Behavioral Patterns of Common Bottlenose Dolphins
(Tursiops truncatus truncatus) Within the Galveston–Port
Bolivar Ferry Lane
Alexandria E. Rivard, Frances P. Gelwick, and Wyndylyn von Zharen
Southeastern Naturalist, Volume 15, Issue 4 (2016): 742–759
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2016 SOUTHEASTERN NATURALIST 15(4):742–759
Behavioral Patterns of Common Bottlenose Dolphins
(Tursiops truncatus truncatus) Within the Galveston–Port
Bolivar Ferry Lane
Alexandria E. Rivard1,*, Frances P. Gelwick2, and Wyndylyn von Zharen3
Abstract - The objective of this study is to assess Tursiops truncatus truncatus (Common
Bottlenose Dolphin) group behavior as a function of spatial, temporal, and vessel proximity
variables within the Galveston–Port Bolivar ferry lane, in lower Galveston Bay, TX. This
area is subjected to vessel traffic entering the Houston, Texas City, and Galveston ship channels
and at risk for environmental accidents. We used the Galveston–Port Bolivar ferries as
a platform of opportunity to observe group behavior within the ferry lane. We conducted
1412 hours of observation between 1 June and 30 November 2013 and then utilized canonical
correspondence analysis to evaluate behavioral state as a function of spatial, temporal,
and vessel-proximity variables. Principal response curve (PRC) analysis showed significant
variation in behavioral states over time in the Bolivar and Galveston zones as compared
to the passage zone. Chi-square goodness-of-fit tests showed significant deviations from
expected behavior observation across zone and time block. These findings demonstrate finescale
behavioral variability in an area of high anthropogenic activity.
Introduction
The number of vessels at sea and in major ports has risen steadily since the 1950s
(Ross 2005). Significant maritime shipping occurs in the Gulf of Mexico, both offshore
and within the Intracoastal Waterway, and accounts for $129 billion of cargo
movement annually (Adams et al. 2004). Several major shipping lanes exist within
the Gulf of Mexico, primarily those through the Florida Straits and the ports of
Houston and New Orleans (Azzara 2012). In addition to commercial traffic, more
than 5 million households in Alabama, Florida, Louisiana, Mississippi, and Texas
participated in recreational boating in 2012 (USCG 2012). Despite the consistent
commercial and recreational traffic, the northern Gulf of Mexico is home to 31
stocks of Tursiops truncatus truncatus (Montagu) (Common Bottlenose Dolphin)
within its bays and estuaries, which are regularly exposed to encounters with vessels
(Waring et al. 2015).
Exposure to vessel traffic can alter habitat use of Common Bottlenose Dolphins.
For instance, a significant decline in dolphin abundance was observed for vessel
tourism sites in Shark Bay, Australia, when compared with control sites where
traffic was lower (Bejder et al. 2006). In Milford Sound, New Zealand, boat traffic
was determined to be the dominant factor responsible for a decline in dolphin visits
1Department of Marine Biology, Texas A&M University at Galveston, Galveston, TX
77553. 2Department of Wildlife and Fisheries Sciences, Texas A&M University, College
Station, TX 77843. 3Department of Marine Science, Texas A&M University at Galveston,
Galveston, TX 77553. *Corresponding author - arivard@email.tamu.edu.
Manuscript Editor: Jeremy Pritt
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(Lusseau 2005). In the short-term, animals may dive longer to avoid vessel encounters
or leave the area as boats arrive (Lusseau 2003). In Sarasota Bay, FL, resident
Common Bottlenose Dolphins exhibited longer inter-breath intervals when approached
by boats compared to when there were no boats within 100 m (Nowacek et
al. 2001). When approached by a vessel, animals changed direction and swimming
speed more frequently than when there were no boats in the vicinity (Nowacek et al.
2001). However, not all behavioral responses are avoidant. In some instances, the
animals may be attracted to vessels to ride the bow wave of the ship (Fish and Hui
1991) or forage behind commercial trawlers (Leatherwood 1975). Nevertheless,
sound exposure, stemming from watercraft and other sources of marine noise, may
cause a rise in stress hormones and potentially lead to long-term health problems
(Romano et al. 2004).
The type of vessel and its operation are also important variables impacting Common
Bottlenose Dolphin behavior (Nowacek et al. 2001, Mattson et al. 2005, Miller
et al. 2008, Weilgart 2007). In Hilton Head Island, SC, group behavior changed following
55% of encounters with watercraft, 67% of encounters with jet skis, 100%
of encounters with shrimp boats, and 11% of encounters with large ships (Mattson
et al. 2005). In the Pelagie Archipelago in Sicily, Italy, animals were disturbed by
fast-moving vessels but not by sailboats (Papale et al. 2012). Approaches by powerboats
can frequently disrupt foraging behavior (Lemon et al. 2008). Such findings
indicate that behavioral disruption is not simply a function of the size or noise
level of a ship, but also the manner in which it is operated. The Marine Mammal
Protection Act of 1972 (MMPA) (16 U.S.C. § 1361 - 1421h) prohibits approaching
marine mammals with a vessel, but enforcement has proven challenging and is
often inconsistent due to the spatial scale over which violations may occur (Roman
et al. 2013). While intentional take of marine mammals is prohibited in the United
States, unnecessary accidental take of individuals should be avoided to the extent
possible (Roman et al. 2013).
Beneficial foraging habitat can be associated with areas of high vessel traffic. In
some areas of the Gulf of Mexico, dolphins have shown a preference for foraging
in dredged channels as compared to natural seagrass beds because their prey are
unable to hide in dredged areas where vegetation was removed (Allen et al. 2001).
Habitat preference depends on a myriad of other factors, but availability of desirable
prey in the vicinity of some Gulf of Mexico habitats does appear to outweigh
the potential negative impacts of heavy vessel traffic (Allen et al. 2001). However,
the level of traffic activity that may cause animals to leave an otherwise desirable
habitat is unknown.
The particularly heavy and steady stream of ships, including regular ferry traffic,
in Galveston, TX (Merrick and Harrald 2007) presents a venue for regular
interactions between vessels and animals. Evidence suggests that habituation to
anthropogenic activity can have adverse effects on cetacean hearing, as well as limit
the effectiveness of their evasive responses to vessels (Richardson and Würsig 1997).
Moreover, because shipping corporations operating in this area often transport hazardous
cargo and traffic is heavy, this location represents an area generally deemed
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as unsafe for vessel operation (Merrick and Harrald 2007). Documenting behavioral
patterns under current conditions will provide a behavioral standard against which
behavior can be compared if vessel traffic changes markedly or an accident occurs.
Within the Gulf of Mexico, fine-scale population structure and patterns of
long-term site fidelity have been documented (Miller and Baltz 2010, Sellas et al.
2005). The Common Bottlenose Dolphin population in Galveston Bay is relatively
small. In one survey, approximately 240 individuals were recorded, though many
were not sighted a second time and may have been transient (Fertl 1994). Seasonal
fluctuations in abundance exist: spring and fall surveys showed higher encounter
rates than summer and winter surveys (Fertl 1994). Weller (1998) supports the low
encounter rate for the winter months, noting that Common Bottlenose Dolphins
move toward inshore waters in the summer in the northern Gulf of Mexico, though
it is unclear why the encounter rate from Fertl (1994) was low in the summer or if
abundance fluctuations can be tied to vessel traffic. There were no significant patterns
of preferred associations with other individuals in Galveston Bay, suggesting
high group fluidity within the region (Bräger et al. 1994).
This study aims to describe patterns for group behaviors of Common Bottlenose
Dolphins as a function of spatial location, group characteristics, month and time
of day, and characteristics of vessel traffic. Using ferry vessels as our observation
platform, we collected data pertaining to dolphin groups within the Galveston–Port
Bolivar ferry lane, which crosses the entrance to the Houston ship channel. These
data describe group behavior in an area of high vessel traffic, and can serve as a
standard for group behaviors in the event of an environmental perturbation.
Materials and Methods
Field site description
Galveston Bay is the largest estuary in the Gulf of Mexico, and home to 3
major ports: Houston, Texas City, and Galveston. These ports are connected by
the Houston Ship Channel, which enters Galveston Bay between the eastern tip
of Galveston Island and the western end of the Bolivar Peninsula (Steichen et al.
2012). This entry point is crossed continually throughout the year by the Galveston–
Port Bolivar ferry. Approximately 7000 ships visit the Port of Houston via the
Houston Ship Channel annually (Merrick and Harrald 2007). The level of traffic
through the port is expected to rise following the completion of the Panama Canal
expansion in 2015; container ship traffic alone is expected to increase by 15 percent
(Harrison and Trevino 2013).
The Galveston–Port Bolivar ferry bridges the gap in State Road 87 by traveling
2.7 miles between the Galveston ferry dock and Port Bolivar ferry dock (Fig. 1;
Weisbrod and Lawson 2003). Ferries cross the same route year round, 24 hours
a day. A one-way trip from dock to dock takes approximately 20–30 minutes.
Figure 1 (following page). The 3 zones of observation: Galveston ferry dock (upper circle),
the passage channel (lines connecting circles), and Bolivar ferry dock (lower circle). (Ferry
channel aerial map from Texas Natural Resources Information System.)
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Between 1 and 5 vessels operate concurrently, with more vessels operating during
times of peak travel. The current ferry fleet consists of 6 vessels, and in 2000,
accommodated over 2 million vehicles and more than 6.5 million passengers (Weisbrod
and Lawson 2003).
Field observations
Data collection took place continually from 7:00 (or sunrise if later) to 19:00 (or
sunset if earlier) between 1 June 2013 and 30 November 2013, weather permitting.
We selected this time period to include periods of high and low sighting rates and
to exclude the springtime calving and breeding season (Fertl 1994, Weller 1998).
We did not conduct observations when visibility was less than 100 m. All observations
were made from the front of the outdoor viewing deck on the second level of
the Galveston–Port Bolivar ferry. This platform was ~20 feet above water level and
provided a 270-degree view. In cases of rain or high wind when visibility was still
greater than 100 m, we conducted observations from the forward windows of the
second-level cabin (visibility 180 degrees), alternating between port and starboard
sides in 2-minute intervals.
We collected data in 3 spatial zones: the passage channel (P), Galveston dock
(G), and Bolivar dock (B) (Fig. 1). We defined the docks as the area within a 100-m
radius of the ferry landing, and the passage channel as the area of ferry operation
more than 100 m from the docks (Fig. 1). The spatial zone recorded for an observation
was the location of the majority of the individuals in the group being observed.
In all zones, we documented only dolphin groups within 100 m of the ferry vessel.
When the group was split evenly at the boundary between 2 observation zones, we
recorded observations only for the group members within the zone nearest to the
ferry.
Groups were defined using a 10-m–chain rule as per Smolker et al. (1992),
wherein we considered all animals within 10 m of another animal part of the same
group. We defined calves as those individuals estimated to be less than 1.5 m in
length (Leatherwood and Reeves 1989). For all sighted groups, we visually estimated
the distance from the dolphin in the group nearest to (a) the shore; (b) all
vessels within 100 m of the group, including other ferries on the route; and (c) the
observer’s ferry. Distance estimation, particularly over the water, is rife with human
error (Baird and Burkhart 2000). To minimize such error, all observers were trained
in distance estimation, and distances were estimated as ranges (Baird and Burkhart
2000). Lemon et al. (2006) estimated the distances at which dolphins change behavior
as a vessel approaches, and ranges were established in accordance with these
findings. Therefore, groups more than 100 m away and vessels more than 100 m
from the group were not documented. All distances were categorized as: very close
(V; ≤ 10 m), close (C; 10–30 m), intermediate (I; 30–50 m), far (F; 50–100 m), or
open water (O; ≥100 m). The open-water category was used only to document the
distance of the group from shore.
We recorded the predominant group activity (PGA) (by ≥50% of the individuals
in the group) when first sighting a group and for every 2-minute interval thereafter
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if the group remained in sight and within 100 m (Mann 1999). When assessing a
small population of a highly mobile species in a dynamic environment with heavy
vessel traffic, it is often difficult to determine when observations become independent.
The 2-minute interval selected provided sufficient time for PGA and other
variable conditions to change and individuals to join or leave the group, as group
fluidity in Galveston Bay is high (Bräger et al. 1994). However, some problems
with independence may persist. PGA was characterized as: resting (R), foraging
(F), socializing (S), or travelling (T) as described in Ballance (1992). We defined
resting as low activity with no coordinated direction of movement by the group;
foraging as the active pursuit of prey; socializing as observable body contact, either
positive or aggressive; and travelling as the coordinated movement of the group in
1 direction (Ballance 1992). All observers were trained extensively in behavioral
identification first in the laboratory and then in the field, until they consistently
identified the correct behavior for all groups sighted.
For each observation, we recorded the following data: time of day sighted; zone
of sighting (P, B, or G); total number of individuals in the group; number of calves;
number of vessels operating an engine in each distance category (V, C, I, or F); ferry
proximity to the group (V, C, I, or F); distance of the group from shore (V, C, I, F,
or O). Observations were distributed equitably across 3 time blocks: morning (M;
7:00 [or sunrise]–11:00), afternoon (A, 11:00–15:00), and evening (E, 15:00–19:00
[or sunset]). In the latter portion of the field season when sunrise occurred after 7:00
and sunset occurred before 19:00, we initiated or terminated observations based on
the sunrise and sunset times.
Analysis
The aim of this study is to describe Common Bottlenose Dolphin behavior as a
function of spatial, temporal, and vessel-proximity variables. To that end, we used
multivariate analysis as a means to evaluate PGA as a function of all candidate
explanatory variables simultaneously. We conducted multivariate analysis using
CANOCO v5.0 and then evaluated variation in PGA based on time block and zone
using Pearson’s chi-square tests in R version 3.2.3 (R Core Team 2015).
In order to create a quantifiable measure of total vessel traffic, we converted the
count of vessels in the vicinity of a group to an ordinal “vessel score”. The count of
vessels in each distance range V, C, I, or F is given by NV, NC, NI, or NF, respectively.
Vessels very close (V) were weighted by 4, close (C) by 3, intermediate (I) by 2,
and far (F) by 1. The ferry score S was assigned the value of 4 if very close (V) to
the group, 3 if close (C), 2 if intermediate (I), and 1 if far (F). The vessel score was
then calculated using the equation:
Vessel score = 4 NV + 3 NC + 2 NI + 1 NF + S
We performed canonical correspondence analysis (CCA) using CANOCO v5.0
(ter Braak and Šmilauer 2012) to evaluate the association of values for candidate
explanatory independent variables with respect to PGA at each sighting (dependent
variable). Such constrained ordination methods result in a best-fit model of
relationships among variables in a dataset; CCA axes correspond to the directional
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gradients of greatest variation in the dependent variable as explained by combinations
of explanatory variables (Van den Brink and ter Braak 1999). We used the
“Forward Selection” procedure in CANOCO to rank the environmental variables
based on the strength of their relationship to gradients in behaviors (ter Braak and
Šmilauer 2012). The initial analysis identified individual significant explanatory
variables in order to quantify their total combined explanatory value. We then classified
explanatory variables into temporal, spatial, and vessel-proximity categories
to partition the variation in PGA that can be explained uniquely by each category
(Table 1).
We assessed the seasonal trends in behaviors using a partial redundancy analysis
(RDA) to construct principal response curves (PRC). The principal response curves
analysis is a multivariate method for the analysis of repeated measurement designs,
and is designed to test and display treatment effects that change across time.
It is based on reduced-rank regression (redundancy analysis) that is adjusted for
changes across time in the base/reference treatment. This protocol allows the method
to focus on the time-dependent treatment effects. The temporal factor (time) is
set as a covariate in the analysis so that the main effect of treatment (location of
observations, with P, passageway, as the base/reference and G and B, Galveston
and Bolivar, as the terminal port locations to be compared to P) and the interaction
with time is included in the ordination model. Each repeatedly recorded location
forms a separate whole-plot and utilizes the observations made in time for its splitplots.
This permutation test then permutes whole-plots, but not the split plots. The
principal component of this analysis (here axes 1 and 2) is plotted against time in
the diagram. (Van den Brink and Ter Braak 1998, 1999).
All multivariate tests were run using Monte Carlo simulations in CANOCO 5
software (ter Braak and Šmilauer 2012) and calculating the F-ratio at the 5% significance
level (comparing the original data to output from 499 permutations for
tests). We used pseudo-F values for partial RDA when controlling for the effects
of covariates. In consideration of the family-wise Type I error rates for multiple
testing, we implemented the false discovery rate in the software program using the
approach of Benjamini and Hochberg (1995).
Table 1. The variable levels included in variance partitioning tests of conditional effects of 3 types
of variable groups: temporal, spatial, and vessel proximity to the group. DS = distance to shore, DF
= distance to ferry.
Temporal Spatial Vessel proximity
Date Bolivar ferry dock DF very close
Morning Passage DF close
Afternoon Galveston ferry dock DF intermediate
Evening Ds very close DF Far
Sight time Ds close Total vessel score
Ds intermediate # vessels very close
Ds far # vessels close
Ds open water # vessels intermediate
# vessels far
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We used Pearson’s chi-square tests to determine how occurrence of PGA deviated
from the expected value on the basis of zone and time block. We calculated
expected values as the product of the row (zone and time block, respectively) and
column (PGA) totals in the contingency table divided by the sample size. The chisquare
test determines whether PGA deviated significantly from these expected
values. This analysis allows for more precise identification of where deviations in
behavior occur within spatial and temporal metrics.
Results
Between 1 June 2013 and 30 November 2013, we conducted 1412 hours of
observations. We documented dolphin groups on 24,780 occasions, in which
calves comprised ~9% of the individuals sighted. Group size ranged from 1 to 74
individuals, and the number of calves in a group ranged from 0 to 15 (Fig. 2). The
greatest number of groups was sighted in the passage zone during the afternoon.
The fewest number of groups was documented at the Galveston ferry dock during
the evening (Table 2).
Figure 2. Frequency histogram of group size (top) and number of calves per group (bottom).
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CCA with forward selection of variables explained 10.2% (adjusted for degrees
of freedom) of the total variation in behavior states across all observations
(Table 3). Variation is due to differences associated with the likelihood that a given
behavior occurs in conjunction with the explanatory variable. All explanatory
variables were significant (Table 3). The largest percentage of the total variation
in behavioral state due to a single variable was from the Bolivar ferry dock, which
accounted for 2.2% of the total variation (21% of the total explained variation). The
open-water distance to shore variable contributed an additional 1.7% of the total
variation (16.4% of the explained variation). With variance partitioned into our 3
sub-groups of explanatory variables (spatial, temporal, and vessel proximity), they
combined to significantly explain 8.5% of the variation in behavior states (Table 4)
as follows: spatial variables uniquely accounted for 3.4% (40.0% of total explained
variation), temporal variables uniquely accounted for 2.6% (30.6% of total explained
variation), vessel-proximity variables uniquely accounted for 1.8% (21.2%
of total explained variation), and variation shared among these groups accounted
for 0.7% (8.2% of total explained variation).
Table 2. The number of dolphin groups documented in each zone by time block.
Time block Bolivar ferry dock Galveston ferry dock Passage
Morning 2286 2090 3706
Afternoon 3692 1305 3961
Evening 3318 658 3764
Table 3. The results of the forward-selection global permutation test CCA showing the total percentage
of variation in behavior and percentage of explained variation that is attributable to each variable.
All selected variables explain 10.3% (adjusted for degrees of freedom to 10.2%) of the total variation
in behavior. DS = distance to shore, DF = distance to ferry.
% of total % of explained
Name variation variation pseudo-F P P (adj)
Bolivar ferry dock 2.2 21.0 548.0 0.002 0.01480
DS Open water 1.7 16.4 436.0 0.002 0.01480
Date 1.5 14.2 382.0 0.002 0.01233
Group size 1.3 13.0 356.0 0.002 0.01057
Total vessel score 1.1 11.1 307.0 0.002 0.00925
Morning 0.8 7.4 206.0 0.002 0.00822
#Calves 0.5 4.5 125.0 0.002 0.00740
Df very close 0.3 3.3 92.6 0.002 0.00673
Ds very close 0.2 2.1 58.5 0.002 0.00617
#Vessels very close 0.1 1.3 38.0 0.002 0.00569
Sight time 0.1 1.1 29.8 0.002 0.00529
Ds far 0.1 1.1 29.8 0.002 0.00493
#Vessels close 0.1 1.1 31.0 0.002 0.00462
Ferry distance less than 0.1 0.7 20.6 0.002 0.00435
Passage less than 0.1 0.7 18.9 0.002 0.00411
Evening less than 0.1 0.6 17.5 0.002 0.00389
Ds intermediate less than 0.1 0.2 7.1 0.002 0.00370
Ds close less than 0.1 0.2 7.1 0.002 0.00370
Df far less than 0.1 0.2 6.8 0.002 0.00322
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The CCA ordination diagrams simultaneously plot the significant associations of
explanatory variables and behavior states (Fig. 3). CCA axis 1 accounted for 5.7%
of total variation in behavioral state (Fig. 3); axis 2 for 3.6% (Fig. 3); and axis 3 for
1% (not shown). Centroids indicate the location of optima for behavioral states as
related to explanatory variables. Longer vector arrows indicate a quantitative explanatory
variable that is more strongly associated with the behavior state centroid
closest to it (positively in the direction of the arrow head, and negatively in the
opposite direction) along the axis. Centroids for categorical explanatory variables
located farthest from the center of the ordination are most strongly associated with
the behavior centroid closest to it along the axis. For example, along CCA axis 1,
foraging and traveling are the 2 behavioral states best explained and most strongly
contrasted with one another, as they sit on opposite sides of the axis. Resting was
best explained along CCA axis 2 and negatively associated with time spent in
traveling or foraging (Fig. 3). Foraging, plotted towards the right on CCA axis 1,
therefore was most strongly associated with morning observations, larger group
sizes, greater numbers of calves, and vessels that were close to the group (Fig 3).
Conversely traveling, plotted towards the left on CCA axis 1, therefore was associated
with open water and groups very close to the ferry in the passage zone in open
water (Fig. 3). In the evening, dolphin groups were resting or socializing farther
from the ferry and farther from shore. Along CCA axis 2, resting was strongly positively
associated with observations in the Bolivar zone and groups farther from the
ferry in the evening, and negatively associated with total vessel score (Fig. 3).
PRC analysis showed significant trends over time (Fig. 4) for variation in
behavior states, comparing spatial distribution of observations in the 2 ports to
observations in the passage zone. When combined, the temporal and spatial explanatory
variables accounted for 8.2% (adjusted 7.0%) of the total variation in
behavioral state. PRC axis 1 accounted for 60% of the total explained variation.
PRC axis 1 shows the dominant behavioral trends over time; behaviors in Galveston
and Bolivar zones were more similar to one another in June, July, and August,
primarily consisting of resting and foraging; socializing was more commonly observed
in the passage zone (Fig. 4A). In July, September, and November, notable
increases in traveling occurred in both ports, but in October foraging and resting increased
in the Galveston zone; smaller increases in traveling occurred in the Bolivar
zone during September, October, and November (Fig. 4A). PRC axis 2 accounted
for 35.5% of explained variation in behavior and indicated the strongest contrasts
Table 4. Percentage of the variation in observed dolphin behaviors that can be explained by each subgroup
of variables (spatial, temporal, vessel proximity), equally by any of the groups (shared), and by
all 3 groups combined (total explained).
Group % of variation % of explained variation Pseudo-F value P value
Spatial 3.4 40.0% 146.0 0.002
Temporal 2.6 30.6% 166.0 0.002
Vessel proximity 1.8 21.2% 66.1 0.002
Shared 0.7 8.2% 136.0 0.002
Total explained 8.5 100.0%
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in behaviors between the two ferry docks, especially in June and July when foraging
and travel were more common in Galveston, and socializing and resting were
more common in Bolivar (Fig. 4B). Behavioral trends were more homogeneous
Figure 3. Results
of canonical
correspondence
analysis
of behavioral
s t a t e (l a rge
circles) as the
dependent variable.
Explanatory
variables
closer to the
centroid for a
dependent variable
are more
likely to be associated
with
the observation
of that behavior.
Explanatory
variables
significant on
CCA axes 1 and
2 were distance
to ferry (Ferry-
Dis; vector head
plotted in upper
left quadrant
and open triangles
for categorical
variables V-, C-, I-, and F-DistFerry plotted from upper left to lower right quadrants),
zone (Bolivar, Galveston, and Passage, dark filled triangles), vessel proximity (VessV4,
VessC3, VessI2, and VessF1; vector heads plotted in upper right quadrant), time of day
(Morning, Aftnoon, Evening;gray filled triangles from upper right to lower left quadrants),
month (gray filled triangles plotted from upper left to lower and upper right quadrants),
total vessel score (TotVessS; vector head plotted in upper right quadrant), group size (Grp-
Size; vector head plotted in lower right quadrant), number of calves (Num-Calf; vector
head plotted in upper right quadrant), and categorical distance to shore (V-, C-, I-, and ODistShore;
open triangles plotted from upper right to lower right quadrants). Total variation
for axes 1, 2, and 3 were, respectively, 5.7%, 3.6%, and 1.0% of total variation; permutation
tests for Axis 1 and all 3 canonical axes together were, respectively, pseudo-F= 1505, P =
0.002 and pseudo-F = 150, P = 0.002. Calf; vector head plotted in upper right quadrant), and
categorical distance to shore (V-, C-, I-, and O-DistShore;open triangles plotted from upper
right to lower right quadrants). Total variation for axes 1, 2, and 3 were, respectively, 5.7%,
3.6%, and 1.0% of total variation; permutation tests for Axis 1 and all 3 canonical axes
together were, respectively, pseudo-F= 1505, P = 0.002 and pseudo-F = 150, P = 0.002.
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over time for observations in Bolivar than Galveston, being more similar to those
in the passage, and consisting primarily of socializing and traveling (Fig. 4B).
PRC axis 3 accounted for only 4.5% of explained variation and was not significant.
PRC results show that, across all behaviors, traveling was most strongly associated
with observations in the passage zone, resting was most strongly associated with
Figure 4. Principal
Response
Curve(PRC) analysis
results using the
passage zone (P) as
the baseline (horizontal
line plotted at
0.0) for comparison
of temporal trends in
behaviors associated
with each zone. The
vertical axis at the
right side of the diagram
shows the gradient
for behaviors
on the ordination
axis aligned with
the respective PRC
axis; Forage (For),
Travel (Tvl), Socialize
(Soc), and Rest
(Rst) across time
periods (months indicated
by arrows
along time axis) for
Bolivar ferry dock
(B) and Galveston
ferry dock (G) zones
on the first (PRC1)
and second (PRC2)
principal responsecurve
axes. The first
and second axes,
respectively, depict
60% and 36% (permutation
tests respectively
pseudo-F
= 1263, P = 0.002
and pseudo-F = 770,
P = 0.002) of the total
explained variation
in behavior.
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2016 Vol. 15, No. 4
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observations at the Bolivar ferry dock, and foraging was most strongly associated
with observations at the Galveston ferry dock.
Chi-square goodness-of-fit tests detected significant deviation from the expected
occurrence of behavioral state across zone (χ2 [6, n = 24,780] = 2175.75, P < 0.01)
and time block (χ2 [6, n = 24,780] = 965.39, P < 0.01), and was consistent with the
PRC trends. We observed resting behavior more frequently than expected and traveling
less frequently than expected in Bolivar (Table 5). Traveling was observed
more frequently than expected in the passage (Table 5). In Galveston, socializing
was less frequent than expected, and foraging more frequent (Table 5). Across time
blocks, foraging was more frequent than expected in the morning and less frequent
in the evening (Table 6). Resting was observed less frequently than expected in the
morning and more frequently in the evening (Table 6).
Discussion
This study represents a relatively fine-scale analysis of Common Bottlenose
Dolphin behavior in an area with high levels of vessel activity. While several
studies suggest that heavy vessel traffic can cause animals to vacate or avoid the
area (Bejder et al. 2006; Lusseau 2003, 2005), Common Bottlenose Dolphins are
sighted regularly in Galveston. However, long-term exposure to vessel traffic can
cause hearing loss and leave individuals vulnerable to long-term health problems
(Richardson and Würsig 1997, Romano et al. 2004).
Ultimately, the variables assessed accounted for 8.5% of the variance in behavior.
Each variable was statistically significant, though the percentage of total variation
explained was low. These findings suggest that while shore position, time of
day, and vessel-proximity variables are important factors in group behavior, they
represent only a small portion of the contributing variables in a complex ecosystem.
The fluid social structure documented in Galveston Bay (Bräger et al. 1994)
may also influence group behavioral changes in a complex way that is difficult to
Table 6. Observed (O) and expected (E) occurrence of each behavioral state based on time block.
Significant deviations in behavior were detected (X2 [6, n = 24,780] = 965.39, P < 0.01).
Foraging Resting Socializing Traveling
Morning O:3804, E:2791.5 O:1141, E:1526.1 O:457, E:640.9 O:2680, E:3123.5
Afternoon O:2771, E:3094.1 O:1673, E:1691.5 O:769, E:710.3 O:3745, E:3462.1
Evening O:1984, E:2673.4 O:1865, E:1461.5 O:739, E:613.8 O:3152, E:2991.4
Table 5. Observed (O) and expected (E) occurrence of each behavioral state based on the zone. Significant
deviations in behavior were detected based on the zone of observation (X2 [6, n = 24,780] =
2175.75, P < 0.01).
Foraging Resting Socializing Traveling
Bolivar O:3077, E:3210.8 O:2833, E:1755.3 O:860, E:737.2 O:2526, E:3592.7
Galveston O:2004, E:1400.0 O:454, E:765.3 O:104, E:321.4 O:1491, E:1566.4
Passage O:3478, E:3948.3 O:1392, E:2158.4 O:1001, E:906.5 O:5560, E:295.3
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2016 Vol. 15, No. 4
capture in brief observations. Variance partitioning showed variation in behaviors
was related primarily to spatial (40.0%) and temporal (30.6%) variables, and to a
lesser extent, to vessel proximity to groups (21.2%). These findings may suggest
that the local population has been to some extent desensitized to vessel traffic.
PRC analysis indicated trends in behaviors across months. The 3 observation
zones deviate most from each other during June, July, and August. Dolphins in
Galveston and Bolivar tended to be resting and foraging, whereas socializing was
more consistently associated with observations in the passage zone. Particularly
early in the summer, dolphins in Galveston were more commonly engaged in foraging
and those in Bolivar were more commonly engaged in socializing. Behavior in
Galveston and Bolivar diverged most from the passage during the summer months,
possibly due to recreational boater disruption of these activities in the passage.
Vessels are known to disturb dolphin groups, increasing dive time and inter-breath
intervals (Lusseau 2003, Mattson et al. 2005, Nowacek et al. 2001). The chi-square
test showed that groups were more likely to rest in Bolivar and forage in Galveston.
Groups in Galveston may be seeking areas of less traffic such as the Bolivar ferry
dock to rest in order to avoid such disruptions, though this does not necessarily
indicate the groups are stressed by vessel traffic. The overall trend suggests that
the Galveston ferry dock and Bolivar ferry dock serve as areas for dolphins to rest,
forage, and socialize, particularly during the summer months when overall vessel
activity is highest.
The impact of vessel traffic on group behavior is further illuminated by vessel
activity within various distance ranges. Groups were frequently recorded foraging
very close (V) and close (C) to vessels, likely indicating that vessel-associated
foraging is common within the survey area and contributing to the high total vessel
score associated with foraging behavior. However, when the ferry was very close
to the group, traveling behavior predominated. The fact that groups foraged in the
presence of trawlers but traveled in the presence of the ferry suggests that the type
of vessel and its operation influence group behavior (Mattson et al. 2005, Miller et
al. 2008, Nowacek et al. 2001, Weilgart 2007). When vessels were far or intermediate
from the group and when the ferry was far from the group, socializing and resting
behaviors predominated, likely due to the minimal vessel disruption. Further
research is necessary to determine how the specific type and activity of the vessel
impacts group behavior in this area.
Distance to shore was significantly related to group behavior. At the open-water
distance (>100 m), traveling behavior dominated. The open-water distance to shore
was only possible in the passage as docks were defined as 100-m circles around
the dock center. Because traveling was often observed in the passage zone, it is unsurprising
that the open-water distance is tightly correlated with traveling. Groups
very close to shore were most often observed foraging. This finding may indicate
a predilection for foraging in shallower waters towards the edges of the dredged
channels, as observed in Clearwater, FL (Allen et al. 2001). In shallow water, prey’s
vertical mobility is limited and may make hunting more efficient. This finding may
also indicate a preference for shrimp-boat associated foraging, which tends to occur
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2016 Vol. 15, No. 4
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in shallower areas. Finally, groups intermediate (30–50 m) and far (50–100 m) from
shore were most often socializing or resting. These ranges likely offers the best
habitat to engage in these behaviors with minimal disruptions from passing vessels,
while also avoiding the hazards of shallower water such as rocks, debris, or water
too shallow to allow for swimming.
A notable shift away from foraging in the evening and with later sighting times
was documented in both the CCA and chi-square results. This preference for foraging
in the morning has been observed in several Common Bottlenose Dolphin
populations globally (Alford 2005, Allen et al. 2001, Bräger 1993). Social behavior
occurred with the expected frequency throughout the day, and may be indicative of
the high group fluidity previously documented in Galveston (Bräg er et al. 1994).
A shift in behavior, away from traveling and towards foraging, resting, and socializing,
occurred moving from summer into fall. Recreational vessel traffic tends
to be high in Galveston during June, July, and August. Watercraft and jet skis tend
to alter behavior more frequently than large ships (Mattson et al. 2005), and powerboat
approaches may cause groups to cease foraging to avoid the oncoming vessel
(Lemon et al. 2008). As the presence of these recreational vessels decreases in the
fall, it is logical that avoidance behavior such as traveling observed in the presence
of these types of vessels would decline and foraging, resting, and socializing would
consequently increase. Further study on how the type and activity of the vessel
impacts group behavior will help to determine whether recreational traffic is more
likely to alter group behavior than commercial boats.
Group size was largest when groups were engaged in foraging, and the number
of calves was higher in larger groups. While larger groups may simply be more
likely to have calves in them, this finding also may be a function of the high level
of ship traffic in the survey area. Mothers with calves are considered the most
vulnerable individuals (Buckstaff 2006). Because the ferry lane is constantly subjected
to a high level of vessel activity as a function of its location at the mouth of
Galveston Bay, adults with calves may be seeking larger social groups for added
protection in response to the greater number of vessels. However, the higher number
of calves in large groups may be an artifact due to mothers with calves bringing
their young into foraging groups as a matter of necessity, because calves remain
with their mothers for 3 to 6 years (Buckstaff 2006). A third potential explanation
is that calves are learning to forage through observation in these large groups. Observational
learning was noted in wild and captive Common Bottlenose Dolphins
(Sargeant and Mann 2009, Yeater and Kuczaj 2010).
The dolphin population in Galveston warrants further research; behavioral patterns
beyond those detected in this study are likely to emerge with further study.
Evaluating the impacts of anthropogenic activity on this population and in similar
high-activity areas will improve understanding of how vessel traffic influences
group behavior, and how groups in areas of heavy traffic are affected. Particularly
because Galveston is a very active commercial port, establishing a standard for
behavioral patterns there will prove useful in the event of an accident that impacts
the marine environment.
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2016 Vol. 15, No. 4
Acknowledgments
We thank the Texas Department of Transportation and the Galveston–Port Bolivar ferry
crewmembers and security officials for permitting the use of their vessels, and research assistants
J. Anklam, M. Bache, K. Clark, G. Giannotti, K. Gillis, E. Jones, A. Love, and L.
Miller for their assistance with data collection.
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