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Green patches among a grey patchwork: the importance of preserving natural habitats to harbour cavity-nesting bees and wasps (Hymenoptera) and their natural enemies in urban areas

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Abstract

Urbanisation is fast-growing worldwide characterised by the conversion of natural vegetation ecosystems into densely paved areas with a high concentration of human constructions and few remnants of natural habitats. This phenomenon may threaten wildlife, especially high trophic level organisms such as predators and parasitoids, which are known to be more sensitive to habitat fragmentation. We investigated the influence of urbanisation on the community of trap-nesting bees and wasps as well as on their parasites in an urban area. Trap-nests were installed in 11 areas within the perimeter of the city of Ribeirão Preto, Brazil. Fourteen land cover categories were distinguished and their percentages calculated for each area from satellite images. The community sampled consisted of 20 wasp and 12 bee species, as well as 25 natural enemy species that attacked 9.75% of the nests. The highest diversity of bees and wasps was observed in areas with higher percentages of natural vegetation, i.e., forests, wastelands, and neighbourhoods with extensive green areas. Rates of parasitism, measured by the abundance and richness of parasites, was positively correlated with the proportion of green areas in the landscape. Even though predatory wasps constitute a higher trophic level than bees they were not more negatively impacted by urbanisation. Our results demonstrate that natural habitats and extensive green areas can host diverse communities of cavity-nesting bees, wasps, and their parasites within a city. The conservation of green areas in urbanised landscapes should be considered as essential to maintain the populations of these important insects.

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Acknowledgements

The authors are grateful to the following taxonomists each for identifying some of the sampled species: Cecília Waichert (Pompilidae), Alexandre Aguiar (Ichneumonidae), Marcelo Tavares (Chalcididae), Daercio Lucena (Chrysididae), Daniele Parizotto (Dicranthidium), Jan Batelka (Ripiphoridae), Marcos Kogan (Xenidae), Barry O'Connor (Pyemotidae), Thiago Izzo (Formicidae), Rogério Lopes and Bolívar Garcete-Barrett (some Vespidae). We thank 'Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES' for providing a scholarship to the first author, Laurence Packer for his careful reading and constructive comments that improved the manuscript, Heraldo Vasconcelos for his valuable assistance with some statistical analysis and all the people who authorised fieldwork in the sampling areas.

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  1. Instituto de Biologia - INBIO, Universidade Federal de Uberlândia - UFU, Uberlândia, MG, 38400-732, Brazil

    Léo Correia da Rocha-Filho

  2. Departamento de Biologia, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto - FFCLRP, Universidade de São Paulo-USP, Ribeirão Preto, SP, 14040-901, Brazil

    Paula Carolina Montagnana, Danilo Boscolo & Carlos Alberto Garófalo

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  1. Léo Correia da Rocha-Filho
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Correspondence to Léo Correia da Rocha-Filho.

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Supplementary file1 (DOC 33 kb)

Supplementary file2 (DOC 29 kb)

Supplementary file3 (DOC 259 kb)

Supplementary file4 (TIF 2294 kb)

Fig. S1. A - Metallic support containing the trap nests. Two wooden plates with 110 black cardboard tubes are located right below the plastic tile whilst 120 bamboo canes are placed within three PVC tubes below. B - MDF box with external measurements of 8 cm x 8 cm x 5 cm and a side hole of 1.2 cm diameter. Two boxes were placed between the two wooden plates (red arrow in A). C - Two Ambay pumpwood petioles attached vertically to the oil chamber of the metallic support (blue arrow in A).

Supplementary file5 (TIF 29178 kb)

Fig. S2. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the Landscape resistance index (LRI) as the predictor variable for bee variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file6 (TIF 29178 kb)

Fig. S3. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the functional connectivity of green areas (GAC) as the predictor variable for bee variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file7 (TIF 29178 kb)

Fig. S4. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the proportion of mixed areas as the predictor variable for bee variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file8 (TIF 29178 kb)

Fig. S5. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the proportion of green areas as the predictor variable for bee variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file9 (TIF 29178 kb)

Fig. S6. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the Landscape resistance index (LRI) as the predictor variable for wasp variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file10 (TIF 29178 kb)

Fig. S7. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the proportion of mixed areas as the predictor variable for wasp variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file11 (TIF 29178 kb)

Fig. S8. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the proportion of green areas as the predictor variable for wasp, natural enemy and whole community variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file12 (TIF 29178 kb)

Fig. S9. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the proportion of mixed areas as the predictor variable for natural enemy variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file13 (TIF 29178 kb)

Fig. S10. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the Landscape resistance index (LRI) as the predictor variable for natural enemy and whole community variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file14 (TIF 29178 kb)

Fig. S11. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the functional connectivity of green areas (GAC) as the predictor variable for wasp, natural enemy, caterpillar- and spider-hunting wasp variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file15 (TIF 13613 kb)

Fig. S12. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the proportion of mixed areas as the predictor variable for whole community variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file16 (TIF 29178 kb)

Fig. S13. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the Landscape resistance index (LRI) as the predictor variable for caterpillar-hunting wasp variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file17 (TIF 29178 kb)

Fig. S14. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the proportion of green areas as the predictor variable for caterpillar- and spider-hunting wasp variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file18 (TIF 29178 kb)

Fig. S15. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the proportion of mixed areas as the predictor variable for caterpillar-hunting wasp variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file19 (TIF 29178 kb)

Fig. S16. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the Landscape resistance index (LRI) as the predictor variable for spider-hunting wasp variables. Dots indicate the sampled areas used in the GLMs.

Supplementary file20 (TIF 13613 kb)

Fig. S17. Best selected Generalised Linear Models (GLMs) according to the second order Akaike's Information Criterion (AICc) with the proportion of mixed areas as the predictor variable for spider-hunting wasp variables. Dots indicate the sampled areas used in the GLMs.

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da Rocha-Filho, L.C., Montagnana, P.C., Boscolo, D. et al. Green patches among a grey patchwork: the importance of preserving natural habitats to harbour cavity-nesting bees and wasps (Hymenoptera) and their natural enemies in urban areas. Biodivers Conserv 29, 2487–2514 (2020). https://doi.org/10.1007/s10531-020-01985-9

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  • DOI: https://doi.org/10.1007/s10531-020-01985-9

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