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Global trends, biases and gaps in the scientific literature about freshwater fish eggs and larvae.

Cleide CarnicerLuciano Benedito LimaFernando Mayer PeliciceDilermando Pereira Lima-Junior
Published in: Journal of fish biology (2022)
Syntheses of knowledge are important to reveal trends, biases and gaps in the scientific literature, indicating main data shortfalls and research needs. In this regard, the authors conducted a broad systematic review on the literature about freshwater fish eggs and larvae to investigate (a) temporal trends in the global scientific production, (b) the scope and habitat types, (c) the spatial distribution of studies, (d) the fish species contemplated and their respective conservation status and (e) the factors associated with the spatial distribution of studies. They analysed 654 studies published between 1950 and 2020. The number of studies has increased over time, but with a weak trend. Most studies investigated basic questions about biology and ecology and were carried out chiefly in rivers and lakes. These studies covered 95 freshwater ecoregions (22.3% of all ecoregions) and recorded 871 fish species (4.8% of all freshwater fish currently described). Most species were assessed by the IUCN and classified into six threat categories, but approximately 35% were not evaluated. The main drivers affecting the spatial distribution of studies were ecoregion area, road density, river volume and the number of hydroelectric plants. Results point to significant biases and gaps in the global scientific literature on fish eggs and larvae, especially associated with habitat type, spatial distribution and target species, emphasizing the need to address specific topics. Such biases and gaps indicate the existence of important data shortfalls, which compromise management and conservation planning, as information on fish eggs and larvae is basic and critical for the assessment of fish recruitment and population dynamics.
Keyphrases
  • systematic review
  • case control
  • healthcare
  • meta analyses
  • gene expression
  • deep learning
  • genome wide
  • artificial intelligence
  • big data
  • genetic diversity
  • single cell