Selection of Non-Crop Plant Mixes Informed by Arthropod-Plant Network Analyses for Multiple Ecosystem Services Delivery Towards Ecological Intensification of Agriculture

• Published in: Sustainability 2022-02, Vol.14 (3), p.1903
• Main Authors: Laha, Supratim, Chatterjee, Soumik, Das, Amlan, Smith, Barbara, Basu, Parthiba
• Format: Article
• Language: English
• Subjects: Agricultural ecosystems; Agricultural land; Agriculture; Candidate species; Crops; Ecosystem services; Ecosystems; Environmental restoration; Flowers & plants; Habitats; Pests; Plant reproduction; Plant species; Pollination; Pollinators; Predators; Refugia; Sustainability; Trap crops
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su14031903

Ecological intensification (EI) of agriculture through the improvement of ecosystem service delivery has recently emerged as the alternative to the conventional intensification of agriculture that is widely considered unsustainable and has negative impacts on the environment. Although tropical agricultural landscapes are still heterogeneous, they are rapidly losing diversity due to agricultural intensification. Restoration of natural or semi-natural habitats, habitat diversity, and provision of multiple benefits have been identified as important targets for the transition to EI. Choosing the right plant mixes for the restoration of habitats that can offer multiple ecosystem service benefits is therefore crucial. The selection of candidate species for plant mixes is generally informed by studies focusing on a specific ecosystem service (e.g., pollination) and not based on the whole arthropod—non-crop plant interactions matrix. In this study, we try to identify non-crop plant mixes that would provide habitat for pollinators, act as refugia for natural pest predators, and also as a trap crop for potential crop pests by studying non-crop plants—arthropod interaction network. We have identified the non-crop plant species mixes by first identifying the connector species based on their centrality in the network and then by studying how their sequential exclusions affect the stability of the network.