|Causal links between North Sea fish biomass trends and seabed structure|Rademaker, M.; Smallegange, I.M.; van Leeuwen, A. (2021). Causal links between North Sea fish biomass trends and seabed structure. Mar. Ecol. Prog. Ser. 677: 129-140. https://dx.doi.org/10.3354/meps13845
In: Marine Ecology Progress Series. Inter-Research: Oldendorf/Luhe. ISSN 0171-8630; e-ISSN 1616-1599, meer
IBTS; International Bottom-Trawl Survey; PCMCI; Seascapes; Spatiotemporal analysis; Time series; Bottom-trawl
|Auteurs|| || Top |
- Rademaker, M.
- Smallegange, I.M.
- van Leeuwen, A., meer
Distinct areas of seabed, called seascapes, are known to shape benthic habitats and communities, yet little is known about the extent to which they affect the dynamics of marine fish populations. We explored the relationship between seascapes and trends in the biomass density of several North Sea fish species. We divided the North Sea into 10 seascapes using standardized methods. Time series of fish biomass density were derived from the North Sea International Bottom-Trawl Survey (NS-IBTS) and aggregated to the seascape level. We analysed the interdependencies between these time series using a causal association network and found independent biomass density trends between adjacent seascapes at a time interval of 0 yr in all species assessed. Long-term causal dependencies in biomass density occurred at time lags of 1-2 yr across different gradients of exchange: (1) both directions from North to South; (2) unidirectional, North-South; (3) unidirectional, South-North; (4) unidirectional, East-West; and (5) no clear direction. Our findings indicate that the separation in (a)biotic conditions between North Sea seascapes can represent relevant barriers to the processes determining the observed fish biomass density. We found that non-fusiform morphology and demersal habitat preferences best explained short-term causal dependencies. This combination is particular to the flatfish and ray species included in the present study. Contrarily, the movement of large, long-lived, benthopelagic species best explained long-term causal dependencies. Our work highlights how causal association networks can be used to study the temporal dependencies between spatial time series in ecology.