Environmental determinants of perch (Perca fluviatilis) growth in gravel pit lakes and the relative performance of simple versus complex ecological predictors

Abstract

Growth of fish is an important contributor to individual fitness as well as fish production. Explaining and predicting growth variation across populations is thus important from fundamental and applied perspectives, which requires knowledge about the ecological factors involved in shaping growth. To that end, we estimated environment‐dependent von Bertalanffy growth models for 13 gravel pit lake populations of Eurasian perch (Perca fluviatilis) from north‐western Germany. To identify the main drivers of perch growth, we evaluated the performance of 16 different biotic or abiotic lake variables in explaining growth variation among lakes. In addition, we compared growth predictions from the best‐performing model incorporating “complex” variables that require intensive sampling effort, with a model using only “simple”, easily measurable lake variables (e.g. shoreline development factor). The derivation of a simple model aimed at future applications in typically data‐poor inland fisheries, predicting expected growth potential from easily measurable lake variables. A model combining metabolic biomass of predators, maximum depth and shoreline development factor performed best in predicting perch growth variation across gravel pits. All three parameters in this model were positively related to perch growth. The best‐performing simple model consisted only of the shoreline development factor. Length‐at‐age predictions from both models were largely identical, highlighting the utility of shoreline development factor in approximating growth potential of perch in gravel pits similar to our study lakes. Our results can be used to inform fisheries management and restoration efforts at existing or newly excavated gravel pit lakes.

Lake depth alters the trajectory of ontogenetic niche shifts in Eurasian perch (Perca fluviatilis) in small lakes

“…Larger and deeper lakes with more heterogeneity in depth are associated with greater biomass of large fishes (Holmgren & Appelberg, 2000), including perch (Blindow et al, 1993;Persson et al, 1991), most likely because the perch can exploit pelagic prey more efficiently than prey that refuges in littoral habitat (Eklöv & Diehl, 1994). Similarly, among the full set of gravel pit lakes that we sampled, habitat heterogeneity, including shoreline development factor and maximum depth, were important predictors of perch growth (Höhne et al, 2020) lakes with more shallow-water habitat suggests that reduced piscivory in shallower lakes may be a mechanism driving a reduction in growth rates as perch feed longer on abundant macroinvertebrates.…”

“…A large gravel pit lake, Meitzer See, with a surface area of 19.5 ha, was, therefore, designated as a reference lake. This lake was not sampled for this analysis, but it was one of a full set of 16 lakes sampled for a wider project (see Höhne et al, 2020;Matern et al, 2019Matern et al, , 2022Nikolaus et al, 2021;Radinger et al, 2023). On this reference lake, 16 nets were deployed to correspond with the standards for 20 ha lakes.…”

“…Gill nets were then placed according to a depth-stratified random sample design. Further details can be found in Höhne et al (2020) and Matern et al (2019). Nets were 40 m in length with mesh sizes of 5, 6.25, 8, 10, 12.5, 15.5, 19.5, 24, 29, 35, 43, 55, 70, 90, 110, and 135 mm and were placed overnight.…”

A new charophyte habitat with a stabilized good ecological potential of mine water

“…The multi-seasonal results proved also that the pond fed with water from the opencast mine drainage system can be safely used for utility purposes, one of which is recreational fishing. All changes in trophic state or ecological status have an impact on the habitat conditions and the growth rate of many fish species, including those in artificial lakes 42 , 43 . Therefore, the stabilization of low trophic level and maximum ecological potential is an inspiration for further research on fish habitat in such ponds.…”

Projected effects of ocean warming on an iconic pelagic fish and its fishery

“…Note that L ∞,(i,age) is not only fish-specific but also varies with age to account for the specific thermal history experienced by each i fish. The temperature effect is incorporated through the accumulated temperature (sum of the average temperature at 00:00 h of each day) experienced by the individual along with its life history over the L ∞ growth parameter considering a linear relationship since similar dependencies in growth model parameters have been suggested 69 : where L ∞,0(i) is the intercept parameter for each i fish, L∞,slope is the regression slope and CummTemperature age is the accumulated temperature from birth to age .…”