Warming Rates Alter Sequence of Disassembly in Experimental Communities.

AbstractThe frequency, intensity, and duration of periods of extreme environmental warming are expected to rise over the next hundred years and play an increasing role in species loss resulting from climate change, and yet we know little about their potential future effects on variability in the composition of communities. This study analyzed patterns of species loss in a community of four rotifers and six ciliates exposed to different rates of extreme warming. Temperature of loss was positively correlated with warming rates for all species, consistent with theoretical frameworks suggesting that lower rates of warming increase exposure time and cumulative thermal stress at each temperature. The sequence of species loss during extreme warming depended on the environmental warming rate (i.e., warming rates had the capacity to drive reversals in the relative thermal tolerances of species), and changes in the sequence of species loss driven by the warming rate resulted in substantial variability in community composition. The results suggest that differences in warming rates across space and time may increase variability in community composition in ecosystems increasingly disturbed by extreme temperature, potentially altering interspecific interactions, the abiotic environment, and ecosystem function. Several ecological mechanisms may be responsible, singly or together, for changes in the sequence of species loss at different rates of warming, including (a) differences among species in their sensitivity to the intensity and duration of heat exposure, (b) the effects of warming rates on temperature-dependent interspecific interactions, and (c) differences in opportunities for evolution among species and across warming rates.

Landscape factors facilitating the invasive dynamics and distribution of the brown marmorated stink bug, Halyomorpha halys (Hemiptera: Pentatomidae), after arrival in the United States.

The brown marmorated stink bug, Halyomorpha halys, a native of Asia, has become a serious invasive pest in the USA. H. halys was first detected in the USA in the mid 1990s, dispersing to over 41 other states. Since 1998, H. halys has spread throughout New Jersey, becoming an important pest of agriculture, and a major nuisance in urban developments. In this study, we used spatial analysis, geostatistics, and Bayesian linear regression to investigate the invasion dynamics and colonization processes of this pest in New Jersey. We present the results of monitoring H. halys from 51 to 71 black light traps that were placed on farms throughout New Jersey from 2004 to 2011 and examined relationships between total yearly densities of H. halys and square hectares of 48 landscape/land use variables derived from urban, wetland, forest, and agriculture metadata, as well as distances to nearest highways. From these analyses we propose the following hypotheses: (1) H. halys density is strongly associated with urban developments and railroads during its initial establishment and dispersal from 2004 to 2006; (2) H. halys overwintering in multiple habitats and feeding on a variety of plants may have reduced the Allee effect, thus facilitating movement into the southernmost regions of the state by railroads from 2005 to 2008; (3) density of H. halys contracted in 2009 possibly from invading wetlands or sampling artifact; (4) subsequent invasion of H. halys from the northwest to the south in 2010 may conform to a stratified-dispersal model marked by rapid long-distance movement, from railroads and wetland rights-of-way; and (5) high densities of H. halys may be associated with agriculture in southern New Jersey in 2011. These landscape features associated with the invasion of H. halys in New Jersey may predict its potential rate of invasion across the USA and worldwide.

Bayesian synthesis for quantifying uncertainty in predictions from process models.

The Bayesian synthesis method is reviewed and judged to be useful for determining posterior distributions and interval estimates for inputs and outputs of process-based forest models. The method furnishes posterior distributions of the values of a model's parameters and response variables. The method also provides estimates of correlation among the parameters and output variables. Bayesian synthesis is the only type of uncertainty analysis that affords incorporation of all the information available to the investigator, in addition to the information contained in the model itself.