# Winter is not coming: evaluating impacts of changing winter conditions on coregonine reproductive phenology

Article in International Journal of Limnology · September 2024

DOI: 10.1051/limn/2024014

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Taylor Stewart

Utah Division of Wildlife Resources

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French National Institute for Agriculture, Food, and Environment (INRAE)

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Juha Sakari Karjalainen

University of Jyväskylä

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French National Institute for Agriculture, Food, and Environment (INRAE)

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Special issue - Biology and Management of Coregonid Fishes - 2023
Guest editors: Orlane Anneville, Chloé Goulon, Juha Karjalainen, Jean Guillard, Jared T. Myers and Jason Stockwell

RESEARCH ARTICLE

OPEN ACCESS

# Winter is not coming: evaluating impacts of changing winter conditions on coregonine reproductive phenology

Taylor R. Stewart<fnref n="1" />,<fnref n="2" />,* ID, Juha Karjalainen<fnref n="3" /> ID, Matteo Zucchetta<fnref n="4" /> ID, Chloé Goulon<fnref n="5" /> ID, Orlane Anneville<fnref n="5" /> ID, Mark R. Vinson<fnref n="6" /> ID, Josef Wanzenböck<fnref n="7" /> ID and Jason D. Stockwell<fnref n="2" /> ID

Received: 21 January 2024; Accepted: 5 July 2024

**Abstract** – Fishes in northern latitude lakes are at risk from climate-induced warming because the seasonality in water temperature is degrading, which can change ecosystem properties and the phenology of life-history events. Temperature-dependent embryo development models were developed for a group of cold, stenothermic fishes (Salmonidae Coregoninae) to assess the potential impacts of climate-induced changes in water temperature on cisco (*Coregonus artedii*) from two populations in Lake Superior (Apostle Islands [USA] and Thunder Bay [Canada]) and one in Lake Ontario (USA), vendace (*C. albula*) in Lake Southern Konnevesi (Finland), and European whitefish (*C. lavaretus*) in lakes Southern Konnevesi, Constance (Germany), Geneva (France), and Annecy (France). Water temperatures for each study group were simulated and changes in reproductive phenology across historic (1900–2006) and three future climatic-warming scenarios (2007–2099) were investigated. Models predicted that increases in water temperatures are likely to cause delayed spawning, shorter embryo incubation durations, and earlier larval hatching. Relative changes increased as warming scenarios increased in severity and were higher for littoral as compared to pelagic populations. Our simulations demonstrated that slower cooling in the autumn and (or) more rapid warming in spring can translate into substantial changes in the reproductive phenology of coregonines among our study groups. We expect that the changes in reproductive phenology predicted by our models, in the absence of thermal or behavioral adaptation, will have negative implications for population sustainability.

**Keywords:** *Coregonus* / climate change / simulation modeling / reproductive phenology / water temperature

## 1 Introduction

Warming lake temperatures worldwide (O'Reilly et al., 2015; Maberly et al., 2020; Woolway, 2023) are an imminent threat to lacustrine fish (Dahlke et al., 2020). Water temperature directly affects the limnological characteristics of lakes and is a master factor in regulating reproduction, development, physiology, and survival of lacustrine fishes (Gillooly et al., 2002; Brown et al., 2004; Cline et al., 2013; Little et al., 2020). Unlike riverine or marine fishes, lacustrine fish are limited in their ability to evade warming habitats. The

vulnerability of fish populations varies among lakes depending on habitat connectivity, the magnitude of thermal regime change, and species and life-stage specific temperature tolerances (Dahlke et al., 2020).

Holarctic fishes are predicted to be at risk because the strong seasonality in water temperature regimes in northern hemisphere lake ecosystems is degrading (Winslow et al., 2017; Sharma et al., 2019; Maberly et al., 2020; Woolway et al., 2021) and their life history is innately linked to seasonal climate patterns (Winder and Schindler, 2004). Disrupted seasonal patterns have resulted in positive feedback loops with warmer winter temperatures and (or) shorter periods of winter ice coverage, earlier and more rapid spring water warming,

<footnote n="1">¹ Department of Biology, University of Vermont, Burlington, Vermont, USA</footnote>

<footnote n="2">² Rubenstein Ecosystem Science Laboratory, University of Vermont, Burlington, Vermont, USA</footnote>

<footnote n="3">³ Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland</footnote>

<footnote n="4">⁴ Institute of Polar Sciences, National Research Council, Venice, Italy</footnote>

<footnote n="5">⁵ French National Research Institute for Agriculture, Food and the Environment (INRAE), University Savoie Mont Blanc, CARRTEL, Thonon-Les-Bains, France</footnote>

<footnote n="6">⁶ United States Geological Survey, Great Lakes Science Center, Lake Superior Biological Station, Ashland, Wisconsin, USA</footnote>

<footnote n="7">⁷ University of Innsbruck, Mondsee, Austria</footnote>

<footnote>*Corresponding author: taylorstewart@utah.gov</footnote>
warmer and prolonged summer growing seasons and stratification periods, and delayed autumn cooling (Sharma *et al.*, 2019; Maberly *et al.*, 2020; Woolway *et al.*, 2021; Woolway, 2023). These changes in seasonal temperatures and lake mixing have modified lake ecosystem productivity (O'Reilly *et al.*, 2015; Yankova *et al.*, 2017), which can have large impacts on ecosystem structure and function (Carpenter *et al.*, 2011; Bhagowati and Ahamad, 2019).

Most observed freshwater fish responses to climate-
induced changes in thermal conditions are shifts in within-
lake habitat distributions (Tunney et al., 2014; Guzzo et al.,
2017) and seasonal timing of life-history events (Parmesan,
2006; Farmer et al., 2015; Slesinger et al., 2021; Woods et al.,
2021). Spawning adults and embryos are hypothesized to be
the most temperature-sensitive life-stages in fishes based on
the concept of limited cardiorespiratory capacity and thermal
tolerance; spawning adults need suitable thermal habitat for
offspring to develop and aerobic capacity improves with
cardiorespiratory development (Dahlke et al., 2020). Warming
waters will also shorten embryo incubation times resulting in
earlier larval hatch dates (Reist et al., 2006; Karjalainen et al.,
2015; Stewart et al., 2021a). Fish reproductive strategies have
generally evolved to account for seasonal changes in resources
and allow for the most energy-intensive period of the
consumer's reproductive phenology to align with the peak
availability of prey resources (Hjort, 1914; Cushing, 1990;
Lowerre-Barbieri et al., 2011; Lyons et al., 2015). The spatio-
temporal synchrony between larval fish and their prey is
believed to be an important driver of interannual variation in
fish year-class strength (Cushing, 1990; Nyberg et al., 2001;
Straile et al., 2015).

Freshwater coregonines, Salmonidae Coregoninae, are a
group of cold, stenothermic fish species of high economic,
ecological, and cultural importance widely distributed
throughout the northern hemisphere (Bogue, 2001; Nyberg
et al., 2001; Zeller et al., 2011; Lynch et al., 2016; Hodgson
et al., 2020; Leppi et al., 2023). Over the past several decades,
many coregonine populations have declined and are the focus
of reintroduction, restoration, and conservation efforts
(Zimmerman and Krueger, 2009; Anneville et al., 2015;
Bronte et al., 2017). Declines in some coregonine populations
throughout their range are related to low early-life survival
(Nyberg et al., 2001; Parks and Rypel, 2018). Reasons for
declining recruitment have been linked to invasive species
(e.g., three-spined stickleback *Gasterosteus aculeatus* in
Lake Constance) and changes in primary production
(e.g., re-oligotrophication in Lake Geneva) at local scales
(DeWeber et al., 2022; Bourinet et al., 2023). However, factors
influencing declining recruitment remain unknown for many
bodies of water, but climate-induced increases in water
temperatures during critical early-life stages may be an
important driver (Nyberg et al., 2001; Jeppesen et al., 2012;
Karjalainen et al., 2015; Bourinet et al., 2023).

Coregonines generally spawn near shore in late-autumn or
winter (i.e., *ca.* October-January in the northern hemisphere);
embryos incubate under ice or in ice-free water and hatch in
late-winter or spring (Anneville *et al.*, 2007; Karjalainen *et al.*,
2015; Eshenroder *et al.*, 2016). For autumn-spawning
coregonines, the seasonal decrease in water temperature
initiates spawning and winter water temperature is positively
related to embryo development rate and negatively related to

incubation duration (Colby and Brooke, 1973; Luczyński and Kirklewska, 1984; Gillet, 1989; Karjalainen et al., 2015; Eshenroder et al., 2016; Stewart et al., 2021a). Climate change projections suggest that warming lake temperatures in autumn and winter could alter spawning phenology, incubation time, and subsequent hatching time and survival of early-life stage coregonines (Nyberg et al., 2001; Karjalainen et al., 2015, 2016; Stewart et al., 2021b; Vinson et al., 2023).

To further investigate how warming water temperatures
may influence coregonine embryo development, we modeled
the response of coregonine spawning phenology and
development time to future climatic-warming scenarios for
coregonine populations across North America and Europe. We
hypothesized that delays in coregonine spawning, as a
consequence of changes in autumn or winter cooling, result
in altered embryo incubation durations (i.e., number of days
between spawning and hatching) and hatching times. Our
predictions were that delayed cooling in autumn or winter
water temperature would cause delayed spawning times, with
one of the following subsequent scenarios for hatching times:
(1) shorter winter duration, higher winter water temperatures,
decreased embryo incubation durations, and earlier hatching
times; or (2) rapid winter cooling, normal winter water
temperatures, typical incubation durations, and delayed
hatching times. We expected coregonine populations that
historically spawn at warmer temperatures (i.e., lower latitude
populations) to have higher relative changes in reproductive
phenology because of climate warming compared to pop-
ulations that historically spawn at colder temperatures and are
adapted to prolonged incubations (i.e., higher latitude
populations).

**2 Methods**

Temperature-dependent embryo development models were
developed for eight coregonine study groups, newly fit models
were validated with existing *in situ* observations, lake bottom
thermal regimes were developed based on climatic-warming
scenarios, and future simulations for study groups were run
with validated models (Fig. 1).

**2.1 Study locations and species**

Study groups included pelagic cisco (*Coregonus artedi*)
from two populations in Lake Superior (Apostle Islands
[United States of America (USA)] and Thunder Bay [Canada])
and one in Lake Ontario (USA), pelagic vendace (*C. albula*) in
Lake Southern Konnevesi (Finland), and the littoral species
within the European whitefish species complex (*C. lavaretus*)
in lakes Southern Konnevesi, Geneva (France), and Annecy
(France), and the pelagic species within the European whitefish
species complex (*C. lavaretus*) in Lake Constance (Germany;
Fig. 2). Study groups were selected based on the availability of
water temperature, spawning, and developmental data.

Lakes Superior and Ontario are large, deep lakes (>19,000 km²; max depths > 244 m) within the Laurentian Great Lakes system with a diverse range of trophic states, thermal conditions, and habitats. Lake Southern Konnevesi is an oligotrophic lake (120 km²; max depth 56 m) in southern Finland with free-flowing inlet and outlet streams. Lakes
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Workflow diagram describing the use of *in situ* spawning, hatching, and daily lakebed water temperature data in the model validation process.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Map showing the location of each study group (LSTB = Lake Superior Thunder Bay, LSAI = Lake Superior Apostle Islands; LOCB = Lake Ontario Chaumont Bay; LSK = Lake Southern Konnevesi; LC = Lake Constance; LG = Lake Geneva; LA = Lake Annecy) sampled in North America (left) and Europe (right).</figcaption></figure>

Constance and Geneva are large, deep, peri-alpine lakes (>530 km²; max depths > 250 m) in central and western Europe. Lake Annecy is a small, peri-alpine lake (28 km²; max depth 82 m) in western Europe. Lakes Constance, Geneva, and Annecy exhibit significant thermal stratification and have been shifting towards an oligotrophic state over the last 40 years (Bourinet *et al.*, 2023).

## 2.2 Temperature-dependent embryo development model description

Embryo development rates were expressed as the reciprocal number of days from fertilization to a given developmental stage (Colby and Brooke, 1973; Luczyński and
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Predicted daily proportion of embryo development at water temperatures (°C) from validated development to 50% hatching (DR<sub>50</sub>) models for vendace (Coregonus albula) from Lake Southern Konnevesi, cisco (C. artedi) from lakes Superior and Ontario, and European whitefish (C. lavaretus) from Lake Geneva, and the pelagic species of European whitefish from Lake Constance. Population-specific DR<sub>50</sub> models were fit to data in Stewart et al., (2021a) for C. artedi from lakes Superior and Ontario and C. albula from Lake S. Konnevesi and Stewart et al., in review) for European whitefish from Lake Geneva. Species-specific DR<sub>50</sub> models were taken from Eckmann (1987) for C. lavaretus (pelagic) from Lake Constance.</figcaption></figure>

Kirklewska, 1984; Eckmann, 1987). The 50% hatching development stage was chosen to be comparable among study groups. The generalized equation relating rate of development to 50% hatching (DR<sub>50</sub>) with respect to incubation temperature (x; °C) is

$$DR_{50} = ab^x c^{x^2}$$

where a, b, and c are polynomial coefficients and the semilog form is

$$\log_{10}DR_{50} = \log_{10}a + x\log_{10}b + x^2\log_{10}c.$$

DR<sub>50</sub> models for cisco from lakes Superior and Ontario and vendace from Lake Southern Konnevesi were fit with experimental incubation duration data on embryos incubated at constant water temperatures of ca. 2.0, 4.5, 7.0, and 9.0 °C (Stewart et al., 2021a; Fig. 3, Tab. 1). An additional model was fit for littoral spawning morphotypes of European whitefish from Lake Geneva using embryo incubation data. Winter temperatures at Lake Geneva in the littoral zone normally decline to only 5–7 °C, so only two incubation temperatures were evaluated (contemporary at 7.0 °C and warmer winters at 9.0 °C). Thus, only a linear model was fit for European whitefish in Lake Geneva. The semilog form (i.e., log<sub>10</sub>DR<sub>50</sub>) was fit to obtain model coefficients for each study group with experimental incubation data using the mean number of days from fertilization at each incubation temperature. Furthermore, published DR<sub>50</sub> models were used for the littoral and pelagic spawning species of European Whitefish (C. lavaretus macrophthalmus and C. lavaretus wartmanni, respectively) from Lake Constance, vendace from Lake Kosno (Poland), and cisco from Pickerel Lake (USA; Colby and Brooke, 1973;

Luczyński and Kirklewska, 1984; Eckmann, 1987; Tab. 1). Hereafter, DR<sub>50</sub> models fit to each study group from experimental data (Stewart et al., 2021a, in review) are referred to as population-specific models while DR<sub>50</sub> models with published polynomial coefficients for species outside of our study groups (Colby and Brooke, 1973; Luczyński and Kirklewska, 1984; Eckmann, 1987) are species-specific models.

To predict development time to 50% hatching, log<sub>10</sub>DR<sub>50</sub> was calculated for each daily mean temperature since spawning and the antilog10 taken to estimate the daily proportion of development. When the cumulative daily proportions of development equaled one, 50% hatching was assumed to have occurred.

## 2.3 Data sources

Fishery-dependent spawning data were available for Lake Superior (Apostle Islands and Thunder Bay; Tab. A1). Commercial fishers from Lake Superior used gillnets to target spawning aggregations and recorded date, sex, maturity, and gonad condition from a subset of their catch (e.g., first 10 fish; Yule et al., 2008).

Fishery-independent spawning data collected during spawning surveys or broodstock collections were available for lakes Southern Konnevesi, Annecy, Geneva, and Ontario (Tab. A1). Spawning adults were collected using gillnets for lakes Southern Konnevesi, Geneva, and Annecy and trap nets for Lake Ontario at known spawning times and locations. Spawning adults collected were either assessed for gonad condition (i.e., green, ripe, spent) if actively spawning or were transported and held in hatchery ponds fed by lake water until spawning was observed. A daily distribution of spawning intensity was calculated, except for Lake Annecy where only peak spawning was reported. Daily number of ripe spawning individuals was reported for Lake Geneva, while daily gonad condition was reported for lakes Southern Konnevesi and Ontario.

Repeat larval abundance data (i.e., ca. weekly) were gathered for each study group to estimate dates of hatching (Tab. A1). Larval collections were made by either a seine or ichthyoplankton net through ice, or along the surface or at a stratified depth when lakes were ice-free (Perrier et al., 2012; Karjalainen et al., 2019; Lucke et al., 2020; McKenna et al., 2020). All larvae collections began prior to the start of hatching or with low abundance so the date of first capture was assumed to be the start hatch date, except for the Lake Superior (Apostle Islands) study group. Larvae from Lake Superior (Apostle Islands) were sampled on fixed dates annually, presumably during or after peak hatching, and thus the hatch date of individual larvae were back-calculated from total lengths assuming an absolute growth rate of 0.18 mm day<sup>-1</sup> (Oyadomari and Auer, 2007) and a length-at-hatch of 9.9 mm (Stewart et al., 2021a). Daily lakebed winter water temperatures were obtained from deployed temperature loggers or sondes (e.g., HOBO® Water Temperature Pro v2) for lakes Superior (Apostle Islands), Ontario, Southern Konnevesi, Geneva, and Annecy on known spawning habitat or depth (Table A.1). Only a single winter (2017-18) of lakebed temperature data was available from Chaumont Bay, Lake
<table><caption>**Table 1.** Development rate equations to 50% hatching *DR*<sub>50</sub> for cisco (*Coregonus artedi*) from lakes Superior, Ontario, and Pickerel, vendace (*C. albula*) from lakes Kosno and Southern Konnevesi, and European whitefish (*C. lavaretus*) from lakes Constance and Geneva. Population-specific *DR*<sub>50</sub> models were fit to data for *C. artedi* from lakes Superior and Ontario, *C. albula* and *C. lavaretus* from Lake S. Konnevesi (Stewart *et al.*, 2021a), and *C. lavaretus* from Lake Geneva (Stewart *et al.*, in review). Species-specific *DR*<sub>50</sub> models were taken for *C. artedi* from Pickerel Lake (Colby and Brooke, 1973), *C. albula* from Lake Kosno (Luczyński and Kirklewska, 1984), and *C. lavaretus* (pelagic and littoral species) from Lake Constance (Eckmann, 1987). The coefficient of determination (*R*<sup>2</sup>) was provided for models fit in this paper. All coefficients are in common logarithm (log10), and coefficients from Eckmann (1987) were transformed from natural logarithm. *x* is the incubation temperature in °C. – indicates no value was reported.</caption><thead><tr><th>Model group</th><th>Species</th><th>log<sub>10</sub>DR<sub>50</sub>=</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>Lake Superior</td><td><i>C. artedi</i></td><td>-2.3289 + 0.0717x - 0.0001x<sup>2</sup></td><td>0.97</td></tr><tr><td>Lake Ontario</td><td><i>C. artedi</i></td><td>-2.3836 + 0.0643x + 0.0008x<sup>2</sup></td><td>0.98</td></tr><tr><td>Pickerel Lake</td><td><i>C. artedi</i></td><td>-2.4088 + 0.0720x + 0.0011x<sup>2</sup></td><td>–</td></tr><tr><td>Lake Kosno</td><td><i>C. albula</i></td><td>-2.3035 + 0.0651x + 0.0004x<sup>2</sup></td><td>–</td></tr><tr><td>Lake S. Konnevesi</td><td><i>C. albula</i></td><td>-2.3664 + 0.0088x + 0.0050x<sup>2</sup></td><td>0.94</td></tr><tr><td>Lake S. Konnevesi</td><td><i>C. lavaretus</i></td><td>-2.4183 + 0.0459x + 0.0032x<sup>2</sup></td><td>0.96</td></tr><tr><td>Lake Constance</td><td><i>C. lavaretus</i> (pelagic)</td><td>-2.2419 + 0.1104x - 0.0033x<sup>2</sup></td><td>–</td></tr><tr><td>Lake Constance</td><td><i>C. lavaretus</i> (littoral)</td><td>-2.3002 + 0.1104x - 0.0031x<sup>2</sup></td><td>–</td></tr><tr><td>Lake Geneva</td><td><i>C. lavaretus</i></td><td>-2.1159 + 0.0528x</td><td>0.86</td></tr></tbody></table>

Ontario, and thus water temperatures from satellite surface water (https://podaac.ipl.nasa.gov/MEaSURES-MUR) and U.S. Geological Survey Oswego River gauge (gauge number 04249000; U.S. Geological Survey, 2024) were compared to the observed lakebed winter water temperatures from Chaumont Bay in 2017-2018. The Oswego River gauge provided the closest representation of the lakebed thermal conditions of Chaumont Bay (mean daily difference = 0.77 °C) and was used as the *in situ* winter water temperatures for the Lake Ontario study group. Daily lakebed winter water temperatures for Lake Superior (Thunder Bay) were provided by the City of Thunder Bay Bare Point Water Treatment Plant, which measures water temperature at intake pipes submerged 733 m from shore at 13-m depth.

## 2.4 Model validation

Population-specific *DR*<sub>50</sub> models fit from experimental incubation duration data (Stewart *et al.*, in review) were validated using *in situ* daily lakebed winter water temperatures at typical spawning depths to compare model outputs (i.e., hatching dates) to the observed hatching data from eight coregonine study groups across North America and Europe. Experimental data were not available to fit population-specific models for lakes Annecy and Superior (Thunder Bay), thus the population-specific model from the closest-related study group and lake was used as the best-approximating model. Our models from Lake Geneva and the Apostle Islands in Lake Superior were therefore applied to Lake Annecy and Thunder Bay, respectively.

Initiation and completion of contemporary spawning were based on observed water temperatures (Fig. 1). Coregonine spawning data to calculate the spawning temperatures were either (1) the daily proportion of ripe females leading to spawning or (2) the daily number of spawning individuals on spawning grounds. The onset of spawning was assumed to start on the date when either >10% of females captured daily were ripe or when the number of daily spawners was >10% of the

total number of spawning individuals annually. The end of spawning was calculated as the nearest date to the start of spawning when either <10% of females were ripe or when the number of daily spawners was <10% of the total number of spawning individuals annually. If the end of spawning period could not be calculated (e.g., commercial roe fishery ceased once eggs were free flowing or only peak spawning was observed), the spawning period was assumed to end 9 days after the start of spawning (Straile *et al.*, 2015). The water temperatures from the first and last date of the spawning period were taken from *in situ* daily lakebed winter water temperatures and averaged across all years available to define contemporary start and ending spawning water temperatures. If the years with spawning data did not overlap the years with water temperature data, the mean starting and ending days of year for the spawning period were calculated from all years of available spawning data, and the water temperatures at each mean starting and ending spawning days of year were averaged across all available water temperature years. For Lake Constance, where the dominant species of European whitefish spawns in pelagic water and embryos sink to a depth of 200–250 m (Straile *et al.*, 2007), spawning start dates were calculated using published equations from the date that the upper 10-m of the water column reached 10 °C isothermal conditions and spawning was assumed to end after 9 days (Straile *et al.*, 2015).

To validate the models for each study group, the mean spawning date was estimated from the calculated spawning start and end water temperatures for each year of available *in situ* water temperature data (Fig. 1). Mean observed hatching days of year were calculated as a weighted mean with daily larval abundance as the weight across all years available for each study group. The predicted development time to 50% hatching for each study group was calculated using daily water temperatures beginning at the mean spawning date for each population-specific model and, if applicable, the respective species-specific models from literature (Colby and Brooke, 1973; Luczyński and Kirklewska, 1984; Eckmann, 1987). The mean degree-days at hatching from all fitted models were
<table><caption>**Table 2.** Model validation results for population-specific development to 50% hatching models (*DR*<sub>50</sub>) for cisco (*Coregonus artedi*) from lakes Superior (Apostle Island and Thunder Bay) and Ontario, vendace (*C. albula*) from Lake Southern Konnevesi, and European whitefish (*C. lavaretus*) from lakes Geneva and Annecy. Starting and ending spawning temperatures (°C) used as model parameters are listed and the model output (i.e., degree-days at hatching) from population-specific and species-specific models were compared to *in situ* hatching data for each study group. N indicates the number of available years with daily *in situ* water temperatures and across which spawning temperatures were averaged. Bolded degree-days at hatching indicates the selected *DR*<sub>50</sub> model for each study group, if one was validated. Population-specific *DR*<sub>50</sub> models were fit to data for *C. artedi* from lakes Superior (Apostle Islands) and Ontario, *C. albula* and *C. lavaretus* from Lake S. Konnevesi (Stewart *et al.*, 2021a), and *C. lavaretus* from Lake Geneva (Stewart *et al.*, in review). Species-specific *DR*<sub>50</sub> models were taken for *C. artedi* from Pickerel Lake (Colby and Brooke, 1973), *C. albula* from Lake Kosno (Luczyński and Kirklewska, 1984), and *C. lavaretus* (pelagic species) from Lake Constance (Eckmann, 1987).</caption><thead><tr><th rowspan="2">Study group</th><th rowspan="2">Species</th><th colspan="2">Mean spawning temperatures</th><th colspan="3">Mean degree-days at hatching</th><th rowspan="2">N</th></tr><tr><th>Start</th><th>End</th><th>Observed</th><th>Pop. model</th><th>Species model</th></tr></thead><tbody><tr><td>Lake Superior (Apostle Islands)</td><td><i>C. artedi</i></td><td>4.3</td><td>3.0</td><td>250.1</td><td><b>255.7</b></td><td>335.0</td><td>3</td></tr><tr><td>Lake Superior (Thunder Bay)</td><td><i>C. artedi</i></td><td>4.9</td><td>4.4</td><td>452.3</td><td>337.9</td><td>405.3</td><td>4</td></tr><tr><td>Lake Ontario</td><td><i>C. artedi</i></td><td>4.1</td><td>2.7</td><td>377.5</td><td><b>359.8</b></td><td>344.1</td><td>10</td></tr><tr><td>Lake S. Konnevesi</td><td><i>C. albula</i></td><td>7.4</td><td>5.6</td><td>390.7</td><td><b>439.0</b></td><td>269.4</td><td>3</td></tr><tr><td>Lake S. Konnevesi</td><td><i>C. lavaretus</i></td><td>6.0</td><td>4.7</td><td>292.5</td><td>395.7</td><td>200.6</td><td>3</td></tr><tr><td>Lake Geneva</td><td><i>C. lavaretus</i></td><td>8.0</td><td>6.6</td><td>792.2</td><td>374.0</td><td>332.6</td><td>26</td></tr><tr><td>Lake Annecy</td><td><i>C. lavaretus</i></td><td>7.9</td><td>6.4</td><td>342.5</td><td>370.3</td><td><b>332.3</b></td><td>16</td></tr></tbody></table>

estimated and compared to the mean observed degree-days at hatching for each study group. Degree-days (DD) were calculated using 0 °C as the reference temperature. A model was considered validated if the difference between the model degree-days at hatching and the mean observed degree-days at hatching was within ±50 DD. The closest validated model (i.e., either the population-specific model or species-specific model) to the mean observed DD at hatching was selected for each study group.

## 2.5 Climate scenarios

Daily lake bottom water temperatures were simulated within the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b) local lake sector (Warszawski *et al.*, 2014), using the SimStrat v2.1 lake model (Gaudard *et al.*, 2019) for years 1900–2099 (Fig. A1). To drive the lake model, EWEMBI bias-corrected (Lange, 2019) climate model projections from ISIMIP2b were used, specifically projections from GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5 for historic (1900–2006) and future periods (2007–2099) under three representative concentration pathways (RCP): RCP 2.6, 6.0, and 8.5. These pathways included a range of potential future global radiative forcing with RCP 2.6 the lowest-emission scenario and RCP 8.5 the highest-emission scenario (IPCC, 2014).

To simulate how warming water temperatures would manifest in each of our study groups, lakes from ISIMIP were used as case studies. A total of 59 modeled case-study lakes from ISIMIP were categorized into climate zones following the definitions of Maberly *et al.* (2020) and maximum depth zones of 0–10, 10–25, 25–50, and 50+ m. Water temperatures from case-study lakes were averaged across all four climate model projections by each date, climate zone, and depth category to provide a simulated daily estimate of thermal spawning habitat for each climate zone and lake depth category combination. Each study group was assigned by climate zone

and typical coregonine spawning depth, and the respective simulated mean daily water temperatures were used for each study group. Only 19 of the 59 available case-study lakes matched the climate zone and depth categories of our study groups and were used (Tab. A2).

Lakes Geneva and Annecy are deep, peri-alpine lakes where coregonine spawning occurs in shallow waters (<5 m). The contemporary thermal spawning habitats for these two lakes were not well represented by the simulated bottom water temperature scenarios. Lakes Geneva and Annecy were available as modeled case-study lakes through ISIMIP and thus the lake-specific simulated daily water temperatures at the 5-m depth stratum were used, instead of the bottom water temperatures (mean maximum depth = 196 m), as the thermal scenarios for these lakes.

## 2.6 Model simulations

For each simulated year in the model, spawning began on the first day that the 5-day running average of simulated water temperatures dropped below the population-specific start spawning temperature and ended on the first day that the 5-day running average of simulated water temperatures dropped below the population-specific end spawning temperature. Eggs were deposited throughout the defined spawning period according to the proportional rate of daily water temperature change, where larger daily decreases in temperature resulted in higher daily spawner abundances. We modeled 500 female spawners each year, with each female depositing 100 eggs to provide a relative index of cohort size. If the simulated water temperatures did not decrease sufficiently to end spawning, spawning was assumed to end after 20 days. A spawning period of this duration is reasonable for high-latitude coregonine populations (Karjalainen and Marjomäki, 2018). Daily cohorts of deposited eggs were run through the respective validated *DR*<sub>50</sub> model for each study group, and the hatch date was estimated.
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Simulation model anomalies (number of days) for spawning date, incubation length, and hatching date for cisco (*Coregonus artedi*) from lakes Superior and Ontario and vendace (*C. albula*) from Lake Southern Konnevesi. Anomalies were calculated for three representative concentration pathways (RCP) from 2007–2099 compared to the mean value from the historical period (1900-2006). The rate of simulated incubation temperature (°C) change per decade is indicated in the top row for each study group and matched to the respective RCP by color. Linear regression equations for each RCP scenario and the coefficient of determination (R²) were provided.</figcaption></figure>

To estimate the magnitude of change in reproductive
phenology, incubation duration was calculated for each
simulated embryo and mean spawning and hatching dates
for each study group across historic (1900–2005) and future
periods (2006–2099). Mean spawning date, incubation
duration, and hatch date anomalies were calculated as
deviations from the respective mean trait value during the
historical period (1900–2005) for each study group, future
year, and RCP scenario. Linear regressions were fit through
simulated years to trait anomalies for each study group and
RCP scenario. The linear models of the trait anomalies for each
RCP scenario were compared using a two-way analysis of
variance (R syntax: *trait anomaly ~ year class + RCP
scenario*) within each study group, and a Tukey post-hoc test
was conducted if a significant difference among slopes was
found (*a* = 0.05) using the *emmeans* package v.1.10.0 (Lenth,
2021).

All simulations and analyses were performed in R version
4.2.3 (R Core Team, 2023).

**3 Results**

Our population-specific models were fit, validated, and
selected for cisco from lakes Superior (Apostle Islands) and
Ontario, vendace from Lake Southern Konnevesi, and
European whitefish from Lake Annecy (Tabs. 1 and 2). No

model was validated for cisco from Lake Superior (Thunder
Bay) and European whitefish from lakes Southern Konnevesi
and Geneva because the difference between predicted and
observed hatching was not within our confidence range (i.e.,
±50 DD), and thus results are not reported for these study
groups (Tab. 2).

Study groups with validated DR<sub>50</sub> models displayed
variable development rates but were similar within species
across populations (Fig. 3). DR<sub>50</sub> models for European
whitefish from Lake Constance had the fastest development
rates while models for vendace from Lake Southern Konnevesi
had the slowest development rates.

**3.1 Spawning time**

Model simulations predicted that spawning will be delayed
for all study groups except the pelagic species of European
whitefish from Lake Constance as climate change scenarios
increase water temperatures (Figs. 4 and 5). The RCP 8.5
scenario resulted in the greatest deviation from the mean
historical spawning time in all study groups (mean increase =
0.28 days year<sup>-1</sup>).

Spawning time of European whitefish from Lake Annecy
had the largest response to increased temperature (0.61 days
year⁻¹ at RCP 8.5), and the RCP 8.5 scenario resulted in
skipped spawning years and the complete absence of adequate
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Simulation model anomalies (number of days) for spawning date, incubation length, and hatching date for European whitefish (*Coregonus lavaretus*) from lakes Constance (pelagic species) and Annecy. Anomalies were calculated for three representative concentration pathways (RCP) from 2007-2099 compared to the mean value from the historical period (1900–2006). The rate of simulated incubation temperature (°C) change per decade is indicated in the top row for each study group and matched to the respective RCP by color. Linear regression equations for each RCP scenario and the coefficient of determination (R²) were provided.</figcaption></figure>

thermal habitat during the reproductive period required to
initiate spawning by 2080 (Fig. 5). Cisco from lakes Superior
and Ontario and vendace from Lake Southern Konnevesi had
similar delayed spawning responses to increased temperatures
within each RCP scenario (Fig. 4). Spawning time of European
whitefish from Lake Constance responded similarly among
RCP scenarios and had minimal change from historical
spawning times (< 0.07 days year⁻¹; Fig. 5). All RCP anomaly
slope pairwise comparisons were significantly different for
spawning date within each study group (*p* < 0.05), except for

vendace from Lake Southern Konnevesi between RCP 2.6 and
6.0 (*p* = 0.060; Fig. 6).

**3.2 Incubation length**

Embryo incubation durations (i.e., number of development
days) were predicted to decrease because of increased water
temperatures from climate change for all study groups
examined (Figs. 4 and 5). The greatest deviation in incubation
length from the mean historical incubation length was the RCP
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Anomaly slopes for spawning date, incubation length, and hatching date among representative concentration pathway (RCP) scenarios. Study groups included vendace (*Coregonus albula*), cisco (*C. artedi*) from lakes Superior and Ontario, the littoral species of European whitefish (*C. lavaretus*) from Lake Annecy, and the pelagic species of European whitefish (*C. lavaretus*) from Lake Constance. The *p*-value from Tukey post-hoc pairwise comparisons within each study group is provided if the RCP *p*-value from the two-way ANOVA main effect was significant. Error bars indicate 95% confidence interval. *** <0.001; ** <0.01; * = <0.05; ns = Not Significant (>0.05).</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Theoretical winter incubation periods and responses of embryo demographics under normal (2.0 °C; blue) and hypothetical warm (5.0 °C; orange) winter thermal regimes. The shaded regions indicate spawning periods (left) and hatching periods (right) that may occur between 4 and 5 °C. The 2.0 °C temperature regime is water temperature data collected from Lake Superior at 10-m depth in 2018.</figcaption></figure>

8.5 scenario for all study groups (mean decrease = 0.29 days year⁻¹). Incubation length had similar responses within the RCP 8.5 scenario to increased temperatures among all study groups, with vendace from Lake Southern Konnevesi having the greatest response (−0.41 days year⁻¹) and the pelagic species of European whitefish from Lake Constance having the smallest response (−0.24 days year⁻¹; Figs. 4 and 5). Furthermore, all study groups responded similarly to increased temperatures within the RCP 6.0 and 2.6 scenarios (mean = −0.16 and −0.04 days, respectively). All RCP anomaly slope pairwise comparisons were significant for incubation length within each study group (*p* < 0.05; Fig. 6).

## 3.3 Hatching

Model simulation outputs for hatching date were variable among study groups (Figs. 4 and 5). Hatching dates of cisco from Lake Ontario, vendace from Lake Southern Konnevesi, and the pelagic species of European whitefish from Lake Constance were earlier in response to increased temperatures, while hatching dates of European whitefish from Lake Annecy were later and did not change for cisco from Lake Superior as water temperatures increased. The hatching date anomaly slopes among RCP scenarios were not significantly different for cisco from Lake Superior (*p* = 0.949). RCP anomaly slope pairwise comparisons between RCP 2.6 and 6.0 for Lake Ontario cisco (*p* = 0.259) were not significant. All other comparisons were significant (*p* < 0.05; Fig. 6).

## 4 Discussion

Our hypothesis that delays in coregonine spawning, as a consequence of changes in autumn and winter cooling, would result in altered embryo incubation durations (i.e., number of days between spawning and hatching) and hatching times was

supported for all study groups. Simulation models predicted that climate-induced increases in water temperatures will shift reproductive phenology of coregonines causing (1) delayed spawning, (2) shorter embryo incubation durations, and (3) varying changes to larval hatching dates (Fig. 7). Relative changes were higher with more severe climate change scenarios for littoral species where embryos have higher contemporary incubation water temperatures (e.g., European whitefish from Lake Annecy).

In our simulations, the timing and number of spawning days were regulated by when, and in some instances if, water temperatures cooled to provide adequate spawning conditions. Oocyte development in fish is driven by energy content and is relative to water temperature during the germ-cell-developing season, and final oocyte maturation is a prerequisite for successful fertilization (Nagahama and Yamashita, 2008; Burt et al., 2011; Im et al., 2016). Elevated summer and autumn temperatures could place additional energy demands on adults and require longer feeding periods later into autumn. By shifting spawning to align with cooler water temperatures, coregonines could delay the high energy demand required for oocyte development to reduce competing energy demands during the summer when metabolic rates are greatest. Spawning at suboptimal water temperatures has the potential to induce considerable fertilization failure or embryo mortality if gametes are not adequately mature prior to spawning (Burt et al., 2011). The pre-fertilization thermal environment can also have intergenerational effects and shape offspring phenotypes, and thermally stressful spawning conditions can reduce the size and swimming performance in European whitefish larvae (Kekäläinen et al., 2018). Thus, delayed spawning could be a more efficient long-term life-history strategy for population persistence than the time-intensive evolutionary process of adults and embryos adapting to increased temperatures.
Shifting reproductive timing is a plausible response to warming temperatures. Contemporary coregonine populations exhibit multiple spawning strategies ranging from autumn to spring-spawning stocks (Eronen and Lahti, 1988; Hénault and Fortin, 1991; Schulz et al., 2006; Ohlberger et al., 2008). Thermal habitat used by high-latitude or deep-spawning populations could be less affected by climate change and may still provide adequate spawning and incubation conditions if water temperatures continue to rise, but this may not be the case for low-latitude or littoral-spawning populations. For example, Lake Annecy is near the southern native extent for European whitefish and had the largest shift in spawning time among our study groups, but adequate thermal spawning temperatures were absent by the middle of the 21st century under the worst-case climate scenario. This projection is likely to be applicable for populations within the same geographic region that inhabit lakes with similar morphology and thermal conditions where population-specific $DR_{50}$ models were not able to be validated, and even more dire projections could be expected for non-native populations south of Lake Annecy (e.g., Lake Garda [Italy]; Volta et al., 2018). However, these southern European lakes are deep (>80 m) with stable thermal refugia outside of the littoral zone (Kelly et al., 2020). Shifting spawning strategies to use deeper habitats could provide adequate thermal conditions for ovulation to initiate, but whether suitable coregonine spawning habitat is available in these deeper waters, including sufficient oxygen levels, is unknown (Jane et al., 2020; Kelly et al., 2020; Desgué-Itier et al., 2023).

Spawning scenarios determined many of the changes in reproduction phenology but embryos are static and unable to evade changes in winter water temperatures post-spawning (Stockwell et al., 2014). Elevated incubation water temperatures accelerated embryo rates of development and hatching time and were not negated by delayed spawning. The frequency of shorter, warmer winters is projected to increase (Sharma et al., 2019), which corresponds with our simulation results suggesting future incubation durations will decrease across all study groups. In the absence of changes to spawning behavior or habitat, warmer and shorter incubations could cause higher coregonine embryo and larvae mortality, increased occurrence of embryo deformities, and smaller and less robust larvae at hatching compared to colder, longer incubations (Stewart et al., 2021a,b). Additionally, reduced ice formation caused by warmer air and water temperatures could decrease embryo survival from increased storm displacement and sediment deposition while decreasing lengths-at-hatching (Kangur et al., 2020; Stewart et al., 2021b). A complex mix of environmental factors during embryogenesis could generate high variability in hatching success and larval fitness - this was not considered in our modeling efforts (Hjort, 1914; Houde, 1989; Marjomäki et al., 2004; Fig. 7).

Coregonine populations where embryos incubate in cold littoral waters (<4 °C) are hypothesized to rely on ice-break up and spring warming to trigger final embryo development stages and hatching (Karjalainen et al., 2015). The rapid increase in spring water temperatures also synchronizes other phenological processes such as the onset of spring plankton blooms (Sommer et al., 2012). Our simulations suggest significant changes in length of incubation and subsequent hatching times, and changes in thermal regimes and time of

hatching may result in temporal separation between coregonine larvae and their zooplanktonic prey. Increases in seasonal temperature variability could also cause mismatches with larval zooplankton prey if temperature changes are not heterogeneous across seasons (e.g., a cold winter followed by a warm spring; Straile et al., 2015).

Fish year-class strength is often dependent on larvae surviving from hatch through the first three to six months of life and successfully transitioning from endogenous to exogenous feeding (Hjort, 1914; Houde and Hoyt, 1987; Cushing, 1990). Numerous size-dependent processes strongly influence when larvae need to first feed and their ability to successfully forage (Miller et al., 1988). Coregonine larval body size at hatching is inversely related to incubation water temperature, and length-at-hatch and yolk-sac volume have a negative relationship (Karjalainen et al., 2015; Stewart et al., 2021a). Warmer incubations result in coregonine larvae hatching earlier with smaller lengths and larger yolk-sacs (Stewart et al., 2021a). Increased yolk-sac energy reserves at hatching may help larvae reduce starvation risk but the rate of endogenous feeding (i.e., yolk consumption) is regulated by metabolic demand (Kamler, 2008). Earlier and warmer spring water temperatures in nursery zones, which induce earlier hatching, could accelerate yolk consumption in newly hatched larvae and counteract the physiological trade-off between length-at-hatching and yolk-sac volume. Larvae hatching earlier in the spring may also have reduced swimming abilities, reduced visual acuity, and more gape limitations, which can impact their ability to evade predators and forage efficiently (Miller et al., 1988; Myers et al., 2014). Determining the physiological stress response of hatching earlier and the impact warming nursery water temperatures may have on yolk conversion efficiency is a logical and necessary next step to build on our simulation results.

Our models extend earlier approaches (Colby and Brooke, 1973; Luczyński and Kirklewska, 1984; Eckmann, 1987) by incorporating mechanistic relationships to future climate-change scenarios from a wider range of populations. Because coregonines are highly developmentally plastic and exhibit diverse spawning behaviors (Muir et al., 2013), the transferability of published models to other populations appears limited. High-quality *in situ* reproductive and embryo development data are critical to fitting and validating new population-specific development models. For example, hatching data for European whitefish in Lake Geneva were only available for a single year with a limited sampling period and likely did not capture the start of hatching. This led to development models underestimating hatching when, in reality, the *in situ* data likely did not represent the true hatching period and created a limitation in our modeling efforts. Increasing field sampling efforts around these critical life stages will benefit future research and climate-change predictability for both applied and modeling-based methods. Our simulations demonstrated that subtle changes in water temperature could translate into substantial changes in the reproductive phenology of coregonines among our study groups. Long-term changes in environmental conditions during reproductive and development periods could play a large role in generating variability in offspring success (Houde and Hoyt, 1987; Little et al., 2020). The results of our modeling efforts highlight how water temperature is
fundamental in regulating biological and physiological processes, but the impact of these changes is difficult to decipher as coregonines are behaviorally and developmentally plastic (Muir *et al.*, 2013; Karjalainen *et al.*, 2015). Quantifying the relationships between water temperature and coregonine reproductive phenology across a wide range of populations will be useful for managers to determine which populations may be more susceptible to the consequences of

climate change and help prioritize conservation and restoration efforts. Unless coregonines exhibit behavioral or thermal adaptations to changing environmental conditions (e.g., McQuinn, 1997; Jørgensen *et al.*, 2006), we expect that the changes in coregonine reproductive phenology predicted by our models will have negative implications for population sustainability throughout the 21st century, even under the lowest climate-emission scenario.

APPENDIX A

<table><caption>**Table A.1.** Years of *in situ* data used for model validation.</caption><thead><tr><th>Study group</th><th>Water temperature</th><th>Spawning</th><th>Hatching</th></tr></thead><tbody><tr><td>Lake Superior (Apostle Islands)</td><td>2016–18</td><td>2016–18</td><td>2016–18</td></tr><tr><td>Lake Superior (Thunder Bay)</td><td>2017–21</td><td>2005; 2007–08; 2010</td><td>2008–09</td></tr><tr><td>Lake Ontario</td><td>2012–21</td><td>2007; 2017; 2019–20</td><td>2004–06</td></tr><tr><td>Lake S. Konnevesi</td><td>2019–21</td><td>2019–21</td><td>2019–21</td></tr><tr><td>Lake Geneva</td><td>2010–21</td><td>2016–19</td><td>2016</td></tr><tr><td>Lake Annecy</td><td>2005–20</td><td>2016–19</td><td>2005–07</td></tr></tbody></table>

<table><caption>**Table A.2.** Study group and modeled case-study lakes from inter-sectoral impact model intercomparison project that match study group parameters (the lakes that were averaged together by category).</caption><thead><tr><th>Lake type</th><th>Lake name</th><th>Latitude</th><th>Longitude</th><th>Climate zone</th><th>Depth bin (m)</th></tr></thead><tbody><tr><td rowspan="10">Group Study</td><td>Geneva</td><td>46.37</td><td>6.45</td><td>Northern Temperate</td><td>0–10</td></tr><tr><td>Annecy</td><td>45.86</td><td>6.17</td><td>Northern Temperate</td><td>0–10</td></tr><tr><td>Ontario</td><td>44.05</td><td>-76.20</td><td>Northern Temperate</td><td>0–10</td></tr><tr><td>Superior (Thunder Bay)</td><td>48.41</td><td>-89.02</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Superior (Apostle Islands)</td><td>46.85</td><td>-90.55</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Constance</td><td>47.64</td><td>9.38</td><td>Northern Temperate</td><td>50+</td></tr><tr><td>Southern Konnevesi</td><td>62.58</td><td>26.58</td><td>Northern Cool</td><td>0–10</td></tr><tr><td>Mueggelsee</td><td>52.43</td><td>13.65</td><td>Northern Temperate</td><td>0–10</td></tr><tr><td>Allequash</td><td>46.04</td><td>-89.62</td><td>Northern Temperate</td><td>0–10</td></tr><tr><td rowspan="19">Modeled Case Study</td><td>Trout Bog</td><td>46.04</td><td>-89.69</td><td>Northern Temperate</td><td>0–10</td></tr><tr><td>Crystal Bog</td><td>46.01</td><td>-89.61</td><td>Northern Temperate</td><td>0–10</td></tr><tr><td>Wingra</td><td>43.05</td><td>-89.43</td><td>Northern Temperate</td><td>0–10</td></tr><tr><td>Esthwaite-Water</td><td>54.37</td><td>-2.99</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Big Muskellunge</td><td>46.02</td><td>-89.61</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Sparkling</td><td>46.01</td><td>-89.70</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Crystal Lake</td><td>46.00</td><td>-89.61</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Two-Sisters</td><td>45.77</td><td>-89.53</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Dickie</td><td>45.15</td><td>-79.09</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Great</td><td>44.53</td><td>-69.89</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Fish</td><td>43.29</td><td>-89.65</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Mendota</td><td>43.10</td><td>-89.41</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Delavan</td><td>42.61</td><td>-88.60</td><td>Northern Temperate</td><td>10–25</td></tr><tr><td>Stechlin</td><td>53.17</td><td>13.03</td><td>Northern Temperate</td><td>50+</td></tr><tr><td>Rappbode</td><td>51.74</td><td>10.89</td><td>Northern Temperate</td><td>50+</td></tr><tr><td>Kuivajarvi</td><td>60.47</td><td>23.51</td><td>Northern Cool</td><td>0–10</td></tr><tr><td>Vortsjaerv</td><td>58.31</td><td>26.01</td><td>Northern Cool</td><td>0–10</td></tr><tr><td>Nohipalo-Mustjaerv</td><td>57.93</td><td>27.34</td><td>Northern Cool</td><td>0–10</td></tr><tr><td>Laramie</td><td>40.62</td><td>-105.84</td><td>Northern Cool</td><td>0–10</td></tr></tbody></table>
<figure><img src="image_12.png" /><figcaption>**Fig. A.1.** Simulated daily lake bottom water temperatures for three representative concentration pathways (RCP) in 2020, 2040, 2060, and 2080 for Lake Superior, Lake Ontario, Lake Southern Konnevesi, Lake Constance, and Lake Annecy.</figcaption></figure>

## Acknowledgments

We thank Jim McKenna (U.S. Geological Survey Tunison Laboratory of Aquatic Science), Mike Connerton (New York State Department of Environmental Conservation Cape Vincent Fisheries Station), Eric Berglund (Ontario Ministry of Natural Resources and Forestry), Scott Sapper and Dray Carl (Wisconsin Department of Natural Resources Bayfield Fisheries Field Station), Jared Myers (U. S. Fish & Wildlife Service Ashland Fish and Wildlife Conservation Office), and Dan Yule (U.S. Geological Survey Lake Superior Biological Station) for providing coregonine spawning and/or hatching data. Brian Weidel (U.S. Geological Survey Lake Ontario Biological Station), Brenda Lafrancois and Jay Glase (National Park Service), and Walter Turek (City of Thunder Bay) provided winter water temperatures from Chaumont Bay, Lake Ontario, Apostle Islands, Lake Superior, and Thunder Bay, Lake Superior, respectively. Spawning data for Lake Geneva were collected during spawning surveys performed by the Observatory on Lakes for the International Commission for the Protection of Waters of Lake Geneva long-term monitoring program. Spawning data for Lake Annecy were collected by the Observatory on Lakes for the SILA-DDT-professional fishermen-ALP association program. Timo Marjomäki, Jonna Kuha, and staff at the Konnevesi Research Station helped obtain temperature, spawning, and hatching data of Konnevesi vendace and whitefish. This work was funded by the U.S.

Geological Survey under Grant No. G16AP00087 to the Vermont Water Resources and Lakes Studies Center. We acknowledge INRAE, French National Research Institute for Agriculture, Food, and Environment, the UMR CARRTEL (INRAE - USMB) and the National Science Foundation (award number 1829451) for supporting a workshop to develop this work. Nicole Berry provided the U.S. Geological Survey solicited review that strengthened the manuscript, as did anonymous peer reviewers. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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**Cite this article as:** Stewart TR, Karjalainen J, Zucchetta M, Goulon C, Anneville O, Vinson MR, Wanzenböck J, Stockwell JD. 2024. Winter is not coming: evaluating impacts of changing winter conditions on coregonine reproductive phenology. *Int. J. Lim.* 60: 17.