Data-driven determination of zooplankton bioregions and robustness analysis.

Identifying biogeographic regions through cluster analysis of species distribution data is a common method for partitioning ecosystems. Selecting the appropriate cluster analysis method requires a comparison of multiple algorithms. In this study, we demonstrate a data-driven process to select a method for bioregionalization based on community data and test its robustness to data variability following these steps: •We aggregated and curated zooplankton community observations from expeditions in the Northeast Pacific.•We determined the best bioregionalization approach by comparing nine cluster analysis methods using ten goodness of clustering indices.•We evaluated the robustness of the bioregionalization to different sources of sampling and taxonomic variability by comparing the bioregionalization of the overall dataset with bioregionalizations of subsets of the data. The K-means clustering of the log-chord transformed abundance was selected as the optimal method for bioregionalization of the zooplankton dataset. This clustering resulted in the emergence of four bioregions along the cross-shelf gradient: the Offshore, Deep Shelf, Nearshore, and Deep Fjord bioregions. The robustness analyses demonstrated that the bioregionalization was consistent despite variability in the spatial and temporal frequency of sampling, sampling methodology, and taxonomic coverage.

Larval connectivity patterns of the North Indo-West Pacific coral reefs.

Coral reefs of the North Indo-West Pacific provide important ecosystem services to the region but are subjected to multiple local and global threats. Strengthening management measures necessitate understanding the variability of larval connectivity and bridging global connectivity models to local scales. An individual-based Lagrangian biophysical model was used to simulate connectivity between coral reefs for three organisms with different early life history characteristics: a coral (Acropora millepora), a sea urchin (Tripneustes gratilla), and a reef fish (Epinephelus sp). Connectivity metrics and reef clusters were computed from the settlement probability matrices. Fitted power law functions derived from the dispersal kernels provided relative probabilities of connection given only the distance between reefs, and demonstrated that 95% of the larvae across organisms settled within a third of their maximum settlement distances. The magnitude of the connectivity metric values of reef cells were sensitive to differences both in the type of organism and temporal variability. Seasonal variability of connections was more dominant than interannual variability. However, despite these differences, the moderate to high correlation of metrics between organisms and seasonal matrices suggest that the spatial patterns are relatively similar between reefs. A cluster analysis based on the Bray-Curtis Dissimilarity of sink and source connections synthesized the inherent variability of these multiple large connectivity matrices. Through this, similarities in regional connectivity patterns were determined at various cluster sizes depending on the scale of interest. The validity of the model is supported by 1) the simulated dispersal kernels being within the range of reported parentage analysis estimates; and, 2) the clusters that emerged reflect the dispersal barriers implied by previously published population genetics studies. The tools presented here (dispersal kernels, temporal variability maps and reef clustering) can be used to include regional patterns of connectivity into the spatial management of coral reefs.