\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,greek,english]{babel}



\usepackage{float}








\begin{document}

\title{Sea otter diet nutrient composition with respect to recolonization, life
history, and season in southern Southeast Alaska}



\author[1]{Nicole LaRoche}%
\author[2]{Sydney King}%
\author[3]{Emily Fergusson}%
\author[1]{Ginny  Eckert}%
\author[2]{Heidi Pearson}%
\affil[1]{University of Alaska Fairbanks}%
\affil[2]{University of Alaska Southeast}%
\affil[3]{NOAA Fisheries Auke Bay Laboratories}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
The sea otter (Enhydra lutris) population of Southeast Alaska has been
growing at a higher rate than other regions along the Pacific coast.
While good for the recovery of this endangered species, rapid population
growth of this apex predator can create a human-wildlife conflict,
negatively impacting commercial and subsistence fishing. Previous
foraging studies throughout the sea otter range have shown they will
reduce invertebrate prey biomass when recolonizing an area. The goal of
this study was to examine and quantify the energetic content of sea
otter diets through direct foraging observations and prey collection.
Our study area, Prince of Wales Island in southern Southeast Alaska,
exhibits a gradient of sea otter recolonization, thus providing a
natural experiment to test diet change in regions with different
recolonization histories. Sea otter prey items were collected in three
seasons (spring, summer, winter) to measure caloric value and lipid and
protein content. We observed 3,523 sea otter dives during the spring and
summer. A majority of the sea otter diet consisted of clams. Sea otters
in newly recolonized areas had lower diet diversity, higher kcal/gram
intake rates, and higher energetic intake rates. Females with pups had
the highest diet diversity and the lowest energetic intake rates
(calories per gram consumed). Sea otter energetic intake rates were
higher in the fall and winter vs. spring and summer. Sea cucumber energy
and lipid content appeared to correspond with times when sea otters
consumed the highest proportion of sea cucumbers. These caloric
variations are an important component of understanding ecosystem level
effects sea otters have in the nearshore environment.%
\end{abstract}%



\sloppy


\textbf{1. Introduction}

The nutritional ecology of marine predators is poorly understood
compared to terrestrial predators. Classic foraging theory suggests that
consumers should target prey that maximizes their net rate of energetic
gain . As energetic intake was the focus, studies addressed consumers'
overall net energetic consumption as a factor in prey selection.
However, more recent studies have focused on predators' nutritional
needs and how prey choice varies according to macronutrient composition
. Consumers may target lower energy prey for various reasons, including
reproductive status. For example, Machovsky-Capuska et al. (2018)
revealed that male Australasian gannets (\emph{Morus serrator} )
consistently foraged for fish with higher protein-to-lipid ratios,
whereas females foraged for fish with higher lipid-to-protein ratios.
One potential explanation is the differential dietary response by female
and male parents according to the changing needs of growing chicks.
Similarly, female sea otters (\emph{Enhydra lutris} ) will switch their
foraging tactics when caring for a pup, which may represent a trade-off
between maximizing potential energy return and meeting pup needs .

Sea otters are a dynamic species in which to study nutritional ecology
because of their unique adaptations to the marine environment. Unlike
most marine mammals, sea otters do not have blubber to keep them warm.
Instead, sea otters maintain very high metabolisms . Various studies
estimate sea otters consume anywhere from 19 to 39\% of their body
weight in food per day to sustain these elevated metabolic costs .
Because of their voracious appetites, sea otters can exert large effects
on the nearshore marine ecosystem within relatively short periods .
These effects are particularly evident in kelp forests, where sea otters
suppress the grazers, sea urchins (\emph{Strongylocentrotus} spp.),
which in turn relieves pressure on kelp. This relief of grazing pressure
helps to increase kelp forest density, which has been shown to increase
juvenile fish habitat and increase overall species diversity in the
system.

Historical records show that sea otters once inhabited nearshore
ecosystems of the Pacific Ocean from Japan to Baja California. However,
by the late 19\textsuperscript{th} Century, there were only 11 remnant
populations within their once continuous distribution due to hunting for
the lucrative fur markets in Russia and China. In 1911, sea otters were
protected from hunting by the International Fur Seal Treaty. By this
time, sea otters were extirpated from Southeast Alaska . To restore sea
otters to their historical range, the Alaska Department of Fish and Game
and the Atomic Energy Commission initiated a translocation program and,
in the 1960s, relocated about 400 sea otters from the Aleutian Islands
to six locations in Southeast Alaska. Since the translocation, sea
otters have expanded their range and increased in numbers. The most
recent range-wide sea otter aerial counts in 2010--2011 estimated that
approximately 25,000 sea otters were present in Southeast Alaska . The
expansion of sea otters from the six translocation sites in Southeast
Alaska into unoccupied habitat over time allows for a `space-for-time'
substitution , in which the longer-term effects (positive, neutral, and
negative) of sea otters on the nearshore ecosystem can be seen in areas
of longer occupation.

Prince of Wales Island (POW), along with its neighboring islands, in
southern Southeast Alaska has two original release locations. Hoyt
(2015) studied sea otter diets around POW for three years (2010--2012)
focusing on sea otter impacts on commercially important species. Hoyt
(2015) found that the number of species consumed by sea otters increased
as time since recolonization increased, and sea otters reduced the
abundance of commercially important species. The sea cucumber
(\emph{Apostichopus californicus} ) fishery is an example of a
commercial shellfishery impacted by sea otters. Previous studies showed
that sea otter presence caused a decline of sea cucumbers in all regions
where sea otters were present for \textgreater{} 15 years . Many sea
cucumber fishery regions have been closed due to declining sea cucumber
abundance after sea otters have recolonized the regions .

Measuring sea otter energetic intake rate is a widely used method to
measure changes in diet and to assign quantitative values to sea otter
nutritional needs . In Alaska, the Alaska Science Center, a part of the
United States Geological Survey (USGS), maintains a database on species-
and size-specific energetic values for sea otter prey items. Many of
these values come from California invertebrate collections and published
literature . These values are used to create energetic models and
biomass estimates for each sea otter prey species; however, using prey
values from other regions could lead to inaccurate consumption estimates
for sea otters in Alaska. Similarly, a comprehensive analysis of the
biochemical composition of sea otter prey was conducted in varying
seasons in California but is absent for Alaskan prey . A preliminary
study in Sitka, Alaska, showed that the preferred prey of sea otters
(clams) was not highest in overall caloric content or lipid content when
compared to all available prey items . This preliminary work is a driver
for further investigation of sea otter diet analysis in Southeast
Alaska.

Our goal for this study was to analyze the relationship between sea
otter diet and prey nutritional composition. Our objectives were to: 1)
investigate the macronutrient quality of sea otter prey in southern
Southeast Alaska across seasons; and 2) examine diet composition
according to prey macronutrient composition across different sea otter
metrics: (i) time since recolonization, (ii) sea otter sex and
reproductive class, and iii) season. This work increases our
understanding of sea otter prey composition according to the energy and
macronutrient content in Southeast Alaska, which, in turn, gives us a
better understanding of the invertebrates removed from the nearshore
system due to sea otter predation, and why. This increases the potential
to predict future impacts of sea otters on nearshore ecosystems,
including changes to kelp forest cover and reductions in commercial and
subsistence species due to sea otter predation.

\textbf{2. Methods}

\emph{2.1 Visual foraging observations}

We observed sea otter foraging behavior from May to August 2018 on the
western side and neighboring islands of POW. Sampling was stratified by
time since recolonization, based on US Fish and Wildlife Service (USFWS)
aerial surveys. Three periods were denoted from the surveys: zone 1
(\textgreater{} 30 years present), zone 2 (\textless{} 30 years and
\textgreater{} 15 years present), and zone 3 (\textless{} 15 years and
\textgreater{} 7 years present) (Fig. 1). In each zone, a minimum of 300
foraging dives were recorded. Because zone 2 makes up a majority of POW,
most foraging dives occurred in this zone.

Foraging observations were made from shore to assess sea otter diet
composition. Questar telescopes (50X) were used to follow individual sea
otters for one foraging bout (up to 20 dives per sea otter). The
observer recorded the following foraging metrics: prey item (to species
level when possible), prey size (based on an estimated sea otter paw
width of 5 cm and categorized into \textless{}  1/3  of the paw,
\textgreater{}  1/3  and \textless{}  2/3  of the paw, or the whole paw), the
proportion of the prey item consumed, GPS location (approximated based
on GPS location of the telescope and distance/bearing to the sea otter),
prey handling time (defined as the amount of time the sea otter spent
manipulating and eating the prey), time spent diving, and total time
spent at the surface. The following sea otter metrics were also recorded
for each foraging bout: sex, reproductive status, and age class. Males
were identified by the presence of a penile bulge, whereas females were
identified if there was a clear lack of penile bulge, or if they had a
pup. If sex was not confirmed, nor pup was observed, the sex was
categorized as ``unknown.'' When possible, age class was determined as
adult or juvenile by visual assessment of size and amount of grizzled
fur .

We calculated the caloric intake for sea otters based on visual foraging
observations using the Sea Otter Foraging Analysis (SOFA) program, which
is based in Matlab (MathWorks) and maintained by the USGS Alaska Science
Center in Anchorage, AK. SOFA uses a Monte Carlo-based simulation to
account for unknown prey items and potential sampling bias. SOFA is a
Bayesian model that provides the estimated biomass for individual prey
types across time since recolonization, reproductive status, and sex.
All SOFA outputs are reported as means with standard deviation. The
consumption rates for each prey species were assigned for each foraging
bout using the estimated prey size relative to a sea otter paw width.
Prey diversity for each region was calculated using the Shannon-Wiener
Index . Success rate, which is defined as the percentage of dives in a
bout where the sea otter came up with food, was calculated for each sea
otter metric.

\emph{2.2 Prey sampling}

Potential prey items to be collected for macronutrient and energetic
analyses were selected based on existing literature on sea otter diets
in Southeast Alaska . Five functional prey groups (crabs, clams, sea
cucumbers, snails, sea urchins) were identified that were composed of 13
target species for analysis. These five functional groups made up 95\%
of sea otter diets (in terms of biomass) from visual foraging
observations. Five individuals of each target species were collected at
two sites (Fig. 1) in May 2018, August 2018, and February 2019. All
samples were collected in the intertidal zone. Two collection sites were
selected that encompassed the foraging observation sites and had
reliable access. Craig (Site 1) and Soda Bay (Site 2) represented
differences in sea otter occupation time (\textgreater{} 15 years for
Craig, and \textgreater{} 7 years for Soda Bay). Where there were more
sea otters present, there were less abundant invertebrate species for
collection. Additional samples were opportunistically collected around
POW if they were not present or in high enough abundance in the two
designated sites. Samples were held in seawater-filled buckets, cleaned
of sand and dirt, and then frozen at -18\selectlanguage{ngerman}°C.

In the lab, samples were thawed, weighed, measured, and separated into
edible and inedible tissues. For bivalves, decapods, gastropods, and sea
urchins, all hard parts were removed and discarded, as they were
considered inedible as the sea otter excretes these contents . For sea
cucumbers, the entire organism was considered edible. Only crabs were
processed separately by sex. Remaining edible tissues were weighed and
homogenized in a Cuisinart Mini-prep food processor. A maximum of 4 g of
tissue was dried in a LECO Thermogravimetric Analyzer 701 (TGA) dryer at
135°C, or in a gravity convection oven (VWR Symphony 414004-552) at
70°C. Standards and duplicates were run with each dryer to confirm
consistent moisture values.

\emph{2.3 Energy content nutritional analysis}

We measured energetic value and proximate composition (the proportion of
protein content, lipid content, moisture, and percent ash) for sea otter
prey items. We used previously established methods to measure energy
density using a Parr 6725 semi-micro bomb calorimeter. Standards and
replicates were used to confirm consistent calorimeter readings. Lipid
content was determined using previously established methods using a
sulfo-phospho-vanillin colorimetric analysis. Protein content was
estimated by multiplying total nitrogen content by 6.25, which accounts
for the nitrogen content of protein . Nitrogen content was measured with
a FlashSmart elemental analyzer coupled to a Delta-V continuous-flow
isotope ratio mass spectrometer (Thermo Scientific, Waltham,
Massachusetts, USA). Carbohydrate content was not assessed as it is
assumed to be negligible in marine invertebrates . Ash content was
processed at 600°C and measured with a LECO Thermogravimetric Analyzer
701 (TGA) dryer. Only samples with \textgreater{} 1 g of dried material
were able to be combusted for percent ash content.

\emph{2.4 Statistical analysis of sea otter prey}

To test our first objective (investigate the macronutrient quality of
sea otter prey across seasons), we calculated the percent protein and
lipid of each prey group using the energy equivalents of 9.5 kcal/g for
lipid and 5.7 kcal/g for protein . We used PRIMER v7 with a one-way
analysis of similarity (ANOSIM) procedure with season as factors for
each prey group, excluding crabs (\selectlanguage{greek}α \selectlanguage{english}= 0.01). Snails had too few samples
to conduct pairwise seasonal comparisons. Mussels were excluded from
results because they were a very small portion (\textless{} 1\%) of the
sea otter diet around POW. Crabs were analyzed separately with season
and sex as factors, using a two-way ANOSIM (\selectlanguage{greek}α \selectlanguage{english}= 0.01).

To test our second objective (compare diet composition and prey
macronutrient composition across different sea otter metrics), we
calculated regional-level concentrations of macronutrient composition of
sea otter diets using established methods . In brief, first, we
converted the dry mass average (kcal, lipid, and protein) for each
functional prey group to a wet mass value. Second, using the proportion
of diet (also in wet mass) from SOFA outputs for each functional prey
group and the average prey value (kcal, lipid, and protein), we
calculated an average for each prey and macronutrient and added all
individual groups together. Finally, we divided this newly calculated
wet mass by dry mass to get the nutrient composition of dry mass.
Statistical comparisons between sea otter metrics tested were not
possible because we were not able to study known individual sea otters
and all data were used at a regional scale (Western POW and surrounding
islands).

Due to weather and light limitations, it was only possible to conduct
visual foraging observations during spring and summer. To estimate
year-round diets, we used results from stable isotope analysis . Diets
estimates were made using stable isotope (\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N) analysis of both sea otter vibrissae and the
prey present around POW using a Bayesian model to estimate percent of
total diet for functional prey groups. These diet estimates were used in
the calculations for seasonal sea otter macronutrient contribution and
comparison to changes in sea otter prey. All datasets are archived in a
publicly accessible database with the Knowledge Network for
Biocomplexity .

\textbf{3. Results}

\emph{3.1 Sea otter diets across Prince of Wales}

Foraging records consisted of 362 foraging bouts. In total, 3,523 dives
were recorded between May 6, 2018, and August 13, 2018. The overall
success rate was 89.9\%, and diet diversity (H') was 0.81. Mean dive
time was 88.4 seconds (\selectlanguage{ngerman}± 44.5), and mean surface time was 56.4 seconds
(± 42.3). The mean energy intake rate was 7.3 ± 0.22 Kcal/min. Sea
otters were observed to consume a total of 44 prey items. When prey
selection was examined irrespective of habitat type or site, the
dominant prey categories (making up 97.5\% of the total diet by biomass)
calculated by SOFA were clams (80.9 ± 2.21\%), sea cucumbers (8.5 ±
1.13\%), crabs (3.8 ± 0.44\%), snails (2.7 ± 0.39\%), and sea urchins
(1.6\% ± 0.28\%). Within the clam category, butter clams
(\emph{Saxidomus gigantea} ) were the predominant species, comprising
36.9 ± 1.61\% of the overall diet.

We observed differences in sea otter diets across recolonization zones
and reproductive status. Species diversity and intake rate (kcal/min)
varied by recolonization zone (Table 1). The newest recolonization zone
(\textgreater{} 7 years) had the lowest species diversity (H' = 0.39)
and highest intake rate (11.06 ± 0.8 Kcal/min). The zone where sea
otters have been present for the longest period of time (\textgreater{}
30 years) had the highest species diversity (H' = 1.19) and lowest
intake rate (5.7 ± 0.8 Kcal/min). Clams were the main prey consumed
across all recolonization zones (Fig. 2A). Species diversity and intake
rate varied by reproductive status (Table 1). Although clams were the
dominant prey, sea otter diets varied by reproductive status; females
with pups had a more varied diet and higher species diversity than
females without pups and males (Table 1, Fig. 2B). Females with pups had
the highest species diversity (H' = 1.23) and highest success rate
(90\%) whereas males had the lowest species diversity (H' = 0.34).

\emph{3.2 Macronutrient content in sea otter diets}

Energetic intake (kcal/gram) and percent protein composition varied by
recolonization zone (Fig. 3A) and reproductive status (Fig. 3B).
Energetic intake, percent lipid, and protein composition varied by
season (Fig. 3D). For example, fall and winter varied from spring and
summer in overall energetic intake (fall: 4.3 Kcal/gram, winter: 4.2
Kcal/gram, spring: 3.8 Kcal/gram, summer: 3.8 Kcal/gram). The variation
in energetic intake is driven by both percent protein and percent lipid
(fall: 8.0\% lipid and 58.4\% protein, winter: 8.1\% lipid and 58.0\%
protein, spring: 7.4\% lipid and 51.3\% protein, summer: 7.5\% lipid and
51.1\% protein). In contrast, females with and without pups had
variation in overall energetic content driven only by the percent
protein in the diet (with pup: 3.9 Kcal/gram, 7.2\% lipid, and 53.6\%
protein, without pup: 4.4 Kcal/gram, 7.4\% lipid, and 61.2\% protein).

\emph{3.3 Energetic content of sea otter prey}

Overall, sea otter prey had a similar makeup of protein and lipid
content; sea urchins were the only prey group significantly different
from other prey groups for lipid-to-protein ratio (Fig. 4, p \textless{}
0.01). Functional prey groups varied in their energy, lipid, and protein
content across seasons (Table 2). Across all seasons, sea cucumbers
exhibited lower energy than all other prey types (Fig. 5), and their
energy and lipid varied significantly by season (Table 2). Sea urchins
had significant variability in lipid content across seasons (Table 2).
Clams exhibited a significant change in energy and lipid over seasons as
well, but R values were low, which means the overall seasonal effect was
low (Table 2). Crabs did not vary significantly across seasons or sex.
Snails did not vary significantly across seasons. Pairwise comparisons
for all prey groups and seasons revealed significant differences in
energy for clams and sea cucumbers (Table 3). Lipid varied for clams,
sea cucumbers, and sea urchins, whereas protein only varied between
seasons for clams. Snails were not compared across seasons due to the
small sample size.

When we compared year-round energetic changes in sea otter prey with
diet proportion estimates, we found consumption rates of most functional
prey groups did not correlate with energy density. Clam consumption, the
major diet item for sea otters in this study, did not correspond with
changes in energy (Fig. 6A) or lipid (Fig. 6C). Based on LaRoche et al.
(2021), sea otters consumed more clams in the fall and winter months,
when the energetic and lipid contents of clams were lower than the
spring season. Sea cucumber consumption, the second most abundant diet
contribution, did appear to correspond with changes in energy (Fig. 6B)
and lipid (Fig. 6D). Sea otters decreased their consumption of sea
cucumber in the fall and winter months, which corresponded with
declining energetic value.

\textbf{4. Discussion}

The main sea otter diet component regardless of location, sex, age, and
season was clams. Sea otter intake rates at the POW regional level were
comparable to studies of sea otters at or near carrying capacity ,
showing that locations around POW may be reaching carrying capacity.
There were differences in the proportion of diet and prey composition
according to time since recolonization, reproductive status, and season.

\emph{4.1 Time since recolonization~}

Time since recolonization affects the prey composition of sea otter
diets. Although clams comprise the majority of the diet in all regions
of POW, there were differences in prey items across recolonization
zones. The areas of POW that have been colonized for the least amount of
time (zone 3, colonized for \textgreater{} 7 years) had the lowest
species diversity in prey and highest energy recovery rates. In this
zone, clams were overwhelmingly present in the diets of sea otters. This
was similar to previous studies in mixed sediment communities in
Southeast Alaska, where sea otters focus on fewer, high-quality prey
species (e.g., sea urchins in rocky habitats, large clams in
soft-sediment habitats) in newly occupied areas, and eventually
diversify prey species as sea otter populations persist . Lipid content
in sea otter diets was consistent across recolonization zones, but
overall energy rate gain was slightly higher in zone 3, which may be due
to the ability to obtain larger prey in newly colonized areas. Foraging
records for the areas where sea otters had been present the longest
(zone 1, colonized for \textgreater{} 30 years) had an intake rate that
was comparable to previous studies where sea otters were at carrying
capacity . This shows that the sea otters in this recolonization zone of
POW are likely at or near carrying capacity, which is in line with
modeling from aerial survey data from the region .

When considering sea otter diets and how sea otter invertebrate removal
can affect the nearshore system, it is important to look at diet
variation across recolonization zones as diets in the newly colonized
areas were less diverse, which is similar to past studies in Alaska . In
rocky habitats of Southeast Alaska and British Columbia, sea otters in
newly colonized regions consumed a majority of red sea urchins . In the
present study, the areas we observed where sea otters are newly
colonizing were soft-sediment habitat ripe with large butter clams,
which were the overwhelmingly predominant prey item.

\emph{4.2 Reproductive status}

Female sea otters with pups had a more varied diet composition than
females without pups and males. The difference in the diet was the
largest shift among all tested metrics. Females with pups ate a higher
proportion of crabs, sea cucumbers, and sea urchins than sea otters
without pups. Sea cucumbers are the functional prey group with the
lowest energetic content, therefore females with pups are obtaining
fewer calories per gram of food consumed. This is likely due to females
with pups opting for a risk-averse strategy, instead of searching for
the highest quality prey and risking no success at all. One possibility
could be that a female with a pup would rather come up with prey every
dive instead of risking no success for a higher effort prey (such as
choosing a slow-moving sea cucumber laying the ocean floor, instead of
digging for a clam or a fast-moving crab). Success rates were high
across the POW region, but females with pups had the highest success
rate. Other studies have reported females to vary their diet according
to reproductive state. In California, tagged female sea otters switched
their foraging strategies and prey types consumed when they had no pup,
small pups, and large pups .

There were other sex-specific differences in prey type. Males were
observed to eat more snails than females, while females with pups were
never observed to eat snails. In previous studies, snail specialists
have been linked to poor overall body condition and higher death rates
due to disease . Because we did not follow individuals in this study, we
were unable to determine if there was a similar pattern in Southeast
Alaska sea otters. In addition, although the prevalence of geoduck clams
(\emph{Panopea generosa} ) in diets was low in our study, with only
seven observations of geoduck clams consumed throughout the region, all
of these were consumed by males. Geoduck clams are higher risk prey
because of the increased effort needed to excavate them, thus, sea
otters generally make several dives to recover one geoduck clam . We did
not observe sea otters foraging for geoduck clams in the most recently
colonized zones, which matches previous studies in the same region .
These areas may have larger clams that reside in shallower areas (e.g.,
butter clams) creating a more efficient risk-reward ratio. Dietary
differences between males and females are worth noting, as males are
more likely to expand into new regions first . As new regions are being
recolonized, knowing the diet preferences of males can help to predict
invertebrate predation with relation to species that are of interest to
humans.

Lipid content and energetic gain per gram were lowest for females with
pups. There were no significant differences in dive or surface times for
females with pups vs. other age/sex classes, which could be an artifact
of eating less calorically-rich prey. However, females with pups have
the highest foraging success rate when compared to females without pups
and males, which could indicate that females with pups select less
energy-rich prey items over the risk of no success. Previous studies of
sea otter energetics showed that female sea otters with large pups
operate at an energetic deficit by the time a pup reaches weaning age .
In the present study, this deficit may be evidenced by the lower
energetic gain per gram for females with pups. However, there are a
multitude of other risks females with pups must contend with while
foraging. In areas where food is limited, females with large pups may
reach a maximum physical amount of time allowable for foraging . They
also encounter constraints with respect to allocating energy to nursing
vs. providing prey to their pup.

\emph{4.3 Seasonal diet shifts}

Sea otters consume prey with higher percent lipid in the fall and winter
months. This change in nutrient composition could be to compensate for
colder temperatures in winter months and the need to obtain more
calories to metabolize for warmth. Average sea surface temperatures
(SST) for nearby Ketchikan range from a low of 6°C in the winter to a
high of 14°C in the summer . Currently, to our knowledge, there are no
published studies that test the density and characteristics of sea otter
fur across seasons, so it is unknown if the fur is thicker and can add
additional warmth in the winter months. Sea otters may need to increase
the consumption of lipids to assist in thermoregulation. The low end of
the water temperature range in which a sea otter can remain in a
thermoneutral zone (i.e., a physiological state whereby the animal
maintains its normal core body temperature without metabolic heat
production or active cooling) is about 15°C . This temperature is
slightly above the typical summer SST on POW, meaning that at
temperatures below this critical level, sea otters must consume more
energy to generate additional heat. Previous studies have shown that sea
otters adjust their foraging patterns to environmental conditions, which
could affect diet composition.

Based on sea otter diet estimates from LaRoche, et al (2021), clam
consumption in spring and summer was lower than fall and winter. This
change, which does not correlate with seasonal changes in energy or
lipid content, could be due to paralytic shellfish poisoning (PSP). PSP
is a toxin in algae that blooms in the spring and summer months along
the Pacific coast . Studies have shown that sea otters will still eat
bivalves that have PSP toxins present, but will avoid bivalves with very
high amounts, as well as only consuming the foot and discarding the
siphon, which usually has the highest concentration of PSP .

Sea otter consumption of sea cucumbers across seasons positively
corresponded with sea cucumber total energy and lipid content. Sea
cucumbers are broadcast spawners. They move into shallow waters in the
late spring to begin spawning in the summer months . During the fall and
winter months, they retreat to deeper water. Their highest percent lipid
and caloric content were observed in the summer when they are preparing
to spawn. Estimates from stable isotope analyses show the highest diet
proportion in the summer, with spring slightly lower, and a drastic drop
in the fall and winter months. This correlates with sea cucumber life
history. The visual foraging observations show higher consumption in the
summer than the spring. This correlation can be due to increased caloric
content. Additionally, their shallow-water summer habitat makes them
more easily obtainable for sea otters. At other times of the year, sea
cucumbers inhabit depths up to 250 m, which is outside of a sea otter's
diving ability .

\emph{4.4 Future Work}

It is important for managers to consider the whole ecosystem and not
only focus on each single species individually. Because sea otters are
removing biomass similar to a fishery, their impact on invertebrate prey
should be considered in management strategies. It is also important to
incorporate how sea otters prioritize and change their diets; for
example, this study showed that sea otters increase their sea cucumber
consumption in the summer months. Currently, there is a lack of
invertebrate surveys to assess current population levels. Dungeness
crabs, butter clams, and other subsistence bivalves like cockles are not
surveyed. Creating a complete ecosystem level management for the Alaska
nearshore system with subsistence foods surveyed would account for the
needs of people both local and commercial fishing, as well as the health
of the ecosystem.

Within our study area only three aerial surveys were conducted over a
30-year period to estimate the sea otter abundance and geographic range
. More fine-scale outcomes of sea otter diet changes were not possible
because of the long gaps in population data. More frequent range-wide
surveys are needed to better understand the population size and
distribution. The current Southeast Alaska population movements and
growth rates are currently estimated on surveys that were conducted a
decade ago .

\textbf{5. Conclusions}

Sea otters can be used as a looking glass into the overall ecosystem due
to their foraging habits. Sea otters sample benthic invertebrates at a
higher rate and with better skill than people can attain with SCUBA
surveys . This study provides quantitative data that can inform an
ecosystem-based management approach that also considers local
subsistence harvest needs and stakeholder input. However, for a more
comprehensive understanding of the sea otter population in Southeast
Alaska, abundance surveys of both sea otters and invertebrates need to
be conducted more frequently. If used in conjunction with more
comprehensive abundance surveys, the quantitative results presented here
regarding sex and location-specific diet composition can be used to
predict current and future sea otter ecosystem effects, thus informing
co-management plans for this apex predator in addition to
commercially-important and subsistence foods.

\textbf{Acknowledgments}

Field and lab assistance from Dan Monson, Todd Miller, Johanna
Vollenweider, Matt Rogers, Corey Fugate, Bryan Cormack, Robert Bradshaw,
Ashley Bolwerk, Maggie Shields, Melanie Borup, Tiffany Stephens, Wendel
Raymond, Lia Domke, and Sarah Peele. Funding from National Science
Foundation (NSF) Coastal SEES (Science, Engineering and Education for
Sustainability, award no. 1600049) and NSF Bio-Oce (Biological
Oceanography, award no. 1600230), and National Oceanic and Atmospheric
Administration, National Marine Fisheries Service, Auke Bay
Laboratories, Earthwatch Institute, the Cooperative Institute for Alaska
Research with funds from NOAA under cooperative agreement NA13OAR4320056
with the University of Alaska and BLaST at UAS, which is supported by
the NIH Common Fund, through the Office of Strategic Coordination,
Office of the NIH Director with the linked awards: TL4GM118992,
RL5GM118990, \& UL1GM118991.

\textbf{Conflict of interest} : none

\textbf{Author contribution} : NLL, HCP, and GLE conceived the ideas and
designed methodology; NLL and SLK collected data; NLL and EF analyzed
the data; NLL led the writing of the manuscript as a part of her
master's thesis. All authors contributed critically to the drafts and
gave final approval for publication.

\textbf{Data availability statement} : Visual foraging observation data
used in this study are published through KNB
at:\emph{https://doi.org/10.5063/F1C53J8X}. Invertebrate biomass data
are published through KNB at:\emph{https://doi.org/10.5063/F1Q23XNG}.

\textbf{References}

\textbf{Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1: Sea otter visual foraging observations were made within three
foraging zones (shaded areas with zone numbers listed) on Prince of
Wales Island in southern Southeast Alaska. Each zone was designated by
time-since-recolonization based on US Fish and Wildlife Service aerial
surveys.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2: Proportion of diet from biomass estimates for
time-since-recolonization zones (A) and sea otter reproductive
status/sex (B). Zones are based on US Fish and Wildlife Service aerial
surveys. Zone 1 is the area occupied for \textgreater{} 30 years, Zone 2
is the area occupied \textgreater{}15 years, and Zone 3 is the area
occupied \textgreater{}7 years.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3: Comparison of the energy, lipid, and protein concentrations in
population-level diets for time-since-recolonization zone (A), sea otter
reproductive status/sex (B), and season (C). All calculations are made
from the wet mass (as a sea otter would eat the item) and converted to
dry mass for comparison. Zones are based on US Fish and Wildlife Service
aerial surveys. Zone 1 is the area occupied for \textgreater{} 30 years,
Zone 2 is the area occupied \textgreater{}15 years, and Zone 3 is the
area occupied \textgreater{}7 years.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4: Proportion of energy in each functional prey group of sea
otters derived from lipid and protein.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5: Seasonal whole-body nutrition of functional prey groups of sea
otters in dry mass. Panel A depicts the energy content in kilocalories
per dry gram (\selectlanguage{ngerman}± one standard error), panel B depicts the lipid content
in dry gram (± one standard error), and panel C depicts the protein
content in dry grams (± one standard error).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 6: Seasonal variation in the nutrition of sea otter prey (A and C
are clams, and B and D are sea cucumbers. Circles are individual samples
and dotted lines are the mean, with percent on the left axes) and
frequency of occurrence of that prey species in the diet (red solid
lines, with percent on the right axes). A and B are energy density
(kilocalorie per dry gram), and C and D are lipid content (percent per
dry gram).





\textbf{Tables}

Table 1: Sea otter intake rates (in kilocalorie per minute of foraging),
species diversity (H', Shannon Weiner Index), and dive statistics by
recolonization zone and reproductive status/sex.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllll@{}}
\toprule
& n (bouts) & n (dives) & Kcal/min intake rate & sd & H' & Mean Dive
Time & sd & Mean Surface Time & sd & Success Rate\tabularnewline
\midrule
\endhead
\textbf{Recolonization zones (number of years occupied)} &
\textbf{Recolonization zones (number of years occupied)} &
\textbf{Recolonization zones (number of years occupied)} &
\textbf{Recolonization zones (number of years occupied)} &
\textbf{Recolonization zones (number of years occupied)} &
\textbf{Recolonization zones (number of years occupied)} &
\textbf{Recolonization zones (number of years occupied)} &
\textbf{Recolonization zones (number of years occupied)} &
\textbf{Recolonization zones (number of years occupied)} &
\textbf{Recolonization zones (number of years occupied)} &
\textbf{Recolonization zones (number of years occupied)}\tabularnewline
Zone 1 (\textgreater{} 30 years) & 34 & 305 & 5.7 & 0.8 & 1.19 & 104.6 &
46.2 & 58.4 & 39.7 & 90.2\%\tabularnewline
Zone 2 (\textgreater{} 15 years) & 270 & 2864 & 7.0 & 0.4 & 0.90 & 83.7
& 43.6 & 52.2 & 40.0 & 89.6\%\tabularnewline
Zone 3 (\textgreater{} 7 years) & 58 & 354 & 11.6 & 0.8 & 0.39 & 101.9 &
41.6 & 85.2 & 50.6 & 90.9\%\tabularnewline
\textbf{Reproductive status/ sex} & \textbf{Reproductive status/ sex} &
\textbf{Reproductive status/ sex} & \textbf{Reproductive status/ sex} &
\textbf{Reproductive status/ sex} & \textbf{Reproductive status/ sex} &
\textbf{Reproductive status/ sex} & \textbf{Reproductive status/ sex} &
\textbf{Reproductive status/ sex} & \textbf{Reproductive status/ sex} &
\textbf{Reproductive status/ sex}\tabularnewline
Female no pup & 37 & 484 & 7.0 & 0.7 & 0.44 & 84.1 & 43.3 & 50.7 & 35.6
& 90.1\%\tabularnewline
Female with pup & 75 & 849 & 6.0 & 0.4 & 1.23 & 80.9 & 45.3 & 53.3 &
38.9 & 93.0\%\tabularnewline
Male & 69 & 821 & 7.1 & 0.5 & 0.34 & 87.4 & 40.7 & 58.9 & 45.4 &
85.7\%\tabularnewline
\bottomrule
\end{longtable}

Table 2: Statistical comparison of sea otter prey groups with (A) season
and (B) sex (for crabs only) as factors using analysis of similarity
(ANOSIM). The R statistic ranges from near 0 (no difference between
groups) and 1 (differences between groups) with bold numbers denoting
significance (p \textless{} 0.01).\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
A. Season & & & &\tabularnewline
\midrule
\endhead
& Functional Prey Group & Energy R statistic & Lipid R statistic &
Protein R statistic\tabularnewline
& Clam & \textbf{0.300} & \textbf{0.213} & 0.058\tabularnewline
& Crab & 0.077 & 0.024 & 0.133\tabularnewline
& Sea Cucumber & \textbf{0.472} & \textbf{0.778} & 0.267\tabularnewline
& Sea Urchin & 0.172 & \textbf{0.404} & 0.067\tabularnewline
& Snail & -0.157 & -0.158 & 0.096\tabularnewline
B. Sex & & & &\tabularnewline
& Functional Prey Group & Energy R statistic & Lipid R statistic &
Protein R statistic\tabularnewline
& Crab & 0.057 & 0.065 & 0.181\tabularnewline
\bottomrule
\end{longtable}

Table 3: Statistical pairwise comparisons of sea otter prey groups with
season and sex (for crabs only) using analysis of similarity (ANOSIM).
The R statistic ranges from near 0 (no difference between groups) and 1
(differences between groups) with bold numbers denoting significance (p
\textless{} 0.01).\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Functional Prey Group & Season & Season & Energy R statistic & Lipid R
statistic & Protein R statistic\tabularnewline
\midrule
\endhead
Clam & Spring & Summer & \textbf{0.470} & 0.082 &
\textbf{0.125}\tabularnewline
& Spring & Winter & \textbf{0.228} & \textbf{0.397} &
0.034\tabularnewline
& Summer & Winter & \textbf{0.117} & \textbf{0.117} &
-0.018\tabularnewline
Crab & Spring & Summer & 0.021 & 0.069 & 0.155\tabularnewline
& Spring & Winter & 0.073 & -0.102 & -0.039\tabularnewline
& Summer & Winter & 0.150 & -0.151 & 0.070\tabularnewline
Sea Cucumber & Spring & Summer & \textbf{0.824} & 0.536 &
0.624\tabularnewline
& Spring & Winter & 0.068 & \textbf{0.672} & 0.016\tabularnewline
& Summer & Winter & 0.472 & \textbf{1.000} & 0.156\tabularnewline
Sea Urchin & Spring & Summer & 0.133 & 0.426 & -0.089\tabularnewline
& Spring & Winter & 0.380 & 0.270 & 0.052\tabularnewline
& Summer & Winter & 0.140 & \textbf{0.410} & 0.205\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
\FloatBarrier
\end{document}

