Foraging Habitat Availability and the Non-Fish Diet Composition of the Grey Heron (Ardea cinerea) at Two Spatial Scales
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area Regions
2.2. Data Collection and Analyses
2.3. Pellet Analyses
- (A)
- Fully/mostly aquatic: Acilius sp., Bivalvia, Carcinus maenas, Colymbetes fuscus, Corixidae, Dytiscus marginalis, Dytiscidae, Dytiscidae larvae, Haliplidae, Haliplus confinis, Ilyocoris cimicoides, Hydrophilidae, Naucoridae, Nepomorpha, Notonectidae, and Odonata;
- (B)
- Terrestrial: Carabidae, Curculionidae, Georissus sp., Graphosoma sp., Gryllotalpa gryllotalpa, Melolontha melolontha, Omophron limbatum, and Scarabeidae.
2.4. Habitat Composition Analyses
- (1)
- Hydrographic network: river/canals, water bodies (i.e., lakes, ponds), inland wetlands (e.g., peat bogs), and an area of the coastal zone (2 m wide; present within the foraging range of four studied colonies). Hydrographic networks serve as important foraging areas for Grey Herons during the breeding season [33], thus constituting an important landscape factor affecting the location of breeding colonies of this species [4,14,34];
- (2)
- Complex cultivation patterns, land principally occupied by agriculture, with significant areas of natural vegetation, non-irrigated arable land, pastures which are area of suboptimal foraging grounds;
- (3)
- Forests (all types combined), often serving as nesting habitats;
- (4)
- Urban zones (airports, construction sites, continuous urban fabric, discontinuous urban fabric, dumpsites, industrial or commercial units, mineral extract sites, port areas, road and rail networks, and sport and leisure facilities), which may be avoided by herons.
2.5. Statistical Analyses and Data Preparation
2.6. Prey–Habitat Relationship
2.7. Multivariate Analyses
- (1)
- Analysis of similarities (ANOSIM) to examine the significance of inter-regional spatial differences [50] in prey item compositions. This analysis is popularly used for testing ecological hypotheses about spatial differences, especially for detecting any environmental impacts on an organism’s assemblages [50]. We applied this method to test for any dissimilarities between regions in prey item contribution and test the impact of environmental variables. ANOSIM creates a value of R between −1 and +1, where 0 represents no impact. Higher positive R values stand for increasing differences between samples [50].
- (2)
- Non–metric multidimensional scaling (nMDS) to summarize patterns of food composition of the diet of the Grey Heron among colonies based on an ordination of pairwise site dissimilarities [51,52] using the Euclidean distance as a measure of dissimilarity and convex hulls. nMDS is an ordination technique using an algorithm that creates the best combination of variables explaining observed differences or similarities among groups [53]. After the ordination procedure conducted by nMDS, prey items and environmental variables were represented on a biplot based on the correlation coefficients between their values and the coordinates of the virtual assemblages of the nMDS axes [54]. The importance of differences among variables was based on calculated coefficients of determination (R2) for each of two axes. We conducted this analysis since the stress value was acceptable (<15) [55].
- (3)
- Similarity Percentage (SIMPER) analysis was used to recognize prey items contributing the most to inter-regional differences. In this technique, the general significance of the difference is commonly assessed by ANOSIM. This difference is based on the observed dissimilarity measured in Euclidean distance. For this analysis, we considered cases with a contribution of 10% ≤ of observed differences [55,56].
3. Results
3.1. Inter-Regional Differences in Diet Composition
3.2. Difference in Diet Composition between Coastal and Inland Colonies in the N Region
3.3. Inter-Regional Differences in Habitats
3.4. Prey–Habitat Relationship
3.5. Multivariate Analyses
3.5.1. ANOSIM
3.5.2. nMDS
3.5.3. SIMPER
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Species | Scientific Name | Code |
---|---|---|
striped field mouse | Apodemus agrarius | AAG |
European water vole | Arvicola amphibius | AAM |
yellow-necked mouse | Apodemus flavicollis | AFL |
aquatic invertebrates | - | AIN |
tundra vole | Alexandromys oeconomus | AOE |
species of genus Apodemus | Apodemus sp. | ASP |
wood mouse | Apodemus sylvaticus | ASY |
bird taxa | - | BIR |
species of genus Crocidura | Crocidura sp. | CSP |
European edible dormouse | Glis glis | GGL |
short-tailed field vole | Microtus agrestis | MAG |
common vole | Microtus arvalis | MAR |
bank vole | Myodes glareolus | MGL |
voles | Microtus sp. | MSP |
European pine vole | Microtus subterraneus | MSU |
species from family of Muridae | Muridae | MUR |
Eurasian water shrew | Neomys fodiens | NFO |
species of genus Neomys | Neomys sp. | NSP |
species of genus Sorex | Sorex sp. | SSP |
European mole | Talpa europaea | TEU |
terrestrial invertebrates | - | TIN |
Code (Habitat) | Short Characteristics |
---|---|
COMCP (Complex cultivation patterns) | Juxtaposition of small parcels of annual crops, city gardens, pastures, fallow lands, and/or permanent crops somewhere with scattered houses. |
FORES (Forests) | Broad-leaved, coniferous, and mixed forests combined. |
LANDP (Land principally occupied by agriculture, with significant areas of natural vegetation) | Land occupied by irrigated cultivated parcels interspersed with significant areas of natural or semi-natural vegetation, e.g., forests, shrubs, vineyards, orchards, or plantations. |
NONIR (Non-irrigated arable land) | Land occupied mainly by rainfed cultivated parcels with a crop rotation system. |
PASTU (Pastures) | Dense grass cover of floral composition, dominated by Poaceae, not under a rotation system. Areas under intense human disturbance are included, mainly for grazing, but the folder may be harvested mechanically. Areas sparsely covered in vegetation or transitional woodland-shrub included. |
RIVER (Rivers) | Natural or artificial water courses serve as water drainage channels. Includes canals and rivers that have been canalized. |
SEACO (Seacoast) | Area of coastal zone of the Baltic Sea (2 m wide). |
URBAN (Urban zones) | Land covered by structures and the transport network (roads, motorways, and railways, including associated installations, e.g., stations), buildings, artificially surfaced areas (e.g., asphalt), infrastructure of port areas, marinas, airports, dump sites, mineral extraction sites, green urban areas, and industrial fabric structures. |
WATBO (Water bodies) | Natural or artificial stretches of water, also with low floating aquatic vegetation, archipelago of lakes inside land areas, coastal lagoons, and fish farms. |
WETLA (Inland wetlands) | Low-lying land (inland wetlands, marshes, swamps) usually flooded in winter and more or less saturated by water all year round. Covered by a specific low ligneous, semi-ligneous, or herbaceous vegetation. |
Prey Item | Regional Prevalence | Counts for Regions | Difference N vs. NE | ||
---|---|---|---|---|---|
N | NE | Test Value | p | ||
European water vole (AAM) | N | 97 | 43 | 10.086 | 0.001 |
Microtus sp. (MSP) | N | 56 | 21 | 9.899 | 0.002 |
aquatic invertebrates (AIN) | NE | 18 | 45 | 11.372 | 0.001 |
terrestrial invertebrates (TIN) | N | 39 | 12 | 10.264 | 0.001 |
tundra vole (AOE) | NE | 4 | 15 | - | 0.015 |
yellow-necked mouse (AFL) | NE | 2 | 9 | - | 0.03 |
Habitat | Area [%] | Difference | ||
---|---|---|---|---|
N | NE | G | p | |
COMCP | 1.6 | 3.7 | 102.75 | <0.0001 |
FORES | 34.0 | 29.9 | 25.05 | <0.0001 |
LANDP | 4.8 | 10.6 | 298.96 | <0.0001 |
NONIR | 44.0 | 35.2 | 105.05 | <0.0001 |
PASTU | 7.8 | 12.4 | 145.2 | <0.0001 |
RIVER | 0.4 | 0.3 | 3.1985 | 0.07 |
SEACO | 0.05 | 0 | Not analyzed | - |
URBAN | 4.4 | 2.25 | 19.717 | <0.0001 |
WATBO | 2.5 | 2.2 | 1.1409 | 0.2855 |
WETLA | 0.5 | 2.48 | 187.86 | <0.0001 |
Model | df | logLik | AICc | ΔAICc | Weight |
AAM~COMCP + PASTU | 3 | −43.370 | 94.2 | 0.00 | 0.618 |
AAM~COMCP + PASTU + SEACO | 4 | −42.305 | 95.1 | 0.96 | 0.382 |
Model | df | logLik | AICc | Delta | Weight |
---|---|---|---|---|---|
MSP~FORES + NONIR | 3 | −42.747 | 92.9 | 0.00 | 1.000 |
Model | df | logLik | AICc | Delta | Weight |
---|---|---|---|---|---|
AIN~FORES + WETLA | 3 | −40.792 | 89 | 0.00 | 1.000 |
Model | df | logLik | AICc | Delta | Weight |
---|---|---|---|---|---|
TIN~COMCP + PASTU | 3 | −35.889 | 79.2 | 0.00 | 1.000 |
Model | df | logLik | AICc | Delta | Weight |
---|---|---|---|---|---|
MGL~NONIR | 2 | −31.987 | 68.6 | 0.00 | 1.000 |
Model | df | logLik | AICc | Delta | Weight |
---|---|---|---|---|---|
MAG~URBAN | 2 | −32.108 | 68.9 | 0.00 | 0.237 |
MAG~INT | 1 | −33.371 | 69.0 | 0.07 | 0.229 |
MAG~SEACO | 2 | −32.219 | 69.1 | 0.22 | 0.212 |
MAG~LANDP | 2 | −32.412 | 69.5 | 0.61 | 0.175 |
MAG~PASTU + SEACO | 3 | −31.206 | 69.8 | 0.94 | 0.148 |
Model | df | logLik | AICc | Delta | Weight |
---|---|---|---|---|---|
AAG~COMCP + PASTU | 3 | −8.387 | 24.2 | 0.00 | 0.321 |
AAG~LANDP + PASTU | 3 | −8.606 | 24.6 | 0.44 | 0.258 |
AAG~COMCP + SEACO | 3 | −8.743 | 24.9 | 0.71 | 0.225 |
AAG~SEACO + WETLA | 3 | −8.881 | 25.2 | 0.99 | 0.196 |
Taxon (Code) | Av. Dissim. | Contrib. % | Cumulative % | Mean N | Mean NE |
---|---|---|---|---|---|
water vole (AAM) | 24.79 | 21.05 | 21.05 | 8.82 | 4.3 |
aquatic invertebrates (AIN) | 21.01 | 17.84 | 38.89 | 1.64 | 4.5 |
striped field mouse (AAG) | 19.5 | 16.56 | 55.45 | 0 | 4.3 |
Microtus sp. (MSP) | 16.55 | 14.06 | 69.5 | 5.09 | 2.1 |
terrestrial invertebrates (TIN) | 11.78 | 10 | 79.51 | 3.55 | 1.2 |
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Cieślińska, K.; Manikowska-Ślepowrońska, B.; Zbyryt, A.; Jakubas, D. Foraging Habitat Availability and the Non-Fish Diet Composition of the Grey Heron (Ardea cinerea) at Two Spatial Scales. Animals 2024, 14, 2461. https://doi.org/10.3390/ani14172461
Cieślińska K, Manikowska-Ślepowrońska B, Zbyryt A, Jakubas D. Foraging Habitat Availability and the Non-Fish Diet Composition of the Grey Heron (Ardea cinerea) at Two Spatial Scales. Animals. 2024; 14(17):2461. https://doi.org/10.3390/ani14172461
Chicago/Turabian StyleCieślińska, Karolina, Brygida Manikowska-Ślepowrońska, Adam Zbyryt, and Dariusz Jakubas. 2024. "Foraging Habitat Availability and the Non-Fish Diet Composition of the Grey Heron (Ardea cinerea) at Two Spatial Scales" Animals 14, no. 17: 2461. https://doi.org/10.3390/ani14172461