U.S. Department of the Interior

U.S. Geological Survey

Model Description and Evaluation of the MarkRecapture Survival Model Used to Parameterize the 

2012 Status and Threats Analysis for the Florida 

Manatee (Trichechus manatus latirostris) 

By Catherine A. Langtimm, William L. Kendall, Cathy A. Beck, Howard I. Kochman, Amy L. Teague, Gaia 

Meigs-Friend, and Claudia L. Peñaloza

Open-File Report 2016–1163
ii

U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016 

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Suggested citation:

Langtimm, C.A., Kendall, W.L., Beck, C.A., Kochman, H.I., Teague, A.L., Meigs-Friend, Gaia, and 

Peñaloza, C.L., 2016, Model description and evaluation of the mark-recapture survival model used to 

parameterize the 2012 status and threats analysis for the Florida manatee (Trichechus manatus 

latirostris): U.S. Geological Survey Open-File Report 2016–1163, 20 p., 

https://dx.doi.org/10.3133/ofr20161163.

ISSN 2331–1258 (online)
iii

Acknowledgments

Conversations with Don DeAngelis, and his papers dedicated to best modeling practices and 

communication, provided the impetus to apply the TRAnsparent and Comprehensive model Evaluation 

(TRACE) approach to the evaluation of manatee survival models. The survival analysis would not have 

been possible without the dedicated effort of a host of field biologists and partners that monitored manatees 

over the years. We are particularly grateful to Bob Bonde, Jim Reid, Susan Butler, Kit Curtin, Wayne 

Hartley, and photographers and data managers with the Fish and Wildlife Research Institute and Mote 

Marine Laboratory. This evaluation benefitted from conversations with Kari Rood and Sheri Barton. We 

thank Mike Runge and Jim Nichols for their technical reviews and improvements to this report and we thank 

Dawn Jennings for her review from the management perspective. 
iv

Contents

Acknowledgments .........................................................................................................................................iii

1 Introduction............................................................................................................................................... 1

2 How to Use This Document ...................................................................................................................... 3

3 Problem Formulation Within the Decisionmaking Context ........................................................................ 3

4 Mark-Recapture Model Description .......................................................................................................... 5

5 Data Evaluation ........................................................................................................................................ 7

6 Model Evaluation .................................................................................................................................... 10

7 Model Implementation Verification.......................................................................................................... 12

8 Model Output Verification ....................................................................................................................... 13

9 Model Analysis Evaluation...................................................................................................................... 13

10 Model Output Corroboration ................................................................................................................. 15

References .................................................................................................................................................. 16

Glossary ...................................................................................................................................................... 19
v 

Abbreviations

Barker RD Barker Robust Design model

cv coefficient of variation

CBM Core Biological Model

CJS Cormack-Jolly-Seber (model) 

FWRI Fish and Wildlife Research Institute (Florida Fish & Wildlife Conservation Commission) 

MIPS Manatee Individual Photo-identification System 

Mote Mote Marine Laboratory

m-r mark recapture

RD robust design closed-population model

TA Threats Analysis

TRACE TRAnsparent and Comprehensive model Evaluation

USFWS U.S. Fish & Wildlife Service

USGS U.S. Geological Survey
Model Description and Evaluation of the MarkRecapture Survival Model Used to Parameterize the 

2012 Status and Threats Analysis for the Florida 

Manatee (Trichechus manatus latirostris) 

By Catherine A. Langtimm,1 William L. Kendall,1 Cathy A. Beck,1 Howard I. Kochman,1 Amy L. Teague,1

Gaia Meigs-Friend,1 and Claudia L. Peñaloza2

1

U.S. Geological Survey. 

2

American Journal Experts, Calle Los Alpes, Quinta Pata-Pata, Prados del Este, Caracas, 

Miranda, 1080-A, Venezuela. 3

Terms in bold are defined in the Glossary. 

1 Introduction

This report provides a TRAnsparent and Comprehensive model Evaluation (TRACE; 

Grimm and others, 2014) of the mark-recapture3

 (m-r) survival estimation model used to 

parameterize components of the 2012 status and threats analysis for the Florida manatee 

(Trichechus manatus latirostris). The aim of a TRACE document is to provide researchers and 

decisionmakers with clear and transparent information about the quality, suitability, and utility of 

a given model to address a specific management objective. Rykiel (1996) lists two questions 

critical to model evaluation: (1) does the model realistically mimic the real world and is therefore 

suitable for its intended purpose; and (2) how much confidence can be placed in inferences about 

the real world that are based on model results? To answer these questions, the TRACE 

evaluation is partitioned into eight elements: problem formulation, model description, data 

evaluation, conceptual model evaluation, implementation verification, model output verification, 

model analysis, and model output corroboration. Standardized protocols are used for evaluation 

reports, which allow researchers to (1) communicate to resource managers consistent evaluation 

information over time; (2) build understanding and expertise on the structure and function of the 

model; (3) document changes in model structures and applications in response to evolving 

management objectives, new biological and ecological knowledge, and new statistical advances; 

and (4) provide greater transparency for management and research review. 

Here, we provide supporting details and evidence for the rationale, validity, and efficacy 

of a new m-r model, the Barker Robust Design (Barker RD, Kendall and others, 2013), to 

estimate regional manatee survival rates and temporal variability necessary to parameterize 

components of the 2012 version of the manatee Core Biological Model (CBM, version 5.03) 

and Threats Analysis (TA). The CBM and TA provide scientific analyses on population viability 

of the Florida manatee subspecies for U.S. Fish and Wildlife Service (USFWS) 5-year reviews of 

 
2 

the status of the West Indian manatee species (Trichechus manatus) as listed under the 

Endangered Species Act. Results of the 2012 CBM and TA were published in 

Runge, M.C., Langtimm, C.A, Martin, J., and Fonnesbeck, C.J., 2015, Status and threats 

analysis for the Florida manatee (Trichechus manatus latirostris), 2012: U.S. Geological 

Survey Open-File Report 2015–1083, 23 p. [Also available at 

http://dx.doi.org/10.3133/ofr20151083.] 

The scientific basis of the Barker RD model has been thoroughly vetted in peer reviewed 

scientific journals and applied to manatee m-r photo-identification monitoring data. Given the 

recent development of the model and its first application in an analysis in a management decision 

context, a review and evaluation of issues specific to its application in the CBM and TA are 

warranted. 

The manatee survival reporting protocols used here were developed from the original 

TRACE rationales first introduced by 

Schmolke A., Thorbek, P., DeAngelis, and D.L., Grimm, V., 2010, Ecological modelling 

supporting environmental decision making—A strategy for the future: Trends in Ecology 

and Evolution, v. 25, p. 479–486. 

and the updated standard document structure and online templates in

Grimm, V., Augusiak, J., Focks, A., Frank, B., Gabsi, F., Johnston, A.S.A., Kułakowska, 

K., Liu, C., Martin, B.T., Meli, M., Radchuk, V., Schmolke, A., Thorbek, P., and 

Railsback, S.F., 2014, Towards better modelling and decision support—Documenting 

model development, testing, and analysis using TRACE: Ecological Modelling, v. 280, p. 

129–139. 

and 

Augusiak, J., Van den Brink, P.J., and Grimm, V., 2014, Merging validation and 

evaluation of ecological models to “evaludation”—A review of terminology and a 

practical approach: Ecological Modelling, v. 280, p. 117–128. 

 

TRACE protocols were designed for the evaluation of ecological projection models. 

Mark-recapture survival models differ in a number of ways, but most importantly in that they 

provide retrospective estimation and inference, which then can be used in a wide variety of 

models to project population dynamics under different management and environmental 

scenarios. To better document and evaluate applications of survival models in general, and 

Florida manatee applications specifically, some modifications were made to the original TRACE

elements in descriptions and format of intended content. We identify this initial standardized 

reporting protocol as TRACE–MANATEE SURVIVAL and this evaluation specifically as 

TRACE–MANATEE SURVIVAL, Barker RD version 1. As survival modeling efforts in 

support of management decisions increase in complexity and specificity to management 

objectives, undoubtedly revisions and refinements to these protocols will be necessary, similar to 

the first TRACE protocols (Schmolke and others, 2010; Grimm and others, 2014). 
3 

2 How to Use This Document

Eight evaluation elements are presented in sections 3–10 below: (3) problem formulation 

within the decisionmaking context, (4) mark-recapture model description, (5) data evaluation, (6) 

model evaluation, (7) model implementation verification, (8) model output verification, (9) 

model analysis evaluation, and (10) model output corroboration. Supporting information for each 

element was derived from peer-reviewed scientific publications of past manatee analyses, classic 

biometrics papers at the foundation of m-r models, the recent publications outlining development 

of the Barker RD model, specifics on the implementation of the model in 2012, and assessments 

of the quality and quantity of the data analyzed and the model output. 

At the beginning of each element is a short explanation of the element and what content 

is provided for evaluation. A short overview follows summarizing the supporting information. 

Full details and discussion are contained in the body of the element. 

The target audience for this document consists of a diverse group of users at varying 

levels of scientific expertise, including resource managers, researchers, policy makers, 

stakeholders, and the public. We have attempted to make the material accessible to all users, but 

there is still considerable technical language and terminology. A glossary of terms is provided at 

the end of the document. The first use of each term in the text is highlighted in bold. Hyperlinks 

embedded in the document provide easy navigation from the table of contents to the element 

headings and Glossary. 

 

3 Problem Formulation Within the Decisionmaking Context

This TRACE element provides supporting information on the decisionmaking context in 

which the model will be used; a precise specification of the objective of the analysis, including a 

specification of necessary model outputs; and a statement of the domain of applicability of the 

model, including the extent of acceptable extrapolations. 

Overview

Adult manatee survival probabilities and female breeding probabilities, estimated 

from photo-identification data of marked individuals monitored state-wide over the course 

of decades, provide the empirical foundation for the manatee CBM and TA. The CBM and 

TA project future growth and population viability under different management and 

environmental scenarios. Outcomes under the different scenarios are considered by the 

USFWS as part of their 5-year reviews of the Florida subspecies, which are required under 

the Endangered Species Act. Population viability analyses in general project future 

population growth based on empirical estimates of past temporal variation around mean 

survival rates and breeding rates. Population growth rates are most sensitive to even small 

changes in adult survival. Therefore, adult survival is the most critical demographic 

parameter in the CBM, and unbiased and accurate estimates are essential to status 

assessments. The Barker RD model used to estimate survival for the 2012 analysis corrects 

for a bias in manatee survival rates identified prior to the first 5-year review in 2007 

(Langtimm and others, 2004; Runge and others, 2007b; Langtimm, 2009). The survival 

parameters required for the CBM take the form of (1) region-specific mean annual 

survival estimated over the years of available monitoring data, and (2) temporal variance 

in survival caused by environmental fluctuations. Estimates of mean and variance are 
4 

region- and time-specific; extrapolations to other locations and time periods should be 

considered with caution.

Unbiased and accurate estimates of regional survival are required for the manatee CBM 

and TA (regions are defined later). Previous analyses have demonstrated that population growth 

rates for large, long-lived vertebrates such as the California condor (Gymnogyps californianus) 

(Mertz, 1971), killer whales (Orcinus orca) (Brault and Caswell, 1993), cheetahs (Acinonyx 

jubatus) (Crooks and others, 1998), and the Florida manatee (Runge and others, 2007a, b) are 

most sensitive to a change in the adult survival rate compared to the same amount of change in 

calf or subadult survival rates or the female breeding rate. 

M-r models were first applied to Florida manatee photo-identification data to estimate 

adult survival probabilities in the 1990s (O’Shea and Langtimm, 1995; Langtimm and others, 

1998). M-r models are highly regarded in the research and management community and have 

been used to estimate survival probabilities for innumerable animal species (and some plants) 

when individuals can be monitored by naturally occurring or artificially applied unique marks 

(Williams and others, 2002). Directly estimating detection probabilities of individuals is a 

hallmark of m-r models and key to estimating survival, because the monitoring of individuals 

during each sampling period is imperfect. If estimation procedures do not account for individuals 

that are alive but not detected, survival and other parameters can be biased. This bias can lead 

to false interpretations of true survival rates or trends in survival and can adversely influence 

management decisions. Detection of marked individuals can be affected by multiple factors, 

including sampling error (that is, observer error, less than ideal survey conditions), and natural 

processes such as permanent dispersal of individuals to another area, temporary movements 

making the individual unavailable for detection, or life history strategies in which individuals use 

spatially separated resources during different monitoring periods. Different types of detection

issues are inherent to different populations and biological systems. 

Over time, more advanced m-r models have been developed with greater realism to 

model, estimate, and account for imperfect detection under the various processes just described. 

The Barker RD model (Kendall and others, 2013) used for the 2012 CBM addressed a detection 

problem that can bias survival estimates for the most recent years at the end of the monitoring 

time series. This terminal bias was specifically identified with manatees but can be common to 

many large, long-lived, mobile vertebrates with high annual survival rates (Langtimm, 2009; 

Bromaghin and others, 2015). More detailed description and discussion of the problem and the 

solutions are provided in other elements herein.

Adult survival parameters required for the CBM take the form of (1) mean annual 

survival estimated over the years of available monitoring data, and (2) the variance in annual 

survival caused by fluctuations in the environment (technical term: “temporal or process 

variance”). Population viability analyses in general project future survival on the basis of 

empirical estimates of past temporal variation around the mean survival rate (McGowan and 

others, 2011). For the 2012 TA, separate estimates of mean annual survival and temporal 

variance were required for the four Florida manatee management regions (Northwest, 

Southwest, Atlantic Coast, and Upper St. Johns River) modeled in the CBM. The estimates 

represent variation in survival as a result of all sources of mortality. Additional estimates were 

required for the Southwest region, where mortality events from toxic red-tide algal blooms occur 

on a frequent basis. The Southwest component of the CBM projects future impacts of these 

severe events with random introductions of a major red-tide year into the analysis, in which 
5 

survival in that year represents red-tide mortality in addition to mortality experienced in nonevent years. The range of possible values for the additional effect of red tide is estimated for 

known event years relative to baseline estimates of survival under nonevent years. 

Survival estimates from a sample of known individuals at specific major winter 

aggregation sites in each region are used to infer survival rates for the broader population of 

adults in the region. The sample of individuals used to estimate adult survival is obtained from 

manatees cataloged in the Manatee Individual Photo-identification System (MIPS). Each 

individual can be identified by its unique scar patterns, which are primarily created by collisions 

with watercraft. Because unscarred individuals are not recognizable and therefore cannot be 

monitored with photo-identification, they are not represented in the analyses. A question posed is 

whether survival estimates are biased if they are only estimated from scarred individuals 

identifiable by marks that can evolve over time; a full discussion of this question is presented in 

the data evaluation element. 

Statistical estimates of survival probabilities are only applicable to adult manatees.

Young individuals (calves and subadults) have a low probability of having healed scars that meet 

the criteria for cataloging in MIPS; thus, data are not sufficient to estimate survival for these age 

classes. We followed conservative criteria for defining adult status, because we expect 

differences in mortality risks among calves, subadults, and adults (see O’Shea and Langtimm, 

1995, for discussion and definitions of age classes). 

Photo-identification data analyzed were collected during specific time periods at specific 

aggregation sites in each management region and thus under specific environmental conditions. 

It may not be possible to extrapolate these estimates to new conditions without further 

justification. A specific case in point is the mortality events from severe cold that occurred 

during the winters of 2009–10 and 2010–11. Although photographs and monitoring data had 

been collected during those years, the extensive postprocessing of data required to accurately 

identify individuals (see data evaluation element) had not yet been completed at the time of the 

2012 CBM. Given the large number of dead manatees documented during those events, 

extrapolation as to their effect on survival is not realistic. 

4 Mark-Recapture Model Description 

This TRACE element provides supporting information on the model. It provides a detailed 

model description and justifies the modeling approach and the degree of complexity. Model 

users should learn what the model is, how it works, and what guided the design or selection of 

the model for this particular analysis and management question. 

Overview

The m-r model used for survival analysis is the Barker RD. Detailed documentation 

for the model is reported in Kendall and others (2013) and a summary is described in this 

section. The model uses repeated encounters of known individuals and jointly models three 

types of data: (1) photo-documented sightings of marked live individuals at the primary 

monitoring areas during winter, (2) auxiliary resightings of those individuals at any 

locations and times other than the standard winter monitoring framework, and (3) any 

identification of individuals from dead recoveries. The model reduces bias in survival 

estimates at the end of the time series by incorporating additional information on true 
6 

survival (dead recoveries or additional sightings of live individuals at times and places 

outside the winter sampling frame).

Justification of the modeling approach and its complexity. The Barker RD model 

(Kendall and others, 2013) used for the 2012 CBM addressed a detection problem that can bias 

survival estimates for the most recent years at the end of the monitoring time series. Langtimm 

and others (2004) identified a decline in adult manatee survival estimates on the Atlantic Coast 

that was probably due to a recent series of warmer winters during which manatees were less 

likely to use the monitored thermal refuges as aggregation sites. Further analyses of both the real 

data and data simulated specific to known survival and individual movement rates confirmed the 

decline was an artifact of the modeling effort (Langtimm, 2009). Bias occurred when some 

individuals did not return the following years to the annually monitored sites. Because survival is 

high, many individuals eventually can return alive to the monitoring areas and estimates made 

for the earlier years were shown to be unbiased; however, at the end of the time series, additional 

information was needed to determine the fate of individuals not seen during the last few periods. 

After conducting a simulation study to compare and contrast the utility of modeling the standard 

winter monitoring data using several types of additional data (Peñaloza and others, 2014), the 

Barker RD model offered the best solution and flexibility to address terminal bias for the Florida 

manatee system. 

Description of the conceptual model and the represented processes. The Barker RD 

model combines robust design capture data (that is, more than one sampling period per primary 

sampling period of interest) collected at focal study sites, with dead recoveries and auxiliary

live observations that can occur outside these focal sites. With this model, survival and two types 

of emigration can be estimated: permanent emigration, whereby an individual that leaves the 

study area has zero probability of returning (for example, dispersal); and temporary emigration, 

whereby an individual that leaves the study area has a positive probability of returning (for 

example, manatees that may not return to a site only used during colder winters). The model 

design incorporating multiple secondary monitoring periods embedded within each primary 

monitoring period (winter season in the case of manatees) was first introduced by Pollock (1982) 

and identified as the robust design, because analyses under a variety of sampling conditions 

showed that results were resistant (robust) to moderate departures from model assumptions. The 

design has since proved useful in extending m-r models to estimate a wide variety of population 

parameters, such as temporary emigration (Kendall and others, 1997). 

The Barker RD model is an extension of the Lindberg model (Lindberg and others, 

2001), which combined the robust design closed population model (RD; Kendall and others, 

1997) with information on known dead individuals (Burnham, 1993), and the Barker model 

(Barker, 1997, Barker and others, 2004), which combined data on known deaths with auxiliary 

live sightings of marked individuals. The Lindberg model and the Barker model were first 

developed to model survival and dispersal of annually hunted waterfowl and fish populations. 

The Barker RD model jointly analyzes three types of data. For the Florida manatee, those 

data for the 2012 CBM analysis consisted of (1) live sightings of individuals photographed 

within the formal winter monitoring design, which is defined by region-specific primary 

aggregation sites and a winter primary period that is divided into two secondary sampling 

periods, (2) auxiliary resightings of those individuals identified at other times or places, and (3) 

dead recoveries of those individuals from any location, at any time. A discussion of the data 

available under those categories is covered below under data evaluation. 
7 

The parameters estimated by the model. The full likelihood for the model and the 

parameter definitions are presented in Kendall and others (2013). Here, we describe the 

parameters and summarize how they are estimated.

• Probability of temporary emigration and probability of availability. Nonrandom 

temporary emigration away from the primary monitoring area is the main source of bias 

in manatee terminal survival rate estimates. Identifying the fate (technical term: “state”) 

of these individuals (dead, alive and available for detection, temporarily unavailable, or 

permanently unavailable at a focal study area) is key to estimating true survival (S), the 

primary demographic parameter of interest for the CBM analysis, separate from 

temporary emigration and dispersal. The model accomplishes this by estimating the 

complement of temporary emigration, which is the probability of availability for 

detection at the winter site. Annual availability is estimated for two individual states: one 

in which individuals available for detection in the study area the previous winter survived 

to the current winter and remained faithful to the winter site (a“); and another in which 

those individuals away the previous year (and thus unobservable) survived to the current 

year and remained faithful to the winter site (a‘). By defining the current year’s 

availability specific to whether an individual was a temporary emigrant or not the 

previous year, subsequent estimates of dead recovery rates and auxiliary live resighting 

rates can be used to draw inferences about the fate of individuals not sighted in the last 

primary periods. The probability of temporary emigration is then estimated as a transition 

from one year to the next from an observable state at the aggregation site to an 

unobservable state away from the site. 

• Detection probabilities. The ability to estimate the probability that an individual is at the 

primary monitoring area and thus available for detection is predicated upon the ability to 

estimate resighting probabilities of individuals from the two secondary periods within 

each annual primary winter period (p). Probability of dead recovery (r) and probability of 

auxiliary live resighting of an individual (R, given it survived to the next period; and R’, 

given it dies before the next period and is not recovered) are estimated between primary 

periods. 

• Fidelity to the primary study site and its complement, permanent emigration. Permanent 

emigration, defined here as ceasing to use the focal winter monitoring sites, with zero 

chance of returning, would negatively bias survival estimates throughout the time series 

if unaccounted for in the model. We don’t believe this phenomenon is likely for 

manatees. The Florida manatee is a nearshore species that forages in shallow waters that 

support their primary food source—submerged aquatic vegetation. They rarely cross deep 

waters, preferring to follow coastlines and natural features, and are limited in distribution 

by accessibility to warm water during cold weather. However, the collection of ancillary 

data on sightings of manatees at other sites during the winter and during other times of 

the year, in addition to dead recoveries, provides the direct information on survival 

needed to mitigate bias that would be induced by permanent emigration. 

5 Data Evaluation

This TRACE element provides supporting information on the quality and sources of 

monitoring data analyzed, the criteria and rationale for selecting data for estimation, and an 

assessment of the validity of the sample to infer estimates for the larger regional population and 

to compare populations. This evaluation will allow model users to assess the scope and the 
8 

uncertainty of the model output as it relates to monitoring processes, post-sampling data 

processing, data selection, and sound statistical inference from the sample to the region. 

Overview

We analyzed long-term data on individual manatees uniquely marked and 

identifiable by scars acquired in the wild and “captured” by photographs during annual 

monitoring at winter aggregation sites. Such data can be subject to misidentification and 

error, but data processing procedures, described below, effectively minimize errors. We 

provide a full discussion regarding possible bias in estimates, given that the sample 

population for inference consists of individuals primarily marked by healed injuries. The 

number of years of data available for analysis varied among regions and did not include 

years of the most recent mortality events of unknown cause in the Indian River Lagoon in 

northeastern Florida or years of severe cold throughout Florida. 

Data processing protocols and quality. Data for these analyses were accessed from the 

MIPS, a database established in 1978 and maintained since 1995 as a cooperative partnership 

with U.S. Geological Survey (USGS) Sirenia Project, Florida’s Fish and Wildlife Research 

Institute (FWRI), and Mote Marine Laboratory (Mote). Since the last manatee 5-year review in 

2007, the database managers at USGS, FWRI, and Mote have completed a major overhaul of 

MIPS, which has resulted in common protocols for collecting and processing data and storing 

data in a common database. Since 2004, digital cameras have been used to photo-document 

individual sightings; legacy images prior to that time have been digitized, increasing efficiency 

and accuracy in data processing and matching photographs to cataloged MIPS individuals. These 

data collection and processing improvements ensure data are comparable among geographic 

regions. 

Individuals in MIPS are identified by healed scars, mutilations, or deformities, typically 

the result of one or more boat strikes, but also by distinct characteristics remaining from healed 

lesions caused by cold damage, injuries caused by entanglements, or other causes. Protocols and 

procedures to process field data for entry into MIPS were designed to minimize misidentification 

and ensure data quality and consistency for analyses. A requirement for inclusion of an 

individual manatee into MIPS is complete photographic documentation of the trunk and tail, 

detailing healed features that are unique enough to be recognized upon subsequent resighting, 

whether that be in the general area of its first capture or an unusual dispersal, such as to Cuba 

(Alvarez-Aleman and others, 2010) or Chesapeake Bay, Maryland (Beck and others, 2011). Each 

resighting of a photographically documented individual is verified by at least two experienced 

observers, and data for each sighting are proofed prior to entry into MIPS. 

The features used for identification may be present in multiple areas on the manatee.

Each healed, permanent feature is coded specifically by type (scar, mutilation, and so forth), by 

location on the manatee (region: flipper, head, trunk, or tail; position: right, left, or dorsal), and 

described subjectively by size (small, medium, or large), and precisely by number (1, 2-3, or 4 or 

more), color (gray or white), and shape (blotch or line). The resulting explicit feature code may 

be common to multiple manatees, but having multiple identical codes assigned to multiple 

manatees is a rare occurrence. Although multiple features may have the same feature code, the 

feature itself has a unique outline or shape that will distinguish it from others upon scrutiny of 

the photographs. Up to five feature codes may be entered into a query of the MIPS user interface 

to generate a list of potential candidates. A wildcard option may be entered for any character(s) 
9 

in the code, and results may be further censored by sex, region, or specific area. A long list of 

potential identifications may thus be reduced to a shorter list for evaluation and ultimate 

determination of a match. 

It is not uncommon for manatees to acquire one or more new features during their 

sighting history. New features in a previously unmarked area on the manatee add useful 

information for subsequent identification. However, new features also may obscure or change the 

appearance of an existing feature, which can make re-identification problematic and could result 

in missing a match to a known individual or misidentification of the individual as another 

manatee. Nonetheless, we believe errors are minimal because nearly all manatees have multiple 

features useful for identification on different areas of the body. Analysis of the database in 2012 

showed that most individuals have at least five features to aid in identification (range 1-21, mean 

6.9, mode 5). Annual resighting rates are high, allowing acquisition of new marks to be 

documented and coded quickly to assist with subsequent identification, and independent 

verification of all matches by two staff members is required before data are accepted in MIPS. 

Inferring survival probabilities from a sample population of scarred manatees: possible 

bias. Given that MIPS consists of individuals primarily marked by permanent scars from 

multiple watercraft encounters, there is the question of whether the estimates are representative 

of the larger population containing both scarred and unscarred adults. The effects of an 

individual’s past collisions with boats on future mortality are unknown. Four scenarios are 

possible: (1) there is no difference between scarred and unscarred individuals with regard to 

future mortality risk and survival estimates are unbiased; (2) scarred individuals recover with 

chronic or debilitating sublethal conditions that reduce future survival, and thus estimates are 

negatively biased relative to survival probabilities in the larger population; (3) scarred 

individuals display behavior that reduces the probability of future watercraft encounters, and 

therefore estimates are positively biased; or (4) scarred individuals have survived past injuries 

because they are the stronger individuals in the population (weaker animals die) and better able 

to survive future threats; therefore, estimates are positively biased. At this point in time, we 

cannot resolve this question. Nonetheless, scarred individuals composed a significant fraction of 

the population in each region, so the magnitude of the bias, if it exists, cannot be large. 

Data availability for the 2012 CBM. The number of years of data available for analysis 

varied among regions on the basis of when consistent monitoring began. For the 2012 CBM, the 

backlog of photographs remaining for processing was reduced but not eliminated. Post-processed 

field data encompassing the winters of 2009–10 and 2010–11, when a large number of manatees 

died from severe cold, were not available for analysis. Data availability was as follows: 

Northwest (NW): 1982–83 through 2008–09

Southwest (SW): 1995–96 through 2008–09

Atlantic Coast (AC): 1982–83 through 2008–09

Upper St. Johns River (USJR): 1985–86 through 2008–09

The formal winter monitoring design targeted the primary winter aggregation sites within 

each region that were historically monitored and the focus of past survival analyses (Langtimm 

and others, 1998; Langtimm and others, 2004; Runge and others, 2007b). Secondary sites that 

do not provide warm water during the coldest days and were not systematically monitored in the 

past were excluded from the winter sampling frame. However photo-documented sightings at 

secondary sites were available for use as auxiliary observations described below. As in past 

analyses, the primary time period for winter monitoring spanned 90 days during the coldest part 
10 

of the year when manatees seek warm water and are most easily photographed. The 90-day 

period yielded an adequate sample of individuals for analysis and excluded early and late 

transients that show strong fidelity over years to other refuge sites. The primary period was 

region-specific and based on evaluations of available data from previously published survival 

analyses (Langtimm and others, 2004). The start of the monitoring period varied by region, 

because of temporal differences in the arrival of the first cold fronts that draw manatees to the 

primary winter monitoring sites.

Auxiliary data consist of all resightings of those individuals identified at the major winter 

monitoring sites at any locations and times outside of the formal winter monitoring design. These 

resightings can be opportunistic, incidental sightings made at any time or place and submitted to 

MIPS by partners, the public, and colleagues; or targeted sightings based on the monitoring plans 

of MIPS partners. Targeted sightings include (1) sightings at the primary aggregation sites before 

or after the winter primary period, mostly when cold fronts draw individuals to the refugia, (2) 

sightings at secondary sites photographed if time is available during winter after the primary sites 

have been covered, and (3) sightings at secondary “shoulder” areas where manatees stage 

seasonally to move between winter and summer habitat (identified by telemetry, aerial surveys, 

and past photo-identification experience). 

Photographs of dead manatees recovered in Florida were provided by the FWRI Carcass 

Recovery Program. Photographs of dead individuals outside of Florida were provided by the 

USFWS and members of the marine mammal stranding network. Carcass images were reviewed 

for a match to a MIPS ID in the same manner as images of live sightings. Features on badly 

decomposed carcasses, even if visible, often precluded identification because of loss of color or 

shape. 

6 Model Evaluation

This TRACE element provides supporting information on the simplifying assumptions 

underlying a model’s design, both with regard to empirical knowledge and general, basic 

principles. It includes explanations relating how the various parts of the model mimic known 

features of the target biological system, and it evaluates model assumptions and criteria for 

censoring data to meet assumptions. This critical evaluation allows model users to understand 

that model design was not ad hoc but based on carefully scrutinized considerations. 

Overview

The Barker RD is built on proven and widely used m-r models. It provides greater 

realism and more accurate and precise estimates of survival for Florida manatees than 

models used in past analyses. The structure allows for the use of all available data, not just 

those collected during the standard winter monitoring design. Analysis of real data 

(Kendall and others, 2013) and a simulation study (Peñaloza and others, 2014) 

demonstrated the ability of the new model to reduce bias in survival estimates at the end of 

the time series.

The Barker RD model provides greater realism and more accurate and precise estimates 

than past efforts with other models. Estimates using the Cormack-Jolly-Seber (CJS) model are 

interpreted as apparent survival, because true survival and temporary or permanent emigration 

cannot be separated. The RD closed population model can directly estimate true survival separate 
11 

from temporary emigration with the exception of the last time period. To estimate the last 

survival rate, the last emigration probability, which is not estimable, must be constrained to equal 

an estimable emigration probability (Kendall and others, 1997). Langtimm (2009) showed that 

for manatees with high annual survival and relatively high probability of nonrandom temporary 

emigration, an inappropriate constraint can produce sufficient bias to warrant ignoring the most 

recent survival estimates when making management decisions. The Barker RD can separately 

estimate true survival, temporary emigration, and permanent dispersal because of the use of 

resightings made at times and locations outside of the winter sampling frame. 

As previously described, the Barker RD is built on proven and widely used existing 

models (Barker, 1997; Kendall and others, 1997; Lindberg and others, 2001; Barker and others, 

2004). The model integrates the salient features of the underlying models to provide greater 

realism specific to manatees but is also applicable to other species. Kendall and others (2013) 

demonstrated the utility of the new model by comparing analyses of the same set of manatee 

encounter histories under models that considered only robust design data (RD model), robust 

design combined with dead recoveries (Lindberg model), and robust design combined with dead 

recoveries and auxiliary resightings (Barker RD model). Additional supporting evidence was 

provided by Peñaloza and others (2014) in analyses of simulated data generated under known 

values of survival, temporary emigration, and detection probabilities that compared the utility of 

joint analyses of several types of ancillary data to reduce terminal bias. This simulation study is 

discussed in more detail under the model analysis evaluation element. 

The value of available manatee auxiliary data for the Barker RD model. Although 

manatee photo-identification monitoring was not originally designed for the Barker RD model, 

data were available for auxiliary resightings. The original objective of the monitoring program 

was to provide individual histories to understand individual movement patterns and to 

characterize population traits of reproduction (gestation period, interbirth interval, litter size, 

fecundity, and crude birth rate) and return rates of individuals as an index of annual survival 

(O’Shea and Hartley, 1995; Rathbun and others, 1995; Reid and others, 1995). As a matter of 

practicality, field photographers focused regular monitoring on the coldest days at the largest 

aggregations when manatees were most easily photographed. Additional sites were soon targeted 

for opportunistic monitoring as aerial surveys and telemetry studies identified other areas that 

manatees frequented during warmer winter days and (or) where manatees staged in smaller 

groups before moving between summer and winter ranges (Deutsch and others, 2003). These 

auxiliary data are useful for determining the fate of individuals undetected at the primary sites 

and are easily collected compared to other species that disperse more widely or migrate long 

distances such as sea turtles or waterfowl (Peñaloza and others, 2014). Additional opportunistic 

photos of manatees taken by the public or local management and research groups in and outside 

of Florida provide additional information. 

Evaluation of model assumptions. The underlying assumptions of the model are similar to 

those of the models from which it was derived (Kendall and others, 2013): 

• The population available for capture within a primary period is demographically closed 

during the primary period (no births, deaths, emigration, or immigration). To best meet 

this assumption, we restricted the primary winter period to 90 days during the coldest part 

of the year, which allowed an adequate sample to be collected before reproduction began 

in the spring and ensured that transients moving to their preferred warm-water refuges 

had already moved through the area. Some deaths do occur during the primary period; 
12 

however, Kendall (1999) demonstrated that closed RD is robust to violations of this 

assumption. 

• Within each group (region), state (available or unavailable), mixture, or detection status 

(previously detected within a primary period or not), each individual has the same 

probability of detection and state transition. To meet this assumption, we carefully 

developed criteria for structuring encounter histories. For example, by restricting the

analyses to adults that only frequented the largest consistently monitored aggregation 

sites, we expected detection to be similar among individuals, as opposed to including 

adults at secondary sites that are only subject to opportunistic monitoring. Restricting the 

primary time period to the coldest days of the year ensured that transient individuals 

(having a low probability of returning the following year compared to winter residents) 

would be few. 

• Identifying marks are retained, recorded correctly, and do not affect survival or behavior. 

Protocols for data collection and post processing minimize errors and ensure individuals 

are still identifiable, even after acquiring new scars. Because manatee identification data 

are “captured” as a digital image, individuals are not physically handled and data 

collection does not disrupt manatee behavior. 

7 Model Implementation Verification

This TRACE element provides supporting information on (1) whether the computer code 

implementing the model has been thoroughly tested for programming errors, (2) whether the 

implemented model performs as indicated by the model description, and (3) how the software 

has been designed and documented to provide necessary usability tools (interfaces, automation 

of experiments, and so forth) and to facilitate future installation, modification, and maintenance.

Overview

Model structure and statistical likelihoods for the Barker RD were officially 

published by Kendall and others (2013) after the completion of the 2012 CBM and TA 

analyses. The model, however, had already been coded and implemented in Program 

MARK (White and Burnham, 1999), the most comprehensive and widely used software 

application currently available to analyze m-r data. Estimates for the 2012 survival 

analyses were conducted using MARK. Program code was thoroughly tested and minor 

problems reported by coauthors and users were corrected and are documented on the 

MARK Web page (http://warnercnr.colostate.edu/~gwhite/mark/mark.htm). The entire history 

of changes to MARK since 1998 is available on the Web page (includes development of the 

Robust Design, Lindberg, and Barker models). MARK is widely used, and continued 

maintenance and incorporation of new statistical and research approaches and models are 

supported by several institutions and numerous researchers. The MARK Web page also 

includes downloading and installation instructions for the free software, program 

documentation, recent changes, and reported known problems. A constantly evolving 

online user’s guide, Program MARK: A Gentle Introduction

(http://www.phidot.org/software/mark/docs/book/), and the online analysis forum, Phi-dot 

(http://www.phidot.org/forum/index.php), provide technical, theoretical, and user support on 

the MARK user interface, core analytical functions, and specific models and applications.

Estimates of mean survival and temporal variance were calculated according to the method 
13 

of Burnham and others (1987), as model output from the variance components estimator in 

MARK. The program allows the user to specify the range of estimates for output or include 

a covariate for specific event years to estimate added or reduced mortality during those 

events. For the Southwest region, we used the covariate option to estimate the additional 

effect in major red-tide years.

8 Model Output Verification

This TRACE element provides supporting information on how well model output matches 

observations. 

Overview

Model output matched several observations of severe mortality events and known 

behavior. 

Although the Barker RD can estimate permanent emigration to new areas outside of 

Florida, true dispersal for manatees is rare (see discussion under model description element).

Therefore, Kendall and others (2013) expected and confirmed that a candidate model that 

precluded permanent emigration (F=1.0) would be the best supported model based on model 

selection criteria. 

Years of lower survival rates were observed during years having known major red-tide 

mortality events. Additional years of photo-identification data added since the 2012 CBM and 

subsequently analyzed showed the expected result that uncertainty in the annual estimates 

decreased in magnitude with additional years of data. 

Comparisons among regions showed the expected survival differences based on 

differences in known risk among regions. 

9 Model Analysis Evaluation

This TRACE element provides supporting information on (1) how sensitive model output is 

to changes in model parameters (sensitivity analysis), and (2) how well the emergence of model 

output has been understood. 

Overview

The relative contribution of dead recoveries and auxiliary live observations to 

reduce terminal bias in manatee survival estimates has been evaluated using real data 

(Kendall and others, 2013) and simulated data (Peñaloza and others, 2014). Both analyses 

showed auxiliary resightings reduced bias the most. Surprisingly, dead recoveries were 

least effective, essentially because of the small number of individuals that die within

populations having high survival rates such as those of Florida manatees. Nonetheless, 

increased precision in estimates was realized when either data type was included in the 

analysis. The simulation study showed that models using RD and auxiliary resightings 

reduce but do not necessarily eliminate terminal bias, particularly for the very last 

estimate. Simulations with higher auxiliary resighting probabilities showed less absolute 

bias and higher precision. Research and evaluation of the quality and quantity of the real 

data available for auxiliary resightings have not yet been conducted and will be required to 
14 

understand and evaluate any remaining terminal bias in the regional survival rates. 

Nonetheless, the published studies demonstrate that with additional years of monitoring 

data, those terminal survival estimates change and stabilize, and the estimates become 

more precise as the fate of additional undetected individuals in those years is determined. 

Analysis of real data. Kendall and others (2013) compared annual survival estimates and 

coefficients of variation (cv) in survival and availability under models that considered (1) only 

robust design data (RD model), (2) robust design data combined with dead recoveries (Lindberg 

model), and (3) robust design data combined with dead recoveries and auxiliary resightings 

(Barker RD model) for a 20-year manatee dataset and the same dataset truncated at 9 years.

Declines in annual survival in later years were most pronounced in the RD only and the Lindberg 

model as compared to the Barker RD model. With respect to precision, the cv for survival was 

typically lowest for the Barker RD model and highest for the RD model. 

Analysis of simulated data. The simulation study by Peñaloza and others (2014) 

generated individual encounter histories under population values for survival, temporary 

emigration, and detection probabilities specified to mimic conditions previously identified by 

Langtimm (2009) as creating pronounced terminal bias in manatee survival estimates. They then 

evaluated the performance of various estimation models that jointly analyzed the encounter 

history robust design data and one additional source of ancillary data. Additional data types 

included dead recoveries, auxiliary resightings, telemetry locations of a small sample of 

individuals (providing information on movements away from the monitoring site), or covariates 

indicative of the magnitude of temporary emigration (that is, winter temperature). The ancillary 

data were simulated using several different levels of detection, or predictive environmental 

covariates, to explore the magnitude of improvements in bias and precision with increasing 

information. 

Although dead recoveries provided direct information on survival, the effect on bias was 

minimal, even if all dead individuals were recovered. With high survival probabilities, few 

individuals died, providing little information on the fate of individuals. Auxiliary observations of 

live individuals also provided direct information on survival, but constituted a potentially larger 

source of information. Unlike recoveries, live individuals can be observed during all future 

occasions, whereas the identification of a carcass can occur only once at death (Kendall and 

others, 2013). Simulations with higher auxiliary resighting probabilities showed less absolute 

bias and higher precision. 

The results of all three studies (Langtimm, 2009; Kendall and others, 2013; Peñaloza and 

others, 2014) showed that models using robust design and auxiliary resightings reduce bias the 

most. Simulations with higher auxiliary resighting probabilities showed less absolute bias and 

higher precision. Furthermore, the separate or combined use of auxiliary resighting data and 

carcass data improved the precision of the survival estimates and other estimates such as 

temporary emigration and detection probabilities. It is important to note that these data did not 

necessarily eliminate terminal bias, particularly for the very last estimate, but with additional 

years of monitoring data, those terminal survival estimates changed and stabilized. The estimates 

became more precise as the fate of additional undetected individuals in previous years was 

determined. For the 2012 CBM, we felt some bias still was possible, and we excluded the last 

estimable survival rate when calculating region-specific mean and temporal variance. Research 

and evaluation of the quality and quantity of the real data available for auxiliary resightings have 
15 

not yet been conducted and will be required to understand and evaluate any remaining terminal 

bias in the regional survival rates. 

10 Model Output Corroboration 

This TRACE element provides supporting information on how model outputs compare to 

independent data and patterns that were not used, and preferably not even known, while the 

model was developed, parameterized, and verified. By documenting model output corroboration, 

model users learn about evidence that, in addition to model output verification, indicates the 

model is structurally realistic and provides results that can be trusted to some degree. 

Overview

The mean survival rates estimated for each region for incorporation into the 2012 

CBM and TA are presented below in table 1, as published in Runge and others (2015). The 

estimates were higher than those estimated in previous studies using other m-r models 

based on fewer years of data (Langtimm and others, 2004; Runge and others, 2007b), but 

there was no evidence of a positive trend in survival rates between the last analysis in 2007 

and the one in 2012. The higher mean survival estimated for the 2012 analysis is the result 

of implementing the new Barker RD model, which directly estimates and accounts for 

annual variation in manatee use of warm-water sites that bias survival estimates. We now 

have a more realistic survival model that accounts for important movement processes that 

affects our ability to monitor manatees. Higher estimates caused by the implementation of 

new models that address bias have been documented in other studies (Lebreton, 2006). 

High adult survival estimates are not unusual for other long-lived vertebrates, as reported

for several studies by population biologists using quantitative techniques.

Table 1. Florida manatee mean adult survival rates by region. [From Runge and others (2015). In all cases, data were analyzed through the winter of 2008–09, but 1–2 annual 

estimates at the end of the timeseries were dropped, owing to concerns about bias. Note that the estimate for the 

Southwest is for years in which there was not a major red-tide event. SE, standard error] 

Region Mean SE Years Source

Atlantic 0.967 0.004 1983–2007 Langtimm and others, this analysis

Upper St. Johns 0.975 0.004 1986–2006 Langtimm and others, this analysis

Northwest 0.977 0.003 1983–2007 Langtimm and others, this analysis

Southwest 0.971 0.004 1996–2007 Langtimm and others, this analysis

Eberhardt (2002) and Gaillard and others (1998, 2003) reviewed published literature for 

studies estimating adult female survival rates from m-r data for long-lived vertebrates. The 

estimates they reported for a diverse group of mammalian species showed the higher survival 

estimates obtained through this analysis are within the range of those found for large long-lived 

vertebrates where m-r data have been analyzed. 
16 

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19 

Glossary

accurate The extent to which a measurement or estimate approaches the true value.

aggregation sites See refugia, below. 

ancillary data Data types considered in analyses in addition to those collected under standard or 

specified monitoring protocols; for example, dead recovery data, telemetry data, and capture 

data.

availability The probability that an individual is in the study area and available for detection 

during the primary monitoring period. 

assumption A simplified representation or description of a system or complex entity, especially 

one designed to facilitate calculations and predictions (see statistical model). 

auxiliary sightings Live sightings obtained outside of the primary winter monitoring period 

either at secondary sites or during a nonwinter period. 

bias A systematic difference between a measured or estimated value and the true value. 

Core Biological Model (CBM) A population model for the Florida manatee developed to project 

population status over 50 to 150 years and to examine the potential impact of different threat 

scenarios.

detection probability In mark-recapture statistical models (see below), the probability that an 

animal will be detected (that is, captured, sighted, or resighted) during the study period. 

fidelity The tendency of some species or individuals to annually return to the same site or range, 

usually at the end of a seasonal migration (see below); also known as philopatry. 

mark-recapture A class of statistical models and related studies based on the estimation of life 

history parameters from multiple re-encounters of marked individually recognizable animals. 

migration A periodic, relatively long-distance movement of animals, usually on an annual or 

seasonal basis, triggered by a variety of environmental and (or) endogenous factors.

Manatee Individual Photo-identification System (MIPS) A computerized database of manatee life 

history records and images providing sighting histories used for manatee population and survival 

modeling. 

parameter A population characteristic, usually unknown, that is estimated in a statistical model 

with a precision indicated by the standard error and related confidence interval.

permanent emigration or dispersal A term used in population biology studies where an 

individual that leaves the study area has a zero probability of returning.

photo-identification The identification of individual animals based on photographs showing 

distinctive external markings or characteristics.

precision The extent to which repeated measurements or estimates of a value produce similar 

results.

primary sampling period A region-specific time period of 90 consecutive days during the 

coldest time of year when manatees are most easily photographed at warm-water refugia. 

primary monitoring site A major warm-water aggregation site where manatees have historically 

been monitored during cold winter periods. 

Program MARK A software application for estimating population parameters in a wide variety 

of mark-recapture statistical models, using individual encounter histories as the basic input.

refugia Natural or artificial warm-water areas where manatees consistently aggregate during 

cold winter periods. 

robust Statistical methods that produce valid results under a variety of sampling conditions by 

being resistant to outliers and moderate departures from model assumptions. 
20 

secondary sampling periods Consecutive 45-day subdivisions of a primary winter sampling 

period during which targeted primary sites should be monitored at least once. 

secondary site Any locality outside of the primary winter aggregation sites that is monitored for 

auxiliary sightings throughout the year, usually at a reduced level of effort.

shoulder area A secondary site where manatees temporarily gather prior to migrating between 

summer and winter ranges.

simulation study The use of computer-generated samples based on predetermined parameter 

values to evaluate the behavior and robustness of statistical models under varying sampling 

scenarios. 

Sirenia Project The research project (1978-present) established by U.S. Fish & Wildlife Service

to support management of manatees and dugongs (scientific classification: Order Sirenia) after 

Congress authorized and listed sirenians under the Endangered Species Act and the Marine 

Mammal Protection Act. The Sirenia Project became part of the U.S. Geological Survey, the 

research arm of the Department of Interior, after a major reorganization of the Department in 

1996. 

statistical model A mathematical equation describing the relationship between two or more 

variables with parameters that can be used for hypothesis testing and predicting future events. 

Most statistical models are based on underlying assumptions that must be met for resulting 

inferences to be valid, with robust models less sensitive to violations (see robust, above).

survival estimation model A statistical model used for estimating survival in a population over 

time and evaluating the relative importance of factors affecting survival.

temporary emigration A term used in population biology studies where an individual that leaves 

the study area has a positive probability of returning (for example, manatees, which may not use 

the site during warmer winters or female sea turtles, which return to monitored nesting beaches 

after several years when in breeding condition). 

time series Data collected through repeated observations of the same individuals and (or) study 

sites over a period of time. 

temporal variance An estimate of the variation in annual survival rates (after removing variance 

caused by sampling error), owing to fluctuations in the environment. 

unbiased See bias, above. 

winter sampling frame A temporal and spatial term corresponding to a primary winter sampling 

period at primary aggregation sites.