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. 2023 Apr 14;16(4):895-910.
doi: 10.1111/eva.13544. eCollection 2023 Apr.

Eco-evolutionary factors that influence its demographic oscillations in Prochilodus costatus (Actinopterygii: Characiformes) populations evidenced through a genetic spatial-temporal evaluation

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Eco-evolutionary factors that influence its demographic oscillations in Prochilodus costatus (Actinopterygii: Characiformes) populations evidenced through a genetic spatial-temporal evaluation

Sandra Ludwig et al. Evol Appl. .

Abstract

The human activity impact on wild animal populations is indicated by eco-evolutionary and demographic processes, along with their survival and capacity to evolve; consequently, such data can contribute toward enhancing genetic-based conservation programs. In this context, knowledge on the life-history and the eco-evolutionary processes is required to understand extant patterns of population structure in Prochilodus costatus a Neotropical migratory fish that has been threatened due to loss and fragmentation of its natural habitat since 1960s promoted by the expansion of hydroelectric power plant construction programs. This study evaluated the eco-evolutionary parameters that cause oscillations in the demography and structure of P. costatus populations. An integrated approach was used, including temporal and spatial sampling, next-generation sequencing of eight microsatellite loci, multivariate genetic analysis, and demographic life-history reconstruction. The results provided evidence of the complex interplay of ecological-evolutionary and human-interference events on the life history of this species in the upper basin. In particular, spawning wave behavior might have ecological triggers resulting in an overlapping of distinct genetic generations, and arising distinct migratory and nonmigratory genetic patterns living in the same area. An abrupt decrease in the effective population size of the P. costatus populations in the recent past (1960-80) was likely driven by environment fragmentation promoted by the construction of the Três Marias hydropower dam. The low allelic diversity that resulted from this event is still detected today; thus, active stocking programs are not effective at expanding the genetic diversity of this species in the river basin. Finally, this study highlights the importance of using mixed methods to understand spatial and temporal variation in genetic structure for effective mitigation and conservation programs for threatened species that are directly affected by human actions.

Keywords: Bayesian Skyline Plot; adegenet; evolution; historical demography; spawning waves.

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Conflict of interest statement

None declared.

Figures

FIGURE 1
FIGURE 1
Distribution of the genetic patterns (= k‐clusters) of Prochilodus costatus populations from the upper São Francisco River basin, when comparing: (a) the hydrological years—SFD: 2013/2014, 2014/2015, 2015/2016; SFU: 2014/2015, 2015/2016, 2016/2017; PA2: 2013/2014, 2014/2015, 2015/2016; PA1: 2014/2015; 2015/2016; and PAO: 2014/2015, 2015/2016. (b) the areas—SFD: São Francisco River downstream; PA1: Pará River section 1; PA2: Pará River section 2; SFU: São Francisco River upstream; and PAO: Paraopeba River; and (c) the periods (migratory—PA2, SFU 2015/2016/2017, PAO, SFD 2016/2017; nonmigratory—SFD 2013/2014/2015/2016, SFU 2013/2014, PA1). The graphs show the membership probability of each individual (represented by one column) to be clustered in its respective genetic pattern (see legend of the colors). The arrows indicate individuals that presented divergent patterns. The sampling details are presented in Table 1
FIGURE 2
FIGURE 2
Past demographic oscillations of Prochilodus costatus effective population size (Ne) assessed by the Bayesian Skyline Plot (using VarEff R package) in the last 100 years (500 generations). The arrows indicate the bottleneck events. The estimated past Ne is shown for P. costatus populations in (a) PA1, (b) PA2, (c) PAO, (d) SFD, (e) and SFU, and (f) shows the estimated Ne to all populations combined. Note that the y‐axis (Ne) has a different scale in each plot

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