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Keywords:

  • Biodiversity;
  • endangered species;
  • folk taxonomy;
  • perceptions;
  • Sebastes;
  • species concept;
  • stakeholder

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Appendix
  9. References

We used folk biological classification as a framework for understanding stakeholder perceptions of marine species diversity and its potential consequences for conservation in Puget Sound, Washington. Respondents (N= 99) classified 46 marine species into folk taxonomies, which diverged substantially from a scientific taxonomy. Variation in folk taxonomy structure was related to respondents’ expertise, suggesting that the ways in which people sampled or observed the marine environment led to different perceptions of species diversity within it. Differences in the degree of aggregation among taxa supported the notion that culturally important species are more identifiable. We focused on rockfishes (Sebastes spp.), long-lived species of conservation concern, to demonstrate how different views of biodiversity could lead to divergent perceptions of risk to rockfish populations. Understanding the connection between people's values, goals, and experience and their underlying views of species diversity may help to reconcile differences between stakeholder and scientific perspectives.

“No one definition has as yet satisfied all naturalists; yet every naturalist knows vaguely what he means when he speaks of a species”(Darwin 1859).

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Appendix
  9. References

The species concept is central to characterizing biological diversity and is arguably the most salient currency of conservation (Wilcove 1994; Brooks et al. 2004). While it is recognized that successful conservation must also consider habitats, landscapes, and whole ecosystems (Franklin 1993), the use of species as a foundation for conservation dominates policy (e.g., U.S. Endangered Species Act, Convention on International Trade in Endangered Species) and many consider species preservation the “heart and soul of ecosystem protection” (Wilcove 1994). Species have remained a conservation focus because they have long been viewed as biologically tractable entities, discrete units on which evolution operates (Mayr 1969). Furthermore, social factors are key determinants of conservation success (Mascia et al. 2003) and species are the biological units that resonate most with policy makers and the public (Mace 2004). Concepts such as “evolutionarily significant unit” may be important in technical discussions but are unlikely to capture public interest (Anderson 2001).

Despite the importance of the species unit in biology and conservation, uncertainty in species identities emerges from empirical limitations in the delineation of taxa and semantic arguments about how “species” is defined (Rojas 1992; Hey et al. 2003). In essence, species are not “real objective units” (Mayr 1942) but human constructs used to characterize and organize diversity (Raven et al. 1971; Levin 1979). Human cognition evolved around the ability to perceive discontinuities in nature and develop classification systems for them (Raven et al. 1971; Anderson 2001). Thus, early scientific taxonomies were derived from innate folk biological understanding of how nature is organized (Raven et al. 1971). Classification systems developed outside of a scientific framework (folk taxonomies) may correspond with contemporary scientific taxonomies (Raven et al. 1971); however, folk taxonomies often reflect an individual's expertise, goals, and values (Medin et al. 1997; Bang et al. 2007). Consequently, the nature of “species” may vary among individuals or groups with different cultural or economic values (Boster & Johnson 1989; Lopez et al. 1997) or social norms (e.g., gender-specific roles in agricultural systems; Boster 1986).

Folk taxonomies not only reflect ways that people observe components of the environment, but also relate to their perceptions and understanding of the natural system as a whole (Atran 1998). People may make biological inductions about an organism based on others they view as similar in nature (i.e., belonging to the same category; Medin et al. 1997; Medin & Atran 2004). Therefore, discrepancies between scientific classification schemes and folk perceptions of biodiversity could lead to a disconnect between scientific views and stakeholder perspectives. Understanding the ways in which stakeholders perceive biodiversity may be particularly important in ecosystems that are not observed or observable by most citizens and where successful conservation depends on willing participation by stakeholders. For example, adherence to species-selective harvest regulations and accuracy of harvest data collected by natural resource agencies depends on the ability of fishers and hunters to recognize and identify managed species (e.g., Haw & Buckley 1968). Furthermore, species that are named, classified, and recognizable elicit stronger support for conservation from the public (Crozier 1997; Agapow et al. 2004). As a result, it may be difficult to garner widespread support for recovery of a species that is morphologically similar to others and unfamiliar to stakeholders.

While simple cognitive models of how people view species do not fully characterize folk biological knowledge that arises from dynamic experiences in nature, they provide a useful system for linking environmental perception with resource management practices (Nazarea 2006). We used folk biological classification as a framework for understanding stakeholder perceptions of marine species diversity and its potential consequences for conservation in Puget Sound, Washington. Puget Sound is home to nine endangered and threatened species and 21 state-listed marine and anadromous fish species of concern (WDFW 2011). Among these are 13 rockfish (Sebastes spp.) species of concern, three of which are federally protected under the Endangered Species Act (NOAA 2010). Rockfishes are morphologically similar (Love et al. 2002), not favored by most recreational and commercial fishers (Williams et al. 2010), and commonly aggregated for management purposes (Palsson et al. 2009). These issues pose challenges to conservation efforts aimed at recovery of rockfish populations.

In this study, we characterized folk taxonomies of individuals with knowledge of Puget Sound marine species acquired through commercial, recreational, and scientific activities and examined structural attributes of these taxonomies. We first determined differences between folk taxonomies and a scientific taxonomy. Second, we evaluated whether variation among folk taxonomies was related to the ways in which respondents gained knowledge of the marine environment (i.e., their expertise). We then quantified the frequency with which respondents identified different species as identical and the extent to which this varied among taxa. Finally, we used rockfishes as a focal group to examine whether differences in species identification could lead to different perceptions of risk for members of this group.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Appendix
  9. References

We used a stratified chain referral approach (Bernard 2006) to identify individuals with specialized knowledge of Puget Sound species acquired through fishing, diving, and research activities (Table 1). Fishing experience was categorized as recreational fishing, commercial fishing, and charter operation. Additional information on respondent characteristics and interview methodology is in the appendix. Respondents completed a pile sort and identification task (e.g., Boster & Johnson 1989; Lampman 2007), in which they were given 46 color photos of marine mammal, fish, and invertebrate species in Puget Sound (Table 2) and asked to “group these according to what belongs together using any criteria you wish” (Bernard 2006). No species was represented more than once. The sorting task was repeated for each group individually until no further subdivisions could be made (i.e., the lowest folk-taxonomic level had been achieved). At this final sorting step, the respondent was asked to identify each organism by name (if any). If multiple species were not separated at the final sorting stage, the respondent was asked to verify whether they were identified as the same organism. We constructed a scientific taxonomy from the literature (Myers et al. 2006) for comparison with folk taxonomies derived from pile sort tasks.

Table 1.  Summary of respondent experience. In-person interviews were conducted with 99 individuals with specialized knowledge of the marine environment acquired through fishing, diving, research, and other activities in Puget Sound, Washington. N is the number of respondents who reported participation in each activity type and Npri is the number of respondents whose principal expertise was determined to be a given activity type based on estimated lifetime days of participation (see the Appendix). Total experience-years was calculated for each activity as the lifetime years of participation summed across respondents
Activity typeParticipantsTotal
NNpriExperience-years
Fishing, recreational92563,627
Fishing, commercial3310  637
Fishing, charter13 1  161
Diving46161,057
Research3614  833
Other24 4  234
Table 2.  Species used in pile sort and identification tasks (N= 46). The percentage of respondents who grouped each species with at least one other at the lowest sorting level is shown as percent frequency. Species are ranked from the highest (rank = 1) to lowest (rank = 33) percent frequency of grouping and those grouped by more than 50% of respondents are in bold type. Note that some species are tied in rank
Accepted common nameaScientific name% FrequencyRank
  1. aAccepted U.S. common names for fish species (Nelson et al. 2004).

Black rockfishSebastes melanops35%17
BocaccioSebastes paucispinis56%7
Brown rockfishSebastes auriculatus65%2
CabezonScorpaenichthys marmoratus10%30
California sea lionZalophus californianus8%31
Canary rockfishSebastes pinniger42%13
Chinook salmonOncorhynchus tshawytscha24%24
Chum salmonOncorhynchus keta36%16
Coho salmonOncorhynchus kisutch30%21
Comb jellyMnemiopsis leidyi43%12
Copper rockfishSebastes caurinus48%9
Dover soleMicrostomus pacificus63%4
Dungeness crabCancer magister3%32
English soleParophrys vetulus63%4
Greenstriped rockfishSebastes elongatus71%1
Harbor sealPhoca vitulina8%31
Kelp greenlingHexagrammos decagrammus15%27
LingcodOphiodon elongatus13%28
Lion's mane jellyfishCyanea capillata29%22
Moon jellyfishAurelia aurita47%10
Northern anchovyEngraulis mordax39%14
OrcaOrcinus orca0%33
Pacific codGadus macrocephalus13%28
Pacific hakeMerluccius productus29%22
Pacific halibutHippoglossus stenolepis36%16
Pacific herringClupea pallasii20%26
Pacific sand lanceAmmodytes hexapterus11%29
Pacific sanddabCitharichthys sordidus64%3
Pacific staghorn sculpinLeptocottus armatus22%25
Pile perchRhacochilus vacca44%11
Pink salmonOncorhynchus gorbuscha32%19
Puget Sound rockfishSebastes emphaeus65%2
Quillback rockfishSebastes maliger49%8
Red rock crabCancer productus3%32
Redstripe rockfishSebastes proriger71%1
Rock soleLepidopsetta bilineata57%6
SablefishAnoplopoma fimbria31%20
Sockeye salmonOncorhynchus nerka38%15
Spiny dogfishSqualus acanthias0%33
Spotted ratfishHydrolagus colliei3%32
Starry flounderPlatichthys stellatus28%23
Striped seaperchEmbiotoca lateralis44%11
Surf smeltHypomesus pretiosus31%20
Walleye pollockTheragra chalcogramma34%18
Yelloweye rockfishSebastes ruberrimus35%17
Yellowtail rockfishSebastes flavidus59%5

The pile sort results were translated into a respondent by species-pair data matrix, in which the elements are folk-taxonomic distances (sensu Lopez et al. 1997) calculated according to

  • image

where Sr is the total number of sorting steps undertaken by a given respondent r and xs is equal to 1 if the species pair was grouped or 0 if it was not grouped in each sorting step s. For example, the scientific taxonomy represented in Figure 2 shows seven levels of taxonomic organization, from phylum (highest level) to species (lowest level). If the tree were derived from a pile sort task, it would have been constructed in a total of S= 6 sorting steps (i.e., subdivisions). In our formulation, folk-taxonomic distance is scaled between 0 (species are identical) and 1 (species are unrelated). Thus, low folk-taxonomic distance corresponds to high folk biological relatedness and the folk-taxonomic distance between a species and itself is 0 (Lopez et al. 1997).

image

Figure 2. Scientific taxonomy of all species used in pile sort task.

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Variation in the structure of scientific and folk taxonomies was evaluated by performing a nonmetric multidimensional scaling on a Euclidean distance matrix calculated from the respondent by species-pairs data (Primer 6 ver. 6.1.11, PRIMER-E Ltd., Plymouth, UK). Groups of respondents with similar folk taxonomies were identified using a hierarchical cluster analysis with group average linking, followed by a similarity profile test (SIMPROF, Primer 6 ver. 6.1.11) to test for significant (P < 0.05) differences among groups (Clarke & Gorley 2006). A separate cluster analysis with SIMPROF was performed to test for differences between the scientific and folk taxonomies. Aggregate folk-taxonomic trees were constructed for groups of respondents whose taxonomies did not differ significantly by performing a hierarchical cluster analysis on a species by species distance matrix calculated for multiple respondents as

  • image

where R is the total number of respondents, Sr is the total number of sorting steps undertaken by a given respondent r, and xr,s is equal to 1 if the species pair was grouped or 0 if it was not grouped in sorting step s by respondent r.

To evaluate the degree to which variation in folk-taxonomic structure was related to respondents’ expertise in the marine environment, we performed a canonical analysis of principal coordinates (canonical correlation-type CAP routine, Primer 6 ver. 6.1.11; Anderson & Willis 2003). In this procedure, an unconstrained ordination (principal coordinates analysis, PCO) was first performed on the respondent by species-pairs matrix. Next, a canonical correlation analysis was used to draw axes through the PCO ordination (i.e., the multivariate cloud of points) that have the strongest correlation with respondents’ relative expertise (see the Appendix). We calculated correlations (loadings) between the canonical axes and two variables—relative expertise and species-pair similarity—and considered loadings with absolute values >0.3 relevant to interpretation of the results (Tabachnick & Fidell 1996).

The extent to which species are distinguishable and identifiable has potential consequences for public perception of their conservation value (Crozier 1997). Therefore, we calculated the percent frequency of occurrence of respondents who grouped each species with at least one other at the lowest level of folk-taxonomic organization (i.e., identified different species as identical). Species were ranked from most to least frequently aggregated (Table 2).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Appendix
  9. References

Differences between scientific and folk taxonomy structure

Folk taxonomy structure diverged substantially from the scientific taxonomy (Figure 1). A hierarchical cluster analysis of taxonomy structure showed that the intercluster distance was maximized between the scientific taxonomy and all folk taxonomies (SIMPROF: π= 0.92, P= 0.01). Scientific and folk taxonomies differed in their structural complexity (i.e., number of sorting levels, number of groups per level) and characteristics of species groupings. The maximum number of taxonomic levels created by respondents in the pile sort task ranged from 4 to 6 (median = 4), while the scientific taxonomy included seven levels of organization (Figure 2). Particular species were grouped by respondents in ways that differed consistently from the scientific taxonomy. For example, Pacific herring (Clupea pallasii), northern anchovy (Engraulis mordax), Pacific sand lance (Ammodytes hexapterus), and surf smelt (Hypomesus prettiosus) are members of different taxonomic orders but were grouped by 90% of respondents into a “forage fish” or “bait fish” category (e.g., Figures 3 and 4).

image

Figure 1. Nonmetric multidimensional scaling ordination (Kruskal stress = 0.16) of respondents’ folk taxonomies (points; N = 99) and scientific taxonomy (triangle; N = 1). Hierarchical cluster analysis revealed 20 significant clusters (P < 0.05) of respondents; the two largest clusters are indicated by open points (Respondent Group A; N = 18) and shaded points (Respondent Group B; N = 5).

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Figure 3. Folk taxonomy calculated from aggregated pile sort data for Respondent Group A.

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Figure 4. Folk taxonomy calculated from aggregated pile sort data for Respondent Group B.

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Differences among folk taxonomies

Folk taxonomies varied among respondents (N= 99), as illustrated by a nonmetric multidimensional scaling (MDS) ordination that shows a scatter of individuals with unique taxonomies diverging from a tight central cluster of respondents whose taxonomies were similar in structure (Figure 1). A hierarchical cluster analysis provided statistical support for this observed pattern, with 69 respondents grouped into 20 significant clusters (similarity profile test: π= 0.85, P= 0.001) and 30 respondents with folk taxonomies that differed from all others. The positions of the two largest clusters (Group A: N= 18 respondents; Group B: N= 5) are shown on the MDS ordination (Figure 1) and aggregate folk taxonomies were constructed for these two groups (Figures 3 and 4). Focusing on rockfishes (Sebastes spp.), Group A showed a greater degree of differentiation among species and species groups than Group B. Group A was composed of respondents whose experience in the marine environment was derived from a range of activities: 46% of the respondents’ lifetime days of experience were attributed to recreational fishing, 21% to research, 16% to diving, 7% to commercial fishing, 5% to charter fishing, and 5% to other activities. Group B was more homogeneous in terms of expertise, with 66% of lifetime experience-days engaged in commercial fishing, 28% in recreational fishing, and 6% in research. Respondents classified organisms according to a range of criteria, including taxonomic relatedness, morphology, ecological factors, behavior, recreational value, and commercial value.

Variation in folk taxonomy structure was significantly related to respondents’ expertise in the marine environment (canonical correlation-type CAP: m= 12, δ1= 0.71, δ2= 0.44, P= 0.002; Figure 5). The first canonical axis primarily described differences between folk taxonomies of respondents with diving (loading =−0.61) and research (−0.22) experience and those of individuals engaged in recreational fishing (0.43) and other activities (0.25). Folk taxonomy structure showed less separation along the second canonical axis, which correlated weakly with research (0.25), commercial fishing (0.22), diving (−0.21), and other activities (−0.20). Separation of folk taxonomies along the canonical axes was also related to differences in particular species-pair groupings among respondents (Figure 5). For example, the second canonical axis was negatively correlated with eight salmon species-pair groupings (loadings < −0.3) and positively correlated with 13 rockfish species-pair groupings (>0.3), suggesting that structural differences among folk taxonomies could be partly explained by the degree to which respondents grouped salmon versus rockfishes. Species-pair similarities with loadings that had absolute values >0.3 are shown in Figure 5.

image

Figure 5. Results of a canonical analysis of principal coordinates describing the relationship between folk taxonomy structure and characteristics of respondent experience in the marine environment. Points represent individual folk taxonomies coded according to respondents’ principal activity types, determined from their total lifetime days of participation in each activity. Species-pair similarities were correlated with the canonical axes; the number of species pairs with loadings that had absolute values >0.3 is shown in parentheses for each species group. For presentation purposes, species pairs were generalized into species groups according to taxonomic membership and/or functional group: flatfishes (Pleuronectiformes), forage fishes (Clupeidae, Osmeridae, Ammodytidae), jellyfishes (Cnidaria), miscellaneous bottomfishes (Anoplopomatidae, Gadidae, Hexagrammidae, Scorpaenidae), rockfishes (Scorpaenidae), salmon (Salmonidae), and sculpins (Cottidae).

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Differentiation and identification of species

The pile sort results revealed a high degree of species aggregation at the lowest level of taxonomic organization. The majority of respondents (93%) did not distinguish between at least two species at the lowest taxonomic level. For instance, Respondent Group B differentiated two rockfish species (yelloweye Sebastes ruberrimus and quillback S. maliger) and grouped the remaining nine rockfishes (Sebastes spp.) into a single identifiable group described as “rockfish,”“rock cod,” or “red snapper” (Figure 4). Respondents formed an average (±SD) of 5.5 ± 2.3 groups, each composed of 3 ± 0.8 species at the lowest level of organization. The degree to which respondents aggregated the organisms varied by taxa. Among the least aggregated taxa (grouped by <10% of respondents) were marine mammals, cartilaginous fishes, and crustaceans; in contrast, more than 50% of respondents grouped 4 of 5 flatfishes (Pleuronectiformes) and 6 of 11 rockfishes with at least one other species at the lowest sorting level (Table 2).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Appendix
  9. References

Systems for classifying species are found across cultures and serve as guides for interpreting the natural world (Medin et al. 1997; Medin & Atran 2004). In this study, people demonstrated diverse ways of organizing species into taxonomies that differed from a scientific taxonomy. Our results are consistent with studies of folk biological classification systems around the world that have found significant variation in taxonomy structure related to both the type and amount of knowledge people possess (e.g., Boster & Johnson 1989; Medin et al. 1997; Shafto & Coley 2003; Bang et al. 2007). Variation among folk taxonomies was related to respondents’ expertise, suggesting that the ways in which people observed the marine environment led to different perceptions of species diversity within it. Among expert types, the greatest separation of folk taxonomy structure occurred between divers and recreational fishers (Figure 5). Discrepancies in the ways individuals grouped organisms might be explained by their goals and observation methods. For example, recreational fishers distinguished among salmon species more often than divers (Figure 5). The majority of recreational fishers (92%) described salmon as primary target species and, therefore, are likely to have a greater familiarity with them than divers, who infrequently observe salmonids underwater. These differences show that the structure of folk taxonomies alone cannot reveal all aspects of how folk biological knowledge is constituted, how it translates into inferences about broader ecological processes, and how it might affect an individual's decisions in the real world. Furthermore, folk taxonomies are an imperfect representation of how people view species in practice because individuals may use a host of features to identify organisms in nature, including size, texture, and behavior, that are inadequately represented by static images in the pile sort task.

Local ecological knowledge is derived from practical experience and situated in a broader sociocultural context (Sillitoe 1998; Lauer & Aswani 2009). Thus, differential opportunities for acquiring knowledge (Boster 1986; Nazarea 2006) and cultural attitudes toward nature can play a role in the way people perceive biodiversity (Boster & Johnson 1989; Bang et al. 2007). This was reflected in the criteria respondents used to classify species, which included biological characteristics of the organisms (e.g., taxonomic relatedness, morphology, food habits, behavior) and also sociocultural attitudes toward them (e.g., sport value, food value, desirability). For instance, “salmon-eaters” such as dogfish and harbor seals were viewed as competitors by many fishers and, therefore, classified as undesirable. Ecological knowledge, and categorizations of nature therein, may therefore respond to changing cultural attitudes toward species and the environment.

Importantly, knowledge of folk taxonomies is of more than academic interest—it provides information about stakeholder perceptions that can inform the communication of conservation science and policy. The structure of people's folk taxonomies extends to their understanding of patterns in nature (Lopez et al. 1997) and different views of how diversity is organized could lead to differences in perception of species extinction risk. Two components of risk are addressed in policy processes: magnitude (i.e., extinction probability) and acceptable level of risk (Tietenberg 2005). Conflict in natural resource management can emerge because stakeholders have different goals and values and, therefore, different degrees of risk tolerance (Stankey & Shindler 2006). Disagreement among stakeholders in their perceptions of extinction risk may not only reflect differing values, but also fundamental differences in how individuals organize diversity. As an illustration, we summarized relative abundance data for two rockfish species based on how they were classified in folk taxonomies. Greenstriped rockfish (Sebastes elongatus) and bocaccio (S. paucispinis) were viewed as the same species by 40% of respondents. Yet, their populations have undergone very different trajectories along the U.S. west coast: greenstriped rockfish increased 7.9% from 1977 to 2001, while bocaccio declined 16.9% over the same period (Levin et al. 2006). To respondents who did not differentiate between the two species (i.e., they are both “rockfish”), the decline of bocaccio would be masked by an increase in the much more abundant greenstriped rockfish (Figure 6). These individuals might conclude that extinction risk to rockfish is quite low, in contrast to those who perceived bocaccio as a distinct species. Thus, stakeholders may perceive risk in different ways because they are using fundamentally different information to assess it. This could lead to divergent beliefs about the need for conservation of particular species.

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Figure 6. Abundance indices for bocaccio (Sebastes paucispinis), greenstriped rockfish (S. elongatus), and both species combined from 1977 to 2009, calculated as the log-transformed mean catch per unit effort (log10(inline image+ 1)) from bottom trawl surveys along the U.S. west coast.

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There are often practical challenges to garnering public support for the conservation of “rare and little-known species” (Stankey & Shindler 2006); however, efforts aimed at increasing stakeholder awareness of these species could improve interest in their conservation. Folk taxonomies reflect people's expertise, goals, and values and can therefore serve as useful tools for gaining insight into the relative knowledge and importance of species to stakeholders. In a study of manioc farmers, respondents provided more specific names and showed more consistent recognition of plants they viewed as familiar and important (Boster 1986). Here, differences in the degree of species aggregation among taxa provide support for the notion that culturally important species are more identifiable. Pacific salmon, a primary target of fisheries and cultural icon in the Pacific Northwest (Montgomery 2003), were less frequently grouped with other species compared to flatfishes and rockfishes, which are of lower value to anglers (Williams et al. 2010). This relative degree of importance is reflected in the local media: over a 10-year period (2000–2010), the Seattle Times published 796 articles related to Chinook salmon (Oncorhynchus tshawytscha) compared to only 22 for bocaccio rockfish (Sebastes paucispinis), both of which are federally listed endangered species. Chinook salmon were among the most and bocaccio the least distinguishable fishes (grouped at the lowest taxonomic level by 24% and 56% of respondents, respectively; Table 2).

The practical problem of species identification becomes increasingly complex when variation in nomenclature is considered (Table A1). Inconsistency in naming may reflect respondents’ uncertainty in species identities (Boster 1986), and the number of different names given by respondents was generally higher for species that were more frequently grouped with others at the lowest taxonomic level (r= 0.41, Table A1). Rockfishes were viewed by many respondents as morphs or varieties of the same species and the dominant name provided for 6 of the 11 rockfishes was the generic “rockfish” or “rockcod” (Table A1). If “a shared understanding of the referential meaning of words seems to be essential to most other forms of human communication,” as Boster (1986) posited, then understanding how people identify and name organisms is critical for effectively communicating regulations to stakeholders and resource use data back to management agencies.

Table A1.  Names provided by respondents for species used in pile sort and identification tasks (N = 46). The number of respondents who used each name is indicated in parentheses; total number of respondents varies across species because some individuals provided more than one name. Names given by fewer than five respondents were categorized as “Other.”
Scientific nameAccepted common nameaRespondent-given names
  1. * Accepted U.S. common names for fish species (Nelson et al. 2004)

Ammodytes hexapterusPacific sand lanceCandlefish (49); Pacific sand lance/Sand lance (40); Needlefish (15); Other (23)
Anoplopoma fimbriaSablefishSablefish (30); Black cod (22); Lingcod/Ling (9); Pacific cod/Cod (8); Other (24); No name provided (27)
Aurelia auritaMoon jellyfishJellyfish/Jelly (49); Moon jellyfish/jelly (14); White jellyfish (7); Aurelia aurita/Aurelia (5); Other (14); No name provided (13)
Cancer magisterDungeness crabDungeness crab/Dungeness (81); Dungie (10); Other (12)
Cancer productusRed rock crabRed rock crab/Rock crab (89); Crab (5); Other (8)
Citharichthys sordidusPacific sanddabPacific sanddab/Sanddab (29); Flatfish (28); Flounder (26); Sole (14); Halibut (8); Other (15); No name provided (2)
Clupea pallasiiPacific herringPacific herring/Herring (88); Other (15)
Cyanea capillataLion's mane jellyfishJellyfish/Jelly (32); Red jellyfish/jelly (22); Lion's mane jellyfish/jelly (15); Man o' war (9); Cyanea capillata/Cyanea (6); Stinging jellyfish (5); Other (10); No name provided (11)
Embiotoca lateralisStriped seaperchPerch/Surfperch/Seaperch (42); Striped perch/surfperch/seaperch (19); Pile perch (14); Blue striped perch/Blue perch (9); Rainbow perch (7); Other (15); No name provided (7)
Engraulis mordaxNorthern anchovyNorthern anchovy/Pacific anchovy/Anchovy (52); Baitfish (14); Herring (12); Smelt (12); Food/Feed fish (6); Sardine (5); Other (16)
Gadus macrocephalusPacific codTrue cod (48); Pacific cod/P cod (22); Cod/Codfish (18); Pacific tomcod/Tomcod (6); Other (9); No name provided (12)
Hexagrammos decagrammusKelp greenlingGreenling (41); Kelp greenling (34); Kelp cod (11); Other (17); No name provided (13)
Hippoglossus stenolepisPacific halibutPacific halibut/Halibut (77); Flatfish (15); Flounder (9); Sole (5); Other (8)
Hydrolagus collieiSpotted ratfishSpotted ratfish/Ratfish (89); Chimaera (6); Other (5); No name provided (6)
Hypomesus pretiosusSurf smeltSmelt (46); Surf smelt (13); Baitfish (12); Food/Feed fish (7); Anchovy (6); Sardine (6); Herring (5); Hooligan (5); Other (15); No name provided (6)
Lepidopsetta bilineataRock soleFlounder (31); Rock sole (24); Flatfish (22); Sole (17); Halibut (10); Pacific sanddab/Sanddab (5); Other (12)
Leptocottus armatusPacific staghorn sculpinBullhead (24); Sculpin (22); Pacific staghorn sculpin/Staghorn sculpin (19); Lingcod/Ling (9); Bottomfish (5); Other (22); No name provided (11)
Merluccius productusPacific hakePacific hake/Hake (51); Pacific whiting/Whiting (7); Stickleback (5); Other (13); No name provided (31)
Microstomus pacificusDover soleFlatfish (28); Flounder (27); Sole (17); Dover sole (15); Pacific sanddab/Sanddab (6); Other (22); No name provided (4)
Mnemiopsis leidyiComb jellyJellyfish/Jelly (58); Ctenophore (10); Comb jellyfish/jelly (8); Other (12); No name provided (14)
Oncorhynchus gorbuschaPink salmonPink salmon (41); Salmon (23); Humpback/Humpy (19); King salmon (12); Chinook salmon (8); Chum salmon (7); Other (14)
Oncorhynchus ketaChum salmonChum salmon (46); Salmon (25); Sockeye salmon (10); Dog salmon (8); Coho salmon (7); Silver salmon (6); Pink salmon (5); Other (4)
Oncorhynchus kisutchCoho salmonCoho salmon (46); Silver salmon (25); Salmon (23); Chum salmon (6); King salmon (5); Other (10)
Oncorhynchus nerkaSockeye salmonSockeye salmon (51); Salmon (26); Chum salmon (8); Silver salmon (8); Coho salmon (5); Pink salmon (5); Other (7)
Oncorhynchus tshawytschaChinook salmonChinook salmon (53); King salmon (45); Salmon (15); Blackmouth (9); Other (12)
Ophiodon elongatusLingcodLingcod (82); Ling (10); Other (9)
Orcinus orcaOrcaOrca (71); Killer whale (47); Blackfish (7); Whale (5); Other (9)
Parophrys vetulusEnglish soleFlatfish (27); Flounder (25); English sole (21); Halibut (16); Sole (13); Other (16)
Phoca vitulinaHarbor sealHarbor seal (70); Seal (20); Other (11)
Platichthys stellatusStarry flounderStarry flounder (53); Flounder (20); Flatfish (18); Sole (6); Halibut (5); Other (8); No name provided (4)
Rhacochilus vaccaPile perchPerch/Surfperch/Seaperch (46); Pile perch/surfperch/seaperch (34); Shiner perch/surfperch (7); Silver perch/surfperch (6); Other (12); No name provided (6)
Scorpaenichthys marmoratusCabezonCabezon (67); Irish lord (7); Sculpin (7); Other (15); No name provided (6)
Sebastes auriculatusBrown rockfishRockfish/Rockcod (37); Brown rockfish/rockcod (24); Copper rockfish/rockcod (12); Quillback (10); Other (12); No name provided (14)
Sebastes caurinusCopper rockfishCopper rockfish/rockcod (40); Quillback rockfish/rockcod (27); Rockfish/Rockcod (22); China rockfish/rockcod (5); Other (11); No name provided (7)
Sebastes elongatusGreenstriped rockfishRockfish/Rockcod (45); Greenstripe(d) rockfish/rockcod (8); Red rockfish/Red snapper (5); Other (19); No name provided (25)
Sebastes emphaeusPuget Sound rockfishRockfish/Rockcod (36); Puget Sound rockfish/rockcod (20); Bottomfish (5); Red rockfish/Red snapper (5); Other (9); No name provided (26)
Sebastes flavidusYellowtail rockfishRockfish/Rockcod (34); Yellowtail rockfish/rockcod (14); Black rockfish/rockcod (9); Black bass/seabass (8); Seabass (7); Other (29); No name provided (13)
Sebastes maligerQuillback rockfishQuillback rockfish/rockcod (44); Rockfish/Rockcod (28); Copper rockfish/rockcod (19); Other (13); No name provided (7)
Sebastes melanopsBlack rockfishBlack rockfish/rockcod (55); Rockfish/Rockcod (16); Seabass (14); Black bass/seabass (13); Blue rockfish (7); Other (12); No name provided (7)
Sebastes paucispinisBocaccioRockfish/Rockcod (38); Bocaccio (26); Other (21); No name provided (22)
Sebastes pinnigerCanary rockfishCanary rockfish (41); Rockfish/Rockcod (21); Red snapper (8); Yelloweye (6); Other (20); No name provided (13)
Sebastes prorigerRedstripe rockfishRockfish/Rockcod (40); Redstripe rockfish/rockcod (12); Red rockfish/Red snapper (9); Other (22); No name provided (22)
Sebastes ruberrimusYelloweye rockfishYelloweye rockfish/rockcod (53); Red rockfish/Red snapper (18); Rockfish/Rockcod (17); Other (17); No name provided (9)
Squalus acanthiasSpiny dogfishSpiny dogfish/Dogfish (73); Mud shark (12); Dog/Dogfish shark (8); Sand shark (6); Shark (6); Other (13)
Theragra chalcogrammaWalleye pollockWalleye pollock/Pollock (36); Pacific hake/Hake (10); Pacific cod/Cod (9); Baitfish (8); Tomcod (6); Other (16); No name provided (23)
Zalophus californianusCalifornia sea lionSea lion (55); California sea lion (20); Seal (11); Steller sea lion (8); Other (7); No name provided (3)

Conservation relies on a common understanding of species identities, but the fundamental nature of species can vary with people's knowledge, goals, and values. If conservation is to proceed from a common ground of biological understanding, it is important to consider the influence of cultural frameworks on the way people organize ecological knowledge (Bang et al. 2007). Furthermore, to the extent that folk taxonomies reveal something about the way people experience the natural system, they may also help to reconcile differences between what science shows and what stakeholders perceive. A species does not have to be charismatic to be preserved: the melding of social and ecological science provides a road into identifying the cultural salience of rare and little-known species and improving the public discourse on their conservation.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Appendix
  9. References

This research was supported by the Fidalgo Chapter of the Puget Sound Anglers Association, the U.S. Environmental Protection Agency, and NOAA Fisheries. We are grateful to the study participants who volunteered their time and knowledge to this research. We thank the editor and two anonymous reviewers for their insightful contributions to the manuscript. The study was conducted in compliance with the University of Washington Human Subjects Division and adhered to the ethical standards established by the American Sociological Association for social science research.

Appendix

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Appendix
  9. References

Interview methodology and respondent characteristics

Following a chain referral (snowball sampling) approach (Bernard 2006), each interview respondent was asked to identify other potential study participants. Initial contacts were made with university and agency scientists working in Puget Sound, recreational fishing and diving club members, and fisheries coordinators for the Northwest Indian Tribes to disseminate information about the study and recruit participants. Respondents were stratified into three broad areas of expertise (fishing, diving, and research) and we interviewed a minimum of 30 respondents (aged 18 + years) per group. This sample size is typical of ethnographic and folk classification studies (e.g., Boster & Johnson 1989; Lopez et al. 1997; Lampman 2007). In-person interviews were conducted individually with each respondent by the same interviewer (A. Beaudreau). Respondents were asked to report the average number of days per year and total years of participation in five activity types (Table 1). Respondents also provided basic demographic information (age, race, and city or town of residence).

Pile sort tasks were completed by 99 individuals residing in 12 counties bordering Puget Sound in western Washington State. Interview respondents ranged in age from 24 to 90 years, with a median age of 60. Respondents demonstrated a wide range of expertise, including commercial and recreational fishing, charter operation, commercial and recreational diving, research, and other professional experience, which included environmental journalism and fishing- or diving-related entrepreneurship. A majority of respondents (84%) indicated that they had experience in two or more of these categories (Table 1). Relative expertise across categories was determined by normalizing the lifetime days of participation in each activity (average days per year × total years) by the total lifetime days in all activities. The category of highest relative expertise was determined to be the principal activity type for each individual. Recreational fishing was the principal activity type for the majority of respondents (55%), followed by recreational diving (16%), research (14%), and commercial fishing (10%; Table 1).

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  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Appendix
  9. References
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