Data exploration on diet, and composition, energy value and functional division of prey items ingested by White Storks Ciconia ciconia in south-western Poland: Dietary variation due to land cover, reproductive output and colonial breeding

The dataset presented in this data paper supports "Linking land cover satellite data with dietary variation and reproductive output in an opportunistic forager: Arable land use can boost an ontogenetic trophic bottleneck in the White Stork Ciconia ciconia" (Orłowski et al. 2019) [1]. Analysis of data on diet and prey composition based on an investigation of 165 pellets of White Storks Ciconia ciconia sampled from 52 nests showed that their diet was based primarily on ‘eurytopic prey’ (embracing taxa from grassland and a variety of non-cropped habitats), the biomass contribution of which in the diet was disproportionately (3–4–fold) higher than the percentage of available corresponding habitats. Similarly, prey items from water/wetland sites prevailed over the availability of corresponding habitats. The opposite pattern characterized prey taxa from arable habitats and forests, the contribution of which was lower than the availability of the corresponding habitats. The total energy content per pellet (calculated by summing the energy content of all individual prey items across one specific prey group) was the most strongly correlated with the biomass of Orthoptera, thereafter with that of mammals, other vertebrates, earthworms and other invertebrates, but not with the biomass of Coleoptera. White Storks from nests of low productivity pairs (i.e. with 1–2 fledglings) consumed a significantly (up to two-fold) higher biomass of Coleoptera, Orthoptera and all invertebrates, which also translated into a higher total biomass and a higher total energy content compared to the diet of high-productivity pairs (i.e. with 3–4 fledglings). Our data, in particular those relating to energy content in a variety of invertebrate taxa, and their body mass and functional division in terms of habitat preferences should be useful for other researchers to calculate energy budgets of predatory animals living in agricultural landscapes in Europe.


a b s t r a c t
The dataset presented in this data paper supports "Linking land cover satellite data with dietary variation and reproductive output in an opportunistic forager: Arable land use can boost an ontogenetic trophic bottleneck in the White Stork Ciconia ciconia" (Orłowski et al. 2019) [1]. Analysis of data on diet and prey composition based on an investigation of 165 pellets of White Storks Ciconia ciconia sampled from 52 nests showed that their diet was based primarily on 'eurytopic prey' (embracing taxa from grassland and a variety of non-cropped habitats), the biomass contribution of which in the diet was disproportionately (3-4-fold) Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/dib higher than the percentage of available corresponding habitats. Similarly, prey items from water/wetland sites prevailed over the availability of corresponding habitats. The opposite pattern characterized prey taxa from arable habitats and forests, the contribution of which was lower than the availability of the corresponding habitats. The total energy content per pellet (calculated by summing the energy content of all individual prey items across one specific prey group) was the most strongly correlated with the biomass of Orthoptera, thereafter with that of mammals, other vertebrates, earthworms and other invertebrates, but not with the biomass of Coleoptera. White Storks from nests of low productivity pairs (i.e. with 1-2 fledglings) consumed a significantly (up to twofold) higher biomass of Coleoptera, Orthoptera and all invertebrates, which also translated into a higher total biomass and a higher total energy content compared to the diet of highproductivity pairs (i.e. with 3-4 fledglings). Our data, in particular those relating to energy content in a variety of invertebrate taxa, and their body mass and functional division in terms of habitat preferences should be useful for other researchers to calculate energy budgets of predatory animals living in agricultural landscapes in Europe.

Value of the data
The data presents the functional classification of prey items into four major habitat categories: (i) arable, (ii) grassland/non-cropped ( ¼ marginal habitats ¼ eurytopic prey), iii) forest; and iv) water/wetland and could be used by others researchers.
For each prey taxa the data on energy content based on ash-free dry mass is given and this data could be re-used in other studies.
The data in this article, in particular those on energy content in a variety of invertebrate taxa, and their body mass and functional division in terms of habitat preferences, should be useful for other researchers to calculate energy budgets of predatory animals.

Data
The data presented here were the basis for the article by Orłowski et al. [1]. The dataset of this article provides detailed information on dietary composition of 165 pellets of White Storks collected in June and July 2012 from 52 nests in 39 villages in south-western Poland (Figs. 1-4, Tables 1-5). The data describes basic dietary indices relating to prey items consumed, including the biomass contribution of invertebrate and vertebrate prey (Figs. 2 and 3, Tables 1-5).

Data on overall diet composition and prey composition in White Storks
The most numerous prey group both by number and biomass was Orthoptera (59.5% and 35.6%, respectively). The following ranking for invertebrate prey items in descending order of their quantitative contribution to the diet was: earthworms (19.5% by number and 16.4% by biomass), Coleoptera (16.2% and 7.3%) and other invertebrates (3.5% and 0.4%). Small mammals and other vertebrates (i.e. fish, reptiles and birds) constituted only 0.7% and 0.6% of all prey items consumed, but the contribution of their biomass was disproportionately high at 24.8% and 15.2%, respectively ( Table 2).
With regard to the functional division of prey, the diet of White Storks was based primarily on 'eurytopic prey' (embracing taxa from grassland and a variety of non-cropped habitats), the biomass contribution of which in the diet was disproportionately (3-4-fold) higher than the percentage of available corresponding habitats (Fig. 2). Similarly, the prey from water/wetland sites prevailed over the availability of corresponding habitats (Fig. 2). The opposite pattern characterized prey taxa from arable habitats and forests, the contribution of which was lower that the availability of the corresponding habitats (Fig. 2).
The total energy content per pellet (calculated by summing the energy content of all individual prey items across one specific prey group) was the most strongly correlated with the biomass of Orthoptera (Pearson r ¼ 0.801, P o 0.0001), thereafter with that of mammals (r ¼ 0.361, P o 0.0001), other vertebrates (r ¼ 0.234, P ¼ 0.002), earthworms (r ¼ 0.214, P ¼ 0.006) and other invertebrates (r ¼ 0.181, P ¼ 0.020), but not with the biomass of Coleoptera (r ¼ 0.024, P ¼ 0.756) (see also Table 3).
Across the 52 nests analysed the diet was based primarily on prey items attributed to the grassland/non-cropped habitat category collectively referred to as 'eurytopic prey', and their biomass contribution to the diet was significantly (indexed via the t-test) -3-4-foldand disproportionately higher than the percentage of available corresponding habitats at each of the three distances (1 km, 2.5 km and 5 km) around the nests (Fig. 2). Similarly, the contribution of prey items from water/ wetland sites prevailed significantly -2-5-foldover the availability of corresponding habitats (Fig. 2). The opposite pattern characterized prey taxa from arable habitats, the contribution of which was markedly -3-5-foldlower than the availability of corresponding habitats. Lastly, the contribution of the prey category attributed to forest habitats was similar to that of the availability of these habitats measured at the distance of 1 km around nests, but was significantly -2-3-foldlower than the availability of forests at the other two distances (Fig. 2).
MANOVA revealed significant differences in dietary composition in terms of the biomass of the six major prey groups (earthworms, Orthoptera, Coleoptera and other invertebrates, other vertebrates and mammals) identified in the 52 nests (MANOVA, Wilks's Lambda, λ 306,653 ¼ 0.007, P o 0.0001). However, further post-hoc analysis (yielding a matrix with 1 326 comparisons between pairs of nests for each individual prey group) confirmed that the contribution of two prey groupssmall mammals and other invertebratesdid not differ between any pairs of nests, indicating similar exploitation of these prey resources across the entire landscape in which our White Stork population foraged. The biomass contribution of the other four prey groups varied between different nests with variable magnitude. So, the negligible variations between nests observed in the case of biomasses of Coleoptera, other vertebrates and earthworms, which varies significantly merely between 3, 5 and 12 pairs of nests, respectively. The most variable contribution was that of the biomass of Orthoptera, which varied between 55 pairs (from all 1326 possible comparisons) of nests.
The variations in biomass of the major prey groups and individual prey mass for different contributions of grassland and arable land measured within three spatial scales around the nests are visualized in Fig. 3. Comparison of the percentage distribution (average 7 1 SE per nest) of prey biomass (n ¼ 20 561 items from 165 pellets) consumed by breeding White Storks, representing taxa classified into four major habitat categories: (i) arable, (ii) grassland/non-cropped ( ¼ marginal habitats ¼ eurytopic prey), iii) forest; and iv) water/wetland (see Table 2) against the corresponding distribution of available landscape/habitat traits within three distances (1 km, 2.5 km and 5 km) determined for the same 52 nests of the species in south-western Poland. The landscape/habitat trait pools the following land cover classes (for land cover codes see Table 1 and water/wetland (WET þ WAT þ large rivers). Note that the t-test for dependent samples comparing the percentage distribution of an individual prey group vs landscape/habitat traits at successive distances for the same nests showed significant differences for most paired comparisons (P r 0.011); the only non-significant comparison was for the prey/habitat category 'forest' at the distance of 1 km (P ¼ 0.305).

Fig. 3.
Biomasses of five major prey groups (earthworms; Coleoptera; Orthoptera; other vertebrates; and mammals) and individual prey mass per pellet (n ¼ 165) compared for three spatial scales (extent/radius: 1 km, 2.5 km and 5 km) around White Stork nests and varying in percentages of grassland and arable land.

Data on reproductive output in relation to colonial breeding and habitat variation
On average, the number of fledglings in solitary nests (n ¼ 36) was nearly 14% higher than in nests with a clumped distribution (n ¼ 16): 2.69 (95% C.I. ¼ 2.45-2.93) vs 2.37 (95% C.I. ¼ 2.04-2.70), respectively; however, this difference was not significant (Mann-Whitney test, Z ¼ 1.46, P ¼ 0.145), presumably because of the small sample size. This result may be explained in part by the fact that there is significantly less %arable land (within 1 km and 2.5 km) around solitary nests than in the vicinity of clumped nests; in contrast, %grassland was higher and there were more aquatic habitats (mostly within 2.5 km and 5 km) around the clumped nests (Table 4).
MANOVA did not show any significant effect (P r 0.251) of the percentage distribution of the eleven land cover types (Fig. 4) or four major habitat categories used for prey classification (i.e. certain land cover types combined; Fig. 2) influencing the number of nestlings in the nest. However, inspection of the distribution of the percentage of the four major land cover types around nests with different numbers of fledglings yielded a single, clear pattern: this tallied only in part with our initial prediction. So, %arable land consistently decreased with the number of fledglings across all three distances (1, 2.5 and 5 km) around nests, whereas %forest exhibited the opposite pattern, this percentage increasing along with the number of fledglings (Fig. 4). Furthermore, we found that in principle, there were no differences between the nests of low productivity pairs (1-2 fledglings) and those of high productivity pairs (3-4 fledglings) in any of the landscape traits analysed (Table 4).
We found marginally significant differences (both at P ¼ 0.08) for two dietary indices, the biomasses of five major prey groups and number of prey taxa, between nests with different numbers of fledglings (Table 5, Fig. 4).
White Storks from the nests of low productivity pairs (i.e. with 1-2 fledglings) consumed a significantly (up to two-fold) higher biomass of Coleoptera, Orthoptera and all invertebrates, which also translated into a higher total biomass and a higher total energy content compared to the diet of highproductivity pairs (Table 5).

Experimental design, materials and methods
As the majority of nestling mortality occurs during the first 20 days after hatching [2][3][4], we considered the number of fledglings present in a nest at the time of ringing to be a proxy of the productivity of a breeding pair of White Storks (hereafter 'reproductive output').
The estimates of earthworm biomass were based on a soil mass of 192 mg per 1000 chaetae (for more details see also Orłowski et al. [5]). For each pellet, the number of prey items representing each invertebrate taxon was established from the numbers of fragments of chitinous body parts (according to [6,7]). Here, we added two new eco-morphological characteristics for each individual prey item: (1) energy content (expressed in kJ) and (2) functional division in terms of habitat preferences ( Table 2). The energy content (ash-free dry mass, AFDM) in prey items of White Storks (see Table 2) followed [8,9], where previous data for specific (or related) prey taxa were used (after [10][11][12][13][14]). Table 1 Major land cover types (class 2 of the Corine Land Cover classification) and traits of hydrographic networks determined for three radii (1 km, 2.5 km and 5 km) around 52 White Stork nests in south-western Poland. More information on the more detailed land cover types representing the class 3 of the CLC classification incorporated into the present class 2 of the CLC classification can be obtained on request from the authors. In the five subplots (2614 km 2 in total; see Fig. 1A) the overall percentages of the major land cover types were: Urban fabric (4.3%); Industrial, commercial and transport units (0.8%); Mine, dump and construction sites (0.3%); Artificial non-agricultural vegetated areas (0.3%); Arable land (38%); Grassland, pasture (7.1%); Heterogeneous agricultural areas (3.1%); Forests (43.4%); Shrub and/or herbaceous vegetation associations (1.2%); Inland wetlands (2.8%); Inland waters (1.2%).    We applied the functional division of the individual prey species/taxa in terms of their habitat preferences (see Table 2), in part basing this classification on our previous detailed per-taxa habitat assignment [15]. Specifically, we classified the individual prey species/taxa into four major habitat categories, taking into account their relationship with the landscape and agricultural activities as the habitat of their development and their association with crop or non-crop habitats [15]. The habitat preferences of prey taxa were based on extensive ecological studies of various invertebrate groups in agricultural regions of south-western Poland after 1960 ( [16][17][18][19][20][21]; summarized in [15]). This yielded four groups of prey from i) non-agricultural/marginal habitats including grassland, ii) crop fields/arable land, iii) wetland and aquatic habitats, and iv) forest and woodland habitats ( Table 1).
The habitat preferences of prey taxa were based on extensive ecological studies of various invertebrate groups in agricultural regions of south-western Poland after 1960 ( [16][17][18][19][20][21]; summarized in [15]). This yielded four groups of prey from i) non-agricultural/marginal habitats including grassland, ii) crop fields/arable land, iii) wetland and aquatic habitats, and iv) forest and woodland habitats (Table 1).

Statistical analysis
The aim of our analysis of data in Fig. 2 was to assess whether the percentage distribution of the biomasses of four functional prey groups representing taxa from major habitat categories (arable; eurytopic ¼ grassland/non-cropped; forest; and waters/wetland) was utilized in proportion to the availability of the corresponding habitats measured at three different distances (1 km, 2.5 km and 5 km) around the same 52 White Stork nests. The corresponding background of available habitats is a synthetic measure combining land cover classes with a similar structure: arable (Arable land þ Heterogeneous agricultural areas), grassland/non-cropped (Urban fabric þ Industrial, commercial and transport units þ Mine, dump and construction sites þ Grassland, pasture þ Shrub and/or herbaceous vegetation associations), forest (Forests þ Artificial non-agricultural vegetated areas) and water/wetland (Inland wetlands þ Inland waters and large rivers) (Table 1; Fig. 1; see also [1]). The percentage distribution of individual prey groups vs available habitat background (Fig. 2) was compared with using the t-test for dependent samples.
Finally, since previous findings on behavioural limitations resulted from colonial breeding leading to decreased reproductive output in White Storks, we compared using the Mann-Whitney test, the landscape traits and dietary indices between nests of low productivity pairs (1-2 fledglings; n ¼ 21 nests) and nests of high productivity pairs (3-4 fledglings; n ¼ 31 nests); and (2) between solitary nests (n ¼ 36) and nests in an aggregation (n ¼ 16; Table 4; Table 5). However, results of the latter  Table 3 All the statistically significant (P r 0.05) results of the Spearman rank correlation coefficient (r s ) testing the relationships between the various dietary indices determined for 165 pellets and landscape/habitat variables (i.e. area of individual land cover type expressed in ha or length of hydrographic networks expressed in m) measured at three spatial scales (1 km, 2.5 km and 5 km) around 52 White Stork nests in south-western Poland; P-values in bold meet the threshold of Bonferroni's correction at α r 0.0036 (k ¼ 14    Table 5 Comparison of dietary indices/variables of breeding White Storks (A) among nests with low productivity (1-2 fledglings; n ¼ 66 pellets) and high productivity (3-4 fledglings; n ¼ 99 pellets) pairs and (B) among solitary nests (n ¼ 125 pellets) and nests in an aggregation (i.e. more than one nest in an individual locality/village; n ¼ 40 pellets); statistically significant results are shown in bold. Note: Thirty-two pellets were collected from 12 nests in Kłopot, a village supporting one of the largest White Stork colonies in Poland, see [1].