Back

Indicators to assess interannual variability in marine connectivity processes: a semi-theoretical approach

Clavel-Henry, M.; Bahamon, N.; Aguzzi, J.; Navarro, J.; Lopez, M.; Company, J. B.

2024-01-15 ecology
10.1101/2024.01.12.575479 bioRxiv
Show abstract

Oceanographic connectivity in an effective network of protected areas is crucial for restoring and stabilising marine populations. However, temporal variability of connectivity is rarely considered as a criterion in designing and evaluating marine conservation planning. In this study, indicators were defined to characterise occurrence, strength and frequency of the temporal variability in connectivity in a northwestern Mediterranean Sea area. Indicators were tested on semi-theoretically-estimated connections provided by the runs of a passive particle transport model in a climatological year and in three years between 2006 - 2020, showing large deviation from the climatological year. The indicators compared the temporal variability in connectivity of four zones, highlighting differences in connectivity due to their locations and the mesoscale hydrodynamics, and identifying areas that require further investigation. The three indicators also showed that the temporal variability in connectivity was influenced by the duration and depth of particle transport, although no consistent pattern was observed in the indicator variations of the compared zones. Provided that specific objectives will be given when parameterising transport models (i.e., selection of focus species and time period), indicators of temporal variability in connectivity have potential to support, and correctly implement, spatial conservation planning, prioritise the protection of marine resources, and measure the effectiveness of Marine Protected Areas, in line with a long-term vision of ocean management.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.