Resource assurance predicts specialist and generalist bee activity in drought

Many short-lived desert organisms remain in diapause during drought. Theoretically, the cues desert species use to continue diapause through drought should differ depending on the availability of critical resources, but the unpredictability and infrequent occurrence of climate extremes and reduced insect activity during such events make empirical tests of this prediction difficult. An intensive study of a diverse bee–plant community through a drought event found that bee specialists of a drought-sensitive host plant were absent in the drought year in contrast to generalist bees and to specialist bees of a drought-insensitive host plant. Different responses of bee species to drought indicate that the diapause cues used by bee species allow them to reliably predict host availability. Species composition of the bee community in drought shifted towards mostly generalist species. However, we predict that more frequent and extended drought, predicted by climate change models for southwest North America, will result in bee communities that are species-poor and dominated by specialist species, as found today in the most arid desert region of North America.     

Chromosomal assignment and genomic structure of Il15

Interleukin-15 (IL-15) is a novel cytokine whose effects on T-cell activation and proliferation are similar to those of interleukin-2 (IL-2), presumably because IL-15 utilizes the β and γ chains of the IL-2 receptor. Murine IL-15 cDNA and genomic clones were isolated and characterized. The murine Il15 gene was found to consist of eight exons spanning at least 34 kb and was localized to the central region of mouse chromosome 8 by interspecific backcross analysis. Intron positions in a partial human IL15 genomic clone were identical with positions of corresponding introns in the murine gene. The human IL15 gene was mapped to human chromosome 4q31 by fluorescence in situ hybridization.     

Structure of the Human Paralemmin Gene (PALM), Mapping to Human Chromosome 19p13.3 and Mouse Chromosome 10, and Exclusion of Coding Mutations ingrizzled, mocha, jittery,andhesitantMice

Paralemmin is a newly identified protein that is associated with the plasma membrane and with intracellular membranes through a lipid anchor. It is abundant in brain, is expressed at intermediate levels in the kidney and in endocrine cells, and occurs at low levels in many other tissues. As it is a candidate for genetic disorders that affect membrane functions, we have determined the structure of the human paralemmin gene,PALM,showing that it is organized into nine exons. Moreover, we have performed chromosomal assignments of the human and mouse paralemmin genes, localizing them to regions of homology at human 19p13.3 and the central mouse chromosome 10. Finally, mutation analysis using RNA from mice homozygous for the mutant genesgrizzled(gr),mocha(mh),mocha 2J(mh^2J),jittery(ji) andhesitant(ji^hes), which map to this area, excluded mutations in theirPalmcoding sequences.     

Local spatial autocorrelation in biological variables

Spatial autocorrelation (SA) methods have recently been extended to include the detection of local spatial autocorrelation at individual sampling stations. We review the formulas for these statistics and report on the results of an extensive population-genetic simulation study we have published elsewhere to test the applicability of these methods in spatially distributed biological data. We find that most biological variables exhibit global SA, and that in such cases the methods proposed for testing the significance of local SA coefficients reject the null hypothesis excessively. When global SA is absent, permutational methods for testing significance yield reliable results. Although standard errors have been published for the local SA coefficients, their employment using an asymptotically normal approach leads to unreliable results; permutational methods are preferred. In addition to significance tests of suspected non-stationary localities, we can use these methods in an exploratory manner to find and identify hotspots (places with positive local SA) and coldspots (negative local SA) in a dataset. We illustrate the application of these methods in three biological examples from plant population biology, ecology and population genetics. The examples range from the study of single variables to the joint analysis of several variables and can lead to successful demographic and evolutionary inferences about the populations studied.