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Responses of Ichthyoplankton Biodiversity and Dynamics to Environmental Change

Cycles between warm and cold regimes occur in the Pacific Ocean on a multidecadal time scale. Those regime shifts can be followed by extensive changes in the aquatic community. We are analyzing the biodiversity and dynamics of ichthyoplankton assemblages in more than 24 years of data collected from the Gulf of Alaska (GOA) assembled by the AFSC Recruitment Processes Program. The goal of our study is to create a variety of sensitive indices to be used in assessing ecosystem integrity and predicting ecosystem shifts due to climate change.

We standardized abundance data for each of 77 ichthyoplankton taxa and clustered them into 18 groups with the Bray-Curtis distance measure and Flexible Beta linkage method. We analyzed the data with a variance partitioning analysis to distinguish between geographical, seasonal, and annual variation.

Figure 4, see caption
Figure 4.  Variance Partitioning Analysis of functional groups in the Gulf of Alaska to distinguish between annual, seasonal, and geographical variance.  Functional Groups were named after dominating species within that group.

For many clusters most variance was explained by geographical region and month in which the samples where taken, while the annual variation only accounted up to 10% (Fig. 4 above). Therefore, consequent analyses focused on the May ichthyoplankton assemblage and on each of seven geographical strata within the GOA individually. Response variables (abundance of species and cluster, diversity, survival index) were then linked to environmental explanatory variables (Pacific Decadal Oscillation index (PDO), temperature, freshwater runoff, total flow and wind indices) by canonical correspondence and canonical correlation analysis. Temperature-related indices seemed to be most closely linked to the response variables and diversity and survival indices were more suitable than abundance indices alone.

In the future, we will strive to achieve improved predictions on community change with dynamic linear modeling and Ecosim.

By Jeff Napp.

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