Macroecology meets macroevolution: evolutionary niche dynamics in the seaweed Halimeda

Abstract

Aim Because of their broad distribution in geographical and ecological dimensions, seaweeds (marine macroalgae) offer great potential as models for marine biogeographical inquiry and exploration of the interface between macroecology and macroevolution. This study aims to characterize evolutionary niche dynamics in the common green seaweed genus Halimeda , use the observed insights to gain understanding of the biogeographical history of the genus and predict habitats that can be targeted for the discovery of species of special biogeographical interest.Location Tropical and subtropical coastal waters. MethodsThe evolutionary history of the genus is characterized using molecular phylogenetics and relaxed molecular clock analysis. Niche modelling is carried out with maximum entropy techniques and uses macroecological data derived from global satellite imagery. Evolutionary niche dynamics are inferred through application of ancestral character state estimation.Results A nearly comprehensive molecular phylogeny of the genus was inferred from a six-locus dataset. Macroecological niche models showed that species distribution ranges are considerably smaller than their potential ranges. We show strong phylogenetic signal in various macroecological niche features. Main conclusionsThe evolution of Halimeda is characterized by conservatism for tropical, nutrient-depleted habitats, yet one section of the genus managed to invade colder habitats multiple times independently. Niche models indicate that the restricted geographical ranges of Halimeda species are not due to habitat unsuitability, strengthening the case for dispersal limitation. Niche models identified hotspots of habitat suitability of Caribbean species in the eastern Pacific Ocean. We propose that these hotspots be targeted for discovery of new species separated from their Caribbean siblings since the Pliocene rise of the Central American Isthmus.

Kelps’ Long-Distance Dispersal: Role of Ecological/Oceanographic Processes and Implications to Marine Forest Conservation

“…However, the near-perfect prediction, indicating that at the spatial scale of our study, the environmental variables used, explained the actual species distributions, and pose the discussion regarding distribution extension [57]. The main predictors which influenced the models were variables related to PP, temperature and, nutrient availability and light.…”

“…The algorithm used in the program was extensively tested in several previous studies [27,77,79] and proved to be a reliable tool to be applied in this context. This method provides a suitable technique, as only data collected from the literature is applied to feed the model [57].…”

“…Statistically independent datasets were produced by partitioning the occurrence records (both presences and pseudo-absences) into 10 distinct latitudinal bands [88]. Models testing different parameters interactively fitted data with one band withheld at a time, where performance was evaluated with the area under the curve (AUC) [57,89] and sensitivity (true positive rate; [89]). Final maps were developed with the combination of parameters retrieving higher average AUC values (identified in cross-validation).…”

“…Graham et al (2007) modeled deep-water kelp refugia, and with the use of the model's predictions they have discovered dense and extensive populations of Eisenia galapagensis in deep waters of the Galapagos archipelago. Although modeling constitutes an essential tool for the discussion of marine macroecological processes [57], there are no published articles with models predicting niche habitat suitability for M. pyrifera and D. antarctica, two of the most important kelp species.…”

Predictive modeling of suitable habitats for threatened marine invertebrates and implications for conservation assessment in Brazil

“…The raster datasets were produced using kriging interpolation, based on readings taken at oceanographic stations throughout the southwestern Atlantic between 1994 and 2000. Although measurements taken near the surface may not represent environmental conditions in the benthic zone, they may be used with reasonable caution, especially for shallow-water species (TITTENSOR et al, 2009;VERBRUGGEN et al, 2009). Bathymetry was derived from data made available by the Database of Exploration and Production of the National Agency for Petroleum, Natural Gas and Biofuels.…”