Acquiring a comprehensive and accurate understanding of habitat preference is essential for species conservation and fishery management,especially for mobile species that migrate seasonally.Presence and absence data f...Acquiring a comprehensive and accurate understanding of habitat preference is essential for species conservation and fishery management,especially for mobile species that migrate seasonally.Presence and absence data from field surveys are recommended when available due to their high reliability.Using field survey data,we investigated seasonal habitat suitability requirements for Tanaka's snailfish(Liparis tanakae)in the Bohai Sea and Yellow Sea(BSYS)via a machine-learning method,random forests(RFs).Five environmental and biologically relevant variables(bottom temperature,bottom salinity,current velocity,depth and distance to shore)were used to build the ecological niches between the presence/absence data and suitable habitat.In addition,the degree to which false absence data might impact model performance was evaluated.Our results indicated that RFs provided accurate predictions,with seasonal habitat suitability maps of L.tanakae differing substantially.Bottom temperature and salinity were identified as important factors influencing the distribution of L.tanakae.False absence data were found to have negative effects on model performance and the decrease in evaluation metrics was usually significant(P<0.05)after 30%or more errors were added to the absence data.Through identifying highly suitable areas within its geographic range,our study provides a baseline for L.tanakae that can be further applied in ecosystem modelling and fishery management in the BSYS.展开更多
基金The National Natural Science Foundation of China under contract No.42176151the Youth Talent Program Supported by Laboratory for Marine Fisheries Science and Food Production Processes,Pilot National Laboratory for Marine Science and Technology(Qingdao)under contract No.2018-MFS-T05the Central Public-Interest Scientific Institution Basal Research Fund,Yellow Sea Fisheries Research Institute,Chinese Academy of Fishery Sciences under contract Nos 20603022019010 and 20603022022022。
文摘Acquiring a comprehensive and accurate understanding of habitat preference is essential for species conservation and fishery management,especially for mobile species that migrate seasonally.Presence and absence data from field surveys are recommended when available due to their high reliability.Using field survey data,we investigated seasonal habitat suitability requirements for Tanaka's snailfish(Liparis tanakae)in the Bohai Sea and Yellow Sea(BSYS)via a machine-learning method,random forests(RFs).Five environmental and biologically relevant variables(bottom temperature,bottom salinity,current velocity,depth and distance to shore)were used to build the ecological niches between the presence/absence data and suitable habitat.In addition,the degree to which false absence data might impact model performance was evaluated.Our results indicated that RFs provided accurate predictions,with seasonal habitat suitability maps of L.tanakae differing substantially.Bottom temperature and salinity were identified as important factors influencing the distribution of L.tanakae.False absence data were found to have negative effects on model performance and the decrease in evaluation metrics was usually significant(P<0.05)after 30%or more errors were added to the absence data.Through identifying highly suitable areas within its geographic range,our study provides a baseline for L.tanakae that can be further applied in ecosystem modelling and fishery management in the BSYS.
