DOI: 10.5061/dryad.zs7h44j5t
title: Central place foraging in a human-dominated landscape - how do common cranes select feeding sites?

description: 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).
description: <i>Study system</i> 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,
 <i>Anser fabilis fabilis,</i> and greylag geese,
 <i>Anser anser</i>. 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 (<i>e.g., </i>scarecrows, pennants and propane
 cannons), diversionary fields (<i>i.e.,</i> 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.   <i>Capturing and
 tagging</i> 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 &lt;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).​​​​​​​
   <i>Field
 surveys</i> 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>i.e.,</i> 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>i.e.,</i> 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>i.e.,</i> 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/dm<sup>2 </sup>in the modeling procedure
   <i>Data
 processing    </i> 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.   <i>Statistical
 analysis</i> 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>i.e,.</i> 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, log<sub>e</sub>(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, log<sub>e</sub> (1+spilled grain availability),
 log<sub>e</sub> (1+distance to human disturbance), the
 interaction effects of spilled grain
 availability<i>*</i>distance to roost site, as well as of
 distance to human disturbance<i>*</i>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>i.e.,</i> overlapping 0) (Table S1 and S2,
 Supporting Information) and were therefore combined into one category in
 the final model setup (<i>i.e.</i>, 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>i.e., </i>RSF-scores. Positive RSF scores
 demonstrate use of the resource in larger proportion than what is
 available, <i>i.e.,</i> 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).