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. 2018 May 20;11(7):1162-1175.
doi: 10.1111/eva.12636. eCollection 2018 Aug.

Relationship between effective and demographic population size in continuously distributed populations

Jennifer C Pierson  1   2 , Tabitha A Graves  3 , Sam C Banks  1 , Katherine C Kendall  3 , David B Lindenmayer  1
Affiliations

Relationship between effective and demographic population size in continuously distributed populations

Jennifer C Pierson et al. Evol Appl. .

Abstract

Genetic monitoring of wild populations can offer insights into demographic and genetic information simultaneously. However, widespread application of genetic monitoring is hindered by large uncertainty in the estimation and interpretation of target metrics such as contemporary effective population size, Ne . We used four long-term genetic and demographic studies (≥9 years) to evaluate the temporal stability of the relationship between Ne and demographic population size (Nc ). These case studies focused on mammals that are continuously distributed, yet dispersal-limited within the spatial scale of the study. We estimated local, contemporary Ne with single-sample methods (LDNE, Heterozygosity Excess, and Molecular Ancestry) and demographic abundance with either mark-recapture estimates or catch-per-unit effort indices. Estimates of Ne varied widely within each case study suggesting interpretation of estimates is challenging. We found inconsistent correlations and trends both among estimates of Ne and between Ne and Nc suggesting the value of Ne as an indicator of Nc is limited in some cases. In the two case studies with consistent trends between Ne and Nc , FIS was more stable over time and lower, suggesting FIS may be a good indicator that the population was sampled at a spatial scale at which genetic structure is not biasing estimates of Ne . These results suggest that more empirical work on the estimation of Ne in continuous populations is needed to understand the appropriate context to use LDNe as a useful metric in a monitoring programme to detect temporal trends in either Ne or Nc .

Keywords: LDNe; effective population size; genetic indicator; genetic monitoring; population trends.

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Figures

Figure 1
Figure 1
Estimates with 95% confidence intervals per method, including LDNe estimates with three different parameters used to remove rare alleles (square: Pcrit = 1/2S, S = median sample size; circle = 1/2S, S = smallest sample size; triangle: Pcrit = 0.05) diamond: CPUE or CMR estimate) and CPUE or CMR estimates for abundance (diamond); CPUE indices do not have errors associated with them. Full error estimates are included in Table 1 and Table S4. (a) Brown antechinus, (b) mountain brushtail possum, (c) Grizzly bears in Glacier National Park and (d) Grizzly bears in the Northern Continental Divide Ecosystem
Figure 2
Figure 2
Predicted population trajectories estimated from linear models based on standardized estimates of N e (based on a range of values to remove rare alleles from data set) and N c (based on either a capture–mark–recapture estimate for mountain brushtail possum or a catch‐per‐unit effort index for brown antechinus and grizzly bears). The x‐axis is trapping session, and the y‐axis is standardized estimates of N e and N c. (a) Brown antechinus, (b) mountain brushtail possum, (c) GNP grizzly bears and (d) Northern Continental Divide Ecosystem (NCDE) grizzly bears. Blue solid line: LDNe Pcrit = 1/2S, S = median sample size; green dotted line: LDNe Pcrit = 1/2S, S = smallest sample size; grey solid line: LDNe Pcrit = 0.05; black dashed line: CMR or CPUE
Figure 3
Figure 3
Temporal patterns in estimates of the inbreeding coefficient (FIS) in each study system
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References

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