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Record W7163990660 · doi:10.82465/159

Urban wildlife species detection, species richness, and community shifts along a gradient of urbanization in an agriculturally embedded landscape using passive and active methods

2025· other· en· W7163990660 on OpenAlexaboutno aff
Jordan Marie Rustad

Bibliographic record

VenueOpen MIND · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSpecies richnessImpervious surfaceUrbanizationWildlifeBiodiversityGeneralist and specialist speciesSparrowHabitatWetland

Abstract

fetched live from OpenAlex

Urbanization is a major contributor to global biodiversity loss, and there are knowledge gaps on how urban wildlife responds to urbanization in prairie cities. I analyzed how species richness of birds and mammals responded to a gradient of urbanization, measured by impervious cover, in Regina, Saskatchewan, Canada. Regina is a prairie city with a large amount of green space of planted trees, a lake, and is surrounded largely by monoculture annual crops. I deployed trail cameras and automated recording units to assess mammal and bird richness, respectively. Equipment was deployed for 30 d seasonally between October 2021 and May 2024. Bird species richness was highest (53 species) at low to intermediate levels (0-20%) of impervious cover and is likely due to edge effects in suburban habitats. Mammal species richness did not respond to any variable, which may be due to the generalist life history of most mammals detected. Most bird species that decreased in response to urbanization were associated with wetlands or grasslands, which generally decline in response to urbanization. Two bird species responded positively to impervious cover but were likely responding to increased tree cover in the city, not increased impervious cover. Individual species of mammals tended to decrease with increasing impervious cover, except for Fox Squirrels (Sciurus niger), which are likely responding to increased tree cover. My research highlights the need to understand species specific responses, taxa specific responses, and how city characteristics influence wildlife’s responses to urbanization. Ecologists use many methods to detect species and determine species richness. The validation of these methods generally has occurred in natural environments rather than urban environments. I analysed two methods, trail cameras and a community science project, to determine factors that influence probability of species detection, understand differences in species detected, and assess how many trail cameras or community science routes are needed to detect target species. Both survey methods were implemented between April and November 2023 in Regina, Saskatchewan, Canada. Trail camera data were collected from previously defined urban and suburban locations. The community science project had survey routes along walking trails and sidewalks. The target species were White-tailed deer (Odocoileus virginianus), White-tailed jackrabbit (Lepus townsendii), American Robin (Turdus migratorius), Rock pigeon (Columba livia), and Fox squirrel. The method and species were the most important factors influencing detection probability, but time was not important for detection probability. This may be due to target species being common and easy to detect, making shorter surveys or deployments just as effective as longer surveys at detecting the species. Trail cameras and community science volunteers detected most species at different proportions, but no differences were significant. Fewer trail cameras, but with long deployment periods, were needed to detect all species compared with community science routes. The two methods detected species differently due to species’ behaviour. The trail cameras detected 57 species compared to community scientists detected 10, but there were species that only community scientists detected. I suggest that researchers would need to use less time and resources to assess species richness with trail cameras but would potentially miss a few species. However, combining trail cameras and community science is a potential solution to maximize species detections without increasing cost and effort in comparison to hiring professional biologists. Researchers conducting long term studies could benefit the most from implementing community science to offset the potentially large up-front investments associated with community science. My study highlights the value of comparing research methods to detect species in an urban setting and how complementary methods can improve detections and measures of species richness in urban environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.332
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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