Skip to contentScholarGate
LibraryBookshelfDeskReview StudioAssistant
Sign in
On this page
IntuitionHow it worksWhen to use itStrengths & limitationsCommon pitfallsApplicationsFrequently asked🔒 Read the full methodSourcesRelated methods
Cite this pageSpotted an issue on this page? Report or suggest a fix →
Home›Veterinary Science›Microhabitat Preference Analysis — Fine-Scale Habitat Selection in Animals
Process / pipelineWildlife ecology and ethology

Microhabitat Preference Analysis — Fine-Scale Habitat Selection in Animals

Microhabitat Preference Analysis · Also known as: habitat selection analysis, microhabitat use analysis, fine-scale habitat preference study, microhabitat utilization assessment

Microhabitat Preference Analysis is a quantitative ecological method used to determine which fine-scale environmental features — such as vegetation structure, substrate type, temperature, or cover — animals actively select beyond what is randomly available to them. Widely applied in veterinary science, wildlife biology, and ethology, it compares the characteristics of locations an animal uses against those of randomly sampled available locations to infer habitat preference, avoidance, or random use.

ScholarGate
  1. Process / pipeline
  2. v1
  3. 2 Sources
  4. PUBLISHED
Cite this page →
Tools & resources
Download slides
Learn & explore

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Method map

The neighbourhood of related methods — select a node to explore.

Microhabitat Preference Analysis
Niche ModelingAcoustic TelemetryElectrofishing

When to use it

Microhabitat Preference Analysis is appropriate when the research question asks which specific environmental features an animal actively selects at a fine spatial scale — relevant in veterinary wildlife assessments, disease reservoir ecology, captive animal welfare studies, and conservation planning. It requires paired use and availability data with matched environmental measurements. The method is not appropriate when animals cannot be individually detected or tracked, when the study area is too small to yield meaningful availability samples, or when interest lies at the landscape scale rather than the immediate surroundings of individuals.

Strengths & limitations

Strengths
  • Directly links animal behavior to measurable environmental features, enabling mechanistic interpretation of habitat selection.
  • Applicable across a wide range of taxa — reptiles, mammals, birds, fish, invertebrates — and habitat types.
  • Multiple selectivity indices allow comparison of preference strength across species, populations, and seasons.
  • Compatible with modern telemetry and remote sensing data, scaling from field plots to GPS-derived fine-scale locations.
  • Results inform practical decisions in wildlife management, captive animal husbandry, and veterinary field assessments.
Limitations
  • Preference estimates are relative to the availability sample; a poor or biased availability design leads to unreliable conclusions.
  • Assumes that animals are free to access all available habitat, which may not hold when competitors, predators, or human disturbance constrain movement.
  • Does not directly measure fitness consequences of habitat choice; preferred microhabitats are not necessarily optimal for survival or reproduction.
  • Sample sizes for both used and available locations must be adequate; small samples produce wide confidence intervals around selectivity indices.

Frequently asked

What is the difference between microhabitat preference and macro-habitat selection?

Macro-habitat selection concerns broad landscape-level choices — which vegetation type or land-cover class an animal occupies relative to what is available regionally. Microhabitat preference operates at the scale of the individual's immediate surroundings, characterizing fine-grained features such as substrate texture, canopy openness, or temperature within a few metres of the animal. Both can be analyzed with similar statistical tools, but the spatial grain of measurement and the biological interpretation differ substantially.

How many used and available locations do I need?

There is no universal minimum, but studies using logistic regression resource selection functions typically require at least 5–10 available locations per used location and enough total observations to estimate model coefficients reliably. For selectivity indices applied to categorical variables, chi-square tests require expected cell counts of at least 5. When GPS telemetry is used, autocorrelation in the location series means that the effective sample size is smaller than the raw number of fixes — accounting for this is essential.

Can the method be applied to captive animals in zoo or clinical settings?

Yes. In enclosure or cage settings, 'available' habitat is operationally defined as all zones within the enclosure, and 'used' habitat is determined by scan sampling or continuous focal observation. The same selectivity indices and statistical comparisons apply. This application is particularly relevant in zoo animal welfare research and in designing enriched environments for animals under veterinary care.

Which selectivity index should I use — Ivlev, Jacob's D, or Manly's alpha?

Ivlev's electivity index is simple but sensitive to the proportion of available habitat. Jacob's D corrects for this sensitivity and is preferred when availability proportions vary widely. Manly's alpha accommodates simultaneous comparison of multiple habitat types and is appropriate when more than two types are contrasted. For continuous environmental variables modeled as a function of use probability, resource selection functions (logistic regression) subsume all these indices and are the current methodological standard.

Sources

  1. Morris, D. W. (1987). Ecological scale and habitat use. Ecology, 68(2), 362–369. DOI: 10.2307/1939267 ↗
  2. Manly, B. F. J., McDonald, L. L., Thomas, D. L., McDonald, T. L., & Erickson, W. P. (2002). Resource Selection by Animals: Statistical Design and Analysis for Field Studies (2nd ed.). Kluwer Academic. ISBN: 978-1402006562

How to cite this page

ScholarGate. (2026, June 3). Microhabitat Preference Analysis. ScholarGate. https://scholargate.app/en/veterinary-science/microhabitat-preference

Related methods

Niche Modeling

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

  • Niche ModelingEcology↔ compare
Compare side by side →

Referenced by

Acoustic TelemetryElectrofishing

Similar methods

Species Distribution Models (MaxEnt)Niche ModelingIndicator ValueFocal Animal SamplingMultiscale Spatial AutocorrelationSuitability AnalysisScan SamplingBeta Diversity Partitioning

Related reference concepts

Animal Adaptation and NicheAnimal Ecology and AdaptationForaging and Optimality TheoryBiogeography and Species DistributionsReserve Design and Systematic Conservation PlanningLandscape Pattern and Connectivity

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — Microhabitat Preference Analysis (Microhabitat Preference Analysis). Retrieved 2026-07-21 from https://scholargate.app/en/veterinary-science/microhabitat-preference · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Multiple contributors (Morris, Manly, Johnson, and others)
Year
1970s–1980s (formalized)
Type
Quantitative observational method
DataType
Spatial use records, vegetation and substrate measurements, behavioral observations
Subfamily
Wildlife ecology and ethology
Related methods
Niche Modeling
ScholarGate

A content-first reference library for research methods — what each one is, how it works, and where it comes from.

Open data (CC-BY)

Explore

  • Library
  • Search the library…
  • Browse by field
  • Fields
  • Journey
  • Compare
  • Which method?

Reference

  • Subjects
  • Atlas
  • Glossary
  • Methodology
  • Philosophy

Your tools

  • Bookshelf
  • Desk
  • Chat

Company

  • About
  • Pricing
  • Contact
  • Suggest a method

Entries are compiled from published sources for reference. Verifying the accuracy and suitability of any information for your own use remains your responsibility.

© 2026 ScholarGate · A research-method reference library
  • Privacy
  • Cookies
  • Terms
  • Delete account