Diving Duck Abundance and Distribution on Lake St

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DIVING DUCK ABUNDANCE AND DISTRIBUTION ON LAKE ST. CLAIR AND WESTERN LAKE ERIE

Brendan T. Shirkey

A THESIS

Submitted to Michigan State University in partial fulfillment of the requirements for the degree of

Master of Science

Fisheries and Wildlife

2012

ABSTRACT

DIVING DUCK ABUNDANCE AND DISTRIBUTION ON LAKE ST. CLAIR AND WESTERN LAKE ERIE

Brendan T. Shirkey

Lake St. Clair and western Lake Erie are important migration staging areas for diving ducks including canvasbacks (Aythya valisineria), redheads (Aythya americana), and lesser and greater scaup (Aythya affinis and Aythya marila). The Michigan DNR has censused diving ducks on the United States portion of Lake St. Clair dating back to 1983, but in 2010 traditional surveys were expanded to cover all of Lake St. Clair and portions of western Lake Erie and distance sampling techniques were adopted in an effort generate statistical estimates of abundance. Furthermore, GPS locations were recorded for all flocks allowing for the development of spatial models to investigate the effects of environmental and anthropogenic variables on diving duck distribution. We found distance sampling techniques to be a viable option for estimating diving duck abundance as long as flock size is accounted for as a covariate affecting the detection function, and we were able to apply distance sampling methods to both spring and fall migration.

Human disturbance (i.e., presence of boats) and environmental variables (i.e., water depth and plant species richness) were predictive of diving duck occurrence. Differences between spring and fall abundance and variables predictive of spring and fall occurrence may indicate diving ducks are adopting different landscape use strategies in fall compared to spring, and this may have significant implications for wetland conservation planning.

DEDICATION

This work is dedicated to my parents, Tom and Wanda Shirkey. Without my mother’s patience and my father’s guidance, I never would have learned the true value of a life spent outdoors. There is no doubt that hunting and fishing fostered the qualities that make me who I am, and it was their appreciation for all living things that drove me to pursue a career in conservation.

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ACKNOWLEDGEMENTS

First, I would like to thank my parents, Tom and Wanda, and my sister Erin because without their support I never would have finished college in the first place. I would also like to thank the punter’s and staff at the Winous Point Marsh Conservancy not only for sharing in many of my most memorable hunting experiences but also for teaching me there is much to be learned outside the classroom.

I thank my funding sources such as The Upper Mississippi River Great Lakes

Region Joint Venture, The Michigan Division of Natural Resources, Michigan State

University, The Safari Club and the Winous Point Marsh Conservancy. I will forever be indebted to all of these great organizations for their support. I thank John Simpson, Dave

Sherman, and Jacob Gray for always lending an ear and some words of guidance to assist in making this project what I hope is a valuable contribution to the waterfowl community.

I thank my fellow observers, Joe Robison, Ernie Kafcas, Mike Wegan, Ryan Boyer,

Dusty Arsenoe, and Howie Singer for all the long and sometimes uneventful hours they spent in an airplane with me. I thank Dr. David Williams for his GIS support and Dr.

Andrew Finley for his spatial statistics expertise. I also thank Dr. Dave Luukkonen and

Dr. Scott Winterstein not only for the statistical support and guidance, but also for forcing me to develop the skills necessary to become a professional in wildlife management. I thank my pilots Derek Deruiter and Bob Anton; without them none of this work would have been possible.

Lastly and most importantly, I thank my wife Kelly. Without her never ending patience, love, and understanding, this work would never have been completed. She

iv volunteered to listen to countless practice presentations and was always willing to listen to me talk about ducks, which I assume must be rather boring for normal folks.

TABLE OF CONTENTS

LIST OF TABLES…………………………………………………………………viii

LIST OF FIGURES………………………………………………………………….x

CHAPTER 1 Introduction: Monitoring Diving Ducks During Migration on Lake St. Clair and Western Lake Erie………………………………………………………………..1 Importance of Lake St. Clair and western Lake Erie…………………………1 Threats posed to diving ducks on the lower Great Lakes……………………..2 Importance and challenges of monitoring diving ducks during migration…...6 Research objectives and thesis organization………………………………….8 Literature Cited………………………………………………………………10

CHAPTER 2 Estimating Diving Duck Abundance During Migration Using Distance Sampling Techniques………………………………………………………..15 Introduction………………………………………………………………….15 Study area and methodology………………………………………………...17 Results……………………………………………………………………….23 Discussion …………………………………………………………………...33 Management Implications…………………………………………………..38 Appendices…………………………………………………………………..40 Literature Cited……………………………………………………………...64

CHAPTER 3 Spatial Modeling of Diving Duck Distributions on Lake St. Clair and Western Lake Erie……………………………………………………………………...... 68 Introduction…………………………………………………………………..68 Study area and methodology…………………………………………………71 Historical survey methods……...... 71 Current survey methods………………………………………………72 Results………………………………………………………………………...76 Historical survey data………………………………………………...76 Current survey data…………………………………………………...77 Discussion…………………………………………………………………….90 Management Implications…………………………………………………….94 Appendices…………………………………………………………………....97 Literature cited………………………………………………………………..103

CHAPTER 4 Summary……………………………………………………………………………….107 Introduction……………………………………………………………………107 Research and Management Implications of Abundance Estimation and Spatial Modeling Techniques for Diving Ducks on Lake St. Clair and Western

Lake Erie……………………………………………………………………….108 Literature Cited………………………………………………………………...115

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LIST OF TABLES

Table 1. Pooled survey effort for Lake St. Clair, western Lake Erie, canvasbacks, redheads, and scaup during each of the 4 migration time frames……………24

Table 2. Survey date, truncation distance (w), number of observations before and after truncation (n), model selected for abundance estimation, p-value obtained from chi-square goodness-of-fit test, detection probability, coefficient of variation for the abundance estimate and model weight for individual surveys conducted over Lake St. Clair and western Lake Erie………………………………………..27

Table 3. Area or species of interest, number of observations before and after truncation (n), model selected for analysis, p-value from chi-square goodness-of-fit test, detection probability, coefficient of variation for abundance estimates and model weight for (a) Fall 2010, (b) Spring 2011, (c) Fall 2011, and (d) Spring 2012 pooled analyses………………………………………………………………28

Table 4. Survey date, 95% lower confidence limit (N LCL), abundance estimate (N), and 95% upper confidence limit (NUPL) for all individual surveys conducted on Lake St. Clair and western Lake Erie during the two year study. Confidence limits obtained for all surveys that included cluster size as a covariate were estimated via the bootstrap option in Distance 6.0………………………………………30

Table 5. Survey date, ocular estimate, distance based estimate, and area covered for flocks containing > 10,000 individuals observed during the two year study…32

Table 7. Survey date, the number of censused large groups (>10,000 birds), and the ocular estimate of the number of individuals contained in those large groups….63

Table 8. Migration period and corresponding percent of sites occupied by diving ducks and recreational boats on Lake St. Clair and western Lake Erie. Total sites on Lake St. Clair were 820 in Fall 2010 and 1025 in all other migration periods, and total sites on Lake Erie were 448 in Fall 2010 and 560 in all other migration periods (see Appendix A for % cells occupied by individual date)……………77

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Table 9. Migration season, survey date, 50% Bayesian quantiles (50% b), and empirical standard deviations (SD) for the intercept, presence of boats, distance to shore, water depth, and plant species richness covariates from hierarchical spatial models predicting diving duck presence on Lake St. Clair (* denotes statistical significance based on Bayesian criterion where the 95% credibility intervals for the posterior distributions of the parameters did not include 0; see Appendix B for Bayesian credibility intervals and parameter estimates for the spatial random effects)………………………………………………………………………83

Table 10. Averaged Bayesian parameter estimates ( B ˆ ) and associated 95% confidence limits from hierarchical spatial models predicting diving duck occurrence for Fall 2010, Spring 2011, Fall 2011, and Spring 2012 on Lake St. Clair. Confidence limits were calculated with empirical standard deviations and methods outlined by Thompson (1992) for linear combinations of random variables……………..84

Table 11. Survey date and corresponding percent of sites occupied by diving ducks and recreational boats on Lake St. Clair and western Lake Erie. The total number of sites on Lake St. Clair was 205 and the total number on Lake Erie was 112….98

Table 12. 2.5 %, 50%, and 97.5% Bayesian quantiles for the intercept term, the 4 non- spatial covariates (boats, distance to shore, water depth, and plant species richness), the spatial random effects (Phi), and the variance of the spatial random 2 effects (Sigma ). All hierarchical models were generated in the spBayes package for the statistical software R and were organized by migration season a) Fall 2010, b) Fall 2011, c) Spring 2011, and d) Spring 2012……………………………99

LIST OF FIGURES

Figure 1. Map of Lake St. Clair and western Lake Erie diving duck survey area with east- west survey transects shown as solid lines. For interpretation of the references to color in this and all figures, the reader is referred to the electronic version of this thesis……………………………………………………………………………19

Figure 2. Illustration of survey area beneath the aircraft showing 5 distance categories established after the first field season and the non-surveyed area directly below the plane that was blocked from view by the aircraft’s floats. Distance categories include: 1) 0-50 m, 2) 51-125 m, 3) 126-225 m, 4) 226-425 m, and 5) >425 m…………………………………………………………………………….20

Figure 3. Estimates of diving duck-use-days and 95% CI’s by lake and migration season with means calculated from pooled abundance estimates divided by the number of surveys (5) and multiplied by the number of days in the migration period (42)...31

Figure 4. Estimates of diving duck-use-days and 95% CI’s by species and migration season with means calculated from pooled abundance estimates divided by the number of surveys (5) and multiplied by the number of days in the migration period (42)……………………………………………………………………….31

Figure 5. Illustration of the effects of flock size when included as a covariate in the detection function. As flock size increases detection probability remains higher at greater distances from the transect line………………………………………….35

Figure 6. Detection probability as a function of average wind speed recorded by the observers on the day of the survey……………………………………………...39

Figure 7. Model fit images for all individual surveys (a-t) conducted over Lake St. Clair and western Lake Erie during the two year study. Detection probability is on the y-axis and distance (m) from the transect is on the x-axis. Binned distance data is shown in blue and the detection function fit to that data by program Distance is shown in red, and each blue bar represents the observed flock density divided by the expected flock density of the first distance category……………………….43

Figure 8. Model fit images for all pooled analyses (a-t) conducted over Lake St. during the two year study. Detection probability is on the y-axis and distance (m) from the transect is on the x-axis. Binned distance data is shown in blue and the detection function fit to that data by program Distance is shown in red, and each blue bar represents the observed flock density divided by the expected flock density of the first distance category…………………………………………….53

Figure 9. Distribution of diving ducks observed on Lake St. Clair and Lake Erie in 4 migration time periods: a) Fall 2010, b) Spring 2011, c) Fall 2011, and d) Spring 2 2012. Abundances (diving ducks/km ) interpolated from kernel density models………………………………………………………………………….79

Figure 10. Model-based predicted probabilities of diving duck occurrence based on parameter estimates for water depth, distance to shore, and plant species richness from averaged hierarchical spatial models for (a) Fall 2010, (b) Spring 2011, (c) Fall 2011, and (d) Spring 2012 on Lake St. Clair……………………………….85

Figure 11. Nocturnal distribution of diving ducks observed on Lake St. Clair on 27 November 2010 using FLIR technology and interpolated using kernel density 2 models (diving ducks/km )…………………………………………………….89

Figure 12. Kernel density map showing areas of recreational boating concentration 2 (boats/km ) on Lake St. Clair in Fall 2010, Spring 2011, Fall 2011, and Spring 2012…………………………………………………………………………….92

Figure 13. Conceptual model showing linkages between dreissenids, disturbance, and fall diving duck distribution and abundance on Lake St. Clair…………….….93

CHAPTER 1. INTRODUCTION: MONITORING DIVING DUCKS DURING MIGRATION ON LAKE ST. CLAIR AND WESTERN LAKE ERIE

The Importance of Lake St. Clair and Western Lake Erie

Lake St. Clair and western Lake Erie are areas of continental significance to waterfowl (NAWMP 2004), and daily waterfowl population estimates on Lake St. Clair have reached as high as 750,000 birds during peak fall migration (Miller 1943). These areas are of principal importance to diving ducks including canvasbacks (Aythya valisineria), lesser scaup (Aythya affinis), greater scaup (Aythya marila), and redheads

(Aythya americana), and as an example, Soulliere et al. (2000) estimated 45% of the entire continental canvasback population may stage on Lake St. Clair during any given fall migration. Furthermore, both canvasbacks and lesser scaup have been identified as species of conservation priority by the Upper Mississippi River and Great Lakes Region

Joint Venture and are considered representatives for wildlife dependent upon deep-water, permanent wetlands (UMRGLJV 2007). In addition, both lesser scaup and canvasbacks have garnered attention in recent years due to population concerns. Lesser scaup have experienced significant population decline since the mid 1980’s with an estimated spring breeding population in 2008 that was 33% below the long-term average (USFWS 2008), and canvasbacks have the distinction of having one of the lowest estimated breeding populations (690,600) of any duck species in the traditionally surveyed region of the

United States Fish and Wildlife Service Waterfowl Breeding Population and Habitat

Survey (USFWS 2011).

In addition to the ecological importance of the region, there are also tremendous economic and social values associated with diving ducks in the lower Great Lakes.

Canvasbacks, scaup, and redheads are all important game birds, and diving duck hunting 1 traditions run deep in the waters of Lake St. Clair and western Lake Erie. Layout hunting, a form of waterfowl hunting that specifically targets diving ducks in open water, dates back over a century and is still actively pursued by many Michigan duck hunters.

Unfortunately for state and federal conservation agencies, duck hunter numbers have declined substantially in recent years. In Minnesota, Wisconsin, and Michigan, three states known for providing outstanding diving duck hunting opportunities, 5 year averages of estimated duck hunter numbers have dropped 23,369, 10,415, and 6,368 respectively (Fronczak 2011). As diving duck hunting traditions continue to weaken, we lose the most outspoken group of advocates for diving duck conservation and those who largely fund wildlife conservation efforts. Without duck hunters, threats posed to diving ducks will only be magnified and protection of their habitat will become increasingly uncertain.

Threats posed to diving ducks in the lower Great Lakes

Both offshore waters and near-shore emergent wetland complexes of Lake St.

Clair and western Lake Erie face a myriad of threats to their ecosystem health despite their ecological and economic value. The southwest Lake Erie marshes that once lined the shores of both Ohio and Michigan experienced some of the most extensive destruction and severe degradation of any marsh ecosystem in the country (Campbell

1995). Pollution, sedimentation, and invasive fish species are largely to blame for reductions in aquatic vegetation abundance and overall plant diversity in the region (Hunt

1963; Hartig et al. 2008; Schloessor and Manny 1990; Sedwick and Kroll 2010). As an example, wild celery, (Vallisineria americana) a preferred food of canvasbacks, declined an estimated 72% between 1950 and 1985 on the lower Detroit River (Schlosser and

Manny 1990). Similar evidence of habitat degradation was documented in the late

1800’s when vast expanses of wild celery and wild rice (Zizania aquatica) were eliminated from wetlands and shallow bays of southwestern Lake Erie due to increases in water turbidity (Sedwick and Kroll 2010). More recently, agricultural runoff has been linked to Microcystis algae blooms and associated hypoxic zones in western Lake Erie resulting in anaerobic conditions near the lake- bottom and subsequent benthic invertebrate die-offs (Bridgeman et al. 2006).

In addition to sedimentation and pollution reducing the quality of waterfowl habitat, human disturbance may be reducing the quantity of available habitat. Lake St.

Clair and western Lake Erie are recreational hotspots for pleasure boaters and fishermen, and over 200 marinas are located on Lake St. Clair (Snider 1999). Intense recreational boating pressure on western Lake Erie and Lake St. Clair could be forcing diving ducks to abandon desirable foraging areas in favor of more secluded but potentially less productive areas. Research conducted on Long Point Bay, Ontario found boating pressure essentially reduced habitat suitability and scaup and canvasbacks were most likely to be disturbed by frequent boating pressure (Knapton et al. 2000). Likewise, recreational boats became such a concern on Navigational Pool #7 of the Mississippi

River that a voluntary waterfowl avoidance area was designated in an attempt to curtail disturbance (Kenow et al. 2003).

Recent pressure to develop alternative energy in the lower Great Lakes may result in further reduction of available diving duck habitat. Offshore wind turbine development is quickly gaining momentum and proposed wind farm sites have already been identified in Canadian waters of Lake St. Clair and Lake Erie (South Point Wind 2010). Wind

3 farms will likely not result in significant levels of direct mortality of migrating waterfowl; however, research has demonstrated waterfowl will actively avoid wind farms and abandon roosting or feeding sites located in close proximity to wind farms (Larsen and Guillemette 2007; Kuvlesky et al. 2007). Thus, given the tendency of waterfowl to avoid large wind farms, saturating the open waters of Lake St. Clair and Lake Erie with windmills could potentially impact diving duck distributions and migration routes.

The causes of habitat degradation in the lower Great Lakes extend beyond the previously mentioned human disturbance factors. Invasive species cause a multitude of issues in the Great Lakes, and their problematic nature is evident throughout the Lake St.

Clair and western Lake Erie basins. Several recent invaders are of special concern to wetland and waterfowl managers because of potential impacts on, and conflicts with, native wildlife. In the mid 1980’s Lake St. Clair was a focal point for the introduction of zebra and quagga mussels (Dreissena polymorpha and Driessena bugensis; hereafter dreissenids). Furthermore, Lake St. Clair and western Lake Erie boast a thriving population of mute swans, (Cygnus olor) a large and highly aggressive species of exotic waterfowl. Each of these invaders presents serious complications for natural resource managers attempting to maintain biodiversity in the region.

Dreissenid mussels have likely had significant impacts on diving duck distributions in the lower Great Lakes. Although most diving ducks are omnivorous, some species like lesser and greater scaup are largely carnivorous, and researchers believe scaup have altered their migration routes to take advantage of the new dreissenid food resource (Petrie and Knapton 1999; Hamilton et al. 1994). After the dreissenid mussel invasion, Custer and Custer (1996) found 98% of scaup collected on western Lake

Erie and 80% of scaup collected on Lake St. Clair contained the mussels. These findings suggested scaup likely benefited from the dreissenid invasion, however there is concern about the dietary implications of feeding heavily on dreissenids. Because dreissenids are filter feeders, they tend to accumulate trace elements, and elevated selenium levels have been detected in scaup feeding on the lower Great Lakes (Petrie et al. 2006). Elevated selenium levels could potentially decrease breeding success in waterfowl, although recent research has not found a strong link between elevated selenium and decreased breeding success in scaup passing through the Great Lakes (Badzinski et al. 2009). Further research is needed to fully understand nutritional impacts of dreissenids that are lower in protein, gross energy value, and lipid content, than many other preferred diving duck foods (Anteau and Afton 2008).

The impacts of dreissenids on more herbivorous species of diving ducks like canvasbacks and redheads are not as direct. Research suggests dreissenids have not become an important food resource as Custer and Custer (1996) found only 9% of canvasbacks collected on Lake St. Clair and western Lake Erie contained the mussels.

However, dreissenids may have indirectly affected redheads and canvasbacks by altering existing submerged aquatic macrophyte communities in the region. Dreissenids increase water clarity by filtering particles from the water column and by taking up enough calcium to prevent episodes of calcite precipitation (Strayer 2009). As evidence, Nalepa et al. (1996) documented a two-fold increase in water clarity on Lake St. Clair from 1986 to 1994. This increase in water clarity allowed for the return of submerged aquatic vegetation (hereafter SAV) in deeper water and changed distribution and species composition of existing SAV (Nalepa et al. 1996; Rybicki and Landwehr 2007, Thomas

5 and Hass 2012). The increase in available SAV as well as shifting species composition could have significant impacts on abundance and distribution of canvasbacks and redheads that feed predominantly on these plants.

In addition to dreissenids, Lake St. Clair and western Lake Erie must also cope with the impacts of a growing mute swan population. Mute swans were introduced to the

United States during the mid-twentieth century and their population has expanded rapidly in recent years. Petrie and Francis (2003) predicted that the Canadian population of mute swans could reach 30,000 individuals even under the most conservative population growth models. Large numbers of mute swans are problematic because these birds are highly aggressive and capable of excluding native waterfowl from high quality habitat

(Conover and Kania 1994). In addition, mute swans can have significant impacts on

SAV. For example, Tatu et al. (2007) documented resident mute swans reducing abundance of SAV by 75% over two years in several locations on Chesapeake Bay.

Furthermore, mute swans eat up to 3.8 kg of SAV per day and often eat less than 50% of what they uproot and kill (Bailey et al. 2008). Potential reduction of SAV on Lake St.

Clair and western Lake Erie could present significant problems to herbivorous native waterfowl that have considerable dietary overlap with mute swans. (Bailey et al. 2008).

Importance and challenges of monitoring diving ducks during migration

Waterfowl migration generally occurs within a series of narrow inter-connected corridors and is an energetically demanding event (Bellrose 1980). Therefore, high quality staging areas along migration corridors are necessary for waterfowl to reach breeding or wintering grounds in good health (Anteau and Afton 2004). For many years researchers believed migration habitat had little effect on waterfowl populations and thus

6 migration areas received little attention in comparison to waterfowl breeding and wintering grounds (Reinecke et al. 1989). More recently, an increased knowledge of interdependence of waterfowl requirements throughout their annual cycle has led to a new-found focus on migration research for both dabbling and diving duck taxa (Reinecke et al. 1989). For example, one of the proposed hypotheses for the decline in the continental lesser scaup population is lack of quantity and quality food at traditional spring migration stop-over sites (Anteau and Afton 2004). Anteau and Afton (2008) hypothesize landscape level declines in amphipods have decreased the quality of spring migration staging areas resulting in female scaup reaching breeding grounds in relatively poorer health than they did historically. This decline in body condition could result in decreased reproductive success and potentially explain declining continental scaup numbers (Anteau and Afton 2008).

All effective monitoring programs that seek to assess potential impacts of management decisions or perceived ecosystem threats depend on long-term data sets

(Gibbs et al. 1999). Although monitoring techniques may differ based on the fundamental objectives of the research, it is difficult to evaluate any changes in either distribution or abundance without long-term and consistently collected data (Lancia et al.

2005). In our specific case, short-term empirical datasets have severe limitations because of the dynamic nature of both diving duck distribution and abundance. We believe long- term monitoring coupled with model-based assessments will allow managers to not only identify diving duck hotspots in the present but also to explore past and predict future areas of diving duck importance. Monitoring at appropriate geographic and temporal scales will enable identification of all habitats important to diving ducks on Lake St. Clair

7 and western Lake Erie. Management of waterfowl habitats typically considers suitability of regional (e.g., Joint Venture scale) landscapes in relation to goals of maintaining desirable continental waterfowl population status and this research has the potential to further scientific understanding of important areas for diving ducks throughout the Great

Lakes.

Research objectives and thesis organization

The purpose of this research is to build upon existing knowledge of migrating waterfowl on Lake St. Clair and western Lake Erie by analyzing historical data collected in fall migration (1983-2008) and by conducting two years of aerial surveys during both spring and fall migration (2010-2012). This research will also address UMRGLJV and

Michigan Department of Natural Resource (MDNR) needs that include effective monitoring of priority waterfowl species during the non-breeding season. The four main objectives of this project are: 1) describe spatial patterns in canvasback, scaup, and redhead distributions from 1983 to 2008 on Lake St. Clair, 2) develop and test a set of candidate models predicting diving duck distribution during both spring and fall migration based on environmental and human disturbance factors on Lake St. Clair, 3) develop aerial survey methodologies using distance sampling techniques that account for imperfect (less than 1.0) detection probability on Lake St. Clair and western Lake Erie, and 4) develop spring aerial surveys that could provide distribution and abundance data comparable to fall data on Lake St. Clair and western Lake Erie.

The remainder of this thesis will focus on Chapters 2 and 3; Chapter 2 will outline our establishment of distance-based aerial survey methodologies for estimating diving duck abundance, and Chapter 3 will explore historical diving duck distributions and

8 explain our development of hierarchical spatial models for current diving duck distributions on Lake St. Clair. In conclusion, Chapter 4 will provide further insight into management implications of research findings. Finally, Chapters 2 and 3 are organized as separate manuscripts for submission to scientific journals so some duplication of text and figures may occur.

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UMRGLRJV. 2007. Upper Mississippi River and Great Lakes Region Joint Venture Waterfowl Habitat Conservation Strategy. U.S. Fish and Wildlife Service, Fort Snelling, Minnesota, USA.

USFWS. 2008. Waterfowl population status, 2008 U.S. Department of Interior, Fish and Wildlife Service, Washington D.C. USA.

USFWS. 2011. Waterfowl population status, 2011 U.S. Department of Interior, Fish and Wildlife Service, Washigton D.C. USA.

CHAPTER 2. ESTIMATIMG DIVING DUCK ABUNDANCE DURING MIGRATION USING DISTANCE SAMPLING TECHNIQUES

Introduction

Lake St. Clair, western Lake Erie, and the lower Detroit River have traditionally harbored the highest concentrations of migrating waterfowl in the Great Lakes Region, and consequently were included in the North American Waterfowl Management Plan

(NAWMP) areas of continental significance (Bookhout et al. 1989; NAWMP 2004).

Historically, daily population estimates on Lake St. Clair have ranged anywhere from

250,000 to 750,000 birds during peak fall migration, and the area is of particular importance to diving ducks that stage by the hundreds of thousands on the open waters and shallow bays of Lake St. Clair and western Lake Erie (Soulliere et al. 2000; Miller

1943). Commonly occurring species include canvasbacks (Aythya valisineria), lesser scaup (Aythya affinis), redheads (Aythya americana), and greater scaup (Aythya marila), and both canvasbacks and lesser scaup have been identified as focal species for management and conservation by the Upper Mississippi River and Great Lakes Region

Joint Venture (UMRGLJV 2007).

The Michigan Department of Natural Resources (MDNR) has collected multi- species diving duck data since 1983 using fixed-winged aerial surveys (Ernie Kafcas, personal communication). Historical surveys covered only United States waters of Lake

St. Clair and counts were assumed to be a complete census, thus no effort was made to account for imperfect detection probability of diving duck flocks. Diving ducks on Lake

St. Clair often occur in large flocks (e.g., up to 100,000 birds) during migration and historic complete fall censuses focused on detecting large flocks and estimating species composition and numbers of birds in these flocks. Recently, the MDNR expanded the 15 traditional survey area to include all of Lake St. Clair and portions of western Lake Erie, and rather than assuming a complete census, decided to test distance sampling techniques in an effort to obtain statistical estimates of abundance. In addition, there was interest among waterfowl managers in better documenting distribution and abundance of diving ducks during spring migration via aerial surveys.

Under some survey conditions, distance sampling is advantageous because it allows researchers to relax the assumption that detection probability is perfect, while still allowing for valid statistical estimates of abundance (Buckland et al. 2000). Distance sampling can be used to model the true detection probability given that 3 assumptions are met: 1) detection on the transect line is 1.0, 2) there is no responsive movement by the objects of interest prior to detection, and 3) distances to detected objects are measured without error (Buckland et al. 2001). In addition to the 3 fundamental assumptions, it is important to note that the objects of interest in our study are diving duck flocks rather than individual birds so some attempt must be made to estimate average flock size or include flock size as a covariate in the detection function (Buckland et al. 2001; Buckland et al. 2004).

The main objectives of this research are to test the assumption implicit in historical survey methodology that every diving duck flock was detected, and to develop aerial survey protocols for open water monitoring of diving ducks if detection probability is in fact less than 1.0. Ultimately, we hope this research establishes a foundation for improved aerial survey techniques allowing for abundance estimates of diving ducks or other waterbirds under open water scenarios.

Study area and methodology

Lake St. Clair and western Lake Erie are shallow, highly productive lake basins dominated by open water < 10 meters deep. Lake St. Clair encompasses approximately

2 1149 km , and the international border divides Lake St. Clair with the northwestern one- third of the lake in the United States and the southeastern two-thirds in Canada.

2 Surveyed area on western Lake Erie includes 621 km extending southward from the mouth of the Detroit River to Maumee Bay near Toledo, Ohio. For the first field season, we used Hawth’s tools in ArcGIS to establish a systematic line transect survey (ESRI

2006, Beyer 2004, Fig. 1). We selected a random shoreline starting point and established

26 line transects, 13 on Lake St. Clair and 13 on western Lake Erie spaced 3.2 km apart.

We established 4 distance categories during the first field season extending out from the transect line 0-75, 76-150, 151-300, and 301-600 m. Based on outcomes of the first five surveys, we adjusted our previously established distance intervals to include five distance categories extending from the transect line 0-50, 51-125, 126-225, 226-425, and >425 m respectively for all other surveys (Fig. 2). We used our target flight altitude and a clinometer to establish declinations from horizontal to associated distance bands and used

5 mm strips of masking tape to mark windows and 25 mm strips of masking tape to mark struts of the plane; this aided observers in recording observations by distance categories.

Observers aligned window and strut marks when recording an observation to prevent inaccurate distance measurements caused by a shift in the observer’s line of sight.

We conducted all flights with an amphibious DHC-2 De Havilland Beaver. All surveys began at approximately 9:00 a.m. with the exception of the 19 March 2012 survey that was delayed nearly 3 hours due to fog. Surveys were generally completed in

17 under 7 hours and flights were not conducted under inclement weather conditions such as excessive wind (>45 km/h), precipitation, or fog. The pilot flew at a target altitude of 90 meters and ground speed of approximately 150 km/h. Two pilots shared command of the aircraft and we used two observers on each flight with each observer being responsible for one side of the plane. Search protocol followed suggestions made by Buckland et al.

(2001) with effort focusing on the transect line to ensure meeting the first and most important assumption of distance sampling theory that every flock on the transect line was detected. In addition, we treated the area directly beneath the plane as non-surveyed because the plane’s floats blocked this area from view. This survey procedure resulted in a left truncation of 56 m on either side of the plane, thus an object recorded at a distance of 1 meter from the transect was actually located 57 meters from the centerline located directly beneath the airplane (Thomas et al. 2010; Buckland et al. 2001; Fig. 2)

We used two different GPS-audio recording devices to record observations due to

GPS failures during the first field season. One observer used a Columbus V-900 data logger while the other used a Trimble Nomad unit connected to a Garmin 10.0 wireless

GPS. When a flock was detected we recorded the appropriate distance category based on location of the approximate center of the flock, we estimated flock size, and we determined species composition. Because lesser and greater scaup are indistinguishable from one another during aerial surveys these two species were combined and collectively recorded as “scaup.” When extremely large groups with ocular estimates > 10,000 birds

20 were encountered, we deviated from the transect line to better estimate total flock size, species composition, and location of the center of the flock. Furthermore, we circled these large groups recording waypoints around the perimeter allowing us to estimate the area covered and then flew a single transect over them at our fixed altitude, counting the number of individuals within each distance band. We then used this count and area information to generate a distance-based estimate of the flock size that could later be compared to an observer’s ocular estimate of flock size.

All distance data was analyzed using the software Distance 6.0 (Thomas et al.

2010). According to Buckland et al.’s (2001) recommendations, all distance observations in the distance category furthest from the transect line (~1% of all flocks recorded) were right truncated after Fall 2010 allowing for better model fit near the transect line. We analyzed the data both by individual survey and by pooling surveys within a migration period (Fall 2010, Spring 2011, Fall 2011, Spring 2012). Pooled analyses facilitated estimation of duck-use-days (use-day defined as one duck spending one day on the study area) by lake or by species over the course of a given migration period. We consider our individual surveys a sample of daily diving duck abundance out of a population of 42 days for each migration season. The total seasonal diving duck use of our study area (i.e., use-days) can be estimated as the product of mean daily abundance and 42 days

(Thompson 1992). We used program distance to analyze pooled daily samples to estimate total duck abundance over 5 daily seasonal surveys and then estimated mean daily abundance by dividing overall estimates of abundance by the number of completed surveys (5). Seasonal duck-use-days were then estimated by multiplying mean daily use

21 by 42 days; variances for duck-use-days were obtained using methodology outlined in

Thompson (1992) for estimation of population totals.

For both individual and pooled surveys, model selection and model fit were assessed using the same methods. A set of a priori candidate models including various combinations of key functions, adjustment terms, and covariates suggested by Buckland et al. (2001) were analyzed using the Multiple Covariate Distance Sampling Engine

(MCDS) in Distance 6.0 (Thomas et al. 2010; see Appendix A for full list of candidate models). We then selected the highest rated model based on Akiake’s Information

Criterion (AIC) for each individual survey date or pooled seasonal analyses, unless visual inspection of frequency histograms showing proportions of flocks observed within each distance category led us to believe the top rated model was an implausible representation of the detection function (Burnham and Anderson 1998). In practice, we selected the lowest AIC value for all surveys except for those individual or pooled surveys conducted in Fall 2010. Model fit was evaluated using the chi-square goodness of fit statistics provided in Distance 6.0 and visual inspection of data binned by distance category in relation to fitted detection functions (Thomas et al. 2010).

We chose a hybrid methodology to generate our final estimates of abundance for both pooled and individual surveys because of extreme variation in observed flock size

(1-90,000 birds/flock). We used the traditional and previously described methods to generate a distance-based estimate for all flocks ranging from 1-9,999 birds in size, and we then added the total number of individuals observed in our ocular estimates of groups of 10,000 or more to the traditional distance-based estimate, thus generating an overall abundance estimate. An important assumption of this hybrid methodology is the

22 detection probability of groups containing > 10,000 birds was 1.0. We believe this assumption was reasonable given the relatively close spacing of transects across the study area; for example, one group with an ocular estimate of 15,000 birds was visible at an approximate distance of 9 kilometers. This distance is 3 times greater than the distance between transects making the probability of missing one of these large groups exceptionally small.

Results

We flew 5 surveys during each migration period (Fall 2010, Spring 2011, Fall

2011 and Spring 2012) for a total of 20 surveys over the course of the study.

Approximately 531.5 km of transect were flown on each individual survey. Pooled survey effort (i.e., km of transect flown) differed between Lake St. Clair and Lake Erie; however, the number of flights conducted was the same on both lakes and during each of the 4 migration time frames for which monitoring occurred (Table 1). Because canvasbacks and redheads were scarcely observed on western Lake Erie, we were not comfortable using the limited number of observations (<10 for each species during each migration period) to generate distance-based estimates of use on western Lake Erie for these two species; hence, we were only able to estimate canvasback and redhead use-days on Lake St. Clair. Sufficient observations were obtained for scaup across the entire study area, which is why pooled survey effort was greater for scaup in comparison to redheads and canvasbacks. It is important to note that individual surveys and pooled analyses on

Lake St. Clair and Lake Erie provide estimates of total diving duck abundance, thus canvasbacks, redheads, and scaup were combined for these analyses. Furthermore,

23 abundance estimates for individual surveys refer to all diving ducks over the entire study area, thus they are an estimate of all canvasback, scaup, and redhead abundances combined on both Lake St. Clair and western Lake Erie on a given date.

Table 1. Pooled survey effort for Lake St. Clair, western Lake Erie, canvasbacks, redheads, and scaup during each of the 4 migration time frames.

lake or species of number of 2 total transect length interest flights area (km ) (km) Lake St. Clair 5 1149 346.2 Western Lake Erie 5 621 185.3 Canvasbacks 5 1149 346.2 Scaup 5 1770 531.5 Redheads 5 1149 346.2

The half normal key function with cluster size or cluster size and observer included as covariates (hereafter half norm+clustersize or halfnorm+clustersize+observer) were the highest ranked models for the detection function on individual surveys after the first field season (Table 2). Cluster size proved to be a significant covariate for all but two individual survey dates both of which occurred during the latter half of spring migration when flocks were small and there was relatively little variation in flock size. For the first 5 surveys, all conducted in Fall 2010,

AIC indicated the hazard rate key function with no covariates (hereafter hazard rate+no covariates) as the top model (Table 2). However, we chose to use the half norm+clustersize model to obtain abundance estimates for 3 of these 5 surveys for two reasons: 1) we were uncomfortable using the regression method available in Distance 6.0 to estimate expected cluster size given the extreme variation in cluster size observed in our data and 2) data collected during the 3 subsequent migration periods demonstrated

24 that the half norm+clustersize model most appropriately represents data when all assumptions of distance sampling were likely met; thus, poor model fit for Fall 2010 surveys was likely the result of violations of distance sampling assumptions caused by inexperienced observers. For example, frequency histograms from Fall 2010 often had higher observed flock densities in the second distance category than the first. Given that historical survey techniques focused on censusing all large groups on the lake, this spike in the second distance category may have been a result of observers focusing search effort away from the transect line in an attempt to avoid missing any large groups. There were no alternatives but to use the hazard rate+no covariates model for the remaining two surveys in Fall 2010 because convergence failures occurred during variance estimation in program Distance when trying to include cluster size as a covariate in the detection model. This error also occurred for one pooled analysis in Spring 2011, for which we used the half norm+no covariates model to estimate number of flocks and the mean of observed group size to generate an abundance estimate.

The half norm+clustersize was the top ranked model for all but three pooled analyses after the of Fall 2010 (Table 3). For those three analyses, the half normal+no covariates model was top-rated (Table 3). It should be noted that for the more robust analyses with the highest numbers of observations the half normal+clustersize model was consistently the highest rated model and model weight often approached 1.0 (Table 3).

Once again we used the half norm+clustersize model to obtain estimates for all pooled data during Fall 2010 for the reasons outlined in the previous paragraph. In addition,

Fall 2010 surveys were right truncated at 600 m, while the truncation distance for all

25 other surveys was 425 m due to the change in distance categories made after the first field season (Tables 2 and 3).

We found model fit improved as the study progressed. P- values obtained from chi-square goodness-of-fit tests for individual surveys ranged from <0.001 to 0.81 with

10 of the 20 surveys having poorly fit models (<0.05 considered poor fit; Table 2). Of the 10 surveys with poorly fit detection models, 4 occurred in Fall 2010 and none occurred in Spring 2012. P- values for pooled analyses ranged from <0.001 to 0.720

(Table 3). Of the 20 pooled analyses performed, 4 had poorly fit detection models, and of these 4 analyses, 3 occurred in Fall 2010.

Abundance estimates for individual surveys ranged from a low of 13,293 on 29

April 2011 to a high of 468,129 on 15 November 2012 (Table 4). Abundance estimates were much higher during fall migration than during spring migration. For example, only once did the abundance estimate exceed 200,000 birds during spring (19 March 2012) in contrast to fall when estimates often exceeded 300,000 birds (Table 4). Pooled duck-use- day estimates by lake exceeded 10 million in both Fall 2010 and Fall 2011 and were under 3 million for both Spring 2011 and Spring 2012 on Lake St. Clair (Fig. 3). The seasonal difference on western Lake Erie was less apparent with use-day estimates remaining between 1 million and 3.7 million throughout the 4 migration periods (Figure

3). All 3 species showed similar trends and were much more abundant in fall than in spring (Fig. 4). Canvasback and scaup use peaked in Fall 2011 with an estimated 4 million and 6.2 million use-days respectively (95% CI: + 3,041,018 and +2,241,565) and redhead use peaked in Fall 2010 with approximately 3.5 million estimated use-days (95%

CI: + 1,824,052; Fig. 4).

Survey n chi-square det. % CV Model date w (m) before n after model selected p prob. (b) weight 10/18/2010 500 18 18 hazard rate + no covariates 0.032 0.30 0.593 0.97 10/29/2010 500 65 65 half norm + clustersize 0.004 0.27 0.347 0.45 11/08/2010 500 122 122 half norm + clustersize 0.090 0.37 0.704 0.74 11/16/2010 500 137 137 hazard rate + no covariates <0.001 0.37 0.393 0.98 12/03/2010 500 159 159 half norm + clustersize 0.011 0.32 0.567 0.83 03/25/2011 425 203 196 half norm + clustersize + observer 0.256 0.41 0.318 0.87 04/01/2011 425 187 182 half norm + clustersize 0.009 0.50 0.535 0.30 04/13/2011 425 150 149 half norm + no covariates 0.296 0.41 0.377 0.59 04/21/2011 425 182 181 half norm + no covariates 0.015 0.39 0.257 0.56 04/29/2011 425 161 160 half norm + clustersize 0.029 0.38 0.327 0.35 10/24/2011 425 104 104 half norm + clustersize 0.023 0.37 0.543 0.68 10/31/2011 425 136 134 half norm + clustersize 0.007 0.38 0.336 0.60 11/15/2011 425 102 99 half norm + clustersize 0.062 0.44 0.328 0.56 11/23/2011 425 145 144 half norm + clustersize + observer 0.044 0.41 0.291 0.48 12/01/2011 425 117 116 half norm + clustersize + observer 0.083 0.34 0.757 0.55 03/19/2012 425 167 165 half norm + clustersize 0.084 0.33 0.185 0.59 03/26/2012 425 241 241 half norm + clustersize 0.818 0.30 0.325 0.58 04/04/2012 425 212 210 half norm + clustersize + observer 0.332 0.30 0.302 0.65 04/11/2012 425 167 167 half norm + clustersize 0.158 0.26 0.297 0.54 04/18/2012 425 133 133 half norm + clustersize 0.542 0.26 0.267 0.44

a Fall 2010 w n n chi- det. % Model (m) before after model selected square p prob. CV weight Lake St. Clair 500 410 410 half norm + clustersize <.001 0.31 0.161 <.01 Lake Erie 500 89 89 half norm + clustersize <.001 0.45 0.441 <.01 Canvasbacks 500 95 95 half norm + clustersize 0.055 0.36 0.264 0.41 Scaup 500 173 173 half norm + clustersize <.001 0.33 0.233 <.01 Redheads 500 72 72 half norm + clustersize 0.108 0.36 0.478 0.20

b. Spring 2011 w n n chi- det. % Model (m) before after model selected square p prob. CV weight Lake St. Clair 425 669 656 half norm + clustersize 0.594 0.41 0.236 0.99 Lake Erie 425 214 212 half norm + clustersize 0.161 0.43 0.358 0.70 Canvasbacks 425 17 17 half norm + no covariates 0.363 0.38 0.403 0.58 Scaup 425 614 608 half norm + clustersize 0.128 0.41 0.625 0.99 Redheads 425 117 116 half norm + no covariates 0.594 0.43 0.238 0.51

c. Fall 2011 w n n chi- det. % Model (m) before after model selected square p prob. CV weight Lake St. Clair 425 488 483 half norm + clustersize 0.515 0.40 0.345 0.99 Lake Erie 425 113 110 half norm + clustersize 0.031 0.34 0.472 0.99 Canvasbacks 425 131 130 half norm + clustersize 0.524 0.42 0.577 0.98 Scaup 425 280 278 half norm + clustersize 0.143 0.37 0.262 0.99 Redheads 425 203 201 half norm + clustersize 0.720 0.40 0.334 0.97

Table 3 (cont’d)

d. Spring 2012 w n chi- det. % Model (m) before n after model selected square p prob. CV weight Lake St. Clair 425 615 609 half norm + clustersize 0.313 0.30 0.233 0.99 Lake Erie 425 316 316 half norm + clustersize 0.318 0.27 0.241 0.79 Canvasbacks 425 59 59 half norm + clustersize 0.681 0.30 0.387 0.42 Scaup 425 609 608 half norm + clustersize 0.545 0.27 0.204 0.99 Redheads 425 20 20 half norm + no covariates 0.231 0.30 0.717 0.43

Survey date N LCL N N UCL 10/18/2010 67,441 173,009 491,100 10/29/2010 186,121 404,689 731,621 11/08/2010 99,007 212,014 701,158 11/16/2010 195,261 390,501 803,236 12/03/2010 144,746 219,845 456,556 03/25/2011 88,049 155,258 239,258 04/01/2011 37,856 95,105 272,086 04/13/2011 15,404 34,696 58,651 04/21/2011 19,821 28,206 37,975 04/29/2011 6,200 13,293 24,868 10/24/2011 135,213 255,781 644,961 10/31/2011 141,582 284,923 510,866 11/15/2011 287,394 468,129 728,703 11/23/2011 141,500 265,040 406,176 12/01/2011 103,525 351,709 875,524 03/19/2012 146,267 214,534 291,114 03/26/2012 42,156 103,149 170,689 04/04/2012 57,042 120,563 191,183 04/11/2012 12,793 28,554 45,184 04/18/2012 8,216 15,799 24,879

We encountered a group of >10,000 birds on at least 1 survey in 3 of the 4 migration periods. Furthermore, no more than 5 of these >10,000 bird groups were detected on any one survey (see Appendix D for all censused large group information).

A total of 26 groups of >10,000 birds were observed and most of these were observed during fall migrations (23 groups: Appendix D). As previously described, when we encountered one of these groups we deviated from the transect line to obtain a better ocular estimate and to obtain a distance-based estimate when possible. In general these two estimates were comparable, although ocular estimates where often lower than distance based estimates (Table 5). A no-intercept linear regression model predicting ocular-estimated size of large groups from distance sampling-based estimates indicated

2 the 2 methods for estimating large group size were highly correlated (R = 0.98); however, the estimate of the slope of the relationship indicated that ocular estimates were  consistently lower than distance-based estimates ( B = 0.74; SE = 0.044).

Table 5. Survey date, ocular estimate, distance based estimate, and area covered for flocks containing > 10,000 individuals observed during the two year study.

area covered Survey ocular distance based 2 date estimate estimate (km ) 04/13/2011 17,500 21,248 3.9 04/21/2011 10,000 9,800 2.6 10/24/2011 55,000 62,249 8.3 10/24/2011 25,000 25,829 5.3 11/15/2011 90,000 130,551 6.5

Discussion

We found the half normal model with flock size included as a covariate to be most appropriate for modeling decreasing detection probabilities with increasing distance from the transect for diving duck flocks on Lake St. Clair and western Lake Erie. We found detection probability often declined rapidly beginning in the third distance category (approximately 125 m from the transect line), especially for groups containing fewer than 100 birds (see Appendix B and C). Given the objective of this research was to establish a novel survey technique, we were unsurprised by the poor model fit that occurred at the outset of this project; however, we did gain experience and proficiency as the study progressed as is evidenced by the much improved model fit that was observed at the end of the project. Based on our results, we believe distance sampling is an improved alternative to census surveys so long as we are able meet the methodological assumptions.

All effective line-transect surveys must meet the 3 assumptions outlined in

Buckland et al. (2001). The first assumption that no objects on the transect line go undetected is most important, and although we were not able to explicitly test this assumption we did not find any evidence of violation of this assumption except in Fall

2010. On all pooled and individual surveys our detection function was relatively constant near the transect with detection probability often exceeding 0.90 at a distance of 50 m

(Appendix A). These results indicate observers were likely not systematically missing diving duck flocks on or near the transect line. Some potential does exist for diving ducks to be underwater and thus unavailable for detection; however, we observed the vast

33 majority of birds were either loafing or ceased any feeding activities when alerted to the presence of the plane making it unlikely we were missing birds due to diving activity.

Given that most diving ducks occurred in flocks, it is even more unlikely that entire groups were missed as birds either swam or flew away from the plane as opposed to diving in unison as the plane approached. Furthermore, our study consisted exclusively of open water, making diving ducks much more visible than other more cryptic species, suggesting that any possible violation of this first assumption was minimal.

Although some birds did flush from the aircraft, we believe any violation of the second assumption of responsive movement was minimal for two reasons: 1) the speed of the aircraft was great enough that evasive movements by flocks did not occur until we were relatively close and 2) the bubbled windows of the aircraft allowed observers to look forward and record the original position of any flushing birds, thus negating the effects of evasive movements. Finally, we met the third assumption of accurate distance measurements by establishing distance bands using marks on the windows and struts of the plane (Buckland et al. 2001). Given our specific situation, the largest obstacle to obtaining abundance estimates was not the violation of one of the three fundamental distance sampling assumptions, but rather extreme variation in observed flock size and the challenges presented by this variation during data analysis.

Distance sampling methodology has readily been adapted to dealing with clustered populations. Under these circumstances the objects of interest become the groups of animals rather than individuals, and to obtain abundance estimates average group size must be estimated in addition to modeling the detection function (Buckland et al. 2001). As one can imagine, a flock of 5,000 ducks likely has a much higher detection

34 probability than does a group of 2 ducks located the same distance from the transect line.

This phenomenon causes an overestimation of the number of large groups and artificially inflates average flock size causing an overestimation of abundance (Buckland et al.

2001). For this reason, we abandoned conventional distance sampling techniques in favor of the Multiple Covariate Distance Sampling (MCDS) analysis approach (Buckland et al. 2004). MCDS allowed us to simultaneously account for distance from groups to the transect line and flock size. Under these circumstances, flock size was treated as a continuous variable and a separate detection function could then be estimated for each individual flock size (Fig. 5; Marques et al. 2007).

Relatively unbiased estimates of flock size are key to our current methodology of estimating diving duck abundance for 2 reasons: 1) they allow for an improved parameter estimate for the effect of flock size on detection probability and 2) without unbiased estimates, our complete count of individuals contained in groups of >10,000 birds is not a valid census. To illustrate potential problems caused by inaccurate or biased estimates of flock size, Frederick et al.’s (2003) simulation study found that observers underestimated true numbers by 29% when dealing with flocks ranging from 200-6000 individuals. The focal point for future improvement of our current methodology is an evaluation of observer’s ability to estimate flock size. Some potential means to accomplish this may involve the development of correction factors based on comparing observer counts to still photographs, the use of infrared videography, or extensive training with the wildlife software Counts (Bajzak and Piatt 1990; Erwin 1982; Haramis et al. 1985; Kinzel et al.

2007; Hodges 1993 ). Another approach that might benefit from additional study is the use of distance sampling of large flocks as we did with a subset of flocks in our study, although it should be noted this often proved difficult if not impossible due to responsive movement of the birds caused by the circling aircraft (Table 5). The wide range of densities we estimated within large flocks (Table 5: 3,769 – 20,084 birds/km2) suggests that spacing of birds is variable and should be considered when estimating flock sizes.

With continued improvement in our ability to estimate flock size, we are comfortable with our hybrid census and distance sampling methodology, and a similar hybrid methodology was used by Thomas et al. (2011) that added complete counts to distance estimates while studying cetaceans in British Columbia, Canada. When hybrid methodologies are used and large flock sizes are estimated (instead of taken as a census),

36 variances of overall abundance estimates should include the contribution of variance associated with large flock size estimation.

The importance of well-trained observers when employing distance sampling techniques cannot be understated. Without proper training, observers have a natural tendency to maximize their number of detections, which is often accomplished by searching for objects located long distances from the transect line (Swann et al. 2002;

Buckland et al. 2001). Although this search behavior results in more detections, it often leads to poor model fit of the detection function or even violation of the first and most important assumption of distance sampling. (Anderson et al. 2001: Buckland et al.

2001).

We believe observer inexperience was at least partially responsible for the poor model fit observed on some of the individual surveys (Table 2). In Fall 2010 we used 4 different combinations of observers on the 5 flights, all of whom were new to the concept of distance sampling. In comparison, we only used 3 different combinations of observers over the final 15 flights, all of whom were gaining experience in distance sampling techniques as the study progressed. Visual inspection of the distance data from Fall 2010 suggests that we encountered problems with both guarding of the transect (evidenced by a spike in the number of detections in the earlier distance categories followed by a rapid drop-off in later distance categories; Appendix B), and attempts to maximize detections likely resulting in a violation of the first and most important assumption of distance sampling (evidenced by a spike in either the second or third distance category with fewer observations recorded in the first distance category; Appendix B; Buckland et al. 2001,

Swann et al. 2002). As a result, rather than trying to fit a model to the poorly collected

37 data from Fall 2010 we chose to fit a model we believe would have best represented our data had we been using sound methodology based on data collected in the following 3 migration periods. We hope the problems we encountered in Fall 2010 serve as caution to those interested in distance sampling techniques. We cannot stress enough the importance of a pilot study that will allow researchers to familiarize themselves with their specific situation, and the importance of using a consistent group of observers who have knowledge of distance sampling’s basic assumptions.

Management Implications

All monitoring programs should be designed to accomplish the fundamental objectives in mind (Lancia et al. 2005). Consequently, careful consideration should be given to the type of monitoring technique employed given the overarching goals of the monitoring program (Lancia et al. 2005). We believe it is the resources manager’s and researcher’s responsibility to use the best available science to develop monitoring programs aimed at protecting resources, maintaining hunting opportunities, and ensuring sustainable harvest for all game species. With this in mind, we believe distance sampling is a viable option and offers an improved alternative to more traditional census counts or fixed-width estimates for open-water surveys of diving ducks or other waterbirds under some survey conditions (Ridgeway 2010). For example, when the target study area is extremely large distance sampling explicitly accounts for missed groups by estimating the detection probability. Furthermore, distance sampling may also account for varying survey conditions. For example, we found a strong negative correlation between wind speed and estimated detection probability on our individual surveys that is likely caused

38 by heavy chop on the lake surface hiding birds that typically would have been detected under calmer conditions (Fig. 6). Thus, poorer sighting conditions correctly resulted in a lower detection probabilities as estimated from the Distance software.

Detection Probability as a Function of Wind

0.6

0.5 R2 = 0.603

0.4

0.3

0.2 DetectionProbability 0.1

0 0 2 4 6 8 10 12 14 Wind Speed (mph)

Figure 6. Detection probability as a function of average wind speed recorded by the observers on the day of the survey.

Given the unknown detection probability and unknown error associated with census counts, these surveys could be of limited use to resource managers (Schmidt et al.

2012). It is becoming increasingly important to develop sound monitoring programs capable of answering the fundamental research and management questions at hand, and as both state and federal wildlife budgets are reduced, programs that are not able to answer these fundamental questions are at risk of being eliminated. We recommend at least a one-year pilot survey to establish distance methodology as every situation and monitoring program is unique. Furthermore, we encourage other agencies to test distance

39 sampling techniques for monitoring waterfowl or other waterbirds in open water situations, especially when the target study area is large and weather variables that could influence detection probability (e.g,. wind and sunlight) are expected to fluctuate among surveys.

The techniques we employed for estimating diving duck abundance during migration will be valuable in future conservation planning. There is growing interest in documenting spatial and temporal variation in waterfowl abundance during migration as resource managers attempt to plan and prioritize habitat conservation in relation to resources required to sustain migratory birds during their entire annual life cycle. It is unlikely that lower estimates of diving duck use of our study area during spring migration can be explained by overwinter mortality (Homan et al. 1993; Haramis et al. 1993). Our seasonal estimates of abundance suggest that diving ducks have fundamentally different habitat selection patterns during fall and spring migration. This has implications for wetland protection, restoration, and enhancement that should be used to meet the full annual life cycle needs of canvasbacks, scaup, and redheads, and we recommend continued monitoring of spring distribution and abundance as several recent studies suggest spring migration may be a limiting factor for waterfowl and especially for diving ducks (Anteau and Afton 2008, Brasher et al. 2007, Straub 2008).

APPENDICES

Appendix A.

Table 6. Candidate models examined prior to selection of models used to perform (a) individual surveys and (b) pooled analyses. We did not consider models for either set of analyses that did not include clustersize as a covariate except for the default hazard rate and half normal models because we were uncomfortable with the regression method for estimating average flock size. We also did not include observer as a covariate for pooled analyses because we had very few observations (>10) per area or species of interest for some observers. a. Candidate models for individual surveys (key function+series expansion+covariates) 1. half normal + cosine 2. hazard rate + cosine 3. half normal + cosine + clustersize 4. hazard rate+ cosine + clustersize 5. half normal + cosine + clustersize and observer 6. hazard rate + cosine + clustersize and observer

b. Candidate models for pooled analyses (key function+series expansion+covariates) 1. half normal + cosine 2. hazard rate + cosine 3. half normal + cosine + clustersize 4. hazard rate + cosine + clustersize

Appendix B.

1.6 1.4

a) 18 October 2010

1.2 1.0 0.8 0.6

0.4 0.2 Detection Probability 0.0 0 100 200 300 400 500 600 Perpendicular Distance (m)

1.2

1.0 b) 29 October 2010

0.8

0.6

0.4

etection Probability 0.2 D 0.0 0 100 200 300 400 500 600 Perpendicular Distance (m)

Figure 7 (cont’d)

1.2

1.0 c) 8 November 2010 0.8

0.6

0.4

0.2

Detection Probability 0.0 0 100 200 300 400 500 600 Perpendicular Distance (m)

1.2

1.0

d) 16 November 2010 0.8

0.6

0.4

0.2

Detection Probability 0.0 0 100 200 300 400 500 600 Perpendicular Distance (m)

Figure 7 (cont’d)

1.0

0.8 e) 3 December 2010

0.6

0.4

0.2

Detection Probability 0.0 0 100 200 300 400 500 600 Perpendicular Distance (m)

1.2

1.0 f) 25 March 2011 0.8

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 7 (cont’d)

1.4

1.2 g) 1 April 2011 1.0 0.8

0.6 0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

1.2

1.0 h) 13 April 2011 0.8

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 7 (cont’d)

1.0

0.8 i) 21 April 2011

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

1.0

0.8 j) 29 April 2011

0.6

0.4

0.2

Detection Probability 0.0

0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 7 (cont’d)

1.0 k) 24 October 2011 0.8

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

0.0

0.0 l) 31 October 2011

0.0

0.0 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 7 (cont’d)

1.2 m) 15 November 2011 1.0

0.8

0.6 0.4

ction ction Probability 0.2

Dete 0.0 0 50 100 150 200 250 300 350 400 450

Perpendicular Distance (m)

1.0

n) 23 November 2011

0.8

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 7 (cont’d)

1.0 o) 1 December 2011 0.8

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

1.2

1.0 p) 19 March 2012

0.8

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 7 (cont’d)

1.0

0.8 q) 26 March 2012

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

1.0

0.8 r) 4 April 2012

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 7 (cont’d)

1.0

0.8 s) 11 April 2012

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

1.0

0.8 t) 18 April 2012 0.6

0.4

0.2 Detection Probability 0.0

0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Appendix C.

1.0

a) Lake St. Clair: Fall 2010 0.8

0.6

0.4

0.2 Detection Probability 0.0 0 100 200 300 400 500 600 Perpendicular Distance (m)

1.2

1.0 b) Lake St. Clair: Spring 2011

0.8

0.6

0.4 0.2

Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 8 (cont’d)

1.0 c) Lake St. Clair: Fall 2011 0.8

0.6

0.4

0.2

Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

1.0

0.8 d) Lake St. Clair: Spring 2012

0.6

0.4 on on Probability

0.2

Detecti 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 8 (cont’d)

1.4 1.2 e) Lake Erie: Fall 2010 1.0 0.8

0.6

0.4

0.2 Detection Probability 0.0 0 100 200 300 400 500 600 Perpendicular Distance (m)

1.2 f) Lake Erie: Spring 2011 1.0

0.8

0.6

0.4

0.2 Detection Probability 0.0

0 50 100 150 200 250 300 350 400 450 Perpendicular0 Distance (m)

Figure 8 (cont’d)

1.0

g) Lake Erie: Fall 2011 0.8

0.6

0.4

0.2

Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

1.0

0.8 h) Lake Erie: Spring 2012

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 8 (cont’d)

1.0

i) Canvasbacks: Fall 2010 0.8

0.6

0.4

0.2

Detection Probability 0.0

0 100 200 300 400 500 600 Perpendicular Distance (m)

1.2

j) Canvasbacks: Spring 2011 1.0

0.8

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 8 (cont’d)

1.0 k) Canvasbacks: Fall 2011 0.8

0.6

0.4

0.2

Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

1.0

l) Canvasbacks: Spring 2012 0.8

0.6

0.4

0.2

Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 8 (cont’d)

1.0

0.8 m) Scaup: Fall 2010

0.6

0.4

0.2

Detection Probability 0.0 0 100 200 300 400 500 600 Perpendicular Distance (m)

1.2 n) Scaup: Spring 2011 1.0

0.8

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 8 (cont’d)

1.0 o) Scaup: Fall 2011 0.8

0.6

Probability 0.4

0.2

Detection 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

1.0

0.8 p) Scaup: Spring 2012

0.6

0.4

0.2

Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 8 (cont’d)

1.0 q) Redheads: Fall 2010 0.8

0.6

0.4

0.2 Detection Probability 0.0 0 100 200 300 400 500 600

Perpendicular Distance (m)

1.2

1.0 r) Redheads: Spring 2011

0.8

0.6

0.4

0.2 Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450 Perpendicular Distance (m)

Figure 8 (cont’d)

1.2

1.0 s) Redheads: Fall 2011

0.8

0.6

0.4 0.2

Detection Probability 0.0 0 50 100 150 200 250 300 350 400 450

Perpendicular Distance (m)

1.0

0.8 t) Redheads: Spring 2012

0.6

0.4

0.2 Detection Probability 0.0

0 50 100 150 200 250 300 350 400 450

Perpendicular Distance (m)

Appendix D.

Table 7. Survey date, the number of censused large groups (>10,000 birds), and the ocular estimate of the number of individuals contained in those large groups.

Survey Number of censused Estimated Number in large date large groups Groups 10/18/2010 1 15,000 10/29/2010 1 20,000 11/08/2010 2 47,000 11/16/2010 1 20,000 12/03/2010 4 93,500 03/25/2011 1 25,000 04/01/2011 0 0 04/13/2011 1 17,500 04/21/2011 1 10,000 04/29/2011 0 0 10/24/2011 2 80,000 10/31/2011 1 10,000 11/15/2011 5 131,000 11/23/2011 2 30,000 12/01/2011 4 61,000 03/19/2012 0 0 03/26/2012 0 0 04/04/2012 0 0 04/11/2012 0 0 04/18/2012 0 0

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CHAPTER 3. MODELING DIVING DUCK DISTRIBUTIONS ON LAKE ST. CLAIR AND WESTERN LAKE ERIE

Introduction

Understanding ecological and anthropogenic factors affecting species distributions is fundamental to wildlife science (Cody 1985; Morrison et al. 1992).

Effective management actions are aided by a clear understanding of mechanisms that ultimately determine whether or not a species can flourish under any given habitat conditions (Morrison et al. 1992), and it is becoming increasingly important to understand the driving factors underlying species distributions as human development continues to alter and destroy wildlife habitat. An ever growing number of wildlife populations are limited by a lack of quality habitat and habitat limitations are further exacerbated by human-induced landscape changes (Hall et al. 2000). Consequently, species specific research is needed to build the scientific foundation for management techniques that can be used to most efficiently and effectively mitigate effects of habitat loss.

Historically, Lake St. Clair and western Lake Erie have daily hosted an estimated

250,000 to 750,000 diving ducks during peak fall migration with canvasbacks (Aythya valisineria), lesser scaup (Aythya affinis), greater scaup (Aythya marila), and redheads

(Aythya americana) being prominent (Soulliere et al. 2000; Miller 1943). As a guild, relatively little research has been conducted on diving ducks in comparison to their dabbling duck counterparts. However, both canvasbacks and lesser scaup have been identified as species of conservation priority by the Upper Mississippi River Great Lakes

Region Joint Venture, and have received additional attention in recent years due to

68 population concerns (UMRGLJV 2007). For example, the breeding population estimate for scaup (lesser and greater combined) in 2011 was 4.3 million birds, which was 15% below the long term average (USFWS 2011). In addition, the breeding population estimate for canvasbacks was 690,600, which was one of the smallest estimated populations of any North American duck species within the traditionally surveyed region of the United States Fish and Wildlife Service Waterfowl Breeding and Habitat Survey

(USFWS 2011).

The historically low canvasback population and continued decline of scaup populations is a concern to conservationists both inside and outside the waterfowl management community. Diving ducks occupy a distinct ecological niche, depending on deep, permanent wetlands for much of their annual life cycle (Korschgen 1989;

UMRGLJV 2007). Few habitat types have undergone as extensive degradation and destruction as wetlands, and by conserving and improving habitat for diving ducks wetland managers can benefit a host of other wetland species (Campbell 1995; Karr et al.

1985; Woltermade 1997). Unfortunately, our efforts to protect and improve diving duck habitat are hampered by a continued decline in the people most interested in conserving diving ducks and who largely fund our conservation efforts. In Minnesota, Wisconsin, and Michigan, three states known for providing exceptional diving duck hunting opportunities, duck stamp sales have decreased approximately 50% from 1970 to 2009 indicating a significant loss in the number of duck hunters within these 3 states (Fronczak

2011).

The 2 main objectives of this research are to: 1) use historical aerial survey data

(1983-2008) to investigate fall diving duck distributions on Lake St. Clair and how

69 distributional changes might have been linked to the driessenid mussel invasion of the late 1980’s, and 2) use current survey data (2010-2012) to map fall and spring diving duck distribution on Lake St. Clair and Lake Erie and to develop explanatory models of diving duck distributions based on anthropogenic and environmental variables. Ducks aggregate in flocks during migration and the second objective offered an opportunity to apply hierarchical Bayesian statistical methods to model inherently contagious distributions of diving ducks during migration. Hierarchical Bayesian models have an advantage over traditional non-spatial models by allowing for explicit accounting of spatial autocorrelation that should reduce the risk of identifying spurious relationships between response and explanatory variables (Cressie et al. 2009; Legendre and

Troussellier 1988).

Detailed results from the first objective are currently in press in the book chapter,

Impacts of dreissenid mussels on diving duck distribution and abundance on Lake St.

Clair in Quagga and Zebra Mussels Biology, Impact, and Control the 2nd Edition

(Luukkonen et al. 2012). Since many of the results that pertain to the first objective are already being published, the historical section of this thesis will be brief and for those seeking more detail we will reference the book chapter; however, we include some historical findings because our second objective builds upon findings from the historic monitoring. Ultimately, our goal is to build a foundation for improved management of diving duck habitat, with the potential to manage and mitigate effects of human disturbance and habitat degradation in the lower Great Lakes.

Study Area and Methodology

Lake St. Clair and western Lake Erie are shallow, highly productive lake basins and the study area is dominated by open water < 10 meters deep. Lake St. Clair

2 encompasses an area approximately 1149 km and the international border divides the lake with the northwestern one-third in the United States and the southeastern two-thirds in Canada. Historical surveys were flown only over U.S. waters of Lake St. Clair during fall (Fig. 7); however, our more recent surveys were expanded to cover all of Lake St.

Clair and portions of western Lake Erie and included fall and spring migration seasons.

2 Surveyed area on western Lake Erie includes 621 km extending southward from the mouth of the Detroit River to Maumee Bay near Toledo, Ohio.

Methodology: Historical Surveys (1983-2008)

Historical surveys (1983-2008) were flown to monitor diving duck abundance within U.S. waters of Lake St. Clair during fall migration. The fall migration period was defined as October 22 to December 2. Survey protocol was similar to protocols used during current surveys; however, observers focused on detecting large diving duck flocks

( > 200 birds) and counts were assumed to be a complete census of the U.S. portion of the lake. Flock locations were recorded on a paper map of Lake St. Clair and then digitized in ArcGIS (ESRI 2006). We used generalized linear models to analyze the effects of dreissenids and recreational boats on diving duck abundance on the U.S. side of the lake, and we included a categorical week variable to account for variation in counts due to the timing of migration. We also used a bathymetric layer (NOAA 2011) to analyze potential shifts in canvasback, scaup, and redhead distribution in relation to water

71 depth, as we hypothesized that increasing water clarity due to the dreissenid mussel invasion could have offered expanded foraging opportunities in deeper water. We also used ArcGIS to map distributions of diving ducks from 1983-1988 (pre-dreissenid),

1989-1994 (post-dreissenid), 1995-2000 (post), 2001-2008 (post). We fit kernel density models by species (canvasback, redhead, and scaup) and year interval and mapped model estimates using the spatial modeling feature in ArcGIS (ESRI 2006).

Methodology: Current Surveys (2010-2012)

We used Hawth’s tools in ArcGIS to establish a systematic line transect survey with a random start point during Fall 2010 (ESRI 2006, Beyer 2004). Total survey area included 1149 km2 on Lake St. Clair and 621 km2 on western Lake Erie. We established

26 line transects oriented in an east west fashion from the northern end of Lake St. Clair to the southern shore of Lake Erie (see Fig. 1 from Chap. 2). Transects on Lake St. Clair ran from shoreline to shoreline and transects on western Lake Erie ran from the western shoreline of Lake Erie to approximately 16 km offshore and transects were spaced 3.2 km apart. Line transects were used to collect distribution data because this study was done in conjunction with research aimed at developing aerial survey protocols for estimating diving duck abundance using distance sampling techniques. We conducted all flights with an amphibious DHC-2 De Havilland Beaver. All surveys began at approximately 9:00 a.m. with the exception of the 19 March 2012 survey that was delayed nearly 3 hours due to fog. Surveys were generally completed in under 7 hours and flights were not conducted under inclement weather conditions such as excessive wind (>30 mph), precipitation, or fog, thus data collected may not be representative of diving duck

72 distributions under inclement weather conditions. The pilot flew at a target altitude of 90 meters and ground speed of approximately 150 km/h. Two pilots shared command of the aircraft and we used two observers on each flight with each observer being responsible for one side of the plane. We flew a total of 20 surveys, five during each of the four monitored migration periods (Fall 2010, Spring 2011, Fall 2011, and Spring 2012), and fall survey dates ranged from 18 October to 3 December while spring survey dates ranged from 19 March to 29 April.

We recorded each observation using a GPS system with audio recording capability. Due to GPS failures during our first field season one observer used a Nomad unit connected to a Garmin 10.0 wireless GPS, while the other observer used a Columbus

V-900 data logger. While flying, we recorded GPS locations for diving duck flocks, recreational boats, and hunting parties. All GPS data was downloaded from either the

Columbus V-900 or the Nomad onto a laptop computer and imported for use in ArcGIS

(ESRI 2006). We used the kernel density routine available in ArcMap to show areas of diurnal diving duck concentration on our study area (ESRI 2006). Pixel size was 60 m by

60 m and we used a search radius of 2500 m to generate the maps. Data within a migration period was pooled to generate kernel density maps for each of the 4 migration periods that were monitored (Fall 2010, Spring 2011, Fall 2011, and Spring 2012).

For predictive modeling purposes, we divided the surveyed area along transects into 1.6 km by 1.6 km sites. We monitored 205 sites on Lake St. Clair and 112 sites on western Lake Erie. Most sites were monitored 19 times (we lost all GPS data for one survey due to GPS failures) over the course of the two year study. Associated with each site were the explanatory variables average water depth, average distance to shore,

73 recreational boat counts, and an aquatic plant species richness count mapped by other researchers (Thomas and Hass 2012; NOAA 2011) and the response variables canvasback counts, scaup counts, and redhead counts. Extreme variation in diving duck count data proved to be problematic for our modeling purposes, and because the main focus of this research was to explore diving duck distribution, we chose to use logistic models and combine all diving duck species into a single presence/absence response variable to develop diving duck distribution models.

The purpose of our hierarchical spatial models was to use observed diving duck distribution data to draw inference about spatial patterns we observed while accounting for any underlying autocorrelation in the data. Each of the 19 surveys was examined independently, and the first stage of the hierarchical model assumes the observed responses are conditionally independent and adds spatial random effects to the structure of the model (Finley et al. 2008). The second stage directly addresses the nature of the association between the spatial random effects and the third and final stage of the model is completed by designating prior distributions for model parameters (Finley et al. 2008).

Thus, for our diving duck models the probability of any given site (i) being occupied by ducks p(si) depends upon the spatially referenced explanatory variables x(si), the transpose (T) of the regression slope parameters θ, and the location specific random effects w(si) which yields:

T p(si) = exp(x(si) θ + w(si)) T 1 + exp(x(si) θ + w(si))

All analyses were implemented in the spBayes package for the statistical software R, and we used Markov chain Monte Carlo methods to generate posterior distributions for the parameters of interests (Finley et al 2007; R Development Core Team 2008). We averaged Bayesian parameter estimates from individual surveys over a given migration period (Fall 2010, Spring 2011, Fall 2011, and Spring 2012), and calculated associated variances based on empirical standard deviations and methods described in Thompson

(1992). We used the Bayesian parameter estimates to develop predictive models for the probability of diving duck occurrence during any given migration period based on the averaged parameter estimates for distance to shore, water depth, plant species richness and the subsequent logistic equation. We then mapped (using ArcGIS: ESRI 2006) predicted probabilities of diving duck occupancy for each migration period based on logistic equations and covariate values of sites across the entire study area.

In addition to our traditional diurnal surveys, we were also able to fly one nocturnal survey using forward-looking infrared videography (hereafter FLIR). We conducted this survey on the 27 November 2010 and were able to cover Lake St. Clair only, flying the same transects that were used during diurnal surveys. The angle of view on the camera was 17.6° and the forward looking angle was 45°. The target altitude was

152 m and based on camera angles and target altitude the strip width of our survey was

67 m. Ducks were readily visible and distinguishable from other bird species on the

FLIR video based on size and flight characteristics, and we made the assumption that ducks observed were diving ducks. We were able to take GPS recordings in-flight, and we were later able to associate a count with each GPS recording by reviewing the video

75 in the lab. This count and GPS data was then used to create a nocturnal kernel density map in ArcGIS (ESRI 2006).

Results

Results: Historical Surveys (1983-2010)

MDNR staff completed 99 diving duck surveys of the U.S. portion of Lake St.

Clair during fall migrations, 1983-2008. We found a significant increase in canvasback and scaup abundance during the post-dreissenid era (1989-2008) while redhead abundance remained largely unchanged (Luukkonen et al. 2012). We also found significant negative effects of recreational boats as the generalized linear models indicated several hundred fewer diving ducks of each species for each additional boat counted during the surveys (Luukkonen et al. 2012). Weekly abundance of canvasback and redhead peaked in mid-November but no significant trend was documented from week to week for scaup (Luukkonen et al 2012). Water depth analyses revealed an increased use by canvasback and scaup of intermediate water depths (2-4 m deep) during the post-dreissenid era but no real shift in redheads, which remained closely tied to shallow water (< 2 m deep) throughout the study (Luukkonen et al. 2012).

In addition to changes in abundance, we observed different spatial use patterns of

Lake St. Clair by diving ducks among years. Canvasback and scaup distribution expanded rapidly after dreissenid invasion with the broadest distribution coinciding with peak abundance estimates obtained in the 1995-2000 year interval (Luukkonen et al.

2012). Although canvasback and scaup concentrated in the relatively shallow Anchor

Bay throughout the study, there was increased use of deeper, offshore waters southeast of

76 the Clinton River in later years of the study (Luukkonen et al. 2012). In contrast, redhead distribution much like redhead abundance remained relatively unchanged after dreissenid invasion and redheads remained closely tied to shallow waters of western Anchor Bay

(Luukkonen et al. 2012).

Results: Current Surveys (2010-2012)

We completed 5 surveys during each of the 4 migration periods (Fall 2010, Spring

2011, Fall 2011, and Spring 2012) for a total of 20 surveys. The pilot flew 531.5 km of transect on each survey. Often we recorded multiple diving duck flocks or multiple recreational boats at the same site, but since we used presence/absence data for predictive modeling purposes we reported the percent of sites occupied by ducks and the percent of sites occupied by recreational boats. Percent of sites occupied by ducks ranged from

17.2% to 22.0% on Lake St. Clair and 7.6% to 18.0% on western Lake Erie.

Furthermore, the percent of sites occupied by boats ranged from 1.2% to 4.0% on Lake

St. Clair and 2.1% to 4.5% on western Lake Erie (Table 8).

Lake St. Clair western Lake Erie % % % % occupied occupied occupied occupied Migration period by ducks by boats by ducks by boats Fall 2010 17.2% 4.0% 7.6% 4.0% Spring 2011 18.8% 1.2% 15.4% 2.1% Fall 2011 22.0% 4.5% 11.4% 3.8% Spring 2012 18.8% 2.3% 18.0% 4.3%

Diving duck use of the study area was higher in fall migration compared to spring

(Fig. 9). Areas of high concentration in fall were quite dynamic with large numbers of birds detected well offshore in Canadian waters of Lake St. Clair, south of the Clinton

River in the U.S. portion of Lake St. Clair, and north of Maumee Bay in offshore waters of Lake Erie (Fig.9). Areas of high concentration in spring often occurred along the eastern shore of Lake St. Clair and along the southern and western shores of Lake Erie around Maumee Bay (Fig. 9). Furthermore, distribution of diving ducks was more restricted on western Lake Erie in comparison to Lake St. Clair as large expanses of water between the lower Detroit River and Maumee Bay were virtually unused (Fig. 9).

From our hierarchical spatial models, we found plant species richness to be the most consistent predictor of diving duck occurrence with higher plant richness being associated with higher probabilities of diving duck occurrence (Table 9). We also found water depth to be a statistically significant predictor on 5 of 10 spring surveys with shallower water being associated with higher probabilities of diving duck occurrence

(Table 9). Diving ducks were consistently associated with shallower water during spring migration; however, no real pattern was detected in relation to water depth during fall migration. Distance to shore was rarely significant, although diving ducks were consistently associated with sites located further from shore in Spring 2012 (Table 9).

Boat presence was often significant with presence of boats essentially eliminating diving ducks use of sites, and we often had surveys where diving ducks were never encountered in the presence of boats (Table 9).

Figure 9 (cont’d)

Distance to shore Plant Species Intercept Boats (Km) Water Depth (ft) Richness Survey Bˆ Season Date 50% b SD 50% b SD 50% b SD 50% b SD 50% b SD Fall 10/18/2010 *-3.42 0.98 *-353 202 0.240 0.139 -0.102 0.081 0.25 0.163 Fall 11/08/2010 *-1.39 0.65 -0.864 1.42 -0.111 0.099 0.008 0.049 0.06 0.113 Fall 11/16/2010 *-4.601 0.95 *-349 206 -0.005 0.030 -0.001 0.057 *0.63 0.150 Fall 12/03/2010 *-2.54 0.58 *-346 204 -0.002 0.079 0.047 0.042 *0.27 0.096 Spring 03/25/2011 *-2.27 0.93 *-351 207 -0.075 0.189 *-0.199 0.086 *0.48 0.161 Spring 04/01/2011 *-1.05 0.65 0.900 1.91 -0.041 0.098 -0.033 0.050 0.06 0.110 Spring 04/13/2011 *-0.87 1.04 *-348 204 -0.113 0.165 -0.120 0.071 *0.32 0.141 Spring 04/21/2011 *-3.09 0.96 *-353 203 0.014 0.122 -0.043 0.061 *0.44 0.139 Spring 04/29/2011 *-2.78 1.18 2.121 1.88 -0.022 0.131 -0.087 0.065 *0.50 0.146 Fall 10/24/2011 *-4.37 1.68 *-348 203 *0.355 0.229 -0.037 0.090 0.43 0.197 Fall 10/31/2011 *-3.32 1.07 -0.515 1.05 0.039 0.101 0.004 0.058 *0.45 0.149 Fall 11/15/2011 *-2.49 0.82 -1.656 1.39 -0.035 0.101 0.018 0.050 *0.30 0.120 Fall 11/23/2011 *-2.36 0.67 *-348 205 -0.079 0.092 0.067 0.050 *0.21 0.111 Fall 12/01/2011 *-4.39 0.89 *-357 203 0.102 0.096 0.007 0.056 *0.58 0.140 Spring 03/19/2012 *-0.91 0.57 -0.700 1.46 0.030 0.098 *-0.102 0.051 0.15 0.113 Spring 03/26/2012 *-3.21 1.00 *-359 205 *0.278 0.125 *-0.139 0.068 *0.48 0.133 Spring 04/04/2012 *-2.06 0.70 *-354 203 *0.281 0.106 *-0.163 0.063 *0.30 0.118 Spring 04/11/2012 *-2.97 0.77 *-351 203 *0.524 0.132 *-0.211 0.066 *0.37 0.155 Spring 04/18/2012 *-2.65 1.41 -0.491 1.60 0.228 0.143 -0.105 0.072 0.26 0.159

By averaging Bayesian parameter estimates over migration periods, we found plant species richness was consistently, positively associated with diving duck occurrence and presence of boats was consistently, negatively associated with diving duck occurrence (Table 10). Diving ducks were positively associated with increasing distance from shore except during Spring of 2011, and diving ducks were most often positively associated with decreasing water depth except during Fall 2011 (Table 10). Predictive probabilities were mapped (Fig. 10) based on season-averaged parameter estimates from

Table 9; however, we fixed the level of the boat covariate at zero because any boat traffic essentially resulted in no probable use of a site by diving ducks and presence of boats was highly variable among surveys and sites.

Fall Spring Fall Spring Covariates 2010 2011 2011 2012 UCL -2.19716 -1.16771 -2.4404 -1.5408 Intercept -2.989 -2.016 -3.391 -2.364 LCL -3.78084 -2.86429 -4.3416 -3.1872 UCL -89.4282 -71.191 -73.151 -75.028 Boats -262.555 -210.155 -211.429 -213.306 LCL -435.682 -349.119 -349.707 -351.584 UCL 0.123296 0.080204 0.194992 0.37482 Dist. To shore 0.03 -0.047 0.077 0.268 (Km) LCL -0.0633 -0.1742 -0.04099 0.16118 UCL 0.046016 -0.03781 0.06688 -0.0852 Water depth (ft) -0.012 -0.097 0.012 -0.144 LCL -0.07002 -0.15619 -0.04288 -0.2028 UCL 0.438536 0.500632 0.5294 0.438952 Plant Spec. 0.308 0.365 0.402 0.319 Richness LCL 0.177464 0.229368 0.2746 0.199048

Figure 10 (cont’d)

Results from our nocturnal survey point toward a much different nocturnal distribution for diving ducks than diurnal distribution. Kernel density estimates for the nocturnal survey are not directly comparable to density estimates obtained for diurnal surveys; however, the nocturnal kernel density map does highlight areas of high concentration that included western Anchor Bay and the near-shore waters of both the

U.S. and Canadian side of the lake (Fig. 11). Few birds were detected in either the offshore waters of Canada or the U.S., which is where the majority of birds were observed during diurnal surveys. These results seem to match local hunter observations of large pre-dawn flights by diving ducks leaving shallow, near-shore waters where they presumably had been feeding during the night.

Figure 11. Nocturnal distribution of diving ducks observed on Lake St. Clair on 27 November 2010 using FLIR technology and interpolated using kernel density 2 models (diving ducks/km ).

Discussion

Historical analyses revealed an increase over time in scaup and canvasback abundance, whereas redhead abundance remained unchanged. In addition, both canvasbacks and scaup concentration areas shifted from shallow water depths (0-2 m) to intermediate water depths (2-4 and 4-6 m), while redheads remained closely tied to shallow water throughout the study period. We found abundance of all three diving ducks species was inversely related to recreational boating pressure within U.S waters of Lake

St. Clair.

We believe increases in scaup and canvasback abundance as well as distributional shifts to deeper water may indicate increased food availability on Lake St. Clair. One possible explanation for increasing amounts of food during the historic study period was the invasion of dreissenid mussels in the mid 1980’s (Luukkonen et al. 2012).

Dreissenids became a new and abundant food source for the more carnivorous scaup as found by Custer and Custer (1996) and Hamilton et al. (1994). Furthermore, dreissenids may have had indirect impacts on more herbivorous diving ducks like canvasbacks and redheads. Although not targeted as a new food resource by these birds, dreissenids were the likely cause of a two-fold increase in water clarity documented on Lake St. Clair from

1986-1994 (Custer and Custer 1996; Nalepa et al. 1996). Increased water clarity resulted in a resurgence in submerged aquatic vegetation (hereafter SAV) documented by MDNR fisheries biologists (Thomas and Hass 2012). This resurgence likely benefited canvasbacks by offering expanded food resources, and increased water clarity may have indirectly benefited all 3 species by reducing the time needed to find novel food patches on the lake. It is unclear why redheads did not respond in a similar fashion to the

90 dreissenid invasion as did canvasbacks, although it may be redheads were simply unable to exploit food available in deeper water or expanding SAV consisted of plant species more preferred by canvasbacks than by redheads. Similar responses of waterfowl abundance to increased water clarity and return of SAV have been documented in other riverine and lacustrine systems (Hanson and Butler 1994; Rybicki and Landwehr 2007).

We believe decreased use of U.S. waters by diving ducks in the last decade as evidenced by our kernel density models and Coordinated Canvasback Survey data

(Cordts 2010) is likely a result of system-wide changes on Lake St. Clair initiated by dreissenids. We hypothesize that Lake St. Clair has transitioned from a relatively food limited system to a food-rich system, and consequently, diving ducks no longer need to tolerate as much risk as they did historically to exploit available food resources (i.e. diving ducks can now avoid disturbance while foraging). Rather than being closely tied to the relatively shallow but highly disturbed U.S. portion of Lake St. Clair, expanded food resources have allowed birds to either loaf in secluded, offshore Canadian waters during the day and forage nocturnally or else forgo using U.S. waters entirely because of increased food abundance in the relatively deeper Canadian waters of Lake St. Clair. We are comfortable in assuming the Canadian side is relatively undisturbed in comparison to the U.S. side because: 1) all duck hunting on the Canadian side must be within 300 m of shore whereas distance to shore is unregulated in the U.S. (Cheskey and Wilson 2001) and 2) over 200 marinas are located on the U.S. portion of Lake St. Clair compared to approximately 13 on the Canadian side (St. Clair River and Lake St. Clair

Comprehensive Management Plan 2004), and 3) our survey data show greater boating traffic in U.S. waters (Fig. 12). To illustrate this dynamic system we have developed a

91 conceptual diagram linking disturbance, food abundance, and diving duck distribution and abundance on Lake St. Clair (Fig.13).

Figure 12. Kernel density map showing areas of recreational boating concentration 2 (boats/km ) on Lake St. Clair in Fall 2010, Spring 2011, Fall 2011, and Spring 2012.

As an extension of our historical analyses, our current data also illustrates the incredibly dynamic nature of diving duck distributions. Kernel density maps show major shifts in areas of concentration from year one to the next, and the nocturnal kernel density map supports the idea that major distributional shifts may occur within a single 24 hour period. Spatial models indicate underlying factors responsible for driving diving duck distributions likely differ depending on migration period. For example, water depth was a more important predictor of distribution on Lake St. Clair during spring in

Figure 13. Conceptual diagram showing linkages between dreissenids, disturbance, and fall diving duck distribution and abundance on Lake St. Clair.

93 comparison to fall (Table 9). Additionally, maps of predicted occupancy probabilities highlight seasonal distributional changes. In the future, we hope to combine individual survey dates in hierarchical spatio-temporal models that account for potential temporal autocorrelation in the data. Furthermore, we recognize the limitations of a logistic modeling approach (i.e., treating a site with 1 duck the same as a site with 10,000 ducks) and we plan to analyze sites in a categorical fashion (e.g., low duck use, moderate duck use, and high duck use) to investigate potential differences in covariate parameter estimates based on the categorical response variables. We also plan to analyze all data collected on western Lake Erie using similar spatial models to further investigate the effects of human disturbance and environmental covariates on diving duck distribution.

Finally, we hope to use additional aquatic macrophyte data (Thomas and Hass 2012) to construct distribution models specific to each individual diving duck species, with a focus on spring migration.

Management Implications

Weekly diurnal monitoring of diving duck distributions coupled with our analyses of a long-term historic dataset and nocturnal survey information highlight the dynamic nature of diving duck distributions during migration. It is this dynamic nature that makes our understanding of the Lake St. Clair and western Lake Erie system as whole so vital, and our development of predictive models critical to understanding environmental or human disturbance factors that are system drivers. Modeling system variables responsible for influencing diving duck distributions provides a more robust basis for assessing areas of importance to diving ducks compared to maps of empirical diurnal

94 diving duck distributions that are relative snapshots of spatial use. As an example, we may observe very high concentrations of diving ducks loafing in off-shore Canadian waters during our diurnal surveys, but may fail to identify the importance of Anchor Bay that could host equally high concentrations of foraging diving ducks every night. It is for this reason we encourage researchers and managers to exercise caution when interpreting species distribution information that makes no attempt to explain underlying factors responsible for observed distributions.

The Lake St. Clair and western Lake Erie region is arguably one of the most important fall and spring migration staging areas for diving ducks in North America. On a continental scale, we know the region attracts tremendous numbers of waterfowl because it is at a crossroads of two major flyways (Atlantic and Mississippi), and offers food resources necessary for waterfowl migration. We cannot create another Lake St.

Clair, but we hope we can maintain and protect the current Lake St. Clair through effective research and management. Disturbance has been proven to reduce habitat suitability for diving ducks, and we believe it is an issue of major concern on Lake St.

Clair and western Lake Erie (Kenow et al. 2003; Knapton et al. 2000). In addition to recreational boating pressure, large-scale wind farm sites have already been identified in

Canadian waters of Lake St. Clair and western Lake Erie (South Point Wind 2010).

Although direct mortality of waterfowl caused by windmills is not a major issue, the birds will actively avoid large wind farms, essentially rendering that site unusable by waterfowl (Larsen and Guillemette 2007; Kuvlesky et al. 2007). We believe that without careful planning the combined effects of alternative energy development and recreational boating could eventually limit the number of diving ducks using the lake. We

95 recommend continued development of predictive models throughout the Great Lakes and a more detailed focus on factors influencing spring distribution, which is potentially a limiting aspect of the annual life cycle of some species of diving ducks (Anteau and

Afton 2008; Straub 2008; Brasher 2007). We hope improved spatial models could be used to aid managers and conservation planners in identifying variables that ultimately drive diving duck distribution in the lower Great Lakes, and consequently can help identify and prioritize areas of potentially important habitat during both spring and fall migration.

APPENDICES

Appendix A.

Lake St. Clair western Lake Erie Survey % occuppied by % occupied by % occuppied by % occupied by date ducks boats ducks boats 10/18/2010 7.3% 9.3% 2.7% 8.0% 11/08/2010 20.5% 3.9% 10.7% 5.4% 11/16/2010 13.2% 1.0% 6.3% 0.9% 12/03/2010 27.8% 2.0% 10.7% 1.8% 03/25/2011 11.2% 0.1% 14.3% 0.0% 04/01/2011 22.0% 1.0% 24.1% 3.6% 04/13/2011 23.9% 1.5% 12.5% 12.5% 04/21/2011 17.6% 1.0% 14.3% 4.5% 04/29/2011 19.5% 2.0% 11.6% 4.5% 10/24/2011 19.5% 5.8% 7.1% 7.1% 10/31/2011 22.9% 4.9% 9.8% 0.9% 11/15/2011 23.4% 4.9% 8.0% 3.6% 11/23/2011 26.3% 2.9% 18.8% 5.4% 12/01/2011 17.6% 3.9% 13.4% 1.8% 03/19/2012 22.4% 2.9% 18.8% 6.7% 03/26/2012 18.0% 0.1% 30.4% 0.0% 04/04/2012 18.0% 4.4% 18.8% 5.4% 04/11/2012 19.5% 1.0% 13.4% 12.5% 04/18/2012 16.1% 2.9% 8.0% 9.8%

Appendix B.

a) date bayesian covariates quantiles 10/18/10 11/08/10 11/16/10 12/03/10 2.5% -5.587 -2.861 -6.653 -3.771 Intercept 50.0% -3.421 -1.394 -4.606 -2.539 97.5% -1.691 -0.259 -2.910 -1.424 2.5% -685.903 -4.354 -688.795 -691.608 Boats 50.0% -353.291 -0.865 -349.475 -346.589 97.5% -19.435 1.208 -15.865 -18.682 2.5% -0.023 -0.331 -0.207 -0.149 Distance to 50.0% 0.245 -0.118 -0.005 -0.002 Shore 97.5% 0.525 0.062 0.208 0.145 2.5% -0.266 -0.087 -0.122 -0.034 Water depth 50.0% -0.102 0.008 -0.001 0.047 97.5% 0.047 0.104 0.105 0.131 2.5% -0.059 -0.149 0.366 0.089 Spec. Richness 50.0% 0.254 0.069 0.636 0.275 97.5% 0.587 0.301 0.952 0.464 2.5% 0.023 0.021 0.020 0.021 Sigma2 50.0% 0.206 0.425 0.221 0.250 97.5% 1.092 4.784 0.951 1.448 2.5% 0.067 0.067 0.067 0.067 Phi 50.0% 0.090 0.084 0.078 0.085 97.5% 6.237 3.539 5.921 6.042

Table 12 (cont’d)

b) date bayesian covariates quantiles 10/24/11 10/31/11 11/15/11 11/23/11 12/01/11 2.5% -10.292 -6.392 -4.656 -3.803 -6.342 Intercept 50.0% -4.377 -3.322 -2.497 -2.363 -4.397 97.5% -2.730 -1.980 -1.216 -1.081 -2.798 2.5% -686.926 -2.838 -5.081 -689.257 -690.467 Boats 50.0% -348.562 -0.515 -1.657 -348.904 -357.507 97.5% -21.108 1.263 0.386 -17.580 -20.369 2.5% 0.148 -0.176 -0.271 -0.267 -0.084 Distance to 50.0% 0.356 0.039 -0.035 -0.079 0.102 Shore 97.5% 0.999 0.236 0.135 0.097 0.285 2.5% -0.293 -0.104 -0.070 -0.040 -0.104 Water depth 50.0% -0.037 0.004 0.018 0.067 0.007 97.5% 0.084 0.136 0.128 0.155 0.116 2.5% 0.150 0.238 0.093 0.002 0.312 Spec. Richness 50.0% 0.437 0.459 0.309 0.216 0.588 97.5% 0.941 0.858 0.573 0.428 0.871 2.5% 0.023 0.020 0.021 0.022 0.019 . Sigma2 50.0% 0.231 0.365 0.238 0.317 0.212 97.5% 17.316 5.050 6.670 5.997 2.782 2.5% 0.067 0.067 0.067 0.067 0.067 Phi 50.0% 0.076 0.110 0.077 0.081 0.076 97.5% 0.454 5.884 5.615 0.520 5.868

100

Table 12 (cont’d)

bayesian c) date covariates quantiles 03/25/11 04/01/11 04/13/11 04/21/11 04/29/11 2.5% -4.371 -2.513 -4.032 -5.525 -6.191 Intercept 50.0% -2.269 -1.052 -0.871 -3.098 -2.789 97.5% -0.737 0.095 0.244 -1.679 -1.354 2.5% -687.751 -2.967 -689.745 -688.463 -0.504 Boats 50.0% -351.745 0.900 -348.482 -353.570 2.121 97.5% -15.347 4.682 -21.254 -17.178 6.808 2.5% -0.471 -0.233 -0.001 -0.233 -0.308 Distance to 50.0% -0.075 -0.041 -0.113 0.014 -0.022 Shore 97.5% 0.273 0.153 0.119 0.252 0.211 2.5% -0.374 -0.135 -0.240 -0.157 -0.210 Water depth 50.0% -0.200 -0.033 -0.120 -0.043 -0.087 97.5% -0.035 0.058 0.059 0.082 0.044 2.5% 0.192 -0.139 0.073 0.169 0.204 Spec. Richness 50.0% 0.481 0.067 0.325 0.446 0.504 97.5% 0.807 0.294 0.616 0.722 0.785 2.5% 0.021 0.023 0.023 0.021 0.019 . Sigma2 50.0% 0.316 0.298 0.264 0.294 0.363 97.5% 4.445 3.485 22.015 6.673 8.715 2.5% 0.067 0.067 0.067 0.067 0.067 Phi 50.0% 0.075 0.078 0.075 0.076 0.082 97.5% 3.692 0.680 0.315 4.189 0.517

101

Table 12 (cont’d)

bayesian d) date covariates quantiles 03/19/12 03/26/12 04/04/12 04/11/12 04/18/12 2.5% -2.055 -5.936 -3.558 -4.598 -7.601 Intercept 50.0% -0.916 -3.216 -2.062 -2.970 -2.654 97.5% 0.167 -1.681 -0.785 -1.608 -1.231 2.5% -4.178 -689.814 -685.604 -689.211 -4.158 Boats 50.0% -0.701 -359.111 -354.977 -351.250 -0.491 97.5% 1.573 -17.373 -20.562 -19.138 2.347 2.5% -0.170 0.054 0.083 0.302 -0.047 Distance to 50.0% 0.030 0.279 0.281 0.524 0.228 Shore 97.5% 0.216 0.551 0.506 0.840 0.480 2.5% -0.203 -0.273 -0.289 -0.344 -0.243 Water depth 50.0% -0.102 -0.139 -0.163 -0.211 -0.105 97.5% -0.001 -0.004 -0.040 -0.087 0.048 2.5% -0.063 0.230 0.081 0.034 -0.067 Spec. Richness 50.0% 0.157 0.489 0.300 0.378 0.269 97.5% 0.362 0.756 0.549 0.639 0.560 2.5% 0.021 0.023 0.021 0.022 0.024 . Sigma2 50.0% 0.339 0.260 0.288 0.230 0.262 97.5% 2.085 5.791 1.464 3.732 24.786 2.5% 0.067 0.067 0.067 0.067 0.067 Phi 50.0% 0.088 0.081 0.100 0.076 0.085 97.5% 5.442 5.898 6.255 5.171 0.423

102

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103

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Chapter 4. Summary

Introduction

Analyses and outcomes from this research address the 4 primary objectives established at the outset of this project. Historical abundance and distributions of canvasback, scaup, and redheads proved to be dynamic with much temporal and spatial variation among surveys and years. In addition, we documented increased abundance and a distributional shift over time from shallow water depths to intermediate water depths for canvasback and scaup that is potentially linked to the dreissenid mussel invasion and subsequent increases in water clarity and SAV. Predictive models generated from current Lake St. Clair-wide survey data indicate that shallow water (<5 m), areas free from disturbance, and aquatic plant diversity are important to migrating diving ducks. Furthermore, our predictive models suggest that relative importance of these factors may change depending on the migration season (e.g., fall or spring).

Aerial surveys and associated distance sampling results suggest that even under relatively close spacing of transects, detection probability of moderate-sized diving duck flocks (i.e. < 10,000 birds) on our study was less than 1.0 (0.26-0.50), and flock size was the most important covariate influencing detection other than distance from the transect.

We believe distance sampling is a viable technique for estimating diving duck abundance, especially if flock sizes are small to moderate (1-10,000 birds). Finally, our spring migration monitoring yielded significantly smaller abundance estimates in comparison to fall. These results support recent research that suggests that spring migration may be more limiting than fall (Anteau and Afton 2008; Brasher et al. 2007;

Straub 2008), perhaps resulting in spring migration behaviors that have evolved in diving

107 ducks that distribute birds in lower densities and across a wider range of habitats. In addition to obtaining resources necessary for surviving spring migration, diving ducks may be storing nutrient reserves at this time of year in preparation for reproduction and this is an additional constraint not present during fall migration (Afton and Ankney 1991;

Ankney et al. 1991).

Research and Management Implications of Abundance Estimation and Spatial Modeling Techniques for Diving Ducks on Lake St. Clair and Western Lake Erie

The motivation for our distance sampling research was to investigate potential use of distance sampling techniques and to establish aerial survey protocols for diving ducks.

The MDNR conducted multi-species aerial surveys of diving ducks from1983 to 2008 over a comparatively small study area that encompassed the United States waters of Lake

St. Clair; however, no attempt was made to account for imperfect detection probability as it was assumed all diving duck flocks were detected. In order to meet UMRGLJV and

MDNR needs that included effective monitoring of priority waterfowl species during migration, the MDNR decided to expand their historical diving duck surveys to encompass all of Lake St. Clair and a large section of western Lake Erie. In addition, there was great interest in establishing a spring survey and ideally techniques would be applicable to fall and spring migration. Given the much larger study area and uncertainties about spring surveys, the idea of achieving a census count seemed impractical and MDNR decided to test distance sampling protocols in an effort to obtain statistical estimates of abundance for migrating diving ducks. Expanded distance-based surveys will benefit UMRGLJV habitat planning and builds a foundation for those interested in improving current open water aerial surveys of diving ducks or other

108 waterbirds.

Our experience with 4 migration seasons of aerial surveys suggests we were able to meet the 3 main assumptions most important to obtaining valid estimates of abundance when using distance sampling methodology. Although model fit of detection functions was poor on some individual surveys, our evidence suggested lack of fit was primarily a result of observer inexperience early in our study. As an example, we found poor model fit for data collected on 4 of 5 individual surveys during our first field season; however, in the final field season model fit was acceptable on all individual surveys. We believe distance sampling is a viable technique for obtaining abundance estimates of diving ducks, and offers an improved alternative to traditional census based method, especially when monitoring is needed at a large geographic scale, because it explicitly accounts for missed groups. We also believe distance sampling offers a more flexible and potentially more precise alternative to fixed-width surveys when sighting conditions (i.e. chop or glare on the water caused by wind and sunlight) are expected to vary among surveys. For example, we found a strong negative correlation (r2 = 0.60) between estimated detection probability and wind speed during our surveys that likely indicates distance sampling is implicitly accounting for poorer sighting conditions caused by high wind speeds and thus heavy chop on the lake surface that hides flocks of diving ducks that would otherwise have been observed under calmer conditions (see Fig. 6 from Chap. 2)

Appropriate modeling of flock size was the most important factor in determining abundance estimates due to extreme variation in observed flock sizes. The vast majority

(94%) of diving flocks encountered ranged anywhere from 1 to 1,000 birds; however, we encountered flocks estimated to contain as many as 90,000 ducks. Intuitively, it makes

109 sense that detection probabilities vary with flock size and we were able to account for this effect by including flock size as a covariate in the detection function. The flexibility of distance sampling modeling techniques allowed us to simultaneously account for variation in flock size and effects of distance from transect lines when fitting detection functions. Eventually, flock size became so large that the detection probability was 1.0 at distances greater than the distance between transects. Under these circumstances, census methodology once again became valid and the focus was shifted to obtaining an accurate total count of the individuals contained in these large flocks.

Future improvement of established survey methodology depends upon our ability to improve observer counts of large flocks of diving ducks. Future research should focus on assessing how accurately we estimate size of flocks that may include more than

10,000, 20,000, or even 50,000 individuals. Additional research on estimation of flock size could improve parameter estimates for the effect of flock size on the detection function and would allow evaluation of our assumption of a complete census of all individuals contained in groups. Future work could include use of aerial photography or infrared videography to correct for observer bias in ocular flock size estimation. We recommend extensive training in both distance sampling search protocol and flock size estimation for anyone interested in adopting a distance-based survey for diving ducks

(Hodges 1993).

Joint Venture-scale habitat management planning considers resources needed by focal waterfowl species throughout their annual life cycle. A common method to determine waterfowl habitat needs during migration is the calculation of duck-use-days to quantify the types and amounts of habitat required to sustain desired abundance over the

110 course of spring or fall migration. We illustrate one application of distance-based abundance estimates for determining diving duck-use days on a waterfowl migration staging area. Our estimates of fall diving duck-use-days ranged from 10.2 to 12 million use-days while our spring estimates ranged from 1.5 to 3 million use-days on Lake St.

Clair. There may be distinctly different management approaches needed to accommodate diving ducks during spring and fall if diving ducks using our study area are representative of diving duck migration behavior across the Upper Mississippi and Great Lakes Joint

Venture Area. For example, fall resources for diving ducks might be met on relatively few large wetland areas like our study area. In contrast, numerous smaller wetlands dispersed across the landscape may better meet the needs of diving ducks during spring migration. Current large-scale wetland conservation programs (e.g., United States

Department of Agriculture, Wetlands Reserve Program) focus on restoring relatively shallow wetlands; future research should examine diving duck use of wetlands during spring migration across the broader landscape to determine if more consideration should be given to restoring or enhancing moderate to deep water wetlands to benefit diving ducks and other wetland-dependent wildlife species.

The main objective of our spatial models was to investigate the effects of various environmental and anthropogenic variables on diving duck distribution on Lake St. Clair during both spring and fall migration while accounting for inherent spatial autocorrelation in the data. We believe hierarchical spatial models offer some distinct advantages for handling spatially referenced observation data and identifying variables that are ultimately driving the system of interest. When dealing with observation data, especially clustered data, not accounting for spatial autocorrelation can result in the

111 identification of spurious correlations between response and predictor variables (Cressie et al. 2009). Furthermore, spatial models should be more robust to predicting effects of system wide changes that might occur and thus more effective at identifying truly important areas for diving ducks than empirical data of diving duck locations.

Our historic and recent diving duck abundance estimates as well as our current distribution models reveal a highly dynamic system within and among migration seasons and these dynamics have implications for protecting the capacity of Lake St. Clair and western Lake Erie to support diving ducks during migration. For example, we know from Thomas and Hass (2012) that submergent vegetation abundance and distribution has changed dramatically from 1983 to the present, and we can hypothesize based on our current predictive models that important areas for diving ducks were likely much different in 1983 than they are in the present. MDNR historical surveys that document a shift in diving duck distribution from shallower water to deeper water and from the highly disturbed U.S portion of the lake to the relatively undisturbed Canadian portion of the lake support this hypothesis. We believe that continued monitoring of the interaction between boating traffic and diving ducks on our study area is warranted as it is difficult to envision a plan for creation of inviolate refuges to accommodate diving ducks that would not be met with strong resistance from the recreational boating community. It is possible that the combination of areas with low boat traffic and relatively undisturbed nocturnal feeding sites will continue to accommodate diving ducks needs during migration.

Uncertainties about future boating traffic patterns, diving duck responses to placement of wind energy developments, water level changes anticipated with climate models, and potential changes in the dreissenid mussel and SAV food bases are additional reasons to

112 consider future diurnal and nocturnal diving duck monitoring. The ever changing and complex nature of wildlife-habitat interactions make the identification of high quality habitat a challenge. As wildlife managers, we do not want to identify and conserve habitat that may be unimportant a decade from now, and we believe hierarchical spatial models offer us the most effective tool for identifying current and past areas of importance and predicting future areas of importance for diving ducks in the lower Great

Lakes.

We plan to expand upon the current spatial modeling work in several ways. We hope to develop spatio-temporal models that will allow us to combine individual surveys over a given migration period in a more robust statistical manner that will account for any potential temporal autocorrelation in the data. Additionally, we plan to apply the methods developed during this thesis to the development of spatial models for the data collected on western Lake Erie. We also hope to use extensive aquatic vegetation data from Thomas and Hass (2012) to develop more detailed predictive models for each diving ducks species. For example, Thomas and Hass (2012) have lake wide estimates of wild celery (Vallisneria americana) abundance that could be used in predictive models for canvasbacks, which are known to feed extensively on wild celery. Finally, we plan to use additional survey data collected in the fall, winter, and spring of 2012 and 2013 to evaluate the accuracy and validity of current predictive models for Lake St. Clair.

Two fundamental questions, the answers to which result in more effective management and more relevant research as they pertain to diving ducks on Lake St. Clair and western Lake Erie are: 1) how many ducks are there and 2) what areas are they using? We believe our current line transect surveys allow us to effectively monitor

113 diving duck abundance and distribution, thus shedding light on these two critical, albeit simple questions. Without a statistically sound means for estimating diving duck abundance it would be difficult to discern trends in abundance over time, and without accurately collected spatial data it would be difficult to identify areas of diving duck concentration lake-wide. If we cannot effectively map diving duck locations we have little ability to test hypotheses about the importance of various environmental or anthropogenic factors that may affect distribution, and without these hypotheses we can hardly offer an explanation for why diving duck numbers may have increased or decreased on our study area in the past and we have no way to predict what may happen in the future. Diving ducks are an important resource both ecologically and socially on

Lake St. Clair and western Lake Erie, and we hope this research lays the groundwork for an effective, long-term monitoring program. May the rafts of 50,000 canvasbacks never disappear.

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LITERATURE CITED

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LITERATURE CITED

Afton, A. D. and C. D. Ankney. 1991. Nutrient-reserve dynamics of breeding lesser- scaup: A test of competing hypotheses. The Condor 93:89-97.

Ankney, C. D., A. D. Afton, and R. T. Alisauskas. 1991. The role of nutrient reserves in limiting waterfowl reproduction. The Condor 93:1029-1032.

Anteau, M. J., and A. D. Afton. 2008. Diets of Lesser Scaup during spring migration throughout the Upper-Midwest are consistent with the spring condition hypothesis. Waterbirds 31:97-106.

Hodges, J. L. 1993. Count- a simulation for learning to estimate wildlife numbers. The Wildlife Society Bulletin 21:96-97.

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