Keyword search (4,164 papers available)

"Yates MC" Authored Publications:

Title Authors PubMed ID
1 eDNA Provides Accurate Population Abundance Estimates With Bioenergetics and Particle Mass-Balance Modelling Beaulieu J; Yates MC; Fraser DJ; Cristescu ME; Derry AM; 41913704
BIOLOGY
2 Evaluating the correlation between genome-wide diversity and the release of plastic phenotypic variation in experimental translocations to novel natural environments. Yates MC, Fraser DJ 33274531
BIOLOGY
3 The relationship between eDNA particle concentration and organism abundance in nature is strengthened by allometric scaling. Yates MC, Glaser D, Post J, Cristescu ME, Fraser DJ, Derry AM 32638451
CONCORDIA
4 Small population size and low genomic diversity have no effect on fitness in experimental translocations of a wild fish. Yates MC, Bowles E, Fraser DJ 31771476
BIOLOGY
5 A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes. Yates MC, Bernos TA, Fraser DJ 29151884
BIOLOGY

 

Title:A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes.
Authors:Yates MCBernos TAFraser DJ
Link:https://www.ncbi.nlm.nih.gov/pubmed/29151884?dopt=Abstract
DOI:10.1111/eva.12496
Publication:Evolutionary applications
Keywords:conservation biologyconservation geneticseffective population sizefisheries managementinventory and monitoringwildlife management
PMID:29151884 Category:Evol Appl Date Added:2019-06-07
Dept Affiliation: BIOLOGY
1 Department of Biology Concordia University Montreal QC Canada.
2 Group for Interuniversity Research in Limnology and Aquatic Environments (GRIL) Universite du Quebec Trois-Rivieres QC Canada.

Description:

A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes.

Evol Appl. 2017 10;10(9):935-945

Authors: Yates MC, Bernos TA, Fraser DJ

Abstract

Technological and methodological advances have facilitated the use of genetic data to infer census population size (Nc) in natural populations, particularly where traditional mark-and-recapture is challenging. The effective number of breeders (Nb) describes how many adults effectively contribute to a cohort and is often correlated with Nc. Predicting Nc from Nb or vice versa in species with overlapping generations has important implications for conservation by permitting (i) estimation of the more difficult to quantify variable and (ii) inferences of Nb/Nc relationships in related species lacking data. We quantitatively synthesized Nb/Nc relationships in three salmonid fishes where sufficient data have recently accumulated. Mixed-effects models were analysed in which each variable was included as a dependent variable or predictor term (Nb from Nc and vice versa). Species-dependent Nb/Nc slope estimates were significantly positive in two of three species. Variation in species slopes was likely due to varying life histories and reinforce caution when inferring Nb/Nc from taxonomically related species. Models provided maximum probable estimates for Nb and Nc for two species. However, study, population and year effects explained substantial amounts of variation (39%-57%). Consequently, prediction intervals were wide and included or were close to zero for all population sizes and species; model predictive utility was limited. Cost-benefit trade-offs when estimating Nb and/or Nc were also discussed using a real-world system example. Our findings based on salmonids suggest that no short cuts currently exist when estimating population size and researchers should focus on quantifying the variable of interest or be aware of caveats when inferring the desired variable because of cost or logistics. We caution that the salmonid species examined share life-history traits that may obscure relationships between Nb and Nc. Sufficient data on other taxa were unavailable; additional research examining Nb/Nc relationships in species with potentially relevant life-history trait differences (e.g., differing survival curves) is needed.

PMID: 29151884 [PubMed]





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