Examining Epibenthic Assemblages Associated with Artificial Reefs Using a Species Archetype Approach. (11th May 2022) - Yu, Haolin; Fang, Guangjie; Rose, Kenneth A.; Tang, Yanli; Song, Xiefa
Abstract: The placement of artificial reefs (ARs) influences, to various degrees, a wide range of epibenthic species, whereas most assessments focus on target or focal species. Methods of capturing the responses of many species can inform management about the full range of likely responses of species to the location and arrangement of ARs. Performing many single‐species analyses presents difficulties in interpretation. We used monitoring data from 14 surveys from June 2017 to August 2020 in an area with ARs deployed in the Bohai Sea, China. Both trap and visual census data were collected, and a suite of environmental variables was also collected or estimated for sampled sites and for spatial cells throughout the study area. The data were used to fit a species archetype model (SAM) that relies on the shared responses of species to environmental variables. The many species were grouped into six distinct archetypes. Species membership in the archetypes was confirmed by comparing results to the isometric feature mapping and partitioning around medoids (ISOPAM) approach, which relied on species co‐occurrence applied to the same data. The SAM results were also validated by comparing archetypes determined with fitting to predictions from a single survey that was not used in fitting. The six archetypes identified by the SAM had member species that differed in dependence on substrate types, distance to the nearest AR, distance to the nearest gravel, temperature, dissolved oxygen, time since AR deployment, and sampling method. The importance of the environmental variables was assessed by computing the changes in predicted occurrence probabilities of the archetypes when environmental variables were varied with the other environmental variables set at their minimum, mean, maximum, or optimal values. Species archetype modeling provides a valuable approach for predicting occurrence probabilities of epibenthic species assemblages in response to the locations and arrangement of ARs, and results can inform management related to fishing enhancement and conservation.