A species distribution model of the giant kelp Macrocystis pyrifera: Worldwide changes and a focus on the Southeast Pacific

Worldwide climate-driven shifts in the distribution of species is of special concern when it involves habitat-forming species. In the coastal environment, large Laminarian algae—kelps—form key coastal ecosystems that   support complex and   diverse food webs.  Among kelps, Macrocystis pyrifera is the most widely distributed habitat-formingspecies and provides essential ecosystem services. This study aimed to establish the main drivers of  future distributional changes on a global scale and use them to predict future habitat suitability. Using species distribution models (SDM), we examined the changes in  global distribution of M. pyrifera under  different emission scenarios with a focus on the Southeast Pacific shores. To constrain the drivers of our simulations to the most important factors controlling kelp forest distribution across spatial scales, we explored a suite of environmental variables and validated the predictions derived from the SDMs. Minimum sea surface temperature was th..., , , This dataset contains what is needed to perform the distribution model of *Macrocystis pyrifera* with Maxent as described in the methods section of the published article (DOI: 10.1002/ece3.10901).

The dataset included: 

1\) Global occurrences of *M. pyrifera* (.csv) extracted from GBIF (www.gbif.org) and following the pretreatment procedures outlined in the methods section.

2\) Global coastline mask to delimit the study area and used to cut all the layers of environmental predictors (.shp). Extracted from www.naturalearthdata.com.

3\) Environmental predictor (.asc; SST = Sea Surface Temperature). They were extracted from BioOracle. For more information on the environmental variables and their origin visit www.bio-oracle.org. 

* Present model: SST_min (ºC), SST_max (ºC), SST_mean (ºC), Salinity, Nitrate (mmol . m-3), Phosphate (mmol . m-3), Calcite (mmol . m-3), and Silicate (mmol . m-3). 
* Model for 2.6 2090-2100 scenario: SST_min_2.6 (ºC), SST_max_2.6 (ºC), SST_mean_2.6 (ºC),...