A Method for Assessing the Vulnerability 

of Sea Turtles to a Changing Climate

Matthew D. Lettrich, Dorothy M. Dick, Christina C. Fahy, Roger B. 

Griffis, Heather L. Haas, T. Todd Jones, Irene K. Kelly, Dennis Klemm, 

Ann Marie Lauritsen, Christopher R. Sasso, Barbara Schroeder, Jeffrey 

A. Seminoff, and Carrie M. Upite

U.S. Department of Commerce

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

NOAA Technical Memorandum NMFS-F/SPO-211

December 2020

A Method for Assessing the Vulnerability of Sea Turtles to a 

Changing Climate

Matthew D. Lettrich, Dorothy M. Dick, Christina C. Fahy, Roger B. Griffis, Heather L. Haas, T. 

Todd Jones, Irene K. Kelly, Dennis Klemm, Ann Marie Lauritsen, Christopher R. Sasso, Barbara 

Schroeder, Jeffrey A. Seminoff, and Carrie M. Upite

NOAA Technical Memorandum NMFS-F/SPO-211

December 2020

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 
ii

Recommended citation:

M. D. Lettrich, D. M. Dick, C. C. Fahy, R. B. Griffis, H. L Haas, T. T. Jones, I. K. Kelly, D.

Klemm, A. M. Lauritsen, C. R. Sasso, B. Schroeder, J. A. Seminoff, and C. M. Upite. 2020. A

Method for Assessing the Vulnerability of Sea Turtles to a Changing Climate. NOAA Tech.

Memo. NMFS-F/SPO-211, 82 p.

Copies of this report may be obtained from: 

Office of Science and Technology

National Oceanic and Atmospheric Administration 

1315 East-West Highway, F/OST

Silver Spring, MD 20910 

Or online at:

http://spo.nmfs.noaa.gov/tech-memos/
iii

Table of Contents

Executive Summary........................................................................................................................ 1

1 Background............................................................................................................................. 3

2 Assessment Methodology ....................................................................................................... 4

2.1 Framework overview and development ........................................................................... 4

2.1.1 Exposure Component.............................................................................................. 4

2.1.2 Sensitivity and Adaptive Capacity Component ...................................................... 6

2.1.3 Method Development: Identifying Climate Exposure Factors and Sensitivity 

Attributes and Establishing Scoring Criteria .......................................................................... 6

2.2 Preparing to Implement the Assessment .......................................................................... 9

2.2.1 Scale and Scope ...................................................................................................... 9

2.2.2 Exposure Maps........................................................................................................ 9

2.2.3 Population Background Narratives....................................................................... 11

2.2.4 Expert Selection.................................................................................................... 11

2.3 The Expert Scoring Process ........................................................................................... 11

2.3.1 Scoring Climate Exposure Factors........................................................................ 12

2.3.2 Scoring Sensitivity Attributes............................................................................... 12

2.3.3 Assessing Data Quality......................................................................................... 12

2.4 Calculating Scores.......................................................................................................... 13

2.4.1 Climate Exposure Factor and Sensitivity Attribute Means................................... 13

2.4.2 Exposure and Sensitivity Component Scores....................................................... 13

2.4.3 Overall Vulnerability ............................................................................................ 14

2.4.4 Response Category Score ..................................................................................... 14

3 Conclusion and Next Steps................................................................................................... 14

3.1 Global Implementation................................................................................................... 16

3.2 Interfacing with Other CVAs......................................................................................... 16

4 Acknowledgements............................................................................................................... 17

5 References............................................................................................................................. 18

Appendix A Climate Exposure Factors................................................................................... A-1

Appendix B Climate Sensitivity Attributes............................................................................. B-1

Appendix C Sample Scoring ................................................................................................... C-1
iv
1

Executive Summary

The National Marine Fisheries Service (NMFS), together with the U.S. Fish and Wildlife Service 

(USFWS), have mandates to protect and recover sea turtle species listed under the Endangered 

Species Act (ESA). Climate change and environmental variability are key stressors affecting sea 

turtle conservation. Sea turtle populations are expected to respond to climate change and 

environmental variability in a variety of ways that may manifest as shifts in distribution, 

population structure, abundance, and/or phenology. However, there is a lack of specific 

information on climate-related impacts for many populations, and there are few methods

specifically designed to assess the vulnerability of sea turtle populations. Vulnerability 

assessments can help provide that climate-related information, and the NMFS Climate Science 

Strategy1and Regional Action Plans2recommend conducting vulnerability assessments for

living marine resources, including sea turtles. Therefore NMFS, with input from USFWS, 

developed a method to simultaneously assess the climate vulnerability of multiple sea turtle

populations to help fill critical information gaps and identify candidate populations for targeted 

climate-related research.

1 https://www.fisheries.noaa.gov/national/climate/noaa-fisheries-climate-science-strategy

2 https://www.fisheries.noaa.gov/national/climate/climate-science-strategy-regional-action-plans

The method described in this study follows the model of the NMFS methods for Marine Fish and 

Shellfish Climate Vulnerability Assessments

3

and the Marine Mammal Climate Vulnerability 

Assessments

4

. It uses existing information and expert elicitation to assess sea turtle populations’ 

exposure, sensitivity, and adaptive capacity to climate change and variability. Exposure to 

climate change is assessed by scoring projected changes in climate conditions within a 

population’s current distribution. Sensitivity and adaptive capacity to climate change are 

assessed based on current understanding of a population’s life history traits.

3 https://www.st.nmfs.noaa.gov/Assets/ecosystems/climate/documents/TM%20OSF3.pdf

4 https://spo.nmfs.noaa.gov/sites/default/files/TMSPO196_508.pdf

An expert working group identified relevant climate exposure factors and life history traits. A 

separate working group defined scoring criteria for each climate exposure factor and life history 

trait to differentiate between sea turtle populations. The assessment method was first pilot-tested 

with sea turtle populations occurring within the Pacific Ocean. We revised and updated the final 

approach used in this study based on input received during the pilot test.

Prior to the full assessment, we acquired maps showing the projected change in the climate 

exposure factors and overlaid current population distribution data. We assembled background 

narratives summarizing the existing literature available for the life history traits for each 

population. 

A team of sea turtle experts individually scored climate exposure as a function of the magnitude 

of projected climate change within the current distribution using a four-point scale. The team 

members then individually scored sensitivity (combined with adaptive capacity) using the 

provided background information and their own knowledge using a similar four-point scale. 

Team members also assessed the quality of the underlying data used to score each factor and 

attribute. After compiling individual scores, the team met to discuss differences in scoring and 

individually revised scores as necessary. The team identified potential differences in the

 
2

interpretation of available information to ensure a common understanding of each attribute and 

factor but did not work toward consensus.

We then aggregated the scores and calculated a weighted mean score for each life history trait 

and climate exposure factor for each population. We combined these weighted mean scores of 

climate exposure factors and life history traits into an overall exposure score and an overall 

sensitivity score, respectively, using a logic model. Finally, we calculated a climate vulnerability 

score for each population by combining a population’s climate exposure score and climate

sensitivity score using a vulnerability matrix.

The assessment method was applied to sea turtle populations defined as Distinct Population 

Segments5(DPSs) under the ESA or Regional Management Units (RMUs) as identified in 

Wallace et al. (2010), where DPSs are not designated. The assessment produced a list of 

populations ranked by vulnerability to climate change. Each population received a vulnerability 

profile that summarized the distribution of expert scores for each life history attribute and 

climate exposure factor, and identified variables that contribute the most to the population’s 

vulnerability. The resulting population-specific profiles will support management decisionmaking by identifying populations most vulnerable to climate change and the underlying causes 

of that vulnerability. Similarly, researchers could use assessment results to prioritize research 

toward specific populations, regions, or attributes to expand our understanding of sea turtle

population responses to climate change and the consequences to the broader marine ecosystem.

This document details the method we used to conduct the climate vulnerability assessment. We 

will endeavor to publish the global and population-specific results in a separate, peer-reviewed 

journal article.

 

5 https://www.federalregister.gov/d/96-2639
3

1 Background

Climate change and environmental variability are impacting species inhabiting coastal and 

marine environments, resulting in range shifts, changes in local abundance, and variation in 

timing of life history events (Pinsky et al. 2013, Poloczanska et al. 2013, Brown et al. 2016, 

Staudinger et al. 2019). Sea turtle populations have been, and will continue to be, affected by 

changing climate conditions (Hawkes et al. 2009, Hamann et al. 2013, Butler 2019). Some sea 

turtle populations have shown climate-related shifts in distribution (Hays et al. 2001, Hamann et 

al. 2007, Maffucci et al. 2016), changes in nesting phenology (Weishampel et al. 2004, Pike et 

al. 2006, Pike 2009, Weishampel et al. 2010, Lamont and Fujisaki 2014, Neeman et al. 2015), 

and changes in reproductive rates (Stokes 2014). Projected climate change effects on sea turtles 

include the alteration of nesting phenology and distribution, reproductive success, hatchling (i.e. 

primary) sex-ratios, foraging distribution, foraging success, and increased cold-stunning (Fish et 

al. 2005, Hawkes et al. 2007, Hawkes et al. 2009, Fuentes et al. 2011, Wyneken and Lolavar 

2015, Griffin et al. 2019). Some populations have been predicted to change in distribution

(Willis-Norton et al. 2015, Wyneken and Lolavar 2015), breeding phenology (Almpanidou et al. 

2016, Patel et al. 2016), and abundance (Saba et al. 2012, Montero et al. 2018).

The National Marine Fisheries Service (NMFS), together with the U.S. Fish and Wildlife Service 

(USFWS), are mandated to protect and recover sea turtle species listed under the Endangered 

Species Act (ESA). These mandates include the issuance of scientific research permits and take 

authorizations, interagency Section 7 consultations, recovery planning and implementation, 

species listings and delistings, critical habitat designations, and status reviews. Consideration of

impacts from all possible natural and human-caused stressors on population viability, which is 

required in ESA-mandated activities, would be incomplete without also considering potential 

climate change effects (McClure et al. 2013, NMFS 2016). An improved understanding of 

species responses to changing climate and ocean conditions, including the magnitude and 

direction of the effect, will help inform and refine management efforts.

Climate vulnerability assessments (CVAs) provide a rapid, general approach to identifying

species that may be most vulnerable to climate change and the potential factors contributing to 

their vulnerability. Typically, CVAs follow a similar framework or structure that combines 

exposure to a threat, sensitivity to a threat, and capacity to adapt to a threat (i.e. adaptive 

capacity) (Schneider et al. 2007, Glick et al. 2011, Foden and Young 2016, Foden et al. 2018). 

To optimize their utility, many CVAs also quantify or qualify the uncertainty associated with the 

respective assessment effort (Foden et al. 2018).

There have been numerous CVA studies of terrestrial species since the 1990s (e.g., Herman and 

Scott 1994, Staudinger et al. 2015, Böhm et al. 2016) but relatively few CVAs for species 

inhabiting marine ecosystems (Pacifici et al. 2015). In the marine environment, fish have been

the most common subjects of CVAs to date (e.g., Chin et al. 2010, Johnson and Welch 2010, 

Foden et al. 2013, Pecl et al. 2014, Hare et al. 2016a), while CVAs for non-fish species have 

been limited in number and scope (e.g., Hamann et al. 2007, Lawler et al. 2007, Laidre et al. 

2008, Fuentes et al. 2011, Sousa et al. 2019, Lettrich et al. 2019). 

Other types of studies (e.g., modeling) can offer insight into potential species-specific responses

to climate change. However, these approaches are generally resource- and data-intensive and 

impractical to perform for multiple species concurrently. In light of these resource constraints, 

CVAs may be used to identify priority species for modeling initiatives (Silber et al. 2017).
4

The NMFS Climate Science Strategy (Link et al. 2015) and other strategies (e.g., NFWPCAP

2012) call for vulnerability assessments as a first step in assessing risks and developing climate 

adaptation strategies for natural resources. CVAs were identified in all NMFS Regional Action 

Plans (Hare et al. 2016b, Lovett et al. 2016, NOAA NW/SW Fisheries Science Centers 2016, 

Polovina et al. 2016, Sigler et al. 2016) designed to implement the NMFS Climate Science 

Strategy (Link et al. 2015).

To provide relevant climate-related information for protected species managers, a team of NMFS 

and USFWS representatives developed a targeted methodology to assess the vulnerability of sea 

turtles to climate change. Using a similar development process and framework as the Marine 

Fish and Shellfish Climate Vulnerability Assessment (FCVA) (Morrison et al. 2015, Hare et al. 

2016a) and Marine Mammal Climate Vulnerability Assessment (MMCVA) (Lettrich et al. 

2019), we adapted the assessment components and scoring criteria to reflect the life histories of 

sea turtles for the Sea Turtle Climate Vulnerability Assessment (STCVA). Here we present the 

method and describe its application.

2 Assessment Methodology

2.1 Framework overview and development

The STCVA was designed using a structure and expert-based scoring approach similar to the 

FCVA and MMCVA and the same nomenclature (i.e., climate exposure factors, sensitivity and 

adaptive capacity attributes) described by Chin et al. (2010). Our method scored multiple 

features for two separate components: 1) exposure to climate change and 2) sensitivity to climate 

change (with adaptive capacity). The framework then combined those separate component 

scores into a relative vulnerability score (Fig. 1). 

2.1.1 Exposure Component

We defined climate exposure factors as measures of the magnitude of climate change a 

population is expected to experience. We scored climate exposure factors as a function of the 

degree of change expected for that factor in areas that overlap with the population’s current 

distribution. For those climate exposure factors that could be modeled spatially, exposure was 

scored by overlaying current range maps of each population onto the projected climate exposure 

factor.

Future climate projections are based on scenarios of greenhouse gas emissions. Climate 

projections use representative concentration pathways (RCPs) to characterize possible 

greenhouse gas emissions through the end of the 21st century using different scenarios of human 

population size, economic activity, lifestyle, energy use, land use patterns, technology, and 

climate policy (Moss et al. 2010, van Vuuren et al. 2011). They are named using numbers that

represent different values of radiative forcing in watts per square meter by 2100 — 2.6, 4.5, 6, 

and 8.5. RCP 8.5 represents a ‘business-as-usual’ scenario and assumes that the fewest 

greenhouse gas mitigation measures will be implemented (Riahi et al. 2011).

NMFS policy guidance states RCP 8.5 be used when considering the treatment of climate change 

in ESA activities (NMFS 2016) and the STCVA therefore used RCP 8.5 for climate change 

projections to maximize the utility of the information produced for management purposes.
5

Experts individually 

score exposure factors

Experts individually score 

sensitivity attributes

Calculate exposure factor

means 

Calculate sensitivity attribute 

means 

Population narratives, 

published data, expert 

judgment 

Vulnerability score and 

vulnerability category

Combine scores with 

vulnerability matrix 

Exposure component score Sensitivity component score 

Combine exposure factor

means with logic model 

Combine sensitivity attribute 

means with logic model 

Abundance, distribution, 

phenology response scores 

Population vulnerability 

profiles 

Figure 1. Climate vulnerability assessment process from information gathering to final products 

(adapted from Lettrich et al. 2019).
6

2.1.2 Sensitivity and Adaptive Capacity Component

We defined sensitivity as the degree to which a population is likely to be affected by climatedriven changes in environmental conditions and adaptive capacity as the ability of a population 

to modify intrinsic characteristics (e.g., behavior, physiology, habitat use) to cope with climatedriven changes in environmental conditions (Glick et al. 2011). Tolerance of a condition and 

adaptation to a condition exist along a spectrum of possible responses to that condition. With 

simple changes in wording, an attribute could be categorized as relating to either sensitivity or 

adaptive capacity (Williams et al. 2008, Hare et al. 2016a). For example, a population with a 

specialist diet could be considered sensitive to climate change due to its reliance on specific prey 

or forage species. Also, it could be considered to have a low capacity to adapt to climate-driven 

changes in the availability of its preferred prey or forage species. Therefore, we combined

sensitivity and adaptive capacity into a single component, hereafter referred to as the “sensitivity 

component,” and the attributes within it referred to as “sensitivity attributes.”

We considered whether each sensitivity attribute related to potential responses in population 

abundance, geographic distribution, and phenology. Some attributes influenced all three response 

categories, while other attributes only influenced one or two response categories. Potential 

responses of sensitivity included: 1) changes in abundance resulting in declines or increases in 

population size, 2) changes in distribution resulting in climate-driven changes in geographic 

ranges, including range expansion, contraction, or shift, and 3) changes in phenology resulting in 

seasonal shifts (earlier or later in the year) or changes in duration (prolonged or shortened) of life 

history events such as breeding or migration.

2.1.3 Method Development: Identifying Climate Exposure Factors and Sensitivity 

Attributes and Establishing Scoring Criteria

We convened an expert workshop with representatives from NOAA and USFWS, other 

governmental agencies, non-governmental organizations (NGOs), and academia to inform the 

selection of relevant climate exposure factors and sensitivity attributes (based on sea turtle life 

history traits). These experts used existing CVA frameworks and approaches (e.g., Thomas et al. 

2011, Pecl et al. 2014, Hare et al. 2016a) as models and sourced attributes from prior syntheses 

of climate impacts on sea turtles (e.g., Hamann et al. 2007, Hawkes et al. 2009, Poloczanska et 

al. 2009, Fuentes et al. 2011).

We identified eight climate exposure factors to be used to score climate exposure (Table 1). 

These were the same climate exposure factors used in the FCVA and MMCVA, with the 

exception of sea ice extent since it is not a feature that sea turtles encounter. The selected climate 

exposure factors are variables that are likely to directly affect sea turtles and also sea turtle prey 

or habitat. Many of these variables have been used in other sea turtle climate-related studies 

(Hamann et al. 2007, Hawkes et al. 2009, Poloczanska et al. 2009, Fuentes et al. 2011, Hamann 

et al. 2013).

We identified 14 life history attributes that could be influenced by climate change to score 

sensitivity (Table 2). We assessed each attribute independently of the other attributes. For 

example, when considering two nearly identical populations for which the only attribute that 

differed was the number of offspring produced, the population that produced more offspring was 

considered to have a lower sensitivity (and higher adaptive capacity) to climate change. 

Although many of these attributes are correlated, we made efforts to reduce “double counting” 

by describing those situations in which an attribute overlapped with another attribute. Our final 
7

set of attributes minimized overlap between attributes. For example, we did not include 

conservation status (e.g., ESA, IUCN Red List) because population abundance and trend were 

already considered during the status determination.

We established criteria to guide the scoring using four bins for each factor (Appendix A) and 

attribute (Appendix B). The criteria used to score exposure are suitable for cross-taxa assessment 

within the geographic scope. The specific criteria used to score each sensitivity attribute were 

selected based on the unique life histories of sea turtles and are not appropriate for cross-taxa 

assessment. 

Table 1. Climate exposure factors included in the STCVA.

Climate Exposure Factor Description

Sea Surface Temperature The temperature of the upper water column (the mixed 

layer) may have direct physiological effects on sea 

turtles and/or prey.

Air Temperature The near-surface air temperature may have direct 

physiological effects on sea turtles, nests, eggs, and/or 

prey. Air temperature serves as a useful proxy for 

estuarine and shallow water temperature.

Precipitation Rain, snow, and ice that affects salinity and serves as a 

delivery mechanism for pollutants and contaminants. 

Rain can affect nesting habitat.

Salinity Surface salt content that can affect sea turtle health 

and/or prey.

Ocean Acidification The ongoing decrease in the ocean’s pH that may affect 

sea turtle prey and habitat.

Dissolved Oxygen The amount of oxygen in surface waters, which may 

affect sea turtle prey.

Circulation The movement of water masses, which may affect sea 

turtle movement and/or prey.

Sea Level Rise The relative change in sea level, which may affect sea 

turtle and/or prey habitat.
8

Table 2. List of sensitivity attributes included in the STCVA.

Sensitivity Attribute Description

Prey/Diet Specificity The breadth of a population’s diet and the ability of individuals to 

shift foraging strategy and/or diet under changing conditions.

Habitat Specificity (inwater)The breadth of habitat used by a population and underlying 

vulnerability of that habitat.

Nesting Beach Type The type of beach individuals of a population use, a proxy for 

nesting habitat vulnerability.

Geographic Extent of 

Nesting

The geographic extent and spatial density of a population’s nesting 

activity.

Nesting Site Fidelity The degree to which individuals of a population use the same 

locations in successive nesting seasons.

Lifetime Reproductive 

Potential

The ability of an individual (and by extension, population) to 

produce offspring that facilitate population growth and avoid 

declines in abundance.

Length of Nesting 

Season

The peak timing and duration of female nesting as a proxy for the 

ability of a population to adapt its reproductive timing to changing 

conditions.

Adult Physiological 

Sensitivity to 

Temperature

The influence of temperature on physiological processes of nesting 

females.

Nest/Egg Sensitivity to 

Temperature

The effect of temperature on the nests and eggs of a population 

based on critical temperature thresholds.

Migration The migratory patterns and pathways of a population.

Foraging Home Range The spatial extent of the foraging range of individuals within a 

population.

Population Abundance The current abundance estimate of nesting females in a population.

Population Abundance 

Trend

The change in a population’s abundance through time.

Cumulative Stressors The level to which a population is impacted by non-climate 

stressors.
9

2.2 Preparing to Implement the Assessment

2.2.1 Scale and Scope

We designed the STCVA for application to sea turtle Distinct Population Segments (DPSs) (50 

CFR § 223.102, 50 CFR § 224.101) and Regional Management Units (RMUs) (Wallace et al. 

2010). These units are subsequently referred to in this document as “populations.” The 

assessment considered the entire life cycle and known geographic ranges of each population. We 

used climate exposure factors projected to the year 2055, a sufficient timeframe for climate

projections to capture climate trends and decadal variability, yet near-term enough to provide 

management-relevant information (based on scoping interviews with NMFS and USFWS 

managers).

Other CVAs and frameworks have included a weighting factor to account for attributes that are 

disproportionately impactful for a species or population (e.g., Thomas et al. 2011, Reece and 

Noss 2014); however, we omitted a weighting factor in the STCVA to reduce complexity (e.g., 

Hare et al. 2016a, Lettrich et al. 2019).

2.2.2 Exposure Maps

Climate exposure factors have been projected and presented in a variety of studies and formats 

(IPCC 2013, Hayhoe et al. 2017). We obtained climate projections for each climate exposure 

factor across the entire geographic scope of the assessment from the Earth Systems Research 

Laboratory (ESRL) web portal (ESRL 2014), following the established approach used in the 

FCVA. The ESRL web portal provided projections for many of the climate exposure factors 

scored in the assessment (see Appendix A, Climate Exposure Factors). Using the ESRL

projections maximized the number of climate exposure factors in the assessment that were 

modeled using the same climate models, timeframe, and spatial resolution. Sea level rise and 

circulation projections were not available through the ESRL web portal.

We obtained customized ESRL web portal projections using the average of all climate models, 

the RCP 8.5 experiment, the entire year seasonal period, and the 21st century time period of 

2006-2055 (Table 3). The ESRL portal produced projection maps for each climate exposure 

factor (sea surface temperature shown as example in Fig. 2). The projected standard anomaly (Z) 

(Fig. 2a) was calculated using the equation:

Z = 

X̅

F − X̅P

σP

where X̅F is the mean of the climate exposure factor during the period 2006–2055, X̅P is the mean 

of the climate exposure factor during the period 1955–2005, and σP is the standard deviation of 

the climate exposure factor during the period 1955–2005. The projected change in variability (F) 

(Fig. 2b) was calculated using the equation:

F =

VF

VP

where VF is the variance of the climate exposure factor during the period 2006–2055 and VP is 

the variance of the climate exposure factor during the period 1955–2005.

The geographic range of the maps can be adjusted to the specific basin or region being assessed 

to provide greater resolution.
10

Table 3. Settings used for ESRL Climate Change Web Portal for each climate exposure factor.

ESRL Field Value

Experiment RCP 8.5

Model Average of All Models

Variable [Based on climate exposure factor]

Statistic Standard Anom (avg historical)

Season Entire year

21st Century Period 2006–2055

Region Scaled to fit entire population distribution

(a)

(b) 

Figure 2. Sample output from ESRL Climate Change Web Portal showing annual sea surface 

temperature projection a) change in mean, top and b) change in variability, bottom. This figure 

uses the following settings: experiment = RCP 8.5, model = average of all models, variable = sea 

surface temperature, statistic = standard anom (avg historical), season = entire year, 21st century 

period = 2006–2055, region = global. The projections are based on the Coupled Model 

Intercomparison Project 5 (CMIP5) ensemble mean (ENSMN) of climate models. 

(http://www.esrl.noaa.gov/psd/ipcc/ocn/) 
11

The ESRL output maps could be used as-is; however, we downloaded and reclassified the legend 

to match the criteria of scoring bins. Doing so presented the exposure maps categorized by

scoring bin and eliminated the need for experts to interpret the climate exposure factor and the 

scoring criteria simultaneously.

Projections obtained from downscaled models or peer-reviewed studies are useful, but the same 

projections for each individual climate exposure factor must be used for all populations that are 

assessed. Finer resolution models are difficult to generate, and are not necessary for a CVA 

conducted at broad geographic scales such as the STCVA.

We supplemented the exposure maps with additional information, such as population range

boundaries, sighting data, and density estimates. Range maps were obtained from a variety of 

sources such as recovery plans (NMFS and USFWS 1991, 1992, 1993, 1998a, 1998b, 1998c, 

1998d, 1998e, 1998f, 2008, 2016, NMFS et al. 2011), status reviews (NMFS and USFWS 2007, 

2013a, 2013b, 2014, 2015, Conant et al. 2009, Seminoff et al. 2015), OBIS SEAMAP (Halpin et 

al. 2009), the International Union for the Conservation of Nature’s Red List (IUCN 2016), and 

Wallace et al. (2010).

2.2.3 Population Background Narratives

We assembled information about each population’s life history attributes, distribution, and 

references to climate change to ensure a common baseline of available information for all 

scorers. We organized this information as population narratives, similar to other CVAs (e.g., 

Chin et al. 2010, Pecl et al. 2014, Hare et al. 2016a, Lettrich et al. 2019). The background 

narratives included information describing the current state of knowledge about each assessed 

life history attribute. When available, the background narratives also highlighted studies 

documenting populations’ responses to changing climate conditions. For poorly studied 

populations, we included life history information from related populations or species. For 

example, if information was lacking for a central Pacific population, information from an eastern 

Pacific population may have been included to help inform experts’ scores.

2.2.4 Expert Selection

For each subpopulation we selected subject matter experts with field or other relevant research 

experience to score the STCVA. While expertise about any given population was valuable, 

having experts that could score a variety of populations allowed us to compare scores across 

populations and avoid scorer bias. If each expert only scored one population, we would have had 

difficulty attributing scores to the population instead of the scorer. We included experts from 

NMFS and USFWS, other governmental agencies, NGOs, and academia.

2.3 The Expert Scoring Process

Each climate exposure factor and sensitivity attribute was scored individually by each member of 

the group of experts for each population. Expert elicitation is an accepted technique with 

established protocols (EPA 2009) that has been utilized by NMFS in ESA activities and climate 

assessment efforts (e.g., Good et al. 2005, Brainard et al. 2011, Hare et al. 2016a, Lettrich et al. 

2019). The optimal number of scorers depends on multiple factors such as breadth of individual 

expertise, breadth of group expertise, and availability (Linstone and Turoff 1975, Hsu and 

Sandford 2007, Mukherjee et al. 2015). To ensure a sufficient number of reviews for each 
12

population while maintaining a reasonable workload for the expert scorers, a minimum of three 

experts scored each population.

Experts scored each climate exposure factor and sensitivity attribute by allocating five points

across four scoring bins (i.e., low, moderate, high, and very high) according to the respective 

scoring criteria for that factor or attribute. Criteria for “Bin 1 – Low” correlated to low exposure, 

low sensitivity, and high adaptive capacity whereas criteria for “Bin 4 – Very High” correlated to 

high exposure, high sensitivity, and low adaptive capacity. 

2.3.1 Scoring Climate Exposure Factors

Experts compared the range maps of each population to the projected exposure level for each 

factor. They then scored each factor by allocating five points across four bins according to the 

magnitude of exposure projected across the entirety of the population’s current distribution (see 

Appendix A, Climate Exposure Factors). For example, if the magnitude of exposure within an 

entire population’s distribution matched the criteria for “Bin 4 – Very High”, all five points were 

placed in “Bin 4”. If the magnitude of exposure in part of a population’s distribution matched the 

criteria for “Bin 4” and part matched the criteria for “Bin 3 – High”, experts placed points

according to the proportion of the distribution that matched each bin.

Some factors did not have modeled projection maps (e.g., circulation and sea level rise), and 

experts scored these factors using expert judgement based on peer-reviewed and grey literature 

about projected impacts.

2.3.2 Scoring Sensitivity Attributes

Experts used their knowledge and population-specific experience combined with the population

narratives to place their five points into each attribute’s four bins based on the bin criteria 

described in Appendix B (Climate Sensitivity Attributes). For example, if all supporting 

evidence for a population matched the criteria in “Bin 4 – Very High”, the experts placed all five 

points in “Bin 4 – Very High.” If evidence for a population ranged across several bins, experts 

could spread their points across multiple bins based on the supporting evidence; the most points

were placed in the bin with greatest support from the literature or based on the expert’s 

knowledge of and experience with the population. Alternatively, if data quality was low, points

could be spread across multiple bins, which would reflect uncertainty for the respective 

factor/attribute. For attributes with multiple criteria, experts used their best judgement to place 

primary emphasis on those metrics with higher quality data and secondary emphasis on other 

metrics less supported by data. Appendix B provides definitions, background, and scoring 

criteria for each attribute, as well as the relationships between each attribute and the response in 

abundance, distribution, and phenology.

2.3.3 Assessing Data Quality

Similar to the FCVA and MMCVA, experts provided a data quality score for each factor and 

attribute. The data quality score represents how much evidence supports the placement of the 

points. Naturally, factor/attribute scores that are associated with higher data quality yield results 

with higher confidence.

Data quality was scored a “3” if there were observed, modeled, or measured data to support the 

placement of points. Data quality was scored a “2” if the score was based on a nearby 

subpopulation or different species, or if conflicts existed in the supporting information that 
13

complicated the ability to assign scores. Data quality was scored a “1” if the expert’s knowledge 

of, and experience with, the population was the sole basis for the score. Data quality was scored 

a “0” if there was no data on which to score, and the expert’s familiarity with that aspect of the 

population was only sufficient to provide a marginally informed opinion. Experts scored data 

quality for climate exposure factors based on the underlying information about the population 

distribution. Sea turtle experts were not asked to assess the data quality of the climate models or 

exposure maps. Experts scored data quality for sensitivity attributes based on their own 

knowledge and on the data provided in the population background narratives. 

2.4 Calculating Scores

2.4.1 Climate Exposure Factor and Sensitivity Attribute Means

We computed mean scores for each climate exposure factor and sensitivity attribute through a 

three-step process. 

First, we combined the points from all experts to produce weighted mean scores for each climate 

exposure factor and each sensitivity attribute. Here, the weighting is for the bins within a factor 

or attribute and does not refer to individual factor or attribute weighting as discussed above

(Section 2.1.3 Method Development: Identifying Climate Exposure Factors and Sensitivity 

Attributes and Establishing Scoring Criteria). Within each factor and attribute, bins are weighted 

according to how the criteria for the bin influence the factor or attribute. We calculated weighted 

mean scores with bin weights corresponding to bin number, using the following equation:

Factor or Attribute Weighted Mean = 

((B1 ∗ 1) + (B2 ∗ 2) + (B3 ∗ 3) + (B4 ∗ 4))

(B1 + B2 + B3 + B4)

where Bn is the number of points in bin n.

Second, for the climate exposure factors that included both change in variability and change in 

mean (i.e., all factors except circulation and sea level rise), we used the greater of the two means 

as the score for that factor. 

Third, we placed mean sensitivity attribute scores with the response categories (abundance, 

distribution, and phenology) identified as relevant to that attribute. For example, if a given 

attribute had influence over all three response categories, then the mean attribute score applied to 

each response category. Alternatively, if a given attribute only had influence over abundance, the 

mean attribute score was applied to abundance, but not to distribution and phenology for that 

attribute. The three response categories remained independent of one another and separate from

the mean sensitivity attribute score.

2.4.2 Exposure and Sensitivity Component Scores 

We determined exposure and sensitivity component scores using the logic model from the FCVA 

(Table 4) and the factor and attribute mean scores for each population. We used a logic model to 

avoid discounting situations in which most factors or attributes scored low while a few factors or 

attributes scored highly and would have a disproportionate effect on the population’s exposure or 

sensitivity.
14

Table 4. Logic model used to determine exposure component score and sensitivity component 

score.

Component Score Criteria

Very High (4) 3 or more attribute or factor mean scores ≥ 3.5

High (3) 2 or more attribute or factor mean scores ≥ 3.0, but does not 

meet threshold for “Very High”

Moderate (2) 2 or more attribute or factor mean scores ≥ 2.5, but does not 

meet threshold for “High” or “Very High”

Low (1) Less than 2 attribute or factor mean scores ≥ 2.5

2.4.3 Overall Vulnerability

We determined the overall vulnerability for a population by multiplying exposure component 

scores and sensitivity component scores to generate a vulnerability rank and place each 

population into a vulnerability category. Higher scores reflected expert opinion depicting greater 

vulnerability. Populations were placed into vulnerability categories using the exposure

component score and sensitivity component score cross-referenced with a vulnerability matrix 

derived from the FCVA (Fig. 3).

2.4.4 Response Category Score

Within the sensitivity component, the three response categories provide additional information 

about anticipated responses. We calculated each population’s response category score using the 

same approach as overall sensitivity, using the weighted means of the individual attribute scores 

for that population while ignoring values of “N/A”. As different attributes influence abundance, 

distribution, and phenology, comparisons were not made across response categories within a 

population.

3 Conclusion and Next Steps

Sea turtle populations are expected to respond to changing climate conditions in a variety of 

ways including range shifts and/or phenological shifts, which may ultimately lead to changes in 

abundance. Climate-related information can help inform management activities under the ESA, 

and CVAs can identify those species and populations most vulnerable to climate change and the 

underlying drivers of that vulnerability. Our method is an early step in evolving efforts to inform 

management measures under changing climate conditions. Here we applied the STCVA method 

at the population level to identify vulnerable populations and describe drivers of climate 

vulnerability on a management-relevant scale. However, this method may be modified to operate 

on differing spatial and temporal scales as needed. Similar to the FCVA, the STCVA was

designed to be repeated at regular intervals to incorporate updated climate projections from new 

IPCC reports and National Climate Assessments. As additional information becomes available 

on sea turtle responses to other climate variables (Appendix A), additional climate exposure 

factors can be added to the process. Likewise, additional sensitivity attributes (Appendix B) may 

be added to future iterations of the assessment as necessary. The results of the STCVA can 

highlight data gaps and help prioritize research needs. As population-specific biological 

information improves, additional information can be incorporated into future assessments to 
15

continue to refine our understanding of sea turtle biology, strengthen the CVAs, and support 

climate-informed management decisions. 

Figure 3. Vulnerability matrix derived from FCVA used to combine sensitivity category 

component score and exposure component score to determine overall vulnerability category. 

Numbers in parentheses represent the factors and product of multiplying sensitivity and 

exposure. Low vulnerability (1-3), moderate vulnerability (4-6), high vulnerability (8-9), and 

very high vulnerability (12-16) can result from multiple combinations of sensitivity and 

exposure. 
16

3.1 Global Implementation

With the method developed and tested, we applied the STCVA method to sea turtle DPSs and 

RMUs. The outputs from this global application will include a ranked vulnerability index, 

response category scores, and population-specific vulnerability profiles. Populations will be 

categorized and ranked by overall vulnerability to support management needs and inform 

decision making and research directions. Each population will have its own graphical 

representation of sensitivity and exposure scores. Corresponding profiles will describe the 

attributes and factors contributing to vulnerability and identify data gaps such as attributes and 

factors with weak supporting evidence. Researchers can use the vulnerability profiles to target 

research toward specific attributes that may be driving the vulnerability of a given population 

and to explore responses to varying magnitudes of change in that driver. Managers can use the 

vulnerability profiles to identify the attributes that contribute most to population sensitivity and 

the types of climate change impacts expected to most affect a population. This information can 

be used to design science-based management strategies and focus efforts on attributes and factors 

that could most reduce vulnerability. We will endeavor to publish the global and populationspecific results in a separate, peer-reviewed journal article.

3.2 Interfacing with Other CVAs

We encourage future iterations of this assessment to interface with other CVAs that characterize 

the vulnerability of protected species as well as their prey and habitat. The STCVA could be 

strengthened by including the results of regional applications of the FCVA and habitat climate 

vulnerability assessments as input to the STCVA’s prey/diet specificity and habitat specificity 

attributes. Developing a plan to integrate the results of the different CVAs will help to describe 

the interconnected and cascading effects of climate change. 
17

4 Acknowledgements

We thank the many contributors and supporters throughout the development and application of 

the STCVA. The members of the Protected Species Climate Vulnerability Assessment steering 

committee guided the initiation of this project: Gregory Balogh, David Gouveia, Jonathan Hare, 

T. Todd Jones, Nathan Mantua, Eric Shott, Gregory Silber, Michael Simpkins, Christopher 

Toole, Carrie Upite, and Kyle Van Houtan. Expert workshop participants provided input on 

assessment framework and initial attribute lists: Robyn Angliss, Jason Baker, Gregory Balogh, 

Steven Bograd, Charlotte Boyd, Erica Fleishman, Mariana Fuentes, Kathy Goodin, John M. 

Halley, Jonathan Hare, Dennis Heinemann, Nathan Mantua, Wendy Morrison, Mark Nelson, 

Aaron Poe, Vincent Saba, Gregory Silber, Michael Simpkins, Mridula Srinivasan, Michelle 

Staudinger, Christopher Toole, and Kyle Van Houtan. Mariana Fuentes, Joanne Braun McNeill, 

and Kyle Van Houtan helped develop initial attribute lists. Jeanette Davis and Laura Ferguson 

tested the framework. Camryn Allen, Summer Martin, Irene Kelly, T.Todd Jones, and Yonat 

Swimmer participated in the Pacific Islands pilot test. Stephen K. Brown, Richard Merrick, 

Kenric Osgood, Mridula Srinivasan, and Donna Wieting provided critical support throughout the 

project. Brian Stacy, Kristin Laidre, and Michelle Staudinger reviewed individual attributes. The 

Protected Resources Board provided feedback and support. The STCVA scoring team revised 

attribute definitions and scoring criteria: Camryn Allen, Paolo Casale, Mariana Fuentes, 

Alexander Gaos, Mark Hamann, Lauren Kurpita, Mike Liles, Summer Martin, Susanna Piovano, 

Earl Possardt, Vincent Saba, Yonat Swimmer, Manjula Tiwari, Jeanette Wyneken, and Thane 

Wibbels. Erin McMichael reviewed this Technical Memorandum. Kate O’Dell provided 

technical edits.

This project was funded by NOAA Fisheries Office of Science and Technology. The views 

expressed herein are the authors’ and do not necessarily reflect the views of NOAA or any of its 

sub-agencies.
18

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A-1

Appendix A Climate Exposure Factors

A.1 Sea Surface Temperature ............................................................................................. A-2

A.2 Air Temperature ........................................................................................................... A-3

A.3 Precipitation ................................................................................................................. A-4

A.4 Sea Surface Salinity ..................................................................................................... A-5

A.5 Ocean pH...................................................................................................................... A-6

A.6 Dissolved Oxygen ........................................................................................................ A-7

A.7 Circulation.................................................................................................................... A-8

A.8 Sea Level Rise.............................................................................................................. A-9

A.9 Appendix A References ............................................................................................. A-10
A-2

A.1 Sea Surface Temperature

Background: Sea surface temperature (SST) is measured using a variety of methods and 

corresponding depths. For the purpose of this assessment, SST refers to the temperature of the 

upper water column, or the mixed layer (ESRL 2014). Sea turtles spend significant time in this 

depth zone when surfacing to breathe and to help regulate their body temperature. Species 

distributions have been correlated with SST (Polovina et al. 2004). Prey abundance and 

distribution also have been correlated with SST (Rutherford et al. 1999). Water temperature 

affects physiology and has implications for survival (Schwartz 1978, Witherington and Ehrhart 

1989).

Scoring: 

Projected change in mean

(Standard Anomaly)

Projected change in variability

(Variance Ratio)

Bin 1: |x| < 0.5 std dev <1.15

Bin 2: 0.5 std dev ≤ |x| < 1.5 std dev 1.15 ≤ x <1.54

Bin 3: 1.5 std dev ≤ |x| < 2.0 std dev 1.54 ≤ x < 1.78

Bin 4: |x| ≥ 2.0 std dev ≥ 1.78

(a) (b)

Figure A-1. a) Projected future sea surface temperature (in 2055) compared to historical 

conditions calculated by subtracting the historical mean from the mean of the future and 

then dividing the difference by the historical standard deviation. b) Projected change in 

sea surface temperature variability, calculated as future variance (in 2055) divided by 

past variance. (source: Earth Systems Research Laboratory 2014)
A-3

A.2 Air Temperature

Background: Near-surface air temperature has been shown to have physiological impacts on sea 

turtles (Sato 2014, Dudley et al. 2016). Near-surface air temperature has been correlated with 

sand temperature (Hays et al. 2003, Laloë et al. 2014, Esteban et al. 2016), which affects sea 

turtle nests and eggs.

Scoring: 

Projected change in mean

(Standard Anomaly)

Projected change in variability

(Variance Ratio)

Bin 1: |x| < 0.5 std dev <1.15

Bin 2: 0.5 std dev ≤ |x| < 1.5 std dev 1.15 ≤ x <1.54

Bin 3: 1.5 std dev ≤ |x| < 2.0 std dev 1.54 ≤ x < 1.78

Bin 4: |x| ≥ 2.0 std dev ≥ 1.78

(a) (b)

Figure A-2. a) Projected future air temperature (in 2055) compared to historical 

conditions calculated by subtracting the historical mean from the mean of the future and 

then dividing the difference by the historical standard deviation. b) Projected change in 

air temperature variability, calculated as future variance (in 2055) divided by past 

variance. (source: Earth Systems Research Laboratory 2014)
A-4

A.3 Precipitation

Background: Precipitation affects surface salinity in the open ocean and coastal areas. It serves 

as a delivery mechanism for pollutants and debris from land-based sources, particularly in 

coastal areas. Precipitation serves as a mediating effect on sand temperatures, and therefore nest

temperatures, and influences moisture conditions within nests (Matsuzawa et al. 2002, Houghton 

et al. 2007, Lolavar and Wyneken 2015, Wyneken and Lolavar 2015).

Scoring: 

Projected change in mean

(Standard Anomaly)

Projected change in variability

(Variance Ratio)

Bin 1: |x| < 0.5 std dev <1.15

Bin 2: 0.5 std dev ≤ |x| < 1.5 std dev 1.15 ≤ x <1.54

Bin 3: 1.5 std dev ≤ |x| < 2.0 std dev 1.54 ≤ x < 1.78

Bin 4: |x| ≥ 2.0 std dev ≥ 1.78

(a) (b)

Figure A-3. a) Projected future precipitation (in 2055) compared to historical conditions 

calculated by subtracting the historical mean from the mean of the future and then 

dividing the difference by the historical standard deviation. b) Projected change in 

precipitation variability, calculated as future variance (in 2055) divided by past variance. 

(source: Earth Systems Research Laboratory 2014)
A-5

A.4 Sea Surface Salinity

Background: Surface salinity is a dynamic property that affects circulation. Salinity has been 

hypothesized as a factor in sea turtle nest site selection (Foley et al. 2006) and hatchling success 

(Wood and Bjorndal 2000), however a conclusive link remains elusive. Salinity may also 

influence sea turtle prey species.

Scoring: 

Projected change in mean

(Standard Anomaly)

Projected change in variability 

(Variance Ratio) 

Bin 1: |x| < 0.5 std dev <1.15

Bin 2: 0.5 std dev ≤ |x| < 1.5 std dev 1.15 ≤ x <1.54

Bin 3: 1.5 std dev ≤ |x| < 2.0 std dev 1.54 ≤ x < 1.78

Bin 4: |x| ≥ 2.0 std dev ≥ 1.78

(a) (b)

Figure A-4. a) Projected future salinity (in 2055) compared to historical conditions 

calculated by subtracting the historical mean from the mean of the future and then 

dividing the difference by the historical standard deviation. b) Projected change in 

salinity variability, calculated as future variance (in 2055) divided by past variance. 

(source: Earth Systems Research Laboratory 2014)
A-6

A.5 Ocean pH

Background: Ocean acidification refers to the decreasing of the ocean’s pH through chemical 

reactions resulting from increased atmospheric carbon dioxide. While no direct physiological 

effects of pH have been documented for sea turtles, pH has been shown to impact habitats (e.g., 

coral reefs) and prey species (Langdon and Atkinson 2005, Fabry et al. 2008).

Scoring: 

Projected change in mean

(Standard Anomaly)

Projected change in variability

(Variance Ratio)

Bin 1: |x| < 0.5 std dev <1.15

Bin 2: 0.5 std dev ≤ |x| < 1.5 std dev 1.15 ≤ x <1.54

Bin 3: 1.5 std dev ≤ |x| < 2.0 std dev 1.54 ≤ x < 1.78

Bin 4: |x| ≥ 2.0 std dev ≥ 1.78

(a) (b)

Figure A-5. a) Projected future sea surface pH (in 2055) compared to historical 

conditions calculated by subtracting the historical mean from the mean of the future and 

then dividing the difference by the historical standard deviation. b) Projected change in 

sea surface pH variability, calculated as future variance (in 2055) divided by past 

variance. (source: Earth Systems Research Laboratory 2014)
A-7

A.6 Dissolved Oxygen

Background: Dissolved oxygen in the surface water varies with temperature and usage by biotic 

elements of the marine ecosystem. While no physiological effects of dissolved oxygen have been 

observed for sea turtles, dissolved oxygen impacts prey species (Craig et al. 2001).

Scoring: 

Projected change in mean

(Standard Anomaly)

Projected change in variability

(Variance Ratio)

Bin 1: |x| < 0.5 std dev <1.15

Bin 2: 0.5 std dev ≤ |x| < 1.5 std dev 1.15 ≤ x <1.54

Bin 3: 1.5 std dev ≤ |x| < 2.0 std dev 1.54 ≤ x < 1.78

Bin 4: |x| ≥ 2.0 std dev ≥ 1.78

(a) (b)

Figure A-6. a) Projected future dissolved oxygen (in 2055) compared to historical 

conditions calculated by subtracting the historical mean from the mean of the future and 

then dividing the difference by the historical standard deviation. b) Projected change in 

dissolved oxygen variability, calculated as future variance (in 2055) divided by past 

variance. (source: Earth Systems Research Laboratory 2014)
A-8

A.7 Circulation

Background: Circulation refers to the movement of water masses. It occurs on the scale from

major currents to estuarine mixing and includes processes such as upwelling, Ekman transport,

and eddies. Circulation contributes to the dispersal of post-hatchling sea turtles (Hays et al. 2010,

Putman et al. 2012, Briscoe et al. 2016, Chambault 2017) and may aid migration of juveniles and

adults (Luschi et al. 2003) and aggregation of prey species. Circulation is difficult to project, and

therefore we use a qualitative approach similar to the Fish Stock Climate Vulnerability

Assessment (Morrison et al. 2015, Hare et al. 2016).

Scoring: 

Bin Criteria

Bin 1: Distribution overlaps almost exclusively with large boundary currents or tidal 

currents

Bin 2: Much of distribution overlaps with large boundary currents or tidal currents 

Bin 3:

Much of distribution overlaps with currents that are expected to have a high 

magnitude of change such as estuarine circulation, and/or nearshore density- and 

wind-driven currents

Bin 4:

Distribution overlaps almost exclusively with currents that are expected to have a 

high magnitude of change such as estuarine circulation, and/or nearshore density- and 

wind-driven currents
A-9

A.8 Sea Level Rise

Background: Sea level rise (SLR) refers to the relative change in sea level and has both a local 

and a global component. Sea level rise comprises thermal expansion of sea water, addition of 

water volume from melting of land-based glaciers, and local changes in land elevation due to 

processes such as subsidence and isostatic rebound. Sea level rise can effectively eliminate some 

shoreline habitat over time and has the potential to exacerbate coastal flooding during storms and 

spring tides. Because sea turtles must interact with terrestrial habitat during hatching and nesting, 

sea level rise will impact all species. We modify the approach of the Fish Stock Climate 

Vulnerability Assessment (Morrison et al. 2015, Hare et al. 2016) to include finer resolution for 

low and moderate exposure. We further modify the FCVA to assess change in sea level rather 

than the rate of sea level rise.

Other assessments of nesting beach vulnerability to sea level rise have taken the approach of 

examining the loss of habitat under varying sea level rise scenarios (Daniels et al. 1993, Fish et 

al. 2005, Baker et al. 2006, Fish et al. 2008, Fuentes et al. 2010, Reece et al. 2013, Garcia et al. 

2015). Habitat loss is a function of local beach characteristics and relative sea level rise. 

Modeling habitat loss of all nesting beaches is beyond the scope of this project. We considered 

the approximate loss in habitat associated with various amounts of sea level rise reported in those 

other assessments to establish our bin breaks.

If local relative sea level rise projections for mid-century using RCP 8.5 were unavailable, we 

used projections from Jackson and Jevrejeva (2016, Figure 2e). The ‘Intermediate’ scenario from 

Sweet et al. (2017), which aligns with recent SLR studies using RCP 8.5, may be used for 

nesting beaches in the United States.

Scoring: 

Bin Criteria

Bin 1: Relative sea level within nesting habitat is expected to increase less than 10 cm by 

mid-century

Bin 2: Relative sea level within nesting habitat is expected to increase 10-20 cm by midcentury

Bin 3: Relative sea level within nesting habitat is expected to increase 20-45 cm by midcentury

Bin 4: Relative sea level within nesting habitat is expected to increase more than 45 cm by 

mid-century.
A-10

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B-1

Appendix B Climate Sensitivity Attributes

B.1 Prey/Diet Specificity .................................................................................................... B-2

B.2 Habitat Specificity (in-water)....................................................................................... B-4

B.3 Nesting Beach Type ..................................................................................................... B-6

B.4 Geographic Extent of Nesting...................................................................................... B-8

B.5 Nesting Site Fidelity................................................................................................... B-10

B.6 Lifetime Reproductive Potential ................................................................................ B-11

B.7 Length of Nesting Season .......................................................................................... B-13

B.8 Adult Physiological Sensitivity to Temperature ........................................................ B-15

B.9 Nest/Egg Sensitivity to Temperature ......................................................................... B-16

B.10 Migration.................................................................................................................... B-18

B.11 Foraging Home Range ............................................................................................... B-21

B.12 Population Abundance ............................................................................................... B-23

B.13 Population Abundance Trend..................................................................................... B-24

B.14 Cumulative Stressors.................................................................................................. B-25

B.15 Attributes considered but omitted .............................................................................. B-27

B.16 Appendix B References.............................................................................................. B-29
B-2

B.1 Prey/Diet Specificity

Goal: To estimate the breadth of a population’s diet and the ability of individuals to shift 

foraging strategy and/or diet under changing conditions.

Background: The foraging behaviors, target prey species, and habitat preferences of a species or 

population depends on a number of variables and differs widely between species and populations 

(Bjorndal 1997, Polovina et al. 2004). In some cases, foraging behaviors and diet vary widely 

between individuals within a single species or population (Polovina et al. 2004, Hawkes et al. 

2006). The diet specificity of a species is described by the diversity of prey the species typically 

consumes. We assess the ability to switch prey by considering the variety of prey types 

historically consumed.

Variability exists among other frameworks as to what constitutes a diet specialist. Laidre et al. 

(2008) used a threshold of one prey type comprising 20% or more of a species’ diet as criteria for 

the most sensitive marine mammal species. Other frameworks (e.g., Cabrelli et al. 2014, Young 

et al. 2015) use a threshold of a prey type comprising 90% or more of a species’ diet to define a 

diet specialist. The differences in the definition of ‘prey type’ each framework uses may account 

for some of this variation and highlights the necessity for consistent usage of terminology among 

scorers.

We consider the number of prey types and the size of prey as primary factors in prey diversity. 

Prey types are considered in terms of broad taxonomic groups (e.g., decapods, bivalves, 

cephalopods, gelatinous zooplankton, grasses, algae, small schooling fish, flatfish). Here, diet 

specialists are species that consume a narrow selection of prey, with a single genus or family 

constituting a majority of the diet. Diet generalists are species that consume a wide variety of 

prey types and sizes. Generalist foragers that can target a variety of prey species and prey sizes, 

utilizing multiple foraging locations, seasons, and/or strategies are more adaptive and resilient to 

direct and indirect impacts from climatic changes (Clavel et al. 2011, Young et al. 2015, Beever 

et al. 2016). 

A species or population that consumes a broad assortment of prey species is more adaptive to 

climate-driven shifts in prey availability because it should be able to more easily switch among 

prey (ZSL 2010), particularly if any one of its prey species is impacted by climate change 

(Laidre et al. 2008). Prey specialists that consume only a single prey species or only a small 

assortment of prey species and prey sizes would likely struggle to find new sources of 

nourishment if any of its prey are impacted by climate change. 

The duration over which the species or population overlaps in space and time with the prey 

species also impacts the sensitivity of the species or population. If a predatory species or 

population and its forage species overlap for only a short duration in time and space, climate 

impacts may create a mismatch and increased vulnerability of the predator species to climatedriven impacts is expected. 

Species targeting abundant and widespread prey are more resilient to climate impacts as the prey 

species itself is likely to be more resilient to environmental changes (Morrison et al. 2015). 

While not formalized here, we encourage future iterations of this assessment to interface with 

vulnerability assessments that score the vulnerability of prey species to climate change.

If a species undergoes a shift in diet between life stages or life history stages, experts score the 

stage that has the most constrained diet.
B-3

Relationship to abundance: Individuals of a population with a more specialized diet are more 

likely to experience declines in abundance due to climate-driven shifts in prey

Relationship to distribution: Individuals of a population with a more specialized diet are more 

likely to experience shifts in distribution due to climate-driven shifts in prey 

Relationship to phenology: Individuals of a population with a more specialized diet are more 

likely to experience shifts in phenology due to climate-driven shifts in prey 

Prey/Diet Specificity Scoring: 

Bin Criteria

Bin 1 (low sensitivity) Generalist; feeds on a wide range of prey types and sizes

Bin 2 (moderate 

sensitivity)

Generalist; feeds on a limited number of prey types, but a wide variety of 

species within those types

Bin 3 (high sensitivity) Specialist; exhibits strong preference for one prey type but is capable of 

switching when necessary

Bin 4 (very high 

sensitivity)

Specialist; reliant on one prey type and unable to switch to other prey types
B-4

B.2 Habitat Specificity (in-water)

Goal: To determine the breadth of habitat used by a population and the underlying vulnerability 

of that habitat.

Background: Sea turtles rely on habitat for shelter, foraging, and breeding throughout various 

life stages. Species that rely on few or highly specific physical and biological features are more 

likely to be sensitive to climate change (Laidre et al. 2008, Chin et al. 2010), especially if the 

features are vulnerable to climate-driven changes (ZSL 2010, Case et al. 2015, Morrison et al. 

2015). Reliance on different types of features is expected to result in different levels of 

sensitivity. 

For the purpose of this assessment, we consider three types of habitat – physical habitat expected 

to be resilient to changing climate conditions, physical habitat expected to be vulnerable to 

changing climate conditions, and biogenic habitat.

Physical features such as depth, bathymetry, submarine canyons and rocky reefs are expected to 

be resilient to climate change and therefore would result in lower sensitivity for those species 

that rely on those types of habitat. Other physical features that are more vulnerable to climate 

changes (e.g., coastal slope) will result in greater impacts to species that rely on those types of 

features. 

Biogenic habitat – habitat created by or consisting of organisms or organism remains – may 

undergo the greatest changes from a changing climate, as both the ecosystem engineers and 

underlying physical conditions may be impacted by changing conditions (e.g., Nelson 2009, 

Doney et al. 2012, Harley et al. 2012). Examples of biogenic habitat include kelp forests, 

mangroves, salt marshes, coral reefs, and seagrass beds (Teck et al. 2010, Okey et al. 2015). 

Thus, species that depend on biogenic habitats are likely more vulnerable to climate change.

While the presence of suitable prey plays a key role in defining a species’ habitat, we consider 

the prey and diet specificity of the species in a separate attribute.

Similar to the prey/diet specificity attribute, we encourage future iterations of this assessment to 

interface with vulnerability assessments that score the vulnerability of habitat to climate change.

For the purposes of this assessment nesting, incubating, and hatching are not considered here.

Relationship to abundance: A population with greater habitat specificity is more likely to 

experience declines in abundance due to climate-driven habitat alterations

Relationship to distribution: A population with greater habitat specificity is more likely to 

experience shifts in distribution due to climate-driven habitat alterations

Relationship to phenology: A population with greater habitat specificity is more likely to 

experience shifts in phenology due to climate-driven habitat alterations
B-5

Habitat Specificity Scoring:

Bin Criteria

Bin 1 (low 

sensitivity)

Population mostly utilizes physical features resilient to climate conditions

Bin 2 (moderate 

sensitivity)

Population mostly utilizes biogenic features or physical features vulnerable to 

climate conditions

Bin 3 (high 

sensitivity)

Population relies on biogenic features or physical features vulnerable to 

climate conditions for critical life stages or events

Bin 4 (very high 

sensitivity)

Population relies on biogenic features or physical features vulnerable to 

climate conditions throughout its entire life
B-6

B.3 Nesting Beach Type

Goal: To assess the type of beach individuals of a population utilize and generalize the 

underlying vulnerability of the nesting habitat.

Background: Sea turtle nesting beaches have characteristics that, while not intrinsic to sea 

turtles, are linked to sea turtle sensitivity to climate change. Sea turtles show preference for a 

variety of beach characteristics such as slope, sediment size and color, and vegetation (Kamel 

and Mrosovsky 2004, Kamel and Mrosovsky 2005, Santidrián Tomillo et al. 2015). Different 

beach locations may have different perceived stability due to geography, topography, and 

underlying geological conditions. This is separate from climate exposure because it is more 

related to the turtles’ preference for a particular type of geologic feature.

Fuentes et al. (2011) identified increased sand temperatures, sea level rise, and cyclonic activity 

as primary drivers of nesting beach vulnerability (see Hawkes et al. 2009, Poloczanska et al. 

2009, Witt et al. 2010) and developed a framework to assess nesting beach vulnerability to 

climate change. Applying the Fuentes et al. (2011) methodology to each individual nesting beach 

may result in double-counting climate exposure factors and is beyond the scope of this 

assessment, though the approach would be worthwhile for finer-scale assessments in the future. 

We use an approach that combines the general topographic and geographic characteristics of the 

nesting beaches to score this attribute. 

Beaches along continents or high-elevation oceanic islands and on the fringes of volcanic islands 

should be generally more resilient. Low-lying oceanic islands (e.g., atolls) and continental 

islands (e.g., barrier islands) are more vulnerable to climate change and sea level rise (Forbes et 

al. 2013, Lorenzo-Trueba and Ashton 2014), and the species or populations that nest there may 

have additional sensitivity due to the consequences of exposure to climate change, sea level rise, 

and natural hazards such as coarse woody debris (e.g., fallen trees, stumps) (Fujisaki and Lamont 

2016). Atolls may undergo significant changes with rising sea levels (Woodroffe 2008, Webb 

and Kench 2010). Geographic isolation of an island increases sensitivity since there are no 

nearby suitable areas for nesting, effectively eliminating the possibility to shift nesting locations.

Adjacent development near or on beaches can cause coastal squeeze, decreasing the available 

habitat for sea turtles to nest (Fish et al. 2005, Fish et al. 2008, Mazaris et al. 2009). This can 

happen on all beach types and confers increased sensitivity in our scoring scheme. In-water 

development (e.g., jetties, groins, breakwaters) affects sediment dynamics and limits the ability 

of the beach to accrete and erode naturally (Nordstrom 2014). These types of structures add 

additional vulnerability to the nesting habitat and serve as obstacles to nesting (Lutcavage et al. 

1997, Matsuzawa 2006). Natural and manmade debris from fallen trees, logging or lost fishing 

gear could also impede nesting activities (Fujisaki and Lamont 2016).

The orientation of island chains and archipelagos may also influence the sensitivity and adaptive 

capacity of a population. Island chains (including barrier islands) in an east-west orientation may 

not afford populations that utilize them the ability to move poleward as temperatures rise and 

some beaches become unsuitable for nesting. Populations that utilize low-lying isolated islands 

may have more difficulty locating new suitable nesting habitat if beaches become submerged or 

otherwise unsuitable.

Relationship to abundance: A population that uses more vulnerable or more low-lying isolated

nesting beaches is more likely to experience declines in abundance due to climate-driven 

changes
B-7

Relationship to distribution: A population that uses more vulnerable or more low-lying isolated

nesting beaches is more likely to experience shifts in distribution due to climate-driven changes. 

Relationship to phenology: N/A. The long-term stability of nesting beaches is not expected to 

influence the phenology of a population. 

Nesting Beach Type Scoring:

Bin Criteria

Bin 1 (low sensitivity) Species nests primarily on continental beaches without adjacent 

development

Bin 2 (moderate 

sensitivity)

Species nests primarily on one of the following:

• high islands without adjacent development or 

• continental beaches with adjacent low-density development

Bin 3 (high 

sensitivity)

Species nests primarily on one of the following:

• non-isolated low-lying islands without adjacent development, 

• high islands with adjacent development, or 

• continental beaches with adjacent high-density development

Bin 4 (very high 

sensitivity)

Species nests primarily on one of the following:

• isolated low-lying islands, 

• non-isolated low-lying islands with adjacent development, 

• high islands with adjacent development and in-water 

development, or 

• continental beaches with adjacent high-density development 

and in-water development
B-8

B.4 Geographic Extent of Nesting

Goal: To estimate the geographic extent and spatial density of the population’s nesting activity.

Background:

Sea turtle populations nest on beaches that, depending on the population, can span a few hundred 

meters to entire coasts (Pike 2013). The geographic extent of nesting and nest density across that 

area affect the population’s vulnerability to climate change.

A small geographic extent is often associated with increased risk (Laidre et al. 2008, Isaac et al. 

2009, Chin et al. 2010, Thomas et al. 2011, Pecl et al. 2014, Stortini et al. 2015). Populations that 

nest across a broad geographic area have greater resilience to climate impacts. Nesting across a 

broader geographic area confers greater diffuseness of available nesting habitat and implies a 

greater diversity of nesting habitat. Other nesting areas are likely to be available if any single 

nesting area becomes unsuitable. In a narrow geographic extent, a change that renders any single 

nesting area unsuitable is likely to impact a greater proportion of the nesting extent.

The density of sea turtle nesting is variable between and within populations. A uniform density 

means that the population’s nesting activity is evenly spread out over an area, while a nonuniform density shows areas of high local density and clustering. Populations that have a nonuniform density may be more sensitive to stochastic events (e.g., storms, pollutant spills, etc.) 

that affect localized areas where the population may be clustered (Pike and Stiner 2007). The 

effect of long-term climatic changes (e.g., increasing temperatures) on populations will have 

varying responses across the distribution of the density (Jensen et al. 2018). When considering a 

given geographic area, a non-uniform density population with greater density near climatic 

tolerance thresholds is likely more sensitive to changes in those conditions (Parmesan 2006, 

Berry et al. 2008). A non-uniform density population with greater density far from climatic 

tolerance thresholds in that same area is likely less sensitive to changes in those conditions. A 

uniform density population in that same area will have equal parts of its population near and far 

from where the climatic tolerance threshold is being passed. In this case, the risk associated with 

a general, non-uniform density is greater while the benefit may not be greater than that of a 

uniform density.

In our scoring, nesting geographic extent plays a greater relative role in sensitivity than nest 

density. Populations with a broad distribution of nesting and uniform density of nests over that 

area are likely least sensitive to climate change impacts. Populations with a narrow distribution 

of nesting and non-uniform density of nests over that area are likely most sensitive to climate 

change impacts.

Other vulnerability assessment frameworks use discrete areal extents (Thomas et al. 2011, Pecl 

et al. 2014), latitudinal extents (Chin et al. 2010, Stortini et al. 2015), or longitudinal extents 

(Laidre et al. 2008) to describe the geographic extent of a population. We use qualitative terms to 

define the geographic extent of nesting of individuals to gain a sense of their potential 

connectivity with other suitable nesting habitat. 

Nesting site locations for each species of sea turtle, compiled from multiple research efforts and 

datasets, are reported on the OBIS-SEAMAP/SWOT website (Halpin et al. 2009).

Relationship to abundance: A population with a broader geographic extent of nesting with 

uniform density is less likely to experience climate-driven declines in abundance because a 
B-9

variety and extensive use of nesting locations reduces the likelihood that climate impacts will 

adversely affect the entire population.

Relationship to distribution: A population with a broader geographic extent of nesting is less 

likely to experience climate-driven shifts in distribution.

Relationship to phenology: N/A. The geographic extent of nesting is not expected to influence 

the phenology of a population.

Geographic Extent of Nesting Scoring: 

Bin Criteria

Bin 1 (low sensitivity) Broad distribution of nests/uniform density

Bin 2 (moderate sensitivity) Broad distribution of nests/non-uniform density

Bin 3 (high sensitivity) Narrow distribution of nests/uniform density

Bin 4 (very high sensitivity) Narrow distribution of nests/non-uniform density
B-10

B.5 Nesting Site Fidelity

Goal: To assess the degree to which individuals of a population utilize the same nesting beach 

locations in successive nesting seasons.

Background: 

Homing in migratory species has been well studied, with individuals returning to natal sites or 

other established areas (e.g., Dittman and Quinn 1996, Bowen et al. 2004, Lohmann et al. 2008, 

Matsumura et al. 2011, Lohmann et al. 2013). Some individuals of a species display strong site 

fidelity by returning to the same nesting complex or foraging grounds year after year, often 

within a few meters or kilometers (depending on the species and population) of previous years. 

Once individuals have returned to the nesting region where they were born and subsequently 

selected a nesting beach, sea turtles will tend to renest in relatively close proximity (0 to 5 km) 

during subsequent nesting attempts and within a nesting season (Miller 1996). Others may select 

different nesting beaches within a season but located in a general proximal region (e.g. 

Santidrián Tomillo et al. 2017) or ranging in distances of a few hundred kilometers (Bjorndal et 

al. 1983). If a site that individuals return to is impacted by climate change, those individuals are 

expected to be impacted as well (Laidre et al. 2008). As the proportion of individuals within a 

species or population exhibiting site fidelity increases, the sensitivity to climate change also 

increases. 

This attribute only considers nesting site fidelity and is not limited to natal nesting site fidelity. 

Relationship to abundance: A population with greater nesting site fidelity is more likely to 

experience declines in abundance due to climate-driven changes.

Relationship to distribution: A population with greater nesting site fidelity is less likely to 

experience shifts in distribution due to climate-driven changes. 

Relationship to phenology: N/A. This attribute considers only geographic parameters and does 

not consider timing. 

Nesting Site Fidelity Scoring: 

Bin Criteria

Bin 1 (low 

sensitivity)

Nesting females display a low degree of site fidelity (nests within 

~100 km in successive nesting seasons)

Bin 2 (moderate 

sensitivity)

Nesting females display a moderate degree of site fidelity (nests 

within ~50 km in successive nesting seasons)

Bin 3 (high 

sensitivity)

Nesting females display a high degree of site fidelity (nests within ~10

km in successive nesting seasons)

Bin 4 (very high 

sensitivity)

Nesting females display extreme site fidelity (nests within 1 km in 

successive nesting seasons)
B-11

B.6 Lifetime Reproductive Potential

Goal: To estimate the ability of an individual (and by extension, population) to produce 

offspring that reach sexual maturity, thereby facilitating population growth and avoiding 

population declines.

Background: The ability of a species to maintain or increase its abundance and to recover from 

disturbance depends on the ability of its individuals to reproduce and replace, through 

recruitment, those individuals lost to mortality (Lande 1993). Among species with similar 

reproductive strategies, if an individual of a species has more offspring, there is a greater 

opportunity to adapt to changes. Species with higher reproductive rates are expected to be less 

sensitive and more adaptive (ZSL 2010, Hagger et al. 2013, Morrison et al. 2015).

Generally, recruitment refers to the addition of individuals to a population (Caley et al. 1996). 

Here, we consider recruitment to be the number of individual offspring produced by one female 

over the course of that female’s lifetime that reach adulthood. All other factors being equal (e.g., 

direct and indirect anthropogenic effects such as fisheries impacts, habitat loss), species and 

populations that have greater recruitment are more buffered to disturbances and more likely to 

adapt to changing conditions. Therefore, those species and populations with greater recruitment 

would be expected to be less sensitive to climate change.

Since recruitment estimates are not available for many sea turtle populations, we use the 

characteristics and processes that determine the number of offspring that an individual of a 

species produces each year or over its lifetime as proxies for recruitment. These proxies include 

metrics such as clutch size, clutch frequency, age at sexual maturity, and juvenile survival.

Clutch size refers to the number of eggs a female lays per nest. All other factors being equal, 

individuals with larger clutch sizes will produce more offspring and would be expected to be less 

sensitive to climate change.

Clutch frequency refers to the number of nests a female lays within a given nesting season. All 

other factors being equal, individuals that lay more nests will produce more offspring and would 

be expected to be less sensitive to climate change. Multiple nests laid over the course of a season

provide more resiliency to nest loss from predation, storms, or other causes.

Hatching success refers to the relative proportion of eggs that produce live hatchlings. All other 

factors being equal, high hatching success would result in low sensitivity to climate change.

Emergence success refers to the relative proportion of eggs that produce live hatchlings that 

leave the nest. All other factors being equal, high emergence success would result in low 

sensitivity to climate change.

Juvenile survival refers to the survival rate of a cohort from the juvenile stage to the adult stage.

Offspring must reach an age at which they can reproduce in order to effectively contribute to the 

longevity of the species. All other factors being equal, species with higher survival rates will 

produce more recruits and would be expected to be less sensitive to climate change.

Remigration interval refers to the number of years between nesting seasons for a female. Species 

or populations that have a shorter remigration interval and therefore nest more frequently are 

considered less sensitive to climate change.

Age at sexual maturity refers to the age at which a female first nests. The time for a generation to 

turn over is shorter for species that reach sexual maturity at a younger age, thereby providing 
B-12

greater opportunity to adapt to changing conditions. All other factors being equal, individuals 

that reach sexual maturity at a younger age will produce offspring earlier and would be expected 

to be less sensitive to climate change.

Reproductive Longevity refers to the fertile years of a female sea turtle. Longevity is directly 

related to age of sexual maturity and reproductive senescence or death (survivability). Other 

physiological mechanisms may also be fixed within narrow age limits. The longer the 

reproductive longevity, the greater the total lifetime reproductive output and, thus, the less 

sensitive the population is.

Climate change is likely to alter many of these proxies (Azanza-Ricardo et al. 2017, Butler 

2019). However, we make no attempt to estimate the magnitude of that change, and assess these 

proxies as they are currently understood rather than how they may change.

Relationship to abundance: A population with greater lifetime reproductive potential is less 

likely to experience declines in abundance due to climate-driven changes.

Relationship to distribution: N/A. Range expansion, contraction, or shift may occur based on 

population sizes, which are mediated by reproductive potential. Therefore, the relationship 

between this attribute and distribution is secondary. Changes in distribution are considered with 

the population abundance attribute rather than here.

Relationship to phenology: N/A. Shifts in the timing of life history events may occur based on 

population sizes, which are mediated by reproductive potential. Therefore, the relationship 

between this attribute and phenology is secondary. Changes in phenology are considered with the 

population abundance attribute rather than here.

Lifetime Reproductive Potential Scoring: 

We provide bookends for a spectrum of reproductive output. Experts should consider the proxies 

for recruitment presented above when estimating reproductive output relative to other sea turtle 

populations.

Bin Criteria

Bin 1 (low sensitivity) High reproductive output and survival to maturity

Bin 2 (moderate sensitivity) Closer to high reproductive output and survival to maturity

Bin 3 (high sensitivity) Closer to low reproductive output and survival to maturity

Bin 4 (very high sensitivity) Low reproductive output and survival to maturity
B-13

B.7 Length of Nesting Season

Goal: To estimate the peak timing and duration of female nesting to infer the ability of a 

population to adapt its reproductive timing to changing conditions.

Background: Reproduction is often associated with seasonal timeframes and temporal 

constraints; however, uninterrupted year-round nesting has been recorded at some locations 

(Hamann et al. 2003). Those populations that have reproductive events that are highly correlated 

with a specific timeframe are expected to be more sensitive to changes in environmental 

conditions while those populations with reproductive events that are loosely correlated to time 

frames are expected to be exhibit more adaptability and be less sensitive (Morrison et al. 2015). 

Here we define the nesting season as the period during which females deposit eggs. We exclude 

incubation and emergence from this timeframe.

A population that can shift reproductive activities to track environmental variables in time and 

space will be better able to adapt. The timing of the activity among individuals influences the 

magnitude of this attribute. For example, an activity that occurs among all individuals within 

days of each other (e.g., arribadas) would cause the population to be more sensitive than if the 

activity occurs across weeks or months.

Sea turtle populations typically nest seasonally, although the timing and duration of nesting 

activities could change in response to environmental disturbances such as climate change that 

can directly affect ocean temperature, biogeochemical composition, and sea level (Dalleau et al. 

2012). Changes in environmental conditions in nesting areas during those months may alter the 

success rates of nests and hatchlings. If conditions at historical nesting beaches become too 

warm during nesting months, populations may shift nesting beaches poleward to cooler 

conditions. Likewise, populations may also shift the seasonal timing of nesting. Nesting 

primarily during warmer months affords populations the opportunity to shift nesting months to 

cooler times of the year if conditions become too warm - assuming populations can respond as 

quickly as climate is changing. Nesting primarily during cooler months does not afford the same 

opportunity to shift the timing of nesting to cooler times of the year, if conditions become too 

warm, thereby making those populations more sensitive. Sea surface temperature has been 

correlated with the length of the nesting season for some populations (Pike et al. 2006, Mazaris 

et al. 2008), but not for others (Pike 2009).

Populations typically exhibit peak nesting periods, with differences in peak intensity and 

duration observed between species, populations, and regions (Bjorndal et al. 1985, Miller 1996, 

Chaloupka 2001, Hamann et al. 2003, Eckert et al. 2012). This temporal density of nesting 

activity contributes to the sensitivity of a population to climate change. Populations with shorter 

peak nesting seasons are more sensitive to destructive stochastic events (e.g., heat waves, storms, 

etc.), while populations with a longer peak nesting season may be more resilient to those events 

(Pike and Stiner 2007).

Relationship to abundance: A population that has a longer nesting season is less likely to 

experience climate-driven declines in abundance because it can more easily shift timing of 

nesting to match favorable conditions, thereby maintaining nesting success.

Relationship to distribution: N/A. While populations may shift the location of nesting, the 

length of the nesting season does not determine the location of nesting.
B-14

Relationship to phenology: A population that has a longer nesting season is less likely to 

experience climate-driven shifts in phenology because its historical nesting seasons are more 

likely to coincide with favorable conditions. A population that has a shorter nesting season is 

more likely to experience shifts in phenology because it would have to shift timing into periods 

outside of historical nesting seasons to match favorable conditions. 

Instructions for expert elicitation: Consider temporal density of nesting activity when placing 

points. Place points to match the highest peak nesting season, when approximately 80% of sea 

turtles are nesting. However, if there is sporadic nesting through the year, for example, also place 

a point in bin 1. 

 

Length of Nesting Season Scoring:

Bin Criteria

Bin 1 (low sensitivity) Population nests 10-12 months per year

Bin 2 (moderate sensitivity) Population nests 7-9 months per year

Bin 3 (high sensitivity) Population nests 4-6 months per year

Bin 4 (very high sensitivity) Population nests 1-3 months per year
B-15

B.8 Adult Physiological Sensitivity to Temperature

Goal: To estimate the influence of temperature on physiological processes of nesting females.

Background: Changes in temperature are one of the most readily observed conditions in a 

changing climate. Temperature has a well-documented impact on the physiology and 

performance of ectothermic species (e.g., Davenport et al. 1997, Hayden and Harrison 2007, 

Madrak et al. 2016). Reaching temperature extremes at either end of the spectrum can result in 

mortality. For ectothermic species, size plays a major role in temperature sensitivity and the 

physiological response to changes in temperature (Stevenson 1985). As temperatures change 

spatially and temporally, individuals may shift their distribution or phenology to avoid extreme 

temperatures (Dudley et al. 2016). 

Some sea turtle species display characteristics of endothermy, raising body temperatures above 

ambient temperatures with metabolic heat (Standora et al. 1982, Sato et al. 1995, Eckert 2002, 

James and Mrosovsky 2004, Bostrom and Jones 2007). Larger species are less susceptible to 

rapid changes in temperature, and therefore have more difficulty shedding heat, particularly 

when on land during daylight hours (Spotila and Standora 1985). When combined with the 

effects of raised ambient temperatures, larger species may suffer from an inability to reduce their 

body temperature, particularly during nesting (Burns et al. 2015). 

This attribute considers only adults; eggs and nests are addressed in nest/egg sensitivity to 

temperature. Hatchlings and juveniles are not considered because populations would not 

differentiate based on size during those life stages.

We assess species or populations using curved carapace length. Straight carapace length may be 

the only metric available for some populations. Relationships between curved carapace length 

and straight carapace length have been established for some species (Bjorndal and Bolten 1989). 

Relationship to abundance: A population with greater sensitivity to temperature is more likely 

to experience climate-driven shifts in abundance. 

Relationship to distribution: A population with greater sensitivity to temperature is more likely 

to experience climate-driven shifts in distribution. 

Relationship to phenology: A population with greater sensitivity to temperature is more likely 

to experience climate-driven shifts in phenology. 

Adult Physiological Sensitivity to Temperature Scoring:

Bin Criteria

Bin 1 (low sensitivity) Average nesting female curved carapace length is less than 80

cm

Bin 2 (moderate sensitivity) Average nesting female curved carapace length is greater than 

or equal to 80 cm but less than 100 cm

Bin 3 (high sensitivity) Average nesting female curved carapace length is greater than 

or equal to 100 cm but less than 150 cm

Bin 4 (very high sensitivity) Average nesting female curved carapace length is greater than 

or equal to 150 cm
B-16

B.9 Nest/Egg Sensitivity to Temperature

Goal: To estimate the effect of temperature on the nests and eggs of a population based on 

critical temperature thresholds.

Background: Temperature has a well-documented impact on the sex-ratio and viability of sea 

turtle eggs (Mrosovsky 1994, Ackerman 1997, Hawkes et al. 2009). Having many nests deviate 

from the pivotal temperature, the temperature at which the female:male hatchling sex ratio is 1:1

(Mrosovsky and Pieau 1991), in the same direction can have lasting impacts on the population 

structure, and surpassing the lethal temperature threshold can result in the mortality of an entire 

nest (Saba et al. 2012, Santidrián Tomillo et al. 2014, Jensen et al. 2018).

The transitional range of temperature (TRT) is a range of incubation temperatures that produces 

hatchlings of both sexes (Mrosovsky and Pieau 1991). Nests at temperatures below the TRT 

result in 100% male hatchlings while temperatures above the TRT result in 100% female 

hatchlings. Populations that have a greater proportion of nests near the upper temperature 

threshold will show greater sensitivity than populations near the pivotal temperature (Santidrián 

Tomillo et al. 2014). Of all the abiotic factors that influence hatching success, particularly in 

some species (i.e., leatherbacks), temperature may singularly be the most important variable 

affecting egg development and hatchling output (Santidrián Tomillo et al. 2012). The effect of 

high temperature accelerates developmental rates, reduces hatching success and emergence rates, 

and may potentially affect hatchling fitness (Poloczanska et al. 2009, Santidrián Tomillo and 

Swiggs 2015). Some populations are already trending toward feminization of the population due 

to temperatures (Jensen et al. 2018).

The impact of air temperature on the incubation temperature of nests may be modified by a 

number of other factors (e.g., precipitation, sand color, groundwater influences) (Tapilatu and 

Tiwari 2007, Santidrián Tomillo et al. 2014) and for this assessment we consider the net effect of 

all of those factors on the temperature of a nest.

The viability of eggs and hatchlings within the nest can be compromised by extreme 

temperatures, with high sand temperatures adding additional stress affecting both hatchling 

behavior as well as overall nest success (Hewavisenthi and Parmenter 2001, Burgess et al. 2006, 

Kobayashi et al. 2017). When temperatures are particularly high in nests, embryonic and 

hatchling deaths may result either directly through an increase into the upper lethal temperature 

range or potentially as a result of behavioral inhibition to the point of non-emergence by 

hatchlings (Milton and Lutz 2003). Species that have lower lethal temperatures would be

expected to have greater sensitivity to climate changes, as increasing temperatures would more 

rapidly impact nests of those species compared to species with higher lethal temperatures 

(Howard et al. 2014). Currently, knowledge of species-specific lethal temperatures is not 

advanced enough to use as a metric for scoring, but future iterations of this assessment are 

encouraged to include lethal temperature as a metric as data improves.

We explored using a metric that measures proximity to upper bounds of the TRT or proximity to 

upper lethal thresholds. However, available information relating to sand temperatures was 

lacking for many nesting sites and required the use of proxies. Deriving those nest temperature 

proxies required a series of steps that resulted in error propagation and poor confidence.

Relationship to abundance: A population with a greater TRT is less likely to experience 

climate-driven declines in abundance.
B-17

Relationship to distribution: A population with a greater TRT is less likely to experience 

climate-driven shifts in distribution. 

Relationship to phenology: N/A. 

Nest/Egg Sensitivity to Temperature Scoring: 

Bin Criteria

(TRT – difference between upper and lower bound)

Bin 1 (low sensitivity): TRT > 5°C

Bin 2 (moderate sensitivity): 3.5°C ≤ TRT < 5°C

Bin 3 (high sensitivity): 2°C ≤ TRT < 3.5°C

Bin 4 (very high sensitivity): TRT <2°C
B-18

B.10 Migration

Goal: To estimate migratory patterns and distribution of a population.

Background:

The impact of migration on a population’s sensitivity and vulnerability to climate change is 

likely the most difficult to characterize of the attributes we assessed.

Migration is characterized by regular, repeated, long-distance movement (Dingle 1996, Stern 

2009) such as between breeding and foraging grounds. Migratory species are often seeking 

specific conditions or abandoning areas that become unsuitable for parts of the year. Species may 

engage in annual or seasonal migrations. Dingle and Drake (2007) define annual migrations as 

round-trip movements synchronized with a yearly pattern and seasonal migrations as the 

individual stages of those annual patterns. Here, we consider seasonal migrations more closely to 

those defined by Dingle and Drake (2007) as “commuting,” movements between discrete areas 

on a more frequent basis than annual migrations. Other frameworks have considered migration as 

a factor contributing to climate sensitivity and/or adaptive capacity for various reasons. As 

creatures that spend their entire lives in marine or estuarine habitats (except for nesting or

basking), sea turtles’ physiological and behavioral adaptations have evolved largely in response 

to selection in the aquatic environment and are resource driven. Data accumulated from years of 

mark-recapture and satellite telemetry studies, as well as sightings and strandings records, 

demonstrate that adult sea turtles travel hundreds to thousands of miles between established 

feeding and breeding areas, at regular or seasonal intervals. While some sea turtle species have 

relatively restricted ranges, foraging as mature animals in neritic zones, others may undergo 

several years of trans-oceanic migrations between nesting seasons, and therefore may have a 

longer remigration interval (i.e., the period (in years) between nesting events) (Plotkin 2003).

Longer reproductive migrations should be differentiated from the shorter-term foraging 

migrations that may occur on a seasonal to annual frequency. 

Migratory species are often considered to be more vulnerable to climate change due to a specific 

temporal or seasonal reliance on a certain habitat (Laidre et al. 2008, ZSL 2010). The reliance on 

specific habitat is considered elsewhere in this assessment (see Habitat Specificity) but the 

temporal aspect and potential for mismatches between the migrant and habitat conditions remain 

important (Laidre et al. 2008, Chin et al. 2010, Gardali et al. 2012, Pecl et al. 2014). 

Environmental cues play a greater role in the life history of migratory species than non-migratory 

species (Bauer et al. 2011, Shuter et al. 2011), therefore making migratory species more sensitive 

to climate-driven shifts in phenology. However, climate-driven shifts in the phenology of 

predators and/or prey may have cascading effects on both migratory and non-migratory species 

(Shuter et al. 2011).

Some frameworks only assessed part of a population’s range and used the migration attribute to 

account for potential impacts in other regions (e.g., Chin et al. 2010, Bagne and Friggens 2011). 

Migratory species may experience varying levels of climate change across their ranges, thereby 

compounding their exposure to climate change. Here we consider the climate exposure 

throughout the entire annual range of the population and therefore do not need to use a proxy for 

areas outside of the scope of the assessment. Those potential changes outside the scope of other 

assessments are explicitly considered in the exposure score of this assessment.

Several frameworks use migration as a proxy for dispersal ability (e.g., Gardali et al. 2012, ZSL 

2010, Hagger et al. 2013). While we also consider the home range of individuals of a population 
B-19

(see Home Range), the fact that a population undergoes a long distance migration and the 

diversity of the pathways the population uses within and between years confer a degree of 

adaptive capacity.

Populations that utilize a variety of migratory routes may be less susceptible to the impacts of 

climate change and likely have evolved migratory flexibility within their large dynamic marine 

ecosystem to adapt to unpredictable environmental changes (Plotkin 2010). Climate change can 

alter currents and other oceanographic features that could, in turn, affect the route that sea turtles 

would take on their migrations. Although adult sea turtles are active swimmers, some species can

utilize or traverse currents to facilitate reaching their final destination (Shillinger et al. 2008) 

while others may be nomadic, displaying little to no fidelity to specific foraging habitats (Plotkin 

2010). Neonate and juvenile sea turtle movement may be aided by currents, though that effect is 

more one of dispersion than migration (Witherington 2002, Kobayashi et al. 2008).

Climate-driven impacts to a migratory pathway could have devastating effects on a population 

that relies solely on that pathway while a population that utilizes a variety of pathways would see 

a reduced impact on the overall population.

Finally, migratory species may be able to escape unfavorable conditions and find new habitat 

more easily than non-migratory species. While most sea turtles are highly mobile and capable of 

traveling long distances, migratory populations tend to engage in behavior that would encourage 

the discovery of new areas.

All aspects of a population’s life history should be considered when scoring this attribute.

Relationship to abundance: N/A. A consistent directional effect could not be established. The 

effect of migratory behavior may add adaptive capacity and sensitivity. We were unable to 

determine whether the added adaptive capacity or the added sensitivity has a greater influence.

Relationship to distribution: A population that does not engage in migratory behavior is more 

likely to require shifts in distribution relative to historical distribution than a population that 

engages in migratory behavior, whereas a population that engages in migratory behavior may 

still require shifts in distribution but these shifts will be less consequential due to the alreadyexpansive distribution of the population.

Relationship to phenology: N/A. A consistent directional effect could not be established. A 

population that engages in migratory behavior is more likely to experience shifts in timing of 

reproduction than a population that does not engage in migratory behavior due to potential 

changes in distant foraging areas.
B-20

Migration Scoring: 

Bin Criteria

Bin 1 (low sensitivity) Reproductive migration; multiple migratory foraging area 

destinations

Bin 2 (moderate sensitivity) Reproductive migration; few or single foraging area 

destinations

Bin 3 (high sensitivity) No reproductive migration; seasonal foraging migration

Bin 4 (very high sensitivity) No migration; local movement only
B-21

B.11 Foraging Home Range

Goal: To estimate the spatial extent of the foraging range of individuals within a population.

Background:

The foraging home range of an individual includes the areas regularly visited to forage (Burt 

1943). The extent of foraging home range differs between individuals of a population and is 

generally smaller than the foraging extent of the population.

Populations with individuals that forage across a broad range likely have greater resilience to 

climate impacts, because foraging across a broader range confers greater redundancy of available 

forage habitat and implies a greater diversity of foraging habitat. Other foraging areas are likely 

to be available if any single foraging area within the range becomes unsuitable. In a narrow 

foraging home range, a change that renders any single foraging area unsuitable is likely to impact 

a greater proportion of the foraging extent. While range distributions may be largely defined by 

temperature, food availability serves as an important factor in defining the foraging home range. 

Sea turtles that have a broad generalist diet (e.g. loggerheads) are less likely to be restricted by 

climate and may have a broad foraging range while those species that are more specialists (e.g., 

hawksbills and green turtles) may have a more restrictive home range that may be less adaptable 

with changing environmental conditions (Witt et al. 2010).

Populations with individuals that have a broad foraging home range may be better suited to adapt 

to changing conditions within their foraging home range and find new foraging areas. These 

individuals have shown the ability to locate resources within a broad area. Individuals with a 

small foraging home range have not historically shown a tendency to forage across broad areas 

and may not regularly encounter new foraging areas. These individuals may also exhibit high 

foraging site fidelity, which would also increase sensitivity and limit the ability to adapt to 

changing conditions within the foraging home range.

Other vulnerability assessment frameworks use discrete areal extents (Thomas et al. 2011, Pecl 

et al. 2014), latitudinal extents (Chin et al. 2010, Stortini et al. 2015), or longitudinal extents 

(Laidre et al. 2008) to describe the geographic extent of a population. We use qualitative terms to 

define the foraging home range of individuals to gain a sense of their potential connectivity with 

other suitable foraging habitat. We consider geographic extent as two separate attributes: 

“Geographic Extent of Nesting” and “Foraging Home Range.”

Considering that sea turtles are long-lived and highly mobile, the foraging home range of the 

individual (including all life stages) is more important than foraging geographic extent of the 

population for the time frame considered in this assessment. Adaptation to changing conditions 

within the foraging grounds is more likely to manifest through individual behavioral changes, 

rather than genetic shifts in the population. Increasing availability of satellite tag data has 

improved our understanding of the movements of sea turtles within the foraging range (Godley et 

al. 2008).

Relationship to abundance: A population consisting of individuals with broad foraging home 

ranges is less likely to experience declines in abundance due to climate-driven changes.

Relationship to distribution: A population consisting of individuals with broad foraging home 

ranges is less likely to require shifts in distribution relative to historical distribution than a 

population of individuals with a narrow foraging distribution. A population with a broad foraging 
B-22

home range may still require shifts in distribution but these shifts will be less consequential due 

to the already-expansive distribution of the population.

Relationship to phenology: N/A. This attribute relates only to geographic extent and does not 

consider shifts in timing. 

Instructions for expert elicitation: If the various life stages of a population would be scored 

differently, score the most restrictive life stage.

 

Foraging Home Range Scoring: 

Bin Criteria

Bin 1 (low sensitivity) Individuals’ foraging home ranges are broad, primarily 

including oceanic pelagic habitat

Bin 2 (moderate sensitivity) Individuals transit coastlines within continental shelf waters to 

forage

Bin 3 (high sensitivity) Individuals typically remain in bays or archipelagos to forage 

but occasionally travel farther and have the capacity to find 

other locations

Bin 4 (very high sensitivity) Individuals’ foraging ranges are narrow, primarily confined to 

bays or archipelagos
B-23

B.11 Population Abundance

Goal: To estimate a population’s current abundance. 

Background: Smaller population sizes have an implied reduced genetic diversity (Frankham 

1996), reduced behavioral diversity (Whitehead et al. 2004), experience more demographic 

stochasticity (Purvis et al. 2000), and are generally at greater extinction risk (Purvis et al. 2000), 

likely because they have lesser adaptive evolutionary capabilities than large populations 

(Frankham et al. 2002). Greater diversity (e.g., genetic, behavioral) confers a greater ability to 

adapt to changing conditions (Morrison et al. 2015). Larger populations presumably are better 

poised to colonize new areas or re-establish in formerly occupied extirpated areas (Laidre et al. 

2008) and buffer inter-annual variability in population size with potentially smaller percentage 

declines in population abundance when experiencing an environmental disturbance. In addition, 

those sea turtle populations that exhibit strong philopatry (natal homing) may limit the 

establishment or exploitation of new areas with suitable habitat, as opposed to populations 

favoring a “dispersal” strategy, where individuals may actively search for new areas to use for 

nesting or foraging (Carreras et al. 2018). 

Abundance estimates of sea turtle populations are most reliably indicated by monitoring the 

number of nests/nesting females on the nesting beaches, although abundance estimates in 

particular foraging areas can be made from aerial surveys and mark/re-capture studies (National 

Research Council 2010). For consistency between populations, we score using nesting females as 

the criterion for population abundance. 

Breaks in the scoring criteria come from generalizations from sea turtle recovery plans (NMFS 

and USFWS 1991, 1992, 1993, 1998a, 1998b, 1998c, 1998d, 1998e, 1998f, 2008, 2016, NMFS 

et al. 2011). Many species and DPS delisting criteria utilize values of 5,000 or 10,000 

nests/nesting females. We use these values to differentiate between low/moderate sensitivity and 

high/very high sensitivity. 

Relationship to abundance: A population with high abundance is less likely to experience 

declines in abundance due to climate-driven changes.

Relationship to distribution: A population with high abundance is less likely to experience 

large relative shifts in distribution due to climate-driven changes.

Relationship to phenology: A population with high abundance is less likely to experience large 

relative shifts in phenology due to climate-driven changes.

Population Abundance Scoring: 

Population abundance is often defined by the survey counts of females as they come to beach. 

Bin Criteria

Bin 1 (low sensitivity) >10,000 nesting females

Bin 2 (moderate sensitivity) 5,000-10,000 nesting females

Bin 3 (high sensitivity) 1,000-5,000 nesting females

Bin 4 (very high sensitivity) <1,000 nesting females
B-24

B.12 Population Abundance Trend

Goal: To identify the recent historical trend of a population’s abundance through time. 

Background: Populations with declining abundance may have a reduced ability to recover from 

disturbances and are more likely to be sensitive to climate change. The “Framework for 

Categorizing the Relative Vulnerability of Threatened and Endangered Species to Climate 

Change” (Galbraith and Price 2009) assesses population size reduction over a 10-year or threegeneration period, derived from one of the criteria used to determine the International Union for 

the Conservation of Nature (IUCN) Red List status (IUCN 2012). Here, we follow the model of 

Thomas et al. (2011) to establish scoring bins. Thomas et al. (2011) use a criterion of 7.5% rate 

of decline to differentiate between ‘declining’ and ‘rapidly declining’. We realize that such 

precision may not be possible for many populations because long time series are not available for 

many populations. While the trend is likely unknown for certain populations, we anticipate 

experts will score based on their scientific knowledge and experience. 

Multiple metrics are available for estimating abundance trend (e.g., aerial survey estimates in 

known foraging hotspots (e.g., Seminoff et al. 2014, Eguchi et al. 2018), number of nests, 

number of nesting females (National Research Council 2010). We ask experts to use trends in 

nesting females (NMFS and USFWS 2016) where available and to use their expert opinion in 

selecting the next best available metric where necessary. 

The period over which a trend is estimated can affect the direction and magnitude of a trend. 

Many sea turtle populations have experienced historical declines, such that when examined on a 

long time scale indicate an overall decline (National Research Council 1990, McClenachan et al. 

2006, IUCN 2016), though the trend in the recent period may indicate an increase. For the 

purposes of this assessment, we ask experts to consider the recent population abundance trend 

(e.g., past decade). We give greater weight to the recent trend over the long-term trend (e.g., 50–

100 years) to reflect the recent advances in conservation. 

Relationship to abundance: A population with a rapidly declining abundance is more likely to 

experience declines in abundance due to climate-driven changes 

Relationship to distribution: A population with a rapidly declining abundance is more likely to 

experience shifts in distribution due to climate-driven changes 

Relationship to phenology: A population with a rapidly declining abundance is more likely to 

experience shifts in phenology due to climate-driven changes 

Population Abundance Trend Scoring: 

Bin Criteria

Bin 1 (low sensitivity) Increasing abundance trend over recent period

Bin 2 (moderate sensitivity) Stable abundance trend over recent period

Bin 3 (high sensitivity) Declining abundance trend over recent period

Bin 4 (very high sensitivity) Rapidly declining abundance trend over recent period or 

deficient data to estimate trend
B-25

B.14 Cumulative Stressors

Goal: To estimate the level to which a population is currently impacted by non-climate stressors.

Background: Climate change is expected to exacerbate the impacts of other non-climate 

stressors that already impact sea turtles. Populations that experience stress from non-climate 

sources will have reduced fitness and capacity to adapt to climate change (Morrison et al. 2015). 

Sea turtles may encounter a wide variety of stressors, both natural and anthropogenic, and these 

may include environmental factors (e.g., salinity, pollution), physiological factors, physical 

factors (e.g., trauma, forced submersion) and biological factors (e.g., disease, toxic blooms). The 

adaptive function of a response to a particular stressor will depend on the intensity, duration, and 

whether populations can adapt (Milton and Lutz 2003). The magnitude of impact experienced by 

additional stressors will vary by species and region.

While any level of a stressor can result in negative impacts on a population, for the purposes of 

this assessment a stressor is considered anything that has population-level effects.

Examples of non-climatic additional stressors include:

 Bycatch or competition with fisheries (e.g., Wallace et al. 2010)

 Direct killing/poaching (e.g., Koch et al. 2006)

 Vessel strike (e.g., Chaloupka et al. 2008, Casale et al. 2010)

 Habitat degradation not due to climate change (e.g., coastal development, beach armoring 

and alteration) (e.g., Fish et al. 2008)

 Disease, parasites, and harmful algal bloom exposure (e.g., Chaloupka et al. 2008)

 Predation by and competition with feral and invasive organisms (e.g., Caut et al. 2008)

 Pollutants/toxins (e.g., Keller 2013)

 Marine debris (e.g., Carr 1987, Bjorndal et al. 1994)

 Natural population interactions such as competition and predation (e.g., Chaloupka et al. 

2008)

Non-climate stressors will react to climate change in different ways. Novel interactions may 

emerge, for example, from responses in fishing (e.g., effort, gear, target species, target areas) as 

fish distributions shift, coastal development and usage as coastal conditions change, and from 

vessel strikes as sea turtle distributions shift. We do not attempt to anticipate how those 

responses will manifest but note that responses to climate change from other sectors and actors 

will be dynamic and, in many cases, difficult to predict.

Studies have characterized (IUCN 2016) and quantified (Wallace et al. 2011) threats to sea 

turtles. Scores from Wallace et al. (2011) could not be used directly because they included 

climate change as a threat, which would confound scores in this assessment. Scores from 

Wallace et al. (2011) should serve as both a baseline and comparison for this assessment.

Relationship to abundance: A population that currently experiences many non-climate stressors 

is more likely to experience declines in abundance due to climate-driven changes.

Relationship to distribution: A population that currently experiences many non-climate 

stressors is more likely to experience shifts in distribution due to climate-driven changes.

Relationship to phenology: N/A
B-26

Cumulative Stressors Scoring: 

Bin Criteria

Bin 1 (low sensitivity) Population currently experiences 2 or fewer additional 

stressors

Bin 2 (moderate sensitivity) Population currently experiences 3 or 4 additional stressors

Bin 3 (high sensitivity) Population currently experiences 5 or 6 additional stressors

Bin 4 (very high sensitivity) Population currently experiences more than 6 additional 

stressors or has one additional stressor that accounts for more 

than half of annual mortality. 
B-27

B.15 Attributes considered but omitted

During the development of this framework, a number of potential attributes were considered to 

score sensitivity and adaptive capacity. We list those that were considered but ultimately omitted 

from the framework such that future iterations may include these attributes if appropriate. Many 

of these attributes were omitted because of a lack of available information or a lack of 

meaningful differentiation in how the attribute would score among species and populations.

Prey availability

Predictability of prey in time and space is an important consideration. Decreased predictability 

would increase sensitivity, making it important to consider search strategies. The attribute is not 

intrinsic to the subject species and would require data on all prey species.

The availability of other prey species of roughly equivalent energetic/nutrient composition adds a 

layer of complexity. If a species is capable of foraging on other species but none are available, or 

only those of considerably deficient nutrient or energetic content, the species would be more 

sensitive to climate impacts. Similarly, if the traditionally targeted prey consists of fewer but 

larger prey items and can only be replaced by smaller prey, additional or different pressures to 

capture more prey items may come at a higher cost to the predator in time, energy, or risk of 

predation. The availability of prey and the relative availability of comparable alternative prey are 

important considerations but beyond the scope of this attribute and therefore not included in the 

scoring. 

Population trend of prey

Species that forage on prey with high variability in abundance may have relatively high 

sensitivity to climate change. The attribute is not intrinsic to the subject species and would 

require data on all prey species.

Distribution trend of prey

Species that forage on prey with high variability in distribution may have relatively high 

sensitivity to climate change. The attribute is not intrinsic to the subject species and would 

require data on all prey species.

Mean trophic level

The primary productivity required to sustain a population can be estimated and may be an 

indicator of sensitivity. While a species that feeds on high trophic levels would likely have a 

different sensitivity than a species that feeds on a lower trophic level, we struggled to formalize 

the relationship between trophic level and sensitivity. This attribute was determined to be similar 

to the more easily measured and qualified metric of ‘prey/diet specificity.’

Influences of changes in trophic web

Climate change could lengthen or shorten the food chain, resulting in changing amounts of 

energy available at the apex levels. Other assessments (see NatureServe’s CCVI6) include 

interspecific interactions but the attribute was deemed too complex for this application.

6

http://www.natureserve.org/conservation-tools/climate-change-vulnerability-index

Complexity in reproductive strategy

Species with highly complex reproductive strategies are more likely to have at least one aspect of 

the strategy impacted by climate change. A lack of variability among taxa rendered this attribute 

impractical.

 
B-28

Environmental variable(s) as a phenological cue for breeding

The triggers/cues for many species are likely unknown. As more data become available, this 

attribute could be reconsidered.

Early life history survival requirements

This attribute was considered too correlated with other attributes, particularly habitat sensitivity 

and reproductive plasticity.

Proximity to limit of thermal tolerance

Separating species’ physiological tolerance from prey tolerance is difficult, at least in terms of 

proximity to a threshold.

Spatial availability of unoccupied habitat for most critical life stage

This attribute most applies for species recovering from past declines with the potential for 

recovering populations to recolonize historical areas where they were previously extirpated or to 

colonize new areas beyond historical ranges. Most populations are not at their carrying capacity, 

suggesting there is unoccupied habitat. This attribute is not intrinsic to the species and would be 

difficult to quantify.

Genetic diversity

Generally, the more genetically diverse the species, the less sensitive the species is to ecosystem 

perturbations. We encourage future iterations of this assessment to consider incorporating this 

attribute.

Temporal mismatches of life-cycle events

This attribute has been incorporated into other attributes through the use of the phenology 

response category.
B-29

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Appendix C Sample Scoring

We show sample exposure scores (Table C1) and sensitivity scores (Table C2) for a hypothetical 

population. Here we highlight the process of calculating the weighted means, the component 

scores, the overall vulnerability score, and the response category scores. 

Factor and Attribute Weighted Means (Section 2.4.1)

Weighted means are calculated for each climate exposure factor and each sensitivity attribute. 

The weighted means are calculated by multiplying the number of points in each bin by the bin 

number (number of points in Bin 1 times 1, number of points in Bin 2 times 2, number of points 

in Bin 3 times 3, number of points in Bin 4 times 4) and then dividing by the total number of 

points for that attribute or factor. The value of these weighted means ranges from 1.0 to 4.0. 

Component Scores (section 2.4.2)

Exposure and sensitivity component scores are calculated using the attribute and factor weighted 

means from above and the component score logic model from Table 4 in section 2.4.2 (also 

below). 

As shown in Table C1, weighted mean scores for sea surface temperature change in mean, air 

temperature change in mean, ocean pH change in mean, and dissolved oxygen change in mean 

are all above 3.5. The exposure component score is therefore “very high (4)." 

Five sensitivity attribute weighted means (geographic extent of nesting, length of nesting season, 

nest/egg sensitivity to temperature, population abundance, and cumulative stressors) are greater 

than 3.5 (Table C2). The sensitivity component score is therefore “very high (4).”

Component score logic model (see Table 4 of section 2.4.2) 

Component Score Criteria

Very High (4) 3 or more attribute or factor mean scores ≥ 3.5

High (3) 2 or more attribute or factor mean scores ≥ 3.0, but does 

not meet threshold for “Very High”

Moderate (2) 2 or more attribute or factor mean scores ≥ 2.5, but does 

not meet threshold for “High” or “Very High”

Low (1) Less than 2 attribute or factor mean scores ≥ 2.5
C-2

Overall Vulnerability Score (Section 2.4.3)

Using the vulnerability matrix (Figure 3 in section 2.4.3) and the component scores from above, 

a “very high (4)” exposure component and a “high (3)” sensitivity component converge at a 

“very high (12)” overall vulnerability score.

Vulnerability matrix (see Figure 3 of section 2.4.3 and)

Response Category Scores (Section 2.4.4)

The distribution, abundance, and phenology response category scores are derived from the 

attribute weighted means calculated according to the relationships with each attribute described 

in Appendix B. The weighted means are used in the component score logic model (Table 4),

similar to the calculation of component scores above.

The distribution response scores as “very high” because geographic extent of nesting, length of 

nesting season, nest/egg sensitivity to temperature, population abundance, and cumulative 

stressors weighted means are greater than or equal to 3.5 (Table C2). The abundance response 

scores as “very high” because geographic extent of nesting, length of nesting season, nest/egg 

sensitivity to temperature, population abundance, and cumulative stressors weighted means are

greater than or equal to 3.5. The phenology response scores as “high” because prey/diet 

specificity, length of nesting season, and population abundance weighted means are greater than 

or equal to 3.0.
C-3

Table C1. Sample exposure scores
C-4

Table C2. Sample sensitivity scores