Developing restoration strategies for dynamic population changes of plant-pollinator networks in a warming climate

Abstract

Ensuring robust pollination service is vital for sustainable food production, as three-quarters of crops require insect pollinators to reproduce, but many insect populations are rapidly declining. Yet, it is widely reported that insect pollinators face increased extinction risk due to habitat loss and warming climate. The biological impact of global mean temperature projections on individual terrestrial ectotherms is often predicted to increase with the rate of warming. However, it also depends on the interdependence of the plant-pollinator network and the physiological sensitivity of ectotherms to temperature change over time. Here, we have used sampled plant-pollinator network data from different climatic zones and the Earth system model projected temperature data of different future projection scenarios. In this study, we present a mathematical framework for modeling species population dynamics using the Lotka-Volterra model, where parameters are integrated from empirical fitness curves of terrestrial insects at different latitudes. This approach also investigates how species abundance evolves in the twenty-first century with and without species management, focusing on maintaining a constant abundance of generalist species to avert sudden ecosystem collapses over declining environmental health. The results show that tropical networks are more sensitive in abundance and extinction to future temperature increase as they live very close to their optimal temperature. In contrast, species of temperate regions have broader thermal tolerance, so the warming may increase their abundance. This study offers insights into how different future temperature projections influence species management, thereby restoring the functional integrity of the entire ecosystem. Also, this study provides region-specific restoration guidelines, offers insights for agro-advisory services, informs sustainable cropping patterns, and optimizes resource allocation.