The effect of nutrients and spacing on neighbor relations in Cardamine pensylvanica

I used a one-dimensional hierarchical design to determine the effect of loc al versus long-distance neighbors on the growth and reproduction of the ann ual weed, Cardamine pensylvanica. I measured the response of target plants to the presence or absence of neighbors lit the first- and second-nearest n eighbor positions and to the overall spatial pattern (clumped, even, or ran dom) of neighbors. In addition, I investigated how variation in interplant spacing and nutrient levels combined with neighbor effects to influence the growth and reproduction of C. pensylvanica. How plants were affected by th eir neighbors depended upon nutrient levels, plant spacing, and the number of first- and second-position neighbors. Under low nutrient conditions and the closest spacing (0.5 cm), plant growth was so restricted that individua l neighbor effects were overshadowed by the overall density effect. At the intermediate spacing (1.0 cm), both the first and the second neighbors infl uenced fruit number, but at the farthest spacing (1.5 cm) only the first ne ighbor was significant. Under high nutrient conditions, at all three spacin gs, first neighbors significantly affected biomass and fruit number, but th ere were differences among the spacing treatments in how target plants were affected by neighbors. Furthermore. under high nutrients there was a signi ficant global pattern x first neighbor interaction. C. pensylvanica grown i n the clumped global pattern with no neighbors at the first position produc ed significantly more fruit than did plants under any of the other global x neighbor combinations. Although long-distance interactions were only impor tant under special circumstances. their effect was dramatic (an almost two- fold increase in fruit production). This study represents the first experimental test of the assumptions underl ying the use of cellular automata models to model plant population dynamics . The results support the basic assumption of cellular automata models, i.e .. that nearest neighbor interactions explain most of the variation in grow th of plants. However, they also show that under certain conditions long-di stance interactions also can have a great influence on the local neighbor e ffects. Before cellular automata models are extensively applied to model pl ant populations. further empirical work is needed to determine how competit ive relationships among plants change with environmental conditions.