Strontium isotopes reveal diverse life history variations, migration patterns, and habitat use for Broad Whitefish (Coregonus nasus) in Arctic, Alaska

Jason C. Leppi; Daniel J. Rinella; Mark S. Wipfli; Randy J. Brown; Karen J. Spaleta; Matthew S. Whitman

doi:10.1371/journal.pone.0259921

Abstract

Conservation of Arctic fish species is challenging partly due to our limited ability to track fish through time and space, which constrains our understanding of life history diversity and lifelong habitat use. Broad Whitefish (Coregonus nasus) is an important subsistence species for Alaska’s Arctic Indigenous communities, yet little is known about life history diversity, migration patterns, and freshwater habitat use. Using laser ablation Sr isotope otolith microchemistry, we analyzed Colville River Broad Whitefish ⁸⁷Sr/⁸⁶Sr chronologies (n = 61) to reconstruct movements and habitat use across the lives of individual fish. We found evidence of at least six life history types, including three anadromous types, one semi-anadromous type, and two nonanadromous types. Anadromous life history types comprised a large proportion of individuals sampled (collectively, 59%) and most of these (59%) migrated to sea between ages 0–2 and spent varying durations at sea. The semi-anadromous life history type comprised 28% of samples and entered marine habitat as larvae. Nonanadromous life history types comprised the remainder (collectively, 13%). Otolith ⁸⁷Sr/⁸⁶Sr data from juvenile and adult freshwater stages suggest that habitat use changed in association with age, seasons, and life history strategies. This information on Broad Whitefish life histories and habitat use across time and space will help managers and conservation planners better understand the risks of anthropogenic impacts and help conserve this vital subsistence resource.

Citation: Leppi JC, Rinella DJ, Wipfli MS, Brown RJ, Spaleta KJ, Whitman MS (2022) Strontium isotopes reveal diverse life history variations, migration patterns, and habitat use for Broad Whitefish (Coregonus nasus) in Arctic, Alaska. PLoS ONE 17(5): e0259921. https://doi.org/10.1371/journal.pone.0259921

Editor: Masami Fujiwara, Texas A&M University, UNITED STATES

Received: October 28, 2021; Accepted: March 25, 2022; Published: May 2, 2022

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This research was funded in-part by the National Science Foundation (Awards Alaska EPSCoR OIA-1208927 and ARCSS-1722572; Wipfli), the state of Alaska (Wipfli), The Wilderness Society (Leppi), U.S. Bureau of Land Management (Interagency Agreement Number L15PG00216; Whitman), and U.S. Geological Survey (Wipfli). In-kind support such as borrowed equipment and staff assistance was provided by U.S. Fish and Wildlife Service and the Native Village of Nuiqsut Tribal Council. Salary support for the research was provided by The Wilderness Society (Leppi), U.S. Geological Survey (Wipfli), U.S. Bureau of Land Management (Whitman), and U.S. Fish and Wildlife Service (Rinella and Brown). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of each author are articulated in the ‘Author Contributions’ section. There was no additional external funding received for this study.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Global freshwater biodiversity is in decline and many fish species are at risk of extinction [1–4]. Freshwater ecosystems cover less than 1% of the global surface but support about half of the 30,000 described fish species [5]. Consequently, environmental impacts on freshwater ecosystems can have a disproportionate effect on interspecific and intraspecific fish diversity [6–8]. Arctic freshwater ecosystems are no exception and recent assessments confirm that ongoing climate-driven changes [9] threaten Arctic freshwater biodiversity [10].

The accelerated impacts of climate change at high latitudes [11, 12] are a major threat to Arctic freshwater ecosystems [13], altering streamflow [14–17], warming [18, 19] and drying [20] aquatic habitats; causing eutrophication [21] and browning of lakes [22, 23]; and allowing for northward range expansion of eurythermic species [24]. To a lesser extent, long-range pollution [7], habitat loss and degradation, and flow modification from oil and gas development [25] can occur independently or interact with climatic change to alter Arctic freshwater ecosystems [26]. New regional and global emerging threats, such as invasive species [27], diseases, or algal blooms [28] may add additional stress to ecosystems [7], potentially reducing genetic and life history diversity within populations and further accelerating biodiversity loss [29–31].

Understanding lifelong habitat use for Arctic fishes is challenging due to their high mobility and our limited ability to track fish as they move among a suite of habitats (i.e., foraging, overwintering, spawning) that are geographically dispersed, change over time, and are often temporary [32]. As a response to dispersed resources and constantly changing conditions, Arctic fish populations have developed numerous life history strategies to exploit food resources, seek refugia from harsh environmental conditions [33], and maximize reproductive success and survival [34, 35].

Foraging strategies include ontogenetic or seasonal movements to marine habitats [36–39] but, due to extreme winter conditions, fish must leave productive marine habitats and find overwintering refugia in freshwater or brackish habitat to avoid lethal low temperatures [40]. An iteroparous reproductive strategy is typical for fishes that use habitats with high environmental variability [41, 42] and, when coupled with a longer life, it hedges against unpredictable conditions [43]. The proximity of safe rearing habitats at or downstream of spawning areas also helps to facilitate juvenile survival [44, 45] and, for anadromous fishes, a diversity of age at ocean entry helps to buffer the effects of unfavorable conditions [29]. This diversity in life history facilitates long-term population stability, helping to stabilize ecosystems and buffer populations from environmental perturbations that may reduce habitat quality and increase mortality [46–48].

Broad Whitefish (Coregonus nasus), a long-lived migratory species, is an important subsistence resource for Alaska’s Indigenous communities. Little is known about the specifics of habitat use within and across life stages in Arctic populations, but previous research in other parts of its range supports the theory of a highly mobile species that utilizes various aquatic habitats [49–52]. Broad Whitefish reach sexual maturity around the age of eight but can live 30 or more years [49]. Habitat use across time and space likely results in a variety of life histories with varying amounts of time spent in freshwater, estuarine, and marine habitats [38, 53]. Broad Whitefish larvae are thought to be passively advected downstream to deltas, estuaries, and nearshore areas by spring breakup flows, based on their hatch timing, inability to resist spring streamflow as larvae, and general abundance in coastal and estuarine habitat [54–57]. Referred to as Aanaakliq in the Iñupiaq language, Broad Whitefish, are valued due to their relatively large size (up to 4.5 kg) and abundance during migrations [58, 59], accounting for about half the total mass of fishes harvested across all Beaufort Sea communities [60]. However, without information on life history diversity and habitat use, land and resource managers lack essential information for the management and conservation of this important subsistence fish.

Otolith microchemistry is an effective tool to understand life history diversity and habitat use of fishes [51, 61–66]. Otoliths, paired inner ear stones used for hearing and balance in all teleost fishes, are laid as concentric layers of metabolically inert biogenic minerals, primarily calcium carbonate. Elements are permanently incorporated into their organic matrix, and compositional changes across the layers reflect changes across an individual’s life [67]. Strontium (Sr), a naturally occurring element derived from geologic material, has four stable isotopes (⁸⁸Sr, ⁸⁷Sr, ⁸⁶Sr, ⁸⁴Sr), in which only ⁸⁷Sr is radiogenic. The ratio of ⁸⁷Sr to ⁸⁶Sr (⁸⁷Sr/⁸⁶Sr), which roughly has similar proportions of elemental Sr abundance and can be measured at similar precisions, reflects Sr released into freshwater sources (e.g., rivers, lakes, streams) and is driven by differences in lithology, age, chemical composition [68–70], and weathering rates of surficial geology [71–73].

Fish take up dissolved Sr through the gills, and the Sr isotopes are incorporated into the otolith matrix [67, 74], forming a continuous record of Sr isotope values that directly reflects habitats inhabited across an individuals’ life [75]. Compared to freshwater habitats, ⁸⁷Sr/⁸⁶Sr in marine habitats are generally lower, homogenous, and constant due to the long residence time and mixing of oceans [71]. The concentrations of total Sr and ⁸⁸Sr, the most abundant of the four stable isotopes and a reliable proxy for total Sr, increase with water salinity and are consistently higher in marine versus freshwater habitats. For diadromous fishes, these relative differences in Sr isotopes can be used to indicate the timing and duration of freshwater, estuarine, and marine habitat use [38, 76].

Broad Whitefish populations use the Colville River watershed for foraging [77], rearing [77], and spawning [78]. With headwaters in the rugged Brooks Range, the Colville River is one of the few rivers in the region that contains abundant gravel substrate and deep channels, which are both likely essential for egg survival [49]. Due in part to its watershed size, the Colville River also has the largest delta on the Alaskan Beaufort Sea coast, which provides abundant rearing habitat for larval and juvenile fishes [79]. Broad Whitefish can live for 30+ years, and they return to the Colville River ecosystem regularly to reproduce [59], likely migrating from a variety of productive foraging areas in coastal lagoons and rivers across the Beaufort Coastal Plain. Thus, by sampling the Colville River’s spawning run, we may learn about life-history patterns of Broad Whitefish at the regional scale.

Conservation of freshwater fish diversity requires an understanding of lifelong habitat use. For many Arctic fishes, this information is lacking because movement between key habitats changes with age and life history strategy and is difficult to monitor. To fill important knowledge gaps about Broad Whitefish habitat use, we used Sr isotope (⁸⁷Sr/⁸⁶Sr, ⁸⁸Sr) otolith chronologies across individuals’ lives to quantify life history attributes and reconstruct migration patterns of migrating fish captured within the Colville River, Alaska. Our specific objectives were to (i) document the range of life history types and explore how individuals are distributed among several predefined life history types, (ii) determine the proportion of Broad Whitefish that are anadromous and investigate the timing of marine habitat use, and (iii) determine if freshwater natal and freshwater juvenile rearing region ⁸⁷Sr/⁸⁶Sr remain constant across life history groups. Understanding the diversity of lifelong habitat use will provide managers and conservation planners essential information to begin to understand Broad Whitefish exposure to changing habitats.

Materials and methods

Study area

The Central Beaufort Sea region study area (Fig 1) contains a diversity of fish habitats. Situated between the Ikpikpuk and Canning rivers in Arctic Alaska, the coastline is a spectrum of bays and inlets, tapped basins (basins that are breached by the sea due to erosion), lagoons behind barrier islands, and exposed bluffs [80]. River deltas are frequent along the coast, the size varying based on physiography and watershed size [80]. Thermokarst and riverine lakes that vary in size, depth, and connectivity [81, 82] cover 30% of the region’s surface area [83]. Stream habitats vary by watershed and geomorphic setting [84, 85], resulting in colluvial channels in foothill and mountainous headwaters, beaded headwater streams in low-gradient coastal plains, and meandering alluvial streams and rivers lower in watersheds [82]. Geologic lithology consists of materials from Precambrian and Paleozoic fragments and continental margin materials dating to the Cretaceous period [68]. Modeled ⁸⁷Sr/⁸⁶Sr are estimated to be highest in the Brooks Range (ca. 0.7200), which contains sedimentary noncarbonate lithology with minor areas of intrusive mafic and ultramafic lithologies, and lowest near the coast (ca. 0.7070) within marine and eolian deposits [69]. Modeled stream ⁸⁷Sr/⁸⁶Sr tend to be higher in watersheds that drain high relief areas of the Brooks Range but also remain relatively high in the mainstem Colville River until the tidally influenced areas of the lower river [68, 69]. Modeled stream ⁸⁷Sr/⁸⁶Sr use bedrock and chemical weathering models within a streamflow accumulation model to estimate values at 1 km grid cell, which have been tested against local field datasets [68, 69], but does not account for modification of isotope values during transport [86].

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Fig 1. Study area.

The Central Beaufort Sea region in Arctic Alaska, situated between the Ikpikpuk and the Canning rivers, AK, contains a diversity of aquatic habitats. The large Colville River, AK, USA (ca. watershed area 60,000 km²), located in the middle of the Central Beaufort Sea coast, AK, contains minor and major tributaries (grey lines) that drain from the Brooks Range, AK that flow toward a large delta on the edge of the Beaufort Sea, near the community of Nuiqsut, AK. We collected fish at three sites (black triangles) within the Colville River (site 1 = Itkillik, site 2 = Puviksuk, site 3 = Umiat).

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The Central Beaufort Sea region, within the Arctic tundra biome, is characterized by permafrost, extreme climate, low-growing plants, and large seasonal variations in day length. The region’s stark seasonality can be divided into two main seasons, cold and warm, but the former controls many of the physical and biological processes. Cold season air temperatures are consistently well below freezing, creating a landscape dominated by snow and ice for about eight months [87]. The warm season is brief but, with 24 hours of daylight and moderate air temperatures [87], the area becomes productive foraging and rearing habitat for many resident and migratory fishes and other animals. Annual precipitation is generally low, with more falling in the foothills than along the coast (30 and 20 cm, respectively) and about half falling as snow [87].

Fish sampling and otolith collection

We collected sagittal otoliths from adult Broad Whitefish migrating up the Colville River (Fig 2A) during 2015. The Colville, the largest river in Arctic Alaska, flows about 560 km northward from its headwaters in the partially glaciated Brooks Range to a large delta on the edge of the Central Beaufort Sea coast, near the Native Village of Nuiqsut (Fig 1). We set gill nets ca. 30 m in length composed of braided nylon and monofilament with 10-cm and 12-cm stretched mesh to target adult fish large enough to spawn (> 35 cm; [49, 88]). We positioned nets at gravel point bars, along eddy lines, and perpendicular to flow in low-gradient reaches at three separate sites (Sites 1–3; Fig 1). We sampled at Puviksuk on July 23–27, at Umiat on August 21–26, and at Itkillik on October 10–11. We euthanized captured Broad Whitefish with a single sharp blow to the cranium and recorded fork length (n = 98, all of which were adults ≥ 39 cm; [49]), total weight, gonad weight, and sex (44 males, 47 females, 7 undetermined; S1 Table). We collected sagittal otoliths from individuals using the Guillotine method [89], rinsed in water, and stored in paper envelopes (Table 1). The planned sample size of 50 individuals per site was smaller than anticipated at Umiat (n = 23) and Itkillik (n = 17), as opposed to Puviksuk (n = 57), due to unexpectedly high streamflow at the former and an early freeze-up at the latter that inhibited our ability to capture fish. Research was conducted under Bureau of Land Management NPR-A permit #FF097006 and the Alaska Department of Fish and Game Fish Resource permit #SF2015-200. All collections were performed using methods to minimize suffering.

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Fig 2. Otolith collection, laser analysis, and life history type classification.

(A) Broad Whitefish (Coregonus nasus) from the Colville River, AK, USA (Photo credit: Jason C. Leppi), (B) Broad Whitefish otolith showing the core, annuli, and laser ablation path (Photo credit: Dan Bogan), (C) Flow diagram showing the life history classification criteria approach used to group Broad Whitefish.

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Table 1. Summary of otoliths collected from Broad Whitefish (Coregonus nasus) caught in the Colville River, AK, USA.

https://doi.org/10.1371/journal.pone.0259921.t001

We caught fish at three locations (Puviksuk, Umiat, Itkillik) in the Colville River, AK, USA. N = number of unique otoliths or individuals, mm = millimeters, g = grams, yrs = years, SD = standard deviation. The sex of seven individuals from Puviksuk capture location were undetermined. Weight and age for one individual from the Puviksuk capture location was undetermined due to predation damage.

Sr isotope analysis of otoliths

Otolith preparation.

We mounted sagittal otoliths on individual microscope slides using Crystalbond^TM 509, smoothed using a 1200-grit grinding wheel, and then thin sectioned in the transverse plane. We then polished thin-sectioned otoliths with 3-μm alumina slurry until the core was visible [90], photographed, and visually aged using standard methods [91] and an Olympus^TM microscope with MicroPublisher^TM imaging attachment. For age analysis we utilized an ad hoc two independent reader approach for older individuals with condensed annuli. In preparation for isotope analysis, we remounted otoliths on petrographic slides (2.7 x 4.6 cm) and repolished using 3-μm alumina slurry. A total of five petrographic slides held all 97 otoliths and each otolith was tracked and labeled using a numerical ID.

Laser ablation.

Due to instrument constraints (i.e., time, funding, instrument availability), we measured Sr isotope concentrations (⁸⁸Sr, ⁸⁷Sr, ⁸⁶Sr, ⁸⁴Sr) from a subset of the otoliths (69 of 98 individuals sampled). To ensure that the full range of life history variation was represented, we subsampled otoliths from across the range of δ¹³Cˈ, δD, and δ¹⁸O tissue values, which we expected to represent time spent in different habitat types (e.g., freshwater, estuarine, marine) over the three months prior to capture (S3 Table; [78]) and ensure that subsampled otoliths were roughly proportional to the number collected at each field site [78]. We acknowledge that our subsampling approach, which favored rare types in the tails of the isotopic distributions at the expense of common ones near the middle, may have biased the proportional abundance of the otolith-derived life history types in our data set.

We used an Analyte G2 Excimer 193-nm Laser Ablation System (LA; Teledyne Photon Machines, Bozeman, USA) with a Helex cell coupled to a Neptune Plus^TM multi-collector inductively coupled plasma mass spectrometer (MC-ICP-MS; Thermo Scientific^TM, Bremen, Germany) for strontium isotope analyses at the University of Alaska Fairbanks’ Alaska Stable Isotope Facility. We selected an ablation path perpendicular to annuli across each otolith and, to ensure that the freshwater natal region was identified, our ablation path encompassed the entire first year of growth (Fig 2B) [63]. We performed an initial ablation cleaning using 80 μm circle spot size and 15 μm/s scan speed over the surface of otoliths prior to analysis. For isotope analysis, the laser was set to 35 μm beam diameter, a pulse rate of 10 Hz, a speed of 5 μm/s, and a laser fluence of 6.64 J/cm². We mixed the laser stream with the output of an Aridus II^TM membrane desolvation system via a mixing chamber immediately prior to the plasma inlet, which allowed for optimization of the signal of the MC-ICP-MS and facilitated external intra-elemental correction of the instrument isotope fractionation (IIF) [92]. We used a National Institute of Standards and Technology (NIST^®) solution of ~13 ng g^-1 SRM 987 (SrCO₃ isotopic standard) and ~20 ng g^-1 Zr standard to optimize the instrument and also aspirated via the Aridus II^TM every 30 minutes for IIF correction via bracketing from standard to sample acquisition. While we ablated samples, the Aridus II^TM aspirated a 2% solution of HNO₃ that was double distilled via sub-boiling distillation (Savillex DST-1000) from trace metal grade concentrated HNO₃. We then used American Society for Testing and Materials (ASTM) Type I water from a Milli-Q^® IQ 7000 system, which had been further purified via a double sub-boiling distillation, for dilution. Instrumental parameters used during laser ablation isotopic analysis are listed in the S2 Table of the Appendix.

Data reduction.

We processed ablation data as outlined in Irrgeher (2016) [93]. We blank-corrected data by subtracting the mean gas blank values obtained during the 30-second laser-warmup period prior to each ablation for all measured isotopes. We calculated calcium (Ca) dimer (CaCa) and Ca argide (CaAr) corrections based on the blank corrected signals at mass to charge number (m/z = 82 and 83) using the natural abundances of Ca and argon (Ar) and applied to the blank corrected signals at m/z 84, 85, 86, 87, and 88. We calculated rubidium (Rb) interference correction on m/z 87 using Russell’s law via mass 85 and applied via peak stripping in which the fractionation of Rb was assumed to be equal to strontium (Sr) due to the low levels of Rb naturally occurring in sample materials like otoliths. We corrected the ⁸⁷Sr/ ⁸⁶Sr ratio via standard/sample bracketing of the NIST SRM^® 987 solution. Instrumental isotopic fractionation (IIF) was corrected externally, also using the NIST SRM^® 987 solution.

Otolith ⁸⁷Sr/ ⁸⁶Sr data post-processing.

We individually inspected strontium data from otolith laser ablation for data quality, trimmed, and adjusted accordingly. We removed otoliths from the dataset if Sr data contained unreliable values due to cracks (n = 9). We used a Leica^® microscope with a micrometer to measure the length of the ablation path from age-1 dorsal side to the otolith edge on the ventral side (Fig 2B). We visually identified otolith core and annuli following standard methods [91] and measured the length of each annuli from the beginning of the ablation transect using a Leica^® microscope with a micrometer. We then cropped data in R statistical program (http://cran.r-project.org/), from the estimated otolith core to the edge, to facilitate comparison between otoliths. Finally, to facilitate classification we inspected strontium data for each otolith and made minor adjustments to the ratio data (⁸⁷Sr/⁸⁶Sr) to align them with the global marine value (GMV = 0.70918 ± 0.00006 2 standard deviations (SD)) when ⁸⁸Sr concentrations were above 12.26 V and ensure that non-elevated ⁸⁸Sr concentrations (< 12.26 V) fell outside of this range.

Statistical analysis.

We used Generalized Additive Models (GAMs) to analyze the ⁸⁷Sr/⁸⁶Sr variation from each otolith core to edge. We fit GAMs to each otolith ⁸⁷Sr/⁸⁶Sr profile using the MGCV package in R, which is similar to previous methods used to analyze otolith ⁸⁷Sr/⁸⁶Sr of Slimy Sculpin (Cottus cognatus) [94] and Chinook salmon (Oncorhynchus tshawytscha) [62]. MGCV implementation of GAMs uses penalized iteratively re-weighted least squares (P-IRLS) to maximize goodness-of-fit, solving the smoothing parameter estimation problem and the Generalized Cross-Validation Criterion to minimize overfitting of the data [95, 96]. We determined GAM model parameters through iterative model fit comparisons and for final GAMs we set the lambda (λ) smoothing parameter to 0.6 and knots parameter (k) to 100. Finally, we calculated Bayesian 95% confidence intervals along each ⁸⁷Sr/⁸⁶Sr profile.

Life history attributes and classification

We visually compared the ⁸⁸Sr concentrations and ⁸⁷Sr/⁸⁶Sr across each otolith core-to-edge chronology and, based on inferred life history patterns, developed a classification criteria approach that grouped individuals into life history types. Initial life history types included anadromous, semi-anadromous, and nonanadromous, but during initial visual inspection of otolith profiles it was apparent that subgroups existed within these groups. We estimated otolith Sr concentrations (Sr mg/kg) by dividing the concentration of Sr in the FEBs-1 standard (i.e., 2055 mg/kg) by the average ⁸⁸Sr FEBs-1 standard value during ablation and multiplying by ⁸⁸Sr otolith values at individual points across the ablation path. We considered a otolith ⁸⁸Sr signal below 6.13 V (corresponding to ca. 850 mg/kg) to be time spent in freshwater habitat, a signal greater than 6.13 V and less than 12.26 V (corresponding to ca.1700 mg/kg) to be time spent in estuarine water and a signal greater than 12.26 V (corresponding to ca.1700 mg/kg) to be marine habitat use [38, 76].

We inferred occupied habitats and behaviors based on otolith ⁸⁸Sr concentrations and ⁸⁷Sr/⁸⁶Sr at different ages, determined by reading annuli and identifying important otolith regions. We defined natal regions as areas distal to the core and without detectable maternal strontium that had ⁸⁸Sr concentrations representative of freshwater and relatively constant ⁸⁷Sr/⁸⁶Sr, which were assumed to precede the onset of exogenous feeding and downstream migration to rearing habitat [97]. We defined freshwater juvenile rearing regions as all values distal to the natal region, but before migration to marine habitats and prior to age-1(i.e., freshwater age-0 juvenile rearing period). We considered freshwater regions to be isotopically distinct from each other, inferring movement to a different habitat, if the difference between mean ⁸⁷Sr/⁸⁶Sr was > 0.00005 [94]. This value was chosen because it was sufficiently greater than the mean ± 2 S.E. of all juvenile and natal region values and would be large enough to detect differences [62, 94]. For individuals that did not migrate to marine habitat at age-0, this region encompassed all values during the growth period (identified as the opaque region with wide growth bands) before the winter period [91]. We determined age at marine entry by comparing ⁸⁸Sr concentrations and ⁸⁷Sr/⁸⁶Sr in relation to annuli and we assigned all anadromous individuals with marine entry values (e.g., age-0, age-1) based on when the first maximum ⁸⁸Sr concentrations were above 12.26 V and ⁸⁷Sr/⁸⁶Sr were near the GMV.

We grouped individuals into six life history types using a classification criteria approach that was informed by visual inspection of otolith ⁸⁸Sr concentrations and ⁸⁷Sr/⁸⁶Sr profiles. We considered individuals with otolith ⁸⁸Sr concentrations above 12.26 V and ⁸⁷Sr/⁸⁶Sr near the GMV anadromous, and we considered all other individuals as nonanadromous [38, 51, 63]. We classified anadromous individuals into four subgroups based on the type of rearing habitat inhabited at age-0 and duration in fresh water prior to marine entry (Fig 2C). We classified individuals as follows: i) early marine anadromous (Type 1) if age at migration from fresh water was less than one; ii) as late freshwater anadromous (Type 2) if age-0 ⁸⁷Sr/⁸⁶Sr represented time spent in fresh water and age at migration was greater than one; and iii) late transitional anadromous (Type 3) if age zero ⁸⁷Sr/⁸⁶Sr represented time spent in both fresh and estuarine water and age at migration was greater than one (Fig 2C). We classified individuals as semi-anadromous if ⁸⁷Sr/⁸⁶Sr at the natal region was near GMV (Type 4; Fig 2C). Semi-anadromous individuals had no detectable age-0 freshwater otolith signature, likely spending limited time in fresh water as larvae and frequently moving between freshwater, estuarine, and marine habitats (Fig 2C). We classified nonanadromous individuals into two subgroups based on the habitat used throughout their life. We classified individuals as freshwater estuarine nonanadromous (Type 5) if they spent time in fresh and brackish water but ⁸⁸Sr concentrations and ⁸⁷Sr/⁸⁶Sr never indicated they entered marine habitats. The remaining nonanadromous individuals showed no evidence of using brackish habitat and therefore we classified these individuals as freshwater residents (Type 6) (Fig 2).

Results

Life history types

Anadromous life history (Types 1–3) comprised a significant proportion of individuals sampled (59%; Fig 3A). Early marine anadromous (Type 1) comprised 21%, late freshwater anadromous (Type 2) comprised 23%, and late transitional anadromous (Type 3) comprised 15% (Fig 3A). All anadromous individuals spent a portion of time (months to years) in fresh water (Fig 4A–4C) and had similar natal (Fig 3B) and juvenile rearing ⁸⁷Sr/⁸⁶Sr (Fig 3C). Following initial migration to marine habitats, our results suggested that numerous anadromous individuals shifted from annual marine habitat use (Fig 5A) to constant freshwater residency (Fig 5B). Semi-anadromous (Type 4) individuals were the most common life history type and comprised 28% of the population sampled (Fig 3A). The majority of these individuals had lower natal (Fig 3B) and juvenile freshwater rearing (Fig 3C) ⁸⁷Sr/⁸⁶Sr reflective of estuarine habitats and ⁸⁷Sr/⁸⁶Sr near the GMV that remained somewhat constant across time (Fig 4D). Nonanadromous individuals (Type 5–6) were comparatively rare (13%; Fig 3A), with freshwater estuarine nonanadromous (Type 5) comprising 3%, and freshwater resident (Type 6) comprising 10% (Fig 3A). These nonanadromous individuals had elevated freshwater natal region ⁸⁷Sr/⁸⁶Sr compared to the other life history groups (Fig 3B) but had similar freshwater juvenile rearing ⁸⁷Sr/⁸⁶Sr (Fig 3C). Freshwater estuarine life history types mainly used habitat with ⁸⁷Sr/⁸⁶Sr within the range of fresh waters, but on occasions these individuals used estuarine habitat (Fig 5C). Within the freshwater resident life history type, some individuals appear to use freshwater habitats across a spectrum of ⁸⁷Sr/⁸⁶Sr (Fig 4F), while others remain in habitat with relatively uniform values (Fig 5D).

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Fig 3. Otolith life history classification.

Classification of 61 Broad Whitefish (Coregonus nasus) from the Colville River, AK, USA into six life history types based on strategy (anadromous, semi-anadromous, nonanadromous), rearing habitat used (marine, brackish, freshwater), and age at marine migration. Anadromous individuals (Types 1–3), semi-anadromous (Type 4), and non-anadromous (Types 5–6) are individually colored by life history type. Fitted Generalized Additive Model values of the first 850 raw ⁸⁷Sr/⁸⁶Sr (solid-colored lines) and 95% confidence interval (grey shading) for each individual and life history type are shown in panel (A). The horizontal light blue line represents the global mean oceanic value (GMV = 0.70918 ± 0.00006 2 SD). Panel (B) and (C) show boxplots of the freshwater natal region and the freshwater juvenile rearing region. Panel (D) shows boxplots for the age of first marine migration for each life history group. Each boxplot shows the median value (⁸⁷Sr/⁸⁶Sr value or age) for each life history type (horizontal black line), IQR (box outline), the maximum value within 1.5 times the IQR (vertical black line), and outside values greater than 1.5 times the IQR (black dots).

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Fig 4. Broad Whitefish life history examples.

Selected otolith ⁸⁷Sr/⁸⁶Sr profiles for six Broad Whitefish (Coregonus nasus) from the Colville River, AK, USA, representative of each life history type. Fitted Generalize Additive Model values (solid-colored lines), 95% confidence interval (grey shading), and raw ⁸⁷Sr/⁸⁶Sr for each life history type (black dots) from the otolith core to edge are shown. Life history types shown; early marine anadromous (A), late freshwater anadromous (B), late transitional anadromous (C), semi-anadromous (D), freshwater estuarine nonanadromous (E), freshwater resident (F). Thin dashed vertical black lines represent the end of each winter annular growth (annuli) for the first four years. The horizontal light blue line represents the global mean oceanic value (GMV = 0.70918 ± 0.00006 2 SD).

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Fig 5. Variation within anadromous and nonanadromous life history types.

Variation within anadromous and nonanadromous life history types. Selected otolith ⁸⁷Sr/⁸⁶Sr profiles of four Broad Whitefish (Coregonus nasus) from the Colville River, AK, USA, displaying unique variation in anadromous and nonanadromous life history types. Fitted Generalized Additive Model values (solid-colored lines), 95% confidence interval (grey shading), and raw ⁸⁷Sr/⁸⁶Sr for each life history type (black dots) from the otolith core to edge are shown. Freshwater late anadromous life history type displaying continued marine habitat use (A), freshwater late anadromous life history type displaying a switch to freshwater habitat later in life (B), freshwater estuarine nonanadromous life history type demonstrating delayed estuarine habitat use (C), and freshwater resident life history type demonstrating relatively stable ⁸⁷Sr/⁸⁶Sr across time (D). The horizontal light blue line represents the global mean oceanic value (GMV = 0.70918 ± 0.00006 2 SD).

https://doi.org/10.1371/journal.pone.0259921.g005

Anadromous migration patterns

Among the 59% classified as one of the three anadromous types, most individuals (67%) initially migrated to sea between ages 0 and 2 (Table 2). All anadromous individuals spent months to years in fresh water, but comparisons of GAM values indicate that distinct patterns exist for anadromous types (Fig 3A). Age of marine migration varied based on life history (Table 2), but we found that 39% of individuals migrated to marine habitats at age-0, 25% migrated at age one, 3% migrated at age two, 6% at age three, no fish migrated at age 4, and 3% migrated at age 5 (Fig 3D). Results also show large variation in the duration of marine habitat use (S1 File), with some individuals using marine habitats for only a few years, while others consistently use marine habitat until captured (Fig 5A; S1 File). Interestingly, our results also show that some adults later in life stop migrating to marine habitats and switch back to using freshwater habitats (Fig 5B; S1 File).

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Table 2. Age at migration for anadromous life history types of Broad Whitefish (Coregonus nasus) caught in the Colville River, AK, USA.

https://doi.org/10.1371/journal.pone.0259921.t002

Freshwater natal and juvenile rearing region ⁸⁷Sr/⁸⁶Sr

A comparison of freshwater natal and juvenile rearing region ⁸⁷Sr/⁸⁶Sr across life history groups revealed several patterns. Anadromous life history types (early marine, late freshwater, late transitional) had similar values with mean natal region ⁸⁷Sr/⁸⁶Sr ranging from 0.71052 to 0.71083 (Table 3). Individuals with the semi-anadromous life history type had natal region ⁸⁷Sr/⁸⁶Sr centered at 0.70961 and were the lowest values observed (Table 3). Nonanadromous individuals (freshwater estuarine, freshwater resident) had mean natal region ⁸⁷Sr/⁸⁶Sr ranging from 0.71106 to 0.71122 and were the highest values observed (Table 3).

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Table 3. Summary of GAM predicted ⁸⁷Sr/⁸⁶Sr for freshwater natal and freshwater juvenile rearing regions across life history types of Broad Whitefish (Coregonus nasus) captured in the Colville River, AK, USA.

https://doi.org/10.1371/journal.pone.0259921.t003

Freshwater juvenile rearing ⁸⁷Sr/⁸⁶Sr remained relatively constant for some life history types, while others varied. Freshwater juvenile rearing ⁸⁷Sr/⁸⁶Sr for anadromous life history types remained similar (range: 0.71048–0.71092), but early marine anadromous life history type values decreased slightly (-0.00010), while late freshwater anadromous and late transitional anadromous ⁸⁷Sr/⁸⁶Sr increased (+0.00009, +0.00004; Table 3). Semi-anadromous life history type ⁸⁷Sr/⁸⁶Sr remained at the lowest values, but freshwater juvenile rearing ⁸⁷Sr/⁸⁶Sr increased from natal values (+0.00005; Table 3). Freshwater estuarine nonanadromous life history type ⁸⁷Sr/⁸⁶Sr decreased by -0.00018 to a mean value of 0.71087, and freshwater resident life history type ⁸⁷Sr/⁸⁶Sr decreased slightly (-0.00009) to 0.71108 (Table 3).

Discussion

Otolith microchemistry revealed a diversity of life histories in Broad Whitefish in the Colville River. Six different ⁸⁷Sr/⁸⁶Sr life history types were evident in the individuals examined, which included three anadromous, one semi-anadromous, and two nonanadromous life history types. Within each life history group, there also appears to be variation within freshwater habitats occupied during juvenile and adult stages and the time and duration spent in marine habitats. The majority of Broad Whitefish had anadromous life history types, but migration timing varied greatly within life history types. These results support the pattern of increased life history diversity among species that live in environments subjected to frequent perturbations and extreme seasonal changes (e.g., shifting river channels, river ice growth up to 2 m thick), which can increase mortality risks [98].

Broad Whitefish life history diversity

The observed diversity of life histories and habitats utilized by Broad Whitefish in this study is generally consistent with other studies conducted on coregonins in Arctic and Boreal regions [38, 51, 63, 99]. Research in the Mackenzie River watershed, in Yukon, Canada, showed multiple Broad Whitefish life histories [100], including anadromous, semi-anadromous, and nonanadromous (riverine and lacustrine) forms [38, 53, 101], with large variability in migration patterns and utilization of the watershed [102]. High variation in the timing of marine entry (years) by anadromous individuals was observed in the Mackenzie River, along with a high degree of variability in freshwater, estuarine, and marine habitat use [38]. In the Yukon River, Alaska, otolith Sr concentrations suggest a slow, gradual downstream movement by some individuals from freshwater to marine habitat over multiple years [51, 88]. Our results generally corroborate this but also provide evidence for increased variation in occupied habitats within anadromous types than previously documented in Broad Whitefish. We found major differences in the duration of freshwater and estuarine use prior to marine migration, which ranged from as little as a month or so to several years. In other fish species, juvenile life history diversity appears to be driven by differences in habitat quality and availability and the response of individuals to maximize fitness [103, 104]. Chinook Salmon, for example, exhibit a continuum of juvenile life history pathways, which are defined by the timing and transition among life stages and freshwater and marine habitats used for rearing [104]. These life history pathways are influenced by local habitat and environmental conditions, such as water temperature, which influences factors such as incubation duration, growth rate, and downstream migration [104]. In many instances, Chinook Salmon that are exposed in warmer post-emergence water temperature display higher growth rates, which generally results in earlier downstream migration [105]. Similar processes along with genetically-controlled traits, such as adult fish spawn timing and adaptation to spawning environments, likely influence the diversity of Broad Whitefish life histories documented here.

Semi-anadromous life history types occurred within our samples of Colville River Broad Whitefish but, to our surprise, the vast majority of these individuals did not exhibit a freshwater natal signature, suggesting they had been advected into estuarine and or marine environments soon after hatching. Subsequently, these individuals frequently moved between estuarine and marine habitats throughout their lives, with minimal time spent in fresh water. This pattern suggests that adult adaptation to spawning environments near coastal estuarine and marine habitats may influence life history trajectory [106], as seen in other fish species such as Atlantic Salmon (Salmo salar) and Brown Trout (S. trutta) [107]. Semi-anadromous individuals were the most common life history type documented, which suggests that nearshore and marine habitats are significantly important for juvenile Broad Whitefish born in the Colville River.

Nonanadromous Broad Whitefish have also been documented in the Yukon and Mackenzie rivers [51, 38, 100]. In the Yukon River drainage, nonanadromous individuals are less common than anadromous individuals [51], with the exception of sites far inland (i.e., 2000 km from the coast) and in upstream tributaries like the Porcupine River. Similar patterns have been observed in the Mackenzie River drainage, where nonanadromous individuals are uncommon and tend to be associated with lentic habitat [38, 101] or smaller tributaries (e.g., Arctic Red River) [102]. Our results show a similar pattern in the Colville River watershed but provide evidence for additional diversity within nonanadromous life history types. The observed habitat use of freshwater estuarine nonanadromous life history types suggests that certain individuals do not remain in one location and rather move between a variety of freshwater and estuarine habitats. Collectively, these findings suggest that migrating to sea is not energetically profitable for all Broad Whitefish, such as those requiring particularly long journeys, and that enough resources to reach sexual maturity can be acquired solely in fresh water.

Age-0 movements

The majority of Broad Whitefish appear to be transported from natal habitat to rearing habitat within the first year of life. Generally, otoliths indicated that individuals with life history types 1–2 and 5–6 left natal habitat and utilized freshwater rearing habitat with isotopically different values. Our results suggest that most larvae do not use natal habitat for rearing and instead are likely transported downstream to new habitat. In contrast to previous research, we found that numerous individuals (n = 13) entered marine habitat at age-0 after spending little time in freshwater. To our knowledge, the rapid transition over days instead of weeks has not been documented but might be due to the proximity of spawning habitat to marine environments and the intensity of spring breakup streamflow on certain years. Across numerous freshwater fish species, there is evidence that spawning areas tend to be associated with shallow habitats [108] and provide refugia for eggs where instream conditions facilitate embryonic development and predation is minimal [109]. For example, salmon and trout commonly use small streams or braided channel networks to spawn, taking advantage of habitat that often contains high hyporheic flow, consistent water temperatures, and gravel substrate [110–112]. Within heterogenous riverine environments, it is common for spawning habitats to differ from juvenile rearing habitats, which may also be distinct from adult habitats [44, 45]. These patterns of habitat use have been documented for Broad Whitefish in the Yukon and Kuskokwim river drainages, where adults feed in lakes during the summer, spawn in upper reaches of the drainage during the fall, and larvae and juveniles rear in downstream delta or estuary habitat [49, 50, 52].

A subset of individuals exhibited minimal isotopic change between natal and juvenile rearing regions. The late transitional anadromous life history type (Type 3), for example, displayed minimal isotopic change, which could be caused by individuals being advected downstream to new rearing habitat that were isotopically similar prior to marine entry. The semi-anadromous type (Type 4) also had minimal isotopic change and, in this case, it is possible that they are not advected far or end up rearing in nearby freshwater habitat with isotopic values similar to natal areas (e.g., lakes, riverine habitat).

Identifying freshwater natal and juvenile rearing specific habitat is difficult due to the lack of empirical ⁸⁷Sr/⁸⁶Sr data, but several broad patterns emerged from comparing our results to coarse estimates of surface water ⁸⁷Sr/⁸⁶Sr (1 km² grids) for surface waters across Alaska [68, 69]. Broad Whitefish are known to spawn in wide channels with moderate braiding and gravel substrate [49, 50, 88]. Recent research in the Colville River estimates mainstem habitat to have the greatest intrinsic spawning potential [78], which is where we would expect natal habitat to be located, as opposed to smaller tributaries with similar ⁸⁷Sr/⁸⁶Sr. Anadromous individuals that migrated to sea at age-0 (Type 1) had natal region ⁸⁷Sr/⁸⁶Sr that were higher than juvenile freshwater rearing region ⁸⁷Sr/⁸⁶Sr, suggesting that individuals were born in the middle watershed where estimated ⁸⁷Sr/⁸⁶Sr is near 0.711 and then gradually decreases downstream. Late migrating anadromous individuals (Type 2–3) had juvenile region ⁸⁷Sr/⁸⁶Sr that were higher than natal regions. This pattern could be explained by individuals hatching from eggs spawned higher in the watershed, where the estimated ⁸⁷Sr/⁸⁶Sr range was near 0.710, drifting downstream to rear in habitats in the middle and lower watershed where estimated ⁸⁷Sr/⁸⁶Sr was generally higher (ca. 0.711). Semi-anadromous individuals (Type 4) had natal and juvenile region ⁸⁷Sr/⁸⁶Sr near the GMV (0.70918 ± 0.00006 2 SD), which suggests they moved between areas in the lower river, delta, and estuary. Nonanadromous individuals (Type 5–6) had higher natal region and lower juvenile rearing values, suggesting they may have hatched in the middle watershed drifted downstream to rear in the lower river or estuary, or in the case of the freshwater resident life history type, remained in the spawning area.

Broad Whitefish conservation

Our research provides new insights into the complex patterns of lifetime habitat use by Broad Whitefish. A portfolio of life history types suggests that Colville River Broad Whitefish largely remain free from anthropogenic impacts that fragment or homogenize habitats. Within life history types, individuals appear to use different habitats (freshwater, estuarine, and marine) for varying durations (months to years) across each life stage, which suggests further complexity. For example, an individual may hatch and spend less than one month or as much as five years in freshwater before heading to sea. Once at sea, some individuals continue annual patterns of marine migrations through life, while others migrate as mature adults to freshwater habitats they have not previously occupied. There is certainly greater life history variation than the small number of otolith ⁸⁷Sr/⁸⁶Sr profiles examined here would suggest. Similar diverse patterns of habitat use have been documented for other fish species. Recent otolith microchemistry work with juvenile Chinook salmon, for example, demonstrated that individuals used a complex array of habitat types to achieve maximum growth prior to ocean migration [113]. The diversity of life histories found here suggests that Broad Whitefish populations experience frequent disturbance or high environmental variability [98, 106, 114].

Our research revealed the complexities of Broad Whitefish life histories and habitat use, but further research is needed for the effective conservation of this important subsistence resource. Understanding the variation in ⁸⁷Sr/⁸⁶Sr across the watershed is important for identifying spawning habitats, which are essential to sustain Broad Whitefish populations and may be disproportionally small [78] compared to other key habitats (e.g., feeding habitat). Once identified, it will also be possible to understand the contribution of each spawning area to the Nuiqsut subsistence fishery and potentially to other fisheries [62]. Broad Whitefish are a highly migratory and long-lived fish, making it conceivable that fish caught outside the watershed (e.g., in the Utqiagvik, AK, fishery), may have originated from the Colville River. Lastly, another challenge is understanding where fish go when they leave the Colville River watershed. Future research that tracks the marine migration patterns and movement between watersheds across the Arctic will likely provide key insights into a diversity of juvenile and adult foraging patterns and migration routes.

Despite its historical and current importance as a subsistence species for Alaska’s Indigenous Beaufort Sea communities, little is known about the nature and distribution of their essential habitats. Currently, infrastructure from oil and gas development is minimal within the Colville watershed. However, as development continues to expand across the region, potentially crossing and altering habitats in the Colville River and other watersheds utilized by Broad Whitefish, it is important for land managers and conservation planners to understand the risks. Oil and gas development in the central Beaufort Sea region has caused cumulative impacts to permafrost, such as flooding and pooling of water, and loss of vegetation due to heavy road dust [115, 116], which can cause flow modifications that affect habitat quality and connectivity. Arctic oil and gas infrastructure (e.g., roads, pipelines) fragments and disrupts aquatic ecosystems along linear paths [25], which can further introduce stressors to juvenile and adult fishes [25, 117], such as increased sedimentation [118–121], modifications of streamflow [122], obstructions to passage [123–125], and reduced instream habitat quality [126], as well as pollution [127]. Climate change is also altering Arctic hydrologic regimes; variability in runoff is increasing [14], and discharge from large Arctic rivers is increasing both annually [15, 16] and during winter [17], and snow-dominated runoff regimes are shifting toward rainfall-dominated regimes [128]. Maintaining habitats to support complex life histories is critical for the long-term conservation of Broad Whitefish and the loss of life history diversity will make the population more susceptible to fluctuations in abundance and will increase the risk of extinction [129–131].

Supporting information

S1 Table. Summary of Broad Whitefish sampled.

Table displaying population sex, age, and size structure, tissues sampled, and sample size for Broad Whitefish (Coregonus nasus) caught at three locations within the Colville River watershed, Alaska, USA.

https://doi.org/10.1371/journal.pone.0259921.s001

(DOCX)

S2 Table. Laser Ablation System (LA) multi collector inductively coupled plasma mass spectrometer (MC-ICP-MS) instrument parameters.

Table displaying the LA MC-ICP-MS instrument parameters used at the Alaska Stable Isotope Facility, University of Alaska Fairbanks, Fairbanks, AK, USA.

https://doi.org/10.1371/journal.pone.0259921.s002

(DOCX)

S3 Table. Summary of Broad Whitefish (Coregonus nasus) sampled in the Colville River, AK, USA.

Table displaying otoliths analyzed, Broad whitefish attributes, and muscle tissue stable isotope values (δ¹³C, δ¹⁵N, δ¹⁸O, and δD).

https://doi.org/10.1371/journal.pone.0259921.s003

(DOCX)

S1 File. Sr otolith plots.

Plots showing ⁸⁸Sr concentration and ⁸⁷Sr/⁸⁶Sr for all Broad Whitefish (Coregonus nasus) caught within the Colville River watershed, Alaska, USA. We cropped strontium data for otoliths analyzed (n = 61) at the otolith core and edge.

https://doi.org/10.1371/journal.pone.0259921.s004

(PDF)

Acknowledgments

We would like to thank the Alaska Cooperative Fish and Wildlife Research Unit (AKCFWRU) staff, Native Village of Nuiqsut (NVN) Tribal Council staff, U.S. Fish and Wildlife Service Fairbanks Fish and Aquatic Conservation Office staff, Bureau of Land Management (BLM) Arctic Office staff, and the Alaska Established Program to Stimulate Competitive Research for assistance with fieldwork and logistics. We also thank Jonah Nukapigak (NVN) for crucial field logistical support and for sharing his traditional knowledge, and Stuart Lehman, Mike Lunde (AKCFWRU), and Kristin Rine (AKCFWRU) for critical assistance in the field with Broad Whitefish capture. We also thank Lonnie Bryant (BLM) for assistance with summer activity permits and Richard Kemnitz (formerly BLM) for logistical help in Umiat, AK. We thank Josh Gage with Gage Cartographic for assistance with R code to help organize and analyze strontium otolith data. We thank Andrew C. Seitz (CFOS, UAF) and Jeffrey A. Falke (USGS AKCFWRU) for reviewing early manuscript drafts and providing constructive comments. We also thank Tracy N. Loewen and one anonymous reviewer for their helpful and constructive comments, which helped us improve the manuscript. Research was conducted under the Alaska Department of Fish and Game Fish Resource Permit #SF2017-200, the Bureau of Land Management NPR-A permit #FF097006, and under the University of Alaska Fairbanks Institutional Animal Care and Use Committee Protocol #901048. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the U.S. Fish and Wildlife Service. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

References

1. Darwall W, Freyhof J. Lost fishes, who is counting? The extent of the threat to freshwater fish biodiversity. In: Closs GP, Olden JD, Krkosek M, editors. Conser Fresh Fish. Cambridge: Cambridge University Press; 2015. pp. 1–36. https://doi.org/10.1017/CBO9781139627085.002
2. Collen B, Whitton F, Dyer EE, Baillie JEM, Cumberlidge N, Darwall WRT, et al. Global patterns of freshwater species diversity, threat and endemism. Glob Ecol Biogeogr. 2014;23: 40–51. pmid:26430385
- View Article
- PubMed/NCBI
- Google Scholar
3. Almond REA, Grooten M, Petersen T. Living planet report 2020—bending the curve of biodiversity loss. WWF, Gland, Switzerland; 2020. Available from: https://f.hubspotusercontent20.net/hubfs/4783129/LPR/PDFs/ENGLISH-FULL.pdf
4. Su G, Logez M, Xu J, Tao S, Villéger S, Brosse S. Human impacts on global freshwater fish biodiversity. Science. 2021;371: 835–838. pmid:33602854
- View Article
- PubMed/NCBI
- Google Scholar
5. Dawson MN. Species richness, habitable volume, and species densities in freshwater, the sea, and on land. Front Biogeogr. 2012;4. https://doi.org/10.21425/f5fbg12675
- View Article
- Google Scholar
6. Palkovacs EP, Kinnison MT, Correa C, Dalton CM, Hendry AP. Fates beyond traits: ecological consequences of human-induced trait change. Evol Appl. 2012;5: 183–191. pmid:25568040
- View Article
- PubMed/NCBI
- Google Scholar
7. Reid AJ, Carlson AK, Creed IF, Eliason EJ, Gell PA, Johnson PTJ, et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol Rev. 2019;94: 849–873. pmid:30467930
- View Article
- PubMed/NCBI
- Google Scholar
8. Dudgeon D, Arthington AH, Gessner MO, Kawabata Z-I, Knowler DJ, Lévêque C, et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol Rev. 2005/12/12. 2006;81: 163–182. pmid:16336747
- View Article
- PubMed/NCBI
- Google Scholar
9. Meredith M, Sommerkorn M, Cassotta S, Derksen C, Ekaykina A, Hollowed A. Polar regions. Intergovernmental Panel on Climate change (IPCC) special report on the ocean and cryosphere in a changing climate. 2019. pp. 203–320. https://doi.org/10.1016/S1366-7017(01)00066-6
- View Article
- Google Scholar
10. Lento J, Goedkoop W, Culp J, Cristoffersen K, Larusson K, Fefilova E, et al. State of the Arctic freshwater biodiversity. Conservation of Arctic Flora and Fauna International Secretariat, Akureyri, Iceland; 2019. Available from: http://www.arcticbiodiversity.is/freshwater
11. Larsen JN, Anisimov OA, Constable A, Hollowed AB, Maynard N, Prestrud P, et al. Polar regions. In: Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, Mastrandrea MD, Mach KJ, Bilir TE, Chatterjee M, Ebi KL, Estrade YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR and White LL, editors. Climate change 2014: impacts, adaptation, and vulnerability part b: regional aspects contribution of working group II to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2014. pp. 1567–1612. Available from: https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-Chap28_FINAL.pdf
12. Overland J, Dunlea E, Box JE, Corell R, Forsius M, Kattsov V, et al. The urgency of Arctic change. Polar Sci. 2019;21: 6–13. https://doi.org/10.1016/j.polar.2018.11.008
- View Article
- Google Scholar
13. Heino J, Virkkala R, Toivonen H. Climate change and freshwater biodiversity: detected patterns, future trends and adaptations in northern regions. Biol Rev. 2009;84: 39–54. pmid:19032595
- View Article
- PubMed/NCBI
- Google Scholar
14. Stuefer SL, Arp CD, Kane DL, Liljedahl AK. Recent extreme runoff observations from coastal Arctic watersheds in Alaska. Water Resour Res. 2017;53: 9145–9163. https://doi.org/10.1002/2017WR020567
- View Article
- Google Scholar
15. Peterson BJ, Holmes RM, McClelland JW, Vörösmarty CJ, Lammers RB, Shiklomanov AI, et al. Increasing river discharge to the Arctic Ocean. Science. 2002;298: 2171–2173. pmid:12481132
- View Article
- PubMed/NCBI
- Google Scholar
16. Shiklomanov AI, Lammers RB. Record Russian River discharge in 2007 and the limits of analysis. Environ Res Lett. 2009;4. pmid:19763186
- View Article
- PubMed/NCBI
- Google Scholar
17. Smith LC, Pavelsky TM, MacDonald GM, Shiklomanov AI, Lammers RB. Rising minimum daily flows in northern Eurasian rivers: a growing influence of groundwater in the high-latitude hydrologic cycle. J Geophys Res Biogeosciences. 2007;112. https://doi.org/10.1029/2006JG000327
- View Article
- Google Scholar
18. Van Vliet MTH, Franssen WHP, Yearsley JR, Ludwig F, Haddeland I, Lettenmaier DP, et al. Global river discharge and water temperature under climate change. Glob Environ Chang. 2013;23: 450–464. https://doi.org/10.1016/j.gloenvcha.2012.11.002
- View Article
- Google Scholar
19. Arp CD, Jones BM, Grosse G, Bondurant AC, Romanovsky VE, Hinkel KM, et al. Threshold sensitivity of shallow Arctic lakes and sub-lake permafrost to changing winter climate. Geophys Res Lett. 2016;43: 6358–6365. https://doi.org/10.1002/2016GL068506
- View Article
- Google Scholar
20. Smol JP, Douglas MS V. Crossing the final ecological threshold in high Arctic ponds. Proc Natl Acad Sci. 2007;104: 12395–12397. pmid:17606917
- View Article
- PubMed/NCBI
- Google Scholar
21. Hayden B, Myllykangas J-P, Rolls RJ, Kahilainen KK. Climate and productivity shape fish and invertebrate community structure in subarctic lakes. Freshw Biol. 2017;62: 990–1003. https://doi.org/10.1111/fwb.12919
- View Article
- Google Scholar
22. Hayden B, Harrod C, Thomas SM, Eloranta AP, Myllykangas J-P, Siwertsson A, et al. From clear lakes to murky waters–tracing the functional response of high-latitude lake communities to concurrent ‘greening’ and ‘browning.’ Ecol Lett. 2019;22: 807–816. pmid:30793453
- View Article
- PubMed/NCBI
- Google Scholar
23. Huser BJ, Futter MN, Bogan D, Brittain JE, Culp JM, Goedkoop W, et al. Spatial and temporal variation in Arctic freshwater chemistry—reflecting climate-induced landscape alterations and a changing template for biodiversity. Freshw Biol. 2020;67: 14–29. https://doi.org/10.1111/fwb.13645
- View Article
- Google Scholar
24. Campana SE, Casselman JM, Jones CM, Black G, Barker O, Evans M, et al. Arctic freshwater fish productivity and colonization increase with climate warming. Nat Clim Chang. 2020;10: 428–433. https://doi.org/10.1038/s41558-020-0744-x
- View Article
- Google Scholar
25. Cott PA, Schein A, Hanna BW, Johnston TA, MacDonald DD, Gunn JM. Implications of linear developments on northern fishes. Environ Rev. 2015;23: 177–190. https://doi.org/10.1139/er-2014-0075
- View Article
- Google Scholar
26. Dudgeon D. Multiple threats imperil freshwater biodiversity in the Anthropocene. Curr Biol. 2019;29: R960–R967. pmid:31593677
- View Article
- PubMed/NCBI
- Google Scholar
27. Goldsmit J, Archambault P, Chust G, Villarino E, Liu G, Lukovich J V, et al. Projecting present and future habitat suitability of ship-mediated aquatic invasive species in the Canadian Arctic. Biol Invasions. 2018;20: 501–517. https://doi.org/10.1007/s10530-017-1553-7
- View Article
- Google Scholar
28. Taranu ZE, Gregory-Eaves I, Leavitt PR, Bunting L, Buchaca T, Catalan J, et al. Acceleration of cyanobacterial dominance in north temperate-subarctic lakes during the Anthropocene. Ecol Lett. 2015;18: 375–384. pmid:25728551
- View Article
- PubMed/NCBI
- Google Scholar
29. Moore JW, Yeakel JD, Peard D, Lough J, Beere M. Life history diversity and its importance to population stability and persistence of a migratory fish: steelhead in two large North American watersheds. J Anim Ecol. 2014;83: 1035–1046. pmid:24673479
- View Article
- PubMed/NCBI
- Google Scholar
30. Hughes JB, Daily GC, Ehrlich PR. Population diversity: its extent and extinction. Science. 1997;278: 689–692. pmid:9381179
- View Article
- PubMed/NCBI
- Google Scholar
31. Greene CM, Hall JE, Guilbault KR, Quinn TP. Improved viability of populations with diverse life history portfolios. Biol Lett. 2010;6: 382–386. pmid:20007162
- View Article
- PubMed/NCBI
- Google Scholar
32. Heim KC, McMahon TE, Calle L, Wipfli MS, Falke JA. A general model of temporary aquatic habitat use: water phenology as a life history filter. Fish Fish. 2019;20: 802–816. https://doi.org/10.1111/faf.12386
- View Article
- Google Scholar
33. Haynes TB, Rosenberger AE, Lindberg MS, Whitman M, Schmutz JA. Patterns of lake occupancy by fish indicate different adaptations to life in a harsh Arctic environment. Freshw Biol. 2014;59: 1884–1896. https://doi.org/10.1111/fwb.12391
- View Article
- Google Scholar
34. Stearns SC. Life history evolution: successes, limitations, and prospects. Naturwissenschaften. 2000;87: 476–486. pmid:11151666
- View Article
- PubMed/NCBI
- Google Scholar
35. Hutchings JA, Morris DW. The influence of phylogeny, size and behaviour on patterns of covariation in salmonid life histories. Oikos. 1985;45: 118–124. https://doi.org/10.2307/3565229
- View Article
- Google Scholar
36. Brown RJ, Courtney MB, Seitz AC. New insights into the biology of anadromous Dolly Varden in the Canning River, Arctic National Wildlife Refuge, Alaska. Trans Am Fish Soc. 2019;148: 73–87. https://doi.org/10.1002/tafs.10122
- View Article
- Google Scholar
37. Brown RJ, Loewen MB, Tanner TL. Overwintering locations, migrations, and fidelity of radio-tagged Dolly Varden in the Hulahula River, Arctic National Wildlife Refuge, 2007–09. Arctic. 2014;67: 149–158. https://doi.org/10.14430/arctic4379
- View Article
- Google Scholar
38. Harris LN, Loewen TN, Reist JD, Halden NM, Babaluk JA, Tallman RF. Migratory variation in Mackenzie River system broad whitefish: insights from otolith strontium distributions. Trans Am Fish Soc. 2012;141: 1574–1585. https://doi.org/10.1080/00028487.2012.713885
- View Article
- Google Scholar
39. Harris LN, Swanson HK, Gilbert MJH, Malley BK, Fisk AT, Moore J-S. Anadromy and marine habitat use of Lake trout (Salvelinus namaycush) from the central Canadian Arctic. J Fish Biol. 2020;96: 1489–1494. pmid:32128819
- View Article
- PubMed/NCBI
- Google Scholar
40. Irvine J, Macdonald R, Brown R, Godbout L, Reist J, Carmack E. Salmon in the Arctic and how they avoid lethal low temperatures. North Pacific Anadromous Fish Comm Bull. 2009;5: 39–50. Available from: https://npafc.org/bulletin-5/
- View Article
- Google Scholar
41. Ranta E, Tesar D, Kaitala V. Environmental variability and semelparity vs. iteroparity as life histories. J Theor Biol. 2002;217: 391–396. pmid:12270282
- View Article
- PubMed/NCBI
- Google Scholar
42. Orzack SH, Tuljapurkar S. Population dynamics in variable environments. VII. The demography and evolution of iteroparity. Am Nat. 1989;133: 901–923. https://doi.org/10.1086/284959
- View Article
- Google Scholar
43. Leggett WC, Carscadden JE. Latitudinal variation in reproductive characteristics of American Shad (Alosa sapidissima): evidence for population specific life history strategies in fish. J Fish Res Board Canada. 1978;35: 1469–1478. https://doi.org/10.1139/f78-230
- View Article
- Google Scholar
44. Schlosser IJ. Stream fish ecology: a landscape perspective. Bioscience. 1991;41: 704–712. https://doi.org/10.2307/1311765
- View Article
- Google Scholar
45. Schlosser IJ. Critical landscape attributes that influence fish population dynamics in headwater streams. Hydrobiologia. 1995;303: 71–81. https://doi.org/10.1007/BF00034045
- View Article
- Google Scholar
46. Hilborn R, Quinn TP, Schindler DE, Rogers DE. Biocomplexity and fisheries sustainability. Proc Natl Acad Sci. 2003;100: 6564–6568. pmid:12743372
- View Article
- PubMed/NCBI
- Google Scholar
47. Schindler DE, Hilborn R, Chasco B, Boatright CP, Quinn TP, Rogers LA, et al. Population diversity and the portfolio effect in an exploited species. Nature. 2010;465: 609–612. pmid:20520713
- View Article
- PubMed/NCBI
- Google Scholar
48. Schindler DE, Armstrong JB, Reed TE. The portfolio concept in ecology and evolution. Front Ecol Environ. 2015;13: 257–263. https://doi.org/10.1890/140275
- View Article
- Google Scholar
49. Brown RJ, Brown C, Braem NM, Carter WK III, Legere N, Slayton L. Whitefish biology, distribution, and fisheries in the Yukon and Kuskokwim River drainages in Alaska: a synthesis of available information. US Fish Wildl Serv Alaska Fish Data Ser 2012–4. 2012. Available from: https://www.fws.gov/alaska/sites/default/files/pdfs/fisheries/data-series/d_2012_4.pdf
- View Article
- Google Scholar
50. Harper KC, Harris F, Miller SJ, Thalhauser JM, Ayers SD. Life history traits of adult broad whitefish and humpback whitefish. J Fish Wildl Manag. 2012;3: 56–75. https://doi.org/10.3996/022011-JFWM-011
- View Article
- Google Scholar
51. Brown RJ, Bickford N, Severin K. Otolith trace element chemistry as an indicator of anadromy in Yukon River drainage coregonine fishes. Trans Am Fish Soc. 2007;136: 678–690. https://doi.org/10.1577/T06-040.1
- View Article
- Google Scholar
52. Brown RJ. Distribution and demographics of whitefish species in the upper Koyukuk River drainage, Alaska, with emphasis on seasonal migrations and important habitats of broad whitefish and humpback whitefish. US Fish Wildl Serv Alaska Fish Tech Rep No 104. 2009. Available from: https://www.arlis.org/docs/vol1/B/495849598.pdf
53. Tallman R, Abrahams M, Chudobiak D. Migration and life history alternatives in a high latitude species, the broad whitefish, Coregonus nasus Pallas. Ecol Freshw Fish. 2002;11: 101–111. https://doi.org/10.1034/j.1600-0633.2002.00001.x
- View Article
- Google Scholar
54. Reshetnikov YS, Bogdanov VD. Features of reproduction of whitefishes. J Ichthyol. 2011;51: 432–456. https://doi.org/10.1134/S0032945211030118
- View Article
- Google Scholar
55. Martin DJ, Whitmus CK, Hachmeister LE, Volk EC, Schroder SL. Distribution and seasonal abundance of juvenile salmon and other fishes in the Yukon Delta. US Dep Commer Natl Oceanogr Atmos Adm Outer Cont Shelf Environ Assess Program, Final Reports Princ Investig. 1987. Available from: https://espis.boem.gov/final%20reports/86.pdf
56. Bond WA. A study of the fishery resources of Tuktoyaktuk Harbour, southern Beaufort Sea Coast, with special reference to life histories of anadromous coregonids. Department of Fisheries and Oceans, Can Tech Rep Fish and Aqut Sci, No. 119, Winnipeg.1982. Available from: https://publications.gc.ca/site/eng/454445/publication.html
57. Næsje TF, Jonsson B, Sandlund OT. Drift of cisco and whitefish larvae in a Norwegian River. Trans Am Fish Soc. 1986;1: 89–93. https://doi.org/10.1577/1548-8659(1986)115<89
- View Article
- Google Scholar
58. Bacon JJ, Hepa TR, Brower HKJ, Pedersen M, Olemaun TP, George JC, et al. Estimates of subsistence harvest for villages on the North Slope of Alaska, 1994–2003. N Slpe Borgh Depart, Barrow, 2011. Available from: http://www.north-slope.org/assets/images/uploads/MASTER%20SHDP%2094-03%20REPORT%20FINAL%20and%20%20Errata%20info%20(Sept%202012).pdf
- View Article
- Google Scholar
59. Brown CL, Braem NM, Kostick ML, Trainor A, Slayton LJ, Runfola DM, et al. Harvests and uses of wild resources in 4 interior Alaska communities and 3 Arctic Alaska communities, 2014. Alaska Dept Fish Game Div Subsis, Tech Pap No. 426, Fairbanks 2016. Available from: https://www.arlis.org/docs/vol1/M/972886420.pdf
60. Alaska Department of Fish and Game Community Subsistence Information System (CSIS). Data for Barrow and Nuiqsut; 2014 [cited 2021 May 19]. Database: CSIS [internet]. Available from: http://www.adfg.alaska.gov/sb/csis/
61. Kennedy BP, Klaue A, Blum JD, Folt CL, Nislow KH. Reconstructing the lives of fish using Sr isotopes in otoliths. Can J Fish Aquat Sci. 2002;59: 925–929. https://doi.org/10.1139/f02-070
- View Article
- Google Scholar
62. Brennan SR, Zimmerman CE, Fernandez DP, Cerling TE, McPhee M V, Wooller MJ. Strontium isotopes delineate fine-scale natal origins and migration histories of Pacific salmon. Sci Adv. 2015;1: e1400124. pmid:26601173
- View Article
- PubMed/NCBI
- Google Scholar
63. Padilla AJ, Brown RJ, Wooller MJ. Determining the movements and distribution of anadromous Bering ciscoes by use of otolith strontium isotopes. Trans Am Fish Soc. 2016;145: 1374–1385. https://doi.org/10.1080/00028487.2016.1225599
- View Article
- Google Scholar
64. Hegg JC, Giarrizzo T, Kennedy BP. Diverse early life history strategies in migratory Amazonian catfish: implications for conservation and management. PLOS ONE. 2015;10: e0129697. pmid:26153984
- View Article
- PubMed/NCBI
- Google Scholar
65. Hobbs JA, Lewis LS, Willmes M, Denney C, Bush E. Complex life histories discovered in a critically endangered fish. Sci Rep. 2019;9: 16772. pmid:31727901
- View Article
- PubMed/NCBI
- Google Scholar
66. Austin CS, Bond MH, Smith JM, Lowery ED, Quinn TP. Otolith microchemistry reveals partial migration and life history variation in a facultatively anadromous, iteroparous salmonid, bull trout (Salvelinus confluentus). Environ Biol Fishes. 2019;102: 95–104. https://doi.org/10.1007/s10641-019-0848-1
- View Article
- Google Scholar
67. Campana SE. Chemistry and composition of fish otoliths: pathways, mechanisms and applications. Mar Ecol Prog Ser. 1999;188: 263–297. https://doi.org/10.3354/meps188263
- View Article
- Google Scholar
68. Brennan SR, Fernandez DP, Mackey G, Cerling TE, Bataille CP, Bowen GJ, et al. Strontium isotope variation and carbonate versus silicate weathering in rivers from across Alaska: implications for provenance studies. Chem Geol. 2014;389: 167–181. https://doi.org/10.1016/j.chemgeo.2014.08.018
- View Article
- Google Scholar
69. Bataille CP, Brennan SR, Hartmann J, Moosdorf N, Wooller MJ, Bowen GJ. A geostatistical framework for predicting variations in strontium concentrations and isotope ratios in Alaskan rivers. Chem Geol. 2014;389: 1–15. https://doi.org/10.1016/j.chemgeo.2014.08.030
- View Article
- Google Scholar
70. Bataille CP, Bowen GJ. Mapping ⁸⁷Sr/⁸⁶Sr variations in bedrock and water for large scale provenance studies. Chem Geol. 2012;304–305: 39–52. https://doi.org/10.1016/j.chemgeo.2012.01.028
- View Article
- Google Scholar
71. Capo RC, Stewart BW, Chadwick OA. Strontium isotopes as tracers of ecosystem processes: theory and methods. Geoderma. 1998;82: 197–225. https://doi.org/10.1016/S0016-7061(97)00102-X
- View Article
- Google Scholar
72. Blum JD, Erel Y, Brown K. ⁸⁷Sr/⁸⁶Sr ratios of Sierra Nevada stream waters: implications for relative mineral weathering rates. Geochim Cosmochim Acta. 1993;57: 5019–5025. https://doi.org/10.1016/S0016-7037(05)80014-6
- View Article
- Google Scholar
73. Horton TW, Chamberlain CP, Fantle M, Blum JD. Chemical weathering and lithologic controls of water chemistry in a high-elevation river system: Clark’s Fork of the Yellowstone River, Wyoming and Montana. Water Resour Res. 1999;35: 1643–1655. https://doi.org/10.1029/1998WR900103
- View Article
- Google Scholar
74. Hüssy K, Limburg KE, de Pontual H, Thomas ORB, Cook PK, Heimbrand Y, et al. Trace element patterns in otoliths: the role of biomineralization. Rev Fish Sci Aquac.2021;29: 445–477. https://doi.org/10.1080/23308249.2020.1760204
- View Article
- Google Scholar
75. Barnett-Johnson R, Pearson TE, Ramos FC, Grimes CB, MacFarlane BR. Tracking natal origins of salmon using isotopes, otoliths, and landscape geology. Limnol Oceanogr. 2008;53: 1633–1642. https://doi.org/10.1111/j.1095-8649.2009.02394.x 10.4319/lo.2008.53.4.1633
- View Article
- Google Scholar
76. Loewen TN, Gillis D, Tallman RF. Ecological niche specialization inferred from morphological variation and otolith strontium of Arctic charr Salvelinus alpinus L. found within open lake systems of southern Baffin Island, Nunavut, Canada. J Fish Biol. 2009;75: 1473–1495. pmid:20738626
- View Article
- PubMed/NCBI
- Google Scholar
77. Alt KT, Kogl DR. Notes on the whitefish of the Colville River, Alaska. J Fish Res Board Canada. 1973;30: 554–556. https://doi.org/10.1139/f73-094
- View Article
- Google Scholar
78. Leppi JC. Broad Whitefish (Coregonus nasus) ecology and habitat use in Arctic Alaska: spawning habitat suitability, isotopic niches, life-history variations, and climate change risks to subsistence fisheries. Ph.D. Dissertation, University of Alaska Fairbanks. 2021. Available from: https://www.proquest.com/openview/0ca0ff9a682772615e3c282ac70132a1/1?pq-origsite = gscholar&cbl = 18750&diss = y
79. Craig PC. Fish use of coastal waters of the Alaskan Beaufort Sea: a review. Trans Am Fish Soc. 1984;113: 265–282. https://doi.org/10.1577/1548-8659(1984)113<265:FUOCWO>2.0.CO;2
- View Article
- Google Scholar
80. Jorgenson MT, Brown J. Classification of the Alaskan Beaufort Sea Coast and estimation of carbon and sediment inputs from coastal erosion. Geo-Marine Lett. 2005;25: 69–80. https://doi.org/10.1007/s00367-004-0188-8
- View Article
- Google Scholar
81. Jorgenson MT, Shur Y. Evolution of lakes and basins in northern Alaska and discussion of the thaw lake cycle. J Geophys Res Earth Surf. 2007;112. https://doi.org/10.1029/2006JF000531
- View Article
- Google Scholar
82. Arp CD, Whitman MS, Jones BM, Kemnitz R, Grosse G, Urban FE. Drainage network structure and hydrologic behavior of three lake-rich watersheds on the Arctic Coastal Plain, Alaska. Arctic, Antarct, Alp Res. 2012;44: 385–398. https://doi.org/10.1657/1938-4246-44.4.385
- View Article
- Google Scholar
83. Grosse G, Jones B, Arp C. Thermokarst lakes, drainage, and drained basins. In: Shroder J, editor in chief, Giardino R, Harbor J, editor. Treatise on Geomorphology. San Diego: Academic Press; 2013. pp. 325–353. https://doi.org/10.1016/B978-0-12-374739-6.00216-5
84. Montgomery DR, Buffington JM. Channel-reach morphology in mountain drainage basins. GSA Bull. 1997;109: 596–611. https://doi.org/10.1130/0016-7606(1997)109<0596:CRMIMD>2.3.CO;2
- View Article
- Google Scholar
85. Knighton D. Fluvial forms and processes: a new perspective. Oxford University Press; 1998.
86. Brennan SR, Torgersen CE, Hollenbeck JP, Fernandez DP, Jensen CK, Schindler DE. Dendritic network models: improving isoscapes and quantifying influence of landscape and in-stream processes on strontium isotopes in rivers. Geophys Res Lett. 2016;43: 5043–5051. https://doi.org/10.1002/2016GL068904
- View Article
- Google Scholar
87. Shulski M and Wendler G. The climate of Alaska. Fairbanks: University of Alaska Press; 2007.
88. Carter WK. Life history and spawning movements of broad whitefish in the middle Yukon River. M.S. Thesis, University of Alaska Fairbanks. 2010. Available from: https://citeseerx.ist.psu.edu/viewdoc/download?doi = 10.1.1.383.4750&rep = rep1&type = pdf
89. Secor D, Dean J, Laban E. Otolith removal and preparation for microstructural examination. In: Stevenson D, Campana S, editors. Otolith Microstructure Examination and Analysis. Can Spec Publ Fish Aquat Sci.1992;117 pp. 13–17. https://doi.org/10.13140/RG.2.2.22258.61127
90. Stevenson DK, Campana SE. Otolith Microstructure Examination and Analysis. Can Spec Publ Fish Aquat Sci. 1992;117. Available from: https://publications.gc.ca/site/eng/9.811462/publication.html
- View Article
- Google Scholar
91. Chilton DE, Beamish RJ. Age determination methods for fishes studied by the groundfish program at the Pacific Biological Station. Can Spec Publ Fish Aquat Sci 60. 1982; 102. Available from: https://publications.gc.ca/site/eng/9.816313/publication.html
- View Article
- Google Scholar
92. Prohaska T, Irrgeher J, Zitek A. Simultaneous multi-element and isotope ratio imaging of fish otoliths by laser ablation split stream ICP-MS/MC ICP-MS. J Anal At Spectrom. 2016;31: 1612–1621. https://doi.org/10.1039/C6JA00087H
- View Article
- Google Scholar
93. Irrgeher J, Galler P, Prohaska T. ⁸⁷Sr/⁸⁶Sr isotope ratio measurements by laser ablation multicollector inductively coupled plasma mass spectrometry: reconsidering matrix interferences in bioapatites and biogenic carbonates. Spectrochim Acta Part B At Spectrosc. 2016;125: 31–42. https://doi.org/10.1016/j.sab.2016.09.008
- View Article
- Google Scholar
94. Brennan SR, Fernandez DP, Zimmerman CE, Cerling TE, Brown RJ, Wooller MJ. Strontium isotopes in otoliths of a non-migratory fish (slimy sculpin): implications for provenance studies. Geochim Cosmochim Acta. 2015;149: 32–45. https://doi.org/10.1016/j.gca.2014.10.032
- View Article
- Google Scholar
95. Wood SN. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J Am Stat Assoc. 2004;99: 673–686. https://doi.org/10.1198/016214504000000980
- View Article
- Google Scholar
96. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B (Statistical Methodol). 2011;73: 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x
- View Article
- Google Scholar
97. Barnett-Johnson R, Grimes C, Royer C, Donohoe C. Identifying the contribution of wild and hatchery Chinook salmon (Oncorhynchus tshawytscha) to the ocean fishery using otolith microstructure as natural tags. Can J Fish Aquat Sci. 2007;64: 1683–1692. https://doi.org/10.1139/f07-129
- View Article
- Google Scholar
98. Lytle DA. Disturbance regimes and life‐history evolution. Am Nat. 2001;157: 525–536. pmid:18707260
- View Article
- PubMed/NCBI
- Google Scholar
99. Stuby L. Contributions to the life history of Kuskokwim River inconnu. Trans Am Fish Soc. 2018;147: 879–890. https://doi.org/10.1002/tafs.10069
- View Article
- Google Scholar
100. Chang-Kue KTJ, Jessop EF. The life history and habitat usage of broad whitefish in the lower Mackenzie River basin. Canadian Technical Report of Fisheries and Aquatic Sciences 2193: the proceedings of the broad whitefish workshop; the biology, traditional knowledge and scientific management of broad whitefish (Coregonus nasus (Pallus)) in the lower Mackenzie River. 1997. pp. 117–146. Available from: https://publications.gc.ca/collections/collection_2014/mpo-dfo/Fs97-6-2193-eng.pdf
101. Millar NP, Harris LN, Howland KL. Seasonal migrations of broad whitefish (Coregonus nasus (Pallas)) in an Arctic lake. Adv Limnol. 2013;64: 91–107. https://doi.org/10.1127/1612-166X/2013/0064-0010
- View Article
- Google Scholar
102. Hodgson EE, Hovel RA, Ward EJ, Lord S, Moore JW. Migratory diversity in an Arctic fish supporting subsistence harvest. Biol Conserv. 2020;248: 108685. https://doi.org/10.1016/j.biocon.2020.108685
- View Article
- Google Scholar
103. Satterthwaite WH, Hayes SA, Merz JE, Sogard SM, Frechette DM, Mangel M. State-dependent migration timing and use of multiple habitat types in anadromous salmonids. Trans Am Fish Soc. 2012;141: 781–794. https://doi.org/10.1080/00028487.2012.675912
- View Article
- Google Scholar
104. Bourret SL, Caudill CC, Keefer ML. Diversity of juvenile Chinook salmon life history pathways. Rev Fish Biol Fish. 2016;26: 375–403. https://doi.org/10.1007/s11160-016-9432-3
- View Article
- Google Scholar
105. Beckman BR, Larsen DA, Lee-Pawlak B, Dickhoff WW. Relation of fish size and growth rate to migration of spring Chinook salmon smolts. N Am J Fish Manag. 1998;18:37–41. https://doi.org/10.1577/1548-8675(1998)018<0537:ROFSAG>2.0.CO;2
- View Article
- Google Scholar
106. Southwood TRE. Habitat, the templet for ecological strategies? J Anim Ecol. 1977;46: 337–365. https://doi.org/10.2307/3817
- View Article
- Google Scholar
107. Jonsson B, Jonsson N. Habitats as template for life histories. Ecology of Atlantic Salmon and Brown Trout: habitat as a template for life histories. Dordrecht: Springer Netherlands; 2011. pp. 1–21. https://doi.org/10.1007/978-94-007-1189-1_1
108. Lobb MD III, Orth DJ. Habitat use by an assemblage of fish in a large warmwater stream. Trans Am Fish Soc. 1991;120: 65–78. https://doi.org/10.1577/1548-8659(1991)120<0065:HUBAAO>2.3.CO;2
- View Article
- Google Scholar
109. Schlosser IJ. The role of predation in age- and size-related habitat use by stream fishes. Ecology. 1987;68: 651–659. https://doi.org/10.2307/1938470
- View Article
- Google Scholar
110. Baxter CV and Hauer FR. Geomorphology, hyporheic exchange, and selection of spawning habitat by bull trout (Salvelinus confluentus). Can J Fish Aquat Sci. 2000; 57:1470–1481. https://doi.org/10.1139/f00-056
- View Article
- Google Scholar
111. Bean JR, Wilcox AC, Woessner WW, Muhlfeld CC. Multiscale hydrogeomorphic influences on bull trout (Salvelinus confluentus) spawning habitat. Can J Fish Aquat Sci. 2014; 72:514–526. https://doi.org/10.1139/cjfas-2013-0534
- View Article
- Google Scholar
112. Bjornn T, Reiser D. Habitat requirements of salmonids in streams. In Meehan W., editor. Influences of forest and rangeland management on salmonid fishes and their habitats. Amer Fish Soc Spec Pub.1991, pp 83–138.
113. Brennan SR, Cline TJ, Schindler DE. Quantifying habitat use of migratory fish across riverscapes using space-time isotope models. Methods Ecol Evol. 2019;10: 1036–1047. https://doi.org/10.1111/2041-210X.13191
- View Article
- Google Scholar
114. Mims MC, Olden JD. Life history theory predicts fish assemblage response to hydrologic regimes. Ecology. 2012;93: 35–45. pmid:22486085
- View Article
- PubMed/NCBI
- Google Scholar
115. Raynolds MK, Walker DA, Ambrosius KJ, Brown J, Everett KR, Kanevskiy M, et al. Cumulative geoecological effects of 62 years of infrastructure and climate change in ice-rich permafrost landscapes, Prudhoe Bay Oilfield, Alaska. Glob Chang Biol. 2014;20: 1211–1224. pmid:24339207
- View Article
- PubMed/NCBI
- Google Scholar
116. Walker DA, Webber PJ, Binnian EF, Evertt KR, Lederer ND, Nordstrand EA, et al. Cumulative impacts of oil fields on northern Alaskan landscapes. Science. 1987;238: 757–761. pmid:17814703
- View Article
- PubMed/NCBI
- Google Scholar
117. Trombulak SC, Frissell CA. Review of ecological effects of roads on terrestrial and aquatic communities. Conserv Biol. 2000;14: 18–30. https://doi.org/10.1046/j.1523-1739.2000.99084.x
- View Article
- Google Scholar
118. Burkhead NM, Jelks HL. Effects of suspended sediment on the reproductive success of the tricolor shiner, a crevice-spawning minnow. Trans Am Fish Soc. 2001;130: 959–968. https://doi.org/10.1577/1548-8659(2001)130<0959:EOSSOT>2.0.CO;2
- View Article
- Google Scholar
119. Kjelland ME, Woodley CM, Swannack TM, Smith DL. A review of the potential effects of suspended sediment on fishes: potential dredging-related physiological, behavioral, and transgenerational implications. Environ Syst Decis. 2015;35: 334–350. https://doi.org/0.1007/s10669-015-9557-2
- View Article
- Google Scholar
120. Sutherland AB. Effects of increased suspended sediment on the reproductive success of an upland crevice-spawning minnow. Trans Am Fish Soc. 2007;136: 416–422. https://doi.org/10.1577/T06-046.1
- View Article
- Google Scholar
121. Chapman DW. Critical review of variables used to define effects of fines in redds of large salmonids. Trans Am Fish Soc. 1988;117: 1–21. https://doi.org/10.1577/1548-8659(1988)117<0001:CROVUT>2.3.CO;2
- View Article
- Google Scholar
122. Flowers HJ, Pine WE III, Dutterer AC, Johnson KG, Ziewitz JW, Allen MS, et al. Spawning site selection and potential implications of modified flow regimes on viability of Gulf sturgeon populations. Trans Am Fish Soc. 2009;138: 1266–1284. https://doi.org/10.1577/T08-144.1
- View Article
- Google Scholar
123. Price DM, Quinn T, Barnard RJ. Fish passage effectiveness of recently constructed road crossing culverts in the Puget Sound region of Washington State. North Am J Fish Manag. 2010;30: 1110–1125. https://doi.org/10.1577/M10-004.1
- View Article
- Google Scholar
124. Gibson RJ, Haedrich RL, Wernerheim CM. Loss of fish habitat as a consequence of inappropriately constructed stream crossings. Fisheries. 2005;30: 10–17. https://doi.org/10.1577/1548-8446(2005)30[10:LOFHAA]2.0.CO;2
- View Article
- Google Scholar
125. MacPherson LM, Sullivan MG, Lee Foote A, Stevens CE. Effects of culverts on stream fish assemblages in the Alberta Foothills. North Am J Fish Manag. 2012;32: 480–490. https://doi.org/10.1080/02755947.2012.686004
- View Article
- Google Scholar
126. Maitland BM, Poesch M, Anderson AE, Pandit SN. Industrial road crossings drive changes in community structure and instream habitat for freshwater fishes in the boreal forest. Freshw Biol. 2016;61: 1–18. https://doi.org/10.1111/fwb.12671
- View Article
- Google Scholar
127. Kime DE. The effects of pollution on reproduction in fish. Rev Fish Biol Fish. 1995;5: 52–95. https://doi.org/10.1007/BF01103366
- View Article
- Google Scholar
128. Arp CD, Whitman MS, Kemnitz R, Stuefer SL. Evidence of hydrological intensification and regime change from northern Alaskan watershed runoff. Geophys Res Lett. 2020;47: e2020GL089186. https://doi.org/10.1029/2020GL089186
- View Article
- Google Scholar
129. Dunham JB, Vinyard GL, Rieman BE. Habitat fragmentation and extinction risk of Lahontan Cutthroat Trout. North Am J Fish Manag. 1997;17: 1126–1133. https://doi.org/10.1577/1548-8675(1997)017<1126:HFAERO>2.3.CO;2
- View Article
- Google Scholar
130. Edge CB, Fortin M-J, Jackson DA, Lawrie D, Stanfield L, Shrestha N. Habitat alteration and habitat fragmentation differentially affect beta diversity of stream fish communities. Landsc Ecol. 2017;32: 647–662. https://doi.org/10.1007/s10980-016-0472-9
- View Article
- Google Scholar
131. Dias MS, Tedesco PA, Hugueny B, Jézéquel C, Beauchard O, Brosse S, et al. Anthropogenic stressors and riverine fish extinctions. Ecol Indic. 2017;79: 37–46. https://doi.org/10.1016/j.ecolind.2017.03.053
- View Article
- Google Scholar

[ref1] 1. Darwall W, Freyhof J. Lost fishes, who is counting? The extent of the threat to freshwater fish biodiversity. In: Closs GP, Olden JD, Krkosek M, editors. Conser Fresh Fish. Cambridge: Cambridge University Press; 2015. pp. 1–36. https://doi.org/10.1017/CBO9781139627085.002

[ref2] 2. Collen B, Whitton F, Dyer EE, Baillie JEM, Cumberlidge N, Darwall WRT, et al. Global patterns of freshwater species diversity, threat and endemism. Glob Ecol Biogeogr. 2014;23: 40–51. pmid:26430385
View Article
PubMed/NCBI
Google Scholar

[3] View Article

[4] PubMed/NCBI

[5] Google Scholar

[ref3] 3. Almond REA, Grooten M, Petersen T. Living planet report 2020—bending the curve of biodiversity loss. WWF, Gland, Switzerland; 2020. Available from: https://f.hubspotusercontent20.net/hubfs/4783129/LPR/PDFs/ENGLISH-FULL.pdf

[ref4] 4. Su G, Logez M, Xu J, Tao S, Villéger S, Brosse S. Human impacts on global freshwater fish biodiversity. Science. 2021;371: 835–838. pmid:33602854
View Article
PubMed/NCBI
Google Scholar

[8] View Article

[9] PubMed/NCBI

[10] Google Scholar

[ref5] 5. Dawson MN. Species richness, habitable volume, and species densities in freshwater, the sea, and on land. Front Biogeogr. 2012;4. https://doi.org/10.21425/f5fbg12675
View Article
Google Scholar

[12] View Article

[13] Google Scholar

[ref6] 6. Palkovacs EP, Kinnison MT, Correa C, Dalton CM, Hendry AP. Fates beyond traits: ecological consequences of human-induced trait change. Evol Appl. 2012;5: 183–191. pmid:25568040
View Article
PubMed/NCBI
Google Scholar

[15] View Article

[16] PubMed/NCBI

[17] Google Scholar

[ref7] 7. Reid AJ, Carlson AK, Creed IF, Eliason EJ, Gell PA, Johnson PTJ, et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol Rev. 2019;94: 849–873. pmid:30467930
View Article
PubMed/NCBI
Google Scholar

[19] View Article

[20] PubMed/NCBI

[21] Google Scholar

[ref8] 8. Dudgeon D, Arthington AH, Gessner MO, Kawabata Z-I, Knowler DJ, Lévêque C, et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol Rev. 2005/12/12. 2006;81: 163–182. pmid:16336747
View Article
PubMed/NCBI
Google Scholar

[23] View Article

[24] PubMed/NCBI

[25] Google Scholar

[ref9] 9. Meredith M, Sommerkorn M, Cassotta S, Derksen C, Ekaykina A, Hollowed A. Polar regions. Intergovernmental Panel on Climate change (IPCC) special report on the ocean and cryosphere in a changing climate. 2019. pp. 203–320. https://doi.org/10.1016/S1366-7017(01)00066-6
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref10] 10. Lento J, Goedkoop W, Culp J, Cristoffersen K, Larusson K, Fefilova E, et al. State of the Arctic freshwater biodiversity. Conservation of Arctic Flora and Fauna International Secretariat, Akureyri, Iceland; 2019. Available from: http://www.arcticbiodiversity.is/freshwater

[ref11] 11. Larsen JN, Anisimov OA, Constable A, Hollowed AB, Maynard N, Prestrud P, et al. Polar regions. In: Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, Mastrandrea MD, Mach KJ, Bilir TE, Chatterjee M, Ebi KL, Estrade YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR and White LL, editors. Climate change 2014: impacts, adaptation, and vulnerability part b: regional aspects contribution of working group II to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2014. pp. 1567–1612. Available from: https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-Chap28_FINAL.pdf

[ref12] 12. Overland J, Dunlea E, Box JE, Corell R, Forsius M, Kattsov V, et al. The urgency of Arctic change. Polar Sci. 2019;21: 6–13. https://doi.org/10.1016/j.polar.2018.11.008
View Article
Google Scholar

[32] View Article

[33] Google Scholar

[ref13] 13. Heino J, Virkkala R, Toivonen H. Climate change and freshwater biodiversity: detected patterns, future trends and adaptations in northern regions. Biol Rev. 2009;84: 39–54. pmid:19032595
View Article
PubMed/NCBI
Google Scholar

[35] View Article

[36] PubMed/NCBI

[37] Google Scholar

[ref14] 14. Stuefer SL, Arp CD, Kane DL, Liljedahl AK. Recent extreme runoff observations from coastal Arctic watersheds in Alaska. Water Resour Res. 2017;53: 9145–9163. https://doi.org/10.1002/2017WR020567
View Article
Google Scholar

[39] View Article

[40] Google Scholar

[ref15] 15. Peterson BJ, Holmes RM, McClelland JW, Vörösmarty CJ, Lammers RB, Shiklomanov AI, et al. Increasing river discharge to the Arctic Ocean. Science. 2002;298: 2171–2173. pmid:12481132
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref16] 16. Shiklomanov AI, Lammers RB. Record Russian River discharge in 2007 and the limits of analysis. Environ Res Lett. 2009;4. pmid:19763186
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref17] 17. Smith LC, Pavelsky TM, MacDonald GM, Shiklomanov AI, Lammers RB. Rising minimum daily flows in northern Eurasian rivers: a growing influence of groundwater in the high-latitude hydrologic cycle. J Geophys Res Biogeosciences. 2007;112. https://doi.org/10.1029/2006JG000327
View Article
Google Scholar

[50] View Article

[51] Google Scholar

[ref18] 18. Van Vliet MTH, Franssen WHP, Yearsley JR, Ludwig F, Haddeland I, Lettenmaier DP, et al. Global river discharge and water temperature under climate change. Glob Environ Chang. 2013;23: 450–464. https://doi.org/10.1016/j.gloenvcha.2012.11.002
View Article
Google Scholar

[53] View Article

[54] Google Scholar

[ref19] 19. Arp CD, Jones BM, Grosse G, Bondurant AC, Romanovsky VE, Hinkel KM, et al. Threshold sensitivity of shallow Arctic lakes and sub-lake permafrost to changing winter climate. Geophys Res Lett. 2016;43: 6358–6365. https://doi.org/10.1002/2016GL068506
View Article
Google Scholar

[56] View Article

[57] Google Scholar

[ref20] 20. Smol JP, Douglas MS V. Crossing the final ecological threshold in high Arctic ponds. Proc Natl Acad Sci. 2007;104: 12395–12397. pmid:17606917
View Article
PubMed/NCBI
Google Scholar

[59] View Article

[60] PubMed/NCBI

[61] Google Scholar

[ref21] 21. Hayden B, Myllykangas J-P, Rolls RJ, Kahilainen KK. Climate and productivity shape fish and invertebrate community structure in subarctic lakes. Freshw Biol. 2017;62: 990–1003. https://doi.org/10.1111/fwb.12919
View Article
Google Scholar

[63] View Article

[64] Google Scholar

[ref22] 22. Hayden B, Harrod C, Thomas SM, Eloranta AP, Myllykangas J-P, Siwertsson A, et al. From clear lakes to murky waters–tracing the functional response of high-latitude lake communities to concurrent ‘greening’ and ‘browning.’ Ecol Lett. 2019;22: 807–816. pmid:30793453
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref23] 23. Huser BJ, Futter MN, Bogan D, Brittain JE, Culp JM, Goedkoop W, et al. Spatial and temporal variation in Arctic freshwater chemistry—reflecting climate-induced landscape alterations and a changing template for biodiversity. Freshw Biol. 2020;67: 14–29. https://doi.org/10.1111/fwb.13645
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref24] 24. Campana SE, Casselman JM, Jones CM, Black G, Barker O, Evans M, et al. Arctic freshwater fish productivity and colonization increase with climate warming. Nat Clim Chang. 2020;10: 428–433. https://doi.org/10.1038/s41558-020-0744-x
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref25] 25. Cott PA, Schein A, Hanna BW, Johnston TA, MacDonald DD, Gunn JM. Implications of linear developments on northern fishes. Environ Rev. 2015;23: 177–190. https://doi.org/10.1139/er-2014-0075
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref26] 26. Dudgeon D. Multiple threats imperil freshwater biodiversity in the Anthropocene. Curr Biol. 2019;29: R960–R967. pmid:31593677
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref27] 27. Goldsmit J, Archambault P, Chust G, Villarino E, Liu G, Lukovich J V, et al. Projecting present and future habitat suitability of ship-mediated aquatic invasive species in the Canadian Arctic. Biol Invasions. 2018;20: 501–517. https://doi.org/10.1007/s10530-017-1553-7
View Article
Google Scholar

[83] View Article

[84] Google Scholar

[ref28] 28. Taranu ZE, Gregory-Eaves I, Leavitt PR, Bunting L, Buchaca T, Catalan J, et al. Acceleration of cyanobacterial dominance in north temperate-subarctic lakes during the Anthropocene. Ecol Lett. 2015;18: 375–384. pmid:25728551
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref29] 29. Moore JW, Yeakel JD, Peard D, Lough J, Beere M. Life history diversity and its importance to population stability and persistence of a migratory fish: steelhead in two large North American watersheds. J Anim Ecol. 2014;83: 1035–1046. pmid:24673479
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref30] 30. Hughes JB, Daily GC, Ehrlich PR. Population diversity: its extent and extinction. Science. 1997;278: 689–692. pmid:9381179
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref31] 31. Greene CM, Hall JE, Guilbault KR, Quinn TP. Improved viability of populations with diverse life history portfolios. Biol Lett. 2010;6: 382–386. pmid:20007162
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref32] 32. Heim KC, McMahon TE, Calle L, Wipfli MS, Falke JA. A general model of temporary aquatic habitat use: water phenology as a life history filter. Fish Fish. 2019;20: 802–816. https://doi.org/10.1111/faf.12386
View Article
Google Scholar

[102] View Article

[103] Google Scholar

[ref33] 33. Haynes TB, Rosenberger AE, Lindberg MS, Whitman M, Schmutz JA. Patterns of lake occupancy by fish indicate different adaptations to life in a harsh Arctic environment. Freshw Biol. 2014;59: 1884–1896. https://doi.org/10.1111/fwb.12391
View Article
Google Scholar

[105] View Article

[106] Google Scholar

[ref34] 34. Stearns SC. Life history evolution: successes, limitations, and prospects. Naturwissenschaften. 2000;87: 476–486. pmid:11151666
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref35] 35. Hutchings JA, Morris DW. The influence of phylogeny, size and behaviour on patterns of covariation in salmonid life histories. Oikos. 1985;45: 118–124. https://doi.org/10.2307/3565229
View Article
Google Scholar

[112] View Article

[113] Google Scholar

[ref36] 36. Brown RJ, Courtney MB, Seitz AC. New insights into the biology of anadromous Dolly Varden in the Canning River, Arctic National Wildlife Refuge, Alaska. Trans Am Fish Soc. 2019;148: 73–87. https://doi.org/10.1002/tafs.10122
View Article
Google Scholar

[115] View Article

[116] Google Scholar

[ref37] 37. Brown RJ, Loewen MB, Tanner TL. Overwintering locations, migrations, and fidelity of radio-tagged Dolly Varden in the Hulahula River, Arctic National Wildlife Refuge, 2007–09. Arctic. 2014;67: 149–158. https://doi.org/10.14430/arctic4379
View Article
Google Scholar

[118] View Article

[119] Google Scholar

[ref38] 38. Harris LN, Loewen TN, Reist JD, Halden NM, Babaluk JA, Tallman RF. Migratory variation in Mackenzie River system broad whitefish: insights from otolith strontium distributions. Trans Am Fish Soc. 2012;141: 1574–1585. https://doi.org/10.1080/00028487.2012.713885
View Article
Google Scholar

[121] View Article

[122] Google Scholar

[ref39] 39. Harris LN, Swanson HK, Gilbert MJH, Malley BK, Fisk AT, Moore J-S. Anadromy and marine habitat use of Lake trout (Salvelinus namaycush) from the central Canadian Arctic. J Fish Biol. 2020;96: 1489–1494. pmid:32128819
View Article
PubMed/NCBI
Google Scholar

[124] View Article

[125] PubMed/NCBI

[126] Google Scholar

[ref40] 40. Irvine J, Macdonald R, Brown R, Godbout L, Reist J, Carmack E. Salmon in the Arctic and how they avoid lethal low temperatures. North Pacific Anadromous Fish Comm Bull. 2009;5: 39–50. Available from: https://npafc.org/bulletin-5/
View Article
Google Scholar

[128] View Article

[129] Google Scholar

[ref41] 41. Ranta E, Tesar D, Kaitala V. Environmental variability and semelparity vs. iteroparity as life histories. J Theor Biol. 2002;217: 391–396. pmid:12270282
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref42] 42. Orzack SH, Tuljapurkar S. Population dynamics in variable environments. VII. The demography and evolution of iteroparity. Am Nat. 1989;133: 901–923. https://doi.org/10.1086/284959
View Article
Google Scholar

[135] View Article

[136] Google Scholar

[ref43] 43. Leggett WC, Carscadden JE. Latitudinal variation in reproductive characteristics of American Shad (Alosa sapidissima): evidence for population specific life history strategies in fish. J Fish Res Board Canada. 1978;35: 1469–1478. https://doi.org/10.1139/f78-230
View Article
Google Scholar

[138] View Article

[139] Google Scholar

[ref44] 44. Schlosser IJ. Stream fish ecology: a landscape perspective. Bioscience. 1991;41: 704–712. https://doi.org/10.2307/1311765
View Article
Google Scholar

[141] View Article

[142] Google Scholar

[ref45] 45. Schlosser IJ. Critical landscape attributes that influence fish population dynamics in headwater streams. Hydrobiologia. 1995;303: 71–81. https://doi.org/10.1007/BF00034045
View Article
Google Scholar

[144] View Article

[145] Google Scholar

[ref46] 46. Hilborn R, Quinn TP, Schindler DE, Rogers DE. Biocomplexity and fisheries sustainability. Proc Natl Acad Sci. 2003;100: 6564–6568. pmid:12743372
View Article
PubMed/NCBI
Google Scholar

[147] View Article

[148] PubMed/NCBI

[149] Google Scholar

[ref47] 47. Schindler DE, Hilborn R, Chasco B, Boatright CP, Quinn TP, Rogers LA, et al. Population diversity and the portfolio effect in an exploited species. Nature. 2010;465: 609–612. pmid:20520713
View Article
PubMed/NCBI
Google Scholar

[151] View Article

[152] PubMed/NCBI

[153] Google Scholar

[ref48] 48. Schindler DE, Armstrong JB, Reed TE. The portfolio concept in ecology and evolution. Front Ecol Environ. 2015;13: 257–263. https://doi.org/10.1890/140275
View Article
Google Scholar

[155] View Article

[156] Google Scholar

[ref49] 49. Brown RJ, Brown C, Braem NM, Carter WK III, Legere N, Slayton L. Whitefish biology, distribution, and fisheries in the Yukon and Kuskokwim River drainages in Alaska: a synthesis of available information. US Fish Wildl Serv Alaska Fish Data Ser 2012–4. 2012. Available from: https://www.fws.gov/alaska/sites/default/files/pdfs/fisheries/data-series/d_2012_4.pdf
View Article
Google Scholar

[158] View Article

[159] Google Scholar

[ref50] 50. Harper KC, Harris F, Miller SJ, Thalhauser JM, Ayers SD. Life history traits of adult broad whitefish and humpback whitefish. J Fish Wildl Manag. 2012;3: 56–75. https://doi.org/10.3996/022011-JFWM-011
View Article
Google Scholar

[161] View Article

[162] Google Scholar

[ref51] 51. Brown RJ, Bickford N, Severin K. Otolith trace element chemistry as an indicator of anadromy in Yukon River drainage coregonine fishes. Trans Am Fish Soc. 2007;136: 678–690. https://doi.org/10.1577/T06-040.1
View Article
Google Scholar

[164] View Article

[165] Google Scholar

[ref52] 52. Brown RJ. Distribution and demographics of whitefish species in the upper Koyukuk River drainage, Alaska, with emphasis on seasonal migrations and important habitats of broad whitefish and humpback whitefish. US Fish Wildl Serv Alaska Fish Tech Rep No 104. 2009. Available from: https://www.arlis.org/docs/vol1/B/495849598.pdf

[ref53] 53. Tallman R, Abrahams M, Chudobiak D. Migration and life history alternatives in a high latitude species, the broad whitefish, Coregonus nasus Pallas. Ecol Freshw Fish. 2002;11: 101–111. https://doi.org/10.1034/j.1600-0633.2002.00001.x
View Article
Google Scholar

[168] View Article

[169] Google Scholar

[ref54] 54. Reshetnikov YS, Bogdanov VD. Features of reproduction of whitefishes. J Ichthyol. 2011;51: 432–456. https://doi.org/10.1134/S0032945211030118
View Article
Google Scholar

[171] View Article

[172] Google Scholar

[ref55] 55. Martin DJ, Whitmus CK, Hachmeister LE, Volk EC, Schroder SL. Distribution and seasonal abundance of juvenile salmon and other fishes in the Yukon Delta. US Dep Commer Natl Oceanogr Atmos Adm Outer Cont Shelf Environ Assess Program, Final Reports Princ Investig. 1987. Available from: https://espis.boem.gov/final%20reports/86.pdf

[ref56] 56. Bond WA. A study of the fishery resources of Tuktoyaktuk Harbour, southern Beaufort Sea Coast, with special reference to life histories of anadromous coregonids. Department of Fisheries and Oceans, Can Tech Rep Fish and Aqut Sci, No. 119, Winnipeg.1982. Available from: https://publications.gc.ca/site/eng/454445/publication.html

[ref57] 57. Næsje TF, Jonsson B, Sandlund OT. Drift of cisco and whitefish larvae in a Norwegian River. Trans Am Fish Soc. 1986;1: 89–93. https://doi.org/10.1577/1548-8659(1986)115<89
View Article
Google Scholar

[176] View Article

[177] Google Scholar

[ref58] 58. Bacon JJ, Hepa TR, Brower HKJ, Pedersen M, Olemaun TP, George JC, et al. Estimates of subsistence harvest for villages on the North Slope of Alaska, 1994–2003. N Slpe Borgh Depart, Barrow, 2011. Available from: http://www.north-slope.org/assets/images/uploads/MASTER%20SHDP%2094-03%20REPORT%20FINAL%20and%20%20Errata%20info%20(Sept%202012).pdf
View Article
Google Scholar

[179] View Article

[180] Google Scholar

[ref59] 59. Brown CL, Braem NM, Kostick ML, Trainor A, Slayton LJ, Runfola DM, et al. Harvests and uses of wild resources in 4 interior Alaska communities and 3 Arctic Alaska communities, 2014. Alaska Dept Fish Game Div Subsis, Tech Pap No. 426, Fairbanks 2016. Available from: https://www.arlis.org/docs/vol1/M/972886420.pdf

[ref60] 60. Alaska Department of Fish and Game Community Subsistence Information System (CSIS). Data for Barrow and Nuiqsut; 2014 [cited 2021 May 19]. Database: CSIS [internet]. Available from: http://www.adfg.alaska.gov/sb/csis/

[ref61] 61. Kennedy BP, Klaue A, Blum JD, Folt CL, Nislow KH. Reconstructing the lives of fish using Sr isotopes in otoliths. Can J Fish Aquat Sci. 2002;59: 925–929. https://doi.org/10.1139/f02-070
View Article
Google Scholar

[184] View Article

[185] Google Scholar

[ref62] 62. Brennan SR, Zimmerman CE, Fernandez DP, Cerling TE, McPhee M V, Wooller MJ. Strontium isotopes delineate fine-scale natal origins and migration histories of Pacific salmon. Sci Adv. 2015;1: e1400124. pmid:26601173
View Article
PubMed/NCBI
Google Scholar

[187] View Article

[188] PubMed/NCBI

[189] Google Scholar

[ref63] 63. Padilla AJ, Brown RJ, Wooller MJ. Determining the movements and distribution of anadromous Bering ciscoes by use of otolith strontium isotopes. Trans Am Fish Soc. 2016;145: 1374–1385. https://doi.org/10.1080/00028487.2016.1225599
View Article
Google Scholar

[191] View Article

[192] Google Scholar

[ref64] 64. Hegg JC, Giarrizzo T, Kennedy BP. Diverse early life history strategies in migratory Amazonian catfish: implications for conservation and management. PLOS ONE. 2015;10: e0129697. pmid:26153984
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref65] 65. Hobbs JA, Lewis LS, Willmes M, Denney C, Bush E. Complex life histories discovered in a critically endangered fish. Sci Rep. 2019;9: 16772. pmid:31727901
View Article
PubMed/NCBI
Google Scholar

[198] View Article

[199] PubMed/NCBI

[200] Google Scholar

[ref66] 66. Austin CS, Bond MH, Smith JM, Lowery ED, Quinn TP. Otolith microchemistry reveals partial migration and life history variation in a facultatively anadromous, iteroparous salmonid, bull trout (Salvelinus confluentus). Environ Biol Fishes. 2019;102: 95–104. https://doi.org/10.1007/s10641-019-0848-1
View Article
Google Scholar

[202] View Article

[203] Google Scholar

[ref67] 67. Campana SE. Chemistry and composition of fish otoliths: pathways, mechanisms and applications. Mar Ecol Prog Ser. 1999;188: 263–297. https://doi.org/10.3354/meps188263
View Article
Google Scholar

[205] View Article

[206] Google Scholar

[ref68] 68. Brennan SR, Fernandez DP, Mackey G, Cerling TE, Bataille CP, Bowen GJ, et al. Strontium isotope variation and carbonate versus silicate weathering in rivers from across Alaska: implications for provenance studies. Chem Geol. 2014;389: 167–181. https://doi.org/10.1016/j.chemgeo.2014.08.018
View Article
Google Scholar

[208] View Article

[209] Google Scholar

[ref69] 69. Bataille CP, Brennan SR, Hartmann J, Moosdorf N, Wooller MJ, Bowen GJ. A geostatistical framework for predicting variations in strontium concentrations and isotope ratios in Alaskan rivers. Chem Geol. 2014;389: 1–15. https://doi.org/10.1016/j.chemgeo.2014.08.030
View Article
Google Scholar

[211] View Article

[212] Google Scholar

[ref70] 70. Bataille CP, Bowen GJ. Mapping ⁸⁷Sr/⁸⁶Sr variations in bedrock and water for large scale provenance studies. Chem Geol. 2012;304–305: 39–52. https://doi.org/10.1016/j.chemgeo.2012.01.028
View Article
Google Scholar

[214] View Article

[215] Google Scholar

[ref71] 71. Capo RC, Stewart BW, Chadwick OA. Strontium isotopes as tracers of ecosystem processes: theory and methods. Geoderma. 1998;82: 197–225. https://doi.org/10.1016/S0016-7061(97)00102-X
View Article
Google Scholar

[217] View Article

[218] Google Scholar

[ref72] 72. Blum JD, Erel Y, Brown K. ⁸⁷Sr/⁸⁶Sr ratios of Sierra Nevada stream waters: implications for relative mineral weathering rates. Geochim Cosmochim Acta. 1993;57: 5019–5025. https://doi.org/10.1016/S0016-7037(05)80014-6
View Article
Google Scholar

[220] View Article

[221] Google Scholar

[ref73] 73. Horton TW, Chamberlain CP, Fantle M, Blum JD. Chemical weathering and lithologic controls of water chemistry in a high-elevation river system: Clark’s Fork of the Yellowstone River, Wyoming and Montana. Water Resour Res. 1999;35: 1643–1655. https://doi.org/10.1029/1998WR900103
View Article
Google Scholar

[223] View Article

[224] Google Scholar

[ref74] 74. Hüssy K, Limburg KE, de Pontual H, Thomas ORB, Cook PK, Heimbrand Y, et al. Trace element patterns in otoliths: the role of biomineralization. Rev Fish Sci Aquac.2021;29: 445–477. https://doi.org/10.1080/23308249.2020.1760204
View Article
Google Scholar

[226] View Article

[227] Google Scholar

[ref75] 75. Barnett-Johnson R, Pearson TE, Ramos FC, Grimes CB, MacFarlane BR. Tracking natal origins of salmon using isotopes, otoliths, and landscape geology. Limnol Oceanogr. 2008;53: 1633–1642. https://doi.org/10.1111/j.1095-8649.2009.02394.x 10.4319/lo.2008.53.4.1633
View Article
Google Scholar

[229] View Article

[230] Google Scholar

[ref76] 76. Loewen TN, Gillis D, Tallman RF. Ecological niche specialization inferred from morphological variation and otolith strontium of Arctic charr Salvelinus alpinus L. found within open lake systems of southern Baffin Island, Nunavut, Canada. J Fish Biol. 2009;75: 1473–1495. pmid:20738626
View Article
PubMed/NCBI
Google Scholar

[232] View Article

[233] PubMed/NCBI

[234] Google Scholar

[ref77] 77. Alt KT, Kogl DR. Notes on the whitefish of the Colville River, Alaska. J Fish Res Board Canada. 1973;30: 554–556. https://doi.org/10.1139/f73-094
View Article
Google Scholar

[236] View Article

[237] Google Scholar

[ref78] 78. Leppi JC. Broad Whitefish (Coregonus nasus) ecology and habitat use in Arctic Alaska: spawning habitat suitability, isotopic niches, life-history variations, and climate change risks to subsistence fisheries. Ph.D. Dissertation, University of Alaska Fairbanks. 2021. Available from: https://www.proquest.com/openview/0ca0ff9a682772615e3c282ac70132a1/1?pq-origsite = gscholar&cbl = 18750&diss = y

[ref79] 79. Craig PC. Fish use of coastal waters of the Alaskan Beaufort Sea: a review. Trans Am Fish Soc. 1984;113: 265–282. https://doi.org/10.1577/1548-8659(1984)113<265:FUOCWO>2.0.CO;2
View Article
Google Scholar

[240] View Article

[241] Google Scholar

[ref80] 80. Jorgenson MT, Brown J. Classification of the Alaskan Beaufort Sea Coast and estimation of carbon and sediment inputs from coastal erosion. Geo-Marine Lett. 2005;25: 69–80. https://doi.org/10.1007/s00367-004-0188-8
View Article
Google Scholar

[243] View Article

[244] Google Scholar

[ref81] 81. Jorgenson MT, Shur Y. Evolution of lakes and basins in northern Alaska and discussion of the thaw lake cycle. J Geophys Res Earth Surf. 2007;112. https://doi.org/10.1029/2006JF000531
View Article
Google Scholar

[246] View Article

[247] Google Scholar

[ref82] 82. Arp CD, Whitman MS, Jones BM, Kemnitz R, Grosse G, Urban FE. Drainage network structure and hydrologic behavior of three lake-rich watersheds on the Arctic Coastal Plain, Alaska. Arctic, Antarct, Alp Res. 2012;44: 385–398. https://doi.org/10.1657/1938-4246-44.4.385
View Article
Google Scholar

[249] View Article

[250] Google Scholar

[ref83] 83. Grosse G, Jones B, Arp C. Thermokarst lakes, drainage, and drained basins. In: Shroder J, editor in chief, Giardino R, Harbor J, editor. Treatise on Geomorphology. San Diego: Academic Press; 2013. pp. 325–353. https://doi.org/10.1016/B978-0-12-374739-6.00216-5

[ref84] 84. Montgomery DR, Buffington JM. Channel-reach morphology in mountain drainage basins. GSA Bull. 1997;109: 596–611. https://doi.org/10.1130/0016-7606(1997)109<0596:CRMIMD>2.3.CO;2
View Article
Google Scholar

[253] View Article

[254] Google Scholar

[ref85] 85. Knighton D. Fluvial forms and processes: a new perspective. Oxford University Press; 1998.

[ref86] 86. Brennan SR, Torgersen CE, Hollenbeck JP, Fernandez DP, Jensen CK, Schindler DE. Dendritic network models: improving isoscapes and quantifying influence of landscape and in-stream processes on strontium isotopes in rivers. Geophys Res Lett. 2016;43: 5043–5051. https://doi.org/10.1002/2016GL068904
View Article
Google Scholar

[257] View Article

[258] Google Scholar

[ref87] 87. Shulski M and Wendler G. The climate of Alaska. Fairbanks: University of Alaska Press; 2007.

[ref88] 88. Carter WK. Life history and spawning movements of broad whitefish in the middle Yukon River. M.S. Thesis, University of Alaska Fairbanks. 2010. Available from: https://citeseerx.ist.psu.edu/viewdoc/download?doi = 10.1.1.383.4750&rep = rep1&type = pdf

[ref89] 89. Secor D, Dean J, Laban E. Otolith removal and preparation for microstructural examination. In: Stevenson D, Campana S, editors. Otolith Microstructure Examination and Analysis. Can Spec Publ Fish Aquat Sci.1992;117 pp. 13–17. https://doi.org/10.13140/RG.2.2.22258.61127

[ref90] 90. Stevenson DK, Campana SE. Otolith Microstructure Examination and Analysis. Can Spec Publ Fish Aquat Sci. 1992;117. Available from: https://publications.gc.ca/site/eng/9.811462/publication.html
View Article
Google Scholar

[263] View Article

[264] Google Scholar

[ref91] 91. Chilton DE, Beamish RJ. Age determination methods for fishes studied by the groundfish program at the Pacific Biological Station. Can Spec Publ Fish Aquat Sci 60. 1982; 102. Available from: https://publications.gc.ca/site/eng/9.816313/publication.html
View Article
Google Scholar

[266] View Article

[267] Google Scholar

[ref92] 92. Prohaska T, Irrgeher J, Zitek A. Simultaneous multi-element and isotope ratio imaging of fish otoliths by laser ablation split stream ICP-MS/MC ICP-MS. J Anal At Spectrom. 2016;31: 1612–1621. https://doi.org/10.1039/C6JA00087H
View Article
Google Scholar

[269] View Article

[270] Google Scholar

[ref93] 93. Irrgeher J, Galler P, Prohaska T. ⁸⁷Sr/⁸⁶Sr isotope ratio measurements by laser ablation multicollector inductively coupled plasma mass spectrometry: reconsidering matrix interferences in bioapatites and biogenic carbonates. Spectrochim Acta Part B At Spectrosc. 2016;125: 31–42. https://doi.org/10.1016/j.sab.2016.09.008
View Article
Google Scholar

[272] View Article

[273] Google Scholar

[ref94] 94. Brennan SR, Fernandez DP, Zimmerman CE, Cerling TE, Brown RJ, Wooller MJ. Strontium isotopes in otoliths of a non-migratory fish (slimy sculpin): implications for provenance studies. Geochim Cosmochim Acta. 2015;149: 32–45. https://doi.org/10.1016/j.gca.2014.10.032
View Article
Google Scholar

[275] View Article

[276] Google Scholar

[ref95] 95. Wood SN. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J Am Stat Assoc. 2004;99: 673–686. https://doi.org/10.1198/016214504000000980
View Article
Google Scholar

[278] View Article

[279] Google Scholar

[ref96] 96. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B (Statistical Methodol). 2011;73: 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x
View Article
Google Scholar

[281] View Article

[282] Google Scholar

[ref97] 97. Barnett-Johnson R, Grimes C, Royer C, Donohoe C. Identifying the contribution of wild and hatchery Chinook salmon (Oncorhynchus tshawytscha) to the ocean fishery using otolith microstructure as natural tags. Can J Fish Aquat Sci. 2007;64: 1683–1692. https://doi.org/10.1139/f07-129
View Article
Google Scholar

[284] View Article

[285] Google Scholar

[ref98] 98. Lytle DA. Disturbance regimes and life‐history evolution. Am Nat. 2001;157: 525–536. pmid:18707260
View Article
PubMed/NCBI
Google Scholar

[287] View Article

[288] PubMed/NCBI

[289] Google Scholar

[ref99] 99. Stuby L. Contributions to the life history of Kuskokwim River inconnu. Trans Am Fish Soc. 2018;147: 879–890. https://doi.org/10.1002/tafs.10069
View Article
Google Scholar

[291] View Article

[292] Google Scholar

[ref100] 100. Chang-Kue KTJ, Jessop EF. The life history and habitat usage of broad whitefish in the lower Mackenzie River basin. Canadian Technical Report of Fisheries and Aquatic Sciences 2193: the proceedings of the broad whitefish workshop; the biology, traditional knowledge and scientific management of broad whitefish (Coregonus nasus (Pallus)) in the lower Mackenzie River. 1997. pp. 117–146. Available from: https://publications.gc.ca/collections/collection_2014/mpo-dfo/Fs97-6-2193-eng.pdf

[ref101] 101. Millar NP, Harris LN, Howland KL. Seasonal migrations of broad whitefish (Coregonus nasus (Pallas)) in an Arctic lake. Adv Limnol. 2013;64: 91–107. https://doi.org/10.1127/1612-166X/2013/0064-0010
View Article
Google Scholar

[295] View Article

[296] Google Scholar

[ref102] 102. Hodgson EE, Hovel RA, Ward EJ, Lord S, Moore JW. Migratory diversity in an Arctic fish supporting subsistence harvest. Biol Conserv. 2020;248: 108685. https://doi.org/10.1016/j.biocon.2020.108685
View Article
Google Scholar

[298] View Article

[299] Google Scholar

[ref103] 103. Satterthwaite WH, Hayes SA, Merz JE, Sogard SM, Frechette DM, Mangel M. State-dependent migration timing and use of multiple habitat types in anadromous salmonids. Trans Am Fish Soc. 2012;141: 781–794. https://doi.org/10.1080/00028487.2012.675912
View Article
Google Scholar

[301] View Article

[302] Google Scholar

[ref104] 104. Bourret SL, Caudill CC, Keefer ML. Diversity of juvenile Chinook salmon life history pathways. Rev Fish Biol Fish. 2016;26: 375–403. https://doi.org/10.1007/s11160-016-9432-3
View Article
Google Scholar

[304] View Article

[305] Google Scholar

[ref105] 105. Beckman BR, Larsen DA, Lee-Pawlak B, Dickhoff WW. Relation of fish size and growth rate to migration of spring Chinook salmon smolts. N Am J Fish Manag. 1998;18:37–41. https://doi.org/10.1577/1548-8675(1998)018<0537:ROFSAG>2.0.CO;2
View Article
Google Scholar

[307] View Article

[308] Google Scholar

[ref106] 106. Southwood TRE. Habitat, the templet for ecological strategies? J Anim Ecol. 1977;46: 337–365. https://doi.org/10.2307/3817
View Article
Google Scholar

[310] View Article

[311] Google Scholar

[ref107] 107. Jonsson B, Jonsson N. Habitats as template for life histories. Ecology of Atlantic Salmon and Brown Trout: habitat as a template for life histories. Dordrecht: Springer Netherlands; 2011. pp. 1–21. https://doi.org/10.1007/978-94-007-1189-1_1

[ref108] 108. Lobb MD III, Orth DJ. Habitat use by an assemblage of fish in a large warmwater stream. Trans Am Fish Soc. 1991;120: 65–78. https://doi.org/10.1577/1548-8659(1991)120<0065:HUBAAO>2.3.CO;2
View Article
Google Scholar

[314] View Article

[315] Google Scholar

[ref109] 109. Schlosser IJ. The role of predation in age- and size-related habitat use by stream fishes. Ecology. 1987;68: 651–659. https://doi.org/10.2307/1938470
View Article
Google Scholar

[317] View Article

[318] Google Scholar

[ref110] 110. Baxter CV and Hauer FR. Geomorphology, hyporheic exchange, and selection of spawning habitat by bull trout (Salvelinus confluentus). Can J Fish Aquat Sci. 2000; 57:1470–1481. https://doi.org/10.1139/f00-056
View Article
Google Scholar

[320] View Article

[321] Google Scholar

[ref111] 111. Bean JR, Wilcox AC, Woessner WW, Muhlfeld CC. Multiscale hydrogeomorphic influences on bull trout (Salvelinus confluentus) spawning habitat. Can J Fish Aquat Sci. 2014; 72:514–526. https://doi.org/10.1139/cjfas-2013-0534
View Article
Google Scholar

[323] View Article

[324] Google Scholar

[ref112] 112. Bjornn T, Reiser D. Habitat requirements of salmonids in streams. In Meehan W., editor. Influences of forest and rangeland management on salmonid fishes and their habitats. Amer Fish Soc Spec Pub.1991, pp 83–138.

[ref113] 113. Brennan SR, Cline TJ, Schindler DE. Quantifying habitat use of migratory fish across riverscapes using space-time isotope models. Methods Ecol Evol. 2019;10: 1036–1047. https://doi.org/10.1111/2041-210X.13191
View Article
Google Scholar

[327] View Article

[328] Google Scholar

[ref114] 114. Mims MC, Olden JD. Life history theory predicts fish assemblage response to hydrologic regimes. Ecology. 2012;93: 35–45. pmid:22486085
View Article
PubMed/NCBI
Google Scholar

[330] View Article

[331] PubMed/NCBI

[332] Google Scholar

[ref115] 115. Raynolds MK, Walker DA, Ambrosius KJ, Brown J, Everett KR, Kanevskiy M, et al. Cumulative geoecological effects of 62 years of infrastructure and climate change in ice-rich permafrost landscapes, Prudhoe Bay Oilfield, Alaska. Glob Chang Biol. 2014;20: 1211–1224. pmid:24339207
View Article
PubMed/NCBI
Google Scholar

[334] View Article

[335] PubMed/NCBI

[336] Google Scholar

[ref116] 116. Walker DA, Webber PJ, Binnian EF, Evertt KR, Lederer ND, Nordstrand EA, et al. Cumulative impacts of oil fields on northern Alaskan landscapes. Science. 1987;238: 757–761. pmid:17814703
View Article
PubMed/NCBI
Google Scholar

[338] View Article

[339] PubMed/NCBI

[340] Google Scholar

[ref117] 117. Trombulak SC, Frissell CA. Review of ecological effects of roads on terrestrial and aquatic communities. Conserv Biol. 2000;14: 18–30. https://doi.org/10.1046/j.1523-1739.2000.99084.x
View Article
Google Scholar

[342] View Article

[343] Google Scholar

[ref118] 118. Burkhead NM, Jelks HL. Effects of suspended sediment on the reproductive success of the tricolor shiner, a crevice-spawning minnow. Trans Am Fish Soc. 2001;130: 959–968. https://doi.org/10.1577/1548-8659(2001)130<0959:EOSSOT>2.0.CO;2
View Article
Google Scholar

[345] View Article

[346] Google Scholar

[ref119] 119. Kjelland ME, Woodley CM, Swannack TM, Smith DL. A review of the potential effects of suspended sediment on fishes: potential dredging-related physiological, behavioral, and transgenerational implications. Environ Syst Decis. 2015;35: 334–350. https://doi.org/0.1007/s10669-015-9557-2
View Article
Google Scholar

[348] View Article

[349] Google Scholar

[ref120] 120. Sutherland AB. Effects of increased suspended sediment on the reproductive success of an upland crevice-spawning minnow. Trans Am Fish Soc. 2007;136: 416–422. https://doi.org/10.1577/T06-046.1
View Article
Google Scholar

[351] View Article

[352] Google Scholar

[ref121] 121. Chapman DW. Critical review of variables used to define effects of fines in redds of large salmonids. Trans Am Fish Soc. 1988;117: 1–21. https://doi.org/10.1577/1548-8659(1988)117<0001:CROVUT>2.3.CO;2
View Article
Google Scholar

[354] View Article

[355] Google Scholar

[ref122] 122. Flowers HJ, Pine WE III, Dutterer AC, Johnson KG, Ziewitz JW, Allen MS, et al. Spawning site selection and potential implications of modified flow regimes on viability of Gulf sturgeon populations. Trans Am Fish Soc. 2009;138: 1266–1284. https://doi.org/10.1577/T08-144.1
View Article
Google Scholar

[357] View Article

[358] Google Scholar

[ref123] 123. Price DM, Quinn T, Barnard RJ. Fish passage effectiveness of recently constructed road crossing culverts in the Puget Sound region of Washington State. North Am J Fish Manag. 2010;30: 1110–1125. https://doi.org/10.1577/M10-004.1
View Article
Google Scholar

[360] View Article

[361] Google Scholar

[ref124] 124. Gibson RJ, Haedrich RL, Wernerheim CM. Loss of fish habitat as a consequence of inappropriately constructed stream crossings. Fisheries. 2005;30: 10–17. https://doi.org/10.1577/1548-8446(2005)30[10:LOFHAA]2.0.CO;2
View Article
Google Scholar

[363] View Article

[364] Google Scholar

[ref125] 125. MacPherson LM, Sullivan MG, Lee Foote A, Stevens CE. Effects of culverts on stream fish assemblages in the Alberta Foothills. North Am J Fish Manag. 2012;32: 480–490. https://doi.org/10.1080/02755947.2012.686004
View Article
Google Scholar

[366] View Article

[367] Google Scholar

[ref126] 126. Maitland BM, Poesch M, Anderson AE, Pandit SN. Industrial road crossings drive changes in community structure and instream habitat for freshwater fishes in the boreal forest. Freshw Biol. 2016;61: 1–18. https://doi.org/10.1111/fwb.12671
View Article
Google Scholar

[369] View Article

[370] Google Scholar

[ref127] 127. Kime DE. The effects of pollution on reproduction in fish. Rev Fish Biol Fish. 1995;5: 52–95. https://doi.org/10.1007/BF01103366
View Article
Google Scholar

[372] View Article

[373] Google Scholar

[ref128] 128. Arp CD, Whitman MS, Kemnitz R, Stuefer SL. Evidence of hydrological intensification and regime change from northern Alaskan watershed runoff. Geophys Res Lett. 2020;47: e2020GL089186. https://doi.org/10.1029/2020GL089186
View Article
Google Scholar

[375] View Article

[376] Google Scholar

[ref129] 129. Dunham JB, Vinyard GL, Rieman BE. Habitat fragmentation and extinction risk of Lahontan Cutthroat Trout. North Am J Fish Manag. 1997;17: 1126–1133. https://doi.org/10.1577/1548-8675(1997)017<1126:HFAERO>2.3.CO;2
View Article
Google Scholar

[378] View Article

[379] Google Scholar

[ref130] 130. Edge CB, Fortin M-J, Jackson DA, Lawrie D, Stanfield L, Shrestha N. Habitat alteration and habitat fragmentation differentially affect beta diversity of stream fish communities. Landsc Ecol. 2017;32: 647–662. https://doi.org/10.1007/s10980-016-0472-9
View Article
Google Scholar

[381] View Article

[382] Google Scholar

[ref131] 131. Dias MS, Tedesco PA, Hugueny B, Jézéquel C, Beauchard O, Brosse S, et al. Anthropogenic stressors and riverine fish extinctions. Ecol Indic. 2017;79: 37–46. https://doi.org/10.1016/j.ecolind.2017.03.053
View Article
Google Scholar

[384] View Article

[385] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Study area

Fish sampling and otolith collection

Sr isotope analysis of otoliths

Otolith preparation.

Laser ablation.

Data reduction.

Otolith 87Sr/ 86Sr data post-processing.

Statistical analysis.

Life history attributes and classification

Results

Life history types

Anadromous migration patterns

Freshwater natal and juvenile rearing region 87Sr/86Sr

Discussion

Broad Whitefish life history diversity

Age-0 movements

Broad Whitefish conservation

Supporting information

S1 Table. Summary of Broad Whitefish sampled.

S2 Table. Laser Ablation System (LA) multi collector inductively coupled plasma mass spectrometer (MC-ICP-MS) instrument parameters.

S3 Table. Summary of Broad Whitefish (Coregonus nasus) sampled in the Colville River, AK, USA.

S1 File. Sr otolith plots.

Acknowledgments

References

Otolith ⁸⁷Sr/ ⁸⁶Sr data post-processing.

Freshwater natal and juvenile rearing region ⁸⁷Sr/⁸⁶Sr