Evidence map›Paper›PMID 31403048›Full record

ArticleFrontiers in veterinary science2019

A Long-Term Lens: Cumulative Impacts of Free-Roaming Cat Management Strategy and Intensity on Preventable Cat Mortalities.

John D Boone, Philip S Miller, Joyce R Briggs, Valerie A W Benka, Dennis F Lawler, Margaret Slater, Julie K Levy, Stephen Zawistowski

Open access · goldAbstract read
In one paragraph

Article in Frontiers in veterinary science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
5.2field-weighted citation impact, top 4% of its field
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

36 citing papers in PubMed, 62 citations in OpenAlex.

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  17. Density and Home Range of Cats in a Small Inhabited Mediterranean Island.Animals : an open access journal from MDPI · 2024
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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

8 authors at 5 institutions in 1 country.

John D BooneGreat Basin Bird Observatory, Reno, NV, United States.
Philip S MillerConservation Planning Specialist Group, Species Survival Commission, International Union for Conservation of Nature, Apple Valley, MN, United States.
Joyce R BriggsAlliance for Contraception in Cats and Dogs, Portland, OR, United States.
Valerie A W BenkaAlliance for Contraception in Cats and Dogs, Portland, OR, United States.
Dennis F LawlerIllinois State Museum, Springfield, IL, United States.
Margaret SlaterStrategy and Research Department, American Society for the Prevention of Cruelty to Animals, Florence, MA, United States.
Julie K LevyMaddie's Shelter Medicine Program, Department of Small Animal Clinical Sciences, University of Florida, Gainesville, FL, United States.
Stephen ZawistowskiAnimal Behavior and Conservation Program, Hunter College, New York, NY, United States.
American Society for the Prevention of Cruelty to Animals · USHunter College · USIllinois State Museum · USInternational Union for Conservation of Nature · USUniversity of Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study used a previously developed stochastic simulation model (1) to estimate the impact of different management actions on free-roaming kitten and cat mortality over a 10-year period. These longer-term cumulative impacts have not been systematically examined to date. We examined seven management scenarios, including: (1) taking no action, (2) low-intensity removal, (3) high-intensity removal, (4) low-intensity episodic culling, (5) high-intensity episodic culling, (6) low-intensity trap-neuter-return (TNR), and (7) high-intensity TNR. For each scenario we tracked within the model the number of kittens born, the number of kittens surviving to adulthood, and the number of adults removed using lethal control over the entire 10-year simulation. We further defined all kitten deaths and lethal removal of adults as "preventable" deaths because they could potentially be reduced by certain management actions. Our simulation results suggested that the cumulative number of preventable deaths over 10 years for an initial population of 50 cats is highest for a "no-action" scenario, estimated at 1,000 deaths. It is lowest for a high-intensity TNR scenario, estimated at 32 deaths, a 31-fold difference. For all management scenarios tested, including removal and culling, the model predicted fewer preventable deaths than for a no-action scenario. For all management scenarios, the model predicted that the higher-intensity option (defined in terms of the proportion of animals sterilized or removed within a given time period) would result in fewer preventable deaths over time than the lower-intensity option. Based on these findings, we conclude that management intensity is important not only to reduce populations more quickly, but also to minimize the number of preventable deaths that occur over time. Accordingly, the lessons for the animal welfare community are both encouraging and cautionary. With sufficient intensity, management by TNR offers significant advantages in terms of combined lifesaving and population size reduction. At lower intensity levels, these advantages are greatly reduced or eliminated. We recommend that those who seek to minimize suffering and maximize lifesaving for free-roaming cats attempt to balance prospective goals (i.e., saving lives tomorrow) with proximate goals (i.e., saving lives today), and recognize that thoughtful choice of management strategies can ensure that both of these complementary goals are achieved.

Indexed as

cat managementfree-roaming catslifesavingpopulation dynamicssimulation modeltrap-neuter-return

Identifiers

PMID31403048
PMCPMC6676151
OpenAlexW2960920126

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.