Data for: Ecological interactions mediate projected loss of kelp biomass under climate change

This dataset contains the data used to define a stacked species distribution model (SDM) for kelp (Ecklonia radiata) and urchins (Centrostephanus rodgersii) in eastern Australia. The spatial extent of this study encompasses the eastern coast of Australia, between 28.0–37.5°S. Within this region, E. radiata is the only laminarian kelp species, while C. rodgersii is the dominant urchin species, with ecological interactions between these two species creating a patchwork of kelp forests and urchin barrens on shallow reefs.

Methods
Modelling of kelp and urchin distributions was conducted using a stacked-SDM which consists of two sub models; a kelp SDM and an urchin SDM, that are integrated to ultimately estimate the distribution of kelp biomass throughout the study extent. The stacked-SDM was constructed using kelp percentage cover data and data on relative densities of the urchin C. rodgersii, from randomly positioned 200 m transects at 23 sites spread along the eastern Australian coastline (see data). The kelp SDM was developed by matching kelp percentage cover data (binomial response variable) to a suite of satellite-derived environmental data of likely importance to kelp persistence and data on relative urchin densities. Explanatory variables for the kelp SDM were selected by trialling explanatory variable combinations in a generalised additive mixed effects modelling (GAMM) framework, with the most parsimonious kelp SDM (optimal model) selected using the Akaike information criteria (AIC). The optimal model predicts the spatial distribution of kelp cover as a function of; 1) summer maximum temperatures at the seabed, 2) average photosynthetically available radiation (PAR) at the seabed, and 3) the density of urchins.

Relative urchin densities utilised in the kelp SDM were calculated from the urchin SDM. This quantified the abundance of urchins throughout the spatial extent of the study for incorporation into the kelp SDM, which is dependent on information on urchin densities to accurately estimate kelp cover. To develop the urchin SDM, data on relative urchin densities from 200 m transects were matched to a suite of potential explanatory variables that are likely to be important predictors of urchin density. Urchin density was modelled using GAMMs fitted using a negative-binomial distribution. The most parsimonious urchin SDM from our set of exploratory models was selected using AIC. This model predicts the spatial distribution of relative urchin densities using two explanatory variables; 1) summer maximum temperatures at the seabed and 2) water depth.