High altitude diving in river otters: coping with combined hypoxic stresses

River otters (Lontra canadensis) are highly active, semi-aquatic mammals indigenous to a range of elevations and represent an appropriate model for assessing the physiological responses to diving at altitude. In this study, we performed blood gas analyses and compared blood chemistry of river otters from a high-elevation (2357 m) population at Yellowstone Lake with a sea-level population along the Pacific coast. Comparisons of oxygen dissociation curves (ODC) revealed no significant difference in hemoglobin-oxygen (Hb-O2) binding affinity between the two populations - potentially because of demands for tissue oxygenation. Instead, high-elevation otters had greater Hb concentrations (18.7 g dl-1) than sea-level otters (15.6 g dl-1). Yellowstone otters displayed higher levels of the vasodilator nitric oxide (NO), and half the concentration of the serum protein albumin, possibly to compensate for increased blood viscosity. Despite compensation in several hematological and serological parameters, theoretical aerobic dive limits (ADL) were similar between high-elevation and sea-level otters because of the lower availability of O2 at altitude. Our results suggest that recent disruptions to the Yellowstone Lake food web could be detrimental to otters because at this high elevation, constraints on diving may limit their ability to switch to prey in a deep-water environment

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Estimating leaf area index in Southeast Alaska: a comparison of two techniques.

The relationship between canopy structure and light transmission to the forest floor is of particular interest for studying the effects of succession, timber harvest, and silviculture prescriptions on understory plants and trees. Indirect measurements of leaf area index (LAI) estimated using gap fraction analysis with linear and hemispheric sensors have been commonly used to assess radiation interception by the canopy, although the two methods often yield inconsistent results. We compared simultaneously obtained measurements of LAI from a linear ceptometer and digital hemispheric photography in 21 forest stands on Prince of Wales Island, Alaska. We assessed the relationship between these estimates and allometric LAI based on tree diameter at breast height (LAIDBH). LAI values measured at 79 stations in thinned, un-thinned controls, old-growth and clearcut stands were highly correlated between the linear sensor (AccuPAR) and hemispheric photography, but the latter was more negatively biased compared to LAIDBH. In contrast, AccuPAR values were more similar to LAIDBH in all stands with basal area less than 30 m(2)ha(-1). Values produced by integrating hemispheric photographs over the zenith angles 0-75° (Ring 5) were highly correlated with those integrated over the zenith angles 0-60° (Ring 4), although the discrepancies between the two measures were significant. On average, the AccuPAR estimates were 53% higher than those derived from Ring 5, with most of the differences in closed canopy stands (unthinned controls and old-growth) and less so in clearcuts. Following typical patterns of canopy closure, AccuPAR LAI values were higher in dense control stands than in old-growth, whereas the opposite was derived from Ring 5 analyses. Based on our results we advocate the preferential use of linear sensors where canopy openness is low, canopies are tall, and leaf distributions are clumped and angles are variable, as is common in the conifer forests of coastal Alaska

Functional and numerical responses of shrews to competition vary with mouse density.

For decades, ecologists have debated the importance of biotic interactions (e.g., competition) and abiotic factors in regulating populations. Competition can influence patterns of distribution, abundance, and resource use in many systems but remains difficult to measure. We quantified competition between two sympatric small mammals, Keen's mice (Peromyscus keeni) and dusky shrews (Sorex monticolus), in four habitat types on Prince of Wales Island in Southeast Alaska. We related shrew density to that of mice using standardized regression models while accounting for habitat variables in each year from 2010-2012, during which mice populations peaked (2011) and then crashed (2012). Additionally, we measured dietary overlap and segregation using stable isotope analysis and kernel utilization densities and estimated the change in whole community energy consumption among years. We observed an increase in densities of dusky shrews after mice populations crashed in 2012 as expected under competitive release. In addition, competition coefficients revealed that the influence of Keen's mice was dependent on their density. Also in 2012, shrew diets shifted, indicating that they were able to exploit resources previously used by mice. Nonetheless, increases in shrew numbers only partially compensated for the community energy consumption because, as insectivores, they are unlikely to utilize all food types consumed by their competitors. In pre-commercially thinned stands, which exhibit higher diversity of resources compared to other habitat types, shrew populations were less affected by changes in mice densities. These spatially and temporally variable interactions between unlikely competitors, observed in a relatively simple, high-latitude island ecosystem, highlight the difficulty in assessing the role of biotic factors in structuring communities

Body mass, mortality of trapped shrews, and relationship to population growth rate.

<p>Average body mass (bars; ± 95% confidence intervals), trap mortality rate (line; top), and the relation between trap mortality and population growth rate (bottom) of dusky shrews captured in 2010–2012 on Prince of Wales Island, Alaska. Letters indicate significant differences.</p

Means (95% confidence intervals) for LAI_DBH and L_e estimates obtained from AccuPAR and hemispheric photographs (Ring 5) from 21 forest stands on Prince of Wales Island, Alaska in summers 2010 and 2011.

<p>For each stand type, the number of paired measurements (<i>n</i>), the correlation coefficient (<i>r</i>) between the two sensors, and statistical difference (paired t-test at α = 0.05) are presented.</p

Relationship between shrew density in the current session vs the previous one.

<p>Density of dusky shrews in the current trapping session (2 or 3) on Prince of Wales Island, Alaska, in relation to their density in the previous session (1 or 2) in each year from 2010–2012. The relationship is shown for all years combined.</p