NOAA TECHNICAL MEMORANDUM NMFS-SEFSC-748

doi:org/10.25923/53z9-nn54

Predicting the effects of low salinity associated with the MBSD project on resident 

common bottlenose dolphins (Tursiops truncatus) in Barataria Bay, LA. 

Lance P. Garrison,1 Jenny Litz1, Carrie Sinclair2

U.S. Department of Commerce 

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Southeast Fisheries Science Center

75 Virginia Beach Drive 

Miami, Florida 33149 

December 2020

NOAA TECHNICAL MEMORANDUM NMFS-SEFSC-748

doi:org/10.25923/53z9-nn54 

Predicting the effects of low salinity associated with the MBSD project on resident 

common bottlenose dolphins (Tursiops truncatus) in Barataria Bay, LA. 

By

Lance P. Garrison,1 Jenny Litz1, Carrie Sinclair2

1

National Marine Fisheries Service, Southeast Fisheries Science Center,

Miami, FL 33149 

2

National Marine Fisheries Service, Southeast Fisheries Science Center,

Pascagoula, MS 39568 

U.S. DEPARTMENT OF COMMERCE

Wilbur L Ross Jr., Secretary 

NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION

Neil A. Jacobs, Ph.D., 

Acting NOAA Administrator

NATIONAL MARINE FISHERIES SERVICE

Chris Oliver

Assistant Administrator for Fisheries

December 2020

This Technical Memorandum series is used for documentation and timely communication 

of preliminary results, interim reports, or special-purpose information. Although the 

memoranda are not subject to complete formal review, editorial control, or detailed 

editing, they are expected to reflect sound professional work 
ii

NOTICE

The National Marine Fisheries Service (NMFS) does not approve, recommend or endorse any 

proprietary product or material mentioned in this publication. No reference shall be made to 

NOAA Fisheries Service, or to this publication furnished by NOAA Fisheries Service, in any 

advertising or sales promotion which would indicate or imply that NOAA Fisheries Service 

approves, recommends or endorses any proprietary product or material herein or which has as its 

purpose any intent to cause or indirectly cause the advertised product to be used or purchased 

because of National Marine Fisheries Service publication.

This report should be cited as follows:

Garrison, L.P, Litz, J. and Sinclair, C. 2020. Predicting the effects of low salinity 

associated with the MBSD project on resident common bottlenose dolphins (Tursiops 

truncatus) in Barataria Bay, LA. NOAA Technical Memorandum NOAA NMFSSEFSC-748: 97 p. https://doi.org/10.25923/53z9-nn54.

Copies of this report can be obtained from: 

Director, Protected Resources and

Biodiversity Division

Southeast Fisheries Science Center

National Marine Fisheries Service

75 Virginia Beach Drive

Miami, FL 33149 

Or online at: 

https://repository.library.noaa.gov/
iii

Executive Summary

In this analysis, we develop a simulation approach to evaluate the probable effects 

of changes in salinity in Barataria Bay, LA associated with the Mid-Barataria Sediment 

Diversion (MBSD) project on the resident common bottlenose dolphin stock. This 

population occurs throughout the Bay with a total estimated population size of 2,071 

(95% CI: 1,832 – 2,309) animals. Dolphins occur in the highest densities near the barrier 

islands and the associated passes. Under various alternative diversion scenarios, the 

MBSD project is expected to reconnect the flows of freshwater, sediment, and nutrients 

from the Mississippi River into the northern portion of the Bay on an annual basis. This 

action is intended to rebuild marsh areas and reduce land loss. The preferred alternative 

(Applicant’s Preferred Alternative) sets a maximum instantaneous inflow from the 

project at 75,000 cubic feet per second (CFS). Two additional alternatives of differing 

discharge capacities are being considered in addition to the “No Action Alternative,” 

where the current and future conditions are considered without the project. Daily salinity 

surfaces from the Delft3D hydrodynamic model were used to assess the changes in the 

distribution of low salinity (< 5 ppt) in the Bay and subsequent projected impacts on the 

bottlenose dolphin population. We used information on the initial spatial distribution of 

dolphins, simulated dolphin movements, modelled exposure to low salinity, and an expert

elicitation-based dose-response curve relating exposure to low salinity to survival to 

estimate expected annual survival rates for the bottlenose dolphin population. 

This document includes three sections. Section 1 describes the results of a photoidentification capture-mark-recapture (CMR) survey of Barataria Bay conducted during 

spring 2019 and provides an updated abundance estimate and an assessment of dolphin 

spatial distribution. Section 2 describes an approach to quantify known prediction biases 

in the Delft3D outputs and account for them in our assessment of bottlenose dolphin 

survival. Finally, Section 3 describes the development and application of the model that 

projects the impacts of exposure to low salinity on the survivorship of bottlenose 

dolphins in the bay under various diversion scenarios. Several sources of uncertainty are 

included in the assessment; however, there remain unquantified sources of uncertainty 

and potential bias that cannot be accounted for in the model. These are discussed in 

detail in section 3. 
iv

The model projects that increasing freshwater input into Barataria Bay will result 

in substantial declines in bottlenose dolphin survival rates. Relative to the No Action 

Alternative, the model projects that the mean population survival rate will decline by an 

estimated 34% (95% CL: 15.3%-62.7%) in any given year in the first decade under the 

Applicant’s Preferred Alternative based upon the representative hydrograph, and the 

greatest impacts would be on dolphins inhabiting the central and western portions of the 

Bay. The projected reductions in survival would likely result in substantial declines in 

bottlenose dolphin population size over the short-term. 
v

Table of Contents

I. Photo-identification Capture-Mark-Recapture and Abundance Estimation.......... 1

I.1 Overview and Objectives........................................................................................... 1

I.2 Methods..................................................................................................................... 1

Photo-identification Surveys....................................................................................... 1

Photo-identification and analysis................................................................................ 4

Abundance Estimation................................................................................................ 5

Spatial Distribution ..................................................................................................... 6

I.3 Results ....................................................................................................................... 7

Photo-identification Surveys....................................................................................... 7

Photo Analysis............................................................................................................ 8

Abundance Estimation................................................................................................ 9

Spatial Distribution ..................................................................................................... 9

I.4 Application .............................................................................................................. 10

II. Prediction Bias and Uncertainty in Delft3D Outputs............................................. 12

II.1 Overview and Objectives....................................................................................... 12

II.2 Methods.................................................................................................................. 14

Regional Cluster Analysis......................................................................................... 15

Regional Mean Bias Estimation................................................................................ 17

II.3 Results.................................................................................................................... 17

Regional Cluster Analysis......................................................................................... 17

Regional Mean Bias Estimation................................................................................ 18

II.4 Application............................................................................................................. 19

III. Low Salinity Exposure Model and Predicted Impacts of the MBSD on Survival 

Rates................................................................................................................................. 21

III.1 Overview and Objectives...................................................................................... 21

III.2 Methods................................................................................................................. 24

Delft3d Model Output Processing ............................................................................ 24

Movement Model and Salinity History..................................................................... 24

Exposure History and Survival Estimates................................................................. 25

III.3 Results................................................................................................................... 26

Initial Distribution and Movement Histories............................................................ 26

Exposure History ...................................................................................................... 27
vi

Survival Rates........................................................................................................... 27

III.4 Discussion............................................................................................................. 29

IV. Literature Cited........................................................................................................ 32

List of Tables and Figures.............................................................................................. 35

Appendix.......................................................................................................................... 89
1

I. Photo-identification Capture-Mark-Recapture and Abundance

Estimation

I.1 Overview and Objectives

The Barataria Bay Estuarine System (BBES) Stock of common bottlenose 

dolphins (Tursiops truncatus) is abundant and widely distributed in Barataria Bay where 

the most recent abundance estimate (~2,300 dolphins) was based on data collected from 

2010‒2014 for the DWH Natural Resource Damage Assessment (NRDA) (McDonald et 

al. 2017). Previous studies have demonstrated that this population maintains a small 

range and is primarily restricted to the waters inside Barataria Bay and in the adjacent 

coastal waters within 2km from shore (Hayes et al. 2019, Wells et al. 2017). A 

comparison of photo-identification databases between Barataria Bay and the adjacent 

Terrebone-Timbalier sound estuarine system demonstrated little movement between these 

neighboring populations (Mullin et al. 2018). 

The most recent estimate of abundance for the BBES dolphin stock was made 

using data collected through 2014. In addition to being outdated, the surveys used for 

those estimates did not cover the entirety of the BBES stock areas and were concentrated 

in the central and western portions of the Bay (Figure I.1). Density estimates for the 

surveyed region were derived using spatially explicit capture-recapture models, and these 

densities were extrapolated to cover the entire range of the stock (McDonald et al. 2017). 

The objective of this study was to conduct capture-mark-recapture surveys of bottlenose 

dolphins to provide a current abundance estimate and characterize spatial distribution 

within the stock range. In particular, the survey area was expanded compared to previous 

surveys to include a larger extent of potential dolphin habitat in the northern and eastern 

portions of Barataria Bay. 

I.2 Methods

Photo-identification Surveys

The surveyed area included modifications to western and central track-lines 

previously covered during studies related to the DWH NRDA (McDonald et al. 2017). 
2

Survey effort was also expanded to include bayous and contours of marsh habitat where 

dolphins have been tracked or sighted using other methodologies including satellite 

telemetry and fecundity surveys (Wells et al. 2017, Lane et al. 2015), as well as far 

eastern portions of the BBES which had not been included in prior surveys (Figure I.1).

The survey window was 19 days from 14 March to 1 April 2019. Surveys were 

conducted using a closed population capture-mark-recapture (CMR) design (e.g. Balmer 

et al. 2019, Mullin et al. 2017, McDonald et al. 2017, Rosel et al. 2011, Speakman et al. 

2010, Balmer et al. 2008). Each regional set of tracklines (Figure I.2) was fully covered 

in each of four “mark” sessions. Each mark session took three to four days to complete 

using three vessels. 

Surveys were conducted from three outboard-powered small boats, the 7.5-m 

NOAA R/V R2 (R2), the 7.3-m NOAA R/V Top Notch (TN) and the 6.4-m NOAA R/V 

Sciaenops (SC), operating in concert. The starting point was weather dependent. A 

combination of marsh contours and open water track-lines were followed to facilitate full 

coverage of the survey area 

Surveys were conducted in Beaufort Sea state (BSS) ≤4 and survey speed was 

approximately 30 km/h. A Survey Effort Log (e.g. on- and off-effort distance segments) 

was completed for each survey and each vessel’s track-line was recorded on a handheld 

global positioning system (GPS). Survey Conditions (i.e. excellent, good, fair, poor) were 

recorded when effort began and any time there was a substantial change in conditions. 

The survey teams consisted of a minimum of three observers per vessel where 

operational duties were driving, photographing and recording data. Observers searched 

visually (with naked eye) for dolphins forward of the vessel’s beam on both sides of the 

boat. When a dolphin group was sighted, it was approached for data collection. A dolphin 

group was defined as all dolphins in <100m proximity, moving in the same direction 

and/or exhibiting similar behavior (Shane 1990). 

Dolphin sighting procedures are given in detail in Melancon et al. (2011), and 

summarized here. Three waypoints were recorded during every encounter: Boat, Start 

and End. The Boat waypoint recorded where the boat was when the dolphins were first 

sighted. It was recorded as soon as a dolphin group was sighted, before the vessel 

departed the trackline to approach the group. The Start waypoint was recorded when the 
3

vessel reached the location of the group to begin data collection. The End waypoint was 

recorded when the encounter was over, before returning to the trackline. The Boat and 

End Distance (daily trip odometer) was recorded when the boat departed (same time as 

Boat waypoint) and returned to the track-line (before effort is resumed), respectively. 

Data associated with each waypoint (waypoint number, time, and trip odometer 

(distance)) were recorded on the Sighting Form. Returning to the trackline depended on 

direction and movement during the sighting. If the sighting was made or moved in a 

direction of previous effort (e.g., sighted abeam, just aft of the beam or moving in a 

direction backwards over track-line already covered), the boat returned to the Boat 

waypoint before resuming effort and recording End Distance. If the sighting continued 

along the track-line and remained forward of the Boat waypoint, the boat returned to the 

track-line at a perpendicular angle once the sighting was complete. Once on track, the 

odometer distance was recorded as the “End Distance” and effort was resumed.

Dolphin groups were approached within 2m at a shallow angle and steady speed. Once in 

the vicinity of the dolphin group, vessel speed and travel direction of the group were 

matched and drastic changes in speed avoided.

Images of dorsal fins were collected with digital cameras equipped with zoom 

lenses (e.g. Canon EOS 7D and a Canon 100-400mm image stabilizing telephoto lens). 

The goal was to collect high quality, left and right side dorsal fin images of every dolphin 

in the group, regardless of fin distinctiveness. A high quality dorsal fin image was 

defined as fully visible (e.g. not obstructed by water, other dorsal fins or Xenobalanus 

spp.), perpendicular to the camera and had good to excellent contrast (lighting) and focus 

(clarity) (Urian et al. 2015). Images other than dorsal fins were taken if any of the 

following were present (but not limited to): 1) aerial or interesting behaviors; 2) freeze 

brands, satellite tags or roto-tags; 3) skin disorders; 4) gear entanglements; and/or 5) body 

condition concerns. Image acquisition was suspended for any of the following reasons: 1) 

all group members were photographed; 2) intractable dolphins; 3) repeatedly adverse 

behavioral reactions by one or more members of the group; 4) weather or safety 

concerns. Maximum time with each sighting was limited to 35 minutes.

The Sighting Form was filled out during the encounter (e.g. survey/sighting 

number, crew, sighting conditions, behaviors, observations, freeze brands) and finalized 
4

at the end of the encounter (e.g. salinity, group size estimates). Estimates of total dolphins 

included all dolphins within the sighting, calves and neonates included. The total calves 

estimate did not include neonates. Calves were defined as dolphins less than 2/3 of the 

presumed mother’s length and observed surfacing in the “calf” or echelon position 

(Shane 1990). Neonates were characterized by fetal folds, dark coloration and 

uncoordinated surfacing pattern (Urian and Wells 1996, Shane 1990). Smaller animals 

not observed alongside a larger animal were noted as a possible calf or neonate.

In the BBES, mom/calf pairs are closely monitored for reproductive rates, including 

fecundity tracking of previous years’ reproductive or pregnant females (Lane et al. 2015). 

In groups where calves and neonates were present, image frame numbers of presumed 

mothers and calves were recorded on the Sighting Form. Typically, this was 

accomplished by communication between the photographer and recorder during the 

encounter or at the end of the encounter, before resuming effort.

In addition to environmental parameters collected during sightings, additional 

salinity and temperature data points were collected along the tracklines at set stations 

(Figure I.3). Some of the points were pre-determined along east-west tracklines. Along 

marsh contours, points were collected at the extremities of the contours (e.g. east, west, 

north and south along the contour and at the opening to bays and deep channels). Data 

points were recorded on the Survey Effort Worksheet. Date, time, station, salinity [in 

parts per thousand (ppt)] and temperature [degrees Celsius (°C)] were recorded in the 

notes section. If there was a sighting within 500m of a salinity and temperature station, 

the temperature and salinity recorded during the sighting was used for that station.

Photo-identification and analysis

GPS data and digital images were downloaded daily to external hard drives (one 

working drive and one backup drive). Data sheets were compared to waypoints and 

camera information to ensure accuracy. All files (camera folders, GPS, data sheets, etc.) 

were named in the same format: survey number, year, month, day, vessel 

(Snnn_YYYY_MMDD_VV). Upon returning from the field, Survey and Sighting Forms 

and GPS files were entered into FinBase, a custom Microsoft Access database (Melancon 

et al. 2011, Adams et al. 2006). All data from salinity and temperature stations were 
5

entered into a centralized spreadsheet. All data forms were scanned and saved in portable 

document format.

Digital images from all surveys were sorted by sighting to find the best right 

and/or left dorsal fin image of each unique individual. All images of the same individual 

were placed in a designated folder within the sighting folder. Poor quality images were 

placed in a separate folder. When necessary, the folder was also labeled with additional 

information pertaining to the individual (mom, calf, skin disorder, etc). Once all images 

were sorted into folders, the best right and/or left from each individuals’ folder was given 

a temporary ID (e.g., A, B, C...Z). Sorted images were rated separately for quality (Q1 = 

excellent, Q2 = average, Q3 = low) (Urian et al. 2014) and distinctiveness (D1 = high, D2 

= average, D3 = Low, D4 = not distinct) (Speakman et al. 2010).

The matching phase consisted of an initial comparison of the sorted images to the 

Barataria Bay master catalog using finFindR software [Western Ecosystems Technology, 

Incorporated (WEST, Inc.); Laramie, WY] (Mullin et al. 2018). Briefly, finFindR was 

used to trace the trailing edge of newly sorted images. Once the traces were saved, they 

were compared to the master catalog images and finFindR displayed a spreadsheet of the 

most similar catalog images to each sorted fin. The “1 per ID” checkbox was selected 

which limits the results table to only one image for each unique ID in the master catalog. 

The first 50 unique catalog images were compared side by side to the sorted fins. If a 

match was found, the existing catalog ID was recorded. If a match was not found, a 

tentative new catalog ID was assigned. Verification of potential matches and tentative 

new fins was performed by trained technicians with naked eye using a user-defined, 

attribute-based ranking system in FinBase. 

Abundance Estimation

The 2019 CMR data were analyzed with the software package MARK version 9.0 

(http://www.phidot.org/software/mark/index.html, accessed 12 Oct. 2019) using closed

population capture-mark-recapture models. The dataset was limited to only images with 

excellent or average photo-quality scores (PQ1 and PQ2, respectively) and only fins with 

high or average distinctiveness (D1 and D2 respectively) were considered marked (Litz et 

al. 2019, Urian et al. 2015, Speakman et al. 2010). The models were analyzed using the 
6

Full-Likelihood approach (Otis et al. 1978) and the conditional approach (Huggins 1989),

and results were similar between these methods. The results of the Full-Likelihood 

approach are presented below. Several model structures were evaluated and the model 

providing the best explanatory power was selected using the minimum Akaike’s 

Information Criterion (AIC, Burnham and Anderson 2004). Using the Otis et al. (Otis et 

al. 1978) notation, the evaluated model structure included: 1) the simple, M0 model where 

capture probability (p) and recapture probability (c) are assumed to be equal and constant 

across mark sessions, 2) the Mt model where the capture probability (p) and the recapture 

probability (c) are assumed to be equal but vary across mark sessions, and 3) the Mtb

model where p and c were allowed to vary across mark sessions and were not equal to 

each other, thus evaluating whether or not there was a behavioral difference between the 

probability of capture and the probability of recapture. 

The derived abundance estimate of the marked animals was then divided by the 

proportion of distinct fins to generate an abundance estimate for the total individuals 

(marked and unmarked) in the sampled population. The proportion marked was 

calculated by dividing the number of marked fins (D1 & D2) in each sighting by the total 

number of animals cataloged in that sighting using the same dataset analyzed (PQ1 & 

PQ2). The proportion marked was calculated only from those sightings with complete 

photo coverage (photo completeness ≥ 1, calculated by the number of cataloged dolphins 

for a sighting divided by the field estimate of the number of dolphins present in that 

sighting; Litz et al. 2019, Balmer et al. 2013).

Spatial Distribution

The relative spatial distribution of bottlenose dolphins during the CMR surveys 

was modeled using a Generalized Additive Model (GAM; Wood et al. 2016, Wood 

2017). Survey tracks were first coded into “on-effort” and “off-effort” segments. “Oneffort” included periods when the vessel was moving along the planned survey tracklines 

and observers were actively searching for dolphin groups. “Off-effort” segments included 

time spent transiting to and from the survey area, during periods when photographic data 

was being collected, and other periods when the vessel was not involved in active 

surveys. A 500 x 500m grid was developed for the survey area, and the length of on 
7

effort trackline, number of dolphin sightings, and total number of dolphins within each 

grid cell was summarized (Figure I.4). The “Sightings Per Unit Effort (SPUE)” metric 

was calculated as the number of dolphins per 100 meters of on-effort trackline. This is a 

metric of relative density assuming that detection probability and the searched strip width

is consistent across all transect segments. SPUE was modeled as a function of spatial 

location in the Bay using the GAM model. The response variable was the number of 

dolphins observed in a given grid cell. The x and y coordinates (UTM15 projection) of 

the centroid of each grid cell and the distance from the Barataria Pass were included as 

smooth terms in the model along with an offset term for survey effort (log(trackline 

length). A Tweedie distribution was used to model the error structure as this is a flexible 

model for count data that allows for overdispersion typical of spatial count data of this 

type (Wood et al. 2016, Wood 2017). The distance from Barataria Pass was included in 

the model to account for high encounter rates of dolphins observed in the pass. Prior 

studies (McDonald et al. 2017) found very high densities of dolphins in passes of 

Barataria Bay and the habitat surrounding the barrier islands. The resulting GAM model 

was used to develop a predicted surface of relative bottlenose dolphin occurrence within 

the stock area including extrapolation to areas that were not surveyed where appropriate.

The GAM model was conducted using package “mgcv” (Wood 2017, version 1.8-31) in 

the R statistical computing language.

I.3 Results

Photo-identification Surveys

A total of 37 surveys (Western: n = 11; Central: n = 13; Southeastern: n = 13) 

covering 4,195km of track-lines were conducted during 14 March – 30 March 2019 in 

Barataria Bay. The number of dolphin groups sighted per day was variable with a range 

of 2-24 groups. Overall there were 368 sightings of bottlenose dolphin groups composed 

of 2,046 dolphins (Table 1). Calves were present in 21% (n = 76) of total sightings and 

comprised 4.7% of the total number of dolphins encountered. Neonates were encountered 

55 times during the survey. 
8

Sightings were distributed throughout the survey area (Figure I.4). Few sightings 

were observed in the northern extent of the Western Area (e.g. Hackberry Bay). In the 

Central area, there were no sightings in the Barataria Waterway adjacent to Hackberry 

Bay. However, there were relatively frequent sightings in the northern portion of this area 

including Bay Batiste. In the Southeastern area, there were few sightings in the 

southeastern area near Bay Jacques (Figure I.4). Group sizes in each of the three regions 

of the study area were similar with a range of 1 to 40 animals and mean group size 

ranging from 4.6 to 6.4 (Table I.2, Figure I.5).

The lowest average salinity was observed in the Western survey area (xx�= 7.6‰, 

range = 0.4 – 18.2‰). Slightly higher average salinity was observed in the Central area 

(xx̅= 9.2‰, range = 1.5 – 20.2‰) and average salinity was highest in the Southeastern 

area (xx̅= 11.4‰, range = 0.6 – 31.0‰). Average temperature was similar amongst the 

three areas (Western: xx̅= 18.9; Central: xx̅= 18.1; Southeastern: xx̅= 18.2). The largest 

range in temperature was observed in the Central area (range = 9.2 – 21.1) (Table I.3). Of 

interest, during the first week of the survey (mark session 1), the salinity in the Western 

area was higher in the northern extent of the survey area and lower in the southern extent, 

closer to the Gulf of Mexico.

Photo Analysis

A total of 27,365 digital photographs were collected during the four mark sessions 

in 97% of sightings (n = 358). Sorting the raw images yielded 1,977 unique dolphins for 

comparison to the Barataria Bay master catalog. A discovery curve indicated there was a 

sharp increase in number of new dolphins added to the master catalog (n = 586) from the 

2019 field effort (Figure I.6).

Photo-identification data were examined to assess the movement of dolphins

between survey regions including comparison to prior year surveys which did not survey 

the Southeast area (McDonald et al. 2017). For this comparison, we used the most 

conservative dataset, which included only photos with excellent or average photo quality 

(PQ1 & PQ2) and individuals with high or average distinct fins (marked animals D1 & 

D2). There were a total of 835 individuals in the dataset from surveys conducted during 

2010-2014, and there were 477 new animals sighted for the first time in 2019 (57%). Of 
9

these new animals, 46.5% (222/477) were first sighted in the southeastern survey area,

and only 4 animals sighted in the southeastern area had been seen in prior years. Of the 

227 animals that were seen in the southeastern survey area, only 6 animals had also been 

seen elsewhere in the Bay, and those additional sightings were all close to the boundary 

of the southeastern survey area. The 2019 data indicate little movement of animals 

between the newly surveyed eastern portion of Barataria Bay and the previously surveyed 

main portion of the Bay. 

Abundance Estimation

The closed capture full-likelihood approach supported the Mt model with slight 

support for the Mtb model (Table I.4). The M0 model was unsupported. The capture 

probabilities for the 4 secondary surveys ranged from 0.15 to 0.22 (Table I.5). The 

resulting abundance estimate was 1,491 marked animals (Standard Error = 64.9). Of the 

210 sightings with complete photographs, the average proportion of individuals marked 

was 0.72 (SE = 0.031). Correcting for the proportion of marked fins, the resulting 

abundance estimate for Barataria Bay dolphins was 2,071 (95% CI: 1,832 – 2,309, Table 

I.6).

Spatial Distribution

The spatial GAM model indicated that all three smooth terms (East, North, and 

distance from Barataria pass) were statistically significant, and the model explained 

14.6% of the residual deviance (Table I.7). The smooth function in the East parameter 

demonstrated lower densities at the eastern and western ends of the survey area with 

higher densities in the central portion. The North parameter indicated higher density in 

the southern portion of the surveyed area, lower density in the middle, and increasing

density toward the northern portion of the range. Finally, the “distance from pass”

parameter demonstrates rapidly increasing density at lower distances from the pass 

(Figure I.7). The rapid increase in uncertainty at the extreme ends of these plots is typical 

of models of this type and caution against extrapolation beyond the range of the collected 

data. This is particularly the case with the North function where the predicted density is 
10

increasing with high uncertainty at extreme values of the explanatory variable (Figure 

I.7). Comparison of model predictions to observed data demonstrate overall good fit and 

explanation of the observed spatial patterns in relative density (Figure I.8). It is notable 

that the relative density increases rapidly near the pass with the SPUE parameter 

increasing from 0.1 dolphins per 100m of trackline at 10,000m from the pass to 0.4 

dolphins per 100m of trackline near the pass (Figure I.8). The predicted spatial pattern in 

density comports well with the location of observed groups with higher relative densities 

in the southern, western, and central portions of the Bay and concentrated around the pass 

(Figure I.9). Higher encounter rates inside smaller embayments in the north-central 

portions of the Bay were also captured by the model (Figure I.9). The resulting surface is 

masked to exclude areas where the coefficient of variation (standard error/mean) 

exceeded 0.4. This level restricted the fitted surface to the data collection area and avoids 

unwarranted extrapolation given the high uncertainty in model predictions outside of this 

range.

I.4 Application

The outcomes from this analysis provide a current abundance estimate for the 

BBES model. The expansion of the survey area to regions in the northern and eastern 

portions of the Bay provides an improved understanding of dolphin spatial distribution 

and habitat use. The newly surveyed southeastern region is an area with generally lower 

densities of dolphins than the central region and areas around the passes. Based on the 

data collected during 2019, there was limited exchange of animals between the 

southeastern area and other portions of the Bay and very few of these animals had 

previously been sighted prior to this survey. Additional surveys would be required to 

confirm that these general patterns are consistent over time. This is consistent with 

previous findings that there is partitioning of the Bay by sub-groups of dolphins and 

home-ranges are relatively small. The spatial model also demonstrates that the highest 

concentrations of dolphins are centered around the Barataria Pass, but it does not appear 

that these very high densities extend all the way along the barrier Island chain to the 

eastern portion of the Bay. Relatively high densities inside small embayments in the 
11

north-central portion of the Bay also had not been documented by previous surveys that 

did not include these habitats. In the dolphin low salinity exposure model, in the initial 

time step, the starting point for the simulated movement patterns are randomly selected 

from the spatial domain of the GAM model. The relative density surface developed in 

this analysis provides a probability weighting for this random selection so that the 

simulated starting positions reflects the observed spatial distribution of the population. 

The movement model is weighted to result in relatively small movements away from 

these starting points, which is consistent with the limited exchange of dolphins between 

regions observed in this study and prior studies showing that animals within the Bay have 

relatively small home ranges. Because the change in salinity expected from the MBSD is 

highly dependent on the location in the Bay, this simulated dolphin spatial distribution is 

an important factor in the model outcomes. 
12

II. Prediction Bias and Uncertainty in Delft3D Outputs 

II.1 Overview and Objectives

Accurate projection of the effects of alternative MBSD scenarios on the future 

survival of Barataria Bay bottlenose dolphins depends upon the accuracy of the projected 

salinity fields produced by the Delft3D model. However, there are several known 

sources of bias and uncertainty in these outputs. If the Delft3D model consistently overor under-estimates salinity, then the resulting prediction projection of future mortality 

rates will be similarly biased. There are two primary types of bias and uncertainty in the 

Delft3D predictions: retrospective prediction bias and future prediction bias.

Retrospective prediction bias reflects the capability of the model to accurately 

predict salinity fields under known conditions within the range of environmental 

variability that has been observed during the historical time series. This type of bias may 

result from simplification in the model formulation relative to the real world (e.g., using 

depth-averaged salinity and thereby ignoring vertical structure in the water column), 

inaccuracy in the boundary conditions used in the model (e.g., time-averaged wind and 

salinity in offshore waters), or inability to capture the influence of short-term or 

stochastic events that influence the salinity within the bay in a given year or shorter time

interval. The Delft3D model uses external data from salinity stations within the bay to 

calibrate model outputs to minimize these potential retrospective biases. Evaluation of 

the effectiveness of this calibration approach for model year 2014 demonstrates that the 

general pattern in salinity levels is well described by the model outputs; however, model 

predictions are more accurate in the upper and middle portion of the basin. This is likely 

due to the influence of offshore boundary conditions and stratification in the deeper, 

lower portions of the bay (Sadid et al. 2018). In general, the calibration based on year 

2014 indicated that salinity in the lower basin was underestimated by between 1-5 ppt. 

Similar results were observed for model year 2016, which was not included in the 

calibration data set and was considered a validation year (Sadid et al. 2018). As this year 

is independent from the model fitting procedure, model predictions for this year are the 

best available metric of retrospective prediction bias. However, it should be recognized 
13

that prediction errors are likely to vary among different years as there is inter-annual 

variation in the underlying processes that may or may not be well captured by the model. 

The second major type of bias and uncertainty is future prediction bias. This is 

unquantifiable bias/uncertainty in the future conditions projected by the Delft3D model. 

There are multiple sources of this type of uncertainty, which are additive to any 

retrospective biases. First, the freshwater inflow conditions expected to be observed 

under various diversion scenarios are likely to be outside of the range of conditions that 

were included in the historical data used to calibrate the Delft3D model. Predictions 

from the model therefore represent extrapolations and therefore are inherently uncertain. 

Second, the future climate, sea level, water flow, and other conditions are unknown. 

Future projections of the model do not attempt to account for expected variability in the 

overall climate outside of projected changes in sea level. This future prediction 

uncertainty cannot be quantified or accounted for. Implicitly, comparisons between 

alternative scenarios assume that the direction and magnitude of future prediction bias 

will be the same for all alternatives.

In this analysis, we develop an approach to account for retrospective prediction 

bias based upon the comparison of model predictions vs. station observations from model 

year 2016. This analysis is similar to that described in Sadid et al. (2018, Appendix 3) 

and uses the same data for both predicted and observed values. However, there are 

several differences between our approach and the characterization of uncertainty 

presented by the Delft3D modelers (Delft3d Model Uncertainty Appendix). First, we are 

interested in a different spatial domain, and therefore observation stations in the extreme 

upper portions of the basin are not included in this analysis. Second, the Sadid et al. 

(2018) analysis only included comparisons between predicted and observed salinity 

values from March – December because the January-February periods of the annual

model runs are heavily influenced by the initial conditions. We used the entire year to

account for uncertainties due to the initial condition applied and because we required a 

full-year simulation to estimate annual survival rate effects. Third, for this analysis, it is 

necessary to estimate a spatially-averaged estimate of bias at a regional level within the 

Bay as opposed to station-by-station estimates presented by Sadid et al. (2018, Delft3d 

Model Uncertainty Appendix). This regional spatial average is required because we are 
14

making predictions of the influence of salinity on dolphin survival rates at all locations in 

the bay, not at specific points corresponding to the locations of the salinity stations used 

in the model validation. Data are not available to adequately characterize the full range of 

uncertainty in predictions at every location within the Bay, and therefore we focused on 

spatial averages accounting for the observed variability and correlation between stations. 

However, the analysis of bias and uncertainty at specific station point locations 

demonstrates that there is variation in uncertainty between stations based on the Root 

Mean Square Error (RMSE) metric to characterize model predictive skill which was 

applied by the Delft3D modelling group (Delft3d Model Uncertainty Appendix). For our 

regional analysis, the standard deviation of the annual bias is the appropriate metric to

characterize uncertainty. Our approach captures the model uncertainties related to model 

skill, but it is not directly comparable with the analysis presented in the Delft3d Model 

Uncertainty Appendix for the reasons presented above.

II.2 Methods

Observed salinity data collected during 2016 from 22 stations dispersed 

throughout Barataria Bay were included in this analysis (Figure II.1, Table II.1). This 

analysis excluded 5 stations that were included in the Sadid et al. (2018) validation 

analysis. Four of these were in the extreme northern portion of the Basin outside of the 

domain of the bottlenose dolphin movement analysis (CRMS278, CRMS3985, 

CRMS287, CRMS4103) and one (CRM282) had an incomplete salinity record and was 

missing data after 04/06/2016. The stations used in this analysis are largely 

representative of the areas where high numbers of dolphins were observed during the 

CMR study. There are stations located near the Barataria Pass, Caminada Bay, and the 

upper portion of the Bay near Bay Batiste where dolphin encounter rates were high 

(Figure I.9). However, the open portion of the central bay is not well represented by the 

available stations. 

Salinities are typically reported at hourly intervals for each station, and these were 

first summarized into daily averages. Hourly predicted salinity values from the Delft3D 
15 

model for the grid cell occupied by each of these stations was also provided and 

summarized into daily means for days from January 4 to December 31 (Figure II.2).

The annual mean observed salinity value (Mean Observed, Table II.1) and three 

metrics of model predictive skill were calculated and summarized for each station: mean 

observation bias (MBE), standard deviation of bias (sd MBE), and root-mean-square 

error (RMSE, Wilmott, 1982). Observation bias is calculated as: 

(1) MMMMMM = NN−1 ∑ (PPii − OOii) NN

ii=1 , 

where N is the number of days, Pi = modeled (predicted) observation, and Oi is the 

observed daily salinity. The standard deviation of the mean bias (sd) is:

(2) ssss = �(NN − 1)−1 ∑ (PPii − OOii − MMMMMM) NN 2

ii=1 . 

Note that the sd estimate assumes independence between the individual observations, 

which is violated for these autocorrelated time series data. Finally, the RMSE is given 

as: 

(3) RRMMRRMM = �NN−1 ∑ (PPii − OOii) NN 2

ii=1 . 

The RMSE indicates the average difference between the observed and predicted values, 

which is a measure of the overall accuracy of the predictions. In contrast, the sd metric 

reflects the variability in the bias estimate. Values of MBE, sd MBE, and RMSE for each 

station are shown in Table 1. These differ from values reported in Sadid et al. (2018) 

because the current analysis includes all dates from January-December, while Sadid et al. 

(2018) included only data from March-December in their summaries.

Regional Cluster Analysis
16

The objective of this analysis is to develop representations of retrospective 

prediction bias across the relevant spatial domain for the bottlenose dolphin stock in 

Barataria Bay. As such, we require a spatially continuous representation of bias in 

predicted salinity values as opposed to the point locations in the validation data sets. 

Ideally, sufficient numbers of point locations would be available to allow the 

development of a surface of bias corrections so that a spatially explicit bias correction 

could be provided at each point in the spatial grid of modeled salinities. However, the 

number of observation stations is too sparse to reliably calculate such a continuous 

gridded surface. Therefore, we took the approach of identifying regional clusters based 

upon the similarities in the time series among the stations. We used k-means clustering

to identify regional groupings of stations with a common pattern in salinity and 

associated biases. K-means clustering is an agglomerative clustering technique that 

combines observations into groups based upon minimizing the within-group variance of 

parameters (Kassambara, 2017). The number of clusters is specified a priori by the 

analyst, and objective measures are used to identify the optimal number of clusters from a 

range of considered values. In this case, we used metrics to identify the optimal number 

of clusters including the “gap” and “silhouette” methods, each of which assess changes in 

the between- and within-cluster variance with changing numbers of clusters 

(Kassambara, 2017). The “gap” statistic (Gap) compares the change in the within cluster 

sum of squares to that of the expectation under a null model. The optimal number of 

clusters is defined as the minimum number of clusters (k) such that Gap(k) ≥ Gap(k+1) –

sk where sk is the expected standard error of the statistic (Tibshirani et al. 2001). The 

silhouette method evaluates the mean distance between members of a cluster and the 

members of a different cluster. The statistic is positive when the members are assigned 

correctly to clusters, and the maximum value indicates the optimal number of clusters 

from a range of k values (Kassambara, 2017). For each statistic, the optimal number of 

clusters was evaluated from k = 1 to 10. The variables for each station that were input 

into the k-means cluster analysis included: mean salinity, RMSE, MBE, sd MBE, East, 

and North (Table II.1). These variables capture the spatial arrangement of the stations, 

the environmental similarity, and the similarity in model performance. Analyses were 
17

conducted in packages “cluster” (version 2.10, Maechler et al. 2019) and “factoextra” 

(version 1.0.6, Kassambara et al. 2019) in the R statistical computing language.

Regional Mean Bias Estimation

Within regional station clusters, the mean and variance of the prediction bias was 

estimated by combining the time series of daily bias across stations. The aggregation of 

annual mean bias, and in particular estimation of appropriate variance, is complicated by 

the time series nature of the data and the correlations among stations. To properly 

account for these correlations, a generalized additive mixed model (GAMM) was used to 

model the effect of day of the year on regional mean bias including an autoregressive 

(AR) error structure to account for serial autocorrelation within the time series. Station 

was included as a random effect in the GAMM. The GAMM method uses smoothing 

splines to fit the daily pattern in bias, while the AR structure accounts for the dependence 

between residuals at different time lags among residuals. GAMM models were fit with 

no autocorrelation and with lags of 1-3 days based upon an initial examination of the 

partial autocorrelation function (PACF) of residuals. The model in which residuals 

showed no lag correlation was selected and used to estimate both the annual mean and 

variance of the model prediction bias. The GAMM analysis was conducted in package 

“mgcv” (Version 1.8-31, Wood 2017). The resulting annual MBE and its variance were 

used to correct predicted salinity fields from the Delft3d model for retrospective bias.

II.3 Results

Regional Cluster Analysis

Examination of the bias time series for each station suggest two general groupings 

of stations. The first (typified by CRMS0226) shows a small positive average bias and 

relatively little variability around the mean. The second group (e.g., CRMS0178) shows 

a negative mean bias greater with variation. There is little indication of linear trends or 

other patterns in the bias time series, thought the greatest variability typically occurs early 

in the year in most cases (Figure II.3). An initial matrix of differences between station 
18

pairs supports this pattern of two clusters of stations that are similar within groups based 

on mean salinity, bias, sd bias, RMSE, and location (Figure II.4).

K-means clustering sorted the stations into similar groups. The centroids of the 

groups are well separated by a single axis with relatively little separation along a second 

dimension (Figure II.5). With 4 or 5 groups specified, there are small clusters of only 2 

or 3 stations, suggesting that this level of division is too fine. Either 2 or 3 clusters appear 

to result in separation of stations (Figure II.5). However, both the “gap” and “silhouette” 

metrics (Figure II.6) indicate that 2 clusters is optimal for these data. 

When classified into the defined clusters, the stations are well separated in both 

environmental and geographic space. Cluster 1 corresponds to stations with a mostly 

positive salinity bias ranging between -0.823 and 1.333 ppt and RMSE ≤ 3.15 (Figure 

II.7). Cluster 2 corresponds to stations with negative bias between -3.779 and -0.4364 

ppt and RMSE ≥ 0.3.367 (Figure II.7). Spatially cluster 1 corresponds to stations that 

are representative of the more northern portion of the Bay that is influenced primarily by 

freshwater inputs and dominated by marsh habitats. Cluster 2 corresponds to the 

southern Bay including the open, estuarine waters and the region near the barrier islands 

and associated channels (Figure II.8). 

Regional Mean Bias Estimation

The GAMM of MBE for cluster 1 fit without an autoregressive error structure 

demonstrated lag correlation in the residuals at days 1-3 (Figure II.9A). Including the 

AR3 structure in the model accounted for this autocorrelation leaving uncorrelated, 

“white noise” in the residual correlation structure (Figure II.9B). The resulting GAMM 

smooth fit for each station closely followed the observed trends in daily bias throughout 

the year (Figure II.10, see Appendix II.A1 for GAMM summary). It is notable that there 

were seasonal trends in the bias with the greatest positive bias occurring during the first 

approximately 100 days of the year for each station. A parametric bootstrap distribution 

of the mean was generated from the GAMM fit accounting for the uncertainty in 

parameter estimates and resulting predictions. As expected, the mean distribution of 

residuals that did not account for spatial autocorrelation had a substantially smaller 
19

variance than for the autoregressive model (Figure II.11). The resulting regional mean 

bias was 0.717 ppt with a standard deviation of 0.0889. 

Results for the GAMM for cluster 2 were similar to those for cluster 1. The initial 

GAMM without the AR error structure indicated significant autocorrelation at lags of 1-3 

days, and this structure was adequately modeled by including the AR3 correlation in the 

error term (Figure II.12, see Appendix II.A2 for GAMM summary). GAMM model fits 

closely followed the patterns in the daily bias value, but there was little to no apparent 

seasonal trend in the magnitude of the bias throughout the year (Figure II.13). There was 

a greater degree of variability around the GAMM fits for Cluster 2 stations, as was 

expected from their greater RMSE values compared to Cluster 1. The parametric 

bootstrap distribution of mean annual bias showed the expected pattern of greater 

variance in the mean once autocorrelation was accounted for (Figure II.14). There was a 

small difference in the estimated mean bias between the two GAMM models for Cluster 

2. The resulting regional mean bias for cluster 2 was -2.316 ppt with a standard 

deviation of 0.168.

II.4 Application

Within the dolphin low salinity exposure model, dolphin movement histories are 

simulated at a daily time step, and the daily salinity value at a location in the Bay on a 

particular day is used to develop an annual exposure history for each simulated 

individual. However, the known retrospective prediction biases in the Delft3D model 

may result in over- or underestimation of the exposure to low salinity. The outcomes of 

this analysis are used to correct for the regional mean annual bias in estimated salinity 

and account for the quantified variation in this parameter. Based upon the regional 

clusters identified here, the grid cells used in the movement simulation were assigned to 

cluster 1 or cluster 2 (Figure II.15). The boundary between the clusters was drawn to be 

approximately equidistant between stations on the boundaries of the two clusters. For 

each location in a movement history, the appropriate bias correction is applied depending 

on which region the location is in. To account for uncertainty in this correction, a 

parametric bootstrap approach is used to develop a distribution of bias corrections by 
20

cluster based upon the estimated standard deviation accounting for spatial 

autocorrelation. Therefore, the approach described here corrects for the retrospective 

prediction bias in modeled salinity and its associated uncertainty at a regional scale 

within Barataria Bay.
21

III. Low Salinity Exposure Model and Predicted Impacts of the MBSD 

on Survival Rates

III.1 Overview and Objectives

The objective of this project is to evaluate the potential impacts of changes in the 

distribution of low salinity (< 5 ppt) water in Barataria Bay on the survival rates of the 

resident common bottlenose dolphin stock. The Mid-Barataria Sediment Diversion 

(MBSD) is an effort to restore the natural deltaic process to the Barataria Basin resulting 

in the creation and maintenance of wetlands and overall reduction in the loss of marsh

and other habitats in Barataria Bay. The project is designed to divert sediment, nutrients 

and freshwater from the Mississippi River into the northeastern portion of the Bay during 

the normal seasonal pulses of the River. This influx of freshwater and associated input of 

sediments is projected to result in the maintenance of wetland, SAV, and associated 

habitats that would otherwise be lost absent the sediment diversion. However, the influx 

of large amounts of freshwater into the system is projected to result in large-scale 

changes in the hydrographic structure of this modified estuary. In particular, it is 

projected that lower salinity water will occur throughout the Bay for longer durations

each year and over a larger spatial area than under current conditions. Here, we describe 

a simulation approach to evaluate the effects of this expansion of low salinity water on 

the survival rates of bottlenose dolphins under the conditions expected for different 

management scenarios and differing degrees of input of freshwater into the Barataria Bay 

estuary.

The Delft3D hydrodynamic model (Sadid et al. 2018) is the primary tool used to 

predict land building and changes in hydrographic structure under different diversion 

alternative describing the magnitude of the diversions. The freshwater input into this 

model is driven by a time-series of annual hydrographs describing river flows into the 

basin. The model simulates the annual time series of daily salinity over the model 

domain for each year. Available Delft3D projections include surface salinity for 5 annual 

hydrographs (A representative year, 1994, 2006, 2010, and 2011) for each of five decades 

(“cycles”). The representative year varies by cycle. Given that there is not a continuous 

prediction of year to year variation in freshwater inputs and the future projections do not 
22

include prediction of likely freshwater inputs, all evaluations in this analysis are done for 

a single year and focus on the comparison of expected outcomes under different diversion 

alternatives for a particular hydrograph.

The simulation approach described here has four primary components. First, the 

daily projected salinity field for a given cycle, hydrograph, and alternative is obtained 

from the Delft3D model. The cycles represent decades: cycle0 – cycle5, and the 

representative year hydrograph varies by cycle: cycle0 = 1970, cycle1 = 1975, cycle2 = 

1985, cycle3 = 1994, cycle4 = 2008, and cycle5 = 2008. Sea level rise becomes more 

apparent in the projections of the last two cycles. Predictions for alternate hydrographs 

(1994, 2006, 2010, and 2011) are also produced for each cycle. The alternatives include 

four different expected flow regimes under different diversion levels: NAA = No Action 

Alternative (Future Without Plan); A3 = discharge from diversion capped at 50K cubic 

feet per second (cfs); APA (Applicants Preferred Alternative) = discharge from diversion 

capped at 75K cfs; and A5 = discharge from diversion capped at 150K cfs. The 

comparison between alternatives within a given cycle/hydrograph combination is the 

primary focus of this analysis.

Second, bias corrections (and the associated uncertainty) are applied to each 

spatial location in a given salinity field based on the region within the Bay (see Section 2 

of this report). Retrospective prediction bias in the daily salinity was evaluated based 

upon comparison to observed data from 2016. The results of the analysis from Section 2 

are applied to develop corrected salinity values.

Third, a simplified movement model is applied to simulate the daily movements 

of individual dolphins for an annual simulation. The daily corrected salinity value for 

each simulated animal position is stored to derive an “exposure history” for each 

simulated animal. The initial positions for the simulated dolphins are set based upon a 

random draw of the spatial cells in the model domain with a probability weighting based 

upon the relative density surface developed in Section 1 of this report.

Fourth, a dose-response curve is applied to each exposure history to estimate an 

annual individual survival rate for the simulated dolphins. The relationship between 

exposure to low salinity and the resulting impact on survival is complex and the data

available to quantify this relationship is limited. Therefore, the relationship between low 
23

salinity exposure and survival was developed through an expert elicitation process. The 

expert elicitation was asked to consider the impacts of “continuous exposure” to low 

salinity (defined as < 5 ppt) on the survival of estuarine dolphins that typically experience 

a range of salinity conditions within a given year. The elicitation focused on the effect on 

survival within a given year and did not consider repeat exposures across years. While 

the experts characterized their uncertainty in the resulting dose-response curve, there is

likely substantial unquantified uncertainty in this relationship (Booth and Thomas

unpublished). 

The average of the annual individual survival rates provides the predicted 

population mean annual survival for the scenario/hydrograph combination. It should be 

noted that the model does not include other processes that may impact dolphin survival or 

a baseline survival rate. Predicted survival rates should therefore be interpreted as the 

change in survival solely due to exposure to low salinity in a given year.

Uncertainty is incorporated into the simulation for each model parameter. The 

uncertainty in the salinity bias correction (see Section 2) is applied by randomly drawing 

bias correction values from the normal distribution with the specific mean and standard 

deviations and applying 1,000 randomized correction biases to each salinity surface. The 

movement histories are based upon the probability weighted randomized placement of 

5,000 simulated dolphins, and a subsample of these random movement histories are 

drawn for each estimate of annual exposure. Finally, the uncertainty in the dose-response 

curve is accounted for by randomly selecting from 10,000 realizations of the curve that 

reflect the uncertainty in parameter estimates derived from the expert elicitation (Booth 

and Thomas unpublished). The distributions of annual survival rates therefore reflect 

1,000 simulations that include uncertainty in salinity bias corrections, initial animal 

location and movement patterns, and the dose-response function. These simulations do 

not represent uncertainty in the future hydrographs or other aspects of future prediction 

uncertainty in the Delft3D projected salinity, possible long-term changes in dolphin 

responses to habitat conditions inside Barataria Bay or the adjacent coastal waters, or 

other factors that may affect dolphin survival rates.
24

III.2 Methods

Delft3d Model Output Processing

Delft model projected salinity values were provided over the native model grid 

and the model spatial domain within Barataria Bay and extending offshore into coastal 

waters for each cycle/hydrograph/alternative. The native model grid has variable spatial 

resolution depending on the distance from the diversion outfall source, and was therefore 

standardized to a nominal 500 x 500 m grid resolution. Cells within this standardized 

grid classified as land (elevation >= 0m) were removed, and the extent of the grid was 

subsampled and classified by areas corresponding to the BBES stock boundary (Figure 

III.1).

The BBES stock area includes the known spatial extent of resident bottlenose 

dolphins including waters extending 1 km seaward from the barrier Islands. Within the 

stock boundary, four strata (West, Central, Southeast, and Island) were defined that 

reflect the restricted movement of different groups of animals based on both photoidentification histories (See Section 1 of this report) and telemetry tag data (Wells et al., 

2017). These strata are consistent with previously defined regional boundaries that had

different animal densities in prior studies (McDonald et al. 2017), with the exception that 

the Island stratum only covers the central and western portions of the barrier islands 

including waters extended 1km from shore into nearshore coastal waters (Figure III.1). 

The strata boundaries extended north into more freshwater areas given that it is possible 

that BBES dolphins occur infrequently in these habitats. However, these areas are 

outside of the boundaries of the 2019 CMR study and the extrapolation of the relative 

density model (Section 1).

Movement Model and Salinity History

To model the exposure to low salinity for individual animals under a given annual 

salinity field, we first randomly selected 5,000 “starting” positions from within the grid 

domain. The initial selection of starting cells was weighted by the predicted relative 

density map described in Section 2 (Figure III.2). The analysis therefore assumes that the 
25

spatial distribution observed during these surveys is representative of the typical 

distribution of the BBES bottlenose dolphin stock. Areas outside of the valid 

extrapolation of this density grid were assigned a sampling probability of zero. 

Probability weighting effectively replicated the expected distribution of animals.

After these initial starting positions were selected, a constrained random walk 

movement was simulated at a daily time step. The maximum daily displacement for an 

individual was set at 5km. At each time step, all of the spatial cells within 5km of the 

simulated individual’s current position were available as a destination. The destination 

cell was selected randomly from this neighborhood, with the probability selection 

weighted by 1/distance2 where the distance is that from the initial starting location. 

Because of the inverse-distance2 weighting, there was a much greater likelihood of 

selecting cells close to the starting location as opposed to those further away. This 

approach effectively constrained the movement of simulated individuals to stay in 

relatively close proximity to the initial starting location. This is consistent with tag 

telemetry studies that demonstrated that bottlenose dolphins had relatively restricted 

home ranges within Barataria Bay and tended to remain within strata (Wells et al. 2017). 

Based on the simulated daily location, each simulated individual was given a daily 

salinity value drawn from the relevant standardized Delft output grid.

For each daily salinity surface, 1,000 randomized bias-corrected surfaces were 

created using a parametric bootstrap based upon the estimated mean and standard 

deviation of salinity bias (see Section 2). A random bias correction value was drawn 

from a normal distribution and applied to all grid cells (as appropriate by spatial location) 

for each daily salinity surface. Thus, each dolphin movement history includes a 

distribution of daily salinity values that represents the uncertainty in the bias correction.

Exposure History and Survival Estimates 

At the end of an annual simulation, each simulated movement history had a time 

series (and distribution) of daily salinity values. This salinity history was used to develop 

exposure indices for comparison with the salinity:survival dose response (DR) curve

(Figure III.3). The expert elicitation (EE) that developed this curve (Booth and Thomas 

unpublished) explicitly considered “continuous exposure” within the context of 
26

environmental variability, movement of individuals within a given estuary, and the 

potential for temporary refugia from low salinities. We considered three possible 

exposure indices as analogous to the “continuous exposure” considered by the EE based 

on the threshold “low salinity” value of 5ppt: 1) the total number of days (per year) with 

salinity <5 ppt, 2) a “maximum count” where each day of salinity <5ppt adds 1 to the 

metric and each day of salinity >5ppt subtracts 1 from the metric (the maximum value 

obtained during the year would therefore reflect extended periods of low salinity 

including intermittent breaks), and 3) the “longest streak” defined as the longest stretch of 

continuous days below 5ppt with breaks of 2 or fewer days. We selected the “longest 

streak” metric as most analogous to the “continuous exposure” situation considered by 

the expert elicitation working group. This is a conservative estimate of exposure as an 

animal may have multiple streaks in a given year, but only the longest one would be 

considered when evaluating the impacts on survival. Further, it is unknown if the effects 

of prolonged exposure to low salinity would be fully resolved in a 48-hour respite 

between repeated exposures, but this metric assumes that the physiological condition of 

the animal is fully reset after this interval regardless of the length of the initial salinity 

exposure.

For each of 1,000 bootstrap iterations of the model, a random realization of the 

salinity:survival DR curve was also selected from the distribution of outcomes provided 

by the expert elicitation using the DR curve for a “compromised” population (Figure 

III.3, EE Scenario 1) since BBES bottlenose dolphins are known to have ongoing health 

impacts from the DWH oil spill. The outcomes of this analysis did not differ appreciably 

if the DR curve for “healthy” populations was used. This curve was then used to predict 

individual survival rates for the exposure histories for that bootstrap iteration, and the 

distribution of the population mean survival rate was calculated. This distribution of 

means was used to evaluate the potential effects of lowered salinity on survival rates of 

Barataria Bay bottlenose dolphins under diversion alternatives.

III.3 Results

Initial Distribution and Movement Histories
27

The probability weighting for selection of starting positions based upon the spatial 

GAM model resulted in initial distributions of simulated dolphins that reflect the 

expected distribution of the population. There were a high number of starting positions 

in the southern part of the Bay near the pass and in the central and northern part of the 

central region (Figure III.4). The restricted random walk movement histories resulted in 

relatively small home ranges of simulated animals within the Bay that were generally 

restricted to within 5-10 km of their original starting point (Figure III.5). These overall 

patterns are consistent with the evidence of limited movement of individuals between 

regions and small home ranges (Wells et al. 2017).

Exposure History

For the representative year, cycle 0, No Action Alternative (NAA), the mean 

longest streak metric was 12.9 days (95% Confidence Limits (CL) 7.5-19.9, Table III.1) 

overall for the whole Bay and ranged between 0.1 and 23.4 for each region (Table III.1, 

Figure III.6, Figure III.7). These exposure levels are expected to result in high survival 

rates for nearly all outcomes of the DR curve (Figure III.3). The long streak metric 

increased for each diversion alternative which the greatest increases occurring in the 

West and Central regions of the Bay (Table III.1, Figure III.7). The mean longest streak 

remained low (< 11 days) in the Island stratum for both A3 and the APA, but increased to 

37.7 days in the alternative with the highest potential freshwater input. Under the APA, 

the longest streak metric overall mean was 51.1 days (95% CL 39.6-62.8, Table III.1) 

with means of 55.1 (95% CL 31.5-83.7, Table III.1) and 87.2 (95% CL 67.0-108.8, Table 

III.1) in the central and west regions, respectively. 

Survival Rates

For the representative year in cycle 0, the mean survival rate under the NAA was 

0.890 (95% CL 0.753-0.982) for the Bay as a whole (Table III.2, Figure III.8). This 

survival rate decreased under each alternative, and the mean annual population survival 

rate under the APA was 0.588 (95% CL 0.281-0.832). Pairwise comparisons between the 

NAA and the other three alternatives demonstrated that the population survival rates were 
28

significantly different based upon the bootstrap distribution of differences between the 

means (Figure III.8). Consistent with the patterns observed in the longest streak metric, 

the survival rates decreased the most for the central and western regions (Table III.2), 

though there were lower survival rates in all regions under the APA (Figure III.9). Under 

the APA, the mean population survival rate was 0.555 (95% CL 0.120-0.885) and 0.292 

(95% CL 0.042-0.683) for the west and central regions, respectively (Figure III.9, Table 

III.2). The survival rates in these regions were extremely low under A5 and declined to 

0.241 (95% CL 0.000-0.658) for the West region and 0.073 (95% CL 0.000-0.403) for 

the central region (Figure III.10, Table III.2).

The annual salinity field is driven primarily by the hydrograph of river outflow. 

During high flow years, there will be a greater amount of freshwater input into the 

Barataria Bay system due to the diversion, and during low flow years it is expected that 

there will be less difference between alternatives. For cycle 0, we compared the exposure 

level between the representative year and four alternative years: 1994, 2006, 2010, and 

2011. Generally, the changes in survival for these alternate years were similar to those 

observed for the representative year (Table III.3, Figure III.11). Population mean survival 

rates for the NAA ranged between 0.837 – 0.950 with the highest survival rates during 

the relatively low flow year of 2006 (Table III.3). Under the APA, mean survival rates 

ranged between 0.319 for 2011 and 0.898 for 2006 (Table III.3, Figure III.11). During 

2006, there was no significant difference between alternatives; however, for the 

remaining years, all alternatives had significantly lower survival rates than the NAA 

(Figure III.11).

It is not possible to explicitly project the expected changes in population size over 

each decade given that the available hydrographs do not form a predictive time series, nor 

does the model developed and applied in this study include food web or reproductive 

information. However, given the extremely low projected survival rates in the west and 

central regions under the alternatives, it is likely that the abundance of animals within 

these regions would decline to near zero after the first 10 years of the project. The only 

portion of the Bay with projected survival rates sufficient to maintain a substantial 

population would be in the Island stratum where survival rates are expected to exceed 0.9 

annually under A3 and the APA. To evaluate the potential future impact of the diversion 
29

on the remaining dolphin population, we simulated annual survival rates for the 

representative year of each future decade (cycle 1-5). It should be noted that the 

representative hydrograph was different for each cycle, and therefore some of the 

observed effects are related to changes in the hydrographs. The NAA alternative in each 

cycle includes dolphins inhabiting the entire Bay as they are currently. Under these 

model conditions, the mean population survival rates in future cycles are lower than those 

under the NAA during cycle 1 and then become slightly higher in cycles 2-5 (for the 

APA scenario), though the differences are not statistically significant (Table III.4, Figure 

III.12). The survival rates under the NAA are projected to decrease over the course of the 

decades which reflects the projected impacts of sea level rise and increased water flow 

due to land loss (Figure III.12). 

III.4 Discussion

This simulation analysis examines the potential effects of future changes in 

salinity under alternative diversion scenarios on the projected annual survival rate of 

bottlenose dolphins in Barataria Bay. The analysis accounts for the initial spatial 

distribution of dolphins in the Bay, variability in individual dolphin movement patterns, 

bias, uncertainty in predicted salinity values, and uncertainty in the expected relationship 

between salinity exposure and survival. Assumptions made in this process are described 

in the sections above. The model is not able to account for uncertainty or bias in the 

future salinity levels experienced by the population. In addition, the model assumes that 

the observed spatial distribution during the spring of 2019 is typical of the overall 

distribution of the stock throughout the entire year and across multiple years and that the 

applied movement model is representative of the scale and movement patterns of BBES 

dolphins.

Projected future scenarios include hydrographic conditions outside of the range of 

natural variability used to calibrate the Delft3d model, and therefore model performance 

is unknown under these conditions. Unknown future conditions in the River hydrograph 

will result in changes in salinity and survival rates that differ from the projections which 

rely on the historical hydrographs. Similarly, future projections rely upon historical 
30

hydrographs. If the future includes a higher proportion of low-flow years, then the 

impacts of the diversion on the bottlenose dolphin population will be lower than 

projected. In contrast, if there is an expectation of higher water flow into the system in 

the future, then the impact could be larger. Available climate prediction models suggest 

that precipitation during winter and spring will be significantly higher over most of North 

America in the coming decades which would suggest an increasing likelihood of highflow years (USGCRP, 2017).

In addition to uncertainty about future climates, several caveats are required when 

interpreting the potential impacts on dolphin survival. The “longest streak” metric 

approximates the intent of the expert elicitation panel consideration of “continuous 

exposure” to salinity less than 5 ppt. However, it is unclear whether this metric ignores

important dynamics that may influence the expected dolphin survival. For example, if an 

animal experienced five 15 day exposures with 3 day breaks in between, the metric 

would have a value of 15, even though the dolphin had experienced a total of 75 days of 

exposure with limited opportunity to recover. Furthermore, it is unknown if a two-day

break from low salinity is sufficient to allow complete recovery, though that is assumed 

by the implemented metric. It is notable that a recent natural event resulting in increased 

freshwater input into Mississippi Sound was followed by substantially increased dolphin 

mortality with as little as 20-40 days of lowered salinity, not all of which were 

consecutively below 5 ppt (Garrison, unpublished data). The observation of an increase 

in mortality associated with extended exposure to freshwater in an estuarine bottlenose 

dolphin population is consistent with the expectations from this model.

The simulation model demonstrates significant reductions in survival rates for 

dolphins under the APA with the highest impacts experienced by dolphins in the central 

and western portions of the Bay. Under the APA, the population mean survival rate is 

expected to decline by an average of 34% (95% CL 15.3%-62.7%, Table III.2) compared 

to the NAA. For comparison, baseline adult survival rates for estuarine bottlenose 

dolphins were estimated to range between 0.88-0.96 for females and 0.85-0.94 for males 

(ages 1-39 years, Schwacke et al., 2017), and these ranges are consistent with our 

estimated NAA survival rates. The projected reduction in survival rates, if realized, 

would result in substantial declines in population size, particularly in the habitats of the 
31

central and western Bay where annual survival rates are expected to decline by 65.9% 

and 41.9%, respectively. If the reduction in survival was sustained over time, then a

smaller dolphin population may be able to persist in Barataria Bay beyond the first 

decade near the barrier islands. However, this population would also experience 

relatively low survivorship associated with exposure to freshwater with annual survival 

rates ranging from 0.681 – 0.901 (Table III.4). By comparison, dolphins near the Islands 

under the no action alternative in cycle 0 are expected to have survival rates approaching 

1 (Table III.2). The projected survival rates for the more broadly distributed population 

in future cycles under the NAA are comparable to those of the Island population under 

alternatives, though there may be some survival benefit 50 years in the future (Figure 

III.12).

There remain significant uncertainties with this analysis, and other factors that 

impact bottlenose dolphin survival are not included in this approach. However, the 

available data and analyses indicate that increased freshwater input into Barataria Bay

associated with the MBSD is projected to result in substantial declines in bottlenose 

dolphin annual survival rates that would not be sustainable by this population over the 

near term. 
32

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34

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35

List of Tables and Figures

Table I.1. Summary of survey effort in Barataria Bay 14 March to 1 April 2019

Table I.2. Group size mean and range per survey area. n = number of groups per area.

Table I.3. Summary of data from temperature and salinity stations collected in Barataria 

Bay 14 March – 1 April 2019

Table I.4. Model results from the closed capture-recapture full-likelihood approach. Mt = 

detection probabilities assumed to be time varying; Mtb model where detection 

probabilities (p) vary with time but also recapture probabilities are different from initial 

capture probabilities; M0= the null model with constant detection probabilities across all 

factors. 

Table I.5. Real function parameters from the closed capture Full-likelihood Mt model. 

Parameters: p = probability of capture and the probability of recapture in each secondary 

session, f0=number of individuals with no sightings during each primary.

Table I.6. Population estimates (N), standard error (SE), coefficient of variation (CV), 

and 95% confidence intervals (95% CI) derived from the closed capture (CC) robust 

design full-likelihood Mt model. Estimated Total N is the estimated marked N divided by 

the proportion marked.

Table I.7. GAM model summary.

Table II.1. Station information for 22 observation stations incuded in the current 

analysis. Station data are the same as those described in Sadid et al. (2018) exluding 5 

stations. East and North represent station coordinates in UTM15 (WGS 84) map 

projection.

Table III.1. Mean (95% Confidence Limits) of the Longest Streak metric by region and 

alternative for cycle 0, representative year. NAA = No Action Alternative, A3 = 

Alternative 3 (50K CFS diversion), APA = Applicant’s Preferred Alternative (75K CFS 

diversion), A5 = Alternative 5 (150K CFS diversion). Overall indicates the average for 

the whole BBES Stock.

Table III.2. Mean (95% Confidence Limits) of population annual survival rates by 

region and alternative for cycle 0, representative year. NAA = No Action Alternative, A3 

= Alternative 3 (50K CFS diversion), APA = Applicant’s Preferred Alternative (75K CFS 

diversion), A5 = Alternative 5 (150K CFS diversion). Overall indicates the average for 

the whole Bay.
36

Table III.3. Mean (95% Confidence Limits) of population annual survival rates by 

alternative for cycle 0, representative year and alternate years. NAA = No Action 

Alternative, A3 = Alternative 3 (50K CFS diversion), APA = Applicant’s Preferred 

Alternative (75K CFS diversion), A5 = Alternative 5 (150K CFS diversion).

Table III.4. Mean (95% Confidence Limits) of population annual survival rates by 

alternative for the representative year in each cycle (decade). For cycles 1-5, the 

simulations for the NAA includes dolphins inhabiting the entire Bay where under the 

other alternatives dolphin starting positions are restricted to the Island habitat with the 

highest survival rates. NAA = No Action Alternative, A3 = Alternative 3 (50K CFS 

diversion), APA = Applicant’s Preferred Alternative (75K CFS diversion), A5 = 

Alternative 5 (150K CFS diversion).

Figure I.1. Barataria Bay in southern Louisiana including the region surveyed during 

photo-id surveys prior to 2019 (hatched area, McDonald et al., 2017).

Figure I.2. Survey track-lines for the Barataria Bay Estuarine System Stock capturemark-recapture project 14 March to 1 April 2019. Prior year surveys did not include the 

“southeast” area or the contour tracklines in the northern portion of the survey area.

Figure I.3 Planned temperature and salinity stations during the spring 2019 CMR survey.

Figure I.4. Executed tracklines, dolphin group sighting locations, and analysis grid.

Figure I.5. Dolphin group sizes during the spring 2019 CMR survey.

Figure I.6. Discovery curve showing the number of new dolphins added to the Barataria 

Bay master photo-identification catalog by survey year. 

Figure I.7. GAM model fit and residuals. The line indicates the model smooth fit for 

each explanatory factor, and the shaded area indicates the 95% confidence limit of the 

model fit.

Figure I.8. Fitted (line) and observed (points) dolphins per 100m of survey effort (SPUE) 

as a function of explanatory factors. The dashed line indicates the 95% confidence limits 

of model fitted values.

Figure I.9. Predicted relative density (SPUE) projected over the spatial grid. Areas with 

CV > 0.4 are masked from the resulting surface given high model uncertainty when 

projecting outside of the range of survey data.
37

Figure II.1. Station locations for 22 salinity observations included in the current 

analysis.

Figure II.2. Daily mean observed (black dots) and Delft3d modelled (red line) salinity 

for each observation sation from 2016.

Figure II.3. Daily bias (red line) for each salinity observation station. The dashed line 

indicates the Mean Bias Estimate for each station.

Figure II.4. Distance plot indicating the Pearson distance between station pairs. Higher 

values indicate greater differences between stations. Pairs are ordered to highlight 

clustering among stations.

Figure II.5. Cluster plots showing the membership and distribution of clusters of 

stations based upon K-means clusters specifying 2-5 clusters. 

Figure II.6. Cluster optimization statistics using the (A) Average silhouette and (B) Gap 

statistics. Both metrics indicate that an optimal number of clusters is 2 (dashed vertical 

line).

Figure II.7. Cluster membership as a function of RMSE and MBE. Cluster 1 includes 

stations with positive salinity biases and lower RMSE values while Cluster 2 includes 

stations with more negative biases and higher RMSE.

Figure II.8. Salinity stations by cluster. Cluster 1 includes stations further north in 

areas of the Bay more heavily influenced by freshwater while Cluster 2 includes stations 

in the middle and lower bay that are more estuarine.

Figure II.9. Partial autocorrelation of residuals from GAMM models for Cluster 1 

stations with (A) no autocorrelation structure and (B) an AR3 autocorrelation structure. 

The dashed line indicates the level for significant autocorrelation in residuals. There is 

evidence for autocorrelation at lags 1-3 days. Accounting for this lag correlation leaves 

no remaining significant autocorrelation in the residuals for the GAMM model.

Figure II.10. Predicted (red line) and observed (points) daily salinity bias values for 

cluster 1 salinity stations. The 95% confidence interval of the GAMM predictions is 

indicated by the dashed lines.

Figure II.11. Parametric bootstrap distribution of mean annual bias for Cluster 1 stations 

derived from the GAMM model. The distribution median is indicated by the dashed 

vertical line. While the mean bias estimates are similar, ignoring autocorrelation in 

residuals (A) underestimates the uncertainty in the estimate while the distribution 

including the AR3 correlation (B) demonstrates higher variance in the mean.
38

Figure II.12. Partial autocorrelation of residuals from GAMM models for Cluster 2 

stations with (A) no autocorrelation structure and (B) an AR3 autocorrelation structure. 

The dashed line indicates the level for significant autocorrelation in residuals. There is 

evidence for autocorrelation at lags 1-3 days. Accounting for this lag correlation leaves 

no remaining significant autocorrelation in the residuals for the GAMM model.

Figure II.13. Predicted (red line) and observed (points) daily salinity bias values for 

cluster 2 salinity stations. The 95% confidence interval of the GAMM predictions is

indicated by the dashed lines.

Figure II.14. Parametric bootstrap distribution of mean annual bias for Cluster 2 stations 

derived from the GAMM model. The distribution median is indicated by the dashed 

vertical line. The mean bias estimate for the AR3 model is slightly higher than that for 

the model including no autocorrelation. Ignoring autocorrelation in residuals (A) 

underestimates the uncertainty in the estimate while the distribution including the AR3 

correlation (B) demonstrates higher variance in the mean.

Figure II.15. Regions within the Barataria Bay bottlenose dolphin stock area 

corresponding to cluster 1 and cluster 2 stations and corresponding mean bias estimates. 

The mean and standard deviation in bias will be applied spatially within these regions to 

account for retrospective prediction bias in outputs from the Delft3D model. Cluster 1 

has a mean bias of 0.717 (sd = 0.0889) while cluster 2 has a mean bias of -2.136 (sd = 

0.168).

Figure III.1. Predicted daily salinity from the Delft model for each cycle, hydrograph, 

and alternative were standardized onto a 500x500m grid and overlaid with the spatial 

boundaries of the BBES bottlenose dolphin stock. The underlying salinity in this image 

is from an example output (Cycle 0, NAA, April 20). The stock boundary and within 

stock strata definitions are outlined in black.

Figure III.2. Relative density (Sightings Per Unit Effort) surface over the model domain 

overlaid with the BBES stratum boundaries. The mean SPUE values (dolphins per 100m 

trackline) by strata are: Island: 0.406, West - 0.0813, Central - 0.203, and Southeast -

0.100

Figure III.3. Dose response curve indicating the relationship between continuous days of 

exposure and individual survival probability. The shaded area indicates the 95% 

confidence limits of a distribution of 10,000 realizations of this curve reflecting 

parameter uncertainty. The solid red line indicates the median of the distribution while 

the blue lines indicate the first and third quartiles of the distribution.
39

Figure III.4. Example distribution of starting positions for simulated dolphins.

Figure III.5. Example movement histories of simulated dolphins. The outline indicates 

the regional boundaries. The green lines indicate the annual locations of selected 

simulated dolphins (numbers).

Figure III.6. Mean longest streak metric for different alternatives during cycle 0, 

representative year. NAA = No Action Alternative, A3 = Alternative 3 (50K CFS 

diversion), APA = Applicant’s Preferred Alternative (75K CFS diversion), A5 = 

Alternative 5 (150K CFS diversion). 

Figure III.7. Mean longest streak metric for different alternatives during cycle 0, 

representative year by region. NAA = No Action Alternative, A3 = Alternative 3 (50K 

CFS diversion), APA = Applicant’s Preferred Alternative (75K CFS diversion), A5 = 

Alternative 5 (150K CFS diversion). 

Figure III.8. Survival rates for different alternatives during cycle 0, representative year. 

P-values indicate results of significance tests comparing survival rates for each 

alternative to the No Action Alternative. NAA = No Action Alternative, A3 = Alternative 

3 (50K CFS diversion), APA = Applicant’s Preferred Alternative (75K CFS diversion), 

A5 = Alternative 5 (150K CFS diversion). 

Figure III.9. Survival rates during cycle 0, representative year comparing the No Action 

Alternative to the Applicants Preferred Alternative by regional stratum. P-values indicate 

results of significance tests comparing survival rates between alternatives within regions. 

NS indicates a non-significant (p > 0.05) proportion of differences in survival rates less 

than zero. NAA = No Action Alternative, APA = Applicant’s Preferred Alternative (75K 

CFS diversion).

Figure III.10. Regional survival rates during cycle 0, representative year for all 

alternatives. NAA = No Action Alternative, A3 = Alternative 3 (50K CFS diversion), 

APA = Applicant’s Preferred Alternative (75K CFS diversion), A5 = Alternative 5 (150K 

CFS diversion). 

Figure III.11. Population survival rates by alternative for additional years in cycle 0. Pvalues indicate results of significance tests comparing survival rates for each alternative 

to the No Action Alternative. NS indicates a non-significant (p > 0.05) proportion of 

differences in survival rates less than zero. NAA = No Action Alternative, A3 = 

Alternative 3 (50K CFS diversion), APA = Applicant’s Preferred Alternative (75K CFS 

diversion), A5 = Alternative 5 (150K CFS diversion). 

Figure III.12. Population survival rates by alternative for the representative year of each 

decade. P-values indicate results of significance tests comparing survival rates for each 
40

alternative to the No Action Alternative. NS indicates a non-significant (p > 0.05) 

proportion of differences in survival rates less than zero. For all alternatives except the 

NAA, the populations for cycles 1-5 were restricted to animals with starting positions in 

the Island strata. NAA = No Action Alternative, A3 = Alternative 3 (50K CFS 

diversion), APA = Applicant’s Preferred Alternative (75K CFS diversion), A5 = 

Alternative 5 (150K CFS diversion). 
41 

Table I.1. Summary of survey effort in Barataria Bay 14 March to 1 April 2019. 

Survey

Area

# of 

Surveys

Survey

Hours

Survey

Distance 

(km)

# of 

Sightings

# of 

Dolphins

Encountered

# of 

Photos 

West 11 76 1,160 85 465 6,746 

Central 13 90 1,504 150 964 9,792 

Southeast 13 90 1,531 133 617 10,827 

Total 37 256 4,195 368 2,046 27,365 
42 

Table I.2. Group size mean and range per survey area. n = number of groups per area. 

Survey Area # of Sightings Mean (SD) Range 

West 86 5.4 (5.58) 1 - 40 

Central 150 6.4 (6.44) 1 - 40 

Southeast 132 4.6 (5.65) 1 - 40 
43 

Table I.3. Summary of data from temperature and salinity stations collected in Barataria 

Bay 14 March – 1 April 2019 

Salinity (ppt) Temperature (°C)

Survey Area Average Range Average Range 

West 7.6 0.4-18.2 18.9 14.2-22.9 

Central 9.2 1.5-20.2 18.1 9.2-21.1 

Southeast 11.4 0.6-31.0 18.2 14.6-22.5 
44 

Table I.4. Model results from the closed capture-recapture full-likelihood approach. Mt = 

detection probabilities assumed to be time varying; Mtb model where detection 

probabilities (p) vary with time but also recapture probabilities are different from initial 

capture probabilities; M0= the null model with constant detection probabilities across all 

factors. 

Model AICc Delta 

AICc

AICc 

Weights

Model 

Likelihood Num. Par Deviance -2log(L)

{Mt} -5911.19 0 0.73129 1 5 31.2111 -5921.2048

{Mtb} -5909.18 2.0029 0.26864 0.3674 6 31.2067 -5921.2091

{M0} -5892.89 18.292 0.00008 0.0001 2 55.5175 -5896.8984
45 

Table I.5. Real function parameters from the closed capture Full-likelihood Mt model. 

Parameters: p = probability of capture and the probability of recapture in each secondary 

session, f0=number of individuals with no sightings during each primary.

95% Confidence Interval

Parameter Estimate Standard 

Error

Lower Upper

1: p 0.1468791 0.0111539 0.1263351 0.1701133 

2: p 0.2152886 0.0141434 0.1888613 0.2443000 

3: p 0.1904734 0.0130884 0.1661299 0.2174537 

4: p 0.1877906 0.0129726 0.1636753 0.2145474 

5: f0 656.02217 64.499169 541.28906 795.07443 
46 

Table I.6. Population estimates (N), standard error (SE), coefficient of variation (CV), 

and 95% confidence intervals (95% CI) derived from the closed capture (CC) robust 

design full-likelihood Mt model. Estimated Total N is the estimated marked N divided by 

the proportion marked. 

CC Full - likelihood

Minimum known 

marked 835 

Estimated Marked N 1491 

SE Marked N 64.5 

95% CI Marked N 1376 – 1630 

Proportion marked 0.72 

SE Proportion Marked 0.031 

Estimated Total N 2071

SE Total N 121.8 

95% CI Marked N 1832 – 2309
47

Table I.7. GAM model summary.

Family: Tweedie(p=1.369)

Link function: log

Formula:

tot.dolphins ~ s(x) + s(y) + s(d.pass) + offset(log(effort))

Parametric coefficients:

 Estimate Std. Error t value Pr(>|t|) 

(Intercept) -6.99656 0.08011 -87.34 <2e-16 ***

Approximate significance of smooth terms:

 edf Ref.df F p-value 

s(x) 5.661 6.656 11.504 1.54e-13 ***

s(y) 3.606 4.554 2.650 0.0256 * 

s(d.pass) 4.152 5.120 2.757 0.0240 * 

R-sq.(adj) = 0.0747 Deviance explained = 14.6%

-REML = 1537.2 Scale est. = 8.3175 n = 1868
48 

Table II.1. Station information for 22 observation stations incuded in the current 

018) exluding 5 

S 84) map 

analysis. Station data are the same as those described in Sadid et al. (2

stations. East and North represent station coordinates in UTM15 (WG

projection. 

Station MBE sd 

MBE

Mean 

Salinity RMSE East North MBE 

Orig. 

RMSE 

Orig. 

CRMS0171 -3.13 3.10 16.8 4.40 811277 3248116 -2.95 4.08

CRMS0172 -1.98 3.03 14.3 3.62 816792 3247185 -1.71 3.17

CRMS0174 -2.78 2.73 12.6 3.89 813953 3256360 -2.27 3.18

CRMS0175 -0.42 3.87 12.7 3.88 777651 3243432 -1.17 3.94

CRMS0176 -2.56 2.71 12.2 3.73 810874 3257567 -2.13 3.19

CRMS0178 -2.66 3.63 13.8 4.50 787006 3243450 -3.16 4.70

CRMS0179 -3.78 2.97 12.3 4.80 820429 3256411 -3.05 3.92

CRMS0181 -1.28 3.12 12.9 3.37 820831 3249682 -0.77 2.55

CRMS0226 0.90 1.95 6.4 2.14 801923 3268796 0.37 1.57

CRMS0232 1.40 2.71 4.7 3.04 789639 3266868 0.80 2.50

CRMS0237 0.22 2.33 6.7 2.34 796361 3264750 -0.37 2.04

CRMS0263 0.59 2.02 4.8 2.10 801493 3273325 0.03 1.61

CRMS0272 -3.05 2.32 10.5 3.83 820801 3259140 -2.43 3.03

CRMS3617 0.54 2.02 5.4 2.09 799087 3272647 -0.02 1.67

CRMS4529 -0.83 1.91 9.1 2.08 808225 3264337 -1.17 2.16

GISL1 -0.44 3.40 9.2 3.42 795884 3258664 -1.12 2.98

USGS1 1.19 1.41 1.1 1.84 774616 3274025 1.05 1.72

USGS3 1.33 1.85 1.2 2.27 773211 3268635 1.07 2.06

USGS5 0.98 3.00 5.4 3.15 789822 3265229 0.34 2.65

USGS6 -1.09 4.36 8.0 4.48 787150 3255761 -1.97 4.66

USGS7 -2.75 2.76 16.4 3.90 797133 3247851 -2.85 3.94

USGS8 -2.21 3.30 18.8 3.97 796670 3242081 -2.02 3.73
49 

Table III.1. Mean (95% Confidence Limits) of the Longest Streak metric by region and 

alternative for cycle 0, representative year. NAA = No Action Alternative, A3 = 

Alternative 3 (50K CFS diversion), APA = Applicant’s Preferred Alternative (75K CFS 

diversion), A5 = Alternative 5 (150K CFS diversion). Overall indicates the average for 

the whole BBES Stock. 

Region NAA A3 APA A5

12.9 44.3 51.1 79.8 Overall (7.5-19.9) (33.1-55.7) (39.6-62.8) (67.0-92.6) 

0.1 6.2 10.8 37.7 Island (0.0-0.5) (4.5-8.4) (7.2-15.1) (28.5-48.4) 

6.3 45.8 55.1 93.6 West (0.6-18.6) (19.7-78.1) (31.5-83.7) (68.8-122.2) 

15.3 74.8 87.2 122.7 Central (7.2-25.7) (55.4-95) (67.0-108.8) (107-139.3) 

23.4 36.5 37.8 56.1 Southeast (10.2-40.7) (19.4-58.5) (20.4-60.5) (36.2-81.8) 

 
50 

Table III.2. Mean (95% Confidence Limits) of population annual survival rates by 

region and alternative for cycle 0, representative year. NAA = No Action Alternative, A3 

= Alternative 3 (50K CFS diversion), APA = Applicant’s Preferred Alternative (75K CFS 

diversion), A5 = Alternative 5 (150K CFS diversion). Overall indicates the average for 

the whole Bay. 

Region NAA A3 APA A5

Overall 0.890 

(0.753-0.982) 

0.665 

(0.423-0.854) 

0.588 

(0.281-0.832) 

0.355 

(0.092-0.694) 

Island 1.000 

(0.999-1.000) 

0.980 

(0.865-1.000) 

0.935 

(0.613-1.000) 

0.605 

(0.094-0.975) 

West 0.955 

(0.855-1.000) 

0.684 

(0.348-0.921) 

0.555 

(0.120-0.885) 

0.241 

(0.000-0.658) 

Central 0.858 

(0.611-0.992) 

0.406 

(0.113-0.746) 

0.292 

(0.042-0.683) 

0.073 

(0.000-0.403) 

Southeast 0.807 

(0.584-0.97) 

0.714 

(0.458-0.938) 

0.683 

(0.368-0.926) 

0.545 

(0.221-0.857) 
51 

Table III.3. Mean (95% Confidence Limits) of population annual survival rates by 

alternative for cycle 0, representative year and alternate years. NAA = No Action 

Alternative, A3 = Alternative 3 (50K CFS diversion), APA = Applicant’s Preferred 

Alternative (75K CFS diversion), A5 = Alternative 5 (150K CFS diversion). 

Year NAA A3 APA A5

0.885 0.567 0.405 0.185 1994 (0.786-0.960) (0.307-0.778) (0.150-0.667) (0.045-0.453) 

0.950 0.897 0.898 0.838 2006 (0.861-0.997) (0.761-0.977) (0.757-0.978) (0.586-0.961) 

0.864 0.577 0.464 0.214 2010 (0.687-0.961) (0.305-0.816) (0.219-0.744) (0.052-0.505) 

0.837 0.461 0.319 0.121 2011 (0.652-0.950) (0.181-0.733) (0.071-0.638) (0.016-0.378) 

Representative 0.890 0.665 0.588 0.355 

Year (0.753-0.982) (0.423-0.854) (0.281-0.832) (0.092-0.694) 
52 

Table III.4. Mean (95% Confidence Limits) of population annual survival rates by 

alternative for the representative year in each cycle (decade). For cycles 1-5, the 

simulations for the NAA includes dolphins inhabiting the entire Bay where under the 

other alternatives dolphin starting positions are restricted to the Island habitat with the 

highest survival rates. NAA = No Action Alternative, A3 = Alternative 3 (50K CFS 

diversion), APA = Applicant’s Preferred Alternative (75K CFS diversion), A5 = 

Alternative 5 (150K CFS diversion). 

Cycle NAA A3 APA A5

0 0.890 

(0.753-0.982) 

0.665 

(0.423-0.854) 

0.588 

(0.281-0.832) 

0.355 

(0.092-0.694) 

1 0.967 

(0.891-1.000) 

0.939 

(0.673-1.000) 

0.811 

(0.345-0.992) 

0.259 

(0.048-0.776) 

2 0.880 

(0.781-0.960) 

0.980 

(0.873-1.000) 

0.901 

(0.563-0.999) 

0.423 

(0.054-0.911) 

3 0.797 

(0.649-0.926) 

0.937 

(0.676-1.000) 

0.854 

(0.368-0.995) 

0.328 

(0.053-0.880) 

4 0.647 

(0.475-0.804) 

0.875 

(0.504-0.989) 

0.681 

(0.162-0.958) 

0.224 

(0.035-0.657) 

5 0.658 

(0.462-0.817) 

0.954 

(0.717-1.000) 

0.864 

(0.362-0.996) 

0.368 

(0.046-0.843) 
Figure I.1. Barataria Bay in southern Louisiana including the region surveyed during photo-id 

surveys prior to 2019 (hatched area, McDonald et al., 2017).
54

Figure I.2. Survey track-lines for the Barataria Bay Estuarine System Stock capturemark-recapture project 14 March to 1 April 2019. Prior year surveys did not include the 

“southeast” area or the contour tracklines in the northern portion of the survey area.
55

Figure I.3 Planned temperature and salinity stations during the spring 2019 CMR survey.
56

Figure I.4. Executed tracklines, dolphin group sighting locations, and analysis grid.
57

Figure I.5. Dolphin group sizes during the spring 2019 CMR survey.
58

Figure I.6. Discovery curve showing the number of new dolphins added to the Barataria 

Bay master photo-identification catalog by survey year. 
59

Figure I.7. GAM model fit and residuals. The line indicates the model smooth fit for 

each explanatory factor, and the shaded area indicates the 95% confidence limit of the 

model fit.
60

Figure I.8. Fitted (line) and observed (points) dolphins per 100m of survey effort (SPUE) 

as a function of explanatory factors. The dashed line indicates the 95% confidence limits 

of model fitted values.
61

Figure I.9. Predicted relative density (SPUE) projected over the spatial grid. Areas with 

CV > 0.4 are masked from the resulting surface given high model uncertainty when 

projecting outside of the range of survey data.
62

Figure II.1. Station locations for 22 salinity observations included in the current 

analysis.
63

Figure II.2. Daily mean observed (black dots) and Delft3d modelled (red line) salinity 

for each observation sation from 2016.
64

Figure II.3. Daily bias (red line) for each salinity observation station. The dashed line 

indicates the Mean Bias Estimate for each station.
65

Figure II.4. Distance plot indicating the Pearson distance between station pairs. Higher 

values indicate greater differences between stations. Pairs are ordered to highlight 

clustering among stations.
66

Figure II.5. Cluster plots showing the membership and distribution of clusters of 

stations based upon K-means clusters specifying 2-5 clusters. 
67

Figure II.6. Cluster optimization statistics using the (A) Average silhouette and (B) Gap 

statistics. Both metrics indicate that an optimal number of clusters is 2 (dashed vertical 

line).
68

Figure II.7. Cluster membership as a function of RMSE and MBE. Cluster 1 includes

stations with positive salinity biases and lower RMSE values while Cluster 2 includes 

stations with more negative biases and higher RMSE.
69

Figure II.8. Salinity stations by cluster. Cluster 1 includes stations further north in 

areas of the Bay more heavily influenced by freshwater while Cluster 2 includes stations 

in the middle and lower bay that are more estuarine.
70

Figure II.9. Partial autocorrelation of residuals from GAMM models for Cluster 1 

stations with (A) no autocorrelation structure and (B) an AR3 autocorrelation structure. 

The dashed line indicates the level for significant autocorrelation in residuals. There is 

evidence for autocorrelation at lags 1-3 days. Accounting for this lag correlation leaves 

no remaining significant autocorrelation in the residuals for the GAMM model.
71

Figure II.10. Predicted (red line) and observed (points) daily salinity bias values for 

cluster 1 salinity stations. The 95% confidence interval of the GAMM predictions is 

indicated by the dashed lines.
72

Figure II.11. Parametric bootstrap distribution of mean annual bias for Cluster 1 stations 

derived from the GAMM model. The distribution median is indicated by the dashed 

vertical line. While the mean bias estimates are similar, ignoring autocorrelation in 

residuals (A) underestimates the uncertainty in the estimate while the distribution 

including the AR3 correlation (B) demonstrates higher variance in the mean.
73

Figure II.12. Partial autocorrelation of residuals from GAMM models for Cluster 2 

stations with (A) no autocorrelation structure and (B) an AR3 autocorrelation structure. 

The dashed line indicates the level for significant autocorrelation in residuals. There is 

evidence for autocorrelation at lags 1-3 days. Accounting for this lag correlation leaves 

no remaining significant autocorrelation in the residuals for the GAMM model.
74

Figure II.13. Predicted (red line) and observed (points) daily salinity bias values for 

cluster 2 salinity stations. The 95% confidence interval of the GAMM predictions is 

indicated by the dashed lines.
75

Figure II.14. Parametric bootstrap distribution of mean annual bias for Cluster 2 stations 

derived from the GAMM model. The distribution median is indicated by the dashed 

vertical line. The mean bias estimate for the AR3 model is slightly higher than that for 

the model including no autocorrelation. Ignoring autocorrelation in residuals (A) 

underestimates the uncertainty in the estimate while the distribution including the AR3 

correlation (B) demonstrates higher variance in the mean.
76

Figure II.15. Regions within the Barataria Bay bottlenose dolphin stock area 

corresponding to cluster 1 and cluster 2 stations and corresponding mean bias estimates. 

The mean and standard deviation in bias will be applied spatially within these regions to 

account for retrospective prediction bias in outputs from the Delft3D model. Cluster 1 

has a mean bias of 0.717 (sd = 0.0889) while cluster 2 has a mean bias of -2.136 (sd = 

0.168).
77

Figure III.1. Predicted daily salinity from the Delft model for each cycle, hydrograph, 

and alternative were standardized onto a 500x500m grid and overlaid with the spatial 

boundaries of the BBES bottlenose dolphin stock. The underlying salinity in this image 

is from an example output (Cycle 0, NAA, April 20). The stock boundary and within 

stock strata definitions are outlined in black.
78

Figure III.2. Relative density (Sightings Per Unit Effort) surface over the model domain 

overlaid with the BBES stratum boundaries. The mean SPUE values (dolphins per 100m 

trackline) by strata are: Island: 0.406, West - 0.0813, Central - 0.203, and Southeast -

0.100
79

Figure III.3. Dose response curve indicating the relationship between continuous days of 

exposure and individual survival probability. The shaded area indicates the 95% 

confidence limits of a distribution of 10,000 realizations of this curve reflecting 

parameter uncertainty. The solid red line indicates the median of the distribution while 

the blue lines indicate the first and third quartiles of the distribution.
80

Figure III.4. Example distribution of starting positions for simulated dolphins.
81

Figure III.5. Example movement histories of simulated dolphins. The outline indicates 

the regional boundaries. The green lines indicate the annual locations of selected 

simulated dolphins (numbers).
82

Figure III.6. Mean longest streak metric for different alternatives during cycle 0, 

representative year. NAA = No Action Alternative, A3 = Alternative 3 (50K CFS 

diversion), APA = Applicant’s Preferred Alternative (75K CFS diversion), A5 = 

Alternative 5 (150K CFS diversion). 
83

Figure III.7. Mean longest streak metric for different alternatives during cycle 0, 

representative year by region. NAA = No Action Alternative, A3 = Alternative 3 (50K 

CFS diversion), APA = Applicant’s Preferred Alternative (75K CFS diversion), A5 = 

Alternative 5 (150K CFS diversion). 
84

Figure III.8. Survival rates for different alternatives during cycle 0, representative year. 

P-values indicate results of significance tests comparing survival rates for each 

alternative to the No Action Alternative. NAA = No Action Alternative, A3 = Alternative 

3 (50K CFS diversion), APA = Applicant’s Preferred Alternative (75K CFS diversion), 

A5 = Alternative 5 (150K CFS diversion). 
85

Figure III.9. Survival rates during cycle 0, representative year comparing the No Action 

Alternative to the Applicants Preferred Alternative by regional stratum. P-values indicate 

results of significance tests comparing survival rates between alternatives within regions. 

NS indicates a non-significant (p > 0.05) proportion of differences in survival rates less 

than zero. NAA = No Action Alternative, APA = Applicant’s Preferred Alternative (75K 

CFS diversion).
86

Figure III.10. Regional survival rates during cycle 0, representative year for all 

alternatives. NAA = No Action Alternative, A3 = Alternative 3 (50K CFS diversion), 

APA = Applicant’s Preferred Alternative (75K CFS diversion), A5 = Alternative 5 (150K 

CFS diversion). 
87

Figure III.11. Population survival rates by alternative for additional years in cycle 0. Pvalues indicate results of significance tests comparing survival rates for each alternative 

to the No Action Alternative. NS indicates a non-significant (p > 0.05) proportion of 

differences in survival rates less than zero. NAA = No Action Alternative, A3 = 

Alternative 3 (50K CFS diversion), APA = Applicant’s Preferred Alternative (75K CFS 

diversion), A5 = Alternative 5 (150K CFS diversion). 
88

Figure III.12. Population survival rates by alternative for the representative year of each 

decade. P-values indicate results of significance tests comparing survival rates for each 

alternative to the No Action Alternative. NS indicates a non-significant (p > 0.05) 

proportion of differences in survival rates less than zero. For all alternatives except the 

NAA, the populations for cycles 1-5 were restricted to animals with starting positions in 

the Island strata. NAA = No Action Alternative, A3 = Alternative 3 (50K CFS 

diversion), APA = Applicant’s Preferred Alternative (75K CFS diversion), A5 = 

Alternative 5 (150K CFS diversion). 
89

Appendix

II.A1: Summary of Generalized Additive Mixed Model for Cluster 1

M1 – GAMM with station random effects, No Autoregressive Term – Cluster 1

Family: gaussian

Link function: identity

Formula:

delta.sal ~ s(jul.day, k = 20) + s(sta.fac, bs = "re")

Parametric coefficients:

 Estimate Std. Error t value Pr(>|t|) 

(Intercept) 0.7013 0.2215 3.166 0.00156 **

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:

 edf Ref.df F p-value 

s(jul.day) 18.596 18.6 192.08 <2e-16 ***

s(sta.fac) 7.883 8.0 74.69 <2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) = 0.568 

Scale est. = 2.2474 n = 3141

M1AR3 – GAMM with station random effects, AR3 Correlation

– Cluster 1

Family: gaussian

Link function: identity

Formula:

delta.sal ~ s(jul.day, k = 20) + s(sta.fac, bs = "re")

Parametric coefficients:

 Estimate Std. Error t value Pr(>|t|) 

(Intercept) 0.7030 0.2168 3.242 0.0012 **

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:

 edf Ref.df F p-value 

s(jul.day) 14.742 14.74 22.759 < 2e-16 ***

s(sta.fac) 6.636 8.00 5.433 1.69e-08 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) = 0.551 

Scale est. = 2.494 n = 3141
90

Appendix II.A2: Summary of Generalized Additive Mixed Model for Cluster 2

M2 – GAMM with station random effects, No Autoregressive Term – Cluster 2

Family: gaussian

Link function: identity

Formula:

delta.sal ~ s(jul.day, k = 20) + s(sta.fac, bs = "re")

Parametric coefficients:

 Estimate Std. Error t value Pr(>|t|) 

(Intercept) -2.1602 0.2826 -7.644 2.57e-14 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:

 edf Ref.df F p-value 

s(jul.day) 17.59 17.59 67.93 <2e-16 ***

s(sta.fac) 11.72 12.00 45.97 <2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) = 0.282 

 Scale est. = 8.1276 n = 4446

M2AR3 – GAMM with station random effects, AR3 Correlation

– Cluster 2

Family: gaussian

Link function: identity

Formula:

delta.sal ~ s(jul.day, k = 20) + s(sta.fac, bs = "re")

Parametric coefficients:

 Estimate Std. Error t value Pr(>|t|) 

(Intercept) -2.1205 0.2883 -7.356 2.25e-13 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:

 edf Ref.df F p-value 

s(jul.day) 11.411 11.41 8.194 3.28e-14 ***

s(sta.fac) 8.912 12.00 3.131 1.21e-06 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) = 0.26 

 Scale est. = 8.7335 n = 4446