Table of Contents

• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Parameters
• Deployments
• Project Information
• Funding

You may access the data via the 'Get Data' button at the top of this page or download the zipped files: HawaiiConnectivityFiles.zip (10 MB).

Related Datasets:
Larval Dispersal Model: reef locations
Larval Dispersal Model: reef names

Related References:

Polovina JJ, Kleiber P, Kobayashi DR (1999) Application of TOPEX-POSEIDON satellite altimetry to simulate transport dynamics of larvae of spiny lobster, Panulirus marginatus, in the Northwestern Hawaiian Islands, 1993-1996. Fish Bull 97:132–143

Wren JLK, Kobayashi DR (2016) Exploration of the “larval pool”: development and ground-truthing of a larval transport model off leeward Hawai‘i. PeerJ 4:e1636

Detailed description of the methods: Wren, J.L.K., Kobayashi, D.R., Jia, Y. and Toonen R.J. (in revision) Modeled population connectivity in the Hawaiian Islands. PLoS ONE.

These data are output from a Lagrangian bio-physical particle tracking model coupled with the flow fields from the MITgcm simulation for the Hawaiian Archipelago (175°E to 150°W and from 15°N to 35°N at a 0.04° resolution). In the model, larvae are moved at each timestep by advective displacement caused by water flow combined with a random displacement caused by diffusion. Current velocities used by the model consist of daily snapshots of u (east) and v (north) velocities obtained from a regional implementation the MITgcm model. Eddy diffusivity was set to 250 m2/sec, consistent with drifters in Hawaiian waters. We released 50 particles daily from May 2, 2009, until April 10, 2014, from 687 coral reef habitat pixels representing of all emergent land in the model domain, totaling just over 62 million released particles for each model run. We used a pelagic larval duration (PLD) of 45 days and for a particle to be considered “settled”, it had to be within a 5 km radius of the center of a habitat pixel on the last day of its PLD. See Polovina et al. 1999 for a description of the model, and Wren and Kobayashi 2016 for modifications done to the model. 

Data are raw model output and unprocessed.

BCO-DMO Processing Notes:
- created toplevel file that calls the 3 text files
- added conventional header with dataset name, PI name, version date
- added parameter names that conform with BCO-DMO naming conventions

Description from NSF award abstract:
The exchange of individuals among populations, termed connectivity, is a central element of population persistence and maintenance of genetic diversity, and influences most ecological and evolutionary processes. To date, field studies of marine connectivity have necessarily focused on one or a few species at a time, providing little understanding of both the extent of variability in connectivity across a whole community and what factors drive that variability. This project will address these questions with population genetic datasets of a diverse marine fauna sampled across the Hawaiian Archipelago. By combining these genetic data with extensive oceanographic, ecological and historical data, this project can potentially transform our understanding of the basis of the genetic structure of populations and the processes influencing genetic patterns. This project will provide unique, and new, knowledge to basic marine ecology and the science of Ecosystem Based Management while incorporating the latest analytical and simulation approaches.

The results will be novel on several fronts: 1) advancing our understanding of community genetics and associated statistical techniques; 2) achieving true integration of genetic, ecological and oceanographic data over large spatial scales for many species simultaneously using a World Heritage Site; the Hawaiian Papahânaumokuâkea Marine National Monument; 3) factoring historical effects into connectivity studies; and 4) providing information on the location of barriers to connectivity, the sources and sinks of individuals and the physical processes influencing ecological patterns at a community level. This project will result in a quantum leap for both the conceptual and empirical understanding of marine connectivity and the utility of population genetic data in basic and applied marine science.