Energetic Consequences for a Northern, Range-Edge Lizard Population

Lizards at the northern, cool edge of their geographic range in the northern hemisphere should encounter environmental conditions that differ from those living near the core of their range. To better understand how modest climate differences affect lizard energetics, we compared daily feeding and metabolism rates of individual Sceloporus occidentalis in two populations during mid-summer. Chuckanut Beach (CB) was a cool, maritime climate in northern Washington State, and Sondino Ranch (SR) was a warmer, drier climate in southern, inland Washington. We found no difference between populations in daily energy expenditure (DEE), as calculated from doubly labeled water estimates. The CB population, however, had significantly higher prey availability and rate of daily energy intake (DEI) as estimated from fecal pellet masses. Consequently, CB lizards had higher size-adjusted body masses than lizards from SR. Within CB, during midsummer, DEE was similar to DEI. Within the SR population, DEE trended higher than DEI during midsummer, but was not significantly different. We found no population differences in lizard activity, active body temperature, or preferred body temperature. Hence, we infer the longer activity season for the SR population may compensate for the low food availability and high daily energy cost of midsummer. Moreover, for the CB population, we infer that cooler temperatures and higher food availability allow the lizards to compensate for the shorter activity. We also suggest the CB population may benefit from the predicted warmer temperatures associated with climate change given the similar activity-period body temperatures and DEE between these lizard populations assuming food availability is sufficient

Integrating Brain and Biomechanical Models—A New Paradigm for Understanding Neuro-muscular Control

To date, realistic models of how the central nervous system governs behavior have been restricted in scope to the brain, brainstem or spinal column, as if these existed as disembodied organs. Further, the model is often exercised in relation to an in vivo physiological experiment with input comprising an impulse, a periodic signal or constant activation, and output as a pattern of neural activity in one or more neural populations. Any link to behavior is inferred only indirectly via these activity patterns. We argue that to discover the principles of operation of neural systems, it is necessary to express their behavior in terms of physical movements of a realistic motor system, and to supply inputs that mimic sensory experience. To do this with confidence, we must connect our brain models to neuro-muscular models and provide relevant visual and proprioceptive feedback signals, thereby closing the loop of the simulation. This paper describes an effort to develop just such an integrated brain and biomechanical system using a number of pre-existing models. It describes a model of the saccadic oculomotor system incorporating a neuromuscular model of the eye and its six extraocular muscles. The position of the eye determines how illumination of a retinotopic input population projects information about the location of a saccade target into the system. A pre-existing saccadic burst generator model was incorporated into the system, which generated motoneuron activity patterns suitable for driving the biomechanical eye. The model was demonstrated to make accurate saccades to a target luminance under a set of environmental constraints. Challenges encountered in the development of this model showed the importance of this integrated modeling approach. Thus, we exposed shortcomings in individual model components which were only apparent when these were supplied with the more plausible inputs available in a closed loop design. Consequently we were able to suggest missing functionality which the system would require to reproduce more realistic behavior. The construction of such closed-loop animal models constitutes a new paradigm of computational neurobehavior and promises a more thoroughgoing approach to our understanding of the brain’s function as a controller for movement and behavior

Seasonal Variability and Drivers of Microzooplankton Grazing and Phytoplankton Growth in a Subtropical Estuary

Rates of microzooplankton grazing and phytoplankton growth are seldom measured with respect to time, yet such estimates may better reflect temporal variability in coastal phytoplankton communities and offer insight into mechanisms that control populations. To assess seasonal patterns in rates, we performed 41, weekly dilution experiments over a full year in the Skidaway River Estuary (GA), measuring rates of phytoplankton growth, microzooplankton grazing, and viral lysis based on total chlorophyll and group-specific abundances (Synechococcus spp., picoeukaryotes, and nanoeukaryotes). Seasonal variability in microzooplankton grazing (0–2.11 day-1) and phytoplankton growth rates (-0.3–2.43 day-1) was observed, with highest values typically recorded in summer and lowest in winter. Grazing pressure was strongest in winter-spring, as phytoplankton accumulation rates were often negative (-0.16–0.28 day-1). Rates varied similarly over seasons for chlorophyll, pico-, and nanoeukaryotes, while rates on Synechococcus spp. were rarely significant in dilutions and did not follow seasonal trends. Few experiments (7%) yielded significant rates of viral lysis. While temperature was an important predictor of phytoplankton rates via PLS analysis, temperature exhibited stronger linearity with growth rates (R2 = 0.46–0.56) compared to grazing rates (R2 = 0.11–0.27), which were more likely driven by observed seasonal shifts in plankton community composition (e.g., fall diatom blooms). Establishing temporal rate measurements is critical to identify factors that drive phytoplankton growth and mortality and accurately predict shifts in phytoplankton population dynamics and food web processes within marine systems

Niche shifts and energetic condition of songbirds in response to phenology of food-resource availability in a high-elevation sagebrush ecosystem

Seasonal fluctuations in food availability can affect diets of consumers, which in turn may influence the physiological state of individuals and shape intra- and inter-specific patterns of resource use. High-elevation ecosystems often exhibit a pronounced seasonal “pulse” in productivity, although few studies document how resource use and energetic condition by avian consumers change in relation to food-resource availability in these ecosystems. We tested the hypothesis that seasonal increases (pulses) in food resources in high-elevation sagebrush ecosystems result in 2 changes after the pulse, relative to the before-pulse period: (1) reduced diet breadth of, and overlap between, 2 sympatric sparrow species; and (2) enhanced energetic condition in both species. We tracked breeding-season diets using stable isotopes and energetic condition using plasma metabolites of Brewer\u27s Sparrows (Spizella breweri), Vesper Sparrows (Pooecetes gramineus), and their food resources during 2011, and of only Brewer\u27s Sparrows and their food resources during 2013. We quantify diet breadth and overlap between both species, along with coincident physiological consequences of temporal changes in resource use. After invertebrate biomass increased following periods of rainfall in 2011, dietary breadth decreased by 35% in Brewer\u27s Sparrows and by 48% in Vesper Sparrows, while dietary overlap decreased by 88%. Energetic condition of both species increased when dietary overlap was lower and diet breadth decreased, after the rapid rise of food-resource availability. However, energetic condition of Brewer\u27s Sparrows remained constant in 2013, a year with low precipitation and lack of a strong pulse in food resources, even though the species\u27 dietary breadth again decreased that year. Our results indicate that diet breadth and overlap in these sparrow species inhabiting sagebrush ecosystems generally varied as predicted in relation to intra- and interannual changes in food resources, and this difference in diet was associated with improved energetic condition of sparrows at least in one year

Bedrock geology of south-central Iowa, Digital geologic map of Iowa, Phase 4: South-Central Iowa

https://ir.uiowa.edu/igs_ofm/1024/thumbnail.jp

Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (1) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (2) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (3) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation

Biohybrid control of general linear systems using the adaptive filter model of cerebellum

© 2015 Wilson, Assaf, Pearson, Rossiter, Dean, Anderson and Porrill. The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks

An internal model architecture for novelty detection: Implications for cerebellar and collicular roles in sensory processing

The cerebellum is thought to implement internal models for sensory prediction, but details of the underlying circuitry are currently obscure. We therefore investigated a specific example of internal-model based sensory prediction, namely detection of whisker contacts during whisking. Inputs from the vibrissae in rats can be affected by signals generated by whisker movement, a phenomenon also observable in whisking robots. Robot novelty-detection can be improved by adaptive noise-cancellation, in which an adaptive filter learns a forward model of the whisker plant that allows the sensory effects of whisking to be predicted and thus subtracted from the noisy sensory input. However, the forward model only uses information from an efference copy of the whisking commands. Here we show that the addition of sensory information from the whiskers allows the adaptive filter to learn a more complex internal model that performs more robustly than the forward model, particularly when the whisking-induced interference has a periodic structure. We then propose a neural equivalent of the circuitry required for adaptive novelty-detection in the robot, in which the role of the adaptive filter is carried out by the cerebellum, with the comparison of its output (an estimate of the self-induced interference) and the original vibrissal signal occurring in the superior colliculus, a structure noted for its central role in novelty detection. This proposal makes a specific prediction concerning the whisker-related functions of a region in cerebellar cortical zone A2 that in rats receives climbing fibre input from the superior colliculus (via the inferior olive). This region has not been observed in non-whisking animals such as cats and primates, and its functional role in vibrissal processing has hitherto remained mysterious. Further investigation of this system may throw light on how cerebellar-based internal models could be used in broader sensory, motor and cognitive contexts. © 2012 Anderson et al