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All issues Volume 61 (2025) Int. J. Lim., 61 (2025) 6 Full HTML
Issue
Int. J. Lim.
Volume 61, 2025
Special issue - Biology and Management of Coregonid Fishes - 2023
Article Number 6
Number of page(s) 19
DOI https://doi.org/10.1051/limn/2025003
Published online 03 April 2025

© EDP Sciences, 2025

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License CC-BY-NC (https://creativecommons.org/licenses/by-nc/4.0), which permits unrestricted use, distribution, and reproduction in any medium, except for commercial purposes, provided the original work is properly cited.

1 Introduction

Lakes are invaluable freshwater systems that provide numerous benefits to humankind, including drinking water, irrigation, transport, recreation and food supply (Jenny et al., 2020; Weyhenmeyer et al., 2024). In the northern hemisphere, coregonines (Salmonidae) are highly prized by commercial and recreational fishers. They have constituted an important protein source for local communities and contributed to substantial revenue incomes to millions of people for centuries (Baer et al., 2017; Tammiksaar and Kangur, 2020; FAO, 2022). Most coregonines are either planktivores or benthivores and their foraging on zooplankton can induce trophic cascades (Vanni et al., 1990). Coregonines also have specific ecological needs. They are cold-water-adapted species with embryos requiring a sufficient oxygenated water-sediment interface for successful development and hatching (Müller, 1992; Eckmann, 2013). Adults require a specific range of oxythermal habitat (Fang et al., 2012; Kumar et al., 2013, Jacobson et al., 2013), although they are more tolerant to hypoxia (dissolved oxygen <2 mg/l) in winter (Rodrigues et al., 2022). These ecological needs make them sensitive to changes in habitat conditions and a useful sentinel of lake water quality and ecological state (Jeppesen et al., 2012).

During the last few decades, scientists have warned that human activities are threatening lakes, and leading to rapid degradation as a result of cumulative impacts combined with other emerging stressors (Jenny et al., 2020). For example, eutrophication, oxythermal habitat loss and inappropriate fisheries management have contributed to the decline of coregonine populations in many lakes (Svärdson, 1976; Vonlanthen et al., 2012; Sandlund et al., 2013; Anneville et al., 2015) and have resulted in major challenges for restoration and management (Bunnell et al., 2024). Furthermore, while local disturbances such as eutrophication and dam construction can be overcome through management actions, climate change is a broader challenge with relatively limited local solutions for natural-resource managers. Climate change is expected to exacerbate the negative impacts of invasive species and anthropogenic stressors, especially eutrophication, with detrimental effects on coregonine populations (Collingsworth et al., 2017). In particular, climate change is expected to induce eutrophication-like problems (Bosch et al., 2014), lower levels of dissolved oxygen (Jane et al., 2021), and reduce ice cover (Jensen et al., 2007; Woolway et al., 2020), each of which could indirectly impair coregonid survival and reproduction success (Brown et al., 1993; Müller and Stadelmann, 2004). In fact, a negative future for coregonines due to climate change is already predicted by in-situ observations (Jacobson et al., 2012; Jeppesen et al., 2012; Kangur et al., 2022), experiments and models (e.g. Lynch et al., 2015; Kao et al., 2015, DeWeber et al., 2022; Stewart et al., 2024a). Warmer summers are likely to impact abundances (Jacobson et al., 2012), with heatwaves reducing oxythermal habitats and inducing strong coregonine mortalities. Temperature affects the fish endocrine system, and high temperatures can inhibit the production of ovarian oestrogen and thus delay the reproduction process (Numann, 1970; Gillet 1991; Pankhurst and Munday, 2011; Servili et al., 2020). Furthermore, warmer temperatures or reduced ice cover during incubation enhance egg mortality and larval malformations (Brooke, 1975; Cingi et al., 2010; Stewart et al., 2021). Those changes also alter the dynamics of egg development (Stewart et al., 2021; Roberts et al., 2024; Stewart et al., 2024b), affecting the hatching time, size-at-hatching and may result in spatiotemporal mismatch between larvae and the emergence of the required prey. Taken together, climate-induced changes to water temperature and quality are predicted to reduce reproductive and recruitment success and induce a global decrease in coregonine populations.

The fate of coregonine catches in response to a changing climate is also highly uncertain due to complex ecosystem processes. Total coregonine catch has decreased at the continental scale (Baer et al., 2021), but aggregated national statistics may not reflect the temporal trends in reported catches of individual lakes. Coregonines exhibit intra- and inter-specific differences in temperature tolerances (Stewart et al., 2021), lake warming is non-uniform across the globe (Wahl and Peeters, 2014; O'Reilly et al., 2015; Öğlü et al., 2020), and lake characteristics such as morphology and stratification patterns are highly variable. Therefore, coregonine populations in nearby lakes might not be equally affected by climate change (Sandström et al., 2014), especially if the populations have access to thermal refuges (Kao et al., 2015) and/or if their egg development has remained unaffected by rising temperatures. Furthermore, warming may benefit coregonines in some cases. For example, a warm spring temperature increased coregonid larvae growth in several lakes (Eckmann and Rösch, 1998; Perrier et al., 2012; Göbel et al., 2017). Faster spring warming rates may enhance larvae growth rates and thus reduce size-dependent mortality and improve recruitment success (Zhu et al., 2015). Also, the number of eggs per spawner and egg size has increased in several whitefish species with increasing growth (Rösch, 1987). Those positive effects of temperature suggest possible increased coregonine production if food resources are available for larvae (Trippel et al., 1991). Finally, some stocks have been maintained or increased because of effective water-quality management strategies, stocking, fishing regulation rules and conservation practices (Bunnell et al., 2023; Bunnell et al., 2024; Rogissart et al., 2024). For example, lake recovery from eutrophication can improve coregonine reproduction success and result in significant increases in coregonine yield (Ludsin et al., 2001; Anneville et al., 2017). Coregonine re-introduction has also been achieved in some ecosystems where the taxon was extirpated (Borcherding et al., 2010; Conley et al., 2021). Accordingly, when implementing appropriate management programs, we might expect stability or recovery of coregonine stocks. Nevertheless, the impacts of ongoing global changes on regulatory mechanisms within populations and communities are likely to result in various outcomes for management efforts across lakes and through time (Baer et al., 2023). Much remains to be learned about the overall trends in coregonine fisheries in this context of multiple anthropogenic pressures and a strong desire to protect aquatic ecosystems.

We collected commercial catch statistics data for coregonines from 27 northern hemisphere lakes (Fig. 1). Long-term changes in harvest are already documented in the literature for some of these lakes, but over different time-periods and using different statistical methods between lakes, making it challenging to extract a global pattern. Consequently, in this study, we applied a consistent methodology for global analysis with the objective of determining whether coregonine catches have a globally consistent downward trends during the first two decades of the 21st century as the negative effects of climate change on lake ecosystems became more prevalent worldwide (Woolway et al., 2020). Furthermore, the integrated dataset was also used to ascertain whether trends are globally associated with environmental conditions or lake characteristics (e.g., geographical location, size, water quality). Finally, we discuss implications of our findings for water quality improvement and coregonine fisheries management.

thumbnail Fig. 1

Map indicating the geographical location of the 27 lakes included in the analysis.

2 Material and methods

2.1 Study sites and data sources

We compiled and harmonized a suite of environmental, and long-term data on climate and coregonine catches from lakes in Asia (n=1), North America (n=6) and Europe (n=20). We used time series of coregonine annual catches in biomass between 2000 and 2019. Any co-occurring coregonine species or morphs were summed due to limited taxonomic resolution in some time series (Supplementary Tab. 1). The data used are therefore total coregonine catches. Catch data came from commercial fisheries statistics (Supplementary Tab. 2), except for Great Slave Lake where all commercial gillnet harvests for coregonines were co-managed by Freshwater Fish Marketing Corporations and Fisheries and Oceans Canada (DFO). Commercial fisheries catches were generally from gillnets and trapnets. Long-term changes in fishing effort or CPUE (Catch Per Unit Effort) were available for only 4 lakes (Supplementary Tab. 1) and good correlations between CPUE and catches were observed for these lakes (Tab. 1). Therefore, although catches do not perfectly reflect population abundance and should be interpreted with caution, we assume, in accordance with previous work, that catch data are an interesting proxy for changes in stock abundance and remain an important data source to be used (Pauly et al., 2013). However, in 3 lakes (Peipsi, Vänern and Vättern) there have been severe restrictions of fisheries, and special attention has been paid to how this might affect our results (see Statistical analysis section and discussion).

Descriptor data for lake morphology, geography, trophic state, and warming rate were compiled (Tab. 2). Fifty-six percent of the lakes had a surface area larger than 100 km2. Lake Baikal was the deepest lake (max depth: 1637 m) and Lake Peipsi was the shallowest (max depth: 15 m). All the lakes, except Great Slave Lake, experienced eutrophication but only four lakes (15%) remain eutrophic. Sixteen lakes (59%) have been restored to an oligotrophic or ultraoligotrophic state (Tab. 2A). The recent lake trophic state communicated by data providers or literature sources were used as an explanatory variable (Steinsberger et al., 2020; Dove and Chapra, 2015; Rimet et al., 2020; MacKenzie et al., 2022). For the analysis, this qualitative variable was converted into a quantitative categorical variable that refers to a phosphorus concentration. Specifically, each lake was given the median value of the theoretical range of total phosphorus (TP) corresponding to its trophic state (Vollenweider and Kerekes, 1982). Eutrophic, mesotrophic, oligotrophic, and ultra-oligotrophic conditions were assigned TP values of 68 μg/L, 23 μg/L, 7 μg/L and 2 μg/L, respectively.

The rate of change in summer lake surface water temperature were integrated from O'Reilly et al. (2015) and were recorded for fourteen lakes (Tab. 2B). In addition, seasonal air temperature anomalies were downloaded from Climate Reanalyzer (Climate Change Institute, University of Maine (US), https://climatereanalyzer.org/research_tools/monthly_tseries/). Data were simulated for six geographical regions corresponding to the location of the lakes (Tab. 2B). The rate of change and direction of trends were computed using the Sen's slope method which estimates the median rate of linear change over a time-period (Sen, 1968; Hirsch et al., 1982). Sen's slope values were used as proxies to quantify the strength of the pressure related to the changes in weather conditions resulting from climate change. While catch data were only from 2000 to 2019, Sen's slopes of temperature anomalies were computed over the last three decades. A 30-year timeframe is usually required to properly capture the global climate-induced changes and better account for cyclic climate variabilities in response to the large-scale variability of the natural teleconnection modes (Hurrell, 1995; Dahlin and Ault, 2018).

Table 1

Average annual catches and standard deviation (2000–2019) in each of the studied lakes. Changes in fishing effort were documented in some lakes (SupTable 1) and, when data were available, correlations were made between catches and catch per unit effort (CPUE).

Table 2

A. Country, (FR: France; RU: Russian Federation ; CH: Switzerland ; DE: Germany ; AT: Austria ; CA: Canada; US: United States of America; IT: Italy ; EE: Estonia ; SE: Sweden), latitude, morphologic characteristics and current trophic status with total phosphorus estimates (TP) of the 27 studied lakes. The lake volumes were estimated as the product of the average depth and the lake area. B: Trends, expressed as Sen's slope values, in lake summer surface water temperature (LSSWT) from O'Reilly et al. (2015), and trend in seasonal air temperatures (Winter: from December of the previous year to February, Spring: March to May; Summer: June to August; Autumn: September to November). Significant trends (p-value<0.05) are indicated with an asterisk.

2.2 Statistical analysis

The analysis focused on the period from 2000 to 2019, which was common to all lakes except Baikal (started in 2007). Because of strong inter-lake differences in the size-related-carrying capacity of the lake (larger lakes are potentially expected to host more fish) and the fishing effort (e.g. number of fishermen, fishing regulations), the range of catch variability differs between lakes (Tab. 1). For this reason, and because of our focus on temporal trends instead of absolute catches, each time series was standardized to zero mean and unit standard deviation prior to the analyses. Analyses were carried out in R version 4.1.0 (R Core Team, 2021) and results were visualized using “ggplot2” (Wickham, 2016).

Our approach combined different methods that provide complementary elements for the analysis of temporal trends in order to obtain a complete overview; accordingly, our study is divided into 3 steps.

In the first step, we compared long-term changes and identified temporal patterns to provide a global picture of inter-annual variabilities and commonalities among lake catches. To determine whether catch rates were synchronous among lakes, we estimated the Pearson's correlation coefficient between catch time series of all possible pairs of lakes. We used a heatmap coupled to hierarchical cluster analysis (R package pheatmap) to quantitatively group lakes into clusters based on levels of similarity (or correlation) between their temporal catch trajectories. Individual lake trends in catches were extracted using a non-parametric regression method, the local polynomial regression fitting, using the LOESS function (smoothing parameter equal to 0.8) in the R package stats (R Core Team, 2021). We used a Dynamic Factor Analysis (DFA), from the R package MARSS (Holmes et al., 2012), to further evaluate the coherence of the temporal trends in catches between lakes and detect common patterns in time series. Dynamic Factor Analysis is a dimension-reduction technique designed for time-series data to estimate underlying common patterns in a set of time series by modelling multiple, nonstationary time series of observed data as a linear combination of shared hidden trends, potential explanatory variables, and observed errors. A matrix of factor loadings on the hidden trends determines the exact form of the linear combinations of the common trends. Comparing factor loadings among lakes can help identify which common trends are important to a particular lake and which lakes are related to the specific common trend.

In the second step, we described the temporal trends of catches in individual lakes based on their direction, their rate of change, and their significance. The direction and rate of change were assessed with the aforementioned Sen's slope method (Sen, 1968; Hirsch et al., 1982) and ran on the standardized catches. We then used the Mann-Kendall trend test to determine whether a time series had a significant monotonic upward or downward trend. The package trend was used for Sen's slope and Mann-Kendall computations.

In the third and final step, we determined lake characteristics and environmental variables that were most important in explaining the rate of change and decrease in coregonine catches. Spearman rank correlations were first used to identify significant relationships between the rate of changes in catches, expressed as Sen's slopes of individual lakes, versus the explanatory variables listed in Table 2 (latitude, elevation, average depth, maximum depth, lake area, trophic state, water and air temperature warming rates). The correlations were computed considering all lakes (except Baikal) and they were validated on the set of lakes excluding the 3 lakes (L. Peipsi, Vänern and Vättern) where important change in fishing effort were documented during our studied period (Tab. 1). The correlations were also tested on the set of lakes presenting significant (p-value<.0.05) trends in the catches (results shown in supplementary Tab. 3). The explanatory variables that were correlated with the rates of change in catches (expressed as Sen's slopes) were then used in an ordination approach to identify whether changes in coregonine catches could be explained by the combination of those environmental variables. We used a Hill and Smith Analysis because the data included a mix of quantitative and qualitative variables (Hill and Smith, 1976). We then used a discriminant analysis to test whether the selected environmental conditions could predict lake memberships to categories based on the magnitude and significance of their temporal trends (according to Sen's slopes and p-value from the Mann-Kendall trend). We used the R package “ade4” to run the Hill & Smith and discriminant analysis.

3 Results

3.1 Long-term trends in coregonine catches

Catch time series exhibited some common trends and patterns. Heat maps revealed negative and positive correlations between lakes (Fig. 2A), some of which were significant. The largest coherent group included 13 lakes that were widespread over North America and Europe, and most were characterized by a continuous decline in catch over the entire studied period according to the Dynamic Factor Analysis (Trend 2; Figs. 2B, C and 3). The Dynamic Factor Analysis highlighted two additional patterns (Fig. 2B). Trend 1 exhibited a decline but then a recovery at the end of the time series and Trend 3 exhibited a positive trend from 2000–2007 followed by a decline. Six lakes were associated with Trend 1 (decreasing, then increasing). When exploring for direction and significance of monotonic trends among Trend 1 lakes, Lake Peipsi presented a strong increased in catches although statistically significant at the 10% level (Fig. 3). In contrast, lakes Brienz and Wallen exhibited decreases in catches (respectively, p-value<0.05 and 0.1) over their time series despite having an increase in catches in the last five or so years. When looking for monotonic trends in the eight lakes associated with Trend 3 (increasing, then decreasing), three of these lakes had overall positive Sen's slopes and significant trends (lakes Morat, Bourget and Lugano), even with steep declines in recent years. Lake Baikal, conversely, had an overall significantly decreasing trend over its shorter time series (Fig. 3).

Overall, almost half of the lakes had significant (p-value<0.05) decline in catches over the period from 2000 to 2019. Four lakes were in North America (Lakes Erie, Huron, Michigan, and Great Slave Lake), five in Western Europe (Bienne, Lucerne, Constance, Brienz, and Thun) and two in Sweden (Vänern and Vättern). Lake Baikal had a shorter time-series (2007–2019) but the observed decline of catches was highly significant (p-value<0.01).

thumbnail Fig. 2

(A) Heatmap to visualize correlations in coregonine catches among lakes over the period 2000 to 2019 (Baikal not included). (B) Three average temporal patterns identified by Dynamic Factor Analysis. (C) Dendrogram showing the gathering of lakes according to their contributions to the construction of the 3 temporal patterns.
thumbnail Fig. 3

Standardized catches and loess (80% smoothing pan). Trend 1, 2 and 3 represent the three patterns identified by the Dynamic Factor Analysis. Significant trends are indicated by *** (p-value<0.05), * (p-value<0.1). Blacks and grey asterisks indicate negative and positive Sen's slope respectively.

3.2 Associating catch trends over 2000–2019 with lake characteristics and environmental variables

Correlations between the rate of changes in catches and lakes characteristics were significant for only three environmental variables (Fig. 4 and Tab. 3). These correlations remain significant (p-value<0.05) in the subset of lakes excluding the 3 lakes (L. Peipsi, Vättern and Vänern) with substantial changes in fishing effort (Tab. 3). Correlations were modest (R=0.48) to strong (R=-0.62) when focusing on lakes where trends were significant (Supplementary Tab. 3). The rates of change in catches were consistently negatively correlated with the lake area and the average lake volume, whereas positively correlated with TP. They differed between small-medium vs. large lakes (p-value<0.1) and between rich vs poor-nutrient lakes (p-value<0.05). Our results indicated that lakes with lower phosphorus concentrations tended to have more negative Sen's slopes in their catch rates. In contrast, the highest positive rates of changes were recorded in small and eu- or mesotrophic lakes (Fig. 4). Spearman correlations revealed no pattern between declining catch rates and geographical area (e.g., latitude).

Significant (p-value<0.05) declines in coregonid catches were observed in specific lakes where the environmental characteristics differed significantly from the other lakes in terms of trophic state and size (Fig. 5). Accordingly, lakes where coregonine catches declined significantly were poorer in nutrients (Wilcoxon rank sum test, p-value<0.05) and larger (Wilcoxon rank sum test, p-value<0.1). Such a difference was significant at only 10% on the subset of lakes excluding the 3 lakes (Tab. 3). The large and oligotrophic Lake Baikal that was not considered for the comparison, also matched well with this pattern.

All the studied lakes were located in geographical areas with positive trends in air temperature over the last 30 years in at least three seasons (Tab. 2B). The magnitude of the air warming differed among geographical areas, but remained relatively low (between 0.005°C/year and 0.04°C/year) over the area of the Laurentian Great Lakes in North America and reached a maximum of 0.06°C/year in Western Europe and the Alpine area (Tab. 2B). The long-term trends in warming air temperature during winter were not correlated with trends in catches (Fig. 6). Correlations were positive between the rates of change in catches and the rates of air warming in spring, summer, and autumn. Results were consistent when correlations were performed on the subset of lakes excluding L. Peipsi, Vättern and Vänern (Tab. 3). Lakes located in geographical areas that suffered more severe air warming in spring, summer, and autumn, presented a wide range of variability in catch trends including increasing catches (Fig. 6). In contrast, catches have been decreasing in all the geographical areas that presented a light warming. Finally, no correlation appeared between the rates of changes in catches and warming rates in lake surface temperature.

Catch trends and trophic state contributed to the formation of the axis 2 while the five other variables (area, average volume, warming rate of air temperature in spring, summer and autumn) contributed to the formation of axis 1 in the Hill & Smith Analysis (Fig. 7A). Axes 1 and 2 represented 85% of the variance in the dataset. The positions of the lakes in the 2-dimensional space enabled the lakes to be visualized and clustered based on their environmental variables (Fig. 7B and C). Discriminant analysis indicated that lakes distributed along axes 1 and 2 according to their membership to groups defined by the rates of changes in catches. This is also the case when the analysis excluded the lakes where significant changes in fishing effort may have influenced the observed trends in catches (Supplementary Fig. 1). The group of lakes with significant catch declines segregated significantly (p-value<0.05) from the other lakes, indicating that the selected variables were strong predictors of decline in coregonine catches. Overall, the rate of change in catch trends was significantly different depending on the trophic state and atmospheric warming (Supplementary Fig. 2A), and in the area with the fastest warming significant differences in the rate of change in catch were observed between eu- or mesotrophic and nutrient-poor lakes (Supplementary Fig. 2B), with decreasing trends more often observed in nutrient-poor lakes. This result remains when the tests were carried out on a set of lakes that excludes Lakes Peipsi, Vänern and Vättern.

thumbnail Fig. 4

Environmental characteristics of lakes showing significant relationships (Spearman p-value< 0.05) with the rate of change in catches computed over the period 2000-2019 (Baikal not included). The color of the circles indicates the level of significance of the long-term trend in catches (p-value<0.05: dark blue; not significant: light blue). Environmental characteristics include logarithm of the lake area (Lake area) and logarithm average volume (Lake volume), total phosphorus concentration according to lake trophic state at the end of the studied period (Reference TP value for trophic status).
Table 3

Correlations analysis between rate of changes in catches and the explanatory variables listed in Table 2. Two-groups Wilcoxon comparisons of rate of changes and selected explanatory variables for A: all lakes except L. Baikal, and B: all lakes except L. Peipsi, Vänern, Vättern and Baikal. Small-medium lakes correspond to lakes whose surface area and volume are smaller than 100km2 and 10km3. High nutrient lakes encompass eu- and mesotrophic lakes while poor-nutrient lakes refer to oligo- and ultraoligotrophic lakes. Rapid warming area corresponds to geographic area where air warming was the fastest (0.06°C.year−1).

thumbnail Fig. 5

Comparison of lake characteristics (displayed as box-whisker plots) between the lakes that displayed a significant decrease in coregonine catches and lakes where the trends were not significant or positive over the period 2000–2019 (Lake Baikal not included). (A) logarithm of lake area (km2), (B) logarithm of average volume (km3) and (C) Reference TP value for trophic status (μg.L−1).
thumbnail Fig. 6

Relationships (Spearman correlation) between the rate of changes in catches and air temperature measured over geographical area of the lakes for different seasons (Winter: December of the previous year to February; Spring: March to May; Summer: June to August; Autumn: September to November). The color of the circles indicates the level of significance of long-term trends in catches computed over the period 2000–2019 (p-value<0.05: dark blue; no significant: light blue).
thumbnail Fig. 7

Hill & Smith analysis of rate of change in catches and environmental variables. (A) projection of the seven variables onto the first factorial plane of the Hill & Smith analysis (axis1 horizontal and axis 2 vertical) with SON-trend, JJA-trend and MAM-trend being for autumn (September-November), summer (July-August) and spring (March-April) air temperature trends respectively. (B–C) projection of the lakes onto the first factorial plane of the Hill & Smith analysis and barycenter of the group of lakes showing significant and non-significant increase or decrease (Increase, IncreaseNs, Decrease, DecreaseNs respectively) in their catches.

4 Discussion

Our long-term datasets from 27 lakes over the Northern Hemisphere offer a unique opportunity to address numerous critical issues related to the coregonine ecology and fisheries management. We examined global trends in catches, with a specific emphasis on the countries with more developed economies where coregonine fisheries are primarily located. Although these lakes are now subject to various monitoring, regulations or protective fisheries management measures, overfishing has sometimes led to stock collapses in the past and, for some lakes, fishing continues to weaken populations and/or influence long-term dynamics by combining with environmental parameters (Allan et al., 2005; Anneville et al., 2015). It is therefore important to know whether certain lakes, because of their morphological or environmental characteristics, are more sensitive and to assess how environmental properties can influence the rate of change in catches. In this study we therefore sought to explore the link between lake characteristics and changes in catches.

4.1 Global changes in catches and suggested reasons for lake-specific trends

Despite management and restoration programs, our results indicated decreases in coregonine catches in 52% of the lakes examined, with significantly declining trends in 44% of them including lakes Baikal, Michigan, Erie, Huron, Great Slave, Vänern, Vättern, Bienne, Lucerne, Brienz, Thun and Constance. We acknowledge that these observed declines could be due to decreases in fishing efforts and other changes associated with the conditions of the fisheries, as our data are total catches and not catch per unit effort. For example, in Lakes Vänern and Vättern, the fishing effort for European whitefish has decreased significantly since the year 2000. One reason for this reduction was due to high levels of dioxins and dioxin-like PCBs (Hållén et al., 2020). Another reason for the decline in coregonine catch in Lake Vättern was that the fishery shifted to the introduced crayfish (Pacifastacus leniusculus) because of markedly better profit margins than coregonines (Blomqvist and Swahnberg, 2020). Thus, the observed declines in coregonine catches might be partially due to a lower fishing effort or changing market conditions.

Catch data are essential for fisheries management and for determining sustainable harvest levels, but catch statistics alone do not provide a correct indication of fish abundance and do not inform on the size of the population (Pauly et al., 2013; Ovando et al., 2022). In our studied lakes, even if some of the observed trends might be influenced by changes in fishing effort, we believe they are representative of changes in stock size. Using reported catch as an index of fish abundance is justified by its practicality, particularly in multi-species fisheries where alternative methods such as detailed stock assessments are not feasible, and indices such as catch per unit effort are difficult to estimate. Pauly et al. (2013) highlighted that while catch data should be interpreted with caution, they still provide valuable insights into fish abundance, especially for data-limited fisheries. Additionally, it is reasonable to assume that fishers adjust their efforts in response to changes in fish abundance. The fishing effort and total catch data across global lakes in Kao et al. (2020) reinforced this idea. While those data showed a positive correlation between fishing effort and total catch, their year-to-year fluctuations suggested that fish abundance likely influenced both of them. This observation further supports the use of catch data as a practical, albeit imperfect, proxy for fish abundance. Furthermore our results are consistent with the literature as many of the observed local trends in the present paper are documented in the literature and induced by changes in environmental conditions. For example, in the 1960–1970s, the catches in Lake Vättern were approximately 50 times higher than they are now. Beside the reduction of fishing effort, other factors, such as a reduction in phosphorus concentrations (Renberg et al., 2003; Norborg Carlsson, 2023), may have contributed to this downward trends (A. Sandström, Swedish University of Agricultural Sciences, Department of Aquatic Resources, Institute of Freshwater Research, written personal communication, 2023). In Lake Vänern, the fishery targets vendace (C. albula) to refine the roe. The catch of vendace also decreased over the study period with a parallel decreasing trend for adult vendace stock biomass assessed by a fisheries independent monitoring program (Rogell and Axenrot, 2023). The cause of this decline is unknown, although recruitment of vendace in Lake Vänern depends on the date of ice-off, with long ice-winters being more favourable for good recruitment (Sandström et al., 2014). Thus, the negative effect of the ongoing reduction in ice duration in combination with lake oligotrophication may explain the observed decline. The main reasons accounting for the collapse of the coregonine populations in Lake Constance are the decrease in food availability due to extreme re-oligotrophication together with competition with other zooplanktivorous species including the introduced three-spined stickleback (Gasterosteus aculeatus, Rösch et al., 2018). In addition, the sticklebacks exert a strong predation pressure on eggs and young whitefish since their population outbreak since 2012 (Gugele et al., 2023). Lake Baikal has been described as a stable and healthy ecosystem (Dobiesz et al., 2010) but in recent years, catches have declined. In Great Slave Lake, the taxonomic changes in algal community, resulting from an increase in air and lake surface temperature, and a decline in ice cover, have implications on the entire food web, providing fewer nutrients for the primary consumers, ultimately affecting the food supply for coregonine fish with consequences for the fisheries (Rühland et al., 2023). In the Laurentian Great Lakes, declines in coregonine catches are likely due to a myriad of potential stressors that are difficult to disentangle but include reduced lake productivity (coincident with invasive mussel proliferation) and potentially lower larval growth and survival (Ebener et al., 2021; Cunningham and Dunlop, 2023), and negative effects from fisheries exploitation in some areas (Rook et al., 2022).

Increases in coregonine catches over the studied period were observed in Lakes Bourget, Lugano, Morat and Peipsi. In Lake Peipsi, an increase in catches might reflect a short-term increase in the vendace stock due to favorable environmental conditions for recruitment success during a few years after a strong vendace fishery regulation that consisted of the closure of the fishery during 6 years in order to help the stock to recover. Nevertheless, the population is still low compared to pre-collapse during the late 1980's (Kangur et al., 2020) and new fishing restrictions are expected soon to sustain the population (Bunnell et al., 2024). In Lake Bourget, the increase in catches was due to a high abundance of Daphnia sp. in spring (Bourinet et al., 2023) and likely the improvement towards a lower trophic state that induced the disappearance of the toxic cyanobacteria Plankthotrix rubescens that used to bloom in that lake (Kerimoglu et al., 2017).

4.2 Drivers of global changes in coregonine catches and implication for lake managements

Our study also indicated that global patterns can be detected among these widely distributed lakes with variation in trophic state and size. We observed strong inter-lake differences in the rates of change in catches and we highlighted a significant correlation between the rate of changes in catches and the trophic state with most of the trends being negative in low nutrient lakes. The rate of changes and direction of the trends were significantly different between high (eutrophic and mesotrophic) and low (oligotrophic and ultra-oligotrophic) productive systems. Lakes with the strongest decrease in catches were nutrient poor lakes while more productive lakes presented no trend or increase in catches. Our results were tested on a smaller subset of lakes (Tab. 3 and supplementary Tab. 3). They remained consistent although increasing risk of type II error due to a smaller number of observations, which in our case means that the role of trophic state is disregarded while it affects the rate of change in catches. Thus, our results highlight the sensitivity of coregonine populations to extreme re-oligotrophication at a global scale. The case of Lake Baikal, which is ultra-oligotrophic and was only considered in the descriptive analysis, corroborates this finding. The highlighted link between the direction of the trends and the trophic state could be explained by resource limitation (e.g., strong bottom-up control) in oligotrophic lakes where primary production has strongly decreased as a result of reduced nutrient loading (Finger et al., 2013). Coregonine communities in very low nutrient lakes are dominated by smaller, slow-growing individuals (Alexander and Seehausen, 2021), however, some exceptions exist (Lake Walchen, e.g. Mayr, 2001). Those slow-growing species are sometimes sparsely rakered indicating benthivory that provides better fitness under low availability of pelagic food (Shimoda et al., 2023). Coregonine size differences between lakes with different trophic state may thus reflects a reduced growth potential because of limited pelagic resources and/or an adaptation to oligotrophic conditions. Re-oligotrophication may act to favor specialized benthic feeding coregonine phenotypes if they are still present in the population (Hirsch et al., 2013). Phenotypic and genetic diversity, such as that documented in Lake Constance, offer the possibility to develop a morphology adapted to benthic feeding (Hirsch et al., 2013) and potentially enhance resilience. However, the declining population has been accompanied by a parallel reduction in coregonine size supporting the hypothesis of resource limitation. Resource limitation has also been emphasized in Lake Brienz, with strong impact on fishing yields, because the smaller sized fish could not be caught by legal gillnets (Müller et al., 2007). A transition from eutrophic to mesotrophic conditions generally benefits coregonine populations (Anneville et al., 2009), yet an even higher reduction in phosphorus could have adverse effects on fisheries. In the four Swiss lakes (Bienne, Lucerne, Brienz, Thun), the threshold below which a positive relationship between phosphorus and coregonine yield is obvious at around 10 μg/L of soluble reactive phosphorus during the spring circulation (Müller et al., 2007). An analysis of catches in 11 peri-alpine lakes suggests the ideal trophic state for European whitefish yield is mesotrophic with high yields recorded for total phosphorus concentrations ranging between 20 and 40 μg/L (Gerdeaux et al., 2006). Accordingly, further phosphorus decrease in oligotrophic lakes is likely to result in a decrease in coregonine populations, especially in lakes suffering parallel outbreaks of invasive species that exert strong exploitative competition or indirect bottom-up trophic cascades (David et al., 2017). For example, invading dreissenid mussels exacerbate the impact that food scarcity already has on larval growth rate and recruitment of coregonines (Cunningham and Dunlop, 2023). Extreme oligotrophy can thus impact abundance and life-traits, highlighting the need to (i) monitor fish populations to provide accurate estimates for fisheries policies that can adapt to changes in the environment, (ii) maintain and protect the possibility of evolutionary divergence by implementing fisheries practices that do not reduce genetic diversity, and (iii) better consider the impacts of bottom-up control on fish populations when managing lake water quality and fisheries.

The link between lake size and the rates of changes requires further investigation. Rate of changes in catches differed slightly between large and small-medium size lakes. Trends are more often declining in large lakes. This pattern could be explained by several hypotheses. First, in economically developed countries, large lakes have usually reached low TP levels (Woelmer et al., 2016), making re-oligotrophication more impactful on fish populations. Furthermore, because larger lakes tend to be deeper and thus typically exhibit a higher ratio of pelagic production relative to benthic production, the more frequent decline observed in large lakes might suggest that pelagic coregonines are more sensitive to the common stressors (climate and re-oligotrophication). Eventually, effective management strategies can be efficiently implemented and show results within a few years in small lakes as was the case in Lake Annecy (Bourinet et al., 2023). Conversely, large lakes have a history of supporting more intense and sometimes industrial fisheries that might have had strong impacts on populations (Rook et al., 2022). Furthermore, large lakes are more likely to provide a higher diversity of ecological niches, allowing co-existence of larger numbers of morphs and several fish stocks that can differ in their response to environmental changes. Such a “response diversity” (Elmqvist et al., 2003) can complicate the assessment of the population if, at the subpopulation scale, the reduction of one or a few populations is balanced by the production of others. These changes in the population composition can pass unnoticed and can delay protection measures that, if established too late, may not prevent the stock to fluctuate at a lower level (Cury and Anneville 1998; Eshenroder et al., 2016). Large lakes are often transboundary, which may also make governance more complex. Nevertheless, given that the majority of lakes are small to medium-sized, prioritizing fisheries management is crucial for safeguarding populations and ensuring stocks sustainability.

The inter-lake differences in the rates of change in catches were not explained by the difference in warming rates of air temperature and lake summer surface temperatures. The number of significantly warming areas were relatively modest, potentially due to the fact that the study period occurred during the "global warming hiatus," which is characterised by a deceleration in global warming (Fyfe et al., 2016). The period considered by O'Reilly et al. (2015) was longer but does not perfectly overlap with the studied period. The characteristics of lakes can act as a mediating factor in climatic effects (O'Reilly et al., 2015). Certain lakes may thus be more vulnerable to warming than others. Consequently, lakes with a high rate of surface temperature change may appear more likely to experience significant alterations in their ecosystems. However, our findings do not support this hypothesis. They were unexpected given the restricted thermal niche of coregonines, but they are in coherence with few studies highlighting non-significant or positive effects of rising lake temperatures (Massol et al., 2007; Anneville et al., 2017). The absence of negative effects of rising temperatures might be due to the statistical approach that focusses on trends instead of the impact of isolated cases or extreme meteorological events, like heatwaves that may have detrimental effects in the large shallow northern lakes like Peipsi. Those shallow lakes are particularly sensitive to short-term extreme weather conditions, to which fish respond surprisingly quickly (Jeppesen et al., 2012; Kangur et al., 2020). Heat waves in summer and changing ice conditions during the winter may have profound detrimental effects on cool-water, autumn-spawning, short-lived fish such as vendace in shallow and polymictic lakes. In such shallow lakes, cool-water and oxygen-rich refuges do not exist in summer and wave action can mechanically damage and displace fish eggs, potentially disrupting their survival and development in spawning grounds during mild winters without permanent ice cover (Kangur et al., 2020). The absence of negative effects of global warming could also be explained by the range of variation in environmental data. Indeed, our results indicate that temperatures have remained within the range of coregonine thermal tolerances and, at least over our study period (2000–2019), temperatures have not yet reached the values that could significantly limit coregonine reproductive success. For example, temperatures for egg incubations should remain below 7–8°C depending on the species (Stewart et al., 2021; Stewart et al., submitted2). Unfortunately, such temperatures are expected to be reached soon even in the most optimistic climatic scenario, with detrimental consequences for reproduction success (Stewart et al., 2024). In lakes where the thermal niche remains within proper conditions for the development and reproduction of the species, warmer conditions might actually favor recruitment. From 2000 to 2019, warming in eu- and mesotrophic lakes Morat, Bourget, Lugano and Peipsi could well have provided a more productive habitat (i.e., warmer and sufficient food). Several papers have reported a positive effect of temperature on larval growth or recruitment (Perrier et al., 2012; Sandström et al., 2014). Accordingly, based on stimulations of larval and juveniles growth, we expect warmer temperatures during spring, summer and autumn to increase growth as long as prey resources are not limiting, potentially resulting in a positive effect on recruitment and on the fishery. Finally, the absence of adverse effects from climate change might also be due to behavioral adaptations of the fish when lake morphology allows fish to seek thermal refuges, especially when lakes are deep and oxygen would not be limiting in deeper waters. Obviously, interplay among lake size, trophic state and climate warming can explain our results and should be considered to properly explain inter-lake differences and to assess the sensitivity of their coregonine populations to global warming.

4.3 Interplay among trophic-state, lake size and air warming jeopardizes future predictions and appeal for a change in management paradigms

Interestingly our results embrace the complexity of confounding and interacting factors that may have strong implications for assessing lake sensitivity to global forcing. For the studied period lake trophic state, lake size and climate pressure explained inter-lake differences in the rate of change in coregonine catches. As previously discussed, decreased catches are not restricted to warming regions. Instead, warming regions are associated with positive trends, especially in nutrient rich lakes. The association between coregonine trends and trophic state is probably strengthened by climate pressure with stronger positive effects of warming in smaller lakes when food is available. These two important lake-characteristics (trophic state and size) are expected to remain relevant and shape coregonine responses to future climatic conditions. Under warmer climate scenarios the stratification behavior of large and deep lakes can change dramatically (Wahl and Peeters, 2014), resulting in changes in plankton phenologies and different food availability for spring-hatching coregonines, which could lead to a decoupling of hatching time and the occurrence of high prey densities of preferred size ranges for newly hatched larvae (Straile et al., 2007). Furthermore, due to climate change, the upper layer of lakes may eventually become too warm for whitefish (DeWeber et al., 2022). In deep lakes the upper water layer is the area with the highest prey density and if whitefish migrate to stay in deeper, cooler regions, where much less food is available, they begin to starve (DeWeber et al., 2022), unless the population has the potential to adapt their feeding behavior and switch to benthic prey. Nevertheless, many oligo- and mesotrophic lakes with higher water clarity also have phytoplankton production at deep water layers (Scofield et al., 2020; Bonnier et al., 2024) and have zooplankton undergoing diel vertical migration up to 125 m (Nowicki et al., 2017; Piton et al., 2024). Further work on the phenology and vertical distribution of plankton communities is necessary to enhance our understanding of how these changes will interact with and impact coregonine communities in a warming world and under diverse trophic conditions. Given the strong link between the direction of the trends and the trophic state (e.g., Freeberg et al., 1990; Müller et al., 2007; Bunnell et al., 2014), we can expect that the limiting effect of food availability might be exacerbated in a warmer climate if metabolism is increased but prey resources are insufficient to approach maximum consumption rates (Kao et al., 2015). Hence, without comprehensive understanding of these complex and potentially interacting dynamics, the certainty of our predictions on the effects of changing trophic state and climate change on coregonine communities will be relatively low.

When temperatures move outside of the ideal range in which gonadal and embryogenic development have been adapted, reduced fertility and reproduction success is likely (Cingi et al., 2010; Eme et al., 2015; Mueller et al., 2015; Servili et al., 2020). Accordingly, depending on whether the fish can adapt its behavior and find thermal refugia, the temperature increase in air temperature will probably not have the same impact in shallow compared to deep lakes. Our study suggests that trophic state together with lake morphology (area and volume) and thus probably the mixing regime of the lake, will be important variables to consider to evaluate the impact of global warming on coregonines. Specific tools such as end-to-end 3D modelling may reproduce biological processes from phytoplankton to fish by taking into account hydrodynamic processes (Bănaru et al., 2019). Such tools may offer interesting possibilities to predict and test the impacts of management solutions and required nutrient thresholds to mitigate the effect of climate changes.

In conclusion, our findings highlight a link between trophic state and trends in coregonine productivity. This link is “shaped” by changing climate and lake size. Our findings also corroborate that coregonines thrive at intermediate levels of productivity (e.g., mesotrophic, Gerdeaux et al., 2006; Anneville et al., 2009), given that the highest declines of catch rates occurred in oligotrophic and ultra-oligotrophic lakes, when resources are likely to become more limited. In developed countries, many efforts have been made to reduce phosphorus loading, control phytoplankton growth, stop algal blooms and recover pristine water quality conditions as required by many legislations. Those efforts were encouraged by lake managers as eutrophication also compromises coregonine reproduction, thus threatening the survival of the populations. Consequently, a transition from eutrophic to mesotrophic conditions resulted in the recovery of coregonine stocks in many lakes. However, lake managers must be aware that aiming for pristine conditions might reduce the productivity of the fisheries (see meta-analysis by Deines et al., 2015). In a world where local food provisioning can be a sustainability goal (see United Nations General Assembly, 2015; opinion of the European Committee of Regions, 2021; European Green Deal; Blue Transformation), lake sources of protein are important to consider and require managers to find the balance in nutrient concentrations that provide acceptably clean water while still providing sufficient fish production. Such an effort requires water and fisheries managers to collaborate effectively to clarify each other's expectations and find common lake-specific management objectives that will meet the sometimes divergent needs of all lake users. This cooperation is even more important and urgent with the intensification of climate change impacts and the introduction of invasive species that can have significant and uncontrolled effects on food availability for coregonines.

Acknowledgments

Authors thank Tim Alexander for providing information on data from Swiss lakes and AO thanks Prof. O. Seehausen for inspiring discussion during the writing of the manuscript. KK acknowledges financial support for this study from the Estonian Research Council (grant PRG 1266). Catch data for Swiss lakes except Lake Geneva were downloaded from the BAFU website (www.fischereistatistik.ch). Catch data for Lakes Geneva, Annecy and Bourget were contributed by the DDT (Direction Départementale des Territoires) of Savoie and Haute-Savoie. Environmental data were contributed by the Observatory of alpine LAkes (OLA), © SOERE OLA-IS, AnaEE-France, INRAE of Thonon-les-Bains, CIPEL, SILA, CISALB. Swedish catch data was provided by the Swedish Agency for Marine and Water Management. 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 and Fisheries and Oceans Canada.

Supplementary Material

Supplementary Figure 1. Hill & Smith analysis of rate of change in catches and environmental variables. The 3 lakes (L. Peipsi, Vänern and Vättern) where trends in catches could be influenced by strong changes in fishing effort were not included in the analysis. A: projection of the seven variables onto the first factorial plane of the Hill & Smith analysis (axis 1 horizontal and axis 2 vertical) with SON-trend, JJA-trend and MAM-trend being for autumn (September-November), summer (July-August) and spring (March-May) air temperature trends respectively. B-C: projection of the lakes onto the first factorial plane of the Hill & Smith analysis and barycenter of the group of lakes showing significant and non-significant increase or decrease (Increase, IncreaseNs, Decrease, DecreaseNs respectively) in their catches. The group of lakes with significant catch declines segregated significantly (Discriminant analysis, p-value<0.05) from the other lakes, indicating that the selected variables were strong predictors of decline in coregonine catches.

Supplementary Figure 2. A. Comparison of rate of changes in catches among four lakes categories (A: nutrient-rich in a fast warming area, B: nutrient-rich in a slow warming area, C: nutrient-poor in a fast warming area, D: nutrient-poor in a slow warming area). B. Comparison of rate of changes between nutrient-rich and nutrient-poor lakes located in fast warming area. The color of the circles indicates the level of significance of the long-term trend in catches (p-value<0.05: dark blue; not significant: light blue).

Supplementary Table 1. Information on the taxonomic composition of coregonines catches (“No”: only one form was recorded; “Yes”: multiple species or forms were recorded; “Not indicated”: no information available), fishing practices and stressors that have been identified as main drivers of coregonine stocks over the last decades.

Supplementary Table 2. Metadata of the datasets used in the analysis and details on data availability.

Supplementary Table 3. A) Mann-Kendal p-values of the long-term trend in catches and Sen’s slopes values for each lake. B) Results of correlation analysis that were performed on the set of lakes showing significant (p-value <0.05) long-term trends in catches.

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Cite this article as: Anneville O, Alric B, Chang C-W, Baer J, Bourinet F, Brinker A, Bunnell DB, Goulon C, Holmgren K, Hsieh C-h, Janjua Y, Kao Y-C, Kangur K, Sandström A, Silow EA, Zhu X. 2025. Are we heading towards a global decrease in coregonine catches? Int. J. Lim. 61: 6: https://doi.org/10.1051/limn/2025003

All Tables

Table 1

Average annual catches and standard deviation (2000–2019) in each of the studied lakes. Changes in fishing effort were documented in some lakes (SupTable 1) and, when data were available, correlations were made between catches and catch per unit effort (CPUE).

In the text
Table 2

A. Country, (FR: France; RU: Russian Federation ; CH: Switzerland ; DE: Germany ; AT: Austria ; CA: Canada; US: United States of America; IT: Italy ; EE: Estonia ; SE: Sweden), latitude, morphologic characteristics and current trophic status with total phosphorus estimates (TP) of the 27 studied lakes. The lake volumes were estimated as the product of the average depth and the lake area. B: Trends, expressed as Sen's slope values, in lake summer surface water temperature (LSSWT) from O'Reilly et al. (2015), and trend in seasonal air temperatures (Winter: from December of the previous year to February, Spring: March to May; Summer: June to August; Autumn: September to November). Significant trends (p-value<0.05) are indicated with an asterisk.

In the text
Table 3

Correlations analysis between rate of changes in catches and the explanatory variables listed in Table 2. Two-groups Wilcoxon comparisons of rate of changes and selected explanatory variables for A: all lakes except L. Baikal, and B: all lakes except L. Peipsi, Vänern, Vättern and Baikal. Small-medium lakes correspond to lakes whose surface area and volume are smaller than 100km2 and 10km3. High nutrient lakes encompass eu- and mesotrophic lakes while poor-nutrient lakes refer to oligo- and ultraoligotrophic lakes. Rapid warming area corresponds to geographic area where air warming was the fastest (0.06°C.year−1).

In the text

All Figures

thumbnail Fig. 1

Map indicating the geographical location of the 27 lakes included in the analysis.
In the text
thumbnail Fig. 2

(A) Heatmap to visualize correlations in coregonine catches among lakes over the period 2000 to 2019 (Baikal not included). (B) Three average temporal patterns identified by Dynamic Factor Analysis. (C) Dendrogram showing the gathering of lakes according to their contributions to the construction of the 3 temporal patterns.
In the text
thumbnail Fig. 3

Standardized catches and loess (80% smoothing pan). Trend 1, 2 and 3 represent the three patterns identified by the Dynamic Factor Analysis. Significant trends are indicated by *** (p-value<0.05), * (p-value<0.1). Blacks and grey asterisks indicate negative and positive Sen's slope respectively.
In the text
thumbnail Fig. 4

Environmental characteristics of lakes showing significant relationships (Spearman p-value< 0.05) with the rate of change in catches computed over the period 2000-2019 (Baikal not included). The color of the circles indicates the level of significance of the long-term trend in catches (p-value<0.05: dark blue; not significant: light blue). Environmental characteristics include logarithm of the lake area (Lake area) and logarithm average volume (Lake volume), total phosphorus concentration according to lake trophic state at the end of the studied period (Reference TP value for trophic status).
In the text
thumbnail Fig. 5

Comparison of lake characteristics (displayed as box-whisker plots) between the lakes that displayed a significant decrease in coregonine catches and lakes where the trends were not significant or positive over the period 2000–2019 (Lake Baikal not included). (A) logarithm of lake area (km2), (B) logarithm of average volume (km3) and (C) Reference TP value for trophic status (μg.L−1).
In the text
thumbnail Fig. 6

Relationships (Spearman correlation) between the rate of changes in catches and air temperature measured over geographical area of the lakes for different seasons (Winter: December of the previous year to February; Spring: March to May; Summer: June to August; Autumn: September to November). The color of the circles indicates the level of significance of long-term trends in catches computed over the period 2000–2019 (p-value<0.05: dark blue; no significant: light blue).
In the text
thumbnail Fig. 7

Hill & Smith analysis of rate of change in catches and environmental variables. (A) projection of the seven variables onto the first factorial plane of the Hill & Smith analysis (axis1 horizontal and axis 2 vertical) with SON-trend, JJA-trend and MAM-trend being for autumn (September-November), summer (July-August) and spring (March-April) air temperature trends respectively. (B–C) projection of the lakes onto the first factorial plane of the Hill & Smith analysis and barycenter of the group of lakes showing significant and non-significant increase or decrease (Increase, IncreaseNs, Decrease, DecreaseNs respectively) in their catches.
In the text

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