Trends and Status of Harbor Seals in Washington State

13 Pages·2003·0.42 MB·English

FEATURE

Trends and Status of Harbor Seals in Washington State: 1978-99
By

Harriet Huber and Jeff Laake

I
n the first half of the twentieth century, Under National Marine Fisheries Service
the number of harbor seals (Phoca vitulina (NMFS) management, Washington harbor
richardsi) in Washington State was severely seals are divided into two stocks: coastal and
reduced by a state-financed population con- inland waters. For both stocks, the observed
trol program, which considered harbor seals population size for 1999 is very close to the
to be predators in direct competition with predicted carrying capacity (K). The current
commercial and sport fishermen. Seals began management philosophy for marine mam­
to recover after the bounty program ended in mals that assumes a density-dependent re-
1960 and the Marine Mammal Protection Act sponse in population growth with MNPL >
was passed in 1972. From 1978 to 1999, biolo- K/2 is supported by growth of harbor seal
gists from the Washington Department of stocks in Washington waters. We expect that
Fish and Wildlife (WDFW) and National Ma- further monitoring of other pinniped and ce­
rine Mammal Laboratory (NMML) flew aerial tacean stocks will also support this concept.
surveys to determine the distribution and This study highlights the importance of
abundance of harbor seals in Washington. We long-term precise monitoring to help under-
used exponential and generalized logistic stand population dynamics and support man-
models to examine population trends and sta- agement decisions.
tus relative to maximum net productivity
level(MNPL)andcarryingcapacity(K). Since
Background
1978, harbor seal counts have increased
threefold, and estimated abundance has in- The U.S. Marine Mammal Protection Act
creased seven- to tenfold since 1970. (MMPA) of 1972 established criteria for man-
AFSC Quarterly Report 1
agement of marine mammals by NMFS and Those “ideal conditions” are rare indeed, but
the U.S. Fish and Wildlife Service that re­ they do exist for harbor seals in Washington
quired that marine mammal populations State. The number of harbor seals residing in
“should not be permitted to diminish beyond Washington State in the early 1970s was esti­
the point at which they cease to be a signifi­ mated to be 2,000-3,000. Beginning in 1978,
cant functioning element in the ecosystem of Steve Jeffries of the WDFW began systematic
which they are a part, and, consistent with surveys of Washington=s harbor seal popula­
this major objective, they should not be per­ tion; the surveys were continued through
mitted to diminish below their optimum sus­ 1999 by the WDFW and NMML.
tainablepopulation.”TheintentoftheMMPA This 22-year time series provides a unique
was clear, but the language was too vague to opportunity to describe population growth of
provide an operational definition for manage­ a recovering and unharvested marine mam­
ment. Ecologist Dr. Lee Eberhardt suggested mal population. Because harbor seals haul
that optimum sustainable population (OSP) out onto land in discrete aggregations at spe­
should be interpreted as the range of popula­ cific times, we were able to count a large pro-
tion sizes from the maximum size (carrying portion of the population to provide a precise
capacity (K)) down to the size which gives measure of population trend. We describe
maximum productivity or maximum sustain- populationgrowthusingexponentialandgen­
able yield (MSY). NMFS adopted the defini­ eralized logistic models and use the models to
tion for OSP as a population level between show that harbor seal populations in Wash­
carrying capacity (K) and the population size ington are above MNPL and near carrying ca­
which provided the maximum net productiv­ pacity. We tested our assumption that there
ity level (MNPL). was no temporal trend in the proportion of
Defining OSP was a first step, but imple­ seals on shore during surveys by comparing
menting the definition proved difficult for re- proportion ashore data collected in 1991-92
searchers because they lacked information with data collected in 1999-2000.
about population parameters and sufficiently
precise data. Difficulties in implementing an
OSP management scheme led to the 1994 METHODS
amendments of the MMPA that provided an
Study Area
alternative approach based on managing inci­
dental take. In this approach, potential bio­ As managed by NMFS, harbor seals in
logical removals (PBR) must remain below a Washington and Oregon have been separated
percentage of a minimum population size. In into coastal and inland stocks because of dif­
the present management scheme, an assess­ ferences in cranial morphology, timing of
ment of population growth rates and status pupping, and genetics. The Washington in-
relative to MNPL can be incorporated into the land stock includes all harbor seals in U.S.
calculation of PBRs. waters east of a line extending north-south
The utility of MNPL was questioned be- between Cape Flattery on the Olympic Penin­
cause it could not be measured precisely in a sula and Bonilla Point on Vancouver Island
well-studied northern fur seal (Callorhinus (Fig. 1). Harbor seals on the outer coast of
ursinus) population. “Under ideal conditions, Washington are part of a stock that includes
MNPL would be determined by accurate and seals in Oregon, from the Columbia River
precise monitoring of a discrete population southward to the Oregon/California border.
unit during natural growth from some level Interchange between inland and coastal
well below MNPL ... to a level above MNPL.”1 stocks is unlikely. No radio-tagged seals from
1
Ragen, T.J. 1995. Maximum net productivity level estimation for the northern fur seal (Callorhinus ursinus)
population of St. Paul Island, Alaska. Mar. Mammal Sci. 11:275-300.
2 October-November-December 2002
Figure 1. Map of harbor seal haul-out sites and survey regions for Washington
coastal and inland stocks. The Washington coastal stock includes Coastal Estuaries
(1) and Olympic Peninsula (2). The inland stock includes Strait of Juan de Fuca (3),
San Juan Islands (4), Eastern Bays (5), Puget Sound (6), and Hood Canal (7).
the inland stock (n = 140) have been observed ined on each census. Because of differences in
in coastal areas or vice versa (n = 188). We di­ timing of pupping, surveys were flown in late
vided harbor seal haul-out sites in Washing- May to mid-June for the coastal stock and Au-
ton into seven survey regions: Coastal gust through September for the inland stock.
Estuaries, Olympic Coast, Strait of Juan de Surveys were scheduled as closely as possible
Fuca, San Juan Islands, Eastern Bays, Puget (tidespermitting)tothetimewhenpeaknum­
Sound, and Hood Canal (Fig.1). Survey re­ bers of pups were expected to be present. All
gions were determined by timing of pupping surveys took place within a week of the peak
and a geographic area that could be surveyed of pupping for each region. Surveys were
within a 3-4 hour tidal window. flown between 2 hours before low tide to 2
hoursafterlowtideinasingleengineplaneat
700 to 800 feet altitude at 80 knots. The total
Survey Methods
number of seals (including pups) present at
Surveys were flown at low tide during the each site was counted from photographic
pupping season when maximum numbers slides.
were onshore. All known haul-out sites were At least two to three surveys were sched­
surveyed and potential new sites were exam­ uled for each region during annual survey
AFSC Quarterly Report 3
windows, though some surveys were canceled density dependent effect and the position of
because of bad weather. A complete survey of MNPL relative to K. The standard logistic
eachregionwasattemptedin1day;ifthiswas curve is obtained when z = 1: per capita pro­
impossiblebecauseofweatherordisturbance, duction is a linear function of N andMNPL/ K
surveys from 2 or 3 days were combined. = 0.5. If z >1, per capita production is a con-
cave (downwards) non-linear function of N
Population Growth Models and MNPL/ K > 0.5 and if z < 1, per capita pro­
duction is a convex (concave upwards)
Two simple non-age-structured determin­
non-linear function of N and MNPL/ K < 0.5.
isticmodelsofpopulationgrowthwereconsid­
An approximate relationship between
ered to represent the growth in the harbor
MNPL/K and z is given by:
sealstocks:exponentialandgeneralizedlogis­
tic. These models are discrete in nature with
an annual time step to represent the annual MNPL / K � (z � 1)�1/ z (3)
pupping pulse. In each case, the population
size (N) in year t is expressed in terms of the
t Incorporating z into the growth model is
population size (N ) in year t-1 plus growth
t-1 important for harbor seal populations be-
(new individuals), which is some fraction of
cause long-lived marine mammals are ex­
N . Exponential growth assumes the popula­
t-1 pected to demonstrate the strongest density
tion grows without limit at a constant annual
dependenteffectcloseto K (z >1). However, in
rate (R ):
max most cases survey data have not been suffi­
ciently precise to estimate z adequately. The
N � N � N R

t t �1 t �1 max (1) parameters Rm ax and z have a strong negative
correlation in the model and diametrically op­
posedparametervaluescanyieldnearlyiden-
Clearly, the exponential model cannot be
ticalpopulationtrajectoriesforportionsofthe
true forever, but populations can experience
overall trajectory (Fig. 2). Without precise
exponential growth prior to approaching K.
population estimates, z will almost surely be
Therefore, it can be used as a null model to
poorly estimated. The correlation between
test for density dependence. In the general­
R and z is lessened by observing the popula­
izedlogisticgrowthmodel,therateofincrease max
tion over a wide-range of growth. Discrimi­
is a function of the population size relative to
nating between the two models in Figure 2
the maximum population size K:
would be nearly impossible if the population
were observed from year 10 and beyond. How-
� � N �z � ever, if it was observed from year 0, the pa­
N � N � N R �1 �� t �1 � � (2) rameterscouldbeestimatedmorepreciselyas
t t �1 t �1 max �� � K � �� the two models imply different starting popu­
lation sizes.
Annual net production is simply the differ­
Growth Model Fitting
ence in consecutive population sizes and the
maximum net productivity level (MNPL) is We based the growth models on our survey
the value of N at which annual net produc­ count data. Fitting growth models to the har­
t-1
tion is maximized. As N /K rangesbetween0 bor seal count data involved finding parame­
t-1
and 1, the realized per capita growth rate val­ ter values which provided the “best fit” to the
ues range between R and 0. data. The best fit depended on the assumed
max
The shape of the growth curve and the per statistical model for the observed data. We
capita production curve is governed by the ex­ used deterministic population growth models
ponent z, which determines the timing of the (i.e., given the parameter values, the popula-
4 October-November-December 2002
tionsizeinyear N determinedexactlythesize
t
in year N ) but our count of harbor seals rep­
t+1
resented only a proportion of the population
because some seals were always in the water
and consequently missed by the aerial sur­
veys. An estimate of the proportion of seals on
land (p) during surveys was determined using
VHF radio tags in three areas in the coastal
stock and three areas in the inland stock in
1991 and 1992 . If we had estimated the pro- Figure 2. Two similar generalized logistic growth
portion ashore for each region in each year, curves achieved by choosing different values for
thegrowthmodelcouldhavebeenbasedones­ z, R , and initial population size.
max
timates of population size. We fitted growth
models to the count data and our inference to inland waters stock did not begin until 1983.
population growth depends on the assump­ A simple solution was to limit counts to years
tion that there was no temporal trend in the in which seals were counted in all regions.
proportion of seals on shore during surveys. However, this would have wasted valuable
We tested that assumption by comparing pro- data and severely restricted the time frame of
portion ashore data collected in 1991-92 with surveys.
data collected in 1999-2000 (see section on Instead, we fitted separate growth curves
Proportion Ashore). for each of the seven regions (Fig. 1) using the
The parameters of the growth model are counts that were available for each region.
R , K, z, and an intercept N , which is an ini­ Fitting separate growth models to the regions
max 0
tial size of the population onshore for some ar­ used only observed data and allowed varia­
bitrarily chosen time designated as t =0. We tion in the number of replicate counts be-
used only the counts to fit the growth models tween regions but required more parameters
but to express initial population size (N ) and that applied to the regions and not the entire
0
carrying capacity (K) in terms of the popula­ population. Any random movement between
tion we multiplied N and K by the correction regions would create additional variation in
0
factor (CF) of 1.53 to account for seals in the counts and any directed movement (i.e., per­
water during surveys. The parameters R manent emigration/immigration) would be
max
and z remain unchanged by the constant scal­ reflectedintheparametersofregionalgrowth
ingbutwouldbeaffectedbyanytrendsin p . models.
t
One of the complications in the harbor seal Fitting the growth models separately for
data was missing counts. While it was not each region expanded the number of esti­
necessary to have a count for each year, ide­ mated parameters substantially. However,
ally, for any one year the entire range should it’s reasonable to hold some of the parameters
have been counted completely. However, in constant for some or all of the regions. In gen­
certain instances some regions were not sur­ eral, z is difficult to estimate and it was un­
veyed due to bad weather, disturbance, logis­ likely that the data would support a different
tical problems, or lack of funding. In other z for each region. Also, R was likely to be
max
instances, surveys were begun in one region constant among regions unless there was a
and then expanded into other regions over strong movement component. However, K
time. For example, the Coastal Estuaries in and N were unlikely to be constant across re­
0
Washington were surveyed as early as 1975 gions because of differences in region size and
but surveys of the Olympic Coast region were habitat quality.
not begun until 1980 (Table 1). Although We fitted a series of models for each of the
there were some counts for inland waters for five regions in the inland Washington stock
1978, consistent counts for all regions in the and separately for the two regions in the
AFSC Quarterly Report 5
Table 1. Average annual counts for the two regions in the coastal stock and the five regions in the inland stock of Washington harbor seals for 1975-99.
Coastal stock Inland stock
Year Coastal Estuaries Olympic Peninsula Strait of Juan de Fuca San Juan Islands Eastern Bays Puget Sound Hood Canal
1975 1,694
1976 1,742
1977 2,082
1978 2,570 417 852 755 337 732
1979
1980 2,864 1,639
1981 4,408 1,677
1982 5,197
1983 4,416 2,359 883 1,688 1,347
1984 4,203 1,025 2,308 1,727
1985 6,008 1,288 1,859 1,416 732
1986 4,807 1,789 849 2,193 1,613
1987 7,600 3,204 1,016 2,179 1,751
1988 6,796 1,518 2,847 1,902
1989 6,475 3,667 1,402 2,884 1,839
1990 1,142 3,157
1991 8,681 3,832 1,238 3,510 1,939 891 1,206
1992 7,761 4,191 1,580 3,640 2,102 708 989
1993 8,161 3,544 2,154 4,524 2,175 972 592
1994 5,786 3,505 1,488 4,529 2,144 854
1995 6,492 4,867 2,281 4,852 2,068
1996 7,191 3,124 1,988 5,330 2,521 1,119 975
1997 7,643 4,221 2,284 4,277 2,008 1,060 695
1998 1,734 4,441 1,810 1,026 577
1999 7,117 3,313 1,752 3,588 1,873 1,025 711
Washington portion of the coastal stock. For Proportion Ashore
each model we assumed that N and K (for the
0 Our growth model based on seal counts
logistic model only) were different for each re­
would only reflect population growth if there
gion. We fitted exponential models that as­
was no trend in the proportion of seals ashore
sumed R was constant or varied by region.
max during the surveys. A trend could occur if,
Likewise, we fitted logistic models that as­
over the two decades of surveys, the seals
sumed R and z were either constant or var­
max spent more or less time ashore as the popula­
ied by region. After selecting the best logistic
tion increased. For example, seals could
model for each stock, we also explored
spend less time on shore because they needed
whether R and z varied by stock. We used
max to spend more time foraging as the population
the small sample Akaike Information Crite­
increased and food resources decreased. We
rion (AICc) to choose the most parsimonious
examined whether the proportion ashore
model (i.e., fewest number of parameters that
changed in Grays Harbor or Boundary Bay
adequately explains the data) with minimum
during the 1990s. VHF radio transmitters
AICc. We evaluated the model goodness of fit
were applied to harbor seals in 1991 at Grays
withtheKolmogorov-Smirnovtesttoexamine
Harbor (coastal stock) and in 1992 at Bound­
whether the standardized residuals were nor­
ary Bay (inland stock) to estimate the propor­
mally distributed. For graphical display of
tion of harbor seals ashore during those
the growth curve for each stock and the entire
surveys. We applied the same techniques at
state we summed the predicted values across
Grays Harbor (GH) in 1999 and Boundary
regions and for observed values, we summed
Bay (BB) in 2000. During each survey, all
the average of regional counts for years in
seals with active tags were determined either
which one or more counts were available for
to be ashore or not. Using each seal as a sam­
each region. To supplement the observed val­
ple, we modeled the number of surveys the
ues for the entire state, we added predicted
seal was ashore using a generalized linear
counts for a few years with missing counts in
model based on a binomial distribution and
one or two areas.
logit link function. We fitted models that in­
cluded four age-sex categories (adult female,
adult male, pup and sub-adult), year (1991-92
Status Determination
versus 1999-2000) and region (GH or BB) and
We considered the coastal and inland har­ their interactions. Using the most general
bor seal stocks to be at OSP if the predicted model with all interactions, we estimated an
population size was above MNPL. This was over-dispersion scale (residual deviance/df) to
most easily determined by comparing popula­ adjustmodelselectionusingminimumQAICc
tion sizes as a proportion of K, because (3) pro­ (quasi likelihood adjustment to AICc). We
vides a simple computation of MNPL/K. For also examined whether any observed annual
each parametric bootstrap, we compared differences in the proportion ashore would in­
N� / K to MNPL/K as given by (3). If fewer fluence our conclusion regarding population
1999
than 5% of the replicates were below MNPL/K growth.
then we concluded that the stock was at OSP.
We also constructed bootstrap confidence in­
tervals for N� / K, MNPL/ K , and RESULTS
1999
�
N 1999 / MNPL. A similar approach was taken Aerial Surveys
to investigate whether a spotted dolphin pop­
ulation was above or below MNPL. Inthe22yearsfrom1978to1999,countsof
harbor seals in Washington State increased
AFSC Quarterly Report 7
gions. Most growth occurred
in the San Juan Islands and
the Strait of Juan de Fuca
andtheleastgrowthoccurred
in Hood Canal (Table 1).
Growth Model
The generalized logistic
model with constant R and
max
z was clearly the best model
(Table 2) to describe the
growth of the inland and
coastal seal stocks. The large
discrepancy in AICc between
exponential and logistic mod­
els provides strong evidence
for a density dependent re­
sponse in population growth
(Table 2). When we examined
models that shared R and z
max
parameters between stocks
the choice was less clear. We
selected the model with sepa­
rate parameters for each
stock because these stocks
are genetically different and
areunlikelytobedemograph­
ically linked. As expected,
the initial size and carrying
capacity of the onshore popu­
lation were estimated with
reasonable precision,
whereas lesser precision was
achieved for R and z. The
max
Figure 3. Generalized logistic growth curves for coastal estimates of initial size using
estuaries and outer Olympic Peninsula coast regions, and 1970 as the base year were
their sum (Washington portion of coastal stock). quite consistent with counts
for 1970-72, with the excep­
tion of the San Juan Islands
nearly threefold—from 6,786 surveys of inland waters did region where our estimate
to 19,379. The earliest sur­ not begin until 1983 (Figs. 3, was more than twice the orig­
veys began in 1975 in the 4). Regions were not always inal estimate. The growth
Coastal Estuaries (Table 1). surveyed annually nor were curves demonstrate apparent
By1978surveyshadbegunin they surveyed an equal num­ slowing of the growth rate as
all regions except the outer ber of times per year. Popula­ numbers approached current
coast of the Olympic Penin­ tion growth between 1978 carrying capacity (Figs. 3-5)
sula where they began in and 1999 was not evenly dis­ and demonstrate a reason-
1980 (Table 1). Consistent tributed throughout all re­ able fit. Pooled standardized
8 October-November-December 2002
Figure 4. Generalized logistic growth curves for Strait of Juan de Fuca, Eastern Bays, San Juan Islands, Hood Canal, and South Puget Sound
regions and their sum (inland Washington stock).
obvious differences in the proportions ashore
(Fig. 6) were associated with the age and sex
of the seal. As expected during the pupping
season, adult males and subadults spent con­
siderably less time ashore than adult females
and pups. The full model with 16 parameters
for age-sex, year, region and their interac­
tions explained 59% of the deviance, and the
residual deviance/df (124.82/113 = 1.11) sug­
gested a minor amount of over-dispersion.
The model with minimum QAICc included all
of the main effects and two-way interactions
Figure 5. Generalized logistic growth curve for
harbor seals in Washington State expressed as (QAICc = 145.6), although a much simpler
population size. The observed values for 1978, modelwithonlyage-sex,year,andtheirinter­
1983, 1986, 1994 and 1995 were supplemented action had a similar value (QAICc = 145.8).
with model predictions for regions with missing The model with age-sex only (QAICc = 150.3)
counts which accounted for 17%, 12%, 13%, 5%
accountedfor63%oftheexplaineddevianceof
and 8% of the total abundance.
the full model.
The influence of year was not consistent
residuals did not differ from the assumed nor­ across the age-sex classes (Fig. 6). Relative to
mal distribution (KS = 0.05, P = 0.21). 1991-92, females and pups spent less time
ashore in 1999-2000, whereas adult males
Status Relative to OSP and subadults spent more time ashore in
those years. Most of the annual difference
Although the evidence is not strong, the
and the interaction resulted from shifts at
growth models of both stocks agree with the
Grays Harbor. We computed an annual aver-
speculationthatMNPLisindeedgreaterthan
age proportion ashore for all seals (Table 4) by
0.5 K (Table 3). The predicted population size
weighting age-sex specific values by the ex­
for 1999 is very close to K for both stocks (Ta­
pected age-sex proportions of seals in the pop­
ble 3), and none of the bootstrap replicates
ulation which adjusted for differences in
predicted a 1999 population size that was be-
sample sizes between the age-sex classes
low MNPL. These results provide overwhelm­
across years. The largest decrease in the av-
ing evidence that both stocks in Washington
are above MNPL and meet the guidelines for
OSP. These stocks could decline or be reduced
by 20% and they would still be above MNPL
with a high degree of certainty (Table 3). The
coastal stock recovered earlier than the in-
land stock as evidenced by the status of the
stocks in 1990 (Table 3).
Proportion Ashore
We tagged 29 seals and conducted five sur­
Figure 6. Average proportion ashore for
veys at Grays Harbor (in the Coastal Estu­
radio-tagged seals in each of the 4 age-sex
aries) in 1999 and tagged 43 seals and categories and a weighted average (Table 4) for
conducted seven surveys at Boundary Bay (in Boundary Bay (BB) in 1992 and 2000 and Grays
the Eastern Bays) in 2000 (Table 4). The most Harbor (GH) in 1991 and 1999.
10 October-November-December 2002