the sea's least desirable. They used derogatory terms, such as "odd-ball species," "dangerous fish," "pisswinks," and "garbage," to describe these species. None were targeted for food or sport. A few fishermen had eaten puffer, calling it "the chicken of the sea," and an occa­sional favorable statement was made about gafftopsail catfish, but generally these fish were considered low on the scale of marine fishes.

Fishermen rejected these species for various reasons. In a few cases, the ugli­ness of these fish were cited. Others of­fered explanations that were, at least su­perficially, more reasonable. Searobins and puffers were said to be "all head and no meat"; puffers, poisonous; sea catfish, poor-tasting scavengers and dangerous to handle because they could use their spines like spears. Fishermen told of bad experiences with catfish, ray stingers, and the spines of searobins.

Species in this last category offended the fisherman's sense of what a fish should be-a scaled, silver, or colorful fish shaped like a grouper or cobia. But fish in category 4 have bumps, wings, stingers, blotchy and smooth skins like salamanders, and spines and whiskers like porcupines. They act strange, puff­ing up, grunting, or flying when tossed in the air.

One of the primary reasons for reject­ing these species, then, is that fishermen tend to associate appearances and odd be­haviors with undesirable characteristics. These findings are reaffirmed below in

Marine Fisheries Review 124

Table 1.-Selected characteristics of fishing club members.

Club membership data

Length of membership (years)

Age

Education in years Percent without HS diploma Percent with HS diploma (only) Percent with <4 years of college Percent with 4 years of college Percent with advanced degrees

Percent who own their own boats

Percent who clean, scale, etc. their fish themselves 0-20% of time 21-50% of time 51-99% of time 100% of time

Percent who have someone else clean, scale, etc. their fIsh 0-20% of time 21-50% of time 51-99% of time 100% of time

Percent who cook their own fish 0-20% of time 21-50% of time 51-99% of time 100% of time

Percent who have another person cook their fish 0-20% of time 21-50% of time 51-99% of time 100% of lime

Cooking styles-1 % of population who: Broil Deep fry Pan fry Bake Barbeque/gnll Smoke Other

the entailment analysis. The notable ex­ception to this is flounder. With two eyes on one side, often blotchy skin, and a flat body like a skate or ray, the flounder qualifies as unusual-looking fish. In fact, one fisherman told of tossing a flounder back before he learned from another fish­erman what it was. The nearly universal utilization of flounder among marine recreational fishermen suggests that a fish which is good tasting and easy to clean will be utilized even if it does not approach the fishermen's ideal.

5) Sharks and dogfish, for obvious rea­sons, were lumped together by nearly every fisherman in the sample. For many, they comprised yet another group of trash fish .

Figure I converts the information from Table 2 into a tree diagram demonstrating

49(2), /987

East Florida West Florida North Carolina Texas

Mean Median Mode % Mean Median Mode % Mean Median Mode % Mean Median Mode %

6.87 7.5 10 5.13 4.12 3 2.3 2 3.7 3.3 3

49.6 44.4 44 48.7 52 52 42.3 39 28 41.2 40 36

15.6 15 16 14.3 14.2 16 17.3 16.4 16 17 16.2 16 4.2 0 0 5

20.8 26.1 6.9 0 29.1 34.7 6.9 5 25.0 30.4 37.9 55 20.9 8.7 48.3 35

91.7 56.5 60 70

96.0 100 93.3 90 4.0 0 0 0

12.0 4.3 3.3 0 8.0 0 0 10

72.0 95.7 90.0 80

28.0 4.3 6.6 20 8.0 0 0 10

16.0 0 33 0 4.0 4.3 0 0 0 0 33 10

72.0 78.3 86.7 55 8.0 17.3 99 5

12.0 17.3 20.0 10 16.0 12.9 19.8 5 36.0 30.4 36.7 35

600 73.9 63.3 65 8.0 8.7 13.2 5 8.0 26.1 26.6 10

20.0 26.1 13.2 5 24.0 13.0 10.0 45

59.1 783 73.3 55 59.1 65.2 73.3 65 22.7 34.8 16.7 5 31.8 39.1 40.0 30 22.7 30.4 36.7 15 31.8 26.1 10.0 5

4.5 4.3 3.3 5

the hierarchical levels at which the spe­cies are more closely or distantly related in terms of perceived and objective crite­ria. While the tree diagram (Fig. I) and Table 2 both show that there is a great deal of agreement h~tween regions in terms of the criteria used to classify salt­water fish, they also show that there is a great deal of overlap between regions in terms of the actual individual species that meet these criteria of sportfish, meatfish, and so on.

We draw three basic points from this information. First, in some cases, fish that are rejected by most fishermen (un­derutilized species) fall into categories with fish that are preferred. For example, while the poor-eating sportfish category in east and west Florida contains mostly underutilized species like amberjack,

crevalle jack, etc., it also contains the highly sought tarpon; in North Carolina we find the generally undesirable pinfish and pigfish in the same pile with desir­able croaker and spot.

Second, some fish that fall into re­jected categories in one area fall into pre­ferred categories in others. Mullet in west Florida is perceived as a higher quality fish than it is in east Florida; in Texas, mullet is considered a trashfish. Smaller species such as croaker and spot, while highly desired in North Carolina, tend to be scorned in east Florida or used only for bait.

Finally, and related to the second point, we see that the sizes and composi­tions of the categories vary greatly be­tween regions. Texas has the largest trashfish or undesirable category and

/25

North Carolina the most meatfish, for ex­ Table 2.-Species clusters by major categories for the four regions: East Florida, West Florida, North Carolina, and

ample. This information tells us that, most im­

portantly, some species have been classi­fied as preferred or undesirable on the basis of local information, rumor, and the general processes that accompany being socialized into recreational fishing, rather than on the basis of more objective criteria. We will see below, in the item­by-use matrices, that fishermen consis­tently said "most people don't eat" about fish that they had never tried eating. In many cases it's obvious that a fish is underutilized in one region primarily be­cause there's no tradition of utilization. Fishermen need only be informed that these fish are perfectly edible, and even good, and they will probably begin utiliz­ing them.

One final point to be made here is that the availability of species tends to be a big factor in whether a fish is targeted or rejected. For instance, smaller species get worse ratings in east Florida than in North Carolina because bigger fish are perceived to be more plentiful and easier to catch in east Florida.

Multidimensional Scaling

Figures 2-5 present the MDS configu­rations for each of the regions5. The find­ings from the clustering analysis are com­plemented by the MDS analysis. Whereas in the clustering analysis we found that the two common categories of meatfish and sportfish came up in every region, in the MDS we found that the most common dimensions in all regions were:

I) Edibility (from good-eating fish to bad-eating or inedible fish) and

2) Sportfish (from large, strong fight­ing fish like wahoo and tarpon to the smaller, panfish types such as spadefish, searobins, and so on).

While these two dimensions appeared in all regions, they were clearest in east and west Florida and least clear in orth Carolina. In orth Carolina, there were a number of other criteria that muddled the

5Stress figures for the scalings in three dimen­sions were: Texas. 0.171; east Florida. 0.170: west Florida. 0.157; and North Carolina. 0.145

Texas.

Major category East Florida West Florida North Carolina Texas

I. Sportfish Amber)ack Amberjack Amberjack Amberjack 8. wPoor·eating." Barracuda Barracuda Barracuda Barracuda2

Tarpon Tarpon Cobia Pompano2

Blue runner Blue runner Linle luna Snook Crevalle jack Crevalle jack Dolphin Tarpon Ladyf,sh Ladyfish Spanish mackerel Cobia Rainbow runner Wahoo Spanish mackerel

King mackerel Wahoo Snook2 King mackerel Tarpon Atlantic mackerel

b. "Good-eating." Cob,a Bluefish Dolphin Wahoo Spanish mackerel Cobia King mackerel Dolphin Wahoo Pompano

Snook2 King mackerel Spanish mackerel

II. Meatfish Black sea bass Black sea bass Black sea bass Jewfish a. Ollshore Jewflsh Nasau grouper Red snapper Red snapper

Gray snapper Red snapper Warsaw grouper Black grouper Red snapper Warsaw grouper Nasau grouper Schoolmaster Schoolmaster snapper Scamp Munon snapper Warsaw snapper Munon snapper Lane snapper Red porgy Nasau grouper Black grouper Jewflsh Jewfish Red grouper Nasau grouper Red grouper Gray snapper Lane snapper Lane snapper Black grouper Lane snapper Gray snapper Red grouper Schoolmaster snapper MuMon snapper Warsaw grouper Black grouper

b. Inshore Bluefish Summer flounder Bluefish Summer flounder Snook Mullet Mullet Sand Irout Southern klngfish Sheepshead Sfriped bass Weakfish Northern klngf,sh Weakfish Weakfish Red drum Summer flounder Sand trout Red drum Southern flounder S::md troul Beach whiting Sponed Irout Sponed Irout Pompano Sponed trout Stnped bass Red drum Croaker Red drum Southern flounder Summer flounder Beach wh,ting Pompano Sponed trout Spot Southern flounder Southern flounder Weakfish Pigfish

Sheepshead White perch Pintish Bunerfish Silver perch Southern kingfish Beach whiting

III. Lower quality or Sheepshead Queen triggerfish Spadefish No Texas clusters fit less well·known meat Tnpletail Schoolmaster snapper Silver Jenny2 these designations. fish3 Scamp Tripletail Tautog/Blackfish

a. Ollshore Gag Gray triggerfish Queen triggerfish Queen trlggertish Gag Scamp2 Gray tnggerf,sh Mutton snapper Gray triggerfish

Gray snapper Tripletail Gag

b. Inshore4 Croaker Croaker Blue runner ("BaiHish") White perch Northern kingfish2 Northern kingfish

Flod,a grunts Silver perch Crevalle jack P'gf,sh Southern puller Rainbow runner Silver perch Pigfish Ladyfish Spot Spadefish Spadelish White perch Mullet Spot BuMerflsh Bunerfish Plntlsh Silver Jenny Sliver Jenny Pinfish

Grunts

(Continued on next page.)

dimensions of edibility and sport, such as Florida (Fig. 2). for example, we can see the size. shape, and habitats of the fish. that the flounders, snappers, and

Examining the MDS figure for east groupers fall to one side of the axis at the

Marine Fisheries Review /26

Table 2.-Conlinued.

Major category East Florida West Florida North Carolina Texas

IV. Trashfish5 Sea catfish Southern putfer Bighead sea robin Smooth puffer Northern sea robin Allantic stingray Gafftopsail caltish

Sea catfish Northern sea robin Gafftopsall Caltish Smooth puffer Bighead sea robin Atlanttc stingray

Sea catfish Smooth puffer Northern sea robin Gafftopsail catfiish Bighead sea robin Atlantic needlefish Red hake Southern puffer Allantic stingray

Black sea bass Queen triggertish Grunts Silver perch Spot Silver Jenny Smooth puffer Gag Northern sea robin Scamp Pinfish Bighead sea robin Southern puffer Blue runner Spadetish Ladyfish Pigfish Northern kingfish Rainbow runner Gray triggertish

Bluefish Sea caltish Crevalle jack Southern kingfish Mullet Gafftopsail catfish Croaker Sheepshead Beach whiting Striped bass Tripletail Stringray

V. Sharks/dogfish6 Blacklip shark (Spinner)

Dusky shark Bull shark Sandbar shark Smooth dogfish

Mako shark Lemon shark Great white shark Sixgill shark Spiny dogtish

'Neither Texas nor North Carolina differentiated between ··good·eating" and "poor-eating" gamefish. 2Not well known In this area. 3These tend to be smaller, if known, and among the inshore species are those which are usually classified as baitfish. Also. because these fish are considered lower quality as food fish, the finer distinctions based on range and sporting qualities are not so strong in differentiating species from one another in these clusters. Fishermen's lack of experience with some of these species could cause the lack of finer distinctions as well. ·Species in this category were generally not well known in North Carolina. The "inshore" mealtish designation probably does not apply here. 5Texas "trashfish" species include species which were generally not well known to Texas fishermen; perhaps a better description of these clusters would be to say that they inClude those species Texas fishermen do not care very much about, nor know much about, nor care to catch. 6With the exception of West Florida. which differentialed the dogfish trom the sharks. all the MCA results contained a cluster Including all the sharks and dogfish. ···Break within a cluster ····Break between clusters.

SALT~ATER FISH

I I

FISH YOU WOULD OR MIGHT KEEP DEFINITE REJECTS

I I~ I I

~ X ~"'" L gOOd poor 10 off 1n off snark. non-sndrk.

''1''"' ,''I'" I I ",:t"r I "..,1I!ffil I

INDIVIDUAL SPECIES

'--------------- ----------­

49(2), 1987

"good-eating" extreme. As we cross the configuration, we encounter progres­sively less desirable species from an edi­bility standpoint. Thus, at the far end of the "poor-eating" fish we find such spe­cies as sharks, searobins, ladyfish, or tar­pon.

The sportfish dimension can be seen from the top to the bottom of the Figure 2. The species get progressively more de­sirable as game fish or fish that are excit­ing to catch as you move from the spade­fish (top) to tarpon (bottom).

The relative placement of underuti­lized species (dots) in relation to the uti­lized or preferred species (circles) was very helpful in developing the educa­tional materials: We can visualize the similarities between species-as per­ceived by recreational fishennen-and then reinforce these similarities between underutilized and preferred species in the brochures, posters, and other educational materials.

The two dimensions of edibility and sport were found in all regions, but again, the precise locations of fish in re­lation to one another change from region to region, just as the species that fell into the clustering analysis categories varied between regions. Comparing east and west Florida, for example, we can see that the species at the extremes are almost identical: Tarpon is considered the most exciting sportfish and spadefish!pinfish the least, and the grouper/snapper species (those with white, delicate meat) are viewed in both regions as the highest qualify foodfish, and the sharks/catfish! searobins the lowest quality foodfish. Between these extremes, however, there are a few differences: Sharks are closer to tarpon in west Florida (indicating that it is considered a higher quality sportfish), and mullet and amberjack are a little closer to the "good-eating" end of the ex­treme in west Florida.

Figure l.-HCL major clusters in tree diagram form.

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North Carolina, on the other hand, is not nearly so well defined as east and west Florida in terms of the edibility and sport dimensions. This is because North Carolina fishermen seemed to group spe­cies on the basis of a variety of criteria, including size and shape, as well as the fight of the fish or its value as a food. In any case, it is still obvious that the hard­fighting fish cluster together at one end, the smaller species at the other, and that the groupers and snappers still fall into the same general region, opposite the trashfish.

Somewhat different than the other re­gions, Texans primarily distinguished between preferred and nonpreferred spe­cies. The three Texas favorites-spotted trout, reddrum, and flounder-all ap­peared together at the preferred end of the figure, and the nonpreferred species con­sist of both the good-fighting fish and the good-eating fish.

These differences reflect local prefer­ences and further support our earlier con­tentions that the general criteria for targeting and rejecting fish remain more or less constant from region to region, while the specific stimuli that meet those

N t: criteria may vary. o

'Vi t: Item-By-Use and Entailmentll)

E 6 The results of the analysis of the item­Vl by-use matrices are similar to what we >

found in the HCL and the MDS analyses. These matrices also have the added ad­vantage of pointing out similarities and differences between the beliefs about fish (or similarities between belief-frames).

For each region, we constructed a ma­trix from the responses to the belief­frame comparisons. These sorted ma­trices are presented in Figures 6-9. Data in this form is useful for providing in­sights into the perceived characteristics of a fish that has an impact on its reputa­tion or image as well as informing us of the combinations of characteristics and attributes that contribute to the clustering of species (and vice versa). In each of the figures, major clusters for belief-frames or sentence-frames are numerically iden­tified along the rows, while major clus­ters for species are identified by letter along the columns.

An alternate way to view or model this data is through entailment analysis. Fig

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Marine Fisheries Review 128

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ures 10 through 13 are entailograms showing both the implicational and con­trast relationships among the belief­frames from the east Florida sample6.

Cluster I (Fig. 10) contains belief-frames that are mostly negative in character. The following are two examples of how to interpret the diagram. The ordered rela­tionship "only eaten by certain classes of people" entails that the fish is a "scavenger," which in turn entails that it "must be skinned." Many infonnants dis­paragingly described certain scavenger fish as being only eaten by certain classes of people. In addition, many of the scav­engers were seen as requiring skinning (e.g., sea catfish) .

A second example is the string "can only be cooked one or two ways" which entails that they "do not freeze well" (don't keep well in the freezer), which in turn entails that the "meat is oily­tasting." In contrast, for example, cluster II (Fig. II) shows the relationship among positive characteristics with respect to freezing. Here the string "meat white when cooked" entails that the meat is "white when raw" which in turn entails that it will "freeze well."

Figure 13 shows examples of contrast relationships. Lines with cross-hatching denote these relationships. Contrast rela­tionships are shown outside the clusters discussed above for the sake of simplicity and readability, but they could have just as well been included. An interesting ex­ample of such a relationship centers around the attribute "easy to clean." If a fish is perceived as "easy to clean," it will not be "poisonous," "ugly," or "slimy."

The importance of both the item-by­use and entailment analyses lies in their ability to infonn and guide us in our at­tempts to change angler attitudes towards the less traditional sport fishes. These analyses, for example, tell us that fisher­men routinely attribute negative culinary characteristics to fish they have never tried. It is much easier to change attitudes in situations where there is some degree

6Data from the east Florida item-by-use was di­chotimized using the following break point: Alpha> 3. The entailogram was produced with the aid of a multidimensional Guttman scaling program written by Doug White at the University of California, Irvine. Relationships shown are with no exceptions.

49(2), 1987 /29

c .....

NON-SPORT"" Atlantic Needlefi~h

Spadefis • Gray Triggerfi~h •

Gag·o • Queen Trig'} - ladyfishJewf i sho o Silver

• Gaff topsail CatBighead Robi"

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eo Ra inbow Runner- Tripletail • Northern Sea

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o lane Snapper

Gray Snapper 00 Hutton Snapper \larsew Grouper OQ ptasau Grouper Blue

eO uroupe~- ~ed Snapper o Northern KingfishBI ad l>l'oupe r

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terfish 0 0Cobia - Amber jack 9 f i ~ h • Be a c h \ltd t • l itt Ie Tuna

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Sheep~head-

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POOR EATING

Spiny Dogfish

usky Shark •• lemo~ Shark Sa ndba r Sha rk

Smooth Dogf ish­

• Stingray

_ Barracuda

GOOD EATING

SPORT

Figure 4.-Multidimensional scaling for North Carolina: Dimension 1 vs. Dimension 2. Dots indicate underutilized species and circles represent utilized or preferred species.

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0.( roater\lhlte Perch. Rainbow Runner 0

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NOT PREFERRED Bluefisht eGafftopsail (at Snook PREFERRED o 0 0 C b'

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Sixyill Shark. • e Stingray Jla rr acuda

0" 11 Shuk.

Grrdt Wli i te Shark•• Mako

•Blaclctip Shark

Figure 5.-Multidimensional scaling for Texas: Dimension I vs. Dimension 2. Dots indicate underutilized species and circles represent utilized or preferred species .

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o 1 2 17 19 6 43 17 8 7J 2} ~ 5 16 34 J;l 40 41 54 55 56 10 32 15 2.3 24 18 52 22 24 Jl 51 53 3 49 25 45 )) )3 J5 9 II 36 50 13 14 34 12 44 J1 46 47 42 48 4;:n 21 Mt')st pe'1[Jle 8at 13000101° 1 000 I 0000 010 01421205621) 4 511 10131412141212131212 l516 17 8 810 9 812 a 91312

I prefer t-, catch )) 4 2 2 ) a 0 1 1 1 0 1 0 1 1 2 0 0 0 0 1 1 1 1 1 3 S ) 1 4 1 2 6 S 710 710 911 10 910 91611 S S ) 4 3 q 8 610 2 Ea'5y t·, clean 11 4 3 3 3 3 313 3 ) 3 3 3 ) 4 ) 1 21 3 ) 3 ) 4 S ) 3 5 6 4 4 ) S 4 510 910 4 6 610 10 10 9 a 12 10 5 2 6 9 811 91011 7

Can get big thick fillets 0r '5t~aks 36 4 1 I) 0 20004 4 40000000000 1 0007 20000 200 1 4 1 S 711 5 5 8 311 8 2 0 1 III 7 114 1

~~~~t f ~~~~C~~ ~ t:~ ~ ~ i ~ ~ ~ 1~1 ~ ~ ~ ~ ~ bgggggl ggg6b ~ g6~ ~ ~ ~ ggi ; ~ iii ~ ~ ; ~ l~ ~ ~ ~ ~ ~ ~ ~ g ~ iIi i ~ ~ Easy t'1 ca tch 6 10 7 8 7 a 11 a 6 5 7 3 4 4 4 5 4 51 3 3 3 S 7 10 6 10 2 5 2 ') ) ) 3 13 a 4 9 2 5 ) 4 ) ) 4 5 5 4 8 6 3 ) ) 6 3 10 6 6

NQt sturdy, durable, q!.Y'ils -3asily 14 3 ) ) 3 ) 3 3 3666 3 ) 3 334 3 3 ) 4 3 ) 4 ) 3 ) 3 3 3 ) 3 3 4 39 3 4 4 4 4 3 3 3 3 4 3 3 ) 9 8 4 38 S 4 MU'5t cut 0ut red <:>teeaks fr ...,rn meat 49 5 4 2 2 4 21 2 2 2 2 2 ) 2 2 2 2 21 2 2 2 2 2 2 2 2 2 2 2 :2 2 2 2 2 2 2 3 2 2 2 3 7 ) 5 2 3 2 4 ? 4 2 2 7 5 13 a 2

F~eeze ....e 11 16 4 ) ) 3 ) ) ) ) 4 ) ) 3 3 ) ) 3 3 3 ) ) 3 3 ) ) 3 3 4 3 3 3 3 3 4 5 4 6 4 7 6 a 6 5 6 7 10 8 3 5 3 4 4 S 4 ) 4 4

Can C'10k a~~'5~a~" y;~ei~~: ;~ ; iii i iJ iii i i ~ ~ ~ ~ i il i ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ j i ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 1~ : ~ ; ~ ~ ~ ~ : j ~ ~ "Ea;ot"'ec;n-::;Owh"'e;-;n;-C~+lgO't~'-'·''';:~~0~;e'C:".~",:+~+~ ~ 6 ~ ~ 6 ~ ~I ~ ~ }i---f ~ ~ ~ gg ~) ggg ~ ~ i i ~ i ~ ; 1 i ~ b~ i }-~ ~ ~ ~ iIi ~ ~ i : i ~ ~ ~ i-+-~-H-~

Can nevee get big t~~~kh~~~l~~~h~~n~t~~~~ ~ 6 ~ g ~ g ?I ~ ~ g ~ g ; ~ : ~ ~ ~I ~ ~ i ; ; ~ : ~ g ~ 6 i j i 6i : ~ ~ i ; ~ ~ ~ i ? ~ i ~ j ~ g ~ ~ ~ i i ~ ~ rnesn't even ~~:~c b~ i~~ i~~:~ ~ ; ~ i ~ ~ ~l t i : ; ~ t ~ ~ ~ ~ ~I ~ ~ i ~ ~ i ~ i i ~ ~ iii i ; ~ ~ i ~ i : : ~ i ~ j i ~ i i t i ~ i ~ 7 ; i

Stu~1y, dueable: d(,esn'tv:~~iie~u~~~~ jj ~ ~ ~ 6 i ~I i ~ i ~ ~ t t ~ ~ ~ il ~ ~ t t t ~ ~ ~ i ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ j : j ~ ~ : : ~ ~ ~ ~ ~ ; ~ ~ ; ~ ~ Meat '"las mi1.1 taste 18 0 1 0 0 0 010 0 0 0 oTO' 0 1 0 ~I 0 0 0 0 1 1 0 1 0 2 0 1 00 1 6 ) 8 7 2 5 4 7 5 6 2 96-7-i-S 0 4 3 0 1 00 1 Meat teKtuec tend~r 44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 2 2 0 0 1 2 0 2 0 1 1 2 5 6 9 2 5 3 ) 7 5 511 2 4 1 2 0 8 6 5 5 1 I 0

Meat ....hite .... hen c<10ked J5 1 0 I 0 1 010 0 0 0 0 0 0 0 0 0 010 0 0 1 2 0 1 0 1 2 0 0 0 1 0 <1 4 4 6 3 S 2 7 5 5 1 9 5 5 0 2 0 ) 2 1 2 1 2 0

Mp.atN~~~t~l:~~n1l~:~ ~ ~ ~ ~ ~ ~ 61 ~ ~ ~ ~ ~ gg? ~ g ~I ggg ~ ~ ~ ~ ? ~ ~ ~ ~ g g~; ; ; i J j ~ ~ : ~ ~1~ ~ : g ~ ~ ~ ~ 6 j g6 ? Most ~e0ple 'in n-)t eat 2 aI41S1314L5141211-fT12TI-u 11 101014 6 95 5 6~12 S 6 7 6 6 4 4 t 2 Ill) 2 2T2:rrt-i14-02""124~

I have neve~ trie<1 eating 4 4 a 912 910[10 9 8 8 II 711 11 11 10 Hill 11 a 5 S 4 S 7 7 7 6 7 6 4 6 1 0 0 0 1 1 1 2 2 1 1 0 1 1 2 2 2 2 1 0 ) 1 1 0 Edible, but the~e ace ~ette~ fish to keep 62 10 6 4 4 8 2 912 5 6 5 5 1 ) 7 7 4 a 0 1 4 ) 4 5 6 4 1 2 4 2 1 0 1 0 1 0 2 1 1 I 0 0 1 1 1 1 1 ) 0 0 0 0 0 0 1 2

Dange~~~~e~'~~~a~1~~ ~ g ~ ?gg ?Ig ~i 1; l~ 1; ~ ~ i i ~ 1~1 ggg ~ gig ~ ~ 6g ggggig g g ~ g g ~ g 6 g ~ g g ~ g g gg g ~ ~ i ~ Only eaten by ce~tain classes "f pe',ple 59 1 ) 0 1 2 019 0 ) ) ) 0 0 0 2 2 1\ 0 a 0 1 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 3

Ha~d t<1 clean 10 0 1 0 0 0 0 4 ') 1 1 1 0 0 2 4 4 2 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 1 1 0 D 6 3 2 0 0 0 0 3 0 2 2 8 0 0 0 0 0 0 0 )

N-1t ~~;~~ ~:~~u~:C~~~:0~~~; ~ ~ 16 ~ gggl i ~ ~ 6 6 g ~ : ~ ~ ~I gggggg ~ gggg ggggggg gg g g6 ggg g gg6g gg g g g ~ ~ 6 Sma 11 ~~~~ ~ ~ 6~ 1; ~ il ; ; ~ ~ ~ ~ g66661ggg1 ~ ~ ~ i ~ gbig gg ~ f g~ ~ TIT~Tg r~ 6i 0 ~ ~ n1-r-r~

Slirny fish 5S 0 1 0 4 1 2[ 2 S 0 0 0 0 0 0 I 0 110 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 2 0 1 0 0 1 0 1 0 0 ) ) G00d pan f i '5h .34 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 ) 5 4 2 4 0 0 0 5 1 0 0 2 2 4 0 1 2 1 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 4

T<,·) ~;:~ 1~'~t~~t~~~ ~~~~ ~ 6gg ~ ~ gl ~ g ~ ; b ~ ~ 1b ~ ;1 ~ ~ ~ ; ~ ~ : ~~ ggg ~ ~ g ~ ~ ; g6g66ggggg ~ gggg ~ gbg? g ~ 'teat 1n ~~~<1q~';t c:~~: i ~ 6gci ~ 16166gg g g 6666 ~I 6g g??g 6 g ~ 6 ~ ? ~ ~ 0 g ~ ~ ~ 6 g g ~ ~ gg ~ ~ ggg ~ ~ ~ ~ ~ ; ~ ~

Eatc"l .... hen sna 11, f\0t hig 7 4 S 1 6 1 0\ 1 1 3 2 0 1 0 0 a 0 21 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 1 0 0 1 1 0 0 0 2 0 anI 1 7 0 0 1 1 0 0 6 3 0 Often have -'''0~rns ')~ paeasit~'5 56 9 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 laO 1 0 0 0 0 1 0 2 0 0 0 0 1 0 ) 0 ) 0 7 0 0 1 1 0 0 0 0 0

________________~~_~_~aJ:~_~,~~_~i ::~_~:~ ~ ~ ~ ggggl g ggg ggg g ~ ggl ~ ~ ggg gg ~ gggg g g g~ gg g ggg6 g ggg66g g g g g ~ i ~ ~ l~ Must ~~ q"aked bef0ee C0·1king 17 1 0 1 0 31ifO-~ -O-O--oooarO 0 0 0 0 0 0 0 0 o""""1f"OOo 0 2 0 0 0 0 0 0 1 1 n 0 1 0 1 0 0 0 0 0 0 1 3 2 0

Big brlnes 31 1 0 1 1 0 0 1 1 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 ) 1 1 1 0 0 1 2 3 1 2 0 1 Q 0 0 0 0 5 1 Medt te>ttuee c-)a~se ...,~ ]rai"lY 42 2 I I 0 1 010 0 ) 3 2 0 0 0 1 0 010 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 ') 0 0 4 0 4 5 4 4 0 0 8 0 1 0 0 0 0 0 0 1 2 0

Meat -,n the hari ';; ide 45 2 0 0 0 I 0 1 1 6 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 3 1 1 1 0 0 1 0 0 0 0 I) 0 0 Ta'ite'5 1 ike i-,d inc 23 0 0 0 0 0 01 0 0 2 1 1 0 0 0 0 0 01 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1

Ileat is ste i"lgy ·11:' t0u1h 47 3 0 0 0 0 0 0 0 ) 1 1 0 0 0 0 0 4 0 0 (II 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 Has muddy t~ste :;:e 0 0 0 0 0 01 ') 3 1 1 1 0 0 0 0 0 01 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 2 0 0 0 0 0 1 0 0 0 0 0 I 1 2 5

_~ ~~.'!.Y_~!'I~~ __~U.!..~ ..'L...=!!?2.~_~~!.._t_~.£~ 60 0 1 0 0 0 1 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 0 I 0 0 0 0 0 I 0 1 0 0 0 0 0 0 0 0, 0 2 0

111ea~~:s n~\~~~~~e s~~ U ~ i ~ ci 6 ~ 6 ~ ~ ~ ~ ~ 66 ~ 66 ?I 6666 ~ 66666666 ~ 6 ~ i ~ 6 i 66 ci 666 ~ 66 i ~ 6 0 f i ~ ; 1 ~ Can on ly C')0~'e·~~e t~.~t~;'\~:~; ~ i ~ ~ ~ ggl gg ~ ~ ~ ~ gggggl ggg ~ g?gg?6 6gg gg ~ ~ 6 ~ ?6 g 6 ~ g g ~ ~ ~ ~ 6 gg g ~ ~ ~ ~ ;

Me~;a~t~.~~~ ~::~t~g ~ ~ i ~ 6 J ~I gg ~ g6 i gg ~ ~ gl g g gggggg6 g ~ g ~ ggg ~ ~ gg g g ggg ~ ggg g g g g g i i it 19 ; Meat dark .... Ilen ra,* 2S 5 2 4 1 13 11 1 1 0 0 0 2 0 0 0 0 01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 D 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 2 IllS 1

BI.ndy meat :B 2 1 ) III 1 J 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 I) 1 1 9 2 1

Figure 6.-East Florida sorted item-by-use matrix based on row-row and column-column similarities.

Marine Fisheries Review 132

la

Can get

o tlr'>st genple c~t 1

ij·)tt")m feedec-, 61 Easy t'? clean 11

E1Isy t,) prer>are 41 free ze we 1 1 16

'''Ieat .... h [te ..... hen ct)t)ked 26 Heat haS' mi 1<1 taste 18

Cafl c,,<")k any way y')u 1 i ke ]7 I prefer t,) catch 3

big thick. fi llets .)[" steaks 3S fI1eat t~'I(tLJre f ir<n 43

StLJr1y, durab~:~e~,);:~~tb~~i~t)~LJ~::t~ l~ ~ ~ ~ ~ ~ ~ ~ ggggg ~ gggggg ~ g?l gg; ; ~ ~ ~ ~ ; ~ ~ ~ ; ~ ; ; ~ ~ ~ ; ~ j ~ b ~ ~ ~ t ~ ~ ~ ~ ~ ~ Ib ~lea\~~~t~l:~~nm~:~ ~ ~ ~ b gi ~ ~ ~ ~ ~ g~ g~ gg~; ~ gg~\g g~ j i; ~ i;:; j;; ~ ~; ~ g;~ ~lr ~ ~ ~ g; ~ ~ t ~: ~

~leat t~:;~r~,) t~~~~~ ~ ~ ~ ~ ; ~ ~ ~ ~ g ~ ~ ~ 19 ~ ~ ~ ~ 77~-t4-H+~ i ~ ~ ; i 6i ~ ; ~ ~ ; ~ ; 1; ~ l~ 1~ ~ ~ i i g i ~ ~ ~~+i ~~: t f ~~~ ~;~J Ei~~~ i~ Ij 1~ ~ ~ i ~ ~ i i ~ ~ ~ ~ i i ~ ~ ~ ~ ~ i il l ~ iii i ~ ~ j ~ ~ iii i i j l~ ; ~ i ~ i ; ~ ? i ~ iii ~ i ~

'''Ieat dark wh~'1 ra .... 25 7 12 1 1 2 2 2 1 2 3 5 2 1 1 2 1 3 I 1 I 1 11 1 1 1 J 1 1 3 2 4 1 1 1 I I 2 3 2 5 2 1 1 1 2 3 2 1 1 1 1 1 1 1 1 2 'l'lt stur'iy, durable, sp0i15 easily 14 3 2 1 1 44 222 1 1 1 1 1 1 1 1 1 1 1 I I 1 1 2 J 6 4 1 2 2 142 1 1 22 146 2 3 8 2 4 1 1 22 1 1 3 1 I 1

mes n~~r~r~~~e~~~~ ~ ~ ~ ~ ~ i ; ~ 66 ~ 6 ~ ~ ~ ~ ~ ~ 6 ~ ~ 6 ~16 ~ 6 1 ~ ~ j ~ i i 6 i j ~ ~ : 6 i ~ ~ ; i ~ 66 ~ 666 6 ~ ~ ~ : Can never Jdt big t~.~~l{h~~~ l~~~h~~n;t~~~~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ i : i ~ ~ ~ g ~ ~ ~ ;1 g g ~ ~ ~ g 666 ~ g ~ ~ ~ ~ 6 ~ ~ 16 ~ ; ~ 66 ~ ; 6 ; ~ i ~ j :

MLlst he s'1aked h~fore cooking 17 2 4 2 2 6 ') ') 3 1 1 2 1 1 1 2 1 1 1 1 1 1 II I 1 I 1 1 1 I 1 2 I 1 1 1 1 I 2 1 2 2 1 1 I I 1 I 2 I I 1 I 1 1 1 1 Only Qaten by certaif\ clas~es 'lE r>e'lple '5B 2 J '5 6 4 4 4 2 3 3 4 ) 4 1 2 3 I 3 2 1 1 I 1 1 1 ) 1 III 1 I 1 1 1 1 1 ) 2 t I 2 1 1 I 3 I 1 1 I I I I 1 2 L

Ta~t~s_like i-1~ ~ ~ ~ i ~ ~ ~ : i ; ~ ~ 16 j ~ i ~ ~ ~ iii j j i i ~ ; ~ iii i ; j ~ ~ ~ ~ 2~1 i 1 il ~ j i ; : iii 2i--3~ ',',-,--,-,:,;,------------~t pe'lple .10 r'\'1t ~at 210114 11 21115 1 i515lOT~-IT-i1i---r-TT5 ~12 2100. 0 ~o-Ori---j----i-a-02 000 I) 100 I .,.­A

I have never tried e:ltl'1') 4 5 810 10 11 712121210 15 12 13 1310 71) lIto 13 9 6 7 7 2 2 1 1 1 1 0 2 0 1 0 0 0 1 0 0 0 0 0 0 0 4 6 J 3 4 6 5 4 4 5 6

~·hble. tn.t there Me ~:~~:~'1~~s~'1t'~a~~~~ ~ ~ ~ i~ ~~ ~ ~ ~ ~ i ~ ~ ~ i ~ ~ i ~ ~ i ~ ; ~1 b ~ ~ ~ b gggggg ~ ~ gg6 i ~ ~ ; b g ~ i ~ ; ~ ~ g ~ g ~ ~ ~ scav~~1~; ~ ~:~ ~ ~ ~ I; l~ ~ ~ ~ ; ~ ~ b ~ i ~ ~ 6 ~ bbg ~ gl gggg ~ gig t ~ gggggig ~ ~ i g ~ ~ ~ g ~ ggggg i ~ t

Thc':) .... back because ugly ')7 t 1 0 8 0 0 1 2 0 1 1 0 010 910 '3 1 S 2 0 310 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 1 1 a 0 0 1 0 1 0 a 0 0 0 0 0 0 (1 0 1'1L.st be .,kinned 12 1 0 4 7 '5 6 5 1 0 0 0 0 0 0 0 2 0 0 1 0 0 '3 0 0 0 I 0 0 I 3 0 6 0 1 0 0 0 0 3 0 0 0 I t I 0 0 1 0 0 0 0 0 0 0 0

-----------''1~at is ~~r~~~~ ,~': ~~~~~ 4j ~ g gg ~ ~ g ~ ~ gg gggg g6 gggg 61 g 6 ~ ~ g g : 6 gI~ i ~ ~ ~ ~ ? ~ 6 g ~ ; gig ~ i ~ ~ gig ~ ~ g ~~~~n~;t:~e~e~:~~: ~~{:.)~.;~: ~ g ~ g g 6 g gIg ~ g g g g ~ 6 ~ g g ~ g g gl g g g g g g6 g g ~ g ~ ~ 66 g g ~ ? ~ ~ g ~ g g g g g g g g ~ ~ ~

Sc<lven~er~, but ricky ab()u~'l~h~~d~~e~a:~~ ~ g g ~ ~ 666g ~ g g g g g g ~ g g ~ g g gl g g g g gg g g ~ g g g g g g g ~ g g ~ g g g ? g g g g g g g ~ gg Edten when smallBi~·)~~~~ Ji J~ 6 ~ ~ J ~ ~ ~ g 6 ~ ~ 66666g 6ggig g ~ g 6g ~ ~ ~ ~ g g g g g j ~ ~ 6g 66 ~ 66; g 66 g 6g 6 g

Often '1ave ....')rms 'lr paras i t<:!'; 56 5 0 I l 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 01 v 0 a 0 0 0 0 0 4 0 0 0 0 0 012 0 0 0 0 0 1 I 0 0 2 0 0 0 0 0 0 a 0

-------'M"'e"a"'t-''1as a H:~~,,~.~ ~;:~~ ~ ~ ~ ~*f_i44+t+-6-~+-~~-~ ggg~ ggggggggig ggg-g-gr4-1gg~+%-H+~-g-g ~-%-f--g ~+~ Medt texture c',arse 'lr grainy 42 2 1 0 0 J 3 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 g: 0 0 0 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 I 0 0 0 0 0 0 0 0 0 0

Meat '1n th~m:~~d ~~~: ~ b g-¥-+-~-6-8t-%-%+-~ g ~ g g g g g g g ~ g g g ~ 6; ~ ~ g g g g g g g g ~ ~ 6b? ~-H~-& g 6g ~~4 %- ~ ~ Go'1d pa'1 ~ i"h 34 0 0 0 0 1 1 I 0 0 0 0 0 4 0 0 0 0 0 0 1 1 01 0 0 1 2 2 1 0 I 0 0 1 0 2 0 0 0 1 I 0 a 2 4 0 I 0 0 0 0 1 0 1 1 5 3

'1eat '1n the s'1ft side 46 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 I 6 6 0 1 0 0 0 0 0 0 1 1 0 0 '3 0 410 1 0 0 0 0 0 0 0 2 0 0 0 r0'1 ~mall t'1 bo)ther .... ith 51 0 0 1 0 2 1 1 0 0 5 1 212 2 0 1 0 0 1 J 4 110 0 3 J 0 I 1 0 0 0 1 0 1 2 1 1 0 0 0 1 2 0 1 0 0 1 1 0 0 I 1 4 7 7

Used m<)stly f'1r bait 54 0 J 0 0 0 0 0 0 0 0 1 316 0 0 0 0 1 0 6 2 0 1 1 0 1 g~ ~ ~ ~ ggg? ~ ~ g~ _~ ~ ~ ~ ? g ~ ?g??g ? g~+_~

Can 'lnly C')'1~e,~~~'1,~~Y t~~S~~~~ ~ ~ ; ~ ~ ~ ~ ~ ~ gg~ g~ ~ ~ ~ g~ 0 gg gl gg~ g I 0 I 4 0 0 2 0 I 2 1 2 1 6 6 4 I 1 0 2 1 0 I 0 0 0 0 0 0 0 "lust cut ';ut red streak fC"'lm meat 49 5 3 1 1 2 2 2 0 0 1 0 0 0 0 1 0 0 0 0 0 0 010 0 0 0 0 0 2 2 3 I 0 0 0 0 0 2 0 6 8 I 0 0 0 3 0 0 0 0 0 0 0 0 0 0

Meat stc',ng tastir'\g 22 4 ) 0 0 0 0 0 J 0 0 2 0 0 0 0 0 000 0 0 0000000000 1 1 100002 1 54 3000 J 0 0 1000000 1 Bl'10dy meat Cl 2 12 tOO 0 0 t 1 2 4 0 0 0 0 0 0 0 0 0 0 01 0 0 0 0 0 0 1 I I 0 I 0 0 0 0 2 1 2 2 1 0 1 0 2 () 0 1 0 0 0 I 0 0 0

Are best sm-)ked )9 10 1 0 0 0 0 0 1 0 0 I 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 4 6 15 0 0 0 0 1 0 0 0 0 0 0 0 0 0 Can be eaten ·)nly if s:Tl'1ked 40 6 1 0 0 0 J J 2 ,) 0 1 0 0 0 0 0 0 0 0 0 0 010 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a 1 1 4 0 0 0 0 0 0 0 J J v v 0 0 0

Figure 7.-West Florida sorted item-by-use matrix based on row-row and column-column similarities.

49(2), J987 J33

I I I I

~ .0,

'" U 0U]

~

~ 'r'

'" " :r: ~" S .~ I..J

.J:: ttl r­ 0 IJ' Q> g ~

x to

~-g

~ t

~ i ~!

~ ~ L.'

-?.J::Q)! .c .c --;;; ~.~ ~ I~ .~.~~:;~~;j~

~ ~ Q)

~-g ~~; ; ~ !i;:;7~ \.:t! ~ c E5:: ~tt>-o=-:

.~I .co>

g ~ fl ~~?

;I..J 'Q) --'

)g~~.c~

1 ~ ~~: I ~ ~.~

~II ~ .... ~ :::!, ~ ~ Ul ~ ~ ::

>oc tl~g' t~ e: ~ ~ :5 .c ~: ~ ttl I..J Cl: ttl

~~:~~.~l~:c::1..J ~~~~c:

~ -g-S c:: 0

~ ~ ~ ~ 3 g:

>­ (f)

6.c~ :: tOtO ..... tV

.c£~Sic~ ';:'~ ~.8~~0:0-S&;5~.;.;~~;:lOx ~~I~ ~locS': ~~~.v o.~~.b ~~~lx·5 ~.~~~ ~-g:<::~ g, 8.g,g('.~ ~ g,~.>g ~

a. ... .., 'C X V!.- C' : C : Q:' 0.. >. ........ "? E E :> c U ttl: U C. c ttl te 0.. c.. t:' Co.w C!l(1) I..J ...... I..J I..J 1..""" _ '-' ::"\ ;:J .w .w .w .., Q) :; ~ ~ ;J U-J .w .J::: I..J I..J c·.... Cl ::J .......... C' "': ttl 0' r::' ttl· .... ":; '- . .0 ." ..... 0' C ;J .... 'C' :t ro I..J C ttl '0 ......... oW ttl _: ttl .... E In r: c C' ;:J ': c._ C C ttl ttl.r::: -::: .... rt· ... "'.J 0

~ ~ ~ ~ 0 ~ ~ ~ (f. (,~~:S; S~~~~ ~:3;; ;-:'O::i"n: i; t:~,:;; (.9)£:;' bl ~V; ~~;;; ~l;;;~ g ~i;:; g-a;~ ;':8 ~ ~ 03. (.~:!i &§ (.~~ g~ o 1 24) 6 16 6.2 17 61 a 40 J6 7 ~ n 13 41 :B Xl 9 1244 2J 21 24 552 I'J 10 :it 22 J6 53 '59 54 55 56 63 60 349 25 42 45 1O 32 4 13 20 II J1 15 14 34 35 o(l so ]5 3347 57 XI

M')st ;>e')plc cat I 0 0 1 1 a 0 0 a 0 0 0 1 1 a a a a () ') 3 4 31 5 1 a a a 1 0 1 2 a a 0 a 1 2 410 IJ 12 16 14 16 IJ 13 10 15 1I 12 12 IJ 12 10 8 6 B a II 7 9 I prefec t·) catt'''' ) 3 laO 0 a 0 a a 0 a a 0 0 a 0 I 0 5 2 1 0" 1 0 0 a 0 a 0 0 I () a a 1 0 a I ) ) 711 IJ 4 4 91212 7 6 5 ') 5 4 8 8 \ ) ') I 0

Easy t) clean 11 a a 0 0 a 0 0 0 a a a a a a a a a a a a 0 III 1 a a a 0 0 0 a 0 a a I 0 a a 4 1 612 6 7 7 910 12 ) ! 1 3 2 ) '] a 2 2 I) 2 I N1t ~t\,.ody, durable, s:}')i 1<; easi ly 14 ) ) '] '] ] ) '] J 3 ) 3 ) 1 ) 3 '] ) J '] 3 ) ) 4 ) 3 ) '] ) 3 ) '] 3 ) ) '] '] ) 3 5 ) 1 4 ') 3 .1\ 11 '] ') 4 ) 4 '] ) ) ) ) ) ) ') '] ']

freeze ',Ie \1 16 2 2 2 2 2 2 2 2 2 2 2 ~ 2 2 2 2 J 2 J. 2 2 21 ) 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ) 4 ) 5 7 7 ) 12 6 6 '] 3 4 4 4 ) '] .3 ) 4 4 2 'teat ....h i te ....hen co)<)k~tj 7.6 2 2 2 2 2 J. 2 2 2 1 2 2 4 2 2 2 '] 1 2 2 3 .3 '] 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 ') 4 7 IS ') 411 7 5 ) 4 4 '] 4 ) 2 4 4 ') ']

Can (,.,·)k any '<lay Y0U 1 i I(e nil I IlL 1 I I 1 1 I 2 I l I 2 1 1 I I 21 2 2 1 I 1 I I 1 I I I I I I 1 1 4 2 5 7 7 4 ') l3 12 8 5 6 1 4 6 a "} ) ) 4 ) 4 Easy t 1 pcepMe 41 I 1 I I I 1 I 1 I 1 1 1 1 1 I 1 1 I I I 2 I I 2 1 1 I I I 1 1 I III I 1 ) 2 2 a 6 4 6 6 9 a ) ) 2 2 I 2 4 1 ? I I 2

'leat has mi 1.1 taste IB 0 a 0 0 0 a a 0 a a a 0 0 I I 0 2 I 1 0 1 ')10 0 l 0 0 0 0 a a 0 a a 1 0 0 '] 4 ') 2 ) 1 2 r) 13 ) 4 2 0 2 ) 1 '] 0 I 0 I 't~at .... h i te .... h~n ra .... 24 0 a 0 0 a I 0 a 0 0 I 0 ) 1 l t 2 1 1 0 2 a 0 a a 0 0 0 a 0 a 0 0 I 0 ') a ] 2 ') 2 4 ') 2 013 4 ') 3 0 1 I '1 2 I 1;) I

Nkoa fldky 'neat 46 a 0 0 0 0 0 a 0 0 a 0 a 0 a ° 0 a 0 1 a I 01 a 1 () a 0 a 1 a a 0 a a a a 0 () ] 1 1 a 4 2 1 I 7 2 2 ') a 2 4 2 1 () 2 1 ')

______ ~ Md'lt t~:~~rTlt~~~7-~ 4: g*g gggg~+_~ ~ ~ ~ ~ 0 0 0 I 0 1 ! 0 0 0 1 gg~ gg~ gog ~-i+H-ci-&~4 '] 1 I ~i-H-~+--H--H e;.,)<1 pa1 f i-;h 34 0 a 0 0 0 0 0 0 0 0 0 0 1 a a t'J a I ggg ~I 2 ~ 6 g gg 1 a a a a a a 1 a 2 IJ 7 2 2 915 ) 5 a a 0 0 0 a a a 0 5 I) 1 a 0

Fhtt)'11 feeders 620 a a 0 2 a 0 0 J 2 ) 4) 2 2 6 1216] 312 4 a a a 2 I () 1 0000 0') ') ') 0 210 7 I 5') () 0 1 6 2 204) 2 2 2 i Eaten when bi'], n,t small 8 a a 0 a a 0 0 0 a 0 a a a 0 001 0 a :) 0 I :2 4 0 0 0 0 0 a 0 0 a 0 0 021 1 a I 46 a 7 0 a 0 I 00 f) a f) 2 a I !) r)

Can rtever ~~t Olg t~~~\~~~1~~~h~~n;t~~~~ ~ r g g g ~ ~ g g ~ ~ ~ ~ t g ~ t g : g g ~ ~I ~ ; g t ~ g 6 g g g g g ~ 1 g ~ iii ~ ~ : ~ ~ gg g g ~ ~ ~ ~ ~ ~ i ~ ~ ~ R'Jny 30 0 1 ] 0 0 a I 0 0 1 I 0 a 1 I I 1 I 0 a 0 21 I 10 1 0 ) 2 1 I 0 a 0 I a 0 0 2 2 1 I n 2 4 I) 1 I a I 0 0 1 1 0 0 4 I f) a t

-roo SM~ 11 t) ~~~~~/,~l~~~ ~ ~ 6 ~ 66~ 6 ~ 6~ 0 666666 ~ ~ 6g ~I ~ 1~ ~ 6 i 6~ ~ 666~ ~ ~ ~ g ~ b~ 6 ~ ~ ~ ~ ~ ggg ~ ggggg ~ ggg ~ --------------~t~)"[)Te-a;~t ·'!at 214 IJ 7 71011 12 8 6 712 5 -li'""m 10 I ' \I I:> ] 6: 5 S6B~)-y-rTl~2 0 1J"'O""0'()o-o""i.JlJOl o..,--rOO-o-j-'-I-r1j"""'7f"Q

I have never tciel"l e1ti:"l') 41214101OlJI2101313I011tl SHUll 777') ') 5114\0121210810 9WlO1211 775 II I 1 '10 l. 0 a a 2 <I () I 0 I S 1 2 2 4 1:2 N·)t eaten becal.<;c ~ lis)n')~<; 20 a a a :) 0 0 0 a 0 J 0 0 () 0 0 0 0 :) a a I 0 0 a 0 0 0 0 0 0 0 a 0 0 0 0 0 0 a 0 0 a 0 0 0 a 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Thn... back be~'au'5e VJ 1Y 57 a 0 a a '] 6 0 a 2 ') 9 2 <I 0 I 8 0 13 0 a 0 Ii a 0 a I a 1 1 a 0 0 1 a a 0 0 0 1 0 0 I) 0 a 0 0 a 0 I) I 0 0 0 0 0 () () 0 a 0 a Hard t) cdt('h 5 2 2 7 I) I t a 2 a L 0 0 I 2 2 I 2 I 7 I 2 0 1 I 0 I 0 1 2 2 1 a :) 0 0 0 0 0 0 0 0 2 2 a 0 1 a 0 1 2 1 I 0 0 4 2 0 0 0 0 7

l)an'.ler)\,.oS t'l nanrlle 9 0 9 0 0 0 I 0 0 0 1 2 4 a B 712 Xl 1 1 0 I 010 1 a a 0 0 0 0 0 a a 0 0 0 0 0 0 0 0 0 0 0 () 10 2 ) a 0 1 () I I 2 0 0 I a 0 0 Hard tl C'lG'l" 10 0 a 0 a 0 0 2 0 I 3 1 2 2 5 OJ I 7 9 :) 2 5 4 I I 0 a a 1 1 0 0 0 a 0 a 1 () 0 a 0 I/)O 0 0 1 0 a a 0 1 0 I 1 a 4 a LOa 2

'lust be <;k inned 12 0 0 a 0 0 0 0 0 6 2 0 9 4 7 6 5 7 6 4 0 5 11 0 0 a a 0 a a a I 0 0 0 a 0 a 0 a 0 0 0 a 0 0 a 1 0 2 a 1 a a a 0 a 0 a 0 a 0 S,'avengec F ish sa 0 0 0 0 I a 0 a 4 0 I) 8 8 5 5 .: 5 a I 2 a .3 I ] 0 0 0 0 0 0 0 a 0 0 a 0 a 2 2 I 0 a 2 1 I 1 () a 0 0 t 1 a a 2 I a 0 () I

_-_"_~dib~~-~"!?~-~-*i{~~~-l~~~~e~-oa~~~~~~~[~ ~~ ~ %-~}--&+H-~ ~-~ ~4-H-g+~~-~-~ *-% gg~-%-~4--~~·%+}g g%g~ gH--+-%--~ %-~-t---g--~-t-~-~-%-%-~·%-~ "1eat "'d'5 .) '5tr'")nr~ sll'ld 1 Z7 a 1 0 a a 0 0 0 0 a a a a 2 2 /) ) a a a 0 Ii 2 a 0 0 0 a 0 a 0 0 0 a 0 0 0 f) 0 I 0 a 0 0 0 ) 0 a ~ a 2 0 0 0 a 0 a 0 0 0 0

""eat )n tn~ hard si le 45 0 0 0 a a 0 0 0 0 a 0 0 2 I l \ 2 0 0 0 a a a a 0 0 0 0 0 0 a a 0 0 0 0 0 0 :) 0 I 0 a 0 0 2 I 1 0 0 a 0 0 I) I 2 0 0 0 0 0 Used tl\')stly f)r bi'lit 54 0 0 0 ) 0 2 0 2 a a 0 0 a 0 0 a 0 0 0 0 0 olD 0 :2 0 I a 0 0 0 0 a 0 0 0 0 0 :) I 0 0 0 0 0 a 0 1 0 0 a 0 0 0 () a a 0 a 0 0

.---~-~~!~--E1~~ctaLnT~~f~-~4i~~ 1-;,~7~~ ~-~*-~-%-ri-}-%-g_·g ~+i+%-fr--b ~+g~~-gt+%-%+-g-~ ~ gg-~-%-~-C\-g ~ ~g g~ gg--+-%-~ %--g-~-~-%-%--~-+-~-%--~-%-% Can be aaten 'lnly if sm0ked 40 a 0 0 a 0 a 0 0 0 0 a 0 a a 0 0 0 0 0 0 0 0 0 a 0 0 :) 0 a a a a 0 0 a 0 a a 0 0 0 0 0 0 0 a 0 a 0 0 0 0 0 a 0 a 0 0 0 0 0

""eat i<:; '5tr'in'1y ')(" t lugh 47 1 0 0 a 0 a 0 0 a a 0 0 a 0 a a 0 0 0 a 0 010 J 0 0 0 a a 0 0 0 0 a 0 0 0 a 0 0 0 a 0 0 a a 0 a 0 a 2 0 a 0 () tOO 0 0 ') Oftoan have w')r'11S ·)C i?aCdo;ltes 5610 0 0 I a 0 0 0 0 a a a a a 0 0 0 0 I 4 0 0 a a 0 0 0 a 0 0 0 a a a a 0 a 0 a 0 a 0 0 a 1 0 a :) a 0 a 0 a 0 1 0 0 0 0 0

IXln't ;'?ven ta'5te til(e fish 'j) a 0 0 a 0 0 0 a 0 a a a I 00 a 0100001000 a a a 1000 a a 0 0 0 a a 000 0 f) 0 a 2 0 2 iJ 010 a a a a 0 '1 0 0 Scawngero;, bl.t [)i~'ky "ll)')ut what t'ley r~<1t 60 0 a I 0 1 0 0 a 0 0 0 a 0 1 I I 1 0 0 1 a a a 0 f) a 0 a 0 0 0 0 a a 0 0 a a 0 1 () a 0 0 a 0 1 0 0 0 0 a 0 a 0 2 ') I) f) a 2

Has m\,.od'1y tasta ;:a a 0 0 0 0 0 a a 2 0 a '] 0 0 a 0 0 0 0 0 a 01 0 1 a 0 0 a f) a a 0 a 0 a 0 0 0 0 0 a 0 0 0 0 a 0 0 0 0 0 0 0 0 0 0 I a I) 0 I)

_ Sli'llt ~i-;'" ~-l 000 a a 0 I 0 01 1 a a 1 0 00 a a 1 a I 000 () a 0 0 0 0 a 0 0 a a 2 a iJ 0 ItO 0 1 000 0 0 0 0 0 0 a 0 0 0

Eatert ·tlhen '5maITOi~')~)~~~ ~ J gTggg ggfg"g ~ g6666 01 ~ 6 ~ ~I ~ ~ og-gg ggggg~ ggg ~ g~ g ~ ~ ~~-rr~ -~-f-~ ~ ~ ~ ~ ifr~ ~ ~ g 6a Mc~t te)(tl.re nac,;<! lC grdi.,y 42 0 0 a 0 0 0 0 0 a 0 0 a 0 0 0 0 0 0 0 2 0 010 0 0 a 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a I 0 a 2 0 2 0 2 I a I I 1 4 a 1 0 a a

Can get ':Jig thick filtH'; lr '5teal(s J5 0 0 0 0 0 000000012206061000000 010000 a 0 0 a 01 000 I 0 \ 042168467) 2 8 sal 000 Hacri flghtlnl fish 5210 S B 2 0 0 3 '] 0 0 \ 0 0 I I 2 3 a 9 2 a q 0 I I 0 0 0 0 i a a 0 0 a a 0 a a 1 2 I I 0 til 1 9 5 4 5 0 a 0 9 6 :) a 0 a a

Stl.rrJy, ,lucable, dln't o;p·)il '1l.l;-Uy 13 0000 a a 0 0000 011102 01102 010 ~O 0 0 a 0 I a a 0 0 a a a a 0 0.3 1 2 3 J.! 4120 ) 012 a 2 a "'eat td)(tve fir'll 4) 0 0 a a 00 0 0 0 0 a a 2 0 0 1 1 0 :) 0 2 0 0 0 0 a a a a a 0 0 0 0 a 0 0 a 0 0 4 1 a a 1 2 4 2 4 2 ) 2 L 1 f) 1 a

6b Vecy fe .... b")n,~s J) 0 a 0 a 0 a 0 0 a a 0 a 2 4 4 1 S J 0 a a 01 1 0 0 0 0 0 a 0 0 0 a 0 0 a a a 0 a a 2 a 0 0 1 7 I) 1 0 2 2 0 0 a 0 0 Can )nly ("') lk )ne Ir t.,,) -Iay<; 13 0 a 0 0 0 a 0 a a 0 a a 0 1. .2 a 1 0 0 0 a a ) 0 a 0 0 0 0 0 0 0 a a 0 a 0 a I III t 1 I 1 1 1 I a 2 I 0 1 0 0 0

~ei'l~ )~'l~h~r:~~~ ~~~~ 1}%-%-g g-~*~ ~ ~ g~-~ ggg~ ~ g~ gg ~ gggg.%-~ ~ ~ ~-~ gg~ gg55~+~H·l~ 6·~6h'-~~g~~+~6-*~g-i--g ~ gg Mu<;t CI,t ')\,.ot re,j stced~ fnm Ille.'lt 49 0 0 a 0 0 0 0 a a 0 a 0 aLl a lOt 0 0 °10 0 0 0 0 a 0 0 0 0 a 0 0 a 0 a 0 a a 0 a 0 012 a ) .3 ) I \ 0 0 0 0 0

Meat jarl( wnen rd"" 2S I a 0 5 a 0 0 ] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 a a a 0 a 0 0 a a 0 0 a 1 a 0 1 a 0 0 7 0 2 1 0 0 a 0 a 1 a 0 a 0 0 61,) ldy meat 2) a 0 0 4 0 0 0 ] I 0 0 1 0 0 0 0 a 0 I I a 01 a a 0 0 a 0 0 0 0 0 a 0 0 0 a 0 a 0 0 0 0 0 a 5 I 0 I/) I 0 a 0 a a 0 a a a

r'eat tao;t(H f i'5ny 19 1 a 0 0 0 0 a a 0 0 a 0 0 () 0 a 0 0 0 0 I I 6 0 0 0 0 1 0 0 0 0 a 0 0 a 0 2 ) a 1 a 0 1 7 0 I 0 a ) 0 a a a a I 0 0 0 a Meat 'li Iy ta-Hill1 11 0 a a 0 0 0 a 0 0 0 0 0 I a a 0 a 0 0 a a ')15 0 a 0 a a 0 a a 0 0 0 a 0 0 0 0 1 a I I I :2 7 a 2 ') 0 LaO 0 0 0 0 0 1 0 0

Medt '5tr"mg t"l,,>tir"lg 22 i () 0 0 0 0 0 0 0 0 a 0 I 0 0 0 2 a 0 0 a 2 4 0 0 a a a 0 0 0 J u U 0 a a a 0 1 0 1 0 I 0 8 0 ) 1 0 a 0 1 0 0 1 I a 0 0 0 ___• __~ "__ Are ~est Sm')k_~ 19 0 0 0 0 0 a 0 0 0 0 0 a 0 0 0 0 0 0 1 0 0 0\ 5 a a 0 0 0 0 0 a 0 0 0 a 0 0 a a I 0 1 a a a 5 0 4 0 0 0 0 0 0 0 0 0 a a 0 0

Figure 8.-North Carolina sorted item-by-use matrix based on row-row and column-column similarities.

Marine Fisheries Review 134

'1ost r>e''}ple o1at I pcef~r t'l catt"h

Easy to) ("lean Mea.t has "'111 taste

la '1ea t wh i to when c"')')ke d Meat ..,hite Ioi'hen caw

Can c'll)k any way y,,>u 1 ike easy t'1 pcepare

IIp..:.tt texture tender Nice flaky meat

l 1 l l l Car) get hig thick. fillets ,r c;t~aks JS 2 0 0 0 01 0 0 0 0 0 0 0 6 5

Hard tl) catch 5 2 2 l 2 2 l l 2 5 l , fiard fightirq fic;h 52 9 6 5 7 9 5 l 0 0 0 0 l , 6

Easy t,) t'atch 6 3 l l 3 01 3 2 l l 3 6 , l 2 0 lO Fhtt'1m feedecs 6l l l 2 l l l 6 l 3 l 6 5 3 J

Sturdy, ducitble, d ..esn't <;p'111 'luickly l3 2 2 2 2 21 ? 2 2 2 2 2 2, 5 , , , 22Sl imi f ic;" 56 2 2 2 2 2 Bll 2 2 ? ~·)t sturdy, dl.rable, <;[)0ils easily l4 5 , , , 'I' , , , , , , 6, 2 , 3 2 ,

6, , 3freeze ~e 11 16 2 2 2 ] 2 2 2 l l 1 l l2 2 l l~"l"l'-------------...,.,Mo'"'s~t;:.~~l~e.~~u~~tf~:'~ 2 =ra 91111 11 91211 6 3 6 8 9 9

I hOl ....e never teien e:!lti"l] "O 9 to II III 6 7 <J 10 8 9 8 6 6 9 ')anger )l.S t·) handlA 9888887 8 l 0 III 9121211

Must be c;k inr'\~<i l 0 0 , 9 8 7~J6gggglg o 6

fiard t") clean l 0 0 8 6 6 5 Edibl"!, blot thece 'lre b~tter fish t'1 Ite~p 62 2 1 1 2 01 1 3 1 6 2 5 2 6 5 4

6 6 5 4 "

---------~--East~~hen~~~~r<;~~~~ ~ 6~ ~ 661 ~ ~-~-H l~ 2 0 0 0 ::dll 'levee get bi!] thicl{ fillets )(" c;tedl(<; 36 0 1 0 1 1 0 0 1 2 3 1 o 0 0 0

\I0t 'li.lc1 fight in.) fish 530000010021'0 o 0 0 0 Small 'nn·::c; 3203000000120 o 0 0 0

(f)')d (Ht'\ ~ ish 34000001000100 o 0 0 0 10'1 <;m3ll t·') bqther with 5401000000103 2 0 0 0

13'lny ll03,Oq,0020, 1 0 0 0 Only eitten by certai" cld.5!>es 'JE peJple 5000200010302 2 2 2 1

DJe'i n'1t freeze .-Ie 11 15000001000000 o 0 0 0 Meat In the 'i'Jft 'ii,],~ 4400000000000 o 0 0 0

-------~eaten ?ec'luscril'lo/),,·.:Il';s 20000001617001 l l 0 0 Thr'") .... back t)eC'lu<;e ufJly 5700000030600 o l , l

T",c;te'i 1 ike i Jdinf1 2300000\000lOO 6 l l 0 Melt is <;tcin'JY 'Jr t0t.qh 4700000000000 o , l

Scave"'Jers, but 9icl(y <lIh"lut what they eat 60000001000l00 0 I l l Has 'Tll.drly taste :EQ,OOOOOOOlO2 l 0 0 0

Dnesn't eve" t",c;te lik~ fis'l 50000001000000 1 0 0 0 Used rrnstly f'1r bait o 0 0 0

__•• • ~~!!.~~~~jl~_kl~.j ~~bb~~I~~~~~~ o 0 0 0 J=:ate" .... hen small, n")t big 7 0 0 0 10200000 o 0 0 0

') 'leat texture coar<;e )r <]rainy 42000001000000 0 1 0 0 Oftl:O!n have ....·Jrms .,r parasit<:l<; 9600000000000 o 0 0 0

--------------------t1eat taste<; fishy 19 0 0 0iggnggg6gi 1 l1il] b'med 3l l 0 l 0

Very few b')rH~<; o l 2 l '1n ly C·J·JOe. 'Jne Jr t"') ways ~g~r~?lg6ggrg l l lCan l

Are best <;m'10e.~1 362 l l l ill l l l I l l l l l -----------;::tL;st~~e:rbehrec')'1kln;l 1700000000000 o 2 3 2

'1eat h-l$ a 'itt" In<J sme 11 o 2 3 2 "1e-lt )0 the hard s i.de ~~~bigl;ggggg o 0 1 0

~1ed.t '>11y ta:;tinq 2l l 0 3 lOIOOOlOO 0 I l l ~leat 5t,. In'J tast i"J 22 0 0 1 30000000 o l I l

''1eat ,1ark when ro'l .... :5305601°00000 o 0 0 0 '31')'1dy '!leat 23316')1100102 I 0 I 0

11lJc;t ct.t <1l.t re.l .;trea~ fr'1'" meat 49101001000000 o 0 0 0

'" 1.0 '"' I'"'

.0 U

u'"U'" '"

l l l 2 6 l l l 2 l l , l l 2 l l l l l 5 l l 5 , l l , l I l 0 0 l l l 3 0 l 2 l 2 0 0 l 'l 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 7 3 6 , 9 , 0 l l 0 0 5 0 , 3 lO 2 °1'0 0, 0 1 0 l l l 0 0 l 0 0 0 0 0 l 0 0 0 0 5 5 l 3 5 5 l I , l l l l l , 7 2 9 3 2ll 7 0 l 0 l Oi' 2 , 0 l l l l 0 0 0 0 0 0 2 0 l 0 l l l I l I 2 3 7 75 5 6 5 2 51' 3, l , I l l 2 l , l 2 2 l l l l l l l l 2 I 2 l l l , 5 l l I 2 5 9 lO l , 5 7 71 2, , , 2 2 21 2 2 2 2 2 2 , 2 2 2 2 , , , , 3 3 3 3 3 , , 2 , 2 3 3 5 3 2 , B , '1 3 3 2 , 2 2 2 2 2 2 2 , 2 , 2 , 2 2 , 2 2 2 2 3 2 '- , , , , 3 1 , 3 , 3 5 , 2 , 3 , 3, , , , ,

'I' , , , , , , , , , , , , , , , , , , 5 , , 5 6 7 5 , ') 610 9 , , 'I' ,

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~-iL; l l 2 l , l l l l l l 1 1 3 3 l l 3 3 3 l 2 l 2 3 3 , , 6 'll 2 _tl.L.~ 2 , 5 6 7 7 7 6 6 , , 5 6 6 6 -6 3 2 3 l l 0 , 0 l 0 l , 3 o 0 0 l 0 l 5 2 3 6 , 7 g 61 g

9 9 7 8 7 8 7 9 7 6 5 6 6 6 6 5 3 3 2 2 l l 3 3 3 l l 0 0 o 0 5 'I' 2 0 0 0 0 0 l 3 0 0 l 0 0 0 0 0 0 l 2 0 0 3 0 l 0 0 0 0 0 0 l 0 l l 0 o 0 5 0

~ gl g 0 l 0 0 0 0 0 0 0 0 0 0 0 0 0 0 l I l l 2 2 0 0 l 0 0 l 3 o 0 l l 3 0 q 6 , 0 0 0 0 2 l 0 2 0 0 0 0 0 0 0 0 l 0 0 0 0 0 l l 0 0 0 0 2 o 0 0 o 3 4 7 2 0 0 0 l 00100"0300000000 2 0 0 0 00000002 :J 0 0 0 I 0 7 91 2 2 l l l 1 I 1 3 1/.221 I 111 1_~ _i_I_'.t +~-.!-~_'_'3+l-~-} 3 ~ 1 2 0 0 l 00j00-0(j()T006ooo-0 0 1 0 0 0 00002003 432262 02]000 0 l 1000211110010000 a 0 0 0 o 0 0 0 3 1 1 2 7 1 7 2 4 0 1 4 0 3 l l l 11102131320000000 o 0 2 0 o 1 1 3 2 0 1 2 2 '5 5 1 3 0 l ,[ 0 0 0 0 2 0100100100000000 o 0 0 0 00000004 'J 0 6 1 1 0 1 0 1 1 0 0 l OOIOOOOOlOOOOOOOO o 0 0 0 00000223 o 1 1 4 3 0 o 01 0 0 0 0 2 0003010100000000 o 0 0 0 00000002 000010 o 0 0 0 0 0 0 I qOl010000000000 tOo 0 00000002 6 0 3 0 t 0 , 'I' 2l 0 0 1011201000000000 o 0 0 0 10001002 2 1 0 1 1 2 4 0 0 0 0 0 0 00100000000000000 o 0 0 a 01000231 o 0 3 1 5 0 o 01 0 0 0 0 l 0000000000000000 o 0 0 0 00001220 000000 o 0 0 0 ° 0 0 0 0 a ~g'ggA~~~~~g~gggg o 0

o 0 0 0 00000000 000000 g ~I g ~ 0 0 00000000 000000

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g gl g go 0 0 0 00000000 000000 0 o 0 0000000000000000 o 0 2 0 0 l 100000 000000 9 1 2 0 0 o 0 00100000000000000 o 0 1 0 00100000 000003 6 31 3 0 0 o 0 0000000100000000 o 0 0 0 00000000 000003 12 2 0 0 0 o 0 i gl g 6~ g g g ~ g g g g g ~ g

2 0 OOlOuOul u 0 0 l l : ~ ~I ~ ;o 0 o 0 3 l 11100000 o 0 1 0 1 0 o 0 001'0000000000 000 o l I 0 3 2 3 0 0 0 1 0 13] ) 21 o II 3 2 l l l l l l ~ ~I ~ ~ ~ ~ ~ ~ ~ iii i ~ i i l l I l \ 1 1 1 1 112 324112 ; ~I ~ ~l2 l l 1 I 1 1 1 III 111111 0 o 0 0000000000000000 l 0 0 0 00000000 o 0 0 0 0 1 0 0 5 1 0 o 0 ggl~ggggggggggggg o 0 0 0 00000000 000010

~ :' i i 0 o 0 l l I 0 01100000 010000 0 o 0 o Oi' 0000000000000 2 0 0 0 00000000 000000 o 'l 8 7 0 o 0 00 I 0 I 00000000000 , I 0 0 00100000 000000 2 2 9 7 0 o 0 ggl~ gggg; ggg; ggggo l 0 l 1 1 I 0 1 0 0 1 000001 o 'l'o 7 0 o 0 o l 0 l 00000000 000001 o 0 <} '5 0 o 0 00\00000000000000 o l 0 0 3 1000000 o 0 0 1 o , o 61'0 ,

Figure 9.-Texas sorted item-by-use matrix based on row-row and column-column similarities.

49(2), 1987 135

II.

NICE FLAKYFigure ll.-Implicational relationships l-'EAT

among positive belief-frames.

cluster

cluster I.

CflLvEATErleV 'VRD TO CERTAIN CLASSES I"EAT HAS A

Cl.UJ I OF PEOPL£ STROl'G Sl"iLl0­/{ATr::-\ OILY TASTING

CArl ONLY COOK ONE 00 'TY(l

,",vs

H.AAD FIGHTII.G FISH cluster

Figure l2.-Implicational relationships among a separate set of more

negati ve belief-frames.

Figure 1O.-lmplicational relationships among negative belief-frames.

I'bST PEOPLE EAT

r II.

Figure l3.-Contrast relationships among belief-frames. Lines with cross-hatching

indicate the contrast relationships.

Marine Fisheries Review 136

of uncertainty; had fishermen actually eaten and rejected these fish, our task would have been much more difficult. In addition, this information helps establish parameters for determining an approach for enhancing the image of a particular underutilized species. Knowing what positive attributes to stress, and in what combinations (e.g., knowing the impor­tance of the implicational relationships between "nice flaky meat," "meat white when cooked," cook any way you like," and" easy to clean"), can make a consid­erable difference in promoting fish. Sim­ilarly, and equally important, knowledge about negative attributes and their per­ceived relationships can help in deter­mining appropriate ways to deal with the negative attributes of a particular species.

Discussion and Conclusions

These findings suggest that most per­ceptions concerning underutilized spe­cies are developed outside actual experi­ences. Beliefs relevant to these species are generally the result of hearsay and rumor perpetuated during a fisherman's socialization into recreational fishing. Ambiguities about the perceptions of un­derutilized species and lack of experience with such species are cognitively dealt with in terms of the general ways that recre­ational fishermen rank and classify fish.

Many of the findings of this study came as no surprise. That recreational fishermen target fish they perceive to be fun to catch, good to eat, easy to cook and clean, etc., are not earth-shattering revelations. Yet we would have sus­pected our techniques and interpretations had we not confirmed such banal knowl­edge. In many ways, this confirmation lends confidence to our findings.

This analysis has placed species of saltwater fish in relation to one another in terms of their similarities and differences as perceived by recreational fishermen. This information has served as the foun­dation for the development of educa­tional/advertising materials designed to improve the reputations of underutilized

species, thereby promoting their use. The methods used in the study complement one another in this regard. While the HCL yielded an understanding of the general perceived similarities and differ­ences among species, the MDS further defined relationships between the species in terms of the specific dimensions of sportfish and meatfish. These relation­ships suggest possible ways that under­utilized species' images may be im­proved via favorable comparisons with preferred species that, in the minds of fishermen, they already resemble. These relationships between species also sug­gest which underutilized species are the most and least likely to improve with an educational program. The item-by-use matrices and entailment analyses further defined relationships between the species in terms of attributes suggested by fisher­men. An understanding of the relation­ships between attributes (belief-frames) suggests the proper and most appropriate ways to present a case for the increased utilization of underutilized species within an educational context. Together, the three types of information provide a clear and workable picture of the domain of saltwater species as perceived by people who regularly and actively deal with them.

Knowing what fishermen like or do not like and understanding the manner in which they express their beliefs concern­ing fish is critical for producing appropri­ate and effective educational materials. In Part II, (Murray et al., 1987) we look at the application of this information to the development of educational materials directed at encouraging marine anglers to better utilize nontraditional fish in the southeastern United States.

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Berkes, F. 1984. Competition between commer­cial and sport fishermen: An ecological analy­sis. Human Ecol. 12(4):413-29

Burton, M. 1972. Semantic dimensions of occu­

pation names. In Romney, A. K., R. N. Shep­erd, and S. B. Nerlove (editors), Multidimen­sional scaling. Seminar Press, N. Y.

.D'Andrade, R. G. 1976. A propositional analy­sis of U.S. American beliefs about illness. In Basso, K. H. and H. A. Selby (editors), Meaning in anthropolgy. Univ. New Mex. Press, Alberquerque.

____, N. R. Quinn, S. B. Nerlove, and A. K. Romney. 1972. Categories of disease in American-English and Mexican-Spanish. In Romney, A. K., R. N. Shepard, and S. B. Nerlove (editors), Multidimensional scaling. Seminar Press, N.Y.

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____, , and J. Murray. In press. Recreational fishermen's perceptions and preferences for marine fish: Some methodological considerations. In Proc. Gulf Caribb. Fish. Inst.

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Murray, J. D., J. C. Johnson, and D. C. Griffith. 1987. Encouraging the use of underutilized marine fishes by southeastern U. S. anglers, Part II: Educational objectives and strategy. Mar. Fish. Rev. 49(2):138-142.

Romney, A. K., R. N. Shepard, and S. B. Neriove (editors). 1972. Multidimensional scaling: Theory and applications in the behav­ioral sciences. Vol. 2. Seminar Press, N.Y.

____, T. Smith, H. E. Freeman, J. Kagan, and R. E. Klein. 1979. Concepts of success and failure. Soc. Sci. Res. 8:302-326.

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Stefflre, V. J. 1972. Some applications of multi­dimensional scaling to social science prob­lems. In Romney, A. K., R. N. Shepard, and S. B. Nerlove (editors), Multidimensional scaling. Seminar Press, N.Y. 1971.

---,---,c:--' P. Reich, and M. McClaran­Stefflre. Some eliciting and computational procedures for descriptive semantics. In Paul Kay (editor), Explorations in mathematical Anthropology. MIT Press, Cambridge.

USDOC. 1980. Marine recreational fishery statistics survey, Atlantic and Gulf coasts, 1979. U.S. Dep. Commer., NOAA, Natl. Mar. Fish. Servo Curro Fish. Stat. 8063, 139 p.

Weller, Susan C. Cross Cultural Concepts of Illness: Variation and validation. American Anthropologist Volume 86, #2, pp. 341-350. 1984.

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49(2), 1987 137