Offshore vagrancy in passerines is predicted by season, wind-drift, and species characteristics

Abstract

Background

Migratory birds accomplish remarkable feats of long-distance navigation. Vagrants, few individuals who migrate to incorrect locations, reveal conditions where orientation and navigation fail. Studies of vagrancy on a continental scale reveal the importance of external factors such as strong winds driving birds off course, clouds obscuring migratory landmarks, and natural disruptions in the Earth’s magnetic field interfering with migratory orientation. Species may also possess characteristics that make them more prone to vagrancy. The external drivers of vagrancy on a smaller scale are less understood.

Methods

I used eBird, a community science dataset comprising millions of bird observations, to study land passerines observed over the Pacific Ocean, here termed offshore vagrants. These data present the opportunity to study a particular case of vagrancy: small-scale displacement into highly inhospitable areas. I modeled how season, wind, lack of visibility, interference with magnetoreception, and species differences may predict offshore vagrancy. Then, I modeled how species vagrancy likelihood is predicted by morphological and life history traits.

Results

Vagrancy was more common in the fall and positively associated with stronger tail winds in the spring. Species with greater preference for understory foraging habitat were less likely to occur as vagrants. Species vagrancy likelihood was higher in birds with a longer migration distance and rounded wings, but the relationship was weaker in birds with a pointed wings. Brown-headed Cowbirds were the most common offshore species in terms of absolute number of records and proportional to onshore frequency.

Conclusions

Offshore community science records proved revealing of mechanisms for small scale vagrancy in passerines. Offshore vagrancy can be driven by wind drift in the spring, but not in the fall despite higher overall levels of vagrancy. Life history characteristics like foraging habitat preference and migration duration may make some species more vulnerable to the effects of wind drift. Species with longer migrations may have more time to encounter vagrancy causing events, but greater aerodynamic efficiency may counteract this effect.

Background

Migratory birds possess remarkable navigational adaptations which allow them to precisely return to breeding territories after migrating thousands of miles. Over 60 years of study continue to unravel sensory, physiological, and genetic adaptations for migratory orientation and navigation [1]. Many birds appear to use magnetic cues to follow genetically coded migration directions over great distances [2,3,4], eventually using learned visual landmarks to find territories with pinpoint accuracy [5]. This remarkable philopatry benefits some birds with increased reproductive success [6, 7].

Bird navigation is not perfect: migratory birds occasionally experience vagrancy, defined as a species straying outside its normal range. Vagrancy can impact birds on a population and community scale. Vagrants may perish in hostile environments [8], expand a species’ range by founding new populations [9, 10] possibly cauing speciation, or they may establish new migration routes [11]. Numerous hypotheses for vagrancy have been proposed including mutations in route encoding genes [12] and even brain parasites distorting orientation senses [13]. Best supported are external factors like weather and geomagnetism interfering with migration. Vagrancy may arise from strong winds driving birds off course [14,15,16], called wind-drift. Cloud or fog cover may obscure landmarks, causing disorientation [9, 12, 17, 18]. Recent study suggests disruptions in the Earth’s magnetic field driven by geomagnetic storms may cause misorientation in free-flying birds in North America [8, 19, 20]. However, increased radiomagnetism from increased solar activity may disrupt avian magnetoreception possibly masking the effects of geomagnetic disturbance, allowing birds to migrate with other cues [8].

Population, life history, and evolutionary characteristics may make vagrancy more common in some species. In vagrant wood warblers in the western United States, population size and migration distance impacts vagrancy likelihood [21]. Species with larger population sizes have more individuals and thus more opportunities for vagrancy. Similarly, species that migrate further may have a higher cumulative probability of vagrancy during their longer migration duration. In a study of offshore vagrants in California, nocturnal migrants may be more prone to vagrancy than diurnal migrants because nocturnal migrants may be unable to see the coastline, thus more easily wandering offshore [17]. The extent to which characteristics make species more prone to vagrancy remain poorly understood.

Defining vagrancy can often prove difficult [10]. The rarity of a species at any given location is often based on observation data limited by low sampling density (e.g., survey or banding data) or inconsistent effort (e.g., community science data). Species abundance is often patchy, making it easy for local range fragments to be overlooked. Any birds dispersing from unknown range fragments may be erroneously identified as vagrants [10]. Additionally, vagrants may represent the extremes of a normal curve of migration orientation, rather than true misorientation [21]. To provide a clear index of vagrancy, I chose to study migratory passerines lost offshore (Fig. 1). While some migratory routes cross bodies of water [22], flying over the open ocean can result in exhaustion and death for a small passerine. Offshore vessels document these passerines, presenting an opportunity to study vagrancy [23]. These vagrants also represent a different scale of vagrancy to many studies, rather than being far outside their normal range [e.g. 8, 21], these represent short distance vagrancy into inhospitable territory. Some studies from islands or peninsulas have attempted to characterize the conditions under which offshore vagrancy occurs [17], but as land-based studies these results are biased to birds that survive water crossings. A geographically broad study of birds observed offshore from boats may provide insight into what drives offshore vagrancy.

Fig. 1

Offshore vagrancy documented from a cruise ship 80 km off the coast of Washington State. a A yellow-rumped warbler flies low over the Pacific Ocean. b A yellow-rumped warbler, Wilson’s warbler, and Lincoln’s sparrow take refuge in an artificial plant. c Two deceased Wilson’s Warblers lay on the deck. d An unwell yellow-rumped warbler perches in an artificial plant. All photographs by Steve Kelling, used with permission

I studied common migratory passerines in the Western United States lost over the Pacific Ocean. I asked the questions: [1] to what extent are patterns in offshore vagrancy explained by external factors or species differences? And [2] what characteristics make a species more likely to experience vagrancy? My analysis revealed that season, wind, and to a lesser extent solar activity, may influence offshore vagrancy. However, the greatest variation in offshore vagrancy occurred between species. Variation between species was best predicted by migration distance and wing pointedness. Brown-headed Cowbirds were a notable outlier, occurring offshore more often than any other species.

Methods

Data acquisition and filtering

The Pacific ocean off western North America is an ideal region to study rarely observed offshore passerines due to its extensive spatial and temporal coverage from birdwatching pelagic trips, repositioning cruises, and research vessels. Observations from these vessels are often submitted by reliable observers to eBird [24], a community science platform for birdwatchers to submit observation data. eBird data is entered in georeferenced checklists containing tallies of species linked to effort data including transect distance, number of observers, and time.

I chose common coastal migratory passerine species to study factors driving offshore vagrancy. Non-migratory and rare species were omitted because there are too few offshore records for analysis. I selected study species by visually inspecting eBird records from reference counties encompassing the West-facing slope of the coastal range in California (Orange, Santa Cruz, Humboldt, and Del Norte), Oregon (Curry, Coos, Lincoln, Tillamook, and Clatsop) and Washington (Pacific and Grays-harbor). I ignored interior counties to avoid introducing extraneous, non-coastal species to the analysis. I selected species reported in > 2% of eBird checklists in any county, a threshold chosen to include all regular species in the region based on prior knowledge. Additionally, I only included species if they have different breeding and non-breeding range in Birds of the World [25] range maps, thus being migratory. This resulted in a set of 49 common costal migratory passerines with a variety of migration distances, diel migration timings, and morphology.

I downloaded the “basic” eBird dataset containing all eBird checklists submitted prior to November 2023, and filtered it only include the 49 study species using a custom AWK script. I then “zero filled” records for all species using the package auk in R v4.3.2 [26, 27]. “Zero-filling” interpolates absences of species left unreported in complete eBird checklists. The function also removes duplicate observations produced when eBird users share their checklists with fellow observers. The resulting dataset contained eBird checklists with each focal species marked as either present “1” or absent “0”.

I thoroughly filtered the raw eBird data to produce a dataset appropriate for studying offshore migration. I selected offshore eBird checklists submitted from southern California to southern British Columbia (32.5–48° latitude) from 10 to 470 km offshore (Western boundary − 130° longitude), removing records within 10 km of land because observers occasionally combine harbor and nearshore data into one eBird checklist. I also selected onshore records up to ~ 10 km inland as a comparison. Within 2.5° latitude slices of the onshore data (width based on weather covariate scales), I calculated observation frequency for each species on each day. Observation frequency is a standard eBird statistic calculated as the percent of all checklists in an area that contain a report of at least one individual of a species [28,29,30]. I then excluded all offshore records (either presence or absence) of species that had an on-land frequency of zero. This ensured I was studying the conditions during the migratory period of that species, avoiding inflating my dataset with uninformative absences when a species isn’t migrating. Additionally, I only included data submitted in March–May and August–November to omit the breeding and non-breeding season of most passerines.

Lastly, I acquired descriptive aspects of the dataset. eBird checklists hold valuable descriptive data entered by users, including various tags and a free text field where users may enter observation comments [31, 32]. I extracted keywords from observation comments and age tags to determine the relative proportion of adults and juveniles offshore. These are only available when noted by the observers, so the relative proportions of each are not necessarily representative of the population.

External drivers of offshore vagrancy

I selected predictors to evaluate four hypotheses for offshore vagrancy. (Hypothesis 1) Vagrancy may be more common in the fall, possibly because naïve juveniles navigate poorly. I included season as a predictor. (Hypothesis 2) Wind-drift may drive birds offshore. I included u-wind (east/west wind vectors) to test if strong east winds drive birds offshore and an interaction between v-wind (north/south wind vectors) and season to account for the different migratory directions during spring and fall migration. I also inlcuded an interaction between u-wind and v-wind to assess the impacts of wind direction and speed simulatenously. (Hypothesis 3) Low visibility caused by fog or low cloud cover may prevent birds from seeing landmarks like the coastline. I used relative humidity as a proxy for fog and low cloud cover because they are generally correlated [33]. I extracted wind and relative humidity NCEP/NCAR weather Reanalysis 1 data from offshore locations using the R package RNCEP [34]. I averaged hourly weather data for an entire 24-h day for each checklist, because weather may have affected birds before the time of observation. (Hypothesis 4) Global changes in geomagnetism or radiation may interfere with magnetoreception. Following Tonelli et al. [8], I included two predictors relating to interference with magnetoreception: geomagnetic disturbance and solar activity. I included geomagnetic disturbance because geomagnetic events may cause misorientation [8, 19, 20]. I downloaded Kp (an average magnetic index from 13 globally distributed geomagnetic observatories) geomagnetic data between 1968-01-01 and 2023-01-01 from the International Service of Geomagnetic Indices (https://isgi.unistra.fr/data_download.php). I then converted Kp to ap, a numeric magnetic index more suitable for statistics. Additionally, radiomagnetism from solar activity may disrupt magnetoreception [8]. I used daily sunspot number as a proxy for solar activity [35]. I downloaded ‘American Relative Sunspot Number-Daily’ data between 1968-01-01 and 2023-02-05 from Laboratory for Atmospheric and Space Physics (https://lasp.colorado.edu/lisird/data/american_relative_sunspot_number_daily/). I also included two predictors to control for other aspects of the data. I included latitude as a predictor because vagrancy may vary geographically. Additionally, the number of offshore vagrants likely varies with the overall number of birds migrating on a given day. To control for migrant abundance, I included daily on-land species frequency, which should scale with the number of migrants. I inspected all possible explanatory variables for outliers and multicollinearity with VIF and plot-correlation matrices. I also checked for missing values.

I fit binomial generalized linear mixed models (GLMM) with the R package lme4 [36] to evaluate the relative role of external drivers and species differences in offshore vagrancy. A binomial GLMM was chosen to fit the binary presence and absence response variable. I chose a mixed effects modeling framework for two reasons. First, I wanted to evaluate the relative contribution of external factors and species differences to offshore vagrancy. To meet this aim, I included species as a random effect. Second, there is inherent non-independence in the eBird dataset. Each checklist has presence and absence of all studied species, so those observations have the same predictor values and are not independent. Additionally, birdwatchers on a single vessel will often submit multiple checklists throughout the day, so passerines riding on board may be reported multiple times. Lastly, some days may have high levels of vagrancy, so checklists from that day may have inflated numbers of offshore passerines. To test the best random effect structure to remedy non-independence, I fit five global models (all fixed effects included) with differing random effect structures (Supplementary Materials). A random intercept for observation date compensated for all three levels of non-independence and had clear support when compared to other random effect structures with AICc. Thus, I opted for a crossed random effect structure with intercepts for species and observation date.

I fit 17 binomial GLMMs with different combinations of eight predictors chosen a prioi to test different hypotheses for offshore vagrancy. To ensure good model fit, I randomly subsampled absences to have the same number as presences. Resampling techniques are commonly used to correct for class imbalance in GLMM and machine learning models across disciplines [12,13,14]. Subsampling was an effective approach to correct for class imbalance when estimating species distribution in a similar community science dataset [37]. I scaled all continuous predictors to ensure model convergence and allow effect size comparison. I fit univariate models to evaluate the individual contribution of all seven predictors and an intercept only model to act as a null hypothesis. I then fit combinations of my overall migration abundance predictor, on-land species frequency, and the most likely predictors based on previous research [17, 21], season, v-wind, and an interaction between v-wind and season. After evaluating model performance, I used the model with these three predictors and interaction as my base model from which to build models testing all combinations of the other five predictors. Models with more than 6 fixed effects were overfit and would not converge. This resulted in 21 candidate models which I compared with AICc. I chose the best model as having the lowest AICc, but any model within 2 values of the best model was considered in the results. I also conducted model averaging with the full model set to assess the importance of each predictor and similarity to estimates in the best model with MuMIn [38]. In the best model, I evaluated the importance of fixed and random effects with conditional and marginal R². As a model diagnostic check, I confirmed the centrality and normality of model residuals by comparing mean residuals to 0, plotting simulated residuals with the R package DHARMa [39], and plotting random effect residuals with the R package sjPlot [40]. I also plotted binned residuals against all predictors, both those included and excluded from the model. I evaluated influential points and dates with cooks distance.

Species characteristics affecting offshore vagrancy likelihood

To evaluate traits that may increase vagrancy I studied interspecific differences in vagrancy likelihood. The frequencies of each species may be heavily influenced by their overall population size. To generate a metric of offshore vagrancy likelihood corrected for differences in relative commonness, I calculated species average of offshore frequency weighted by one minus the daily on-land frequency. Thus, common species on land would have a lower adjusted offshore frequency.

I tested whether morphological and life history traits drive species differences in offshore vagrancy likelihood. I downloaded hand-wing index (HWI) and mass data from the AVONET dataset [41]. HWI is a measure of wing pointedness, calculated as the factor of the longest primary and secondary feather length. I included HWI because it is widely considered a proxy for aerodynamic efficiency [42,43,44], thus possibly impacting migratory ability. I included mass to consider an additional morphological trait. I downloaded Elton traits to extract foraging habitat trait data [45]. Species occupying habitats with different levels of exposure may be differently impacted by wind and other weather events. I included foraging preference for understory, midstory, canopy, and aerial strata, which are expressed as a percentage of time spent foraging in that habitat. Ground foraging preference was omitted as it was highly negatively collinear with understory and midstory foraging preference. I retrieved migration traits from Birds of the World [25]. I recorded if birds migrated during the day, night, or both. To estimate migration distance, I averaged both breeding and non-breeding latitude and longitude on range maps, and then calculated spherical distance in km between the points. I included migration distance because birds that migrate further tend to be more prone to vagrancy [21]. I fit 15 multiple linear regression models using adjusted vagrancy likelihood as a response variable, normalized with a log transformation. In the initial modeling phases, I tested all four combinations of HWI and migration distance, including an interaction term between HWI and migration distance to test if birds with greater aerodynamic efficiency may be more able to compensate with the vagrancy causing events. This initial phase revealed the importance of including the interaction between HWI and migration distance, which I used as the base model for future models. Later, I added the other five variables in univariate models and combined with the base model. I verified the normality of the best model residuals and checked influential points with Cook’s distance.

I conducted a phylogenetic analysis to test if related species had similar vagrancy likelihood due similar life history characteristics. I downloaded 1000 phylogenetic tree subsets with a Hackett backbone from birdtree.org and generated a 50% majority-rule consensus tree [46]. I made the tree ultrametric and dichotomous to meet the assumptions of standard phylogenetic analyses and tested for phylogenetic signal of offshore vagrancy likelihood with Blomberg’s K using the R package phytools [47, 48].

Results

I downloaded 15,804 offshore eBird checklists from 1968 to 2023, comprising 23,492.03 observation hours and 310,630.7 km of travel distance. Roughly 27% of checklists were submitted at 13 heavily used offshore eBird locations, the rest being distributed over 8,992 locations. Offshore passerine vagrants occurred on just 2.74% of offshore eBird checklists totaling ~ 1606 individual birds (Fig. 2). Mean distance offshore was 41.0 (± 27.1) km, but the furthest offshore vagrant was observed 202.7 km offshore. In observer comments, juveniles were noted 33.33% more than adults (species frequencies in Supplementary materials Table S3).

Fig. 2

Offshore eBird data used in this study. a The raster grid shows the sampling coverage of eBird data, with the color of each cell representing the number of eBird checklists in that cell. Each triangle is a single offshore observation of a passerine. b A histogram depicts the distribution of records in terms of distance from shore

External drivers of offshore vagrancy

The best model explained much of the variance in the dataset (Conditional R² = 0.741), however little of this predictive power was due to the fixed effects (Marginal R² = 0.137). Species identity and observation date were large sources of non-independence in the dataset. Variance in offshore vagrancy between dates was 5.10 and between species was 2.59. On-land species frequency, solar activity, season, v-wind, and the interaction between v-wind and season, were included as fixed effects in the best model (Fig. 3, Table S2). Higher on-land observation frequency had a large effect size and was positively correlated with probability of offshore vagrancy. The interaction between v-wind and season also had a large effect size. Stronger south winds (positive v-wind) correlated with higher vagrancy probability in the spring, but there was no relationship in the fall. Season had a large effect size, with more vagrants occurring in the fall (Fig. 4). Solar activity had a small effect size, with slightly more vagrants occurring under high relative sunspot abundance. Five other models were within 2 AICc values of the best model (Table 1). These models also included on-land species frequency, season, v-wind, and an interaction between v-wind and season. Geomagnetic disturbance, wind, visibility, and latitude all appeared in one to two of the other top models. A model with no additional predictor had the second lowest AICc value and the fewest predictors. The model averaged parameter estimates closely match those from the best model, apart from solar radiation, which was lower than the best model additionally suggesting it is not an important predictor.

Fig. 3

External drivers of offshore vagrancy. Predicted offshore vagrancy probability response curves and 95% confidence intervals are shown under the studied range of each predictor in the best model (a–c). Binned frequencies of the raw data are included as points with standard deviation bars to show model fit. Predicted level of vagrancy in spring and fall are shown with separate curves and binned frequencies are separated by season for the interaction with season and v-wind (c). The model was built from subsampled data to ensure model fit, so all observation frequency values are rescaled to match the probabilities of the full dataset. The effect size of each predictor in the best model is shown with a caterpillar plot (D). On-land observation frequency (land obs freq), season, and the interaction between v-wind and season had large effect sizes. Solar activity (Ra) and v-wind had relatively small effect sizes. The model averaged effects sizes are similar to the best model (E)

Fig. 4

Species vagrancy likelihood by season. Two versions of offshore observation frequency are shown for each passerine species: raw observation frequency and adjusted for on-land species commonness (adjusted observation frequency). Observation frequency is the percentage of all eBird checklists that contain a report of a given species. Proportion of observations in spring and fall migration are overlaid. Note the high frequency of brown-headed cowbirds, particularly in the fall

Table 1 Candidate external vagrancy models ranked by AICc value

Species characteristics affecting offshore vagrancy likelihood

Offshore vagrancy likelihood varied dramatically between species (Fig. 4), mirroring the importance of the species random effect in the external driver GLMM. There was clear AICc support for a multiple linear regression including understory foraging preference and the interaction between migration distance and HWI (Table 2). The best model moderately explained species variance in offshore vagrancy likelihood (Fig. 5; Multiple linear regression, R² = 0.165, F_{4, 55} = 3.616, p = 0.0072): understory foraging preference was positively correlated (β =  − 0.008, t = –2.227, p = 0.0301), HWI and migration distance were both positively correlated (β = 0.03, t = 1.629, p = 0.1089; β = 0.0006, t = 2.954, p = 0.0046), and HWI and migration distance had a significant negative interaction (t =  − 2.589, p = 0.0123). For birds with less pointed wings, a longer migration distance increased the likelihood of vagrancy. For birds with more pointed wings, vagrancy was only slightly more likely with longer migration distances. Vagrancy propensity was not predicted by phylogenetic structure. A Blomberg’s K of 0.246 indicates phylogenetic repulsion, where related species are more different than expected under Brownian Motion (K = 1), however the relationship was not significant (Blomberg’s test, P = 0.262; Supplementary materials Figure S1).

Table 2 Candidate species models ranked by AICc value

Fig. 5

Species-wise vagrancy likelihood in response to hand-wing index (HWI) and migration distance. Each point represents a different species. Vagrancy likelihood was calculated as the observation frequency offshore divided by the observation frequency on land, normalized with a log transformation. The independent effects of understory foraging preference (a) and HWI, a measure of wing pointedness which correlates to aerodynamic efficiency (b), are shown. The effect of migration distance is shown with its interaction with HWI (c). In birds with a pointed wing shape (high HWI, demonstrated by a Horned Lark wing), the effects of migration distance are reduced. In birds with a rounded wing shape (low HWI, demonstrated by a Wilson’s warbler), longer migration was strongly correlated with increased vagrancy likelihood

Brown-headed cowbirds were more common offshore than all other studied species both in absolute number of records and proportional to onshore frequency (Fig. 4). A group of three Brown-headed Cowbirds also represented the passerine recorded furthest offshore at 202.7 km off the coast of British Columbia. Juveniles represented 4 of the 7 Brown-headed Cowbirds records where observers noted age (Table S3).

Discussion

I found strong support for two vagrancy hypotheses, season and wind-drift, and weak support for solar activity. Consistent with prior research, I found that vagrancy is more common in the fall [17, 21]. Additionally, wind appears to be an important factor, again supporting previous research [14,15,16]. South winds in the spring were associated with more vagrancy, perhaps because tailwinds encourage more migration. However, no such relationship with vagrancy occurs in the fall. I found a trend with low model support of greater offshore vagrancy during periods of higher solar activity, but the small effect size and absence in most top models suggests this may not be a real effect.

Differences with previous research may be reflective of differences in migration scale. Relative humidity, which is expected to correlate with fog and low cloud cover, had no impact on vagrancy, despite previous studies finding clouds to reduce visibility and increase vagrancy [9, 17]. Similarly, increased geomagnetic activity produced no change in vagrancy, in contrast to Tonelli et al. [8]. Most studies focus on species detected 100 s of km out of range [8, 21], not 10 s of km as in this study. DeSante’s analysis of landbirds on the Farallon Islands is more similar in vagrancy scale, as these are birds that had to displace ~ 30 km offshore to arrive on the island [17]. Drivers of offshore vagrancy in common landbirds appear similar in this analysis, DeSante hypothesized these birds are responding small changes in weather and electromagnetic disturbance [17]. As most offshore landbirds were within 25 km of land, perhaps due to wind, it raises the question of whether passerines observed from boats are in the process of returning to land. Surveillance radar data suggests that migratory birds can compensate for wind drift, reorienting to land in morning flight [49, 50].

I found clear evidence of species differences in vagrancy likelihood. Species differences in offshore vagrancy appear related to an interaction between migration distance and wing pointedness. Birds that migrate further tend to be more prone to vagrancy [21], likely because more time spent migrating provides more opportunities to encounter vagrancy causing factors. However, the impact of migration distance was reduced in birds with more pointed wings and exaggerated in birds with more rounded wings. It is possible that the increased aerodynamic efficiency conferred by pointed wings [42,43,44] makes species more resilient to factors like wind. Another possible explanation is that lower aerodynamic efficiency makes species less able to maintain altitude, thus dropping lower where observers on boats are more likely to see them. Species with preference for understory foraging strategy were less likely to occur as vagrants. Preference for ground foraging was removed because it was negatively correlated with understory preference, but this also suggests that vagrancy is positively related to ground foraging. These results may suggest that species in more protected habitats are less exposed to winds. Additionally, foraging strategy may be correlated with dispersal ability or migration behavior. Crucially, there was much variance in species offshore vagrancy likelihood left unexplained by the species model, suggesting there may be other characteristics that make species more prone to vagrancy.

It is possible the passerines detected offshore are not vagrants, but instead birds undertaking over-water migration. Birds may undertake over-water migration to shorten their journey, as seen in birds crossing the Gulf of Mexico or North Sea [22, 51]. Given the abundance of offshore records in Southern California, where over-water migration gives no opportunity for a short cut, this seems a poor explanation for this system. Additionally, greater occurrence of juveniles and increased vagrancy in species with long migrations are results in common with other vagrancy studies [10].

Brown-headed Cowbirds were an intriguing outlier in my dataset, occurring offshore more than other passerines, even when correcting for on land frequency. Greater vagrancy likelihood in juveniles could result from high post-natal dispersal, but this explanation may not fit Brown-headed Cowbirds as they show equivalent levels of natal philopatry to other North American passerines [52]. Brown-headed Cowbirds are unique in our dataset as brood parasites, meaning females lay eggs in other species nests allowing that species to rear their offspring for them. It is possible that their brood parasitism relates to vagrancy likelihood in Brown-headed Cowbirds, however such a relationship requires direct study.

Conclusions

Records from offshore community science have revealed mechanisms for small-scale vagrancy in passerines. Taken together, external factors, life-history, and morphological traits may influence vagrancy likelihood of passerines. Vagrancy is more common in the fall and during strong wind conditions in the spring. Preference for understory habitat is associated with lower vagrancy. Migration distance and HWI appear to contribute to how prone a species is to vagrancy. Species with longer migrations may encounter more vagrancy-causing events, though greater aerodynamic efficiency might mitigate this effect. Intriguingly, offshore vagrancy appears to be more common in Brown-headed Cowbirds than any other passerine.