Temperate-breeding Canada Geese (Branta canadensis maxima; hereafter Canada Geese) in the Mississippi Flyway recovered from near-extinction by the late 1930s to over 1.4 million individuals in 2016 (Luukkonen and Leafloor 2017). Concurrently, Canada Goose abundance in urban centers increased, resulting in conflicts between geese and people (USFWS 2005). In 2020, United States Department of Agriculture Wildlife Services conducted activities in 49 U.S. states resulting in over 0.75 million Canada Geese being dispersed and over 22,000 euthanized (USDA 2021). Goose harvest regulations were liberalized to increase opportunity for hunters and to help address human-goose conflict. Mississippi Flyway Canada Goose management thus shifted focus from limiting harvest and maximizing abundance of subarctic-breeding geese to increasing harvest of temperate-breeding Canada Geese (Leafloor et al. 2004).
Hunting can be used to reduce overabundant wildlife populations (Conover 2001, Calvert and Gauthier 2005) and is an important component of Canada Goose mortality (Trost and Malecki 1985, Rextad 1992). Many Mississippi Flyway states initiated special Canada Goose harvest seasons, typically occurring before regular seasons, to help address local goose population conflicts (Sheaffer et al. 2005). However, efficacy of special early seasons on Canada Goose survival varied among states and was confounded by changes in harvest levels during regular seasons (Sheaffer et al. 2005). Canada Geese using urban areas may be less available for harvest by hunters because urban areas are often effectively closed to hunting (Luukkonen et al. 2008, Balkcom 2010, Dorak et al. 2017), complicating the use of hunting to address conflict in urban areas.
Urban areas may serve as refuges for Canada Geese when municipal ordinances prohibit hunting or firearm discharge, or when other safety concerns and logistical difficulties make hunting close to developed areas infeasible. During the non-breeding period, Canada Geese and other waterfowl species generally select areas that provide forage resources and minimize mortality risk (Gates et al. 2001, Dorak et al. 2017, Palumbo et al. 2019). Urban areas in agricultural landscapes provide geese with opportunity to minimize mortality risk while acquiring necessary energy, possibly contributing to higher survival than geese using rural areas. Additionally, urban areas provide valuable resources to breeding geese and thus may also be selected as favorable breeding areas (Guerena et al. 2016). Goose home ranges are likely related to distribution of resources in urban and rural landscapes, and seasonal variability in home-range areas could inform when geese are more likely available to hunters. Although some studies have reported higher Canada Goose survival in urban areas than in rural areas (Balkcom 2010, Dorak et al. 2017), others have found survival was similar or different only for certain cohorts (Heller 2010, Groepper et al. 2012, Beston et al. 2014, Shirkey et al. 2018).
Although a number of studies have estimated demographic parameters of Canada Geese marked in urban areas, few have examined fine-scale movement data to assess how movement ecology and harvest availability of Canada Geese breeding in urban areas may differ from those nesting in rural settings. We marked Canada Geese during brood rearing with GPS-GSM (global system for mobile communications) transmitters in urban and rural locations in Iowa, USA to assess efficacy of hunter harvest in managing urban goose populations. Our objectives were to estimate the proportion of time geese were available to harvest, home-range area, and habitat selection during the 2018-2019 and 2019-2020 Mississippi Flyway Canada Goose hunting season frameworks (range of dates during which goose hunting seasons can occur under federal law) for Canada Geese marked in urban and rural areas of Iowa. We hypothesized urban-marked geese would be less available for harvest by hunters than rural-marked geese. Thus, we predicted urban-marked Canada Geese would have a lower proportion of GPS locations in areas where they were available for harvest, smaller home ranges, and would be more likely to select developed areas that serve as refuges from hunting during the non-breeding season than rural-marked geese.
Canada Geese were marked at urban and rural sites in Iowa and goose movement was monitored within and outside of the state. We captured and marked geese at urban sites in metropolitan Des Moines, the most populous city in Iowa with over 214,000 people (U.S. Census Bureau 2021). More nuisance Canada Goose complaints now occur in urban areas than in rural areas in Iowa. In 2020, 69 formal complaints were issued for Canada Geese in urban areas, comprising 73% of the total complaints (O. Jones, Iowa Department of Natural Resources [DNR], unpublished data). The Iowa DNR established urban Canada Goose management zones around metropolitan Des Moines and Cedar Rapids/Iowa City in 2003 and around Waterloo/Cedar Falls in 2008 (Fig. 1). Canada Goose harvest regulations were liberalized in these areas with the goal of increasing goose harvest and reducing conflict. During the 2018-2019 and 2019-2020 goose hunting seasons, urban Canada Goose management zone regulations consisted of a nine-day season in early September with a daily bag limit of five Canada Geese. This early September season around large urban areas was initiated in 2003 with a three-goose daily bag limit that was increased to a daily bag limit of five geese in 2007. We marked geese during brood rearing in approximate proportion to their distribution and abundance observed while scouting for banding locations in metropolitan Des Moines, within the Des Moines urban Canada Goose management zone and municipal city limits, so movement data were representative of geese nesting throughout the urban area.
Rural capture sites were on or adjacent to state wildlife management areas (WMAs) managed by the Iowa DNR that had an area closed to Canada Goose hunting. The Iowa DNR established areas closed to Canada Goose hunting during efforts to restore Canada Geese to the state, and 14 of these areas remain (Jones and Hancock 2014). Areas closed to goose hunting typically contain part of a WMA and surrounding agricultural land, ranging in size from approximately 10-114 square kilometers. These areas serve as goose refuges and attract large concentrations of migrant geese during autumn and winter (Jones and Hancock 2014). We marked geese at or adjacent to 10 closed areas (Fig. 1) distributed across the state so movement data were representative of Canada Geese nesting in rural areas of Iowa.
Our sample of geese marked in urban and rural areas each had a refuge available. Many sites provide escape from hunting disturbance, although these areas may not be easily identified (e.g., a private landowner who does not allow hunting on their property). We chose to estimate use of known refuges so that we could accurately compare movement and availability to hunters for Canada Geese marked in urban and rural areas. Because rural closed areas and urban city limits each provided refuges, the primary difference between these areas was land cover. Little or no developed land cover, defined as low, medium, and high intensity developed using the 2016 USGS National Land Cover Database (Yang et al. 2018), occurred in rural closed areas. Developed land cover including buildings, parking lots, other impervious surfaces, parks, golf courses, and lawns were extensive in Des Moines. Therefore, this allowed us to assess harvest availability and other movement characteristics for geese breeding in these areas of varying landscape characteristics.
We captured Canada Geese during June and July in 2018 and 2019, during the brood rearing period when adult geese were molting remiges and juveniles had not yet attained flight capability. We targeted brood flocks to capture adults with juvenile geese. We captured geese by driving birds over land or water into a portable pen using kayaks and small motorboats when necessary. Authority to capture, band, and mark geese with GPS-GSM transmitters was provided by Iowa State University (ISU) Institutional Animal Care and Use Committee (IACUC) permits 4-18-8741-Q and IACUC-19-068 and banding permit #06790 from the United States Geological Survey (USGS) Bird Banding Lab (BBL). We aged and sexed individuals using plumage characteristics and cloacal examination (Elder 1946). We examined all adult females captured for a brood patch, presence of which indicated the female had attained breeding status and had likely attempted nesting that spring. At each capture location, we marked one-five adult females (Table 1) with GPS-GSM transmitters (n = 45 in 2018 and n = 26 in 2019). Each goose was also fit with a size eight standard aluminum USGS leg band and recaptures of previously-banded geese were recorded. Geese were released at the capture site immediately after banding and data collection. In 2019, one goose was recaptured approximately two weeks after initial marking to replace a failed transmitter, and one non-functioning transmitter was removed from a recaptured goose that had been marked in 2018.
Canada Geese are highly philopatric (Hanson 1997), therefore adult females are likely to return to the same areas to nest each year. Adult females and their mates nesting in urban areas likely contribute most to human-goose conflict by continually returning to and producing young at these locations. Additionally, we expected movements of adult females to be similar to males because of long-term, year-round pair bonding (Rutledge et al. 2015); movements of juveniles were expected to be similar given the tendency of young geese to remain in family units for most of the first year of life, especially in their first autumn. Therefore, we chose to mark only adult females with GPS-GSM transmitters.
In 2018, GPS-GSM transmitters (CTT-ES400; Cellular Tracking Technologies [CTT], Rio Grande, New Jersey, USA) were attached to black plastic neck collars (48 mm diameter) with white alphanumeric codes. In 2019, 11 CTT transmitters recovered from hunter harvest (n = 5) and other mortalities (n = 6) were redeployed on new individuals, and a different model of GPS-GSM transmitter (OrniTrack-N44 3G; Ornitela, Vilnius, Lithuania) was used to mark additional geese (n = 15). Transmitters weighed 67 g and 40 g for the CTT and Ornitela models, respectively. We programed transmitters to record a location every 15 minutes, 24 hours per day except when conditions limited solar recharging, primarily during December through May, when transmitters were programmed to record a location every 30 minutes, 24 hours per day. Data were stored internally on transmitters and uploaded once daily to online databases when the unit was in an area with adequate cellular coverage. Data from each transmitter were automatically forwarded to Movebank (Wikelski and Kays 2019) for retrieval and analysis.
We remotely monitored goose movement daily using data uploaded via cellular connection. We promptly investigated when data indicated a transmitter had been stationary for more than 24 hours (except during the period when geese were nesting and thus stationary during incubation) by visiting the transmitter’s last known location and attempting to observe the individual. When goose mortalities were observed, we attempted to determine cause of death based on the condition of the carcass and surrounding evidence (hunting mortality, predation, ice accumulation, other winter mortality). Transmitters from goose mortalities were collected so they could be redeployed on other individuals in the future. Transmitters were inscribed with text indicating a reward (replica transmitter and a $50 USD Bass Pro Shops gift card) and providing contact information in the event a goose wearing a transmitter was harvested by a hunter or otherwise found.
We retrieved data from Movebank for analysis using the move package (Kranstauber et al. 2018) in Program R (R Core Team 2019). For the 2018-2019 hunting season analyses, we included locations collected between September 1, opening day of goose season in Iowa, and February 15, the latest permitted date for Canada Goose hunting in the Mississippi Flyway federal migratory bird hunting regulation frameworks (USFWS 2018). During the 2019-2020 goose hunting season, one individual migrated to a Central Flyway state which had a goose season open until February 16th (Oklahoma), thus we included data from September 1 to February 16 in 2019-2020. We censored locations with only one satellite fix, a latitude or longitude of 0 (indicating a failed fix), and/or a horizontal dilution of precision (HDOP; D’Eon and Delparte 2005; A. McGann, Cellular Tracking Technologies, personal communication).
To compare availability to harvest by waterfowl hunters, we estimated availability, which we defined as the proportion of locations outside of city limits and outside of areas closed to Canada Goose hunting during daytime for urban- and rural-marked geese, respectively. Although some urban municipalities permitted waterfowl hunting within city limits, opportunities to do so were restricted, and quantifying hunter effort on a fine scale was not feasible. Therefore, we considered areas within city limits effectively closed to hunting. We used the R package amt (Signer et al. 2019) to designate each location as occurring during day or night. Crepuscular locations (recorded during dawn or dusk) were included with daylight locations, corresponding with waterfowl hunting regulations allowing goose harvest to occur from 30 minutes before sunrise to sunset. We excluded night locations because harvest regulations did not permit goose harvest at night.
We obtained spatial data for the urban Canada Goose management zones and areas closed to Canada Goose hunting from the Iowa DNR and city limit boundaries online from Iowa GeoData (State of Iowa 2019). We used the R package sp (Bivand et al. 2013) to clip the city limit data to include cities within the urban Canada Goose management zones, then extracted whether each goose location was within or outside urban city limits, urban Canada Goose management zones, and rural areas closed to Canada Goose hunting (Fig. 1). Because we were also interested in whether geese moved outside the urban Canada Goose management zones during the special urban goose hunting season, we calculated the proportion of locations outside the urban goose management zones during the special urban season each year. We used general linear models via the lme4 package (Bates et al. 2015) to compare harvest availability in relation to the categorical variables of hunting season framework (2018-2019 or 2019-2020) and site (urban or rural). Preliminary analyses of the global model (interactive effects of hunting season and site) using a binomial distribution and logit link revealed data were overdispersed. To account for overdispersion, we included two random effect terms in all models: we used a random intercept for individuals to account for variability between geese and because some individuals were monitored in both years, and we used an observation-level random effect to account for overdispersion. We also chose not to include week as a model variable and instead plotted mean weekly availability separately to examine temporal trends in availability. In addition to the global model, we included all combinations of additive effects of hunting season framework and site for a total of four models. We ranked models using Akaike’s Information Criterion (Burnham and Anderson 2002) adjusted for small sample size (AICc) using the package MuMIn (Bartón 2019). We plotted estimated proportion of locations in areas where harvest could occur and 95% confidence intervals from the top model.
We estimated home range (95% utilization distribution) area for urban- and rural-marked Canada Geese during hunting seasons to compare home ranges using an auto correlated kernel density estimator (AKDE) in the R package ctmm (Calabrese et al. 2016). The AKDE estimates the autocorrelation structure in the data by fitting continuous-time movement models and ranking models using AIC. Home range estimates were derived from the selected model and included an area correction to further reduce bias (Fleming and Calabrese 2017). An extensive review of methods to estimate home-range area with minimal bias using auto-correlated data found AKDE to be the only estimator capable of producing accurate home-range area estimates, and AKDE does not assume independent and identically distributed (IID) data (Noonan et al. 2019).
We determined if individuals were range-resident (plots of variance in position over time reach an asymptote, indicating the amount of space used eventually becomes constant) during the time periods for which we were interested in calculating home ranges by examining a variogram to verify asymptotic space use over time (Noonan et al. 2019). Locations during time periods in which geese were not range-resident (i.e., during migration) were excluded from analyses for those individuals. We calculated home-range areas throughout the entire goose hunting season and for three seasonal time periods within federal goose hunting frameworks: autumn (September-October), winter (November-December), and late winter (January through the framework end date [February 15th in 2019 and February 16th in 2020]). This allowed us to compare home-range area for urban- and rural-marked birds over the period in which geese are subject to harvest, and also to examine how home ranges varied seasonally during the hunting period. We implemented linear mixed effect models using the R package lme4 (Bates et al. 2015) to test for differences in home-range area in relation to urban/rural status and time period. To account for individual heterogeneity and because home ranges of some individuals were estimated in both hunting seasons, we incorporated a random effect of goose ID. Inspection of the distribution of home-range areas and QQ plots suggested a log transformation of home-range area was appropriate because individual variation was high and some home ranges were large. Thus, we used log home-range area as the dependent variable with site, time period, and hunting season (2018-2019 or 2019-2020) as categorical independent variables. We ran all combinations of additive effects for site, time period, and year, and included a global model with additive effects of all three variables for a total of seven models. We inspected residuals to confirm our assumption of normality was sound and calculated overdispersion for the global model. Random effect structure was a random intercept for each goose in all models. We ranked models using Akaike’s Information Criterion (Burnham and Anderson 2002) adjusted for small sample size (AICc)) using the package MuMIn (Barton 2019). We obtained estimated model coefficients and calculated median home-range area and subsequent 95% confidence intervals for urban and rural geese during each time period.
GPS fixes collected at high frequency inherently contain temporal and spatial autocorrelation, complicating inference (de Solla et al. 1999). Multiple methods exist for minimizing bias associated with autocorrelation in movement datasets, although explicitly modeling autocorrelation is often preferred (Fieberg et al. 2010). Step selection analysis provides a method for incorporating the movement process into estimation of resource selection coefficients (Avgar et al. 2015). Modeling the animal’s movement characteristics and the serial structure of GPS data allows for unbiased estimates of habitat selection (Forester et al. 2009, Avgar et al. 2015). Step selection analyses compare where an animal chose to move with possible options that were not selected. Used steps, or consecutive observed locations, are compared with multiple possible but unused steps, thus incorporating the movement process into a used versus available design (Avgar et al. 2015). This integrated approach is biologically relevant because movement and habitat selection are linked: an animal’s movement patterns affect which resources it uses, and resource availability affects how an animal moves across a landscape (Avgar et al. 2012). We followed a similar approach to the analysis of Karelus et al. (2019) in which we incorporated goose movement patterns into habitat selection analysis using hidden Markov models (HMMs; Appendix 1).
We implemented a two-step mixed conditional logistic regression using the TwoStepCLogit package (Craiu et al. 2016) in R, setting strata as step ID and cluster as individual goose ID as described in Appendix 1. This computational method allows estimation of individual parameters via maximum likelihood prior to estimation of population parameters using an expectation maximization algorithm and conditional restricted maximum likelihood (Duchesne et al. 2010, Craiu et al. 2011). This two-step procedure avoids convergence issues in numerical maximization associated with unconditional mixed effect logistic regression for situations in which there are multiple observations per stratum as is common in analyses of wildlife habitat selection (Craiu et al. 2011). We used models with fixed effects of land-cover type and random effects of goose to account for differences in habitat selection between individuals. Because we were interested in comparing habitat selection between the urban and rural groups, and throughout time periods within each goose hunting season, we first fit models for urban- and rural- marked geese for the entire hunting season (data for both hunting season frameworks combined) and then fit models for each combination of urban/rural and time period (September-October, November-December, and January-mid-February). This allowed for comparison of selection coefficients and odds ratios of selection between urban- and rural-marked geese and how these changed through time. We selected open water as the reference category because this was the most-selected land-cover type. Selection coefficients less than zero with confidence intervals non-overlapping zero indicate that land-cover type was less likely to be selected than open water whereas selection coefficients with confidence intervals overlapping zero indicate that land-cover type was selected similarly to open water. Odds ratios less than one with confidence intervals non-overlapping one indicate that land-cover type was less likely to be selected than open water whereas odds ratios with confidence intervals overlapping one indicate that land-cover type was selected similarly to open water.
We used the 2016 USGS national land-cover database (Yang et al. 2018) to extract the land-cover type for each actual and random step, using the land-cover type of the step endpoint. We reclassified land-cover data into the following land-cover types relevant to Canada Goose resource use: open water, wetlands (emergent herbaceous, and woody wetlands), developed open space (urban greenspace such as lawns, parks, golf courses), developed (low, medium, and high intensity developed), barren land (exposed soil with little vegetation), forest (deciduous, evergreen, and mixed forest), pasture/grassland (scrub/shrub, grassland/herbaceous, pasture/hay), and agriculture. We also added a category for urban agriculture to classify agricultural land use that occurred inside city limits in the three urban Canada Goose management zones (Des Moines, Iowa City/Cedar Rapids, Waterloo/Cedar Falls). All other agriculture was classified as rural agriculture. We were specifically interested in whether selection differed between urban and rural agriculture because agricultural fields inside city limits were numerous and often were unavailable to hunters, providing geese with quality forage in areas with minimal hunting disturbance. We calculated resource selection coefficients and odds ratios of selection for the urban and rural groups for the entire hunting period (September 1 to February 15 in 2018-2019 and September 1 to February 16 in 2019-2020) and during autumn, winter, and late winter. We included locations of migrant geese because we were interested in resource selection by individuals that both remained in and left Iowa during the hunting period, and it is unknown whether geese nesting in urban areas would select urban areas during migration.
We marked 45 (n = 30 urban, n = 15 rural) adult females with GPS-GSM transmitters in 2018 and marked 26 (n = 15 urban, n = 11 rural) in 2019 (Table 1). Fifteen individuals marked in 2018 and six individuals marked in 2019 were geese previously banded in Iowa. A total of 83% of marked females had a brood patch. GPS-GSM transmitters collected 465,881 locations from 45 (30 urban, 15 rural) Canada Geese during September 1, 2018 to February 15, 2019 and 522,496 locations from 46 (30 urban, 16 rural) geese during September 1, 2019 to February 16, 2020. Twenty-one transmitters (16 urban, 5 rural) remained active from the first year of marking and these individuals were monitored for a second goose hunting season in 2019-2020. Failed (latitude or longitude of 0) or inaccurate (HDOP > 5 m) fixes numbered 10,527 (2.2%) during 2018-19 and 5437 (1.0%) during 2019-2020. This resulted in 972,413 useable GPS locations collected during open goose hunting seasons. There were 92% of transmitters with < 5% and 88% with < 2% unusable fixes. Mean proportion of unusable fixes collected by transmitters were 0.02 (range: 0-0.16) for rural transmitters and 0.01 (range: 0-0.09) for urban transmitters. After removing locations with HDOP, useable fixes had an average HDOP of 1.65 m (range: 0-5).
On average, transmitters were deployed on an individual goose for 253 (range: 1-533) days over the course of the study. Eleven transmitters were recovered from hunter harvest (n = 5 in 2018-2019 and n = 6 in 2019-2020) and nine transmitters (n = 6 in 2018-2019 and n = 3 in 2019-2020) were recovered from other mortalities (Table 1). Ten geese with transmitters harvested by hunters were marked in urban locations (22%), while one goose marked with a transmitter in a rural area was harvested (4%). All harvest of marked geese occurred in Iowa except one urban-marked individual that was harvested in southern Manitoba. We were unable to document the fate of 18 transmitters throughout the duration of the study that stopped connecting with no apparent sign of mortality (i.e., lack of movement) for those individuals (Table 1). For these cases, transmitter failure or undocumented mortality are most likely. We had little reason to suspect transmitter loss because inspection of transmitters present on geese recaptured a year after initial transmitter deployment showed no sign of cracking or other structural deterioration. Some transmitter battery levels slowly declined despite reducing duty cycles in an attempt to allow recharging, indicating transmitter failure was likely and loss of ability to track these individuals made confirmation difficult.
The proportion of locations available to hunter harvest (outside city limits or closed areas for urban or rural geese, respectively) were distributed similarly for each hunting season framework (2018-19 or 2019-20). Correspondingly, the top model included only an effect of urban-rural status on proportion of available locations and received 66% of the model weight (Table 2). The second-ranked model included additive effects of site and season and received 23% of the model weight, however, season was an uninformative parameter (Arnold 2010). Harvest availability estimates, or the estimated proportion of locations outside of city limits for urban geese or closed areas for rural geese, were 0.07 (95% CI: 0.04-0.13) and 0.56 (95% CI: 0.34-0.76), respectively.
We also found availability varied temporally within each hunting season, with the largest variation occurring for rural-marked geese (Fig. 2). Geese marked at both urban and rural sites were generally most likely to be outside city limits or closed areas in September and early October, or in late January and early February. During the 2018-2019 goose hunting season, urban-marked geese had greater temporal variation in availability than during 2019-2020 (Fig. 2), although trends over time were similar between hunting seasons. Rural-marked geese also exhibited similar trends in availability during both hunting seasons.
During the special September Canada Goose hunting seasons within the urban Canada Goose management zones, some geese were unavailable for harvest due to movement outside hunting zone, although the majority of urban-marked geese stayed inside the urban goose management zone and were available to harvest during the special urban season. Ten (33%) urban-marked individuals had at least one daytime location outside the urban Canada Goose management zone in 2018 while four (13%) did in 2019. Mean proportion of locations outside the urban goose management zone for individuals leaving the zone were 0.24 (range: 0.19-0.29) and 0.29 (range: 0.13-0.44) in 2018 and 2019, respectively. Mean proportion of locations outside the urban goose management zone during the special September Canada Goose seasons for all individuals were 0.08 (range: 0.07-0.09) and 0.04 (range: 0.03-0.05) in 2018 and 2019, respectively. Geese that moved outside the urban Canada Goose management zone left city limits (and did not enter other municipalities), but were still unavailable for harvest during the special urban season due to regulations that restricted hunting to occur within urban goose management zones.
There was no evidence of lack of fit for the global model. Models explaining log home range area as a function of time period (autumn, winter, late winter) were most supported; models without time period as an explanatory variable received almost no support (Table 3). The top model included time period only and received 64% of the model weight while the second-ranked model included time period and site, receiving 22% of the model weight (Table 3). However, in the second-ranked model, 95% confidence intervals on the site coefficient included zero, indicating site was an uninformative parameter (Arnold 2010). Median home-range areas for the entire goose hunting season were not significantly different for urban-marked (30.05 [95% CI: 21.0-41.23]) and rural-marked (37.97 [95% CI: 24.55-58.71]) geese. Models did support differences in median home-range area across the three time periods: home-range area decreased from autumn to early and late winter. Median home-range area estimates were lower in late winter than autumn and early winter for rural geese and significantly lower in early and late winter than in autumn for urban geese (non-overlapping 95% confidence intervals).
Open water was the most-selected land-cover type for both urban- and rural-marked Canada Geese and thus open water was the reference category for our analysis. Following open water, barren, urban agriculture, and pasture were most selected by both groups, and wetlands were selected similarly to barren land by rural geese. Forest land cover was the type least likely to be selected by both urban and rural geese (Table 4; Figure 3).
Several land-cover types had significantly different odds ratios of selection for urban and rural geese (Fig. 3). Urban Canada Geese had greater odds of selecting developed and developed open land-cover types than rural geese. Rural geese had substantially greater odds of selecting wetlands than urban geese. Confidence intervals on odds of selecting rural and urban agriculture overlapped for urban- and rural-marked geese. However, urban-marked geese were more likely to select agriculture in urban areas than agriculture outside city limits.
Selection coefficients and odds ratios did not differ significantly across time periods within goose hunting seasons for urban or rural geese. Few rural geese selected or had urban agriculture available during autumn, thus uncertainty on selection coefficients and odds ratios was high for this time period. Selection of developed areas increased for rural geese during late winter because some individuals moved into cities during winter. However, these only represented a small proportion of the rural geese and thus odds of selecting developed areas in January and February were only slightly higher than earlier in goose hunting seasons.
Canada Geese that nested in urban areas used areas that were open to hunting far less than geese that nested in rural areas (Fig. 2). Because GPS-GSM transmitters collected locations equally spaced through time, our measure of availability can also be viewed as the approximate proportion of time spent outside refuge areas (urban city limits or rural closed areas) during daylight in goose hunting seasons, meaning rural birds were in areas available to hunters significantly more often than urban geese. Due to difficulties quantifying hunting effort at a fine scale throughout goose hunting seasons, we did not directly measure hunting pressure on urban and rural Canada Geese. However, band recovery data and Iowa DNR engagement meetings with goose hunters suggest hunting intensity (e.g., number of hunters, number of days hunted) is greater around metropolitan Des Moines than near the rural closed areas included in our study (C. Ensminger, personal communication). This perhaps partially explains why urban geese leave city limits less often than rural geese leave refuge areas because waterfowl seek areas with minimal disturbance, especially hunting disturbance (Roy et al. 2014, Palumbo et al. 2019). Additionally, lower availability of Canada Geese using urban areas agrees with some literature suggesting higher survival for geese in urban areas (Balkcom 2010, Heller 2010, Dorak et al. 2017), although results are mixed with others finding urban goose survival was similar or even lower and with effects sometimes depending on cohort (Beston et al. 2014, Shirkey et al. 2018, Ladin et al. 2020). In our study, the percentage of locations geese were in areas unavailable to hunters was higher for urban geese (93%) but lower for rural geese (44% [95% CI: 34-76%]) compared to Canada Geese marked in southern Quebec, where 73.2% ± 6.2% of locations were in areas where hunting was prohibited (Beaumont et al. 2013). Furthermore, although we expected movement of adult females with young to be representative of their mates (paired adult males) and juveniles, Beaumont et al. (2013) reported adult females attending young were less likely to use areas in which harvest could occur than adult females without young. This may suggest that our estimates of availability based on adult females with young are minimums, and that adults without young and subadults may be more likely to leave refuge areas. However, there are a multitude of factors that affect goose availability to hunters, such as skill and equipment of hunters (Harvey et al. 1995), goose body condition, and weather. Despite lower harvest availability, a higher proportion of geese marked in urban areas were harvested by hunters, suggesting availability for harvest does not directly translate to realized harvest. Band recovery data show adult survival and recovery rates are similar for geese banded in urban and rural areas of Iowa (Luukkonen et al. 2021), whereas movement data collected in this study show geese that enter areas open to hunting have higher mortality than geese that stay in areas closed to hunting. Observations and interactions with goose hunters suggested that hunters in the Des Moines area were actively targeting neck-banded geese, although there was no evidence of targeting at rural sites (C. Ensminger, personal communication). This along with high goose hunter effort around Des Moines and higher visibility of urban rather than rural geese may partially explain higher harvest of urban-marked individuals. Although harvest depends on many factors in addition to availability, results show that geese nesting in urban areas are available to hunters and contribute to goose harvest.
Metropolitan Des Moines provided a much larger area of refuge than any single rural closed area, thus urban-marked geese had a larger refuge area available. However, because wildlife managers cannot easily affect the amount of land area currently developed, we did not see relevancy in adjusting availability metrics to the amount of refuge available and were instead interested in how often geese leave urban areas under current conditions and despite a larger available area. Furthermore, our estimate of availability is likely a minimum estimate, and in reality, is likely greater for the population of Canada Geese nesting in metropolitan Des Moines. Goose hunting was permitted under city or town ordinances within a few municipalities, with some requiring special authorization of city councils or other governing bodies, thus a small proportion of locations occurring inside city limits are in areas where goose hunting could occur. However, ordinances enforced by individual municipalities limited opportunities to hunt within city limits and due to difficulty in measuring hunter activity on fine spatial and temporal scales comparable to our location data, we elected to use city limits as a conservative measure of availability for harvest.
We also found that geese in both urban and rural areas were most likely to be available to hunters during the early and late portions of goose hunting seasons. These time periods generally coincide with the periods when geese also had the largest home ranges early in the hunting seasons, and when geese, particularly from rural areas, migrated later in goose seasons. Geese are likely more naive to hunters early in the hunting season, and early seasons have been successfully used to focus harvest on temperate-breeding goose populations (Sheaffer et al. 2005). Most recoveries of Canada Geese banded in Iowa 1999-2019 occurred early in goose hunting seasons, further suggesting geese are most available for harvest in September and October (Luukkonen 2020). Recently, an option for the August take of Canada Geese outside of the Migratory Bird Treaty Act framework has become available (USFWS 2022). However, Mississippi Flyway states have only relatively recently liberalized Canada Goose seasons to make use of the entire 107-day framework and these changes need evaluation. August is not a desirable time for Canada Goose harvest for hunters or wildlife managers, and little crop (primarily corn and soybeans) harvest occurs in August in Iowa, resulting in few areas where urban-nesting geese are available for harvest (O. Jones, personal communication).
Availability was slightly higher during 2018-2019 than during 2019-2020 for urban geese, potentially because crop harvest was delayed in autumn 2019 due to weather conditions. This resulted in lower availability of agricultural fields early in the goose season. Increased availability late in the hunting period was primarily due to migration, and likely partially a result of closure of Iowa’s goose hunting season, eliminating hunting disturbance. Cold temperatures and reduced availability of forage due to snow cover could lead to some individuals leaving city limits to forage in agricultural fields and food resources in city limits may become depleted late in the season, although we did not observe higher selection for agriculture in late winter. Nonetheless, some individuals left city limits more often during this period, presumably to find food. Although Canada Geese breeding in urban areas are likely less available to goose hunters than geese breeding at rural sites, urban geese do leave city limits, and potential exists to increase their harvest availability. Expanded hunting seasons during September would allow increased opportunity during a period when urban geese were most likely to use areas available to hunters. January and February are another time period of higher availability for urban geese, although presence of Canada Geese from other populations or of other sub-species may buffer harvest of locally nesting geese during winter.
Home ranges were generally similar for urban and rural Canada Geese, contrary to our prediction that urban geese would have smaller home ranges than rural geese. Models indicated time period was most influential on home-range size, and home ranges decreased from autumn into winter and late winter for both urban and rural geese. There was relatively high individual variation in home-range size for both urban and rural geese, suggesting individuals use landscapes differently. Urban geese may experience a higher variability in spatial distribution of resources across individual home ranges, likely requiring some individuals to move greater distances to obtain necessary resources. Likewise, some urban-marked geese had large home ranges that covered a large portion of metropolitan Des Moines.
Our home-range area estimates during winter and late winter are similar to those reported for Canada Geese marked during winter in the greater Chicago metropolitan area (Dorak et al. 2017). However, our estimates of median home range were larger than mean home range estimates reported for Canada Geese in suburban areas of North Carolina, although time periods during which home ranges were calculated differed (Rutledge et al. 2015). During hunting seasons and excluding migratory movement, Canada Geese are likely most mobile during autumn (September and October). Decrease in home-range areas from autumn to winter periods were also observed in Canada Geese in suburban North Carolina (Rutledge et al. 2015). Although urban geese used smaller areas and were less likely to leave city limits during hunting seasons, individuals did leave city limits to forage in agricultural fields, whereas no individuals were observed doing so during winter in Chicago (Dorak et al. 2017). Because metropolitan Des Moines is much smaller than Chicago and contains agricultural fields within city limits, geese likely have more opportunity to use agricultural fields within and around Des Moines while avoiding hunting disturbance and minimizing energy expenditure. However, individual variation is high and some individuals in our study rarely left city limits to forage while others readily ventured away from developed areas, resulting in greater harvest risk. As median home-range size and trends in area throughout the hunting period were similar for urban and rural geese, home ranges may be influenced by similar processes that are at least partially independent of hunting pressure or land cover. Urban environments may provide adequate resources such that geese using these areas do not need to adjust home ranges to acquire necessary resources. Additionally, winter weather affects both urban and rural geese and thus may explain seasonal reduction in home ranges. Reduced daily movement in winter may reflect behavioral changes whereby Canada Geese attempt to conserve energy and feed less when food is difficult to obtain due to frozen water and deep snow, which make field foraging more energetically costly (Gates et al. 2001).
Habitat selection results suggest that Canada Geese marked in urban and rural areas selected some habitats differently. Unsurprisingly, open water was the most-selected land-cover type overall, regardless of time period for urban and rural geese. Although wetlands had relatively high selection by rural-marked geese, selection of wetlands was low for urban geese (Table 4, Fig. 3). Open water is used extensively during roosting and loafing and functions to reduce vulnerability of geese to terrestrial predators. Another important difference between urban and rural geese were significantly greater odds of geese marked in urban areas selecting developed and developed open land-cover types (Fig. 3). Geese using urban areas have adapted to using non-traditional habitats and appear able to obtain necessary resources even with reduced use of wetlands. Although the developed land-cover category included more intensely developed areas, the developed open land-cover type included areas such as golf courses, parks, lawns, and other urban greenspaces (Yang et al. 2018) that are attractive to Canada Geese and provide resources for activities such as foraging or resting. Making these areas less appealing to geese by planting vegetated buffers around water areas may reduce use by geese (Jones and Handcock 2014). Urban geese were more likely to select agricultural fields within city limits than agricultural fields outside city limits. Agricultural fields in city limits likely provide a relatively safe and high energy foraging option for geese, and although less available, were also selected similarly by rural geese. In Des Moines, approximately 20% of the land area within city limits consists of agricultural fields (Yang et al. 2018), representing an opportunity to increase hunter access to geese where hunting can safely be conducted. Urban agriculture was available for some rural-marked individuals when geese made large movements to winter in an urban area, although urban agriculture was not available for rural geese during most of the year. Both urban and rural geese had high odds of selecting barren land, despite relatively low availability. This primarily included areas such as gravel pits, mud flats, riparian areas, and idle crop fields, particularly in rural areas, whereas in urban areas included new residential or commercial development.
In this study, Canada Geese that nested in urban areas used developed areas during goose hunting seasons, resulting in these individuals generally being less available for harvest than geese using rural areas. Selection of some other habitats typically important in the Canada Goose’s lifecycle, such as wetlands, were lower for urban-marked geese than for rural-marked geese. This complicates use of hunter harvest to manage urban goose populations. However, use of developed habitats inside city limits in the non-breeding period was not as extreme as geese wintering in Chicago (Dorak et al. 2017) and individuals monitored in our study routinely left city limits. Although it is established that geese and other waterfowl use developed areas (Conover and Chasko 1985, Ankney 1996, Beaumont et al. 2013, Palumbo et al. 2019) and that Canada Geese in urban areas may have different demographic parameters than geese in more rural habitats (Heller 2010, Beston et al. 2014, Dorak et al. 2017, Shirkey et al. 2018, Ladin et al. 2020), opportunity exists to increase harvest of geese using urban areas, particularly for metropolitan areas of similar size or smaller than Des Moines.
Although Canada Geese using urban areas may be less available to hunters, movement data revealed where and when opportunity exists to increase harvest, addressing multiple goals of helping to reduce conflict while providing recreational opportunity. Geese have the largest home ranges and are most likely to be outside refuge areas, including city limits, early in the goose hunting season. Therefore, early seasons with liberal bag limits in September and early October are most likely to affect urban geese. Migrant geese from other populations begin arriving during October, peak in late November and early December and are likely to buffer harvest of locally breeding geese later in the season (Iowa DNR, unpublished data). Geese in this study left city limits late during federal hunting frameworks, and although this is partially due to migratory movement, late seasons may be another option to direct harvest on urban geese. However, the scale of urban development may be a factor affecting harvest availability because some geese wintering in extremely large metropolitan areas such as Chicago rarely leave (Dorak et al. 2017). Liberalizations to bag limits or seasons could be implemented at smaller spatial scales than the state or hunting zone levels if it is unnecessary or undesirable to expose geese that do not nest in large urban areas to additional harvest pressure.
Movement data indicated geese in urban areas select agricultural fields within city limits. These may be areas in which hunting could safely occur, although hunting may also be logistically difficult or illegal due to local ordinances. Implementing policies allowing waterfowl hunting within municipalities where safe to do so would increase hunter access to agricultural fields within city limits and would create capacity for additional harvest. To achieve a balance between minimizing conflict in urban areas while managing Canada Geese as a valuable resource, managers may consider hunter harvest as a primary method of population management while using Canada Goose ecology to inform harvest prescriptions and other management actions to reduce conflict.
Any mention of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by
the United States Government. We thank Iowa DNR staff and volunteers who helped capture and band geese. We also thank the hunters and others who reported banded geese. Without their efforts, this research would not be possible. Comments and reviews provided by P. M. Dixon, A. K. Janke, and K. A. Abraham greatly improved this manuscript. C. I. Ensminger provided helpful assistance in administrative and many other aspects of the project. Funding was provided by Iowa DNR, Department of Natural Resource Ecology and Management, Iowa State University, and through the Federal Aid in Wildlife Restoration Act, grant F18AF00313.
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