The Effect of Species Diversity and Shade on Milkweed Growth and Cardenolide Concentration

Monarch butterflies (Danaus plexippus) rely upon milkweed plants for survival, as it is the only plant upon which female monarchs oviposit and it is the sole food supply for monarch larvae. Additionally, cardenolide, a defense toxin within milkweed tissue, is sequestered by monarchs, making them unpalatable to most predators. Eastern and Western monarch populations in the United States are in sharp decline, in part due to the growing scarcity of milkweed, and reestablishing milkweed habitat is crucial to the monarch’s long-term survival. This study aimed to study the effects of plant species diversity and shade on the growth and cardenolide content of two species of milkweed common in the Pacific Northwest (PNW). Milkweed plants were grown outdoors over three summers at the University of Portland, under varying levels of plant species diversity and shade. Four growth measurements and cardenolide concentration were analyzed. Species diversity affected several growth measurements but not cardenolide concentration. Medium shade conditions resulted in increases in three growth measurements and cardenolide concentration compared to low and high shade conditions. The results suggest PNW milkweed can grow under more diverse conditions than previously known, which may be useful information for reestablishment of milkweed habitat supporting the highly imperiled Western monarch population

Simple rules to contain an invasive species with a complex life cycle and high dispersal capacity

Free to read at publisher Designing practical rules for controlling invasive species is a challenging task for managers, particularly when species are long-lived, have complex life cycles and high dispersal capacities. Previous findings derived from plant matrix population analyses suggest that effective control of long-lived invaders may be achieved by focusing on killing adult plants. However, the cost-effectiveness of managing different life stages has not been evaluated. We illustrate the benefits of integrating matrix population models with decision theory to undertake this evaluation, using empirical data from the largest infestation of mesquite (Leguminosae: Prosopis spp) within Australia. We include in our model the mesquite life cycle, different dispersal rates and control actions that target individuals at different life stages with varying costs, depending on the intensity of control effort. We then use stochastic dynamic programming to derive cost-effective control strategies that minimize the cost of controlling the core infestation locally below a density threshold and the future cost of control arising from infestation of adjacent areas via seed dispersal. Through sensitivity analysis, we show that four robust management rules guide the allocation of resources between mesquite life stages for this infestation: (i) When there is no seed dispersal, no action is required until density of adults exceeds the control threshold and then only control of adults is needed; (ii) when there is seed dispersal, control strategy is dependent on knowledge of the density of adults and large juveniles (LJ) and broad categories of dispersal rates only; (iii) if density of adults is higher than density of LJ, controlling adults is most cost-effective; (iv) alternatively, if density of LJ is equal or higher than density of adults, management efforts should be spread between adults, large and to a lesser extent small juveniles, but never saplings. Synthesis and applications.In this study, we show that simple rules can be found for managing invasive plants with complex life cycles and high dispersal rates when population models are combined with decision theory. In the case of our mesquite population, focussing effort on controlling adults is not always the most cost-effective way to meet our management objective

Factors and mechanisms explaining spatial heterogeneity: A review of methods for insect populations

1. The spatial distribution of populations is affected by the dispersal abilities of the species, interactions among individuals, or habitat selection. Linking these ecological processes to spatial patterns is of primary importance for understanding and prediction purposes. 2. We review both statistical and mechanistic methods for studying the spatial distribution of populations. Statistical methods, such as spatial indexes and nearest-neighbour analyses help characterizing the spatial pattern. They allow testing the effect of environmental variables on spatial patterns using regression analyses. 3. Mechanistic modelling can be used to analyse the effect of mechanisms underlying the spatial pattern. Wereview mechanistic models (e.g. metapopulation, individual-based and cellular automaton models) devoted to represent dispersal abilities, interactions among individuals and habitat selection. 4. We illustrate each method by works on insects, which cover a broad range of spatial patterns. Strengths and limitations of methods are discussed according to the process and type of data set. 5. Scientists can use statistical or mechanistic methods in an iterative manner to infer process from spatial pattern. New approaches such as 'pattern-oriented modelling' or 'space as a surrogate framework' determine whether alternative models reproduce an observed pattern. It allows selection of the process that best explain the observed pattern. (Résumé d'auteur