DOI: 10.5061/dryad.3ffbg79hf
title: Hull fouling marine invasive species pose a very low, but plausible, risk of introduction to East Antarctica in climate change scenarios

description: Aims: To identify potential hull fouling marine invasive species that
 could survive in East Antarctica presently and in the future. Location:
 Australia's Antarctic continental stations: Davis, Mawson and Casey,
 East Antarctica; and subantarctic islands: Macquarie Island and Heard and
 McDonald Islands. Methods: Our study uses a novel machine-learning
 algorithm to predict which currently known hull fouling MIS could survive
 in shallow benthic ecosystems adjacent to Australian Antarctic research
 stations and subantarctic islands, where ship traffic is present. We used
 gradient boosted machine learning (XGBoost) with four important
 environmental variables (sea surface temperature, salinity, nitrate and
 pH) to develop models of suitable environments for each potentially
 invasive species. We then used these models to determine if any
 Australia's three Antarctic research stations and two subantarctic
 islands could be environmentally suitable for MIS now and under two future
 climate scenarios. Results: Most of the species were predicted to be
 unable to survive at any locations between now and the end of the century,
 however, four species were identified as potential current threats, and
 five as threats under future climate change. Asterias amurensis was
 identified as a potential threat to all locations. Main conclusions: This
 study suggests that the risk are very low, but plausible, that known hull
 fouling species could survive in the shallow benthic habitats near
 Australia's East Antarctica locations and suggest a precautionary
 approach is needed by way of surveillance and monitoring in this region,
 particularly if propagule pressure increases. Whilst some species could
 survive as adults in the region, their ability to reach these locations
 and undergo successful reproduction is considered unlikely based on
 current knowledge.
description: A full description of the methods can be found in the manuscript.
 A short summary is provided here: 1. As we were
 interested in hull fouling species, the global port network was used to
 build the model of environmental suitability for each species. Port
 occurring in fresh water environments, for example, the North American
 Great Lakes, or ports that had insufficient data were excluded from the
 dataset. 2. The Global Invasive Species Database
 (iucngisd.org) was used to identify marine invasive species with an
 association to hull fouling. If there was insufficient data on the GISD
 record, primary literature was used to ascertain any hull fouling
 association. Whilst many of the species identified have a stronger
 association with ballast water as a means of transport, all species
 identified in this study do have an association with hull fouling also.
 This yielded a list of 160 species, though most belonged to the
 <em>Didemnum spp. </em>group and were not adequately resolved
 to species level. As most <em>Didemnum </em>species have no
 evidence of being invasive, only the known invasive member of this group
 <em>Didemnum vexiluum</em> was included in this
 study. 3. The Ocean Biogeographic Information System
 mapper was used to find occurrence records for each of the species
 identified. These occurrences were overlaid with the port locations and
 species were matched to ports when they were within 1.0 decimal degrees of
 each other. Data was condensed by port so that only one record of
 occurrence per species per port was taken. Where a species was matched to
 10 or fewer ports, it was excluded as we did not have enough data to build
 the model. 4. Species were only considered for model
 building when: For the subantarctic
 locations - the species had a recorded distribution where the minimum
 temperature was 11°C or less to align with maximum temperatures expected
 in the subantarctic with climate change. For the
 Antarctic locations -  the species had a recorded distribution that
 included sub-freezing temperatures. This resulted in
 a total of 33 species for consideration for the subantarctic and a subset
 of 20 species considered for the Antarctic locations.
 5. We used four environmental variables for our
 models: Sea surface temperature, sea surface salinity, nitrate, and
 pH. To build the model: SST and SSS were
 from the World Ocean Atlas version 2 (http://www.nodc.noaa.gov/OC5/woa13/)
 at 1° resolution using objectively analysed means and monthly averages for
 2005 - 2012, inclusive. To build the model:
 NO<sub>3</sub> and pH were from the CMIP5 CanESM2 RCP4.5 model
 as monthly averages for 2006-2012, inclusive. Two
 models were built: one with annual aggregation of the environmental
 variables, and seasonal aggregation of the environmental
 variables. Predictions were made for the current
 environment at each of the five Australian Antarctic locations.
 6.  The same four environmental variables were used
 to make our predictions. Two future climate models were considered: RCP
 4.5 (curbing of emissions) and RCP 8.5 (business-as-usual). These were all
 from the CMIP5 CanESM2 family of models, for RCP 4.5 and RCP 8.5.
 Predictions were made for: 2030 - using
 monthly averaged data for 2026-2030 2050 - using
 monthly averaged data for 2046-2050 2100 - using
 monthly averaged data for 2096-2100, except for November and December as
 the RCP 8.5 models did not cover these dates so these were average monthly
 data for 2096-2099. 7. Data for model
 building were divided into training (70%) and testing (30%), using
 'createDataPartition' in the R package
 'caret'. 8. Due to imbalanced data,
 resampling using four methods was done to each training dataset for each
 species. The resampling methods were: 
 Over-sampling of the minority class
 Under-sampling of the majority class Both
 over- and under-sampling Synthetic minority
 over-sampling Further details of each of these
 resampling methods is found in the Supplementary Material of the
 manuscript. 9. Extreme gradient boosting
 (XGBoost) was used to build the models. Examples of the code used are
 found in the Supplementary Material of the manuscript.
 10. The resampled model which most accurately predicted the
 presence of species was chosen, as long as the ability to predict absences
 was also high. That model was then used for predictions.
 11. Predictions were made for the five Australian Antarctic and
 subantarctic locations.
description: All data in this dataset is from free, publically available
 datasets. Researchers looking to do similar work are advised to ensure the
 data they are using is from the latest version of these datasets. For
 example, World Ocean Atlas 2013 was used in this study, however, there is
 now a World Ocean Atlas 2018. Further, future predictions should make use
 of the CMIP6 datasets which are being made available now.