# Best practices for genetic indicator estimation from DNA-based data

Marie-Gabrielle Harribey, Joachim Mergeay, Gernot Segelbacher, Anja Westram, Peter Galbusera, Alexander Kopatz, Cristiano Vernesi, Leslie Noble, Frédéric Raspail, Sean Hoban, et al.

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Marie-Gabrielle Harribey, Joachim Mergeay, Gernot Segelbacher, Anja Westram, Peter Galbusera, et al.. Best practices for genetic indicator estimation from DNA-based data. 7. European Congress of Conservation Biology, Jun 2024, Bologna, Italy. hal-04627693

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# Best practices for genetic indicator estimation from DNA-based data

Marie-Gabrielle Harribey<fnref n="1" />, Joachim Mergeay², Gernot Segelbacher<fnref n="3" />, Anja Westram<fnref n="4" />, Peter Galbusera<fnref n="5" />, Alexander Kopatz<fnref n="6" />, Cristiano Vernesi<fnref n="7" />,
Leslie Noble<fnref n="4" />, Frédéric Raspail<fnref n="1" />, Sean Hoban<fnref n="8" />, Joost Raeymaekers<fnref n="4" />, Myriam Heuertz<fnref n="1" />, Pauline Garnier-Géré<fnref n="1" />

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INRAO

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GINAMO

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biodiversa+
European Biodiversity Partnership

## Recent progress in biodiversity conservation policies

* In 2022, a timely **new headline genetic diversity indicator** for application to natural populations (Kunming-Montreal Global Biodiversity Framework - KMGBF): the **proportion of populations with an effective population size (Ne) > 500** <fnref n="1" />

* A Ne > 500 has long been recognized as a minimum threshold to ensure species' survival in the long-term, and potential for adaptation and resilience ²⁻<fnref n="3" />

→ **How to estimate recent Ne?** Either with demographic data or with genetic data: recent methods from one sample only, using **Linkage Disequilibrium statistics** (i.e. statistical association between alleles at different loci, e.g. LDNe)

### GINAMO Genetic Indicators for Nature Monitoring

Coord. Christina Hvilsom, Copenhagen Zoo - 11 Partners in Europe and the USA - 2024 - 2027

* General aim: facilitate the implementation of the KMGBF by improving the estimation, application and integration into biodiversity observation workflows of genetic diversity indicators

* This work focuses on developing best practices for conservation managers to obtain accurate and robust Ne estimates from DNA-based data (WP2)

<figure><img src="image_5.png" /></figure>

## Data for Q1: Choosing species across life-history trait diversity and population demographic history

Choice strategy for legacy datasets (i.e. large population genomic datasets with metadata - e.g. census sizes, pedigrees)

→ **Categorization framework** proposed that includes **3 organizing principles** based on meta analyses/reviews of multidimensional representations ⁹⁻¹⁰⁻¹¹⁻¹², combined across plant and animal phyla

### The GINAMO "cube"

<figure><img src="image_6.png" /></figure>

Coordinates on axis "Fast-slow continuum" and "reproductive strategies"

obtained from Paniw et al. 2018

## Scientific Issues

* Data from natural populations depart from LD-based method assumptions

* Several factors can bias estimation of Ne:

* **Population structure**, demography history (e.g. migration rates in a metapopulation)<fnref n="4" />⁻<fnref n="5" />⁻<fnref n="6" />

* **Complex life cycles** (e.g. overlapping generations) <fnref n="7" />⁻<fnref n="8" />

* **Technical data features** (e.g. number of sampled individuals and SNPs, quality of SNPs, missing data...) <fnref n="7" />⁻<fnref n="8" />

## Questions

Q1 - How do these factors affect Ne estimates in species with contrasted life-history traits and evolutionary scenarios?

Q2 - Knowing the true Ne, what are the impacts of these factors on genetic Ne estimates, as well as method assumptions and practical sampling schemes?

## Preliminary results for Q1: Ne estimates in 2 contrasted forest tree species

<figure><img src="image_7.png" /></figure>

**Pinus sylvestris:**
Continuous populations
very low IBD, mean
pairwise Fst of 0.01

**Pinus pinaster:**

Fragmented populations
Mean pairwise Fst of 0.13,
with higher Fst among
several population pairs

Figures after Milesi et al. 2023 (BioRxiv¹³). Gene pools defined with STRUCTURE analyses.

* Putatively neutral SNPs with <50% missing data across samples at each SNP, filtered for quality & depth (> 8)

### DATA

* Ne estimated with: 1000 randomly chosen SNPs per location (≈20 individuals), and for 20 individuals across the range (harmonic mean, 3 replicates)

<table><thead><tr><th rowspan="2">Species<br>Location<br>(country)</th><th colspan="5">Pinus sylvestris</th><th colspan="5">Pinus pinaster</th></tr><tr><th>Spain</th><th>Swiss</th><th>Lithuania</th><th>Finland</th><th>Random<br>inds.</th><th>Spain</th><th>France<br>(West)</th><th>France<br>(East)</th><th>Italy</th><th>Moroc-<br>co</th><th>Random<br>inds.</th></tr></thead><tbody><tr><td>LDNe</td><td>7247.9<br>(32-Inf)</td><td>Inf<br>(32-Inf)</td><td>Inf<br>(32.3-Inf)</td><td>Inf<br>(3.3-Inf)</td><td>Inf<br>(18.3-Inf)</td><td>Inf<br>(30.1-Inf)</td><td>Inf<br>(28.9-Inf)</td><td>851.1<br>(36.2-Inf)</td><td>102.6<br>(25.8-<br>Inf)</td><td>2.5<br>(2.3-5.2)</td><td>14.4<br>(5.7-265.5)</td></tr><tr><td>CurrentNe</td><td>13.7<br>(10.2-<br>18.2)</td><td>15,5<br>(11.4-<br>21.1)</td><td>15.2<br>(11.1-<br>20.6)</td><td>5.3<br>(5-7.6)</td><td>9.7<br>(7.2-13)</td><td>12.2<br>(9.2-16.2)</td><td>9.8<br>(7.3-<br>13.2)</td><td>7.4<br>(5.9-9.3)</td><td>15.3<br>(11.5-<br>20.3)</td><td>4<br>(5-5)</td><td>5.9<br>(5.1-7.6)</td></tr></tbody></table>

LDNE (in NeEstimator¹⁴): singletons pruned, jackknife confidence intervals (CIs).

currentNe¹⁵: polymorphic SNPs, assuming random pairing. CIs determined with Artificial Neural Network

First observations and **possible** interpretations:

* **Downward bias in currentNe** vs LDNe → technical issues, e.g., SNP quality (testing higher depth & lower % missing data filters), dealing with pseudoreplication? <fnref n="7" />⁻<fnref n="8" />

* **Inf. values with LDNe**: genetic drift signal might be very low or not captured in small sample sizes in a continuous distribution? <fnref n="7" />

* **Downward bias due to localized sampling** in populations with IBD → may reflect the **neighbourhood size** <fnref n="5" />⁻<fnref n="6" />

## Ongoing work and Perspectives

* **For Q1:** Choose additional legacy datasets (aim at ~25-50) that would be both representative of a large range of taxa (i.e. in the "Cube" space) & of IUCN conservation status → compare Ne estimates and how they are impacted by various factors across contrasted species

* **For Q2:** Design simulation scenarios matching case studies from legacy datasets → study interactions between factors affecting Ne estimates

* Provide workflows to biodiversity observation networks (e.g. GEO BON, French BON)

<footnote n="1">¹ Biogeco, INRAE, Univ. Bordeaux, France; ² Evolution and Biodiversity Conservation, KU Leuven, Belgium</footnote>

<footnote n="3">³ Chair of Wildlife Ecology and Management, Univ. Freiburg, Germany</footnote>

<footnote n="4">⁴ Faculty of Biosciences and Aquaculture, Nord Univ., Norway</footnote>

<footnote n="5">⁵ Centre for Research and Conservation, Royal Zoological Society of Antwerp, Belgium</footnote>

<footnote n="6">⁶ Norwegian Institute for Nature Research, Trondheim, Norway</footnote>

<footnote n="7">⁷ Forest Ecology Unit, Research and Innovation Centre - Fondazione Edmund Mach, Italy</footnote>

<footnote n="8">⁸ The Morton Arboretum, Center for Tree Science, Lisle, United States</footnote>

<footnote>**References** ¹ Hoban et al. 2020, *Biological Conservation* 248(8):108654; ² Franklin 1980, *Conservation Biology: An Evolutionary-Ecological Perspective* 135-49; ³ Frankham et al. 2013, *Trends in Ecology & Evolution* 28 (4): 187-88; ⁴ Ryman et al. 2019, *Molecular Ecology* 28 (8): 1904-18; ⁵ Santos-del-Blanco et al. 2022, *Biological Conservation* 273 (9):109704; ⁶ Neel et al. 2013, *Heredity* 111 (3): 189-99; ⁷ Waples 2024, *Molecular Ecology Resources* 24 (1): e13879; ⁸ Gargiulo et al. 2024, *Evolutionary Applications* 17 (5): e13691; ⁹ Paniw et al. 2018, *Ecology Letters* 21 (2): 275-86; ¹⁰ Capdevila et al. 2020, *Functional Ecology* 34 (8): 1613-25; ¹¹ Gaillard et al. 2016, *Encyclopedia of Evolutionary Biology*, 312-23; ¹² De Kort et al. 2021, *Nature Communications* 12 (1): 516; ¹³ Milesi et al. 2013, *BioRxiv*; ¹⁴ Do et al. 2014, *Molecular Ecology Resources* 14 (1): 209-14; ¹⁵ Santiago et al. 2024, *Molecular Ecology Resources* 24 (1): e13890</footnote>

<footnote>**Acknowledgments** We thank Dr. Salguero-Gomez for access to data and advice. This work benefits from financial support of Agence Nationale de la Recherche, for the project GINAMO - Biodiversa + (ANR-23-EBIP-0003-06).</footnote>