CRAMP Rapid Assessment. Fish Results
A total of 184 fish transects were sampled at 56 sites over a four year period from May, 9, 2000 to April 29, 2004 on the MHI. The vast majority (95%) of transect locations were chosen using randomly selected, predetermined points generated from habitat maps; or in areas where habitat maps were not available, stations were selected haphazardly in the field. A small number of transects (1.6%) were located in habitats of geological or biological interest or at stations of scientific interest with previously established instrumentation (3.3%).
Overall, 153 species of fishes from 31 families were quantified. The mean number of species recorded per transect is approximately 17, ranging from 1 to 33. The average number of individuals pr hectare is 9820, ranging from 0 to 588. The mean biomass of 0.54 ±1.16 Mg/ha (0.6 tons/ha) ranged from 0 to 14.5 Mg/ha (16.1 t/ha). Range for all parameters measured is wide, exhibiting considerable variability.
Summary of Top Species
The most abundant fish species in the state is Chromis vanderbilti, the black-finned chromis, even though its frequency of occurrence is 52%, recorded in 92 of 184 transects (Fig. 1).
The high ranking is due to the large number of individuals occurring in a school. Two other Chromis species rank within the top ten species in abundance, yet are found on only 13% of transects statewide. These planktivores can be found in large schools throughout the state and are especially abundant along the Kona coast of the island of Hawai’i. Although Chromis spp. ranks high in total numbers of individuals, their biomass is relatively low, due to their small size (Table 1).
The second most abundant species is the Lavender Tang, occurring on 72% of the transects (Fig. 1). This frequency of occurrence is surpassed only by the Saddle Wrasse, which ranked third in abundance and was recorded from more transects than any other species (87%). Other common species found on approximately half of all transects include the Gold-ring Surgeonfish, (52%) and the Yellow Tang, (41%) (Table 1).
Two endemic species are included in the top ten species overall, the Saddle Wrasse, and the Oval Chromis. The Bluestripe Snapper is the only alien species with numbers of individuals large enough to be included in the top ten. Several extremely large schools, found on few transects (12%), account for the high abundance. This is similar to juvenile Parrotfishes, with a frequency of occurrence of only 8%, but ranking sixth in abundance overall, due to large numbers found at a few locations (Table 1). Adult Parrotfishes are identified at the species level while juvenile Parrotfishes are recorded at the genus level due to difficulty in identification.
Figure 1: 10 fish species with the highest abundance (mean number of individuals as a % of the total ± SD, n=184).
The species with the highest biomass is the non-native snapper, Lutjanus kasmira (ta’ape) (Fig. 2). Originally from the Marquesas, this species was introduced by the Hawai‘i Fish and Game for commercial purposes. Contrary to the motive of government introduction, this prolific snapper has not been widely accepted as a food fish among the local population. This consumer resistance has contributed to its widespread ecological success. Its range extends from the shoreline to several hundred meters in depth. They are often abundant in bays, as recorded by Friedlander et al. (2002) who found this species to have the second highest numerical and biomass densities in Hanalei Bay, Kaua‘i. The high ranking of the Bluestripe Snapper in our surveys can be attributed to very large schools on few transects (Table 2).
The Black Durgon has the second highest biomass in the state with a frequency of occurrence of 29%.
The three species of Acanthurids within the top ten species cumulatively place Acanthuridae as the family with the largest biomass (Table 2). The lucrative aquarium fish trade in Hawai‘i included 103 species that were collected statewide in 1995 (Tissot and Hallacher 2003). Eleven of these species accounted for over 90% of the fishes collected in that year. Three of these are included in the rankings of this research for the top ten species with the highest biomass. The Yellow Tang, which accounts for over half of all aquarium fish collected, ranked fifth in abundance and seventh in biomass. Other highly prized aquarium species, the Orangespine Unicornfish and the Goldring Surgeonfish also rank within the top ten species with the highest biomass statewide.
Figure 2: Top 10 fish species with the greatest mean
biomass (% ± SD) (n=184).
Numerical and biomass densities by depth
Although both biomass and numbers of fishes have greater densities at deeper sites, diversity and evenness are slightly lower with depth. Evenness is a component of diversity, where diversity is divided by the total number of species present, for an expression of the abundance of different species. Biomass is higher at depths greater than 10 m (653 kg/ha, 0.72 t/ha) relative to shallower stations (517 kg/ha, 0.57 t/ha). The pattern continues with abundance values at deeper sites (>10 m) having higher numerical densities (11,100/ha) than at shallower sites (<10 m) (9,700/ha).
Summary of Top Families
The family with the greatest recorded abundance is Pomacentridae (Fig. 3, Table 3). The overwhelming majority of the individuals in this family are from the five species in the genus Chromis (80%). Six other species from other genus in this family account for the remaining 20%.
Acanthurids rank nearly as high as Pomacentrids in number of individuals recorded. Although twenty species were recorded within this family, just two species, the Lavender Tang and the Goldring Surgeonfish, comprise over half of all Acanthurids.
The families Labridae and Scaridae are also important in their abundances (Fig. 3). Of the 24 recorded species from the family Labridae, the Saddle Wrasse accounts for nearly 65%, while juvenile parrotfishes, comprise almost half of the fishes in the family Scaridae.
Other dominant families of interest include the Chaetodons and Lutjanids. The fishes in the family Chaetodontidae, commonly found throughout the state, include of 16 recorded species of butterflyfishes. The introduced snapper, Lutjanus kasmira accounts for over 97% of the individuals in the family Lutjanidae.
Figure 3: Top 10 fish families with the highest abundance
(mean number of individuals as a % of the total ±SD).
Nine of the families that rank in the top 10 in abundance are also within the top 10 families in biomass (Table 3). By far the family with the greatest recorded biomass is Acanthuridae with 20 recorded species. Other families with large biomass include Lutjanidae, Balistidae and Scaridae (Fig 4).
The majority (86%) of the biomass in the family Lutjanidae is from a single species, the Bluestripe Snapper. The Black Durgon accounts for 67% of the family Balistidae and the dominant species influencing the biomass of the Scarids is the Bullethead Parrotfish (33%). The inclusion of the family Pomacentridae in the top ten of total biomass is mainly influenced by the 5 species of Chromis (38%) and the Sergeant Major (37%).
Figure 4: Ten fish families with the greatest biomass (% of total ± SD).
Summary of Trophic Levels
The organization of fish assemblages such as trophic structure is more dependant on local than regional conditions. This makes such assemblages more susceptible to local disturbances of overfishing, pollution, and eutrophication, which can cause shifts in trophic levels. Declines in apex predators are highly evident when comparing feeding guilds in the MHI with the Northwestern Hawaiian Islands (NWHI). Large apex predators, primarily jacks and sharks, comprise over half of the total biomass in the NWHI (54%), while only a small percentage (3%) is represented in the MHI (Friedlander and DeMartini 2002). Other fish assemblage characteristics (density, diversity, endemism, and richness) are also dramatically different, pointing to the heavy exploitation in the MHI.
This study is in concordance with previously published data in the MHI. It recorded habitats that are heavily dominated by herbivorous fishes and significantly fewer piscivorous fishes, in both numbers of individuals and biomass (Figs. 5, 6).
Figure 5: Mean abundance (mean number of individuals as a % of total) by trophic levels in MHI
Figure 6: Mean biomass as a percentage of total by trophic levels in MHI
This is in sharp contrast to the NWHI where piscivores dominate, comprising
nearly 75% of the fish biomass. Typical of the MHI, the percentage of
piscivores in this study is only 1.4% of the total number of individuals and
11.7% of the total biomass. Planktivores make up nearly a third (30.1%) of
the abundance due to the large number of Chromis but only 6.7% of the total
biomass, due to their small size. Approximately a quarter of the total
numbers (25.7%) and biomass (23.3%) are invertebrate feeders. As a result of
human fishing pressure and environmental degradation in recent decades,
herbivores clearly dominate in the MHI, with well over half of the total
biomass (58.3%) and an overwhelming percentage of individuals (42.8%)
Summary of Endemic Status
Both terrestrial and marine endemism in the Hawaiian Islands is high compared to the rest of the world, due to geographic isolation which restricts gene flow and favors speciation.
Endemism is a biologically relevant attribute in examining fish assemblages. It relates to conservation of biodiversity, genetic connectivity and spatial patterns of recruitment. Historically, endemic comparisons have been based solely on presence/absence data due to lack of quantitative data. Yet, endemism evaluations are more statistically meaningful when incorporating numerical and biomass densities which allow for elucidation of spatial patterns (Friedlander and DeMartini 2004).
Endemism recorded in this study (23.0%) is highly consistent with published values for fish endemism (23.1%) in Hawai‘i based on the most comprehensive estimate of reef and shore fishes (Randall 1998). This provides supporting evidence that the sample size of this study was large enough to accurately determine endemic status.
A total of 32 endemic species were recorded in the transect sampling. The species contributing the majority of individuals (36%) and biomass (20%) is the Saddle Wrasse, commonly observed at more stations than any other species (frequency of occurrence=87%).
Indigenous fish species, which are taxa native but not unique to the
Hawaiian Islands marine environment, comprise the vast majority of the
abundance (7.2 per ha x 1000 and 73% of the total) and biomass 417 kg/ha
(0.46 t/ha) and 77% of the total of fishes recorded (Table 5 and Fig.
7).Only 4% of the total abundance and 11% of the total biomass can be
attributed to non-native species
(Fig. 7). The alien species recorded include two introduced snappers,
the Bluestripe Snapper, Lutjanus kasmira, (ta’ape) and the Blacktail
Snapper, L. fulvus (to‘au) and a grouper, the Peacock Grouper,
Cephalopholis argus (roi). Since most snappers occurring in Hawai’i have
historically been highly prized food fish (‘opakapaka, ehu, onaga), but
inhabit depths of over 60 m, the Hawai‘i Fish and Game introduced three
shallow water snappers from the South Pacific and Mexico in the mid 1950s
and early 1960s in hopes of stimulating the commercial fisheries. These are
among the 11 demersal species introduced within a 5 year period. L.
kasmira and L. fulvus (to’au) have become widely established,
while the third species, L. gibbus is extremely rare. None of these
species has been widely accepted as a food fish among the local population
or become successful in the commercial fisheries and the ecological effects
of these aliens have only recently been realized. Histological reports from
Work et al. (2003) found that nearly half of the ta’ape examined from O‘ahu
were infected with an apicomplexan protozoan. Furthermore, 26% were infected
with an epitheliocystic-like organism with potential transmission to endemic
reef fishes. In Addition, ta‘ape from Hilo were found to host the nematode
Spirocamallanus istiblenni (Font and Rigby 2000). Species of goatfish
(weke and kūmū), a popular food fish for humans, may be displaced by ta’ape,
which has also expanded its range into deeper water where ‘opakapaka reside.
Friedlander and Parrish (1998a) looked at patterns of habitat use to
determine predation and resource competition between ta’ape and several
native species within Hanalei Bay, Kaua’i, but found no strong ecological
Figure 7: Biomass (%) and number of individuals (%) by endemic status.
The more common of the non-native snappers, ta‘ape, was introduced from the Marquesas in 1958, while to’au was imported two years earlier in 1956. Although only 3,200 ta’ape were released on the island of O‘ahu, they have increased their range to include the entire Hawaiian archipelago. The peacock grouper, Cephalopholis argus introduced by the state for commercial purposes in 1956 from Moorea, French Polynesia, has had more popularity as a food fish than the introduced snappers. The large size of this species is responsible for a biomass percentage that is 3 times the percent abundance (Table 5).
There are higher numerical and biomass densities of endemic and indigenous fishes at shallower depths. In contrast, introduced species are more prevalent in deeper waters. Endemism is twice as high at depths <10 m (14.3%) than at depths >10 m (7.9%) while introduced species have more than 10 times the densities at sites > 10 m (K-W test, p<0.01).
Summary of Size Classes
Size structure of fish populations can be an informative means of characterizing fish communities both spatially and temporally. Variations in recruitment processes such as production, transport, settlement, and mortality, can be revealed in missing or reduced size classes. Lack of recruitment can limit population size. Variations in size categories can explain variation in site attached fishes. The condition of different size assemblages can provide clues to causal mechanisms and links to environmental factors. Certain anthropogenic impacts can be detected, including the most influential impact of overfishing, by quantifying absence or highly reduced abundance of food fishes in the larger size classes.
Absence or overabundance in certain size groups can predict future trophic structure and species composition. Size classes can directly influence competition, predation, and shifts in community structure.
The high abundance of fishes in the smaller size class (15.6%) is due to a large numbers of Chromis. Although there are large numbers in the smallest size class, they comprise a very small percentage of the total biomass (0.3%). The opposite effect is represented in the largest size class where few fishes (19%) account for nearly 70% of the total biomass. The majority of fish abundance is in the 5-15 cm range (Fig. 8).
Figure 8: Size classes of fishes by biomass (% of total) and abundance (% of total).
Summary Statistics by Transect and Location
Transects within sites vary due to depth and substrate. All site information is reported by averages of pooled transects. Several sites rank consistently high in many of the fish parameters. Molokini Island, Maui ranks within the top five for diversity, evenness and biomass and Kanahena Bay (Āhihi Kīna‘u), Maui scored consistently high for diversity, evenness, and number of species. Hanauma Bay, O‘ahu also ranks within the highest in the state for diversity and number of species. These three locations are all fully protected marine reserves. Molokini and Hanauma Bay are both state Marine Life Conservation Districts, where fishing is strictly prohibited, and Kanahena Bay is located within a federal Natural Area Reserve, where only extremely limited subsistence fishing is allowed.
In contrast, sites with a history of high anthropogenic impacts scored consistently low among the 56 locations surveyed. Waikīkī, O‘ahu and Pelekane Bay, Hawai‘i are among the bottom five sites for all five parameters summarized. Kamiloloa, Moloka‘i ranks in the bottom five for abundance, biomass and mean number of species. Pelekane Bay and Kamiloloa have a long history of human induced sedimentation while Waikīkī has had chronic, sustained anthropogenic impacts, both of which clearly affect fish populations.
Mean number of species
Of 56 sites, the sites with the highest mean number of species are Molokini (28), Kanahena Bay (Āhihi Kīna‘u) (28) and Honolua (27), Maui, Hanauma Bay, O‘ahu (27), and Nenue, Hawai‘i (25). These top five sites are all marine protected areas. In contrast, the bottom five sites with the fewest mean number of species recorded all have open access to fishing. High sedimentation due to runoff and nearby dredging of Kawaihae Harbor placed Pelekane Bay, Hawai‘i at the bottom of the hierarchy with an average of 1.7 species per transect. The majority of transects at Pelekane Bay had no recorded fish. Due to the heavy anthropogenic use of Ala Wai (12) and Waikīkī (8.8), areas of O‘ahu ranked in the bottom five. Unlike the other locations with the fewest species, Ka‘alaea (9.5) (Waiāhole) and Manana Island (10.5) (Rabbit Island), O‘ahu have few species but high numbers of fishes.
Diversity is an important factor in many ecological and conservation issues. It can be an important factor in assessing the effectiveness of management regimes. Reductions in diversity can be indicative of overfishing, which selectively removes specific species. Other anthropogenic impacts, such as eutrophication, can result in phase shifts that strongly affect fish diversity.
The highest diversity is found at Molokini (3.0), Kanahena Bay (Āhihi Kīna‘u) (3.0) Maui, Hanauma Bay (2.5) and Kahe Point, O‘ahu (2.5), and Leleiwi, Hawai‘i (2.5). Three of these five locations are marine protected areas. The lowest diversity is found at Pelekane Bay (0.25) with few recorded fishes and Pu‘uhonua o Honaunau (1.27), Hawai‘i due to a small sample size and a large school of ta‘ape recorded in the survey of that site. On the island of O‘ahu, Ka‘alaea (1.1), and Waikīkī (1.2) rank low in diversity. The sand substrate encountered on the majority of the transects at Hulopo‘e, Lāna‘i had few fishes and thus, low diversity (1.1).
The top evenness scores are from Molokini (0.9) and Kanahena Bay (0.9) on Maui. Kamiloloa (0.9) on Moloka‘i, Ka‘apahu (0.9), located on the northwest side of Lāna‘i, and Nualolo Kai (0.8) on the Na Pali coast of Kaua‘i also rank high. The sites with the lowest evenness are Hulopo‘e (0.4) on Lāna‘i, Pelekane Bay (0.4) and Pu‘uhonua o Honaunau (0.4) on Hawai‘i, and He‘eia (0.5) and Waikīkī (0.5) on O‘ahu.
The top five sites with the highest numerical densities are Hulopo’e (26,520/ha) on Lāna’i, Ka’alaea (26,080/ha) and Moku o lo’e (18,560/ha) on O’ahu, Pu’uhonua o Honaunau (20,190/ha) and Kawaihae (19,600/ha) on Hawai’i. These sites have an order of magnitude higher abundance than the sites at the other end of the range (Figure 4.9). In sharp contrast, the 5 sites that have the lowest fish abundance are Waikīkī (3220/ha) on O’ahu, Hanalei (3960/ha) on Kaua’ ‘i, Kamiloloa (4640/ha) on Moloka’i, Lehua Island (5000/ha) near Ni’ihau, and Laupāhoehoe (5160/ha) on Hawai’i. Kamiloloa and Waikīkī have high levels of anthropogenic impacts that reduce fish populations, while Lehua and Laupāhoehoe are exposed to high wave energy and have low coral cover (<10%) and spatial complexity (Fig. 9).
Figure 9: Number of fishes (ha x 1000) by location
These factors are all correlated to fish densities. The low biomass of fishes in Hanalei Bay is probably related to station selection and a low sample size that does not represent the bay as a whole. The two stations surveyed are located on the reef flat in close proximity to one another. Friedlander and Parrish (1998) found the lowest biomass to occur on the reef flats.
The sites with the greatest biomass include Molokini Island (2,500 kg/ha), Kakahai’a (2,000 kg/ha) on Moloka’i, Honolua N (1,600 kg/ha) on Maui, Kawaihae (1,500 kg/ha) on Hawai’i, and Ki’eki’e (1,400 kg/ha) on Ni’ihau. These sites have nearly 20 times the biomass of those ranking near the bottom. Sites in the lower end of the range include: Pelekane Bay (54 kg/ha) on Hawai’i, Ma’alaea (127 kg/ha) on Maui, Kamiloloa (127 kg/ha) on Moloka’i, and Ala Wai (145 kg/ha) and Waikīkī (154 kg/ha) on O’ahu (Fig. 10).
Figure 10: Biomass of fishes (Mg/ha) by location.
The sites with the lowest biomass in the state all have historically, or are currently, experiencing strong anthropogenic influences. Dredging (Ma’alaea and Pelekane), sedimentation from runoff (Pelekane and Kamiloloa), and overfishing (Waikīkī and Ala Wai) can have lasting effects on fish populations.
Summary Statistics by Island
The island of Hawai’i has the highest number of fishes per hectare (11.6) (Fig. 11). This is due to the high number of Chromis species that are particularly abundant on the Kona coast. This may also be attributed to a disproportionate sample size, with the majority of the sites on that Maui has the highest biomass, followed by the more remote islands of Ni’ihau, Moloka’i, and Kaho’olawe. A large sample size and evenly spread distribution of sites may be a factor in Maui’s high rating. The islands of Kaho’olawe and Moloka’i also have the high fish diversity.
Although the island of O’ahu ranks fifth in abundance, it is last in order for all other parameters explored, well below state averages for all fish assemblage characteristics. The slightly elevated numerical density rating is in large part due to high numbers of juvenile parrotfishes at the Kāne’ohe Bay sites. Although over half of sites surveyed on O’ahu are marine protected areas (56%), the low biomass (0.43 Mg/ha) attests to the heavy fishing pressure overall compared to the outer islands. O’ahu is also at the bottom of the hierarchy for fish diversity and evenness (Fig. 11). Although overfishing is the overwhelming cause of reduced fish populations, other contributing factors include pollution, coastal development, dredging, and sedimentation along with poor management practices.
Figure 11: Mean summary statistics by island.
Fish Abundance, Biomass, and Diversity
Non-parametric Spearman correlations were used to determine which variables were most strongly correlated with fish assemblage characteristics (Table 6). There is a moderate negative correlation between macroalgae and herbivores (r2=0.46). Many sites without macroalgae had a high number of herbivores. Over half (62.5%) of the stations had no recorded macroalgae.
General linear multiple regression was used to determine the best model for predicting fish biomass, numerical abundance and diversity. To obtain a parsimonious model, many of the variables that made only a small contribution to explaining the variability were excluded. This facilitates ecological interpretation and management application.
The regression model using fish biomass as the response variable was significant among the stations (R2 (adj)=58.6%, p=<0.001). The variation in biomass is best explained by 9 variables: organics, rugosity, calcareous and turf algae, total coral cover and diversity, silt, human population within 5 km, and management status. A negative relationship exists between biomass and human population within 5 km and organics, while all other variables are positively correlated with the response (Table 7).
Multiple regression, with numerical abundance of fishes as the response
identifies 8 explanatory variables: rugosity, organics, total coral cover
and diversity, coralline and turf algae, Montipora capitata, and management
status. This model was statistically significant (p=<0.001) and explained
54.1% of the variation in fish abundance. All significant variables except
organics and Montipora capitata are positively correlated with the number of
fishes observed (Table 7).
The factors that most strongly influence fish diversity are organics, human
population, coral cover, wave direction, turf, sand, rugosity, and coralline
algae (R2 (adj)=49.2%, p=<0.001).
Fish populations are strongly related to coral communities. A non-metric multi-dimensional scaling (MDS) plot of 154 stations are ordinated by the fish community factors of biomass, number of individuals, and diversity. Those stations with low fish assemblage values cluster away from the majority of the stations. Total coral cover is superimposed on the fish characteristics to depict a relationship between them, as shown by the gradient of the size of bubbles increasing towards the right. Impaired sites are characterized by low fish community characteristics (sites in upper left) and low coral cover (smaller bubble size).
Figure 12: Non-metric multi-dimensional scaling (MDS) plot of stations ordinated by fish community factors and showing coral cover gradient.
To determine if fish abundance and/or biomass can determine the degree of overfishing, a Kruskal-Wallis test was used. All species of fishes and select target species that represent popular food fish were used in the analyses. The target species included: surgeonfishes: palani and pualu, snappers: (uku and wahanui), grouper: roi, jacks: (papio, opelu and kahala), parrotfishes: (uhu), squirrel and soldierfishes (menpachi and ‘ala ‘ihi), big-eyes: (‘āweoweo), goatfishes: (weke, weke ula and moano), rudderfish: (nenue) and unicornfish: (kala). Degree of fishing pressure at each site was based on marine protection status and subjective expert knowledge. Sites were placed into one of three levels of fishing pressure: high, medium, and low.
Fishing pressure and protection status used as categorical variables were statistically significant (p=0.03) for numerical fish abundances although not for fish biomass. This pattern is consistent when using all species of fishes or only selected target species. Post-hoc Mann-Whitney tests found high/low fishing pressure for numerical abundances (p=0.02) and biomass (p=0.03) to be statistically different. The highest level of protection was also found to be statistically different from the lowest level for both numbers of fishes (p=0.03) and biomass (p=0.03). High variability between samples prevents stronger correlations. As sample size increase, this variability will decrease, allowing stronger gradients to be revealed.
Power and Sample Analysis
To determine whether the current sample size is sufficient to detect important differences with high probability in fish assemblage variables, a retrospective examination of statistical test power and sample size was conducted, with Minitab 13.2.
Fish counts ranged from 0 to 638 individuals per transect and biomass ranged from 0 to 14.6 (Mg/ha). Two influential observations were detected for fish biomass. An anomalous school of 500 Lutjanus kasmira (ta’ape) were recorded from Kakahai‘a, Moloka‘i and 400 Decapterus macarelus (ōpelu) were recorded from Pelekane Bay on the island of Hawai‘i. Eight abundance outliers with counts over 300 included mainly small fishes from the genus Chromis.
In testing the power for detecting differences in counts of fishes, the level of significance was set at 0.05, to allow for a 95% chance of detecting an effect if one does exist (Sheppard 1999). The standard deviation was high (7,900), close to the mean of the population (9,800 individuals). At the sample size tested (184), a detection difference of 1,900 individuals per ha or 1/5th of the mean is possible over 90% of the time. This shows some confidence that the sample mean represents the true population mean. This level of confidence is adequate in detecting relative values of fishes to compare between sites, as was done in this study. To quantify absolute values by detecting a difference from the true population mean of 1,000 individual per ha, or 1/10th of the mean, 658 transects would be necessary. At the current sample size of 184, there is only a 40% ability to detect differences correctly at this level.
The level of power in detecting fish biomass is much lower than in detecting numerical abundances. The variability is extremely high, over three times the mean (SD=1.8), with a sample mean of 0.5 Mg/ha. At a sample size of 184 (as used in these analyses) a power value of 0.9 and a level of significance of 0.05, the difference detectable is approximately half the mean. To detect a 0.2 Mg/ha difference, which is one-third of the mean, 431 transects would be necessary.
Fish biomass and abundance are strongly correlated with topographical relief, coral cover and diversity, human population, organics, management status, and coralline and turf algae.
In concert with this study, prior research has recognized the importance of topographic relief in the structure of fish assemblages throughout the world (Carpenter 1981; Holbrook et al. 1990) and in Hawai‘i (Friedlander and Parrish 1998a). It is evident that fish populations are highly associated with spatial relief for several reasons.
Since habitat heterogeneity is important in structuring fish assemblages, an index of fish abundance may be obtained through rugosity measurements. There are clear advantages to this indirect measure of abundance.
A large sample size is necessary due to the high variability among fish populations, many rare, cryptic or mobile species can be under reported, and the power to accurately detect absolute fish abundances can be extremely low. Although the use of a rugosity index can not substitute for fish abundance data, it can serve as a relative indicator of differences between sites over large spatial scales where abbreviated surveys are necessary.
Spatial complexity can be an indicator in determining the distribution of fish size. For optimum protection, fishes select shelter that complements their size, reducing the risk of predation. Size of voids in reef structure are positively correlated to numerical and biomass densities (Hixon and Beets 1989).
Rugosity measurements are heavily influenced by coral cover and diversity, which are also found in this study to be highly correlated with fish populations. Thus, measurements of spatial complexity may prove to be a rapid way to assess both coral and fish communities.
Anthropogenic effects of overfishing of Hawaiian reef fishes have been extensively documented (Shomura 1987; Gulko et al. 2000; Brown et al. 2003; Friedlander et al. 2003). Results from this study determined that heavily populated areas near marine environments appear to have a large, negative effect on near-shore fish stocks on a statewide scale. This was also found to be true for coral community characteristics.
Organic material, which can be derived from anthropogenic sources, is also negatively correlated with fish populations, while marine protected sites, with reduced human impact, are shown to have the opposite effect.
Regions sheltered from high wave energy have previously been reported to maintain higher fish populations and exhibited greater species diversity in Hawai‘i (Friedlander and Parrish 1998b; Friedlander et al. 2003). This inverse relationship between wave exposure and fish standing stock is most likely attributed to reduced habitat complexity in high energy environments. The seasonal variability in wave impacts can structure the physiography of reefs, reducing habitat and spatial complexity through a dominance of encrusting coral forms of corals. Although the influence of wave height in this research is related to fish assemblage characteristics, it is not among the most highly correlated factors controlling fish communities.
Herbivorous Association with Algae
No strong correlation between macroalgae and herbivores was found. Large numbers of herbivores were recorded from sites lacking macroalgae. This reflects the fact that most of the Acanthurids and Scarids which comprise a large percentage of the fishes recorded on the transects do not feed on macroalgae. Many of the fishes in these families feed on turf and filamentous algae. This is supported with statistically significant correlations between turf and fish densities.
Windward and Leeward sides of islands were found to have significant differences in some assemblage characteristics. More fishes in the smallest size class (<5 cm) occur on the Windward sides of islands (K-W test, p=0.009).
Fish abundance and distribution is stratified by depth, making this habitat variable an important measure. Consistent with other research (Friedlander and Parrish 1998b), fewer species, but more fishes were found at deeper depths. The mean number of fishes observed per transect, and the numerical and biomass densities, are considerably higher at stations >10 m than in shallower waters, while diversity and evenness are statistically similar.
High variability exists among fish populations. Spurious results from small sample sizes can occur due to the high spatial and temporal variability of fish populations. The power to detect relative differences between sites for numerical abundance of fishes is sufficient; however, sample size must be substantially increased to confidently determine differences in fish biomass among sites. Thus, confidence in the validity of the formal tests conducted for biomass is low. The power to detect differences in fish biomass in this study is extremely low, due to high variability. The high variability is due to the different substrates selected at each site, which strongly correlate with fish populations. Although this is useful in habitat classification for the development of bioindicators to detect anthropogenic effects, and increases the statistical power in developing reference sites, it substantially reduces the statistical power of other tests conducted by increasing the variance.
To provide data that is truly representative of the average biomass, the sampling design would have to include several hundred more transects. Projected, continued surveys of additional sites will provide the increased statistical power needed to detect important differences in biomass and reduce variability. The sampling time, effort and cost involved in most surveys is typically restrictive in conducting assessments over large areas, due to the large number of transects necessary to detect differences. It is possible that fewer transects coupled with rugosity measurements can detect relative differences between sites, although intermittent, large schools of fish may provide anomalous outliers that can strongly influence results.
Last Update: 04/21/2008
By: Lea Hollingsworth
Hawai‘i Coral Reef Assessment & Monitoring Program
Hawai‘i Institute of Marine Biology
P.O. Box 1346
Kāne‘ohe, HI 96744