Environmental Biology of Fishes

, Volume 88, Issue 4, pp 349–359 | Cite as

Utility of mesohabitat features for determining habitat associations of subadult sharks in Georgia’s estuaries

Article

Abstract

We examined the affects of selected water quality variables on the presence of subadult sharks in six of nine Georgia estuaries. During 231 longline sets, we captured 415 individuals representing nine species. Atlantic sharpnose shark (Rhizoprionodon terranovae), bonnethead (Sphyrna tiburo), blacktip shark (Carcharhinus limbatus) and sandbar shark (C. plumbeus) comprised 96.1% of the catch. Canonical correlation analysis (CCA) was used to assess environmental influences on the assemblage of the four common species. Results of the CCA indicated Bonnethead Shark and Sandbar Shark were correlated with each other and with a subset of environmental variables. When the species occurred singly, depth was the defining environmental variable; whereas, when the two co-occurred, dissolved oxygen and salinity were the defining variables. Discriminant analyses (DA) were used to assess environmental influences on individual species. Results of the discriminant analyses supported the general CCA findings that the presence of bonnethead and sandbar shark were the only two species that correlated with environmental variables. In addition to depth and dissolved oxygen, turbidity influenced the presence of sandbar shark. The presence of bonnethead shark was influenced primarily by salinity and turbidity. Significant relationships existed for both the CCA and DA analyses; however, environmental variables accounted for <16% of the total variation in each. Compared to the environmental variables we measured, macrohabitat features (e.g., substrate type), prey availability, and susceptibility to predation may have stronger influences on the presence and distribution of subadult shark species among sites.

Keywords

Essential fish habitat Subadult sharks Canonical correlation analysis Discriminant analysis 

Introduction

Along the east coast of the United States and the Gulf of Mexico, shark nurseries have been documented in shallow bays, estuaries, and lagoon systems for a number of species. Nurseries can be classified as primary or secondary: primary nurseries are areas where parturition occurs and neonates are present, and secondary nurseries being areas used by older juveniles (Bass 1978). The use of nursery areas by many shark species is influenced by both biotic and abiotic factors; however, current studies suggest biotic factors, specifically food abundance and predator avoidance, are the primary reasons for nursery use (Branstetter 1990; Castro 1993; Heupel and Hueter 2002; Simpfendorfer and Milward 1993).

Multi-species nurseries have been reported off the South Carolina coast (Castro 1993; McCandless et al. 2007), in Florida’s Indian River Lagoon (Snelson and Williams 1981), and in the Gulf of Mexico (Carlson and Brusher 1999; McCandless et al. 2007). Single species nurseries that have received some intensive study include: the lemon shark nursery in the Bahamas (Morrissey and Gruber 1993), sandbar shark nurseries in areas such as the Gulf of Mexico (Carlson 1999), Chesapeake Bay (Conrath and Musick 2007; Grubbs and Musick 2007), and Delaware Bay (Merson and Pratt 2001; Rechisky and Wetherbee 2003), the blacktip shark nursery near Tampa Bay, Florida (Heupel and Hueter 2002), and the Atlantic sharpnose shark nursery in the Gulf of Mexico (Parsons and Hoffmayer 2005). To date, much of this work has focused on identifying spatial and temporal aspects of the nurseries as well as occurrence and distribution of various species.

Although abiotic factors may have limited effects on nursery use, they are potentially useful for defining the physical boundaries for some nurseries (Simpfendorfer et al. 2005; Grubbs and Musick 2007). As the presence of anthropogenic factors associated with water use and coastal development increases, knowing what abiotic factors influence the distribution of fish species is becoming increasingly important.

Although the importance of certain abiotic factors (e.g., water temperature, salinity, and dissolved oxygen) has been inferred in some studies of subadult sharks (i.e., neonates and/or juveniles), a few have quantitatively assessed the effects of these factors on the presence or abundance of shark species. Salinity affects the presence and number of neonate bull shark in estuaries in southwest Florida (Simpfendorfer et al. 2005) and juvenile sandbar shark in the Cheaspeake Bay (Grubbs and Musick 2007). Depth affects the distribution of both juvenile lemon shark (Morrissey and Gruber 1993) and juvenile sandbar shark (Rechisky and Wetherbee 2003; Grubbs and Musick 2007). Dissolved oxygen levels affect the distribution of juvenile sandbar shark along the eastern shore of Virginia (Conrath and Musick 2007). Temperature affects the occurrence and number of juvenile sandbar shark in the bays along the east shore of Virginia (Conrath and Musick 2007) and in the northeastern Gulf of Mexico (Carlson 1999), as well the presence of juvenile blacktip shark on the west coast of Florida (Heupel and Hueter 2002) and juvenile lemon shark in the Bahamas (Morrissey and Gruber 1993); however, temperature correlations with abundance can be temporal as well as spatial.

During the late spring and summer, Georgia’s estuaries are inhabited by multiple species of subadult sharks. Prior to Belcher (2008), these potential nursery areas had not been investigated. The purpose of this study was to determine if environmental variables, specifically salinity, water temperature, dissolved oxygen, current speed, water depth, and turbidity, could be used to define the habitat use of subadult sharks in Georgia’s estuaries.

Materials and methods

Study area

Georgia’s coastline is approximately 161 km (100 miles) long and extends from the St. Marys River in the south (dividing GA and FL) to the Savannah River in the north (dividing GA and SC). The outer coastline is comprised of eight barrier islands that separate the mainland from the Atlantic Ocean (Johnson et al. 1974). The openings between the islands form the entrances to nine estuaries. The nine estuaries are interconnected by a maze of tidal creeks and rivers, many of which are navigable and are part of the Intra-Coastal Waterway (Fig. 1).
Fig. 1

Map of study area and longline stations fished in Georgia’s estuaries during 2001–2002. Insert shows location of study site relative to the southeastern region of the United States

To ensure sampling provided adequate spatial coverage, longline sets were made in multiple sound systems and in different areas within the sound. Six of the nine Georgia estuaries were studied from April 15 through September 30 during 2001 and 2002. The period from April 15 through September 30 corresponds with the pupping seasons of shark species commonly captured in Georgia’s waters. Sampling occurred in Doboy, Sapelo, St. Catherines, and Ossabaw systems in 2001, and in Wassaw and Cumberland systems in 2002. Each sound system was sampled two days during each month, with four sets made each day. Sampling areas in all estuaries were divided into sound sites (closest to the mouth) and creek/river sites in the lower reaches adjacent to the estuary. Typically, two longline sets were made in the sound areas, and two were made in the river areas each day. Any deviations from this protocol were because of inclement weather.

Sampling methodology

Longline sampling was conducted under the COASTSPAN protocol established by the Apex Predator Investigation (USDOC 1997). Stations were sampled during daylight hours with a hand-retrieved longline, which consisted of 305 m of 6.4 mm braided mainline and 50 gangions. Each gangion was comprised of a longline snap attached to 50 cm of 6.4 mm braided nylon connected to 50 cm of 1.6 mm stainless steel cable with a non-offset 12/0 Mustad® circle hook with a depressed barb at the terminal end. Hooks were baited with pieces of squid, and gangions were attached to the main line at 4.5–6.1 m increments. The gear was set along the bottom and secured via standard Danforth® multi-purpose anchors (4.1 kg) at both ends. The mainline was marked with two fluorescent buoys (30.9 kg buoyancy rating) attached to each anchor. The longline was deployed and recovered as described in Belcher (2008).

Initially, the longline was allowed to fish for one hour. The 1-h set time maximized the catch as very few recovered sets had hooks with remaining bait (less than 10% of hooks). However, during June 2001, soak time was reduced to 30 min because of high mortalities in the smaller shark species. The shorter set time reduced mortality and did not appear to affect catch rates, as the number of baited hooks recovered after 30 min remained negligible.

Shark sampling

All sharks collected during a longline set were identified to species. All sharks captured were removed from the hook and returned to the water as quickly as possible to ensure maximum survival. All sharks were sexed, measured for both fork and total lengths (FL and TL in cm, respectively), weighed (kg), and umbilical scar characteristics were recorded. All sharks capable of swimming were tagged prior to release. Sharks were classified as neonates or juveniles based on the presence of an umbilical scar and the degree of healing. Small sharks with umbilical scars that were open or incompletely healed (i.e., black in color but not “open” or a gray line is visible) were classified as neonates. All others were classified as juveniles based on species- and sex-specific lengths-at-maturity presented in Castro (1983). Catch per unit effort (CPUE) was calculated as the number of subadult sharks per 50 hooks (i.e., the number of subadult sharks caught during each longine set), and were calculated for the commonly occurring species as well as for the total catch of subadults of all species. Because CPUEs calculated for the subadult shark data were highly skewed and could not be normalized with traditional transformations, catches were coded as binomial variables representing presence and absence for both the aggregate catch of subadults and for the commonly encountered species.

Environmental data

Georgia’s estuaries are classified as well-mixed estuaries that demonstrate homogeneous measures of water quality between the surface and bottom of the water column (Verity et al. 2006). Five environmental variables were measured at the beginning of each longline set. Dissolved oxygen (mg·L−1), water temperature (°C), and salinity (ppt) were measured within 1 m of the surface with a YSI® 85 m. Current velocity (measured in ft·s−1; converted to m·s−1) also was measured within 1 m of the surface with a Marsh-McBirney® model 511 current meter. Turbidity (NTU) was measured with a Lamotte® series 2020 handheld turbidity meter. Turbidity was measured for samples collected with a Van Dorn bottle within 1 m of the bottom. Water depth (measured in ft; converted to m) was measured at the beginning and end of each set with a fathometer and a stern-mounted transducer. The average depth was calculated for each set and used in the analyses.

Environmental data were evaluated for normality, linearity, homogeneity of variance and multicollinearity. Normality was evaluated by examining skewness and kurtosis values for each variable. When both values fell between ±1, the assumption of normality was supported (Mertler and Vannatta 2005). Linearity was evaluated qualitatively by using bivariate scatterplots wherein if the shape of the cloud of data points was not elliptical, the relationship was determined to lack linearity (Mertler and Vannatta 2005). Data transformations were applied to those variables that were non-normal or exhibited nonlinear relationships with the remaining variables. Once the data were corrected for normality and linearity, they were examined for homogeneity of variance. In the multivariate setting, homogeneity of variance is evaluated via Box’s M test for equality of variance-covariance matrices (Mertler and Vannatta 2005). In the case of the canonical correlation analysis, multicollinearity was examined via the computation of the squared multiple correlation for each individual environmental variable with the remaining environmental variables. If the value is close to 1, the variable is considered strongly related to the others, thus indicating multicollinearity and redundant information among the variables (Mertler and Vannatta 2005).

Turbidity was the only environmental variable with values that were not normally distributed; these data were transformed by taking the square root of the value to correct for a moderately positive skew (Mertler and Vannatta 2005). Bivariate plots for each of the pairings of the environmental variables in the model, including the transformed turbidity, indicated that linearity was present among the variables. The assumptions of homogeneity of variance and absence of mutlicollinearity were supported. Mahalanobis distances for environmental data were calculated for all longline sets prior to analysis to determine the presence of multivariate outliers (Tabachnick and Fidell 1996). Multivariate outliers were not detected.

Two multivariate analyses were used to analyze the data from this study. Canonical correlation analysis was used to describe the association between the water chemistry, depth, and current velocity and the presence/absence of common shark species. Canonical correlation analysis is akin to multiple regression analysis, except that more than one dependent variable is predicted (Tabachnick and Fidell 1996). Canonical variates, similar to those produced in principal component analysis, are produced for each dataset, with the additional caveat that the resulting variates are strongly correlated with each other (Manly 2005). Canonical redundancy analysis also was applied to determine how much variance the canonical variates from the environmental set extract from the species presence/absence set.

Discriminant analyses were conducted for each individual species included in the canonical correlation analysis to determine how well water chemistry, depth, and current speed determine the presence of each of those species. An additional discriminant analysis was conducted to determine if the environmental variables could be used to determine the presence of subadult sharks in general. The significant discriminant functions were cross-validated to determine the adequacy of the functions for correctly classifying the sample data (Tabachnick and Fidell 1996). Analyses were conducted with SAS1 9.1 and were evaluated at α = 0.05. Variable importance in the canonical correlation analysis and the discriminant analyses was evaluated by examining the corresponding correlations within the resulting functions. Based on criteria presented in Tabachnick and Fidell (1996), only variables with correlations above 0.45 were considered significant and were included as predictor variables for each function.

Results

A total of 415 subadult sharks representing nine species were captured during 231 longline sets (Table 1). The four most abundant species, which represented a combined 96.1% of the total number caught, were Atlantic sharpnose shark (Rhizoprionodon terranovae), bonnethead (Sphyrna tiburo), blacktip shark (Carcharhinus limbatus) and sandbar shark (C. plumbeus) (Table 1). Atlantic sharpnose shark was the most frequently captured species and was caught on approximately 52% of the longline sets. The other three species were captured on less than 20% of the longine sets (Table 1). The total number of subadults caught per set ranged from 0 to 16 (mean = 2, SD = 2.2), and the number of species captured per set ranged from 0 to 4 (mean = 1, SD = 0.84). Because of low encounter rates (represented in <5% of the sets); Table 1) the additional species encountered (i.e., scalloped hammerhead shark (S. lewini), lemon shark (Negaprion brevirostris), finetooth shark (C. isodon), bull shark (C. leucas), and spinner shark (C. brevipinna)) were not included in the canonical correlation analysis. However, these species were included in the discriminant analysis examining the effects of the environmental variables on the presence or absence on the subadult sharks in general. The mean, minimum and maximum values for each of the environmental variables are presented in Table 2.
Table 1

Numbers, frequencies of occurrence, encounter rates and total length (TL) ranges for subadult shark species captured on longlines in Georgia’s estuaries during April through September 2001 and 2002

Species

Number caught

Percent of total catch

Encounter ratea (%)

Range of total lengths (cm)

Atlantic sharpnose shark Rhizoprionodon terraenovae

305

73.49

52.4

28.1–98.3

Bonnethead Sphyrna tiburo

62

14.81

18.2

45.5–100.5

Blacktip shark Carcharhinus limbatus

16

3.88

6.5

60.9–161.0

Sandbar shark C. plumbeus

16

3.88

6.9

60.6–118.0

Scalloped Hammerhead S. lewini

7

1.70

2.6

46.0–68.8

Finetooth shark C. isodon

5

1.21

1.7

62.3–154.5

Spinner shark C. brevipinna

2

0.49

0.9

69.1 and 86.0

Bull shark C. leucas

1

0.24

0.4

160.0

Lemon shark Negaprion brevirostris

1

0.24

0.4

66.0

Overall

  

66.7

 

a–Calculated as the number of positive stations divided by the total number of stations sampled (n–231)

Table 2

Mean, minimum and maximum values for environmental variables measured for all longline sets and for longline sets where subadult sharks were collected

Species

 

Salinity (ppt)

Water temperature (degrees C)

Dissolved oxygen (mg·l–1)

Turbidity (NTU)

Current speed (m·s–1)

Depth (m)

Atlantic

sharpnose shark

Mean

31.21

28.39

4.93

21.1

0.27

5.4

Minimum

23.30

23.30

3.45

0.2

0.03

1.7

Maximum

35.30

30.40

6.40

131.0

0.79

10.2

Bonnethead

Mean

32.03

28.76

4.77

16.3

0.24

5.2

Minimum

27.50

25.10

3.45

7.4

0.02

1.8

Maximum

35.60

30.10

5.80

70.6

0.50

9.8

Blacktip shark

Mean

30.70

29.10

4.82

25.7

0.28

5.9

Minimum

28.00

27.70

3.93

10.1

0.04

2.7

Maximum

32.40

30.10

6.02

70.9

0.74

10.2

Sandbar shark

Mean

30.09

28.73

4.34

34.2

0.30

7.3

Minimum

24.30

23.20

3.53

13.6

0.02

3.5

Maximum

34.50

30.00

5.91

131.0

0.73

10.2

All species

Mean

31.23

28.45

4.84

22.3

0.27

5.6

Minimum

23.30

23.20

3.45

0.2

0.02

1.7

Maximum

35.60

30.40

6.40

131.0

0.79

10.2

All longline sets

Mean

31.15

28.13

4.89

26.1

0.28

5.5

Minimum

23.30

22.90

3.45

0.2

0.02

1.7

Maximum

35.70

30.40

6.41

131.0

0.79

10.2

Canonical correlation analysis

The canonical correlation analysis of the four most common species and environmental variables indicated that 87.6% of the variance was explained by the first two canonical correlations (Table 3). Although significant, neither of the two canonical correlations represented a substantial relationship between the pairs. The percent of variation explained between the first pair of variates was 18.71%, with 13.27% explained between the second pair of variates (Table 3).
Table 3

Canonical variate results and significance associated with the canonical correlation analysis conducted to examine the relationship between subadult shark species and their corresponding environmental variables in Georgia estuaries

Canonical variate

Eigenvalue

Proportion

Cumulative

Squared canonical correlation

Significance (p–value)

1

0.2301

0.5258

0.526

0.1871

<0.0001

2

0.1531

0.3499

0.876

0.1327

0.0179

3

0.0531

0.1214

0.997

0.0505

0.4566

4

0.0012

0.0029

1.000

0.0012

0.9803

The first canonical variate calculated for the species data was positively correlated with the presence of subadult bonnethead and negatively correlated with the presence of subadult sandbar shark (Table 4). The first canonical variate calculated for the environmental data was negatively correlated with depth (Table 4). Taken as a pair, these variates indicated that the presence of subadult bonnethead correlates negatively with water depth, which suggests that subadult bonnethead are found in shallower waters. The same pair of variates indicated subadult sandbar shark are positively correlated with water depth and suggests that they are present in deeper waters. The second canonical variate calculated for the species data was positively correlated with the presence of both subadult bonnethead and subadult sandbar shark (Table 4). The second canonical variate calculated for the environmental data was positively correlated with salinity and negatively correlated with dissolved oxygen levels (Table 4). This pair of variates indicates the presence of both subadult bonnethead and subadult sandbar shark is positively correlated with salinity and negatively correlated with dissolved oxygen levels. These results suggest that higher salinity and lower dissolved oxygen levels influence those sets where subadult sandbar shark and subadult bonnethead co-occur. The results of the redundancy analysis indicated that the two environmental variates account for only 8.3% of the total variation in the species presence dataset.
Table 4

Factor loadings, amount of explained variance and redundancy values for the canonical variates examining the relationship between shark species (subadults) and associated environmental variables in Georgia estuaries. Bolded values indicate variables that were considered for interpretation based on a ±0.45 cutoff

   

First canonical variate

Second canonical variate

   

Correlation

Coefficient

Correlation

Coefficient

Species Set

 

Atlantic Sharpnose Shark

0.271

0.542

0.044

0.087

 

Bonnethead

0.585

1.434

0.770

1.886

 

Blacktip Shark

0.021

0.087

0.241

1.020

 

Sandbar Shark

–0.684

–2.228

0.682

2.223

  

Percent of variance

0.285

 

0.225 Total =

0.510

  

Redundancy

0.053

 

0.030 Total =

0.083

Environmental Set

 

Salinity (ppt)

0.425

0.173

0.477

0.195

 

Water temperature (°C)

0.437

0.243

0.354

0.197

 

Dissolved Oxygen (mg/l)

0.173

0.261

–0.668

–1.010

 

Average Depth (m)

−0.559

−0.299

0.355

0.190

 

Current Speed (m/s)

−0.046

−0.238

−0.269

−1.389

 

Transformed Turbidity

0.229

−0.227

−0.192

−0.104

  

Percent of variance

0.223

 

0.212 Total =

0.434

  

Redundancy

0.042

 

0.028 Total =

0.070

Canonical correlation

 

0.433

 

0.364

 

Discriminant analyses

Five independent discriminant analyses were performed with the environmental variables as predictors of presence and absence for the four commonly encountered species and for the presence of subadult sharks in general. Two of the five analyses yielded significant discriminant functions. Similar to the results of the canonical correlation analysis, the presence of subadult sandbar shark (λ = 0.842, χ2(6, n = 153) = 25.374, p < 0.0001) and subadult bonnethead (λ = 0.852, χ2(6, n = 153) = 23.772, p = 0.001) were correlated with environmental variables; whereas, the presence of subadult sharks in general (λ = 0.923, χ2(6, n = 153) = 11.813, p = 0.066), subadult Atlantic sharpnose shark (λ = 0.947, χ2(6, n = 153) = 8.113, p = 0.230) and subadult blacktip shark (λ = 0.969, χ2(6, n = 153) = 4.634, p = 0.592) were independent of the environmental variables examined.

The discriminant function generated for the presence of subadult bonnethead accounted for 14.82% of the function variance. Standardized function coefficients and correlation coefficients indicated that transformed turbidity and salinity were most associated with the presence of this species (Table 5). Stations where subadult bonnethead were present had lower turbidities (mean = 16.3 NTU) and higher salinities (mean = 32.03 ppt) than stations where they were absent (mean turbidity = 24.7 NTU and mean salinity = 30.9 ppt). Original classification results showed that 96.7% of the stations where subadult bonnethead were absent were correctly classified, whereas only 15.6% of the stations where subadult bonnethead were present were correctly classified. For the overall sample, 79.7% of presence/absence determinations were correctly classified. Cross-validation derived similar accuracy for the overall sample, with a correct classification rate of 79.1%.
Table 5

Discriminant function factor loadings associated with significant relationships between presence data and environmental variables for subadult sharks in Georgia’s estuaries. Bolded values indicate variables included for interpretation; (a) Bonnethead, and (b) Sandbar shark

 

Correlation coefficients with discriminant function

Standardized function coefficients

(a)

 Current Speed

0.277

0.183

 Average Depth

0.214

0.216

 Salinity

−0.454

−0.736

 Dissolved Oxygen

0.222

0.546

 Turbidity

0.503

0.555

 Water Temperature

−0.439

−0.384

(b)

 Current Speed

0.053

−0.148

 Average Depth

0.779

0.688

 Salinity

−0.344

−0.096

 Dissolved Oxygen

−0.683

−0.524

 Turbidity

0.471

0.255

 Water Temperature

0.263

−0.148

The discriminant function generated for the presence of subadult sandbar shark accounted for 15.76% of the function variance. Standardized function coefficients and correlation coefficients indicated that transformed turbidity, depth, and dissolved oxygen were most associated with the function (Table 5). Stations where subadult sandbar shark were present had higher turbidities (mean = 34.2 NTU), were deeper (mean = 7.28 m) and had lower dissolved oxygen levels (4.34 mg·L−1) than stations where they were absent (mean turbidity = 21.6 NTU, mean depth = 5.33 m, and mean dissolved oxygen = 4.95 mg·L−1). Original classification results showed that 100% of the stations where subadult sandbar shark were absent were correctly classified, whereas only 12.5% of the stations where subadult sandbar shark were present were correctly classified. For the overall sample, 90.8% were correctly classified. Cross-validation derived similar accuracy for the overall sample, with a correct classification rate of 88.9%.

Discussion

Results from the canonical correlation analysis provided a synoptic view of the suite of shark species that use Georgia’s estuaries and which environmental variables affect that assemblage. Only subadult bonnethead and subadult sandbar shark were affected by environmental variables. Generally, the two species were separated by depth preference, with subadult sandbar shark found in deeper water than the subadult bonnethead; however, when the two species co-occurred, they were found in areas characterized by high salinity and low dissolved oxygen levels. In Georgia’s estuaries, subadult bonnethead occurred commonly in small marsh creeks or feeder creeks that are surrounded by marsh grass, or along the shallows where they can be seen feeding on low tide, whereas subadult sandbar shark frequented large creeks and the open areas of the lower sound.

Similar patterns of habitat use for bonnethead and sandbar shark have been reported in other areas. For example, bonnethead frequented shallow water areas near seagrass beds in Charlotte Harbor, FL (Heupel et al. 2006). Heupel et al. (2006) also documented that bonnethead tend to be localized residents within an estuary; however, attachment to specific sites within a given estuary was not observed. Further, catch rates of both neonate and small juvenile sandbar sharks found along the eastern shore of Virginia were correlated with sites located farther from the inlet and with warmer temperatures and lower dissolved oxygen levels (Conrath and Musick 2007). In another example, neonate and juvenile sandbar sharks in Chesapeake Bay were most abundant in areas of salinity greater than 20.5 and in depths greater than 5.5 m (Grubbs and Musick 2007).

The results of the canonical correlation analysis in the present study also suggest that neither subadult Atlantic sharpnose shark nor subadult blacktip shark presence was influenced by any of the environmental variables investigated. Similar results were found for immature Atlantic sharpnose shark in the Gulf of Mexico (Parsons and Hoffmayer 2005) and juvenile blacktip shark on the west coast of Florida (Heupel and Hueter 2002). However, Heupel and Hueter (2002) also suggested that water temperature could be a migratory cue for blacktip shark, indicating that temperature acts more on a temporal scale than a spatial one.

Although the canonical correlation was able to account for some of the variability in presence and absence of shark species found in Georgia’s estuaries, the amount of variation explained was minimal at best. Redundancy analysis indicated that less than 10% of the variation in the species set was explained by the environmental variables examined during this study.

Whereas the discriminant analysis is a special case of the canonical correlation analysis (Tabachnick and Fidell 1996), it allows for a species-specific examination of how the environmental data affect an individual species, minus any interspecies relationships. Additionally, the discriminant analysis was able to examine the effects of habitat variables on the presence/absence of subadult sharks in general.

The results of the discriminant analyses applied to the presence/absence data of the four common species support the general conclusions of the canonical correlation analysis and provide insight into the relative importance of those environmental variables for each species. Although salinity, average water depth, and dissolved oxygen were able to define how two co-occurring species partition habitat use, the influence of these variables at the species level differs. With the species interactions removed, average depth and dissolved oxygen defined the presence of subadult sandbar shark; whereas, salinity defined the presence of subadult bonnethead. Areas where subadult sandbar shark were present were deeper and had and lower dissolved oxygen than areas where they were absent. Grubbs and Musick (2007) and Conrath and Musick (2007) present similar findings. Salinities were higher in areas where subadult bonnethead occurred than in areas where they were not. Generalized habitat use for bonnethead suggests that they use shallow coastal areas and estuaries (Castro 1983; Compagno 1984), which are areas characterized by high salinities.

Turbidity, though not identified in the canonical correlation analysis, was an additional environmental variable that was correlated with the presence of both subadult sandbar shark and subadult bonnethead. Subadult sandbar shark were present in areas with relatively higher turbidity, whereas subadult bonnethead were found in areas of relatively lower turbidity. Because of Georgia’s high tide amplitude, the estuaries usually are well mixed (Johnson et al. 1974). Why these species exhibit their respective turbidity preferences is unknown, but may be related to their general habitat preferences. Sandbar shark are found over sandy or muddy bottoms in the mouths of river systems and bays (Compagno 1984), whereas bonnethead have been documented frequenting shallow waters, sometimes in conjunction with seagrass beds (Heupel et al. 2006), which are areas with lower turbidities.

Although mesohabitat features may be too fine a scale for analyzing habitat associations for subadult sharks, examination of macrohabitat may provide stronger associations for habitat use. Other studies have found relationships between specific macrohabitat types and life history stages for a variety of shark species. For example, older juvenile and adult bonnethead frequented seagrass beds in a Florida estuary (Heupel et al. 2006). Pratt and Carrier (2007) found that small juvenile nurse shark used coral patch reefs on the edge of a lagoon, channel edges, and mangrove roots as shelter in the Dry Tortugas; whereas, larger juveniles and adults preferred octocoral (i.e., soft corals composed of polyps that have eight tentacles) hard bottom areas, which have more exposure to waves and currents than the other habitats. Lemon shark in the Bahamas preferred shallow waters over rocky or sandy substrate, possibly to avoid predators (Morrissey and Gruber 1993). Georgia has very few unique habitat types in its inshore waters, yet there are many hydrologic and geologic characteristics that could provide similar forms of refuge/protection. Identification of macrohabitat features (e.g., intertidal oyster reefs, in channel and off channel sites, bank characteristics, across-creek gradient) either through empirical methods or through the use of GIS analyses would be useful avenues for future research of shark habitat use in Georgia and along the SE Atlantic coast of the US.

Biotic factors such as predator avoidance and prey availability may have a stronger effect than abiotic factors on the presence and abundance of subadult sharks (Branstetter 1990; Castro 1993; Simpfendorfer and Milward 1993). Other studies that have examined the effects of abiotic factors on the presence and abundance of subadult sharks have concluded that biotic factors have a stronger effect than environmental ones (Heupel and Hueter 2002; Conrath and Musick 2007). Understanding how prey density and the presence of predators, specifically larger sharks, affect habitat use for subadult sharks would help with future identification of essential shark habitat in Georgia.

Current federal fisheries management requires that Essential Fish Habitat (EFH) be defined in all management plans for fisheries occurring in the Exclusive Economic Zone (NMFS 1997). EFH is defined as those waters and substrate necessary to fish for spawning, breeding, feeding or growth to maturity (NMFS 2002). The difficulty experienced defining EFH for subadult sharks in Georgia estuaries with mesohabitat variables suggests that shark nurseries, especially multi-species nurseries, may be better managed as Habitat Areas of Particular Concern (HAPC). HAPCs are subsets of EFH and are areas that serve extremely important ecological functions or are especially vulnerable to degradation (NMFS 2002). This designation can be based on one or more of the following criteria: importance of the ecological function provided by the area, extent to which the area is sensitive to human induced environmental degradation, rarity of a particular habitat type, whether and to what extent development activities are or will be stressing to the habitat (NMFS 2002). HAPC designation is used to prioritize conservation efforts and does not provide additional protection or restriction on a given area (NMFS 2002). Both Chesapeake Bay and Delaware Bay have been designated HAPCs for sandbar shark, as they are considered the primary nursery grounds for this species (Conrath and Musick 2007). Georgia’s estuaries fits the HAPC criteria because they provide an important ecological role for at least four species of sharks and because of the potential negative effects to these areas caused by human activities, including dredging of shipping channels and waterways, as well as coastal development.

Footnotes

  1. 1.

    Reference to trade names does not imply Government endorsement of commercial products.

Notes

Acknowledgements

The University of Georgia Marine Extension Service provided field support and personnel needed to conduct this project. Robert Cooper, Gary Grossman, and Randy Walker provided useful comments to an earlier draft of this manuscript. We would like to extend our appreciation to the three reviewers who provided additional comments and edits to this manuscript. Funding for this project was administered through the National Marine Fisheries Service’s Apex Predator Program in Narragansett, RI as part of a larger cooperative grant sponsored by National Marine Fisheries Service’s Highly Migratory Species Division in Silver Spring, MD. The Georgia Cooperative Fish and Wildlife Research Unit is sponsored jointly by the US Geological Survey, the U.S. Fish and Wildlife Service, GA Department of Natural Resources, the University of Georgia, and the Wildlife Management Institute.

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Marine Fisheries Section, Coastal Resources DivisionGeorgia Department of Natural ResourcesBrunswickUSA
  2. 2.U.S. Geological Survey, Georgia Cooperative Fish and Wildlife Research Unit, D.B. Warnell School of Forestry and Natural ResourcesUniversity of GeorgiaAthensUSA

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