Predicting the response of communities to climate change is a major challenge for ecology. Communities may well not respond as entities but be disrupted, particularly if trophic levels respond differently, but as yet there is no evidence for differential responses from natural systems. We therefore analyzed unusually detailed plant and animal data collected over 20 years from two grassland communities to determine whether functional group climate sensitivity differed between trophic levels. We found that sensitivity increases significantly with increasing trophic level. This differential sensitivity would lead to community destabilization under climate change, not simple geographical shifts, and consequently must be incorporated in predictive ecological climate models.

Summary1. Management decisions are increasingly based on matrix models intended to predict the long-term fate of endangered species. However, certain elements of these models, such as life-state transition probabilities (vital rates), are difficult to parameterize and their values may vary depending on external conditions such as weather. Details of how weather might influence population performance are rare, yet necessary to assess the effects of global climate change on a species' distribution. 2. Based on a 26-year data set of a population of Himantoglossum hircinum in a nature reserve in Germany, variations of life-history traits and vital rates were studied. Matrix analysis was used to identify the most important life-state transitions for population growth. Multiple linear regression was used to quantify the response of population traits and vital rates to changing weather conditions. 3. Population size increased exponentially and density effects could not be observed. Flowering plants and large plants had the highest and second highest reproductive value, respectively. The population's finite rate of increase fluctuated strongly among years; life-history traits varied strongly and were interlinked, thereby violating the assumptions of matrix modelling in a population viability analysis. 4. Some vital rates and the population growth rate showed a trend over the total period. A certain and sometimes large amount of that variability could be attributed to variability of weather conditions, with warmer winter conditions favouring population performance. Prediction of population size was fairly accurate within a time frame of 10 years, but size class structure was not. Synthesis and applications.Matrix modelling proved to be unreliable for predicting long-term population dynamics, despite the long-term data set used for matrix construction. This can be explained by weather-dependent variability of vital rates driving population dynamics. A minimum study period of 4 years is necessary to produce relevant information for model development. Our study emphasizes the need for critical evaluation of management decisions based only on single short-term studies and for studies covering longer time intervals than 2-3 years.

The flowering pattern of plant species, including orchid species, may fluctuate irregularly. Several explanations are given in the literature to explain that pattern, including: costs associated with reproduction, herbivory effects, intrinsically triggered unpredictable variation of the system, and external conditions (i.e. weather). The influence of age is discussed, but is difficult to determine because relevant long-term field observations are generally absent in the literature. The influence of age, size, reproductive effort and climatic conditions on flowering variability of Himantoglossum hircinum are examined using data collected in a long-term project in Germany. PCA and multiple regression analysis were used to analyse variability in flowering pattern over the years as a function of size and weather variability. We studied future size after flowering to quantify costs of reproduction. Flowering probability was strongly determined by plant size, while there was no significant influence of age class on flowering probability of the population. Costs associated with reproduction resulted in a decrease in plant size, causing reduced flowering probability of the plants in the following year. The weather explained about 50% of the yearly variation in the proportion of large plants and thus had an indirect, strong influence on the flowering percentage. We conclude that variability in flowering is caused mainly by the variability of weather conditions in the previous and current year, whereby reproductive effort causes further variability in flowering at the individual and, consequently, the population levels.

T. H. 2003. Responses of arthropods to plant diversity: changes after pollution cessation. -Ecography 26: 788-800.Data collected from three different polluted sites in the vicinity of a phosphate fertilizer factory that was closed in 1990 are used to test with Mantel tests and smoothing techniques whether the rapid increase of plant species richness following cessation of pollution enhanced associated arthropod assemblage diversity. 132 plant species (between 1990 and 1999) and 66 413 individuals of 680 arthropod species (using sweep net sampling between 1990 and 1996) were recorded. Using top soil pH as a representative pollution parameter we detected an increase of plant species richness, effective diversity and evenness of plant community with decreasing pH both in space and time. While the richness of all studied functional groups of herbivores increased with plant species richness, only the richness of one carnivore functional group showed a similar pattern. Plant species richness was significantly correlated to the abundance patterns of two herbivore and two carnivore groups. But contrary to theoretical predictions consumer abundance tended to decrease with increasing plant diversity only between a plant species richness range of 10 to ca 35. Our results support the findings of previous studies that highlight how increased plant species and functional group richness may result in higher herbivore species richness, and that carnivore richness may be influenced by herbivore and detritivore richness. The functional group approach used in this study has enabled us to detected the very individual interaction patterns that occur between different groups within the same trophic level.

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