State University Capstones, Theses and Graduate Theses and Dissertations Dissertations

2020

Wetland use by spring migrating ducks in Iowa’s Prairie Pothole

Derek Ballard Iowa State University

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Recommended Citation Ballard, Derek, " use by spring migrating ducks in Iowa’s Prairie Pothole Region" (2020). Graduate Theses and Dissertations. 18091. https://lib.dr.iastate.edu/etd/18091

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Wetland use by spring migrating ducks in Iowa’s Prairie Pothole Region

by

Derek C. Ballard

A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE

Major: Wildlife Ecology

Program of Study Committee: Adam K. Janke, Major Professor Stephen J. Dinsmore Grace Wilkinson Orrin Jones

The student author, whose presentation of the scholarship herein was approved by the program of study committee, are solely responsible for the content of this thesis. The Graduate College will ensure this thesis is globally accessible and will not permit alterations after a degree is conferred.

Iowa State University

Ames, Iowa

2020

Copyright © Derek C. Ballard, 2020. All rights reserved. ii

TABLE OF CONTENTS Page ACKNOWLEDGEMENTS ...... iv ABSTRACT ...... vi CHAPTER 1. GENERAL INTRODUCTION ...... 1 Background ...... 1 Objectives ...... 3 Thesis Organization ...... 3 Literature Cited ...... 4 CHAPTER 2. FACTORS AFFECTING WETLAND USE BY SPRING MIGRATING DUCKS IN THE SOUTHERN PRAIRIE POTHOLE REGION ...... 6 Abstract ...... 6 Introduction ...... 7 Study Area ...... 11 Methods ...... 13 Focal Area and Wetland Selection ...... 13 Duck Surveys ...... 16 Vegetation Surveys ...... 17 Geospatial Analysis ...... 18 Analysis ...... 20 Results ...... 28 Wetland and Duck Observations ...... 28 Comparing Duck Use of Wetland Types ...... 29 Factors Affecting Use between Wetland Types ...... 31 Discussion ...... 36 Management Implications ...... 45 Literature Cited ...... 46 Tables ...... 55 Figures ...... 61 CHAPTER 3. EVALUATING DIURNAL WETLAND ACTIVITY BUDGETS AMONG SPRING MIGRATING DUCKS ...... 72 Abstract ...... 72 Introduction ...... 73 Methods ...... 75 iii

Study Area ...... 75 Wetland Categories and Selection ...... 76 Camera Placement ...... 78 Video Processing ...... 78 Statistical Analysis ...... 79 Results ...... 82 Dabbling Duck Activity...... 82 Diving Duck Activity ...... 85 Discussion ...... 87 Management Implication ...... 93 Literature Cited ...... 94 Tables ...... 99 Figures ...... 101 CHAPTER 4. GENERAL CONCLUSIONS ...... 109 Farmed ...... 110 Seasonal Wetlands ...... 112 Semi-permanent Wetlands ...... 112 Lakes ...... 114 Conclusions ...... 115 Literature Cited ...... 117 Tables ...... 119

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ACKNOWLEDGEMENTS

Financial support for this research was provided by the U.S. Fish and Wildlife Service through the Prairie Pothole and Upper Mississippi River Great Lakes Migratory Bird Joint

Ventures Improving the Science Foundation for Bird Conservation grant program in collaboration with the U.S. Geological Survey Iowa Cooperative Fish and Wildlife Research

Unit (Cooperative Agreement 1434-17-HQ-RU-01560). Additional financial and in-kind support for the research was provided by the Iowa Department of Natural Resources and the Natural

Resources Ecology and Management Department at Iowa State University.

I first thank all of the private landowners that allowed us to conduct this research on their land. I enjoyed the conversations and discussions and without them, and this research wouldn’t have been possible without them. I would also like to thank the Iowa DNR, U.S. Fish and

Wildlife Service (USFWS), and the many different Iowa County Conservation Boards (CCB)

that also allowed access for duck and wetland sampling during the course of this study.

I sincerely thank my advisor, Adam Janke, for the unbelievable amount of knowledge,

advice, and support provided during this project. I believe I’ve developed into a better individual

and wildlife professional under his mentorship. Without his assistance, my time pursuing this

master’s program wouldn’t have been nearly as rewarding. I’d also like to thank my graduate

committee for their time and direction, Dr. Steve Dinsmore, Dr. Grace Wilkinson, and Orrin

Jones. They provided assistance with the direction and offered various expertise over the course of this project. I’d also like to specifically thank Orrin Jones, Iowa’s waterfowl biologist, who was instrumental in the success of this project. His insight and knowledge of Iowa’s wetlands and waterfowl were a real asset to this study. I‘d like to also thank Dr. Philip Dixon, who provided needed knowledge and advice in during my analysis. v

I also thank the many people that assisted with both duck and wetland surveys. My

technicians, Blake Mitchell and Heather Sanders were essential to this project, and without them,

we wouldn’t have been able to sample nearly the number of wetlands we were able to. I’d also

like to thank an Iowa State undergraduate student, Connor Langen, who helped with lab work

such as GIS mapping and the tedious work of going through thousands of activity budget videos.

I thank my family, who have provided continuous support throughout my life. Without them, I wouldn’t have my interest in wildlife and the outdoors. My mother, who tolerated many muddy boots and noises early in the morning during my early experiences of the outdoors. I’d also like to thank my father, who made the time and trip to help with wetland sampling for both years of this study. The time spent with him in Iowa’s wetlands is something I’ll always remember.

Finally, the many people in the Natural Resources and Ecology Management Department at Iowa State University, specifically my officemates and other graduate students, were helpful and made my time there memorable. They provided much needed laughter, support, and knowledge during my time at ISU. My time wouldn’t have been nearly as enjoyable without the interactions and experiences shared with these people.

vi

ABSTRACT

Wetlands in the Prairie Pothole Region (PPR) provide important stopover areas for spring

migrating ducks in transit to northern breeding areas. However, how ducks use and distribute

among different wetland types found in the agriculturally-dominated landscapes of the southern

PPR is unknown. The goals of this study were to (1) determine factors affecting the use of spring-migrating ducks on wetland types present in the PPR and to (2) calculate activity budgets of ducks on different wetland types within the Iowa Prairie Pothole Region. We conducted weekly duck surveys during the springs of 2018-19 and surveyed a total of 1,061 wetlands within the PPR of Iowa. We observed approximately 131,000 ducks for a total of more than 1 million duck use- days. The majority of ducks counted in the study concentrated on a few key wetlands, primarily large semi-permanent wetlands. Semi-permanent wetlands provided the most duck use days overall and by a per unit area compared with the other wetland types including farmed wetlands, seasonal wetlands, and lakes. In addition to wetland area, duck use- days were influenced by wetland depth (mean, maximum), and the structure and percent of emergent vegetation on semi-permanent wetlands. We found dabbling ducks had a quadratic relationship with the percent of emergent vegetation while diving ducks were negatively related to the percent of vegetation. The structure of emergent vegetation had variable relationships with the abundances of duck species, although we observed the highest abundances on wetlands with areas of interspersed emergent vegetation and open water. Seasonal and farmed wetlands had inconsistent duck use, apparently due to variable water presence and duration in these wetland types. The number of visits flooded (+) and crop type were important factors for farmed wetlands while wetland area (+), percent vegetation (-) and vegetation height (+) were important for seasonal wetlands. Despite relatively low use, we found these wetlands may still contribute vii

important habitat for spring migrants, with high percentages of time spent feeding and resting

compared to semi-permanent wetlands. These results suggest that large, semi-permanent wetlands, which provided the most duck use, were important for transitioning ducks through this region. However, the other wetland types may also provide important habitat, especially because of the abundance on the landscape. 1

CHAPTER 1. GENERAL INTRODUCTION

Background

The Prairie Pothole Region (PPR) was once one of the largest marsh grassland complexes

found in the world. The PPR contained more than 70 million ha and extended to parts of Iowa,

Minnesota, North and , , and into prairie Canada (van der Valk 2005). The receding Wisconsin Pleistocene created a flat to gently rolling landscape scattered with millions of palustrine depressions colloquially called “prairie potholes” (Johnson et al. 2008).

Prairie potholes are typically small depressions that largely rely on precipitation as the source for flooding as well as input from in more permanent wetland types (Euliss et al. 2004).

However, the PPR provides an abundance of diverse wetland types, primarily from differences in water permanence and accordingly vegetation communities or zones.

These dynamic and diverse wetland types provide important habitat for many breeding and migratory avian species such as waterfowl and shorebirds (Stewart and Kantrud 1971). For

waterfowl, prairie potholes host between 50–80% of the North American duck breeding

populations annually depending on wetland conditions (Batt et al. 1989). In wet years, the PPR

can contribute to over 70% of the total duck production of species that comprise the majority of

the North American harvest (Batt et al. 1989). However, the PPR also provides important

stopover sites for waterfowl migrating to multiple northern breeding areas and the quality and quantity of stopover habitat can affect speed and success of migration. In addition to ecological

functions, prairie potholes provide a multitude of other functions such as acting as sinks for

nutrients and sediments, flood control, recharging groundwater, storing large amounts of organic

carbon, as well as other environmental and socio-economic values (Johnson et al. 2008). 2

The portion of the PPR that extends into Iowa is called the Des Moines Lobe (DML), and historically contained approximately 3.08 million ha of native prairie pothole complex (Bennet

1938). However, by the 1980’s, it was estimated that between 90% to 99% of Iowa’s wetlands had been lost, primarily due to (van der Valk. 2005, Miller et al. 2009, Dahl 2014,

Van Meter and Basu 2015). Fewer than 12,000 ha of prairie wetland habitat was estimated to remain by this time, with only about 2,000 ha in private ownership (Bishop et al.1998). The northern region of the DML was documented as having the most extensive wetland loss throughout the entire PPR (Bishop et al.1998). Implementation of drainage districts for agriculture was the principal cause for the conversion of land and loss of wetlands in the PPR, with the goal of increasing production and efficiency of crops on the landscape (Miller et al.

2012). Consequently, more than 1.4 million ha of wetlands were drained throughout the PPR of

Iowa (Dahl 1990; McCauley et al. 2015).

Wetlands with shorter hydroperiods such as temporary wetlands were the most abundant wetland type (Miller et al. 2012) and were targeted by the extensive drainage in this region.

Consequently, semi-permanent wetlands make up a much larger proportion of wetlands on the landscape (Miller et al. 2012). However, the drainage of wetlands with shorter hydroperiods negatively impacted the remaining larger, more permanent wetland types as well (McCauley et al. 2015). Recent restoration efforts within the Des Moines Lobe of Iowa have focused on the degraded semi-permanent and permanent wetlands types (Miller et al. 2012). However, little is known about the relative value that these wetland types provide for spring migrating waterfowl.

Overall duck use and species abundance on wetland types encountered within the PPR has not been documented in previous studies in association with the wetland size and vegetation community. 3

Objectives

The goal of this research was to address information gaps in duck use of modern prairie wetland landscapes during spring migration to inform wetland conservation priorities in intensively farmed of the PPR. These landscapes are traditionally out of the scope of breeding-focused habitat conservation programs in the region and therefore may be best-served by an alternative conservation and management paradigm than typically used across the PPR landscape for breeding habitat conservation. This goal will be accomplished by addressing the following objectives:

1) Document the relative use of key wetland types by spring-migrating ducks to understand the contribution of specific wetland classes to migrants at the landscape scale.

Identify wetland factors associated with use of spring migrating ducks, including surrounding landscape, vegetation composition, wetland size, and basin characteristics.

2) Document activity budgets by ducks on different wetland types within the Iowa

Prairie Pothole Region.

Thesis Organization

This thesis is organized by journal papers as chapters. Chapter 1 provides general background information and a general introduction to the thesis, Chapters 2 through 3 address the research objectives outlined in the section above, and Chapter 4 provides a general conclusion for the three chapters addressing the stated research objectives.

4

Literature Cited

Batt, B. D., M. G. Anderson, C. D. Anderson, and F. D. Caswell. 1989. The Use of Prairie Potholes by North American Ducks. Northern Prairie Wetlands: 204-227.

Bennett, L. J. 1938. The blue-winged teal: its ecology and management. Iowa State University.

Bishop, R. A., J. Joens, and J. Zohrer. 1998. Iowa's Wetlands, Present and Future with a Focus on Prairie Potholes. The Journal of the Iowa Academy of Science: JIAS 105:89-93.

Dahl, T.E., 1990, Wetlands-Losses in the United States, l 780's to l 980's: Washington, D.C., U.S. Fish and Wildlife Service Report to Congress, 13 p.

Dahl, T. E. 2014. Status and trends of prairie wetlands in the United States 1997 to 2009. U.S. Department of the Interior, Fish and Wildlife Service. Washington D.C.

Euliss, N. H., J. W. LaBaugh, L. H. Fredrickson, D. M. Mushet, M. K. Laubhan, G. A. Swanson, T. C. Winter, D. O. Rosenberry, and R. D. Nelson. 2004. The Wetland Continuum: A Conceptual Framework for Interpreting Biological Studies. Wetlands 24:448-458.

Johnson, R. R., F. T. Oslund, and D. R. Hertel. 2008. The past, present, and future of prairie potholes in the United States. Journal of Soil and Water Conservation 63:84A-87A.

McCauley, L. A., M. J. Anteau, M. P. van der Burg, and M. T. Wiltermuth. 2015. Land use and wetland drainage affect water levels and dynamics of remaining wetlands. Ecosphere 6:122.

Miller, B. A., W. G. Crumpton, and A. G. van der Valk. 2009. Spatial Distribution of Historical Wetland Classes on the Des Moines Lobe, Iowa. Wetlands 29:1146- 1152.

Miller, B. A., W. G. Crumption and A. G. van der Valk. 2012. Wetland hydrological class change from prior to European settlement to present on the Des Moines Lobe, Iowa. Wetland Ecology and Management 20:1-8.

Van der Valk, A. 2005. The prairie potholes of . Pages 393-423 in Cambridge University Press: Cambridge, UK. 5

Van Meter, K. J., and N. B. Basu. 2015. Signatures of human impact: size distributions and spatial organization of wetlands in the Prairie Pothole landscape. Ecological Applications 25:451-465.

6

CHAPTER 2. FACTORS AFFECTING WETLAND USE BY SPRING MIGRATING

DUCKS IN THE SOUTHERN PRAIRIE POTHOLE REGION

A paper to be submitted to The Journal of Wildlife Management

Derek C. Ballard1, Orrin Jones2, and Adam K. Janke1

1Department of Natural Resource Ecology and Management, Iowa State University, Ames, Iowa 50011, USA 2 Department of Natural Resources, Clear lake, Iowa, 50428, USA

Abstract

There is increasing recognition of the importance of wetlands in the prairie pothole region of the for stopover habitat for spring-migrating waterfowl because the quality and quantity of stopover habitat can affect speed and success of migration. However, conservation and management of wetlands in the region has traditionally focused narrowly on reproductive phases of the life cycle, and little to no research has examined how ducks use the diversity of available wetlands during migration. We conducted weekly surveys on 1,061 wetlands during the springs of 2018 and 2019 to examine factors affecting use of wetlands in the intensively modified southern PPR landscape of Iowa to inform restoration and conservation strategies. We compared wetland types while accounting for area, which included farmed, seasonal, and semi-permanent wetlands, and lakes and found highest duck use on semi- permanent wetlands followed by seasonal and then farmed wetlands. We examined how local and landscape factors within and around wetlands influenced duck use during spring migration.

We found multiple factors related to duck use at both local and landscape scales, such as the vegetation structure and density, depth, and number and area of wetlands on the surrounding landscape. Among the most used wetlands, semi-permanent wetlands, we found local factors 7

within wetlands were more important than landscape factors in determining duck use.

Collectively, our findings suggest semi-permanent wetlands within the PPR play a key role in transitioning birds across this landscape during migration and that management there to promote interspersion of emergent vegetation and open water and submerse aquatic plants could improve their function for migrants.

KEY WORDS: duck, Iowa, Prairie Pothole Region, spring migration, wetland

Introduction

Stopover sites provide essential refueling habitat for many migratory species as they transit from wintering to breeding areas. Thus, conditions experienced during migration and at stopover sites may influence population growth and trajectory for migratory species (Newton

2006). However, stopover sites may be limited, especially for waterfowl and shorebird species that use widely dispersed and highly modified wetland ecosystems. This limited number of stopover sites may cause high densities of birds, which deplete food resources as well as increase competition (Newton 2004). Consequently, habitats used during this period should be of interest for conservation efforts and if not included, may limit annual population productivity.

Nonetheless, the migratory periods of many avian species, including waterfowl, are understudied

(Lindström 1995, Hutto 1998, Stafford et al. 2014). Within the last few decades, there has been increasing recognition that these brief periods are critical for avian populations as the number of studies conducted during migration have also increased.

Among migrating ducks, nutrient acquisition and maintenance during spring can influence subsequent reproductive success (Heitmeyer and Fredrickson 1981, Devries et al. 2008, Stafford et al. 2014). Primary objectives of spring migrating ducks on stopover habitats 8 have been found to largely include refueling and resting (Guillemain et al. 2004). In addition to the challenges of fueling migration alone, ducks perform additional energetically costly activities during migration including courtship (Weller 1965), molt (Anteau et al. 2011), and unpredictable weather (Janke et al. 2019). Therefore, quality stopover sites must satisfy nutrient needs while also providing space for courtship, and minimizing disturbance and other threats (Newton 2006).

However, availability of food on stopover sites during spring is potentially at its lowest compared with other portions of the annual cycle (Anteau and Afton 2009, Brasher et al. 2010) due to food depletion during fall migration, absence of new vegetative or invertebrate growth or reproduction, and constraints imposed by ice conditions (Stafford et al. 2006, Greer et al. 2007,

Straub et al. 2012, Stafford et al. 2014).

Quality stopover habitat must therefore facilitate safe and swift migration as weather conditions allow. Krementz et al. (2011) noted that ducks that arrive on the breeding grounds in better condition have multiple advantages such as larger clutch size (Krapu 1981), earlier nest initiation (Dubovsky and Kaminski 1994), and higher breeding propensity (Alisauskas and

Ankney 1992). Anteau and Afton (2004) found that lesser scaup (Aythya affinis) arrived on the breeding grounds with decreased body mass and lipid reserves and postulated that changes in condition were related to degradation of mid- spring migration habitat over the period of their study. Ducks arriving on the breeding grounds may be required to spend time foraging thus delaying clutch formation due to decreases in body condition from insufficient stopover habitats, consequently affecting survival as well as reproductive success (Afton and Ankney

1991).

Wetlands provide the primary stopover sites for waterfowl staging and refueling and have therefore been the focus of research and management in many different landscapes across the 9

U.S. These landscapes span longitudinal extremes of the continental U.S., including western stopover areas such as southern Oregon and northeastern California (SONEC) (e.g., Miller et al.

2005, Fleskes and Yee 2007), central stopover areas of Nebraska’s Rainwater Basin (RWB)

(e.g., Webb et al. 2010, Pearse et al. 2011), the Prairie Pothole Region (PPR) (e.g., Austin et al.

2002, Anteau and Afton 2009, Janke 2019), the Upper Mississippi River and

(UMR/GLR) (e.g., Hitchcock 2009, Straub et al. 2012), as well as eastern coastal areas of the

Atlantic Flyway (e.g., Lewis Jr 2016). Each of these ecosystems share a common role in facilitating the transition of ducks from wintering areas in the southern U.S. and to breeding areas in northern U.S., Canada, and Alaska. However, each landscape differs in respect to the nature of the wetlands used there, their dispersion across the landscape, their ownership and management, and behavior of ducks. For example, SONEC historically supported large concentrations of both fall and spring migrating waterfowl in the Pacific flyway, and has been managed through creation of multiple refuges of large wetland and lake complexes (Gilmer et al. 2004). Similarly, areas used by spring migrants in the Atlantic flyway are comprised of large expanses of coastal habitat also publicly owned and managed. These landscapes differ from the RWB which is characterized by private land ownership and widespread wetland loss and degradation. Approximately only 11,500 ha of wetland area remain in the RWB (Smith and

Higgins 1990). Similarly, the PPR is dominated by privately owned land, with the majority of wetland lose due to conversion of land for agriculture. However, the area of the PPR historically contained over 70 million ha of prairie wetland complexes, with 3.5 million in Iowa (van der

Valk 2005, Miller et al. 2009). A multitude of wetland types are encountered and used by spring migrating ducks in the PPR, including farmed wetlands, seasonal wetlands, riverine wetlands, and semi-permanent and permanent wetlands (Heitmeyer and Vohs 1984, Krementz et al. 2011, 10

Straub et al. 2012, Murphy and Dinsmore 2018). These wetlands provide a diversity of resources, similar to that from other landscapes used by migrants, during a time of high energy expenditure. However, unlike these other regions, a unique feature of the DML is its proximity to the rest of the PPR for breeding ducks or the ‘duck factory’ and its sheer size. Consequently, the importance of prairie landscapes and the range of wetland types they provide for migrating waterfowl across mid-latitude North America may be unparalleled.

The majority of research, management, and conservation strategies developed for prairie landscapes focus on breeding (e.g., Swanson and Meyer 1977, Delphey and Dinsmore 1993,

Austin 2002, Cordts et al. 2002, Walker et al. 2013). However, the PPR also provides important stopover sites for waterfowl migrating to multiple northern breeding areas such as the northern continental U.S., the Boreal forest, , and Alaska. Recently, studies have documented widespread use of stopover sites by radiomarked spring migrating waterfowl in the

PPR (Miller et al. 2005, Haukos et al. 2006, Gray 2010, Krementz et al. 2011). Similarly, some research in the region has considered the distribution of a few species of migrant ducks (Austin et al. 2002, Anteau and Afton 2009, Janke et al. 2019) or a single wetland type used by ducks

(LaGrange and Dinsmore 1988, Murphy and Dinsmore 2018, Vanausdall and Dinsmore 2019).

However, no research has synchronously examined patterns of duck use across the range of wetland types found in the region and available for use by spring migrating ducks. Wetland types encountered may provide different resources, and therefore ducks may use and distribute across wetland types differently.

Conservation efforts aimed at improving conditions for migrating ducks in this landscape would therefore benefit from an understanding of how ducks distribute across a range of wetland types, sizes, and management regimes that are characteristic of the southern PPR landscape. 11

There, we find a wide diversity of wetland types, many of which exist in a matrix of intensively

farmed uplands. Recent work suggests extant wetlands in these landscapes have the capacity to

support migrants (Janke et al. 2019), but understanding which wetlands are used and what factors

drive variation in use among them is lacking. Wetland conditions have changed considerably

since European settlement in this landscape and shifted from millions of small upland

depressions to a relatively disproportionate number of large semi-permanent and permanent

wetlands (Miller et al. 2012) likely through the process of consolidating seasonal or temporary

wetlands into larger, more permanent wetlands through drainage (McCauley et al. 2015). Recent

restoration efforts within the Des Moines Lobe of Iowa have also focused on the more permanent

wetlands types (Miller et al. 2012). However, little is known about the relative value that these

wetland types provide for spring migrating waterfowl. Therefore, our objectives were to

simultaneously document use of a range of wetland types by spring-migrating ducks to understand the contribution of specific wetland types to migrants across the southern PPR. We aimed to understand factors associated with wetland use and selection amongst a diversity of wetland types present in the modern, intensively farmed prairie pothole landscape.

Study Area

We conducted our research in the Prairie Pothole Region (PPR) of Iowa, USA

(42.580°N, 93.709°W; approximately 365 m elevation), during the springs of 2018 and 2019.

The PPR that extends into Iowa is called the Des Moines Lobe (DML) and is the southern extent of the PPR (Figure 2.1). It is distributed within the north-central part of the state and consist of

29 counties. The DML is characterized by the typical knob and topography of the PPR with ridges of high relief interspersed between expanses of flat glacial till. This landscape was created by the relatively recent receding of the Wisconsin Pleistocene glacier 12 approximately 12,000- 14,000 years ago. The retreat of the glacier created a flat to gently rolling landscape scattered with millions of palustrine depressions colloquially called “prairie potholes”

(Johnson et al. 2008). Historically, approximately 3.08 million ha of native prairie pothole complex were found in the DML of Iowa (Bennet 1938). The majority of wetlands in this region had temporary or seasonal wetland hydroperiods (Miller et al. 2009). By the 1980’s, it was estimated that approximately 95% to 99% of Iowa’s wetlands had been lost due to draining for agriculture (van der Valk 2005; Miller et al. 2009, Van Meter and Basu 2015). The region has an extensive network of surface and subsurface drainage systems that removes runoff after spring thaw and precipitation events. Of the approximate 3.10 million ha in the Iowa PPR, 83.5% is dominated by row crop production, mainly corn and soybeans, with grassland (5.4%), forest

(2.1%), and development (6.4%) contributing proportionally less of the landscape (NLCD 2016).

Because of this, farmed wetlands are the most abundant type within Iowa, with an estimated potential wetland area of 7% to 12.2% of the total area of the DML (Van Meter and Basu 2015,

McDeid et al. 2019). Richardson et al. (1994) defined the climate of the PPR as a cool continental climate with hot summers and cold winters with extreme variations in precipitation and temperature. This area has an average annual temperature of 7.3°C and rainfall of 83 cm.

The mean annual high temperatures during the spring migratory season are -1.67°C in February,

9.17°C in March, 13.88°C in April, and 26.11°C in May (National Oceanic and Atmospheric

Administration 2020).

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Methods

Focal Area and Wetland Selection

The Iowa Department of Natural Resources defined 101 habitat focal areas in the region

for implementation of habitat goals under the state’s tactical plan as part of the Prairie Pothole

Joint Venture (PPJV) Implementation Plan. Focal areas were determined through landscape

analyses and expert opinion and are the focus of PPJV implementation in the state. We selected

a systematic random sample of these focal areas using a Generalized Random Tessellation

Stratified Sample (GRTS; Stevens and Olsen 2004) that generated a spatially balanced sample of

focal areas to focus our work. The GRTS sampling approach guards against oversampling in

areas with higher densities of focal areas such as northern Iowa by systematically stratifying sample units across the entire study area, thus ensuring we captured variability in migrant use and wetland characteristics across the entire study area. We used R to draw the GRTS sample in

2 panels with a total of 20 sites and included an oversample of 16 sites to use as replacements if

the initial sample was logistically infeasible or logistics allowed for >10 sites in a year. Focal

areas were weighted by area to ensure larger focal areas had equal opportunity to be chosen,

rather than being outnumbered by numerically abundant small focal areas. We used the ordered

GRTS sampling frame to select 25 focal areas to visit during the 2 years of the study (n = 11

during 2018, n = 14 during 2019; Figure 2.1).

We used a combination of National Wetlands Inventory (NWI; Wilen and Bates 1995),

LiDAR-derived drained wetland maps (following McCauley and Jenkins 2005), hydric soil maps

(Miller et al. 2009), and historical and current aerial imagery to map all wetlands within each

focal area. We mapped wetlands with an area greater than 100 m2 in order to increase the

likelihood that depressions would have water present and thus be used by waterfowl. We 14 categorized all wetlands within each focal area into 4 types: farmed wetlands, seasonal wetlands, artificial wetlands, and semi-permanent and permanent wetlands (here forward semi-permanent wetlands). We classified wetlands by inspecting historical aerial imagery as well as inspecting growing-season true color imagery taken during a wet (2008) and average to dry year (2017). We excluded artificial wetlands, such as stormwater storage , from our sample because we were focusing on duck use of “naturally” occurring wetlands that may have opportunities for management or restoration. For each focal area, we drew a simple random sample of 20 semi- permanent wetlands and 10 seasonal wetlands, weighted by maximum wetland area. We sought permission to sample wetlands in the randomized list and when we were denied access, we replaced the wetland with the next in the sample order until achieving our intended sample size for each wetland type.

Seasonal Wetlands

Seasonal wetlands included wetlands that had water present at some time during the growing season, but not for extended periods. This hydrology is necessary for growth and persistence of wetland obligate plants (Stewart and Kantrud 1971). These wetlands primarily depend on precipitation and had variable hydroperiods between years and within seasons. Many of these wetlands were on land formally under cultivation but recently enrolled within the

Conservation Reserve Program or used for haying or pasture. We sought to sample 5 seasonal wetlands on each focal area.

Semi-permanent Wetlands

The semi-permanent wetland category included wetland types with comparatively longer hydroperiods typically present in semi-permanent and permanent wetlands (Stewart and Kantrud 15

1971, Cowardin et al. 1979). We categorized these differently from seasonal wetlands by the presence of a zone of emergent wetland obligate plants (Stewart and Kantrud 1971). Although extensively drained and modified, semi-permanent wetlands today comprise a greater percentage of remaining wetland types in the Iowa PPR (ca. 40%) than they did historically (6%; Miller et al. 2012) because of extensive drainage to less permanent wetland types and consolidation drainage of many small wetlands into single, more permanent basins. Semi-permanent wetlands were also the focus of many wetland restoration and management projects on private and publicly owned land in the study area. We sought to sample 10-12 semi-permanent wetlands on each focal area.

Farmed Wetlands

Farmed wetlands were temporarily flooded depressions that were actively under cultivation (e.g., Murphy and Dinsmore 2018). The majority of these wetlands were artificially drained through surface or subsurface drainage systems to reduce water depth and permanence for agriculture. The size and density of farmed wetlands was variable across the study area depending on geomorphology and precipitation (Miller et al. 2009). Due to how hydrologically dynamic and abundant farmed wetlands were, we developed a roadside survey method for monitoring duck use on them inside 1 square mile sections in the focal areas. We only considered sections that had a minimum of three usable roads around the perimeter of the section. We mapped farmed wetlands generally following the methods of McCauley and Jenkins

(2005) and only included sections with ≥10 potential farmed wetlands in the sampling pool. We then performed a simple random sample and selected 2-5 sections per focal area, based on focal area size.

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Duck Surveys

We visited wetlands weekly from the start of the spring migration period in March to completion in May. We initiated surveys as early as ice conditions allowed for duck use.

Locally-breeding ducks complicated inferring the exact endpoint of migration, but we ended surveys when we observed locally-breeding ducks only (i.e., no migrant species such as ; Anas acuta) and when pairs began to replace flocks of locally breeding species (e.g., blue-winged teal; Spatula discors). We recorded abundance of all duck species present during each survey. We conducted surveys 30 minutes after sunrise to 30 minutes before sunset.

Surveys were not conducted during severe weather, such as snow or rain to ensure suitable conditions for observing wetlands and ducks. At each visit, we documented the area of open water and ice in each wetland by estimating on a paper map and later entering it in ArcGIS. The survey frequency of approximately 7 days allowed us to survey a higher number of wetlands and corresponded with known minimum stopover duration of 12 days (Krementz et al. 2011) among (Anas platyrhynchos) migrating from Arkansas. Similarly, spring migrating northern pintails had residency times of approximately 7 days in the Rainwater Basin (Pearse et al. 2011).

We surveyed seasonal and semi-permanent wetlands primarily through ground surveys from key vantage points with binoculars and spotting scopes as well as flushing when visibility of the wetland was limited due to location or vegetation. Multiple observers used a small canoe to survey large wetlands.

For farmed wetland roadside surveys, we drove routes at a speed appropriate

(approximately ≤50 km/hour) to effectively detect and observe wetlands with water and then stopped to count ducks. Field observers used 10 cm topographic contour maps inside all potential farmed wetlands within each randomly selected section to uniquely identify basins and help 17 estimate water surface area upon each visit by marking extent within each depression that was later entered into ArcGIS.

Our survey and analytical approach assumed we had perfect detection of all ducks across wetland types, or that biases in our counts were consistent among wetland types. On small wetlands or those with little vegetation, complete censuses were likely, whereas the accuracy of counts likely changed as wetland area increased or had more isolated patches of open water interspersed with emergent vegetation. However, we had few observers in the study (3 total over

2 years) and each was trained in the same way to survey wetlands and attempt to enumerate all individuals.

Vegetation Surveys

We characterized wetland depth and vegetation in seasonal and semi-permanent wetlands with multiple transects that radiated from the center of the wetland. We randomly selected the first bearing and sampled two other transects 120 degrees apart in order to space transects evenly across the wetland. We sampled three to five points on each transect that were equally distanced from each other until we reached the edge of the wetland. We determined the number of sampling points on a transect (2-5) based on the size and heterogeneity of vegetation within each wetland. At each point, we measured the depth of water and visually estimated percent cover of vegetation for a 1 m radius around the point. We identified and categorized wetland plant species present from the previous growing season and estimated percent cover of each. We used four dominance categories to characterize plant cover at each point; <10% (present), >10 to 40%

(subdominant), >40% to 70% (codominant), and 100% (dominant). Categories included species of persistent emergent macrophytes, grasses, and other prairie and wetland obligate vegetation.

Common persistent emergent macrophytes included cattail (Typha spp.), common reed 18

(Phragmites australis), and bulrushes (Schoenoplectus spp.). We categorized dominant grass

communities as native warm season, cool season, annual, and reed canary grasses (Phalaris

arundinacea). We also documented presence of forbs and woody species within the wetland as

well as any moist-soil plants such as smartweed (Polygonum spp.), arrowhead (Sagittaria spp.),

and spikerush (Eleocharis spp.). We took submersed aquatic vegetation (SAV) samples using the

lake rake method (Kenow et al. 2007) in all semi-permanent and permanent wetlands. We took

three pulls in three random directions at each sampling point and took the sum of tines covered

with SAV in order to get a score of relative abundance as the percent of tines (out of 45)

obstructed by SAV. We also documented the species and number of species present of SAV

within each wetland. Common SAV species encountered included coontail (Ceratophyllum demersum), pondweeds (Potamogeton and Stuckenia spp.), naiads (Najas spp.), and bladderwort

(Ultricularia macrorhiza). We recorded three measurements of vegetation height at each sampling location in seasonal wetlands to capture variability in plant management among basins.

We also included the presence of disturbances, which included any mowing, burning, or grazing that occurred recently or during the field season.

For farmed wetlands, we recorded the crop type and tillage practice present within all wetlands in each section. Tillage practices within each basin were categorized as no till and till.

We classified till as having any post-harvest soil disturbance including conventional and strip tillage. Farmed wetlands that included more than a single crop type or tillage practice were characterized based on whichever was dominant.

Geospatial Analysis

We classified all wetlands using the Stewart and Kantrud (1971) cover types (1-4) using

ArcGIS, aerial imagery from the spring of 2018, and visual observations while in the field each 19

spring. These types characterize emergent vegetation structure within the wetland from “choked”

wetlands (Cover type 1) to wetlands with little to no emergent vegetation (Cover type 4).

Breeding dabbling ducks select for “hemi-marsh” wetlands with ~50:50 interspersed vegetation and open water (Weller and Spatcher 1965). Murkin et al. (1982) found that dabbling duck pairs

selected experimental plots with 50:50 vegetative cover to open water. Thus, we predicted

vegetative cover and structure within a wetland may potentially impact use of spring migrants.

We calculated three landscape scale buffers for each of the wetlands in our sampling

frame. We created local 1 km, intermediate 5 km, and large 10 km buffers that radiated out from

the perimeter of each wetland. Buffer distances were determined with known flight distances of

waterfowl and waterbird species throughout spring. Northern pintails in Nebraska made foraging

flights of approximately 4 km (Pearse et al. 2011). Brown and Dinsmore (1986) used similar

buffer covariates. Within each buffer we calculated a number of landscape covariates predicted

to influence duck use of wetlands. We calculated the percent wetland area as well as the number

of unique wetlands within each buffer by manually digitizing all semi-permanent wetlands from

2017 spring aerial imagery and National Wetlands Inventory maps (NWI; Wilen and Bates

1995). We used the Cropland Data Layer (USDA 2018) to calculate the percent area of cropland,

grassland (including CRP and hay/pasture) as well as any development present in each buffer.

We included the proportion of the area of development as a metric for disturbance.

We used an Iowa statewide 3 m digital elevation model (DEM) and calculated maximum water depths for seasonal and farmed wetlands. For farmed wetlands, we also determined the distance to the nearest road by creating centroids in each farmed wetland and calculating the shortest distance to a road to include as a covariate in our analyses.

20

Analysis

Use-days

We calculated use-days for each species by multiplying the mean number of individuals

observed on 2 consecutive counts by the number of days between those counts (Rundle and

Fredrickson. 1981, O’Neal et al. 2008). We then summed across weeks to calculate a total

number of use-days for each wetland by species. Interpolating between counts allowed us to characterize the total use of wetlands by ducks during the migration period and account for variable return intervals and total sampling occasions among years. Use-days (by species or guild) became the response variable used in all subsequent analyses.

Comparing duck use of wetland types

Our initial analysis step was to evaluate mean responses of ducks to different wetland types, while accounting for variable sample sizes and wetland areas among 4 primary wetland types in our sample. The four wetland types were farmed wetlands, seasonal wetlands, semi-

permanent wetlands, and lakes. Analyses on farmed wetlands only occurred on wetlands that

were visible and had presence of water at least once during the field season. Lakes were a subset

of semi-permanent wetlands defined as > 30 ha and classified as a Stewart and Kantrud (1971)

cover type 4.

We considered many regression models in initial exploratory data analyses, including

Poisson and negative binomial regressions, zero-inflated regression models with these

distributions, and hurdle models. We found zero-inflated regression models with a negative

binomial distribution fit our data best because they allowed for high quantities of zeros by

linking a discrete distribution (e.g., negative binomial) with a binomial distribution and allowing 21

for zeros to be from both distributions. In the first part of the model the binomial distribution

(zero-inflation logit model) models the probability of obtaining a zero count. The second part, the count model, examines the relationship between non-zero counts and selected covariates. In

our first step, we combined all wetlands and associated duck use-day metrics into a single

analysis framework and tested a series of zero-inflated negative binomial regression models to compare among wetland types and control for wetland areas. In our baseline null model, we fit two terms for both the logistic regression (probability of obtaining a zero count) and count model: year and log-transformed wetland area. Next, we tested whether inclusion of the wetland classification (farmed, seasonal, semi-permanent, or lake) would improve model fit and thus

reveal differences in expected wetland use (logistic model) and duck use (count model). Because

all lakes had non-zero counts, we ignored the coefficient (of infinity) of lakes in the logistic

model and forced farmed wetlands to be the reference level. We used a chi-squared test on the

difference of log likelihoods to test for model improvements over the null model without wetland

type and a more constrained model with wetland type (additive model). We then tested for

whether the slopes of the relationship to log-transformed wetland area were different among

farmed, seasonal, and semi-permanent wetland types with an interaction term between wetland

type and wetland area. Because sample sizes of lakes were so small (n = 11), we constrained the

slope for the lake interaction to be 0. We used a chi-squared test on the difference of log

likelihoods to test for model improvement with the interaction model compared to the less

constrained additive model. We reported regression coefficients and associated measures of

precision for the model with the best support.

Direct comparisons of predicted abundance of ducks between lakes and other wetland

types were not feasible because lakes were consistently larger than wetlands within the 22

observational ranges of other wetland types. Therefore, interpretation of lake data was restricted

only to comparisons of models and associated regression equations. However, farmed, seasonal, and semi-permanent wetlands had observations across a range of wetland sizes. We generated predictions from the best model of each wetland type ranging from 0.3 to 6 ha, a range that comprised the majority of the inter-quartile range of wetland areas in each wetland type in our

sample. We generated and combined predictions from the count and logistic regression models

for an estimated size-specific abundance based on the sum of 1 minus the probability of

obtaining a zero count multiplied by the predicted count. To generate 95% confidence intervals

on the prediction, we ran a bootstrapping routine to resample the data 1,000 times and showed predictions inside 2.5 and 97.5 percentiles of bootstrapped predictions.

To test whether duck use differed among private and publicly owned wetlands, we tested for differences in duck use while accounting for wetland areas within on all semi-permanent wetlands in our sample. We followed similar methods from the comparisons of wetland types, and compared models using AIC to determine whether inclusion of ownership (public or private) would improve model fit and thus reveal differences in expected wetland use (logistic model) and duck abundance (count model) among private and publicly owned wetlands. We excluded other wetland types from this analysis because they were generally all in one ownership category; farmed wetlands were 100% private, lakes were 100% public, and seasonal wetlands were 70% private.

To qualitatively compare distribution of ducks among wetlands in our sample, we ordered wetland use-days of all wetlands, in sequential order, and reported wetland numbers and

areas comprising four 25% quantiles of total use-days.

23

Analysis of factors affecting duck use

We used zero-inflated negative binomial regression models with various combinations of

covariates to understand how wetland vegetation, depth, and landscape conditions influenced

duck use-days. In our regression models, we tested multiple covariates that we predicted a priori

would influence duck use on a wetland by fitting all possible model combinations of covariates

for each wetland type. First, we determined covariates that we believed determined the presence

of ducks on a wetland (logit model). We assumed that both duck presence (logit model) and

abundance (count model) would be affected by the area of the wetland. We included multiple

potential area covariates to reduce the influences of extreme observations in our study. We

calculated the wetland area and maximum open water area for all wetlands in all wetland types.

Wetland area was the total extent of the wetland, including emergent vegetation and dry areas,

determined from GIS maps. The maximum open water area was the maximum area of open

water recorded weekly on paper maps concurrent with duck surveys. We also calculated

perimeter to area ratios for seasonal and semi-permanent wetlands. We calculated these ratios for

the wetland area to wetland perimeter, and the maximum open water area to maximum open

water perimeter. We log transformed each area covariate and tested quadratic functions. For

semi-permanent wetlands, we additionally included maximum depth as a potential covariate explaining duck presence. We tested for effects of year in both the logit and count models for all wetland types. We found significant differences (P < 0.01) for all wetland types, farmed, semi- permanent and seasonal wetlands, in the count model and thus included year in all count models as a nuisance term. Once best fit terms were determined for each wetland type’s logit model, we examined local and landscape covariates for associations with our various response variables in the count models. For semi-permanent wetlands, analyses were performed with total ducks, as 24

well as for each guild (diving and dabbling ducks) and individual species’ use-days as the

response variable. For analysis of seasonal and farmed wetlands, we used total duck use-days as the response variable due to low total abundance and number of wetlands within the sample.

We calculated Akaike’s Information Criterion (AIC) values and weights for each model.

For the initial logit model analysis of area terms, we calculated AIC scores to compare models

and determined the model with the lowest AIC value was best supported and used those area

terms as offset terms in all future models. We calculated the overall relative importance of all

covariates by taking the sum of the AIC weights of all models that included the covariate

(Burnham and Anderson 2002). We used AIC scores to compare local and landscape models for

the various response variables. In comparing landscape and local terms, we determined the

model with the lowest AIC value and assumed this model was the best supported for each scale.

We calculated correlation coefficients for all covariates and did not include highly correlated

covariates (|r| > 0.6) within the same model. We z-standardized all continuous covariates, by

centering covariates on the mean and then scaling by one standard deviation (Schielzeth 2010).

We included a suite of vegetation and wetland covariates at local and landscape scales that we

predicted may influence duck use based on previous research (Table 2.1). The rationale for

including each of these covariates and the procedures used to calculate them for each wetland are

described below.

Wetland and landscape covariates

We used a weighted average of vegetation and water depth measurements from sampling

points to account for the non-random distribution of transect sampling locations within the

wetland. We created a 1m x 1m grid within each wetland in R and assigned weights to each grid

cell based on the distance of the cell to the edge of the wetland, such that cells that were more 25 abundant in the wetland (i.e., those along the wetland perimeter) were given more weight than those less represented (i.e., those clustered near the center). We then plotted transect sampling locations on the grid and used the corresponding weights to calculate a weighted average for each metric from the transects. Failing to conduct this weighting routine would have over- represented points clustered in the middle of the wetlands and underrepresented edge conditions in means. We used this method for percent vegetation, percent vegetation by species such as percent cattail, percent SAV, and water depth. To create a quantitative measure of vegetation abundance for analysis, we multiplied the vegetation dominance categories taken in the field by assigned percentages; 25% (subdominant), 50% (codominant), and 100% (dominant). Then, we multiplied by the corresponding wetland transect point weight and took the sum for each vegetation dominance category. Lastly, we took the sum of all vegetation dominance categories and divided by the total percent of vegetation within the wetland, which was calculated as the sum of the percent of vegetation multiplied by the corresponding point weights.

We included a linear and quadratic function for the percent of emergent vegetation within semi-permanent wetlands. Many studies have considered the percent of emergent vegetation

(percent vegetation) in wetlands and its influence on duck use. Anteau and Afton (2009) estimated the density of vegetation as well as identified emergent species in wetlands that lesser scaup foraged in. Webb et al. (2010) estimated the percent of emergent vegetation and assigned them into bins for wetlands within the Rainwater Basin and found abundance of dabbling ducks was highest at approximately 50% emergent vegetation. Similarly, Brasher (2010) assumed that vegetative cover had a quadratic relationship with duck abundance on wetlands in Ohio. We hypothesized that dabbling duck use–days would be highest when the percent vegetation was approximately 50%, while for diving ducks, use-days would increase as the percent vegetation 26 decreased within the wetland. McKinstry and Anderson (2002) found that dabbling ducks migrating through Wyoming selected wetlands with high amounts of submersed vegetation and were larger and deeper than wetlands with little duck use. The total amount of submersed vegetation within a wetland was found to be related with migrating dabbling duck use

(McKinstry and Anderson 2002). Other studies such as Pöysä (1983) found that dabbling and diving duck species used areas with high SAV densities and that body and neck lengths of dabbling ducks correlated with water depths at foraging sites. We hypothesized that both dabbling and diving ducks would be positively correlated with the SAV while dabbling ducks would be negatively correlated with mean depth of the wetland as ducks wouldn’t be able to feed in deeper wetlands. In the Rainwater Basin, Webb et al. (2010) found that diving duck abundance was positively correlated with wetland depth. Thus, we predicted that diving ducks would be positively correlated with maximum depth of the wetland.

We summarized vegetation data to characterize vegetation communities within wetlands for dominant plant species along with metrics for wetland plant diversity including species evenness, Shannon diversity index, and richness for vegetation within seasonal and semi- permanent wetlands (Table 2.1). For seasonal wetlands, we calculated the same covariates as semi-permanent wetlands, excluding SAV. We additionally calculated the mean height of vegetation (height) within seasonal wetlands because we hypothesized that seasonal wetlands with taller heights would have lower duck use. We characterized the grass composition within seasonal wetlands by creating a categorical covariate based on the dominant type of grass present. We created five categories: annual grass, cool season grass, native warm season grass, reed canary grass, and determined which was dominant for each seasonal wetland. 27

Studies on farmed wetlands such as LaGrange and Dinsmore (1989) and Murphy and

Dinsmore (2018) found duck use was positively related to area of farmed wetlands. LaGrange

and Dinsmore (1989) found farmed wetlands in corn had higher use than other crop

types during spring in Iowa. Grazed, no-till corn was also found to be the dominant crop type used by mallards in Nebraska during winter and spring (Jorde 1981). LaGrange and Dinsmore

(1989) found that no tilled wetlands were used most. They suggested that wetlands that underwent tillage potentially had less food availability than no till wetlands. Consequently, we hypothesized that duck use would be greatest in farmed wetlands where corn and no tillage were

present, with wetland area influencing abundance. We also calculated the number of visits that a

farmed wetland was wet, which acted as a proxy for the hydroperiod and water availability

throughout the study period.

For landscape covariates, we hypothesized that wetland density and area would be

negatively associated with duck use within our wetlands. The presence of other wetlands nearby

may influence the duck use of our study wetlands, potentially allowing for distribution of

migrants away from the sampled wetland. Disturbances such as hunting pressure and vehicle

traffic can affect habitat use and distribution of waterfowl during migration (Madsen and Fox

1995, Dooley et al. 2010, Webb et al. 2011). Therefore, we included the intensity of

development within 1 km of the wetland as a proxy for human disturbance. We only noted

hunters present in a wetland on 3 occasions during our study so we chose not to include any

terms for hunter disturbances in our analysis.

28

Results

Wetland and Duck Observations

We surveyed 1,061 wetlands and made 8,982 unique observations (n = 1,935 during

2018, n = 7,047 during 2019). During 2018, we surveyed 100 semi-permanent wetlands, 4 lakes,

31 seasonal wetlands, and 169 farmed wetlands that had water on ≥ 1 occasion (304 wetlands

total). During 2019, we surveyed 139 semi-permanent wetlands, 7 lakes, 49 seasonal wetlands,

and 562 farmed wetlands (757 wetlands total). Of all wetlands other than farmed wetlands which

were private, 200 (n = 62 during 2018, n = 138 during 2019) were located on public land,

primarily on land managed by the State of Iowa (Iowa Department of Natural Resources) and

County Conservation Boards. The remaining 130 (n = 73 during 2018, n = 57 during 2019)

wetlands were on land owned by 91 private landowners (n = 47 during 2018, n = 44 during

2019). All farmed wetlands were on privately owned land. For our farmed wetlands, we

surveyed an average of 2.27 sections per focal area in 2018 (range = 2 - 4) and 4.36 per focal

area in 2019 (range = 4 - 5). We conducted surveys every 7.03 days on average (SD = 1.13,

range = 6.5 - 8 days) from when ducks arrived on 5 March 2018 to when migration had completed on 11 May 2018. We conducted surveys every 6.99 days (SD = 0.56, range = 6 - 9

days) from 12 March 2019 to 10 May 2019. We counted 130,157 ducks (n = 75,179 during 2018,

n = 54,978 during 2019) between the two years of the study, which accounted for more than

1,014,959 duck use-days (UD’s). We observed 20 duck species between the 2 years of the study,

18 of which were common migrants observed during spring in Iowa. Mallards (n = 295,995

UD’s) were the most abundant species for both years followed by lesser scaup (n = 113,380

UD’s), ring-necked ducks (Aythya collaris) (n = 104,333 UD’s), and blue-winged teal (n =

71,742 UD’s; Figure 2.2). 29

Comparing Duck Use of Wetland Types

Semi-permanent wetlands contributed approximately 826,914 total use-days (81.47 % of

total) on 239 semi-permanent wetlands between the two years of the study (Table 2.2). We

calculated 549,886 dabbling duck use-days and 277,047 diving duck use-days. Similar to the

use-days for all wetland types, mallards (n = 287,148), ring-necked ducks (n = 105,496), and

lesser scaup (n = 102,436) were the most abundant species on semi-permanent wetlands (Figure

2.3a). Seasonal wetlands contributed approximately 20,000 total use-days between the two years

of the study (Table 2.2). We documented 15,966 dabbling duck use-days and 3,581 diving duck

use-days on seasonal wetlands. Mallards (8,788 UD’s), ring-necked ducks (3,341 UD’s), and blue-winged teal (3,224 UD’s) were the most abundant species on seasonal wetlands (Figure

2.4a). Of the surveyed area, there were 2,226 unique farmed wetlands (n = 589 during 2018, n =

1637 during 2019), with only 744 ever containing any presence of water (n = 186 during 2018, n

= 558 during 2019). We only included wetlands with water during at least one visit within our sample for analysis. Farmed wetlands contributed 36,182 total use-days between the two years of the study (Table 2.2). We observed 30,042 dabbling duck use-days and 6,140 diving duck use- days. The most abundant species were mallards (17,296 UD’s), northern shovelers (Spatula clypeata) (3,558 UD’s), green-winged teal (2,991 UD’s), and (2,891 UD’s; Figure 2.4b).

We observed a total of 132,317 use-days on 11 lakes (2018 = 4, 2019 = 7). Although we surveyed 3 more wetlands in 2019 (17,323 UD’s), we observed much more use in 2018 (n >

100,000 UD’s). The majority of use-days observed in 2018 were observed from 1 lake (85,234

UD’s). We observed more diving ducks (n = 77,866 UD’s) than dabbling ducks (n = 54,452

UD’s) on lakes. The top 5 most abundant species were mallards (n = 40,690 UD’s), lesser scaup 30

(n = 27,065 UD’s), (Aythya valisineria) (n = 12,297 UD’s), ring- necked ducks (n

= 9,827 UD’s), and ruddy ducks (Oxyura jamaicensis) (n = 8,819 UD’s) (Figure 2.3b).

Migrants were clustered on a few wetlands within the study area. Of all wetlands in this

study (n = 1,061), we observed approximately 25% of total use-days on three wetlands: two

semi-permanent wetlands and one lake. These three wetlands comprised 7% of total wetland area

surveyed. Fourteen wetlands comprised 50% of all duck use and 30% area surveyed. This

quantile consisted of 12 semi-permanent wetlands and two lakes. Thirty seven wetlands

comprised 75% of all use-days on 41% of all wetland area. The majority of wetlands within this

quantile were semi-permanent wetlands, with three lakes, and 34 semi-permanent wetlands. The

other 1,024 wetlands contributed the remaining use-days. No seasonal or farmed wetlands were

present in the top 75% quantile of use-days.

In the analysis comparing all 4 wetland types, inclusion of the wetland type term in an

additive model with log-transformed wetland area significantly improved model fit over the

baseline without wetland type (P < 0.001). Estimation of unique slopes for wetland area for the

semi-permanent, seasonal, and farmed wetland categories in the interaction model did not

improve model fit (P = 0.858). Therefore, we interpreted the model with a universal area response for the logit and count model and unique wetland type intercepts. Log-transformed wetland area was negatively associated with the probability of a zero-count on a wetland (β = -

0.702, SE = 0.081). The seasonal wetland type was slightly less likely to have a zero count than farmed wetlands (β = -0.599, SE = 0.313) whereas semi-permanent wetlands were much less likely to have zero counts (β = -2.817, SE = 0.236). In the count model, abundance was positively associated with log-transformed wetland area (β = 0.779, SE = 0.0612). Lakes (β = -

0.892, SE = 0.549) and seasonal wetlands (β = 0.354, SE = 0.338) did not have greater mean 31

counts after controlling for wetland area over farmed wetlands in the reference group. Semi- permanent wetlands had greater predicted counts above the reference and other wetland types (β

= 0.465, SE = 0.217). Combining predictions of probability of non-zero counts and abundance on occupied wetlands of the same size revealed the cumulative impact of wetland type classifications demonstrating greater use and abundance on semi-permanent wetlands over farmed and seasonal wetlands across a range of observed wetland sizes (Figure 2.5).

Factors Affecting Use between Wetland Types

Semi-permanent wetlands

We found multiple local and landscape covariates were associated with duck use-days on semi-permanent wetlands (Table 2.3). In the logit model, maximum depth (P < 0.001) and log of maximum area of open water (P = 0.006) had a negative relationship with probability of a zero count. All logit models contained these two terms. Log-transformed maximum area of open water (P < 0.001) was the best fit for the area term in the count model and was included in all subsequent count models. Additional local effects in the count model included cover type (P <

0.001) and the quadratic function of percent of emergent vegetation (P < 0.001). With area accounted for, highest mean total duck abundance occurred on cover type 2 and 3, with the lowest abundance in cover type 1 wetlands (Figure 2.6). We found that the number of semi- permanent wetlands within a 1km buffer (P = 0.007) was negatively related with total duck use and was the only landscape covariate that influenced duck use on semi-permanent wetlands

(Table 2.3). We compared final fitted local and landscape models and found that the local model with wetland-specific covariates better predicted total duck use on semi-permanent wetlands with over 99.9% of the AIC weight compared with the model with only landscape terms. 32

Analysis for the dabbling duck guild found that maximum depth and log maximum area of open water were the best covariates for the logit model. Maximum depth and log maximum area of open water in the logit model both had a negative relationship with the probability of a zero count for dabbling ducks. In the count model, we found additional significant effects of cover type (P < 0.001), the quadratic function of percent of emergent vegetation (P = 0.005;

Figure 2.8), and mean depth (P = 0.009) (Table 2.3). Log-transformed maximum area of open water was included in the count models for dabbling duck use. All models including cover type were above the null model and along with percent of emergent vegetation had the most evidence of relative importance in predicting dabbling duck abundance. Cover type 1 had the lowest predicted abundance of dabbling ducks while cover type 2 had the highest with decreasing abundance for types 3 and 4 (Figure 2.6). Among species within the dabbling duck guild, we saw similar trends with all species containing cover type within associated final models except for wood ducks (Aix sponsa) and northern shovelers. Mean depth was negatively related with dabbling duck use-days (Figure 2.7) and was a factor for all dabbling duck species except northern pintails and gadwall (Mareca strepera). The percent of SAV (P = 0.064) had weak evidence of importance within our models but was not included in final fitted models as confidence intervals contained zero (Table 2.3). Analysis by dabbling duck species found that the percent of SAV was significant for gadwall and green-winged teal (Anas carolinensis), although relationships were different for these two species (Table 2.3). Landscape covariates associated with dabbling duck use-days included number of semi-permanent wetlands within a 1 km buffer (β = -0.211, SE = 0.084, P < 0.001), the percent area of grass within a 1 km buffer (β

= -0.200, SE = 0.092, P < 0.001), and the percent area of crop within a 5 km buffer (β = 0.185,

SE = 0.089, P < 0.05). These relationships were primarily negative except for 33

(Mareca americana) and green-winged teal (Table 2.3). Of dabbling ducks that were significant for the percent of land use around the wetland in crop, mallards were the only one with a positive relationship, with the majority of species negatively related. The percent of area of crop had no influence at either the 1 or 5 km buffers, while the area of grass had the opposite effect. The percent of area in grass was significant and negatively related for blue-winged and green-winged teal and wood ducks in the 1 and 5 km buffers (Table 2.3). Our final fitted local model better explained dabbling duck use compared to our landscape model. When tested against the landscape model, the final fitted local model made up 99.8% of AIC model weights.

Logit model covariates of maximum depth (P < 0.001) and log maximum area of open water (P = 0.004) were important for predicting diving duck presence and use-days. Maximum depth and log maximum area of open water in the logit model both had a positive relationship with diving duck use. In the count model, we found evidence that log maximum area of open water (P < 0.001) was an important factor in predicting diving duck use-days and thus was present in all count models. Log-transformed maximum area of open water was positively related to diving duck use-days in the count model. While the null model was ranked relatively high, we found significant effects of the percent of SAV (P < 0.05; Figure 2.9) and maximum depth (P <

0.05; Figure 2.10) for predicting diving duck use-days (Table 2.3). Both SAV (β = 0.221, SE =

0.105) and maximum depth (β = 0.325, SE = 0.146) had a positive relationship with diving duck use-days. For diving duck species, SAV was only significant for lesser scaup (P < 0.05), which was the most abundant diving duck. Cover type was not associated with diving duck use-days at the guild level. However, similar to dabbling ducks, some species were influenced by cover type.

While the highest use was also on cover type 2, the next highest use was on cover type 4 followed by 3 with no use of cover type 1. Ring- necked ducks had very little evidence of 34 influence of our local covariates, with over 99.7 % of models below the null model. For landscape covariates, we found that the percent of grass within 10 km (β = -0.246, SE = 0.11, P

= 0.01) was the only significant landscape covariate. The null model, which only included log maximum area of open water and year in the count model, was the next best fit for predicting diving duck abundance. However, when analyzed by individual diving duck species, we found that the area of semi-permanent wetlands within 1 km was significant and positively related for bufflehead. While ruddy ducks had a significant and negative relationship with the number of semi-permanent wetlands in the 10km buffer. Lastly, the area of semi-permanent wetlands within

10 km was significant and negatively related for ring- necked ducks. Comparison of top local and landscape models indicated that local model better explained diving duck use compared to our landscape model, which was below the null. The local model made up approximately 91% of the AIC weight.

Seasonal Wetlands

Log-transformed maximum area of open water and depth were positively related to duck presence on seasonal wetlands. Log-transformed maximum area of open water was the best fit and was positively related to total duck abundance. For local covariate models, total duck use on seasonal wetlands were best modeled by the percent of vegetation (P = 0.005 and height (P =

0.018). Model showed that total duck use was positively related with the height of vegetation within the wetland and negatively related with the percent of vegetation (Table 2.5). While the null model was within the top 50% of all local models, the final fitted local model, which contained height and the percent of vegetation, made up 95.7% of AIC weight and a difference of 6.21 ΔAIC over the null model. For our landscape covariate models, we found that the number of semi-permanent wetlands within a 1km buffer was the only significant (β = -0.682, SE = 35

0.231, P = 0.003) covariate for predicting duck abundance on seasonal wetlands. The number of

semi-permanent wetlands within the smallest buffer, 1 km, had a negative relationship with duck

abundance (Table 2.4). Comparisons between best fit local and landscape models showed similar

support for each; the local model contained just over 67% of the AIC weight.

Farmed Wetlands

For the logit model, the best fit area term explaining total duck presence on farmed

wetlands was log maximum water area (P < 0.01). Log maximum area had a negative

relationship with the probability of obtaining a zero count in the logit model. For our count

model, the best fit area term that explained duck abundance was a quadratic function of the log

wetland area (P < 0.01). The quadratic function of the log wetland area term for our count model

also had a positive relationship with predicting duck abundance. The final fitted regression

models for farmed wetlands included the number of visits wet (P = 0.009), crop type (P < 0.001),

and nearest distance to a road (P = 0.042) as significant factors. Models showed that the distance

to the road was positively related with predicting duck use on farmed wetlands (β = 0.693, SE =

0.282). The final fitted model indicated that the number of visits wet had a positive relationship

and was the most important factor for use-days of ducks on farmed wetlands (β = 0.507, SE =

0.150; Figure 2.11). Crop type was an important covariate with corn (P < 0.001) having a positive relationship with duck use while beans (β = -1.25, SE = 0.434) were negatively related.

Highest duck use occurred on farmed wetlands with wetlands that had no-till tillage practices and where the crop type was corn (Table 2.5). Thus, we included tillage practice due to evidence from relative importance weights and that 75% of models with this covariate included were above the null model. Tillage practice had a weak relationship (P = 0.07) for wetlands where the tillage practice was no till, while farmed wetlands with tillage were not significant (P = 0.33). 36

Depth in farmed wetlands had a negative relationship with total duck use (β = -0.244, SE =

0.2127).

Discussion

We found that large abundances of ducks distributed across the southern PPR landscape in what seemed to be a highly clustered way, primarily using a few large wetlands with over 50% of all duck use-days documented in our study occurring on only 1.2% of unique wetlands surveyed and 30% of the surveyed wetland area. These few wetlands were primarily publicly- managed semi-permanent wetlands, and contributed the majority of duck use while comprising a proportionally small percentage of the wetland area and a fraction of the overall wetland basins surveyed. Consistent with this pattern, we also found highest predicted duck abundances occurred on semi-permanent wetlands after controlling for wetland area. Further, we found multiple local and landscape covariates were associated with variability in duck use with local wetland characteristics consistently most important in determining spring duck use in the PPR.

Our finding of a clustered distribution of migrants on a few wetlands in our study area is somewhat consistent with previous work on migrants and an important consideration for management and restoration of wetlands for migrants in the study area. While not clustered to the degree we observed, other studies have documented large concentrations of waterfowl on mid-latitude spring staging areas (SONEC; Fleskes and Yee 2007, RWB; Bishop and Vritiska

2007, UMR/GLR; Hitchcock 2009). While only reported by , research conducted in

SONEC found that certain did have higher percentages of use-days (Fleskes and Yee

2007). Perhaps more similar to the landscape of the PPR, the RWB provides important spring stopover habitat on a relatively small area on the landscape. Previous research in the RWB has 37

documented use by an abundance of migrants during spring (Brennan 2006), especially on semi-

permanent wetlands (Webb et al. 2010). In our study, distribution of migrants occurred with a

few wetlands contributing a large portion of use-days on relatively little wetland area in a

landscape containing an abundance of wetlands and wetland types. Ducks were clustered on

these wetlands, which were primarily larger semi-permanent wetlands, and provided evidence that these wetlands delivered important habitat for migrants through this region. Although area alone did not predict duck use across wetland types, it was an important factor, as has been shown in many other studies (e.g., Hagy et al. 2014, Webb et al. 2011, Brasher 2010). However, the distribution of ducks within our study suggest that there were differences between and within wetlands and wetland types of the PPR beyond area alone that may be important focal points for restoration and management.

Our analysis comparing wetland types found while semi-permanent wetlands were not the most abundant wetland type, they had the highest duck use per unit area of all wetland types in our study. The majority of this region in private ownership due to agriculture, and therefore many of the remaining semi-permanent wetlands occur on publicly managed land, which is where the majority of management and conservation activities are focused because of this extensive loss. While we had a limited sample size, lakes provided similar estimates of predicted use-days per unit area as farmed wetlands. Lake areas were also variable and ranged from 31 to

337 ha and were characterized by Stewart and Kantrud cover type 4, very little to no emergent vegetation. Many of these were surrounded by large, trees and rocky shores, with some disturbance present such as houses, roads, and boat ramps. As seen in these results, with two lakes within the 50% of total use-days quantile, as well as other studies (Vanausdall and

Dinsmore 2019), management of this wetland type can result in high abundances and diversity of 38

many waterfowl and waterbirds during spring. However, our results suggest that due to the

higher use of ducks on semi-permanent wetlands, conservation and management would be more

efficient per unit area for semi-permanent wetlands with an interspersion of emergent water and

vegetation.

Accordingly, we found multiple local factors that were associated with duck use on semi-

permanent wetlands. The vegetation community, in terms of the percent and structure of

emergent vegetation, were among the primary factors that influenced duck use. The structure of

the emergent vegetation, categorized by Stewart and Kantrud (1971) cover types, was in all final

models for analysis of semi-permanent wetlands and was high in relative importance calculations

for the majority of duck species (Table 2.3). Highest abundance of ducks occurred on cover type

2 wetlands, which has interspersed vegetation throughout the wetland similar to a ‘hemi-marsh’.

Other studies have documented that ‘hemi-marshes’ have higher waterfowl abundance and diversity for breeding and migrating ducks (Weller and Fredrickson 1974, Murkin et al.1982,

Smith et al. 2004). Cover type 3 semi-permanent wetlands were the most abundant cover type within our study and had the second highest total duck use following cover type 2. Cover type 3 is characteristic of a ‘typical’ prairie pothole wetland with a ring of emergent vegetation surrounding an open water deep-marsh zone which is the deepest portion of the wetland (Stewart and Kantrud 1971). These different zones provide habitats that different species can use based on morphological differences as well as forage type and selection (Pöysä 1983). Thus, the more zones a wetland provides, the more opportunities for a diversity of waterfowl species to use that wetland. Semi-permanent wetlands with cover type 4 also contributed relatively high duck use for total ducks, with the majority of those being diving ducks. Cover type 4 wetlands are mostly open water and typically have very little emergent vegetation, if any, on the surrounding edge. 39

Variable detection rates in our weekly counts may complicate comparisons of relative duck use between wetland types. We predicted that the most likely bias in our count method would occur on relatively large wetlands with highly interspersed vegetation that obscured ducks from observers. However, we found that these semi-permanent wetlands, with cover types 2 and

3, also had the highest duck use of all cover and wetland types. Since any bias of counts occurred on wetlands with the highest use, this may increase the number of ducks counted, and provide further evidence of differences in use between cover types.

Emergent vegetation in wetlands provides cover against harsh weather as well as protection against predators when compared to open water areas, and the lack of emergent vegetation may increase the risk on these wetlands (Pöysä 1983). On the other hand, wetlands with cover type 1 are dominated by emergent vegetation with very little open water. These wetlands contributed little to duck use, potentially due to the limited availability of open water and that the majority of these wetlands are small. We observed cover types of some semi- permanent wetlands influenced by spring flooding such as typically emergent vegetation dense or “choked” wetlands (cover type 1) becoming more open or similar to cover types 2 and 3.

Some of these wetlands flooded outside the perimeter edge of emergent vegetation, where forbs and grasses were present. This may benefit ducks during spring as it provides new habitat and food resources that most likely would not have been used without flooding. However, this may make it challenging for landowners and managers in providing proposed optimal cover types and percent vegetation of wetlands during spring. In addition to the cover type, the percent of emergent vegetation is also important, and most likely influences the cover type of a wetland.

Similar to previous work, the percent of emergent vegetation had a quadratic relationship with dabbling ducks and a negative relationship with diving ducks (Brasher 2010, Webb et al. 2010). 40

Other studies such as Webb et al. (2010) found that dabbling duck abundance was highest with

wetlands that contained approximately 50% emergent vegetation. Similarly, Vanausdall and

Dinsmore (2019) found the greatest number of diving ducks on shallow lakes in Iowa were on

those with 40-50% emergent vegetation cover. Our results were similar, with an average of 37% emergent vegetation on the top 20 semi-permanent wetlands with the highest total use-days.

However, diving duck use-days were not associated with cover type which suggested the structure of emergent vegetation may not be as important as the area of open water.

The percent of submerged aquatic vegetation and number of SAV species were important for predicting duck abundance at guild and species levels. At the guild level, diving ducks were positively related to the percent of SAV while dabbling ducks was not significant but had evidence of importance for both number of SAV species and percent of SAV. Individual species that had significant effects of SAV were gadwall, green-winged teal, and lesser scaup. The percent of SAV was positively related for all species except for green- winged teal whose diets include primarily plant matter such as seeds and some animal matter during spring (Klimas et al.

2020). The number of SAV species may provide different habitats for different aquatic invertebrate species and densities (Krull 1970). The effects of water clarity are positively related to the presence and amount of SAV (Madsen et al. 2001) in multiple wetland types including wetlands in the PPR (Antaeu and Afton 2008). For gadwall, one of the most herbivorous waterfowl species, this may be due to diets primarily composed of submerged vegetation and algae during winter and spring (Paulus 1982; Stafford et al. 2007). SAV provides important habitat for aquatic invertebrates. Voigts (1976) found peak abundance of invertebrates when the

SAV was also at peak abundance in Iowa. Zones of open water with SAV and open water with emergent vegetation were also found to produce the most common invertebrate species (Voigts 41

1976). Diets of lesser scaup during spring in the PPR have historically been dominated by

amphipods, however there has been a decline in aggregate percentages of amphipods which is

hypothesized to be one of the factors for the decline of the scaup population. Thus, species such

as lesser scaup may be using wetlands with SAV due to the likelihood that there’s an abundance

of aquatic invertebrates.

Lastly, depth was also important for duck use on semi-permanent wetlands. While maximum depth was significant for predicting presence of all species, we found depth was variable for predicting the abundance of various duck species. Overall, for the count models, mean depth of the wetland was a better fit for the dabbling duck guild as a whole, however, maximum depth was a better predictor for American wigeon, blue-winged and green-winged teal. This is most likely due to foraging strategies and food selection during spring. Studies such as Pöysä (1983) have found that dabbling duck species forage in habitats where they can most efficiently use the available resources. Both teal species are among the smallest dabbling ducks in North America and are thus more constrained by wetland depths than larger dabbling ducks

(e.g., mallards) for foraging. Green (1998) found that smaller dabbling ducks used deeper microhabitats than mallards, which supports that the maximum depth of a wetland may impact the foraging ability of these smaller species. For individual species of dabbling ducks, depth had no significant effects for gadwall or northern pintails for predicting abundance. This may be due to larger body sizes that allow them to forage in deeper water zones which our maximum depth covariate was able to capture. Maximum depth was better associated with the abundance of diving ducks, however, analysis for diving duck species found significance for only lesser scaup and redheads (Aythya americana). Mean depth was not significant for any diving duck species within this study, most likely due to fewer foraging depth constraints. Our results found local 42 wetland factors were more important than the landscape for predicting duck use. Similarly, Janke et al. (2019) found that wetlands in the PPR with surrounding intensive agriculture were no worse in quality, thus suggesting that local factors were driving wetland use. Thus, when managing habitats for spring migrants, local factors should first be considered. Differences between the various response variables with local factors should also be considered when managing these wetlands for desired conditions.

Traditionally, restoration and conservation within the PPR focuses on providing a high density of ‘natural’ wetlands with multiple hydroperiods, especially for breeding waterfowl that use surrounding grasslands for nesting (Kantrud and Stewart 1977). However, duck use on wetlands within seasonal wetlands was relatively low, although variable. The majority of seasonal wetlands had very short water availability or were never observed inundated which limits the potential for duck use within our study. The average number of visits wet was two survey weeks for seasonal wetlands, or approximately 14 days. Average maximum depth of seasonal wetlands was 58 cm, but wetlands used by ducks averaged 115 cm. Seasonal wetlands in this study differed from wetlands in that Swanson et al. (1974) studied in which wetlands contained water long enough for an abundance of invertebrates and spring migrants to utilize. The majority of wetlands that were drained in this region were temporary and seasonal wetlands which explains the limited availability of this wetland type on the landscape and thus in our study (Miller et al. 2009). Also, with less than 6% of the DML currently in grassland, the availability of this wetland type is even more limited in providing habitat for waterfowl during spring.

In contrast, farmed wetlands were much more abundant on the landscape. However, the majority of farmed wetlands were also intermittently inundated, with many containing no water 43 for the whole study period. We had similar farmed wetland area and depth variability as

LaGrange and Dinsmore (1989), who observed farmed wetlands with areas of 5 ha go completely dry within a few days in the DML during spring. Euliss and Mushet (1996) documented depths of multiple wetland types fluctuated more in landscapes with high agriculture, which is characteristic of many farmed and seasonal wetlands within Iowa’s landscape. Many of these wetlands were inundated early in spring due to initial thaw and then flashed with precipitation events. We found that this initial flooding was still important for earlier migrants as the majority of semi-permanent wetlands had yet to become available due to ice. Early migrants such as northern pintails and mallards were observed on farmed wetlands and were the first ducks observed on any wetland type in both years. Also, use of farmed wetlands followed precipitation events and potentially allowed ducks to distribute into new habitats that weren’t available before, perhaps to forage. The local factors we found influenced duck use on farmed wetlands were similar to other studies conducted on this wetland type and in this region.

Multiple studies have found that crop and tillage types impact waterbird use on farmed wetlands during spring (LaGrange 1985, LaGrange and Dinsmore 1989, Murphy and Dinsmore 2018).

These studies also found higher use in corn and no-till farmed wetlands similar to our results. We also found highest use on farmed wetlands where corn was present with some use of beans.

LaGrange (1985) found that mallards were primarily eating moist soil plants, tubers, and corn in farmed wetlands with no beans consumed. We found foraging was occurring in farmed wetlands although what food resources ducks were consuming was unknown (Chapter 3). Ducks in this study selected wetlands that had no tillage and corn, due to the relative available area and associated duck abundance. LaGrange (1989) found similar results with the highest use of mallards using no-tilled wetland with corn as well. While the duration of flooding in farmed 44

wetlands was important for duck use, of wetlands that had water at one point within our study, the mean number of visits inundated was only 1.79 (SE = 1.20), which is approximately 11 days.

Many of these farmed wetlands had drainage inlets with the goal of removing water rapidly, thus these wetlands may provide potential habitat for even less time. The number of visits wet may be also impacted by the tillage type, further evidence that tillage practice is important for spring migrating duck use of farmed wetlands. Land under conventional till are believed to drain water

faster as pore size is increased (Blanco‐Canqui 2011). Areas where conservation practices are

performed such as no till reduced erosion and retained water longer (Busari et al. 2015). We

observed higher mean visits wet in no till wetlands in all three crop types which further gives

evidence that no till wetlands provide water on the landscape longer than other tillage practices.

Although duck use was primarily on large semi-permanent wetlands and lakes, these wetland types are limited within this region. Historically much less abundant compared to wetlands with less permanent hydroperiods, semi-permanent wetlands presently make up a greater proportion of wetlands available in the PPR of Iowa (Galatowitsch and van der Valk

1996). However, farmed wetlands are still more abundant on this landscape, and the abundance across the PPR may provide more duck use than captured in this study, compared with the limited amount of other wetland types available. Nonetheless, our results provide evidence that conservation and management for spring migrating ducks is best placed on managing local wetland factors on semi-permanent wetlands. Future research should focus on a landscape scale analysis and comparison of wetland types in this region while considering different spring conditions.

45

Management Implications

Results from this study demonstrated that multiple wetland types provide stopover habitat for spring migrating ducks in the Prairie Pothole Region of Iowa but variation in wetland size and characteristics that may be subject to management or restoration control have important impacts on duck use. While duck use was variable within and between the different wetland types, our results suggest the per-unit area gain in conservation efforts would benefit primarily from a focus on restoration and management of semi-permanent wetlands on public and private land. In the Des Moines Lobe, semi-permanent wetlands are the most commonly restored wetland type and even work on shallow lake restoration mimics wetland functions (Vanausdall and Dinsmore. 2019). The management of semi-permanent wetlands should consider multiple local factors when trying to promote habitat for spring migrating ducks. The overall area of open water, percent emergent vegetation and the structure of emergent vegetation (cover type) within semi-permanent wetlands should be taken into account. Efforts should also aim to provide semi- permanent wetlands and lakes with interspersed emergent vegetation, which would provide habitat for both spring migrants as well as breeding waterfowl later in the year. Depth of the wetland should also be considered and can help control the wetland plant community and structure as well. Lastly, we encourage managers to provide an abundance of these wetland habitats due to potential weather conditions that may increase the abundance and duration that migrants are utilizing this region. Providing an abundance of these habitats would allow for migrants to reach the breeding grounds in the best possible body condition, even during severe spring conditions. Work to restore semi-permanent wetland distribution and function in this landscape could dovetail with ongoing efforts in the region to address flood control, water 46 quality, and carbon sequestration – all important ecosystem services provided by wetlands that could also provide critical stopover habitat for spring migrating ducks.

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Tables

Table 2.1. Local and landscape covariates tested for examining patterns of duck use among different wetland types during the spring of 2018 and 2019 in the Prairie Pothole Region of Iowa.

Wetland type Local Covariates Landscape Covariates Wetland area, max area, crop type, tillage practice, depth, number * Farmed wetlands of visits weight, distance to nearest road

Wetland area, max area of open water, percent veg., grass com., Cropland %, Grassland %, Development %, Seasonal wetlands species evenness, diversity, and richness, % forb , % woody, depth semi-permanent wetland density and area at 1, 5, 10 km buffers

Wetland area, max area of open water, percent emergent veg., % Cropland %, Grassland %, Development %, 55

Semi-permanent wetlands cattail, % rush, mean & max depth, % SAV, number of SAV sp. semi-permanent wetland density and area at 1, 5, 10 km buffers * * Lakes

*Denotes fields were no covariates were tested 56

Table 2.2. Number, total area, and total use-days of wetland types monitored for spring migrating ducks in the Iowa Prairie Pothole Region during spring 2018 and 2019.

Wetland type n Total area (ha) Range (ha) Use-days

Farmed 744 517.80 36,182

2018 186 79.44 0.01 - 11.12 15,838

2019 558 438.36 0.01 - 25.36 20,344

Seasonal 80 80.08 19,546

2018 31 19.96 0.10 - 21.79 1,628

2019 49 60.12 0.11 - 10.49 17,918

Semi-permanent 239 2,294.54 826,914

2018 100 803.71 0.28 - 222.94 452,254

2019 139 1,490.83 0.11 - 197.76 374,660

Lake 11 1,501.66 132,317

2018 4 490.91 31.55 - 274.27 114,994

2019 7 1,010.75 40.67 - 336.73 17,323

Table 2.3. Tested local covariates and the associated relative importance weights for total ducks, dabbling and diving guilds, and each duck species on semi-permanent wetlands surveyed during spring migration in Iowa during 2018 and 2019. Significant covariates are bolded and relationship, positive (+) and negative (-) are placed to the right. Species in red, null models were best fit, thus covariates had little to no significance. Asterisks (*) for cover type, a factor, indicate significance, with subsequent columns indicating relationship (positive (+) and negative (-)) for each cover type. Asterisks (*) for percent vegetation indicate a quadratic relationship.

Local Covariates Veg Cover CT CT CT CT SAV # Max Mean % Type 1 2 3 4 Cattail Rush Forbs Polyg. Woody % SAV Depth Depth Diversity Evenness Richness TOTAL1 0.91* 0.87 * - + - 0.31 0.35 0.32 0.39 0.27 0.57 0.41 0.18 0.27 0.28 0.16 0.18

Dabblers1 0.90 * 1 * - + - - 0.36 0.7 0.27 0.43 0.28 0.77 0.53 0.06 0.84 - 0.31 0.18 0.16

AMWI1 0.30 0.86 * + 0.33 0.42 0.29 0.95 + 0.28 0.69 0.55 - 0.56 - 0.24 0.23 0.2 0.22

BWTE1 0.29 0.99 * - + - 0.36 0.64 0.68 + 0.66 0.44 0.63 0.43 0.69 - 0.32 0.23 0.18 0.22

GADW1 0.37 0.96 * - + - 0.67 0.31 0.3 0.31 0.32 0.67 + 0.48 0.18 0.35 0.31 0.58 0.07

57 AGWT1 0.29 1.00 * - + - 0.88 + 0.75 0.38 0.37 0.28 1.00 - 1.00 + 0.97 - 0.03 0.2 0.19 0.22

MALL1 0.85 * 0.99 * - + - 0.31 0.38 0.37 0.71 0.28 0.63 0.34 0.1 0.73 - 0.15 0.16 0.28

NOPI1 0.72 * 0.89 * + - - 0.33 0.31 0.36 0.31 0.35 0.58 0.3 0.54 0.14 0.77 - 0.1 0.07

NSHO2 0.80 * 0.11 + 0.56 0.42 0.31 0.36 0.59 - 0.67 0.33 0.17 0.77 - 0.44 0.09 0.46

WODU2 0.52 0.13 + 0.3 0.69 0.31 0.27 0.49 0.8 0.33 0.18 0.5 - 0.49 0.09 0.28

Divers1 0.28 0.08 0.32 0.38 0.31 0.29 0.39 0.85 + 0.46 0.55 + 0.19 0.17 0.16 0.29

BUFF1 0.3 0.15 0.28 0.3 0.29 0.96 - 0.79 + 0.49 0.29 0.43 0.22 0.26 0.35 - 0.3

CANV 0.58 * 0.56 0.30 0.32 0.83 - 0.34 0.3 0.54 0.37 0.22 0.21 0.17 0.21 0.18

LESC1 0.49 1.00 * - + + 0.3 0.32 0.43 0.4 0.42 0.97 + 0.29 0.84 + 0.11 0.18 0.06 0.62 -

REDH1 0.29 0.65 * - + - 0.36 0.85 - 0.52 0.61 0.28 0.49 0.28 0.55 + 0.19 0.25 0.16 0.24

RNDU 0.28 0.11 0.44 0.32 0.69 0.27 0.36 0.63 0.44 0.39 0.22 0.18 0.18 0.2

RUDU1 0.71 * 0.77* - + 0.42 0.43 0.45 0.50 - 0.47 0.49 0.29 0.47 0.2 0.19 0.18 0.19

1 – Denotes local model importance over landscape model; 2 – Denotes landscape model importance over local model Veg %: percent vegetation; Cover Type: Stewart and Kantrud (1971) cover type: Cattail: percent cattail; Rush: percent rushes; Forbs; percent forbs; Polyg.: percent Polygonum spp.; Woody: percent woody vegetation; SAV %: percent SAV; # SAV: number of SAV species; Max Depth: maximum depth; Mean Depth: mean depth; Diversity: Shannon’s Diversity Index; Evenness: species evenness; Richness: species richness

Table 2.3. (cont.)

Landscape Covariates B1 Count B1 Area B1 Crop B1 Grass B1 Dev. B5 Count B5 Area B5 Crop B5 Grass B10 Count B10 Area B10 Crop B10 Grass

TOTAL 0.82 - 0.46 0.13 0.43 0.52 0.04 0.09 0.45 0.17 0.05 0.23 0.13 0.22

Dabblers 0.84 - 0.46 0.10 0.48 - 0.51 0.03 0.13 0.59 + 0.25 0.04 0.20 0.09 0.14 AMWI 0.19 0.02 0.24 0.15 0.32 0.30 0.04 0.10 0.13 0.41 + 0.90 - 0.42 + 0.37 BWTE 0.22 0.23 0.07 0.88 - 0.30 0.12 0.18 0.25 0.08 0.46 0.27 0.59 + 0.01 GADW 0.37 0.25 0.13 0.25 0.29 0.14 0.14 0.21 0.16 0.17 0.28 0.40 + 0.19 AGWT 0.24 0.53 + 0.30 0.01 0.29 0.69 - 0.23 0.21 0.95 - 0.07 0.12 0.21 0.02 MALL 0.24 0.11 0.17 0.12 0.28 0.19 0.13 0.14 0.28 0.18 0.47 - 0.56 - 0.38 NOPI 0.15 0.26 0.25 0.24 0.37 0.18 0.39 - 0.21 0.16 0.28 0.18 0.18 0.20 NSHO 0.87 - 0.02 0.36 0.44 0.31 0.04 0.02 0.13 0.09 0.05 0.90 - 0.21 0.23 58 WODU 0.62 - 0.21 0.22 0.68 - 0.31 0.11 0.17 0.42 0.07 0.09 0.31 0.13 0.09

Divers 0.16 0.10 0.29 0.11 0.27 0.27 0.15 0.17 0.08 0.17 0.43 0.16 0.70 BUFF 0.41 0.66 + 0.21 0.40 0.31 0.23 0.24 0.20 0.14 0.09 0.03 0.18 0.13 CANV 0.59 0.16 0.31 0.07 0.47 0.11 0.20 0.64 0.21 0.10 0.31 0.02 0.54 LESC 0.15 0.13 0.12 0.19 0.31 0.27 0.32 0.13 0.30 0.19 0.22 0.43 0.16 REDH 0.17 0.13 0.37 0.17 0.34 0.32 0.15 0.25 0.08 0.19 0.44 0.13 0.67 RNDU 0.16 0.05 0.30 0.20 0.33 0.27 0.08 0.16 0.20 0.20 0.75 - 0.20 0.20 RUDU 0.06 0.20 0.15 0.19 0.50 0.33 0.32 0.28 0.17 0.48 - 0.17 0.19 0.20

Count: number of semi-permanent wetlands; Area: total area of semi-permanent wetlands; Grass: total area of grass; Dev.: total area of development Species codes: AMWI = American wigeon, BUFF = bufflehead, BWTE = blue-winged teal, CANV = , GADW = gadwall, AGWT = American green-winged teal, LESC = lesser scaup, MALL = mallard, NOPI = northern pintail, NSHO = , REDH = , RNDU = ring-necked duck, RUDU = ruddy duck, WODU = wood duck.

Table 2.4. Tested local and landscape covariates and the associated relative importance weights for total ducks on seasonal wetlands surveyed during spring migration in Iowa during 2018 and 2019. Significant covariates are bolded and relationship, positive (+) and negative (-) are placed to the right.

Seasonal wetlands

Local Covariates Number Grass Veg % Height Forbs of visits Disturbance Depth Diversity Evenness Richness Category wet Total 0.77 - 0.69 + 0.22 0.32 0.33 0.34 0.34 0.22 0.16 0.23

Landscape Covariates 59

B1 B1 B1 B1 B1 B5 B5 B5 B5 B10 B10 B10 B10 Count Area Crop Grass Dev. Count Area Crop Grass Count Area Crop Grass

Total 0.91 - 0.14 0.3 0.26 0.3 0.01 0.13 0.22 0.17 0.07 0.46 0.26 0.22 60

Table 2.5. The number of farmed wetlands, duck use-days, mean visits wet, and total area in ha for the different tillage and crop type combinations we observed during the springs of 2018 and 2019 in the PPR of Iowa.

Tillage Mean area Mean Visits Crop Type Practice n (ha) Wet Use-days Corn No-till 95 70.98 2.07 10,731 Conventional Corn 248 185.70 1.79 13,679 till Beans No-till 298 197.21 1.83 4,761 Conventional Beans 103 63.91 1.50 217 till

61

Figures

Figure 2.1. Location of the study area where we surveyed duck use on wetlands within focal areas for wetland conservation in the Prairie Pothole Region of Iowa during springs 2018 and 2019.

25

20

15 days

2018 10 2019 62

Percent of total use - 5

0

Species Figure 2.2. Percentage of each species from all wetland types for the springs of 2018 and 2019 in the PPR of Iowa. Species codes: AMWI = American wigeon, BUFF = bufflehead, BWTE = blue-winged teal, CANV = Canvasback, GADW = gadwall, AGWT = American green-winged teal, LESC = lesser scaup, MALL = mallard, NOPI = northern pintail, NSHO = northern shoveler, REDH = redhead, RNDU = ring-necked duck, RUDU = ruddy duck, WODU = wood duck. 63

40 a) 35

30 days - 25

20

15

10 Percent of total use 5

0 MALL LESC RNDU BWTE AGWTGWTE GADW Species

35 b)

30

25 days - 20

15

10 Percent of total use 5

0 MALL LESC CANV RNDU RUDU REDH Species

Figure 2.3. The percent use-days of the top 6 duck species observed on semi-permanent wetlands (a) and on lakes (b) during the springs of 2018 and 2019 in the PPR of Iowa. Species codes: MALL = mallard, LESC = lesser scaup, RNDU = ring- necked duck, BWTE = blue-winged teal, AGWT = American green-winged teal, ADW = gadwall, CANV = Canvasback, RUDU = ruddy duck, REDH = redhead. 64

45 a) 40 35 days

- 30 25 20 15

Percent of total use 10 5 0 MALL BWTE RNDU NSHO WODU AGWTGWTE Species 60 b)

50

d

days 40 -

30

20 Percent of total use 10

0 MALL NSHO AGWTGWTE GADW BWTE LESC Species

Figure 2.4. The percent use-days of the top 6 duck species observed on seasonal (a) and farmed (b) wetlands during the springs of 2018 and 2019 in the PPR of Iowa. Species codes: MALL = mallard, BWTE = blue-winged teal, RNDU = ring- necked duck, NSHO = northern shoveler, WODU = wood duck, AGWT = American green-winged teal, GADW = gadwall, BWTE = blue-winged teal, LESC = lesser scaup.

65

Figure 2.5. Predicted duck use-days in relation to the wetland area of farmed, seasonal and semi-permanent wetland types surveyed in Iowa’s Prairie Pothole Region during spring migration in 2018 and 2019. 66

Figure 2.6. Predicted use-days of total ducks with 95% confidence intervals of the mean in relation to the Stewart and Kantrud (1971) cover types on semi-permanent wetlands during spring of 2018-19 in the PPR of Iowa. Predictions with wetland area of 10 ha and max depth of 80 cm.

67

Figure 2.7. Total predicted use-days of dabbling ducks with 95% confidence intervals in relation to the mean depth on semi- permanent wetlands during spring of 2018-19 in the PPR of Iowa. Predictions with wetland area of 10 ha and max depth of 80 cm.

68

Figure 2.8. Total predicted use-days of dabbling ducks with 95% confidence intervals in relation to the percent of emergent vegetation on semi-permanent wetlands during spring of 2018-19 in the PPR of Iowa. Predictions with wetland area of 10 ha and max depth of 80 cm.

69

Figure 2.9. Predicted use-days of diving ducks with 95% confidence intervals in relation to the percent of SAV on semi- permanent wetlands during spring of 2018-19 in the PPR of Iowa. Predictions with wetland area of 10 ha and max depth of 80 cm.

70

Figure 2.10. Predicted use-days of diving ducks with 95% confidence intervals in relation to the max depth on semi-permanent wetlands during spring of 2018-19 in the PPR of Iowa. Predictions with wetland area of 10 ha and max depth of 80 cm.

71

Figure 2.11. Predicted total use-days of ducks with 95% confidence intervals in relation to the number of visits a farmed wetland was wet during spring of 2018-19 in the PPR of Iowa. Predictions with wetland area of 9.6 ha. 72

CHAPTER 3. EVALUATING DIURNAL WETLAND ACTIVITY BUDGETS AMONG

SPRING MIGRATING DUCKS

A paper to be submitted to Waterbirds

Derek C. Ballard1, Orrin Jones2, and Adam K. Janke1

1Department of Natural Resource Ecology and Management, Iowa State University, Ames, Iowa 50011, USA 2 Department of Natural Resources, Clear lake, Iowa, 50428, USA

Abstract

Spring migrating ducks are engaged in multiple energetically expensive activities as they transition from wintering to breeding areas, where they initiate clutch formation soon after arrival. However, how ducks allocate time in different activities on wetland types present in the

PPR during spring is unknown. Understanding the behavior of ducks at stopover sites would help inform management and conservation used to provide and improve habitat and assist in successful migration. We placed trail cameras within wetlands in order to determine activity budgets of spring migrating ducks explain duck use (2018-19). Ducks spent the majority of time in locomotion (38%) followed by feeding (28%) and resting (25%). However, we found differences by guild (P < 0.001) and thus analyzed the various time and wetland variables by dabbling and diving ducks. We found that diving ducks (mean = 38.46, SD = 2.29) spent more time feeding than dabbling ducks (mean = 18.74, SD = 2.08). Feeding was most prevalent on seasonal wetlands for dabbling ducks (mean = 26.92, SD = 0.90) and highest on semi-permanent wetlands for diving ducks (mean = 28.72, SD = 0.91). Depth was negatively related to the percent of time feeding for dabbling ducks, while positively related with diving ducks. However, we found that the cover type of semi-permanent wetlands had more evidence of importance for 73 most activities for diving ducks while depth was more important for dabbling ducks. However, our results suggest that further research needs to be done in order to determine other factors that may be affecting the activities on these wetland types encountered during spring in the PPR.

KEY WORDS: activity budget, duck, Iowa, Prairie Pothole Region, spring migration, wetland

Introduction

Animals divide their time into periods of different activities in order to meet daily and seasonal requirements for survival, breeding and reproductive success (Nielsen 1984). Time activity budgets characterize how individuals allot time to different activities, and have been calculated for many avian species, including passerines (Barnard 1980) and shorebirds (De Leon and Smith 1999). Time activity budgets are useful for answering multiple research questions, such as how animals use habitats, how they meet energetic demands, and how activities change across temporal and spatial scales (Maxson and Pace 1992).

Migration is energetically demanding and nutrient acquisition en route is necessary to complete migration among most ducks (Stafford et al. 2014). Additionally, synchronous energetically expensive activities such as courtship (Weller 1965) and molt (Anteau et al. 2011) occur during migration, and unpredictable fluctuations in weather create additional energetic burdens (Janke et al. 2019). Consequently, stopover habitat must assist in safe and swift migration as weather conditions allow. The quality of stopover habitat during migration may influence nutrient acquisition and maintenance during spring as well as allow individuals to arrive early to the breeding grounds, influencing subsequent reproductive success (Heitmeyer and Fredrickson 1981, van Noordwijk et al. 1995, Devries et al. 2008, Stafford et al. 2014).

However, waterfowl species also rely on spring stopover sites when food resources are at their 74 most scarce compared to other parts of the annual cycle (Brasher et al. 2010). This may be due to food resources consumed by fall migrants and no new plant resources produced over winter

(Brasher et al. 2010, Anteau and Afton 2009). In addition to food resources, the quality of stopover sites may depend on other factors such as levels of competition and disturbances

(Newton 2006). However, for waterfowl and shorebirds, there may be potential limitations due to a limited number and area of adequate stopover sites for refueling and resting (Newton 2006).

The limited number of stopover sites may cause high densities, which deplete food resources as well as an increase of competition (Newton 2004).

The majority of research, management, and conservation strategies developed for prairie landscapes focus on the breeding ecology of waterfowl (e.g., Swanson and Meyer 1977, Delphey and Dinsmore 1993, Austin 2002, Cordts et al. 2002, Walker et al. 2013). However, the PPR also provides important stopover habitat for waterfowl migrating to multiple northern breeding areas such as northern continental U.S., the Boreal forest, Canadian prairies, and Alaska. Similar to the general lack of information of duck ecology during spring, the available literature of behavioral ecology from activity budgets during this period and in this region are limited as well

(Fredrickson and Drobney 1979). The limited amount of past work that has conducted activity budgets within the PPR include very few species of migrants (e.g., mallards) or single wetland types (e.g., farmed wetlands; LaGrange 1985). The wetland types that waterfowl encounter in an agriculture dominated landscape such as that found in the southern PPR may provide different resources for migrants. The presence and abundance of ducks on these wetland types differ and thus how ducks are utilizing them in activities may as well (Chapter 2). Activity budgets may further provide insight on wetland and food quality and the success of programs through observation and amount of time spent in major activities. For example, in some waterfowl 75 species, time spent feeding may be associated with the quality and abundance of food resources

(Brodsky and Weatherhead 1985). Understanding how ducks use wetland types may help management and conservation by providing habitats needed for important activities such as feeding and resting. This increase of knowledge would have multiple benefits, especially where goals of providing optimal stopover habitat that provide opportunities for increasing and maintaining body condition are important. Changes in behavior and habitat use have been documented during spring for some waterfowl species, such as snow geese (Gauthier et al.

1988). As a result, ducks may spend varying amounts of time towards major activities based on the weather conditions during spring.

Our study sought to further address this information gap and document how ducks used wetlands throughout spring migration. Our objectives were to determine the behavior of spring migrating ducks on different wetland types encountered in the southern PPR by calculating activity budgets acquired through remote cameras unlikely to influence duck behavior. We further aim to understand how local wetland factors may influence behaviors, as well as how behaviors change throughout spring.

Methods

Study Area

This study was conducted throughout the Des Moines Lobe (DML) of Iowa (42.580°N,

93.709°W; approximately 365 m elevation), during the springs of 2018 and 2019. The DML represents the southernmost extent of the PPR and is located in the north-central part of the state and includes 29 counties. Similar to the rest of the PPR, the DML is characterized by typical knob and kettle topography with moraine ridges of high relief. The area of the PPR contains over 76

70 million ha of prairie wetland complexes, with 3.5 million in Iowa (van der Valk 2005, Miller et al. 2009). Iowa’s PPR is dominated by row crop with remaining land covers including grassland (5.4%), forest (2.1%), and development (6.4%; NLCD 2016). In the PPR, a gradient of precipitation and temperature occurs from west to east, with precipitation of the eastern prairies exceeding that of the west. However, average temperature is also higher in the easternmost region as well. The PPR of Iowa had an average annual temperature of 7.3°C and rainfall of approximately 83 cm. The mean annual high temperatures during spring during the two years of the study was -1.67°C in February, 9.17°C in March, 13.88°C in April, and 26.11°C in May

(National Oceanic and Atmospheric Administration 2020).

Wetland Categories and Selection

We used a subsample of wetlands selected from a sample used during a concurrent study

documenting duck abundance across a diversity of wetland types in our study area during spring

2018 and 2019 (Chapter 2). In our sampling design, we classified wetlands into 3 categories;

seasonal wetlands, semi-permanent wetlands, and farmed wetlands. Seasonal wetlands included

wetlands where the deepest zone consisted of either the wet-meadow zone or shallow-marsh

zone (Stewart and Kantrud 1971). The hydroperiod in this wetland type is relatively short, with

water absent for extended periods that were necessary for growth and persistence of wetland

obligate plants (Stewart and Kantrud 1971). Semi-permanent wetlands were those with

comparatively longer hydroperiods conducive for growth of emergent and submersed wetland-

obligate plants (Stewart and Kantrud 1971, Cowardin et al. 1979). These wetlands include deep

marsh zone or permanent open water zones, found in both semi-permanent and permanent

wetlands within the PPR (Stewart and Kantrud 1971). Semi-permanent wetlands were the 77 primary focus of wetland restoration and management projects on private and publicly owned land in the study area.

Farmed wetlands were temporarily flooded depressions actively under cultivation (e.g.,

Murphy and Dinsmore 2018). Similar to seasonal wetlands, the hydroperiods in these wetlands were short, although slightly more ephemeral than seasonal wetlands. In order to reduce water depth and permanence for agricultural practices, many farmed wetlands were artificially drained through surface or subsurface drainage. Farmed wetlands were the most abundant wetland within

Iowa, with an estimated potential wetland area of 7% to 12.2% of the total area of the DML (Van

Meter and Basu 2015, McDeid et al. 2019). The size and density of farmed wetlands was variable and depended on geomorphology and precipitation (Miller et al. 2009).

In 2018, we randomly selected a sample of semi-permanent wetlands that had open water and an area less than 50 ha on which to make behavioral observations with remote cameras.

Once cameras were placed, they remained for the duration that spring migrants were present within the study area. During 2019, to better observe how ducks were using wetlands, we selected wetlands based on duck use, rather than a random sample. We identified wetlands with duck use based on concurrent weekly ground surveys and sought permission to place cameras.

Seasonal wetlands and farmed wetlands were difficult due to the short or infrequent periods of flooding as well challenges presented by private ownership. Because of this, the sample of wetlands within these two wetland types in our study were limited. Cameras were placed in wetlands as soon as access was made available and ducks were present and removed by the third week of May in both years.

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Camera Placement

We programmed trail cameras (Bushnell Trophy Cam Aggressor) to record 15 sec videos

that started 1 hr before sunrise and continuing at 30 minute intervals until 1 hr after sunset.

Cameras were placed at the edge of water in farmed and seasonal wetlands, and at the edge of emergent vegetation in semi-permanent wetlands. In seasonal and farmed wetlands, we moved cameras so they remained at the edge of water as wetland area changed with fluctuating water levels during the season. We placed all cameras facing north in order to reduce glare that would inhibit identification of activity, species, or sex of individuals. In order to ensure cameras were secure and stable for identification, we placed cameras on fence posts pounded firmly into the ground and then secured cameras using bolts and zip ties. We placed cameras approximately 1 m above the water level to allow for a clear view of ducks in high densities. We placed a 1-inch diameter, 1 m tall PVC pipe (hereafter depth poles) attached to a fence post with zip-ties 30 m in front of the camera to gauge water depth and sampling range of the cameras. In shallow water the depth poles touched the bottom of the basin, whereas in deeper sites they straddled the water line. Depth poles had 2.5 cm increments marked in alternating colored tape used to monitor water depth.

Video Processing

We defined the sampling area for each video as the visible area from the camera to the depth pole (30 meters; Figure 3.1). The angle of the field of view of cameras was 35°. Therefore, the area sampled was approximately 280 m2 (Figure 3.1). Each video was processed in the

laboratory by trained observers. Observers recorded the date, start time, species, sex, and amount

of time spent in each of 5 behavioral categories for each individual present within the sampling

area during the 15 second video. The 5 behavioral categories were based on previous studies that 79 conducted activity budgets of waterfowl (Paulus 1988, Bergan et al. 1989) and included feeding, resting, comfort movements, locomotion, and courtship. Feeding included surface feeding, tipping, and diving, as well as time spent submerged, and drinking. When diving ducks appeared from below the water during the video and had not been seen before, we assumed the individual was present and had been foraging from the start of the video. Resting included sleeping or loafing, stationary position with head tucked or relaxed. Comfort movements included preening and feather maintenance, stretching, and body shake. Locomotion included swimming, walking and flying. While we documented the time spent in flight, this activity was excluded from further analysis because we were focused on how ducks used the wetland itself. Courtship included social displays such as head bobbing as well as aggressive behavior towards another individual.

For each video, we determined the depth by counting the number of depth increments above the water surface. We then subtracted that from 40, which was the total number of increments on the depth pole, and multiplied that by 2.5, which was the width of each increment, in order to determine the depth in centimeters. When increments on the depth pole were difficult to see due to weather conditions, we used VLC media player and adjusted the contrast and sharpness of the video. This made the alternating increments on the depth pole more distinguishable and thus easier to determine depths.

Statistical Analysis

Activity budget studies ordinarily employ human observers and sample behavior either by instantaneous sampling or focal animal sampling. For focal animal sampling, observers document all activities of an individual or group during a specified time period (Altmann 1974).

For instantaneous scan sampling observers simultaneously document the activity at a fixed occasion for all individuals observed within a group (Altmann 1974). The 15 sec video clips used 80 in our study in place of human observers lend themselves to an analytical approach analogous to instantaneous sampling, where each bird’s behavior in the field of view is classified at the instantaneous 15 sec period we observed them. To reduce issues of non-independence among ducks in the same video, we characterized the behavior of all birds in the sampling area and placed them into their corresponding guild (dabbling or diving ducks). For each video, we summed the amount of time all individuals within a guild were engaged in each activity. Then, in order to get the total observation time in each video, we summed the time observed across all activity categories for each guild. Lastly, we calculated the percentage of time spent in each activity category by dividing the sum time for each activity by the total observation time. Thus, for a video, the percentage of time all individuals within a guild were in each activity were considered independent observations and the basis of subsequent analyses.

We performed an additive log-ratio transformation on each activity category in order to meet the assumption of normality and to preserve the sum-to-1 constraint for our percent of time variables. Variation in duck behavior and how it relates to wetlands type, time of day, and season were tested by conducting multivariate analysis of variance (MANOVA). For significant interactions and activities, we used ANOVAs and Tukey- Kramer multiple comparison test to determine differences in means. We used a linear regression to compare time spent in each activity category with the depth of water at the sampling location. To compare whether depth or wetland cover type was important in determining these categories, we compared Akaike’s

Information Criterion (AIC) values and weights for a model with a linear term for depth with a model with a categorical term for wetland cover type (semi-permanent wetlands). We reported which model had the most weight as a test of which term was most important in predicting the behavior of ducks in each guild. 81

We categorized observation times into 5 bins to compare activity budgets throughout the

day: early morning (600-859), mid-morning (900-1159), midday (1200-1459), afternoon (1500-

1759), and evening (1800-2059). The range of sunrise times throughout focal areas and spring were approximately 25 minutes (6:15 AM to 6:50 AM; 1 hour gained due to DST), while sunsets had more variation as spring progressed, with a range of approximately 1 hour and 10 minutes

(6:05 PM to 8:15 PM; 1 hour gained due to DST). Videos that didn’t meet any of the above bins were placed in (6) nocturnal and excluded due to a small sample size and the diurnal focus of this study. To analyze whether there were differences in activity by ducks throughout spring, we categorized each spring into season time bins: early spring (March 21th – April 7th), mid-spring

(April 8th – April 24th), and late spring (April 25th – May 15th). We classified all semi-permanent

wetlands using Stewart and Kantrud (1971) cover types (1-4) using ArcGIS, aerial imagery for

the spring of 2018, and visual observations while in the field each spring. These types

characterized the emergent vegetation structure within the wetland from “choked” wetlands

(Cover type 1) to wetlands with little to no emergent vegetation (Cover type 4) and have been

shown to influence duck use of wetlands in our previous work (Chapter 2). Our sample of

wetlands did not contain semi-permanent wetlands with cover type 1, due to little to no open

water area. Thus, semi-permanent wetlands were categorized as cover type 2 through 4. We

excluded videos where the total time of all individuals was ≤ 1 second, due to little time spent in

the sampling area and thus creating bias for higher percentages of the activity.

For each guild, we tested for differences in the percent of time in each activity category

and 4 different time scales. These time scales were the five diurnal time bins, weekly, season

time bins, and yearly differences. We tested for differences in the percent of time in each activity

category and the wetland type, as well as cover type for semi-permanent wetlands. We also 82

determined differences in percent of time in each activity category for individual species as well

as between sexes.

Results

We observed an average of 3 individuals (SD = 2.28, range = 1-30 ducks) within each video. The average time that an individual was present within a video was 13.7 seconds (range=

2-15 seconds). The average total time observed in each video was 31.27 (SD = 34.1) seconds for dabbling ducks and 30.96 (SD = 28.3) for diving ducks. During spring 2018, we placed cameras in 15 semi-permanent wetlands and observed activities of 1,659 individual ducks. During 2019, we placed cameras in 15 semi-permanent wetlands, 2 seasonal wetlands, and 2 farmed wetlands and observed activities of 2,671 individual ducks. We observed 16 species. Mallards were the most abundant species (992 observations) followed by ring-necked ducks (977 observations) and blue-winged teal (397 observations; Figure 3.3). Ducks spent 91% of their time in either locomotion, feeding, or resting (Table 3.1). We found differences (P < 0.05) in time spent in each activity category for duck guilds, thus we separated all species into their corresponding guild for analysis as presented below.

Dabbling Duck Activity

We observed 8 dabbling duck species, with the 3 most observed species including mallards, blue-winged teal, and northern shoveler (Spatula clypeata). We observed ≥2,300

individual dabbling ducks between the two years of the study. Dabbling duck activity was

primarily composed of locomotion, followed by resting and feeding (Table 3.1). Comfort

movements and courtship combined contributed approximately 10% of time. For individual

dabbling duck species, percent of time of activity was highest for locomotion for all species other 83 than northern pintails, where feeding was most prevalent, although sample size was limited

(Table 3.1). While the percent of time resting for the dabbling duck guild was higher than percent of time feeding, this was only found for mallards and wood ducks when analyzed by species. All other dabbling duck species had a higher percent of time feeding. We found differences in the percent of activity and the sex in dabbling ducks. Feeding (P = 0.003) was greater among females, while locomotion (P = 0.03) was greater among male dabbling ducks.

We observed dabbling ducks on all three wetland types with the majority of observations on semi-permanent wetlands followed by seasonal and farmed wetlands. Time spent by dabbling ducks in each activity category was different between wetland types (P < 0.001) for all activities except courtship (P = 0.85; Figure 3.4). For feeding, we found differences between the three wetland types with a higher mean of feeding on seasonal (P < 0.001) and farmed wetlands (P =

0.002) compared to semi-permanent wetlands (Figure 3.7). For resting, we found significant differences between seasonal wetlands (P < 0.001) compared with semi-permanent wetlands, with lower time spent resting on semi-permanent wetlands. Locomotion was most prevalent on semi-permanent wetlands (P < 0.001) compared to both farmed and seasonal wetlands. While time spent in comfort movements made up little of the total percent of activity, it was highest on seasonal wetlands followed by farmed wetlands (Figure 3.4). Time spent in the activity categories were significantly different with the cover types of semi-permanent wetlands for feeding (P < 0.001) and locomotion (P < 0.01) activity categories. We found significant differences of feeding between cover type 4 and both cover types 3 (P < 0.001) and 2 (P <

0.001), with feeding highest on cover type 4. Locomotion was also significantly different between cover type 4 and cover type 3 (P < 0.01), with less time spent in locomotion on cover type 4 wetlands (Figure 3.4). 84

We found differences in the multiple time covariates and time spent in the activities. For

year, we found differences (P < 0.001) between the year and the percent of time of feeding and

locomotion for dabbling ducks. We found significant difference in the percent of time feeding (P

< 0.001), with higher feeding in 2019 than 2018, while locomotion was significant (P < 0.001) and higher in 2018 (Figure 3.5). Feeding was the only activity with evidence of difference in the season time bins, with significant difference between seasons bin 3 and 2 (P < 0.001; Figure

3.6). Thus, more time spent feeding during mid-spring compared to late-spring. For analysis by week, we found differences (P < 0.05) for all activities but courtship. The percent of time feeding (P < 0.001) was highest in the second and third weeks of the study, with minor peaks in week five and eight. The percent of time spent resting was highest in the first and last weeks of our survey season for dabbling ducks. We found differences (P < 0.05) in the percent of time resting and in comfort movements for dabbling ducks and the diurnal time bins in this analysis.

Comfort movements were highest at time bin 4, the afternoon period, and significantly different from the earlier time bins (Figure 3.7). For resting, we only found a significant difference in time bin 5 compared to time bin 2, with higher mean time spent resting in time bin 5. While not significantly different with the time bins, feeding gradually increased throughout the day, with highest time spent feeding during mid-morning (time bin 2) and midday (time bin 3) (Figure

3.7).

The analysis on depth with the activity categories found that depth was significant (P <

0.01) for all activities except for courtship. As depth decreased, time spent feeding (β = -0.499,

SE = 0.042) (Figure 3.8), resting (β = -0.102, SE = 0.044), and in comfort movements (β = -

0.061, SE = 0.026) increased. The time spent in locomotion was positively related with depth for dabbling ducks (β = 0.638, SE = 0.055). For all activities except for courtship, the comparison of 85

depth models to the cover type models found cover type had greater evidence of determining

duck behavior for most activity categories. On semi-permanent wetlands, the depth model was

more important in predicting time spent feeding with the depth model making up 99.9% of AIC

model weights and a ΔAIC of 21.02 over the model with cover type alone.

Diving Duck Activity

We observed 8 species of diving ducks that primarily included ring-necked ducks,

bufflehead (Bucephala albeola), and lesser scaup (Aythya affinis) making up over 1,900 individuals between the two years of the study. Overall, diving ducks spent the majority of time feeding (38.5%) followed by resting (31%). Similar to dabbling ducks, courtship wasn’t abundant in observations and contributed little to the overall percent of time spent in activities for diving ducks. We tested for differences of both sex and species of diving ducks and percent of time of each activity. We only found a difference (P = 0.05) in locomotion and the sex of diving ducks, with a higher percentage of locomotion in males. Other activities (P > 0.1) were similar between both sexes of diving ducks. We found differences (P < 0.001) in percent of time

of each activity and diving duck species. For individual diving duck species, percent of time

resting was the dominant activity for all species other than bufflehead and ruddy ducks, where

feeding was more abundant. Bufflehead were observed feeding more than any other diving duck

species which contrasted redheads which were observed primarily resting and in locomotion with

very little time expended on feeding and courtship (Table 3.1). We combined hooded mergansers

(Lophodytes cucullatus) and red- breasted mergansers (Mergus serrator), with locomotion and

feeding the dominant activities.

Diving ducks weren’t observed on seasonal wetlands, and <5% were observed outside

semi-permanent wetlands. Thus, analysis with diving ducks was only conducted on and between 86

farmed wetlands and semi-permanent wetlands. We found no difference (P > 0.05) in diving

duck activity between wetland types for the majority of activity categories. We did find

evidence of differences (P = 0.012) in activity between wetland types for comfort movements,

which were more prevalent in farmed wetlands (18.9%) compared to semi-permanent wetlands

(7%). Within semi-permanent wetlands, we found differences of cover type and activity categories for feeding, resting, and locomotion (Figure 3.4). We found that time spent feeding was greater on cover type 4 (54.9%) compared to both cover types 2 (26.6%) and 3 (32.5%).

However, time spent resting was significantly different and greatest on cover type 3 (35.4%) wetlands compared to cover type 4 (24.4%) and 2 (32.3%). We found differences in locomotion and cover type, with a greater percent of time spent in locomotion on cover type 2 (35.7%) compared with both cover type 3 (24.0%) and 4 (15.1%; Figure 3.4).

The percent of time in each activity was similar (P = 0.14) between years for diving ducks for all activity categories except resting (P = 0.02), which was more prevalent in 2018

(Figure 3.5). We found feeding (P < 0.001) and locomotion (P = 0.013) were significantly

different between season bins. We found differences in feeding (P < 0.001) between mid-spring

(bin 2) and early-spring (bin 1), with higher time spent feeding in mid-spring (Figure 3.6). We

also found evidence that feeding was significantly different (P = 0.04) and greater in mid-spring

(bin 2) compared to late spring (bin 3). For locomotion, the only significant difference (P =

0.009) occurred between mid- spring (bin 2) and early-spring (bin 1), with higher time spent in

locomotion in early-spring. We found differences (P < 0.001) in feeding and locomotion of

diving ducks and the diurnal time bins in this analysis (Figure 3.7. Feeding throughout the day

contributed approximately 60% of the time observed in diving ducks, with greater time and

significant differences of early morning (diurnal time bin 1) compared to midday (diurnal time 87 bins 3) and afternoon (diurnal time bin 4). The percent of time in locomotion was highest during early morning (diurnal time bin 1) and evening (diurnal time bin 5) and lowest during the middle bins (Figure 3.7).

Similar to dabbling ducks, all activities other than courtship were significant with depth

(P < 0.01). Feeding was positively related with depth (β = 0.586, SE = 0.060), thus an increase in time spent feeding as depth increased (Figure 3.8). Also similar to dabbling ducks, other significant activity categories had a negative relationship with depth, thus the time resting (β = -

0.232, SE = 0.058) and in locomotion (β = -0.278, SE = 0.060) decreased as depth increased. The comparison of depth models to cover type models found cover type had greater evidence of determining duck behavior for time spent feeding, resting and in locomotion. Time spent in courtship and comfort movements were negatively associated with depth and had greater support than cover type models with the depth models making up approximately 75% of AIC model weights and a ΔAIC of 2.50.

Discussion

Our results illustrate that ducks used wetlands in our study for all major diurnal activities but varied in how these behaviors played out through time, among guilds, wetland types, and wetland characteristics. Variation in activities primarily occurred in the three dominant activity categories – feeding, locomotion, and resting. Notable differences in activity by wetland types and depth suggests ducks used wetland types differently during spring and may offer insights into effectively constructing or managing wetlands to provide appropriate resources for spring migrating ducks in the study area. 88

We found the percent of time in major activities during spring in the PPR were

comparable with other stopover sites and portions of the annual cycle. Excluding locomotion,

feeding and resting were the most prevalent activities for total, guilds, and individual duck

species, which was consistent with other studies that found dabbling ducks increase and spend

most of their time feeding during spring (Tamisier 1972, Paulus 1983, Miller 1985). An increase

in time feeding may be due to an increase in the demand or time spent foraging as quality or

density of food resources decreases (Brodsky and Weatherhead 1985, Paulus 1983). Waterfowl

are trying to accumulate nutrient reserves at this time in order to complete migration as well as

reach breeding areas in optimal body condition, and thus spend more time feeding. Thus, the

availability and quality of food resources in this region may influence the amount of time spent

feeding. Higher estimates in the amount of time feeding on some wintering areas for mallards

and gadwall (Mason et al. 2013) suggest that migrants were arriving in a good enough body

condition from the wintering areas that they required less food resources and time feeding in

order to replenish reserves.

For some species of diving ducks, the percent of time feeding during winter were similar

to our results during spring with approximately 20-35% of time spent feeding (Bergan et al.

1989, Crook et al. 2009). Similarly, bufflehead spent the majority of their time feeding with over

50% of their time allotted during winter in South Carolina (Bergan et al. 1989), while canvasbacks had much lower estimate for time spent feeding in northeastern Texas (e.g., 18%;

Crook et al. 2009). We observed more time spent feeding in diving ducks compared to dabbling

ducks (Table 3.1), which may be due to the feeding strategy of the guild. The amount of time

spent searching and acquiring food is thought to be greater for diving ducks, and they may be

selecting for higher quality food resources, such as invertebrates. Somewhat different from our 89

results, mallards spent the majority of time in comfort movements and resting in Nebraska

during spring (Jorde 1981). While not as prevalent, we found mallards had among the highest

percent of time for comfort movements of all species and the highest time spent resting for all

dabbling ducks. Courtship, which accounted for little activity across all response variables, was

found to be much higher in winter than spring for mallards (Jorde 1981), suggesting that majority

of courtship occurred before individuals arrived in spring in both studies. Our results suggest

there are similarities in behavior between stopover sites and other portions of the annual cycle.

While a limited sample size of wetlands, we found evidence that migrating ducks used

wetland types differently during spring. Feeding was most prevalent on seasonal wetlands,

potentially due to the availability of food resources, such as seeds that are used by many species

during spring (Hitchcock 2009). Swanson et al. (1974) found that blue-winged teal feeding in seasonal wetlands primarily consumed seeds during spring. Similar to LaGrange (1985) who found resting was prevalent for female mallards on farmed wetlands in Iowa, we found that dabbling ducks spent more time resting on farmed wetland compared to semi-permanent wetlands. This may be due to the landscape characteristics of farmed wetlands, with high visibility that may provide safety from disturbances or predators. For both dabbling and diving ducks, our results suggest that there is no difference between the times spent feeding in farmed and semi-permanent wetlands. However, the availability of these wetlands were very different, with much higher abundance of farmed wetlands available for use on the landscape. Our sample size on these wetlands was limited as well, and should be bolstered in future work. Although the percent of time of feeding on semi-permanent wetlands between guilds were different, highest time feeding occurred on cover type 4 for both guilds. The depth of semi-permanent wetlands was more important than the cover type for feeding for dabbling ducks, suggesting depth may be 90

limiting the accessibility of food resources. For activities where cover type was more important

than depth, differences may be due to the amount and structure of emergent vegetation within

wetlands. Emergent vegetation on the edge of wetlands provide cover against harsh weather as

well as protection against predators compared to open water areas (Pöysä 1983).

There may be potential bias of our observations due to the placement of cameras on

wetlands where relatively high duck use was observed. The placement of cameras in highly

productive farmed and seasonal wetlands suggests there are other factors, such as an abundance

of food resources, which may affect the presence and activities of ducks. While these wetlands

with cameras had concentrations of ducks, we observed many farmed and seasonal wetlands

have little to no duck use across the study period in both years (Chapter 2). Thus, ducks may be

selecting and concentrating on certain wetlands that have an abundance of available food

resources, and using the other wetland types to fulfill other requirements (i.e., resting).

Differences in time feeding and in locomotion for dabbling ducks and resting for diving

ducks between years are potentially due to the weather conditions migrants encountered. In the

first year of the study (2018), weather conditions were much more variable, with multiple late

spring freeze events that caused migrants to stage within the study area for much longer than a

more average spring (2019). Differences in the season time bins found variable percent of time

feeding among dabbing ducks and feeding and locomotion among diving ducks through the migration period. The trend of an increase in the percent of time feeding for both guilds correlate

with peak abundance of migrants in the region in the concurrent study (Chapter 2). The time in

activity compared to the diurnal time bins were mostly consistent throughout the day. We didn’t

observe many changes in the activities and the diurnal time bins. Perhaps the largest differences

in the diurnal time bins was the percent of time feeding in diving ducks, which increased 91 throughout the day until the evening time bin. Paulus (1988) found that the amount of time spent in an activity varies within and between different duck species. We found similar results with differences between and among species during spring as well. This may be due to different migration strategies and habitat utilization by different species.

Camera placement in this study was primarily on semi-permanent wetlands. There were multiple logistic constraints for placing cameras on seasonal and farmed wetlands, which impacted sample sizes of these wetland types. Overall the number of observations or duck use on farmed and seasonal wetlands was also considerably less in this study, which parallels other research in this region that found higher duck use relative to unit of area on semi-permanent wetlands compared to these wetland types. The presence and abundance of species was also similar to those results, with mallards and ring-necked ducks among the most abundant species in both studies (Chapter 2).

The use of trail cameras provided many advantages. We were able to constantly observe wetlands while limiting the amount of disturbance within the wetlands. We were also able to review duck activities multiple times at later dates. This means we were able to accurately categorize the activity of all ducks instead of having to estimate it for a flock or select a single focal individual. Perhaps the greatest advantages of using trail cameras was the constant ability to observe ducks while only visiting the wetland to check batteries and memory cards.

Limitations of traditional methods for waterfowl activity budgets include the ability to only observe focal individuals or need to generalize a flock’s activity, and ability to observe wetlands consistently over a period of time, day, and season. Of studies that have calculated activity budgets on waterfowl, many have focused on activities of only few or individual species of waterfowl (Afton 1979, Miller 1985, Turnbull and Baldassarre 1987). Also, the majority of 92 previous studies only calculated activity budgets for single wetlands or wetland types (Brown and Fredrickson 1987, Bergan et al. 1989, Crook et al. 2009, Mason et al. 2013). This may be due to limitations from traditional observation methods used to calculate activity budgets.

Traditional methods employed by these studies require extensive time and effort that may not be feasible for long term monitoring, especially on multiple wetlands. While few studies have used various camera and video recording devices for waterfowl (Cowardin and Ashe 1965, Brown and

Fredrickson 1987, Croston et al. 2018), cameras alleviate some of these limitations and improve our understanding of behavioral ecology by also allowing for a multiple species approach

(McKinney 1973).

Limitations of using trail cameras primarily include the limited sampling area of the camera view and video length. Because of this some activities such as locomotion may have higher percentages. Traditional methods allow for a longer duration and larger sampling area of observed individuals, with the ability to follow individuals throughout the wetland. The main limitation with video length was primarily for diving ducks that may be mid-dive and thus not visible to the camera until surfacing. When diving ducks appeared from underwater, we assumed they had been feeding the time from the beginning of the video. A potential resolution may be increasing video length to ensure dive durations are accounted for, which may increase the monitoring effort of battery life. However, we observed diving ducks before and then reappear from diving, suggesting our video length was long enough to capture these individuals.

Furthermore, wetland characteristics may limit the effectiveness of trail cameras, primarily on semi-permanent wetlands, such as large wetlands and wetlands with interspersed emergent vegetation. Wetlands with large areas may not be feasible as the sampling area of the trail cameras is proportionally small. For farmed wetlands, all potential wetlands were located on 93

private land and thus required permission to access. This was an issue due to the intermittent

presence of water and thus ducks on this wetland type. On many farmed wetlands, delay of

permission occurred until after wetland area and duck abundance began to decline. We had

similar farmed wetland area and depth variability as LaGrange and Dinsmore (1989), who

observed farmed wetlands with areas of 5 ha go completely dry within a few days in the DML

during spring. Consequently, keeping the cameras on the edge of water in farmed wetlands was

difficult as the wetland size varied. Higher camera monitoring and placement efforts was

required in this wetland type and thus may not be as feasible if monitoring effort is a concern.

Management Implication

Results from this study revealed that spring migrating ducks used all three wetland types

for major activities as they transitioned through the southern PPR to northern breeding areas.

However, higher percent of time feeding occurred on the more ephemeral wetland types

(seasonal and farmed wetlands), suggesting these wetlands were important for accumulation and

maintenance of nutrients during spring. Management of seasonal and farmed wetlands within the

PPR should primarily focus on seasonal wetlands, due to the limited availability of grassland in

this region compared to the abundance of farmed wetlands. Similarly to Chapter 2, depth should

be considered when providing optimal habitats for various activities, especially feeding in

dabbling ducks. Management should focus on providing an abundance of ephemeral wetlands

where major activities and relatively high use can occur, while also providing semi-permanent habitats for when ephemeral wetlands aren’t available due to the variable yearly and seasonal flooding conditions. In addition to Chapter 2, these results suggest that spring migrants use a diversity of wetland habitats, and may best benefit by providing an abundance of wetland types 94 that allow for the performance of the different major activities and continuation and success of migration.

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Tables

Table 3.1. Mean percent of time (+- SD) spent in each of 5 behavioral activity categories by spring migrating ducks observed on wetlands in the Prairie Pothole Region of Iowa during spring migration 2018-19 Species codes: AMWI = American wigeon, BUFF = bufflehead, BWTE = blue- winged teal, CANV = Canvasback, GADW = gadwall, AGWT = American green-winged teal, LESC = lesser scaup, MALL = mallard, NOPI = northern pintail, NSHO = northern shoveler, REDH = redhead, RNDU = ring-necked duck, RUDU = ruddy duck, WODU = wood duck.

n Feeding Resting Locomotion Courtship Comfort mov. Total 4,330 28.39 ± (1.61) 25.16 ± (1.49) 37.89 ± (1.87) 0.91 ± (0.25) 7.65 ± (0.64)

Dabblers 2,369 18.74 ± (2.08) 19.53 ± (2.04) 52.15 ± (2.70) 1.57 (0.48) 8.00 ± (0.75) AMWI 18 31.30 ± (18.27) 16.48 ± (13.77) 41.85 ± (22.07) 0.00 ± (0) 10.37 ± (8.30) BWTE 410 17.06 ± (3.28) 14.23 ± (2.92) 58.10 ± (4.14) 3.27 ± (1.13) 5.51 ± (1.79) GADW 127 10.97 ± (4.95) 8.81 ± (4.31) 73.30 ± (6.65) 2.78 ± (2.61) 3.93 ± (2.49) 99

AGWT 202 11.27 ± (4.62) 10.68 ± (3.79) 68.38 ± (5.70) 2.64 ± (1.39) 5.53 ± (2.76) MALL 1,063 17.70 ± (2.14) 30.24 ± (2.46) 37.90 ± (2.61) 1.88 ± (0.58) 13.08 ± (1.68) NOPI 7 78.10 ± (34.64) 7.62 ± (13.98) 7.14 ± (17.48) 0.00 ± (0) 7.14 ± (17.48) NSHO 391 33.47 ± (4.06) 12.02 ± (2.60) 45.82 ± (4.13) 3.14 ± (1.78) 5.26 ± (1.08) WODU 151 4.13 ± (2.60) 10.48 ± (3.74) 79.31 ± (5.26) 1.81 ± (1.06) 4.04 ± (2.63)

Divers 1,961 38.46 ± (2.29) 31.04 ± (2.12) 22.99 ± (2.20) 0.23 ± (0.13) 7.29 ± (1.04) BUFF 383 46.81 ± (3.62) 24.84 ± (2.97) 20.55 ± (3.16) 0.42 ± (0.32) 7.09 ± (2.34) CANV 79 19.58 ± (6.64) 34.59 ± (8.79) 34.04 ± (9.75) 3.01 ± (2.00) 8.78 ± (5.99) LESC 371 22.81 ± (3.20) 41.64 ± (4.20) 26.36 ± (3.88) 0.65 ± (0.65) 8.54 ± (2.53) REDH 49 6.33 ± (5.99) 35.61 ± (13.04) 42.35 ± (13.18) 0.14 ± (0.27) 15.58 ± (9.54) RNDU 977 34.34 ± (2.05) 36.55 ± (2.21) 24.48 ± (2.27) 0.17 ± (0.12) 4.45 ± (1.09) RUDU 72 25.96 ± (6.71) 22.80 ± (7.32) 46.57 ± (10.35) 1.30 ± (1.44) 3.28 ± (3.26) MERG 30 32.68 ± (13.76) 11.78 ± (9.63) 44.07 ± (17.87) 0.00 ± (0) 4.79 ± (8.79)

Table 3.2. Number of observations (videos), individuals, and mean depths (+- SD) for wetland types and semi-permanent cover types by spring migrating ducks observed on wetlands in the Prairie Pothole Region of Iowa during spring migration 2018-19.

# of # of Wetland Type Mean depth (cm) ± SD observations individuals

Semi-permanent wetlands 1,590 3,522 78.58 ± 20.60

- Cover Type 2 299 686 77.04 ± 15.53

- Cover Type 3 787 1,718 73.75 ± 12.39

- Cover Type 4 504 1,118 86.91 ± 29.02

Seasonal wetlands 139 656 36.13 ± 5.87

Farmed wetlands 68 152 42.41 ± 10.63

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Figures

Figure 3.1. Example of placement of camera and depth pole with the associated sampling area used to determine activity budgets for spring migrating ducks in the Prairie Pothole Region of Iowa during spring migration 2018-19.

102

Figure 3.2. Still image of sample video used to collect data of spring migrating ducks observed on wetlands in the Prairie Pothole Region of Iowa during spring migration 2018-19. Depth pole in center of video and outlined.

103

Figure 3.3. The 10 most observed duck species detected by cameras in the Prairie Pothole Region of Iowa during spring migration 2018-19. Species codes: AGWT = American green- winged teal, BUFF = bufflehead, BWTE = blue-winged teal, CANV = Canvasback, GADW = gadwall, LESC = lesser scaup, MALL = mallard, NSHO = northern shoveler, RNDU = ring- necked duck, WODU = wood duck.

104

Figure 3.4. The mean percent of time spent in each of 5 behavioral activity categories by wetland type cover type with 95% confidence intervals for dabbling and diving ducks observed on wetlands in the Prairie Pothole Region of Iowa during spring migration 2018-19. Cover types categorize the structure of emergent vegetation of semi-permanent wetlands.

105

Figure 3.5. The mean percent of time spent in each of 5 behavioral activity categories by year with 95% confidence intervals for dabbling and diving ducks observed on wetlands in the Prairie Pothole Region of Iowa during spring migration 2018-19.

106

Figure 3.6. The mean percent of time spent in each of 5 behavioral activity categories by season time bins with 95% confidence intervals for dabbling and diving ducks observed on wetlands in the Prairie Pothole Region of Iowa during spring migration 2018-19. Season time bins: bin 1 = early spring, bin 2 = mid-spring, bin 3 = late spring.

107

Figure 3.7. The mean percent of time spent in each of 5 behavioral activity categories by diurnal time bins with 95% confidence intervals for dabbling and diving ducks observed on wetlands in the Prairie Pothole Region of Iowa during spring migration 2018-19. Time bins: Bin 1 = early morning (600-859), Bin 2 = mid-morning (900-1159), Bin 3 = midday (1200-1459), Bin 4 = afternoon (1500-1759), Bin 5 = evening (1800-2059).

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Figure 3.8. The mean percent of time spent feeding in relation with depth with 95% confidence intervals for dabbling and diving ducks observed on wetlands in the Prairie Pothole Region of Iowa during spring migration 2018-19.

CHAPTER 4. GENERAL CONCLUSIONS

Traditionally, the majority of research, conservation, and management of wetlands within

the Prairie Pothole Region (PPR) have focused on breeding waterfowl (e.g., Swanson and Meyer

1977, Delphey and Dinsmore 1993, Cordts et al. 2002, Walker et al. 2013). This is due to the

importance of this region for waterfowl production, with prairie potholes hosting between 50–

80% of the North American duck breeding populations annually (Batt et al. 1989). Thus, the

region has rightfully earned the moniker ‘North America’s Duck Factory’. However, the PPR, particularly the southern PPR, can also provide important habitat for spring migrating ducks in transit to the prairie, boreal, or breeding grounds. This study provides evidence that a diversity of wetlands in the southern PPR provide important habitat for migrating ducks as they transition to northern breeding areas. Although duck use occurred on all studied wetland types, large, managed semi-permanent wetlands and shallow lakes accounted for the majority of duck use- days on an area and per-wetland basis. Migrants clustered on these wetlands and while not abundant on the landscape, they were used extensively by all species common during spring in

Iowa. Thus, our results suggest the majority of migrants cluster on a few important wetlands, and the rest distribute across wetlands of varying types and sizes, potentially constrained by the amount of water on the landscape.

Determining what role individual wetland types play in the annual transition of ducks from wintering to breeding areas through the southern PPR depends heavily on their abundance and annual availability. Thus, in this final chapter, we used National Wetlands Inventory (NWI;

Wilen and Bates 1995), hydric soil maps (Miller et al. 2009), and current aerial imagery to attempt to estimate the availability of unique wetland types we studied across the entire DML and then calculated the number of use-days on wetlands by applying the proportion of duck use- 110

days on the wetland area we surveyed in Chapter 2. These ‘back-of-the-envelope’ estimates make many assumptions and would be aided by additional years of research and improved estimation (and accounting for uncertainty) of wetland availability. However, they serve as an initial attempt to understand how ducks may distribute across this important migratory area, and offer guidance on the relative importance of different wetland types found there.

Farmed Wetlands

The abundance of farmed wetlands leaves unanswered questions pertaining to the cumulative number of duck use-days these wetlands provide across the southern PPR.

Historically more abundant (Galatowitsch and van der Valk 1996), wetlands with shorter hydroperiods such as farmed wetlands still make up the majority of wetlands on the landscape today and are clearly used by migrants (Murphy and Dinsmore 2018, Chapter 2). Previous work in addition to the results presented in Chapter 2 suggest that factors such as wetland area

(Murphy and Dinsmore 2018), crop type and tillage practice (LaGrange and Dinsmore 1989) impacts duck use on farmed wetlands. Evidence of higher duck use on farmed wetlands when the crop type was corn has also been found by multiple studies in the RWB (Jorde 1981, Pearse et al.

2011) and PPR (LaGrange and Dinsmore 1989, Chapter 2), with corn documented in diets of spring migrating ducks (Hitchcock 2008). Thus, the area planted in corn or availability of farmed

wetlands with corn as the dominant crop type may be another factor in estimating potential for

duck use of farmed wetlands in addition to the water availability. Therefore, both spring weather

conditions and landscape factors may further influence the overall potential duck use on farmed

wetlands across the landscape.

We surveyed a total of 3,244 ha of (n = 589 during 2018, n = 1,637 during 2019) farmed

wetlands between both years and observed 36,182 duck use-days. We found that wetlands with 111

shorter hydroperiods of farmed and seasonal wetlands were seldom flooded, if ever (Chapter 2).

Of the surveyed area, there were 2,226 unique farmed wetlands (n = 589 during 2018, n = 1637

during 2019), with only 744 ever containing any presence of water (n = 186 during 2018, n =

558 during 2019). We combined areas from both years of wetland surveys and used that as the

sample of wetland areas. This allowed us to account for the variation between spring wetland

conditions and years. We followed this method for all subsequent wetland types below. We

found the wetland area that farmed wetlands contributed and the number of potential wetlands

were much lower than the potential area flooded from both NWI and soil mapping delineations

used in Chapter 2. Total farmed wetland availability is thus challenging to predict, and as a

result, a total estimate of annual duck use is challenging. Murphy and Dinsmore (2018) found

that although they are abundant, farmed wetlands rely heavily on precipitation and are primarily

small. Thus, the potential for duck use on farmed wetlands is annually variable and dependent on

spring weather conditions.

McDeid et al. 2019 estimated there were approximately 226,848 ha of depressional

wetlands within the DML. Using the proportion of observed wetland area compared to the

potential area from McDeid et al. (2019), we estimated that approximately 42,000 ha of farmed

wetland area may potentially occur within the DML, depending on spring weather conditions.

Then using the proportion of duck use-days on the total wetland area, we could then estimate the

duck use-days across the DML. Assuming the same proportion of wetland area and relationships with duck use, we estimated that 2,530,152 duck use-days potentially occurred on farmed wetlands throughout the entire DML during 2018 and 2019 (Table 4.1).

112

Seasonal Wetlands

The majority of wetlands drained within the PPR were wetlands with shorter

hydroperiods (Miller et al. 2009). Our definition of seasonal wetlands were those with short

hydroperiods lacking wetland obligate plants. We found hydroperiods of seasonal wetlands were

similar to farmed wetlands, with the presence and area of water there being highly variable, often

without water for long periods of time. Available grassland area is approximately 5.5% within

the DML (NLCD 2016), further limiting the landscape area for seasonal wetlands to occur.

Factors we found important for predicting duck use on seasonal wetlands included local

vegetation management such as the percent of vegetation and the height of vegetation in addition

to the wetland area and depth. Thus, these factors should be considered when estimating the

contribution of seasonal wetlands across the landscape. In Chapter 2, we observed approximately

19,500 use-days on 80 ha of seasonal wetlands. We then estimated the area of seasonal wetlands within the DML by determining wetlands with seasonal hydroperiods in NWI and soil maps.

Then, we excluded wetlands that were categorized as farmed in the wetland subcategory of NWI

and used aerial imagery to confirm whether wetlands overlapped grassland. We found that 8,300

ha were classified as seasonal wetlands within the DML. Then using the proportion of duck use- days on the surveyed wetland area, we estimated that 2,023,125 duck use-days occurred on seasonal wetlands across the DML (Table 4.1).

Semi-permanent Wetlands

While still a fraction of what historically occurred, semi-permanent wetlands within the

DML currently make up a greater proportion on the landscape than historically occurred, primarily due to recent focus of restoration (Miller et al. 2012). Many semi-permanent wetlands were affected by the drainage of wetlands with shorter hydroperiods, many becoming more 113

permanent and deeper due to the consolidation of water (McCauley et al. 2015). Estimating the

duck use on wetland types with longer hydroperiods such as semi-permanent wetlands provide multiple challenges such as the variability in the vegetation community and wide ranges of wetland sizes and depths. Wetlands with longer hydroperiods have a diversity of species, densities, and structures of vegetation. The vegetation community is much simpler within farmed wetlands because the vegetation community consists primarily of the crop type. Previous studies have documented the positive relationship of wetland area and waterfowl abundance (LaGrange and Dinsmore 1989, Webb et al. 2010, Chapter 2). The majority of semi-permanent wetlands

within the DML are relatively small in size, with the average size of semi-permanent wetlands

surveyed approximately 9.50 ha and many with limited areas of open water. However, we

observed high concentrations of duck use primarily on a few large semi-permanent wetlands,

which may be limited within the DML. Estimating duck use on semi-permanent wetlands should

consider these factors when considering a landscape scale analysis.

We estimated the area of semi-permanent wetlands by determining wetlands with semi-

permanent hydroperiods using NWI and soil maps. Then, we used aerial imagery and checked

each potential wetland and determined whether persistent emergent vegetation was present,

which was how semi-permanent wetlands were categorized in Chapter 2. While the majority of

wetlands within the PPR have shorter hydroperiods, we found approximately 11,000 ha

classified as semi-permanent wetlands within the DML. Our results from Chapter 2 of semi-

permanent wetlands found over 820,000 use-days (452,254 UD’s during 2018, 374,660 UD’s

during 2019) on approximately 2,300 ha of semi-permanent wetland area. However, using the

proportion of duck use-days on the surveyed wetland area, we estimated that 4,198,456 duck

use-days occurred on semi-permanent wetlands across the DML (Table 4.1). 114

Lakes

Previous studies have found that management of shallow lakes in Iowa can provide

important habitat for migrating waterfowl during spring (Vanausdall and Dinsmore 2019). Due

to the large areas of this wetland type, lakes can provide a diversity of microhabitats due to

different depths and vegetation zones, providing habitat for both duck guilds and other waterbird

species (Brown and Smith. 1998, Vanausdall and Dinsmore 2019). Restoration of shallow lakes

include installations of water control structures which allow for an improvement in water quality

and the ability to mimic the natural wet-dry cycles of prairie wetlands. This restoration has

occurred on 38 shallow lakes within the DML and have been found to positively impact the use

of both dabbling and diving ducks in Iowa (Vanausdall and Dinsmore 2019). Manipulation of

water levels can also help control vegetation as well as provide optimal foraging depths since it

has been documented as a constraint in some duck species (Pöysa 1983). However, how lakes

were categorized in Chapter 2 differed somewhat from other studies, with many shallow lakes

falling into the semi-permanent wetland category above. This is due primarily to the emergent

vegetation structure and wetland area used to differentiate them from semi-permanent wetlands

(Chapter 2). Many shallow lakes that have been found to support large abundances of ducks were most similar to large semi-permanent wetlands previously discussed, with interspersed emergent vegetation (Vanausdall and Dinsmore 2019). The majority of lakes were characterized by large, trees and rocky shores, with some disturbance present such as houses, roads, and boat ramps, and thus we believed acted differently than large semi-permanent wetlands for migrants. We surveyed approximately 1,500 ha of lake area and totaled over 130,000 duck use-days. However, the majority of duck use occurred on two lakes, which contributed over 80% of total lake use- days. We then estimated the area of lakes within the DML by determining wetlands with 115

permanent hydroperiods using NWI and had an area of greater than 30 ha. We then used aerial

imagery to determine the Stewart and Kantrud (1971) cover type, as the criteria of lakes required

a cover type of 4, with little to no emergent vegetation (Chapter 2). We found approximately

18,500 ha classified as lakes or large permanent wetlands within the DML. We estimated that

lakes may have contributed 1,630,105 duck use-days across the DML (Table 4.1).

Conclusions

A core challenge in comparing duck use of different wetland types across the PPR is in accounting for the diversity of sizes and abundances of any one wetland type. Previous studies have examined only single wetland types or compared relative use of wetland types, as we did in

Chapter 2. However, due to the abundance of wetlands on a landscape like the PPR, in order to understand the contribution of wetlands for migrants, a landscape scale approach is valuable.

What we present here is an initial, crude attempt at accounting for variable wetland abundances and duck use patterns to estimate how ducks distribute across this heterogeneous landscape during migration. Using our results from Chapter 2, our calculations presented here suggest that wetlands within the entire DML provided over 10.4 million duck use-days for spring migrating ducks, distributed among our 4 wetland types. Among these wetland types, semi-permanent wetlands provided the most use-days for spring migrants on both a per-unit area basis (Chapter

2) and after accounting for the area of each wetland type in the study area, as presented here. We estimated that 40% of all use-days in the study area occur on semi-permanent wetlands, even though they make up only 4% of the wetland area in the DML. However, we documented use

(Chapter 2) and important behaviors (Chapter 3) on wetlands with shorter hydroperiods such as farmed and seasonal wetlands, which accounted for an estimated 24% and 19% of total duck use- days, respectively across the DML. Thus, if these wetlands are available, there is an opportunity 116 for an abundance of habitat for migrants across the DML. Finally, lakes, which we defined as open water bodies over 30 ha and little to no emergent vegetation (Cover type 4), had high duck use but were relatively rare in the study area (6.97% of total wetland area) and thus only comprised 16% of duck use-days in this region.

These coarse estimates of duck use-days support results on how migrants use wetlands and distribute across the landscape (Chapter 2) and thus should help direct conservation and management of wetland types encountered in the PPR. However, multiple assumptions were made that need to be accounted for if true estimated are desired. For example, multiple factors may affect the availability of wetland habitat, and thus the distribution and abundances of wetland types throughout the DML. We found that all wetlands within this region provided habitat for spring migrants, although focusing on wetlands with longer hydroperiods, which may persist even in the harshest years, may be best when restoration and management is limited. That way, even during years with bad springs, quality habitat will still be on the landscape that can facilitate spring migrating ducks to breeding areas. The current climate produces variable wetland conditions, especially for wetlands with shorter hydroperiods. Changes in the timing, seasonality, and extent of precipitation within this region could change the wetland availability and consequently the use by spring migrants across the landscape. However, further research is needed in order to determine the true contributions of wetland types at the landscape scale. In order to do achieve this, improvements in wetland mapping is needed in order to accurately estimate the availability of wetlands on the landscape in a given year, with different spring conditions in mind. Additionally, continued monitoring of the abundance and diversity of wetlands on this landscape offer an opportunity to further explore how migrants engage with the 117 diverse wetlands of the PPR, and thus better understand the roles of these wetlands for spring migrating ducks.

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Tables

Table 4.1. Estimates and proportion of the number of basins, total area, and use-days of wetland types relative to the results of wetlands monitored for spring migrating ducks in the Iowa Prairie Pothole Region during spring 2018 and 2019.

Surveyed Estimated in DML

Wetland Type Area (ha) Basins (n) Use-days Area (ha) Basins (n) Use-days

Farmed 3,244 (45.56%) 2,226 (87.09%) 36,182 (3.57%) 226,848 (85.52%) 173,171 (93.14%) 2,530,152 (24.37%) 119

Seasonal 80 (1.12%) 80 (3.13%) 19,500 (1.92%) 8,300 (3.13%) 9,993 (5.37%) 2,023,125 (19.49%)

Semi-permanent 2,294 (32.23%) 239 (9.35%) 826,914 (81.48%) 11,600 (4.37%) 2,618 (1.41%) 4,198,456 (40.44%)

Lakes 1,501 (21.09%) 11 (0.43%) 132,317 (13.04%) 18,500 (6.97%) 143 (0.08%) 1,630,105 (15.70%)

Total 7,120.28 (100%) 2,556 (100%) 1,014,913 (100%) 265,248 (100%) 185,925 (100%) 10,381,960 (100%)