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Abstract
Knowing the distribution of fish larvae can inform fisheries science and resource management in several ways, by: 1) providing information on spawning areas; 2) identifying key areas to manage and conserve; and 3) helping to understand how fish populations are affected by anthropogenic pressures, such as overfishing and climate change. With the expansion of industrial fishing activity after 1945, there was increased sampling of fish larvae to help better understand variation in fish stocks. However, large-scale larval records are rare and often unavailable. Here we digitize data from Nishikawa et al. (1985), which were collected from 1956–1981 and are near-global (50°N–50°S), seasonal distribution maps of fish larvae of 18 mainly commercial pelagic taxa of the families Scombridae, Xiphiidae, Istiophoridae, Scombrolabracidae, and Scomberesocidae. Data were collected from the Pacific, Atlantic, and Indian Oceans. We present four seasonal 1° × 1° resolution maps per taxa representing larval abundance per grid cell and highlight some of the main patterns. Data are made available as delimited text, raster, and vector files.
Measurement(s) | larva abundance |
Technology Type(s) | tows |
Factor Type(s) | location of tows |
Sample Characteristic - Organism | fish taxa |
Sample Characteristic - Environment | open-ocean |
Sample Characteristic - Location | near-global (Atlantic, Pacific, and Indian Oceans) |
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1 The University of Queensland, School of Earth and Environmental Sciences, St Lucia, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537); The University of Queensland, School of Mathematics and Physics, St Lucia, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537)
2 The University of Queensland, School of Mathematics and Physics, St Lucia, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537); Queensland Biosciences Precinct (QBP), Commonwealth Scientific and Industrial Research Organization (CSIRO) Oceans and Atmosphere, St Lucia, Australia (GRID:grid.1016.6) (ISNI:0000 0001 2173 2719); The University of New South Wales, Centre for Marine Science and Innovation (CMSI), Sydney, Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432)
3 The University of Queensland, School of Earth and Environmental Sciences, St Lucia, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537)
4 The University of New South Wales, Centre for Marine Science and Innovation (CMSI), Sydney, Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432)
5 The University of New South Wales, Centre for Marine Science and Innovation (CMSI), Sydney, Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432); Sydney Institute of Marine Science, Mosman, Australia (GRID:grid.493042.8)
6 The University of Queensland, School of Earth and Environmental Sciences, St Lucia, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537); The University of Queensland, School of Mathematics and Physics, St Lucia, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537); Université Libre de Bruxelles - ULB, Systems Ecology and Resource Management, Department of Organism Biology, Faculté des Sciences, Brussels, Belgium (GRID:grid.4989.c) (ISNI:0000 0001 2348 6355); Vrije Universiteit Brussel - VUB, Ecology and Biodiversity, Laboratory of Plant Biology and Nature Management, Biology Department, Brussels, Belgium (GRID:grid.8767.e) (ISNI:0000 0001 2290 8069)
7 The University of Queensland, School of Mathematics and Physics, St Lucia, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537); Queensland Biosciences Precinct (QBP), Commonwealth Scientific and Industrial Research Organization (CSIRO) Oceans and Atmosphere, St Lucia, Australia (GRID:grid.1016.6) (ISNI:0000 0001 2173 2719)