Research Priorities for Migratory Birds Under

Climate Change—A Qualitative Value of Information 

Assessment

National Climate Adaptation Science Center

Circular 1472

U.S. Department of the Interior

U.S. Geological Survey
Cover. Anas acuta (Linnaeus, 1758) (northern pintail). Photograph by J. Kelly, 

U.S. Fish and Wildlife Service. Public domain.

Back cover. Anas acuta (Linnaeus, 1758) (northern pintail). Photograph by 

Andrea Mott, U.S. Geological Survey. Public domain.

Back cover. Calidris pusilla (Linnaeus, 1766) 

(semipalmated sandpiper). Photograph by the

U.S. Fish and Wildlife Service. Public domain.
Research Priorities for Migratory Birds 

Under Climate Change—A Qualitative 

Value of Information Assessment

By Madeleine A. Rubenstein, Clark S. Rushing, James E. Lyons, Michael C. Runge

National Climate Adaptation Science Center

Circular 1472

U.S. Department of the Interior

U.S. Geological Survey Sterna maxima (Boddaert, 1783) (royal tern). 

Photograph by Jeff Corbett, U.S. Geological Survey.
U.S. Department of the Interior

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2020

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Suggested citation:

Rubenstein, M.A., Rushing, C.S., Lyons, J.E., and Runge, M.C., 2020, Research priorities for migratory birds under 

climate change—A qualitative value of information assessment: U.S. Geological Survey Circular 1472, 18 p., 

https://doi.org/10.3133/cir1472.

ISSN 2330-5703 (online)
iii

Acknowledgments

We thank participants of two workshops for valuable contributions to the conceptual basis 

for this paper. In particular, we thank M. Rice, S. Covington, K. Kriese, and K. Fowler of the 

U.S. Fish and Wildlife Service for their insight on the process and context of land acquisition 

decision making.

v

Contents

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

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

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

Background.....................................................................................................................................................3

Climate Change and Migratory Birds ................................................................................................3

Sources of Uncertainty ........................................................................................................................3

Decision Context—Land Acquisition ................................................................................................4

Methodology...................................................................................................................................................6

Workshops and Expert Elicitation ......................................................................................................6

Problem, Objectives, Alternatives, Consequences, and Tradeoffs Framework .........................6

Literature Review ..................................................................................................................................7

Hypothesis Evaluation ..........................................................................................................................7

Hypothesis Ranking ..............................................................................................................................9

Results ...........................................................................................................................................................11

The Effects of Rising Temperatures on Spatial Distributions of Migratory Birds 

During the Breeding and Nonbreeding Seasons (Hypotheses 1a and 1b) ..................10

Magnitude of Uncertainty ........................................................................................................10

Relevance to Land Acquisition Programs .............................................................................10

Reducibility..................................................................................................................................11

Climate-Driven Changes to Avian Community Composition Through Homogenization 

and Loss of Specialists (Hypothesis 4b) ............................................................................12

Magnitude of Uncertainty ........................................................................................................12

Relevance to Land Acquisition Programs .............................................................................12

Reducibility..................................................................................................................................12

The Effects of Decreased Precipitation on Abundance in the Breeding Season 

(Hypothesis 2b) ......................................................................................................................12

Magnitude of Uncertainty ........................................................................................................12

Relevance to Land Acquisition Programs .............................................................................12

Reducibility..................................................................................................................................13

The Effects of Rising Temperatures on Abundance in the Nonbreeding Season 

(Hypothesis 2a) ......................................................................................................................13

Magnitude of Uncertainty ........................................................................................................13

Relevance to Land Acquisition Programs .............................................................................13

Reducibility..................................................................................................................................13

Conclusions...................................................................................................................................................14

References Cited..........................................................................................................................................15

Figures

1. Graph showing 11 hypotheses about climate change effects on migratory 

birds plotted according to their reducibility and their qualitative value of 

information score ........................................................................................................................10
vi

Tables

1. Uncertainties characterizing migratory bird ecology and management in the

context of climate change ...........................................................................................................4

2. Key types of migratory bird management decisions ..............................................................5

3. Major Federal land acquisition programs for migratory birds ..............................................5

4. Common evaluation criteria in Federal land acquisition programs .....................................6

5. Proposed climate-relevant evaluation criteria ........................................................................8

6. Climate change-related hypotheses and associated rankings ............................................8

7. Hypothesis scoring rubric ...........................................................................................................9

Abbreviations

CBC Christmas Bird Count

ProACT Problem, Objectives, Alternatives, Consequences, and Tradeoffs

QVoI qualitative value of information

Ardea herodias (Linnaeus, 1758) (great blue heron). 

Photograph by Jeff Corbett, U.S. Geological Survey.
Research Priorities for Migratory Birds Under Climate 

Change—A Qualitative Value of Information Assessment

By Madeleine A. Rubenstein,1 Clark S. Rushing,1,2 James E. Lyons,1 Michael C. Runge1

Executive Summary

The mission of the U.S. Geological Survey National 

Climate Adaptation Science Center is to provide actionable, 

management-relevant research on climate change effects on 

ecosystems and wildlife to U.S. Department of the Interior 

bureaus. Providing this kind of useful scientific information 

requires understanding how natural-resource managers make 

decisions and identifying research priorities that support those 

decision-making processes. Migratory bird management and 

conservation of migratory bird habitat are central components 

of the U.S. Department of the Interior’s mission. In particular, 

the U.S. Fish and Wildlife Service has an intensive, complex 

decision-making process for identifying high-priority parcels 

of land that will contribute to migratory bird conservation 

through permanent acquisition or easement. Climate change 

introduces several uncertainties into this decision-making 

process, and additional climate change research should help 

to support more informed decision making regarding habitat 

acquisition.

Not all climate change related uncertainties, however, 

will have a meaningful effect on acquisition decisions; 

therefore, understanding which uncertainties have the most 

potential to alter decision making is crucial. This document 

summarizes a multiyear effort to clarify the major sources of 

climate change uncertainty that affect migratory bird management and to articulate related research priorities. We worked 

with U.S. Fish and Wildlife Service staff to assess the primary 

ways in which climate change is likely to affect migratory 

birds and their habitats; to clarify uncertainties surrounding 

these effects; and to assess how uncertainties may affect habitat acquisition decisions. Using a modified structured decisionmaking approach, we assessed a set of hypotheses about how 

climate change will affect migratory birds and their habitats. 

Then, we used a qualitative value of information assessment to 

rank the most important topics for future research. The ranking 

process was built on an assessment of three primary characteristics: the magnitude of uncertainty, the topic’s relevance 

to habitat acquisition decision making, and the feasibility of 

reducing the uncertainty. Based on the results of this process, 

high-priority topics for future research include the following:

• The effects of rising temperatures on spatial distributions of migratory birds during the breeding and 

nonbreeding seasons;

• Climate-driven changes to avian community composition through homogenization and loss of specialists;

• The effects of decreased precipitation on abundance in 

the breeding season; and

• The effects of rising temperatures on abundance in the 

nonbreeding season.

In addition to describing high-priority research needs, 

this document provides a summary of the methodology 

used to identify, assess, and rank uncertainties. This method 

was developed for a climate change related topic where a 

full quantitative value of information approach may not be 

feasible. The results and methodology described here may be 

useful for U.S. Geological Survey and other science-funding 

agencies interested in improving the applicability of their 

research to natural-resource management decision making.

Introduction

Natural-resource management decisions are often characterized by uncertainty, including uncertainty surrounding 

environmental variability and uncertainty about the efficacy 

of management strategies (Nichols and others, 2011). Climate 

change introduces additional sources of uncertainty that affect 

decision making in multiple ways, including through inherent uncertainties about the trajectory of climate change, the 

response of natural systems, and the efficacy of management 

interventions. Traditional approaches to natural-resource management often fail to account for the uncertainties introduced 

by climate change, thereby posing a challenge to current 

management and decision systems. Despite the risks posed by 

climate change, and the potential for climate change effects 

to render existing management approaches substantially less 

effective, most natural-resource decisions are made under 1U.S. Geological Survey.

2Utah State University.
2 Research Priorities for Migratory Birds Under Climate Change—A QVoI Assessment

assumptions that current ecological, biological, and physical 

processes will remain constant (in other words, stationarity; 

Nichols and others, 2011). Finding ways to effectively account 

for climate-driven uncertainty within the context of naturalresource management is therefore critical to meeting management objectives.

Migratory birds are an extensively managed resource, 

serving as the focus of several landmark international laws 

and treaties (for example, the Migratory Bird Treaty Act of 

1918 [16 U.S.C. 703–712] and the Bald and Golden Eagle 

Protection Act [16 U.S.C. 668–668c]), executive orders (for 

example,, Executive Order 13186), and conservation programs (for example, North American Waterfowl Management 

Plan). Although multiple long-standing research programs are 

in place to support effective monitoring and management of 

migratory birds, substantial uncertainty remains about how 

climate change is likely to affect migratory birds and about the 

expected effect of management interventions in the context of 

climate change (Knudsen and others, 2011).

Effective management of migratory birds in the context of climate change, therefore, requires addressing and 

accounting for novel sources of uncertainty in a rigorous, 

systematic process. Multiple strategies have been developed 

to support decision making in the face of uncertainty, including the fields of adaptive management and structured decision 

making. Structured decision making is a systematic, analytical method used to break down decision making into a set of 

discrete components (Runge and others, 2011) and is useful 

for identifying priority scientific and research topics in decision making. Although the potential sources of uncertainty 

related to climate change and migratory birds are seemingly 

endless, it is important for managers and researchers in this 

field to focus their efforts on the particular sources of uncertainty that are most likely to improve decision outcomes and 

support management objectives. This focus requires considering which management actions are available to managers, 

assessing likely outcomes or consequences of those actions, 

and comparing differences in expected outcomes. In some 

cases, resolving uncertainty about a climate change effect may 

not alter decision making because a preferred management 

action will be favored in all scenarios; in other cases, resolving 

uncertainty may change which management action is favored, 

rendering the uncertainty more relevant to decision making. 

Establishing a management-relevant research program for 

migratory birds and climate change, therefore, requires identifying and understanding the most relevant sources of climate 

change uncertainty for migratory bird management.

In this document, we explore the major sources of uncertainty regarding how climate change is affecting migratory 

birds in an effort to create an effective, management-relevant 

research program within the U.S. Geological Survey. Through 

a series of workshops and discussions with migratory bird and 

habitat managers, we gathered information and conducted a 

semistructured expert elicitation to identify which sources of 

uncertainty had the greatest potential to affect management 

decisions, particularly those regarding land and habitat acquisition decisions. We then conducted a systematic literature 

review and qualitative value of information (QVoI) analysis 

to identify which sources of uncertainty are most significant 

according to three criteria: magnitude of uncertainty, relevance 

to land acquisition programs, and reducibility. We synthesize 

these findings into a description of top research priorities and 

review the implications of those sources of uncertainty for 

land acquisition decisions. In addition, we describe a methodology for identifying management-relevant research priorities 

that can be applied to other systems, even in the absence of 

data required for a formal value of information assessment. 

This document is intended to be useful for research program 

managers and for any scientist interested in understanding how 

their research can be as relevant as possible to natural-resource 

managers.

Anas acuta (Linnaeus, 1758) (northern pintail). 

Photograph by Andrea Mott, U.S. Geological Survey. Public domain.
Background 3

Background

Climate change is one of the most substantial drivers of ecological change facing natural-resource managers 

today (Weiskopf and others, 2020). Effective management of 

migratory birds in the face of climate change is particularly 

challenging because effects happen across multiple biological 

scales, from individuals to populations and species; multiple 

spatial scales, reflecting breeding, stopover, and nonbreeding grounds; and multiple dimensions, including phenological changes, distributional shifts, and demographic effects. 

Migratory birds are a high priority for many natural-resource 

managers and represent a particularly important area of focus 

for Federal managers within the U.S. Department of the 

Interior. A clear understanding of how climate change is affecting migratory birds, and what those changes mean for management decisions, is therefore critical to achieving the goals and 

mandates of Federal, State, and Tribal managers.

Climate Change and Migratory Birds

One of the primary effects of climate change is geographic range shifts; in other words, as species respond to spatially variable changes in temperature and precipitation, their 

current and future distributions may no longer align with the 

historical ranges they once occupied (Chen and others, 2011). 

These changes are not occurring evenly for all species, taxonomic groups, or regions; indeed, high variability in climatedriven range shifts has been observed even within guilds of 

migratory birds (La Sorte and Thompson, 2007; Tingley and 

others, 2012; Rushing and others, 2020). Therefore, some species may maintain historical ranges, but their optimum habitat 

may shift, resulting in a net loss of resources. Alternatively, 

they may effectively track climatic shifts faster than a predator 

or competitor, thereby benefiting from competitive release.

Phenological shifts are another widely recognized effect 

of climate change on migratory birds; for example, as temperatures have risen, multiple species have demonstrated 

advances in the timing of migration, arrival on breeding 

grounds, nesting, and egg laying (Wood and Kellermann, 

2015). However, the cues driving the phases of migration and 

reproduction in migratory birds are complex, including exogenous signals (that is, photoperiod) that are not affected by 

climate change and climatic conditions experienced on wintering grounds, which may be many thousands of miles from 

breeding grounds. Many migratory birds, therefore, have not 

advanced their migration and breeding phenology sufficiently 

to track changes in the emergence of the plant and insect 

resources they rely on to nourish themselves and their offspring. This asynchrony with critical food sources may depress 

reproductive success or recruitment (Mayor and others, 2017), 

although population-level effects have been difficult to demonstrate (Miller-Rushing and others, 2008). The spatial and 

temporal alignment of competitors, predators, and pathogens 

may also similarly shift, resulting in novel interactions.

Rising temperatures and altered precipitation patterns 

may also have more direct effects on the abundance and population levels of migratory birds. As temperate winters become 

milder, winter mortality may decline for some species, resulting in increased abundance on temperate wintering grounds. 

Decreased precipitation, however, may decrease abundance in 

the breeding and nonbreeding season, particularly for species 

that overwinter in tropical areas.

Sources of Uncertainty

Although each of these climate change effects has 

received attention from the research community, numerous 

uncertainties remain (table 1), which have the potential to 

affect management decisions.

Firstly, there are significant uncertainties regarding the 

fundamental ecology of many migratory bird species, even 

without considering the potential effects of climate change. 

The current distribution during the breeding, nonbreeding, and 

migratory seasons is not known with high confidence for all 

species, and for many species it is not clear which season or 

habitat type currently limits population growth (that is, “limiting habitat;” table 1, nos. A1–A3). Because limiting factors 

are not known for all migratory bird species, the relationship 

between quantity and quality of habitat and population vital 

rates (for example, recruitment and survival; table 1, no. A4) is 

uncertain.

Climate change introduces additional uncertainties: 

climate change is likely to affect the spatial and temporal distribution of priority species and their habitats, but 

the magnitude, extent, and rate of these changes is unclear 

(table 1, nos. B1–B2). If the spatial or temporal distributions 

of limiting resources shift, it is unclear how closely migratory birds will be able to track these changes (table 1, no. B3). 

Climate change will also affect other important ecological 

relationships, including those between migratory birds and 

their predators, competitors, and pathogens, yet considerable 

uncertainties remain (table 1, no. B4).

Finally, climate change will affect social and management 

systems in ways that will affect migratory bird populations. 

Climate change is projected to affect the economic drivers 

that determine land-use patterns (table 1, no. C1), potentially altering the dynamics of risk facing habitat acquisition 

managers when they approach acquisition decisions, yet how 

land-use patterns will change is uncertain. Climate change 

highlights the substantial uncertainties facing managers about 

the effects of management interventions on management 

objectives (table 1, no. C2). The result of various management 

actions, such as acquisition of limiting habitat or reduction in 

harvest activities, may affect demographic rates or population 

levels, but the precise effects are often uncertain, and climate 

change may exacerbate these uncertainties by altering the 

effectiveness of previously well characterized management 

interventions.
Decision Context—Land Acquisition

Managers face many decisions about how to manage 

migratory bird populations and their habitats to reach desired 

outcomes, generally expressed as population objectives. From 

acquiring new habitat for protection to managing or restoring 

existing habitat, the decision landscape facing managers is 

complex. Yet in many ways the landscape is relatively limited 

because only a few key types of interventions are available to 

managers to achieve their goals (table 2).

These possible decisions include purchasing new land to 

place under permanent protection (acquisition); restoring or 

improving the quality of existing habitat (restoration); altering 

direct mortality rates (harvest and take limits); and setting 

priorities for future management actions (prioritization). 

Although we initially assessed the effects of climate change on 

all these decisions, we decided to focus our structured QVoI 

analysis on habitat acquisition decisions. This focus was partially because of the importance of habitat acquisition as a key 

management intervention, as well as the substantial and welldocumented effects of climate change on spatial distribution 

of migratory birds and associated implications for long-term 

acquisition decisions.

Although land acquisition decisions are made by many 

organizations, land acquisition specifically for migratory bird 

populations is a central focus of the U.S. Fish and Wildlife 

Service. We therefore focused our analysis on major land 

acquisition programs used by U.S. Department of the Interior 

and specifically by the U.S. Fish and Wildlife Service: the 

Migratory Bird Conservation Fund, the Land and Water 

Conservation Fund, and the North American Wetlands 

Conservation Act (103 Stat. 1968; 16 U.S.C. 4401–4412) 

Grant program (table 3). Although these land acquisition 

programs are all different in several important ways, they 

each rely on a set of foundational conservation plans to define 

fundamental program elements, including priority species, 

priority regions for management and acquisition, definitions 

of limiting habitat and seasons, and population goals. These 

plans play a vital role in decision making for migratory bird 

management because they set boundaries around the decision 

context (in other words, by defining priority species and population objectives), and they provide definitions for some of the 

evaluation criteria (that is, priority regions and habitat types) 

used in land acquisition programs.

We investigated the decision criteria (that is, evaluation 

criteria) essential to each of these land acquisition programs 

as a central part of our QVoI analysis. By understanding these 

criteria, we were able to understand what sources of information were valuable to decision makers within the context of 

these decisions. We identified four core evaluation criteria that 

are shared by these programs to prioritize habitat for migratory 

bird conservation (table 4). These evaluation criteria include 

current distribution of a focal species, current abundance of a 

focal species, limiting habitat for a focal species, and risk of 

loss. By focusing our analysis on the sources of uncertainty 

relevant to these evaluation criteria, we ensured that our 

assessment would identify sources of uncertainty relevant to 

the specific acquisition context.

4 Research Priorities for Migratory Birds Under Climate Change—A QVoI Assessment

Faced with these multiple sources of uncertainty, managers and researchers alike are challenged to identify which 

research questions should receive priority. One of the central 

tenants of decision analysis is that reducing uncertainty is 

only valuable if doing so will lead to different management 

decisions and the potential for better management outcomes 

(Runge and others, 2011). Our goal was to use the tools of 

decision analysis to untangle these sources of uncertainty 

and to identify those that have the potential to alter management decisions and that could be effectively reduced through 

research, thereby establishing a research program that would 

have high utility for natural-resource managers.

Table 1. Uncertainties characterizing migratory bird ecology and management in the context of climate change.

Questions about sources of uncertainty

Underlying ecology Climate change effects to natural systems Climate change effects to social and 

management systems

A1. What constitutes limiting habitat for 

focal species?

A2. What is the current distribution (spatial 

and temporal) of limiting habitat?

A3. In which season (wintering, breeding, or 

stopover) does limiting habitat occur?

A4. How will increased availability or 

conservation of limiting habitat through 

land acquisition affect vital rates?

B1. How will climate change affect the 

distribution (spatial and temporal) of focal 

species?

B2. How will climate change affect the 

distribution (spatial and temporal) of 

limiting habitat?

B3. How responsive are focal species to 

shifts in habitat?

B4. How will climate change alter the 

dynamics of migratory bird predators, 

competitors, pathogens, and invasive 

species?

C1. How will climate change alter landuse patterns, and how will that affect 

distribution of limiting habitat?

C2. How will climate change affect 

the effectiveness of management 

interventions?
Background 5

Table 2. Key types of migratory bird management decisions.

Management decision type Question to consider

Habitat acquisition Where should habitat be acquired for permanent protection?

Habitat management and restoration Where and how should existing habitat be managed, restored, or both?

Setting harvest and take limits What are appropriate limits for annual take, either through purposeful harvest or incidental 

take?

Planning and prioritization How should species and geographies be prioritized for management intervention?

Table 3. Major Federal land acquisition programs for migratory birds.

[FWS, U.S. Fish and Wildlife Service; T&E, threatened and endangered]

Program 

information

Federal land acquisition program

Migratory Bird Conservation Fund 

(MBCF)

Land and Water Conservation Fund 

(LWCF)

North American Wetlands Conservation 

Act (NAWCA, 103 Stat. 1968; 16 U.S.C. 

4401–4412) Grant program

Program 

description

Created in 1934 as part of the 

Migratory Bird Hunting and 

Conservation Stamp Act 

(16 U.S.C. 718–718j, 48 Stat. 

452). Provides funds for the 

protection of migratory bird 

habitat. Financed primarily 

through the sale of Migratory 

Bird Hunting and Conservation 

Stamps (“duck stamps”). Funds 

are used to protect critical habitat 

within the FWS Refuge System 

for priority waterfowl.

Established in 1964 to protect public 

lands and waters, and financed 

by earnings from offshore oil 

and gas leasing. LWCF funds are 

used across U.S. Department of 

Interior bureaus for conservation. 

LWCF is used to protect migratory 

waterfowl habitat within the FWS 

Refuge System.

Established in 1989 to protect wetland habitats across North America. This program 

focuses primarily on migratory waterfowl, but protection of critical habitat for 

wetland-associated migratory birds and 

T&E species is also considered. Revenue 

from the Pittman-Robertson Act (The 

Federal Aid in Wildlife Restoration Act, 

16 U.S.C. §§669 et seq.; an excise tax on 

firearms and ammunition) is the primary 

source of funding for NAWCA grants.

Focal species Primary focus is waterfowl. 

Secondary focus is T&E 

species.

Primary focus is waterfowl. 

Secondary focus is migratory birds 

of conservation concern and T&E 

species.

Primary focus is waterfowl. Secondary 

focus is wetland-associated migratory 

birds and T&E species.

Decision maker FWS leadership; FWS refuge 

and regional managers; State 

governments

FWS leadership; FWS refuge and 

regional managers

NAWCA council: FWS leadership; State 

representatives; National Fish and 

Wildlife Federation; other nongovernmental organizations

Jurisdiction United States (Refuge System or 

Waterfowl Protection Area only)

United States (Refuge System only) North America (United States, Mexico, & 

Canada)

Foundational plan North American Waterfowl 

Management Plan (NAWMP)

NAWMP; Landbird, waterbird, & 

shorebird conservation plans; T&E 

species listings

NAWMP; Landbird, waterbird, & shorebird conservation plans; T&E species 

listings; State wildlife action plans

Criteria 1. Limiting factor mitigation

2. Relative abundance for priority 

species

3. Limiting factor and relative 

abundance for secondary species

4. For Waterfowl Production 

Areas: Risk of loss/conversion

1. Limiting factor mitigation

2. Relative abundance for priority 

species

3. Relative abundance for secondary 

species

4. Priority geography

5. T&E species recovery plan 

directive

1. Abundance for priority species

2. Abundance for secondary species

3. Priority geography

4. Wetland status and trend

5. Long-term conservation and climate 

change benefit

6. Partnerships

Climate change Not explicitly considered. Not explicitly considered. Considered in evaluation of long-term 

benefit and whether actions are adaptive 

for climate change effects.
6 Research Priorities for Migratory Birds Under Climate Change—A QVoI Assessment

Methodology

Our QVoI analysis used a hybrid model of expert elicitation and a systematic literature review to collect information 

about important uncertainties constraining decision making 

related to migratory birds. Although we describe our methodology here, additional details are in Rushing and others (2020).

Workshops and Expert Elicitation

We held a series of workshops and in-person meetings 

with Federal migratory bird and habitat managers to identify 

major types of decisions that managers make with regards to 

migratory bird populations and habitats, to articulate the major 

mechanisms by which climate change may affect migratory 

birds, and to discuss major sources of uncertainty about how 

climate change is likely to affect migratory birds in ways that 

may affect management decisions.

In July 2016, we held our first workshop with scientists 

and migratory bird managers. Our objective was to identify the 

most consequential management decisions that affect migratory birds in the context of climate change by answering these 

questions: what decisions do migratory bird and habitat managers make? What interventions are available to them? How 

are these decisions and interventions potentially affected by 

climate change? Participants identified four key decision types 

(table 2) and described how each is likely to be affected by 

climate change. We then explored major sources of uncertainty 

surrounding these potential effects (table 1). Having identified the major decision types facing migratory bird and habitat 

managers, we were then able to advance our analysis to assess 

uncertainties and associated value of information.

In January 2017, we held a subsequent workshop focused 

exclusively on habitat acquisition and specific decisions 

made within the context of the three primary land acquisition 

programs that were the focus of our analysis (Land and Water 

Conservation Fund, Migratory Bird Conservation Fund, and 

North American Wetlands Conservation Act Grants program; 

table 3). This workshop was also focused on understanding 

how climate change introduces uncertainty into this decisionmaking process and on identifying which sources of uncertainty would be the most fruitful areas of research. Participants 

included Federal migratory bird and habitat managers, particularly those involved in decision making related to the three 

primary land acquisition programs. Our ultimate objective 

was to understand how research within the U.S. Geological 

Survey and other science agencies could directly inform 

these programs by reducing important uncertainties related 

to climate change. Therefore, we concentrated our efforts on 

understanding how uncertainties about climate change effects 

to migratory birds may affect habitat acquisition decision making within the context of these three specific land acquisition 

programs.

Problem, Objectives, Alternatives, 

Consequences, and Tradeoffs Framework

To identify important uncertainties associated with these 

acquisition programs, we adopted a modified decision analysis approach based on the Problem, Objectives, Alternatives, 

Consequences, and Tradeoffs (PrOACT) framework 

(Hammond and others, 2015). For each program, we analyzed 

each component of the PrOACT framework: the general 

decision context (problem framing), the objectives of the 

decision makers, the alternative potential actions to achieve 

Table 4. Common evaluation criteria in Federal land acquisition programs.

[LWCF, Land and Water Conservation Fund; MBCF, Migratory Bird Conservation Fund; NAWCA; North American Wetlands Conservation Act (103 Stat. 1968; 

16 U.S.C. 4401–4412)]

Evaluation criteria Description Example from a specific program

Current species distribution of a focal speciesIs the focal species currently 

distributed on the parcel?

Decision tree used in evaluating parcels for LWCF asks whether a priority species 

(for example, Bird of Conservation Concern) is present on the land.

Current species 

abundance of a 

focal species

Is the focal species currently 

abundant on the parcel?

Decision tree used in evaluating parcels for MBCF acquisition asks whether the 

refuge in question overlaps with high abundance areas for a priority species.

Limiting habitat for a 

focal species

Does the parcel represent 

limiting habitat for the focal 

species? Does the parcel 

provide high-quality habitat 

for the focal species?

Evaluation criteria for LWCF program ask whether the parcel would mitigate 

a population-limiting factor for a priority species, as determined by subjectmatter experts.

Risk of loss Is the parcel at risk of being 

converted, lost, or otherwise 

degraded if not acquired?

Decision tree used in Waterfowl Production Area program of MBCF asks 

whether the parcel is at risk of being converted to agriculture if not acquired.

Evaluation criteria for NAWCA grants ask whether the parcel represents a 

type of wetland generally in decline.
Methodology 7

these objectives, and the evaluation process used by decision 

makers to assess the consequences of each alternative. For the 

purposes of this workshop, we did not focus on the tradeoffs 

(optimization) component of PrOACT.

We were particularly interested in the evaluation (consequences) step of PrOACT, in which we wanted to answer this 

question: of the available parcels for acquisition (alternatives), 

how do decision makers assess which will best achieve their 

goals? The criteria used to evaluate parcels for acquisition 

reflect management objectives and underlying assumptions 

about the ecological system. Climate change is generally not 

systematically or explicitly included in these criteria (except 

for the North American Wetlands Conservation Act Grant program; see table 3), which poses a challenge to incorporating 

climate science into acquisition decision making. Participants 

suggested several potential additional evaluation criteria that 

could help to incorporate the effects of climate change into the 

evaluation process (table 5).

Literature Review

After the workshop, we reviewed literature to assess the 

state of the science surrounding major sources of uncertainty, 

which we expressed as a series of hypotheses. We assumed 

a null hypothesis (H0) that climate change will not affect the 

particular ecological or demographic process of interest (for 

example, climate change will not affect spatial distributions) 

and treated each proposed hypothesis as an alternative (H1) to 

the null, which assumes an ecological or demographic effect 

of climate change (for example, climate change will result in 

range shifts). Building on the uncertainties identified in the 

previous workshops, we were able to articulate a set of specific hypotheses that could form the basis of a managementrelevant research program (table 6; see table 7 for more information on how hypotheses were evaluated). These specific 

hypotheses include those related to species distribution (for 

example, how species distributions may shift in response to 

climate change), abundance (for example, how demographic 

rates and population levels are affected by precipitation and 

temperature), migration and phenology (for example, how 

climate change will alter the spatial and temporal characteristics of migration), and community responses (for example, 

how climate change may affect community composition and 

species interactions).

Using Google Scholar and Web of Science, we searched 

for papers published in the academic literature using the following keywords: “migratory bird*” and “climate,” “climate 

change,” “temperature,” “precipitation,” or “phenology.” 

We also used a modified network association methodology 

(Lecy and Beatty, 2012), whereby we reviewed the citations 

within the papers located via these searches to find additional 

papers that the searches missed. Our review was not meant 

to be exhaustive; instead, we aimed to review a representative sample of published papers to draw general conclusions 

about the current state of knowledge regarding climate change 

effects on migratory birds.

Hypothesis Evaluation

Once the hypotheses were identified (table 6) and the 

literature compiled, we used three criteria to evaluate each 

hypothesis: magnitude of uncertainty, relevance to land acquisition programs, and reducibility. We established a standard 

rubric by which to evaluate each hypothesis on each of these 

criteria (table 7).

In evaluating hypotheses for the magnitude of uncertainty, we assumed that, all else equal, research and monitoring will have a larger effect on improving management outcomes when focused on hypotheses that are more uncertain (in 

other words, whether the null or alternate hypothesis is true is 

uncertain). We used the results of the literature review to score 

each hypothesis based on the current magnitude of uncertainty. 

We then evaluated hypotheses based on their relevance to 

decision making (that is, the relevance of resolving uncertainty 

to meeting fundamental management objectives). We scored 

each hypothesis based on the degree to which resolving uncertainty is expected to alter the outcomes of land acquisition 

decisions. Hypotheses in this category were scored as either 

1, 2, or 3 depending on whether we judged the difference in 

performance to be small, moderate, or large, respectively, 

relative to natural variability. Finally, we assessed whether 

the uncertainty expressed in the hypothesis could be reduced 

through research and monitoring; all else equal, sources of 

uncertainty that can be reduced with existing data and analytical methods are easier to resolve than sources of uncertainty 

that will require additional long-term data collection. Although 

we recognize that many of these uncertainties are interrelated, 

we considered each source of uncertainty independently.

We, the four authors, served as an expert panel to score 

the hypotheses, following a formal process of expert judgment (Hanea and others, 2018). First, we each independently 

scored the hypotheses using the rubrics described in table 7. 

We then compared our scores, discussed any differences, 

and independently revised scores where necessary. To gather 

further insight, we convened a subset of U.S. Fish and Wildlife 

Service managers for a second in-person meeting to review 

our relevance scores. We discussed how the various hypotheses either were, or were not, likely to alter land acquisition 

decisions. After hearing those discussions, we reviewed our 

scores for each hypothesis. Finally, individual scores across 

experts were aggregated by finding a consensus score.
8 Research Priorities for Migratory Birds Under Climate Change—A QVoI Assessment

Table 5. Proposed climate-relevant evaluation criteria.

[—, not applicable]

Current evaluation criteria Effect of climate change Potential climate-focused evaluation criteria

Current species distribution Climate change may alter current spatial and 

temporal distribution of focal species.

Future distribution: Is the focal species likely to be present 

on the parcel under future climate scenarios?

Limiting habitat Climate change may alter the distribution and 

quality of limiting habitat or alter which 

resources or seasons are limiting.

Future limiting habitat: Is the habitat likely to remain high 

quality under future climate scenarios?

Risk of loss Climate change may alter land-use patterns and 

practices, thereby affecting the risk of habitat 

loss for a given parcel.

Future risk: Is the risk of conversion or loss likely to 

change because of climate effects?

— — Landscape resilience: Would the acquisition increase 

overall heterogeneity of the broader landscape, increase 

climate resilience of the landscape, or both?

— — Refugia: Does the parcel incorporate characteristics of 

climate refugia for focal species?

Table 6. Climate change-related hypotheses and associated rankings.

[See table 7 for the scoring rubric used to determine the uncertainty, relevance, and reducibility rankings]

Hypothesis Uncertainty Relevance Reducibility Priority ranking

Species distribution

1a. Breeding distributions will shift poleward because of 

warming temperatures. 2 3 3 Highest

1b. Nonbreeding distributions will shift poleward because 

of warming temperatures. 2 2 2 Highest

1c. Distributions will shift towards higher elevations 

because of warming temperatures. 3 1 2 Medium

Abundance

2a. Warmer temperatures on temperate nonbreeding 

grounds will increase abundance. 4 1 1 High

2b. Decreased precipitation during the breeding season 

will lower abundance. 2 3 1 High

2c. Reduced precipitation on the nonbreeding grounds 

will decrease abundance of species that overwinter in 

tropical areas.

3 0 1 Low

Migration and phenology

3a. Migration distances will shorten for short-distance 

migrants. 3 0 1 Low

3b. Migration distances will lengthen for long-distance 

migrants. 3 1 1 Low

3c. Climate-driven phenological mismatches will lower 

abundance of migratory birds through their effects on 

reproductive success.

2 1 2 Medium

Community responses

4a. Climate change will result in a lower proportion 

of migratory species in temperate avian breeding 

communities because of increased competition with 

resident species.

2 1 1 Low

4b. Climate change will lead to more homogeneous bird 

communities that are dominated by a small number of 

generalist species.

2 2 3 Highest
Methodology 9

Hypothesis Ranking

In conventional decision analyses, the expected value 

of information is the expected outcome of the optimal action 

after uncertainty has been reduced minus the expected outcome of the optimal action based on the current knowledge 

of the system (Schlaifer and Raiffa, 1961). Using our qualitative criteria, we conducted a QVoI assessment whereby the 

expected value of information is equivalent to the product of 

our uncertainty and relevance scores. Taking the product of 

the scores ensured that any hypothesis that scored a 0 in either 

of these categories automatically received a QVoI score of 0. 

This property is desirable because if, for example, a hypothesis was judged to be irrelevant to management decisions (score 

of 0 for relevance criteria), even a high degree of uncertainty 

would not make the hypothesis a high priority for additional 

research. QVoI scores therefore provide a principled method to 

rank hypotheses that is consistent with formal decision theory. 

Our additional criterion (reducibility) captures the degree 

to which uncertainty can be reduced through research and 

monitoring, which is an important consideration for prioritizing research needs. Reducibility is not directly incorporated 

in the QVoI analysis, and we therefore included these scores 

into our ranking as a second axis along which hypotheses 

were ranked (fig. 1). Hypotheses that rank highly for QVoI and 

reducibility are obvious priorities for future research, whereas 

hypotheses that rank poorly in both categories should not be 

priorities for research that aims to improve land acquisition 

decisions. To create our final prioritization of research topics, 

we placed hypotheses into four categories, according to how 

they ranked in terms of QVoI and reducibility (fig. 1). Highest 

priority hypotheses are those that rank highly in terms of 

QVoI and reducibility. These hypotheses are highly relevant 

to managers, have substantial uncertainty that needs to be 

reduced, and rank relatively highly in terms of reducibility. We 

define these as having a QVoI of greater than or equal to (≥) 

4 and a reducibility of ≥2. High priority hypotheses are those 

that rank highly in QVoI but have low reducibility. Resolving 

these uncertainties would be valuable to management decision 

making, but the challenges associated with reducibility would 

render this task onerous. We define these as having a QVoI 

of ≥4 and a reducibility of less than (<) 2. Medium priority 

hypotheses have a relatively low QVoI but high reducibility. 

These are uncertainties that may not be of highest priority to 

Table 7. Hypothesis scoring rubric.

[—, not applicable]

Score Magnitude of uncertainty Relevance to land acquisition 

programs Reducibility

0 Firm theoretical foundation 

and several empirical studies 

(>10) that support theoretical 

predictions

Preferred management action will 

be favored regardless of whether 

hypothesis is true.

Data necessary to reduce uncertainty does not 

currently exist and will be prohibitively 

difficult and expensive to collect given current 

technologies.

1 Firm theoretical foundation with 

robust empirical support; or 

several (>10) of consistent 

empirical studies

Reducing uncertainty is predicted to 

improve management outcomes, 

but range of outcomes will be 

swamped by natural variability 

and other uncertainties.

Data to reduce uncertainty exist but only for a limited 

taxonomic, geographic, or temporal scope; or 

cannot resolve the specific mechanisms; collection 

of additional data needed to discriminate among 

alternative mechanisms will be difficult and 

expensive or cannot be collected in timeframe 

relevant to decision.

2 Firm theoretical foundation with 

moderate empirical support; 

or Moderate number (5–10) of 

consistent empirical studies

Reducing uncertainty is predicted to 

improve management outcomes; 

range of outcomes will be 

small to moderate compared 

to natural variability and other 

uncertainties.

Data to reduce uncertainty exist but only for a limited 

taxonomic, geographic, or temporal scope or data 

only allow weak inference about mechanisms; 

collection of additional data needed to discriminate 

among alternative mechanisms is feasible given 

current technologies.

3 Firm theoretical foundation with no 

empirical support; or a few (<5) 

consistent empirical studies

Reducing uncertainty is predicted to 

improve management outcomes, 

and the range of outcomes will 

be the same order of magnitude 

as natural variability and other 

uncertainties.

Data to reduce uncertainty exist across a large 

taxonomic, geographic, or temporal scope; and 

credible inference can be made from these data.

4 Large disagreement between theory 

and empirical studies; or no 

theoretical basis and inconsistent 

empirical studies

— —
10 Research Priorities for Migratory Birds Under Climate Change—A QVoI Assessment

management, or may have only a small uncertainty surrounding their likelihood of being true, but could be easily resolved. 

If reducibility is a high priority, these might be appealing 

candidate hypotheses. We define these as having a QVoI <4 

and a reducibility ≥2. Low priority hypotheses have a low 

QVoI, indicating that they are not particularly relevant to land 

acquisition decision making, or have low remaining uncertainty based on the literature. In addition, they will be difficult 

to reduce. Therefore, these are not high priority topics. We 

define these as having a QVoI <4 and a reducibility <2.

Results

Of the 11 hypotheses we evaluated, we identified 4 highpriority research topics, which combined moderate to high 

scores in magnitude of uncertainty, relevance to land acquisition programs, and reducibility (fig. 1; note that in reporting 

high-priority topics, we have combined hypothesis 1a and 1b 

because they overlap in the peer-reviewed literature) . For a 

complete discussion of all 11 hypotheses, see Rushing and 

others (2020). The four high-priority research topics represent 

sources of uncertainty that have substantial scientific disagreement that needs to be resolved and that are relevant to land 

acquisition decision making as currently practiced in Federal 

programs. A high reducibility score indicates that these 

relevant topics are top candidates for future research efforts. 

A low score for reducibility indicates that, although possible, 

reducing these uncertainties will be more difficult and potentially require additional resources.

The Effects of Rising Temperatures on Spatial 

Distributions of Migratory Birds During 

the Breeding and Nonbreeding Seasons 

(Hypotheses 1a and 1b)

Magnitude of Uncertainty

Breeding and nonbreeding distributions for migratory 

birds are widely expected to shift poleward in response to 

rising temperatures (Chen and others, 2011). Although a large 

body of research on climate-driven range shifts has developed 

over the past two decades, and evidence of poleward range 

shifts has been well documented across regions and taxa, 

moderate uncertainty still remains. In particular, constructing accurate projections of future range shifts at spatial scales 

relevant to habitat managers remains challenging. In addition, the empirical and theoretical knowledge base regarding 

climate-driven range shifts along southern (that is, trailing) 

edges of species’ ranges is small. Furthermore, range shifts in 

nonbreeding distributions are generally less well understood; 

although strong theoretical evidence indicates winter temperature plays an important role in determining temperate winter 

distributions (Swanson and Olmstead, 1999; Canterbury, 

2002), the environmental processes that limit nonbreeding 

distributions have received less theoretical attention than 

breeding season processes. Finally, variability in species’ 

range shift responses to rising temperature is highly interspecific, and although some traits have been identified that seem 

to be associated with greater or lesser responses (that is, body 

size and dispersal capacity), considerable uncertainty remains 

about which species are most adaptive or vulnerable. Overall, 

the theoretical links between temperature and species distributions, combined with the many studies that have documented 

northward range shifts across many avian species, results 

in relatively high certainty that range limits will continue to 

shift northwards. But high interspecific variability, increased 

uncertainty about southern range edges, and lack of clarity 

about trait-level indicators of adaptive capacity suggest that 

considerable uncertainty remains.

Relevance to Land Acquisition Programs

This research priority has high relevance to managers 

within the context of land acquisition decision making. The 

presence of one or more priority species is a key criterion for 

land acquisition decisions, and it is clear that future range 

shifts are likely for many species. Parcels that are currently 

important because of the presence or abundance of priority 

species may no longer support those species in the future. 

Likewise, parcels that are currently uninhabited or support 

Medium priority Highest priority

Low priority High priority

3

2

1

Reducibility

1 2 3

Qualitative value of information score

4 5 6

3a 2c 4a 3b 2a 2b

3c 1c 1b

4b 1a

Figure 1. Eleven hypotheses about climate change effects on 

migratory birds plotted according to their reducibility (y axis) and 

their qualitative value of information score (x axis). Hypotheses 

in upper right quadrant are high priority because they are both 

highly reducible and have high value of information. Low priority 

hypotheses (lower left quadrant) have low value of information 

and low reducibility. See table 6 for hypothesis numbering and 

table 7 for details of scoring system. Note that a small jitter has 

been added to the qualitative value of information scores of 

hypotheses 2c and 3a to avoid overlapping points.
Results 11

low-density populations may become critical to the maintenance of priority species under future climate conditions. 

Additional information about these projected changes could 

therefore fundamentally alter the outcome of management 

decisions if uncertainties were sufficiently reduced. Changes 

in breeding distribution are likely to be more relevant than 

changes in nonbreeding distributions, given that nonbreeding distributions occur outside the jurisdiction of Federal land 

acquisition programs for many priority species. In addition, 

shifts in nonbreeding distributions are projected to be smaller 

than breeding-season distributional shifts (Langham and others, 2015), resulting in less of a potential effect on acquisition 

decisions. Research on breeding season distributional shifts is 

therefore likely to have a greater relevance to managers than 

nonbreeding season shifts. Because the rate, direction, and 

magnitude of distributional shifts will vary across space, time, 

and species, the ability to identify parcels that will or will not 

support long-term management objectives before making land 

acquisition decisions is expected to have high value.

Reducibility

This research priority has relatively high reducibility 

compared to other priorities. The ability to quantify breeding distribution shifts has been facilitated by the availability 

of large-scale monitoring data for many species, including 

through the North American Breeding Bird Survey (Sauer and 

others, 2005) and the Audubon Christmas Bird Count (CBC; 

Niven and others, 2004). These structured monitoring programs are increasingly being supplemented by unstructured 

citizen-science initiatives (for example, eBird; Sullivan and 

others, 2014), which provide millions of additional observations from across the globe each year. Although observation 

networks for breeding distributions are more widespread than 

for nonbreeding distributions, several large-scale programs 

have been documenting the winter distribution of many bird 

species for decades (for examples, CBC). Augmenting these 

data with unstructured citizen-science initiatives like eBird, 

within-species distribution models will further reduce uncertainty about how winter distributions will respond to future 

climate change. In both seasons, recent advances in species 

distribution modeling have provided new methods for estimating range shifts that improve upon earlier methods by taking 

into account imperfect detection (Guillera‐Arroita, 2017), 

nonequilibrium dynamics (Clement and others, 2016; Rushing 

and others, 2019), and spatial autocorrelation (Johnson and 

others, 2013). Given the quantity of existing data, the large 

temporal, spatial, and taxonomic scope of these data, and 

increasingly powerful species distribution models, reducing 

uncertainty about how breeding distributions will respond to 

future climate change should be highly feasible.

Recurvirostra americana (Gmelin, 1789) (American avocet). Photograph by Douglas Barnum, U.S. Geological Survey. Public domain.
12 Research Priorities for Migratory Birds Under Climate Change—A QVoI Assessment

Climate-Driven Changes to Avian Community 

Composition Through Homogenization and Loss 

of Specialists (Hypothesis 4b)

Magnitude of Uncertainty

Ecological specialization affects many processes and 

characteristics that relate to species’ adaptive capacity in the 

face of climate change, including range size, dispersal dynamics, and population viability (Kisdi, 2002; Kotiaho and others, 

2005; Slatyer and others, 2013). In general, specialists are 

thought to favor stable environments and be poor colonizers of 

new habitat, whereas generalists are able to thrive in disturbed 

and newly created habitat (Levins, 1968). As climate changes, 

ecological communities are predicted to become more homogeneous as generalist species flourish and specialists dwindle 

(McKinney and Lockwood, 1999). The relations between 

ecological specialization and range size, dispersal, and population viability have a strong tradition in the theoretical literature 

(McKinney, 1997; Büchi and Vuilleumier, 2014), providing a 

firm theoretical foundation for predicting how climate change 

will affect biotic homogenization. A growing body of empirical studies has also found evidence that avian communities 

have become more homogenous because of the increase of 

generalist species and decline of specialists (Davey and others, 

2012; Le Viol and others, 2012). Although changes in community composition have coincided with changes in several 

climate variables, the role of climate versus land-use change 

has not been fully explored in the existing literature, leading to 

moderate uncertainty surrounding this climate change effect.

Relevance to Land Acquisition Programs

Because many of the priority species are restricted in 

range or low in abundance, loss of these species because 

of climate-driven homogenization is potentially relevant to 

land acquisition decisions. Reducing uncertainty about this 

hypothesis could allow researchers to better predict which 

priority species may be unable to adapt to future conditions 

and where these species may remain present or abundant in the 

face of climate disruptions. Additionally, because community 

homogenization is also driven by nonclimate drivers, such as 

urbanization and land-use change, land acquisition managers 

could potentially take these other drivers into account during 

acquisition decision making as a buffer against climate-driven 

homogenization.

Reducibility

The widespread availability of monitoring data from programs like the North American Breeding Bird Survey, CBC, 

and eBird provide ample opportunities to estimate changes in 

community composition of North American birds over long 

timescales and across multiple spatial scales. When combined 

with species distribution models that can account for dispersal 

ability (Bateman and others, 2013), these datasets may provide 

opportunities to predict how individual species and entire 

communities may be disrupted by changing patterns of abundance between generalists and specialists.

The Effects of Decreased Precipitation 

on Abundance in the Breeding Season 

(Hypothesis 2b)

Magnitude of Uncertainty

Precipitation across North America has changed in spatially variable ways; for example, precipitation has decreased 

in the West, Southwest, and Southeast and increased in the 

southern Great Plains, Midwest, and Northeast (Easterling 

and others, 2017). Changes in total precipitation, as well 

as changes in the frequency and severity of precipitation 

extremes, can affect the productivity of many terrestrial 

and aquatic habitats that are important to the survival and 

reproductive success of migratory birds. This importance 

is particularly true for the billions of waterfowl and other 

wetland-dependent species that rely on permanent and ephemeral wetlands for successful breeding (Johnson and others, 

2005; Millett and others, 2009). The relationship between 

wetland productivity and recruitment and abundance of 

wetland-dependent migratory birds is well established from 

first principles (Johnson and others, 1987) and decades of 

long-term empirical studies (Markham, 1982; Zhao and others, 

2016; Specht and Arnold, 2018). The effects of precipitation 

on nonwetland species is less certain, but evidence suggests 

that decreased precipitation can lead to lower reproductive 

success (Öberg and others, 2015; Conrey and others, 2016), 

juvenile survival (Yackel Adams and others, 2006; Vernasco 

and others, 2018), and adult survival (Öberg and others, 2015) 

in landbirds; therefore, little uncertainty remains that reduced 

breeding season precipitation will negatively affect these species. However, substantial uncertainty remains about spatial 

and temporal changes in precipitation patterns. Although 

most climate models agree that the frequency and severity of 

extreme precipitation events will increase throughout North 

America, models disagree substantially about regional changes 

in the amount and timing of precipitation (Stephens and Hu, 

2010; Pierce and others, 2013; Wuebbles and others, 2017). 

Therefore, despite well-understood relationships between 

precipitation and the demography of migratory birds, the 

uncertainty about spatial variation in the amount, timing, and 

frequency of future precipitation creates substantial uncertainty regarding this hypothesis.

Relevance to Land Acquisition Programs

Given the centrality of waterfowl to many Federal land 

acquisition programs, future reductions in precipitation and 

increasing droughts have the potential to dramatically alter 

abundances of the priority species that drive land acquisition 
Results 13

decisions in North America (Bethke and Nudds, 1995; 

Sorenson and others, 1998; Haig and others, 2019). The high 

spatial variability in precipitation changes makes this hypothesis particularly relevant to land acquisition decisions because 

understanding this spatial variation will be critical for accurately predicting which areas will provide high-quality habitat 

for priority species into the future.

Reducibility

The reducibility for this hypothesis is relatively low. 

Although the relationships between precipitation, wetland 

quality, and breeding success of wetland-dependent species are 

relatively well understood, improving management actions in 

the face of future precipitation changes will require improving predictions of future precipitation changes at high spatial 

resolutions. Although climatologists are developing a better 

understanding of the physical processes that affect variation in 

precipitation patterns (Meehl and others, 2005; Stephens and 

Hu, 2010; Muller and others, 2011), the predictive ability of 

precipitation models is still relatively low (Chen and Knutson, 

2008; Wehner and others, 2010). Reducing uncertainty about 

this hypothesis will therefore fall heavily on the ability of 

climatologists to improve the predictive ability of climate 

models. Because of the inherent complexity of precipitation modeling, reducing uncertainty about this hypothesis 

will require continued long-term data collection and model 

development, both of which render this source of uncertainty 

difficult to reduce.

The Effects of Rising Temperatures on 

Abundance in the Nonbreeding Season 

(Hypothesis 2a)

Magnitude of Uncertainty

Climate conditions during the nonbreeding season can 

also affect abundance through survival and reproductive 

success of resident and migratory birds (Harrison and others, 

2011). For residents and migrants that overwinter in temperate 

areas, adverse winter weather (for example, low temperatures 

and duration of frost or snow) can lower survival through 

direct thermoregulatory costs (Swanson and Olmstead, 1999) 

or food limitation (Jansson and others, 1981) and can lower 

subsequent reproductive success via carry-over effects on body 

condition or phenology (Robb and others, 2008). Increasingly 

mild winter temperatures in temperate areas could therefore 

result in higher survival and reproductive success of species 

that winter in these areas, potentially resulting in higher abundance if species were previously limited in the nonbreeding 

season. Although studies have linked improved demographic 

rates to warmer winter temperatures, and a few have demonstrated positive effects on overall changes in abundance, the 

validity of this hypothesis is conditional on winter limitation; 

that is, abundance of migratory bird populations will only 

increase in response to warming winters if winter temperature 

limits population growth. The degree to which populations 

of migratory birds are winter limited is poorly understood for 

most species. Collectively, the lack of theory, combined with 

the lack of empirical evidence regarding winter limitation and 

the few studies that have directly linked changes in abundance 

to winter temperature, leads to a large degree of uncertainty 

regarding this hypothesis.

Relevance to Land Acquisition Programs

The links between this hypothesis and land acquisition 

decision making is weaker than the links with some of our 

other hypotheses. If populations are currently winter limited, 

parcels that currently support high-abundance populations of 

priority species during the winter will likely remain important sites in the future regardless of whether this hypothesis 

is true. As a result, management decisions based on current 

abundance are unlikely to be detrimental under this hypothesis if winter remains the limiting season. Therefore, reducing 

uncertainty about this hypothesis is not likely to change which 

acquisition actions produce the highest benefits. However, 

warming temperatures may relax winter limitation enough that 

breeding-season processes become more limiting in the future. 

In this case, current means objectives used by land acquisition programs may become unreliable predictors of the parcels 

that are most likely to meet fundamental objectives long term. 

Populations are unlikely to become completely released from 

winter limitation, so protecting land that currently supports a 

high abundance of individuals is unlikely to have major downsides under future climate scenarios.

Reducibility

This hypothesis has relatively low reducibility. 

Measuring breeding versus winter limitation has been a central 

focus of avian population ecology for decades (Newton, 

2004), yet researchers still lack a solid theoretical or empirical 

understanding of this issue because linking population processes to seasonal vital rates and environmental processes is 

exceedingly difficult. However, the same monitoring programs 

that have proven useful for modeling species distributions, 

including the North American Breeding Bird Survey, CBC, 

and eBird, provide data that can be used to quantify changes 

in abundance across space and time (Niven and others, 2004; 

Link and Sauer, 2007; Robinson and others, 2018). Combined 

with large-scale remote sensing data, these data can be used 

to model the relation between abundance and winter climate 

for many species (Wilson and others, 2011; Rushing and others, 2016). In cases where demographic data are also available, integrated population models could provide a powerful 

method to link climate variation to changes in abundance via 

demographic vital rates (Weegman and others, 2017), which 

should improve predictions about future dynamics (Zipkin and 

Saunders, 2018). Continued development of these data sources 
14 Research Priorities for Migratory Birds Under Climate Change—A QVoI Assessment

and analytical methods may provide important insights into 

this hypothesis in the future, but progress will likely be limited 

by the intrinsic difficulty of the questions.

Conclusions

An actionable science program must consider the information needs of decision makers within a particular decision 

context, as well as the feasibility of reducing uncertainty 

through scientific research. By using a modified structured 

decision-making approach, and a qualitative value of information analysis, we identified four high priority topics for 

research on migratory birds and climate change: the effects 

of rising temperatures on spatial distributions of migratory 

birds in the breeding and nonbreeding seasons, climate-driven 

changes to avian community composition through homogenization and loss of specialists, the effects of decreased precipitation on abundance in the breeding season, and the effects of 

rising temperatures on abundance in the nonbreeding season. 

Because we began with a thorough understanding of land 

acquisition programs and built our assessment on the evaluation criteria used to make acquisition decisions, our rankings are specifically tailored to the information needs of land 

acquisition program managers. The research priorities identified through this process are important scientifically, in that 

substantial uncertainty still needs to be reduced, are relevant 

to decision makers within the land acquisition context, and are 

reducible given existing methods and data.

The high-priority uncertainties we identified emphasize 

potential changes in spatial distribution of focal species or 

changes in their abundance, rather than sources of uncertainty focused on temporal changes in phenology. Although 

climate-driven phenological shifts remain an active area of 

research, we determined that this topic was not highly relevant 

to land acquisition decision making given the evaluation criteria currently in use. Federal land acquisition programs generally protect parcels of land in perpetuity, so climate-driven 

range shifts have potential to substantially alter which parcels 

will help meet fundamental objectives in the future. The structure of current land acquisition programs makes incorporating 

phenological shifts difficult, thereby reducing the management 

relevance of uncertainty related to phenology.

Our work focused on the specific context of habitat 

acquisition for migratory birds, but this approach could be 

used to identify important research priorities in other fields. 

Natural-resource managers often make decisions in complex 

and highly uncertain ecological systems, and available management interventions are often rare. In these situations, the 

qualitative value of information approach outlined here, combined with expert elicitation and a review of the existing literature, can be an important tool for improving management decisions in the face of uncertainty. By providing a replicable and 

transparent approach to prioritizing research efforts in cases 

where traditional quantitative value of information analysis 

is not possible, qualitative value of information is a valuable 

tool for prioritizing research and for improving the relevance 

of research products to management decisions. Results from 

this analysis can provide Government, academic, and nongovernmental organizations with a list of priority topics for future 

research and a clear rationale for allocating research efforts.

Anas platyrhynchos (Linnaeus, 1758) (mallard). Photograph by 

Andrew Reeves, U.S. Geological Survey. Public domain.

Ammodramus henslowii (Audubon, 1829) 

(Henslow’s sparrow). Photograph by 

Jim Hudgins, U.S. Fish and Wildlife Service. 

Public domain.
References Cited 15

References Cited

Bateman, B.L., Murphy, H.T., Reside, A.E., Mokany, K., and 

VanDerWal, J., 2013, Appropriateness of full‐, partial‐and 

no‐dispersal scenarios in climate change impact modelling: Diversity & Distributions, v. 19, no. 10, p. 1224–1234. 

[Also available at https://doi.org/10.1111/ddi.12107.]

Bethke, R.W., and Nudds, T.D., 1995, Effects of climate 

change and land use on duck abundance in Canadian 

prairie‐parklands: Ecological Applications, v. 5, no. 3, 

p. 588–600. [Also available at https://doi.org/10.2307/

1941969.]

Büchi, L., and Vuilleumier, S., 2014, Coexistence of specialist and generalist species is shaped by dispersal and 

environmental factors: American Naturalist, v. 183, 

no. 5, p. 612–624. [Also available at https://doi.org/

10.1086/675756.]

Canterbury, G., 2002, Metabolic adaptation and climatic 

constraints on winter bird distribution: Ecology, v. 83, no. 4, 

p. 946–957. [Also available at https://doi.org/10.1890/0012-

9658(2002)083[0946:MAACCO]2.0.CO;2.]

Chen, C., and Knutson, T., 2008, On the verification and comparison of extreme rainfall indices from climate models: 

Journal of Climate, v. 21, no. 7, p. 1605–1621. [Also available at https://doi.org/10.1175/2007JCLI1494.1.]

Chen, I., Hill, J.K., Ohlemuller, R., Roy, D.B., and Thomas, 

C.D., 2011, Rapid range shifts of species associated with

high levels of climate warming: Science, v. 333, no. 6045,

p. 1024–1026. [Also available at https://doi.org/10.1126/

science.1206432.]

Clement, M.J., Hines, J.E., Nichols, J.D., Pardieck, K.L., and 

Ziolkowski, D.J., 2016, Estimating indices of range shifts 

in birds using dynamic models when detection is imperfect: 

Global Change Biology, v. 22, no. 10, p. 3273–3285. [Also 

available at https://doi.org/10.1111/gcb.13283.]

Conrey, R.Y., Skagen, S.K., Yackel Adams, A.A., and Panjabi, 

A.O., 2016, Extremes of heat, drought and precipitation

depress reproductive performance in shortgrass prairie passerines: The Ibis, v. 158, no. 3, p. 614–629. [Also available

at https://doi.org/10.1111/ibi.12373.]

Davey, C.M., Chamberlain, D.E., Newson, S.E., Noble, D.G., 

and Johnston, A., 2012, Rise of the generalists—Evidence 

for climate driven homogenization in avian communities: Global Ecology and Biogeography, v. 21, no. 5, 

p. 568–578. [Also available at https://doi.org/10.1111/

j.1466-8238.2011.00693.x.]

Easterling, D.R., Kunkel, K., Arnold, J., Knutson, T., 

LeGrande, A., Leung, L., Vose, R., Waliser, D., and Wehner, 

M., 2017, Precipitation change in the United States, chap. 

7 of Wuebbles, D.J., Fahey, D.W., Hibbard, K.A., Dokken, 

D.J., Stewart, B., and Maycock, T., eds., Climate science

special report—Fourth National Climate Assessment,

volume I: Washington, D.C., U.S. Global Change Research

Program, p. 207–230. [Also available at https://doi.org/

10.7930/ J0H993CC.]

Guillera‐Arroita, G., 2017, Modelling of species distributions, range dynamics and communities under imperfect 

detection—Advances, challenges and opportunities: 

Ecography, v. 40, no. 2, p. 281–295. [Also available at 

https://doi.org/10.1111/ecog.02445.]

Haig, S.M., Murphy, S.P., Matthews, J.H., Arismendi, I., 

and Safeeq, M., 2019, Climate-altered wetlands challenge 

waterbird use and migratory connectivity in arid landscapes: 

Scientific Reports, v. 9, no. 1, p. 1–10. [Also available at 

https://doi.org/10.1038/s41598-019-41135-y.]

Hammond, J.S., Keeney, R.L., and Raiffa, H., 2015, Smart 

choices—A practical guide to making better decisions: 

Boston, Mass., Harvard Business Review Press, 256 p.

Hanea, A.M., Burgman, M., and Hemming, V., 2018, IDEA 

for uncertainty quantification, in Elicitation: Springer, 

International Series in Operations Research & Management 

Science, v. 261, p. 95–117. [Also available at https://doi.org/

10.1007/978-3-319-65052-4_5.]

Harrison, X.A., Blount, J.D., Inger, R., Norris, D.R., and 

Bearhop, S., 2011, Carry‐over effects as drivers of fitness 

differences in animals: Journal of Animal Ecology, v. 80, 

no. 1, p. 4–18. [Also available at https://doi.org/10.1111/

j.1365-2656.2010.01740.x.]

Jansson, C., Ekman, J., and von Brömssen, A., 1981, mortality and food supply in tits Parus spp: Oikos, v. 37, no. 3, 

p. 313–322. [Also available at https://doi.org/10.2307/

3544122.]

Johnson, D.H., Sparling, D.W., and Cowardin, L.M., 1987, A 

model of the productivity of the mallard duck: Ecological 

Modelling, v. 38, no. 3–4, p. 257–275. [Also available at 

https://doi.org/10.1016/0304-3800(87)90100-1.]

Johnson, D.S., Conn, P.B., Hooten, M.B., Ray, J.C., and 

Pond, B.A., 2013, Spatial occupancy models for large data 

sets: Ecology, v. 94, no. 4, p. 801–808. [Also available at 

https://doi.org/10.1890/12-0564.1.]

Johnson, W.C., Millett, B.V., Gilmanov, T., Voldseth, R.A., 

Guntenspergen, G.R., and Naugle, D.E., 2005, Vulnerability 

of northern prairie wetlands to climate change: Bioscience, 

v. 55, no. 10, p. 863–872. [Also available at https://doi.org/

10.1641/0006-3568(2005)055[0863:VONPWT]2.0.CO;2.]
16 Research Priorities for Migratory Birds Under Climate Change—A QVoI Assessment

Kisdi, É., 2002, Dispersal—Risk spreading versus local adaptation: American Naturalist, v. 159, no. 6, p. 579–596. [Also 

available at https://doi.org/10.1086/339989.]

Knudsen, E., Lindén, A., Both, C., Jonzén, N., Pulido, F., 

Saino, N., Sutherland, W.J., Bach, L.A., Coppack, T., 

Ergon, T., Gienapp, P., Gill, J.A., Gordo, O., Hedenström, 

A., Lehikoinen, E., Marra, P.P., Møller, A.P., Nilsson, 

A.L.K., Péron, G., Ranta, E., Rubolini, D., Sparks, T.H.,

Spina, F., Studds, C.E., Sæther, S.A., Tryjanowski, P., and

Stenseth, N.C., 2011, Challenging claims in the study of

migratory birds and climate change: Biological Reviews

of the Cambridge Philosophical Society, v. 86, no. 4,

p. 928–946. [Also available at https://doi.org/10.1111/

j.1469-185X.2011.00179.x.]

Kotiaho, J.S., Kaitala, V., Komonen, A., and Päivinen, J., 

2005, Predicting the risk of extinction from shared ecological characteristics: Proceedings of the National Academy 

of Sciences of the United States of America, v. 102, no. 6, 

p. 1963–1967. [Also available at https://doi.org/10.1073/

pnas.0406718102.]

Langham, G.M., Schuetz, J.G., Distler, T., Soykan, C.U., and 

Wilsey, C., 2015, Conservation status of North American 

birds in the face of future climate change: PLoS One, v. 10, 

no. 9, p. 1–16. [Also available at https://doi.org/10.1371/

journal.pone.0135350.]

La Sorte, F.A., and Thompson, F.R., 2007, Poleward shifts 

in winter ranges of North American birds: Ecology, v. 88, 

no. 7, p. 1803–1812. [Also available at https://doi.org/

10.1890/06-1072.1.]

Lecy, J.D., and Beatty, K.E., 2012, Representative literature reviews using constrained snowball sampling and citation network analysis: SSRN, research 

paper, 15 p. [Also available at https://doi.org/ 

10.2139/ ssrn.1992601.]

Le Viol, I., Jiguet, F., Brotons, L., Herrando, S., Lindström, 

Å., Pearce-Higgins, J.W., Reif, J., Van Turnhout, C., and 

Devictor, V., 2012, More and more generalists—Two 

decades of changes in the European avifauna: Biology 

Letters, v. 8, no. 5, p. 780–782. [Also available at 

https://doi.org/10.1098/rsbl.2012.0496.]

Levins, R., 1968, Evolution in changing environments—

Some theoretical explorations: Princeton, N.J., Princeton 

University Press, Monographs in Population Biology, v. 2, 

120 p. https://doi.org/10.1515/9780691209418.

Link, W.A., and Sauer, J.R., 2007, Seasonal components 

of avian population change—Joint analysis of two 

large-scale monitoring programs: Ecology, v. 88, no. 1, 

p. 49–55. [Also available at https://doi.org/10.1890/0012-

9658(2007)88[49:SCOAPC]2.0.CO;2.]

Markham, B.J., 1982, Waterfowl production and water level 

fluctuation: Canadian Water Resources Journal, v. 7, 

no. 4, p. 22–36. [Also available at https://doi.org/10.4296/

cwrj0704022.]

Mayor, S.J., Guralnick, R.P., Tingley, M.W., Otegui, J., 

Withey, J.C., Elmendorf, S.C., Andrew, M.E., Leyk, S., 

Pearse, I.S., and Schneider, D.C., 2017, Increasing phenological asynchrony between spring green-up and arrival of 

migratory birds: Scientific Reports, v. 7, no. 1, art. 1902, 

10 p. [Also available at https://doi.org/10.1038/s41598-017-

02045-z.]

McKinney, M.L., 1997, Extinction vulnerability and 

selectivity—Combining ecological and paleontological 

views: Annual Review of Ecology and Systematics, v. 28, 

no. 1, p. 495–516. [Also available at https://doi.org/10.1146/

annurev.ecolsys.28.1.495.]

McKinney, M.L., and Lockwood, J.L., 1999, Biotic 

homogenization—A few winners replacing many losers in 

the next mass extinction: Trends in Ecology & Evolution, 

v. 14, no. 11, p. 450–453. [Also available at https://doi.org/

10.1016/S0169-5347(99)01679-1.]

Meehl, G.A., Arblaster, J.M., and Tebaldi, C., 2005, 

Understanding future patterns of increased precipitation intensity in climate model simulations: Geophysical 

Research Letters, v. 32, no. 18, 4 p. [Also available at 

https://doi.org/10.1029/2005GL023680.]

Miller-Rushing, A.J., Lloyd-Evans, T.L., Primack, R.B., and 

Satzinger, P., 2008, Bird migration times, climate change, 

and changing population sizes: Global Change Biology, 

v. 14, no. 9, p. 1959–1972. [Also available at https://doi.org/

10.1111/j.1365-2486.2008.01619.x.]

Millett, B., Johnson, W.C., and Guntenspergen, G., 2009, 

Climate trends of the North American prairie pothole region 

1906–2000: Climatic Change, v. 93, no. 1–2, p. 243–267. 

[Also available at https://doi.org/10.1007/s10584-

008-9543-5.]

Muller, C.J., O’Gorman, P.A., and Back, L.E., 2011, 

Intensification of precipitation extremes with warming in 

a cloud-resolving model: Journal of Climate, v. 24, no. 11, 

p. 2784–2800. [Also available at https://doi.org/10.1175/

2011JCLI3876.1.]

Newton, I., 2004, Population limitation in migrants: The Ibis, 

v. 146, no. 2, p. 197–226. [Also available at https://doi.org/

10.1111/j.1474-919X.2004.00293.x.]

Nichols, J.D., Koneff, M.D., Heglund, P.J., Knutson, M.G., 

Seamans, M.E., Lyons, J.E., Morton, J.M., Jones, M.T., 

Boomer, G.S., and Williams, B.K., 2011, Climate change, 

uncertainty, and natural resource management: The Journal 

of Wildlife Management, v. 75, no. 1, p. 6–18. [Also available at https://doi.org/10.1002/jwmg.33.]
References Cited 17

Niven, D.K., Sauer, J.R., Butcher, G.S., and Link, W.A., 

2004, Christmas Bird Count provides insights into population change in land birds that breed in the boreal forest: 

American Birds, v. 58, 104th Christmas Bird, p. 10–20.

Öberg, M., Arlt, D., Pärt, T., Laugen, A.T., Eggers, S., and 

Low, M., 2015, Rainfall during parental care reduces reproductive and survival components of fitness in a passerine 

bird: Ecology and Evolution, v. 5, no. 2, p. 345–356. [Also 

available at https://doi.org/10.1002/ece3.1345.]

Pierce, D.W., Cayan, D.R., Das, T., Maurer, E.P., Miller, N.L., 

Bao, Y., Kanamitsu, M., Yoshimura, K., Snyder, M.A., 

Sloan, L.C., Franco, G., and Tyree, M., 2013, The key role 

of heavy precipitation events in climate model disagreements of future annual precipitation changes in California: 

Journal of Climate, v. 26, no. 16, p. 5879–5896. [Also available at https://doi.org/10.1175/JCLI-D-12-00766.1.]

Robb, G.N., McDonald, R.A., Chamberlain, D.E., Reynolds, 

S.J., Harrison, T.J.E., and Bearhop, S., 2008, feeding of

birds increases productivity in the subsequent breeding season: Biology Letters, v. 4, no. 2, p. 220–223. [Also available

at https://doi.org/10.1098/rsbl.2007.0622.]

Robinson, O.J., Ruiz-Gutierrez, V., Fink, D., Meese, R.J., 

Holyoak, M., and Cooch, E.G., 2018, Using citizen science 

data in integrated population models to inform conservation decision-making: Biological Conservation, v. 227, 

p. 361–368. [Also available at https://doi.org/10.1016/

j.biocon.2018.10.002.]

Runge, M.C., Converse, S.J., and Lyons, J.E., 2011, Which 

uncertainty? Using expert elicitation and expected value 

of information to design an adaptive program: Biological 

Conservation, v. 144, no. 4, p. 1214–1223. [Also available 

at https://doi.org/10.1016/j.biocon.2010.12.020.]

Rushing, C.S., Royle, J.A., Ziolkowski, D.J., and Pardieck, 

K.L., 2019, Modeling spatially and temporally complex

range dynamics when detection is imperfect: Scientific

Reports, v. 9, no. 1, p. 1–9. [Also available at https://doi.org/

10.1038/s41598-019-48851-5.]

Rushing, C.S., Rubenstein, M.A., Lyons, J.E., and Runge, 

M.C., 2020, Using value of information to prioritize

research needs for migratory bird management under climate change—A case study using Federal land acquisition

in the United States: Biological Reviews of the Cambridge

Philosophical Society, v. 95, no. 4, p. 1109–1130. [Also

available at https://doi.org/10.1111/brv.12602.]

Rushing, C.S., Ryder, T.B., and Marra, P.P., 2016, 

Quantifying drivers of population dynamics for a migratory bird throughout the annual cycle: Proceedings 

of the Royal Society B: Biological Sciences, v. 283, 

no. 1823, 10 p. [Also available at https://doi.org/ 

10.1098/ rspb.2015.2846.]

Sauer, J.R., Link, W.A., Nichols, J.D., and Royle, J.A., 

2005, Using the North American Breeding Bird Survey 

as a tool for conservation—A critique of Bart et al. 

(2004): The Journal of Wildlife Management, v. 69, no. 4, 

p. 1321–1326. [Also available at https://doi.org/10.2193/

0022-541X(2005)69[1321:UTNABB]2.0.CO;2.]

Schlaifer, R., and Raiffa, H., 1961, Applied statistical decision 

theory: The American Mathematical Monthly, v. 69, no. 1, 

p. 72–73.

Slatyer, R.A., Hirst, M., and Sexton, J.P., 2013, Niche breadth 

predicts geographical range size—A general ecological 

pattern: Ecology Letters, v. 16, no. 8, p. 1104–1114. [Also 

available at https://doi.org/10.1111/ele.12140.]

Sorenson, L.G., Goldberg, R., Root, T.L., and Anderson, 

M.G., 1998, Potential effects of global warming on waterfowl populations breeding in the northern Great Plains:

Climatic Change, v. 40, no. 2, p. 343–369. [Also available

at https://doi.org/10.1023/A:1005441608819.]

Specht, H.M., and Arnold, T.W., 2018, Banding age ratios 

reveal prairie waterfowl fecundity is affected by climate, 

density dependence and predator-prey dynamics: Journal of 

Applied Ecology, v. 55, no. 6, p. 2854–2864. [Also available at https://doi.org/10.1111/1365-2664.13186.]

Stephens, G.L., and Hu, Y., 2010, Are climate-related changes 

to the character of global-mean precipitation predictable?: 

Environmental Research Letters, v. 5, no. 2, 7 p. [Also 

available at https://doi.org/10.1088/1748-9326/5/2/025209.]

Sullivan, B.L., Aycrigg, J.L., Barry, J.H., Bonney, R.E., Bruns, 

N., Cooper, C.B., Damoulas, T., Dhondt, A.A., Dietterich, 

T., Farnsworth, A., Fink, D., Fitzpatrick, J.W., Fredericks, 

T., Gerbracht, J., Gomes, C., Hochachka, W.M., Iliff, M.J., 

Lagoze, C., La Sorte, F.A., Merrifield, M., Morris, W., 

Phillips, T.B., Reynolds, M., Rodewald, A.D., Rosenberg, 

K.V., Trautmann, N.M., Wiggins, A., Winkler, D.W., Wong,

W.-K., Wood, C.L., Yu, J., and Kelling, S., 2014, The eBird

enterprise—An integrated approach to development and

application of citizen science: Biological Conservation,

v. 169, p. 31–40. [Also available at https://doi.org/10.1016/

j.biocon.2013.11.003.]

Swanson, D.L., and Olmstead, K.L., 1999, Evidence for a 

proximate influence of winter temperature on metabolism 

in passerine birds: Physiological and Biochemical Zoology, 

v. 72, no. 5, p. 566–575. [Also available at https://doi.org/

10.1086/316696.]

Tingley, M.W., Koo, M.S., Moritz, C., and Rush, A.C., 2012, 

The push and pull of climate change causes heterogeneous shifts in avian elevational ranges: Global Change 

Biology, v. 18, no. 11, p. 3279–3290. [Also available at 

https://doi.org/10.1111/j.1365-2486.2012.02784.x.]
18 Research Priorities for Migratory Birds Under Climate Change—A QVoI Assessment

Vernasco, B.J., Sillett, T.S., Marra, P.P., and Ryder, T.B., 2018, 

Environmental predictors of nestling condition, postfledging movement, and postfledging survival in a migratory 

songbird, the wood thrush (Hylocichla mustelina): The Auk, 

v. 135, no. 1, p. 15–24. [Also available at https://doi.org/

10.1642/AUK-17-105.1.]

Weegman, M.D., Arnold, T.W., Dawson, R.D., Winkler, D.W., 

and Clark, R.G., 2017, Integrated population models reveal 

local weather conditions are the key drivers of population 

dynamics in an aerial insectivore: Oecologia, v. 185, no. 1, 

p. 119–130. [Also available at https://doi.org/10.1007/

s00442-017-3890-8.]

Wehner, M.F., Smith, R.L., Bala, G., and Duffy, P., 2010, The 

effect of horizontal resolution on simulation of very extreme 

US precipitation events in a global atmosphere model: 

Climate Dynamics, v. 34, no. 2–3, p. 241–247. [Also available at https://doi.org/10.1007/s00382-009-0656-y.]

Weiskopf, S.R., Rubenstein, M.A., Crozier, L.G., Gaichas, 

S., Griffis, R., Halofsky, J.E., Hyde, K.J.W., Morelli, T.L., 

Morisette, J.T., Muñoz, R.C., Pershing, A.J., Peterson, 

D.L., Poudel, R., Staudinger, M.D., Sutton-Grier, A.E., 

Thompson, L., Vose, J., Weltzin, J.F., and Whyte, K.P., 

2020, Climate change effects on biodiversity, ecosystems, 

ecosystem services, and natural resource management 

in the United States: Science of the Total Environment, 

v. 733, 18 p. [Also available at https://doi.org/10.1016/

j.scitotenv.2020.137782.]

Wilson, S., LaDeau, S.L., Tøttrup, A.P., and Marra, P.P., 2011, 

Range‐wide effects of breeding‐and nonbreeding‐season 

climate on the abundance of a Neotropical migrant songbird: Ecology, v. 92, no. 9, p. 1789–1798. [Also available at 

https://doi.org/10.1890/10-1757.1.]

Wood, E.M., and Kellermann, J.L., eds., 2015, Phenological 

synchrony and bird migration—Changing climate and 

seasonal resources in North America: Boca Raton, Fla., 

CRC Press, Cooper Ornithological Society Studies in Avian 

Biology, v. 47, 246 p. [Also available at https://doi.org/

10.1201/b18011.]

 Wuebbles, D.J., Fahey, D.W., Hibbard, K.A., Dokken, D.J., 

Stewart, B.C., and Maycock, T.K., eds., 2017, Climate 

science special report—Fourth national climate assessment v. I: Washington, D.C., U.S. Global Change Research 

Program, 470 p. [Also available at https://doi.org/10.7930/

J0J964J6.]

Yackel Adams, A.A., Skagen, S.K., and Savidge, J.A., 

2006, Modeling post‐fledging survival of lark buntings 

in response to ecological and biological factors: Ecology, 

v. 87, no. 1, p. 178–188. [Also available at https://doi.org/

10.1890/04-1922.]

Zhao, Q., Silverman, E., Fleming, K., and Boomer, G.S., 2016, 

Forecasting waterfowl population dynamics under climate 

change—Does the spatial variation of density dependence 

and environmental effects matter?: Biological Conservation, 

v. 194, p. 80–88. [Also available at https://doi.org/10.1016/

j.biocon.2015.12.006.]

Zipkin, E.F., and Saunders, S.P., 2018, Synthesizing multiple 

data types for biological conservation using integrated 

population models: Biological Conservation, v. 217, 

p. 240–250. [Also available at https://doi.org/10.1016/

j.biocon.2017.10.017.]

Phalacrocorax auritus (Lesson, 1831) (double-crested cormorant). 

Photograph by Jeff Corbett, U.S. Geological Survey.
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