Multiscale assessment of patterns of avian species richness

The search for a common cause of species richness gradients has spawned more than 100 explanatory hypotheses in just the past two decades. Despite recent conceptual advances, further refinement of the most plausible models has been stifled by the difficulty of compiling high-resolution databases at continental scales. We used a database of the geographic ranges of 2,869 species of birds breeding in South America (nearly a third of the world's living avian species) to explore the influence of climate, quadrat area, ecosystem diversity, and topography on species richness gradients at 10 spatial scales (quadrat area, ≈12,300 to ≈1,225,000 km2). Topography, precipitation, topography × latitude, ecosystem diversity, and cloud cover emerged as the most important predictors of regional variability of species richness in regression models incorporating 16 independent variables, although ranking of variables depended on spatial scale. Direct measures of ambient energy such as mean and maximum temperature were of ancillary importance. Species richness values for 1° × 1° latitude-longitude quadrats in the Andes (peaking at 845 species) were ≈30–250% greater than those recorded at equivalent latitudes in the central Amazon basin. These findings reflect the extraordinary abundance of species associated with humid montane regions at equatorial latitudes and the importance of orography in avian speciation. In a broader context, our data reinforce the hypothesis that terrestrial species richness from the equator to the poles is ultimately governed by a synergism between climate and coarse-scale topographic heterogeneity.

A general two-process model describes the hydrogen exchange behavior of RNase A in unfolding conditions.

When NMR hydrogen exchange was used previously to monitor the kinetics of RNase A unfolding, some peptide NH protons were found to show EX2 exchange (detected by base catalysis) in addition to the expected EX1 exchange, whose rate is limited by the kinetic unfolding process. In earlier work, two groups showed independently that a restricted two-process model successfully fits published hydrogen exchange rates of native RNase A in the range 0-0.7 M guanidinium chloride. We find that this model predicts properties that are very different from the observed properties of the EX2 exchange reactions of RNase A in conditions where guanidine-induced unfolding takes place. The model predicts that EX2 exchange should be too fast to measure by the technique used, whereas it is readily measurable. Possible explanations for the contradiction are considered here, and we show that removing the restriction from the earlier two-process model is sufficient to resolve the contradiction; instead of specifying that exchange caused by global unfolding occurs by the EX2 mechanism, we allow it to occur by the general mechanism, which includes both the EX1 and EX2 cases. It is logical to remove this restriction because global unfolding of RNase A is known to give rise to EX1 exchange in these unfolding conditions. Resolving the contradiction makes it possible to determine whether populated unfolding intermediates contribute to the EX2 exchange, and this question is considered elsewhere. The results and simulations indicate that moderate or high denaturant concentrations readily give rise to EX1 exchange in native proteins. Earlier studies showed that hydrogen exchange in native proteins typically occurs by the EX2 mechanism but that high temperatures or pH values above 7 may give rise to EX1 exchange. High denaturant concentrations should be added to the list of variables likely to cause EX1 exchange.

Transforming neural computations and representing time

Motifs of neural circuitry seem surprisingly conserved over different areas of neocortex or of paleocortex, while performing quite different sensory processing tasks. This apparent paradox may be resolved by the fact that seemingly different problems in sensory information processing are related by transformations (changes of variables) that convert one problem into another. The same basic algorithm that is appropriate to the recognition of a known odor quality, independent of the strength of the odor, can be used to recognize a vocalization (e.g., a spoken syllable), independent of whether it is spoken quickly or slowly. To convert one problem into the other, a new representation of time sequences is needed. The time that has elapsed since a recent event must be represented in neural activity. The electrophysiological hallmarks of cells that are involved in generating such a representation of time are discussed. The anatomical relationships between olfactory and auditory pathways suggest relevant experiments. The neurophysiological mechanism for the psychophysical logarithmic encoding of time duration would be of direct use for interconverting olfactory and auditory processing problems. Such reuse of old algorithms in new settings and representations is related to the way that evolution develops new biochemistry.