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Central place foraging in a human-dominated landscape - how do common cranes select feeding sites?

Cite this dataset

Nilsson, Lovisa; Persson, Jens; Bunnefeld, Nils; Månsson, Johan (2020). Central place foraging in a human-dominated landscape - how do common cranes select feeding sites? [Dataset]. Dryad.


Human infrastructure and disturbance play an important role when animals select resources in human-modified landscapes. Theory predicts that animals trade food intake against costs of movement or disturbance to optimize net energy gain and fitness, but other necessary resources may also constrain the decisions, e.g. when animals repeatedly need to return to a central location, such as a nest, waterhole or night roost. Central place foraging theory states that the probability of occurrence of an animal decreases with the distance to the central location while selectivity for food items or foraging sites providing high net energy gain should increase with distance. We studied foraging patterns of common cranes Grus grus feeding in an agricultural landscape adjacent to a wetland to which they return for night roost. We used availability of spilled grains on harvested fields and distance to human settlement as proxy for site quality (i.e. increased likelihood of increased net energy gain with increased food availability and less disturbance).  As predicted by theory, our results clearly show that cranes were more likely (more than twice as high resource selection function scores) to select foraging sites close to roosts. However, contrary to predictions, the selection of high quality sites in terms of high food availability decreased with distance to roost sites. Nevertheless, our results indicate that cranes were more likely to select sites with low risk of human disturbance far from roost sites, and were more tolerant to disturbance close to roost sites. How different species respond to the local and environmental conditions will increase the understanding of the species’ resource requirement, and also where in the landscape to prioritize conservation or management actions (e.g., mitigation of human disturbance and crop damage prevention to sustain agricultural production).


Study system

We conducted the study in Kvismaren (59°10´N/15°22´E), in the boreonemoral zone of south-central Sweden (Fig.1). The area is flat and dominated by productive farmland (~66 %), well suited for cultivating cereals, ley and potatoes. Crops are generally harvested between August and early October, but the timing varies due to crop type and weather. The average precipitation in September is 50-75 mm (SMHI 2017). The core of the area is a nature reserve consisting of two shallow eutrophic lakes, 2.5 km apart, surrounded by narrow strips of grazed wetlands. The area is assigned under the Ramsar convention of wetlands and as an EU Natura 2000 special protection area. The shallow lakes and the surrounding agricultural landscape provide both suitable roost sites and favorable foraging conditions for cranes and other large grazing birds such as bean geese, Anser fabilis fabilis, and greylag geese, Anser anser. Kvismaren has been a staging site for large grazing birds for the last 30 years and is the site in Sweden hosting most cranes during autumn, with maximum numbers of 15 500-24 200 cranes in 2009-2019 (Månsson & Nilsson, unpubl.). The number of cranes in Kvismaren progressively increases from August to the end of September and all cranes leave at about the same time when the weather conditions become suitable in the beginning of October. The large concentration of foraging cranes and geese causes damage to crops (Frank et al. 2019, Montràs‐Janer et al. 2019). Commonly used preventive measures are scaring (e.g., scarecrows, pennants and propane cannons), diversionary fields (i.e., supplying food at undisturbed locations) and occasional local culling (Hake et al. 2010, Cusack et al. 2018). The level of scaring activity was however hard to quantify as it was an uncoordinated activity carried out by farmers and managers.


Capturing and tagging

We captured and tagged 19 juvenile cranes with backpack GPS-transmitters between 2012 and 2014 (for details of the methods, see: Månsson et al. 2013). Fourteen juvenile cranes were tagged with Vectronic GPS-plus bird backpacks (Vectronic Aerospace, Berlin, Germany) and five cranes with solar-cell transmitters (Cellular Tracking Technologies, Rio Grande, U.S.). The captures were conducted within a 30 km radius of Grimsö Wildlife Research Station (59°43´N/15°28´E, 85 km north of Kvismaren, see Fig.1) in July and early August (late part of the breeding period). The juvenile cranes migrated with their parents to Kvismaren in late August or early September before continuing their migration. During the staging period in Kvismaren, the transmitters were programmed for 8 days of intensive positioning (1 location per 30 min from dawn to dusk) evenly distributed during the period from when the cranes arrived to the study area until they left. Each individual was only monitored during its first autumn staging period in Kvismaren. The family group normally split at the wintering grounds in January, and we thus assume that each juvenile was accompanied by the parental pair and occasionally by one sibling during the study period (Alonso et al. 1984). The effect of the GPS transmitters on foraging and movement of cranes are not known. However, previous studies on birds show that the amount of discomfort and time spent preening may increase (Robert et al. 2006). However, studies have also shown non-significant effect on reproductive success or foraging behavior as long as the transmitter load was low in relation to body weight (Phillips and Croxall 2003, Burnside et al. 2019). To minimize the potential effects of the transmitters, we kept transmitter weights <3% of crane body weights (Phillips and Croxall 2003). All captures and tagging fulfilled ethical requirements for research on wild animals after approval from the Animal Ethics Committee of central Sweden (C104/10 and C53/13).​​​​​​​


Field surveys

To study individual selection of foraging sites and food availability within arable stubble fields, we surveyed locations (n=124) used by the cranes for crop type and spilled grain availability, during the days of intensive positioning. To exclude flight locations, only fields with two or more consecutive crane locations were chosen (i.e., fields visited less than 30 min were excluded). At each field, one of the locations was randomly selected for survey. An equal number of random locations representing availability in the landscape (hereafter ‘available locations’) were surveyed. Available locations were randomly distributed within stubble fields less than 11 km from the two wetland roost sites, corresponding to the maximum daily flight distance from the wetland roosts earlier defined by the studied cranes. Both used and available locations were surveyed after sunset the same day as visited by the targeted individuals i.e., immediately when the cranes had left for night roost to avoid disturbance. At each location we noted crop type and counted number of unsprouted grains (i.e., spilled grain availability) at three plots (0.2x0.2 m), at the focal location, 5 m north and south of the location, respectively. The mean spilled grain availability of the three plots was used in the analysis and was rescaled to kernels/dm2 in the modeling procedure


Data processing   

Crop types were pooled into three categories; barley, wheat and other. The category ‘other’ included oat, rye and cereal mix, because each of these types had too few observations to be included separately. Distance to human disturbance was assessed as the shortest distance to roads (excluding agricultural roads only passable with tractors and 4WD vehicles), houses or farms derived from the GSD Terrain map (Lantmäteriet 2016) in ArcGIS version 10.3.1. Similarly, the distance from locations to the roost site was calculated in ArcGIS.


Statistical analysis

Selection of foraging sites was analyzed with a resource selection function, where used locations were compared to available locations (Lele and Keim 2006). As we could not exclude the possibility that available locations were actually used by cranes (Lele and Keim 2006, Lele et al. 2013), we assessed relative resource selection estimates (i.e,. RSF scores) and fitted a generalized linear mixed model with a binomial error structure and a logit link function (R package lme4; Bates et al., 2015). Spilled grain availability and distance to human were log-transformed to reduce skewness of distribution, loge(x+1) (Zuur et al. 2010). For the model selection, we included used and available locations (binomial) as response variable and crop type (categorical), distance to roost site, loge (1+spilled grain availability), loge (1+distance to human disturbance), the interaction effects of spilled grain availability*distance to roost site, as well as of distance to human disturbance*distance to roost site as explanatory variables. Crane identity was added as random intercept to account for unbalanced number of locations per individual. The results from a first model setup showed that the estimates for barley and wheat were comparable (i.e., overlapping 0) (Table S1 and S2, Supporting Information) and were therefore combined into one category in the final model setup (i.e., in total two categories: wheat/barley and other) Model estimates from the categorical variables represent absolute numbers rather than the difference to the estimated intercept, i.e., RSF-scores. Positive RSF scores demonstrate use of the resource in larger proportion than what is available, i.e., selection, a negative score demonstrates underuse in relation to availability, i.e. avoidance, and a score of zero indicates that animals do not select or avoid the resource. Model selection was carried out according to Burnham & Anderson (2002) using the function ‘dredge’ (R package MuMIn: Barton, 2013). The top-ranked model was selected based on AIC and was used to model the associated fitted values and their 95% confidence intervals after repeated simulations (n=1000) (R package arm: Gelman et al., 2014). All analyses were done in R version 3.2.3 (R Core Team, 2015).


Swedish Research Council for Environment Agricultural Sciences and Spatial Planning, Award: 2018-00463

Swedish Research Council for Environment Agricultural Sciences and Spatial Planning, Award: 942‐2015‐1360

Marie-Claire Cronstedts Stiftelse

C.F. Lundströms Stiftelse

Swedish Environmental Protection Agency