INTRODUCTION

Many conservation decisions and predictive models require a detailed understanding of habitat associations at individual and population scales. Unlike survey or mark–recapture techniques, telemetry-based studies (Boyd et al. 2004) provide continuous data on the movements of known individuals over time, offer individual- and location-specific information on preferred habitat characteristics, and can be used to identify remote areas of particular conservation importance that might not otherwise be recognized (Tancell et al. 2013, Lamb et al. 2019). These advantages, along with advances in the accessibility and miniaturization of individual-borne telemetry devices, have contributed to the growing importance of telemetry data in wildlife ecology (Geen et al. 2019).

To ensure that telemetry studies are safe for their subjects, researchers and veterinarians generally seek to minimize mortality risks associated with radio-marking by following standard guidelines (e.g., limiting transmitter weight to less than 3–5% of an individual's body mass; Kenward 2001) and measuring survival of radio-marked birds in captive or field trials to identify factors contributing to mortalities (e.g., Hatch et al. 2000, Sexson et al. 2014, Le Net et al. 2019). However, even in cases where transmitter attachment conforms to recommendations and does not result in mortality, the energetic cost of carrying a payload can affect energetics, physiology, and life history in captive (Latty et al. 2010, 2016) and free-living birds (Calvo and Furness 1992, Barron et al. 2010, Vandenabeele et al. 2012). If the sublethal effects of radio-marking on individual behavior or fitness are not incorporated into data analysis, they may subsequently bias population-level inferences on habitat use and distribution derived from tracking data (Igual et al. 2005, Hebblewhite and Haydon 2010). Studies of sublethal transmitter effects have found that energetic consequences of transmitters may include alterations in day-to-day movements and activity budgets (e.g., individual behavior: Hamel et al. 2004, Latty et al. 2010, Enstipp et al. 2015, Kenow et al. 2018) or fitness (e.g., survival and reproduction: Fast et al. 2011, Schacter and Jones 2017, Lameris et al. 2018) and may vary across short- and long-term timescales and among species, individuals, transmitter types, and attachment techniques (Barron et al. 2010, Fast et al. 2011, Vandenabeele et al. 2012, Lameris and Kleyheeg 2017). Factors such as handling time, sex, breeding location, and timing of capture have previously been linked to variation in negative effects of capture and tagging among groups of individuals (Lamb et al. 2016, Snijders et al. 2017). Therefore, to ensure that data obtained from tracked individuals accurately represent the general population, it is important to consider sublethal impacts of capture and tagging, and their variation among individuals, in analyses and interpretation of telemetry data.

Despite potential tag effects, the majority of researchers reporting data from movement studies do not include any assessment of how sublethal transmitter effects may influence their findings (Calvo and Furness 1992, Vandenabeele et al. 2011, Geen et al. 2019). This may be due in part to the difficulty of observing behavioral changes in free-living individuals. Since telemetry studies often target individuals or species whose life histories make them difficult to observe directly (Hussey et al. 2015, Kays et al. 2015), fully controlled observational studies comparing the behavior of radio-marked individuals with controls may not be feasible, particularly over wide areas or long timescales. However, in the absence of such systematic study, telemetry data themselves provide an opportunity to investigate long-term patterns of individual behavior following capture and radio-marking. Process models and algorithms (e.g., state-space models [Jonsen et al. 2005], hidden Markov models [Patterson et al. 2009], expectation maximization binary clustering [Garriga et al. 2016]), which treat data as discrete samples of continuous underlying movement states, are frequently used to identify and classify the behavioral processes that generate observed locations. While not as informative as a fully controlled comparative study, such models can be used to infer changes in individual post-capture behavior over time based on movement data, and subsequently compare patterns of behavioral change among individuals and species.

Rather than directly evaluating the sublethal effects of transmitters, analysis of telemetry data often assumes that immediate effects of capture and radio-marking last for a fixed amount of time in all individuals, after which effects are negligible (e.g., Nenno and Healy 1979). This practice is commonly used in movement studies of sea ducks (Tribe: Mergini), which are frequently targeted for telemetry, and, like other waterfowl, may be especially vulnerable to tag effects (Lameris and Kleyheeg 2017). Recent movement analyses of sea ducks with implanted transmitters (e.g., De La Cruz et al. 2009, Loring et al. 2014, Beuth et al. 2017, Meattey et al. 2018, Lamb et al. 2019) have censored data from the 14-day period immediately following transmitter attachment. The decision to remove the first 14 days of movement data is based on a study of Harlequin Ducks (Histrionicus histrionicus), in which the authors observed that most mortality of transmitter-equipped individuals occurred within 14 days of release (Esler et al. 2000). However, no assessment has yet been made to determine whether this window is also appropriate to account for sublethal behavioral effects of transmitter attachment. Previous research has suggested that waterfowl and other waterbirds with implanted transmitters may experience changes in behaviors such as foraging, migration, and reproduction that extend beyond the immediate post-capture period (e.g., Meyers et al. 1998, Hupp et al. 2003, Latty et al. 2010, Hooijmeijer et al. 2014). Thus, depending on the specific research question of interest, sublethal effects of transmitter implantation may affect inferences derived from telemetry data even after accounting for the initial post-capture adjustment period.

The objective of this study was to evaluate the effects of capture and transmitter implantation on short- and long-term behavioral and movement patterns derived from telemetry data. We used telemetry data from a long-term sea duck tracking dataset, the Atlantic and Great Lakes Sea Duck Migration Study (AGLSDMS), which includes data from hundreds of individual sea ducks with coelomically implanted satellite transmitters in eastern North America representing 5 high-priority species (Common Eider [Somateria mollissima], Surf Scoter [Melanitta perspicillata], White-winged Scoter [M. deglandi], Black Scoter [M. americana], and Long-tailed Duck [Clangula hyemalis]). The AGLSDMS dataset is useful for examining transmitter effects as it includes multi-year data from individuals across a variety of capture locations, capture times, species, ages, and sexes. We used state-space models to test for the presence and duration of altered behavior immediately following capture and transmitter implantation, and compared subsequent phenological parameters (breeding site attendance, breeding initiation dates, timing and duration of migration) among post-capture years to evaluate the potential effects of transmitter attachment on movement, behavior, and reproduction. We further examined relationships of behavioral responses to individual covariates including capture season, species, age, and sex. Our goal is to provide guidelines for incorporating effects of capture and transmitter implantation into analysis of movement patterns across various temporal scales, while also accounting for inter- and intraspecific variation.

METHODS

Study Area

Biologists and collaborators with the AGLSDMS project captured sea ducks in multiple areas along the Atlantic Coast and Great Lakes of North America during the molting, staging, and wintering time periods (August–March) from 2002 to 2016 (Figure 1). To maximize capture efficiency, sampling locations were selected to represent locations and time periods of particularly high nonbreeding concentrations of each species. This approach resulted in a lack of sampling efforts in less-utilized winter and staging sites; however, given the tendency of waterfowl to form large aggregations during the nonbreeding time period (Weller and Batt 1988), sampling known areas of high sea duck concentrations allowed AGLSDMS collaborators to efficiently capture individuals and deploy transmitters. Capture efforts for Long-tailed Ducks and Surf Scoters focused primarily on wintering sites where high bird concentrations are typically observed during the Atlantic Winter Sea Duck Survey (Silverman et al. 2013). Additional captures of Surf Scoter (40%) were carried out during fall migration in the St. Lawrence Estuary, Quebec, and additional sampling of Long-tailed Ducks wintering on Lake Michigan was added late in the project to account for areas of known use not represented by locations of individuals captured elsewhere. Capture efforts for Black Scoters focused on spring staging sites on Resticouche River and Chaleur Bay between New Brunswick and Québec, in the Gulf of St. Lawrence. Over 60% of White-winged Scoter captures were conducted on molting grounds in the St. Lawrence Estuary. Sampling of Common Eiders was limited to 1 of 3 eastern subspecies (S. m. dresseri) during breeding and wintering periods.

FIGURE 1.

Locations, capture years, and sample sizes by species of sea duck telemetry studies used to analyze transmitter effects, eastern North America, 2002–2016 (BLSC = Black Scoter; COEI = Common Eider; LTDU = Long-tailed Duck; SUSC = Surf Scoter; WWSC = White-winged Scoter).

Effects of individual covariates.

We used mixed effects models to evaluate the relationship of behavior and phenology to individual covariates following capture and transmitter implantation. After using histograms to examine the distributions of individual response variables and determine the most appropriate distribution for each model, we fit generalized linear mixed models (GLMMs) to 11 response variables: time-to-normal (exponential distribution, log link), breeding site attendance (binomial distribution, logit link), breeding initiation date, and start dates and durations of breeding, molt, winter, and spring migrations (Gaussian distribution, identity link). Predictors included fixed effects of years post-capture, capture season (fall migration/molt, winter, or spring), transmitter manufacturer, transmitter weight as a percentage of body weight, age (subadult or adult), species, sex, and their interactions, as well as individual, capture site, and year as random factors. After testing the fit of the global model for each response variable, we dropped random terms whose variance values were close to zero, and generated candidate models using a backward stepwise selection of the remaining fixed terms. When all remaining terms were significant (P < 0.05), we considered this our final model.

TABLE 1.

Sample sizes for transmitter deployments on 5 species of sea ducks in eastern North America, 2002–2017.

RESULTS

Of 672 individual radio-marked sea ducks (deployed), 476 had sufficient data for our analysis (retained; Table 1). Sample sizes for each species ranged from 76 (Common Eider) to 139 (Surf Scoter), and sampling for each species included individuals of both sexes. The sample included 424 adults and 52 subadults at capture. Transmitter masses varied from 1.4 to 5.7% of body mass, with species-specific means ranging from 2.3 to 4.3%, and an overall mean of 3.6 ± 0.9% (Table 1).

Behavioral state b-values were generally below seasonal averages (i.e. more dispersive) immediately following capture, but increased to seasonally average levels within ∼5 days (Figure 2). After reaching normal levels, behavioral state values for all species remained within the normal range through the rest of the 14-day post-capture period.

Our final model for time-to-normal included species, age, and capture season, with random effects of year and capture region (Table 2A). Median time-to-normal was highest (∼6 days) and most variable for Common Eiders, intermediate (∼5 days) for Long-tailed Ducks and White-winged Scoters, and lowest for Black and Surf Scoters (∼4 days) (Figure 3). Common Eiders took significantly longer than the other 4 species to reach b_norm, while the 3 scoter species and Long-tailed Duck intervals were similar (Table 2A). Males were slightly quicker than females to return to normal behavior, although the 95% confidence interval of the coefficient estimate overlapped zero. Birds captured during winter took longer to return to normal behavior than birds captured during breeding or migration (Table 2A), with effects varying by species (Figure 3).

FIGURE 2.

Differences in daily behavioral (movement pattern) states from seasonal averages for the first 14 days after transmitter implantation for 5 sea duck species tracked between 2002 and 2017 in eastern North America. Values below 0 indicate more dispersive behavior, while values above 0 indicate more sedentary behavior. Dots represent individual values (see Table 1 for sample sizes by species). Box plots show median (center bar), interquartile range (box), and minimum/maximum (vertical bars: ± 1.5 times interquartile range) values.

Our final model of breeding site attendance included years post-capture, age, species, sex, and the interaction of species and sex, with a random term for year (Table 2B). Breeding site attendance was positively correlated with the number of years post-capture, indicating lower breeding site attendance in the first breeding season after capture compared with subsequent years. Breeding site attendance also increased with age, and subadults consistently showed lower breeding site attendance than adults. Males of the 3 scoter species attended breeding sites at lower rates than females, while breeding site attendance was similar between male and female Common Eiders and Long-tailed Ducks (Figure 4).

Our final models for breeding initiation date included years post-capture, capture season, transmitter weight, and species (Figure 5A). Our final models for start date and duration of migrations included years post-capture, capture season, sex, and species (Figure 5B, C). Number of years post-capture affected breeding initiation date and the timing of spring staging and breeding migrations, with both breeding and migration dates delayed during the first year after capture relative to subsequent years (Figure 5A, B; Figure 6). Molt and wintering migrations also tended to occur later during the first year after capture compared with subsequent years, although these differences were not significant (Figure 6). Duration of winter, spring staging, and breeding migrations also increased with years post-capture, indicating quicker migrations immediately following transmitter attachment than in subsequent years (Figure 5C). Individuals captured during spring staging or breeding had delayed breeding initiation dates compared with individuals captured in fall or winter (Figure 5A). Effects of capture timing on migration start date and duration were variable (Figure 5B, C). Males had later and shorter breeding migrations than females but started fall molt and winter migrations earlier (Figure 5B, C). Birds with heavier transmitters relative to body weight began breeding slightly earlier than those with lighter transmitters (Figure 5A). Finally, after adjusting for species-specific baseline differences in phenology, species varied in their phenological parameters following transmitter implantation (Figure 5A–C).

DISCUSSION

While sea ducks resumed normal movement patterns relatively quickly following implantation of coelomic transmitters with percutaneous antenna, long-term effects including reductions in breeding site attendance and delayed breeding and migration phenology occurred in the year following transmitter attachment.

TABLE 2.

Coefficient values for the best models of (A) time to normal movement patterns and (B) breeding site attendance for 5 species of sea ducks, 2002–2017. Covariates for which the 95% confidence intervals (CI) of the coefficient estimates do not overlap zero are listed in bold.

Across all species, capture and transmitter attachment initially increased dispersive behavior in radio-marked individuals (n = 476), but movement patterns generally returned to normal within ∼5 days of release. This period of altered movement is considerably shorter than the 14-day post-capture censorship window recommended by Esler et al. (2000) and frequently applied to sea duck movement analyses (e.g., Loring et al. 2014, Beuth et al. 2017, Meattey et al. 2019). Thus, removing data collected up to 14 days post-capture may eliminate locations unaffected by short-term effects of capture and tagging on movement. The duration of immediate capture and tagging effects was longer and more variable in Common Eiders than in the other 4 species. This accords with previous work suggesting that eiders may experience significant short- and medium-term effects that varied by individual (Latty et al. 2010, 2016, Fast et al. 2011), although none of these studies included other sea duck species for comparison. However, the difference in median values between the longest and shortest adjustment times was relatively small (2 days). This suggests that, at least in the short term, no species experienced dramatically greater effects than others of capture and tagging on movement patterns.

It is important to note that the 14-day censorship window originally proposed by Esler et al. (2000) was based on observations of elevated mortality during the period immediately following transmitter attachment, and not on any assessment of movement or behavior. By contrast, our study evaluated movement patterns but does not account for effects of transmitters on survival. Since sensor data from our transmitters were often insufficient to distinguish mortality from transmitter failure (Brodeur et al. 2008), we did not directly assess survival rates in any of the study species, and due to the requirements of our models, we did not include individuals whose transmitters failed within a month of capture. In practice, elevated mortality rates shortly after tagging have been observed in Long-tailed Ducks (Sea Duck Joint Venture 2015) and Surf Scoters (Le Net et al. 2019), possibly due to issues associated with capture and surgery (Mulcahy and Esler 1999, Iverson et al. 2006, Ford et al. 2011, Sexson et al. 2014). However, for individuals that survived the initial post-release period, we were unable to detect abnormal day-to-day movement patterns after a few days from release regardless of species. Thus, while it seems reasonable to continue excluding individuals whose transmitters go offline within a few weeks to a month of capture from further analysis, it may be necessary to censor only the first 5–6 days of data for individuals whose transmitters last months to years. Including 8–10 additional days of data may not dramatically affect conclusions at annual or multi-annual timescales; however, analyses of habitat use frequently focus on a single season (e.g., Loring et al. 2014, Beuth et al. 2017, Meattey et al. 2019). Migratory sea ducks occupy habitat areas for periods ranging from weeks (staging sites) to months (wintering areas), meaning that even a few days of additional data could potentially affect results at the focal timescales. Given that analyses of sea duck habitat use based on telemetry data have already had significant implications, including informing siting of offshore energy infrastructure (Olsen et al. 2014), incorporating all available information into such analyses is critical to ensuring the best possible conservation outcomes.

FIGURE 3.

Average number of days after transmitter attachment that daily behavioral (movement pattern) state assignment probability matched the seasonal average by species and capture season for 5 sea duck species tracked between 2002 and 2017 in eastern North America. Box plots show sample size (text), median (center bar), interquartile range (shaded box), minimum/maximum (vertical bars: ± 1.5 times interquartile range), and outlier (dots) values.

FIGURE 4.

GLMM-predicted probabilities of males and females of 5 species of sea ducks attending breeding sites after capture and transmitter implantation, 2002–2017, eastern North America. Dots indicate mean probability of attendance predicted from the final models, and lines are 95% confidence intervals of predicted values.

In addition to species-level effects, we also assessed whether variation in individual characteristics, capture timing, or external conditions affected the duration of change in movement patterns following capture and transmitter attachment. While sex and age differences did not contribute significantly to inter-individual variation, capture season helped to explain observed variation, as short-term changes in behavioral state lasted slightly longer during winter than during breeding and migration. However, as not all species were sampled in all seasons, it is difficult to fully separate the effects of species-specific differences in sensitivity from seasonal differences. For example, Black Scoters captured in winter exhibited normal movement patterns more quickly than Black Scoters captured during spring staging, while Surf and White-winged Scoters captured in winter showed similar adjustment times to those captured during fall staging; as the 3 species were not all sampled during the same seasons, however, it is impossible to assess whether these differences are the result of greater sensitivity to transmitter implantation during spring staging or species-specific differences in sensitivity. Finally, we found that unmeasured among-year and among-site differences could affect the duration of short-term effects of transmitters. Previous studies of other avian taxa and transmitter types have shown that transmitter effects may vary depending on external conditions (e.g., Hamel et al. 2004, Snijders et al. 2017), and our work further supports the need to account for environmental heterogeneity in assessing the potential intensity and duration of transmitter effects.

FIGURE 5.

Effect sizes for fixed covariates in GLMMs of (A) breeding initiation date, and (B) start date and (C) duration of migration, for 5 sea duck species tracked between 2002 and 2017 in eastern North America. Dots represent coefficient estimates, and lines are 95% confidence intervals of coefficients.

FIGURE 6.

Differences between migration start dates during the first (gray) and second (orange) years after capture for 5 sea duck species (Common Eider, Surf Scoter, White-winged Scoter, Black Scoter, and Long-tailed Duck) tracked between 2002 and 2017 in eastern North America. Pairs marked with asterisks (**) indicate periods for which migration start date varied significantly with years post-capture. Box plots show median (center bar), interquartile range (shaded box), minimum/maximum (bars: ± 1.5 times interquartile range), and outlier (dots) values.

Despite the fact that sea ducks appeared to resume normal day-to-day movement patterns within less than a week after radio-marking, we observed longer-term effects on migration patterns and breeding site attendance. Probability of breeding site attendance in the first breeding season following transmitter attachment was significantly reduced compared with the subsequent season across all individuals. Moreover, individuals fitted with transmitters migrated later and more quickly between winter, staging, and breeding sites during the first year after transmitter implantation compared with subsequent years, while the timing and duration of migrations between breeding, molt, and wintering sites were not affected. Prolonged migration chronology due to tagging could introduce biases into habitat-use analysis. Delayed breeding migration was most evident in individuals captured during spring staging, followed by those captured in winter, while individuals captured during the previous fall or breeding season showed little to no delay. Later breeding migrations immediately following transmitter implantation could result from spending additional time at nonbreeding sites to augment energy reserves, possibly to compensate for decreases in foraging efficiency while adjusting to transmitters (Latty et al. 2010), and individuals may have migrated more quickly to reduce the resulting delays in breeding initiation. However, the increased speed of migration did not fully outweigh the later onset of migration, resulting in later arrival at breeding sites during the first year after implantation. As nest success in sea ducks and other migratory birds is often negatively correlated with nest initiation date (Grand and Flint 1997, Blums et al. 2002, Morrison et al. 2019), this further suggests a potential reduction in reproductive output in the season following implantation compared with subsequent years. Since breeding site attendance and phenology alone do not provide definitive information on whether individuals succeeded in raising young, additional measures of breeding success would be necessary to fully quantify the effects of transmitters on reproduction. Direct observations have suggested that reduced breeding success immediately after transmitter implantation in Common Eiders may be followed by successful breeding in subsequent seasons (Fast et al. 2011); however, further observational study is needed to assess whether the same is true across other species and sites.

We also found evidence that interannual effects of capture and transmitter implantation varied among species and between sexes. This variation did not appear to result from species- or sex-specific differences in body size, since transmitter weight as a percentage of body weight was relatively consistent among individuals and was not a strong predictor of breeding site attendance or most aspects of annual-cycle phenology, aside from a slight association between heavier transmitters and earlier breeding initiation. No individual species showed consistently greater phenological change across all migrations; however, Black Scoters tended to migrate later and more quickly in spring relative to the other 4 species. This could indicate an interannual effect of differences in capture timing, as Black Scoters were the only species captured during spring staging; it may also be related to baseline differences in phenology among species at the sampled sites.

Overall, radio-marked males attended breeding sites less frequently than females and experienced greater delays in breeding migration. While underlying differences in migration timing between males and females likely account for our observations of earlier molt and winter migrations in males, previous studies suggest that unmarked males and females typically arrive at breeding sites at similar times (e.g., Savard et al. 2007), suggesting that breeding migration chronology may be particularly affected in males following transmitter implantation. Sex-specific breeding attendance also varied by species: while male Common Eiders and Long-tailed Ducks attended breeding sites at similar rates to females, males of the 3 scoter species showed markedly lower attendance rates. This may result in part from an underlying male bias in waterfowl populations (Bellrose et al. 1961), which may be especially pronounced in scoters (Rodway et al. 2015). Depending on the timing of capture relative to pair formation, delays in migration and breeding phenology resulting from capture and implantation could further decrease the probability that a male will successfully find a breeding partner or may disrupt pair bonds that have already formed, whereas radio-marked females might be able to find breeding opportunities among the surplus males regardless of any phenological delays associated with the transmitter. Our ability to detect sex-specific trends was limited by high levels of breeding site attendance in both years for Common Eider, as well as relatively small sample sizes for males of some species (Common Eider, White-winged Scoter), meaning that further tracking of males may be needed to fully assess sex-specific variation in breeding responses for these species.

It is important to note that there were no true controls in our study; thus, our assessment of transmitter effects was limited to information available from marked individuals and may not represent population-wide norms if marked and unmarked individuals differ systematically. In particular, while the multi-year duration of transmitters provided opportunities to examine intra-individual differences in behaviors over time, these effects are necessarily confounded with age. Since age at capture could not be precisely determined for after-hatch-year individuals, we were unable to account for any potential underlying variation in parameters (e.g., phenology, breeding site attendance) with age beyond the first year. Hatch-year and after-hatch-year sea ducks did not appear to differ in any of our measured parameters for sensitivity to transmitter implantation except for breeding site attendance, which may be related to underlying differences in movement patterns between adult and subadult sea ducks (e.g., Bentzen and Powell 2015). Observational studies at breeding sites could potentially be used to compare individuals with implanted transmitters with unmarked individuals in order to remove the confounding effect of time and directly compare phenology, attendance, and breeding success between sea ducks with and without transmitters. In addition, our ability to detect behavioral change was limited by the spatiotemporal scale of data collection, which would not have identified fine-scale changes such as altered dive behavior (such as those observed in captive birds by Latty et al. [2010]) or diurnal patterns of marine habitat use (e.g., Lewis et al. 2005, Merkel and Mosbech 2008). Given the rapid increases currently occurring in both the accuracy and frequency of data from bird-borne telemetry, our approach could be used in future studies to analyze behavioral effects at finer scales.

Despite these limitations, our analysis allows us to identify several potential species- and individual-level correlates of sensitivity to tagging effects, and to provide more rigorous guidelines for future tracking studies. In particular, our results suggest that a data censorship window of 5–6 days, rather than the commonly used 14-day period, is likely sufficient to eliminate most short-term changes in movement patterns resulting from capture and tagging at the timescale of our data. We also demonstrate that tagging can affect the phenology of migration and breeding propensity, although such effects were more likely when birds were captured at spring staging sites just before their migration to breeding areas. This suggests that potential effects of capture and tagging on reproductive output could be minimized by timing captures during fall molt or winter and emphasizes the importance of collecting data across multiple years for comparison.