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Applying network methods to acoustic telemetry data: Modeling the movements of tropical marine fishes
Finn, J.T.; Brownscombe, J.W.; Haak, C.R.; Cooke, S.J.; Cormier, R.; Gagne, T.O.; Danylchuk, A.J. (2014). Applying network methods to acoustic telemetry data: Modeling the movements of tropical marine fishes. Ecol. Model. 293: 139-149. https://dx.doi.org/10.1016/j.ecolmodel.2013.12.014
In: Ecological Modelling. Elsevier: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0304-3800; e-ISSN 1872-7026
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| Trefwoord |
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| Author keywords |
Acoustic tagging; Fish movement; Social network analysis; Bipartite graphs; Directed graphs |
| Auteurs | | Top |
- Finn, J.T.
- Brownscombe, J.W.
- Haak, C.R.
- Cooke, S.J.
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- Cormier, R.
- Gagne, T.O.
- Danylchuk, A.J.
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| Abstract |
Modeling animal movements is fundamental to animal ecology as it provides the foundation for furtherexploration into mechanisms affecting individual and population-level processes. In the last few decades,biotelemetry has enabled scientists to track the movements of marine life across a variety of scales.However, the use of such technology is progressing faster than the analytical techniques for modelingmovement patterns. In summer 2012, we deployed an acoustic telemetry array around Culebra, PuertoRico, consisting of 48 remote receivers that can detect coded transmissions sent by tags implanted infish. We surgically implanted transmitters in bonefish (n = 28), great barracuda (n = 2) and permit (n = 1)as part of a multi-year study. In January 2013, we downloaded over 850,000 detections from 39 receiversfor 31 fish (several receivers had zero fish detections, and two receivers were not downloaded), and usedthat six-month data set to explore how graph theory and network analysis can be used to model themovement ecology of the tagged fish. We analyzed this data as two types of graphs. First, a bipartitegraph was constructed by linking each fish with an edge weighted by the number of detections of thatfish by that receiver. Bipartite graphs are not explicitly spatial, but rather represent which fish associatewith which receivers. Second, spatial movement graphs for individuals were built by linking receivers(nodes) by edges with the number of times each fish moved along that edge as weights. The bipartitegraph identified groups of fish visiting the same sites, and groups of sites visited by the same fish. Ofthe six community detection algorithms used, Multilevel, Fast-Greedy, and Walk-Trap performed best,with similar module partitions and modularity scores. All three of these algorithms produced modules(groups) that appear to reflect working hypotheses related to the coastal bathymetry, habitat types, andassociated movement ecology of the tagged species. Spatial movement graphs were very different foreach fish examined and reflect behavioral differences. Fish exhibited various movement patterns, someshowing the pattern of a central place forager (bonefish), while others cruised along a territory (greatbarracuda and permit). |
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