Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Fitting functional response surfaces to data: a best practice guide
Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences.
Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2020 (English)In: Ecosphere, ISSN 2150-8925, E-ISSN 2150-8925, Vol. 11, no 4, article id e03051Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2020. Vol. 11, no 4, article id e03051
Keywords [en]
data transformation, functional response, heteroscedasticity, Hill exponent, ingestion rate, parameter correlation, parameter estimation, prey density, temperature, type III
National Category
Ecology
Identifiers
URN: urn:nbn:se:umu:diva-172511DOI: 10.1002/ecs2.3051ISI: 000536583400024Scopus ID: 2-s2.0-85084508099OAI: oai:DiVA.org:umu-172511DiVA, id: diva2:1450902
Funder
Swedish Research Council, 621-2010-5316Available from: 2020-07-02 Created: 2020-07-02 Last updated: 2020-07-02Bibliographically approved

Open Access in DiVA

fulltext(5126 kB)337 downloads
File information
File name FULLTEXT01.pdfFile size 5126 kBChecksum SHA-512
4cbbd4f2be59c02db17464be4675358ad530afff3d5eddbeaa9cfe55288401f4f04f858ce0181f6db8dbcceb48e247065648ba9e1fe39bfa34b9d59bb945f781
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Uszko, WojciechDiehl, SebastianWickman, Jonas

Search in DiVA

By author/editor
Uszko, WojciechDiehl, SebastianWickman, Jonas
By organisation
Department of Ecology and Environmental SciencesDepartment of Mathematics and Mathematical Statistics
In the same journal
Ecosphere
Ecology

Search outside of DiVA

GoogleGoogle Scholar
Total: 337 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 369 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf