Expected minimum population size as a measure of threat

By M. McCarthy, C. Thompson. 2001

152 citations recorded by the source corpus.

Subfields: Genetics, Nature and Landscape Conservation, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/eb08aa187604c3fe5b07f495f298634ad260d63b

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In Genetics

In Nature and Landscape Conservation

In Public Health, Environmental and Occupational Health

Citations in the corpus

The impact of cold water releases on the critical period of post‐spawning survival and its implications for Murray cod (Maccullochella peelii peelii): a case study of the Mitta Mitta River, southeastern Australia (2005, background): “…risk curves examining alternative scenarios is called added risk (Burgman et al., 1993) and a method for quantifying added risks is to calculate the average minimum population size for each curve and compare these values (McCarthy, 1995; McCarthy and Thompson, 2001; Todd et al., 2002, 2004).”
Integrating Landscape and Metapopulation Modeling Approaches: Viability of the Sharp‐Tailed Grouse in a Dynamic Landscape (2004, result): “Other measures of viability—expected minimum Conservation Biology Volume 18, No. 2, April 2004 abundance (McCarthy & Thompson 2001) and total abundance at the end of the simulation—gave similar results (Table 4).”
The theta‐logistic is unreliable for modelling most census data (2010, unknown): “We then compared expected minimum abundance (EMA), which is a robust index of extinction risk (McCarthy & Thompson 2001), between the two RAMAS runs: those based on the initial h value and those using the plug-in estimate.”
Population Viability Analysis (2022, background): “The area to the left of a quasi-extinction risk curve is equal to the expected minimum population size, which usefully summarizes the curve [21].”
Tools for integrating range change, extinction risk and climate change information into conservation management (2013, background): “occur between 2020 and 2080 (expected minimum abundance; McCarthy and Thompson 2001), which is a continuous metric reflecting risks of both declines and extinction risk.”
Fire frequency matters more than fire size: Testing the pyrodiversity-biodiversity paradigm for at-risk small mammals in an Australian tropical savanna (2015, background): “The EMA, the average minimum size of simulation replicates, has been shown to equate to the area under the quasi-extinction curve and thus captures a large amount of information in a single metric (McCarthy and Thompson, 2001).”
Disease prevention and resistance in social insects: modeling the survival consequences of immunity, hygienic behavior, and colony organization (2007, methodology): “Due to the highly stochastic nature of our models, we employed an iterated Monte Carlo method to statistically evaluate results (McCarthy and Thompson 2001).”
Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? (2012, methodology): “…measures of change in range area are used to infer extinction likelihood (Thomas et al., 2004) and (ii) direct measures of extinction risk, which can only be estimated when stochastic-demographic models are part of the estimation method, i.e., expected minimum abundance (McCarthy & Thompson, 2001).”
Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models (2008, methodology): “The viability of species' populations was assessed using expected minimum abundance (EMA; McCarthy & Thompson 2001 ) and the probability that the population would fall below 50% of its initial abundance.”