Vendace populations on the life table : between-lake variation and the association between early life and mature survival and growth

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Vendace populations on the life table : between-lake variation and the association between early life and mature survival and growth © T.J. Marjomäki et al., Published by EDP Sciences, 2024 Published version Marjomäki, Timo J.; Auvinen, Heikki; Helminen, Harri; Huusko, Ari; Huuskonen, Hannu; Hyvärinen, Pekka; Jurvelius, Juha; Karels, Aarno; Sarvala, Jouko; Valkeajärvi, Pentti; Karjalainen, Juha Marjomäki, T. J., Auvinen, H., Helminen, H., Huusko, A., Huuskonen, H., Hyvärinen, P., Jurvelius, J., Karels, A., Sarvala, J., Valkeajärvi, P., & Karjalainen, J. (2024). Vendace populations on the life table : between-lake variation and the association between early life and mature survival and growth. International Journal of Limnology, 60, Article 11. https://doi.org/10.1051/limn/2024011 2024 RESEARCH ARTICLE Vendace populations on the life table: between-lake variation and the association between early life and mature survival and growth Timo J. Marjomäki 1,* , Heikki Auvinen 2 , Harri Helminen 3 , Ari Huusko 2 , Hannu Huuskonen 4 , Pekka Hyvärinen 2 , Juha Jurvelius 2 , Aarno Karels 5 , Jouko Sarvala 6 , Pentti Valkeajärvi 2 and Juha Karjalainen 1 1 University of Jyväskylä, Department of Biological and Environmental Science, PO-BOX 35, 40014 Jyväskylän yliopisto, Finland 2 Natural Resources Institute, Finland 3 Centre for Economic Development, Transport, and the Environment, Turku, Finland 4 University of Eastern Finland, Department of Environmental and Biological Sciences, PO-BOX 111, 80101 Joensuu, Finland 5 South Karelian Fisheries Centre, Karels Oy 6 University of Turku Received: 18 January 2024; Accepted: 18 June 2024 Abstract –The vital rates related to reproduction and survival dictate the resistance and persistence of a population under perturbations. Freshwater fishes perform high levels of phenotypic plasticity thus these rates may differ widely between populations and temporally within a population. Knowledge of their ranges enables understanding the scope of population persistence and predicting the effects of environmental stressors. Time series of vendace (Coregonus albula) catch samples from 22 lakes were applied to estimate the lake-specific average length-at-age and survival in mature age groups (mS). Assuming an age-atmaturity of 2 yr and a constant length–fecundity relationship, survival from spawning to age 1 (firstS) and 2 (premS, prematurity survival) were estimated using a life table assuming a stable state. The average length at age 2 yr (L2) varied two-fold between populations, <100 –>200 mm, and the estimated fecundity approximately eight-fold. Also, mS varied considerably, <10–70%a 1 . L2 and mS were positively associated. The premS estimate varied ∼30-fold among lakes, <0.01 –>0.2% per 2 yr, being highest in populations with low L2 and fecundity combined with low mS. The range of firstS estimate was even higher, 0.01–2%. This high between-lake variability seems to occur especially after hatching during the first summer. Its level is set by the factors external to the population, e.g., the abundance of key predators. Persistence with low early life survival is possible because of the wide scope of compensation in the sizeand fecundity-at-age and mS. Early life survival is expected to decrease due to climate change while the compensation has its limits, increasing the risk of local extinctions. Keywords: Early life stages / global warming / life history / mortality / persistence / population regulation 1 Introduction A sufficient level of net reproduction rate (e.g.,Krebs, 1985) is essential to the persistence of populations. In the long run, this rate depends on the combination of several vital rates: survival in prematurity and mature life stage as well as fecundity-at-age and quality of the sexual products. In fish populations, since the age of recruitment to fishing, average survival can be strongly reduced from its natural level by fishing mortality. To maintain population persistence, corresponding compensatory change is required in other vital rates. On the other hand, the average level of early life survival is determined by various environmental factors of the ecosystem, e.g., oxygen concentration at egg incubation sites and abundance of predators during egg, larval and juvenile stages. These factors may vary considerably between ecosystems. They are not temporally constant either, but may, in addition to their unpredictable short-term, interannual variation, change more permanently directionally due to ecosystem changes, e.g., due to eutrophication or climate change, and/or perform autocorrelated variability dominated by long-period cycles, “coloured”spectrum variability (Steele and Henderson, 1984; Vasseur and Yodzis, 2004), e.g., the density fluctuations of a long-lived key predator. To maintain persistence, the effects of these differences and changes on average early life survival 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 *Corresponding author: timo.j.marjomaki@jyu.fi Int. J. Lim. 2024, 60, 11 ©T.J. Marjomäki et al., Published by EDP Sciences, 2024 https://doi.org/10.1051/limn/2024011 Available online at: www.limnology-journal.org This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. must be successfully compensated at later life stages by phenotypic plasticity (Nussey et al., 2007) and/or genotypic changes in some characteristics affecting the vital rates. Vendace (Coregonus albula) is renowned for strong compensatory density-dependent variability of its vital rates in different life stages, e.g., prerecruitment survival (Valtonen and Marjomäki, 1988;Auvinen, 1988, 1994;Helminen and Sarvala, 1994; Salmi and Huusko, 1995; Marjomäki, 2003), growth rate (Järvi, 1919,1920;Lind, 1976;Hamrin and Persson, 1986;Viljanen, 1986;Auvinen, 1994;Marjomäki and Kirjasniemi, 1995;Salmi and Huusko 1995) and consequently age-specific fecundity (Karjalainen et al., 2016) as well as post-recruit natural mortality (Marjomäki et al., 2021a). Consequently, vendace populations are harnessed with a high level of resistance, the capacity to withstand disturbances, and resilience, the capacity to recover to its original state after perturbation (Westman 1978;Webster et al., 1983), creating the scope of compensation against high levels of fishing mortality (but see Sarvala et al., 2020) or changes in early life survival. The vital rates are thus strongly regulated by the actual population density at different life stages (e.g., review by Marjomäki, 2003;Marjomäki et al., 2014) within the preconditions set by the typical levels of the factors external to the vendace population within the ecosystem. These external factors are far from being constant temporally. In addition to strong inter-annual variability, they may induce permanent changes in the level of vendace population abundance or shifts between alternating low and high abundance regimes (e.g., Valkeajärvi and Marjomäki, 2004). Some of these factors may even be partly regulated by vendace density, e.g., perch population dynamics (e.g.,Marjomäki et al., 2021a) or predation by brown trout (Hyvärinen and Huusko, 2006). Data on the vital rates of vendace at the life stages that are potentially targeted by fishing, typically from the late juvenile stage at the age of 1 yr (from spawning to the autumn after the first growing season), are available from annual catch samples in many lakes. However, early life survival has been quantified to a lesser extent because its quantification requires data on population density indices, which are less often available. When the density data has been available, quantitative studies have often concentrated on analysing the form of spawner– recruit relationship, the level of density-dependent compensation and the level and causes of short-term, inter-annual variability in early life stage survival, i.e., recruitment variability, typically under the assumption of temporally stable average spawner–recruit-relationship (e.g.,Valtonen and Marjomäki, 1988;Viljanen, 1988a, b;Auvinen, 1988, 1994;Marjomäki, 2003; Marjomäki et al., 2014). Much less attention has been paid to the average level of early life survival itself and its periodical variability within a lake (Marjomäki et al., 2021a) or differences in average early age survival between different lakes (Karjalainen et al., 2000, Marjomäki 2003). Relevant questions in this context are to what extent the differences in average early-life survival are or could be compensated by the later-life vital rates and what is their scope of compensation that determines the scope of persistence of the population. Marjomäki et al. (2021a) applied retrospective life table analysis to quantify and compare the vital rates in one vendace population between periods of very high and low abundance regimes, finding a large difference in average prematuration survival and consequently large compensatory changes in lengthand fecundity-at-age as well as survival at the mature life stage. The present study aims to expand that analysis to several vendace populations, quantifying the average level of their vital rates and in some populations even separately for periods of very sparse and abundant vendace population as well as the association between the rates. The geographical range of the lake ecosystems reaches from 60°30' N, in the southern edge of the boreal zone, to over 66°N, close to the Polar Circle, their trophy from ultra oligotrophic to meso-eutrophic and their vendace population abundance from sparse to abundant, providing a possibility to estimate the full scope of the variability of vendace vital rates, especially its seldom studied early life survival. 2 Material and methods The vendace data were collected from 22 lakes or basins of large lakes (Fig. 1,Tab. 1) in Finland (one lake located presently in Russia but part of Finland during the data collection). The lakes were selected based on the sufficient length of the time series, the minimum being 9 yr. The oldest data were collected in the first part of the 20th century, 1908 at the earliest, but most of the data stem from the period from the 1970s to the 2000s (Tab. 2), the newest from 2022. Random samples have been collected from vendace catches either close to spawning in autumn, typically October, and/or during winter fishing season (under ice seining, typically from January to April). In most lakes, samples were collected from seine catches but, in some lakes consistently and in some lakes occasionally from trawl and gill nets (Tab. 2). All samples were accepted for the analysis. In different years they may stem from different fishers and basins within the lake which may affect the lake-specific variance of the variables (e.g.,Lind, 1976), but this is considered negligible compared to the actual between-year variability. The mesh size of the gear was usually set so that the size and age structure of the catch represented well those in the population, except for the youngest fish (age 1) which may have been underrepresented because of being very small in some years (especially trawl catches), and in autumn because of not gathering in the spawning and fishing sites in the same proportion as the mature individuals. In Lake Vesijärvi, the samples were partly selected in 3 yr, thus not thoroughly random (Järvi, 1947). Still, they were accepted for the analysis because the average survival estimate was not very sensitive to data from those years. Fish were measured for their total length (L, mm), except for the samples by Järvi (1942a,b,1947) where fork length (FL) was measured. FL was converted to total length by multiplying it by 1.1 (e.g.,Keränen and Lind, 1973; Czerniejewski and Filipiak, 2002;Fiszer et al., 2012). The age was determined from scales and the sex of mature individuals was visually determined. For each lake, the grand average over the annual estimates of average length was calculated for each age group. For Lake Pyhäselkä, back-calculated length was used instead of the observed because several samples were not collected at the end of the growing season. The oldest age groups, typically 4–6yr Page 2 of 14 T.J. Marjomäki et al.: Int. J. Lim. 2024, 60, 11 onwards, with a low number of annual estimates (typically <5) wereomitted,becausetheseweremostlyavailableonlyforslowgrowing abundant year classes. The von Bertalanffy (1938) growth function was fitted to the age-specific grand averages of length starting from age 1 by the iterative nonlinear least squares method assuming normally distributed residuals. The lake-specific parameter estimates are given in Table 2. The prominent fluctuations in the population abundance (Tab. 2) were interpreted roughly based on the annual average length of 2 yr old individuals (L2). The growth of vendace is strongly density-dependent (e.g.,Järvi 1919,1920;Viljanen, 1986) and the growth of both the young-of-the-year and older subpopulations is affected by the density of both (Marjomäki and Kirjasniemi, 1995). Thus, the temporal variability of L2 bears information on the density, relative to lake-specific resources, of the whole population during a preceding 2 yr period. The rough growth-based interpretation of density fluctuations is in good agreement with vendace density indices in the lakes where these are available (e.g., Helminen et al., 1993;Marjomäki and Kirjasniemi, 1995;Salmi and Huusko 1995;Valkeajärvi et al., 2012;Marjomäki et al., 2021a,b). The age of maturity for every female vendace was assumed to be 2 yr (e.g.,Järvi, 1919,1920;Lind, 1976,Viljanen, 1986; Karjalainen et al.,2016). It was assumed that a general average length-at-age (L i )–fecundity-at-age (f i , eggs per individual) equation, fi¼0:000583L3:0626 i; Fig. 1. Study lakes. Page 3 of 14 T.J. Marjomäki et al.: Int. J. Lim. 2024, 60, 11 applies to all populations (data from Karjalainen et al., 2016; Marjomäki et al., 2021a). It was further assumed that 50% of the eggs were females. The average fecundity-at-age of female eggs was estimated for each population using the von Bertalanffy model -predicted average length-at-age. For each lake, the age distributions of all seasonal (autumn and/or winter) vendace samples were standardised to percentages and the age-specific percentages were averaged. Then, these seasonal percentages were averaged over the study period. These grand average percentages were logarithmised and the constant total mortality was estimated using linear regression, lnðpiÞ¼aþZi; mS ¼expðZÞ; where p i = age-specific grand average percentage, Z= instantaneous total mortality (a 1 ), i= age, minimum 2 yr, and mS = annual proportional survival for mature age groups, age 2 and older). In Southern Konnevesi, age group 2 was not used in the estimation due to low catchability (Marjomäki et al., 2021a). The oldest age groups with only a few observations and p i <1% (typically from age 4–6 onwards, as for length above) were omitted to minimise bias (see Marjomäki et al., 2023) in the mortality estimate. Assuming a population steady state, the prematurity survival (premS) from fertilisation to first spawning in the second autumn at age 2 yr was estimated for each population based on the fecundity-at-age estimates of female eggs, and constant annual survival in mature age groups (mS) using a static life table (e.g.,Krebs, 1985) as described in detail by Marjomäki et al. (2021a). Further, assuming that the survival for the second year of life equals mS, the first-year survival (firstS) was estimated by, firstS ¼premS=mS: A comparison between the life-table-based steady-state estimates of first-year mortality (Z) and direct estimates from certain lakes is presented in Appendix 1. Lakes Southern Konnevesi and Puruvesi experienced long both very abundant and extremely sparse population periods. For those lakes, all parameters were estimated separately for these extreme states, in addition to the whole study period, to illustrate the within-lake differences in population parameters during different abundance regimes. Details for Southern Konnevesi are given in Marjomäki et al. (2021a). 3 Results The whole study period average length at age 2 yr (L2) for different populations had a very wide range, from less than 100 mm in Yli-Kitka to over 200 mm in Pyhäjärvi southwest Finland (Fig. 2a), the average being 141 mm. L2 was lower in the northernmost populations than in the southernmost ones. However, the lake-specific range of annual average length at age 2 yr was quite wide as well in many lakes, e.g., Kerojärvi, Table 1. The morphometrics of the study lakes, range of their total phosphorus concentration (Tot. P) and colour content for the water sampling period. For the period, the first and the last year of water samples are given, but sampling did not occur necessarily every year. The selected water samples were taken during winter and from the middle of the water column, if available. N.A. = not available, * Values from a much more recent period than the vendace study period. Lake Area km 2 Ave. depth m Max. depth m Tot. P ml –1 Color content mg Ptl –1 Period, depth Yli-Kitka 237 6.6 41 2–10 5–20 1974–1992, 15 m Kiitämö 19 6.7 22 4 5 1973, 10 m Kirpistö 21 N.A. 17 6 14 1973, 1 m, 10 m Kuusamo 47 3.4 18 6–17 15–30 1979–1991, 10 m Muojärvi 55 5.4 36 5–10 10–20 1976–1992, 10 m Kostonjärvi 44 5.1 17 8–28 35–59 1979–1987, 5 m Kerojärvi 21 N.A. 17 8–11 29–40 1973–1985, 1 m Irnijärvi 32 5.6 24 4–18 15–60 1973–1992, 10 m Oulu 887 7.6 35 5–28 30–80 1974–2006, 15 m Ylä-Keitele 79 8.8 66 3–10 30–50 1991–2004*, 10 m Keski-Keitele 327 6.6 33 5 25–38 1965–1970*, 10 m Southern Konnevesi 122 12.5 57 4–715–30 1984–2009, 25 m Onkamo 32 3.6 12 6–21 10–25 1980–2012, 1 m Pyhäselkä 361 8.8 67 5–11 50–110 1990–2009, 30–34 m Puruvesi 416 8.8 61 3–85–10 1973–2017, 15 m Pyhäjärvi SE Finland 248 7.8 27 3–75–20 1984–2017, 13–15 m Puula 331 9.2 62 2–510–32 1984–2022, 25 m Päijänne, Tehinselkä 250 >16.0 61 7–11 24–40 1981–1991, 25 m Southern Saimaa 621 8.4 70 4–10 25–45 2001–2021, 20 m Vesijärvi 108 6.1 40 20–25 10–20 1967–1970*, 15 m Pyhäjärvi SW Finland 155 5.5 26 10–34 10–40 1978–2021, 15 m Pyhäjärvi Kar. Isthm. 68 N.A. 30 N.A. N.A. Page 4 of 14 T.J. Marjomäki et al.: Int. J. Lim. 2024, 60, 11 Table 2. The vendace sampling period (L = length, mS = mature survival) and reference to more detailed material and methods, gear (S = seine, G = gill net, T = trawl), the von Bertalanffy growth equation parameter estimates and trends in population abundance as interpreted from size at age (þ= increasing, = decreasing, ! = strongly). varia =Keränen and Lind, 1973, 1975;Ellonen and Lind, 1974;Hyrynkangas and Lind, 1976;Hanski and Lind, 1977,1978,1980,1984,1985,1987 a,b,c,1988,1989,1990,1991,1992;Myllylä and Lind, 1982,1983; Piekkola and Lind, 1980. Lake Period (samples) Data Gear Von bertalanffy equation Trend Lmax K t0 Yli-Kitka 1973–1978, 1980–1992 varia;Salmi and Huusko, 1995 S 234 0.13 –2.22 no Kiitämö 1974–1978, 1980–1992 varia;Salmi and Huusko, 1995 S 160 0.47 –0.71 þ Kirpistö 1973–1978, 1980–1992 varia;Salmi and Huusko, 1995 S 157 0.55 –0.48 no Kuusamo 1973–1978, 1980–1992 varia;Salmi and Huusko, 1995 S 178 0.75 –0.21 – Muojärvi 1973–1978, 1980–1992 varia;Salmi and Huusko, 1995 S 140 0.56 –0.54 – Kostonjärvi 1978, 1980–1992 varia;Salmi and Huusko, 1995 S 179 0.30 –1.07 –! Kerojärvi 1976–1978, 1980–1992 varia;Salmi and Huusko, 1995 S 229 0.25 –1.11 –! Irnijärvi 1973–1978, 1980–1990, 1992 varia;Salmi and Huusko, 1995 S 198 0.26 –1.12 þ! Oulu 1973–1984, 1986–2017 varia;Salmi and Huusko, 1995 S, T 166 0.57 –0.53 þ! Ylä-Keitele 1908, 1911–1941 Järvi, 1942a S 169 0.57 –0.55 –þ Keski-Keitele 1908, 1917, 1919–1931, 1941 Järvi, 1942a S 165 0.45 –0.71 no Southern Konnevesi 1984–2009 Marjomäki et al., 2021a S, G 182 0.80 –0.07 –!þ! Onkamo 1980–1993, 1995–2012 Auvinen et al., 2000, unpubl. S 205 0.50 –0.82 no Pyhäselkä 1989–1990, 1992–2009 A. Huuskonen, unpubl. T 197 0.67 –0.39 þ! Puruvesi 1973–1990, 1992–2017 varia; Auvinen, unpubl. S 480 0.08 –2.78 –!þ! Pyhäjärvi SE Finland 1978–1992, 1994–2000, 2004, 2008, 2010, 2014 Auvinen et al., 1987, unpubl. S 229 0.54 –0.55 –! Puula 1984–2022 Marjomäki et al., 2014, unpubl. S 152 0.72 –0.30 no Päijänne, Tehinselkä L 1981–2009, mS 1984–2010 Valkeajärvi et al., 2012 T 233 0.84 0.06 no Southern Saimaa 2001–2021 A. Karels, unpubl. T 206 0.52 –0.29 þ! Vesijärvi 1914–1916, 1919, 1920, 1929, 1940, 1943, 1945 Järvi, 1947 S, G? 237 0.97 0.15 – Pyhäjärvi SW Finland 1978, 1980–2020 Sarvala et al., 2020 S 288 0.46 –0.57 –þþ Pyhäjärvi Karelian Isthmus 1913–1917, 1919, 1920, 1922–1938 Järvi, 1942b S, G 231 0.61 –0.10 –þ Page 5 of 14 T.J. Marjomäki et al.: Int. J. Lim. 2024, 60, 11 Irnijärvi, S. Konnevesi, Puruvesi and Pyhäjärvi southeast Finland, that have presumably (Tab. 2) experienced wide density variation during the study period. In S. Konnevesi and Puruvesi, abundant and sparse population regime-specific averages of L2 differed as much as over 70mm (Fig. 2a). Assuming a general length–fecundity relationship for all populations, the whole time-series average fecundity at age 2 varied almost by an order of magnitude between lakes from 370 female eggs in Yli-Kitka with the lowest L2 to 3300 female eggs in Pyhäjärvi SW Finland with the highest L2 (Fig. 2a). The difference in average fecundity at age 2 was 4–5fold between the abundant and sparse population regimes in Puruvesi and S. Konnevesi. Also, the average proportional annual survival of mature fish (mS) varied considerably between lakes (Fig. 2b) being lowest in intensively fished populations of Pyhäjärvi SW Finland and certain Northern Finnish lakes and highest in the fast-growing population of Vesijärvi with very sparse population and hence low fishing mortality. The geometric mean of lake-specific mS estimates was 27%. There was also a considerable difference in mS between the abundant population regime with commercial vendace fishing and the sparse regime with very low fishing mortality in Puruvesi and S. Konnevesi. The general association between lake-specificmS and growth-related variables L2 (Fig. 3) and K was not a decreasing one, but rather increasing (Spearman; mS vs. L2, r= 0.41, p= 0.056; mS vs. K, r= 0.56, p= 0.007). The life-table-based estimate of average premature S (two first years, premS) required for population steady state varied between lakes enormously from as high as over 0.2% in YliKitka to less than 0.01% in Vesijärvi, a 30-fold difference (Fig. 4a), the geometric mean being 0.05% (mean Z= 7.5). The within-lake differences between the sparse and abundant regimes in S. Konnevesi and Puruvesi were high as well, about an order of magnitude. The range of the survival estimate for the first year (firstS, from spawning to the first autumn before the onset of any considerable fishing mortality) was even higher, more than two orders of magnitude, from about 2% in Yli-Kitka to as low as about 0.01% in Vesijärvi, (Fig. 4b). The geometric mean first-year survival was 0.2% (mean Z= 6.2). The large between-population variability in premS required for population steady state for certain fixed mS (Fig. 4) results from large differences in size and thus fecundity between populations. While the mS estimates e.g., in Päijänne (Tehinselkä), S. Konnevesi and Puula were at a similar level, much lower premS is required for steady state in Päijänne than in the latter two because of the large size-at-age (Fig. 5). The difference in size-at-age also is the reason for the premS required in Pyhäjärvi SW Finland being much less than in Yli-Kitka even though the mS in Pyhäjärvi is lower than that in Yli-Kitka (Figs. 4a and 5). The proportional variability in prematuration S due to differences in mature survival at certain fixed L2 was considerable, too (Fig. 5). 4 Discussion The quite exceptional density-dependent variability in growth that is not limited to early life stages only, and thus fecundity (e.g.,Lind, 1976;Viljanen, 1986;Helminen et al., 1993;Marjomäki and Kirjasniemi, 1995;Marjomäki, 2003; Karjalainen et al., 2016;Marjomäki et al., 2021a) facilitates a wide scope of compensation within a typical vendace population. Compensatory reserve is defined as the “sum of all density-dependent phenomena that affect the size and growth of individuals in a population”(Goodyear, 1980), referring mostly to the level of intra-specific food competition, and it reflects the degree of population resilience to perturbations (Youn, 2017). Because of considerably smaller size-at-age at higher densities and therefore likely higher predation mortality (Marjomäki et al., 2021a), some scope of compensation seems also to take place in the natural mortality, adding to the capacity for resilience. The scope of Fig. 2. a) The von Bertalanffy model -calculated average length at maturity (age 2, L2) and fecundity (female eggs) estimates for different vendace populations put in the order of their location from north to south. The bars show the minimum and maximum annual values. b) The constant survival estimates for mature (2 yr and older, mS) vendace. The symbols þand –show the averages for abundant and sparse population regimes, respectively, in Lake S. Konnevesi and Puruvesi. Page 6 of 14 T.J. Marjomäki et al.: Int. J. Lim. 2024, 60, 11 compensation in a population is further facilitated by the considerable compensatory density dependence of first-year survival in vendace (e.g.,Auvinen 1988;Valtonen and Marjomäki, 1988;Viljanen, 1988a;Helminen and Sarvala, 1994;Salmi and Huusko, 1995;Helminen et al., 1997; Marjomäki, 2003,2004;Marjomäki et al., 2014). All these phenomena make the vendace populations very resilient, resistant, and consequently persistent towards high and variable mortality at different life stages, as well as fishing mortality, the typical cause of human-induced perturbation. The average size-at-age is an important variable for determining the average fecundity-at-age and consequently, the estimate of prematurity survival (premS) from a lifetable. In our data, the average size at age 2 yr (L2) decreased with the increase in latitude. This can be partly because of the latitudinal trend in the temperature and the length of the growing season. Lind (1976) and Viljanen (1986 review and references therein) noted the same but suggested that differences in lake productivity and vendace population density are also responsible for the observed latitudinal differences. The same conclusion holds for our data. The phosphorus concentration in the southern lakes was typically higher than that in the northern lakes. Further, the vendace density in the northern Finnish lakes has very likely been higher in proportion to resources than that in the southern Finnish lakes. The severe prolonged vendace population decline at the turn of the 1980s and 1990s, thus within our study period in many lakes, occurred in central and southern Finland while the populations in northern Finland remained abundant (Valkeajärvi et al., 2002). Growth of vendace equally fast as the fastest in southern Finland has also been recorded in northern Finland: in the oligotrophic–mesotrophic Lake IsoVenejärvi, 80 km north of our northernmost study lake, YliKitka, the average length at age 2 was about 200 mm (Alanne, 2004). A long day length with a higher level of illumination at night, facilitating feeding and a lower, and thus more suitable epilimnion temperature during the summer, may compensate for the lower temperature sum during the growing season and food production in higher latitudes to a certain extent. One might expect based on the general theory of exploitation that the relationship between mature survival (mS) and length-at-age is a decreasing one: the lower the average survival, the lower the average population density and the faster the growth, at least within a population. This was not the case in our data of average lake-specific mature S and L2 estimates from several lakes but rather the opposite. The relationship and causality appear more complicated: First, the combination of high mature S and large L2 is typical for the population experiencing a sparse population regime, the reason for which is a period of very low prematuration S induced by environmental factors, e.g., sparse periods in S. Konnevesi (Marjomäki et al., 2021a), Puruvesi and Vesijärvi. During the sparse population regime, the fishing mortality is very low because vendace fishing is not profitable, while the natural adult mortality may also be low due to the large size of fish (Marjomäki et al., 2021a), consequently high mature S. Simultaneously, the strongly density-dependent size-at-age and fecundity are maximal due to minimal intra-population competition for food. Together high mature S and length-atage, the compensatory capacity being in full use, can compensate for the low prematuration S and facilitate the persistence of the population. But there is a maximum for the Fig. 3. The lake-specific von Bertalanffy model -predicted average length at age 2 (L2) vs. the annual constant mature survival (mS) and instantaneous mortality (Z, purple axis) estimate for different vendace populations. Page 7 of 14 T.J. Marjomäki et al.: Int. J. Lim. 2024, 60, 11 compensatory capacity of growth, fecundity and mature survival, and the lake ecosystems with even lower prematuration S, dictated by environmental variables, cannot appear in the data because their vendace populations must have gone extinct if ever established in the first place. At the other extreme, very low mature S, mostly due to the very intensive fishing starting already in the first winter has kept the density of Pyhäjärvi SW Finland vendace at so low level that very fast growth was possible (Sarvala et al., 2020). Meso–eutrophy of this lake and its location in southern Finland may also have facilitated fast growth there. However, during a period of very intensive fishing (extremely low mature S, << 10%) with simultaneous poor environmental conditions for newly hatched larvae (lower than average prematuration S), the population collapsed as there was no compensatory reserve available in the growth and fecundity of fish. Fig. 4. a) The average prematurity survival estimates (premS, the first 2 yr) b) the average first-year survival estimates (firstS) vs. constant mature survival estimates for different vendace populations and those during the abundant (þ) and sparse (–) population regimes in lakes Southern Konnevesi and Puruvesi. The grey dashed lines are the premS and firstS that would be required for a stable state in different levels of mature survival (mS) for Lake Vesijärvi vendace with the observed fast growth. Page 8 of 14 T.J. Marjomäki et al.: Int. J. Lim. 2024, 60, 11