Estimating acridid densities in grassland habitats: a comparison between presence-absence and abundance sampling designs

Are dominant plant species more susceptible to leaf-mining insects? A case study at Saihanwula Nature Reserve, China

Dominant species significantly affect interspecific relationships, community structure, and ecosystem function. In the field, dominant species are often identified by their high importance values. Selective foraging on dominant species is a common phenomenon in ecology. Our hypothesis is that dominant plant groups with high importance values are more susceptible to leaf-mining insects at the regional level. Here, we used the Saihanwula National Nature Reserve as a case study to examine the presence-absence patterns of leaf-mining insects on different plants in a forest-grassland ecotone in Northeast China. We identified the following patterns: (1) After phylogenetic correction, plants with high importance values are more likely to host leafminers at the species, genus, or family level. (2) Other factors including phylogenetic isolation, life form, water ecotype, and phytogeographical type of plants have different influences on the relationship between plant dominance and leafminer presence. In summary, the importance value is a valid predictor of the presence of consumers, even when we consider the effects of plant phylogeny and other plant attributes. Dominant plant groups are large and susceptible targets of leaf-mining insects. The consistent leaf-mining distribution pattern across different countries, vegetation types, and plant taxa can be explained by the "species-area relationship" or the "plant apparency hypothesis."

Simulating small-scale climate change effects-lessons from a short-term field manipulation experiment on grassland arthropods

Climate change is expected to cause major consequences on biodiversity. Understanding species-specific reactions, such as species shifts, species declines, and changes in population dynamics is a key issue to quantify large-scale impacts of climate change on biotic communities. As it is often impossible or at least impracticable to conduct large-scale experiments on biotic responses to climate change, studies at a smaller scale may be a useful alternative. In our study, we therefore tested responses of grassland arthropods (carabid beetles, spiders, grasshoppers) to simulated climate change in terms of species activity densities and diversity. We conducted a controlled field experiment by changing water and microclimatic conditions at a small scale (16 m(2) ). Roof constructions were used to increase drought-like conditions, whereas water supply was enhanced by irrigation. In all, 2 038 carabid beetles (36 species), 4 893 spiders (65 species), and 303 Orthoptera (4 species) were caught using pitfall traps from May to August, 2010. During our experiment, we created an artificial small-scale climate change; and statistics revealed that these changes had short-term effects on the total number of individuals and Simpson diversity of the studied arthropod groups. Moreover, our results showed that certain species might react very quickly to climate change in terms of activity densities, which in turn might influence diversity due to shifts in abundance patterns. Finally, we devised methodological improvements that may further enhance the validity of future studies.

Spatial autocorrelation in farmland grasshopper assemblages (Orthoptera: Acrididae) in western France

Agricultural intensification in western Europe has caused a dramatic loss of grassland surfaces in farmlands, which have resulted in strong declines in grassland invertebrates, leading to cascade effects at higher trophic levels among consumers of invertebrates. Grasshoppers are important components of grassland invertebrate assemblages in European agricultural ecosystems, particularly as prey for bird species. Understanding how grasshopper populations are distributed in fragmented landscapes with low grassland availability is critical for both studies in biodiversity conservation and insect management. We assessed the range and strength of spatial autocorrelation for two grasshopper taxa (Gomphocerinae subfamily and Calliptamus italicus L.) across an intensive farmland in western France. Data from surveys carried out over 8 yr in 1,715 grassland fields were analyzed using geostatistics. Weak spatial patterns were observed at small spatial scales, suggesting important local effects of management practices on grasshopper densities. Spatial autocorrelation patterns for both grasshopper taxa were only detected at intermediate scales. For Gomphocerinae, the range of spatial autocorrelation varied from 802 to 2,613 m according to the year, depending both on grasshopper density and on grassland surfaces in the study site, whereas spatial patterns for the Italian locust were more variable and not related to grasshopper density or grassland surfaces. Spatial patterns in the distribution of Gomphocerinae supported our hypothesis that habitat availability was a major driver of grasshopper distribution in the landscape, and suggested it was related to density-dependent processes such as dispersal.

A comparative analysis of fine-scale genetic structure in three closely related syntopic species of the grasshopper genus Calliptamus

Landscape analysis and genetics can allow a better understanding of grasshoppers for which ecology is not very well known. We analysed landscape changes between 1990 and 2006 at two areas from southern France where three grasshopper species ( Calliptamus italicus (L., 1758), Calliptamus wattenwylianus Pantel, 1896, and Calliptamus barbarus (Costa, 1836)) occur. We then applied microsatellite markers to the study of 1200 georeferenced samples collected over both areas. We used a recent Bayesian clustering method with correlated allele frequencies to detect weak population genetic structure. We found evidence of breaks in gene flow only in C. wattenwylianus, thought to be sedentary relative to its congenerics. By using different allele frequency models and prior information to different levels of genetic differentiation for our six real data sets, our study also informs on the ability of the newly available Bayesian clustering methods model to detect weak genetic structure in natural field populations.