Using species distribution modelling to predict future distributions of phytoplankton: case study using species important for the biological pump

TY - JOUR

T1 - Using species distribution modelling to predict future distributions of phytoplankton

T2 - case study using species important for the biological pump

AU - Ørsted Jensen, Lasse

AU - Mousing, Erik Askov

AU - Richardson, Katherine

PY - 2017/6

Y1 - 2017/6

N2 - Some phytoplankton species have been predicted to contribute more to the biological pump than others. In this study, we examine the potential of species distribution modelling (SDM) for describing current and predicting future global distributions of two phytoplankton species: the diatom Chaetoceros diadema and the coccolithophore Emiliania huxleyi. Species distribution models (SDMs) were constructed using species data from the Ocean Biogeographic Information System and environmental layers from the Coupled Model Intercomparison Project Phase 5. The resulting distributions were evaluated by comparing predicted distributions with those found via a literature survey. The developed SDMs were then applied to predict future changes in the distributions of these species using environmental conditions based on the Intergovernmental Panel on Climate Change's Representative Concentration Pathways scenario 8.5 climate scenario, predicted for the year 2100. The model predicts that the total ocean area covered by C. diadema and E. huxleyi will decline under the examined climate scenario by 8% and 16%, respectively. Furthermore, the future ranges above depths >1,000 m are predicted to decline by 28% for C. diadema. As the biological pump is most active in deep ocean waters, this projected decrease in range in the deeper regions of the ocean may have implications for carbon sequestration, particularly for the diatom species. We conclude that SDM appears to be a robust tool for examining phytoplankton distributions. If the predicted changes in ranges of these two species under future ocean conditions are realised, this may result in a reduced contribution of these two phytoplankton species to carbon sequestration via the biological pump.

AB - Some phytoplankton species have been predicted to contribute more to the biological pump than others. In this study, we examine the potential of species distribution modelling (SDM) for describing current and predicting future global distributions of two phytoplankton species: the diatom Chaetoceros diadema and the coccolithophore Emiliania huxleyi. Species distribution models (SDMs) were constructed using species data from the Ocean Biogeographic Information System and environmental layers from the Coupled Model Intercomparison Project Phase 5. The resulting distributions were evaluated by comparing predicted distributions with those found via a literature survey. The developed SDMs were then applied to predict future changes in the distributions of these species using environmental conditions based on the Intergovernmental Panel on Climate Change's Representative Concentration Pathways scenario 8.5 climate scenario, predicted for the year 2100. The model predicts that the total ocean area covered by C. diadema and E. huxleyi will decline under the examined climate scenario by 8% and 16%, respectively. Furthermore, the future ranges above depths >1,000 m are predicted to decline by 28% for C. diadema. As the biological pump is most active in deep ocean waters, this projected decrease in range in the deeper regions of the ocean may have implications for carbon sequestration, particularly for the diatom species. We conclude that SDM appears to be a robust tool for examining phytoplankton distributions. If the predicted changes in ranges of these two species under future ocean conditions are realised, this may result in a reduced contribution of these two phytoplankton species to carbon sequestration via the biological pump.

U2 - 10.1111/maec.12427

DO - 10.1111/maec.12427

M3 - Journal article

SN - 0173-9565

VL - 38

JO - Marine Ecology

JF - Marine Ecology

IS - 3

M1 - e12427

ER -