Potential sites for Asparagopsis spp. cultivation on Australia’s East Coast in 2100

Potential sites for Asparagopsis spp. cultivation on Australia’s East Coast in 2100 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Potential sites for Asparagopsis spp. cultivation on Australia’s East Coast in 2100 Gabrielle King, Tristan Salles, Ana Vila-Concejo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7784217/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Asparagopsis , a red macroalgae native to Australia’s East Coast (10° S, 142° E to 44 ° S, 154° E), has shown the potential to reduce livestock methane emissions by up to 98%, facilitating climate change mitigation. Ensemble species distribution models were used to predict suitable cultivation sites for A. armata and A. taxiformis along the East Coast of Australia in 2100 under three climate scenarios (SSP-1, SSP-2, SSP-5). Results show a substantial loss of highly suitable habitat by 2100, with only moderately suitable areas remaining; one location for A. armata under SSP1-2.6, and four locations across multiple climate scenarios for A. taxiformis . Cultivating Asparagopsis in these locations can contribute to global methane mitigation efforts. Species distribution models (SDM) Asparagopsis Net Zero Cultivation Methane Livestock Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 INTRODUCTION Livestock production is the largest contributor to anthropogenic methane emissions (Camer-Pesci et al. 2023 ). Additionally, methane has a 28 times greater global warming potential than carbon dioxide over a 100-year period (Myhre et al. 2014 ). Further, demand for livestock derived products such as meat and milk is projected to increase by almost 40% by 2050 relative to 2020 levels (Komarek et al. 2021 ), accentuating the need to lower emissions from the livestock sector. More than 140 countries have committed to limiting global warming to a maximum of 2°C by 2100 under the Paris Agreement (UNFCCC n.d.), and major emitters, such as the United States of America and China, as well as Australia (the 14th greatest emitter), have set targets of Net Zero by 2050 (Friedrich et al. 2023 ; Department of Climate Change n.d.). One promising methane mitigation strategy involves the use of the two species of the red macroalgae Asparagopsis, A. armata and A. taxiformis , as a livestock feed additive. Supplementing ruminants’ diets with as little as 0.2% of this macroalga can reduce methane emissions by up to 98% (Kinley et al. 2020 ). Considering its potential, Australia’s national science agency the CSIRO established the FutureFeed initiative in 2020 to commercialise Asparagopsis as a feed additive. FutureFeed currently holds the global intellectual property rights for its application and has identified the need to significantly scale up production to meet global demand and reduce costs. Yet, climate change adds complexity, as it is expected to alter macroalgal physiology and geographic distribution (Assis et al. 2016 ; Laeseke et al. 2020 ; Gonzalez-Aragon et al. 2024 ). Currently, only two of the nine licensed Asparagopsis farms worldwide are ocean-based, despite the greater costs associated with land-based cultivation (GreenerGrazing n.d.), underscoring the importance of identifying additional ocean-based cultivation sites under future climate conditions. Five of FutureFeed’s licensed farms are located in Australia, including one on the East Coast which lies within the native range of both A. armata and A. taxiformis (Zanolla et al. 2022 ; FutureFeed 2024 ). Both species do not generally occur simultaneously due to thermal tolerance differences. A. armata is principally found in cooler waters in the south and A. taxiformis in warmer waters in the north (Zanolla et al., 2022 ), thereby increasing the spatial extent of potentially suitable sites for cultivation by 2100. Yet, this region is undergoing rapid oceanographic changes due to the intensification of the East Australian Current (EAC), which is projected to strengthen and extend southward while weaken in the north (Ridgway and Hill 2009 ; Oliver and Holbrook 2014 ; Bull et al. 2020 ). As a result of this, sea surface temperatures (SST) along Australia’s East Coast are expected to increase by 2.5°C by 2050 and by up to 3.0°C by 2070 (Hobday and Lough 2011 ), with implications for the future distribution and viability of Asparagopsis cultivation. This study aims to assess the impact of climate change on the distribution of Asparagopsis spp. along Australia’s East Coast under three Shared Socioeconomic Pathways (SSPs): SSP-1(2.6) (low carbon emissions), SSP-2 (medium carbon emissions), and SSP-5 (high carbon emissions). Using species distribution models (SDMs) that correlate known species occurrences with environmental factors (Chaabani et al. 2019 ; Blanco et al. 2021 ; O’Mahony et al. 2021 ), the current (2010) and projected (2100) distributions of A. armata and A. taxiformis were modelled. The results identify locations that may remain suitable for cultivation in 2100, helping guide future investment in ocean-based Asparagopsis farming, and supporting methane mitigation efforts in the livestock sector. 2 MATERIAL & METHODS Our study area extends over Australia’s East Coast, ranging from 10° S, 142° E to 44 ° S, 154° E (Fig. 1 ). This region was chosen as it lies within the native distribution of both A. armata and A. taxiformis. 2.1 Species Occurrence Data 2.1.1 Data sources Occurrence records for both species were obtained from the Global Biodiversity Information Facility (GBIF) (gbif.org) as well as from the Atlas of Living Australia (ALA) (ala.org). Both are open-access biodiversity databases that collate and store species presence data from multiple sources. Both databases were used to maximise spatial coverage and to capture any differences in occurrence records across repositories. 2.1.2 Processing To ensure ecological relevance, occurrence points located on land, often originating from herbarium or museum collections, were removed. Likewise, occurrence points located below the depth range of both species (25 m for A. armata and 30 m for A. taxiformis (Zanolla et al. 2022 )) were excluded to reduce outliers. Duplicate records were removed to prevent oversampling of specific sites. To address spatial autocorrelation and reduce sampling bias, the study area was rasterised at a 1km resolution using bathymetric data, and limiting only one presence point was retained per grid cell (Varela et al. 2014 ; Castellanos et al. 2019 ). Overall, this resulted in 60 points for A. armata and 33 records for A. taxiformis (Fig. 1 ). 2.2 Environmental Data 2.2.1 Sources and Datasets Environmental predictor variables were obtained from Bio-Oracle v3.0, a global marine dataset tailored for species distribution modelling (Tyberghein et al. 2012 ; Assis et al. 2024 ). A summary of the selected environmental variables, their temporal resolution, units, and ecological justification is provided in Table 1 . Long-term minimum and maximum layers which represent the “long-term average of the yearly maxima and minima of a given decade” (Assis et al. 2024 ) were chosen as the aim of the study was to determine the long-term impact of climate change on the species distribution by 2100. Only surface layers were used, as both species are typically found at depths shallower than 30 m (Zanolla et al. 2022 ). The variable “depth” itself was excluded from the modelling process, as Asparagopsis is cultivated on suspended ropes of fixed lengths, rendering bathymetric depth less relevant to cultivation feasibility. Table 1 Environmental factors and associated aggregations, units and timestep used as well as justification for inclusion in the model Factor (abbreviation) Aggregation/s Unit Present Timestep Justification Ocean temperature (thetao) ltmax, ltmin °C 2010–2019 One of most determining factors of growth and reproduction of Asparagopsis (Zhu et al. 2021 ; Mihaila et al. 2024 ) Nitrate (no3) ltmax, ltmin mmol m − 3 2010–2018 Most growth limiting nutrient for seaweed (Roleda and Hurd 2019 ) Phosphate (po4) ltmax, ltmin mmol m − 3 2010–2018 Second most growth limiting nutrient for seaweeds (Roleda and Hurd 2019 ) Dissolved iron (dfe) ltmax, ltmin mmol m − 3 2010–2018 Essential nutrient in algae; affects electron transport chains in photosynthesis and respiration (Rijkenberg et al. 2014 ; Schoffman et al. 2016 ). Seawater speed (sws) ltmax m s − 1 2010–2019 Positive relationship with growth in Asparagopsis (Mihaila et al. 2024 ) Salinity (so) ltmax, ltmin psu 2010–2019 Affects osmosis and turgor pressure (Nejrup and Pedersen, 2012 ; Pereira et al., 2017 ). pH (ph) ltmin pH 2010–2018 Affects photosynthetic rates (Wootton et al. 2008 ; Britton et al. 2016 ). 2.2.2 Processing All environmental layers were cropped to the defined study area and limited to ocean regions shallower than 200 m to focus on coastal and shelf habitats. The layers were then interpolated to a spatial resolution of 1 km and extrapolated landward to extend to the coastline, ensuring continuous coverage across nearshore environments. Collinearity between environmental variables was not explicitly assessed, as several algorithms used within the ensemble SDM framework inherently account for multicollinearity. These include Random Forest (Boulesteix et al. 2012 ), eXtreme Gradient Boosting Training (Montomoli et al. 2021 ), and Maximum Entropy (Feng et al. 2019 ). In the context of predictive modelling rather than interpretive analysis, Multiple Adaptive Regression Splines (MARS) is also considered robust to collinearity (Li et al. 2020 ). In addition, studies have found that collinearity has less substantial effects on species extent when modelled by ensemble models rather than simple models (De Marco and Nóbrega 2018 ). For baseline modelling, environmental data from 2010–2018 (or 2010–2019, depending on availability see Table 1 ) were used to approximate current species distributions. Future projections for 2100 (Table 2 ) were based on three Shared Socioeconomic Pathways (SSPs) from the CMIP6: SSP-1(2.6), SSP-2, and SSP-5. Table 2 Chosen SSP scenarios with associated predicted warming and emission trends SSP Warming Emissions track 1(2.6) 2°C Low radiative forcing. Emission reductions in near future 2 3°C Intermediate radiative forcing. Emissions increasing until 2040 and then decrease 5 4°C Increasing emissions until 2080 2.3 Modelling Species distribution models were constructed using the biomod2 R package, an ensemble modelling platform that integrates multiple statistical and machine learning techniques (Thuiller et al. 2016 ). One ensemble model was developed for each species ( A. armata and A. taxiformis ) across the East Coast of Australia using 11 commonly applied SDM algorithms; Artificial Neural Network (ANN), Classification Tree Analysis (CTA), Generalized Additive Model (GAM), Maximum Entropy (MAXENT), Generalized Boosting Model (GBM), Multiple Adaptive Regression Splines (MARS), Flexible Discriminant Analysis (FDA), Random Forest (RF), Surface Range Envelop (SRE), eXtreme Gradient Boosting Training (XGBOOST), Maximum Entropy (MAXNET). 2.3.1 Pseudo absences As no true absence data was available, 10,000 pseudo-absence points were randomly drawn from the study area and within the same depth range as each species. From these, a random subset of 1,000 pseudo-absences was chosen for model training for each species. 2.3.2 Model training and cross-validation Each of the 11 modelling algorithms was trained using a repeated, random sub-sampling validation approach. Models were cross-validated by randomly partitioning the presence and pseudo-absence data into training and testing sets over ten iterations, as recommended in the biomod2 documentation (Thuiller et al. 2016 ). This procedure was repeated three times, each time using one-third of the pseudo-absence dataset. In total, 533 individual models were generated for each species. 2.3.3 Ensemble modelling and performance assessment The projections derived from the individual models were then coalesced into an ensemble model by averaging the predictions across all qualified models (O’Mahony et al. 2021 ). This ensemble approach improves prediction accuracy and robustness, offering more realistic estimates of potential species distribution (Thuiller et al. 2009 ; Chaabani et al. 2019 ). The performances of the single and ensemble models were assessed by comparing for each algorithm, the Receiver Operating Characteristic curve (ROC), mean True Skill Statistic (TSS) and Cohen’s Kappa Coefficient (Kappa). The main index used for evaluating the models’ performance was TSS, which accounts for both sensitivity and specificity. Only models with a TSS score above 0.6 were included to build the ensemble model (Thuiller et al. 2009 ). The performances of the ensemble models were evaluated using the same criteria. The TSS metric was selected over commonly used alternatives such as ROC and Kappa, due to known limitations: ROC measures a model’s ability to discriminate between presence and absence locations across all possible thresholds, but it is sensitive to the spatial extent of the modelling domain (Lobo et al. 2008; Beck et al. 2014). Kappa compares the observed accuracy of the model to what would be expected by chance, but it is highly sensitive to species prevalence, particularly when the number of occurrence points available is small. In contrast, TSS is not sensitive to extent or prevalence (Allouche et al. 2006). 2.4 Current and future predictions Using the 2010 distribution predictions for each species, projections for their distribution under SSP-1 (2.6), SSP-2 and SSP-5 in 2100, were run. Habitat suitability scores produced by the ensemble models ranged from 0 to 1000 and were then categorized into four categories using equal interval classification; 0–250 as unsuitable, 250–500 as poorly suitable 500–750 as moderately suitable, and 750–1000 as highly suitable. Since each grid cell represents 1 km 2 , the total area per suitability class corresponds directly to the number of pixels in that class. 3 RESULTS 3.1 Model performance The ensemble models were evaluated using three aggregation approaches: the median, mean, and weighted mean of probabilities (wmean). For both A. armata and A. taxiformis , all three ensemble approaches achieved excellent performance with TSS scores above 0.95, ROC scores above 0.79, Kappa scores above 0.98 (Fig. 2 ), indicating excellent agreement between predicted and observed occurrences. Among the aggregation methods, the mean probability approach yielded the best overall performance across metrics. As such, subsequent figures and analyses are based on mean ensemble predictions (Fig. 2 ). 3.2 Environmental drivers of Asparagopsis spp. distributions The optimal conditions of the five most important environmental factors affecting the distribution of both species can be seen in Table 3 . As can be seen from Table 3 and Fig. 3 , A. armata and A. taxiformis were influenced by distinct environmental variables, reflecting their contrasting thermal niches and habitat preferences (Zanolla et al. 2022 ). For both species, long-term dissolved iron and long-term maximum sea temperature are within the five most important environmental factors (Table 3 ; Fig. 3 ). The thermal differences between the two species can be seen from the optimal long-term maximum sea temperature (Table 3 ). Table 3 Optimum conditions and standard deviation (SD) for five most important environmental factors (in descending order) affecting the distribution of A. armata and A. taxiformis respectively A. armata Environmental factor Unit Optimum ± SD long-term minimum pH pH 8.053 0.016 long-term maximum dissolved iron mmol m − 3 0.003 0.001 long-term maximum sea temperature ° C 20.508 2.033 long-term minimum phosphate mmol m − 3 0.119 0.051 long-term minimum nitrate mmol m − 3 0.167 0.403 A. taxiformis Environmental factor Unit Optimum ± SD long-term maximum dissolved iron mmol m − 3 0.004 0.001 long-term maximum sea temperature ° C 28.661 1.092 long-term minimum sea temperature ° C 21.449 1.493 long-term minimum salinity psu 34.682 0.550 long-term maximum salinity psu 35.715 0.142 3.3 Suitable habitat for Asparagopsis spp. in 2010 The areas with suitable habitat for Asparagopsis spp. have been named with the label ROI (Region Of Interest) with an A (for A. armata ) or T (for A. taxiformis ) and numbered chronologically from north to south (Table 4 ; Fig. 4 ). Table 4 Regions Of Interest (ROI) of A. armata (A) and A. taxiformis (T) and associated toponym and approximate coordinates Label Toponym Coordinates A. armata ROIA1 NSW coast between 32 and 38°S, 150°40’ E ROIA2 Melbourne 38°08’S, 144°50’E ROIA3 Tasmania east coast between 40 and 43°S, 148°06’ E A. taxiformis ROIT1 Lizard Island 14°45’ S, 145°25’ E ROIT2 Townsville 18°46’ S, 146°23’ E ROIT3 Gladstone 23°37’ S, 151°18’ E ROIT4 Brisbane 27°20’ S, 153°17’ E Overall, in 2010, A. armata exhibited the greatest habitat suitability in the southern half of the study area (Fig. 4 a). Highly suitable habitat (scores between 750–1000) accounted for 0.30% of the study area, equivalent to 12,240 km 2 , and was concentrated in ROIA1, ROIA2 and ROIA3. Surrounding these zones were moderately suitable areas (scores between 500–750) which made up an additional 0.56% of the study area. In contrast, A. taxiformis showed greatest suitability in the northern half of the study area, consistent with its higher thermal tolerance (Zanolla et al. 2022 ). Notable clusters of high suitability (750–1000) were identified at ROIT1, ROIT2, ROIT3, and ROIT4 (Fig. 4 b; Table 4 ). This area covered 0.13% of the total study area, or 5,304 km 2 . This is approximately 60% less than the area for A. armata . Areas of moderate (500–750) suitability accounted for an additional 0.36% and extended around the regions of high suitability. 3.3.1 Factors driving A. armata habitat suitability Suitability in ROIA1 was driven by long-term minimum pH, phosphate and nitrate concentrations and long-term maximum sea temperatures which were optimal for A. armata according to the model. The suitability in ROIA2 and ROIA3 was driven by all five factors. Long-term minimum pH in the suitable areas of ROIA1, ROIA2 and in ROIA3 correspond to the optimal long-term minimum pH conditions of 8.053 ± 0.016 pH. In the three ROIs, long-term maximum dissolved iron concentrations are approximately 0.0015, 0.0025 and 0.002 mmol m − 3 respectively. The latter two (ROIA2 and ROIA3) correspond to the optimal concentrations for the growth of A. armata as determined by the model. Long-term maximum temperatures in the three ROIs all correspond to the optimal long-term maximum sea temperature conditions of 20.508 ± 2.033°C. Finally, in these regions, the long-term minimum concentrations of phosphate and nitrate are within the optimal conditions of 0.119 ± 0.051 mmol m − 3 and 0.167 ± 0.403 mmol m − 3 respectively. 3.3.2 Factors driving A. taxiformis habitat suitability Suitability at ROIT1 was driven by long-term maximum and minimum sea temperature as well as long-term minimum and maximum salinity. Suitability at ROIT2 and ROIT3 was driven by all five factors. Suitability at ROIT4 was driven by long-term maximum sea temperature as well as long-term minimum and maximum salinity. Long-term maximum sea temperature at all four ROIs had the optimal conditions for the growth of A. taxiformis of 28.661 ± 1.092°C as determined by the model. The suitability in all ROIs were also all driven by long-term maximum salinity which had the optimal conditions of 35.715 ± 0.142 psu. The suitability in ROIT1, ROIT3 and ROIT4 was also driven by long-term minimum salinity with the optimal conditions of 34.682 ± 0.550 psu. The suitability in ROIT1, ROIT2 and ROIT3 was also driven by long-term minimum sea temperature with the optimal conditions of 21.449 ± 1.493°C. Long-term maximum dissolved iron at ROIT2 and ROIT3 was approximately 0.0038 and 0.003 mmol m − 3 respectively which corresponds to the optimal conditions of 0.004 ± 0.001 mmol m − 3 . 3.4 Projected suitable Habitat for Asparagopsis spp. in 2100 By 2100, suitable habitat for both species of Asparagopsis is projected to decline substantially under all climate scenarios compared to 2010. Notably, no highly suitable habitat (750–1000) remains for either species under any of the SSP projections (Fig. 5 ; Fig. 6 ). For A. armata , only under SSP-1 (2.6) does some area of moderate (500–750) suitability persist in ROIA3, specifically at 49°55’ S, 147°22’ E (Fig. 5 ), comprising less than 0.01% of the total study area (Fig. 6 ). Under both SSP-2 and SSP-5, A. armata loses all moderately or highly suitable habitat. Long-term maximum temperature in ROIA1 is 25°C under SSP-1 and increases to 26°C under SSP-2 and up to 28°C under SSP-5. This is above the optimal long-term maximum temperature determined by the model. Other factors have also made this area unsuitable; although the long-term minimum pH is suitable under SSP-1, it drops under the optimal pH of 8.053 ± 0.016 under SSP-2 and SSP-5. Further adding to the loss in suitability of the area is maximum dissolved iron concentration which in 2100 appears to be slightly lower than the optimal long-term maximum concentration of dissolved iron as predicted by the model of 0.003 ± 0.001 mmol m − 3 . For ROIA2 and ROIA3 the loss in suitability can be attributed in part to a rise in temperature; long-term maximum temperature in ROIA2 and ROIA3 ranges between 17–22°C under SSP-1, 18–23°C under SSP-2, and 20–25°C under SSP-5. These values under SSP-2 and SSP-5 in parts exceeds the optimal temperature of 20.508 ± 2.033°C as determined by the model. Therefore, the increasing temperature may be driving the loss of A. armata in this region as it starts to exceed the optimal conditions determined by the model. The reduction in suitability in these regions could also be attributed to changes in pH especially under SSP-2 and SSP-5 which show pH values below 8, lower than the optimal value. A. taxiformis retains a slightly broader range of moderately suitable habitat (500–750) by 2100. Such areas account for 0.06% of the study area under SSP-1 (2.6), decreasing to 0.01% under both SSP-2 and SSP-5 (Fig. 6 ). Under SSP-1(2.6), small patches of moderate suitability for A. taxiformis remain near ROIT1, ROIT2, ROIT3, and ROIT4. Under SSP-2, only ROIT1 and ROIT2 maintain residual moderately suitable habitat. Under SSP-5, the area near ROIT1 maintains moderate suitability and a small area of moderate suitability reappears near ROIT4 (Fig. 5 ). In ROIT1, the loss in suitability from high to moderate between 2010 and 2100 can first be attributed to the rise in temperature above the optimal long-term maximum temperature. A second reason can be that long-term maximum salinity is optimal in 2010 as well as under SSP-1, however, under SSP-2 and SSP-5, the salinity increases out of the optimal long-term maximum salinity range for A. taxiformis. In ROIT2 the loss in suitability between SSP-1 and SSP-2 can be attributed to the optimal long-term minimum and maximum temperatures being exceeded. The continued loss under SSP-5 can be attributed to long-term maximum salinity exceeding the optimal condition of 35.715 ± 0.142 psu. The complete loss between SSP-2 and SSP-5 of moderate suitability in ROIT3 can be attributed to long-term maximum temperature exceeding the optimal values as determined by the model. For ROIT4 it is difficult to determine a reason for the loss in suitability from high to moderate between 2010 and SSP-1 as well as loss from moderate to mainly poor between SSP-1 and SSP-2 in ROIT3, as between these scenarios there is not much fluctuation between the variables. 4 DISCUSSION The aim of this study was to identify key locations most suitable for the growth of Asparagopsis armata and taxiformis on the East Coast of Australia in 2100 under three climate scenarios SSP-1(2.6), SSP-2 and SSP-5. These locations could then inform decisions cultivation sites of Asparagopsis in the future to help production demand for the livestock industry globally. 4.1 Potential cultivation sites in 2100 for Asparagopsis spp. In 2100, no areas of high suitability exist in the study area for either species of Asparagopsis . Areas of moderate suitability exist for both species. Under SSP-1(2.6), there is less than 408 km 2 of moderately suitable area available in ROIA3 for A. armata. However, this is only a viable option if we follow the climate trajectories of SSP-1(2.6) as there are no moderately suitable areas left under the more intense climate scenarios. A. taxiformis has more suitable area than A. armata . Under SSP-1(2.6), 2,448 km 2 of moderately suitable area is available. Under the other climate scenarios more than 400 km 2 is available for A. taxiformis . The best locations for A. taxiformis cultivation would be near ROIT1 as it remains moderately suitable under all climate scenarios, ROIT2 remains moderately suitable under SSP-1(2.6) and SSP-2, ROIT3 that has moderate suitability under SSP-1(2.6), and ROIT4 remains moderately suitable under SSP-1(2.6) and SSP-5. It should be considered that whilst areas of moderate suitability will be limited in 2100, not much area is necessary for productive cultivation of Asparagopsis . In 2004, eight tonnes of A. armata wet biomass was harvested from 14 km of cultivation rope in an area of 0.02 km 2 (Werner et al. 2004 ). According to FutureFeed ( 2024 ), to meet global demand, production of 100,000 tonnes per year will be needed. From this, assuming such values are based off bromoform concentrations as found in A. armata , a very rough estimate of area required to meet global demand results in a minimum of 250 km 2 . As A. taxiformis has 8.5 times less bromoform than A. armata more than 2000 km 2 would be needed. Although the available areas are only moderately suitable in 2100, they are large enough to contribute significantly to the production of Asparagopsis globally. Other locations Australia-wide and globally may be available for Asparagopsis cultivation as currently Asparagopsis is cultivated in the ocean on Australia’s west and south coast, in Vietnam and tests undergoing currently in south Korea indicating its widespread suitability. 4.2 Factors explaining the loss of suitable locations in 2100 Compared to 2010, in 2100 all highly suitable habitat is lost for both species under all climate scenarios. In terms of moderately suitable habitat, for A. armata more than 98% is lost under SSP-1(2.6) and 100% is lost under the more intense climate scenarios compared to 2010. For A. taxiformis , approximately 83% of moderate suitable habitat is lost under SSP-1(2.6) and more than 97% is lost under the more intense climate scenarios compared to 2010. This general loss in suitability can be attributed to unfavourable changes in environmental conditions. In 2010, areas were moderately or highly suitable if at least three of the five most important environmental factors affecting the distribution of the Asparagopsis species (Table 3 ) were in their optimal condition as determined by the model. If between 2010 and 2100 some of the environmental factors were no longer in their optimal conditions this resulted in a loss of suitability. For both species, the loss of moderately and highly suitable area in 2100 can be particularly attributed to a rise in sea temperature. A. armata has an optimal long-term maximum temperature of 20.508 ± 2.033°C as determined by the model, and an upper thermal tolerance of either 21°C (Mihaila et al. 2024 ) or 24°C (Chualáin et al. 2004 ). Yet in 2100, in ROIA1, all these values are largely surpassed. In ROIA2 and ROIA3 under the different climate scenarios certain areas surpass the optimal temperature determined by the model but they never surpass the upper thermal tolerance as found by previous studies (Chualáin et al. 2004 ; Mihaila et al. 2024 ). Temperature may however not be driving the loss as sea temperature never completely exceeds the predicted optimal maximum temperature for A. armata or the upper thermal tolerances as found by previous studies (Chualáin et al. 2004 ; Mihaila et al. 2024 ). In addition, typically, seaweed growth increases with increasing temperature up to a certain limit which is the thermal tolerance of the species (Mihaila et al. 2024 ). A. taxiformis ’ optimal long-term maximum temperature is 28.661 ± 1.092°C and an upper thermal tolerance of 25–28°C (Zanolla et al. 2022 ). In 2100, both these values are surpassed for ROIT1, ROIT2 and ROIT3. This rise in temperature throughout the study region is probably a consequence of the lengthening and strengthening of the EAC bringing warmer water further south. In fact, the decline of giant kelp forests by more than 50% in 50 years in Tasmanian (around ROIA2 and ROIA3) waters has been attributed to the lengthening of the EAC bringing warmer, nutrient poor waters towards the south of the east coast (Hobday et al. 2006 ; Ling 2008 ; Vergés et al. 2016 ). Globally, the loss of macroalgae due to rising temperatures has been well studied (Wilson et al. 2019 ). Temperature affects all aspects of macroalga growth as it regulates enzyme activity and diffusion of nutrients (Roleda and Hurd 2019 ; Theobald et al. 2024 ). Warmer water has a further detrimental impact on algae due generally to lower nutrient concentrations (Kämpf and Chapman 2016 ). The general loss in suitability is not just due to rising sea temperature but also due to the compounding effect of multiple environmental factors no longer being in the optimal conditions. pH, the environmental factor which affected the distribution of A. armata the most according to the model, decreased in 2100 compared to 2010. According to Bio-Oracle, in 2100, under all climate scenarios, pH drops below the optimal long-term minimum pH of 8.053 ± 0.016 in all locations. Ocean acidification can limit the bio-availability of nutrients (Asadian et al. 2018 ). Nevertheless, multiple studies have found increases in non-calcifying macroalgae growth with reduced pH (Beardall et al. 1998 ; Cornwall et al. 2012 ). For A. taxiformis , the loss can also be partially attributed to salinity increases, surpassing the optimal long-term maximum salinity of 35.715 ± 0.142 psu in ROIT1 and ROIT2. Salinity is responsible for the distribution of algae globally and locally (Nejrup and Pedersen 2012 ; Pereira et al. 2017 ) as it plays a key role in osmosis and turgor pressure (Pereira et al. 2017 ). Pereira et al. ( 2017 ) showed that for a red macroalga, salinity level outside of 25–40 psu were very damaging, often resulting in reduced branching and thalli bleaching. pH, salinity and iron are considered to be critical to the growth of algae. The importance of pH and salinity has been discussed previously. Iron is essential in plants, including algae, playing a vital role in electron transport chains in photosynthesis and respiration (Rijkenberg et al. 2014 ; Schoffman et al. 2016 ). The high importance in determining suitable habitat for Asparagopsis spp. that these factors were attributed by the model is surprising, given that other factors such as nitrate and phosphate have been shown to be extremely important variables affecting the growth of Asparagopsis (Roleda and Hurd 2019 ; Zhu et al. 2021 ). This could be a result of being inflated as these pH, salinity and iron do not fluctuate substantially within the study area and therefore between occurrence points (Harisena et al. 2021 ). This means that these environmental conditions represent more the conditions of the study area than the optimal growth conditions for the species. 4.3 Limitations and future research This study has limitations sourced from the raw data and methodology which provides multiple opportunities for future research to build upon. Future studies will benefit from more precise data for the environmental factors. Further, the Bio-Oracle v3.0 environmental factors, derived from Earth System Models (ESMs) come with additional assumptions and limitations (Flato 2011 ; Heavens et al. 2013 ). Asparagopsis is clearly an adaptable genus, as attested by its invasive capabilities (Taylor and Kumar 2013 ; Blanco et al. 2021 ; Silva et al. 2021 ). Therefore, it is possible Asparagopsis spp. could undergo niche shifts under the future conditions (Laeseke et al. 2020 ). Future studies should integrate biological processes such as adaptation as they are fundamental to producing more accurate projections of the distribution of a species (especially adaptable ones), such as by using ΔTraitSDMs (Benito Garzón et al. 2019 ). Overall, this study demonstrated that the East Coast of Australia is a suitable area for the cultivation of Asparagopsis spp. under climate change in 2100. Under all climate scenarios, for A. armata , the southern region near ROIA3 will remain moderately suitable for cultivation under low emission scenarios (SSP-1(2.6)), and for A. taxiformis a few key sites, namely near ROIT1, ROIT2, ROIT3 and ROIT4, remain moderately suitable for cultivation under some or all climate scenarios in 2100. Increased production globally would render Asparagopsis products more accessible to the livestock industry, in turn assisting emissions reductions, facilitating countries to reach targets of NetZero. Declarations Funding :No funds, grants, or other support was received. Competing Interests: The authors have no competing interests to declare that are relevant to the content of this article. Data availability The data and notebooks will be made available via Zenodo [https://zenodo.org/]. Authors' contributions Gabrielle King, Tristan Salles and Ana Vila-Concejo contributed to the study conception and design. 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1","display":"","copyAsset":false,"role":"figure","size":369531,"visible":true,"origin":"","legend":"\u003cp\u003eThe study area with \u003cem\u003eA. armata \u003c/em\u003e(pink) and\u003cem\u003e A. taxiformis\u003c/em\u003e (orange) occurrence points\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7784217/v1/158c6dc74e92466ab503182a.png"},{"id":94856669,"identity":"0e08b572-f6f3-4b21-b16f-415e5d76970a","added_by":"auto","created_at":"2025-10-31 12:20:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45630,"visible":true,"origin":"","legend":"\u003cp\u003eThree ensemble model performance evaluation methods with the median of probabilities, mean of probabilities and weighted mean of probabilities for \u003cem\u003eA. armata \u003c/em\u003eand\u003cem\u003e A. taxiformis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7784217/v1/7695afc8276aac8436c21f74.png"},{"id":94985525,"identity":"cca072a5-9708-4d4e-9495-12eb2302f30f","added_by":"auto","created_at":"2025-11-03 06:58:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81229,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot representing the 25\u003csup\u003eth\u003c/sup\u003e to 75\u003csup\u003eth\u003c/sup\u003e Interquartile Range (IQR) of the relative importance of the environmental factors that contribute to the habitat distribution in the ensemble model for A. armata and A. taxiformis in 2010. Whiskers represent 1.5 times IQR, black lines represent median, and circle represent outliers.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7784217/v1/e2abad3c968ae8e299bd46cf.png"},{"id":94985308,"identity":"f2610440-306c-4914-99e6-dbaabb1371c5","added_by":"auto","created_at":"2025-11-03 06:57:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":523315,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of mean habitat suitability (0 – 1000) for \u003cem\u003eA. armata \u003c/em\u003e\u003cstrong\u003e(a)\u003c/strong\u003e\u003cem\u003e \u003c/em\u003eand\u003cem\u003e A. taxiformis \u003c/em\u003e\u003cstrong\u003e(b) \u003c/strong\u003eon Australia’s East Coast with overlayed occurrence data of the respective species.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7784217/v1/095e7bbd174371d16a09c118.png"},{"id":94856672,"identity":"7eb93fdb-eb21-4515-b511-bffcec1ed10b","added_by":"auto","created_at":"2025-10-31 12:20:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":522732,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of mean habitat suitability (0 - 1000) of \u003cem\u003eA. taxiformis\u003c/em\u003e (top) and \u003cem\u003eA. armata\u003c/em\u003e (bottom) in 2100 under SSP-1, SSP-2, and SSP-5. Regions of interest (ROI) have been boxed and named with an A for \u003cem\u003eA. armata\u003c/em\u003e or T for \u003cem\u003eA. taxiformis\u003c/em\u003e and numbered from north to south. Red circles highlight areas of moderate suitability within ROIs.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7784217/v1/674764cd1be89a9757cb613f.png"},{"id":94856675,"identity":"2eb9f225-4e69-40f2-a706-8e2632557019","added_by":"auto","created_at":"2025-10-31 12:20:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":74478,"visible":true,"origin":"","legend":"\u003cp\u003eBar graphs of the percentage of study area which is highly (750 – 1000) and moderately (500 – 250) suitable in 2010 and 2100 under SSP-1, SSP-2 and SSP-5 for \u003cem\u003eA. taxiformis \u003c/em\u003e(top) and \u003cem\u003eA. armata\u003c/em\u003e (bottom)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7784217/v1/880a49140294abba6bd78280.png"},{"id":100357122,"identity":"268f47bd-567c-4fb0-bd30-40a0322ea49e","added_by":"auto","created_at":"2026-01-16 07:18:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2483227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7784217/v1/383afcfa-624d-4b54-96b8-bb7b17d9f33f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potential sites for Asparagopsis spp. cultivation on Australia’s East Coast in 2100","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eLivestock production is the largest contributor to anthropogenic methane emissions (Camer-Pesci et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, methane has a 28 times greater global warming potential than carbon dioxide over a 100-year period (Myhre et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Further, demand for livestock derived products such as meat and milk is projected to increase by almost 40% by 2050 relative to 2020 levels (Komarek et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), accentuating the need to lower emissions from the livestock sector. More than 140 countries have committed to limiting global warming to a maximum of 2\u0026deg;C by 2100 under the Paris Agreement (UNFCCC n.d.), and major emitters, such as the United States of America and China, as well as Australia (the 14th greatest emitter), have set targets of Net Zero by 2050 (Friedrich et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Department of Climate Change n.d.).\u003c/p\u003e\u003cp\u003eOne promising methane mitigation strategy involves the use of the two species of the red macroalgae \u003cem\u003eAsparagopsis, A. armata\u003c/em\u003e and \u003cem\u003eA. taxiformis\u003c/em\u003e, as a livestock feed additive. Supplementing ruminants\u0026rsquo; diets with as little as 0.2% of this macroalga can reduce methane emissions by up to 98% (Kinley et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Considering its potential, Australia\u0026rsquo;s national science agency the CSIRO established the FutureFeed initiative in 2020 to commercialise \u003cem\u003eAsparagopsis\u003c/em\u003e as a feed additive. FutureFeed currently holds the global intellectual property rights for its application and has identified the need to significantly scale up production to meet global demand and reduce costs. Yet, climate change adds complexity, as it is expected to alter macroalgal physiology and geographic distribution (Assis et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Laeseke et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gonzalez-Aragon et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Currently, only two of the nine licensed \u003cem\u003eAsparagopsis\u003c/em\u003e farms worldwide are ocean-based, despite the greater costs associated with land-based cultivation (GreenerGrazing n.d.), underscoring the importance of identifying additional ocean-based cultivation sites under future climate conditions.\u003c/p\u003e\u003cp\u003eFive of FutureFeed\u0026rsquo;s licensed farms are located in Australia, including one on the East Coast which lies within the native range of both \u003cem\u003eA. armata\u003c/em\u003e and \u003cem\u003eA. taxiformis\u003c/em\u003e (Zanolla et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; FutureFeed \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Both species do not generally occur simultaneously due to thermal tolerance differences. \u003cem\u003eA. armata\u003c/em\u003e is principally found in cooler waters in the south and \u003cem\u003eA. taxiformis\u003c/em\u003e in warmer waters in the north (Zanolla et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), thereby increasing the spatial extent of potentially suitable sites for cultivation by 2100. Yet, this region is undergoing rapid oceanographic changes due to the intensification of the East Australian Current (EAC), which is projected to strengthen and extend southward while weaken in the north (Ridgway and Hill \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Oliver and Holbrook \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bull et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a result of this, sea surface temperatures (SST) along Australia\u0026rsquo;s East Coast are expected to increase by 2.5\u0026deg;C by 2050 and by up to 3.0\u0026deg;C by 2070 (Hobday and Lough \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with implications for the future distribution and viability of \u003cem\u003eAsparagopsis\u003c/em\u003e cultivation.\u003c/p\u003e\u003cp\u003eThis study aims to assess the impact of climate change on the distribution of \u003cem\u003eAsparagopsis spp.\u003c/em\u003e along Australia\u0026rsquo;s East Coast under three Shared Socioeconomic Pathways (SSPs): SSP-1(2.6) (low carbon emissions), SSP-2 (medium carbon emissions), and SSP-5 (high carbon emissions). Using species distribution models (SDMs) that correlate known species occurrences with environmental factors (Chaabani et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Blanco et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; O\u0026rsquo;Mahony et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the current (2010) and projected (2100) distributions of \u003cem\u003eA. armata\u003c/em\u003e and \u003cem\u003eA. taxiformis\u003c/em\u003e were modelled. The results identify locations that may remain suitable for cultivation in 2100, helping guide future investment in ocean-based \u003cem\u003eAsparagopsis\u003c/em\u003e farming, and supporting methane mitigation efforts in the livestock sector.\u003c/p\u003e"},{"header":"2 MATERIAL \u0026 METHODS","content":"\u003cp\u003eOur study area extends over Australia\u0026rsquo;s East Coast, ranging from 10\u0026deg; S, 142\u0026deg; E to 44 \u0026deg; S, 154\u0026deg; E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This region was chosen as it lies within the native distribution of both \u003cem\u003eA. armata\u003c/em\u003e and \u003cem\u003eA. taxiformis.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Species Occurrence Data\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1 Data sources\u003c/h2\u003e\u003cp\u003eOccurrence records for both species were obtained from the Global Biodiversity Information Facility (GBIF) (gbif.org) as well as from the Atlas of Living Australia (ALA) (ala.org). Both are open-access biodiversity databases that collate and store species presence data from multiple sources. Both databases were used to maximise spatial coverage and to capture any differences in occurrence records across repositories.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2 Processing\u003c/h2\u003e\u003cp\u003eTo ensure ecological relevance, occurrence points located on land, often originating from herbarium or museum collections, were removed. Likewise, occurrence points located below the depth range of both species (25 m for \u003cem\u003eA. armata\u003c/em\u003e and 30 m for \u003cem\u003eA. taxiformis\u003c/em\u003e (Zanolla et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)) were excluded to reduce outliers. Duplicate records were removed to prevent oversampling of specific sites. To address spatial autocorrelation and reduce sampling bias, the study area was rasterised at a 1km resolution using bathymetric data, and limiting only one presence point was retained per grid cell (Varela et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Castellanos et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Overall, this resulted in 60 points for \u003cem\u003eA. armata\u003c/em\u003e and 33 records for \u003cem\u003eA. taxiformis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Environmental Data\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Sources and Datasets\u003c/h2\u003e\u003cp\u003eEnvironmental predictor variables were obtained from Bio-Oracle v3.0, a global marine dataset tailored for species distribution modelling (Tyberghein et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Assis et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A summary of the selected environmental variables, their temporal resolution, units, and ecological justification is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Long-term minimum and maximum layers which represent the \u0026ldquo;long-term average of the yearly maxima and minima of a given decade\u0026rdquo; (Assis et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) were chosen as the aim of the study was to determine the long-term impact of climate change on the species distribution by 2100. Only surface layers were used, as both species are typically found at depths shallower than 30 m (Zanolla et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The variable \u0026ldquo;depth\u0026rdquo; itself was excluded from the modelling process, as \u003cem\u003eAsparagopsis\u003c/em\u003e is cultivated on suspended ropes of fixed lengths, rendering bathymetric depth less relevant to cultivation feasibility.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEnvironmental factors and associated aggregations, units and timestep used as well as justification for inclusion in the model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003cp\u003e(abbreviation)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAggregation/s\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePresent Timestep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJustification\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOcean temperature\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(thetao)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eltmax, ltmin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOne of most determining factors of growth and reproduction of \u003cem\u003eAsparagopsis\u003c/em\u003e (Zhu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mihaila et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNitrate\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(no3)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eltmax, ltmin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2010\u0026ndash;2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMost growth limiting nutrient for seaweed (Roleda and Hurd \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePhosphate\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(po4)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eltmax, ltmin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2010\u0026ndash;2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSecond most growth limiting nutrient for seaweeds (Roleda and Hurd \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDissolved iron\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(dfe)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eltmax, ltmin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2010\u0026ndash;2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEssential nutrient in algae; affects electron transport chains in photosynthesis and respiration (Rijkenberg et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schoffman et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSeawater speed\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(sws)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eltmax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003em s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePositive relationship with growth in \u003cem\u003eAsparagopsis\u003c/em\u003e (Mihaila et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSalinity\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(so)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eltmax, ltmin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epsu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAffects osmosis and turgor pressure (Nejrup and Pedersen, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pereira et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003epH\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(ph)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eltmin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2010\u0026ndash;2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAffects photosynthetic rates (Wootton et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Britton et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Processing\u003c/h2\u003e\u003cp\u003eAll environmental layers were cropped to the defined study area and limited to ocean regions shallower than 200 m to focus on coastal and shelf habitats. The layers were then interpolated to a spatial resolution of 1 km and extrapolated landward to extend to the coastline, ensuring continuous coverage across nearshore environments. Collinearity between environmental variables was not explicitly assessed, as several algorithms used within the ensemble SDM framework inherently account for multicollinearity. These include Random Forest (Boulesteix et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), eXtreme Gradient Boosting Training (Montomoli et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Maximum Entropy (Feng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the context of predictive modelling rather than interpretive analysis, Multiple Adaptive Regression Splines (MARS) is also considered robust to collinearity (Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, studies have found that collinearity has less substantial effects on species extent when modelled by ensemble models rather than simple models (De Marco and N\u0026oacute;brega \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For baseline modelling, environmental data from 2010\u0026ndash;2018 (or 2010\u0026ndash;2019, depending on availability see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were used to approximate current species distributions. Future projections for 2100 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were based on three Shared Socioeconomic Pathways (SSPs) from the CMIP6: SSP-1(2.6), SSP-2, and SSP-5.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChosen SSP scenarios with associated predicted warming and emission trends\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWarming\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmissions track\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e1(2.6)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow radiative forcing. Emission reductions in near future\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntermediate radiative forcing. Emissions increasing until 2040 and then decrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncreasing emissions until 2080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Modelling\u003c/h2\u003e\u003cp\u003eSpecies distribution models were constructed using the biomod2 R package, an ensemble modelling platform that integrates multiple statistical and machine learning techniques (Thuiller et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). One ensemble model was developed for each species (\u003cem\u003eA. armata\u003c/em\u003e and \u003cem\u003eA. taxiformis\u003c/em\u003e) across the East Coast of Australia using 11 commonly applied SDM algorithms; Artificial Neural Network (ANN), Classification Tree Analysis (CTA), Generalized Additive Model (GAM), Maximum Entropy (MAXENT), Generalized Boosting Model (GBM), Multiple Adaptive Regression Splines (MARS), Flexible Discriminant Analysis (FDA), Random Forest (RF), Surface Range Envelop (SRE), eXtreme Gradient Boosting Training (XGBOOST), Maximum Entropy (MAXNET).\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Pseudo absences\u003c/h2\u003e\u003cp\u003eAs no true absence data was available, 10,000 pseudo-absence points were randomly drawn from the study area and within the same depth range as each species. From these, a random subset of 1,000 pseudo-absences was chosen for model training for each species.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Model training and cross-validation\u003c/h2\u003e\u003cp\u003eEach of the 11 modelling algorithms was trained using a repeated, random sub-sampling validation approach. Models were cross-validated by randomly partitioning the presence and pseudo-absence data into training and testing sets over ten iterations, as recommended in the biomod2 documentation (Thuiller et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This procedure was repeated three times, each time using one-third of the pseudo-absence dataset. In total, 533 individual models were generated for each species.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Ensemble modelling and performance assessment\u003c/h2\u003e\u003cp\u003eThe projections derived from the individual models were then coalesced into an ensemble model by averaging the predictions across all qualified models (O\u0026rsquo;Mahony et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This ensemble approach improves prediction accuracy and robustness, offering more realistic estimates of potential species distribution (Thuiller et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chaabani et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The performances of the single and ensemble models were assessed by comparing for each algorithm, the Receiver Operating Characteristic curve (ROC), mean True Skill Statistic (TSS) and Cohen\u0026rsquo;s Kappa Coefficient (Kappa). The main index used for evaluating the models\u0026rsquo; performance was TSS, which accounts for both sensitivity and specificity. Only models with a TSS score above 0.6 were included to build the ensemble model (Thuiller et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The performances of the ensemble models were evaluated using the same criteria. The TSS metric was selected over commonly used alternatives such as ROC and Kappa, due to known limitations: ROC measures a model\u0026rsquo;s ability to discriminate between presence and absence locations across all possible thresholds, but it is sensitive to the spatial extent of the modelling domain (Lobo et al. 2008; Beck et al. 2014). Kappa compares the observed accuracy of the model to what would be expected by chance, but it is highly sensitive to species prevalence, particularly when the number of occurrence points available is small. In contrast, TSS is not sensitive to extent or prevalence (Allouche et al. 2006).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Current and future predictions\u003c/h2\u003e\u003cp\u003eUsing the 2010 distribution predictions for each species, projections for their distribution under SSP-1 (2.6), SSP-2 and SSP-5 in 2100, were run.\u003c/p\u003e\u003cp\u003eHabitat suitability scores produced by the ensemble models ranged from 0 to 1000 and were then categorized into four categories using equal interval classification;\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e0\u0026ndash;250 as unsuitable,\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e250\u0026ndash;500 as poorly suitable\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e500\u0026ndash;750 as moderately suitable, and\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e750\u0026ndash;1000 as highly suitable.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eSince each grid cell represents 1 km\u003csup\u003e2\u003c/sup\u003e, the total area per suitability class corresponds directly to the number of pixels in that class.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Model performance\u003c/h2\u003e\u003cp\u003eThe ensemble models were evaluated using three aggregation approaches: the median, mean, and weighted mean of probabilities (wmean). For both \u003cem\u003eA. armata\u003c/em\u003e and \u003cem\u003eA. taxiformis\u003c/em\u003e, all three ensemble approaches achieved excellent performance with TSS scores above 0.95, ROC scores above 0.79, Kappa scores above 0.98 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating excellent agreement between predicted and observed occurrences. Among the aggregation methods, the mean probability approach yielded the best overall performance across metrics. As such, subsequent figures and analyses are based on mean ensemble predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Environmental drivers of \u003cem\u003eAsparagopsis spp.\u003c/em\u003e distributions\u003c/h2\u003e\u003cp\u003eThe optimal conditions of the five most important environmental factors affecting the distribution of both species can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, A. \u003cem\u003earmata\u003c/em\u003e and \u003cem\u003eA. taxiformis\u003c/em\u003e were influenced by distinct environmental variables, reflecting their contrasting thermal niches and habitat preferences (Zanolla et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For both species, long-term dissolved iron and long-term maximum sea temperature are within the five most important environmental factors (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The thermal differences between the two species can be seen from the optimal long-term maximum sea temperature (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOptimum conditions and standard deviation (SD) for five most important environmental factors (in descending order) affecting the distribution of \u003cem\u003eA. armata\u003c/em\u003e and \u003cem\u003eA. taxiformis\u003c/em\u003e respectively\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA. armata\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnvironmental factor\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eUnit\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eOptimum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e\u0026plusmn; SD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elong-term minimum pH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elong-term maximum dissolved iron\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elong-term maximum sea temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026deg; C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elong-term minimum phosphate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elong-term minimum nitrate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eA. taxiformis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnvironmental factor\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eUnit\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eOptimum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e\u0026plusmn; SD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elong-term maximum dissolved iron\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elong-term maximum sea temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026deg; C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elong-term minimum sea temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026deg; C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elong-term minimum salinity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epsu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elong-term maximum salinity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epsu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Suitable habitat for \u003cem\u003eAsparagopsis spp.\u003c/em\u003e in 2010\u003c/h2\u003e\u003cp\u003eThe areas with suitable habitat for \u003cem\u003eAsparagopsis spp.\u003c/em\u003e have been named with the label ROI (Region Of Interest) with an A (for \u003cem\u003eA. armata\u003c/em\u003e) or T (for \u003cem\u003eA. taxiformis\u003c/em\u003e) and numbered chronologically from north to south (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegions Of Interest (ROI) of A. armata (A) and A. taxiformis (T) and associated toponym and approximate coordinates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLabel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eToponym\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoordinates\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eA. armata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROIA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNSW coast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ebetween 32 and 38\u0026deg;S, 150\u0026deg;40\u0026rsquo; E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROIA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMelbourne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u0026deg;08\u0026rsquo;S, 144\u0026deg;50\u0026rsquo;E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROIA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTasmania east coast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ebetween 40 and 43\u0026deg;S, 148\u0026deg;06\u0026rsquo; E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cem\u003eA. taxiformis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROIT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLizard Island\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u0026deg;45\u0026rsquo; S, 145\u0026deg;25\u0026rsquo; E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROIT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTownsville\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u0026deg;46\u0026rsquo; S, 146\u0026deg;23\u0026rsquo; E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROIT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGladstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u0026deg;37\u0026rsquo; S, 151\u0026deg;18\u0026rsquo; E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROIT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBrisbane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27\u0026deg;20\u0026rsquo; S, 153\u0026deg;17\u0026rsquo; E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOverall, in 2010, \u003cem\u003eA. armata\u003c/em\u003e exhibited the greatest habitat suitability in the southern half of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Highly suitable habitat (scores between 750\u0026ndash;1000) accounted for 0.30% of the study area, equivalent to 12,240 km\u003csup\u003e2\u003c/sup\u003e, and was concentrated in ROIA1, ROIA2 and ROIA3. Surrounding these zones were moderately suitable areas (scores between 500\u0026ndash;750) which made up an additional 0.56% of the study area.\u003c/p\u003e\u003cp\u003eIn contrast, \u003cem\u003eA. taxiformis\u003c/em\u003e showed greatest suitability in the northern half of the study area, consistent with its higher thermal tolerance (Zanolla et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notable clusters of high suitability (750\u0026ndash;1000) were identified at ROIT1, ROIT2, ROIT3, and ROIT4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This area covered 0.13% of the total study area, or 5,304 km\u003csup\u003e2\u003c/sup\u003e. This is approximately 60% less than the area for \u003cem\u003eA. armata\u003c/em\u003e. Areas of moderate (500\u0026ndash;750) suitability accounted for an additional 0.36% and extended around the regions of high suitability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Factors driving \u003cem\u003eA. armata\u003c/em\u003e habitat suitability\u003c/h2\u003e\u003cp\u003eSuitability in ROIA1 was driven by long-term minimum pH, phosphate and nitrate concentrations and long-term maximum sea temperatures which were optimal for \u003cem\u003eA. armata\u003c/em\u003e according to the model. The suitability in ROIA2 and ROIA3 was driven by all five factors. Long-term minimum pH in the suitable areas of ROIA1, ROIA2 and in ROIA3 correspond to the optimal long-term minimum pH conditions of 8.053\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016 pH. In the three ROIs, long-term maximum dissolved iron concentrations are approximately 0.0015, 0.0025 and 0.002 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively. The latter two (ROIA2 and ROIA3) correspond to the optimal concentrations for the growth of \u003cem\u003eA. armata\u003c/em\u003e as determined by the model. Long-term maximum temperatures in the three ROIs all correspond to the optimal long-term maximum sea temperature conditions of 20.508\u0026thinsp;\u0026plusmn;\u0026thinsp;2.033\u0026deg;C. Finally, in these regions, the long-term minimum concentrations of phosphate and nitrate are within the optimal conditions of 0.119\u0026thinsp;\u0026plusmn;\u0026thinsp;0.051 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 0.167\u0026thinsp;\u0026plusmn;\u0026thinsp;0.403 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Factors driving \u003cem\u003eA. taxiformis\u003c/em\u003e habitat suitability\u003c/h2\u003e\u003cp\u003eSuitability at ROIT1 was driven by long-term maximum and minimum sea temperature as well as long-term minimum and maximum salinity. Suitability at ROIT2 and ROIT3 was driven by all five factors. Suitability at ROIT4 was driven by long-term maximum sea temperature as well as long-term minimum and maximum salinity. Long-term maximum sea temperature at all four ROIs had the optimal conditions for the growth \u003cem\u003eof A. taxiformis\u003c/em\u003e of 28.661\u0026thinsp;\u0026plusmn;\u0026thinsp;1.092\u0026deg;C as determined by the model. The suitability in all ROIs were also all driven by long-term maximum salinity which had the optimal conditions of 35.715\u0026thinsp;\u0026plusmn;\u0026thinsp;0.142 psu. The suitability in ROIT1, ROIT3 and ROIT4 was also driven by long-term minimum salinity with the optimal conditions of 34.682\u0026thinsp;\u0026plusmn;\u0026thinsp;0.550 psu. The suitability in ROIT1, ROIT2 and ROIT3 was also driven by long-term minimum sea temperature with the optimal conditions of 21.449\u0026thinsp;\u0026plusmn;\u0026thinsp;1.493\u0026deg;C. Long-term maximum dissolved iron at ROIT2 and ROIT3 was approximately 0.0038 and 0.003 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e respectively which corresponds to the optimal conditions of 0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Projected suitable Habitat for \u003cem\u003eAsparagopsis spp.\u003c/em\u003e in 2100\u003c/h2\u003e\u003cp\u003eBy 2100, suitable habitat for both species of \u003cem\u003eAsparagopsis\u003c/em\u003e is projected to decline substantially under all climate scenarios compared to 2010. Notably, no highly suitable habitat (750\u0026ndash;1000) remains for either species under any of the SSP projections (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For \u003cem\u003eA. armata\u003c/em\u003e, only under SSP-1 (2.6) does some area of moderate (500\u0026ndash;750) suitability persist in ROIA3, specifically at 49\u0026deg;55\u0026rsquo; S, 147\u0026deg;22\u0026rsquo; E (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), comprising less than 0.01% of the total study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Under both SSP-2 and SSP-5, \u003cem\u003eA. armata\u003c/em\u003e loses all moderately or highly suitable habitat.\u003c/p\u003e\u003cp\u003eLong-term maximum temperature in ROIA1 is 25\u0026deg;C under SSP-1 and increases to 26\u0026deg;C under SSP-2 and up to 28\u0026deg;C under SSP-5. This is above the optimal long-term maximum temperature determined by the model. Other factors have also made this area unsuitable; although the long-term minimum pH is suitable under SSP-1, it drops under the optimal pH of 8.053\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016 under SSP-2 and SSP-5. Further adding to the loss in suitability of the area is maximum dissolved iron concentration which in 2100 appears to be slightly lower than the optimal long-term maximum concentration of dissolved iron as predicted by the model of 0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor ROIA2 and ROIA3 the loss in suitability can be attributed in part to a rise in temperature; long-term maximum temperature in ROIA2 and ROIA3 ranges between 17\u0026ndash;22\u0026deg;C under SSP-1, 18\u0026ndash;23\u0026deg;C under SSP-2, and 20\u0026ndash;25\u0026deg;C under SSP-5. These values under SSP-2 and SSP-5 in parts exceeds the optimal temperature of 20.508\u0026thinsp;\u0026plusmn;\u0026thinsp;2.033\u0026deg;C as determined by the model. Therefore, the increasing temperature may be driving the loss of \u003cem\u003eA. armata\u003c/em\u003e in this region as it starts to exceed the optimal conditions determined by the model. The reduction in suitability in these regions could also be attributed to changes in pH especially under SSP-2 and SSP-5 which show pH values below 8, lower than the optimal value.\u003c/p\u003e\u003cp\u003e\u003cem\u003eA. taxiformis\u003c/em\u003e retains a slightly broader range of moderately suitable habitat (500\u0026ndash;750) by 2100. Such areas account for 0.06% of the study area under SSP-1 (2.6), decreasing to 0.01% under both SSP-2 and SSP-5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Under SSP-1(2.6), small patches of moderate suitability for \u003cem\u003eA. taxiformis\u003c/em\u003e remain near ROIT1, ROIT2, ROIT3, and ROIT4. Under SSP-2, only ROIT1 and ROIT2 maintain residual moderately suitable habitat. Under SSP-5, the area near ROIT1 maintains moderate suitability and a small area of moderate suitability reappears near ROIT4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn ROIT1, the loss in suitability from high to moderate between 2010 and 2100 can first be attributed to the rise in temperature above the optimal long-term maximum temperature. A second reason can be that long-term maximum salinity is optimal in 2010 as well as under SSP-1, however, under SSP-2 and SSP-5, the salinity increases out of the optimal long-term maximum salinity range for \u003cem\u003eA. taxiformis.\u003c/em\u003e In ROIT2 the loss in suitability between SSP-1 and SSP-2 can be attributed to the optimal long-term minimum and maximum temperatures being exceeded. The continued loss under SSP-5 can be attributed to long-term maximum salinity exceeding the optimal condition of 35.715\u0026thinsp;\u0026plusmn;\u0026thinsp;0.142 psu. The complete loss between SSP-2 and SSP-5 of moderate suitability in ROIT3 can be attributed to long-term maximum temperature exceeding the optimal values as determined by the model. For ROIT4 it is difficult to determine a reason for the loss in suitability from high to moderate between 2010 and SSP-1 as well as loss from moderate to mainly poor between SSP-1 and SSP-2 in ROIT3, as between these scenarios there is not much fluctuation between the variables.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eThe aim of this study was to identify key locations most suitable for the growth of \u003cem\u003eAsparagopsis armata\u003c/em\u003e and \u003cem\u003etaxiformis\u003c/em\u003e on the East Coast of Australia in 2100 under three climate scenarios SSP-1(2.6), SSP-2 and SSP-5. These locations could then inform decisions cultivation sites of \u003cem\u003eAsparagopsis\u003c/em\u003e in the future to help production demand for the livestock industry globally.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Potential cultivation sites in 2100 for \u003cem\u003eAsparagopsis spp.\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eIn 2100, no areas of high suitability exist in the study area for either species of \u003cem\u003eAsparagopsis\u003c/em\u003e. Areas of moderate suitability exist for both species. Under SSP-1(2.6), there is less than 408 km\u003csup\u003e2\u003c/sup\u003e of moderately suitable area available in ROIA3 for \u003cem\u003eA. armata.\u003c/em\u003e However, this is only a viable option if we follow the climate trajectories of SSP-1(2.6) as there are no moderately suitable areas left under the more intense climate scenarios. \u003cem\u003eA. taxiformis\u003c/em\u003e has more suitable area than \u003cem\u003eA. armata\u003c/em\u003e. Under SSP-1(2.6), 2,448 km\u003csup\u003e2\u003c/sup\u003e of moderately suitable area is available. Under the other climate scenarios more than 400 km\u003csup\u003e2\u003c/sup\u003e is available for \u003cem\u003eA. taxiformis\u003c/em\u003e. The best locations for \u003cem\u003eA. taxiformis\u003c/em\u003e cultivation would be near ROIT1 as it remains moderately suitable under all climate scenarios, ROIT2 remains moderately suitable under SSP-1(2.6) and SSP-2, ROIT3 that has moderate suitability under SSP-1(2.6), and ROIT4 remains moderately suitable under SSP-1(2.6) and SSP-5.\u003c/p\u003e\u003cp\u003eIt should be considered that whilst areas of moderate suitability will be limited in 2100, not much area is necessary for productive cultivation of \u003cem\u003eAsparagopsis\u003c/em\u003e. In 2004, eight tonnes of \u003cem\u003eA. armata\u003c/em\u003e wet biomass was harvested from 14 km of cultivation rope in an area of 0.02 km\u003csup\u003e2\u003c/sup\u003e (Werner et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). According to FutureFeed (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), to meet global demand, production of 100,000 tonnes per year will be needed. From this, assuming such values are based off bromoform concentrations as found in \u003cem\u003eA. armata\u003c/em\u003e, a very rough estimate of area required to meet global demand results in a minimum of 250 km\u003csup\u003e2\u003c/sup\u003e. As \u003cem\u003eA. taxiformis\u003c/em\u003e has 8.5 times less bromoform than A. armata more than 2000 km\u003csup\u003e2\u003c/sup\u003e would be needed. Although the available areas are only moderately suitable in 2100, they are large enough to contribute significantly to the production of \u003cem\u003eAsparagopsis\u003c/em\u003e globally. Other locations Australia-wide and globally may be available for \u003cem\u003eAsparagopsis\u003c/em\u003e cultivation as currently \u003cem\u003eAsparagopsis\u003c/em\u003e is cultivated in the ocean on Australia\u0026rsquo;s west and south coast, in Vietnam and tests undergoing currently in south Korea indicating its widespread suitability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Factors explaining the loss of suitable locations in 2100\u003c/h2\u003e\u003cp\u003eCompared to 2010, in 2100 all highly suitable habitat is lost for both species under all climate scenarios. In terms of moderately suitable habitat, for \u003cem\u003eA. armata\u003c/em\u003e more than 98% is lost under SSP-1(2.6) and 100% is lost under the more intense climate scenarios compared to 2010. For \u003cem\u003eA. taxiformis\u003c/em\u003e, approximately 83% of moderate suitable habitat is lost under SSP-1(2.6) and more than 97% is lost under the more intense climate scenarios compared to 2010. This general loss in suitability can be attributed to unfavourable changes in environmental conditions. In 2010, areas were moderately or highly suitable if at least three of the five most important environmental factors affecting the distribution of the \u003cem\u003eAsparagopsis\u003c/em\u003e species (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were in their optimal condition as determined by the model. If between 2010 and 2100 some of the environmental factors were no longer in their optimal conditions this resulted in a loss of suitability.\u003c/p\u003e\u003cp\u003eFor both species, the loss of moderately and highly suitable area in 2100 can be particularly attributed to a rise in sea temperature. \u003cem\u003eA. armata\u003c/em\u003e has an optimal long-term maximum temperature of 20.508\u0026thinsp;\u0026plusmn;\u0026thinsp;2.033\u0026deg;C as determined by the model, and an upper thermal tolerance of either 21\u0026deg;C (Mihaila et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or 24\u0026deg;C (Chual\u0026aacute;in et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Yet in 2100, in ROIA1, all these values are largely surpassed. In ROIA2 and ROIA3 under the different climate scenarios certain areas surpass the optimal temperature determined by the model but they never surpass the upper thermal tolerance as found by previous studies (Chual\u0026aacute;in et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mihaila et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Temperature may however not be driving the loss as sea temperature never completely exceeds the predicted optimal maximum temperature for \u003cem\u003eA. armata\u003c/em\u003e or the upper thermal tolerances as found by previous studies (Chual\u0026aacute;in et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mihaila et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, typically, seaweed growth increases with increasing temperature up to a certain limit which is the thermal tolerance of the species (Mihaila et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eA. taxiformis\u003c/em\u003e\u0026rsquo; optimal long-term maximum temperature is 28.661\u0026thinsp;\u0026plusmn;\u0026thinsp;1.092\u0026deg;C and an upper thermal tolerance of 25\u0026ndash;28\u0026deg;C (Zanolla et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In 2100, both these values are surpassed for ROIT1, ROIT2 and ROIT3. This rise in temperature throughout the study region is probably a consequence of the lengthening and strengthening of the EAC bringing warmer water further south. In fact, the decline of giant kelp forests by more than 50% in 50 years in Tasmanian (around ROIA2 and ROIA3) waters has been attributed to the lengthening of the EAC bringing warmer, nutrient poor waters towards the south of the east coast (Hobday et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ling \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Verg\u0026eacute;s et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Globally, the loss of macroalgae due to rising temperatures has been well studied (Wilson et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Temperature affects all aspects of macroalga growth as it regulates enzyme activity and diffusion of nutrients (Roleda and Hurd \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Theobald et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Warmer water has a further detrimental impact on algae due generally to lower nutrient concentrations (K\u0026auml;mpf and Chapman \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe general loss in suitability is not just due to rising sea temperature but also due to the compounding effect of multiple environmental factors no longer being in the optimal conditions. pH, the environmental factor which affected the distribution of \u003cem\u003eA. armata\u003c/em\u003e the most according to the model, decreased in 2100 compared to 2010. According to Bio-Oracle, in 2100, under all climate scenarios, pH drops below the optimal long-term minimum pH of 8.053\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016 in all locations. Ocean acidification can limit the bio-availability of nutrients (Asadian et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nevertheless, multiple studies have found increases in non-calcifying macroalgae growth with reduced pH (Beardall et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Cornwall et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For \u003cem\u003eA. taxiformis\u003c/em\u003e, the loss can also be partially attributed to salinity increases, surpassing the optimal long-term maximum salinity of 35.715\u0026thinsp;\u0026plusmn;\u0026thinsp;0.142 psu in ROIT1 and ROIT2. Salinity is responsible for the distribution of algae globally and locally (Nejrup and Pedersen \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pereira et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) as it plays a key role in osmosis and turgor pressure (Pereira et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Pereira et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) showed that for a red macroalga, salinity level outside of 25\u0026ndash;40 psu were very damaging, often resulting in reduced branching and thalli bleaching.\u003c/p\u003e\u003cp\u003epH, salinity and iron are considered to be critical to the growth of algae. The importance of pH and salinity has been discussed previously. Iron is essential in plants, including algae, playing a vital role in electron transport chains in photosynthesis and respiration (Rijkenberg et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schoffman et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The high importance in determining suitable habitat for \u003cem\u003eAsparagopsis spp.\u003c/em\u003e that these factors were attributed by the model is surprising, given that other factors such as nitrate and phosphate have been shown to be extremely important variables affecting the growth of \u003cem\u003eAsparagopsis\u003c/em\u003e (Roleda and Hurd \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This could be a result of being inflated as these pH, salinity and iron do not fluctuate substantially within the study area and therefore between occurrence points (Harisena et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This means that these environmental conditions represent more the conditions of the study area than the optimal growth conditions for the species.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Limitations and future research\u003c/h2\u003e\u003cp\u003eThis study has limitations sourced from the raw data and methodology which provides multiple opportunities for future research to build upon. Future studies will benefit from more precise data for the environmental factors. Further, the Bio-Oracle v3.0 environmental factors, derived from Earth System Models (ESMs) come with additional assumptions and limitations (Flato \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Heavens et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). \u003cem\u003eAsparagopsis\u003c/em\u003e is clearly an adaptable genus, as attested by its invasive capabilities (Taylor and Kumar \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Blanco et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, it is possible \u003cem\u003eAsparagopsis spp.\u003c/em\u003e could undergo niche shifts under the future conditions (Laeseke et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Future studies should integrate biological processes such as adaptation as they are fundamental to producing more accurate projections of the distribution of a species (especially adaptable ones), such as by using ΔTraitSDMs (Benito Garz\u0026oacute;n et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, this study demonstrated that the East Coast of Australia is a suitable area for the cultivation of \u003cem\u003eAsparagopsis spp.\u003c/em\u003e under climate change in 2100. Under all climate scenarios, for \u003cem\u003eA. armata\u003c/em\u003e, the southern region near ROIA3 will remain moderately suitable for cultivation under low emission scenarios (SSP-1(2.6)), and for \u003cem\u003eA. taxiformis\u003c/em\u003e a few key sites, namely near ROIT1, ROIT2, ROIT3 and ROIT4, remain moderately suitable for cultivation under some or all climate scenarios in 2100. Increased production globally would render \u003cem\u003eAsparagopsis\u003c/em\u003e products more accessible to the livestock industry, in turn assisting emissions reductions, facilitating countries to reach targets of NetZero.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e:No funds, grants, or other support was received.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting Interests:\u003c/u\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eData availability\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe data and notebooks will be made available via Zenodo [https://zenodo.org/].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGabrielle King, Tristan Salles and Ana Vila-Concejo contributed to the study conception and design. The first draft of the manuscript was written by Gabrielle King, Tristan Salles and Ana Vila-Concejo commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsadian M, Fakheri BA, Mahdinezhad N, Gharanjik S, Beardal J, Talebi AF (2018) Algal communities: An answer to global climate change. CLEAN\u0026ndash;Soil, Air, Water vol. 46, no. 10, pp. 1800032.\u003c/li\u003e\n\u003cli\u003eAssis J, Fern\u0026aacute;ndez Bejarano SJ, Salazar VW, Schepers L, Gouv\u0026ecirc;a L, Fragkopoulou E, Leclercq F, Vanhoorne B, Tyberghein L, Serr\u0026atilde;o EA (2024) Bio‐ORACLE v3. 0. Pushing marine data layers to the CMIP6 Earth System Models of climate change research. 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Algal Research vol. 56, no., pp. 102319.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Species distribution models (SDM), Asparagopsis, Net Zero, Cultivation, Methane, Livestock","lastPublishedDoi":"10.21203/rs.3.rs-7784217/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7784217/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eAsparagopsis\u003c/em\u003e, a red macroalgae native to Australia\u0026rsquo;s East Coast (10\u0026deg; S, 142\u0026deg; E to 44 \u0026deg; S, 154\u0026deg; E), has shown the potential to reduce livestock methane emissions by up to 98%, facilitating climate change mitigation. Ensemble species distribution models were used to predict suitable cultivation sites for \u003cem\u003eA. armata\u003c/em\u003e and \u003cem\u003eA. taxiformis\u003c/em\u003e along the East Coast of Australia in 2100 under three climate scenarios (SSP-1, SSP-2, SSP-5). Results show a substantial loss of highly suitable habitat by 2100, with only moderately suitable areas remaining; one location for \u003cem\u003eA. armata\u003c/em\u003e under SSP1-2.6, and four locations across multiple climate scenarios for \u003cem\u003eA. taxiformis\u003c/em\u003e. Cultivating \u003cem\u003eAsparagopsis\u003c/em\u003e in these locations can contribute to global methane mitigation efforts.\u003c/p\u003e","manuscriptTitle":"Potential sites for Asparagopsis spp. cultivation on Australia’s East Coast in 2100","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 12:20:25","doi":"10.21203/rs.3.rs-7784217/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41383f1d-6a7a-4e3c-807c-a803f54adb40","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-09T00:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 12:20:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7784217","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7784217","identity":"rs-7784217","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}