Using long‐term data for a reintroduced population to empirically estimate future consequences of inbreeding

Armstrong, Doug P.; Parlato, Elizabeth H.; Egli, Barbara; Dimond, Wendy J.; Kwikkel, Renske; Berggren, Åsa; McCready, Mhairi; Parker, Kevin A.; Ewen, John G.

doi:10.1111/cobi.13646

Cited by 4 publications

(10 citation statements)

References 30 publications

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“…To simulate the increase in inbreeding over the 26 years, we assumed the first juvenile cohort would be completely outbred

()(, F = 0)

, and following (Crow & Kimura, 1970) calculated the expected average inbreeding coefficient of subsequent cohorts to be.

{\bar{F}}_{y + 1} = 1 ‐ (1 ‐ {\bar{F}}_{y}) {()(, 1 ‐ \frac{1}{2 N_{y} * Ne false/ normalN})}^{1 / GT},

where

N_{y}

is the population size in year y ,

Ne / N

is the ratio of effective population size to census population size, and

GT

is the generation time. The

Ne / N

parameter was sampled from a normal distribution with mean 0.561 and SD 0.010 based on analysis of the observed changes in inbreeding coefficients over time (Armstrong, Parlato, Egli, Dimond, Kwikkel, et al., 2021), and GT set to 3.1 years (average age of breeding adults). To allow for demographic stochasticity in inbreeding coefficients, the actual average coefficient for each cohort was selected from a normal distribution with mean

{\bar{F}}_{y}

and SD

\sqrt{{\bar{F}}_{y} ()(, 1 ‐ {\bar{F}}_{y}) c}

, where the parameter c was in turn selected from a normal distribution with mean 0.111 and SD 0.002 based on the variance in observed inbreeding coefficients (Armstrong, Parlato, Egli, Dimond, Kwikkel, et al., 2021).…”

Section: Methodsmentioning

confidence: 99%

“…An individual's inbreeding coefficient can range from 0 to 1, where F i = 0 means the individual's ancestors are unrelated (assumed to be the case for founders) and parent‐offspring or sibling‐sibling cross between two such individuals produces offspring with F i = 0.25. We calculated inbreeding coefficients from pedigrees using PMx 1.0 (Ballou et al., 2011), with unknown portions of pedigrees modelled using multiple imputation (this involved sampling from a beta distribution defined by the mean and standard deviation [ SD ] of known inbreeding coefficients in the individual's cohort; see Armstrong, Parlato, Egli, Dimond, Kwikkel, et al., 2021, for details).…”

Section: Methodsmentioning

confidence: 99%

“…where the parameter c was in turn selected from a normal distribution with mean 0.111 and SD 0.002 based on the variance in observed inbreeding coefficients (Armstrong, Parlato, Egli, Dimond, Kwikkel, et al, 2021). ( 14) ø. ad…”

Section: Yearmentioning

confidence: 99%

“…Data available from the Dryad Digital Repository https://doi.org/10.5061/dryad.kkwh70s5f (Armstrong, Parlato, Egli, Dimond, Berggren, et al., 2021).…”

Section: Data Availability Statementmentioning

confidence: 99%

“…where N y is the population size in year y, Ne∕N is the ratio of effective population size to census population size, and GT is the generation time. The Ne∕N parameter was sampled from a normal distribution with mean 0.561 and SD 0.010 based on analysis of the observed changes in inbreeding coefficients over time (Armstrong, Parlato, Egli, Dimond, Kwikkel, et al, 2021), and GT set to 3.1 years (average age of breeding adults). To allow for demographic stochasticity in inbreeding coefficients, the actual average coefficient for each cohort was selected from a normal distribution with mean ‼ F y and SD…”

Section: Yearmentioning

confidence: 99%

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Capturing the dynamics of small populations: A retrospective assessment using long‐term data for an island reintroduction

Armstrong

Parlato

Egli

et al. 2021

Journal of Animal Ecology

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1. The art of population modelling is to incorporate factors essential for capturing a population's dynamics while otherwise keeping the model as simple as possible.However, it is unclear how optimal model complexity should be assessed, and whether this optimal complexity has been affected by recent advances in modelling methodology. This issue is particularly relevant to small populations because they are subject to complex dynamics but inferences about those dynamics are often constrained by small sample sizes.2. We fitted Bayesian hierarchical models to long-term data on vital rates (survival and reproduction) for the toutouwai Petroica longipes population reintroduced to Tiritiri Matangi, a 220-ha New Zealand island, and quantified the performance of those models in terms of their likelihood of replicating the observed population dynamics. These dynamics consisted of overall growth from 33 (±0.3) to 160 (±6) birds from 1992-2018, including recoveries following five harvest events for further reintroductions to other sites.3. We initially included all factors found to affect vital rates, which included inbreeding, post-release effects (PRE), density-dependence, sex, age and random annual variation, then progressively removed these factors. We also compared performance of models where data analysis and simulations were done simultaneously to those produced with the traditional two-step approach, where vital rates are estimated first then fed into a separate simulation model. Parametric uncertainty and demographic stochasticity were incorporated in all projections.4. The essential factors for replicating the population's dynamics were densitydependence in juvenile survival and PRE, i.e. initial depression of survival and reproduction in translocated birds. Inclusion of other factors reduced the precision of projections, and therefore the likelihood of matching observed dynamics.However, this reduction was modest when the modelling was done in an integrated framework. In contrast, projections were much less precise when done with a two-step modelling approach, and the cost of additional parameters was much higher under the two-step approach. | MATERIAL S AND ME THODS | Species and siteThe toutouwai Petroica longipes is a small (26-32 g) insectivorous forest passerine endemic to the North Island of New Zealand and 5. These results suggest that minimization of complexity may be less important than accounting for covariances in parameter estimates, which is facilitated by integrating data analysis and population projections using Bayesian methods.

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“…To simulate the increase in inbreeding over the 26 years, we assumed the first juvenile cohort would be completely outbred

()(, F = 0)

, and following (Crow & Kimura, 1970) calculated the expected average inbreeding coefficient of subsequent cohorts to be.

{\bar{F}}_{y + 1} = 1 ‐ (1 ‐ {\bar{F}}_{y}) {()(, 1 ‐ \frac{1}{2 N_{y} * Ne false/ normalN})}^{1 / GT},

where

N_{y}

is the population size in year y ,

Ne / N

is the ratio of effective population size to census population size, and

GT

is the generation time. The

Ne / N

{\bar{F}}_{y}

and SD

\sqrt{{\bar{F}}_{y} ()(, 1 ‐ {\bar{F}}_{y}) c}

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Yearmentioning

confidence: 99%

“…Data available from the Dryad Digital Repository https://doi.org/10.5061/dryad.kkwh70s5f (Armstrong, Parlato, Egli, Dimond, Berggren, et al., 2021).…”

Section: Data Availability Statementmentioning

confidence: 99%

Section: Yearmentioning

confidence: 99%

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