Machine learning applied to species occurrence and interactions:the missing link in biodiversity assessment and modelling of Antarctic plankton distribution  

作　　者：Marco Grillo Stefano Schiaparelli Tiziana Durazzano Letterio Guglielmo Antonia Granata Falk Huettmann 

机构地区：[1]Department of Physical Sciences,Earth and Environment(DSFTA),University of Siena,Siena,Italy [2]Italian National Antarctic Museum(MNA,Section of Genoa),University of Genoa,Genoa,Italy [3]Department of Earth,Environmental and Life Sciences(DISTAV),University of Genoa,Genoa,Italy [4]Department of Arctic and Marine Biology,UiT The Arctic University of Norway,Tromsø,Norway [5]Integrative Marine Ecology Department,Stazione Zoologica Anton Dohrn,Naples,Italy [6]Institute of Polar Sciences,National Research Council(CNR),Messina,Italy [7]Department of Biological,Chemical,Pharmaceutical,and Environmental Sciences(ChiBioFarAm),University of Messina,Messina,Italy [8]-EWHALE Lab-,Biology&Wildlife Department,Institute of Arctic Biology,University of Alaska Fairbanks,Alaska,USA

出　　处：《Ecological Processes》2024年第3期192-209,共18页生态过程(英文)

摘　　要：Background Plankton is the essential ecological category that occupies the lower levels of aquatic trophic networks,representing a good indicator of environmental change.However,most studies deal with distribution of single species or taxa and do not take into account the complex of biological interactions of the real world that rule the ecological processes.Results This study focused on analyzing Antarctic marine phytoplankton,mesozooplankton,and microzooplankton,examining their biological interactions and co-existences.Field data yielded 1053 biological interaction values,762 coexistence values,and 15 zero values.Six phytoplankton assemblages and six copepod species were selected based on their abundance and ecological roles.Using 23 environmental descriptors,we modelled the distribution of taxa to accurately represent their occurrences.Sampling was conducted during the 2016–2017 Italian National Antarctic Programme(PNRA)‘P-ROSE’project in the East Ross Sea.Machine learning techniques were applied to the occurrence data to generate 48 predictive species distribution maps(SDMs),producing 3D maps for the entire Ross Sea area.These models quantitatively predicted the occurrences of each copepod and phytoplankton assemblage,providing crucial insights into potential variations in biotic and trophic interactions,with signifcant implications for the management and conservation of Antarctic marine resources.The Receiver Operating Characteristic(ROC)results indicated the highest model efciency,for Cyanophyta(74%)among phytoplankton assemblages and Paralabidocera antarctica(83%)among copepod communities.The SDMs revealed distinct spatial heterogeneity in the Ross Sea area,with an average Relative Index of Occurrence values of 0.28(min:0;max:0.65)for phytoplankton assemblages and 0.39(min:0;max:0.71)for copepods.Conclusion The results of this study are essential for a science-based management for one of the world’s most pristine ecosystems and addressing potential climate-induced alterations in species interaction

关 键 词：Copepoda PHYTOPLANKTON Ross sea Terra Nova Bay Species Distribution Model Marine trophic web 

分 类 号：P73[天文地球—海洋科学]