DOI: 10.5061/dryad.x69p8czm5
title: Spatial datasets for Victorian kelp dynamics

description: Aim: Kelp forests throughout temperate regions of the world serve as
 foundation species that play a critical role in sustaining the health and
 function of marine ecosystems but are experiencing declines in abundance
 due to loss in resilience as the ocean climate changes. Ocean warming
 along southeast Australia has already been linked to dramatic losses of
 kelp species and is contributing to the range expansion and population
 increases of two species of sea urchin. The purpose of this research is to
 understand the impact of multiple stressors on the decline in kelps in
 this region. Location: Coastal waters off Victoria, Australia Methods: In
 this study, we use long-term (&gt; 20 years) datasets on biological
 observations across Victorian waters to determine trends in coverage and
 the impact of multiple environmental variables (temperature, habitat,
 currents, waves, connectivity, urchin abundances) on two important kelps
 that serve as foundation species (Phyllospora comosa and Ecklonia radiata)
 using boosted regression trees. These models were then used to develop
 predictive distribution models for each species and also to predict areas
 of future risk. Results: We found that both kelp species are decreasing in
 percent coverage over time and multiple environmental variables, including
 increasing temperatures, intensifying wave energy, changes in currents and
 recruitment patterns, and increases in urchin populations are all
 contributing to the declines of kelps. Additionally, future projections of
 temperature and wave energy show that these species will likely continue
 to decrease across 71% of Victorian waters. Main Conclusions: This
 information can help to better manage these important foundation species
 by providing maps of their current and past distributions, along with
 projections of climate change, to target different areas for urchin
 culling or macroalgae restoration to reduce future losses.
description: This dataset contains all the spatial data used in the model
 creation and predictions across Victoria for kelps and urchins. 
 <em>Ecklonia
 radiata</em> and
 <em>Pyllospora comosa</em>, and estimates of abundance of
 <em>H. erythrogramma</em> and <em>C.
 rodgersii</em> were recorded annually by SCUBA at &gt;200
 locations over 22 years by three different sampling programs: Parks
 Victoria Subtidal Reef Monitoring Program (SRMP), Reef Life Survey (RLS),
 and Victorian Fisheries Authority (VFA) fishery independent abalone
 surveys. To combine all these datasets together for analyses, all datasets
 were processed and standardised to matching units. Below gives a brief
 overview of each of the methods used.
 Subtidal Reef Monitoring Program Parks Victoria established the SRMP for the
 purpose of monitoring the status of reef-associated kelp,
 macroinvertebrates, and fish within the coastal waters of Victoria.
 Through long-term surveys, the SRMP tracks the trends in species
 abundances, species diversity, and reef community structure through time.
 The sites surveyed for the SRMP include sites inside Victorian marine
 protected areas (MPAs) and in areas adjacent to them. These datasets allow
 for comparison between highly protected areas (e.g., Marine National Parks
 and Marine Sanctuaries, IUCN Category II reserves) and unprotected reefs
 (Edgar and Barrett 1997,
 Edgar and Barrett 1999).
 The visual census methods used in these surveys were developed by Edgar
 and Barrett (Edgar and
 Barrett 1997, Edgar et al. 1997, Edgar and Barrett
 1999). Each site is
 located using a global positioning system (GPS) unit and a 200 m transect
 line is laid along a shallow (&lt;10 m) contour with its centre on the
 GPS point. Three different methods are used for each transect to collect
 information on 1) mobile fishes and cephalopods, 2) benthic invertebrates
 and cryptic fishes, and 3) kelp and sessile invertebrates. We are using
 the data from the invertebrate and kelp surveys in this
 study. The methods used to capture the abundances of
 invertebrates are restricted to within one metre of one side of the
 transect line, and all invertebrates within that 1 m band are counted
 across four 50 m transects. We extracted all observations of <em>C.
 rodgersii</em> and <em>H. erythrogramma</em> from these
 surveys and then calculated their densities by dividing the counts by the
 area surveyed. To quantify kelp, 0.25 m<sup>2</sup> quadrats
 are placed at 10 m intervals along the transect line. These quadrats are
 divided into a grid of 7 x 7 perpendicular wires to provide 49 points plus
 one corner, making 50. The cover of kelp is then estimated by counting the
 number of times a species intersects with one of the points.
 Reef Life Survey
 Reef Life Survey (RLS) is
 citizen-science program that trains volunteer SCUBA divers to conduct
 visual surveys of underwater reefs. RLS started surveying along the
 Victorian coast in 2008 and continues to extend the time series of
 observations of fish, invertebrates, and kelps in the region. Detailed
 methods for how RLS data are acquired are described in
 Edgar and Stuart-Smith
 (2009). Briefly, each site
 is surveyed using a 50 m line transect along a depth contour with two or
 more depth contours targeted at each site. Invertebrates are counted
 within 1 m of either side of the transect. Densities for <em>C.
 rodgersii</em> and <em>H. erythrogramma</em> were
 calculated for the RLS sites by dividing the counts by the area surveyed.
 Additionally, at 2.5 m intervals along the transect line, digital
 photo-quadrats are acquired. These photos are taken downward from a 50 cm
 height above the seabed. These photo quadrats are then classified into 16
 functional or morphological categories of algae using CATAMI
 (Althaus et al.
 2015), which is a standard
 image classification system for Australia. Percent coverage of
 <em>E. radiata</em> and <em>P. comosa</em> were
 calculated based on these photo quadrats.
 Victorian Fisheries Authority To supplement the kelp data from the RLS and
 SRMP datasets, we used estimates of <em>E. radiata</em> and
 <em>P. comosa</em> percent cover and densities of <em>H.
 erythrogramma</em> and <em>C. rodgersii</em> from
 fishery independent diver surveys conducted annually by the VFA at 195
 monitoring sites along the coast of Victoria from 2002 to 2016. At each of
 these sites, the divers estimate percent coverage of <em>E.
 radiata</em> and <em>P. comosa</em> and count the
 urchins within six 30 m by 1 m transects at random cardinal directions
 from the central site coordinates. The estimates for percent cover are
 based on visual approximation within 30 sections along each transect and
 then averaged to the nearest 10 % across all transects for each site. We
 divided the urchin counts by the area surveyed to provide density
 estimates per metre squared for both species of urchin at each
 site. Data Collation
 Across all datasets, we
 calculated the density of urchins per 2500 m<sup>2</sup> to
 standardise all datasets to the total number of urchins within a 50 m
 resolution grid. Percent cover was left as percent cover. Once all the
 datasets were standardised, they were combined into a single dataset for
 analyses. This final dataset includes a time series from 1998 to
 2020. We calculated variables describing the structure
 of the seafloor using the Benthic Terrain Mapper toolbar
 (Wright et al.
 2005) and Spatial Analyst
 within ArcGIS (ESRI
 2011). All derivatives
 extracted for each site and their descriptions are shown in Table 1. Those
 variables with a temporal component (wave orbital velocity, current speed,
 SST, connectivity parameters) were extracted for each year in the
 biological sampling while accounting for a 1-year lag in recruitment for
 kelp and a 2-year for urchins when applying the connectivity variables.
 These lags accounted for the lag in time before these species were counted
 across the different sampling programs due to recruitment and size. The
 resolution of all environmental variables was converted to 50 m resolution
 for analysis. The seafloor variables were at a finer resolution (2.5 m)
 and zonal statistics was used to find the mean and standard deviation of
 each of them in a 50 m resolution grid. The oceanographic variables ranged
 from 500 m resolution (the hydrodynamic variables) to 2000 m resolution
 (SST). These variables were converted to 50 m resolution using Empirical
 Bayesian Kriging to interpolate between cell centres. Further information
 on the sources and methods to create the environmental parameters used in
 this study can be found in the Supplementary Information or in
 Ierodiaconou et al.
 (2018).
description: The Biological data has a separate file for each species. These
 each contain the observation location (lat/long), the year, and the
 percent cover (both decimal and integer) of kelps or density of urchins at
 both 1 m<sup>2</sup> and 2500
 m<sup>2</sup>. The Environmental data
 contains all the seafloor and environmental data for the modelling of
 species/habitat associations. The naming conventions are below (see
 manuscript for more details on how each of these layers was
 calculated. AverageAnnualCurrentSpeeds
 a_ACS_[year].tif - average annual current speed and
 the year associated with that data layer (m/s) 
 Connectivity 
 InDegree_BU_[year].tif - average in degree for black urchins
 (<em>C</em>. <em>rodgersii</em>) and the year
 associated with that data layer
 InDegree_Kelp_[year].tif - average in degree for kelps (both
 species) and the year associated with that data layer
 InDegree_PurpleUrchin_[year].tif - average in degree for purple
 urchins (<em>H</em>. <em>erythrogramma</em>) and
 the year associated with that data layer
 LocRet_BU_[year].tif - average local retention for black urchins
 (<em>C</em>. <em>rodgersii</em>) and the year
 associated with that data layer
 LocRet_Kelp_[year].tif - average local retention for kelps (both
 species) and the year associated with that data layer
 LocRet_PurpleUrchin_[year].tif - average local retention for
 purple urchins (<em>H</em>.
 <em>erythrogramma</em>) and the year associated with that data
 layer SlfRec_BU_[year].tif - average self-recruitment
 for black urchins (<em>C</em>. <em>rodgersii</em>)
 and the year associated with that data layer
 SelfRec_Kelp_[year].tif - average self-recruitment for kelps
 (both species) and the year associated with that data layer
 SelfRec_PurpleUrchin_[year].tif - average self-recruitment for
 purple urchins (<em>H</em>.
 <em>erythrogramma</em>) and the year associated with that data
 layer TtlInfl_BU_[year].tif - average total inflow
 for black urchins (<em>C</em>. <em>rodgersii</em>)
 and the year associated with that data layer 
 MaxWinterWaveOrbitalVelocities 
 w_MOV_[year].tif - average winter wave orbital velocities and
 the year associated with that data layer (m/s) 
 Seafloor BioUnt_W2E.tif - the
 biounits for Victoria numbered from west to east
 bpi125_fs.tif - the bathymetric position index at a 125 m scale
 calculated at 2.5 m resolution and then averaged within a 50m moving
 window btystd_fs.tif - the bathymetric standard
 deviation calculated at 2.5 m resolution and then averaged within a 50m
 moving window rug_fs.tif - the rugosity calculated at
 2.5 m resolution and then averaged within a 50m moving window
 slope_fs.tif - the slope calculated at 2.5 m resolution and then
 averaged within a 50m moving window
 Victoria_MBES_LiDAR_50m_bathy_AHD_VG94_fs50m_m.tif - the 50 m
 bathy data for Victoria 
 Summer_SST s_SST_C_[year].tif - average
 summer SST and the year associated with that data layer (degrees
 Celsius) The
 Future predictions downloaded from CSIRO are included in the
 "FuturePredictions" folder. "Hs" is wave height and
 "tos" is sea surface temperature. See manuscript for more detail
 and to access the locations where these data were downloaded
 from.