DOI: 10.5061/dryad.r4xgxd2dm
title: Data for: Ecological interactions mediate projected loss of kelp biomass under climate change

description: 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.
description: 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 <em>C</em>.
 <em>rodgersii</em>, 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.
description: The spreadsheet containing the data has the following
 columns. Site = Unique site name
 Transect = Transect number at site Name =
 Unique transect name Sample  = sample number on
 transect Latitude = Latitude of sample in decimal
 degrees Longitude = Longitude of sample in decimal
 degrees Depth = Average depth of sample
 (m)      Kelp pts = Number of random points in sample
 images overlaid on kelp (out of 50) Kelp cover = Kelp
 cover for sample (%) Urchin density = Urchin density
 in sample (Urchins/m<sup>2</sup>)               
 PARaz = Average photosynthetically available radiation (PAR) at
 the seabed at the sample location
 (Einstein.m<sup>-2</sup>.d<sup>-1</sup>)              Temperature (Tmx) = Summer maximum temperatures at the seabed (Deg. C)