The Temporal Dynamic of Young Fish Communities in the Water Bodies of the �Dnipro-Orylskiy� Nature Reserve

Bondarev, D.; Fedyushko, M.; Gubanova, N. L.; Жуков, О. В.

doi:10.32819/020017

Cited by 1 publication

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“…To be successful, living organisms must coordinate their life cycles. Such coordination is the result of long evolutionary processes, and the the oscillatory processes in the environment are a regulatory factor (Bondarev et al, 2020;Fedushko et al, 2021). The ability to receive information from environmental factors to correct the rate of various stages of the life cycle is due to the regular repeatability of the rhythmic regimes of the environment.…”

Section: Discussionmentioning

confidence: 99%

Multiscale oscillations of the annual course of temperature affect the spawning events of rudd (Scardinus erythrophthalmus)

Кунах

Bondarev

Gubanova

et al. 2022

Regul. Mech. Biosyst.

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Identifying climate impacts on ecosystems and their components requires observing time series of sufficient length to ensure adequate statistical power and reasonable coverage of the historical range of variability inherent in the system. The complexity of the hierarchy of climate effects reflected in temporal patterns in time series creates a need to be accurately modeled. The life cycle phenomena of living organisms, including fish spawning, have the character of one-time or time-limited events in time. An approach to finding the relationship between continuous components of time dynamics of environment properties and life cycle events of living organisms was proposed. This approach allowed us to evaluate the role of temperature patterns in the phenology of spawning rudd (Scardinus erythrophthalmus Linnaeus, 1758) in the Dnipro River basin water bodies. The atmospheric temperature time series may be decomposed into the following components: trend, annual cycle, episodic component, harmonic component, extreme events, and noise. Systematically low water temperatures at the beginning of the spawning period were observed in the Protoka River system and the Obukhov floodplain, and systematically elevated temperatures were recorded in the Dnipro River. The annual temperature dynamics was shown to be presented as a composition of oscillatory processes of different scale levels. The sinusoidal trend was previously extracted from the temperature series data. The average annual temperature, amplitude, and phase shift were calculated on the basis of the sinusoidal regression model. The residuals of the sinusoidal trend were processed by means of redundancy analysis with variables derived from symmetric distance-based Moran’s eigenvector maps as explanatory predictors. A set of 104 orthogonal dbMEM variables was extracted from the annual time series. These temporal variables were divided into the broad-, medium-, and fine-scale components. The parameters of temperature dynamics and biotope type are able to explain 51–72% of variability of spawning event. The time of spawning in water bodies corresponds to the time of spawning start: the earlier spawning starts, the earlier it ends. The duration of the spawning season is influenced by the patterns of different scale levels, as well as the amplitude and shift of phases. In this case, the duration of spawning in all water bodies does not differ. Spawning temperature depends on medium- and fine-scale temperature patterns, but does not depend on the characteristics of the sinusoidal annual trend. The annual temperature variation has been shown to be such that it can be decomposed into a sinusoidal trend, patterns of a multiscale nature, and a random fraction. Over the time range studied, the trend of increasing mean annual temperature was not statistically significant for spawning events. The sinusoidal trend explains 78.3–87.6% of the temperature variations and depends on the mean annual temperature, the amplitude of temperature variations during the year, and the earlier or later seasons of the year. Amplitude and phase shift play a role in describing spawning phenology. The residuals of the sinusoidal trend have been explained using dbMEM variables. This variation was decomposed into large-scale, medium-scale, and small-scale components. Winter and spring temperature fluctuations prior to spawning initiation had the greatest effect on spawning. Water temperature determines the lower possible limit for the start of spawning, but the actual start of spawning is determined by the preceding temperature dynamics. The results of the study have implications for understanding the dynamics of fish populations and assessing the influence of environmental conditions on the harmonization of the various components of ecosystems.

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Section: Discussionmentioning

confidence: 99%