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Global estimates of the extent and production of macroalgal forests

Publication . Duarte, Carlos M.; Gattuso, Jean‐Pierre; Hancke, Kasper; Gundersen, Hege; Filbee‐Dexter, Karen; Pedersen, Morten F.; Middelburg, Jack J.; Burrows, Michael T.; Krumhansl, Kira A.; Wernberg, Thomas; Moore, Pippa; Pessarrodona, Albert; Ørberg, Sarah B.; Pinto, Isabel S.; Assis, Jorge; Queirós, Ana M.; Smale, Dan A.; Bekkby, Trine; Serrao, Ester; Krause‐Jensen, Dorte; Field, Richard

Aim Macroalgal habitats are believed to be the most extensive and productive of all coastal vegetated ecosystems. In stark contrast to the growing attention on their contribution to carbon export and sequestration, understanding of their global extent and production is limited and these have remained poorly assessed for decades. Here we report a first data-driven assessment of the global extent and production of macroalgal habitats based on modelled and observed distributions and net primary production (NPP) across habitat types. Location Global coastal ocean. Time period Contemporary. Major taxa studied Macroalgae. Methods Here we apply a comprehensive niche model to generate an improved global map of potential macroalgal distribution, constrained by incident light on the seafloor and substrate type. We compiled areal net primary production (NPP) rates across macroalgal habitats from the literature and combined this with our estimates of the global extent of these habitats to calculate global macroalgal NPP. Results We show that macroalgal forests are a major biome with a global area of 6.06-7.22 million km(2), dominated by red algae, and NPP of 1.32 Pg C/year, dominated by brown algae. Main conclusions The global macroalgal biome is comparable, in area and NPP, to the Amazon forest, but is globally distributed as a thin strip around shorelines. Macroalgae are expanding in polar, subpolar and tropical areas, where their potential extent is also largest, likely increasing the overall contribution of algal forests to global carbon sequestration.

2022Journal article

Open access

Artificial intelligence convolutional neural networks map giant kelp forests from satellite imagery

Publication . Marquez, L.; Fragkopoulou, Eliza; Cavanaugh, K. C.; Houskeeper, H. F.; Assis, J.

Climate change is producing shifts in the distribution and abundance of marine species. Such is the case of kelp forests, important marine ecosystem-structuring species whose distributional range limits have been shifting worldwide. Synthesizing long-term time series of kelp forest observations is therefore vital for understanding the drivers shaping ecosystem dynamics and for predicting responses to ongoing and future climate changes. Traditional methods of mapping kelp from satellite imagery are time-consuming and expensive, as they require high amount of human effort for image processing and algorithm optimization. Here we propose the use of mask region-based convolutional neural networks (Mask R-CNN) to automatically assimilate data from open-source satellite imagery (Landsat Thematic Mapper) and detect kelp forest canopy cover. The analyses focused on the giant kelp Macrocystis pyrifera along the shorelines of southern California and Baja California in the northeastern Pacific. Model hyper-parameterization was tuned through cross-validation procedures testing the effect of data augmentation, and different learning rates and anchor sizes. The optimal model detected kelp forests with high performance and low levels of overprediction (Jaccard's index: 0.87 +/- 0.07; Dice index: 0.93 +/- 0.04; over prediction: 0.06) and allowed reconstructing a time series of 32 years in Baja California (Mexico), a region known for its high variability in kelp owing to El Nino events. The proposed framework based on Mask R-CNN now joins the list of cost-efficient tools for long-term marine ecological monitoring, facilitating well-informed biodiversity conservation, management and decision making.

2022Journal article

Open access

Global seaweed productivity

Publication . Pessarrodona, Albert; Assis, Jorge; Filbee-Dexter, Karen; Burrows, Michael T.; Gattuso, Jean-Pierre; Duarte, Carlos M.; Krause-Jensen, Dorte; Moore, Pippa J.; Smale, Dan A.; Wernberg, Thomas

The magnitude and distribution of net primary production (NPP) in the coastal ocean remains poorly constrained, particularly for shallow marine vegetation. Here, using a compilation of in situ annual NPP measurements across >400 sites in 72 geographic ecoregions, we provide global predictions of the productivity of seaweed habitats, which form the largest vegetated coastal biome on the planet. We find that seaweed NPP is strongly coupled to climatic variables, peaks at temperate latitudes, and is dominated by forests of large brown seaweeds. Seaweed forests exhibit exceptionally high per-area production rates (a global average of 656 and 1711 gC m-2 year-1 in the subtidal and intertidal, respectively), being up to 10 times higher than coastal phytoplankton in temperate and polar seas. Our results show that seaweed NPP is a strong driver of production in the coastal ocean and call for its integration in the oceanic carbon cycle, where it has traditionally been overlooked.

2022Journal article

Open access

Global impacts of projected climate changes on the extent and aboveground biomass of mangrove forests

Publication . Gouvêa, Lidiane; A, Serrão; Cavanaugh, Kyle; Gurgel, Carlos F. D.; Horta, Paulo A.; Assis, Jorge

Aim: Over the past 50 years, anthropogenic activities have led to the disappearance of approximately one-third of the world's mangrove forests and their associated ecosystem services. The synergetic combined effect of projected climate change is likely to further impact mangroves in the years to come, whether by range expansions associated with warming at higher latitudes or large-scale diebacks linked to severe droughts. We provide an estimate of future changes in the extent and aboveground biomass (AGB) of mangrove forests at global scales by considering contrasting Representative Concentration Pathway scenarios (decade 2090-2100 under RCP 2.6 in line with the Paris Agreement expectations, and RCP 8.5 of higher emissions). Location: Global. Methods: Boosted regression trees fitted occurrence and AGB of mangroves against high-resolution biologically meaningful data on air temperature, precipitation, wave energy, slope and distance to river Deltas. Results: On the global scale, models produced for present-day conditions retrieved high accuracy scores and estimated a total area of 12,780,356 ha and overall biomass of 2.29 Pg, in line with previous estimates. Model projections showed poleward shifts along temperate regions, which translated into comparable gains in total area, regardless of the RCP scenario (area change RCP 2.6: 17.29%; RCP 8.5: 15.77%). However, biomass changes were dependent on the emission scenario considered, remaining stable or even increasing under RCP 2.6, or undergoing severe losses across tropical regions under RCP 8.5 (overall biomass change RCP 2.6: 12.97%; RCP 8.5: -11.51%). Such losses were particularly aggravated in countries located in the Tropical Atlantic and Eastern Pacific, and the Western and Eastern Indo-Pacific regions (regions with losses above similar to 20% in overall biomass). Conclusions: Our global estimates highlight the potential effect of future climate changes on mangrove forests and how broad compliance with the Paris Agreement may counteract severe trajectories of loss. The projections made, also provided at the country level, serve as new baselines to evaluate changes in mangrove carbon sequestration and ecosystem services, strongly supporting policy-making and management directives, as well as to guide restoration actions considering potential future changes in niche availability.

2022Journal article

Open access

How experimental physiology and ecological niche modelling can inform the management of marine bioinvasions?

Publication . Koerich, Gabrielle; Assis, Jorge; Costa, Giulia Burle; Sissini, Marina Nasri; Serrao, Ester; Rorig, Leonardo Rubi; Hall-Spencer, Jason M.; Barufi, Jose Bonomi; Horta, Paulo Antunes

Marine bioinvasions are increasing worldwide by a number of factors related to the anthroposphere, such as higher ship traffic, climate change and biotic communities' alterations. Generating information about species with high invasive potential is necessary to inform management decisions aiming to prevent their arrival and spread. Grateloupia turuturu, one of the most harmful invasive macroalgae, is capable of damaging ecosystem functions and services, and causing biodiversity loss. Here we developed an ecological niche model using occurrence and environmental data to infer the potential global distribution of G. turuturu. In addition, ecophysiological experiments were performed with G. turuturu populations from different climatic regions to test predictions regarding invasion risk. Our model results show high suitability in temperate and warm temperate regions around the world, with special highlight to some areas where this species still doesn't occur. Thalli representing a potential temperate region origin, were held at 10, 13, 16, 20 and 24 degrees C, and measurements of optimal quantum field (Fv/Fm) demonstrated a decrease of photosynthetic yield in the higher temperature. Thalli from the population already established in warm temperate South Atlantic were held at 18, 24 and 30 degrees C with high and low nutrient conditions. This material exposed to the higher temperature demonstrated a drop in photosynthetic yield and significant reduction of growth rate. The congregation of modelling and physiological approach corroborate the invasive potential of G. turuturu and indicate higher invasion risk in temperate zones. Further discussions regarding management initiatives must be fostered to mitigate anthropogenic transport and eventually promote eradication initiatives in source areas, with special focus in the South America. We propose that this combined approach can be used to assess the potential distribution and establishment of other marine invasive species. (C) 2019 Elsevier B.V. All rights reserved.

2020-01Journal article

Restricted access