Voting with their feet: Spring staging geese increasingly select for cereal crops over grassland at an important central European stopover site

Voting with their feet: Spring staging geese increasingly select for cereal crops over grassland at an important central European stopover site | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Voting with their feet: Spring staging geese increasingly select for cereal crops over grassland at an important central European stopover site Michał Polakowski, Łukasz Jankowiak, Monika Broniszewska, Michał Fabiszewski, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4189509/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Northern hemisphere wild geese have increasingly shifted in the non-breeding season from feeding on natural wetlands to grazing agricultural grasslands and, since the 1960s, to green cereals, enhancing agricultural conflicts. From an agricultural perspective, it is important to know if this shift is a response to availability (i.e. grassland conversion to arable) or a response to the enhanced feeding opportunities provided by cereals and hence goose food preferences. In an important Polish goose spring staging area, we showed no change in the extent of cereal production area between 2012 and 2018, but based on repeated goose counts, a significant increase in goose use from a mean of 20.7% (± 0.04 se during 2007–2015) use of cereal fields to 33.5% in 2023. Larger flocks of the numerically dominant Greater White-fronted Geese Anser albifrons (hereafter White-fronted) and Bean Goose Anser fabalis showed greater preference for feeding on cereals in 2023, increasingly through the staging period, whereas larger flocks preferred grass in the period 2007–2015 (although patterns differed in less numerous species). Previous studies have shown that geese can maintain higher food intake rates on monospecific cereal fields than on grass swards, suggesting the recent transition is due to improved foraging efficiency, since their availability relative to grassland and other habitats has not changed. We predict that more geese will make this transition in this study area and in many other European farmland landscapes as long as there are no fitness penalties to doing so, potentially increasing level of agricultural conflict as more geese in larger flocks depredate cereal fields. Biological sciences/Ecology Biological sciences/Zoology Branta Anser field use agriculture habitat availability food preferences Figures Figure 1 Figure 2 Introduction Prior to the Neolithic clearance of woodland in Northern Europe, overwintering wild geese were inevitably constrained to feed on natural habitats (usually wetlands). Indeed, until recently, several species have continued to feed on what were likely to have been their traditional pre-agriculture food resources (e.g. Dark-bellied Brent Geese Branta bernicla bernicla on intertidal Eelgrass Zostera noltii and Greylag Geese Anser anser on Alkali Bulrush Bolboschoenus maritimus in brackish marshes) [1-3]. Geese are specialist herbivores, which, lacking a rumen, are unable to digest plant fibre [4]; this necessitates relatively rapid throughput of highly digestible plant material to maintain nutrition and energetic balance. For this reason, many species have likely grazed short cropped grassland facilitated by larger, mammalian herbivores from long before the advent of contemporary human agriculture transformed our landscape. Modern pastoral agriculture, however, has increasingly provided such short-grazed grasslands in abundance, which have progressively become important foraging grounds for geese due to the appropriate bite size and nutrient content of such short swards [e.g. 5,6]. Increasingly, improvements in grassland husbandry (especially through inorganic fertilisation) [7,8] has enhanced the carrying capacities of agricultural grasslands for wild geese and likely contributed to their recent population expansions [9]. The circumpolar Arctic-nesting White-fronted Goose shows this transition to different degrees through its range. In East Asia, the species feeds on sedge Carex spp. meadows exposed by water table drawdown in natural wetlands [10,11]. Similarly, the Greenland subspecies A. a. flavirostris still feeds in winter on below-ground storage organs of Common Cotton-grass Eriophorum angustifolium and other plants in natural peatlands [12], but increasingly exploits agricultural grasslands, apparently with associated fitness benefits [13]. At the same time, in most of Europe, this goose species is now a classic pastoral grassland specialist [e.g. 14-16]. Other goose species in western Europe have show similar tendencies to specialise as grazers of agricultural grassland and as their populations have increased, so has the grazing pressure there increased, together with the potential for agricultural damage [9] [16,17]. Recently, with the increasing autumn sowing of cereals (especially since the early 1970s) [18,19], many goose species have shown a transition from grassland and pasture feeding to winter and spring feeding on cereal crops, particularly autumn sown crops because of the winter green above ground biomass they provide, a shift considered due to the superior nutrient and energy intake rates that these dense, single species crop swards provide [20-22]. With this transition inevitably comes increased agricultural conflict, since cereal crops tend to be more economically important and more evidently affected by geese than grasslands [23]. It is therefore important to understand if geese are making this transition because of conversion of grassland to cereals (i.e. physical loss of pasture to croplands), or if the geese are in the process of shifting from grassland to cereal because of the superior levels of energetic and nutritional intake rates they can sustain there [21,24,25]. In recent years, Central European grasslands have been increasingly converted to croplands [26], so it is natural to assume that the movement of spring staging geese to cereals from grasslands is a function of their increasing availability, but is this really the case? Could increasing numbers of geese be exploiting cereals because of the superior intake rates they can sustain on such crops, independent of their relative availability? Given that cereal crops support higher intake rates and cereal fields are frequently larger than grasslands, could the transition also be supporting changes in flock size? We looked to answer these questions in a key central European stopover site for many thousands of White-fronted and other goose species in the Biebrza Basin in NE Poland, an extensive area of grassland and cereals [15,27]. Migrating Greylag Geese arrive to stage here early in the season (February and early March; some stay to breed locally) and tend to feed mainly on cereal fields that are exposed early in the season (when grasslands are usually covered by snow) [15]. The more numerous White-fronted Geese pass through on migration later and, in the past at least, preferred grasslands, foraging in areas nearer the roosting sites, where they were less exposed to human disturbance, an important factor influencing the distribution of the geese [28]. Methods Study area This study was conducted in the Biebrza Basin, NE Poland, in the floodplains of two river systems, the Biebrza and Narew (100 km and 65 km long, respectively) a designated Important Bird Area [29]. It is one of the main spring staging sites in central Europe for White-fronted Geese on their way to Siberian breeding areas, but also stopover site for staging Barnacle Branta leucopsis , Bean Anser fabalis sensu lato and Greylag Geese [15,27,30,31]. Land use change Traditionally, the floodplain grasslands have been mowed by farmers and grazed by cows and such grassland currently covers about 58% of the total area, with cereal cultivation covering a further 37% [32,33], and remaining land cover features (such as sedge meadows, scrub, woodland and buildings) in the landscape not exploited by geese. Most of the grasslands are located nearer to the rivers, while the arable farmland tends to lie towards the upper edges of the Basin. The grasslands in the valleys are flooded annually for up to 150 days, mostly as a result of the spring snowmelt [15]. Because of the limited availability of land cover data, we were constrained to use CORINE Land Cover data [32] from the years 2012 for our goose observations from 2007-15 and 2018 [33] for those from 2023 to characterise the extent of different land use types in relation to our goose counts from that time. However, our frequent observations in this area confirm that there have been no major cropping or other land cover changes in the study area between 2018 and 2023. In both 2012 and 2018, we extracted land cover data within a radius of 200 m of 30 points selected throughout the study area, which covered equal areas of grassland and cereals, where half (i.e. 15) were regularly used by geese for foraging and in half where geese were absent. These areas extended to about 20% of all fields used by geese during spring staging. We then used generalized linear mixed models (GLMMs) with a negative binomial distribution, using the MASS package [34] in R to compare statistically the percentage of cereal cover within these sampled areas in 2015 and 2018, using cereal percentage cover as dependent variable, period as fixed effect and point id as random repetitive effect (the 30 sites in 2012 and 2018). For GIS analyses we used R software, employing packages sf [35] and sp [36]. Goose data collection Observations were carried out during the spring migration periods from 12 February to 10 May in 2007–2015 (in 2009 and 2015 single observations were taken also in January) and from 7 February until 22 April 2023 (Table 1), covering the main goose migration period, which peaks in March [37]. Data from 2007-2015 included 2,187 observations of 1,011,304 geese and from 2023 with 678 observations of 233,019 geese (Table 1). Although the majority of the geese were White-fronted Goose, we assigned all counts to species where these occurred, namely Greylag Goose, Pink-footed Goose A. brachyrhynchus , Barnacle Goose and Bean Goose. The latter species Anser fabalis was split into Anser fabalis and Anser serrirostris [38] during the study, so even with the almost all observations were of A. serrirostris, they are aggregated here as Anser fabalis sensu lato . Observations were carried out throughout the entire study area, beginning each day from a different starting point to avoid bias, with the main focus to cover all the important areas where geese concentrated annually [15,21]. Surveys aimed to assign the maximum possible number of geese flocks to habitat type (cereal or grassland), regardless of size, on every occasion. We attempted to obtain equal coverage of feeding grounds close to flooded areas (which were used as roosting sites) as well as the more distant cereal fields [37]. High-quality spotting scopes were used to avoid disturbance and observe birds from distances of up to 500 m. During all surveys (2007-2015 and 2023), we identified each group of geese in a discrete unit in a field to species (our definition of a flock), counted individuals and the foraging habitat where they fed (either grassland, i.e. mowed meadows and pastures grazed by domestic animals or arable land, principally winter cereals). Censuses classified most foraging birds to grass/crops in 2008 and 2023, fewest in 2013 (when a record long winter resulted in most birds migrating without stopping through the Biebrza Basin, see also Table 1) [15]. Statistical procedures We extracted the annual percentage use of cereals and grass by all geese (regardless of species) in each of the years 2007-2015 and again in 2023. We checked the data for normality using qqnorm function in R and then used One Sample student’s t-tests to compare the mean observed use of cereals and grass from 2007-2015 with those from 2023. After log-transformation of flock size to normalise their distributions, we tested foraging ground preferences for each species of goose with sufficient sample size as a binomial variable (one indicates complete preference for cereals, zero indicates only use of grass) and the following predictor variables: Period (old 2007-2015 vs new 2023), Day Of Year and Flock Size. We fitted generalised linear models (GLMs) with a binomial distribution to assess the relationship of foraging behaviour with different predictors. For each species, we compared between different models, including those with interaction terms between the predictors using Akaike Information Criterion corrected for small sample sizes (AIC c see Supplementary materials Table S1-S5). After selecting model of the lowest AIC c score we assessed significance of each term in the models using the drop1 function with a Chi-squared test. These analyses were carried out for: White-fronted (n = 856 flocks), Greylag (n = 875), Pink-footed (n = 58), Bean (n = 632), and Barnacle Geese (n = 234). For each species, similar GLM models were fitted and evaluated. The emmeans package was used for post-hoc comparisons where necessary. The statistical analyses were performed using R software using lme4 [39], emmeans [40], ggplot2 [41]. We tested for changes in overall mean flock size on grass and cereals in 2007-2015 combined versus the mean flock sizes there in 2023 using Mann-Whitney tests, because the data were not normally distributed. Results Changes in extent of grassland and cereals in 2012 and 2018 There was no difference in the percentage of grassland (37.2% and 37.2%) or cereal cover (57.6% and 57.6%) within 200 m around the 30 random points between 2012 and 2018 (GLMM, p = 1). Changes in goose use of grassland and cereals between 2007-2015 and 2023 Censuses were conducted on 231 days (mean 23.1 per season) and a mean of 126,468 geese were observed annually (Table 1). There was a significantly higher percentage of geese on cereals in 2023 (33.5%) than the mean value from the period 2007-2015 (20.7%; Figure 1; t = -3.173, df = 8, p = 0.013) and a significantly lower percentage of geese on grass in 2023 (65.6%) compared to 2007-2015 (77.0%; Figure 1; t = 2.661, df = 8, p = 0.029). Modelled changes in goose counts on grass and cereals 2007-2015 and 2023 Analysis of the selected binomial model (Table S1) for White-fronted Goose showed that both interactions were preferred (Table 2). The first interaction Period × Flock size (Fig 2A) showing greater flock size in 2023 corresponded with a stronger preference for cereals, while the converse was true in 2007-2015 although this interaction failed to reach significance despite AIC favouring this model (Table 2). The preference for grass in 2007-2015 had also showed a pronounced increase with flock size. This shows a potential shift in habitat utilisation to cereals but especially in larger flocks in recent years. The second interaction (Period × DayOfYear) revealed an increasing preference for cereals in 2023, while the converse was true in 2007-2015 (Figure 2B) and this interaction was significant (Table 2). The AIC c selected binomial model for Bean Goose indicated that both interactions were preferred and both were significant (Table S2 and Table 3). As with the preceding species, this indicates larger Bean Goose flocks selected to feed on cereals in 2023 and did so increasing as the season progressed, whereas larger flocks had tended to feed on grass in 2007-2015 and increasing so with date in that period (Figures 2C and 2D). The most parsimonious model for Barnacle Goose (Table S4), indicated a significant interaction Period × DayOfYear (Table 5). In 2023, Barnacle Geese showed a preference for cereals later in the migration period, whereas they had increasingly exploited grasslands later in the season during 2007-2015 (Figure 2H) and there was a tendency for larger flocks to forage on grassland (Figure 2G). For Greylag Goose, the selected binomial model also included both interactions (Table S3) and both were significant (Table 4). However, the results are the opposite to those of White-fronted and Bean Geese, showing significantly less preference for cereals in 2023 and greater section of grassland with increasing flock size through the season than during 2007-2015 when these relationships held more for cereal and in the case of greater flock size as well (Figure 2E and 2F). Pink-footed Geese were never numerous in the study area and model section suggested that null model was the best one, so there were no apparent differences in foraging preference according to the proposed parameters (Table S5). Changes in mean flock size from 2007-2015 to 2023 Mean goose flock size (averaged across all species and back transformed after logarithmic transformation) significantly increased between 2007-2015 and 2023 from 61 to 261 on grass (Mann-Whitney test, W = 46361, p < 0.001) and from 69 to 237 on cereals (Mann-Whitney test, W = 16987, p < 0.001). Discussion Our results showed no change in the area of grassland and cereals in the CORINE database for 2012 and 2018 in the Biebrza Basin study area and our ground examination of the extent of agricultural land cover for 2023 confirms that the CORINE cover estimates from 2018 were also largely unchanged for 2023. Based on changes in regional availability of cereals versus grass to staging geese, there was therefore no reason to expect any increase in their use of cereals between the two periods. Nevertheless, the proportion of geese using cereal fields significantly increased from 21% during 2005-2017 to 34% in 2023. The two most abundant species, White-fronted and Bean Geese show a strong increased preference for foraging on cereals, especially at larger flock sizes, in 2023 compared to during 2007-2015, when larger flocks had tended to forage more on grasslands. Geese were aggregated in greater average flock sizes across both habitats in 2023 compared to 2007-2015 and both tended to show stronger preference for cereals as the season progressed. Because there was no change in the relative availability of grassland versus cereals, we conclude that in the Biebrza Basin study area, spring staging White-fronted and Bean Geese are showing increased selection for cereals over grassland, independent of their relative availability and the model results showed this was especially the case at larger flock sizes. Despite this overall pattern, sympatric spring staging Barnacle and Greylag Geese showed different habitat preferences between species and over time. Relatively modest numbers of Barnacle Geese tended to feed in both periods on grass in larger flock sizes, but whereas they showed a preference for cereals early in the stopover period in 2005-2017 and grass later, this pattern was reversed in 2023. Greylag Geese showed significantly less preference for cereals in 2023 (and even less so as the season progressed) and greater selection of grassland with increasing flock size through the season than during 2007-2015 when they preferred cereals also increasingly so with greater flock size. We speculate that this observed effect may be the result of interspecific interactions because the Greylag Goose is a short-distance migrant and partially local breeding species compared to the other long-distance migratory species that pass through the Biebrza Basin relatively quicker en route to nest in the Arctic. Greylag Geese arrive before the other species and remain in the Biebrza Basin study area later after their departure. In 2023, we observed that Greylag Geese conspicuously switched to feeding on grasslands when the large numbers of other species arrive and pass through. Locally breeding birds are typically distributed in individual pairs, where dominant, aggressive alert males protect their females while they feed intensively in the prelude to egg-laying and incubation to accumulate energy and nutrients to invest in reproduction. Small groups of such pairings cannot gain feeding domination over very large flocks of intensively foraging staging boreal and Arctic nesting geese, since large groups of geese are dominant over smaller groups, pairs and singles [e.g. 42,43]. This mechanism might also explain the slightly different patterns shown by the staging Barnacle Geese that also occur in far lesser numbers than the two most numerous goose species. Hence, to avoid continuous agonistic interactions, particularly with the numerically highly abundant White-fronted Geese during their stay in the Biebrza Basin, Greylag Geese most likely trade-off much reduced loss of feeding time for females pre-laying by feeding on less profitable grasslands where they can accumulate stores of fat and nutrients at less cost to aggressive interactions with other goose species in superior densities. Although we can find no references to this type of displacement of less numerous species by more abundant ones in the literature, locally summering Greylag Geese were also conspicuously more numerous before and after the spring passage of large dense flocks of Greenland White-fronted Geese though Hvanneyri in western Iceland as if also avoiding aggression from the far more numerous species [44; pers obs.]. Although wild geese have been attracted to agricultural crops after, during or near the time of harvest since mediaeval times [45], waterbirds (and especially the Anatidae) in general have benefited mostly from the increasing quality of agricultural grasslands that have occurred since the 1920s as a result of their fertilisation, selective breeding and reseeding [7,8,46]. Only since the introduction of autumn sown cereal from the 1960s and their widespread adoption through the 1970s in Europe have these monospecific swards of high biomass, energy and nutritional quality become an attractive and more profitable alternative to agricultural grasslands in the farmland landscape and ultimately the focus of agricultural conflict [e.g. 47]. The relative profitability of green cereals versus grassland seems to be high seasonally determined [e.g. 48,49], likely reflecting changes in the relative profitability of green cereal laminae in relation to other profitable sources of food (such as highly energetically dense maize cobs used by autumn staging White-fronted Geese in Poland in autumn when autumn sown cereals were avoided [50]. Indeed, our results also showed a general increase in preference for cereals as the migration season progressed, indicating a temporal shift in habitat utilization from grasslands, which was statistically significant and consistent across the species, excepting Greylags. Whether this was due to heavy grazing removing the available grass biomass and making cereals even more attractive later in the season, or to shifts in the relative quality of the two food resources as the season progresses is the subject of continuing research. Generally, however, there is no doubt that the sustained green above ground primary production and high protein content of winter cereals in winter and spring makes them highly attractive to foraging geese compared to even heavily fertilised reseeded grassland swards available in the same farmland landscape [e.g. 21,24,25]. Indeed, under certain circumstances, sacrificial autumn sown cereals have been used to attract geese off of perennial forage grasses which are otherwise economically highly susceptible to such goose grazing [51]. We infer, but cannot know for sure, that the normally highly site faithful White-fronted Geese return to the same wintering and staging areas in the Biebrza Basin to forage outside the breeding season (as they do elsewhere) [e.g. 52,53]. However, within such generally highly conservative goose flocks, subordinate individuals (by virtue of their low dominance status) are often explorative and the first to find enhanced foraging opportunities, to which they subsequently attract other geese [54]. These mechanisms could explain the lag in exploitation of new foraging opportunities which we are experiencing in spring in the Biebrza Basin, as geese gradually learn about new foraging opportunities provided by cereals grown adjacent to the grassland staging areas that they have traditionally occupied. Because geese show family cohesion over one or more years [55], such new feeding traits become learned and spread by family associations, leading to widespread adoption of novel food items, as in this case. Conclusions Given that this study provides a clear case of geese physically shifting from grassland to cereals in the absence of changes in the relative availability and accessibility of these two field types, we predict that more and more geese will make this transition in spring in the Biebrza Basin (as elsewhere) in the future and that potentially the level of agricultural conflict will increase as more geese in larger flocks (as also demonstrated here) depredate cereal fields there. Declarations Acknowledgements We are very grateful to volunteer observers who helped in the field studies. The research was co-financed by the Minister of Science under the "Regional Excellence Initiative" Program. Author contributions Concept & design: M.P. and A.D.F. Data acquisition: M.P., M.B. and M.F. Analysis & data interpretation: M.P., A.D.F. and Ł.J. Article drafting: A.D.F.,M.P. and Ł.J. Critical supervision and revision: M.P., Ł.J., M.B., M.F., A.D.F. Data availability Data will be made available on request. To request the data, corresponding author should be contacted. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical approval This study did not involve any experiments on live vertebrates according Polish law ( Dz. U. 2015 poz. 266 Ustawa z dnia 15 stycznia 2015 r. o ochronie zwierząt wykorzystywanych do celów naukowych lub edukacyjnych ), but observational research. According to Polish law, local committee approval is not required for observational studies. We followed all applicable institutional and national guidelines for the care and use of wild animals. Funding The research was co-financed by the Minister of Science under the "Regional Excellence Initiative" Program. References Zwarts, L. 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Wickham, H. ggplot2: Elegant Graphics for Data Analysis . (Springer-Verlag, 2016). Boyd, H. On encounters between wild white-fronted geese. Behaviour 5, 85–129 (1953). Raveling, D. G. Dominance relationships and agonistic behavior of Canada geese in winter. Behaviour 37, 291–318 (1970). Kristiansen, J. N., Fox, A. D., Boyd, H. & Stroud, D. A. Greenland White-fronted Geese Anser albifrons flavirostris benefit from feeding in mixed-species flocks. Ibis 142, 142–144 (2000). Kear, J. Three early medieval accounts of agricultural damage by wild geese. Arch. Nat. Hist. 28, 245–255 (2001). Jefferies, R. L., Drent, R. H. & Bakker, J. P. 13. Connecting Arctic and Temperate Wetlands and Agricultural Landscapes: The Dynamics of Goose Populations in Response to Global Change . In: Verhoeven, J.T.A., Beltman, B., Bobbink, R., Whigham, D.F., (Eds.) Wetlands and Natural Resource Management . (Springer, 2006). Patterson, I. J., Jalil, S. A. & East, M. L. Damage to winter cereals by greylag and pink-footed geese in north-east Scotland. J. Appl. Ecol. 26, 879–895 (1989). McKay, H. V., Bishop, J. D. & Ennis, D. C. The possible importance of nutritional requirements for dark-bellied brent geese in the seasonal shift from winter cereals to pasture. Ardea 82, 123–132 (1994). Strong, E. A., Redpath, S. M., Montrŕs-Janer, T., Elmberg, J. & Månsson, J. Seeking greener pastures: crop selection by Greylag Geese ( Anser anser ) during the moulting season. Ornis Fennica 98, 16–32 (2021). Rosin, Z. M. et al. Landscape structure, human disturbance and crop management affect foraging ground selection by migrating geese. J. Ornithol. 153, 747–759 (2012). Bradbeer, D. & Halpin, L. Managing cereal grasses as waterfowl lure crops: investigating planting dates and waterfowl feeding ecology. Delta Farmland and Wildlife Trust Report, Delta, BC, Canada. (Delta Farmland and Wildlife Trust, 2012). Wilson, H. J., Norriss, D. W., Walsh, A., Fox, A. D. & Stroud, D. A. Winter site fidelity in Greenland White-fronted Geese Anser albifrons flavirostris : implications for conservation and management. Ardea 79, 287–294 (1991). Fox, A.D. et al. Phenology and distribution of Greenland White-fronted Geese Anser albifrons flavirostris staging in Iceland. Wildfowl 50, 29–43 (1999). Stahl, J., Tolsma, P. H., Loonen, M. J. & Drent, R. H. Subordinates explore but dominants profit: resource competition in high Arctic barnacle goose flocks. Anim. Behav. 61, 257–264 (2001). Weegman, M. D. et al. Should I stay or should I go? Fitness costs and benefits of prolonged parent-offspring and sibling associations in an Arctic-nesting goose population. Oecologia 181, 809–817 (2016). Tables Table 1. Number of observations of geese from each species contributing to the analysis presented here, based on the number of census days in each year during the study period in the Biebrza Basin, 2007-2023. Year Number of days of census Number of birds Anser albifrons Anser fabalis sensu lato Anser anser Anser brachyrhynchus Branta leucopsis 2007 17 130226 8510 610 3 105 2008 27 198662 18957 538 17 189 2009 18 76427 8384 3089 6 36 2010 23 124857 5178 2412 1 37 2011 18 62153 2613 2020 0 52 2012 19 54200 9807 3956 9 67 2013 22 19688 5504 10906 2 57 2014 28 61524 8430 2498 7 44 2015 31 181037 4463 3577 4 442 Mean 22,6 100974,9 7982,9 3289,6 5,4 114,3 2023 28 193442 34555 4687 32 303 Table. 2. Results from the most favoured binomial model evaluating estimates and effects of period, flock size, and day of the year on the preference for cereals (coded as 1) over grasslands (coded as 0) by White-fronted Geese Anser albifrons . Fixed effects Estimate SE z value Pr(>|z|) (Intercept) -0.777 0.784 -0.991 0.322 Periodold 2.524 0.933 2.706 0.007 Flock_size -0.119 0.171 -0.697 0.486 DayOfYear 0.007 0.010 0.646 0.518 Periodold:Flock_size -0.312 0.191 -1.633 0.103 Periodold:DayOfYear -0.024 0.012 -2.013 0.044 Effects Df Deviance AIC LRT P 1079.686 1091.686 Period:Flock_size 1 1082.349 1092.349 2.662 0.103 Period:DayOfYear 1 1083.758 1093.758 4.071 0.044 Significant results are marked in bold Table 3. Results from a binomial model evaluating estimates and effects of period, flock size, and day of the year on the preference for cereals (coded as 1) over grasslands (coded as 0) by Bean Geese Anser fabalis . Fixed effects Estimate SE z value Pr(>|z|) (Intercept) -3.072 1.087 -2.826 0.005 Periodold 3.346 1.216 2.750 0.006 Flock_size 0.507 0.227 2.228 0.026 DayOfYear 0.027 0.012 2.227 0.026 Periodold:Flock_size -0.655 0.258 -2.534 0.011 Periodold:DayOfYear -0.033 0.014 -2.356 0.018 Effects Df Deviance AIC LRT P 848.351 860.351 Period:Flock_size 1 855.078 865.078 6.727 0.009 Period:DayOfYear 1 854.173 864.173 5.822 0.016 Significant results are marked in bold Table 4. Results from binomial model evaluating estimates and effects of period, flock size, and day of the year on the preference for cereals (coded as 1) over grasslands (coded as 0) by Greylag Geese Anser anser. Fixed effects Estimate SE z value P (Intercept) 3.802 1.765 2.154 0.031 Periodold -4.593 1.806 -2.543 0.011 Flock_size -0.999 0.517 -1.933 0.053 DayOfYear -0.052 0.020 -2.657 0.008 Periodold:Flock_size 2.183 0.533 4.095 <0.001 Periodold:DayOfYear 0.041 0.020 2.053 0.04 Effects Df Deviance AIC LRT P 1,049.494 1,061.494 Period:Flock_size 1 1,067.239 1,077.239 17.745 <0.001 Period:DayOfYear 1 1,054.190 1,064.190 4.696 0.03 Significant results are marked in bold Table 5. Results from a binomial model evaluating estimates and effects of period, flock size, and day of the year on the preference for cereals (coded as 1) over grasslands (coded as 0) by Barnacle Geese Branta leucopsis . Fixed effects Estimate SE z value Pr(>|z|) (Intercept) -4.615 2.109 -2.188 0.029 Periodold 7.263 2.470 2.940 0.003 DayOfYear 0.057 0.027 2.160 0.031 Flock_size -1.363 0.489 -2.787 0.005 Periodold:DayOfYear -0.098 0.031 -3.198 0.001 Effects Df Deviance AIC LRT P 231.487 241.487 Flock_size 1 239.841 247.841 8.354 0.004 Period:DayOfYear 1 243.356 251.356 11.869 0.001 Significant results are marked in bold Additional Declarations No competing interests reported. Supplementary Files Supmat.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4189509","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":326312564,"identity":"f5a775fc-6d74-477b-bd2f-966e8aae1c89","order_by":0,"name":"Michał Polakowski","email":"data:image/png;base64,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","orcid":"","institution":"University of Szczecin","correspondingAuthor":true,"prefix":"","firstName":"Michał","middleName":"","lastName":"Polakowski","suffix":""},{"id":326312567,"identity":"be9b359f-a787-42b2-8504-6991adaf23a7","order_by":1,"name":"Łukasz Jankowiak","email":"","orcid":"","institution":"University of Szczecin","correspondingAuthor":false,"prefix":"","firstName":"Łukasz","middleName":"","lastName":"Jankowiak","suffix":""},{"id":326312568,"identity":"f97a7aea-7235-4556-a9db-4b5aaeb108e4","order_by":2,"name":"Monika Broniszewska","email":"","orcid":"","institution":"Jestem Na pTAK! Society","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Broniszewska","suffix":""},{"id":326312569,"identity":"d7b2825d-f639-4567-8433-87a9da89344f","order_by":3,"name":"Michał Fabiszewski","email":"","orcid":"","institution":"Biebrza National Park","correspondingAuthor":false,"prefix":"","firstName":"Michał","middleName":"","lastName":"Fabiszewski","suffix":""},{"id":326312570,"identity":"7c984794-d814-41ac-bac8-155e16d76e57","order_by":4,"name":"Anthony David Fox","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"David","lastName":"Fox","suffix":""}],"badges":[],"createdAt":"2024-03-29 19:29:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4189509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4189509/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60367368,"identity":"697631f6-f34b-4f0b-8c27-dfba931f8cfc","added_by":"auto","created_at":"2024-07-16 04:04:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59801,"visible":true,"origin":"","legend":"\u003cp\u003eObserved mean percentage of geese counted on cereals (left) and grass (right) from 2007-2015 against cereal and grass cover from 2023 (dashed lines). Whiskers indicate 95% confidence intervals for 2007-2015. Differences between time periods were significant for both habitats (see text for details).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4189509/v1/d625a5bf7c0666a580c3fc77.png"},{"id":60367980,"identity":"e1476037-3493-4d9d-a71a-21524502938c","added_by":"auto","created_at":"2024-07-16 04:12:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":525713,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant predictions (solid lines, shaded area indicates 95% confidence intervals) of different models explaining goose preference for cereals (coded as 1) over grassland (coded as 0) based on count data from 2007-2015 (red) and 2023 (green). Circles represent observed data, jittered around 0 or 1.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4189509/v1/e43fab9b87efd095625f8da1.png"},{"id":71473944,"identity":"8ece17f6-da14-410d-8019-0f64de130001","added_by":"auto","created_at":"2024-12-16 04:39:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1118257,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4189509/v1/ab324ae9-00bb-4454-8f30-1f28d3cac98f.pdf"},{"id":60367369,"identity":"8952c528-fa9f-46e1-88d6-a99329f5235c","added_by":"auto","created_at":"2024-07-16 04:04:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23528,"visible":true,"origin":"","legend":"","description":"","filename":"Supmat.docx","url":"https://assets-eu.researchsquare.com/files/rs-4189509/v1/c567ca5b8bc21e02438957a4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Voting with their feet: Spring staging geese increasingly select for cereal crops over grassland at an important central European stopover site","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrior to the Neolithic clearance of woodland in Northern Europe, overwintering wild geese were inevitably constrained to feed on natural habitats (usually wetlands). Indeed, until recently, several species have continued to feed on what were likely to have been their traditional pre-agriculture food resources (e.g. Dark-bellied Brent Geese \u003cem\u003eBranta bernicla bernicla\u0026nbsp;\u003c/em\u003eon intertidal Eelgrass \u003cem\u003eZostera noltii\u003c/em\u003e and Greylag Geese \u003cem\u003eAnser anser\u003c/em\u003e on Alkali Bulrush \u003cem\u003eBolboschoenus maritimus\u003c/em\u003e in brackish marshes) [1-3]. Geese are specialist herbivores, which, lacking a rumen, are unable to digest plant fibre [4]; this necessitates relatively rapid throughput of highly digestible plant material to maintain nutrition and energetic balance. For this reason, many species have likely grazed short cropped grassland facilitated by larger, mammalian herbivores from long before the advent of contemporary human agriculture transformed our landscape.\u003cem\u003e\u0026nbsp;\u003c/em\u003eModern pastoral agriculture, however, has increasingly provided such short-grazed grasslands in abundance, which have progressively become important foraging grounds for geese due to the appropriate bite size and nutrient content of such short swards [e.g. 5,6]. Increasingly, improvements in grassland husbandry (especially through inorganic fertilisation) [7,8] has enhanced the carrying capacities of agricultural grasslands for wild geese and likely contributed to their recent population expansions [9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe circumpolar Arctic-nesting White-fronted Goose shows this transition to different degrees through its range. In East Asia, the species feeds on sedge \u003cem\u003eCarex\u003c/em\u003e spp. meadows\u003cem\u003e\u0026nbsp;\u003c/em\u003eexposed by water table drawdown in natural wetlands [10,11]. Similarly, the Greenland subspecies \u003cem\u003eA. a. flavirostris\u003c/em\u003e still feeds in winter on below-ground storage organs of Common Cotton-grass \u003cem\u003eEriophorum angustifolium\u0026nbsp;\u003c/em\u003eand other plants in natural peatlands [12], but increasingly exploits agricultural grasslands, apparently with associated fitness benefits [13]. At the same time, in most of Europe, this goose species is now a classic pastoral grassland specialist [e.g. 14-16].\u0026nbsp;Other goose species in western Europe have show similar tendencies to specialise as grazers of agricultural grassland and as their populations have increased, so has the grazing pressure there increased, together with the potential for agricultural damage [9] [16,17]. Recently, with the increasing autumn sowing of cereals (especially since the early 1970s) [18,19], many goose species have shown a transition from grassland and pasture feeding to winter and spring feeding on cereal crops, particularly autumn sown crops because of the winter green above ground biomass they provide, a shift considered due to the superior nutrient and energy intake rates that these dense, single species crop swards provide [20-22]. With this transition inevitably comes increased agricultural conflict, since cereal crops tend to be more economically important and more evidently affected by geese than grasslands [23]. It is therefore important to understand if geese are making this transition because of conversion of grassland to cereals (i.e. physical loss of pasture to croplands), or if the geese are in the process of shifting from grassland to cereal because of the superior levels of energetic and nutritional intake rates they can sustain there [21,24,25].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn recent years, Central European grasslands have been increasingly converted to croplands [26], so it is natural to assume that the movement of spring staging geese to cereals from grasslands is a function of their increasing availability, but is this really the case? Could increasing numbers of geese be exploiting cereals because of the superior intake rates they can sustain on such crops, independent of their relative availability? Given that cereal crops support higher intake rates and cereal fields are frequently larger than grasslands, could the transition also be supporting changes in flock size? \u0026nbsp;We looked to answer these questions in a key central European stopover site for many thousands of White-fronted and other goose species in the Biebrza Basin in NE Poland, an extensive area of grassland and cereals [15,27]. Migrating Greylag Geese arrive to stage here early in the season (February and early March; some stay to breed locally) and tend to feed mainly on cereal fields that are exposed early in the season (when grasslands are usually covered by snow) [15]. The more numerous White-fronted Geese pass through on migration later and, in the past at least, preferred grasslands, foraging in areas nearer the roosting sites, where they were less exposed to human disturbance, an important factor influencing the distribution of the geese [28].\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy area\u003c/p\u003e\n\u003cp\u003eThis study was conducted in the Biebrza Basin, NE Poland, in the floodplains of two river systems, the Biebrza and Narew (100 km and 65 km long, respectively) a designated Important Bird Area [29]. It is one of the main spring staging sites in central Europe for White-fronted Geese on their way to Siberian breeding areas, but also stopover site for staging Barnacle \u003cem\u003eBranta leucopsis\u003c/em\u003e, Bean \u003cem\u003eAnser fabalis\u003c/em\u003e \u003cem\u003esensu lato\u0026nbsp;\u003c/em\u003eand Greylag Geese [15,27,30,31]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLand use change\u003c/p\u003e\n\u003cp\u003eTraditionally, the floodplain grasslands have been mowed by farmers and grazed by cows and such grassland currently covers about 58% of the total area, with cereal cultivation covering a further 37% [32,33], and remaining land cover features (such as sedge meadows, scrub, woodland and buildings) in the landscape not exploited by geese. Most of the grasslands are located nearer to the rivers, while the arable farmland tends to lie towards the upper edges of the Basin. The grasslands in the valleys are flooded annually for up to 150 days, mostly as a result of the spring snowmelt [15].\u003c/p\u003e\n\u003cp\u003eBecause of the limited availability of land cover data, we were constrained to use CORINE Land Cover data [32] from the years 2012 for our goose observations from 2007-15 and 2018 [33] for those from 2023 to characterise the extent of different land use types in relation to our goose counts from that time. However, our frequent observations in this area confirm that there have been no major cropping or other land cover changes in the study area between 2018 and 2023. In both 2012 and 2018, we extracted land cover data within a radius of 200 m of 30 points selected throughout the study area, which covered equal areas of grassland and cereals, where half (i.e. 15) were regularly used by geese for foraging and in half where geese were absent. These areas extended to about 20% of all fields used by geese during spring staging. We then used generalized linear mixed models (GLMMs) with a negative binomial distribution, using the MASS package [34] in R to compare statistically the percentage of cereal cover within these sampled areas in 2015 and 2018, using cereal percentage cover as dependent variable, period as fixed effect and point id as random repetitive effect (the 30 sites in 2012 and 2018). \u0026nbsp;For GIS analyses we used R software, employing packages sf [35] and sp [36]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGoose data collection\u003c/p\u003e\n\u003cp\u003eObservations were carried out during the spring migration periods from 12 February to 10 May in 2007\u0026ndash;2015 (in 2009 and 2015 single observations were taken also in January) and from 7 February until 22 April 2023 (Table 1), covering the main goose migration period, which peaks in March [37]. Data from 2007-2015 included 2,187 observations of 1,011,304 geese and from 2023 with 678 observations of 233,019 geese (Table 1). Although the majority of the geese were White-fronted Goose, we assigned all counts to species where these occurred, namely Greylag Goose, Pink-footed Goose \u003cem\u003eA. brachyrhynchus\u003c/em\u003e, Barnacle Goose and Bean Goose. The latter species \u003cem\u003eAnser fabalis\u003c/em\u003e was split into \u003cem\u003eAnser fabalis\u003c/em\u003e and \u003cem\u003eAnser serrirostris\u003c/em\u003e [38] during the study, so even with the almost all observations were of \u003cem\u003eA. serrirostris,\u0026nbsp;\u003c/em\u003ethey are aggregated here as \u003cem\u003eAnser fabalis sensu lato\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObservations were carried out throughout the entire study area, beginning each day from a different starting point to avoid bias, with the main focus to cover all the important areas where geese concentrated annually [15,21]. Surveys aimed to assign the maximum possible number of geese flocks to habitat type (cereal or grassland), regardless of size, on every occasion. We attempted to obtain equal coverage of feeding grounds close to flooded areas (which were used as roosting sites) as well as the more distant cereal fields [37]. High-quality spotting scopes were used to avoid disturbance and observe birds from distances of up to 500 m. During all surveys (2007-2015 and 2023), we identified each group of geese in a discrete unit in a field to species (our definition of a flock), counted individuals and the foraging habitat where they fed (either grassland, i.e. mowed meadows and pastures grazed by domestic animals or arable land, principally winter cereals). Censuses classified most foraging birds to grass/crops in 2008 and 2023, fewest in 2013 (when a record long winter resulted in most birds migrating without stopping through the Biebrza Basin, see also Table 1) [15]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical procedures\u003c/p\u003e\n\u003cp\u003eWe extracted the annual percentage use of cereals and grass by all geese (regardless of species) in each of the years 2007-2015 and again in 2023. We checked the data for normality using qqnorm function in R and then used One Sample student\u0026rsquo;s t-tests to compare the mean observed use of cereals and grass from 2007-2015 with those from 2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter log-transformation of flock size to normalise their distributions, we tested foraging ground preferences for each species of goose with sufficient sample size as a binomial variable (one indicates complete preference for cereals, zero indicates only use of grass) and the following predictor variables: Period (old 2007-2015 vs new 2023), Day Of Year and Flock Size. We fitted generalised linear models (GLMs) with a binomial distribution to assess the relationship of foraging behaviour with different predictors. For each species, we compared between different models, including those with interaction terms between the predictors using Akaike Information Criterion corrected for small sample sizes (AIC\u003csub\u003ec\u003c/sub\u003e see Supplementary materials Table S1-S5). After selecting model of the lowest AIC\u003csub\u003ec\u003c/sub\u003e score we assessed significance of each term in the models using the drop1 function with a Chi-squared test. These analyses were carried out for: White-fronted (n = 856 flocks), Greylag (n = 875), Pink-footed (n = 58), Bean (n = 632), and Barnacle Geese (n = 234). For each species, similar GLM models were fitted and evaluated. The emmeans package was used for post-hoc comparisons where necessary. The statistical analyses were performed using R software using lme4 [39], emmeans [40], ggplot2 [41]. We tested for changes in overall mean flock size on grass and cereals in 2007-2015 combined versus the mean flock sizes there in 2023 using Mann-Whitney tests, because the data were not normally distributed.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eChanges in extent of grassland and cereals in 2012 and 2018\u003c/p\u003e\n\u003cp\u003eThere was no difference in the percentage of grassland (37.2% and 37.2%) or cereal cover (57.6% and 57.6%) within 200 m around the 30 random points between 2012 and 2018 (GLMM, p = 1). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChanges in goose use of grassland and cereals between 2007-2015 and 2023\u003c/p\u003e\n\u003cp\u003eCensuses were conducted on 231 days (mean 23.1 per season) and a mean of 126,468 geese were observed annually (Table 1). There was a significantly higher percentage of geese on cereals in 2023 (33.5%) than the mean value from the period 2007-2015 (20.7%; Figure 1;\u003cem\u003e\u0026nbsp;t\u003c/em\u003e = -3.173, df = 8, p = 0.013) and a significantly lower percentage of geese on grass in 2023 (65.6%) compared to 2007-2015 (77.0%; Figure 1; \u0026nbsp;\u003cem\u003et\u003c/em\u003e = 2.661, df = 8, p = 0.029).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModelled changes in goose counts on grass and cereals 2007-2015 and 2023\u003c/p\u003e\n\u003cp\u003eAnalysis of the selected binomial model (Table S1) for White-fronted Goose showed that both interactions were preferred (Table 2). The first interaction Period \u0026times; Flock size (Fig 2A) showing greater flock size in 2023 corresponded with a stronger preference for cereals, while the converse was true in 2007-2015 although this interaction failed to reach significance despite AIC favouring this model (Table 2). The preference for grass in 2007-2015 had also showed a pronounced increase with flock size. This shows a potential shift in habitat utilisation to cereals but especially in larger flocks in recent years. The second interaction (Period \u0026times; DayOfYear) revealed an increasing preference for cereals in 2023, while the converse was true in 2007-2015 (Figure 2B) and this interaction was significant (Table 2).\u003c/p\u003e\n\u003cp\u003eThe AIC\u003csub\u003ec\u003c/sub\u003e selected binomial model for Bean Goose indicated that both interactions were preferred and both were significant (Table S2 and Table 3). As with the preceding species, this indicates larger Bean Goose flocks selected to feed on cereals in 2023 and did so increasing as the season progressed, whereas larger flocks had tended to feed on grass in 2007-2015 and increasing so with date in that period (Figures 2C and 2D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe most parsimonious model for Barnacle Goose (Table S4), indicated a significant interaction Period \u0026times; DayOfYear (Table 5). In 2023, Barnacle Geese showed a preference for cereals later in the migration period, whereas they had increasingly exploited grasslands later in the season during 2007-2015 (Figure 2H) and there was a tendency for larger flocks to forage on grassland (Figure 2G).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor Greylag Goose, the selected binomial model also included both interactions (Table S3) and both were significant (Table 4). However, the results are the opposite to those of White-fronted and Bean Geese, showing significantly less preference for cereals in 2023 and greater section of grassland with increasing flock size through the season than during 2007-2015 when these relationships held more for cereal and in the case of greater flock size as well (Figure 2E and 2F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePink-footed Geese were never numerous in the study area and model section suggested that null model was the best one, so there were no apparent differences in foraging preference according to the proposed parameters (Table S5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChanges in mean flock size from 2007-2015 to 2023\u003c/p\u003e\n\u003cp\u003eMean goose flock size (averaged across all species and back transformed after logarithmic transformation) significantly increased between 2007-2015 and 2023 from 61 to 261 on grass (Mann-Whitney test, W = 46361, p \u0026lt; 0.001) and from 69 to 237 on cereals (Mann-Whitney test, W = 16987, p \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results showed no change in the area of grassland and cereals in the CORINE database for 2012 and 2018 in the Biebrza Basin study area and our ground examination of the extent of agricultural land cover for 2023 confirms that the CORINE cover estimates from 2018 were also largely unchanged for 2023. Based on changes in regional availability of cereals versus grass to staging geese, there was therefore no reason to expect any increase in their use of cereals between the two periods. \u0026nbsp;Nevertheless, the proportion of geese using cereal fields significantly increased from 21% during 2005-2017 to 34% in 2023. \u0026nbsp;The two most abundant species, White-fronted and Bean Geese show a strong increased preference for foraging on cereals, especially at larger flock sizes, in 2023 compared to during 2007-2015, when larger flocks had tended to forage more on grasslands. Geese were aggregated in greater average flock sizes across both habitats in 2023 compared to 2007-2015 and both tended to show stronger preference for cereals as the season progressed. Because there was no change in the relative availability of grassland versus cereals, we conclude that in the Biebrza Basin study area, spring staging White-fronted and Bean Geese are showing increased selection for cereals over grassland, independent of their relative availability and the model results showed this was especially the case at larger flock sizes.\u003c/p\u003e\n\u003cp\u003eDespite this overall pattern, sympatric spring staging Barnacle and Greylag Geese showed different habitat preferences between species and over time. Relatively modest numbers of Barnacle Geese tended to feed in both periods on grass in larger flock sizes, but whereas they showed a preference for cereals early in the stopover period in 2005-2017 and grass later, this pattern was reversed in 2023. Greylag Geese showed significantly less preference for cereals in 2023 (and even less so as the season progressed) and greater selection of grassland with increasing flock size through the season than during 2007-2015 when they preferred cereals also increasingly so with greater flock size. We speculate that this observed effect may be the result of interspecific interactions because the Greylag Goose is a short-distance migrant and partially local breeding species compared to the other long-distance migratory species that pass through the Biebrza Basin relatively quicker \u003cem\u003een route\u003c/em\u003e to nest in the Arctic. Greylag Geese arrive before the other species and remain in the Biebrza Basin study area later after their departure. In 2023, we observed that Greylag Geese conspicuously switched to feeding on grasslands when the large numbers of other species arrive and pass through. Locally breeding birds are typically distributed in individual pairs, where dominant, aggressive alert males protect their females while they feed intensively in the prelude to egg-laying and incubation to accumulate energy and nutrients to invest in reproduction. Small groups of such pairings cannot gain feeding domination over very large flocks of intensively foraging staging boreal and Arctic nesting geese, since large groups of geese are dominant over smaller groups, pairs and singles [e.g. 42,43]. This mechanism might also explain the slightly different patterns shown by the staging Barnacle Geese that also occur in far lesser numbers than the two most numerous goose species. Hence, to avoid continuous agonistic interactions, particularly with the numerically highly abundant White-fronted Geese during their stay in the Biebrza Basin, Greylag Geese most likely trade-off much reduced loss of feeding time for females pre-laying by feeding on less profitable grasslands where they can accumulate stores of fat and nutrients at less cost to aggressive interactions with other goose species in superior densities. Although we can find no references to this type of displacement of less numerous species by more abundant ones in the literature, locally summering Greylag Geese were also conspicuously more numerous before and after the spring passage of large dense flocks of Greenland White-fronted Geese though Hvanneyri in western Iceland as if also avoiding aggression from the far more numerous species [44; pers obs.]. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough wild geese have been attracted to agricultural crops after, during or near the time of harvest since mediaeval times [45], waterbirds (and especially the Anatidae) in general have benefited mostly from the increasing quality of agricultural grasslands that have occurred since the 1920s as a result of their fertilisation, selective breeding and reseeding [7,8,46]. Only since the introduction of autumn sown cereal from the 1960s and their widespread adoption through the 1970s in Europe have these monospecific swards of high biomass, energy and nutritional quality become an attractive and more profitable alternative to agricultural grasslands in the farmland landscape and ultimately the focus of agricultural conflict [e.g. 47]. The relative profitability of green cereals versus grassland seems to be high seasonally determined [e.g. 48,49], likely reflecting changes in the relative profitability of green cereal laminae in relation to other profitable sources of food (such as highly energetically dense maize cobs used by autumn staging White-fronted Geese in Poland in autumn when autumn sown cereals were avoided [50]. Indeed, our results also showed a general increase in preference for cereals as the migration season progressed, indicating a temporal shift in habitat utilization from grasslands, which was statistically significant and consistent across the species, excepting Greylags. Whether this was due to heavy grazing removing the available grass biomass and making cereals even more attractive later in the season, or to shifts in the relative quality of the two food resources as the season progresses is the subject of continuing research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenerally, however, there is no doubt that the sustained green above ground primary production and high protein content of winter cereals in winter and spring makes them highly attractive to foraging geese compared to even heavily fertilised reseeded grassland swards available\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ein the same farmland landscape [e.g. 21,24,25]. Indeed, under certain circumstances, sacrificial autumn sown cereals have been used to attract geese off of perennial forage grasses which are otherwise economically highly susceptible to such goose grazing [51].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe infer, but cannot know for sure, that the normally highly site faithful White-fronted Geese return to the same wintering and staging areas in the Biebrza Basin to forage outside the breeding season (as they do elsewhere) [e.g. 52,53]. However, within such generally highly conservative goose flocks, subordinate individuals (by virtue of their low dominance status) are often explorative and the first to find enhanced foraging opportunities, to which they subsequently attract other geese [54]. These mechanisms could explain the lag in exploitation of new foraging opportunities which we are experiencing in spring in the Biebrza Basin, as geese gradually learn about new foraging opportunities provided by cereals grown adjacent to the grassland staging areas that they have traditionally occupied. Because geese show family cohesion over one or more years [55], such new feeding traits become learned and spread by family associations, leading to widespread adoption of novel food items, as in this case.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eGiven that this study provides a clear case of geese physically shifting from grassland to cereals in the absence of changes in the relative availability and accessibility of these two field types, we predict that more and more geese will make this transition in spring in the Biebrza Basin (as elsewhere) in the future and that potentially the level of agricultural conflict will increase as more geese in larger flocks (as also demonstrated here) depredate cereal fields there.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very grateful to volunteer observers who helped in the field studies. \u0026nbsp;The research was co-financed by the Minister of Science under the \u0026quot;Regional Excellence Initiative\u0026quot; Program.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept \u0026amp; design: M.P. and A.D.F. Data acquisition: M.P., M.B. and M.F. Analysis \u0026amp; data interpretation: M.P., A.D.F. and Ł.J. Article drafting: A.D.F.,M.P. and Ł.J. Critical supervision and revision: M.P., Ł.J., M.B., M.F., A.D.F.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request. To request the data, corresponding author should be contacted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve any experiments on live vertebrates according Polish\u0026nbsp;law (\u003cem\u003eDz. U. 2015 poz. 266 Ustawa z dnia 15 stycznia 2015 r. o ochronie zwierząt wykorzystywanych do cel\u0026oacute;w naukowych lub edukacyjnych\u003c/em\u003e), but observational research. According to Polish law, local committee approval is not required for observational studies. We followed all applicable institutional and national guidelines for the care and use of wild animals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was co-financed by the Minister of Science under the \u0026quot;Regional Excellence Initiative\u0026quot; Program.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZwarts, L. De Grauwe Ganzen \u003cem\u003eAnser anser\u003c/em\u003e van het brakke getijdegebied de Ventjagersplaten. Limosa 45, 119\u0026ndash;134 (1972).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanter, B. 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N. \u0026amp; Ripley, B. D. \u003cem\u003eModern Applied Statistics with S\u003c/em\u003e. (Springer, 2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePebesma, E. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439\u0026ndash;446 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePebesma, E. J. \u0026amp; Bivand, R. S. S classes and methods for spatial data: the sp package. R news 5, 9\u0026ndash;13 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolakowski, M., Broniszewska, M., Jankowiak, Ł., Ławicki, Ł. \u0026amp; Siuchno, M. Numbers and dynamics of spring migration of geese in the Biebrza Basin. Ornis Polonica 52, 159\u0026ndash;180 (2011). [in Polish with English summary]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGill, F. \u0026amp; Donsker, D. (Eds.) \u003cem\u003eIOC World Bird List (v8.2)\u003c/em\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates, D., Maechler, M., Bolker, B. \u0026amp; Walker, S. \u003cem\u003elme4: Linear mixed-effects models using Eigen and S4. R package version 1.1\u0026ndash;7.\u003c/em\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenth, R. emmeans: \u003cem\u003eEstimated Marginal Means, aka Least-Squares Means. R Package. version 1.5.1\u003c/em\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham, H. \u003cem\u003eggplot2: Elegant Graphics for Data Analysis\u003c/em\u003e. (Springer-Verlag, 2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyd, H. On encounters between wild white-fronted geese. Behaviour 5, 85\u0026ndash;129 (1953).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaveling, D. G. Dominance relationships and agonistic behavior of Canada geese in winter. Behaviour 37, 291\u0026ndash;318 (1970).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKristiansen, J. N., Fox, A. D., Boyd, H. \u0026amp; Stroud, D. A. Greenland White-fronted Geese \u003cem\u003eAnser albifrons flavirostris\u003c/em\u003e benefit from feeding in mixed-species flocks. Ibis 142, 142\u0026ndash;144 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKear, J. Three early medieval accounts of agricultural damage by wild geese. Arch. Nat. Hist. 28, 245\u0026ndash;255 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJefferies, R. L., Drent, R. H. \u0026amp; Bakker, J. P. \u003cem\u003e13. Connecting Arctic and Temperate Wetlands and Agricultural Landscapes: The Dynamics of Goose Populations in Response to Global Change\u003c/em\u003e. In: Verhoeven, J.T.A., Beltman, B., Bobbink, R., Whigham, D.F., (Eds.) \u003cem\u003eWetlands and Natural Resource Management\u003c/em\u003e. (Springer, 2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatterson, I. J., Jalil, S. A. \u0026amp; East, M. L. Damage to winter cereals by greylag and pink-footed geese in north-east Scotland. J. Appl. Ecol. 26, 879\u0026ndash;895 (1989).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKay, H. V., Bishop, J. D. \u0026amp; Ennis, D. C. The possible importance of nutritional requirements for dark-bellied brent geese in the seasonal shift from winter cereals to pasture. Ardea 82, 123\u0026ndash;132 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrong, E. A., Redpath, S. M., Montrŕs-Janer, T., Elmberg, J. \u0026amp; M\u0026aring;nsson, J. Seeking greener pastures: crop selection by Greylag Geese (\u003cem\u003eAnser anser\u003c/em\u003e) during the moulting season. Ornis Fennica 98, 16\u0026ndash;32 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosin, Z. M. \u003cem\u003eet al.\u003c/em\u003e Landscape structure, human disturbance and crop management affect foraging ground selection by migrating geese. J. Ornithol. 153, 747\u0026ndash;759 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradbeer, D. \u0026amp; Halpin, L. \u003cem\u003eManaging cereal grasses as waterfowl lure crops: investigating planting dates and waterfowl feeding ecology. Delta Farmland and Wildlife Trust Report, Delta, BC, Canada.\u003c/em\u003e (Delta Farmland and Wildlife Trust, 2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson, H. J., Norriss, D. W., Walsh, A., Fox, A. D. \u0026amp; Stroud, D. A. Winter site fidelity in Greenland White-fronted Geese \u003cem\u003eAnser albifrons flavirostris\u003c/em\u003e: implications for conservation and management. Ardea 79, 287\u0026ndash;294 (1991).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox, A.D. \u003cem\u003eet al.\u003c/em\u003e Phenology and distribution of Greenland White-fronted Geese \u003cem\u003eAnser albifrons flavirostris\u003c/em\u003e staging in Iceland. Wildfowl 50, 29\u0026ndash;43 (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStahl, J., Tolsma, P. H., Loonen, M. J. \u0026amp; Drent, R. H. Subordinates explore but dominants profit: resource competition in high Arctic barnacle goose flocks. Anim. Behav. 61, 257\u0026ndash;264 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeegman, M. D. \u003cem\u003eet al.\u003c/em\u003e Should I stay or should I go? Fitness costs and benefits of prolonged parent-offspring and sibling associations in an Arctic-nesting goose population. Oecologia 181, 809\u0026ndash;817 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Number of observations of geese from each species contributing to the analysis presented here, based on the number of census days in each year during the study period in the Biebrza Basin, 2007-2023.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.278145695364238%\" rowspan=\"2\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.721854304635762%\" rowspan=\"2\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003eNumber of days of census\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"75%\" colspan=\"5\"\u003e\n \u003cp\u003eNumber of birds\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.740088105726873%\"\u003e\n \u003cp\u003e\u003cem\u003eAnser albifrons\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.92511013215859%\"\u003e\n \u003cp\u003e\u003cem\u003eAnser fabalis sensu lato\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.722466960352424%\"\u003e\n \u003cp\u003e\u003cem\u003eAnser anser\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.550660792951543%\"\u003e\n \u003cp\u003e\u003cem\u003eAnser brachyrhynchus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.061674008810574%\"\u003e\n \u003cp\u003e\u003cem\u003eBranta leucopsis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e130226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e8510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e198662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e18957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e76427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e8384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e3089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e124857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e5178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e2412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e62153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e2613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e54200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e9807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e3956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e19688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e5504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e10906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e61524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e8430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e2498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e181037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e4463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e3577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e22,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e100974,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e7982,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e3289,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e5,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e114,3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.264462809917354%\" style=\"width: 7.7815%;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.694214876033058%\" style=\"width: 14.9006%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\"\u003e\n \u003cp\u003e193442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e34555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e4687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.173553719008265%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.553719008264462%\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable. 2. Results from the most favoured binomial model evaluating estimates and effects of period, flock size, and day of the year on the preference for cereals (coded as 1) over grasslands (coded as 0) by White-fronted Geese \u003cem\u003eAnser albifrons\u003c/em\u003e.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"537\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.28491620111732%\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.573556797020483%\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15270018621974%\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91806331471136%\"\u003e\n \u003cp\u003ez value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.070763500931097%\"\u003e\n \u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.28491620111732%\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.573556797020483%\"\u003e\n \u003cp\u003e-0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15270018621974%\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91806331471136%\"\u003e\n \u003cp\u003e-0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.070763500931097%\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.28491620111732%\"\u003e\n \u003cp\u003ePeriodold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.573556797020483%\"\u003e\n \u003cp\u003e2.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15270018621974%\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91806331471136%\"\u003e\n \u003cp\u003e2.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.070763500931097%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.28491620111732%\"\u003e\n \u003cp\u003eFlock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.573556797020483%\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15270018621974%\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91806331471136%\"\u003e\n \u003cp\u003e-0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.070763500931097%\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.28491620111732%\"\u003e\n \u003cp\u003eDayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.573556797020483%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15270018621974%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91806331471136%\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.070763500931097%\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.28491620111732%\"\u003e\n \u003cp\u003ePeriodold:Flock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.573556797020483%\"\u003e\n \u003cp\u003e-0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15270018621974%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91806331471136%\"\u003e\n \u003cp\u003e-1.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.070763500931097%\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.28491620111732%\"\u003e\n \u003cp\u003ePeriodold:DayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.573556797020483%\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15270018621974%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.91806331471136%\"\u003e\n \u003cp\u003e-2.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.070763500931097%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"536\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.544776119402986%\"\u003e\n \u003cp\u003eEffects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.395522388059701%\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.485074626865671%\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.298507462686567%\"\u003e\n \u003cp\u003eLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.350746268656717%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.544776119402986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;none\u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.395522388059701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.485074626865671%\" valign=\"top\"\u003e\n \u003cp\u003e1079.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e1091.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.298507462686567%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.350746268656717%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.544776119402986%\" valign=\"top\"\u003e\n \u003cp\u003ePeriod:Flock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.395522388059701%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.485074626865671%\" valign=\"top\"\u003e\n \u003cp\u003e1082.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e1092.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.298507462686567%\" valign=\"top\"\u003e\n \u003cp\u003e2.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.350746268656717%\" valign=\"top\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.544776119402986%\" valign=\"top\"\u003e\n \u003cp\u003ePeriod:DayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.395522388059701%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.485074626865671%\" valign=\"top\"\u003e\n \u003cp\u003e1083.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e1093.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.298507462686567%\" valign=\"top\"\u003e\n \u003cp\u003e4.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.350746268656717%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eSignificant results are marked in \u003cstrong\u003ebold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Results from a binomial model evaluating estimates and effects of period, flock size, and day of the year on the preference for cereals (coded as 1) over grasslands (coded as 0) by Bean Geese \u003cem\u003eAnser fabalis\u003c/em\u003e.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003ez value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e-3.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e-2.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003ePeriodold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e3.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e1.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e2.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003eFlock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e2.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003eDayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e2.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003ePeriodold:Flock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e-0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e-2.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003ePeriodold:DayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e-2.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.77304964539007%\"\u003e\n \u003cp\u003eEffects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.957446808510639%\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.73049645390071%\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003eLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.77304964539007%\"\u003e\n \u003cp\u003e\u0026lt;none\u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.957446808510639%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.73049645390071%\"\u003e\n \u003cp\u003e848.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\"\u003e\n \u003cp\u003e860.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.77304964539007%\"\u003e\n \u003cp\u003ePeriod:Flock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.957446808510639%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.73049645390071%\"\u003e\n \u003cp\u003e855.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\"\u003e\n \u003cp\u003e865.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e6.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.77304964539007%\"\u003e\n \u003cp\u003ePeriod:DayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.957446808510639%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.73049645390071%\"\u003e\n \u003cp\u003e854.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\"\u003e\n \u003cp\u003e864.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e5.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003eSignificant results are marked in \u003cstrong\u003ebold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Results from binomial model evaluating estimates and effects of period, flock size, and day of the year on the preference for cereals (coded as 1) over grasslands (coded as 0) by Greylag Geese \u003cem\u003eAnser anser.\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"536\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.03731343283582%\" colspan=\"2\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.619402985074627%\" colspan=\"2\"\u003e\n \u003cp\u003ez value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.873134328358208%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.03731343283582%\" colspan=\"2\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003e3.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003e1.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.619402985074627%\" colspan=\"2\"\u003e\n \u003cp\u003e2.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.873134328358208%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.03731343283582%\" colspan=\"2\"\u003e\n \u003cp\u003ePeriodold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003e-4.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003e1.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.619402985074627%\" colspan=\"2\"\u003e\n \u003cp\u003e-2.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.873134328358208%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.03731343283582%\" colspan=\"2\"\u003e\n \u003cp\u003eFlock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.619402985074627%\" colspan=\"2\"\u003e\n \u003cp\u003e-1.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.873134328358208%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.03731343283582%\" colspan=\"2\"\u003e\n \u003cp\u003eDayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.619402985074627%\" colspan=\"2\"\u003e\n \u003cp\u003e-2.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.873134328358208%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.03731343283582%\" colspan=\"2\"\u003e\n \u003cp\u003ePeriodold:Flock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003e2.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.619402985074627%\" colspan=\"2\"\u003e\n \u003cp\u003e4.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.873134328358208%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.03731343283582%\" colspan=\"2\"\u003e\n \u003cp\u003ePeriodold:DayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.619402985074627%\" colspan=\"2\"\u003e\n \u003cp\u003e2.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.873134328358208%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"77.23880597014926%\" colspan=\"9\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.761194029850746%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.492537313432837%\"\u003e\n \u003cp\u003eEffects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.485074626865671%\" colspan=\"2\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003eLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.552238805970148%\" colspan=\"2\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.492537313432837%\"\u003e\n \u003cp\u003e\u0026lt;none\u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003e1,049.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.485074626865671%\" colspan=\"2\"\u003e\n \u003cp\u003e1,061.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.552238805970148%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.492537313432837%\"\u003e\n \u003cp\u003ePeriod:Flock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003e1,067.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.485074626865671%\" colspan=\"2\"\u003e\n \u003cp\u003e1,077.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003e17.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.552238805970148%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.492537313432837%\"\u003e\n \u003cp\u003ePeriod:DayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.738805970149254%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.604477611940297%\" colspan=\"2\"\u003e\n \u003cp\u003e1,054.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.485074626865671%\" colspan=\"2\"\u003e\n \u003cp\u003e1,064.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.126865671641792%\" colspan=\"4\"\u003e\n \u003cp\u003e4.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.552238805970148%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.54477611940298%\" colspan=\"10\"\u003e\n \u003cp\u003eSignificant results are marked in \u003cstrong\u003ebold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.455223880597014%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Results from a binomial model evaluating estimates and effects of period, flock size, and day of the year on the preference for cereals (coded as 1) over grasslands (coded as 0) by Barnacle Geese \u003cem\u003eBranta leucopsis\u003c/em\u003e.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003ez value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e-4.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e2.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e-2.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003ePeriodold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e7.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e2.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e2.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003eDayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e2.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003eFlock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e-1.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e-2.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.345070422535212%\"\u003e\n \u003cp\u003ePeriodold:DayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.767605633802816%\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.077464788732396%\"\u003e\n \u003cp\u003e-3.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.612676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.77304964539007%\"\u003e\n \u003cp\u003eEffects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.957446808510639%\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.73049645390071%\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003eLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;none\u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.957446808510639%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.73049645390071%\" valign=\"top\"\u003e\n \u003cp\u003e231.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003e241.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003eFlock_size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.957446808510639%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.73049645390071%\" valign=\"top\"\u003e\n \u003cp\u003e239.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003e247.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e8.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003ePeriod:DayOfYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.957446808510639%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.73049645390071%\" valign=\"top\"\u003e\n \u003cp\u003e243.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.425531914893616%\" valign=\"top\"\u003e\n \u003cp\u003e251.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"top\"\u003e\n \u003cp\u003e11.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003eSignificant results are marked in \u003cstrong\u003ebold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Branta, Anser, field use, agriculture, habitat availability, food preferences","lastPublishedDoi":"10.21203/rs.3.rs-4189509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4189509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNorthern hemisphere wild geese have increasingly shifted in the non-breeding season from feeding on natural wetlands to grazing agricultural grasslands and, since the 1960s, to green cereals, enhancing agricultural conflicts. From an agricultural perspective, it is important to know if this shift is a response to availability (i.e. grassland conversion to arable) or a response to the enhanced feeding opportunities provided by cereals and hence goose food preferences. In an important Polish goose spring staging area, we showed no change in the extent of cereal production area between 2012 and 2018, but based on repeated goose counts, a significant increase in goose use from a mean of 20.7% (\u0026plusmn;\u0026thinsp;0.04 se during 2007\u0026ndash;2015) use of cereal fields to 33.5% in 2023. Larger flocks of the numerically dominant Greater White-fronted Geese \u003cem\u003eAnser albifrons\u003c/em\u003e (hereafter White-fronted) and Bean Goose \u003cem\u003eAnser fabalis\u003c/em\u003e showed greater preference for feeding on cereals in 2023, increasingly through the staging period, whereas larger flocks preferred grass in the period 2007\u0026ndash;2015 (although patterns differed in less numerous species). Previous studies have shown that geese can maintain higher food intake rates on monospecific cereal fields than on grass swards, suggesting the recent transition is due to improved foraging efficiency, since their availability relative to grassland and other habitats has not changed. We predict that more geese will make this transition in this study area and in many other European farmland landscapes as long as there are no fitness penalties to doing so, potentially increasing level of agricultural conflict as more geese in larger flocks depredate cereal fields.\u003c/p\u003e","manuscriptTitle":"Voting with their feet: Spring staging geese increasingly select for cereal crops over grassland at an important central European stopover site","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 04:04:48","doi":"10.21203/rs.3.rs-4189509/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a69a839-bb2a-44bc-a34b-76e6ea428733","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34532459,"name":"Biological sciences/Ecology"},{"id":34532460,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2024-12-16T04:38:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-16 04:04:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4189509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4189509","identity":"rs-4189509","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}