Plant fitness is determined by the outcome of allocations to growth, survival, and reproduction. Reproductive allocations can be further divided into allocations to female versus male function and it is generally thought that female function entails greater costs and stronger trade-offs with growth and survival. However, most previous studies have had limited scope, both in terms of the ‘currencies’ (carbon, nitrogen, etc.) and timescales used to measure reproductive costs. The long-term, demographic costs of allocations to male function remain poorly understood. The objective of this study was to assess the direct and indirect (demographic) costs of reproduction via female and male sex functions using biomass and nitrogen as currencies. We grew Broadleaf Arrowhead (Sagittaria latifolia), a dioecious clonal plant in a common garden over two growth periods and experimentally manipulated nitrogen availability and reproductive investment. We measured the direct and indirect costs of reproductive allocations via female versus male function using assessments of plant physiology (including photosynthetic rates), the carbon and nitrogen content of perennating structures, investment in clonal growth, and indices of survival.
Results/Conclusions
The costs of reproduction via female and male sex functions depended on the environments in which plants were grown. Under nitrogen-limited conditions, males experienced much higher direct and indirect costs than females. The reverse was true when plants were grown in nitrogen-supplemented conditions, particularly in terms of indirect (demographic) costs - females produced fewer clonal offspring than males under these conditions. This is the first study to demonstrate the environmental-dependence of the costs of reproduction and we show that under certain conditions males bear substantially greater reproductive costs than females. Previous findings that females pay greater reproductive costs might - to some extent - be an artefact of conducting experiments using plants grown under optimal conditions.
