Condition-dependent foraging in the wolf spider Hogna baltimoriana

Lyon, Shelby; Sjulin, Charlotte A.; Sullivan, Kirsten M.; DeLong, John P.

doi:10.1016/j.fooweb.2017.12.003

Cited by 8 publications

(11 citation statements)

References 27 publications

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“…Because both condition indices provide quantitatively similar information (Lyon et al, 2018), we used condition ratio here due to the ease of visualizing body shape via the condition ratio (i.e. To further confirm that our choice of metric for condition did not influence our results, we ran the best linear model using residuals from a carapace-abdomen regression instead of condition ratio and found similar results using both approaches.…”

Section: Methodsmentioning

confidence: 76%

“…Mass and metabolic rate are both closely linked to population abundance, foraging rates, and even rates of evolution (Damuth, 1981;Martin & Palumbi, 1993;Ernest et al, 2003;DeLong & Vasseur, 2012). Mass, condition, and taxonomic identity all contribute to foraging success (Rall et al, 2012;Kalinoski & DeLong, 2016;Lyon et al, 2018), suggesting that variation in metabolic rate (whether an inherent trait or a response to food intake) may also be important in determining interaction strengths and structuring food webs. A population in better condition may ultimately produce more biomass and more offspring, increasing population sizes above what might be expected for a given body size.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, understanding metabolic rate variation could also help to explain variation around allometric scaling of predator functional responses (McCann et al, 1998;Rall et al, 2011). Mass, condition, and taxonomic identity all contribute to foraging success (Rall et al, 2012;Kalinoski & DeLong, 2016;Lyon et al, 2018), suggesting that variation in metabolic rate (whether an inherent trait or a response to food intake) may also be important in determining interaction strengths and structuring food webs.…”

Section: Discussionmentioning

confidence: 99%

“…To further confirm that our choice of metric for condition did not influence our results, we ran the best linear model using residuals from a carapace-abdomen regression instead of condition ratio and found similar results using both approaches. Because both condition indices provide quantitatively similar information (Lyon et al, 2018), we used condition ratio here due to the ease of visualizing body shape via the condition ratio (i.e. a ratio of 1.5 refers to a spider with an abdomen 1.5 times wider than its carapace).…”

Section: Methodsmentioning

confidence: 99%

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Body condition helps to explain metabolic rate variation in wolf spiders

Uiterwaal

DeLong

2019

Ecological Entomology

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1. Metabolism is the fundamental process that powers life. Understanding what drives metabolism is therefore critical to our understanding of the ecology and behaviour of organisms in nature.2. Metabolic rate generally scales with body size according to a power law. However, considerable unexplained variation in metabolic rate remains after accounting for body mass with scaling functions.3. We measured resting metabolic rates (oxygen consumption) of 227 field-caught wolf spiders. Then, we tested for effects of body mass, species, and body condition on metabolic rate.4. Metabolic rate scales with body mass to the 0.85 power in these wolf spiders, and there are metabolic rate differences between species. After accounting for these factors, residual variation in metabolic rate is related to spider body condition (abdomen:cephalothorax ratio). Spiders with better body condition consume more oxygen.5. These results indicate that recent foraging history is an important determinant of metabolic rate, suggesting that although body mass and taxonomic identity are important, other factors can provide helpful insights into metabolic rate variation in ecological communities.

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Section: Methodsmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Body condition helps to explain metabolic rate variation in wolf spiders

Uiterwaal

DeLong

2019

Ecological Entomology

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“…where a is the space clearance rate of the predator, h is the handling time for one prey item, x is the number of prey items available, and y is the per capita rate of prey consumption (Holling, 1959). The handling time and space clearance rate of a predator are affected by several factors, including the body size of the predator and the prey, the body condition of the predator, temperature, and the predator's foraging strategy (Lyon et al, 2018;Pawar et al, 2012;Rall et al, 2011;Uiterwaal et al, 2017). Foraging strategy may be a fixed aspect of a predator's behavior, such as sit-and-wait versus active pursuit predation, but foraging behaviors also may be influenced by the energetic needs of the organism.…”

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confidence: 99%

Gravid Tetragnathid spiders show an increased functional response

Boswell

DeLong

2019

Food Webs

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Spiders in the genus Tetragnatha feed on emerging aquatic insects, including mosquitoes and midges, but there is little known about the foraging behavior of these spiders. We hypothesized that female spiders actively developing egg sacs would increase food consumption to provide more energy to produce and provision their eggs. We tested this hypothesis by measuring foraging rates of Tetragnatha spiders kept in jars and provisioned with different levels of midges. We then tested for a difference in the functional response of spiders that did or did not lay egg sacs in their jars. Egg-laying and non-egg-laying spiders showed significantly different functional responses, indicating that Tetragnatha spiders can change their behavior or web structure to increase their foraging rate, presumably to accommodate increased energy demand for reproduction.

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Fitting functional response surfaces to data: a best practice guide

2020

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Describing how resource consumption rates depend on resource density, conventionally termed "functional responses," is crucial to understanding the population dynamics of trophically interacting organisms. Yet, accurately determining the functional response for any given pair of predator and prey remains a challenge. Moreover, functional responses are potentially complex surfaces in multidimensional space, where resource density is only one of several factors determining consumption rates. We explored how three sources of error can be addressed in the design and statistical analysis of functional response experiments: ill-chosen spacing of prey densities, heteroscedastic variance in consumption rates, and non-independence of parameters of the function describing prey consumption in relation to prey density and additional environmental factors. We generated extensive, virtual data sets that simulated feeding experiments in which both prey density and environmental temperature were varied, and for which the true, deterministic functional response surface was known and realistic variance had been added. We compared eight different methods of functional response fitting, one of which stood out as best performing. We subsequently tested several conclusions from the simulation study against experimental data of zooplankton feeding on algae across a broad range of temperatures. We summarize our main findings in three best practice guidelines for the experimental estimation of functional response surfaces, of which the second is the most important: (1) space prey densities logarithmically, starting from very low densities; (2) log-transform prey consumption data prior to fitting; and (3) fit a multivariate functional response surface to all data (including all prey densities and other factors, in our case temperature) in a single step. We also observed that functional response surfaces were fitted more accurately and precisely than their component parameters. The latter occurred because parameter estimates were non-independent, which is an inevitable feature of fitting complex nonlinear functions to data: A given response surface can often be described with near-equal accuracy by multiple parameter combinations. We therefore conclude that fitted functional response models perform better at optimizing the fit of the overall response surface than at determining how component parameters, such as the attack rate or handling time, depend on environmental factors such as temperature.

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Condition-dependent foraging in the wolf spider Hogna baltimoriana

Cited by 8 publications

References 27 publications

Body condition helps to explain metabolic rate variation in wolf spiders

Body condition helps to explain metabolic rate variation in wolf spiders

Gravid Tetragnathid spiders show an increased functional response

Fitting functional response surfaces to data: a best practice guide

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