Abstract

Midcontinent populations of arctic nesting geese (hereafter, arctic geese), including greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii, have increased in abundance and shifted their winter distribution in recent decades. Consequently, the number of arctic geese wintering in the Mississippi Alluvial Valley (MAV) has increased since the 1980s. Stored endogenous nutrients are critically important to the life cycle of arctic geese as the geese use these stored nutrients to complete long-distance migration events, survive harsh winters, and supplement nutrients needed for reproduction. This study tracked temporal changes in body condition of arctic geese during the wintering period. We collected arctic geese from October–February 2015–2016 and 2016–2017 in eastern Arkansas. We used proximate analysis to determine size of lipid and protein stores as an index of body condition. Protein stores were more stable through time than lipids, but we observed a slight increase in all species as winter progressed. Mean lipid stores were dynamic and were highest in November and lowest in February. Greater white-fronted geese arrived earliest to the MAV and experienced an increase in endogenous lipid stores during early winter when high-energy food resources were most abundant. Conversely, snow and Ross's geese arrived to the MAV later and did not appear to increase their lipid stores upon arrival. All three species experienced a decline in stored lipid mass as winter progressed; a combination of factors such as resource depletion, a shift in dietary needs, physiological factors, hunting pressure, and increased energetic demands may have driven the decline. An improved understanding of the role that “nontraditional” wintering grounds exert on the nutrient dynamics of arctic geese may aid in the management of growing and shifting populations.

Introduction

By biological definition, body condition is the nutritional state of an individual and its ability to meet energetic demands (Owen and Cooke 1977). The energetics and nutrient dynamics of individuals drive body condition (Biebach 1996). Waterfowl, including arctic geese, experience fluctuations in body condition throughout the annual cycle in response to changes in energetic demands (e.g., migration and thermoregulation) and availability of nutrients on the landscape. To survive winter, arctic geese must obtain resources from the environment to sustain metabolic processes (Baldassarre and Bolen 2006). When excess resources are available, arctic geese may store them as lipid or protein reserves known as endogenous nutrient stores (Raveling 1979; Lindstrom 1991; Biebach 1996). Wildlife biologists can use the relative size of an animal's endogenous nutrient stores as an index of body condition (Johnson et al. 1985). Understanding changes in body condition and the factors influencing their dynamics may be critical to successful management of migratory waterfowl, especially considering climatological and land use changes (Johnson et al. 1985).

Long-distance migration is energetically demanding (Lindstrom 1991), but migration offers arctic geese an escape from the harsh winter of northern latitudes and access to energetic resources needed to survive winter. To reach their wintering grounds, arctic geese may utilize large amounts of stored energy (Lindstrom 1991). Winter can also be energetically demanding, particularly when resources become scarce and lower temperatures increase the energy required for thermoregulation (Hobaugh 1985; Ely and Raveling 1989; Biebach 1996). Other factors such as predator avoidance, courtship, and exploration of new territories may also increase energy expenditure during winter. If resources are insufficient, arctic geese may rely on endogenous nutrient stores to maintain metabolic processes and survive the winter (Hobaugh 1985; Biebach 1996).

Greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens (hereafter, snow geese), and Ross's geese Anser rossii (hereafter, arctic geese when referred to as a group) have shifted their winter distribution in recent decades from historical wintering areas to the agricultural landscape of the Mississippi Alluvial Valley (MAV; Baldassarre 2014; Jónsson and Afton 2015; Askren 2016). Historically, greater white-fronted geese and snow geese wintered predominately in the coastal marshes and prairies along the Gulf Coast of Louisiana and Texas (Chabreck et al. 1989). Ross's geese, on the other hand, wintered primarily in the Central Valley of California until around the 1980s when they began expanding their winter distribution east to include the Central and Mississippi flyways (Alisauskas et al. 2006; Baldassarre 2014). Along with distributions shifting into the MAV, populations of arctic geese have also increased in size (Alisauskas et al. 2009; CAFF 2018; USFWS 2017a). For example, midcontinent greater white-fronted goose populations quadrupled between 1970 and 2017 while the midcontinent snow goose population increased almost exponentially from the 1990s to the 2000s before leveling off with an estimated adult population of around 12.8 million (CAFF 2018). The combined effects of shifting winter distributions and increasing population sizes have caused the number of arctic geese wintering in Arkansas to increase steadily since the 1980s (USFWS 2017b).

Though all three species have adapted to wintering on the agricultural landscape of the MAV, they vary widely in migration chronology, foraging strategy, and body size. For example, greater white-fronted geese are early-arriving migrants to the MAV, arriving about 1 mo earlier than snow and Ross's geese (Askren 2016). Greater white-fronted and Ross's geese have delicate mandibles best suited for grazing and gleaning, whereas snow geese have stout mandibles to aid in the excavation and extraction of roots and tubers in a marsh environment (Baldassarre 2014). According to the body-size hypothesis, smaller-bodied geese (e.g., Ross's geese) deplete endogenous reserves faster than larger-bodied geese (e.g., greater white-fronted and snow geese; Thompson and Raveling 1987; Jónsson 2005). Due to life history and morphological differences, it is likely that these three species have different strategies for utilizing available resources while wintering in the MAV of Arkansas.

Numerous studies have investigated body condition of arctic geese on the breeding grounds (Ankney and MacInnes 1978; Budeau et al. 1991), on traditional wintering grounds (Hobaugh 1985; Alisauskas 1988; Ely and Raveling 1989), and during spring migration (Alisauskas 1988, 2002; Krapu et al. 1995; Pearse et al. 2011; Fowler 2018). These studies have shown the importance of endogenous nutrients to survival and reproductive success during various stages of the annual life cycle. To our knowledge, no researchers have evaluated body condition of wintering arctic geese since their distributions shifted into the MAV. The objective of our research was to investigate temporal patterns in body condition of arctic geese wintering in the MAV of Arkansas. Baseline data of body condition for arctic geese in the MAV may be useful to monitor long-term changes in nutrient dynamics and resource availability as climate, population abundance, and landscape composition fluctuate.

Methods

Study site

We conducted this study on private agricultural lands in Drew, Desha, Lincoln, Arkansas, Jefferson, and Chicot counties in southeastern Arkansas (Figure 1). These counties are contained within the MAV and border the eastern edge of the West Gulf Coastal Plains. Collectively, land cover for the portion of these six counties contained within the MAV consisted of 63.4% agriculture, 2.0% forested areas, 25.4% wetlands, and 3.4% developed areas (USDA 2016) at the time of the study. Agriculture was predominately soybeans (51.1%), rice (17.1%), and corn (15.3%), while the remaining agricultural area consisted of less-abundant crops such as winter wheat, cotton, and sorghum (16.5%). Historically, bottomland hardwood forests dominated the MAV and served as floodplain wetlands for semiannual flooding events (Reinecke et al. 1989; King and Keeland 2002). Agricultural activity has vastly altered the composition of the landscape and natural hydrology of the MAV from its historical state, resulting in a predominately agriculture-dominated landscape interspersed with fragmented blocks of bottomland hardwood forest (Reinecke et al. 1989).

Figure 1.

Six-county study area (Drew, Desha, Lincoln, Arkansas, Jefferson, and Chicot counties) and collection locations of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii used in proximate analysis of winter body condition during October–February 2015–2016 and 2016–2017.

Figure 1.

Six-county study area (Drew, Desha, Lincoln, Arkansas, Jefferson, and Chicot counties) and collection locations of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii used in proximate analysis of winter body condition during October–February 2015–2016 and 2016–2017.

Field collection and lab processing

We used an opportunistic sampling approach to lethally collect arctic geese using modern shotguns and small-caliber rifles between the dates of October 5 and February 22 of 2015–2016 and 2016–2017. We collected arctic geese that were on the ground actively feeding, typically within 4 h of sunrise or sunset. Opportunistic sampling was necessary because arctic geese are highly mobile on the wintering grounds and can exhibit a mean daily movement distance of > 32 km (Askren 2016). Furthermore, once we located foraging geese, it was logistically challenging to acquire timely access to private land.

Although informative for resource managers (Raveling 1979; Miller 1986; Alisauskas 1988; Ballard et al. 2006), evaluating the dynamics of nutrient deposition and catabolization by collecting specimens through time necessitates a few assumptions. In particular, our approach has the potential to misinterpret changes in the sample population due to migration events as true changes in body condition over time. For example, if a cohort of individuals in better body condition migrates into the study area after collections have begun, study results may indicate an overall increase in the body condition of the population. For this study, we assumed that individuals migrating into the study area were of comparable body condition with the overall population. To satisfy this assumption and attempt to obtain a representative sample of the overall population wintering in this area, we distributed sampling efforts over space and time during this multiyear study.

At the time of collection, we labeled specimens with a unique identifier and placed them in plastic zip-tight bags. We transported specimens in an ice chest to the laboratory and then kept them frozen until processing. All collection and laboratory procedures followed state (Arkansas Game and Fish Commission permit 081220151) and federal scientific collection permits (U.S. Fish and Wildlife Service permit MB65905B-0). In the laboratory, we recorded body mass and morphometric measurements (Table S1, Supplemental Material) according to Dzubin and Cooch (1992). We removed body feathers using electric shears (Oster Shearmaster, Boca Raton, FL), and clipped wing and tail feathers. We removed the gastrointestinal tract and emptied its contents. We placed all organs in the cavity of the carcass and weighed it to obtain an overall ingesta- and feather-free wet carcass weight. To obtain dry carcass mass, we dried the carcass to stable mass at 80°C. We homogenized dried carcasses with a two-step process using a commercial grade blender (Vitamix XL, Cleveland, OH) and an electric coffee grinder. We used homogenized samples for proximate analysis.

Proximate analysis of carcass

We conducted proximate analysis to determine total lipid, total ash, and total protein content as an index of body condition (Johnson et al. 1985). Methods generally followed Ballard et al. (2006), but we have detailed laboratory methods in the supplemental material (Text S1, Supplemental Material) because we used new equipment: an Ankom XT15 Lipid Extractor (Ankom Technology, Macedon, NY). We calculated ash and protein content (Table 1) based on incinerated lipid-free mass values and equations provided in Text S1.

Table 1.

Summary statistics for total lipid (g), total protein (g), and total ingesta-free body mass (g) by species, sex and age-class from proximate analysis of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. Summary statistics provided include the number of individuals in each group (n), the minimum value (min), the arithmetic mean (mean), the maximum value (max), the range (difference between the maximum and minimum value), and the standard deviation (SD). Note: This table reports lipid and protein values that we have not corrected for body size and only contains the 355 individuals included in the analysis.

Summary statistics for total lipid (g), total protein (g), and total ingesta-free body mass (g) by species, sex and age-class from proximate analysis of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. Summary statistics provided include the number of individuals in each group (n), the minimum value (min), the arithmetic mean (mean), the maximum value (max), the range (difference between the maximum and minimum value), and the standard deviation (SD). Note: This table reports lipid and protein values that we have not corrected for body size and only contains the 355 individuals included in the analysis.
Summary statistics for total lipid (g), total protein (g), and total ingesta-free body mass (g) by species, sex and age-class from proximate analysis of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. Summary statistics provided include the number of individuals in each group (n), the minimum value (min), the arithmetic mean (mean), the maximum value (max), the range (difference between the maximum and minimum value), and the standard deviation (SD). Note: This table reports lipid and protein values that we have not corrected for body size and only contains the 355 individuals included in the analysis.

Statistical analysis

As an intermediate step, we performed a principal component analysis (PCA) using five morphological measurements (culmen, head length, tarsus length, middle toe length, and wing chord) to create a composite variable (i.e., principal component) to represent structural size (Alisauskas and Ankney 1987). We conducted a PCA for each species and generated eigenvalues to assess the proportion of total variance in structural size explained by each of the principal components. Subsequently, we conducted a regression analysis to examine the relationship of the principal component that explained the most variation in structural size (PC1) with lipid and protein content (Ankney and Alisauskas 1991. If we detected a significant linear relationship (P ≤ 0.05), we applied a correction factor to adjust lipid and protein for within-species differences in structural size (Ankney and Alisauskas 1991). The correction factor derived by Ankney and Alisauskas (1991) used the residuals from the linear regression equation to correct for structural size. We used the corrected values in further analyses. We used the Ankney and Alisauskas (1991) equation:  
formula
where y1 = corrected value, a = y-intercept of regression line, b = the slope of the regression line, yobs = actual value, obs = mean of actual values.

We conducted an analysis of variance to test for differences in corrected lipid and protein content among species and sex/age classes and within species. We used Tukey–Kramer least square means multiple comparison tests to quantify differences between groups. Next, using data pooled for all species, we tested the effects of demographic variables and time of collection. We used body size corrected lipid and body size corrected protein mass as continuous response variables. We used species, age, sex, year of study, and month of collection as categorical predictor variables, and day of year of collection as a continuous predictor variable. Species contained three levels (greater white-fronted, snow, and Ross's), age contained two levels (adult and juvenile), sex contained two levels (male and female), year of study contained two levels (winter 2015–2016 and winter 2016–2017), and month of collection contained five levels (October, November, December, January, and February) where samples from both years of the study were pooled by month. For the continuous predictor variable, we used the day of year on which the specimen was collected, wherein October 2 was equal to day 275 and the year of the study was not considered (both years were pooled). We used forward stepwise selection to investigate contributive predictors and relevant interactions between fixed effects based on presence in the top-performing model. We determined top models using corrected Akaike Information Criteria (AICc). Models with a ΔAICc < 2 were considered equivalent, and from equivalent models, we selected the most parsimonious model. All statistical analyses were conducted in R 3.6.0 (R Development Core Team, 2019).

Results

We collected 371 arctic geese, of which 95.7% (n = 355) were retained for the analysis (Table S1). Overall, 49.0% of all arctic geese collected were greater white-fronted geese (n = 174), 40.6% were snow geese (n = 144), and 10.4% were Ross's geese (n = 37). Principal component analysis for each species effectively reduced five morphological measurements to a single composite variable used as an index of body size. For greater white-fronted geese, PC1 accounted for 67.2% of the variation in the structural measurements. For snow and Ross's geese PC1 accounted for 73.0% and 70.1% of the variation, respectively. For all three species, PC2 accounted for less than 15.0% of the variation in the structural measurements, thus we elected to use PC1 as the index of structural size.

Total lipids (g) were correlated with body mass for all species but only weakly related to PC1 representing structural size (Table 2). Total protein (g) was highly correlated with total body mass and structural size (PC1) for all species (Table 2). To control for differing body size among individuals, we corrected total lipid and protein for structural size (PC1) by species.

Table 2.

Linear relationships (R2 [P]) between indices of body condition (lipid mass and protein mass), total body mass, and structural size (PC1) of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. Reported values include the adjusted coefficient of determination (R2), in parentheses, the probability value (P) of each model.

Linear relationships (R2 [P]) between indices of body condition (lipid mass and protein mass), total body mass, and structural size (PC1) of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. Reported values include the adjusted coefficient of determination (R2), in parentheses, the probability value (P) of each model.
Linear relationships (R2 [P]) between indices of body condition (lipid mass and protein mass), total body mass, and structural size (PC1) of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. Reported values include the adjusted coefficient of determination (R2), in parentheses, the probability value (P) of each model.

Total lipids differed among species (F = 82.53, P < 0.001), and on average, were higher in greater white-fronted geese (mean = 321.8 g, SE = 8.9 g) than in snow geese (mean = 205.7 g, SE = 6.3 g) and Ross's geese (mean = 147.7 g, SE = 9.5 g). Total lipid mass (structural size corrected) did not differ among sex/age classes within species (F = 0.61, P = 0.60). Lipid mass of greater white-fronted geese was highest in November (mean = 442.6 g, SE = 17.3 g) and subsequently declined by 51.4% to the lowest lipid levels in February (mean = 215.1 g, SE = 15.6 g; Figure 2). For greater white-fronted geese, pair-wise comparisons confirmed that mean monthly lipid mass was higher in November than all other months (Figure 3) and lower in January and February than all other months, but lipid mass was similar in January and February (P = 0.30).

Figure 2.

Temporal trends in the lipid stores (g) of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in southeast Arkansas from October–February 2015–2016 and 2016–2017. The x-axis represents the day of year on which the specimen was collected, wherein October 2 is equal to day 275 and March 1 is equal to day 60.

Figure 2.

Temporal trends in the lipid stores (g) of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in southeast Arkansas from October–February 2015–2016 and 2016–2017. The x-axis represents the day of year on which the specimen was collected, wherein October 2 is equal to day 275 and March 1 is equal to day 60.

Figure 3.

Results of Tukey–Kramer multiple comparisons test of lipid stores (g) by collection month for arctic geese collected in southeast Arkansas from October–February 2015–2016 and 2016–2017. For greater white-fronted geese Anser albifrons frontalis, mean monthly lipid mass was higher in November than all other months and lower in January and February than all other months. For lesser snow Anser caerulescens caerulescens and Ross's geese Anser rossii mean monthly lipid mass was lower in January and February than in November and December. Black error bars represent statistically significant differences between mean monthly values (P < 0.05).

Figure 3.

Results of Tukey–Kramer multiple comparisons test of lipid stores (g) by collection month for arctic geese collected in southeast Arkansas from October–February 2015–2016 and 2016–2017. For greater white-fronted geese Anser albifrons frontalis, mean monthly lipid mass was higher in November than all other months and lower in January and February than all other months. For lesser snow Anser caerulescens caerulescens and Ross's geese Anser rossii mean monthly lipid mass was lower in January and February than in November and December. Black error bars represent statistically significant differences between mean monthly values (P < 0.05).

Temporal trends in lipid mass of snow and Ross's geese differed from greater white-fronted geese. Unlike greater white-fronted geese, there was no evidence of an early winter increase in lipids for snow geese and Ross's geese, though sample sizes were limited in October (mean = 290.5 g, SE = 56.3 g, n = 3 for snow geese; mean = 178.2 g, SE = 30.9 g, n = 3 for Ross's geese) and November (mean = 307.9 g, SE = 47.7 g, n = 8 for snow geese; only one Ross's goose was collected; Figure 2). Similar to greater white-fronted geese, lipid stores for snow and Ross's geese declined as winter progressed, with lowest lipid mass in February (mean = 142.1 g, SE = 6.3 g for snow geese; mean = 103.0 g, SE = 17.3 g for Ross's geese). Post-hoc comparisons for snow and Ross's geese demonstrated that mean monthly lipid mass was lower in January and February than in November and December (Figure 3).

Trends in endogenous protein stores differed among species (F = 269.9, P < 0.001). Even after body size corrections, protein mass differed by sex/age class (F = 72.8, P < 0.001), but protein mass was more stable over the winter than endogenous lipid mass (Figure 4). Post-hoc tests showed minimal differences among months; the only significant monthly differences were that January and February protein mass was greater than October protein mass for greater white-fronted geese (Figure 5). The percentage of increase in protein content (structural size corrected) from October to February was 7.4% (33.6 g) for white-fronted geese, 9.4% (36.7 g) for snow geese, and 13.1% (31.9 g) for Ross's geese.

Figure 4.

Temporal trends in protein stores by month of collection for greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in southeast Arkansas and used in our analysis of winter body condition from October–February 2015–2016 and 2016–2017.

Figure 4.

Temporal trends in protein stores by month of collection for greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in southeast Arkansas and used in our analysis of winter body condition from October–February 2015–2016 and 2016–2017.

Figure 5.

Results of Tukey–Kramer multiple comparisons test of protein stores (g) by collection month for arctic geese collected in southeast Arkansas and used in our analysis of winter body condition from October–February 2015–2016 and 2016–2017. For greater white-fronted geese Anser albifrons frontalis, mean monthly protein mass was higher in January and February than in October. There was no difference in monthly mean protein mass for lesser snow Anser caerulescens caerulescens or Ross's geese Anser rossii. Black error bars represent statistically significant differences between mean monthly values (P < 0.05).

Figure 5.

Results of Tukey–Kramer multiple comparisons test of protein stores (g) by collection month for arctic geese collected in southeast Arkansas and used in our analysis of winter body condition from October–February 2015–2016 and 2016–2017. For greater white-fronted geese Anser albifrons frontalis, mean monthly protein mass was higher in January and February than in October. There was no difference in monthly mean protein mass for lesser snow Anser caerulescens caerulescens or Ross's geese Anser rossii. Black error bars represent statistically significant differences between mean monthly values (P < 0.05).

Based on model selection, we found evidence that lipid mass, but not protein, was related to the date on which the individual was collected. For lipids, the top model included collection month and species (Table 3). For protein, the top model included species, age, and sex, and the interaction between age and sex (Table 4).

Table 3.

The four top-performing models (based on Akaike Information Criterion [AIC] model selection) for endogenous lipid mass and protein mass of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. These models are reported with the natural logarithm of the likelihood function (logLik), corrected AIC (AICc), AICc difference (ΔAICc), degrees of freedom (df), and model weight (wi). We considered all relevant and logical variables of interest (including interactions) during the forward stepwise model selection process.

The four top-performing models (based on Akaike Information Criterion [AIC] model selection) for endogenous lipid mass and protein mass of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. These models are reported with the natural logarithm of the likelihood function (logLik), corrected AIC (AICc), AICc difference (ΔAICc), degrees of freedom (df), and model weight (wi). We considered all relevant and logical variables of interest (including interactions) during the forward stepwise model selection process.
The four top-performing models (based on Akaike Information Criterion [AIC] model selection) for endogenous lipid mass and protein mass of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. These models are reported with the natural logarithm of the likelihood function (logLik), corrected AIC (AICc), AICc difference (ΔAICc), degrees of freedom (df), and model weight (wi). We considered all relevant and logical variables of interest (including interactions) during the forward stepwise model selection process.
Table 4.

Parameter estimates, standard errors (SE) and t values (t) for the fixed effects in the best-fitting linear models of endogenous nutrient stores (selected by corrected Akaike Information Criterion [AICc]) of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. The model for lipid (r2 = 0.57, where r2 is the adjusted coefficient of determination) contained two categorical variables: species and month of collection. Species contained three levels (greater white-fronted, snow, and Ross's) and month of collection (October, November, December, January, and February). The model for protein (r2 = 0.66) contained three categorical variables plus an interaction term. Species contained three levels (greater white-fronted, snow, and Ross's), age contained two levels (adult and juvenile), and sex contained two levels (male and female). This model also included an interaction between age and sex.

Parameter estimates, standard errors (SE) and t values (t) for the fixed effects in the best-fitting linear models of endogenous nutrient stores (selected by corrected Akaike Information Criterion [AICc]) of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. The model for lipid (r2 = 0.57, where r2 is the adjusted coefficient of determination) contained two categorical variables: species and month of collection. Species contained three levels (greater white-fronted, snow, and Ross's) and month of collection (October, November, December, January, and February). The model for protein (r2 = 0.66) contained three categorical variables plus an interaction term. Species contained three levels (greater white-fronted, snow, and Ross's), age contained two levels (adult and juvenile), and sex contained two levels (male and female). This model also included an interaction between age and sex.
Parameter estimates, standard errors (SE) and t values (t) for the fixed effects in the best-fitting linear models of endogenous nutrient stores (selected by corrected Akaike Information Criterion [AICc]) of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. The model for lipid (r2 = 0.57, where r2 is the adjusted coefficient of determination) contained two categorical variables: species and month of collection. Species contained three levels (greater white-fronted, snow, and Ross's) and month of collection (October, November, December, January, and February). The model for protein (r2 = 0.66) contained three categorical variables plus an interaction term. Species contained three levels (greater white-fronted, snow, and Ross's), age contained two levels (adult and juvenile), and sex contained two levels (male and female). This model also included an interaction between age and sex.

Discussion

Increasing population abundance and shifting winter distributions (Alisauskas et al. 2009; Baldassarre 2014; Jónsson and Afton 2015; Askren 2016; USFWS 2017a) have made the MAV an increasingly important wintering area for arctic geese (USFWS 2017b). The MAV is an agriculturally dominated landscape (USDA 2016) that provides abundant energetic resources for arctic geese and other wintering waterfowl. In the context of shifting distributions, our objective was to quantify nutrient dynamics of three species of arctic geese that now commonly winter in the MAV of Arkansas.

Temporal trends of endogenous lipid stores during winter differed among species in our study, suggesting that these species may use the MAV to meet different energetic needs. The Arkansas MAV may be more energetically important for greater white-fronted geese than for snow and Ross's geese. Early-arriving greater white-fronted geese substantially increased lipid mass upon their arrival, whereas snow and Ross's geese did not. Greater white-fronted geese arrived in Arkansas as early as October 5 with a mean arrival date of October 30 (Askren 2016). Few snow geese arrived in Arkansas before November 14, and mean arrival was November 30 (D.C. Osborne, University of Arkansas Division of Agriculture, personal communication). The abundance of waste grain available in October and November (Massey 2017) likely contributed to the rapid accumulation of lipid stores by greater white-fronted geese during early winter. Early-arriving migrants may have a competitive advantage for obtaining energetic resources on the wintering grounds, specifically in terms of the acquisition of waste grain prior to flooding or depletion, which typically occurs by midwinter (Stafford et al. 2006). Despite early-winter lipid increases for greater white-fronted geese, all three species experienced a decline in endogenous lipids throughout the winter.

Many factors could be interacting to cause the observed loss of lipid mass of all species during the late winter including rice depletion, a shift in diet, physiological factors, hunting pressure, and increased energetic demands due to colder temperatures (Owen and Cook 1977; Miller 1986; Loesch et al. 1992; Lovvorn 1994). Researchers have proposed two competing hypotheses to explain the dynamics of waterfowl endogenous nutrients: the endogenous-rhythm hypothesis and the energy-deficit hypothesis. The endogenous-rhythm hypothesis proposes that waterfowl have evolved to reduce endogenous nutrient stores at times when it isn't advantageous to sustain them, even if there are sufficient resources to do so (Reinecke et al. 1982; Baldassarre et al. 1986; Loesch et al. 1992). In contrast, the energy-deficit hypothesis proposes that environmental factors (availability of nutrients, temperature, and weather events) regulate the size of nutrient reserves (Joyner et al. 1984; Miller 1986; Lovvorn 1994). Both may affect the dynamics of arctic goose body condition in Arkansas. If this is the case, environmental factors likely affect the magnitude of the nutrients lost, but nutrients would be reduced regardless of those factors (Baldassarre and Bolen 2006). The timing of the decline in lipid stores of the arctic geese in this study coincides with a reduction in food availability (Stafford et al. 2006; Massey 2017) as well as the coldest months of the year in Arkansas. Because this study did not consider environmental factors such as daily temperature, precipitation, or large-scale rice depletion, further investigation may be required to understand how environmental and endogenous factors interact and influence the nutrient dynamics of arctic geese wintering in the MAV.

Protein stores did not change significantly over the wintering period. This is not surprising because protein stores are likely a less important energy source for winter survival. Due to the complexity of the processes involved in both creating and catabolizing proteins, waterfowl typically only use protein stores as a source of energy in extreme cases when lipid stores are diminished (Blem 1990).

Based on these findings, it appears that arctic geese use Arkansas an area to merely survive the winter rather than to build large nutrient stores for breeding. Lipids gained by greater white-fronted geese in the MAV of Arkansas are mostly depleted in Arkansas, while snow and Ross's geese do not appear to store a substantial amount of lipids during their stay in the MAV. These results are consistent with the findings of other midcontinent arctic goose body condition studies conducted on the historical wintering grounds and in spring staging areas (Hobaugh 1985; Alisauskas 1988; Krapu et al. 1995; Alisauskas 2002; Pearse et al. 2011). Snow geese wintering on a rice-producing landscape in southeast Texas in the late 1970s showed a similar decline in lipid levels though the winter, with Hobaugh (1985) observing the lowest lipid levels in February. Alisauskas (1988) found that the lipid stores acquired by snow geese on the historical wintering grounds in Louisiana and Texas were negligible compared to the stores that were acquired during spring migration. Lipid levels of greater white-fronted geese collected in February in our study were comparable with greater white-fronted geese arriving in Nebraska in February after departing historical wintering grounds (Krapu et al. 1995; Pearse et al. 2011). Based on our results, it seems that the nutrient dynamics of arctic geese wintering in the MAV are very similar to the nutrient dynamics observed on the historical wintering grounds, despite differences in landscape composition and climate. Midcontinent arctic geese likely continue to obtain most of the nutrients they will use during the breeding season while staging in agricultural habitats during spring migration (Alisauskas 1988, 2002; Pearse et al. 2011).

Though our results demonstrate that arctic geese may not carry nutrients gained in Arkansas to the breeding grounds, there may be other indirect effects of winter body condition on breeding propensity. Presumably, individuals in better body condition with larger nutrient stores have higher chances of surviving winter, finding or retaining a mate, and completing migration to the breeding grounds (Ward et al. 2005; Sedinger et al. 2011). In this way, the nutrient dynamics of arctic geese on the wintering grounds may have some effect on the fecundity and breeding propensity of arctic geese on the breeding grounds (Ankney and MacInnes 1978; Sedinger and Alisauskas 2014).

Supplemental Material

Please note: The Journal of Fish and Wildlife Management is not responsible for the content or functionality of any supplemental material. Queries should be directed to the corresponding author for the article.

Table S1. Microsoft Excel file containing a summary of data (n = 366) for collection date, species, age, sex, body weight, muscles masses, and weight of nutrient stores used in the analysis of body condition of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. The file also includes dissection and proximate analysis data for each of the 366 geese collected.

Found at DOI: https://doi.org/10.3996/062018-JFWM-047.S1 (59 KB XLSX).

Text S1. Text document containing a detailed protocol for conducting proximate analysis using an Ankom XT15 Lipid Extractor. We used the lipid mass and protein mass measured by these analyses as an index of body condition for greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017.

Found at DOI: https://doi.org/10.3996/062018-JFWM-047.S2 (21 KB DOCX).

Reference S1. Askren RJ. 2016. Migration chronology, distribution, and winter habitat selection of the Midcontinent population of greater white-fronted geese. Master's thesis. Monticello: University of Arkansas at Monticello.

Found at DOI: https://doi.org/10.3996/062018-JFWM-047.S3 (2.22 MB PDF).

Reference S2. Dzubin A, Cooch EG. 1992. Measurements of geese: general field methods. Sacramento: California Waterfowl Association.

Found at DOI: https://doi.org/10.3996/062018-JFWM-047.S4 (30.72 PDF).

Reference S3. Massey ER. 2017. Winter diet and body condition of arctic geese wintering in the Mississippi Alluvial Valley of Arkansas. Master's thesis. Monticello: University of Arkansas at Monticello.

Found at DOI: https://doi.org/10.3996/062018-JFWM-047.S5 (1.81 MB PDF).

Reference S4. [USFWS] U.S. Fish and Wildlife Service. 2017a. Waterfowl Population Status, 2017. Washington, D.C.: U.S. Department of the Interior.

Found at DOI: https://doi.org/10.3996/062018-JFWM-047.S6 (3.96 MB PDF).

Archived Material

Please note: The Journal of Fish and Wildlife Management is not responsible for the content or functionality of any archived material. Queries should be directed to the corresponding author for the article.

To cite this archived material, please cite both the journal article (formatting found in the Abstract section of this article) and the following recommended format for the archived material.

Data A1. Dryad data repository including fields such as collection date, species, age, sex, body weight, muscles masses, and weight of nutrient stores used in the analysis of body condition of greater white-fronted geese Anser albifrons frontalis, lesser snow geese Anser caerulescens caerulescens, and Ross's geese Anser rossii collected in Arkansas during October–February 2015–2016 and 2016–2017. The archive also includes dissection and proximate analysis data for each of the geese collected as well as metadata for each field. Available at: https://doi.org/10.5061/dryad.0gb5mkkwq

Code A1. GitHub repository containing the fully reproducible statistical analysis associated with this manuscript. Code is written in R programming language and saved as an Rmd (RMarkdown) file. Available at: https://github.com/LGCarlson/10.3996-062018-JFWM-047

Acknowledgments

We extend our appreciation to the private landowners who allowed us access to their property. We thank numerous undergraduate students, graduate students, and program technicians who assisted field collection and lab processing including C. Martin, D. Oden, R. Askren, C. Watt, A. Humphrey, J. Ballard, K. Minor, J. Sas, and N. Graves. We also thank the editors and anonymous reviewers for their contributions to improving this manuscript. Financial and logistical support for this research was provided by Ducks Unlimited, Inc., the University of Arkansas–Division of Agriculture, and University of Arkansas–Monticello College of Forestry, Agriculture, and Natural Resources. The funders of our research had no influence on the content of this manuscript. None of the funders require approval of the final manuscript to be published. This research was conducted in accordance to the University of Arkansas Code of Ethical Conduct.

Any use of trade, product, website, or firm names in this publication is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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Author notes

Citation: Massey ER, Carlson LG, Osborne DC. 2020. Temporal trends in body condition of arctic geese wintering in the Mississippi Alluvial Valley. Journal of Fish and Wildlife Management 11(1):11–21; e1944-687X. https://doi.org/10.3996/062018-JFWM-047

Competing Interests

The findings and conclusions in this article are those of the author(s) and do not necessarily represent the views of the U.S. Fish and Wildlife Service.