Evidence map›Paper›PMID 41907160›Full record

ArticleEnvironment and planning. E, Nature and space2022

Individual animal geographies for the more-than-human city: Storying synanthropy and cynanthropy with urban coyotes.

Lauren E Van Patter

Abstract read
In one paragraph

Article in Environment and planning. E, Nature and space, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Lauren E Van PatterQueen's University, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent efforts within geography to deconstruct anthropocentric readings of the urban and explore the city as 'multispecies' or 'more-than-human' face substantial methodological challenges. This paper contributes an empirical case study of human-coyote urban cohabitations in the Greater Toronto Area, Canada, using a 'hybrid' methodological approach to individual animal geographies. It builds on dialogues surrounding animals' geographies that centre individual animal lifeworlds and experiences, exploring coyotes as subjects and actors who participate in the co-creation of shared urban worlds. A methodological approach based on collaboration and storying recounts the tales of two coyotes - Urban10 and Blondie - and their kin whose stories are gleaned by weaving together diverse social and ecological research tools, including: participant observation with Coyote Watch Canada, document review, semi-structured interviews, GPS collar data, field investigations, ethological observations, and trail cameras. The discussion details implications in terms of cynanthropy - 'becoming-canid' as methodology, delving into coyote lifeworlds using hybrid tools - as well as synanthropy - coyote synurbization and more-than-human urban belongings. Dwelling with Urban10 and Blondie in cynanthropic exploration makes visible opportunities for multispecies researchers to generate knowledge collaboratively with other-than-humans. Findings surrounding synanthropy highlight the practices involved in adapting to and participating, ecologically and socially, in life in the multispecies city. Overall, this paper advances efforts aimed at developing innovative and experimental hybrid methodologies for animal geographies, and theoretical discussions around re-storying the more-than-human city towards livable multispecies futures.

Indexed as

animal geographiescoyoteIndividual animal geographiesmore-than-human geographymultispecies city

Identifiers

PMID41907160
PMCPMC13032836

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.