Date Published: May 10, 2019
Publisher: Public Library of Science
Author(s): Gisele P. M. Dantas, Larissa R. Oliveira, Amanda M. Santos, Mariana D. Flores, Daniella R. de Melo, Alejandro Simeone, Daniel González-Acuña, Guillermo Luna-Jorquera, Céline Le Bohec, Armando Valdés-Velásquez, Marco Cardeña, João S. Morgante, Juliana A. Vianna, Sam C Banks.
The upwelling hypothesis has been proposed to explain reduced or lack of population structure in seabird species specialized in food resources available at cold-water upwellings. However, population genetic structure may be challenging to detect in species with large population sizes, since variation in allele frequencies are more robust under genetic drift. High gene flow among populations, that can be constant or pulses of migration in a short period, may also decrease power of algorithms to detect genetic structure. Penguin species usually have large population sizes, high migratory ability but philopatric behavior, and recent investigations debate the existence of subtle population structure for some species not detected before. Previous study on Humboldt penguins found lack of population genetic structure for colonies of Punta San Juan and from South Chile. Here, we used mtDNA and nuclear markers (10 microsatellites and RAG1 intron) to evaluate population structure for 11 main breeding colonies of Humboldt penguins, covering the whole spatial distribution of this species. Although mtDNA failed to detect population structure, microsatellite loci and nuclear intron detected population structure along its latitudinal distribution. Microsatellite showed significant Rst values between most of pairwise locations (44 of 56 locations, Rst = 0.003 to 0.081) and 86% of individuals were assigned to their sampled colony, suggesting philopatry. STRUCTURE detected three main genetic clusters according to geographical locations: i) Peru; ii) North of Chile; and iii) Central-South of Chile. The Humboldt penguin shows signal population expansion after the Last Glacial Maximum (LGM), suggesting that the genetic structure of the species is a result of population dynamics and foraging colder water upwelling that favor gene flow and phylopatric rate. Our findings thus highlight that variable markers and wide sampling along the species distribution are crucial to better understand genetic population structure in animals with high dispersal ability.
In species with high dispersal ability and no geographical barriers in their distribution, it is expected found low genetic population structure. For instance, weak or no population genetic structure has been frequently recorded for seabird species along the Atlantic coast of South America (e.g. Kelp gull, Larus dominicanus [1,2]; Magellanic penguin, Spheniscus magellanicus ; South-American tern, Sterna hirundinacea ), along the Pacific coast of South America (e.g. Peruvian pelican, Pelecanus thagus ), and around Antarctica (Emperor penguin, Aptenodytes forsteri, [6, 7]; Adélie penguin, Pygoscelis adelia ; P. antarticus Chinstrap penguin ). Therefore, relative importance of factors that influence the population structure of seabirds have been under debate, such as the presence of physical or non-physical barriers [10,11], the foraging ecology of the species , and/or their philopatric behavior .
Our study reveals that the Humboldt penguin exhibits a clear population genetic structure along the Pacific coast of the South America, as observed in other previous studies on Humboldt penguins . However, our outcome did not corroborate isolation by distance pattern , probably due to a gap of sampling. The present study used of several markers, a higher sample size, the distribution and the number of breeding colonies sampled throughout the whole range, and the new methods of data analysis such as the Bayesian methods applied in this study. The combination of all these appeared to overcome the effects of large population size and pulses of migration related to climate oscillations (e.g. ENSO) in the Humboldt penguin, which frequently limit the power of detection of population genetic structure. Bayesian genetic structure analyses revealed three genetic clusters in the Humboldt penguin: 1) Peru (Punta San Juan), 2) north Chile (region of Pan de Azucar and Isla Grande de Atacama), and 3) Central-South of Chile (Pajaros, Chañaral, Tilgo, Choros, Cachagua, Algarrobo) (Fig 2). This structure needs to be considered while implementing management and conservation action plans for the Humboldt penguin along the Southern Pacific coast. Also, taking into account the population data for Humboldt penguin (numbers of breeding pairs in each colony) that indicate Punta San Juan as a key colony in Peru, supporting around 3,160 breeding pairs ; and Pajaros, Chañaral, Tilgo and Choros together supporting around 21,700 breeding pairs (14,000 at Chañaral, 1,860 at Choros, 2,640 at Tilgo, and 1,200 at Pajaros), and Pan de Azucar with 3,000 breeding pairs. These regions need to be monitored to avoid population decline.