Snyder_etal_TunaAtFronts

Matlab workspace containing the archival tag data used in paper. The timeseries data is provided in a structure while the daily locations and their associated dates are provided in separate matrices. Each matrix has 4 columns which are associated with the four tags - the order of which is listed in the 'Front' structure. The 'Readme.m' file contains the code used to analyze this data

Results from GAMM indicating degrees of freedom for each parameter identified as influential by random forest.

<p>Results from GAMM indicating degrees of freedom for each parameter identified as influential by random forest.</p

Gini index of relative parameter importance.

<p>Gini index of importance scores for all of the potential parameters in the ultimate permuted Random Forest run (including the single most influential environmental index, MEI). Red indicates a parameter that significantly partitioned the data.</p

An analytical approach to sparse telemetry data

<div><p>Horizontal behavior of highly migratory marine species is difficult to decipher because animals are wide-ranging, spend minimal time at the ocean surface, and utilize remote habitats. Satellite telemetry enables researchers to track individual movements, but population level inferences are rare due to data limitations that result from difficulty of capture and sporadic tag reporting. We introduce a Bayesian modeling framework to address population level questions with satellite telemetry data when data are sparse. We also outline an approach for identifying informative variables for use within the model. We tested our modeling approach using a large telemetry dataset for Shortfin Makos (<i>Isurus oxyrinchus</i>), which allowed us to assess the effects of various degrees of data paucity. First, a permuted Random Forest analysis is implemented to determine which variables are most informative. Next, a generalized additive mixed model is used to help define the relationship of each remaining variable with the response variable. Using jags and rjags for the analysis of Bayesian hierarchical models using Markov Chain Monte Carlo simulation, we then developed a movement model to generate parameter estimates for each of the variables of interest. By randomly reducing the tagging dataset by 25, 50, 75, and 90 percent and recalculating the parameter estimates, we demonstrate that the proposed Bayesian approach can be applied in data-limited situations. We also demonstrate how two commonly used linear mixed models with maximum likelihood estimation (MLE) can be similarly applied. Additionally, we simulate data from known parameter values to test each model’s ability to recapture those values. Despite performing similarly, we advocate using the Bayesian over the MLE approach due to the ability for later studies to easily utilize results of past study to inform working models, and the ability to use prior knowledge via informed priors in systems where such information is available.</p></div

Bayesian posterior distribution plots with expanded, flat priors.

<p>Bayesian posterior distributions for all of the parameters included in the ultimate model with vague priors. All priors were defined as normal distributions with a mean of 0 and a variance large enough to not influence the posterior distribution for that parameter. Red indicates the model with both data and priors and blue indicates the prior-only model.</p

Trace plots and Bayesian posterior plots for the final model.

<p>Trace plots indicating good mixing of the four model chains (a) and posterior distribution plots indicating parameter estimates produced by the model (b).</p

Comparison of Bayesian parameter estimates given varying amount of data.

<p>Accuracies of parameter estimates with different amounts of data. Accuracy was determined by subtracting the parameter value estimated using the full dataset from the estimate using reduced datasets. Columns are arranged by parameter and rows indicate that size of the dataset used.</p

Results from GAMM indicating degrees of freedom for each parameter identified as influential by random forest.

<p>Results from GAMM indicating degrees of freedom for each parameter identified as influential by random forest.</p

Map of Mako locations.

<p>Aggregated SLRT locations for 34 individual Makos (9440 total locations). Points in red are east of 125°W longitude boundary. Points in blue are in west of the boundary.</p

Bayesian posterior distribution plots with identical priors.

<p>Bayesian posterior distributions for all of the parameters included in the model with identical priors. All priors were defined as normal distributions with a mean of 0 and a variance of 10. Red indicates the model with both data and priors and blue indicates the prior-only model.</p