scholarly journals Considerations When Applying the Consumer Functional Response Measured Under Artificial Conditions

2021 ◽  
Vol 9 ◽  
Author(s):  
Blaine D. Griffen
Keyword(s):  
Functional Response ◽  
Response Curve ◽  
Mathematical Formulation ◽  
Field Conditions ◽  
Functional Responses ◽  
Considerable Effort ◽  
Natural Conditions ◽  
Ecological Processes ◽  
Foraging Patterns ◽  
Foraging Decisions

Since its creation, considerable effort has been given to improving the utility of the consumer functional response. To date, the majority of efforts have focused on improving mathematical formulation in order to include additional ecological processes and constraints, or have focused on improving the statistical analysis of the functional response to enhance rigor and to more accurately match experimental designs used to measure the functional response. In contrast, relatively little attention has been given to improving the interpretation of functional response empirical results, or to clarifying the implementation and extrapolation of empirical measurements to more realistic field conditions. In this paper I explore three concepts related to the interpretation and extrapolation of empirically measured functional responses. First, I highlight the need for a mechanistic understanding when interpreting foraging patterns and highlight pitfalls that can occur when we lack understanding between the shape of the functional response curve and the mechanisms that give rise to that shape. Second, I discuss differences between experimental and real-world field conditions that must be considered when trying to extrapolate measured functional responses to more natural conditions. Third, I examine the importance of the time scale of empirical measurements, and the need to consider tradeoffs that alter or limit foraging decisions under natural conditions. Clearly accounting for these three conceptual areas when measuring functional responses and when interpreting and attempting to extrapolate empirically measured functional responses will lead to more accurate estimates of consumer impacts under natural field conditions, and will improve the utility of the functional response as a heuristic tool in ecology.

scholarly journals Functional response of Harmonia axyridis preying on Acyrthosiphon pisum nymphs: the effect of temperature

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yasir Islam ◽  
Farhan Mahmood Shah ◽  
Xu Rubing ◽  
Muhammad Razaq ◽  
Miao Yabo ◽  
...  
Keyword(s):  
Functional Response ◽  
High Performance ◽  
Acyrthosiphon Pisum ◽  
Harmonia Axyridis ◽  
Effect Of Temperature ◽  
Functional Responses ◽  
Aphid Control ◽  
Host Aphid ◽  
Predator Foraging

AbstractIn the current study, we investigated the functional response of Harmonia axyridis adults and larvae foraging on Acyrthosiphon pisum nymphs at temperatures between 15 and 35 °C. Logistic regression and Roger’s random predator models were employed to determine the type and parameters of the functional response. Harmonia axyridis larvae and adults exhibited Type II functional responses to A. pisum, and warming increased both the predation activity and host aphid control mortality. Female and 4th instar H. axyridis consumed the most aphids. For fourth instar larvae and female H. axyridis adults, the successful attack rates were 0.23 ± 0.014 h−1 and 0.25 ± 0.015 h−1; the handling times were 0.13 ± 0.005 h and 0.16 ± 0.004 h; and the estimated maximum predation rates were 181.28 ± 14.54 and 153.85 ± 4.06, respectively. These findings accentuate the high performance of 4th instar and female H. axyridis and the role of temperature in their efficiency. Further, we discussed such temperature-driven shifts in predation and prey mortality concerning prey-predator foraging interactions towards biological control.

scholarly journals Applying predator-prey theory to modelling immune-mediated, within-host interspecific parasite interactions

Parasitology ◽  
2010 ◽  
Vol 137 (6) ◽  
pp. 1027-1038 ◽  
Author(s):  
ANDY FENTON ◽  
SARAH E. PERKINS
Keyword(s):  
Functional Response ◽  
Interspecific Interactions ◽  
Parasite Load ◽  
Multiple Infections ◽  
Functional Responses ◽  
Predator Prey ◽  
Immune Activity ◽  
Parasite Communities ◽  
Immune Mediated ◽  
Predator Prey Models

SUMMARYPredator-prey models are often applied to the interactions between host immunity and parasite growth. A key component of these models is the immune system's functional response, the relationship between immune activity and parasite load. Typically, models assume a simple, linear functional response. However, based on the mechanistic interactions between parasites and immunity we argue that alternative forms are more likely, resulting in very different predictions, ranging from parasite exclusion to chronic infection. By extending this framework to consider multiple infections we show that combinations of parasites eliciting different functional responses greatly affect community stability. Indeed, some parasites may stabilize other species that would be unstable if infecting alone. Therefore hosts' immune systems may have adapted to tolerate certain parasites, rather than clear them and risk erratic parasite dynamics. We urge for more detailed empirical information relating immune activity to parasite load to enable better predictions of the dynamic consequences of immune-mediated interspecific interactions within parasite communities.

Effect of predation on the low-density dynamics of vendace: significance of the functional response

2001 ◽  
Vol 58 (10) ◽  
pp. 1909-1923 ◽  
Author(s):  
Outi Heikinheimo
Keyword(s):  
Functional Response ◽  
Brown Trout ◽  
Salmo Trutta ◽  
Dynamic Modelling ◽  
Perca Fluviatilis ◽  
Low Density ◽  
Functional Responses ◽  
Density State ◽  
Brown Trout Salmo Trutta ◽  
Stock Recruitment

During the past 20 years, there have been prolonged vendace (Coregonus albula) recessions in several Finnish lakes. Hypotheses have been proposed that predation by brown trout (Salmo trutta m. lacustris) or perch (Perca fluviatilis) on young-of-the-year vendace could prevent the recovery of the vendace stocks from a low-density state. In this study, dynamic modelling was applied to examine the effect of predation, assuming a dome-shaped spawning stock–recruitment relationship for vendace, type II or III functional responses to predation by brown trout and perch, and a constant rate of fishing. The results showed that the form of the functional response is crucial in determining the significance of the predation on vendace stocks that have a steep dome-shaped stock–recruitment relationship. In all cases, however, predation by perch had more effect than that by brown trout, probably due to perch occupying the pelagic zone when the vendace stock is sparse. This may make the mortality of vendace increase with decreasing population density (depensatory mortality) at certain density levels.

scholarly journals Permanence of Periodic Predator-Prey System with Functional Responses and Stage Structure for Prey

2008 ◽  
Vol 2008 ◽  
pp. 1-15 ◽  
Author(s):  
Can-Yun Huang ◽  
Min Zhao ◽  
Hai-Feng Huo
Keyword(s):  
Functional Response ◽  
Prey Species ◽  
Necessary Conditions ◽  
Stage Structure ◽  
Functional Responses ◽  
Predator Prey ◽  
Predator Prey Model ◽  
Prey System ◽  
Sufficient And Necessary Conditions ◽  
Holling Ii Functional Response

A stage-structured three-species predator-prey model with Beddington-DeAngelis and Holling II functional response is introduced. Based on the comparison theorem, sufficient and necessary conditions which guarantee the predator and the prey species to be permanent are obtained. An example is also presented to illustrate our main results.

Use of Functional Response Modeling to Evaluate the Effect of Temperature on Predation of Amblyseius swirskii (Acari: Phytoseiidae) Adults Preying on Tetranychus urticae (Acari: Tetranychidae) Nymphs

2021 ◽  
Author(s):  
Azadeh Farazmand ◽  
Masood Amir-Maafi
Keyword(s):  
Functional Response ◽  
Tetranychus Urticae ◽  
Attack Rate ◽  
Handling Time ◽  
Effect Of Temperature ◽  
Coefficient Of Determination ◽  
Temperature Threshold ◽  
Amblyseius Swirskii ◽  
Functional Responses ◽  
Wide Range

Abstract In this research, functional responses of Amblyseius swirskii Athias-Henriot preying on different Tetranychus urticae Koch nymphal densities (2, 4, 8, 16, 32, 64, and 128) were studied at eight constant temperatures (15, 20, 25, 27.5, 30, 32.5, 35 and 37.5°C) in a circular Petri dish (3-cm diameter × 1-cm height) under lab conditions. At all temperatures, the logistic regression showed a type II functional response. A nonlinear relationship was found between temperature and attack rate and the reciprocal of handling time. The reciprocal of handling time decreased exponentially with increasing temperature. In contrast, the attack rate grew rapidly with increasing temperatures up to an optimum, showing a decreasing trend at higher temperatures. In order to quantify the functional response of A. swirskii over a broad range of temperatures and to gain a better estimation of attack rate and handling time, a temperature-settled functional response equation was suited to our data. Our model showed that the number of prey consumed increased with rising prey density. Also, the predation rates increased with increasing temperatures but decreased at extremely high temperatures. Based on our model, the predation rate begins at the lower temperature threshold (11.73°C) and reaches its peak at upper temperature threshold (29.43°C). The coefficient of determination (R2) of the random predator model was 0.99 for all temperatures. The capability of A. swirskii to search and consume T. urticae over a wide range of temperatures makes it a good agent for natural control of T. urticae in greenhouses.

scholarly journals Fitting functional responses: Direct parameter estimation by simulating differential equations

2017 ◽  
Author(s):  
Benjamin Rosenbaum ◽  
Bjoern C. Rall
Keyword(s):  
Parameter Estimation ◽  
Functional Response ◽  
Marine Biology ◽  
Prediction Models ◽  
Functional Responses ◽  
Food Web Structure ◽  
Ode Models ◽  
Background Mortality ◽  
Functional Response Models ◽  
Scientific Fields

The feeding functional response is one of the most widespread mathematical frameworks in Ecology, Marine Biology, Freshwater Biology, Microbiology and related scientific fields describing the resource-dependent uptake of a consumer. Since the exact knowledge of its parameters is crucial in order to predict, for example, the efficiency of biocontrol agents, population dynamics, food web structure and subsequently biodiversity, a trustful parameter estimation is of utmost importance for scientists using this framework. Classical approaches for estimating functional response parameters lack flexibility and can often only serve as approximation for a correct parameter estimation. Moreover, they do not allow to incorporate side effects such as resource growth or background mortality. Both call for a new method to be established solving these problems. Here, we combined ordinary differential equation models (ODE models), that were numerically solved using computer simulations, with an iterative maximum likelihood fitting approach. We compared our method to classical approaches of fitting functional responses, using data both with and without additional resource growth and mortality. We found that for classical functional response models, like the often used type II and type III functional response, the established fitting methods are reliable. However, using more complex and flexible functional responses, our new established method outperforms the traditional methods. Additionally, only our method allows to analyze experiments correctly when resources experience growth or background mortality. Our method will enable researchers from different scientific fields that are measuring functional responses to estimate parameters correctly. These estimates will enable community ecologists to parameterize their models more precisely, allowing for a deeper understanding of complex ecological systems, and will increase the quality of ecological prediction models.

scholarly journals Towards a function-first framework to make soil microbial ecology predictive

2021 ◽  
Author(s):  
Johannes Rousk ◽  
Lettice Hicks
Keyword(s):  
Microbial Community ◽  
Environmental Factors ◽  
Microbial Communities ◽  
Functional Response ◽  
Community Composition ◽  
Ecosystem Function ◽  
Bacterial Growth ◽  
Response Curve ◽  
Ecosystem Functions ◽  
Soil Microbial

<p>Soil microbial communities perform vital ecosystem functions, such as the decomposition of organic matter to provide plant nutrition. However, despite the functional importance of soil microorganisms, attribution of ecosystem function to particular constituents of the microbial community has been impeded by a lack of information linking microbial function to community composition and structure. Here, we propose a function-first framework to predict how microbial communities influence ecosystem functions.</p><p>We first view the microbial community associated with a specific function as a whole, and describe the dependence of microbial functions on environmental factors (e.g. the intrinsic temperature dependence of bacterial growth rates). This step defines the aggregate functional response curve of the community. Second, the contribution of the whole community to ecosystem function can be predicted, by combining the functional response curve with current environmental conditions. Functional response curves can then be linked with taxonomic data in order to identify sets of “biomarker” taxa that signal how microbial communities regulate ecosystem functions. Ultimately, such indicator taxa may be used as a diagnostic tool, enabling predictions of ecosystem function from community composition.</p><p>In this presentation, we provide three examples to illustrate the proposed framework, whereby the dependence of bacterial growth on environmental factors, including temperature, pH and salinity, is defined as the functional response curve used to interlink soil bacterial community structure and function. Applying this framework will make it possible to predict ecosystem functions directly from microbial community composition.</p>

Predator Ecology

2021 ◽  
Author(s):  
John P. DeLong
Keyword(s):  
Food Webs ◽  
Functional Response ◽  
Behavioral Ecology ◽  
Evolutionary Dynamics ◽  
Ecological Systems ◽  
Ecological Communities ◽  
Functional Responses ◽  
Predator Prey ◽  
Top Predators ◽  
Integrative Science

Predator-prey interactions form an essential part of ecological communities, determining the flow of energy from autotrophs to top predators. The rate of predation is a key regulator of that energy flow, and that rate is determined by the functional response. Functional responses themselves are emergent ecological phenomena – they reflect morphology, behavior, and physiology of both predator and prey and are both outcomes of evolution and the source of additional evolution. The functional response is thus a concept that connects many aspects of biology from behavioral ecology to eco-evolutionary dynamics to food webs, and as a result, the functional response is the key to an integrative science of predatory ecology. In this book, I provide a synthesis of research on functional responses, starting with the basics. I then break the functional response down into foraging components and connect these to the traits and behaviors that connect species in food webs. I conclude that contrary to appearances, we know very little about functional responses, and additional work is necessary for us to understand how environmental change and management will impact ecological systems

An inexpensive portable freezer for in situ freezing in the field

2002 ◽  
Vol 32 (12) ◽  
pp. 2160-2168 ◽  
Author(s):  
Yves Dubuc ◽  
Jean Dubuc ◽  
Francine J Bigras
Keyword(s):  
Picea Glauca ◽  
White Spruce ◽  
Field Conditions ◽  
Natural Conditions ◽  
Dry Ice ◽  
Ambient Temperatures ◽  
Programmable Controller ◽  
Controlled Conditions ◽  
Large Trees

A portable freezer was developed to apply frost to branches of large trees to study their growth and recuperation after frost application under natural conditions. The freezer measures 37.5 × 63.5 × 31.5 cm and weighs approximately 3 kg. It consists of two compartments, a freezing compartment and a dry ice compartment. The portable freezer provides a ramp-and-soak freezing pattern using a programmable controller. The nonfreezing temperature plateaus can be set from 1 to 6°C and maintained for 0 to 12 h. The cooling and warming rates can be programmed from 1 to 12°C·h–1. Test temperatures can be maintained for a period of time ranging from 0 to 12 h at set temperatures. Freezers were tested without samples under controlled conditions at ambient temperatures of 0, –5, –10, 5, 15, 20, and 25°C. Under these conditions, the cooling and warming rates showed a deviation of less than ±1°C·h–1 at a set rate of 2°C·h–1. The freezer provides test temperatures as low as –38°C and –47°C at ambient temperatures of 20 and –10°C, respectively. Freezers were also tested under field conditions on attached branches in mature white spruce (Picea glauca (Moench) Voss) trees under hardening conditions.

Prey–predator model for optimal harvesting with functional response incorporating prey refuge

2015 ◽  
Vol 09 (01) ◽  
pp. 1650014 ◽  
Author(s):  
G. S. Mahapatra ◽  
P. Santra
Keyword(s):  
Functional Response ◽  
System Parameter ◽  
Optimal Harvesting ◽  
Functional Responses ◽  
Prey Refuge ◽  
Numerical Examples ◽  
Pictorial Presentation ◽  
Constant Form ◽  
Predator Model ◽  
Predator System

This paper presents a prey–predator model considering the predator interacting with non-refuges prey by class of functional responses. Here we also consider harvesting for only non-refuges prey. We discuss the equilibria of the model, and their stability for hiding prey either in constant form or proportional to the densities of prey population. We also investigate various possibilities of bionomic equilibrium and optimal harvesting policy. Finally we present numerical examples with pictorial presentation of the various effects of the prey–predator system parameter.

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