Report of a Workshop on Best Approaches 

and Needs for Projecting Marine Mammal

Distributions in a Changing Climate

Santa Cruz, California, USA

12-14 January 2016

Gregory K. Silber, Mathew Lettrich, and Peter O. Thomas (eds.)

Contributors and Workshop Participants:

Jason Baker, Mark Baumgartner, Elizabeth A. Becker, Peter Boveng, Dorothy Dick, Jerome 

Fiechter, Jaume Forcada, Karin A. Forney, Roger Griffis, Jon Hare, Alistair Hobday, Daniel 

Howell, Kristin Laidre, Nate Mantua, Lori Quakenbush, Jarrod Santora, Paul Spencer, Charles 

Stock, Kate Stafford, William Sydeman, Kyle Van Houtan, and Robin Waples

U.S. Department of Commerce

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

NOAA Technical Memorandum NMFS-OPR-54

May 2016

Report of a Workshop on Best Approaches 

and Needs for Projecting Marine Mammal

Distributions in a Changing Climate

Santa Cruz, California, USA

12-14 January 2016

Gregory K. Silber, Mathew Lettrich, and Peter O. Thomas (eds.)

Contributors and Workshop Participants:

Jason Baker, Mark Baumgartner, Elizabeth A. Becker, Peter Boveng, Dorothy Dick, Jerome 

Fiechter, Jaume Forcada, Karin A. Forney, Roger Griffis, Jon Hare, Alistair Hobday, Daniel 

Howell, Kristin Laidre, Nate Mantua, Lori Quakenbush, Jarrod Santora, Paul Spencer, Charles 

Stock, Kate Stafford, William Sydeman, Kyle Van Houtan, and Robin Waples

NOAA Technical Memorandum NMFS-OPR-54

May 2016

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:

Silber, G.K., M. Lettrich, and P.O. Thomas (eds.). 2016. Report of a Workshop on Best 

Approaches and Needs for Projecting Marine Mammal Distributions in a Changing Climate. 12-

14 January 2016, Santa Cruz, California, USA. U.S. Dep. Commer., NOAA. NOAA Technical 

Memorandum NMFS-OPR-54, 50 p.

A copy of this report may be obtained from:

Office of Protected Resources

NOAA, NMFS, F/PR2

1315 East-West Highway

Silver Spring, MD 20910

Or online at:

http://www.nmfs.noaa.gov/pr/publications

Acknowledgements

The workshop was jointly funded and co-convened by NOAA’s Office of Protected 

Resources and Office of Science and Technology, and the U.S. Marine Mammal Commission. 

We are grateful for the work of the workshop Steering Committee: Mark Baumgartner, Peter 

Boveng, Roger Griffis, Nate Mantua, Gregory Silber, Peter Thomas, Kyle Van Houtan, and 

Robin Waples. We are indebted to Steven Brown and Nancy Young for improving drafts of this 

report with their careful reviews. 
iii

Table of Contents

Acronyms.................................................................................................................................... v

Definitions.................................................................................................................................. vi

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

Introduction and Background...................................................................................................... 4

Purpose of the Workshop ............................................................................................................ 6

Regulatory Context and the Science/Management Interface ...................................................... 6

Relevant Legislation and Federal Agency Activities.............................................................. 7

Science/Management Interface................................................................................................ 9

Aligning Resource Management Needs and Modeling Time Periods – A Visual Approach 10

Communicating Management and Research Needs.............................................................. 12

Case Study: Projecting Ringed Seal Distributions.................................................................... 13

Types of Models and Selecting Appropriate Models................................................................ 13

Typology of Models .............................................................................................................. 13

Expert-Based Models ............................................................................................................ 16

Statistical Models .................................................................................................................. 16

Process-Based or Mechanistic Models.................................................................................. 17

Choosing a Model.................................................................................................................. 18

Choosing a Model – Modeling Issues and Obstacles............................................................ 18

The Continuum of Existing Knowledge................................................................................ 20

Uncertainty and Model Limitations.......................................................................................... 21

Case Study: Modeling Cetacean Density in the Pacific Ocean................................................. 23

Data Types, Data Evaluation, and Data Limitations................................................................. 25

Physical Data as Proxies........................................................................................................ 25

Modeling with Proxy Variables............................................................................................. 26

Beyond Physical Data – Modeling with Tractable and Intractable Features ........................ 27

Exemplar Species/Population(s) for Assessing Modeling Methodologies............................... 28

Highly Vulnerable Species or Populations............................................................................ 29

Management Priority ............................................................................................................. 30

Populations Inhabiting Rapidly Changing Environments..................................................... 30

Data-rich Populations............................................................................................................ 31
iv

Species Complexes................................................................................................................ 31

Confounding Factors for Species Selection........................................................................... 32

Summary – Exemplar Species............................................................................................... 32

Next Steps ................................................................................................................................. 32

Suggested Research and Study Needs and Policy Considerations........................................ 32

Manager-Scientist Interface................................................................................................... 33

Forecasting Distributions of Illustrative Marine Mammal Populations – Conduct Modeling 

Case Studies........................................................................................................................... 33

Model Validation................................................................................................................... 33

Ongoing Data Collection....................................................................................................... 34

Time Scales for Modeling ..................................................................................................... 34

The Value of Inter-/Multi-disciplinary Teams ...................................................................... 34

Fiscal Planning ...................................................................................................................... 35

Literature Cited ......................................................................................................................... 36

Workshop Participants.............................................................................................................. 45

Agenda ...................................................................................................................................... 47
v

Acronyms

CCE – California Current Ecosystem

CHL – Chlorophyll Concentration

ESA – Endangered Species Act

ETP – Eastern Tropical Pacific

FWS – U.S. Fish and Wildlife Service

IPCC – Intergovernmental Panel on Climate Change

IWC – International Whaling Commission

LMR – Living Marine Resources

MLD – Mixed Layer Depth

MMC – Marine Mammal Commission

MMPA – Marine Mammal Protection Act

MSE – Management Strategy Evaluation

NEPA – National Environmental Policy Act

NMFS – National Marine Fisheries Service

NOAA – National Oceanic and Atmospheric Administration

OSP – Optimal Sustainable Population

SDM - Species Distribution Model

SSH – Sea Surface Height

SST – Sea Surface Temperature
vi

Definitions 

Analogous species – similar or related species to a data-deficient species being modeled that 

provide insights to expected responses

Correlative models – species distribution models that relate species occurrence to environmental 

parameters

Empirical relationships – a correlation or relationship based solely on observation rather than 

theory

Envelope models – use of the observed range of environmental conditions as the definition of 

habitat

Forecast – most likely prediction (http://sciencepolicy.colorado.edu/zine/archives/1-

29/26/guest.html)

Fundamental (potential) niche – The range of physiological tolerances for a species in the 

absence of interactions with other species (e.g., competition, predation)

Generalized linear or additive models – statistical models that can be used to describe functional 

relationships between species distribution (occurrence or abundance) and environmental 

variables

Hindcast – a forecast made for a period in the past using only information available before the 

beginning of the forecast. (http://www.euporias.eu/taxonomy/term/89)

Model – a representation of reality

Nowcast – forecast on time scales of days to weeks

Prediction – probabilistic statement that an event will occur or condition will be present in the 

future based on initial conditions (http://sciencepolicy.colorado.edu/zine/archives/1-

29/26/guest.html)

Process-based, or mechanistic, models – models built on assumptions about how a system works 

and are grounded in ecological theory

Projection – probabilistic statement that an event may occur or condition may be present in the 

future based on initial and future conditions 

(http://sciencepolicy.colorado.edu/zine/archives/1-29/26/guest.html)

Proxy variables – physical, environmental, or biological parameters that are readily observed or 

modeled and used as indicators for more complex processes or variables that are difficult 

to measure or model

Quasi-extinction threshold – an abundance below which the persistence of a species is unlikely
vii

Realized (actual) niche – the environmental space where a species actually occurs; considers 

availability of environmental conditions and biotic interactions that influence a species 

distribution

Species distribution models – Extrapolates species distribution data in space and time from 

observations of species occurrences and environmental variables believed to influence the 

distribution individuals of that species. SDMs are usually based on a statistical model

Uncertainty – the degree to which a given state or outcome is not known, accounting for 

imperfect and unknown information
1

Executive Summary

Climate-related changes in marine and coastal ecosystems are already affecting fish, seabirds, 

and marine mammals. While the eventual magnitude of these climate-driven impacts remains 

unknown, alteration of oceanographic conditions and processes from global climate change are 

expected to profoundly influence ecosystems and marine mammals in the foreseeable future. As 

marine habitats undergo change, efforts to conserve marine mammals and manage marine 

ecosystems would benefit from the ability to anticipate likely responses of marine mammals to 

these changes. Research in this arena is in its infancy despite the critical need for methodologies 

to help anticipate changes in occurrence, distribution, phenology, and relative abundance of 

marine mammal populations so that conservation measures and ecosystem management can be 

developed accordingly.

The National Marine Fisheries Service (NMFS) and Marine Mammal Commission (MMC)

convened a workshop in January 2016 to determine to what extent it is plausible to project

climate-related changes in marine mammal distribution and relative abundance based on existing 

modeling practices and the current state of marine mammal science, and to identify the 

methodologies available and their limitations. The five workshop objectives were to:

a) explore modeling or other means to assess future changes in distribution and abundance 

of marine mammal species;

b) identify metrics relevant to assessing physical changes and biological responses of 

marine mammals;

c) identify one or more exemplar species/population(s) for assessing methodologies or 

metrics;

d) identify next steps for performing these studies; and

e) produce draft and final reports of the findings.

Given the global distribution and high mobility of many marine mammal species, the workshop 

assembled experts from various disciplines and provided the opportunity for collaboration 

among scientists (ecologists and modelers from marine mammal, fisheries, and climate science) 

and managers from the United States, Europe, and Australia. Though primarily focused on a U.S. 

perspective, the issues discussed in this workshop report can be applicable to other regions of the 

world and a number of marine mammal taxa. 

Legislation in the United States and other nations specifically addresses the conservation and 

management of marine mammals. At the same time, governments are requiring increasing focus 

on ecosystem considerations within fisheries management, and marine mammals are often major 

predators in such ecosystems. The responsibility of managing marine mammals in the context of 

climate change will be driven by the quality of information available to managers. Effective 

communication between managers and scientists will be integral to developing and delivering 

information in management-relevant contexts. Resource managers must be able to identify 

research and analytical products needed from scientists, and scientists need to be able to convey 
2

to managers the assumptions, strengths, and weaknesses in modeling studies and empirical 

research, and to provide clear statements regarding levels of uncertainty in projections.

A range of models, varying in levels of complexity, have been developed that can be used to 

generate projections of climate change impacts on living marine resources. These models vary 

in objective, form, informational needs, computational needs, and trade-offs. The three general 

classes of models commonly used to guide management decisions are expert opinion, statistical 

extrapolation, and process-based models. Expert opinion, or rule-based, models integrate

information from a history of management applications and can be developed relatively rapidly 

in most cases; however, the assumptions used in these models are not always apparent. Statistical 

extrapolation models draw on species occurrence and data from past conditions of a particular 

system to project future conditions. Although the assumptions in extrapolation models are often 

transparent, past behavior of a system may not be suitable for projecting forward if the

underlying ecological relationships are also sensitive to climatic conditions. Process-based, also 

called mechanistic, models are built on causal mechanisms grounded in ecological theory rather 

than correlation, which can increase the level of confidence in extrapolating beyond historical 

conditions. Mechanistic approaches have high computational costs and need to convey

uncertainty regarding ways in which ecological processes are represented.

Choosing an appropriate model type for predicting future conditions is an important step in 

advancing research/management actions for a particular situation. Model selection will be based 

on criteria such as the extent of existing data related to the life history, distributional ecology, 

and population dynamics of the species; space and time scales of the required prediction; and 

whether existing models are based on environmental factors that can be projected into the future. 

Levels of uncertainty inherent to habitat and population dynamics models, in turn, are directly 

related to the amount known about a particular species’ ecology and demography.

Because it is not possible to know precisely what the future holds, any prediction should identify 

some level of confidence in the likelihood that prediction will be realized. Estimating 

uncertainties in projected marine mammal responses to climate change and in the climate models

(i.e., various emission scenarios) themselves should consider whether and to what extent the 

model: a) is based on physiological and ecological principles expected to hold over the space and 

time scales of interest; b) adequately re-creates past (i.e., hindcast) marine mammal responses to 

climate change over the space and time scales of interest; and c) captures primary uncertainties 

in marine mammal responses.

Data used in models are diverse (e.g., physical, biological, biogeochemical) and the amount of 

data available to address a particular question may vary. Situations can be characterized as data 

or model rich/poor. Mechanistic processes within a food web or ecosystem can be difficult to 

measure, and model and biological parameters or environmental data may be few or unavailable. 

These limitations have led modelers to search for simpler means, or proxies, to approximate 

certain processes. Physical oceanographic processes are more readily quantified and modeled 
3

than are ecological systems, and food web interactions and can serve as useful proxies for those 

processes. The breadth of data sources results in a variety of observed temporal and spatial 

resolutions and a range of confidence levels, but generally physical data can be measured with 

finer resolution and greater confidence than exist for the biological/ecological data. Physical 

proxies tend to accurately represent linkages to the physical environment for species feeding at 

low trophic levels, but may inadequately characterize relationships for marine mammals feeding 

at higher trophic levels.

A range of factors other than the physical environment will affect marine mammal modeling 

efforts, including trophic interactions, short-term behavioral responses, and anthropogenic 

influences. Some factors may be tractable within current modeling approaches, and they might 

be included in the initial baseline model. Some generalizations can be made for all marine 

mammals (e.g., long generation times may make behavioral plasticity more important than 

evolutionary responses over the short term), while other features may be species specific (e.g., 

degree of site fidelity to haul-out sites or migration routes). In contrast, some factors are difficult 

to model and might significantly alter model results. In particular, the ability of marine mammals 

to alter their behavior or food sources can act as a buffer to measurable climate-change induced 

changes, and can delay any adverse effects, or mask them until critical thresholds are reached.

Exemplar species should be identified now and used as case studies to begin forecasting future 

distributions. The workshop concluded that species or populations exposed to existing threats 

and vulnerable to large scale ecosystem shifts, represent a current management priority, or occur 

in rapidly changing ecosystems should receive most emphasis in the consideration of nextgeneration forecasting studies.

Discussion throughout the workshop identified activities and outcomes that would represent 

progress toward improving capacity to predict marine mammal distributions as the climate 

changes. It is essential to identify appropriate time scales for which a model will apply, 

recognizing that short-term “nowcasts” are likely to be more accurate than those projections out 

to decades or centuries. As data become available via ongoing monitoring and research efforts, 

model-based forecasts should undergo empirical validation. Given the global scale and 

complexity of both climate change impacts and marine mammal distributions, multidisciplinary 

collaborations will be necessary to develop new and improve existing models to enable a better 

understanding of the interplay between these processes. Finally, long-term fiscal planning will be 

critical to ensure scientists and managers are equipped with the tools and resources needed 

address the challenges of managing marine mammal populations in the future.
4

Introduction and Background

Marine mammals are exposed to a variety of threats and habitat perturbations from human 

activities on regional and global scales. Among these, alteration of oceanographic conditions and 

processes due to global climate change is expected to profoundly influence ecosystems and, in 

turn, marine mammal populations in the foreseeable future. While the nature and scope of these 

climate-driven impacts on marine mammals are uncertain, changes in the ranges and relative 

abundances of species or stocks are expected.

Long observational time series and modeling studies have illustrated distributional changes in 

some marine fish and invertebrate populations (e.g., Nye et al. 2009, Pinsky et al. 2013, 

Poloczanska et al. 2013, Walsh et al. 2015) and climate change projections suggest the likelihood 

of further shifts (e.g., Cheung et al. 2009, Stock et al. 2011, Gutt et al. 2012, Hare et al. 2012a, 

Hare et al. 2012b, Lynch et al. 2014). Some researchers have predicted that distributional shifts 

will occur in marine vertebrates, especially upper trophic level predators, due to climate change 

(IWC 2010, Hazen et al. 2013). Changes in regional abundance, distribution, and range in some 

marine mammal populations have been observed (e.g., Hansen 1990, Angunuzzi and Buckland 

1994, Heyning and Perrin 1994, Kovacs et al. 2011, Clarke et al. 2013), and there is a growing 

body of research on predictive studies involving marine mammals (e.g. Gilles et al. 2011, Becker 

et al. 2012, Keller et al. 2012, Gregr et al. 2013, Mannocci et al. 2014). Efforts to conserve 

marine mammals, and to manage human activities in marine ecosystems in general, would 

benefit from an improved ability to anticipate the response of marine mammal distributions to 

climate change. 

Conservation of living marine resources (LMR) is often the responsibility of federal, state, and 

provincial governments. Legislation, such as the Marine Mammal Protection Act (MMPA) and 

Endangered Species Act (ESA) in the United States, authorizes agencies to develop regulations 

and protective (mitigation) measures for marine mammals. While agencies have broad authority

to reduce threats, they may not always have the ability to do so quickly, due to limited resources, 

resistance to change by resource users, or regulatory inertia. In addition, threat-reduction

measures may become obsolete, and it may be necessary to develop new or modified approaches

in situations where LMR are undergoing shifts in regional abundance, under conditions of 

changing physical or biological ocean conditions and processes. Therefore, anticipating these 

changes through modeling would enhance near- and long-term planning by wildlife or resource 

management agencies.

Historically, ocean resource management practices have relied on site-specific “static 

management approaches” (e.g., marine protected areas, coral reefs) (Hyrenbach et al. 2000) that

precluded planning on time scales appropriate to ocean resource uses, dynamic ocean processes, 

and management of highly mobile organisms (Hobday et al. 2014, Lewison et al. 2015, Maxwell 

et al. 2015). As marine mammal habitats undergo change, wildlife management agencies will
5

need to pursue conservation approaches that are responsive and flexible. However, strategies to 

pro-actively address changes on large geospatial scales are not easily developed.

For these reasons, there is a need to identify 

methodologies to aid in anticipating changes in 

occurrence, distribution, phenology, and relative 

abundance of marine mammal populations, so 

conservation and management measures can be 

developed accordingly. For example, in locations 

and situations where permitting (e.g., for oil and 

gas, or offshore renewable energy lease-sales) is 

required, or where endangered species recovery 

planning is occurring, wildlife management 

agencies would benefit from the ability to predict 

changing marine mammal occurrence. Forecasts 

of future occurrences could inform the evaluation 

of the risks and benefits of various management 

responses. In some settings, marine mammals 

may also compete with humans for commercially 

valuable fish species. Therefore, advance knowledge concerning likely future shifts in 

distributions of significant predator species would be of benefit in deciding how to focus 

research to support fisheries and ecosystem management. In addition, for locations undergoing 

rapid ecosystem changes, including those in high-latitudes (Forcada et al. 2006, Laidre et al. 

2008, Ragen et al. 2008), or where populations are in decline (e.g., Shelden et al. 2015), resource 

management agencies will face challenges in responding quickly with effective protective 

measures.

These and related issues were highlighted by the National Marine Fisheries Service (NMFS) in 

its recently released Climate Science Strategy (Link et al. 2015), which identifies priority 

objectives and activities for managing LMR in a changing climate. One objective is to “identify 

future states of marine, coastal, and freshwater ecosystems, LMRs, and LMR-dependent human 

communities in a changing climate”. Near-term actions include adequately funding processoriented research to improve understanding of climate impacts on these resources.

To investigate ways to address these needs, NMFS and the Marine Mammal Commission 

(MMC) convened a workshop on 12-14 January 2016 in Santa Cruz, CA, USA to determine 

whether projecting changes in marine mammal distribution and relative abundance was plausible 

based on existing modeling practices and current state of marine mammal science, and if so, to 

identify the considerations needed when making such projections.

Forecast – prediction with highest 

likelihood of occurrence

Prediction – probabilistic statement 

that an event will occur or condition 

will be present in the future based on 

initial conditions

Projection – probabilistic statement 

that an event may occur or condition 

may be present in the future based on 

initial and future conditions 

(http://sciencepolicy.colorado.edu/zine

/archives/1-29/26/guest.html)
6

Purpose of the Workshop

The purpose of the workshop was to identify and evaluate the analytical tools available for 

predictive modeling of marine mammal population distribution and assess the relative utility and 

precision of these tools as related to the conservation of marine mammal populations.

The stated workshop objectives were:

• Explore modeling or other means to assess future changes in distribution and 

abundance of marine mammal species;

• Identify metrics relevant to assessing physical changes and biological responses 

of marine mammals;

• Identify one or more exemplar species/population(s) for assessing methodologies 

or metrics;

• Identify next steps for performing these studies; and

• Produce draft and final reports of the findings.

The workshop consisted of ten invited presentations (see “Agenda”, provided at the end of this 

report), associated discussion, and break-out group discussions. Presentations and subsequent 

discussions provided an overall background so participants with varied expertise had a baseline

context on climate change modeling, ocean resource management, and marine mammal 

modeling studies to facilitate later discussions.

Here, we provide a report of workshop findings and conclusions that: a) synthesizes key points

and conclusions from the invited presentations and break-out group discussions; b) provides 

context in regard to relevant literature; and c) identifies plausible next steps in forecasting marine 

mammal distributions. Rather than provide a meeting chronology, the report is organized by 

sections providing discussions of:

• regulatory context and the science/management interface; 

• types of models and the considerations for selecting an appropriate model;

• uncertainty and model limitations; 

• data types, data evaluation, and data limitations; 

• characteristics of marine mammal species suitable for further modeling studies; and

• next steps and policy considerations.

In each of these sections, we provide descriptions of the background and context of a particular 

issue and approaches for addressing the issue.

Regulatory Context and the Science/Management Interface

The protection of LMR – including marine mammals and their habitats – is often legislativelymandated to federal governments. In many instances, delegation of responsibility to agencies 

provides considerable latitude and a number of avenues for establishing protective measures 
7

(e.g., McClure et al. 2013). It is under these jurisdictions that climate change-related impacts 

must also be addressed.

Relevant Legislation and Federal Agency Activities

In the United States, the U.S. Fish and Wildlife Service (FWS) and NMFS are the primary 

agencies charged with the protection and recovery of organisms designated as threatened or 

endangered on the ESA List of Endangered and Threatened Wildlife (50 CFR 17.11) 

(http://www.fws.gov/endangered/laws-policies/). The MMPA “prohibits, with certain 

exceptions, the ‘take’ of marine mammals in U.S. waters and by U.S. citizens on the high seas” 

(http://www.nmfs.noaa.gov/pr/laws/mmpa/). Among other things, the MMPA allows the 

granting of “Incidental Take Authorizations” to activities such as oil and gas development, 

exploration, and production; geophysical surveys; and other activities when no more than 

a “negligible impact” on those marine mammal species or “stocks” is expected to occur. The 

MMPA also requires that all marine mammal stocks are identified, their status monitored, and 

measures taken to ensure that stocks return to, or do not decline from, their Optimum Sustainable 

Population levels. Marine mammal stocks are typically defined by static geographic ranges.

In 1970, the U.S. Congress passed the National Environmental Policy Act (NEPA) into law; its 

stated goal is to “formulate and recommend national policies which ensure that the programs of 

the federal government promote improvement of the quality of the environment” 

(www.gsa.gov/nepa). Federal entities such the U.S. Army Corps of Engineers, the U.S. Navy, 

and the Department of the Interior’s Bureau of Ocean Energy Management are responsible for 

adhering to requirements of NEPA, which includes, among other things, assessments of 

cumulative impacts from all sources including climate change. Under the ESA, these entities are 

to consult with the appropriate agency (i.e., FWS or NMFS) “to ensure that actions they 

authorize, fund, or carry out do not jeopardize the existence of any species listed under the ESA, 

or destroy or adversely modify designated critical habitat of any listed species.” 

Related to this, interactions between marine mammals and fisheries fall under the jurisdiction of 

the federal government. Regulations under the MMPA and Magnuson-Stevens Fishery 

Conservation and Management Act aim to reduce incidental bycatch of marine mammals by 

fisheries. Fisheries stock assessment reports include ecosystem effects, such as depredation by 

marine mammals or competition between marine mammals and the fishery.

Other nations (as well as state or provincial governments) have analogous legislation and 

capacities to establish protective measures, but with varying levels of scope. In Canada, marine 

mammals are managed under the Species At Risk Act and by development of Marine Mammals 

Regulations under authority of the Fisheries Act. Australia manages marine mammals under the 

Environment Protection and Biodiversity Conservation Act of 1999. New Zealand has managed

marine mammals since 1978 under the Marine Mammals Protection Act. Marine mammal 

management in the United Kingdom is authorized by the Offshore Marine Conservation 

Regulations of 2007 and the 2010 Conservation of Habitats and Species Regulations (as 
8

amended). These are but a few examples; many nations have related laws. Therefore findings 

presented here are likely to have application for resource management in waters of a number of 

countries.

In most cases, in the United States in particular, these laws, and the policies developed under 

them, not only provide considerable leverage in developing conservation programs, but also 

require federal agencies to take into account all forms of potential impacts on marine mammal 

populations that may arise from planned activities (e.g., industrial, military, or research 

operations). Among these is the impact of global climate change. For example, under NEPA, 

ESA, and MMPA, NMFS is expected to consider climate change in recovery planning, 

assessments of threats with regard to permits for endangered species and habitat conservation 

plans, and the permitting of various ocean-based human activities. Under the ESA in particular, a 

recent agency-wide directive requires that NMFS take into account factors related to climate

change when making determinations regarding:

• adding a species to (or removing it from) the ESA’s List of Endangered and Threatened 

Wildlife;

• conducting listed endangered species status reviews;

• designating critical habitat;

• undertaking recovery plan evaluation and analyses;

• permitting take of listed species; and

• consultations with other agencies regarding the impacts from various activities.

Agency management actions occur on varying temporal and spatial scales. Mitigation of marine 

mammal exposure to military operations (e.g., NOAA 2015), measures to reduce large whale 

entanglement in commercial fishing gear, and ship strike prevention are often established in 

specific areas and seasons (e.g., Silber et al. 2012), and may only be implemented on the scale of 

seasons or a few years (NOAA 2013). Endangered species recovery plans are developed with 

annual cost estimates for the first five years and are to be updated at least once every five years. 

Planning and permitting for industrial activities such as the siting or construction of offshore 

renewable energy facilities and oil and gas exploration and development tend to be on the order 

of decadal periods. On still longer time scales, ESA listing decisions often assess the prospects 

for species persistence on the order of a century (e.g., Angliss et al. 2002). For example, one of 

the recovery criteria for the North Atlantic right whale (Eubalaena glacialis) is “...no more than 

a 1% chance of quasi-extinction in 100 years” (NMFS 2005). 

Endangered marine mammal ESA listing determinations can have management implications that 

are global and multi-decadal in scale. Dozens of marine mammal species were designated as 

endangered with enactment of the ESA over 40 years ago. Listed large whale species 

designations were range-wide, i.e., global. Although some populations have been re-designated 

(e.g., eastern North Pacific Steller sea lion (Eumetopias jubatus) and gray whale (Eschrichtius 

robustus)) or proposed for re-designation (humpback whales (Megaptera novaeangliae)), most 
9

other listed species are expected to retain their current designations for the foreseeable future. 

Therefore, these designations will involve ongoing management responsibilities that are global in 

scope and lasting for decades.

Thus, in the context of the scope of responsibilities entrusted to wildlife conservation agencies, 

an ability to forecast marine mammal relative abundance and distribution across relevant spatial 

and temporal scales have the potential to enhance marine mammal conservation practices. Given

the range in spatial and temporal scales embedded in these mandates, scientists and resource 

managers can expect to face challenges in selecting appropriate temporal and spatial scales for 

modeling studies as these animals’ habitats undergo change. 

Science/Management Interface

In any science-based resource management endeavor, research enterprises should be aligned with 

management needs and objectives. Managers, policy-makers, and researchers all have critical 

roles to play in understanding the impacts of climate change and managing marine mammal 

populations. Managers are guided by legislative, policy, and operational objectives using data

collected and analyzed by researchers combined with social, economic, and political 

considerations. Researchers seek to gather information needed to inform, elucidate uncertainties,

and make recommendations around decision-making. 

There is not a one-to-one or perfect connection between scientific inquiry and production of

scientific results relevant to specific decision-making challenges. In terms of modeling studies, 

temporal and spatial scales of modeled outputs may be difficult to match to the temporal scales

or spatial resolution required for management decisions (e.g., Griffis et al. 2008). For example, 

managers may be seeking information about future prospects for a marine mammal stock 

occupying a small range, but available habitat and distribution models may only provide 

projections for much larger study areas and spatial scales. In another example, a model with the 

power to predict presence or absence of a species from one season, or year, to another may not 

serve to inform long-term trends in the presence of that species required for making a listing 

determination under the ESA. 

In addition to these differing objectives, disconnects between managers and researchers occur for 

several reasons. First, scientists might build distribution models outside of decision-relevant 

contexts. Second, managers may not clearly articulate their needs and communicate these to 

scientists so that models and analyses best meet those needs. Finally, even if decision-makers

and researchers are in close consultation on research needs, only limited data may be available.

In each of these cases, model output, no matter how robust, may be of limited use to specific 

management challenges. 

It is also essential that policy-makers understand the role of uncertainty in modeling (discussed 

below). Scientists need to ensure that the uncertainty inherent in a modeling effort to inform a 

given management issue is presented in a way that: a) is simple to understand; b) is based on 
10

parameters that can be estimated readily and evaluated; and c) provides metrics that can be 

translated into transparent, rational management decisions or action thresholds (Taylor et al. 

2000). Ultimately, doing so allows the development of more informed management approaches 

and decision criteria, despite the fact that some sources of uncertainty may be difficult or 

impossible to reduce within timeframes required for management. Managers may wish to 

evaluate a range of decision criteria to assess likely success or failure of a variety of possible 

management approaches within a risk analysis (cost-benefit, or worst case scenario) framework. 

Scientists can aid the development of management strategies by conducting targeted simulation 

studies or sensitivity analyses to assess probabilities of achieving management objectives under

various decision thresholds (e.g., ‘minimum population size’ under the MMPA; Wade 1998). 

In addition, data-collection needs chronically outpace available funding. Given that climate 

change is not the only threat confronting marine mammal conservation, it is critical that agencies

allocate resources for research and monitoring to provide the best possible information to inform 

management decisions. Alignment of science and policy needs will support allocation decisions

where funding is available and may help identify future funding needs.

Given the considerations discussed above, the workshop addressed the interplay of information 

needs with the current realities of modeling horizons, in time and geographical scale, and levels 

of uncertainty as a means to characterize the framework for such efforts. These issues are 

discussed in the sections that follow.

Aligning Resource Management Needs and Modeling Time Periods – A Visual 

Approach

Aligning research objectives, particularly modeling efforts, with resource management needs can 

best be accomplished by explicitly identifying the temporal and spatial scales over which both

operate. Here, we graphically present various management activities for which future marine 

mammal distribution is relevant and the research approaches for predicting distribution along 

spatial and temporal axes (Fig. 1). The approach is inspired by the Stommel (1963) diagram 

which has been used to map fisheries management activities in a similar fashion (Dunn et al. 

2016); however, we believe there has been no previous attempt to plot management and research 

activities in this way. We note that this approach could be valuable for numerous types of 

scientific endeavors, not just distribution projections.

As indicated by the graphic, modeling on a long time frame, as in the case of evaluating threats 

under the ESA for long-lived species, leads to broad, general predictions that may be most 

applicable to large geographic areas and at species levels. Such long-term modeling does not 

have equal precision in discerning trends for smaller populations within a species. Population 

trends or changes in geographic distribution at the smaller scale may be driven by more 

proximate factors which, while important to populations, have less effect on larger species-wide 

trends. At the opposite end of the spectrum, short-term changes in population size or geographic 
11

distribution may be discernible at small geographic scales, but carry little predictive power for 

long-term trends.

At large-scales, direct use of global climate change projections can be tractable (Randall et al. 

2007, Stock et al. 2011) but applications at local scales may require investment in highresolution climate models and/or downscaling exercises (Saba et al. 2016). There are certain 

limitations involving assumptions associated with downscaling discussed later in this report. 

Nonetheless, other cases exist in which marine mammal-associated modeling and management 

align well, such as for large whale ship strike reduction efforts that require short-term projections 

of distribution over a small area (e.g., Redfern et al. 2013). These cases have proven tractable at

relevant spatial scales for some environmental drivers (e.g., sea surface temperature (SST)

anomalies, Hobday et al. 2011, Spillman et al. 2013, Stock et al. 2015).

Figure 1. Conceptually, several illustrative marine mammal management activities are mapped 

along temporal/spatial scales, overlain (gray arrow) with a generalized characterization of the 

state of species distribution models. The temporal and spatial precision of predictive efforts 

decreases at longer time scales and larger spatial scales. Short-term projections on relatively 
12

small spatial scales will have greater granularity than projections on long time and large spatial 

scales. Management and research objectives on appropriate spatial/temporal scales will dictate 

model type selection and data used. 

Communicating Management and Research Needs

Clear articulation of temporal and spatial scales of analysis of future marine mammal distribution 

required to inform specific management decisions will assist the development of relevant 

modeling efforts. Likewise, clear indications by scientists of the limitations of predictive power 

at different scales will improve the tailoring of research to management requirements and 

accelerate the use of research results in management action. Awareness of these challenges will 

also improve communication of management needs, and the nature of the science required to 

meet those needs, to funders, the public, and other stakeholders (including other researchers and 

agencie). Scientists preparing research proposals will be better able to tie proposed activities to 

identified management needs, and agencies will be better able to articulate funding requests on 

the basis of clearly identified priorities. NMFS Science Centers, for example, may be able to use 

this process to identify existing and ongoing research programs that may need adjustment to be 

better take into account management needs.

Another benefit of this conceptual approach is that it helps guide research prioritization in at 

least three areas. First, studies are needed to test model assumptions. For example, a model that 

predicts future habitat for an Arctic species based on future projections of sea ice inevitably 

involves assumptions about how the species (and individuals) will respond to those changes in 

sea ice, particularly at key life stages. These assumptions can be tested by conducting “process” 

studies to determine how the species (or individual) actually does respond in areas where ice loss 

is occurring most rapidly within their range. Ideally, such results help to clarify functional 

responses of species and can be incorporated to improve model projections. Similar evaluation is 

needed to assess the relevance of any underlying physical data or models. These evaluations will 

largely be conducted by physical oceanographers, climate scientists, and biological 

oceanographers on collaborative modeling teams. A second element is that of basic monitoring, 

which can be directed to testing model predictions. For example, if a model being considered as 

a basis for management decisions makes specific predictions about marine mammal range 

change, field studies should be directed to determine whether those predictions are being 

realized. Targeted studies involving specific data collection needs (quantifying the occurrence of 

a particular species in a particular location, for example) might be identified. Third, model 

results can also be used to direct research priorities in the absence of information. In this 

instance, provisional models with recognized shortcomings and untested assumptions may 

arguably be an improvement over acting on the basis of little information of any kind. For 

example, such a model may assist in identifying stocks and geographic areas where the rates of 

distribution changes are projected to be most rapid. This can focus the design of research and 

monitoring efforts to document climate-driven changes in distribution.
13

Case Study: Projecting Ringed Seal Distributions

In some cases, particularly for ESA listing decisions, the ‘best available science’ will include a 

broad consensus among climate scientists about future trends in key factors that will influence a 

species’ or population’s future distribution.

Ringed seal distributions were projected through the 21st Century by using a highly simplified 

definition of habitat required by the species for the critical life history functions of whelping 

pups and nursing them to independence. These functions occur during spring, a period when the 

pups are highly vulnerable to predation and hypothermia if there is insufficient snow cover in 

which the mothers can construct and maintain lairs on top of the ice. Studies of lair construction 

indicated that accumulated snow depths of at least 20 cm are required for drifts to form that are 

sufficiently deep (50–65 cm) for adequate birth lairs. Therefore, ringed seal habitat and breeding 

distribution were assumed to be those areas of the Arctic where at least 20 cm of snow depth 

could be expected in the month of April. Output from global climate models predict that 

although precipitation in the Arctic is expected to increase, much of it will fall as rain, and 

delayed autumn ice formation will mean that some of the snow will fall into open water rather 

than accumulating on the ice surface. The area with snow depths above 20 cm in April was 

projected to decline under a broad range of plausible greenhouse gas emission scenarios, up to 

70% by the end of the 21st Century under one emissions scenario (Hezel et al. 2012). This would 

reflect a substantial loss of ringed seal reproductive habitat.

The strength of an approach like this is that it is built upon climate projections that are, 

qualitatively at least, broadly agreed to represent the best available science. One significant 

limitation is that characterization of ringed seal habitat has been reduced to just one or a couple 

of dimensions (sea ice extent and snow accumulation).

Types of Models and Selecting Appropriate Models

A primary objective of this workshop was to identify approaches that can be used to project 

marine mammal distributions. In this section, we discuss a variety of modeling approaches that 

can be and have been used to forecast species distributions. We further discuss factors that 

should be considered when selecting the most appropriate model for a given application.

Typology of Models

A broad range of models has been developed that can be used to generate projections of climate 

change impacts on LMR. Ranging from simple to complex (Table 1), these models have 

different objectives and form; they also have different trade-offs (Morin and Thuiller 2009, Stock 

et al. 2011). Traditional single-species stock assessment models are the basis for many 

management efforts and focus on the dynamics of a target organism. Other modeling approaches 

emphasize interactions between organisms and between organisms and their environment.
14

Table 1. Model types. Examples of models and their various characteristics.

Model Approach Characteristics Selected References

Observation

Historical records Presence only data, point maps of observations, broad spatial 

scales (e.g., museum collections, ships’ logs)

Range maps or kernel 

density

Presence only data, converts point data to a continuous 

surface of point density, interpolation, broad spatial scales

(e.g., home range maps)

Grid-based surveys Divides study area into equal-area grid cells, systematic data 

collection, generally presence only data due to coarse spatial 

scale

Expert Based

Expert opinion, 

published literature

Observations on species abundance are lacking but 

environmental data exist, uses simple decision rules to define 

species habitat requirements

Vulnerability 

assessment

Life history characteristics and predicted exposure to climate 

change are combined for a number of species within a region 

to create a relative vulnerability index

Statistical

Envelope methods Uses statistical correlations between observed species 

distribution(s) and environmental variables to define species 

tolerance, envelope drawn around some percentage of 

observations (often 95%) (e.g., BIOCLIM, broad spatial 

scale)

Hazen et al. 2013

Hobday 2010

Kaschner et al. 2006

Quotient Numerically calculates probability of species occurrence or 

abundance along an environmental gradient.

Eveson et al. 2015

Environmental Niche Uses computer algorithms to predict distribution of species 

in geographic space based on a mathematical representation 

of known distribution in environmental space (realized 

ecological niche), presence only data, allows for 

interpolation if data are few (e.g. MaxEnt)

Merow et al. 2013

Druon et al. in press

GLMs (generalized 

linear models)

Linear regression based models, uses a transformation to 

cope with non-normal distributions of response variable (x)

Becker et al. 2010

Forney et al. 2012

GAMs (generalized 

additive models)

Non-parametric extensions of GLMs, flexible and automated 

way to identify/describe non-linear relationships between 

predictors and response

Becker et al. 2012, 2014, 

2016

Ferguson et al. 2006

Forney 2000
15

Spatial regression Regression method that explicitly includes spatial 

information related to observations, important for 

highlighting spatial processes in data, spatial effects 

incorporated via spatially lagged independent variable, 

spatially lagged dependent variable, or spatially lagged error 

term

NOAA 2012

See “Case Study: Projecting 

ringed seal distributions” 

text box in this report

Machine Learning

Regression Trees Numeric or continuous response variable, fits regression 

model to response variable using each independent variable, 

for each independent variable data are split recursively

Dell et al. 2015

Process-Based 

(Mechanistic)

Atlantis A modular whole-ecosystem model based on the 

Management Strategy Evaluation approach that incorporates 

biophysical, economic, and social processes

Fulton et al. 2004

Models of Intermediate 

Complexity for 

Ecosystem assessment 

(MICE)

More complex than single-species models but less complex 

than ecosystem models. Includes ecological processes and 

can be connected to non-ecological models

Plaganyi et al. 2014

SEAPODYM (Spatial 

Ecosystem and 

Population Dynamics 

Model)

Numerical model initially developed for investigating 

physical-biological interactions between tuna populations 

and the pelagic ecosystem of the Pacific Ocean

Lehodey et al.

http://www.spc.int/ofp/seap

odym

APECOSM (Apex 

Predators ECOSystem 

Model) 

Goal to represent spatialized dynamics of open ocean pelagic 

ecosystems in global ocean 

Maury et al.

http://meece.eu/library/APE

COSM/apecosm.html

There are three general classes of models commonly used to guide management decisions: 

expert opinion, statistical extrapolation, and process-based models. Expert opinion or rule-based 

models are used most where a need exists for repeated response to management problems that 

remain similar over time. This approach facilitates integration of legacy information from a 

history of management applications and can be developed relatively rapidly in most cases. 

However, there may be little potential for evaluating the assumptions used in these models 

because assumptions behind expert opinion are not always apparent.

Statistical extrapolation models draw on data from past conditions of a particular system to 

project future conditions. These models may utilize, for example, species occurrence data, and 

climatic conditions at those locations, to create correlations that are used to extrapolate the future 

range of species under climate change. Although the underlying assumptions of extrapolation 

models are often transparent, models based on the past behavior of a system may not be suitable 

for projecting forward if these underlying ecological relationships are also sensitive to climatic 

conditions. 
16

Process-based, also called mechanistic, models rely on explicit assumptions about how a system 

works and are grounded in ecological theory. These are built on causal mechanisms rather than 

correlation which can increase the level of confidence in extrapolating beyond known data and 

can allow for the partitioning of uncertainty in predictions. Nonetheless, use of mechanistic 

approaches has constraints due to high computational costs (owing to the model’s complexity), 

reliance on assumptions that conditions will persist into the future, and the need to incorporate 

uncertainty regarding ways in which ecological processes will respond to novel global 

conditions.

Below, we provide descriptions of some models discussed at the workshop with application to 

modeling marine mammal occurrence. A fuller description of the range of models available and 

their uses and limitations can be found in Redfern et al (2006), Plaganyi et al. (2007), Stock et al. 

(2011), and Cuddington et al. (2013).

Expert-Based Models

Modeling options are limited for those species for which little ecological information is known, 

and in such cases, conceptual models based on expert opinion are the best choice. In general, 

forecasts from such efforts will be based on a conceptual distribution map and expert opinion on 

how the distribution may be affected by climate change. Uncertainty in such predictions will be 

high and difficult to describe quantitatively.

An example of a simple conceptual model that relies only on a description of a species’ 

distribution is the seasonal distribution of North Atlantic right whales described by Winn et al. 

(1986), who identified the regions where the animal occurs at different times of the year based 

on known sightings records. In this case, relatively simple characterizations of seasonal 

occurrence were displayed visually.

Vulnerability assessments often utilize expert judgment to cope with gaps in observational data

and predictive capability. Vulnerability assessments incorporate exposure, sensitivity, and 

adaptive capacity of a suite of species to generate an overall vulnerability rank. Laidre et al. 

(2008) provided an example of assessing one of the components of climate vulnerability, 

sensitivity to climate change, for Arctic marine mammal species. NMFS is currently developing 

a trait-based, semi-quantitative climate vulnerability assessment methodology for marine 

mammals that uses a mixture of expert opinion and empirical data to generate relative 

vulnerability ranks among species and highlight those traits and exposure factors that most 

contribute to species climate vulnerability.

Statistical Models

The availability of sighting data and environmental information increases the variety of statistical 

approaches that can be used to construct habitat models. Each type of modeling approach seeks 

to relate the occurrence or abundance of a species with particular environmental conditions; 

suitable habitat is assumed to occur where those conditions exist. The simplest of the approaches 
17

are correlative envelope models, which use the observed range of environmental conditions 

where species occur to define habitat. For example, if most bottlenose dolphins (Tursiops 

truncatus) occur between SST of 10-15o C, then a map of all locations with surface temperatures 

between 10-15o C is a map of suitable bottlenose dolphin habitat. Kaschner et al. (2006) used 

envelope models based on observed relationships between basic environmental conditions and a 

given species’ presence to predict the average annual geographical ranges of marine mammal 

species on a global scale.

In essence, these models are descriptive – based on a statistical relationship between a species 

and the environment. Species distribution can then be “predicted” under specific environmental 

conditions based on the statistical relationship. These models assume that the statistical 

relationship is unchanging (i.e., stationary). Such models are effective at predicting a species’ 

fundamental niche (the set of environmental conditions under which a species can survive; 

Hutchinson 1957) but may not accurately capture the species’ realized niche (i.e., the species’ 

actual distribution that reflects predator and prey dynamics, interactions with the physical 

environment, historical events, and other factors). The realized niche is most relevant in the 

conservation and management context. 

More sophisticated statistical techniques, such as generalized linear or additive models, fit

functional relationships between species distribution (occurrence or abundance) and 

environmental variables (e.g., as surface temperature increases from 10 to 15o C, the abundance 

of bottlenose dolphins increases in areas exhibiting those temperatures, but in waters with 

surface temperature greater than 15o C, bottlenose dolphin abundance remains constant). Maps of 

habitat showing spatial/temporal variability in the probability of occurrence or abundance can be 

generated from these statistical relationships. Predictions of a species’ distribution can be 

generated using expected future values of the environmental variables used in habitat models 

(e.g., Becker et al. 2012). As an example, predictive habitat-based models for 11 cetacean 

species in the California Current Ecosystem were developed using a variety of dynamic 

environmental variables, including temperature, salinity, sea surface height, and mixed layer 

depth (Becker et al. 2016; see “Case Study: Modeling cetacean density in the Pacific Ocean” 

below).

Process-Based or Mechanistic Models 

Process-based models are based on a theoretical understanding of relevant underlying ecological 

processes. These models provide a way to predict an organism’s specific response to altered 

environmental conditions and an ability to extrapolate beyond known conditions. These features 

make process-based approaches well-suited to guiding management decisions under conditions 

of rapid global change (Cuddington et al. 2013).

In the last two decades, technical advances in observational data collection and access to large 

environmental databases have enabled shifts from simple exploration of correlations between 

marine mammals and their environment toward models that established functional relationships 
18

with the physical and biological underpinnings of habitat utilization (Gregr et al. 2013, Cribb et 

al. 2015). This, in turn, has led to increased use of process-based modeling and increasingly 

robust predictions of species distributions rooted in ecological understanding (Palacios et al. 

2013). When constructing process-based models it is essential to consider ecological processes 

relevant to the species under study, the scientific questions being addressed, and appropriate 

spatial/temporal scales.

Recent examples of this approach include modeling California sea lion (Zalophus californianus) 

habitat utilization and foraging success based on biogeochemical, regional ocean circulation, and 

forage fish submodels (Fiechter et al., in review). Baumgartner and Mate (2005) used water 

depth, depth gradient, bottom hydrographic properties, SST, chlorophyll concentration, and other 

features to characterize North Atlantic right whale habitat. 

Choosing a Model

Choosing the appropriate model to assist in predicting future conditions is an important step in 

pursuing a research/management endeavor for a particular situation. As indicated earlier in this 

report, developing various models and model type selection will be based on such things as a 

priori knowledge of and existing data related to the life history, distributional ecology (e.g., prey 

preferences), and population dynamics of the species of concern; the state of model development 

for the species of interest; the space and time scales of the required prediction; observational 

constraints for evaluating past model performance; and whether existing models are based on 

environmental factors that can be projected into the future (Fig. 2). Levels of uncertainty inherent 

to habitat and population dynamics models, in turn, are directly related to the amount known 

about a particular species’ ecology and demography. In some instances, use of proxies for certain 

unknown variables may be the best (or only) approach. If sufficient analytical time and funding 

are available, and the abundance, distribution, and behavior of a species and relevant other 

variables (e.g., oceanographic) are known, multi-disciplinary teams should be assembled to 

assess modeling options and the ranges of forecasting ability (Fig. 2).

Choosing a Model – Modeling Issues and Obstacles 

In this section we review some of the major elements and concerns involved in modeling. We 

first discuss general conclusions about the difficulties of modeling marine mammal distribution 

and response to climate change; then, address uncertainty and model limitations, data types and 

their limitations, and use of physical data proxies. The section concludes with a summary of 

features unique to marine mammals which may be intractable in modeling. 

A key discussion area at the workshop was the extent to which certain SDMs can be useful for 

predicting future marine mammal distribution. Participants indicated that a variety of marine 

mammal characteristics introduce uncertainty into modeling efforts. As long-lived endotherms, 

marine mammals are not tied tightly to specific physical environmental characteristics, and the 

behavior of individuals -- and the ability of individuals to respond to change within the course of 

a season or lifetime -- introduces variability that may be difficult to capture in modeling efforts 
19

encompassing only a few marine mammal generations. As discussed below, this limits use of 

proxy variables that might otherwise be effective in modeling low trophic level responses that 

influence marine mammal distributions. While some groups, such as the baleen whales that feed 

on lower trophic levels may exhibit tight coupling to environmental variables, odontocetes 

feeding at higher trophic levels may not. These considerations need to be addressed in any 

modeling study, and tempered the workshop’s overall expectations about the ease and utility of 

modeling approaches to predict future distributions of marine mammals at the scale of climate

change.

Various models used to predict impacts of climate change on marine organisms (and discussed in 

this report) may not be appropriate for all marine mammal species. In an important example, the 

primary climate-related threat to Hawaiian monk seals (Monachus schauinslandi) will likely be 

sea level rise and loss of vital haul-out areas. Therefore, a wholly different type of modeling 

(dynamic shoreline evolution) would be appropriate for forecasting their future distribution.

Participants reviewed case studies presented at the meeting. In particular, the work of Forney et 

al. (2012) provided state-of-the-art modeling in well-characterized ecosystems with species for 

which distribution and abundance are known from systematic survey efforts (see text box on 

“Case study: Modeling Cetacean Density in the Pacific Ocean”). This work is of value in 

providing real-time and seasonal or annual predictions of the presence of a number of marine 

mammal species in the California Current ecosystem. On a much longer time scale, biological 

opinions developed in support of ESA listing decisions for Arctic marine mammal species 

provided the most relevant example of modeling on large spatial and long temporal scales (see 

text box). 

Between these two extremes there are few examples of modeling and prediction in the medium 

term. In fact, the workshop drew to a large extent on studies involving marine fish and 

invertebrate species to provide context for this type of analysis. In these studies, historical catch 

data and other information were used to quantify shifts in distribution to date and to project 

expected distributional changes (e.g., Hare et al. 2012a, Pinsky et al. 2013). However, because 

marine mammals differ in life histories from these taxa, and due to a paucity of ecological or 

(regional-scale) distributional data for many marine mammal populations, this type of analysis 

may not be possible for marine mammals in the near term. Therefore, the workshop concluded 

that one of the most effective ways forward for studies involving marine mammals will be to 

continue to expand the capabilities of current short-term modeling efforts to encompass longer 

time-frames and larger areas, and perhaps to narrow the geographical scale of modeling (e.g., 

from circumpolar to regional basins) for populations for which broad scale predictions have been 

developed on decadal or centurial time frames.
20

Figure 2. Decision matrix for choosing an approach based on project timeline and available data.

The Continuum of Existing Knowledge

Redfern et al. (2006) described habitat modeling along a continuum of ecological knowledge for 

a given cetacean species (Fig. 3). For species about which little knowledge exists, habitat models 

based on scant data may be strengthened by the use of expert opinion to describe species’ 

distributions on coarse time and space scales. As knowledge improves, habitat models can be 

made more detailed and quantitative, and can be used to predict distribution over short temporal 

and small spatial scales. For some species (e.g., a suite of cetacean species occurring in waters 

off the U.S. west coast (Becker et al. 2012, Forney et al. 2012), and in New England waters (Best 

et al. 2012)), considerable work has been done to develop habitat models; these species are good 

candidates for making limited projections of future distributions. With a high level of ecological 

knowledge, habitat models can be used to develop specific testable hypotheses for process-based

studies, which would in turn provide data and results to improve habitat models and lead to 

refined hypotheses for future testing. 
21

Models based on empirical relationships (i.e., involving a correlation or relationship based solely 

on observation rather than theory) are considered reliable for short time-scale predictions where 

conditions over the predicted time-scale are not expected to evolve beyond the range of 

observations. Myers (1998) noted that such relationships may break down even over short timescales; thereby indicating the importance of understanding mechanistic relationships (i.e., those 

founded on ecological processes) for even short time horizons. Therefore, for multi-decadal 

climate change applications, mechanistically informed empirical relationships provide a starting 

point, but projections based on robust physiological and ecological principles expected to hold 

under climate change are essential for building confidence in future projections (Stock et al. 

2011). Mechanism-driven models that identify trophic links between oceanic processes, prey,

and cetaceans have been limited to planktivorous species with very short trophic linkages (e.g., 

Baumgartner et al. 2003, Croll et al. 2005). Iterative sequences of hypothesis testing and 

improvement of habitat models enhance spatial ecology predictions (Palacios et al 2013). The 

addition of improved physiological/ecological elements should be done iteratively as new 

insights are gained (e.g., Cheung et al. 2009, 2010) as long as a frank assessment of remaining 

uncertainties is always provided, including those in the climate (i.e., emissions) scenarios 

themselves. 

Irrespective of the current understanding regarding a particular group and its relationship to its 

physical or biological environment along the knowledge continuum, the goal of an incipient 

modeling effort is to build knowledge to the point where a model of a species’ distribution can 

be developed and its “nowcast” capability validated. At this point the potential for using the 

model for decadal or centennial forecasts can begin to be explored.

Figure 3. Models can be used to increase ecological knowledge through iterative hypothesis

(from Redfern et al. 2006).

Uncertainty and Model Limitations

Best practices for developing and using projections of marine mammal responses to climate 

variability and change include careful assessments and communication of confidence and 

uncertainty inherent in those analyses (see Stock et al. 2011). Quantitative and qualitative results 

of confidence assessments are essential for informing management decisions, evaluating 

management strategies, and providing a basis for risk analyses (Smith et al. 1999). The most 

successful management approaches are those that explicitly incorporate uncertainty (e.g. Taylor 

et al. 2000). 
22

Strengths and weaknesses inherent to both the information incorporated into models and the 

ensemble of outputs determine the role of uncertainty in quantities of interest (e.g., future 

population size, likelihood of ice loss). As noted, a clear understanding of these factors helps 

managers interpret the scientific information and also helps prioritize data needs.

Because it is not possible to know precisely what the future holds, any prediction should identify 

some level of confidence in the likelihood that prediction will be realized. People are provided 

with this kind of information in short-term weather forecasts that are presented in terms of 

“percent chance of rainfall” for specific places and time frames. Likewise, seasonal climate 

forecasts are couched in terms of probabilities for specific outcomes with, for example, 

statements like “the upcoming winter has a 60% chance of being wetter than normal in southern 

California, a 25% chance of being normal, and a 15% chance of being dryer than normal.”

Statements about uncertainty might also include indications of levels of confidence around an 

expected percent change in a particular forecast. Following the previous illustration, this would 

be expressed as “the upcoming winter has a 60 ± 5% chance of being wetter than normal.”

Three main sources of uncertainty in global climate projections are the evolution of factors 

forcing climate change (e.g., greenhouse gas emissions), climate model differences, and natural 

variability. Multiple forcing scenarios might be used to account for these uncertainties and are

generally constructed using projections with the same climate model and emissions scenarios

(e.g., Nakicenovic et al. 2000, IPCC 2007, Moss et al. 2010) but different initial conditions. Even 

slight differences in initial conditions can result in rapid divergence of outputs. The result is an 

array of climate change scenarios with a range of outcomes (Knutti and Sedláček 2013) where, in 

some cases, even the direction of change in key variables may differ. Uncertainties in the 

biological response to climate variability compound uncertainties in projected climate forcing 

(Cheung et al. 2016). Downscaling global climate projections to regional scales, in which 

regional-scale climate projections are extended to biological impacts, may also increase this 

uncertainty.

Estimating uncertainties in projected marine mammal responses to climate change and in the 

climate models themselves should consider whether and to what extent the model: a) is based on 

physiological and ecological principles expected to hold over the space and time scales of 

interest; b) adequately re-creates past (i.e., hindcast) marine mammal responses to climate 

change over the space and time scales of interest; and c) captures primary uncertainties in marine 

mammal responses.

Predictions over short time-scales (i.e., days to seasons) generally allow for testing of the 

stability of ecological principles over time and space and, because predicted conditions are often 

within the range of past variations. Climate change projections, in contrast, can often be tested 

against very limited knowledge of past long-term trends and must project into novel 

oceanographic conditions. Testing for confidence must thus rely heavily on the stability of 
23

ecological underpinnings and confidence is reduced by empirical relationships that lack strong 

mechanistic foundations.

Assessment of modeled marine mammal response to climate change should emphasize testing 

over similar space- and time-scales in a manner that mimics the eventual prediction as closely as 

possible. Use of separate data sets to test and train a model (or, out-of-sample testing) is 

important for short time-scale predictions. Testing against existing knowledge of past climate 

change responses or observed responses is emphasized for longer time-scale projections. 

Consultation with experts provides the initial foundation for assessing each of these variables

and should allow development of a clearly articulated rationale for the model structure and 

permutations in its structures. Sensitivity analyses may be needed to assess uncertainty under a 

number of alternate scenarios. However, even a single projection can be useful in elucidating the 

magnitude, direction, and potential mechanisms underlying projected changes. 

Case Study: Modeling Cetacean Density in the Pacific Ocean

NOAA’s Southwest Fisheries Science Center 

(SWFSC) has developed and validated predictive 

habitat-based density models for cetaceans using an 

extensive line-transect survey data set that covers 

broad areas of the eastern Pacific Ocean (Fig. A). 

Their work includes a variety of projects that have 

advanced the science of species distribution 

modeling in several key areas, including comparing 

the effectiveness of various modeling frameworks, 

evaluating different spatial and temporal resolutions 

of input variables, and developing methods to 

characterize uncertainty in model predictions 

(Barlow et al. 2009). Models have been developed 

and validated for 25 species or species groups in 

three broad study areas - the California Current 

Ecosystem (CCE), the eastern tropical Pacific 

(ETP), and the central Pacific (Fig. A; Forney et al. 

2012, 2015; Becker et al. 2016). Model validation has included internal cross-validation during 

the model selection process (Forney 2000, Barlow et al. 2009, Becker et al. 2010, Forney et al. 

2012), predictions based on novel data sets (Barlow et al. 2009, Becker et al. 2012, Forney et al. 

2012, 2015, Becker et al. 2014), and expert opinion (Barlow et al. 2009, Forney et al. 2012). 

All of the above models were developed within a generalized additive modeling framework 

(Hastie & Tibshirani 1990) at ecosystem-dependent scales ranging from 2 km to 120 km (e.g., 

Forney 2000, 2012, Ferguson et al. 2006, Redfern et al. 2008, Becker et al. 2010, 2012, 2016). 

Dynamic environmental covariates used in the models included remotely sensed data (e.g., sea 

Figure A. Transect coverage for 

surveys conducted by SWFSC between 

1986 and 2006 in three broad study 

areas in the eastern North Pacific.
24

surface temperature [SST] and its standard deviation, chlorophyll concentration [CHL], sea 

surface height [SSH], and SSH root-mean-square variation), and data collected in situ during the 

line-transect surveys (SST, CHL, sea surface salinity [SSS], mixed layer depth [MLD], and 

thermocline depth and strength). More recently, dynamic variables from ocean models have been 

used as potential predictors in the habitat models, including SST and its standard deviation, SSS, 

MLD, SSH and the standard deviation of SSH (Becker et al. 2016). The habitat predictors 

included in these models all serve as proxies for unmeasured underlying ecological processes 

linking cetaceans to their prey.

Models have successfully captured variability in 

cetacean density and distribution at seasonal and 

interannual time scales (e.g. Fig. B, Forney et al. 

2012, Becker et al. 2014, 2016). While the multiyear average models provide a valuable tool for 

managers (e.g., Redfern et al. 2013), they only 

reflect historical data and the variation therein and 

do not take into account current or future 

conditions. Given emerging emphases on dynamic 

ocean management (e.g., Hobday et al. 2014, 

Maxwell et al. 2015) and the need for addressing 

species distribution relative to climate change,

habitat predictors from ocean circulation models 

offer opportunities for dynamic predictions. 

Becker et al. (2012) demonstrated that advanced 

satellite data and forecasts from ocean models 

allow “nowcasts” of marine mammal distributions 

on time scales of days to weeks and forecasts on 

time scales of 3-4 months. While the SWFSC 

studies have not yet directly addressed climate 

change, they have developed robust methods and extensively validated habitat-based density 

models for cetaceans, primarily in the CCE. A recent study demonstrated that ocean circulation 

models provide robust predictive models of cetacean distributions, showing promise for future 

predictions of marine mammal distributions in a changing climate (Becker et al. 2016). However, 

the models rely on proxy variables (i.e., they are not mechanism-driven models), and future 

forecasts can fail if the proxy relationships change. Future steps require additional model

validation, particularly at different spatial resolutions and longer temporal scales.

Figure B. Model-based estimates of fin 

whale summer/fall density (animals/km-2)

and distribution for six different years, 

1991-2008, in the California Current 

Ecosystem. Black dots show actual 

sighting locations during ship surveys 

conducted in each year. (Details provided 

in Becker et al. 2016).
25

Data Types, Data Evaluation, and Data Limitations

In this section, we describe a conceptual framework and rationale for evaluating data available 

for incorporating into forecast modeling. We define data broadly to include in-situ observations, 

remotely-sensed data, model output, and proxies, among others. 

Data used in models are diverse (e.g., physical, biological, biogeochemical) and the amount of 

data available to address a particular question may vary. Situations can be characterized as data 

or model rich/poor. Science strives to move from data, model, and prediction poor scenarios to 

data, model, and prediction rich conditions (Fig. 4). In any modeling exercise, data limitations

and gaps should be identified and considered when determining appropriate modeling 

approaches. As discussed above, even some data poor and model poor approaches can be 

valuable and may serve management needs in specific situations (see section on “Types of 

Models, and Selecting Appropriate Models” above). And, as also previously indicated in this 

report, expanding collaborators and disciplines engaged in the effort and seeking input from 

multidisciplinary teams can be vital to building a robust forecast modeling endeavor.

Figure 4. Model and data characteristics and their relationship to model prediction and 

forecasting capabilities. The goal of science is to move from data, model and prediction poor 

scenarios to data, model and prediction rich scenarios. 

Physical Data as Proxies

Forecasting future species distributions requires the use of habitat variables and ecological 

processes that can be suitably and confidently projected into the future. Mechanistic processes 

within a food web or ecosystem can be difficult to measure and model and biological parameters 

or environmental data may be few or unavailable. Highly detailed models will be of limited 

utility for future projections if the model variables cannot be reliably projected in ways that 
26

anticipate future climate change. These challenges have led modelers to search for simpler 

means, or proxies, to approximate certain processes. Physical oceanographic data are more 

readily observed and modeled than ecological, physiological, and food web processes and can 

serve as useful proxies for those processes.

Physical data are obtained from a variety of sources including observing stations, ship surveys, 

drifters, and satellites. The breadth of data sources results in a variety of observed temporal and 

spatial resolutions and a range of confidence levels, but generally physical data can be measured 

with finer resolution and greater confidence than exist for the biological/ecological data. 

Observational bases for physical data are more comprehensive than for ecosystems, and physical 

data can also be modeled more easily than biological data, with several models providing 

physical proxy projections that can be used as inputs for biological models (e.g., Stock et al. 

2015, Saba et al. 2016).

Physical parameters discussed during presentations at the workshop included:

• Chlorophyll concentration

• Distance to mainland

• Distance to shelf or isobath

• Mixed layer depth

• Salinity

• Sea floor aspect

• Sea floor slope

• Sea ice concentration

• Sea surface height

• Sea surface temperature

• Snow depth 

• Water depth

Physical variables form the basis for correlative envelope models (discussed above in the section 

on “Types of Models and Selecting Appropriate Models) that establish a statistical relationship

between a modeled species and a variable or set of variables in its physical environment. Species 

distribution modeling assumes those relationships will continue into the future (i.e., relationships 

remain stationary) and distributions can be projected into novel conditions (e.g., extrapolation). 

However, there may be no assurances that the relationship will hold under new or changing 

conditions or that the relationship is causative.

Modeling with Proxy Variables

Physical proxies tend to accurately represent linkages to the physical environment for species 

feeding at low trophic levels. Therefore, well-documented relationships with specific parameters 

(e.g., phytoplankton) can be approximated using physical parameters. Many fish species exhibit 

tight linkages to physical oceanographic features because they are ectothermic and may be 

limited to particular water masses or temperatures. Some marine mammal species have some 

degree of flexibility and may be able to tolerate a wider range of temperature regimes than many 

fish or invertebrate species. Nonetheless, some species such as blue whales (Balaenoptera 

musculus) have shown tight coupling to certain physical oceanographic features (Fiedler et al. 

1998, Moore et al. 2002), likely because they feed on low trophic level organisms that are 

themselves tightly linked to the physical environment. Other marine mammal species likely 
27

exhibit similar relationships, e.g., bowhead (Balaena mysticetus) (George et al. 2015) and right 

whales and thus the use of proxy variables may be appropriate. Modelers can expect far less 

success with dependence on proxy variables with higher trophic level predators such as sperm 

whales (Physeter macrocephalus) or small odontocete species that are prey generalists, because 

such efforts are complicated by multiple trophic level interactions and the subject organism’s

prey may be only indirectly linked to the physical environment.

A given projection model may not specifically include chlorophyll concentration, for example,

but this feature might serve as a proxy for higher trophic level relationships in modeling future 

conditions. Similarly, bottom water temperatures as related to animal occurrence may not be well 

captured in a forecasting model, but statistical downscaling can be used to develop an indicator 

of bottom water temperature in the future. The same holds true for biological parameters which 

may not be available for the species of interest, but information from a closely related species or 

various environmental factors could be used as a proxy instead. 

In some cases, it may be worthwhile to develop more “naïve” models that use proxy variables

that are both currently available and can be incorporated into future projections, as opposed to 

the use of more conceptually “correct” variables (e.g., environmental variables on a small spatial 

scale) that are not available for future projections. An alternative to using proxy variables 

directly is to identify statistical relationships between proxy variables and the variables of 

interest. For example, Spencer et al. (In press) found a relationship between the distribution of 

arrowtooth flounder (Atheresthes stomias) in the eastern Bering Sea and the “cold pool” (bottom 

water ≤ 2 °C). Projections of the future size of the cold pool were obtained from a statistical 

relationship between cold pool area (the variable of interest) and sea level pressure and sea ice 

extent (the proxies), which were available from global climate models. Thus, the location and 

extent of these cold water features provided a proxy by which the expected distribution of the 

flounder might be forecast.

Beyond Physical Data – Modeling with Tractable and Intractable Features

A range of factors other than the physical environment will affect marine mammal modeling 

efforts, including trophic interactions (distribution of predators, competitors, and prey), disease 

and parasites, and anthropogenic factors (e.g., disturbance, fishing pressure, shipping). The

possible effect of these factors might be broadly characterized by running a range of simulations 

of their impact on potential future distributions. Some factors may be tractable within current 

modeling approaches, and they might be included in the initial baseline model. In other words,

some generalizations can be made for all marine mammals (e.g., long generation times may 

make behavioral plasticity more important than evolutionary responses over the short term), 

while other features may be species specific (e.g., degree of site fidelity to haul-out sites or 

migration routes). 

In contrast, some factors (e.g., species plasticity, prey switching, resilience, evolutionary effects

(Forcada et al. 2006), behavioral changes, and site fidelity) are difficult to model and might 
28

significantly alter model results. In particular, the ability of marine mammals to alter their 

behavior or food sources can act as a buffer to measurable climate-change induced changes, and 

can delay any adverse effects, or mask them until critical thresholds are reached. Ignoring such 

behavioral effects in models can result in overly pessimistic projections, with effects predicted as 

occurring too rapidly. At the same time, models may provide a longer term, more visionary 

picture that uncovers larger trends or underlying directional processes, which may be otherwise 

masked in observations of near-term marine mammal behavior. Such a mismatch between 

projections and reality can undermine faith in the model projections.

In addition, “surprise” or stochastic events cannot be forecast. Variables such as the potential for 

a very warm year or a year with large-scale sea ice loss may adversely affect model outputs. 

These might be modeled generally, but the precise timing and magnitude may not be predictable.

Therefore, the key is to identify and incorporate the well-understood variables during initial 

model development and then include those that are more intractable to provide a selection of 

plausible alternative responses. 

Exemplar Species/Population(s) for Assessing Modeling Methodologies

One of the workshop’s principal objectives was to “identify one or more exemplar 

species/population(s) for assessing [modeling] methodologies or metrics.” We approached this 

by first discussing features of various marine mammal populations, complexes of species, or 

regions that might represent good candidates for further study. The goal was to identify 

populations (e.g., data rich, highly vulnerable) most suitable for proof-of-concept forecast 

modeling, and conversely those that might have a somewhat lower priority (e.g., high abundance 

or low management needs). 

The workshop concluded that species or populations exposed to existing threats and vulnerable 

to large scale ecosystem shifts, that represent a current management priority, or occur in rapidly 

changing ecosystems should receive most emphasis in consideration of next-generation 

forecasting studies. In addition, selection of marine mammal populations for modeling studies 

might be based on a balance of interacting (or competing) factors. For example, it may be of 

relatively limited added value to devote resources to a population that is abundant and increasing 

in size and/or of relatively low management need priority, and for which modeling capabilities 

may be limited (e.g., sperm whales) (Fig. 5). Conversely, greater emphasis might be placed on 

species where, in combination, both modeling capability is high and the management need is 

great (e.g., ice or Hawaiian monk seals). 
29

Figure 5. The range of increasing modeling capability and management need for some 

illustrative marine mammal populations. Species and their relative positions are provided for 

illustration purposes and may not indicate an official agency prioritization.

Highly Vulnerable Species or Populations

Projecting the effects of climate change on highly vulnerable populations, particularly in cases 

where known threats are already occurring, should be a high priority. Some marine mammal 

populations are experiencing known or suspected population declines or slow population growth 

(e.g., vaquita (Phocoena sinus), Cook Inlet beluga whale (Delphinapterus leucas), Hawaiian 

monk seal). Species or populations with limited ranges, specialized diets, or similarly limiting

ecological features may be particularly vulnerable to a changing climate. Such species may also 

be exposed to additional known anthropogenic impacts (e.g., incidental by-catch in commercial 

or artisanal fisheries) or habitat perturbations, thereby increasing their susceptibility to ecosystem 

changes should they occur within their ranges. Future effects of climate change on these and 

other vulnerable populations are not known, but the possibility of such effects should be 

evaluated.
30

A recent workshop and related follow-up discussions focused specifically on identifying which 

protected marine vertebrates (i.e., pinnipeds, cetaceans, and turtles) were likely to be vulnerable 

to climate change, and identifying the attributes that make them vulnerable (NOAA, in draft). To 

the extent possible, those marine mammal populations deemed most at risk as a result of these 

vulnerability assessment efforts should receive high priority when considering future forecasting 

exercises. The workshop we describe here avoided duplication of that discussion – but the 

findings of that work, when completed, should be factored into decisions regarding next step 

modeling studies. 

Management Priority

As indicated in the “Resource Management Needs and Modeling Horizons” section above, a

number of depleted or recovering populations should be afforded high priority when considering 

climate change-related modeling work. Among these are ESA-listed marine mammal species. 

By way of example, NMFS recently identified three “Species in the Spotlight” marine mammal 

populations -- those most at risk of extinction in the near future: Cook Inlet (Alaska) beluga 

whales, Hawaiian monk seals, and southern resident killer whales (Orcinus orca). These 

populations are likely candidates for modeling studies because they fit a number of criteria 

identified here (i.e., small population sizes, exposure to known threats, and relatively data rich). 

In any case, government-fostered and funded forecasting research on climate change impacts on 

marine mammals might focus first and foremost on those populations with the greatest 

management needs (Fig. 5).

Populations Inhabiting Rapidly Changing Environments

Plans for predictive modeling efforts might also include species that are residents or seasonal 

migrants in high latitudes. In fact, forecasting work has already been done on polar bears (Ursus 

maritimus), ice seals, and walruses (Odobenus rosmarus) in the context of developing biological 

opinions for proposed ESA listings (Jay et al. 2011). High-latitude regions are exhibiting more 

rapid climate change than low-latitude regions (e.g., Doney et al. 2012, MMC 2012, Thomas et 

al. 2015). Marine mammal populations (and their prey) occurring in these locations may exhibit 

changes in relative abundance and possible range expansions or contractions, or other ecological 

disruptions sooner than ecosystems at lower latitudes (e.g., Laidre et al. 2008, Moore and 

Huntington 2008, Gilg et al. 2012). Rapid shifts in occurrence or availability of key prey species 

in these areas may likewise be expected in the foreseeable future (Thomas et al. 2015). Perhaps 

most important among these are ice-obligate species, particularly when ice provides a platform 

for raising young (e.g., ringed seal (Phoca hispida) pupping lairs) (NOAA 2012), a source of 

prey (e.g., polar bears), or where key prey species are closely or directly linked to ice (e.g., 

Antarctic fur seals (Arctocephalus gazella)) (Forcada et al. 2008).

In addition, indications regarding the magnitude of climate change are themselves evolving. As 

an example, a recent high-resolution climate model has indicated that the northeast U.S. shelf 

and specifically the Gulf of Maine may warm about two times faster than previously thought 
31

(Saba et al. 2015). This enhanced warming results from a climate-forced change in circulation 

which is not simulated in models with lower resolution. Based on new information about the 

magnitude of change in certain locations, or in similar situations where model capabilities are 

improving, evaluation of the updated impact to marine mammals in waters off New England and 

eastern Canada should be a priority.

Data-rich Populations

Much more is known about the ecology of and the drivers of distribution and occurrence for

some populations and in some locations (e.g., blue whales (Fiedler et al. 1998, Croll et al. 2005) 

and North Atlantic right whales (Baumgartner et al. 2003)) than for others. As indicated 

elsewhere in this report, in cases where abundant data exist on marine mammal occurrence, and 

the environmental features (prey, in particular) linked to seasonal and annual occurrence, 

forecasting will be more robust and uncertainties fewer than for data-poor populations.

Therefore, modeling data-rich populations is highlighted given the relative precision in 

predictive capabilities is high. These subjects are of particular importance as providing testing 

grounds to identify and test methods and parameters which might then be used for less tractable 

species and areas. In addition, these studies are cost-effective inasmuch as data already exist for 

these populations.

Species Complexes

To maximize use of limited (analytical) resources, consideration should be given to systems

where distributional shifts of multiple species, even at multiple trophic levels, could be modeled

(for a discussion, see section “Analytical Tools: Selecting Appropriate Models” above). If the 

ecology of several species, particularly as they are interrelated (e.g., predator-prey relationships), 

is relatively well-known, managers may benefit from predictions of expected changes occurring 

in suites of species in a given system. 

Given the level of effort that has been devoted to characterizing their physical and biological 

components, as well as their marine mammal populations, the California Current and Bering Sea 

ecosystems are particularly important areas for such modeling efforts. Regional marine mammal 

species complexes have been the subject of predictive modeling studies is in the California 

Current (Redfern et al. 2013, Becker et al. 2012, 2014, 2016, Dransfield et al. 2014). Strong 

consideration should be given to continuing these studies because adequate environmental and

species distribution data already exist, linkages between species occurrence and their 

environmental drivers have been studied, and these systems are relatively well-known and have 

been well-modeled. Inasmuch as these are ongoing studies, they are a more cost-effective (i.e., 

relatively less commitment of resources up front) means of ongoing proof-of-concept and model 

validation than situations where data collection and model development/evaluation might require 

a costly initiation of research. They provide a means to capitalize on interdisciplinary work 

already conducted on other taxa and will maximize use of available resources.
32

Confounding Factors for Species Selection

As noted above, the capacity for a species or individual to accommodate changing ecosystems 

may depend in the short term on aspects of its behavior and ecology (Trathan et al. 2007). For 

example, species that feed on diverse prey types (e.g., bottlenose dolphins, humpback whales), 

with relatively flexible phenologies, or are not dependent on a specific substrate or location may 

experience relatively fewer impacts from changing ecosystems than those that are reliant on a 

particular prey (e.g., crabeater seal (Lobodon carcinophaga), North Atlantic right whales), 

habitat type, or location. Therefore, those species with relatively narrow behavioral or 

phenotypic plasticity might also receive priority in future modeling studies.

Climate change impacts may have disruptive influences at multiple trophic levels (Doney et al. 

2012, Sydeman et al. 2015). Complex food webs may experience trophic cascades and food web 

disruptions at drastically different scales and rates than those with only primary or secondary 

trophic interactions. In addition, some commercially valuable mid-trophic level fish species may 

be exploited by fisheries – a situation with added ramifications for marine mammal species that 

prey on the same resource (Forcada et al. 2012). Top-level predators such as killer whales, polar 

bears, and leopard seals (Hydrurga leptonyx) may be impacted differently than those preying at

lower trophic levels, such as right whales and walruses. Therefore, systems with relatively few 

trophic connections (e.g., those involving some baleen whale species) may be relatively simpler 

to model (where data are available) than those with complex food webs. 

Summary – Exemplar Species

In sum, next-generation predictive modeling exercises might reasonably focus first on 

species/populations a) that are depleted or currently undergoing declines in abundance; b) that 

represent high management priority because they are exposed to multiple anthropogenic threats; 

c) for which aspects of their ecology are already quantified; or d) that occur in high latitude 

species. 

Next Steps

Suggested Research and Study Needs and Policy Considerations

Given workshop discussions and the findings described above, a number of short- and longterms actions might be pursued. 

Workshop Follow-up

By way of follow-up, agency leadership might consider constituting groups (particularly 

interdisciplinary groups), encouraging additional studies or data collection, engaging in advance 

budget planning to foster further work, and undertaking related actions subsequent to this

workshop in the following areas.
33

Manager-Scientist Interface

Ideally, processes for marine resource managers to effectively communicate research and 

analytical needs to researchers should be developed and/or improved. Fora for researchers to 

communicate to managers the strengths and weaknesses inherent in modeling studies, the 

importance of uncertainties in modeling exercises, and to help provide an awareness about 

assumptions and values that contributed to model outputs are also needed.

Methods to encourage this dialog might include establishing a working group or organizing a

follow-on workshop to address specific issues identified here. Goals for a workshop might 

include identifying prioritized lists of species (or populations), species complexes, or regions for 

specific modeling studies. These might be based, at least initially, on the criteria discussed above 

in the “Exemplar species/population(s) for assessing modeling methodologies” section and in 

conjunction with marine protected species vulnerability assessment work. These discussions 

should also include processes to identify specific modeling needs as changes in marine mammal 

distribution expose these animals to impacts from particular human activities (e.g., renewable 

energy lease sales) or as the need arises to develop new management measures (e.g., establishing 

critical habitat). It may also include identifying initial modeling trials involving both 

species/populations readily modeled as well as those representing greater challenges and 

uncertainties, which incorporate testing and validation of each. In this regard, attention might 

also be paid to the collaborations that support successful modeling efforts and consideration 

given to how to facilitate such teams (see below).

Forecasting Distributions of Illustrative Marine Mammal Populations – Conduct 

Modeling Case Studies

The workshop identified a number of marine mammal populations that, for various reasons 

(described in general terms in the “Exemplar Species/population(s) for Assessing Modeling 

Methodologies” section above), might serve well as case studies to project future distributions

and illustrate the state-of-play for current modeling capabilities. Consideration should be given 

to advancing these and related studies. These studies might focus intensive work on a few 

species (or suite of species) one by one – an approach that might then inform the modeling of 

other species. By way of analogy, “Potential Consequences of Disturbance” (PCoD) models 

(Harwood et al. 2014) have been used for in-depth study of individual marine mammal

populations (e.g., Costa et al. 2016). 

Model Validation

Modeling is an iterative process. Therefore, as a general matter, and as a follow-up to studies

described above, it is essential to plan for the testing or validation of these forecasts as additional 

data become available and as analytical approaches are refined (Fig. 3). Because every model 

contains some level of uncertainty, the workshop concluded that efforts are needed to continually 

refine models to diminish uncertainty to the extent possible. Therefore, processes should be 

developed for identifying future analytical work that includes systematic feedback with an 

emphasis on hypotheses testing and model validation using, for example, species-habitat models 
34

as well as hybrid (mechanistic and correlative) models to predict spatial distributions, and 

continually applying new information as it becomes available. These steps will improve overall 

modeling rigor and resolution and should be regarded as an ongoing exercise.

Ongoing Data Collection

As noted in the paragraph immediately above, predictive models are improved as new data are 

incorporated. Therefore, plans should be made, where practicable, to collect and make available 

additional marine mammal occurrence and environmental correlate data in locations and 

involving species where critical needs exist or where modeling studies are underway.

A central message from the workshop was that marine mammals may respond more to the 

distribution of their prey than to the environment. Therefore, studies should be fostered that 

provide increased observations and improved modeling of prey resources as drivers of marine 

mammal distribution (as well as the physical and biological features that dictate prey 

occurrence). In addition, although marine mammals are distributed broadly, including in habitats 

well outside the continental shelf, most monitoring programs are largely coastal in focus. As a 

result, it may be difficult to validate models and to detect changes in distribution and regional 

abundance of many species. Consideration should be given to developing (or prioritizing) 

monitoring networks that incorporate additional assets outside areas where marine mammals are 

traditionally studied. Passive acoustic technologies, including, for example, autonomous 

underwater vehicles and underwater gliders equipped with marine mammal acoustic detection 

capabilities – are technologies well-suited for this purpose, both because they can reach remote 

areas, and because they can collect important physical and oceanographic data along with 

biological data.

Time Scales for Modeling

For many marine mammal populations, “nowcasts” and projections in the short term (months to 

years) can be made with relative certainty, but projections in the longer term (decades to 

centuries) have less certainty. Many management needs are on relatively long time scales – so, 

uncertainties inherent to these scales are likely to remain a challenge for the foreseeable future. 

Therefore, intra- and inter-agency, multidisciplinary working groups might be established to 

design studies to address the level of uncertainties in projections on mid- an long-time scales, to 

identify specific ways in which next-generation marine mammal modeling studies might be 

refined, and determine ways in which long-time scale uncertainties can be addressed in 

management decisions.

The Value of Inter-/Multi-disciplinary Teams

A common theme among the presentations and in subsequent discussions was the importance of 

engaging multiple disciplines when establishing a project team. Including expertise in disciplines 

such as climate science, ecology, physical oceanography, statistics, marine resource 

management, and social science strengthens the separate components of the project to improve 

the overall utility and confidence in the project outputs. Project leads and research scientists 
35

should consider including expertise from related disciplines when planning modeling projects. In 

addition, seeking multi-disciplinary expertise should also be a practice fostered by agency 

leadership when modeling projects are under development.

Fiscal Planning

The influence of climate change on oceanographic processes and living marine resources will 

present challenges for managers into the foreseeable future. Accordingly, relevant agencies 

should begin now to engage in long-term fiscal planning to equip scientists and managers with 

the tools and resources needed to address these challenges. This should include planning for new 

and enhanced data collection and enhancing ongoing studies, and model development and 

refinement studies, particularly in settings where conservation/management needs are greatest.
36

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45

Workshop Participants 

* Member of the Workshop Steering Committee

Jason Baker, Ph.D

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

Seattle, WA, USA

Mark Baumgartner, Ph.D. *

Woods Hole Oceanographic Institute

Woods Hole, MA, USA

Elizabeth Becker, Ph.D.

Contractor to the 

Southwest Fisheries Science Center

National Marine Fisheries Service

Santa Barbara, CA, USA

Peter Boveng, Ph.D. *

Alaska Fisheries Science Center 

Marine Mammal Laboratory

National Marine Fisheries Service

Seattle, WA, USA

Dorothy Dick

Sea Grant Knauss Fellow

Office of Protected Resources

National Marine Fisheries Service

Silver Spring, MD, USA

Jerome Fiechter

Institute of Marine Sciences

University of California, Santa Cruz

Santa Cruz, CA, USA

Jaume Forcada, Ph.D.

British Antarctic Survey

Natural Environment Research Council, UK

Cambridge, UK

Karin Forney, Ph.D.

Southwest Fisheries Science Center

National Marine Fisheries Service

Santa Cruz, CA, USA

Roger Griffis *

Office of Science and Technology

National Marine Fisheries Service

Silver Spring, MD, USA

Jon Hare, Ph.D.

Northeast Fisheries Science Center

National Marine Fisheries Service

Narragansett, RI, USA

Alistair Hobday, Ph.D.

Commonwealth Scientific and Industrial 

Research Organisation (CSIRO)

Hobart, Tasmania, Australia

Daniel Howell, Ph.D.

Institute of Marine Research, Norway

Bergen, Norway

Kristin Laidre, Ph.D.

Applied Physics Laboratory

University of Washington

Seattle, WA, USA

Mattew Lettrich

Contractor to the

Office of Science and Technology

National Marine Fisheries Service

Silver Spring, MD, USA
46

Nate Mantua, Ph.D. *

Southwest Fisheries Science Center

National Marine Fisheries Service

Santa Cruz, CA, USA

Lori Quakenbush, Ph.D.

Alaska Department of Fish & Game

Fairbanks, AK, USA

Jarrod Santora, Ph.D.

University of California, Santa Cruz

Santa Cruz, CA, USA

Gregory Silber, Ph.D. *

Office of Protected Resources

National Marine Fisheries Service

Silver Spring, MD, USA

Paul Spencer, Ph.D.

Alaska Fisheries Science Center

National Marine Fisheries Service

Seattle, WA, USA

Charles Stock, Ph.D.

Geophysical Fluid Dynamics Laboratory

Ocean and Atmospheric Research

Princeton, NJ, USA

Kate Stafford, Ph.D.

Applied Physics Laboratory

University of Washington

Seattle, WA, USA

William Sydeman, Ph.D.

Farallon Institute

Petaluma, CA, USA

Peter O. Thomas, Ph.D. *

Marine Mammal Commission

Bethesda, MD, USA

Kyle Van Houtan, Ph.D. *

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

Honolulu, HI, USA

Robin Waples, Ph.D. *

Northwest Fisheries Science Center

National Marine Fisheries Service

Seattle, WA, USA
47

Agenda

Workshop on

Best Approaches and Needs for Projecting Marine Mammal

Distributions in a Changing Climate

12-14 January 2016

Santa Cruz, California

Workshop Objectives:

• Explore modeling or other means to assess future changes in distribution and 

abundance of marine mammal species;

• Identify metrics relevant to assessing physical changes and biological responses 

of marine mammals;

• Identify one or more exemplar species/population(s) for assessing methodologies 

or metrics (depending on the timing, exemplar species might include those 

considered most vulnerable in the climate change vulnerability assessments);

• Identify next steps for performing these studies;

• Produce draft and final reports of the findings.

Day 1

8:30-9:00

Opening, introductions, charge to the workshop, housekeeping

9:00-10:30 

Assessing the effects of climate change on marine ecosystems (15 minute presentations)

“Approaches/models used to characterize, and to forecast, physical and biological shifts 

in marine ecosystems in a changing climate” (Alistair Hobday, Commonwealth 

Scientific and Industrial Research Organisation (CSIRO), Canberra, Australia)

“Advances in seasonal to century-scale global climate and earth system predictions for 

marine resource applications” (Charles Stock, NOAA’s Geophysical Fluid Dynamics 

Laboratory, Princeton University)

Assessing the effects of climate change on marine vertebrate occurrence and distribution 

“Climate change and marine vertebrates” (William Sydeman, Farallon Institute for 

Advanced Ecosystem Research, Petaluma, CA)

“Brief overview of literature related to approaches used to predict marine mammal 

distribution; and conclusions of the July 2015 ‘Protected Species Climate Vulnerability 

Assessment’ Workshop” (Matt Lettrich, NMFS Office of Science and Technology)

"Using environmental correlates to forecast marine mammal distributions" (Karin 

Forney and Elizabeth Becker, NMFS/SWFSC)
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10:30-10:45 Coffee Break 

10:45-12:00 

“Understanding climate responses from genes to predator communities in the Southern 

Ocean” (Jaume Forcada, British Antarctic Survey) 

“Using global climate models to project bearded and ringed seal distribution through the 

21st century” (Peter Boveng, NMFS National Marine Mammal Laboratory)

“Drivers that influence phenological changes in marine mammals, particularly in the 

Arctic and sub-Arctic Pacific” (Kate Stafford, University of Washington)

“Barents Sea marine mammals: understanding and modeling ecosystem interactions” 

(Daniel Howell, Norway Institute of Marine Research)

Discussion: 

Begin synthesizing list of models/approaches that might be useful in predicting 

marine mammal distribution based on the morning’s presentations 

12:00-1:00 Lunch

1:00-2:30

Additional presentations (if needed)

Discussion:

Identify any additional models/approaches not discussed thus far in the workshop that 

might be useful in predicting marine mammal distribution.

Begin developing a list of the features of the various models that might be useful in 

predicting marine mammal distribution. Which features would not apply? Are there other 

useful features not discussed thus far? 

2:30-2:45 Coffee Break

2:45-4:30

Discussion:

Continue discussing features of various models most salient to forecasting marine 

mammal distribution.

Assess strengths/weaknesses of the various models/approaches identified thus far. 

Begin discussion of constructing a matrix that summarizes features of various models and 

helps describe advantages/disadvantages of each.
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Day 2

9:00- 10:30 

Review of Day 1. Complete anything not addressed in Day 1. 

"A new perspective on the foraging ecology of apex predators in the California Current: 

results from a fully coupled ecosystem model” (Jerome Fiechter, UC, Santa Cruz)

Additional presentations, if needed, on approaches/models used to characterize marine 

vertebrate distribution. Complete list of approaches/models that might be used in 

predicting marine mammal distribution. 

Continue identifying/assessing, and complete a list of desirable attributes of models 

useful in characterizing marine mammal distribution. 

Construct matrix of approaches/models and strengths/weaknesses of each.

10:30-10:45 Coffee Break

10:45-12:00

Discuss/decide whether the next few items should be addressed in break-out groups, as a 

workshop as a whole, or in combination of both

Discussion of species/populations that might be used in assessing or illustrating use of 

approaches for predicting distribution (i.e., which marine mammal populations would be 

suitable for future modeling case studies and analytical exercises? Which populations 

most vulnerable? Which are data-rich, which are data-poor? )

Pinniped vs. cetacean break-out groups (??) Which models would lend themselves to 

predicting pinniped vs. cetacean distributions? Which populations are good candidates 

for future modeling case studies?

Continue constructing a matrix of strengths/weaknesses of various approaches/models.

12:00-1:00 Lunch

1:00-2:30

In break-out groups or as a workshop as a whole.

Work to complete strengths/weaknesses matrix. Prioritize/rank the various models; rank 

model attributes.

Develop list of illustrative species that might reasonably be used in modeling exercises.

Develop framework and main components of a final report; begin developing suggested 

guidance for post-workshop action/analyses.

Determine initial report drafting assignments; if time allows, begin initial drafting.
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Day 3 

9:00-10:30 

Continue developing, fine-tuning matrix.

Concluding discussion regarding work to date; work to finalize strengths/weaknesses 

matrix, prioritized list of approaches/models (and attributes), and list of 

species/populations for future analysis.

10:30-10:45 Coffee Break

10:45-12:00

Finalize ranked model/attribute matrix, and list of species/populations for future analysis.

Next steps, plans for workshop report preparation, drafting assignments, timelines, etc.

Misc.

12:00 Adjourn