The Indo-Pacific Ocean region is a hotspot of marine diversity and is the optimal environment for the growth and speciation of tropical marine macroalgae. However, there are currently only various hypotheses about why macroalgae are so abundant and diverse in this region, and the underlying drivers of this diversity are still unknown (Leliaert et al., 2018). Climate change will further complicate these hypotheses, and there is uncertainty as to how macroalgae biodiversity will respond to climate change-induced biogeochemical fluctuations. This study aims to evaluate the role of different abiotic environmental variables in determining macroalgae genus richness to provide some insight into the potential drivers of genera diversity in the present day. It also aims to predict future macroalgae richness under one of the representative concentration pathways (RCPs) for greenhouse gases and climate change.

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The differences of the environmental variables across space and time are what defines the premise of this investigation. For this analysis, we utilized the geostatistical method of geographically weighted regression (GWR), which takes into consideration spatial variance. GWR is a local model that allows a regression equation to be fitted onto data that exhibits non-stationarity. A regression equation can be calculated for each point that allows its nearest neighbours to have a much stronger impact than values that are geographically distant based on a distance decay function. Global models such as the generalized or ordinary least-squares (OLS) linear regressions would not be good predictors as they would discount the important environmental variation that determines macroalgal ecology.