Supplement 1. R code used for simulation.

<h2>File List</h2><blockquote> <p><a href="Simulation code.r">Simulation code.r</a> – Source code to run simulation analysis</p> </blockquote><h2>Description</h2><blockquote> <p>This is the complete code for simulating usage under different environmental scenarios of food/cover availability and fitting GFR models to the synthetic data. The end of the listing contains the following three functions:</p> <p>environ(d,x): Creates a square dxd arena containing a total of x units of resource. The function introduces spatial autocorrelation via kernel smoothing of the resource units. Kernel smoothed map is re-normalized to ensure that x is conserved.</p> <p>movement(d, env1, env2) : This function contains the movement simulation. The parameters pertaining to animal behavior are locally defined, so the function operates as a wrapper. Its input is the dimensionality d of the square arena and the two environmental layers (env1 and env2). Its output is a map of usage.</p> <p>predict.lmer(mod, newdat) : Generates predictions from the fixed effects of mixed model.</p> <p>The main body of the code has the following parts:</p> <p>1. Initialization</p> <p> Parameters regulating the arena size, number of environmental scenarios and overall availabilities of each resource in the arena for each scenario.</p> <p>2. Simulation</p> <p>The functions environ() and movement() are used to generate resource distributions and resulting usage for each environmental scenario.</p> <p>3. Model fitting</p> <p>Here, the data frame is extended with columns for expectations of the two resources in each scenario and four GFR models are fit to the augmented data frame.</p> <p>4. Model validation</p> <p> This part first inspects the goodness-of-fit of the models. It then generates data for a new environmental scenario with unobserved availabilities. The model fitted in part 3 is then used to make predictions about the new scenario. Then follow several different outputs that aim to visualize and quantify the quality of the resulting predictions.</p> </blockquote

Appendix C. Model comparisons for individual wolves.

Model comparisons for individual wolves

Appendix B. Scatterplot of median grain size (MGS) and inundation time (IT).

Scatterplot of median grain size (MGS) and inundation time (IT)

Forward variable selection based on models fitted to harbour porpoise data, based on the cross-validation log-Likelihood (CVLL).

<p>ΔCVLL is the change in CVLL by adding a (smooth of the) covariate. Sea state and Site entered the model as factor variables. “te(X,Y)” represents a tensor product smooth of X and Y coordinates (Bardsey projection). “s” represents a thin plate regression spline smoother (or cubic regression spline for cyclic smoothers, i.e. for the covariates Lunar cycle, aspect and tidal state). The best model contained all variables up to slope.</p

The dominating current speed (m/s) measured for Bardsey Island.

<p>The current speeds for different areas to the West, East and to the North (Bardsey Sound) of Bardsey Island are shown.</p

Overview of different survey sites regarding height, sector coverage (size) and summary of systematic effort (number of 10-min scans) during sea states 0–2 with number of harbour porpoise and Risso’s dolphin sightings relative to corresponding inflection points.

<p>Overview of different survey sites regarding height, sector coverage (size) and summary of systematic effort (number of 10-min scans) during sea states 0–2 with number of harbour porpoise and Risso’s dolphin sightings relative to corresponding inflection points.</p

Kernel density utilisation grids.

<p>Risso’s dolphin: All data (A); August (B); September (C) and Harbour porpoise: All data (D); July (E); August (F); September (G). Densities are presented in percentiles (50; 60; 75; 95%). Sighting locations are indicated by small circles.</p

The estimated effect of environmental covariates on the observed harbour porpoise sighting rate.

<p>Predictions were made by varying the variable of interest (e.g. Lunar cycle in the first figure), but keeping the other values fixed at median values at which they occur in the model data.</p

The location of Bardsey Island within Cardigan Bay (Wales).

<p>The four different viewing points (A-D) and corresponding survey sectors are also shown.</p

The estimated effect of environmental covariates on the observed Risso’s dolphins sighting rate.

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086331#pone-0086331-g005" target="_blank">Figure 5</a> for more details.</p