DataSheet_1_Multi-event modeling of true reproductive states of individual female right whales provides new insights into their decline.pdf

Abundance and population trends of Critically Endangered North Atlantic right whales (Eubalaena glacialis, NARW) have been estimated using mark-recapture analyses where an individual’s state is based upon set delineations of age, using historical estimates of age at first reproduction. Here we assigned individual females to states based upon their reproductive experience, rather than age. We developed a Bayesian mark-recapture-recovery model to investigate how survival, recapture, site-fidelity and dead-recovery probabilities vary for female NARW in different states, using data collected from 1977-2018. States were assigned as calves for individuals in their first year; pre-breeder for individuals greater than one year of age who had yet to produce a calf, or breeder if an individual had reproduced. A decline in abundance of female NARW was seen starting in 2014, with 185 females declining yearly to 142 by 2018. The largest decline was seen in breeding females, with 72 estimated to be alive at the beginning of 2018, while female pre-breeder abundance plateaued at around 70 between 2011- 2018. Females born from 2000 onwards had an average 4% (95% CI:0.03-0.06) chance of transitioning from pre-breeder to breeder, compared to 8% (95%CI:0.06-0.1) for females born prior. This reduction in transition rate from non-breeder to breeder for the current cohort resulted in breeding females declining to 51% of the female population by 2018. We show that a collapse in fecundity of breeding females, and the failure of pre-breeders to start breeding, is an important factor in understanding the current decline in abundance of the NARW.</p

electronic supplementary material for "Disentangling the influence of entanglement on recruitment in North Atlantic right whales"

ESM includes information on entanglement severity determination, description of the model, model output, code used and additional figures

Fulmar tracking data

The spreadsheet contains the fulmar tracking data used for the study, collected on Eynhallow (Scotland) in July 2009. The data were filtered and interpolated at a 10-minute resolution using R, as described in the paper. The data set includes: the bird and trip ID numbers, the longitude and latitude coordinates of a bird at each time step of a trip, the corresponding date and time, the step length (in degrees), the bearing (measured from 0 radians), and the closest boat's longitude, latitude and distance (in km) from the bird

The data on golden eagle exposure used to construct the prior for <i>λ</i> (bird-min hr^-1 km^-3).

<p>Data on the mean and variance of <i>λ</i> were collected at nine independent sites within the U.S., all with varying levels of exposure. Site names are redacted in order to protect proprietary information.</p

Posterior-prior plots for the golden eagle collision risk model.

<p>The prior (black line) and posterior (dashed line) distributions for the exposure rate (<i>λ</i>) (a) and collision probability (<i>C</i>) (b) of golden eagles at a wind facility in Wyoming. Both plots demonstrate how the inclusion of data results in a posterior distribution with reduced uncertainty that is more specific to the given wind facility.</p

Diagram of a wind turbine and proposed project layout.

<p>The total hazardous volume (dotted line) for each turbine (a) is calculated using the rotor radius and the turbine height rather than just the rotor swept area. The total hazardous volume informs a wind facility’s hazardous footprint (circles) once the number of turbines within the project’s boundaries (solid line) is taken into account (b).</p

Rcpp model code and associated files

The archive includes the Rcpp code to run the Stochastic Dynamic Programming model, forward Monte Carlo simulations and sensitivity analysis. The data files required to run the model are also attached. These include the coordinates of model locations (file locs_100 km), the number of hours available to feed at different locations over time (file twilight_times_byLoc_forModel) and the upwelling index per location over time (file UpwellingIndex1). The details of how these data were generated are reported in the manuscript

A comparison of risk functions relating the probability of disturbance to received level for beaked whales exposed to sonar signals.

<p>The current step function used by the U.S. Navy is shown by a green line and the historical function by a blue-dashed line. The empirical function developed in this paper is shown by a solid black line. A solid red line marks the.5 probability of disturbance.</p

The probability of disturbance (<i>D_rms</i>) as a function of sonar RL_rms.

<p>The GAM fit to the recorded data is shown in red with the bootstrap mean shown by the green with the point-wise 95% confidence limits indicated by dotted lines from the bootstrap. The parametric GLM approximation is shown in black. There is a.5 probability of disturbance at a RL_rms of 149.8 dB; this is indicated in blue.</p

The start and stop times of six sonar scenarios during a multi-ship exercise on the AUTEC range in 2009 with the duration of gap periods with no sonar.

<p>The start and stop times of six sonar scenarios during a multi-ship exercise on the AUTEC range in 2009 with the duration of gap periods with no sonar.</p