Connectivity as a Management Tool for Coastal Ecosystems in Changing Oceans

Corre, Nicolas Le; Guichard, Frédéric; Johnson, Ladd E.

doi:10.5772/27704

Cited by 2 publications

(1 citation statement)

References 104 publications

(149 reference statements)

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“…Metapopulation structure has been described for the bay scallop Argopecten irradians in the eastern GOM (Bert et al 2014), a species exhibiting local and mesoscale distribution similar to that of the oyster. To the extent that oysters in the eastern GOM do exhibit metapopulation structure, that structure is poorly understood and is only recently developing as a consideration in the plight of the eastern oyster in Florida and throughout the speciesÕ range (Haase et al 2012, LeCorre et al 2012, Munroe et al 2012, Kjelland et al 2015. At present, evidence for oyster metapopulation structure is primarily based on genetic studies, which reveal complex larval exchange patterns among estuaries spanning the entire Gulf (Gaffney 2006, Varney et al 2009.…”

Section: Introductionmentioning

confidence: 99%

Applying a Coupled Biophysical Model to Predict Larval Dispersal and Source/Sink Relationships in a Depleted Metapopulation of the Eastern OysterCrassostrea virginica

Arnold

Meyers

Geiger

et al. 2017

Journal of Shellfish Research

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Section: Introductionmentioning

confidence: 99%

Applying a Coupled Biophysical Model to Predict Larval Dispersal and Source/Sink Relationships in a Depleted Metapopulation of the Eastern OysterCrassostrea virginica

Arnold

Meyers

Geiger

et al. 2017

Journal of Shellfish Research

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The Use of Kernel Density Estimation With a Bio-Physical Model Provides a Method to Quantify Connectivity Among Salmon Farms: Spatial Planning and Management With Epidemiological Relevance

et al. 2018

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Connectivity in an aquatic setting is determined by a combination of hydrodynamic circulation and the biology of the organisms driving linkages. These complex processes can be simulated in coupled biological-physical models. The physical model refers to an underlying circulation model defined by spatially-explicit nodes, often incorporating a particle-tracking model. The particles can then be given biological parameters or behaviors (such as maturity and/or survivability rates, diel vertical migrations, avoidance, or seeking behaviors). The output of the bio-physical models can then be used to quantify connectivity among the nodes emitting and/or receiving the particles. Here we propose a method that makes use of kernel density estimation (KDE) on the output of a particle-tracking model, to quantify the infection or infestation pressure (IP) that each node causes on the surrounding area. Because IP is the product of both exposure time and the concentration of infectious agent particles, using KDE (which also combine elements of time and space), more accurately captures IP. This method is especially useful for those interested in infectious agent networks, a situation where IP is a superior measure of connectivity than the probability of particles from each node reaching other nodes. Here we illustrate the method by modeling the connectivity of salmon farms via sea lice larvae in the Broughton Archipelago, British Columbia, Canada. Analysis revealed evidence of two sub-networks of farms connected via a single farm, and evidence that the highest IP from a given emitting farm was often tens of kilometers or more away from that farm. We also classified farms as net emitters, receivers, or balanced, based on their structural role within the network. By better understanding how these salmon farms are connected to each other via their sea lice larvae, we can effectively focus management efforts to minimize the spread of sea lice between farms, advise on future site locations and coordinated treatment efforts, and minimize any impact of farms on juvenile wild salmon. The method has wide applicability for any system where capturing infectious agent networks can provide useful guidance for management or preventative planning decisions.

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Connectivity as a Management Tool for Coastal Ecosystems in Changing Oceans

Cited by 2 publications

References 104 publications

Applying a Coupled Biophysical Model to Predict Larval Dispersal and Source/Sink Relationships in a Depleted Metapopulation of the Eastern OysterCrassostrea virginica

Applying a Coupled Biophysical Model to Predict Larval Dispersal and Source/Sink Relationships in a Depleted Metapopulation of the Eastern OysterCrassostrea virginica

The Use of Kernel Density Estimation With a Bio-Physical Model Provides a Method to Quantify Connectivity Among Salmon Farms: Spatial Planning and Management With Epidemiological Relevance

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