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Understanding life-history diversity in a population is imperative to developing effective fisheries management and conservation practices, particularly in degraded environments with high environmental variability. Here, we examined variation in habitat use and migration patterns of White Sturgeon (Acipenser transmontanus), a long-lived migratory fish that is native to the San Francisco Estuary, CA, United States. Annual increment profiles were combined with respective geochemical (87Sr/86Sr) profiles in sturgeon fin rays to reconstruct annual salinity chronologies for 112 individuals from 5 to 30 years old. Results indicated a complex and diverse amphidromous life history across individuals, characterized largely by estuarine residence, a general ontogenetic trend toward higher-salinity brackish habitats, and high variability in habitat use across all age groups. Hierarchical clustering based on fin ray geochemistry during the first 10 years of life, prior to sexual maturation, indicated at least four distinct migratory phenotypes which differed largely in the timing and duration of juvenile to subadult movements between fresh- and brackish-water habitats. This study provides information regarding habitat use and migration in sub-adult fish that was previously lacking. Different migratory phenotypes vary in exposure to stressors across time and space and populations. Understanding White Sturgeon habitat distributions through space and time at different life stages can help identify areas where habitat restoration would be most effective and develop management actions to reduce stressors associated with specific areas where White Sturgeon are present.
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ORIGINAL RESEARCH
published: 17 March 2022
doi: 10.3389/fmars.2022.859038
Edited by:
Patrick Reis-Santos,
University of Adelaide, Australia
Reviewed by:
John Austin Mohan,
University of New England,
United States
Matthew McMillan,
Department of Agriculture
and Fisheries, Queensland
Government, Australia
*Correspondence:
Kirsten Sellheim
kirstens@fishsciences.net
These authors have contributed
equally to this work and share first
authorship
Specialty section:
This article was submitted to
Marine Ecosystem Ecology,
a section of the journal
Frontiers in Marine Science
Received: 20 January 2022
Accepted: 22 February 2022
Published: 17 March 2022
Citation:
Sellheim K, Willmes M, Lewis LS,
Sweeney J, Merz J and Hobbs JA
(2022) Diversity in Habitat Use by
White Sturgeon Revealed Using Fin
Ray Geochemistry.
Front. Mar. Sci. 9:859038.
doi: 10.3389/fmars.2022.859038
Diversity in Habitat Use by White
Sturgeon Revealed Using Fin Ray
Geochemistry
Kirsten Sellheim1*, Malte Willmes2,3, Levi S. Lewis4, Jamie Sweeney1, Joseph Merz1,4
and James A. Hobbs4,5
1Cramer Fish Sciences, West Sacramento, CA, United States, 2Institute of Marine Sciences, University of California, Santa
Cruz, Santa Cruz, CA, United States, 3Southwest Fisheries Science Center, National Marine Fisheries Service, Santa Cruz,
CA, United States, 4Department of Wildlife, Fish, and Conservation Biology, University of California, Davis, Davis, CA,
United States, 5California Department of Fish and Wildlife, Stockton, CA, United States
Understanding life-history diversity in a population is imperative to developing
effective fisheries management and conservation practices, particularly in degraded
environments with high environmental variability. Here, we examined variation in habitat
use and migration patterns of White Sturgeon (Acipenser transmontanus), a long-lived
migratory fish that is native to the San Francisco Estuary, CA, United States. Annual
increment profiles were combined with respective geochemical (87Sr/86Sr) profiles in
sturgeon fin rays to reconstruct annual salinity chronologies for 112 individuals from 5 to
30 years old. Results indicated a complex and diverse amphidromous life history across
individuals, characterized largely by estuarine residence, a general ontogenetic trend
toward higher-salinity brackish habitats, and high variability in habitat use across all age
groups. Hierarchical clustering based on fin ray geochemistry during the first 10 years
of life, prior to sexual maturation, indicated at least four distinct migratory phenotypes
which differed largely in the timing and duration of juvenile to subadult movements
between fresh- and brackish-water habitats. This study provides information regarding
habitat use and migration in sub-adult fish that was previously lacking. Different
migratory phenotypes vary in exposure to stressors across time and space and
populations. Understanding White Sturgeon habitat distributions through space and
time at different life stages can help identify areas where habitat restoration would be
most effective and develop management actions to reduce stressors associated with
specific areas where White Sturgeon are present.
Keywords: White Sturgeon, life history diversity, microchemistry, habitat use, migration, San Francisco Estuary
INTRODUCTION
Many species have evolved diverse migratory behaviors to persist within dynamic environments
(Lundberg et al., 1988;Schindler et al., 2010). For example, populations of fishes composed of
both migratory and non-migratory phenotypes (i.e., partial migration) or exhibiting variation
in habitat use patterns (i.e., habitat mosaics) can enhance stability and resilience to natural and
anthropogenic disturbances (Greene et al., 2010;Schindler et al., 2010). Such phenotypic diversity
can serve as a bet-hedging strategy that spreads risk and enhances resilience to environmental
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Sellheim et al. White Sturgeon Habitat Use Diversity
variation (Rochet, 2000;Schindler et al., 2010;Moore et al.,
2014). Describing and translating such life history diversity into
management-relevant tools remains key to developing effective
fisheries management and conservation policies. For example,
stocks or contingents that express different patterns likely occupy
distinct habitats throughout their lives and thus experience
different natural and anthropogenic stressors. Quantification and
incorporation of complex life histories into management and
conservation is needed in order to maximize population stability
in the long-term (Greene et al., 2010;Schindler et al., 2010;
Brennan et al., 2019).
Migratory fish management is often limited by a lack
of information about how environmental variability and
management actions intersect with natural complexity in habitat
use, movements, and population status of a species (Nelson
et al., 2013). This knowledge gap is largely due to the difficulty
in reconstructing spatial distributions and habitat associations
throughout individuals’ lifespans, especially for long-lived species
that make large migrations between habitats (Grande et al.,
2009;Blechschmidt et al., 2020). A poor understanding of
migration and habitat use patterns for long-lived species makes it
difficult to improve or maintain the quality of habitats essential
to key life stages and can lead to unanticipated population
crashes or extinction events whose causes can only be assessed
after the fact (Brainwood et al., 2006;Spencer et al., 2018;
Cramer et al., 2020). For example, long-lived species often
mature late in life, reproduce infrequently, and are reliant
on older-larger females to contribute disproportionately to
future populations (Stearns, 1992;Caswell, 2000;Jager et al.,
2008). However, spatial and temporal variation in fecundity,
density dependence, and survival, can also drive population
dynamics (Genovart et al., 2018). Given the speed and extent
of anthropogenic environmental change, novel approaches are
necessary to improve our understanding regarding complex
patterns in habitat use and migration and to apply this knowledge
to enhance management and conservation (Heppell et al., 2005;
Nowacek et al., 2016;Parsons et al., 2018).
Sturgeons (Acipenseridae) are ancient chondrostean fishes
that inhabit aquatic environments throughout the Northern
Hemisphere (Birstein et al., 1997;Nelson et al., 2013). Individuals
may live over 100 years, mature as late as age 20–25, and
reproduce infrequently (Billard and Lecointre, 2001). This
longevity, slow growth, and delayed maturation make sturgeons
highly vulnerable to overfishing (Rieman and Beamesderfer,
1990;Rochard et al., 1990;Birstein, 1993). Furthermore,
sturgeons spawn exclusively in freshwaters of major river
systems, migrating across expansive geographic areas and
habitats, including estuarine and marine environments. Thus, in
addition to fishing, sturgeons are exposed to a broad range of
environmental conditions and anthropogenic stressors including
habitat loss, degradation, and alteration (Birstein, 1993;Bemis
and Kynard, 1997;Heublein et al., 2009;Poletto et al., 2018).
This combination of factors make sturgeons both difficult
to study and highly vulnerable to extinction (Jager et al.,
2008). Due to increasing numbers of imperiled and extirpated
sturgeon populations around the globe, sturgeons have become
the subject of major conservation efforts in recent decades
(Rochard et al., 1990;Birstein, 1993;Bemis and Kynard, 1997;
Birstein et al., 1997;Lower Columbia Fish Recovery Board, 2004).
White Sturgeon (A. transmontanus) native to the
San Francisco Estuary (SFE), CA, United States (Figure 1A):
are believed to spawn in lower stretches of major rivers (e.g.,
Sacramento River) and spend most of their lives in estuarine
habitats of the SFE, with some also moving into coastal waters
(Lower Columbia Fish Recovery Board, 2004;Miller et al., 2020).
White Sturgeon make spawning migrations every 1–4 years
starting at age 10–15, depending on sex and environmental
conditions, and very little is known about juvenile habitat use
and migratory patterns (Chapman et al., 1996;Miller et al., 2020).
White Sturgeon have evolved physiologically and behaviorally to
survive and reproduce in a highly dynamic estuary; however, as is
occurring globally, anthropogenic alterations to the hydrograph
and local climate are reducing the quality and extent of suitable
habitats for SFE sturgeons (Cloern et al., 2011;Poletto et al.,
2018). Much of historical California freshwater spawning and
rearing habitat is now either inaccessible or severely degraded due
to impassable barriers, insufficient freshwater flows, agricultural
diversions, elevated water temperatures, invasive species, and
environmental contaminants such as selenium (Billard and
Lecointre, 2001;Mussen et al., 2014;Zeug et al., 2014;Gundersen
et al., 2017;National Marine Fisheries Service [NMFS], 2018).
SFE White Sturgeon are also exposed to a broad range of direct
mortality risks that vary spatially, temporally, and ontogenetically
including predation, ship strikes, recreational fishing, and by-
catch during commercial fishing (Balazik et al., 2012;Blackburn
et al., 2019;Richerson et al., 2019;Baird et al., 2020;Demetras
et al., 2020;Doukakis et al., 2020). SFE White Sturgeon are not
listed in the federal or state Endangered Species Act but are
categorized as a California Species of Special Concern. While
White Sturgeon have experienced marked declines over the past
century, they continue to support an important recreational
fishery throughout the west coast of North America (Moyle et al.,
2015;National Marine Fisheries Service [NMFS], 2015). Thus,
identifying key environmental stressors and how they intersect
spatially or temporally with sturgeon life histories remains a key
priority for resource managers (Nelson et al., 2013).
Detailed studies of fish movement patterns, such as those
using artificial (e.g., acoustic) and natural (e.g., otolith
geochemistry) tags, can be used to describe how different
life-history phenotypes contribute to specific populations,
year-classes, and cohorts, and how this may vary in response to
extreme or changing conditions (Hall et al., 2016;Brennan et al.,
2019;Hobbs et al., 2019;Sturrock et al., 2020). For example,
Rainbow Trout (Oncorhynchus mykiss) exhibit life-history
diversity expressed as a range of anadromous and resident
behaviors, depending on extrinsic (e.g., environmental) and
intrinsic (e.g., body condition) factors (Beakes et al., 2010;Evans
et al., 2014;Kendall et al., 2015;Hall et al., 2016). Although several
species of Pacific Salmon (Oncorhynchus spp.) and Atlantic
Salmon (Salmo salar) exhibit more fixed migratory traits relative
to Rainbow Trout (Crozier et al., 2008;Kovach et al., 2012),
even these taxa demonstrate diversity in spawning run timing
and locations, and dispersal timing from freshwater habitats
(Crozier and Hutchings, 2014;Jonsson and Jonsson, 2018). Even
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Sellheim et al. White Sturgeon Habitat Use Diversity
FIGURE 1 | (A) Overview of the four subregions of the San Francisco Estuary (SFE) and fin ray collection sites used in this study. The three lower panels display
annual average salinity conditions in the SFE during (B) flood (2011), (C) average (2010), and (D) drought years (2008). Data adapted from MacWilliams et al. (2016).
Legend acronyms are California Department of Fish and Wildlife (CDFW) and U.S. Fish and Wildlife Service (USFWS).
smaller-bodied forage fishes such as Delta Smelt (Hypomesus
transpacificus) have been found to employ complex resident
and migrant behaviors in the SFE (Hobbs et al., 2019). These
intraspecific differences have been described as “behavioral
syndromes” and have been demonstrated to impact individual
reproduction, population structure and distribution, and survival
(Cote et al., 2010;Sih et al., 2012;Moiron et al., 2020). Identifying
behavioral syndromes in natural populations can help resource
managers support diverse life history profiles within a population
and determine how resilient populations may be to increased
temperatures and changing precipitation conditions predicted
by future climate models (Mann and Gleick, 2015;McCabe et al.,
2018).
Habitat use research of SFE White Sturgeon has previously
been limited to recreational catch data, short-term artificial
(acoustic) tags studies, video and eDNA monitoring, and
opportunistic data from monitoring programs designed
to target other species (Miller, 1970;Schaffter, 1997;
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Sellheim et al. White Sturgeon Habitat Use Diversity
Billard and Lecointre, 2001;Pikitch et al., 2005;Heublein
et al., 2009;Lindley et al., 2011;Nelson et al., 2013;Poytress et al.,
2015;Anderson et al., 2018). Thus, our current understanding
is largely based on data that exhibit numerous limitations
in extent and scope. Though acoustic tagging studies can
provide very detailed information on timing and frequency of
individual’s movements through locations where receivers are
placed (Johnston et al., 2020;Miller et al., 2020) they are often
limited in temporal and spatial scope, thus likely limiting our
understanding to a subset of migratory behaviors or habitats for
a small portion of an individual’s lifespan.
Unlike these previous methods, natural tags stored in calcified
structures of fishes (e.g., fin rays and otoliths) provide a valuable
tool for examining the life history of many individuals of
a species throughout its entire life (Campana, 1999;Walther
et al., 2017). For example, geochemical analysis of calcified
structures via laser-ablation inductively coupled plasma mass
spectrometry (LA-ICP-MS) has become a valuable tool for
reconstructing the migratory histories of individual fish from
several species. Strontium isotope ratios (87Sr/86 Sr), in particular,
have been used extensively in Chinook Salmon, Delta Smelt,
and other fishes of the SFE to identify migratory life-history
diversity; determine natal origins (e.g., tributaries and hatchery
versus wild); and provide information on how habitat use,
environmental variability, and growth interact throughout the
life history of individual fish (Hobbs et al., 2005;Feyrer et al.,
2007;Barnett-Johnson et al., 2008;Sturrock et al., 2015;Phillis
et al., 2018;Willmes et al., 2018a).
Fin rays are better suited compared to otoliths for studying
sensitive populations because they can be collected non-
lethally with undetectable effects on growth, survival, or
swimming performance (Collins and Smith, 1996;Nguyen et al.,
2016). Fin rays are comprised largely of a calcium-phosphate
(hydroxyapatite) and protein matrix, with a large range of
elements commonly incorporated as substitutes for calcium,
trapped in interstitial spaces, or bound within protein-rich
layers of the matrix (Tzadik et al., 2017). Fin rays accrete
annual bands and appear reliable for aging sturgeon (Kohlhorst
et al., 1980;Rien and Beamesderfer, 1994;Jackson et al., 2007).
Chemical composition is controlled by several factors including
the chemical composition of the water, ambient water salinity
and temperature, diet, and fish physiology, including metabolic
rate, growth rate, and reproductive state (Clarke et al., 2007;Kerr
and Campana, 2014;Sweeney et al., 2020). Unlike otoliths, fin
rays are susceptible to resorption, particularly in the vascularized
core of the ray (Beamish, 1981;Campana and Thorrold, 2001;
Tzadik et al., 2017). This may reduce precision and accuracy
of age and growth estimates and limit the identification of
natal origins or detection of short-duration migrations such as
freshwater spawning excursions, particularly if individuals are
not feeding enough to support fin ray growth during these
migrations (Paragamian and Beamesderfer, 2003). However,
previous studies have demonstrated that fin rays have minimal
tissue turnover and that trace elements and stable isotopes remain
stable over time once they are incorporated into the molecular
matrix (Lida et al., 2014;Tzadik et al., 2017). Furthermore, recent
experiments suggest that environmental signals are archived in
White Sturgeon fin rays starting at 3 weeks post hatch and
that detection of fine-scale freshwater movements (i.e., weeks) is
possible in juvenile sturgeon (Sellheim et al., 2017;Sweeney et al.,
2020). Sturgeon pectoral fin ray trace elemental ratios (e.g., Sr/Ba,
Br/Ca, Sr/Ca) have been successfully used to detect movements
between ocean and freshwater habitats (Arai et al., 2002;Allen
et al., 2009;Veinott et al., 2011).
Here, we combined annual increment profiles with
geochemical (87Sr/86 Sr) profiles in White Sturgeon fin rays
to examine life-history diversity expressed by this species
in the SFE. Salinity chronologies were reconstructed to
examine fine-scale and broad-scale ontogenetic patterns in
habitat use for individuals ranging from 5 to 30 years old.
Furthermore, hierarchical clustering was used to identify unique
life history phenotypes from the first 10 years of life before sexual
maturation, which was the timeframe that supported a robust
statistical analysis given the sample size available. Our aim was to
improve our understanding of phenotypic plasticity in managed
species, such as White Sturgeon, to better inform predictions
of environmental change effects and how specific management
actions are likely to influence populations.
MATERIALS AND METHODS
Study Site
Environmental conditions in the SFE are highly dynamic
(Kimmerer, 2004), with a salinity gradient from upstream
freshwater habitats in the Sacramento-San Joaquin River Delta
to marine habitats in San Francisco Bay, and variable brackish
conditions through Suisun and San Pablo Bays (Figures 1B–D).
The SFE exhibits California’s Mediterranean climate exemplified
by warm, dry summers and cool, wet winters. Furthermore,
interannual variation in precipitation and subsequent freshwater
runoff is high, and similarly, water temperatures and clarity
vary spatially and seasonally (Kimmerer, 2004;Cloern, 2019).
Native species, such as White Sturgeon, have physiologically and
behaviorally evolved to thrive within this dynamic landscape;
however, anthropogenic alterations to the local hydrograph and
climate are increasingly compressing available habitat quantity
and quality (Cloern et al., 2011). For example, as the SFE
continues to warm, thermal stress is likely to increasingly impact
population dynamics of sensitive species (Brown et al., 2016).
Reductions in freshwater outflow, combined with sea level rise,
are likely to push salinity gradients further inland, reducing the
amount of estuarine habitat, further exacerbating warming trends
(Feyrer et al., 2006;Brown et al., 2016). Continuing weather
pattern changes, coupled with upstream reservoir sediment
capture and expanding aquatic macrophytes, are also likely to
impact SFE water turbidity and temperature (Hestir et al., 2016;
Bever et al., 2018).
Field Collection and Laboratory
Processing
White Sturgeon fin rays were collected opportunistically by the
U.S. Fish and Wildlife Service during sturgeon sport fishing
derbies from Rio Vista on the Lower Sacramento River to San
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TABLE 1 | White Sturgeon fin ray sample sources, including sample size, years collected, mean and range of fish age and fork length (cm), and sex.
Source Sex Year(s) Mean age in years (range) Mean fork length in cm (range) Sample size
CDFW Sturgeon Study UNK 2014 10.2 (4–17) 115 (66–178) 9
USFWS sturgeon fishing derby UNK 2012–2016 12.5 (12–13) 146 (141–151) 2
F 12.4 (10–15) 134 (112–154) 34
M 12.6 (9–17) 130 (104–165) 41
USFWS San Joaquin River surveys UNK 2015–2016 14 142 1
F 14.0 (12–18) 145 (132–170) 5
M 15.5 (10–30) 144 (105–216) 20
More specific sample locations for each source are displayed on the map in Figure 1.
FIGURE 2 | Example fin ray with microstructure and microchemistry analysis
trajectories indicated. The white dashed line follows an annulus from the
transect used for geochemistry to the transect used for aging. Drawing of
White Sturgeon by Adi Khen.
Pablo Bay, during telemetry studies in the San Joaquin River, and
from the California Department of Fish and Wildlife’s trammel
net surveys for adult sturgeon in Suisun Bay (Table 1). During
field collection, each sturgeon’s fork length was measured to the
nearest centimeter (cm). The most anterior fin ray was removed
from a pectoral fin of each fish by cutting the ray at the point
where the ray articulated with the pectoral girdle (Koch et al.,
2008). Fin rays were either collected non-lethally during trammel
net surveys and the telemetry study, or were taken from fish
that had already been harvested during fishing derbies. Fin rays
were stored in separate, labeled bags in a freezer until processed.
Fin rays (n= 112) were prepared and processed according to
established methods (Koch and Quist, 2007). Briefly, fin ray
sections were cleaned of tissue, air dried, mounted in epoxy resin,
and thin sectioned from the proximal end of each fin ray using
an IsoMet low-speed saw (Buehler, Lake Bluff, IL, United States)
with a width from 0.8 to 1.3 millimeter (mm). The saw blade
was cleaned between fin rays to prevent contamination. The thin
sections were mounted on glass slides and polished until annual
increments were visible throughout the section (Figure 2).
Microstructure Analysis
Sectioned fin rays were imaged before and after microchemistry
analysis with Image Pro-Premier R
using a Motic BA310
compound microscope with a Motic Cam 5+ camera attached to
the trinocular port. Each fin ray was calibrated, photographed,
and measured under 40X total magnification. Annual bands
were annotated and counted on digital images using Image-Pro
Plus software (Media Cybernetics, Rockville, MD, United States)
following methods used by Blackburn (2018;Figure 2). The
annulus represents the winter, slow growth period prior to
reproduction in the late spring (Rien and Beamesderfer, 1994).
Therefore, if an individual was captured between September
and October (CDFW survey), the edge was not included in
the annulus count, as the “annulus” portion of its seasonal
growth had not yet occurred. If an individual was captured
during the winter or spring (January–April; Derby collections
and San Joaquin River surveys), the fin ray edge was counted
as an annulus and incorporated into the age estimate. Initial age
estimates were completed by the Quist lab, University of Idaho.
To assess the precision of sturgeon age estimates, at least three
additional independent age reads were conducted by University
of California, Davis and Cramer Fish Sciences biologists and
evaluated using the FSA package (Ogle, 2016)inR(R Core
Team, 2021). If ages did not agree across the three readers, the
images were reviewed by all three readers together to determine
whether agreement could be achieved or whether the fin ray
should be excluded from analysis due to poor sample quality
or indistinct annuli (e.g., presence of accessory lobes or other
structural features).
Microchemistry Analysis
In situ 87Sr/86 Sr values were measured at the University of
California, Davis Interdisciplinary Center for Plasma Mass
Spectrometry (Table 2). A Nu Plasma HR (Nu032) multi-
collector inductively coupled plasma mass spectrometer (MC-
ICP-MS) was interfaced with a Nd:YAG 213 nm laser (New
Wave Research UP213). A laser beam of 40 µm diameter was
traversed across the fin ray from the core to the ventral edge
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TABLE 2 | Instrument operating conditions of the Nu Plasma HR (Nu032) and
New Wave Research UP213 Nd:YAG 213 nm laser.
Instrument parameters
Nu Plasma HR (Nu032) MC-ICP-MS
Forward power 1300 W
Extraction voltage 6000 V
Analyzer pressure <5e-8 mbar
Cones Nickel dry plasma sampler cone + high sensitivity
skimmer cone
Torch depth 5 mm
Mass resolution 8000 (0.05 mm source slit, alpha slits each set with
25% beam reduction)
Detector array Faraday cups, 1011 resistors Ion Counter
Detector configuration H4 (88), H2 (87), Ax (86), L2 (85), L3 (84), L4 (83)
Gas flows
Coolant gas 13 l/min
Argon makeup gas 1.05 l/min
Helium gas to cell 0.85 l/min
New Wave Research UP213 laser
Nd:YAG 213 nm
Supercell Low volume laminar flow cell
Laser fluence 3–7 J/cm2
Repetition rate 10 Hz
Spot size 40 µm
Scanning speed 5 µm/s
TABLE 3 | Strontium isotope in-house reference materials.
Reference material Type 87Sr/86 Sr ±2σn
Modern marine coral (South
China Sea)
Coral (Aragonite) 0.70917 0.00003 22
White Seabass (Atractoscion
nobilis)
Otolith (Aragonite) 0.70917 0.00008 52
Green Sturgeon (Acipenser
medirostris)
Fin ray (Bioapatite) 0.70917 0.00014 50
at 5 µm/s, with the laser pulsing at 10-Hz frequency and
5–15 J/cm2photon output. The instrument was operated in
medium resolution mode (7500 M/1M) to avoid polyatomic
spectral interferences, i.e., 40Ca31 P16O, or 40 Ar31P16O, which
can overlap with 87Sr, and consequently cause inaccurate results.
Correction and monitoring for potential interferences followed
established protocols (Vroon et al., 2008;Lewis et al., 2014;
Willmes et al., 2016;Lugli et al., 2017;Griffin et al., 2021). Data
reduction was carried out in IsoFishR (Willmes et al., 2018b),
and included a background subtraction (30 s, laser off), an
exponential mass bias correction, assuming 86Sr/88 Sr = 0.1194,
and a 87Rb correction, by monitoring the 85 Rb signal and
applying the same mass bias correction as determined for Sr.
A 5-point average was applied to the raw data collected by the
mass spectrometer with an integration time of 0.2 s resulting
in 1 datapoint per second. Outliers were removed based on 2
IQR outlier criterion using a 40-point moving average window
(Hoaglin et al., 1986). Accuracy and reproducibility of the LA-
MC-ICP-MS were evaluated using in-house reference materials
consisting of a modern marine coral (Acropora sp.) from the
South China Sea, a modern marine otolith from a White Seabass
(Atractoscion nobilis) collected offshore of Baja California, and a
marine Green Sturgeon (Acipenser medirostris) fin ray. Replicate
analyses of the different in-house reference materials (Table 3)
were in good agreement with the global average 87Sr/86Sr value
of modern seawater of 0.70918 (McArthur et al., 2001;Mokadem
et al., 2015). 87Sr/86 Sr profiles were then smoothed using a thin-
plate regression spline (k= 60) and Generalized Cross Validation
to optimize the effective degrees of freedom using the mgcv
package in R (Wood, 2017).
Salinity Movement Reconstructions
From Strontium Isotopes
To estimate salinity from 87Sr/86 Sr values in fin rays, we applied
the published salinity mixing model for the SFE (Hobbs et al.,
2019;Figure 3A). A two-endmember mixing model was used to
describe the freshwater to ocean salinity gradient based on water
samples previously analyzed for the SFE (Hobbs et al., 2019). To
define the freshwater endmember we used a long-term average of
70% Sacramento and 30% San Joaquin River relative contribution
(Chen et al., 2018;Hutton et al., 2019). To assess the sensitivity
of our salinity reconstructions to variable Sacramento River and
San Joaquin River influences we compared two scenarios, where
the freshwater endmember is either only the Sacramento River
(100%) or only the San Joaquin River (100%). We found that this
introduced only small uncertainties in our ability to reconstruct
salinity (Figure 3B).
Salinity reconstructions using 87Sr/86 Sr have high precision
at low salinity values, however; uncertainty increases rapidly
above 5 psu (practical salinity units) (Figure 3B), due to the
low concentration of Sr in freshwater compared to ocean. These
uncertainties make it increasingly difficult to distinguish between
higher salinities (>10 psu) found in the estuary and those
found in the ocean (Figure 1). Based on these uncertainty
thresholds and known management and ecological boundaries
we divided the SFE into several salinity zones including
freshwater (<0.5 psu), low salinity (0.5–2 psu), medium salinity
(2–10 psu), and high salinity (>10 psu) which includes ocean
values (Allen et al., 2009;MacWilliams et al., 2016).
We then reconstructed temporally resolved salinity histories
for each White Sturgeon examined in this study following the
approach described by Hobbs et al. (2019). We fit the geochemical
data onto the aging transect for each fin ray by aligning the
proportional distances (from core to edge) of each age band to the
matching proportional distances along the laser-ablation profile.
Salinities were then calculated along each merged profile using
the 87Sr/86 Sr to salinity mixing model.
Annual Average Salinities and Clustering
Salinity values falling within each annual fin ray band were
averaged, generating annual mean salinity profiles. We then used
these annual averages in an agglomerative hierarchical cluster
analysis. The cluster analysis was used to identify dominant
ontogenetic patterns in habitat use during the juvenile-subadult
life stages (ages 0–10), prior to maturation which typically occurs
at 10–15 years of age, depending on sex and environmental
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FIGURE 3 | The 87Sr/86 Sr to salinity mixing model based on Hobbs et al. (2019).(A) The 87Sr/86 Sr to salinity relationship across the 87Sr/86 Sr values for the entire
SFE. Blue line representing the 70-30 Sacramento to San Joaquin River freshwater endmember and the inner gray band representing the uncertainty introduced by
varying between 100% Sacramento and 100% San Joaquin River water. The outer gray band represents the uncertainty introduced into predicted salinities based
on the propagated uncertainties from the geochemical analyses. (B) The relationship between predicted versus measured salinity values, showing that prediction
uncertainty increases asymmetrically with salinity. Dashed lines show the 0.5, 2, and 10 salinity cutoffs used to identify different habitats. Note that due to these
increasing uncertainties salinities >10 psu were treated here as “high salinity” and include salinity values from 10 to 32 (seawater).
FIGURE 4 | (A) Histogram of number of fish in each age bin, and (B) fork length to fish age relationship (R2= 0.77). Study designates the origin of the samples
collected from the California Department of Fish and Wildlife (CDFW) trawling surveys, sturgeon sport fishing derbies (Derby), or the San Joaquin River (SJR) gill and
trammel net surveys.
conditions (Moyle, 2002;Hildebrand et al., 2016;Miller et al.,
2020). We chose this age cutoff because it allowed for a large
enough samples size (n= 105) to detect patterns using the
hierarchical clustering method, and because relatively little is
known about White Sturgeon migratory behavior during the
first decade of life prior to spawning. We used the “agnes”
function from the cluster package (Maechler et al., 2015) in R
(R Core Team, 2021) to compare the different agglomerative
methods (“average,” “single,” “complete,” “ward”) and found the
“ward” method, which uses the minimum variance method
and squared dissimilarities for cluster updating, to provide the
strongest internal clustering structure. Next, we used the NbClust
package (Charrad et al., 2014) to determine the optimal number
of clusters by majority vote and decided to use four clusters
though both 3 and 5 could also have been chosen based on
similar vote counts.
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To test whether sample location introduced bias into our
cluster analysis, we used a chi-square test to determine whether
the proportion of fish assigned to a given cluster differed
significantly across capture locations. Initially we tested for
differences across four locations: Delta, San Joaquin River,
Suisun Bay, San Pablo Bay; however, sample sizes were too
low for a robust statistical analysis. Therefore, we combined
the capture locations into “upstream” (Delta and San Joaquin)
and “downstream” (San Pablo Bay and Suisun Bay) groups.
A threshold of α= 0.05 was used to determine significance,
but we also considered α= 0.10 threshold to be conservative
in our assessment.
RESULTS
Age and Growth
Fin ray annuli counts provided precise White Sturgeon age
estimates. Age estimations between three age readers across
all samples, reached an initial agreement of 76% (n= 112,
ACV = 1.14, APE = 0.87). Fork lengths ranged from 66 to 216 cm
and estimated ages ranged from 4 to 30 years with an average
of 12.9 years (Table 1 and Figure 4A). As expected, fork length
was positively correlated with fish age (Figure 4B,R2= 0.77), and
growth appeared to be constant at approximately 5.89 (±0.31)
cm/year from age 4 to 30. Overall, 61 males and 39 females were
collected, and sex could not be determined for 12 fish.
Interannual Patterns in Salinity
Movements
We observed broad diversity in the fine-scale salinity histories
of individual White Sturgeon (Figure 5). For example, one
of the youngest fish sampled (age 4) demonstrated freshwater
habitat residency during most of the first year of life, moving
to low and medium salinity at the end of its first year and
remaining there through age 4 (Figure 5A). Some individuals
spent their entire lives in fresh- or low-salinity water (Figure 5B)
while others made short high salinity excursions (Figure 5C)
or migrated quickly to higher-salinity brackish water in their
first year and remained in these habitats (Figure 5D). The
oldest fish sampled (a male, age 30 years) spent the last
14 years of his life in medium salinity habitat and showed no
freshwater signature at any point in its life history (Figure 5E).
Overall, movements into high salinity (>10 psu) were generally
infrequent, short duration, and occurred in 52% of the fish.
Most individuals remained in salinities less than 10 psu, and
6% of fin rays never recorded salinities >2 psu. No fin rays
appeared to demonstrate prolonged marine residency although,
as described above, salinity estimates above 10 psu have high
uncertainty (Figure 3).
Of 112 fin rays analyzed, most demonstrated brief
excursions among different salinity habitats, which reflects
their amphidromous migratory behavior (Figure 5). Annual
salinity ranges, defined as the maximum-minimum salinity
experienced by individual sturgeon at a given age, were highly
variable and did not show an age trend (Figure 6).
Annual Mean Salinity Habitat Use
Adequate samples were available to assess annual salinity
exposure to 18 years of age. Medium salinities were most
common across all annuli for all fish (48–75%, depending on
age), followed by low salinity (7–48%), and high salinity (2–
29%). Freshwater only accounted for 1–12% of salinity habitat
use, and there was no general trend with age. However, there
were trends in the dominant salinity value by age group. For
age 0–3, for example, an average of 42% (range 32–48%) of
fish exhibited low-salinity values, while only 4% (range 0–6%)
exhibited high salinity values (Figure 7). Beyond age 3, there
was a trend toward a greater proportion of fish inhabiting higher
salinity waters (19%, range 2–29%), with only 17% (range 7–33%)
in low salinity (Figure 7).
Sub-Adult Habitat Clusters
Mean annual salinity values revealed a diversity of ontogenetic
patterns in White Sturgeon habitat use. Several distinct statistical
clusters were identified based on mean annual salinity values
across the first 10 years for each individual (Figure 8). Individuals
within Cluster 1 generally remained in fresh- to low-salinity
(<2) habitats throughout their lives, with a few individuals
demonstrating occasional medium-salinity migrations but no
ontogenetic movements into higher salinities. Individuals in
Cluster 2 spent most of their lives in medium salinity (2–
5 psu), with ontogenetic movements to higher salinities after
age 7. In contrast, individuals in Clusters 3 and 4 exhibited
ontogenetic movements from lower to higher salinities, with
Cluster 3 inhabiting lower salinities until around year 2, when
they migrated to medium and high salinity, and Cluster 4
exhibiting rapid early movements toward higher salinity values
(generally >5 psu) with little or no evidence of low salinity
residence across individuals (Figure 8).
Proportion of Cluster Across Capture
Locations and by Sex
We observed some evidence for salinity cluster composition
differences across sample collection locations (Figure 9). When
locations were combined into “downstream” (San Pablo and
Suisun Bay) and “upstream” (Delta and Lower San Joaquin River)
locations, the chi-square was not significant at α= 0.05 but was
significant at α= 0.10 (χ2= 7.15, df = 3, p= 0.07). There were no
significant differences in cluster assignment between males and
females (χ2= 3.87, df = 3, p= 0.28).
DISCUSSION
We quantified life-long variation in exposure to salinity
concentrations in White Sturgeon to reconstruct movements
and habitat use in the SFE using fin ray Sr isotope (87Sr/86 Sr)
geochemistry. Although previous studies have described habitat
use of White Sturgeon in the SFE using baited hooks, telemetry,
and mark recapture techniques (Nelson et al., 2013;Miller
et al., 2020;Patton et al., 2020), our study is the first to
employ a natural geochemical tracer to examine the ontogenetic
movements and habitat use patterns of over 100 individual
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FIGURE 5 | Salinity profile examples showing variability in use of different salinity habitats across a range of ages. The white line is the salinity spline, and the dark
shaded area is the associated estimated uncertainty. Note patterns at higher salinity values are less certain due to the non-linear nature of the mixing curve
(Figure 3). Dashed vertical age lines represent the opaque (summer) portion of the annuli. Note the x-axis range varies among different plots because of fish age and
because fin ray sections were cut from different fin ray regions across individual fish. The fish ID is shown at the top of each graph and links to the Supplementary
Table 1. Profiles for all fish are shown in Supplementary Figure 1 and annual average salinities for each fish are in Supplementary Figure 2. An image of each fin
ray section is included to the right of each figure to provide a representation of size variability. The white line on the fin rays shows the location of the geochemistry
transect. Fin ray images were taken by Shannon Blackburn. (A) is the youngest fish sampled, (B) resided in predominantly fresh- or low-salinity water (B),(C) made
short high salinity excursions, (D) exhibited early migration to higher-salinity brackish water, and (E) is the oldest fish sampled.
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FIGURE 6 | Intra-annual variation (range) in salinity by age for each White Sturgeon. Points represent the salinity range for each year of each fish’s life
(maximum-minimum salinity at that age). Box plots show the salinity ranges across all fish within each age class.
FIGURE 7 | Proportion of White Sturgeon 18 years of age observed in each of four salinity zones by year-post-hatch. Salinity zones were assigned to each year for
each fish by converting the mean annual 87Sr/86 Sr value into salinity estimates using the mixing model (Figure 3).
White Sturgeon over their lifespan prior to capture. Our
results largely corroborated prior observations of amphidromous
migratory behaviors (Bemis and Kynard, 1997;Patton et al.,
2020) with prolonged periods of estuarine residence (DeVore
et al., 1999;Miller et al., 2020). However, we also observed
broad variation among individual fish. The fin ray geochemistry
migratory profiles supported identification of at least four distinct
habitat use patterns expressed by White Sturgeon in the SFE.
Such diversity in “behavioral syndromes” is likely to influence
species distribution and abundance patterns, species interactions,
population dynamics, responses to anthropogenic impacts, and
ecological invasions (Sih et al., 2012). Given these considerations,
it is imperative that future studies quantify variation in the habitat
use of managed species to improve population model accuracy
and support effective management decisions.
Life History Diversity and Population
Resilience
The salinity clusters observed in this study suggest that, although
subadult amphidromous movements were quite diverse across
individuals, there were statistically distinct behavioral types,
ranging from those that primarily inhabited low salinity waters
to those who resided in high salinity water following a few years
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FIGURE 8 | Individual salinity chronologies and ontogenetic salinity clusters for San Francisco Estuary White Sturgeon. (A) Individual (gray) and mean (black) salinity
splines for all fish, (B) overlayed mean loess smooths (span = 0.3) for each of the four statistical life history clusters, (C) overlayed mean loess smooths (span = 0.3)
and individual (gray) salinity splines for all fish in each cluster.
in low or medium salinity. Environmental stressors vary spatially
and temporally within the SFE, and the relative population
impacts of different stressors are poorly understood (Light and
Marchetti, 2007;Null and Viers, 2013;Gundersen et al., 2017).
Different ontogenetic stages and habitat use strategies may expose
individuals to distinct types and degrees of risk. For example,
sturgeon inhabiting primarily freshwater habitats may be more
exposed to summer high temperatures or poor water quality due
to agricultural runoff and entrainment (Grimaldo et al., 2009;
Mussen et al., 2014). Conversely, sturgeon inhabiting higher
salinity waters may be disproportionately exposed to shipping
channel impacts including boat strikes and channel dredging,
particularly with their preference of deep, open water habitats
(Demetras et al., 2020;Patton et al., 2020). Because these risks
vary across annual or seasonal time scales and are influenced
by anthropogenic activities and climate change, some behavioral
types may be more resilient than others.
Sturgeon Fin Ray Microchemistry vs.
Other Microchemistry Studies
Our study contributes to a growing body of literature that
leverages microchemistry data in bony parts to document life
history diversity and migratory behavior in anadromous
fish. Most prior studies have reconstructed migratory
histories for anadromous species with a more limited life
span than White Sturgeon or focused on a particular life
stage. For example, Hermann et al. (2016) used otolith
microchemistry to compare migratory behaviors in two long-
distance migratory catfishes (Brachyplatystoma rousseauxii and
Brachyplatystoma filamentosum), and found that B. rousseauxii
resided in the Amazon estuary for its first few years, while
B. filamentosum did not enter the estuary during its entire
life history. Yokouchi et al. (2012) used otolith Sr:Ca ratios
to document a high degree of phenotypic plasticity in fresh
and estuarine habitat use in Japanese eels (Anguilla japonica)
from the Hamana Lake system of central Japan, but also
found that early salinity experience influenced individual
migratory trajectories. Feutry et al. (2012) used otolith
microchemistry and microstructure to compare juvenile
migratory histories of three Kuhlia species (Teleostei) from
fresh and brackish environments, providing basic biological
information about the similarities and differences in habitat
use across the three species. Like White Sturgeon, the focal
species of each of these studies were commercially exploited
populations exposed to a range of stressors. Understanding basic
migratory patterns and life history diversity has global fisheries
management implications, in particular if these patterns can
be translated into specific management tools and approaches
(Schindler et al., 2010).
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FIGURE 9 | Regional variation in migratory cluster composition of San Francisco Estuary White Sturgeon. The proportion of White Sturgeon belonging to each of the
four sub-adult life-history clusters is shown for each region. Regions are defined in Figure 1 and clusters are defined in Figure 8.
Several previous studies of anadromous fish that migrate
through the SFE have used microchemistry studies to underscore
the importance of maintaining life history diversity portfolios
to enhance population resilience under a range of future
environmental conditions. For example, Hobbs et al. (2019)
used laser-ablation otolith strontium isotope microchemistry
to uncover three distinct life-history phenotypes for an
endangered Delta Smelt, including freshwater resident, brackish-
water resident, and semi-anadromous fish, and that the semi-
anadromous phenotype could be categorized into at least four
additional life-history phenotypes that varied by natal origin,
dispersal age, and adult salinity history. Several Chinook Salmon
microchemistry studies have documented diverse out-migration
strategies in juveniles and have highlighted the importance
of maintaining high life history diversity in order to support
viable populations under a variable and changing climate (Phillis
et al., 2018;Sturrock et al., 2020;Cordoleani et al., 2021;
Willmes et al., 2021). Our study suggests that sturgeon may be
more behaviorally plastic and have broader life history diversity
compared with fish species in these previous microchemistry
studies which tend to have shorter lifespans, smaller body
size, and more defined ontogenetic migratory patterns that
vary on a smaller spatial and temporal scale. This complicates
sturgeon population management, as highly plastic behavior
reduces predictability of sturgeon distributional patterns over
space and time and makes it more difficult to determine
how the population will react to a particular environmental
change, stressor, flow management decision, or restoration
action. Structuring future population-level studies specifically to
include all life-history strategies and determining the relative
success of each under varying environmental conditions would
aid in understanding life-history expressions and better inform
management of these iconic fish.
Evidence for the Use of Coastal Marine
Habitats
Based on mark-recapture data, White Sturgeon have been known
to migrate across thousands of kilometers of ocean habitat
(DeVore et al., 1999), and specimens have been found as far
south as the Pacific coast of Mexico (Ruiz-Campos et al.,
2011). These observations have been supported by estimates of
genetic isolation and gene flow (Anders, 2002). Nevertheless,
little remains known about the ocean migratory behavior of this
species. Here, we used the salinity chronologies reconstructed
from fin ray Sr isotope profiles to assess the use of coastal
marine habitats by White Sturgeon in the SFE. Though several
profiles indicated short excursions into relatively high salinity,
such excursions were generally brief and rare, and never resulted
in a mean annual signal indicating year-round marine residence.
This, in part, could be due to the limitations of the Sr isotope-
salinity mixing model, where confidence decreases rapidly and
non-linearly with increasing salinity values. However, though
confidence might decrease with increasing salinity, Sr isotope
ratios of 0.70918 (the global ocean value) are commonly observed
in calcified structures of other species (e.g., Chinook Salmon)
that are known to migrate to the coastal ocean (Kennedy et al.,
2002;Johnson et al., 2016). Our results suggest that none of the
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112 White Sturgeon examined in this study spent a significant
portion of their life in fully marine habitats, and all largely
remained within fresh- to brackish-water habitats within the SFE.
Limited ocean migration was also observed in previous studies
of White Sturgeon in the Columbia River. For example, of the
69,609 White Sturgeon originally tagged in the unimpounded
Columbia River below the Bonneville Dam from 1976 to 1997,
only 471 were recaptured outside of the Columbia River Basin
(DeVore et al., 1999). Furthermore, statistical comparisons of
mitochondria DNA (mtDNA) haplotype frequency distributions
indicated significant isolation between populations separated by
1,000 km of ocean habitat (Anders, 2002). Together, these results
further suggest White Sturgeon are mostly estuarine residents
throughout much of their lives with limited use of coastal
marine habitats.
Capture Location Effect on Migration
Strategy
Many sturgeon sampling efforts and long-term tagging studies
target sampling within Suisun and San Pablo bays (Miller et al.,
2020;Patton et al., 2020). For example, Miller et al. (2020) used
acoustic telemetry arrays to track large juvenile, subadult, and
adult sturgeon over 5–7 years and found that juveniles and sub-
adults were detected almost exclusively in San Pablo Bay, Suisun
Bay, and the Delta, with only very rare incidences of ocean
movements (1 of 160 fish). Patton et al. (2020) used baited hooks
(set lines) to examine habitat use patterns in eastern Suisun Bay,
finding that fish were present year-round in shoal and channel
habitats, but not shallow wetland habitats. The geographically
limited nature of sampling, although often necessary due to
funding or time limitations, introduces potential bias into studies
attempting to document habitat utilization. Our study examined
sturgeon collected from throughout the central Delta and San
Joaquin River, filling in a key gap in sampling range for sturgeon
movement and habitat use studies. We observed weak differences
in the distribution of clusters between individuals collected in
San Pablo and Suisun bays and those collected in the Delta and
San Joaquin River, which may have been due to low sample
size in particular locations. Even so, our study did not collect
individuals from the Sacramento River or its tributaries, which
is a key spawning reach for White Sturgeon (Kohlhorst and
Cech, 2001;Miller et al., 2020). Further, our sample size from
the Delta was also relatively low (<4%). These areas should be
sampled in future studies to further expand on the distribution
of migratory strategies present in the SFE. In more extensively
studied anadromous fishes such as Rainbow Trout, as many as
32 different life-history strategies have been described (Thorpe,
1998;Moore et al., 2014). As White Sturgeon samples are
collected from a broader geographic area, additional strategies
may be documented.
Migratory History Versus Salinity History
We used 87Sr/86 Sr in fin rays to generate time-resolved salinity
histories for individual White Sturgeon. Given that White
Sturgeon are known to move across broad geographic salinity
gradients, we assumed that fin ray salinity histories reflect
geographic movements for each fish. At finer scales, however,
reconstructed salinity patterns in fin rays may not reflect
geographic movements. For example, SFE salinity gradients
are dynamic, moving tidally, seasonally, and interannually,
depending on the balance between freshwater outflow and tidal
conditions. It is possible, therefore, for a fish to remain at a
single geographic location and exhibit a variable salinity history
in its fin rays. In our study, we used the annual mean salinity
value to estimate the average position of each fish across the
salinity gradient for each year of its life; therefore, the patterns
we report herein are integrated across the full year and are
not sensitive to seasonal variation in geography. The mean
values, however, could reflect different geographic locations in
different years, depending on the water year type and total
freshwater outflow. At smaller scales, this could be important;
however, at larger scales, the mean salinity gradient across
wet and dry years remains relatively stable in the SFE. To
improve confidence in inferred geographic positions based on
geochemically reconstructed salinity profiles, future efforts could
aim to contrast finer-scale spatiotemporal patterns in salinity
gradients with higher-resolution salinity profiles in fin rays.
Further, finer-scale spatio-temporal environmental variability
could be overlaid with fin ray growth increments to examine how
environmental conditions influence individual growth.
Temporal Resolution
This study focused on categorizing dominant movement patterns
for the sub adult (<10 years) component of sturgeon life history,
as the sample size of adult spawning-aged sturgeon was not
large enough to support a hierarchical clustering analysis. For
the adult sturgeon in the dataset, which are expected to make
spawning runs into freshwater, we observed little evidence of
freshwater excursions. Similarly, we observed little evidence of
freshwater residence in age-0 fish, which presumably are exposed
to freshwater habitats for at least a short period during the
larval stage (Bemis and Kynard, 1997). These results suggest
that salinity reconstructions from fin rays are time-averaged
measurements that can represent mean patterns in habitat use
across months or seasons throughout an individual’s life but
may not always capture very fine scale movement patterns. For
example, freshwater rearing or spawning (Miller et al., 2020)
for only 1 month would represent <10% of the annual mean
fin ray chemistry value, and even less if growth is reduced
during these periods. Thus, short-term movements may not be
detectible in fin rays using the techniques defined here including
biologically important but low frequency or duration events such
as early freshwater residency, freshwater spawning migrations,
or occasional excursions into coastal marine habitats. Other
methods, such as acoustic tagging studies, may be better suited to
capturing spawning migrations or sturgeon movements through
particular locations of interest (e.g., habitat restoration sites,
reaches with high mortality risk such as shipping channels).
These methods, however, only capture a small portion of an
individual’s life history (Balazik et al., 2012). In contrast, fin ray
geochemistry may be the best tool for examining the life history
of White Sturgeon over broader temporal and spatial scales.
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Future Opportunities
The SFE is a highly altered system with impassable barriers
blocking most historic habitat and widespread degradation of
remaining accessible habitat, and it is likely that the current
population represents a small subset of historical life history and
genetic diversity. Therefore, it is imperative that we improve
understanding of habitat use, stressors impacting the remaining
population, and the genes driving life history expression to
prevent further loss of diversity, and subsequently, population
resilience in the face of changing climatic conditions and
increased environmental variability (Greene et al., 2010;Moore
et al., 2014;Rundio et al., 2021). Future studies linking
ontogenetic migratory patterns to inter-annual variation in
temperature, stream flow, or other environmental factors that
reflect climate regimes (flood/drought) or other temporally
variable stressors would elucidate whether specific annual
behavioral movements between salinity zones are correlated
with shifts in environmental conditions. Incorporating growth
using fin ray increments into these analyses could improve
understanding of what specific environmental factors drive
growth patterns, and how these vary spatially and temporally
(Rundio et al., 2021). Ultimately, understanding how individual
life history types interact spatially and temporally with specific
habitats and stressors will support management decisions meant
to benefit SFE White Sturgeon, including prioritizing habitat
restoration efforts and developing policies to remedy stressors in
areas where sturgeon are likely to occur.
Conclusion
This study demonstrated that White Sturgeon exhibit a wide
range of migratory behavior during the first decade of life and that
most individuals exhibit an amphidromous life history, moving
between fresh and brackish water. We revealed four distinct
behavioral types that utilized brackish habitats for different
portions of their first 10 years. These data fill an important gap
in basic biological knowledge of subadult sturgeon migratory
behavior and habitat utilization. Individuals or groups that
express different migratory behaviors will be exposed to distinct
environmental stressors over time and space, and White Sturgeon
populations will be more resilient if their diverse ontogenetic
migratory behaviors are incorporated into habitat conservation
and management strategies.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
Ethical review and approval was not required for the animal study
because the samples used in this study were either taken during
previous surveys by state or federal entities, or were taken from
fish already harvested by recreational anglers.
AUTHOR CONTRIBUTIONS
KS: conceptualization, project administration, funding
acquisition, supervision, and writing and revision. MW:
conceptualization, methodology, formal analysis, investigation,
data curation, writing and revision, and visualization. LL:
conceptualization, formal analysis, writing and revision, and
visualization. JS: investigation, data curation, visualization, and
revision. JM: conceptualization, funding acquisition, supervision,
and revision. JH: conceptualization and revision. All authors
contributed to the article and approved the submitted version.
FUNDING
This research was funded by the U.S. Fish and Wildlife Service
grant # F16AC01081-03.
ACKNOWLEDGMENTS
California Department of Fish and Wildlife and U.S. Fish
and Wildlife Service provided fin rays and metadata for this
study. Zachary Jackson, Laura Heironimus, and Cramer Fish
Sciences technical staff supported field collections. Zachary
Jackson also supported early concept development. Shannon
Blackburn and Michael Quist from University of Idaho
supported fin ray aging. Michael MacWilliams provided support
for spatial salinity analysis and visualization. Justin Glessner
from the University of California, Davis Interdisciplinary
Center for Plasma Mass Spectrometry lab provided analytical
support. Numerous California sturgeon anglers donated fin
rays and supported collection of metadata on the sturgeon
analyzed in this study.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmars.
2022.859038/full#supplementary-material
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