Methodology for Assessing the Vulnerability 

of Marine Fish and Shellfish Species to a 

Changing Climate

Wendy E. Morrison, Mark W. Nelson, Jennifer F. Howard, Eric J. 

Teeters, Jonathan A. Hare, Roger B. Griffis, James D. Scott, and 

Michael A. Alexander

U.S. Department of Commerce 

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

NOAA Technical Memorandum NMFS-OSF-3 

October 2015 

Methodology for Assessing the Vulnerability of Marine Fish 

and Shellfish Species to a Changing Climate

Wendy E. Morrison1, Mark W. Nelson1, Jennifer F. Howard2, Eric J. Teeters1, 

Jonathan A. Hare3

, Roger B. Griffis4,James D. Scott5,6, and Michael A. Alexander6

1

Earth Resources Technology, Inc. Under contract to NOAA, National Marine Fisheries Service, 

Office of Sustainable Fisheries, 1315 East-West Highway, Silver Spring, MD 20910 

2

Conservation International, 2011 Crystal Drive, Arlington, VA 

3

NOAA, National Marine Fisheries Service, Northeast Fisheries Science Center, 28 Tarzwell 

Dr., Narrangansett, RI 02882 

4

NOAA ,National Marine Fisheries Service, Office of Science and Technology, 1315 East-West 

Highway, Silver Spring, MD 20910 

5

Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, 

216 UCB, Boulder, CO 80309 

6

NOAA Earth System Research Laboratory, 325 Broadway, Boulder, Colorado 80305

NOAA Technical Memorandum NMFS-OSF-3 

October 2015 

U.S. Department of Commerce 

Penny S. Pritzker, Secretary

National Oceanic and Atmospheric Administration

Kathryn D. Sullivan, Administrator 

National Marine Fisheries Service

Eileen Sobeck, Assistant Administrator for Fisheries
ii

Recommended citation: 

Morrison, W.E., M. W. Nelson, J. F. Howard, E. J. Teeters, J. A. Hare, R. B. Griffis, J.D. Scott, 

and M.A. Alexander. 2015. Methodology for Assessing the Vulnerability of Marine Fish and 

Shellfish Species to a Changing Climate. U.S. Dept. of Commer., NOAA. NOAA Technical 

Memorandum NMFS-OSF-3, 48 p. 

Copies of this report may be obtained from: 

Domestic Fisheries Division

Office of Sustainable Fisheries, National Marine Fisheries Service

National Oceanic and Atmospheric Administration

1315 East-West Highway, SSMC3

Silver Spring, MD 20910 

Or online at:

http://www.nmfs.gov/sfa/tech_memo.html
iii

TABLE OF CONTENTS

1.0 INTRODUCTION ........................................................................................................... 1

2.0 DEVELOPMENT OF METHODOLOGY ...................................................................... 2

3.0 VULNERABILITY ASSESSMENT DESIGN ............................................................... 3

3.1 Vulnerability Components ........................................................................................... 3

3.2 Expert Based Scoring ................................................................................................... 4

3.3 Uncertainty and Data Quality ....................................................................................... 5

3.4 Calculating Vulnerability Ranks .................................................................................. 6

4.0 VULNERABILITY ASSESSMENT PROCESS ............................................................ 8

4.1 Scoping and Planning: .................................................................................................. 8

4.2 Assessment Preparation................................................................................................ 9

4.3 Scoring Process .......................................................................................................... 10

4.4 Assessment Results .................................................................................................... 10

4.5 Communication and Application of the Vulnerability Assessment Results .............. 11

5.0 CONCLUSIONS............................................................................................................ 12

ACKNOWLEDGEMENTS ...................................................................................................... 13

REFERENCES ......................................................................................................................... 13

TABLES

Table 1 A summary of the 12 sensitivity attributes, including the goal of the attribute and brief 

descriptions of what would be considered a low and a high score ........................... 19 

Table 2 Data quality scoring guidelines ................................................................................ 20 

Table 3 Logic rules for determining each species’ sensitivity and exposure component scores

 .................................................................................................................................. 21 

Table 4 Potential uses and misuses of assessment results. .................................................... 21 

FIGURES

Figure 1 Four design components for the climate vulnerability assessment .......................... 22 

Figure 2 A species’ vulnerability is based on a combination of its sensitivity and exposure. 

Exposure is determined by the overlap of the species’ current distribution and the 

magnitude of the expected climate change. Twelve sensitivity attributes characterize 

life history characteristics believed to be indicative of how much a species may be 

affected by a changing climate ................................................................................. 23 

Figure 3 The five process steps for the climate vulnerability assessment .............................. 24 

Figure 4 Matrix for determining a species, vulnerability rank based on component scores for 

exposure and sensitivity ........................................................................................... 25 

APPENDIX A - Sensitivity Attribute Definitions .................................................................... 26 

1 

ABSTRACT

Climate change is already affecting fishery resources and the communities that depend on 

them. Climate change and multidecadal variability have been implicated in the shifting 

distributions, abundances, and phenology of fish and shellfish species in many marine 

ecosystems. These impacts are expected to intensify in the future, increasing the need to 

understand which species may be most vulnerable to climate-related environmental change. We 

have developed a vulnerability assessment that uses expert elicitation methods to quantify a 

species’ exposure and sensitivity to expected climate change. Vulnerability, as used here, refers 

to a reduction in a species’ productivity and or abundance associated with a changing climate, 

and includes both climate change and multidecadal climate variability. This methodology uses a 

vulnerability assessment framework, which is applicable across multiple species and provides a 

relative rank of vulnerability to climate change and variability.as well as information about why 

a species may or may not be vulnerable. The results can help fishery managers and researchers 

identify highly vulnerable species and more effectively target research and assessment resources 

on species of highest concern. 

1.0 INTRODUCTION

Changes in global climate patterns are driving changes in ecosystems around the world, 

including marine and coastal oceans. These changes result from both from climate change and 

multidecadal climate variability. Coastal and open oceans are undergoing significant physical 

and chemical changes, including increases in ocean temperature, increases in acidification, 

changes in circulation, decreases in dissolved oxygen, and changes to freshwater inputs (Stock et 

al., 2011; Melillo et al., 2014; Doney et al., 2012; Rhein et al., 2013; Howard et al., 2013). 

Climate variability is also pronounced in ocean systems including the 50-90 year Atlantic 

Multidecadal Oscillation and the 20-30 year Pacific Decadal Oscillation (Mantua and Hare, 

2002, Edwards et al., 2013). Changes in climate can influence the physical and chemical 

conditions of the marine environment on a variety of spatial and temporal scales, which can 

affect vital rates of marine organisms (e.g., growth, reproduction, consumption, and respiration), 

and through time may reduce a species’ ability to survive (O’Connor et al., 2007; Badjeck et al., 

2010; Ottersen et al., 2010; Stock et al., 2011). Climate-related changes—have been implicated

in the shifting abundances and distributions of fish species. These changes are already being 

observed worldwide (Perry et al., 2005; Brierley and Kingsford, 2009; Cheung et al., 2010; 

Hoegh-Guldberg and Bruno, 2010), including several important commercial and recreational fish 

species in the United States (Nye et al., 2009; Mills et al., 2013; Pinksy et al., 2013; Hollowed et 

al., 2012). Climate change will affect fisheries by both the amount of fish being caught and the 

species composition of the catch (Sumaila et al., 2011). 

The impacts of climate on marine resources will not be uniform across species or regions 

(Fulton, 2011). Due to the spatial heterogeneity of climate change and ocean conditions, some 

species will be exposed to greater levels of change than others (Hobday and Pecl, 2014), and 

biological or behavioral characteristics may make a species in a given region more or less 

sensitive to change, while other characteristics could make a species more adaptable (Pecl et al. 

2014, Sunday et al. 2015). Several in-depth investigations regarding climate change impacts on 

various economically important fish species have been completed (Hollowed et al., 2009; Hare 

et al., 2010; Hazen et al., 2012; Plaganyi et al., 2013; Wayte, 2013). These in-depth studies are 
2 

valuable but require considerable investments in time, resources and data collection. It is not 

feasible to conduct this type of extensive analysis for all managed fisheries; in the U.S. alone 

there are more than 230 managed fish stocks. Thus, there is a need to develop a faster way to 

assess the vulnerability of a wide range of fishery resources in a changing climate. While 

vulnerability assessment tools have been well developed for use in conservation and 

management of terrestrial resources, there have been a limited number of vulnerability 

assessments in marine ecosystems (e.g., Chin et al., 2010; Johnson and Welch, 2010; Foden et 

al., 2013; Mathis et al. 2015, Pecl et al. 2014, Stortini et al., 2015). Scientists and managers 

need to be able to identify species vulnerable to decreases in abundance and productivity to 

inform decisions about where to invest resources for more detailed and in-depth analyses. 

Failure to anticipate or prepare for these changes could negatively affect species productivity 

resulting in negative social and economic impacts (Cooley and Doney 2009; Madin et al., 2012; 

Mills et al., 2013). 

We have developed a transparent assessment methodology to determine the relative 

vulnerability of fish stocks to a changing climate that may result in changes in abundance or 

productivity. The assessment includes both anthropogenic climate change as well as natural 

climate variability. Our assessment methodology leverages existing knowledge—both published 

sources and expert opinion—and can be used to assess data-rich as well as data-poor 

species. We designed the assessment methodology to generate transparent, easily understood 

outputs that will provide insight into which species are likely to be the most vulnerable to 

decreases in abundance due to climate change and identify the key drivers behind the 

vulnerability. High vulnerability, in this study, does not imply extinction risk, as a vulnerable 

species could continue to persist, just at a lower biomass level. Scientists, managers, and 

fishermen can use this information as they prepare for and adapt to future conditions. 

 

2.0 DEVELOPMENT OF METHODOLOGY

We organized a working group consisting of expert scientists and managers from across 

the United States to create a vulnerability assessment methodology applicable across tropical, 

temperate, and high latitude marine systems that can address a wide range of fish and shellfish 

life history characteristics. We combined characteristics from three existing vulnerability 

assessments: we modified the logic model from Chin et al. (2010), and we revised the extensive 

list of biological sensitivities from Johnson and Welch (2010), and Pecl et al. (2014). We 

concurred with Pecl et al (2014) that life history attributes should be based on current biological 

characteristics versus predicted changes, as in Johnson and Welch (2010). 

Our methodology assumes that current biological parameters and expected exposure to 

climate change can be used to evaluate the relative vulnerability of a species (Chin et al., 2010; 

Johnson and Welch, 2010; Foden et al., 2013; Pearson 2014; Pecl et al. 2014). Species’ 

responses to climate can be complex, and our ability to predict which species will be able to 

adapt via genetic change is low and is thus not explicitly included. The criteria we include in 

this methodology are specific to life history characteristics of marine fish and shellfish species. 

With the current set of attributes, this methodology is not applicable to marine mammals, seabirds, or sea turtles. However, the general framework of the methodology could be adapted for 

these groups. We note that our analysis of sensitivities at the species (or stock) level will not 
3 

identify ecosystem changes. For example, changes to ecosystem primary productivity will 

indirectly impact species throughout the food web, but this kind of indirect effect is not included 

in the relative ranks provided by this methodology. 

The goal of the vulnerability rank is to identify species that may respond to climate 

change with a decrease in productivity and/or abundance. In addition, understanding and 

predicting species that are likely to shift their distribution is also important for managers, as these 

range shifts can have large impacts on fishing communities and natural resource allocations. 

Pecl et al. (2014) use four life history traits to predict species that are at risk for a shift in 

distribution: larval dispersal, adult or juvenile mobility, physiological tolerance (predicted by 

species range), and the availability of unoccupied habitat. These categories can be supported and 

updated by recent data that show a correlation between three life history traits and range 

expansions in SE Australia: omnivory (i.e. diet generalists), adult mobility and species range

(Sunday et al. 2015). Therefore, a combination of the scores from a subset of our species 

attributes can be used to estimate a species’ ability or potential to shift distributions: high adult 

mobility, high dispersal at early life stages, habitat and temperature generalist. Diet 

generalization could also be included as a predictor. Species that can adapt to climate change 

and have a high potential for distributional shifts will be more likely to receive a lower 

vulnerability score. 

The time-scale involved with fisheries management (next 1-5 years), interannual climate 

variability (1-50+ years), and climate change (often projected changes are for the next 50-100 

years) cancreate a mismatch in managers and scientists ability to act or detect change in the 

system. Climate projections are often given with a 40–100 year time horizon; this time scale is 

needed to detect a climate change signal from climate variability. However, climate has been 

changing and will continue to change into the foreseeable future. Biological or ecological 

responses to these changes are hard to predict and have the potential to occur at any point in 

time. Fishery managers and scientists need to be prepared to anticipate these changes even 

though they are typically making decisions on a much shorter timeframe (1-3 years). 

3.0 VULNERABILITY ASSESSMENT DESIGN

Our vulnerability methodology has four main design features (Figure 1), described 

below. 

3.1 Vulnerability Components 

There is considerable diversity in the design of vulnerability assessments (Glick et al., 

2011); however, nearly all derive vulnerability by considering at least two components: exposure 

and sensitivity. We follow the terminology in Chin et al. (2010) where the sensitivity component

is divided into twelve sensitivity attributes and the exposure component is divided into a number 

of exposure factors (Figure 2). We define climate exposure as the overlap between the species 

distribution and the magnitude of the expected change in climate. Exposure factors (e.g.,

temperature, acidification, rate of ice melt, changes in circulation, decreases in dissolved oxygen 

etc.) considered will differ depending on what climate factors are important to the region of 

interest (see below). These exposure factors could include changes in means or changes in 
4 

variability (i.e. extremes) depending on what is appropriate for the region. The magnitude of 

change can be determined using numerous methods (e.g., climate models, quantitative analyses, 

and qualitative evaluations). 

Our sensitivity attributes are based on the current biological attributes of a species that 

are indicative of their ability/inability to respond to potential environmental changes. 

Some vulnerability assessments also use a third component, adaptive capacity (Chin et 

al., 2010; Johnson and Welch, 2010; Glick et al., 2011), which accounts for biological responses 

that could reduce or mitigate the sensitivity or exposure, and includes dispersal ability, genetic 

diversity, and phenotypic plasticity (Beever et al. 2015). Our methodology follows a similar 

approach to the one described in Williams et al. (2008) that uses a sensitivity component that 

includes the adaptive capacity. We chose this approach after a thorough analysis of possible 

adaptive capacity attributes. Many biological attributes contribute to both sensitivity and 

adaptive capacity, creating methodological difficulties in disentangling sensitivities from 

adaptive capacity. We found that we could express the adaptive capacity attributes in terms of 

sensitivity by inversing the scale (e.g., a species that is a habitat specialist shows high sensitivity 

and/or low adaptive capacity). Analysis of data from a pilot study highlighted how results were 

affected depending on how the biological attributes were distributed into the two components. 

Therefore, combining the biological attributes into a single sensitivity component simplified our 

analysis and increased our confidence in the results. Other studies have had similar concerns 

with adaptive capacity and have either included it within sensitivity as we have, or have not 

included it at all (e.g., Pecl et al., 2014; Gardali et al., 2012). A disadvantage of combining 

adaptive capacity within sensitivity is that the results may not highlight the need for more 

research into adaptive responses or genetic adaptation (Beever et al. 2015). 

With this methodology, the exposure factors included may differ across regions (e.g., 

importance of ice melt), but the sensitivity attributes included should stay consistent as we

developed them to be applicable to most marine fish and shellfish species (e.g., prey specificity 

is consistently used for all species because all animals eat). There should be 8-14 exposure 

factors so that scoring of exposure is comparable to the scoring of the 12 sensitivity attributes. 

We have developed a detailed definition for each sensitivity attribute that includes the rationale 

for including the attribute, a description of the relationship to climate change, and guidance on 

how to score it. A summary of the 12 sensitivity attributes are provided here (Table 1), and full 

attribute definitions are available in Appendix 1. 

3.2 Expert Based Scoring 

Our methodology relies on technical experts using species profiles, scientific literature, 

and general knowledge to provide a score for each species for each sensitivity attribute and for 

each exposure factor. Although these scores are based on well-defined scoring bins, assigning a 

score is still a subjective process. This methodology uses both individual and group expert 

elicitation practices to minimize bias and increase precision of the results (Burgman et al., 2011). 

Assembling the right group of experts for the project can influence the results and buy-in from 

the stakeholders (EPA, 2009). A good expert has technical knowledge of the subject, the ability 

to extrapolate information to new situations, and the capacity to clearly articulate the reasoning 

behind their decisions (EPA, 2009). The number of experts will vary depending on the 
5 

application (time and money, number of available experts, etc.). If a topic is highly 

controversial, care needs to be taken to ensure all viewpoints are represented and there is 

transparency in the selection process (EPA, 2009). Scoring of climate exposure and sensitivity 

attributes can be performed by the same group of experts or by a different group of experts. 

Specific biases are introduced via the use of expert opinion (EPA, 2009). Our methodology was 

created to minimize these biases to the extent possible. Clearly defining the attributes and the 

scoring bins establishes a clear baseline so all experts will interpret the questions similarly, 

which can reduce unnecessary variability in results (see Appendix 1). Each species should be 

scored by multiple experts to reduce individual bias and increase confidence in the results. There 

is no optimum number of experts but the literature suggests a range of four to seven experts is 

appropriate (Linstone and Turoff, 2002; Angus et al., 2003). We recommend having some 

overlap in experts across species so that individual expert biases can be identified and scores 

standardized for comparison. 

Experts assign a score based on four scoring bins (low, moderate, high, very high) for 

each exposure factor or sensitivity attribute assigned to them based on existing information and 

expert judgment. Pecl et al. (2014) found that a 3 bin scale (low, medium, high) was sufficient 

for showing resolution between species. We opted to add a bin for “very high” to pull out those 

most at risk. We recommend that summaries of known life history information be compiled into 

species profiles (Pecl et al. 2014) which can be referenced by experts when assigning scores (see 

Vulnerability Process step 2: Assessment Preparation). Experts should also incorporate their 

expert knowledge on the species. In data-poor situations, the expert may use information on 

other similar species or general ecological principles to provide a score. 

Experts also indicate whether a species is expected to respond positively, negatively, or 

neutrally to the effects of climate change. This “direction of effect score” will provide initial 

feedback to managers on the species that might increase abundance and productivity or expand 

into the region in response to future changes. 

3.3 Uncertainty and Data Quality

Life history traits in marine species are diverse, and our understanding of these traits 

varies across species. For many species, there is little direct knowledge of the life history 

characteristics that contribute to sensitivity attributes. Even when the life history characteristics 

of a species are well known, their complexity can lead to difficulty providing a score for a 

particular attribute. Similarly, future projections of climate change can also have a high 

uncertainty or vary across the range of a species. Our methodology allows experts to account for 

their uncertainty when assigning sensitivity, exposure and direction of effect scores. Experts 

have five “tallies” for each sensitivity attribute and exposure factor, which they distribute among 

the four scoring bins depending on their confidence in the score (Good et al., 2005; Brainard et 

al., 2011). Experts who are certain about a score may place all five tallies in one bin (e.g., all 

five tallies can be placed in the very high bin). Conversely, experts who are unsure about a score 

may spread all five tallies across the relevant bins (for example, they can put two tallies in the 

high bin, and three in the very high bin). Distributing five tallies across four bins forces the 

expert to choose one bin as the most likely. This is a transparent method that clearly shows the 

expert’s uncertainty about each score. In addition, uncertainty across experts can also be 
6 

informative. Experts will also score the direction of effect using a similar tally system. Four 

tallies will be distributed across the three classifications: expected positive, negative or neutral 

response. 

Experts also provide a data quality score for each attribute. This score is based on the 

guidelines shown in Table 2. Understanding the type and quality of information used to score 

the attribute allows end users to identify data gaps and areas for future research. We suggest a 

summary data quality score (e.g. number of attributes with a data quality score > 2.0) that can be 

used to compare available information among species. Together, the data quality score and the 

distribution of the tallies that make up the sensitivity attribute and exposure factor score are 

useful in characterizing the uncertainty in the overall vulnerability. 

3.4 Calculating Vulnerability Ranks 

We use three steps to combine expert tallies into a final vulnerability rank for each 

species. These are: 1) calculating a sensitivity attribute and exposure factor mean, 2) 

determining a sensitivity and exposure component score, and 3) assigning an overall climate 

vulnerability rank. 

First, we calculate the sensitivity attribute and exposure factor means based on the 

distribution of all expert tallies across the four scoring bins. We assign the low, moderate, high 

and very high scoring bins the values of 1, 2, 3, and 4; respectively. The attribute/factor mean is 

calculated as the weighted mean of the number of tallies in each scoring bin and the value of 

each bin (Equation 1). For example, an attribute/factor with 10 tallies in the moderate scoring 

bin and 15 tallies in the high scoring bin would have a mean of 2.6. 

((L * 1) + (M * 2) + (H * 3) + (VH * 4)) / (L + M + H +VH) = Attribute or Factor Mean

where:

L = # of tallies in “low” scoring bin

M = # of tallies in “moderate” scoring bin

H = # of tallies in “high” scoring bin 

VH = # of tallies in “very high” scoring bin (1)

Second, a component score is calculated for sensitivity and exposure based on a logic 

model (i.e. decision rule): results are dependent on the number of attribute/factor means above a 

certain threshold (Table 3). We use a logic model, rather than averages (e.g., Johnson and 

Welch 2010, Patrick et al. 2010) because our attributes and factors are not intended to be 

correlated. Averaging tends to minimize the importance of high scoring sensitivity attributes or 

exposure factors. Our logic model contains strict criteria for receiving a “very high” component 

score: the species must receive a “very high” attribute mean (>3.5) in three or more of the 

individual sensitivity attributes or exposure factors. We created this high threshold to pull out 

those species with multiple risks (i.e., they will experience large changes in multiple 

environmental parameters, or they have specific life history requirements where environmental 

change could impact productivity through multiple mechanisms). The cut-offs for receiving 

“high” and “moderate” component scores follow similar but less strict criteria. A “high” 
7 

component score requires at least two sensitivity attributes or climate factors receive a “high” 

attribute or factor mean (> 3.0). Similarly, a “moderate” component score requires at least two 

sensitivity attributes or climate factors to have an attribute or factor mean > 2.5. Any species 

that does not meet or exceed the criteria for moderate will receive a “low” score. These 

thresholds were developed using data from pilot studies. 

Third, the overall vulnerability rank is determined by multiplying exposure and 

sensitivity. Low, moderate, high and very high component scores are assigned 1, 2, 3, and 4 

respectively. The product is then classified where 1-3 results in a low vulnerability rank, 4-6 a 

moderate vulnerability rank, 8-9 a high vulnerability rank, and 12-16 a very high vulnerability 

rank. Results can be displayed visually using a vulnerability matrix, to show final ranks as well 

as component scores. For the most part, the overall vulnerability rank ends up being the lesser of 

the sensitivity or exposure scores (Chin et al., 2010). The theory is that the species needs to be 

sensitive to a change, as well as be exposed to a change, to be affected. For example, a species 

with a high sensitivity to temperature located in an area where the water temperature is not 

predicted to significantly increase will not be affected. Conversely, if temperature is expected to 

change but the species is not sensitive to temperature it also would not be affected. The 

vulnerability matrix diverges in one case from this simple logic. When one of the two scores is 

“very high”, the overall vulnerability is increased by one rank. This is a precautionary way to 

identify the species that have the potential for unexpected responses due to very high levels of 

exposure or sensitivity. 

To test the robustness of the results to different scoring techniques, we used the expert 

scores collected from the first full implementation of the methodology (see Hare et al., in prep) 

to compare how the final vulnerability ranks would change if the scoring were based on means 

(final vulnerability was calculated as the average of mean sensitivity and mean exposure and 

divided into quartiles for assigning vulnerability rank). Sixty-one percent of the species fell into 

the same final vulnerability rank when using logic and when using means. Species with a few 

very high attribute scores had a higher vulnerability rank using our logic model when compared 

to using averages. This reinforced our decision to use logic because we want to pull out species 

with two to three high sensitivity attributes from those with moderate scores throughout. 

While we believe the scoring rubric we have developed performs well, there are some 

caveats. First, by using a logic model that pulls out situations where a species is vulnerable from 

multiple attributes or factors, it can create a disconnect between attribute scores and component 

scores. For example, a species that experts score as moderate on all sensitivity attributes will 

receive a sensitivity component score of low. Second, the assessment can be sensitive to slight 

changes in scores if there are a limited number of high scoring sensitivity attributes or exposure 

factors. Third, the logic model can create artificial differences between species with very 

similar attribute/factor means. Theoretically, it is possible that a change in one tally on one 

attribute could increase or decrease the overall vulnerability rank one level. We recommend 

using a bootstrap simulation to identify situations where slight changes in expert scores can 

impact the overall vulnerability rank. By resampling the expert tallies (with replacement), the 

bootstrap simulation estimates the uncertainty in the final vulnerability rank, given the experts’ 

scores (Hare et al., in prep.). 
8 

4.0 VULNERABILITY ASSESSMENT PROCESS

The vulnerability assessment process involves five steps (Figure 3): scoping and 

planning, assessment preparations, scoring process, assessment results, and communicating 

results. 

4.1 Scoping and Planning:

Three fundamental decisions need to be made when developing a new assessment: define 

the study area, select species, and determine which climate exposure factors are appropriate to 

include in the assessment. To ensure the assessment meets stakeholder needs, it is important to 

involve managers, scientists, decision-makers, and key stakeholders when developing the scope 

of the assessment.

When defining the study area, choose an area large enough to encompass most of the 

range of the species being studied. Using only a subsection of the range or life-cycle of the 

species can cause key vulnerabilities to be missed (Small-Lorenz et al., 2013). A large marine 

ecosystem (Sherman and Hemple, 2009) is an appropriate scale for many species, but several 

large marine ecosystems may need to be considered for certain species (e.g., highly migratory 

species). 

A number of factors should be considered when selecting species to include in the 

assessment, such as presence in the study area and ecological, cultural, or economic importance. 

The methodology can accommodate species with a wide range of available data. There is an 

economy of scale in developing the materials for the assessment; therefore, it may be beneficial 

to be more inclusive rather than overly selective when choosing species to analyze. Keystone 

species, such as forage fish, should be included as changes in their abundance could have strong 

impacts on other species. 

This methodology can be applied either at the level of species or stock. In some cases, 

stocks of the same species may have different vulnerabilities due to geographic variability in 

exposures and/or biological attributes. For this paper, we have consistently described the 

methodology for species, but implementation at the stock level is encouraged if pertinent stock 

specific biological information exists. Things to consider when deciding to assess vulnerability 

at the stock versus species level include: 1) does the species range extend beyond the boundaries 

of the assessment study area 2) does stock specific information exist, and do life history 

characteristics or exposure differ substantially between stocks, and 3) at what level (stock or 

species) would the information be the most useful to management decisions. 

When deciding which exposure factors should be included in the assessment, the project 

developers should consider: the magnitude of expected changes in the region, the importance of 

the climate factor to the biology of the species, and the confidence in future projections. For 

example, the extent of sea ice may be used in Arctic areas, while the number of coral bleaching 

events may be important in tropical areas (Donner, 2009). Project leaders will also have to 

decide if annual means, seasonal means, and/or variance is the most appropriate measure for 

each climate factor. 
9 

4.2 Assessment Preparation

The vulnerability assessment described in this paper is a “rapid” assessment compared to 

the in-depth single species investigations that have been described elsewhere (Hollowed et al., 

2009; Hare et al., 2010; Hazen et al., 2012; Plaganyi et al., 2013; Wayte 2013). However, this 

method still requires resources, including staff time to prepare for and conduct the assessment. 

A summary of the pertinent life history characteristics is needed to score each species and 

should be compiled in a document prior to the assessment. These “species profiles” do not need 

to be exhaustive, but should provide information on the characteristics needed to score each 

attribute. If information is not available for a species, data on a related species or data from 

outside the study area may be used with a lesser degree of certainty. Developing species profiles 

is a time consuming step (approximately 4 hours per species; J. Hare, pers. comm.) in the 

assessment process; however, pilot testing showed that species profiles are critical to the scoring 

process. These species profiles can use the scientific literature as well as the numerous summary 

documents generated as part of the scientific advice and fisheries management process (e.g., 

stock assessments, essential fish habitat documents, environmental impact statements). 

Similar to a species profile, information about predicted climate change must be 

compiled prior to implementing this vulnerability methodology. Exposure factors can be based 

either on qualitative descriptions or quantitative outputs from climate models. In part to support 

the methodology reported here, NOAA’s Earth System Research Laboratory (ESRL) developed

an online tool to provide the climate projections needed to score exposure factors

(http://www.esrl.noaa.gov/psd/ipcc/). This tool can provide climate projection outputs from a 

number of climate models for a range of time frames (including seasonal variations) and for a 

range of factors. It is envisioned that this website could provide most of the climate exposure 

information needed in an implementation of this methodology. However, if downscaled models 

exist, they should be used as marine organisms have a stronger response to local vs global 

change (Pinsky et al. 2013). When possible, ensemble averages should be used, as they are 

often more accurate at matching past conditions than any one model (Stock et al., 2011). Choose 

models that perform best in hindcasting recent conditions in the study area of the assessment. It 

is also important to consider the timeframe of the climate projections. If the timeframe is too 

long, (e.g., 100 years), the results are less relevant for current management decisions. If the 

timeframe is too short, (e.g., 10 years), the results will be dominated by interannual variability

and not long-term change. We suggest using model averaged outputs for the past 50 years for 

determining current conditions and the next 50 years for the future condition. 

We recommend that when data are available, the scoring bins for the exposure factors use 

a measure of the magnitude of change as a function of past variability. This is because change 

relative to natural fluctuations can be more important than absolute change (Richardson et al., 

2010, Tewksbury et al. 2008). For example, in terrestrial ectotherms, tropical species have 

adapted to low variability in temperature and many live near their thermal maximum, while 

temperate species with a wider thermal tolerance have a larger ability to adapt (Tewkesbury et al. 

2008). We recommend the bin for a very high exposure factor represent projected means that are 

at or exceed historic extremes (see example in Hare et al., in prep). We note that there may be 

some cases where changes in extremes may be a more suitable measure of change; for example, 
10

changes in temperature variability could be used to predict changes in extreme events (Hare et 

al., in prep). 

Some projected climate changes expected to affect fish species (e.g. changes in 

circulation) are not captured by climate models in a quantifiable way due to model resolution 

issues. For these exposure factors, qualitative descriptions of the expected changes can be used

(Hare et al., in prep), but they should provide as much spatial information as possible (e.g. any 

expected latitudinal or inshore/offshore gradients). 

Knowledge of a species’ distribution is needed to determine the area over which to 

evaluate the exposure factors. Distributional maps of species are available from a variety of 

sources including existing literature, survey databases or public online databases such as 

FishBase (www.fishbase.org) or the Ocean Biogeographic Information System (www.iobis.org). 

If possible, maps that overlay the current species distribution on top of the expected climate 

change would allow quick quantification of exposure scores. If the magnitude of exposure varies 

across the range of the species, the experts scoring exposure can spread their five tallies 

appropriately. 

4.3 Scoring Process

We recommend the expert scoring process be split into two rounds: a preliminary round 

of individual scoring and a final round that includes a group discussion and the option to change 

their score. The preliminary stage ensures each expert has an equal opportunity to provide an 

initial score that is not influenced by other opinions (Ariely, 2009). During the final round the 

experts compare and discuss their scores in a workshop setting. 

Experts have the chance to explain the rationale behind their scores and can change their 

scores based on new information provided during the workshop (for a similar approach, see 

Patrick and Damon-Randall, 2008; McDaniels et al., 2010). This process is not intended to 

garner consensus among the group; rather, it helps identify and fix errors, reduce individual bias, 

encourage buy-in from the experts, and increases the precision of the final scores. Experts may 

or may not choose to change their scores based on what they heard, and final results do not 

identify which tallies came from which expert, allowing some anonymity. 

4.4 Assessment Results 

The assessment’s outputs include an overall relative vulnerability rank for each species, 

information on the key sensitivity attributes and exposure factors that contributed to that rank, 

details on major data gaps, and a rank for the potential for shifts in distribution. The bootstrap 

analysis (see vulnerability assessment design component on calculating vulnerability ranks) 

describes the uncertainty associated with each ranking and shows where small changes in expert 

scores could result in a change in the overall vulnerability rank. Additional analyses such as 

jackknife analyses (systematically removing scores from one attribute, factor, or expert one at a 

time and re-running the analysis) can show the influence of each of these components on the 

final result. These subsequent analyses are very important, as they provide information 

regarding uncertainty in the final vulnerability rank, guidance as to data gaps and future research 
11

needs, and information about the importance of individual biological attributes and exposure 

factors. 

4.5 Communication and Application of the Vulnerability Assessment Results

It is important to communicate results from the assessment in a way that maximizes the 

usefulness to the end users and limits misinterpretation (Table 4). The vulnerability matrix 

(Figure 4) is an effective tool for displaying the relative vulnerability of the species. Species 

with high sensitivity and high exposure are clearly identified in the matrix and should be the 

initial focus for both scientists and managers. Species that have high sensitivity scores and low 

exposure scores can have high latent vulnerability (sensu Foden et al. 2013). These species may 

not be vulnerable at this time but there is the potential for the species to be impacted if actual 

climate change differs substantially from predicted climate change. Species with high exposure 

and low sensitivity are potential persisters (sensu Foden et al. 2013). These species tend to have 

life history traits with high plasticity that allow them to adapt to environmental changes and 

possibly exploit newly created or vacated niches. Although, species with low sensitivity and low 

exposure may not be of the highest concern, there could still be key climate impacts on these

species. In addition, there could be additional stressors (e.g., overharvest, pollution, and habitat 

loss) affecting the productivity and abundance of these species. 

Although the vulnerability matrix provides a good overview of the results, species 

specific vulnerability narratives are a critical communication tool. The vulnerability narratives 

should not only identify the sensitivity attributes and exposure factors driving the score but 

should also explore the interplay between these factors and where important data gaps exist. 

Certain combinations of attributes, when found together, may either mitigate or exacerbate each 

other. For example, species experiencing high exposure changes in ocean acidification may not 

be impacted if their sensitivity to ocean acidification is low. 

These vulnerability narratives add important context to the species’ vulnerability rank 

and help managers and scientists identify specific areas for further analysis and potential 

management actions. Scientists can use the results to identify stocks that can benefit from 

additional research such as examining potential adaptive responses, developing mechanistic 

models, or incorporating environmental variability into stock assessments. Scientists can also 

use the results in combination with other information, such as social and economic importance, 

to identify research priorities (from data gaps) and species that could benefit from increased 

monitoring (and at what life stage). By identifying the key drivers for these species, scientists 

can investigate the development of mechanistic vulnerability models. 

In the face of a changing climate, fisheries management can focus on two overarching 

strategies: increasing the resilience of the stock or system to predicted changes, or increasing the 

adaptability of the fishery or system. Managers can look at existing fishery management plans to 

determine if modifications are needed in light of climate change. There is debate over the 

definition of resilience as it relates to climate change. For the purposes of this paper, resilience 

is defined as the capacity of an ecosystem to absorb recurrent disturbances or shocks and 

reorganize or adapt to change while retaining essentially the same function and structure (Walker

et al. 2004; McClanahan et al. 2012); and thus includes both resistance to change and recovery 
12

back to the original form. The species specific vulnerability narratives can provide context for 

the results and help managers identify species-specific attributes that make a particular species 

more or less resilient to climate change. For example, long-lived species tend to have one of two 

main life-history strategies: consistent low reproductive output (e.g., elasmobranchs) or high 

recruitment variability (e.g., Sebastes species) where the species withstands periods of poor 

environmental conditions by surviving until favorable conditions produce a strong year class—

often called the storage effect (Chesson, 1984). The latter life history strategy could become less 

favorable with climate change, as the conditions required for successful recruitment may become 

less frequent or disappear altogether. Both these life-history strategies may benefit from 

management options that protect age class structure (Stevens et al., 2000; Field and Francis, 

2002) such as marine protected areas, reverse slot limits, or area closures based on distribution of 

older adults. 

The second overarching fisheries management strategy is to increase the adaptability of 

the fishery or fishing community. Adaptation includes pro-active changes to the social or 

ecological systems that will improve their ability to adjust as environmental changes affect the 

system. “Adaptation strategies and actions can range from short-term coping to longer-term, 

deeper transformations, aim to meet more than climate change goals alone, and may or may not 

succeed in moderating harm or exploiting beneficial opportunities” (Moser and Eckstrom 2010). 

Management regimes that increase flexibility may increase adaptability of the fishermen or 

community when changes occur. Flexibility in fisheries management can be separated into three 

hierarchical levels: management within fisheries, management between fisheries, and 

management across jurisdictions. Flexibility to adapt across all these levels will be needed as 

species abundances and distributions change through time. For example, species with specific 

environmental requirements for the egg, larval, and settlement stages could be more susceptible 

to match-mismatch dynamics (including prey, predators, favorable transport, etc.), where 

necessary resources are not available at the correct time (Kristiansen et al., 2011; Peck et al., 

2012). Increased monitoring may allow scientists to identify when a recruitment failure occurs. 

The management framework for these species should be tailored to quickly respond to the 

recruitment failure as decreasing the lag time between changes in recruitment and management 

response decreases the probability for population collapse (Brown et al., 2012). Alternatively, to 

increase the adaptability of the fishing community, the information on species-specific 

vulnerabilities from this assessment can be combined with information about a community’s 

dependence on fishing (e.g. Jacob and Jepson 2009) to determine which communities may be 

more vulnerable. Vulnerable communities may require alternative management measures that 

allow fishermen to switch target species as the abundances change, or to increase the availability 

of alternative livelihoods that are not dependent on fishing. 

5.0 CONCLUSIONS

Global climate change is affecting the physical, chemical, and biological characteristics 

of the world’s oceans. These impacts are expected to increase in the future with continued 

changes in the planet’s climate system. To effectively prepare for and respond to these impacts, 

the fisheries management community needs information on the potential impacts of climate 

change on important fisheries resources. Fishermen also need to know the impacts of climate 

change on fish species when making decisions (e.g., whether to buy or sell a permit or 
13

transferable quota). The methodology described here applies across multiple species and 

provides a relative rank of vulnerability to climate change as well as information about why a 

species may or may not be vulnerable to it. However, this methodology is not the only step 

necessary to prepare for climate change impacts on living marine resources. Rather, it is 

designed to be an early-step in the conversation about how to manage fisheries resources with

changing climate and ocean conditions, and help guide the science needed to design and 

implement effective management actions. A better understanding of the vulnerability of fish and 

shellfish species will help fisheries scientists and managers identify and implement actions that 

increase the resiliency of managed fisheries.

ACKNOWLEDGEMENTS

A great many people contributed to this work. We would like to thank our expert 

working group that helped get this project off the ground: B. Arnold, R. Brainard, F. Bowers, J. 

Brodziak, M. Clark, T. Curtis, Y. deReynier, K. Gore, R. Hart, J. Lindsay, M. Pentony, J. 

Phinney, P. Spencer, N. Tolmeri, and B. Weidoff. The many participants of the pilot tests 

conducted in the Northeast and Caribbean also contributed a good deal of input to the project. 

We appreciate the constructive feedback from M. Stachura, G. Tromble and K. Abrams on 

earlier drafts of this paper. B. Young and K. Goodin from NatureServe provided great advice 

and lessons learned from terrestrial systems. We would also like to thank H. Jabalpurwala, K. 

Butala, and D. Bento for developing the web enabled scoring database. Finally, thanks to W. 

Patrick for his sage advice throughout this project. This project was funded by NMFS Office of 

Sustainable Fisheries and Office of Science and Technology. The views expressed herein are the 

author's and do not necessarily reflect the views of NOAA or any of its sub-agencies.

REFERENCES

Angus A. J., Hodge I. D., McNally S., Sutton M. A. 2003. The setting of standards for 

agricultural nitrogen emissions: A case study of the Delphi technique. Journal of 

Environmental Management. 69:323–337. 

Ariely, D. 2009. Predictably Irrational. Harper Collins Publishers, New York, NY. 349 pp. 

Badjeck M., Allison E. A., Halls A. S., Dulvey N. K. 2010. Impacts of climate variability and 

change on fishery-based livelihoods. Marine Policy. 34:375-383. 

Beever E., O'Leary J., Mengelt C., West J., Julius S., Green N. et al. 2015. Improving 

conservation outcomes with a new paradigm for understanding species’ fundamental and 

realized adaptive capacity. Conservation Letters. doi: 10.1111/conl.12190

Brainard R. E., Birkeland C., Eakin C. M., McElhany P., Miller M. W., Patterson, M., and 

Piniak, G. A. 2011. Status review report of 82 candidate coral species petitioned under 

the U.S. Endangered Species Act. U.S. Department of Commerce, NOAA Technical 

Memorandum, NOAA-TM-NMFS-PIFSC-27. 530 pp. 

Brierley A. S. and Kingsford M. J. 2009. Impacts of climate change on marine organisms and 

ecosystems. Current Biology. 19(14):R602-R614. 
14

Brown C. J., Fulton E. A., Possingham H. P., and Richardson A. J. 2012. How long can fisheries 

management delay action in response to ecosystem and climate change? Ecological 

Applications. 22(1): 298-310. 

Burgman M. A., McBride M., Ashton R., Speirs-Bridge A., Flander L., Wintle, B., et al. 2011. 

Expert status and performance. PLoS ONE. 6(7):e22998. 

Chesson P. L. 1984. The storage effect in stochastic population models. Mathematical Ecology 

Lecture Notes in Biomathematics. 54: 76-89. 

Cheung W. W. L., Lam V. W. Y., Sarmiento J. L., Kearney K., Watson R., Zeller D., Pauly, D. 

2010. Large-scale redistribution of maximum fisheries catch potential in the global ocean 

under climate change. Global Change Biology. 16(1):24-35. 

Chin A., Kyne P. M., Walker T. I., McAuley R. B. 2010. An integrated risk assessment for 

climate change: analyzing the vulnerability of sharks and rays on Australia’s Great 

Barrier Reef. Global Change Biology. 16(7):1936-1953. 

Cooley S. R. and Doney, S. C. 2009. Anticipating ocean acidification’s economic consequences 

for commercial fisheries. Environmental Research Letters. 4(2):024007. 

Doney S. C., Ruckelshaus M. H., Duffy J. E., Barry J. P., Chan F., English C., et al. 2012. 

Climate change impacts on marine ecosystems. Annual Review of Marine Science. 

4(1):11-37. 

Donner S. D. 2009. Coping with commitment: Projected thermal stress on coral reefs under

different future scenarios. PLoS ONE. 4(6):e5712. 

Edwards M., Beaugrand G., Helaouët P., Alheit J., Coombs S. 2013. Marine ecosystem response 

to the Atlantic Multidecadal Oscillation. PLoS ONE. 8(2):e57212. 

Environmental Protection Agency (EPA). 2009. Expert Elicitation Task Force White Paper. 

External Review Draft, January 2009. 

Field J. C. and Francis R. C. 2002. Cooperating with the environment: Case studies of climate 

and fisheries in the Northern California current. In: McGinn N. editor. Fisheries in a 

changing climate. Bethesda, MD: American Fisheries Society. 32:245-260. 

Foden W. B., Butchart S. H. M., Stuart S. N., Vie J., Akcakaya H. R., Angulo A., et. al. 2013. 

Identifying the World’s most climate change vulnerable species: A systematic trait-based 

assessment of all birds, amphibians and corals. PLoS ONE. 8(6):e65427. 

Fulton E. A. 2011. Interesting times: winners, losers, and system shifts under climate change 

around Australia. ICES Journal of Marine Science. 68(6):1329-1342. 
15

Gardali T., Seavy N. E., DiGaudio R. T., Comrack L. A. 2012. A climate change vulnerability 

assessment of California’s at-risk birds. PLoS ONE. 7(3):e29507. 

Glick P., Stein B. A., Edelson N. A. editors. 2011. Scanning the Conservation Horizon: A Guide 

to Climate Change Vulnerability Assessment. National Wildlife federation, Washington 

DC. 168 pp. 

Good T. P., Waples R. S., Adams P. editors. 2005. Updated status of federally listed ESUs of 

West Coast salmon and steelhead. U.S. Department of Commerce, NOAA Technical 

Memorandum, NMFS-NWFSC-66. 598 pp. 

Hare J., Alexander M., Fogarty M., Williams E., Scott J. 2010. Forecasting the dynamics of a 

coastal fishery species using a coupled climate-population model. Ecological 

Applications. 20(2):452-464. 

Hare J., Morrison W., Nelson M. W., Stachura M., Griffis R., Alexander M., et al. In Prep. 

Northeast Fisheries Climate Vulnerability Assessment (NEVA): an application of the 

NMFS Climate Vulnerability Protocol. 

Hazen E. L., Jorgensen S., Rykaczewski R. R., Bograd S. J., Foley D. G., Jonsen I. D. et al.

2012. Predicting habitat shifts of Pacific top predators in a changing climate. Nature 

Climate Change. 3(3):234-238. 

Hobday A. J. and Pecl G. T. 2014. Identification of global marine hotspots: sentinels for change 

and vanguards for adaptation action. Reviews in Fish Biology and Fisheries. 24(2):415-

425. 

Hoegh-Guldberg O. and Bruno J. F. 2010. The impact of climate change on the world’s marine 

ecosystems. Science. 328(5985):1523-1528. 

Hollowed A., Bond N., Wilderbuer T., Stockhausen W., Amar Z., Beamish R. et al. 2009. A 

framework for modelling fish and shellfish responses to future climate change. ICES 

Journal of Marine Science. 66:1584-1594. 

Hollowed A. B., Barbeaux S. J., Cokelot E. D., Farley E., Kotwicki S., Ressler P. H., et al. 2012. 

Effects of climate variations on pelagic ocean habitats and their role in structuring forage 

fish distributions in the Bering Sea. Deep-Sea Research Part II. 65-70:230-250. 

Howard J., Babij E, Griffis R., Helmuth B., Himes-Cornell A., Niemier P. et al. 2013. Oceans 

and marine resources in a changing climate. Oceanography and Marine Biology: An 

Annual Review. 51:71-192 

Jacob S. and Jepson M. 2009. Creating a community context for the fishery stock sustainability 

index. Fisheries. 34:228-231. 
16

Johnson J. E. and Welch, D. J. 2010. Marine fisheries management in a changing climate: A 

review of vulnerability and future options. Reviews in Fisheries Science. 18(1):106-124. 

Kristiansen T., Drinkwater K., Lough R., Sundby, S. 2011. Recruitment variability in North 

Atlantic cod and match-mismatch dynamics. PLoS ONE. 6(3):e17456. 

Linstone H. A. and Turoff, M. editors. 2002. The Delphi method: Techniques and applications. 

<http://www.is.njit.edu/pubs/delphibook/>. Accessed July 2, 2014. 

Madin E. M. P., Ban N. C., Doubleday Z. A., Holmes T. H., Pecl G. T., Smith F. 2012. Socieconomic and management implications of range-shifting species in marine systems. 

Global Environmental Change. 22(1):137-146. 

Mantua N. J. and Hare S. R. 2002. The Pacific decadal oscillation. Journal of Oceanography, 

58(1):35-44. 

Mathis J. T., Cooley S. R., Lucey N., Colt S., Ekstrom J., Hurst T. et. al. 2015. Ocean 

acidification risk assessment for Alaska’s fishery sector. Progress in Oceanography.

136:71-91.

McClanahan T. R., Donner S. D., Maynard J. A., MacNeil M. A., Graham N. A. J., Maina J., et 

al. 2012. Prioritizing key resilience indicators to support coral reef management in a 

changing climate. PLoS ONE. 7(8):e42884. 

McDaniels T., Wilmot S., Healey M., Hinch S. 2010. Vulnerability of Fraser River sockeye 

salmon to climate change: A life cycle perspective using expert judgments. Journal of 

Environmental Management. 91(12):2771-2780. 

Melillo J. M., Richmond T., Yohe G. W., Eds. 2014: Climate change impacts in the United 

States: The third national climate assessment. U.S. Global Change Research Program. 

841 pp. doi:10.7930/J0Z31WJ2. 

Mills K. E., Pershing A. J., Brown C. J., Chen Y., Chiang F. S., Holland D. S. et al. 2013. 

Fisheries management in a changing climate: Lessons from the 2012 ocean heat wave in 

the Northwest Atlantic. Oceanography. 26(2):60-64. 

Moser S. C. and Ekstrom J. A. 2010. A framework to diagnose barriers to climate change 

adaptation. Proceeding of the National Academy of Sciences. 107(51):22026-22031. 

Nye J. A., Link J. S., Hare J. A., Overholtz W. J. 2009. Changing spatial distribution of fish 

stocks in relation to climate and population size on the Northeast United States 

continental shelf. Marine Ecology Progress Series. 393:111-129. 

O’Connor M. I., Bruno J. F., Gaines S. D., Halpern B. S., Lester S. E. 2007. Temperature control 

of larval dispersal and the implications for marine ecology, evolution, and conservation. 

Proceedings of the National Academy of Sciences. 104(4):1266-1271.
17

Ottersen G., Kim S., Huse G., Polovina J. J., Stenseth N. C. 2010. Major pathways by which 

climate may force marine fish populations. Journal of Marine Systems. 79(3-4):343-360. 

Patrick W. S. and Damon-Randall K. 2008. Using a five-factored structured decision analysis to 

evaluate the extinction risk of Atlantic Sturgeon (Acipenser oxyrinchus oxyrinchus). 

Biological Conservation. 141(11):2906-2911. 

Patrick W. S., Spencer P., Link J., Cope J., Field J., Kobayashi D. et al. 2010. Using productivity 

and susceptibility indices to assess the vulnerability of United States fish stocks to 

overfishing. Fisheries Bulletin. 108:305-322. 

Pearson R.G., Stanton J.C., Shoemaker K.T., Aiello-Lammens M.E., Ersts P.J., Horning N.

2014. Life history and spatial traits predict extinction risk due to climate change. Nature 

Climate Change. 4(3):217-221. 

Peck M., Huebert K., Llopiz J. 2012. Intrinsic and extrinsic factors driving match-mismatch 

dynamics during the early life history of marine fishes. Advances in Ecological Research. 

47:177-302. 

Pecl G., Ward T., Doubleday Z., Clarke S., Day J., Dixon C. et al. 2014. Rapid assessment of 

fisheries species sensitivity to climate change. Climate Change 127(3-4):505-520. 

Perry A. L., Low P. J., Ellis J. R., Reynolds J. D. 2005. Climate change and distribution shifts in 

marine fishes. Science. 308(5730):1912-1915. 

Pinsky M., Worm B., Fogarty M., Sarmiento J., Levin S. 2013. Marine taxa track local climate 

velocities. Science. 341:1239-1242. 

Plagányi É. E., Skewes T. D., Dowling N. A., Haddon M. 2013. Risk management tools for 

sustainable fisheries management under changing climate: a sea cucumber example. 

Climate Change. 119(1):181-197. 

Rhein M., Rintoul S.R., Aoki S., Campos E., Chambers D., Feely R.A., et al. 2013. 

Observations: Ocean. In: Climate Change 2013: The Physical Science Basis. 

Contribution of Working Group I to the Fifth Assessment Report of the 

Intergovernmental Panel on Climate Change. Edited by Stocker, T.F., Qin, D., Plattner, 

G. K., Tignor, M., Allen, S.K., Boschung, J., et al. Cambridge University Press, 

Cambridge, and New York. 

Richardson A. J., Hobday A. J., Poloczanska E. S. 2010. A short critique of vulnerability 

assessments for climate change and marine ecosystems. Report to the Department of 

Climate Change and Energy Efficiency, Canberra, Australia. May 2010. 29 pp. 

Sherman K. and Hempel, G. editors. 2009. The UNEP large marine ecosystem Report: A 

perspective on changing conditions in LMEs of the world’s regional seas. UNEP 
18

Regional Seas Report and Studies No. 182. United Nations Environmental Programme. 

Nairobi, Kenya. 

Small-Lorenz S. L., Culp L. A., Ryder T. B., Will T. C., Marra P. P. 2013. A blind spot in 

climate change vulnerability assessments. Nature Climate Change. 3(2):91-93. 

Stevens J. D., Bonfil R., Dulvy N. K., Walker P. A. 2000. The effects of fishing on sharks, rays 

and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES 

Journal of Marine Science. 57:476-494. 

Stock C. A., Alexander M. A., Bond N. A., Brander K. M, Cheung W. W. L., Curchitser E. N. et 

al. 2011. On the use of IPCC-class models to assess the impact of climate on living 

marine resources. Progress in Oceanography. 88(1-4):1-27. 

Stortini C. H., Shackell N.L., Tyedmers P., Beazley K. 2015. Assessing marine species 

vulnerability to projected warming on the Scotian Shelf, Canada. ICES Journal of Marine 

Science. 72(6):1731-1743. 

Sumaila U. R., Cheung W. W. L., Lam V. W. Y., Pauly D., Herrick S. 2011. Climate change 

impacts on the biophysics and economics of world fisheries. Nature Climate Change 

1(9):449-456. 

Sunday J., Pecl G., Frusher S., Hobday A., Hill N., Holbrook N., et al. 2015. Species traits and 

climate velocity explain geographic range shifts in an ocean-warming hotspot. Ecology 

Letters. 18:944–953. 

Tewksbury J. J., Huey R. B., Deutsch C. A. 2008. Putting the heat on tropical animals. Science. 

320(5881):1296-1297. 

Walker B., Holling C. S., Carpenter S. R., Kinzig A. 2004. Resilience, adaptability and 

transformability in social-ecological systems. Ecology and Society. 9(2):5. 

Wayte S.E. 2013. Management implications of including a climate-induced recruitment shift in 

the stock assessment for jackass morwond (Nemadactylusmacropterus) in south-eastern 

Australia. Fisheries Research. 142:47-55. 

Williams S., Shoo L., Isaac J., Hoffman A., Langham, G. 2008. Towards an integrated 

framework for assessing the vulnerability of species to climate change. PLoS Biology, 

6(12):e325 
19

Table 1. A summary of the 12 sensitivity attributes, including the goal of the attribute and brief 

descriptions of what would be considered a low and a high score. Detailed attribute 

definitions can be found in Appendix 1. 

Attribute Goal Low Score High Score

Stock 

Size/Status

To determine if the stock's 

resilience is compromised due to 

low abundance

High abundance Low abundance

Other Stressors To account for other factors that 

could limit population responses 

to climate change

Low levels of other stressors High levels of other 

stressors

Population 

Growth Rate

Estimate the productivity of a 

stock

High productivity Low productivity

Complexity in

Reproductive 

Strategy

Identify reproductive strategy that 

may be disrupted by climate 

change

Low complexity High complexity

Spawning Cycle Identify spawning strategies that 

are more sensitive to changes

Year round spawners Short duration aggregate 

spawners

Early Life 

History Survival 

and Settlement 

Requirements

Determine the relative importance 

of early life history requirements 

for a stock

Larval requirements are 

relatively resistant to 

environmental change

Larval requirements are 

specific and likely to be 

impacted by environmental 

change

Sensitivity to 

Ocean 

Acidification

Determine the stock's relationship 

to "sensitive taxa"

Is not a sensitive taxa or rely 

on a sensitive taxa for food 

or shelter

Stock is a sensitive taxa

Habitat 

Specificity

Determine the relative 

dependence a stock has on habitat 

and the abundance of the habitat

Habitat generalist with 

abundant habitat available

Habitat specialist on a 

limited habitat type

Prey Specificity Determine is the stock is a prey 

generalist or a prey specialist

Prey generalist Prey Specialist

Sensitivity to 

Temperature

Known temperature of occurrence 

or distribution as a proxy for 

sensitivity to temperature

Species found in wide 

temperature range or has a 

distribution across wide 

latitudinal range and depths

Species found in limited 

temperature range or has a 

limited distribution across 

latitude and depths

Adult Mobility Determine the ability of the stock 

to move if their current location 

becomes unsuitable

Highly mobile adults Sessile adults

Dispersal of 

Early Life 

Stages

Estimate the ability of the stock 

to colonize new habitats

High dispersal Low dispersal
20

Table 2. Data quality scoring guidelines. 

Data 

Quality 

Score

Description

3 Adequate Data. The score is based on data 

which have been observed, modeled or 

empirically measured for the species in 

question and comes from a reputable source.

2 Limited Data. The score is based on data 

which has a higher degree of uncertainty. The 

data used to score the attribute may be based 

on related or similar species, come from 

outside the study area, or the reliability of the 

source may be limited.

1 Expert Judgment. The attribute score 

reflects the expert judgment of the reviewer 

and is based on their general knowledge of the 

species, or other related species, and their 

relative role in the ecosystem.

0 No Data. No information to base an attribute 

score on. Very little is known about the 

species or related species and there is no basis 

for forming an expert opinion.
21

Table 3. Logic rules for determining each species’ sensitivity and exposure component scores. 

Component 

Score Scoring Criteria

Very High 3 or more mean attribute or factor scores ≥ 3.5

High 2 or more mean attribute or factor scores ≥ 3.0

Moderate 2 or more mean attribute or factor scores ≥ 2.5

Low Less than 2 or more mean attribute or factor scores ≥ 2.5

 

Table 4. Potential uses and misuses of assessment results. Appropriate uses 

Inform stakeholders as to the relative vulnerability of species. 

Identify important climate exposure factors and sensitivity attributes.

Inform data gaps and contribute to setting research priorities. 

Identify species where mechanistic models are needed.

Suggest species that could benefit from management strategy evaluations.

Potential 

misuses 

Vulnerability rank does not indicate magnitude of effects, and therefore, results do 

not suggest appropriate catch levels or harvest control rules. 

Vulnerability rank does not replace need for mechanistic models. 

Results are region specific and may not extrapolate to regions outside of the 

designated study area
22

Figure 1. Four design components for the climate vulnerability assessment. 
23

Figure 2. A species’ vulnerability is based on a combination of its sensitivity and exposure. 

Exposure is determined by the overlap of the species’ current distribution and the 

magnitude of the expected climate change (exposure factors used for the first full 

implementation (Hare et al., in prep) are listed). Twelve sensitivity attributes 

characterize life history characteristics believed to be indicative of how much a species 

may be affected by a changing climate. 
24

Figure 3. The five process steps for the climate vulnerability assessment.
25

Figure 4. Matrix for determining a species, vulnerability rank based on component scores for 

exposure and sensitivity. Component scores are given a value of 1-4 (in brackets). 

Vulnerability rank is determined by multiplying the two component scores (in 

parenthesis). 
26

APPENDIX A – Sensitivity Attribute Definitions and Bins

TABLE OF CONTENTS

Habitat Specificity ........................................................................................................................27 

Prey Specificity .............................................................................................................................28 

Adult Mobility ..............................................................................................................................29 

Dispersal of Early Life Stages .....................................................................................................30 

Early Life History Survival and Settlement Requirements .....................................................31 

Complexity in Reproductive Strategy .......................................................................................33 

Spawning Cycle ............................................................................................................................35 

Sensitivity to Temperature ..........................................................................................................36 

Sensitivity to Ocean Acidification...............................................................................................39 

Population Growth Rate..............................................................................................................41 

Stock Size/Status ..........................................................................................................................42 

Other Stressors .............................................................................................................................43 

References .....................................................................................................................................44 
27

Habitat Specificity

Goal: To determine, on a relative scale, if the stock is a habitat generalist or a habitat specialist 

while incorporating information on the type and abundance of key habitats. 

Relationship to climate change: Generalists stocks should be more resilient to changing 

resource availability (habitat and food) than specialists (Wilson et al. 2008, Clavel et al. 2011, 

Graham et al. 2011, Pecl, 2014). This is because specialists are dependent on not only their own 

response to climate change, but also the impact on their habitat (EPA 2009). Note: the type and 

distribution of these habitats should be considered for this attribute. 

Background: Changes in climate are expected to alter marine and coastal habitats that fish 

stocks depend upon. Species that are habitat generalists (can utilize several different habitat 

types) are expected to be more likely to succeed in a changing environment (Wilson et al. 2008, 

Clavel et al. 2011). The more a species specializes on a specific habitat, the more likely the 

species will be impacted by an environmental change. However, not all habitats are expected to 

be impacted equally. Species that depend on habitats that are abundant and wide ranging are less 

likely to be impacted by changes than species that depend on habitats that are limited in scope. 

We expect habitats that are created by disturbances (e.g., coral rubble or edge habitats) to 

increase with climate change. In addition, biological habitats (i.e., live coral reefs, deep water 

corals, mangroves, salt marshes, sea grass beds) are more likely to be impacted by the changes 

than physical habitats (sand, mud, rocky bottom). When considered together, these three criteria 

(habitat specialist or generalist; whether or not the stock depends on biological habitats; and 

habitat availability) are indicative of how a stock will be impacted by climate-induced changes 

on habitat. 

How to use expert opinion: This attribute will be scored using a combination of the three 

criteria described above: habitat specialist or generalist; whether or not the stock depends on 

biological habitats (i.e., live coral reefs, deep water corals, mangroves, salt marshes, sea grass 

beds); and habitat availability (limited vs. abundant). It is understood that these criteria are not 

dichotomous but are a continuum. Stocks that are dependent on “disturbed” habitats should do 

fine or increase with climate change, so put these species in the “low” bin. If you think that a 

stock fits in multiple scoring bins, weight your 5 tallies between the appropriate bins. Using 

your expert opinion, account for any lifespan or ontogenetic shifts in diet; however, limit your 

response to the juvenile and adult life stages as larvae are considered under the attribute “early 

life history survival and settlement requirements.” 

 

Habitat Specificity Bins:

1. Low: The stock is a habitat generalist and/or utilizes very common abiotic habitats. 

Occurrences of the stock have been documented in diverse habitats. Also, included in 

this bin are stocks that are restricted to one abiotic habitat which is widespread and 

common (e.g., vast stretches of sandy bottom, or pelagic waters over a large range).

2. Moderate: The stock strongly prefers a particular habitat. The stock prefers a 

particular habitat, but can survive in other habitats (with possible impacts to their fitness). 

3. High: The stock is a specialist on an abundant biological habitat. The stock is a 

specialist that is restricted to a specific, but common biological habitat.

4. Very High: The stock is a specialist on a restricted biological habitat. The stock is a 

specialist that is restricted to a specific and uncommon biological habitat.
28

Prey Specificity

Goal: To determine, on a relative scale, if the stock is a prey generalist or a prey specialist.

Relationship to climate change: Generalists stocks should be more resilient to changing 

resource availability (habitat and food) than specialists (Wilson et al. 2008, Clavel et al. 2011, 

Graham et al. 2011, Pecl et al. 2014). Understanding how reliant a stock is on specific prey 

species could predict its ability to persist as the climate changes. Specialists (who have specific 

prey requirements) are likely to be more vulnerable to climate change because their persistence is 

dependent on not only their own response to climate change, but also the response of their prey. 

During mass extinction events of the past, diet specialists were more prone to extinction than diet 

generalists (Clavel et al. 2011). 

Background: Climate change impacts extend beyond the stock in question to include species 

within its food web (e.g., prey, predators and competitors). 

How to use expert opinion: The scoring bins below estimate the stocks’ relative distribution 

along a continuum that runs between prey specialists and prey generalists. Using your expert 

opinion, account for any lifespan or ontogenetic shifts in diet; however, limit your response to 

the juvenile and adult life stages as larvae are considered under the attribute “early life history 

survival and settlement requirements.” For this attribute, prey type refers to groups of similar 

species; copepods, krill, forage fish, etc., for example, are each categorized as a prey type. 

Prey Specificity Bins:

1. Low: The stock eats a large variety of prey. The stock can eat a variety of prey types 

depending on what is available. Include detritivores, herbivores, and omnivores in this 

bin. 

2. Moderate: The stock eats a limited number of prey types. The stock can feed on a 

wide variety of prey species, but are restricted to a limited number (~3) of prey types 

(copepods, krill, forage fish, etc.).

3. High: The stock is partial to a single prey type. The stock’s diet is composed of one 

main prey type. The stock is able to switch to a different prey type if the preferred food is 

unavailable, but this may negatively impact fitness. 

4. Very High: The stock is a specialist. The stock is dependent on one prey type and is 

unable to switch to alternatives if the preferred prey is unavailable. 
29

Adult Mobility

Goal: To estimate the ability of the stock to move to a new location if their current location 

changes and is no longer favorable for growth and/or survival. 

Relationship to climate change: Site-dependent species that are unable to move to better 

habitat when a location becomes unfavorable are less able to adapt to environmental change than 

highly mobile species (Foden et al. 2013). 

Background: As climate change occurs, habitats that were once suitable may change and no 

longer be able to sustain a given stock of fish. Similarly, what was once unsuitable habitat may 

become suitable. A stock can survive changes in habitat as long as they have the ability to 

disperse from unsuitable habitat and find new, suitable habitat; and dispersal ability can be used 

as a proxy for the capacity to change distribution (Pecl et al. 2014). This can occur through 

larval dispersal and settlement (covered under the “Dispersal of Early Life Stages” attribute) or 

through adult mobility. Species can be limited in their mobility by physical or behavioral (e.g., 

won’t swim across open ocean) barriers. 

How to use expert opinion: This attribute represents a continuum from sessile to highly 

migratory organisms. Use your expert opinion to place the stock in question in the appropriate 

bin according to its physical and behavioral ability to move. Homing behavior for spawning 

should not be considered here as it is accounted for in the “Complexity in Reproductive 

Strategy” attribute. For this attribute, we define site-dependent stocks as those whose adults are 

site-attached (i.e., spend their entire adult phase in one limited location). 

Adult Mobility Bins: 

1. Low: Non-site dependent. The stock is highly mobile and non-site dependent. 

2. Moderate: Site dependent but highly mobile. The stock has site-dependent adults 

capable of moving from one site to another if necessary.

3. High: Site dependent with limited mobility. The stock has site-dependent adults that 

are restricted in their movement by environmental or behavioral barriers.

4. Very High: Non-mobile. The stock has sessile adults.
30

Dispersal of Early Life Stages 

Goal: To estimate the ability of the stock to colonize new habitats when/if their current habitat 

becomes less suitable.

Relationship to climate change: In general, the greater the dispersal of larvae, the better its 

ability to respond to climate change. Wide distribution of eggs and larvae can lead to greater 

ability to colonize new habitats in areas that are suitable for survival. Conversely, if a stock has 

limited larval distribution and the habitat in the localized area becomes unsuitable, then the stock 

is more likely to be negatively affected.

Background: For marine species, extended larval dispersal is an important strategy for 

colonizing new areas. Duration of the larval stage may impact dispersal distance and stock 

persistence. Jablonski and Lutz (1983) found that marine invertebrates with relatively long 

planktonic larval stages were more persistent in the fossil record than those species with nonplanktonic larvae and had lower extinction rates. Early life stage dispersal is affected by a 

number of factors including spawning, advection, diffusion, larval behavior, planktonic duration, 

planktonic survival, and settlement habitat (Pineda et al. 2007; Hare and Richardson 2014). In 

general, studies have found that spawning time and place and planktonic duration are key factors, 

but the other factors can be important in specific situations. 

How to use expert opinion: The main point of this attribute is to estimate dispersal ability. If 

no information is known about actual dispersal distances, capacity for larval dispersal can be 

estimated by a stock’s larval duration (hatching to settlement in benthic species and hatching to 

yolk-sac re-absorption in pelagic species) (Pecl et al. 2014). However, if information about 

actual dispersal distances are known, use that information. If a stock has a relatively short larval 

duration, but is known to disperse large distances, or if the larvae are able to influence dispersal 

through selective tidal stream transport, adjust your tallies accordingly. Keep in mind that longdistance dispersal of only a small fraction of the larvae could still be adequate for colonization of 

new areas in a changing climate. We note that since elasmobranchs have evolved life history 

strategies that produce a smaller number of well-developed offspring, the impact of this attribute 

will be reduced: 1) for elasmobranchs with live birth, dispersal will occur while in utero and 

should be scored as low to moderate, 2) for elasmobranchs with egg cases, egg dispersal will be 

more limited, but juveniles will have the ability to disperse if needed so these stocks should be 

scored as moderate to high. 

Dispersal of Early Life Stages Bins

Larval durations utilized in Bins are adapted from Pecl et al. (2014); distances are provided on a 

log-scale to show general/large changes in magnitude.

1. Low: Highly dispersed eggs and larvae. Duration of planktonic eggs and larvae 

greater than 8 weeks and/or larvae are dispersed >100 km from spawning locations. 

2. Moderate: Moderately dispersed eggs and larvae. Duration of planktonic eggs and 

larvae less than 8 but greater than 2 weeks and/or larvae are dispersed 10-100 km from 

spawning locations. 

3. High: Low larval dispersal. Duration of planktonic eggs and larvae less than 2 weeks 

and/or larvae typically found over the same location as parents. 

4. Very High: Minimal larval dispersal. Benthic eggs and larvae or little to no 

planktonic early life stages.
31

Early Life History Survival and Settlement Requirements

Goal: To determine the relative importance of early life history requirements for a stock. 

Relationship to climate change: In general, the early life stages (eggs and larvae) of marine 

fish are characterized by high mortality rates, via predation, starvation, advection, or unsuitable 

conditions. Small changes in the environment can lead to large changes in early life survival, 

which can affect recruitment and year-class strength. Large scale climate change could have a 

greater impact on species that have more specific early life history and settlement requirements.

Background: Close to 100 years ago, fisheries scientists recognized the importance of 

recruitment variability in fish populations (Hjort 1914). Despite considerable research devoted 

to fisheries recruitment, there is still considerable uncertainty about how environmental 

variability impacts recruitment (Punt et al. 2013). Scientists now understand that multiple 

processes are important during the egg and larval stages (Houde 2008). Conditions that can lead 

to decreased or negligible recruitment include: 

• Larvae that are dependent on specific biological conditions in the water column during 

their larval stage. For example, if the larvae are dependent on the presence of food at a 

specific point in development, different emergence of the larvae and the food (due to 

dependence on different cues) could result in a mismatch in availability. Alternatively, if 

the larvae have evolved to survive in low predator (and low food) conditions, a change in 

predation pressure could impact survival (Bakun 2010). 

• Larvae or eggs that are dependent on specific physical conditions to survive (e.g., specific 

temperature requirements for eggs, temporary gyres that provide food and retention for 

larvae, calm conditions that allow for concentration of larval prey, specific transport 

pathways to nursery habitats, etc.) (Houde 2008). 

• Larvae that are dependent on a cue for settlement or metamorphosis that could be 

impacted by a changing climate (Pecl et al. 2014). 

For the purpose of this assessment, early life history requirements include the environmental 

conditions necessary for larval survival, and encompass the eggs, pelagic larvae stages, and 

settlement. The more specific the early life history requirements, the more precise the 

environmental conditions may need to be, and thus the more vulnerable the stock may be in a 

changing environment. Note: some fish species, namely elasmobranchs, have evolved life 

history traits which minimize or eliminate early life stages either by birthing well-developed 

young or by laying egg cases that allows embryos to fully develop before hatching. Therefore, 

elasmobranchs should be ranked as “Low.” 

How to use expert opinion: Marine species are largely dependent on both physical and 

biological conditions during their larval stage. However, the reliance on specific conditions 

varies between stocks. For the bins below, recruitment can be characterized as low variability 

when there is relatively constant recruitment events every 1-2 years, and high variability when 

the stock experiences highly episodic recruitment events (Pecl et al. 2014). If no citable 

reference is available on a stock’s early life history survival and settlement, the score may be 

based on expert opinion. 
32

Early Life History Survival and Settlement Bins:

1. Low: Larval requirements are minimal. Stock has general requirements for the larval 

stage that are relatively resilient to environmental change. Elasmobranchs should be 

ranked as “Low.” 

2. Moderate: Larval requirements are minimal or unknown. Stock requirements are 

not well understood and recruitment is relatively constant, suggesting limited 

environmental influence. 

3. High: Larvae have some specific requirements. Stock requirements are not well 

understood, but recruitment is highly variable and appears to have a strong dependence 

on environmental conditions. 

4. Very High: Larvae have multiple specific requirements. Stock has specific known 

biological and physical requirements for larval survival.
33

Complexity in Reproductive Strategy 

Goal: To determine how complex the stock’s reproductive strategy is and how dependent 

reproductive success is on specific environmental conditions. 

Relationship to climate change: Species that have complex reproductive strategies (that 

require a series of events or special conditions) are more likely have these conditions disrupted 

by changes in the environment. 

Background: There is great diversity in reproductive strategies in marine fishes. The more 

complex the reproductive strategy, the more precise the conditions may need to be, and thus the 

more vulnerable the stock may be to environmental change. For our purposes, complexity in 

reproductive strategy is defined as reproductive behaviors, characteristics or cues that create 

specific requirements that must be met in order for reproduction to be successful. Species with 

reproductive events that are dependent on temperature (vs. day-length) cues will be more 

sensitive to climate change (Pecl et al. 2014). 

How to use expert opinion: A list of common reproductive characteristics that may affect the 

reproductive capacity of a stock in a changing climate is provided below. To score, determine if 

any of these examples apply to the stock. Note: this is not intended to be an exhaustive list. If 

other characteristics exist that may affect a stock’s reproduction capacity in a changing climate, 

incorporate that information and adjust your score appropriately. 

Example reproductive characteristics that create “complexity”:

• The stock has known temperature effects on reproduction. Examples include 

temperature-dependent sex changes, and temperature cues that impact spawning, gonad 

development, etc. 

• The stock uses large spawning aggregations. Large spawning aggregations can 

contribute to a high sensitivity because a large number of individuals must get to the 

spawning area simultaneously (i.e., migration or cues to migrate may be impeded by a 

change in the environment), the spawning area has to retain the environmental conditions 

that made it successful in the past, and the reproductive success for that year is dependent 

on the conditions present at one time period. 

• The stock experiences decreased recruitment per spawner, or a weakening in the strength 

of density dependence, at low stock sizes, potentially because of depensation/Allee 

effects. If unknown, does the stock share life history characteristics that would predict 

depensation effects (e.g., significant changes in the relative abundance of the stock’s 

predators/prey at low stock densities, decreased fertilization success at low stock sizes)?

• The reproductive success of the stock requires the use of vulnerable habitats (freshwater, 

estuaries, mangroves, salt marshes, corals) for spawning or rearing of young. Vulnerable 

habitats are likely to experience larger climate change impacts (such as changes in 

salinity, dissolved oxygen, pollution, sedimentation, or water depth), and stocks that 

require these habitats for successful reproduction will likely be impacted. 
34

Complexity in Reproductive Strategy Scoring Bins:

If a particular characteristic is suspected to have a large impact on the stock, adjust the score 

appropriately. 

1. Low: Simple reproductive strategy. The stock contains no more than one 

characteristic that suggest complexity in reproductive strategy.

2. Moderate: Slight complexity. The stock has two characteristics that suggest 

complexity in reproductive strategy. 

3. High: Complex reproductive strategy. The stock has three characteristics that suggest 

complexity in reproductive strategy. 

4. Very High: Very complex reproductive strategy. The stock has four or more 

characteristics that suggest complexity in reproductive strategy. 
35

Spawning Cycle

Goal: To determine if the duration of the spawning cycle for the stock could limit the ability of 

the stock to successfully reproduce if necessary conditions are disrupted by climate change. 

Relationship to climate change: It is assumed that stocks that spawn throughout the year will 

be more likely to be successful in a changing environment: “Protracted spawning is believed to 

enhance offspring survival by allowing the stock to “hedge its bet” against adverse 

environmental conditions” (Marteinsdottir and Thorarinsson 1998). Conversely, stocks that 

spawn all at once in major events are more likely to experience recruitment failure with potential 

changes in environmental conditions. 

Background: Spawning characteristics describe the spawning activity of a stock (in aggregate, 

not individually) over a particular time frame. If a stock spawns several times per year across a 

variety of seasons, then they will likely be less susceptible to climate change because their 

reproductive events are not dependent on just one set of very specific conditions (e.g., 

phenological events). Increased spawning events, also help to protect against vulnerabilities 

associated with single spawning aggregations (see the “Complexity in Reproductive Strategy” 

attribute). Similarly, stocks that reproduce seasonally are also less likely to adapt to climate 

change as they are dependent on environmental conditions historically present during a given 

season that may not persist through time. For example, spring-like conditions and related 

activities have occurred progressively earlier since the 1960s (Walther et al. 2002) and changes 

in spawning season and location have already been observed and predicted to continue (Shoji et 

al. 2011; Rijnsdorp et al. 2009). Note: We are describing the spawning activity of the entire 

stock, not the individual. In other words, we are interested in the time from when spawning 

commences until when it ends, not how long a single individual spawns. 

How to use expert opinion: It is impossible to distill every potential spawning cycle into 4 

scoring bins. The below bins are rough breaks in a continuum of possibilities. If a species does 

not fit the below bins, use your expert judgment to best score the species based on the above 

discussion. For stocks (such as elasmobranchs) that are born as fully developed juveniles capable 

of long distance movements, there is less concern over a short hatching/mating period, and these 

stocks should be ranked low to moderate. 

Spawning Characteristics Bins: 

1. Low: Consistent throughout the year. Stocks that spawn continuously throughout the 

year without a defined “spawning season” are less likely to suffer spawning failure. 

Example: a stock that spawns daily or monthly. 

2. Moderate: Several spawning events throughout the year. Stocks that spawn several 

times per year and spawn across more than one season have a moderate likelihood of 

spawning success to be impacted by climate change. Example: a stock that spawns in 

both the spring and summer. 

3. High: Several spawning events per year within a confined time frame. Stocks that 

may spawn several times per year but all spawning events in that year take place in one 

season have a higher likelihood of being affected by climate change. Example: the 

spawning season occurs once a year and lasts over a period of less than 3 months. 

4. Very High: One spawning event per year. Stocks that require very specific 

environmental/social cues to initiate spawning and that only spawn once per year have the 

highest likelihood of being affected by climate change. Example: the spawning season 

occurs once a year over a brief period of time.
36

Sensitivity to Temperature

Goal: To use information regarding temperature of occurrence or the distribution of the species 

as a proxy for its sensitivity to temperature. 

Relationship to climate change: Species that experience a wide range of temperature regimes 

are more likely to persist in a warming ocean. 

Background: A species temperature requirements can be a good predictor of how it will 

respond to climate change. For species that lack specifics on temperature requirements, the 

latitudinal coverage of the species can be a proxy for temperature tolerance (Pecl et al. 2014). 

Since species can cover a wide tropical latitude but still have a limited temperature tolerance, 

distribution of a species within or across provinces can be used instead. Spalding et al. (2007) 

(Figure A1) divides coastal waters of the world into 62 provinces and 232 ecoregions. Even 

though Spalding’s provinces are not specifically based on temperature (they also consider 

upwelling, currents, salinity, nutrients, etc.), they can be used to delineate areas with similar 

thermal conditions. 

In addition, a species’ distribution in the water column and seasonal movements can indicate its 

sensitivity to temperature. Species that make large diurnal migrations across the thermocline 

have lower sensitivities to changing temperatures than species that have limited depth 

distributions. Additionally, species that make large seasonal migrations and track seasonally 

changing water temperatures may have more sensitivity to temperature than indicated by range 

alone.

How to use expert opinion: Use known temperature requirements to score this attribute when 

available. When temperature information is not known, use the species distribution, along with 

Figure A1 to determine if a species is found across >1 province. Also use knowledge of seasonal 

and diurnal movements to adjust the tallies. Keep in mind that you can adjust your tallies 

depending on the distribution of the species relative to the area of interest (i.e., if the area of 

interest is at the edge of the distribution of the species, consider if the species is expected to 

move out of or expand into the area of interest). Spalding et al. (2007) only characterize coastal 

environments; therefore, use your expert opinion for open ocean species. If information about 

temperature requirements or depth distributions is available, use this to modify your response. 

For example, if a species is found across 2 provinces, but it has a limited depth distribution, the 

expert could distribute the 5 tallies between bins 2 and 3. If a species’ sensitivity changes with 

ontogeny, consider the most limited stage when determining the most appropriate bin(s). Given 

that a stock range will always be less than a species range, if scoring temperature dependence for 

a stock, consider not only the stock range, but also the species range as the species range may 

predict the stock’s ability to adapt. Consideration of the species distribution relative to the study 

area is also important. Stocks at the cold edge of the species range would be expected to fare 

well, while stocks at the warm edge of its species range may not (Planque and Fredou 1999, 

Drinkwater 2005). 
37

Temperature Sensitivity Bins: 

1. Low: Large temperature range. Species occurs in a wide range of temperatures 

(>15o

C), or is found across 3 or more provinces. 

2. Moderate: Moderate temperature range. Species occurs in a moderately wide range 

of temperatures (10-15oC), or is found across 2 provinces. 

3. High: Somewhat limited temperature range. Species occurs in a moderately narrow 

range of temperatures (5-10oC), or is found within one province but has a variable depth 

distribution. 

4. Very High: Very limited temperature range. Species occurs in a narrow range of 

temperatures (<5oC), or is found within one province and has a limited depth distribution 

(i.e., depth range is <100 m).
38

Figure A1. Originally published in Spalding et al. 2007. Provinces are provided in color with ecoregions outlined.
39

Sensitivity to Ocean Acidification

Goal: To estimate a stock’s sensitivity to ocean acidification (OA) based on its relationship with 

“sensitive taxa.”

Relationship to climate change: Impacts of OA on marine organisms can be highly variable, 

with considerable variability between taxa and species. Therefore, we are estimating impact of 

OA by examining the dependence of the stock on sensitive taxa. For example, current research 

shows a consistent negative impact of OA on mollusks, corals, calcified algae and echinoderms 

(Kroeker et al. 2013), so species in these classes or dependent on species in these classes should 

be considered more sensitive to changes in ocean pH. We expect the volume of research into 

ocean acidification to increase in the near future, so this attribute will be updated as new 

information becomes available. 

Background: Ocean acidification is often called “the other carbon dioxide problem,” and is the 

term given to the chemical changes in the ocean as a result of carbon dioxide emissions (Wicks 

and Roberts 2012). While initial research suggested that the majority of species that have 

calcium carbonate or chitin shells or those that lay down calcium carbonate skeletons (corals) 

will be negatively impacted by ocean acidification (Arnold et al. 2009; Hoegh-Guldberg et al. 

2007; Honisch et al. 2012; Kawaguchi et al. 2011; Orr et al. 2005), recent studies have 

highlighted a high variability in response between different shelled organisms and suggest that 

not all shelled species will be impacted to the same degree and not all impacts will be negative. 

(i.e., Ries et al. 2009; Kroeker et al. 2013). For example, Kroeker et al. (2013) in a metaanalysis of 228 studies found significant and consistent negative impacts of OA on the larval 

stages of mollusks and corals. However, recent research suggests soft corals may not be as 

sensitive as stony corals (Gabay et al. 2014) In contrast, high variability in the responses of 

crustaceans suggests impacts may be species specific within this group, with brachyuran 

crustaceans showing a higher resistance (Kroeker et al. 2013). 

The direct effect of ocean acidification on finfish is not well understood. Recent research 

suggests impacts on finfish stocks will be most prevalent at the egg and early larval stages 

(Baumann et al. 2011; Franke and Clemmenssen 2011; Frommel et al. 2011), but juvenile and 

adult olfaction and behavior may also be affected (Munday et al. 2009; 2014). Despite these 

studies, not enough is known to be able to predict which finfish stocks will be more sensitive. 

This attribute will be updated when more information is available on which finfish stocks are 

more likely to be directly impacted by ocean acidification.

How to use expert opinion: Use current information on a species’ reliance on sensitive taxa 

(e.g. corals, mollusks, echinoderms, or calcified algae; see Kroeker et al. 2013) to bin species. 

When scoring, base your score on the most sensitive life stage, if appropriate. In cases where 

research has shown that the effects of OA may be positive or mitigated by biological processes 

(e.g., reduced OA by plant absorption of CO2), use your expert judgment to inform the score. 

We have binned sensitive taxa which are directly impacted by changes in OA as “very high” and 

those dependent on sensitive taxa as “high” due to the indirect impact. However, use your expert 

opinion to place your tallies between these groups depending on your perception of the species’ 

adaptability. 
40

Sensitivity to Ocean Acidification Bins:

Sensitive taxa are taxa that consistently show negative effects from OA, such as hard corals, 

mollusks, calcified algae, and echinoderms (Kroeker et al. 2013). 

1. Low: Stock either does not use sensitive taxa, or is expected to respond positively to 

ocean acidification. The stock does not utilize sensitive taxa for food or habitat. Species 

expected to respond positively to ocean acidification should be scored as low. 

2. Moderate: Stock is somewhat reliant on sensitive taxa. The stock utilizes sensitive 

taxa as either food or habitat, but can switch to non-sensitive taxa when necessary. This 

can include omnivores and species that prefer coral habitats but can utilize any rigid 

structure. 

3. High: Stock is reliant on sensitive taxa. The stock is dependent on sensitive taxa for 

either food or habitat (i.e., cannot switch to a non-sensitive alternative). 

4. Very High: Stock is a sensitive taxa. The stock is a sensitive taxa (such as hard corals, 

mollusks, calcified algae, and echinoderms) that have been shown to have a consistent 

negative impact of OA on survival. 
41

Population Growth Rate 

Goal: To estimate the relative productivity of the stock. 

Relationship to climate change: More productive stocks are, in general, more resilient to long 

term changes in the environment, such as climate change (Lande 1993; Pecl et al. 2014). 

Background: 

Productivity is a measure of the capacity of the stock to reproduce and recover if the population 

is reduced. In general, it is thought that highly productive stocks are more resilient to change 

because they are quicker to respond to impacts, such as fishing, or catastrophic events (Lande 

1993; Pecl et al. 2014). In fisheries, productivity can be measured as the maximum intrinsic rate 

of increase (rmax). We are interested in the maximum intrinsic rate of increase as it describes 

how fast a population is able to recover from a disturbance. Given density dependence, the 

classic model of population growth can be given by: ∂∂ ∂∂ ⁄ = rmmm (1 − N

K), where K is 

carrying capacity and for which population growth rate is maximized at 0.5K. 

If a direct measurement of the maximum intrinsic rate of increase (rmax) is unavailable, other 

biological reference points that are correlated with population growth rate can be used: von 

Bertalanffy growth rate (k), age at maturity, maximum age, natural mortality and maximum 

length (Patrick et al. 2010; Hutchings et al. 2012). Scoring bins for these proxies were 

developed from an analysis of 141 marine fish species that were considered to be representative 

of U.S. fisheries (Patrick et al. 2010). 

How to use expert opinion: Multiple proxies may be used to inform the final score, but the 

accuracy and precision of the different proxies should be considered. For example, a stock with a 

“good” estimate of age at maturity is in the range for a “High” score, and a “fair” estimate of 

maximum age is in the range for the “High” scoring bin. In that case, the scorer should use their 

expert opinion to weight their response according to their confidence in the estimates. If no 

estimates are available, estimate a relative score for the stock across a continuum of r-selected 

(low) vs. k-selected (high) species. 

Population Growth Rate Bins:

Parameter Low Moderate High Very High

Maximum growth rate 

(rmax) > 0.50 0.16 - 0.50 0.05 - 0.15 < 0.05 

von Bertalanffy K > 0.25 0.16 - 0.25 0.11 - 0.15 <= 0.10

Age at maturity < 2 yrs 2 - 3 yrs 4 - 5 yrs > 5 yrs

Maximum age < 10 yrs 11 - 15 yrs 15 - 25 yrs > 25 yrs

Natural mortality (M) > 0.50 0.31 - 0.50 0.21 - 0.30 < 0.2

Maximum length < 55 cm 55 – 85 cm 85 – 150 cm > 150cm
42

Stock Size/Status

Goal: To estimate stock status to clarify how much stress from fishing the stock is experiencing 

and to determine if the stock’s resilience or adaptive capacity are compromised due to low 

abundance. 

Relationship to climate change: It is assumed that a stock that has a large biomass is more 

resilient to changes in climate. Conversely, stocks with very low biomass are likely to be in a 

compromised ecological position and therefore may have a diminished capability to respond to 

climate change (Rose 2004). The genetic diversity, as well as the abundance, of a stock can 

impact its susceptibility. The assumption is that species with a limited genetic diversity could be 

more negatively impacted by climate change as their offspring would be less variable and thus 

less likely to have the combination of genes needed to adapt to changes in the environment. 

Note: stocks that are at historical high biomass levels may be an indication of a net positive 

effect to an environmental change. 

Background: Fish stocks that are already being affected by other stressors are likely to have 

faster and more acute reactions to climate change. Fishing is the largest stressor currently 

impacting fish stocks (Jackson et al. 2001), and the magnitude of the stress can be estimated 

through the status of the stock. Stock size/status can be measured as a ratio of the current stock 

size (B) over the biomass at maximum sustainable yield (BMSY) and is a commonly used 

biological reference point for U.S. federally managed stocks. For other areas, Bmax may be 

available and can also be used. Use the following link for information on current estimates of 

B/BMSY in U.S. species: http://www.nmfs.noaa.gov/sfa/statusoffisheries/SOSmain.htm. 

Low genetic variation can decrease a species’ ability to adapt to climate change. Large variation 

in reproductive success between individuals, large fluctuations in population size, and frequent 

local extinctions can all decrease genetic diversity (Grosberg and Cunningham 2001). Presence 

of these characteristics could suggest a decreased ability to adapt to changes in the environment. 

Beyond stock status and genetic diversity, there are additional concerns for stocks that are 

particularly rare. The IUCN classifies stocks with a population <10,000 mature individuals as 

vulnerable (IUCN 2015). Therefore, for the purposes of this attribute, stocks with population 

sizes less than 10,000 individuals are considered to have significantly reduced ability to adapt to 

climate change and should be scored as “Very High.” 

How to use expert opinion: If a direct measure of biomass is not available, biomass proxies 

(such as survey indices or spawning stock biomass) may be used. For data-poor stocks with an 

unknown status, or stocks that are analyzed as part of a species group, use your expert opinion to 

estimate the stock size and rate the data quality accordingly. We note that BMSY can change 

(NEFSC 2012), which will affect B/BMSY ratio and thus vulnerability scores. In situations where 

BMSY has been recently updated, use your expert opinion to adjust your scores appropriately. 

Also, if a stock has known low genetic diversity, adjust your ranks accordingly.

Stock Size/Status Bins:

1. Low: B/BMSY ≥ 1.2 (or proxy)

2. Moderate: B/BMSY ≥ 0.8 but < 1.2 (or proxy) 

3. High: B/BMSY ≥ 0.5 but < 0.8 (or proxy)

4. Very High: B/BMSY < 0.5 (or any stock below <10,000 mature individuals) 
43

Other Stressors

Goal: To account for conditions that could increase the stress on a stock and thus decrease its 

ability to respond to changes.

Relationship to climate change: In most cases but not all, climate change is predicted to 

exacerbate the effects of other stressors. Fish stocks that are already being affected by other 

stressors are likely to have faster and more acute reactions to climate change. 

Background: Scientists theorize that species experiencing additional stressors are more likely to 

have faster and more acute reactions to climate change (Stein et al. 2013, Sumaila et al. 2011). A 

stress is an activity that induces an adverse effect and therefore degrades the condition and 

viability of a natural system (Groves et al. 2000; EPA 2008). This attribute attempts to take into 

account interactions between climate change and other stressors already impacting fish stocks. 

Some examples of other stressors include: habitat degradation, invasive species, disease, 

pollution, and hypoxia. Although climate change is not currently the biggest threat to many 

natural systems, its effects are projected to be an increasingly important source of stress in the 

future (Mooney et al. 2009). Consideration of observed and projected impacts of climate change 

in the context of other environmental stressors is essential for effective planning and 

management (Tingley et al. 2014). 

How to use expert opinion: For the purpose of this assessment, we are looking for detrimental 

impacts from other stressors. We have provided examples of other stressors that may be 

impacting stocks, but the list is not exhaustive. If the stock being scored is suffering from a 

known or suspected stressor that is not listed below, adjust the score appropriately. The 

magnitude of the stressors should also be considered. If a single stressor is suspected of a large 

impact on the stock, adjust the score appropriately. It is expected that in some cases, impacts of 

climate change could create positive impacts (e.g., reduction in predators). If you suspect 

positive impacts, adjust tallies toward the lower bins as appropriate. We are not including 

fishing pressure as a stressor here as it is covered under the “stock size/status” attribute.

Example of stressors the stock may be experiencing: 

• The habitat on which the stock depends is degraded. Examples include anthropogenic 

effects or changes to freshwater input, stratification, storm intensity, and hypoxia. 

• The stock is currently exposed to detrimental levels of pollution (chemical and/or 

nutrient). 

• The stock has experienced a known increase in parasites, disease, or harmful algal bloom 

exposure. 

• The stock has experienced a detrimental impact due to a change in the food web. 

Examples include increases in the abundance of predators or competitors, or the 

introduction of an invasive species that negatively impacts the stock. Do not include 

changes to prey here as they are covered under the “prey specificity” attribute.
44

Other Stressors Bins:

If a single stressor is suspected of a large impact on the stock, adjust the score appropriately. 

1. Low: Stock is experiencing no known stress other than fishing. Stock is 

experiencing no more than one known stressor. 

2. Moderate: Stock is experiencing limited stress other than fishing. Stock is 

experiencing no more than two known stressors. 

3. High: Stock is experiencing moderate stress other than fishing. Stock is 

experiencing no more than three known stressors.

4. Very High: Stock is experiencing high stress other than fishing. Stock is 

experiencing four or more known stressors. 

References

Arnold K., Findlay H., Spicer J., Daniels C., Boothroyd D. 2009. Effect of CO2 related 

acidification on aspects of the larval development of the European lobster, Homarus 

gammarus. Biogeosciences. 6:1747-1754. 

Bakun, A. 2010. Linking climate to population variability in marine ecosystems characterized by 

non-simple dynamics: Conceptual templates and schematic constructs. Journal of Marine 

Systems. 79:361-373. doi: 10.1016/j.jmarsys.2008.12.008. 

Baumann H., Talmage S., Gobler C. 2011. Reduced early life growth and survival in a fish in 

direct response to increased carbon dioxide. Nature Climate Change. 2:38-41. doi: 

10.1038/nclimate1291. 

Clavel J., Julliard R., Devictor V. 2011. Worldwide decline of specialist species: toward a global 

functional homogenization? Frontiers in Ecology and the Environment. 9:222-228. doi: 

10.1890/080216. 

 

Drinkwater K. 2005. The response of Atlantic cod (Gadus morhua) to future climate change. 

ICES Journal of Marine Science. 62:1327-1337. doi: 10.1016/j.icesjms.2005.05.015. 

Environmental Protection Agency (EPA). 2008. U.S. EPA's 2008 Report on the Environment 

(Final Report). EPA/600/R-07/045F (NTIS PB2008-112484). Washington, DC. 

Environmental Protection Agency (EPA). 2009. A framework for categorizing the relative 

vulnerability of threatened and endangered species to climate change. National Center for 

Environmental Assessment. EPA/600/R-09/011. Washington, DC. 

Foden W. B., Butchart S. H. M., Stuart S. N., Vie J., Akcakaya H. R., Angulo A., et. al. 2013. 

Identifying the World’s most climate change vulnerable species: A systematic trait-based 

assessment of all birds, amphibians and corals. PLoS ONE. 8(6):e65427. 
45

Franke A., Clemmesen C. 2011. Effect of ocean acidification on early life stages of Atlantic 

herring (Clupea harengus L.). Biogeosciences. 8:3697–3707. doi: 10.5194/bg-8-3697-

2011. 

Frommel A., Maneja R., Lowe D., Malzahn A., Geffen A., Folkvord A., et al. 2011. Severe 

tissue damage in Atlantic cod larvae under increasing ocean acidification. Nature Climate 

Change. 2:42-46. doi: 10.1038/nclimate1324. 

Gabay Y., Fine M., Barkay Z., Benayahu Y. 2014. Octocoral tissue provides protection from 

declining oceanic pH. PLoS ONE 9:e91553. doi: 10.1371/journal.pone.0091553. 

Graham, N., Chabanet, P., Evans, R., Jennings, S., Letourneur, Y., MacNeil, M. A., et al. 2011. 

Extinction vulnerability of coral reef fishes. Ecology Letters. 14:341-348. doi: 

10.1111/j.1461-0248.2011.01592.x. 

Grosberg R., Cunningham C. 2001. Genetic structure in the sea: From populations to 

communities. In: Bertness M, Gaines S, Hay M. Marine community ecology. Sinauer 

Associates, Sunderland, Mass. 

Groves C., Valutis L., Vosick D., Neely B., Wheaton K., Touval J., et al. 2000. Designing a 

geography of hope: A practitioner’s hand book for ecoregional conservation planning 2nd 

edition. 2000. The Nature Conservancy, Arlington VA. 

Hare J, Richardson D. 2014. The use of early life stages in stock identification studies. In: Cadrin 

S., Kerr L. editors. Stock identification methods. Academic Press. London. 

Hjort J. 1914. Fluctuations in the great fisheries of northern Europe viewed in the light of 

biological research. Rapports et Proces-verbaux des Reunions. Conseil International pour 

l’Exploration de la Mer. 20: 1-228. 

Hoegh-Guldberg O., Mumby P., Hooten A., Steneck R., Greenfield P., Gomez E., et al. 2007. 

Coral reefs under rapid climate change and ocean acidification. Science. 318:1737-1742. 

doi: 10.1126/science.1152509. 

Hönisch B., Ridgwell A., Schmidt D. N., Thomas E., Gibbs S. J., Sluijs A., et al. 2012. The 

geological record of ocean acidification. Science. 335(6072):1058-1063. doi: 

10.1126/science.1208277. pmid:22383840. 

Houde E. 2008. Emerging from Hjort's Shadow. Journal of Northwest Atlantic Fishery Science. 

41: 53-70. doi: 10.2960/j.v41.m634. 

 

Hutchings J., Myers R., García V., Lucifora L., Kuparinen, A. 2012. Life-history correlates of 

extinction risk and recovery potential. Ecological Applications. 22:1061-1067. doi: 

10.1890/11-1313.1. 

 
46

International Union for Conservation of Nature. 2015. The IUCN Red List of Threatened Species 

[Internet]. 2015 [cited 3 April 2015]. Available from: http://www.iucnredlist.org. 

Jablonski D., Lutz R. 1983. Larval ecology of marine benthic invertebrates: Paleobiological 

implications. Biological Reviews. 58:21-89. doi: 10.1111/j.1469-185x.1983.tb00380.x. 

Jackson J. 2001. Historical overfishing and the recent collapse of coastal ecosystems. Science. 

293:629-637. doi: 10.1126/science.1059199. 

Kawaguchi S., Kurihara H., King R., Hale L., Berli T., Robinson J., et al. 2011. Will krill fare 

well under Southern Ocean acidification? Biology Letters. 7:288-291. doi: 

10.1098/rsbl.2010.0777. 

Kroeker K., Kordas R., Crim R., Hendriks I., Ramajo L., Singh G., et al. 2013. Impacts of ocean 

acidification on marine organisms: Quantifying sensitivities and interaction with 

warming. Global Change Biology. 19:1884-1896. doi: 10.1111/gcb.12179. 

 

Lande, R. 1993. Risks of population extinction from demographic and environmental 

stochasticity and random catastrophes. The American Naturalist. 142:911. doi: 

10.1086/285580. 

Marteinsdottir G., Thorarinsson K. 1998. Improving the stock-recruitment relationship in 

Icelandic cod (Gadus morhua) by including age diversity of spawners. Canadian Journal 

of Fisheries and Aquatic Sciences. 55:1372-1377. doi: 10.1139/f98-035. 

Mooney, H., Larigauderie, A., Cesario, M., Elmquist, T., Hoegh-Guldberg, O., Lavorel, S., et al. 

2009. Biodiversity, climate change, and ecosystem services. Current Opinion in 

Environmental Sustainability. 1(1):46-54. doi:10.1016/j.cosust.2009.07.006 

Munday P., Dixson D., Donelson J., Jones G., Pratchett M., Devitsina G., et al. 2009. Ocean 

acidification impairs olfactory discrimination and homing ability of a marine fish. 

Proceedings of the National Academy of Sciences. 106:1848-1852. doi: 

10.1073/pnas.0809996106. 

Munday P., Cheal A., Dixson D., Rummer J., Fabricius K. 2014. Behavioural impairment in reef 

fishes caused by ocean acidification at CO2 seeps. Nature Climate Change. 4:487-492. 

doi: 10.1038/nclimate2195. 

Northeast Fisheries Science Center (NEFSC). 2012. Southern New England Mid-Atlantic 

yellowtail flounder (Limanda ferruginea) stock assessment for 2012, updated through 

2011. http://www.nefsc.noaa.gov/publications/crd/crd1218/partb.pdf 

Orr J., Fabry V., Aumont O., Bopp L., Doney S., Feely R., et al. 2005. Anthropogenic ocean 

acidification over the twenty-first century and its impact on calcifying organisms. Nature. 

437:681-686. doi: 10.1038/nature04095. 
47

Patrick W. S., Spencer P., Link J., Cope J., Field J., Kobayashi D., et al. 2010. Using 

productivity and susceptibility indices to assess the vulnerability of United States fish 

stocks to overfishing. Fishery Bulletin. 108:305-322. 

Pecl G, Ward T, Doubleday Z, Clarke S, Day J, Dixon C, et al. 2014. Rapid assessment of 

fisheries species sensitivity to climate change. Climatic Change. 127:505-520. doi: 

10.1007/s10584-014-1284-z.

Pineda J., Hare J., Sponaugle S. 2007. Larval transport and dispersal in the coastal ocean and 

consequences for population connectivity. Oceanography. 20:22-39. doi: 

10.5670/oceanog.2007.27. 

Planque B., Frédou T. 1999. Temperature and the recruitment of Atlantic cod (Gadus morhua). 

Canadian Journal of Fisheries and Aquatic Sciences. 56:2069-2077. doi: 10.1139/f99-

114. 

Punt A., A'mar T., Bond N., Butterworth D., de Moor C., De Oliveira J., et al. 2013. Fisheries 

management under climate and environmental uncertainty: control rules and performance 

simulation. ICES Journal of Marine Science. 71:2208-2220. doi: 10.1093/icesjms/fst057. 

Ries J., Cohen A., McCorkle D. 2009. Marine calcifiers exhibit mixed responses to CO2-induced 

ocean acidification. Geology. 37:1131-1134. doi: 10.1130/g30210a.1. 

Rijnsdorp A., Peck M., Engelhard G., Mollmann C., Pinnegar J. 2009. Resolving the effect of 

climate change on fish populations. ICES Journal of Marine Science. 66:1570-1583. doi: 

10.1093/icesjms/fsp056. 

Rose G. 2004. Reconciling overfishing and climate change with stock dynamics of Atlantic cod 

(Gadus morhua) over 500 years. Canadian Journal of Fisheries and Aquatic Sciences. 

61:1553-1557. doi: 10.1139/f04-173. 

Shoji J., Toshito S., Mizuno K., Kamimura Y., Hori M., Hirakawa K. 2011. Possible effects of 

global warming on fish recruitment: shifts in spawning season and latitudinal distribution 

can alter growth of fish early life stages through changes in daylength. ICES Journal of 

Marine Science. 68:1165-1169. doi: 10.1093/icesjms/fsr059. 

Spalding M. D., Fox H. E., Allen G. R., Davidson N., Ferdaña Z. A., Halpern B., et al. 2007. 

Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. 

Bioscience. 57(7):573-583. doi: 10.1641/B570707 

Stein B., Staudt A., Cross M., Dubois N., Enquist C., Griffis R., et al. 2013. Preparing for and 

managing change: climate adaptation for biodiversity and ecosystems. Frontiers in 

Ecology and the Environment. 11:502-510. doi: 10.1890/120277. 
48

Sumaila U., Cheung W., Lam V., Pauly D., Herrick S. 2011. Climate change impacts on the 

biophysics and economics of world fisheries. Nature Climate Change. 1:449-456. doi: 

10.1038/nclimate1301. 

Tingley M., Darling E., Wilcove D. 2014. Fine- and coarse-filter conservation strategies in a 

time of climate change. Annals of the New York Academy of Sciences. 1322:92-109. 

doi: 10.1111/nyas.12484. 

Walther G., Post E., Convey P., Menzel A., Parmesan C., Beebee T., et al. 2002. Ecological 

responses to recent climate change. Nature. 416:389-395. doi: 10.1038/416389a. 

Wicks L., Roberts J. 2012. Benthic invertebrates in a high-CO2 world. In: Gibson R. 

Oceanography and marine biology. CRC Press, Boca Raton, FL.

Wilson S., Burgess S., Cheal A., Emslie M., Fisher R., Miller I., Polunin N., et al. 2008. Habitat 

utilization by coral reef fish: Implications for specialists vs. generalists in a changing 

environment. Journal of Animal Ecology. 77:220-228. doi: 10.1111/j.1365-

2656.2007.01341.x.