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Developmental response to low diets by giant Nephila clavipes females (Araneae: Nephilidae)

Journal of Arachnology, , 399-408, 2011


Status: Published

Citations:

Cite: [bibtex]


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Abstract: Female-biased sexual size dimorphism is common in arthropods, apparently driven by fecundity selection in females. Selective pressures that limit growth are less often considered. One factor that researchers have rarely considered is the possible role of energetic limits on growth. The orb weaving spider Nephila clavipes (Linnaeus 1767) is extremely sexually size dimorphic. Males are "normal" sized spiders and females are up to ten times longer, having passed through several additional juvenile instars. This extreme size dimorphism presents the opportunity to test for intrinsic energetic costs of gigantism. Prior studies have shown that males successfully reach maturity on a range of diets, while female dietary requirements increase rapidly with increasing size. We here examine the effects of variation in food availability on juvenile female development by randomly assigning spiderlings from six different families (from six distinct populations) to quantitatively varying but qualitatively identical diets. Based upon field observations, we expected that dietary restrictions would have the greatest effect on duration of instars, particularly later instars, and on instar number (because longer total development would lead to curtailment of growth at an earlier stage), with relatively little effect on growth per molt. Because the diets ranged from higher than mean intake observed in the field to well below mean intake, we expected females to mature at a wide range of instars (and sizes). Our results support the functional relationship among food intake, instar duration, and fixed growth per molt (although growth per molt was less canalized than suggested by field observations). However, we observed no variation in number of instars, and we suggest that these data provide additional support for the importance of rare, large prey in the diets of web-building spiders.



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Joshua Bongard - Department of Computer Science, Associate Professor

Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.


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Chris Danforth -Department of Mathematics and Statistics, Flint Professor of Mathematical, Natural, and Technical Sciences

Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.

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Laurent Hébert-Dufresne - Assistant Professor, Computer Science

Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.

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Paul Hines - School of Engineering, Associate Professor

Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.

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James Bagrow - Assistant Professor, Department of Mathematics and Statistics

Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).

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