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Quantitative genetics of extreme insular dwarfing: the case of red deer (Cervus elaphus) on Jersey

Citation

Diniz-Filho, José Alexandre Felizola et al. (2021), Quantitative genetics of extreme insular dwarfing: the case of red deer (Cervus elaphus) on Jersey, Dryad, Dataset, https://doi.org/10.5061/dryad.47d7wm3cf

Abstract

Aim: The Island Rule – i.e. the tendency for body size to decrease in large mammals and increase in small mammals on islands has been commonly evaluated through macroecological or macroevolutionary, pattern-orientated approaches, which generally fail to model the microevolutionary processes driving either dwarfing or gigantism. Here, we seek to identify which microevolutionary process could have driven extreme insular dwarfism in the extinct dwarf red deer population on the island of Jersey.

Location: Jersey, UK (Channel Islands).

Taxon: Red deer ( Cervus elaphus)

Methods: We applied an individual-based quantitative genetics model parameterized with red deer life-history data to study the evolution of dwarfism in Jersey’s deer, considering variations in island area and isolation through time due to sea level changes.

Results: The body size of red deer on Jersey decreased fast early on, due to phenotypic plasticity, then kept decreasing almost linearly over time down to the actual body size of the Jersey deer (36 kg on average). Only 1% out of 10,000 replicates failed to reach that size in our simulations. The distribution of time to adaptation in these simulations was right-skewed, with a median of 395 generations (equivalent to roughly 4 ky years), with complete dwarfism effectively occurring in less than 6 ky 84.6% of times. About 72% of the variation in the time to adaptation between simulations was collectively explained by higher mutational variance, the number of immigrants from the continent after isolation, available genetic variance, heritability, and phenotypic plasticity.

Main Conclusions: The extreme dwarfing of red deer on Jersey is an expected outcome of high mutational variance, high immigration rate, a wide adaptive landscape, low levels of inbreeding, and high phenotypic plasticity (in the early phase of dwarfing), all occurring within a time window of around 6 ky. Our model reveals how extreme dwarfism is a plausible outcome of common, well-known evolutionary processes.

Methods

The dataset was generated by an individual-based quantitative genetics model parameterized with red deer life-history data gathered from the literature. The changes of carrying capacity and colonization probability through time were calculated based on sea-level changes of Jersey Island (Lister 1995) and allometric equations proposed by Carbone & Gittleman (2002).

Carbone, C., & Gittleman, J. L. (2002). A common rule for the scaling of carnivore density. Science, 295, 5563, 2273-2276.

Lister, A. (1995). Sea-levels and the evolution of island endemics: the dwarf red deer of Jersey. In: Preece, R. (ed). Island Britain: a quaternary Perspective. Geological
Society Special Publications 96, 151-172.

Usage Notes

This zip directory contains the data, R codes, and simulation results required to reproduce analyses reported in the associated paper.

Data

The file contains folders for each simulation scenarios (1) Baseline, (2) constant_k, (3) no_plasticity, (4) no_recolonization and (5) no_reproduction_system. There is also a file named island_area.txt, which contains data of the island area (Kisl) and isolation (migr) changes through time. 

Folders

Baseline - Script to run baseline models. The baseline model includes non-constant carrying capacity, phenotypic plasticity, island recolonization, and sexual dimorphism.

 

constant_k - Script to run baseline models with constant carrying capacity through time.

 

no_plasticity - Script to run baseline models without phenotypic plasticity.

 

no_recolonization - Script to run baseline models without island recolonization.

 

no_reproduction_system - Script to run baseline models without sexual dimorphism.

 

All folders contain R codes to run simulations for each scenario and the tables of simulation results:

AdaptSS_cervus_(scenario).R - Script to run adaptation through generations. The function AdaptSS runs a simulation of trait adaptation for each generation step and the function run_generation updates simulation's parameters for each generation and calls AdaptSS.

parallel_function_cervus.R - Set simulation parameters and run simulations.

Cervus_meanP.txt - Mean body size evolution through time. Rows are time steps and columns are simulation replicates.

Cervus_results_(scenario).txt - Table of simulation results. Rows are simulation replicates and columns simulation outputs.

Column Legend:

h2 - heritability

cv - phenotypic coefficient of variation

vm -  mutational variance

Ancestral - Initial trait

meanK - mean carrying capacity

Ni - initial population size

Nrecol - number of recolonization

Precol - probability of colonization

w2 - Length of adaptive landscape

time_adap - time until adaptation

selgrad - selection gradient

meanG -  mean genotype

varG - genotypic variance

N_end - Final population size

F_Peak - adaptive landscape optimum (optimum trait)

meanP - Final mean trait

bp.max - maximum phenotypic plasticity

bp - phenotypic plasticity

Funding

MCTIC/CNPq/FAPEG, Award: 465610/2014-5

CNPq PVE, Award: 314523/2014-6

Spanish MICIU Juan de la Cierva-Incorporación, Award: IJCI-2014-19502

MCTIC/CNPq/FAPEG, Award: 465610/2014-5

CNPq PVE, Award: 314523/2014-6

Spanish MICIU Juan de la Cierva-Incorporación, Award: IJCI-2014-19502