| Literature DB >> 32934310 |
Hafiz Sohaib Ahmed Saqib1,2,3, Junhui Chen1,2,3, Wei Chen1,2,3, Gabor Pozsgai1,2,3, Komivi Senyo Akutse4, Muhammad Furqan Ashraf5, Minsheng You6,7,8,9, Geoff M Gurr10,11,12,13.
Abstract
Both field- and landscape-scale factors can influence the predator communities of agricultural pests, but the relative importance and interactions between these scales are poorly understood. Focusing on spiders, an important taxon for providing biological control, we tested the influence of field- and landscape-scale factors on structuring the spider communities in a highly dynamic brassica agroecosystem. We found that local factors (pesticide-use and crop type) and forested landscape significantly influenced the abundance and species richness of spiders, whilst grassland patches significantly affected the spider species richness. Correlation results demonstrated that assemblage patterns of most spider families positively responded to the interplay between local factors and forest patches in the landscape. The spiders abundance was greatest in cauliflower crops surrounded with forest and grassland patches in landscape. Similarly, ordination analyses revealed that organic fields of cauliflower in forested landscapes had a strong positive association with the abundance and species richness of spiders. In contrast, insecticide and synthetic fertilizer-treated fields of Chinese cabbage in landscapes with little non-crop habitat reduced the abundance and species richness of spiders. Our results highlight the extent of interaction between local- and landscape-scale factors, help explain recently reported inconsistent effects of landscape factors on conservation biological control.Entities:
Year: 2020 PMID: 32934310 PMCID: PMC7493935 DOI: 10.1038/s41598-020-71888-w
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Figure 1Species accumulation curves correspond to different taxa of spider samples collected from (a) two Brassica crops types under (b) the organic and conventional management practices. Curves represent the randomized samples.
Figure 2The relationship of spider (a) taxa abundance and (b) species richness with the proportions of different land use variables (“BUP” = built-up, “CUL” = cultivated, “FOR” = forest, “GRA” = grassland, “ORC” = orchard, “UNU” = unused, “WAT” = water) in the landscape based on correlation at levels of management practices (organic “OR” and conventional “CO”). The abundance and species richness of each spider taxa is correlated with each of the environmental variables. “*” is indicating the significant correlation.
Figure 3The relationship of spider (a) taxa abundance and (b) species richness with the proportions of different land use variables (“BUP” = built-up, “CUL” = cultivated, “FOR” = forest, “GRA” = grassland, “ORC” = orchard, “UNU” = unused, “WAT” = water) in the landscape based on correlation at levels of Brassica crop types (Chinese cabbage “CC” and cauliflower “CF”). The abundance and species richness of each spider taxa is correlated with each of the environmental variables. “*” is indicating the significant correlation.
Permutation test for Constrained Correspondence Analysis (CCA) of spider abundance and richness in Brassica crop types (cauliflower or Chinese cabbage) managed under different management practices (conventional or organic) in sites with varying proportions of different land-uses.
| Factors | Abundance | Richness | ||||
|---|---|---|---|---|---|---|
| Chi-square | F-value | Pr(> F) | Chi-square | F-value | Pr(> F) | |
| Management practices | 0.057 | 2.803 | 0.010** | 0.023 | 1.943 | 0.050* |
| Crop types | 0.068 | 3.354 | 0.002** | 0.055 | 4.647 | 0.001*** |
| Forest | 0.054 | 2.667 | 0.014* | 0.025 | 2.143 | 0.042* |
| Cultivated | 0.007 | 0.346 | 0.968 | 0.003 | 0.281 | 0.970 |
| Grassland | 0.024 | 1.189 | 0.277 | 0.030 | 2.568 | 0.021* |
| Unsued | 0.007 | 0.362 | 0.961 | 0.009 | 0.744 | 0.641 |
| Water | 0.015 | 0.765 | 0.591 | 0.006 | 0.518 | 0.832 |
| Orchard | 0.014 | 0.712 | 0.670 | 0.007 | 0.562 | 0.806 |
| CCA model | 0.247 | 1.525 | 0.013* | 0.158 | 1.676 | 0.006* |
The significance of constraint variables were tested by performing 999 permutations, “*”, “**” and “***” is indicating the significant constraints.
Figure 4CCA ordination diagram with type II scaling represents the association of (a) abundance and (b) richness of 12 spider families found in Brassica crop types (cauliflower and “Chin.cab.” = Chinese cabbage) grown under different management practices (organic and conventional) across varying proportions of land-use variables. The arrow length and direction represent the magnitude of variance that can be explained by the explanatory and response variables. The perpendicular distance between spider families and explanatory variables reflects their correlations (below-90° = positive correlation and above-90° = negative correlation). The smaller the perpendicular distance, the stronger the correlation.
Figure 5(a) Locations of focal brassica fields in the region of Fuzhou City, Fujian province, China (image obtained from Google satellite map using Google earth software https://earth.google.com/web/). Pies show the composition of the (b) landscape at 130 m radius around the focal fields. (c) Mapping of drone-based georeferenced (using QGIS https://qgis.osgeo.org) high-resolution image of a focal sampling site in the region of Fuzhou City, Fujian province, China.