# Dealing With the Complexity of Effective Population Size in Conservation Practice

Ancuta Fedorca, Joachim Mergeay, Adejoke O Akinyele, Tamer Albayrak, Iris Biebach, Alice Brambilla, Pamela A Burger, Elena Buzan, Ino Curik, Roberta Gargiulo, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Ancuta Fedorca, Joachim Mergeay, Adejoke O Akinyele, Tamer Albayrak, Iris Biebach, et al. Dealing With the Complexity of Effective Population Size in Conservation Practice. Evolutionary Applications, 2024, 17 (12), pp.e70031. 10.1111/eva.70031. hal-04884184

HAL Id: hal-04884184

https://hal.science/hal-04884184v1

Submitted on 13 Jan 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# Dealing With the Complexity of Effective Population Size in Conservation Practice

Ancuta Fedorca¹,² ID | Joachim Mergeay³,⁴ ID | Adejoke O. Akinyele⁵ ID | Tamer Albayrak⁶,⁷ ID | Iris Biebach⁸ ID |

Alice Brambilla⁸,⁹ ID | Pamela A. Burger¹⁰ ID | Elena Buzan¹¹,¹² ID | Ino Curik¹³,¹⁴ ID | Roberta Gargiulo¹⁵ ID |

José A. Godoy¹⁶ ID | Santiago C. González-Martínez¹⁷ ID | Christine Grossen¹⁸ ID | Myriam Heuertz¹⁷ ID |

Sean Hoban¹⁹,²⁰ ID | Jo Howard-McCombe²¹ ID | Maria Kachamakova²² ID | Peter Klinga²³,²⁴ ID | Viktoria Köppä²⁵ ID |

Elenora Neugebauer²⁶,²⁷ ID | Ivan Paz-Vinas²⁸ ID | Peter B. Pearman²⁹,³⁰,³¹ ID | Laia Pérez-Sorribes¹⁶ ID |

Baruch Rinkevich³² ID | Isa-Rita M. Russo³³ ID | Adélaïde Theraroz¹⁷ ID | Nia E. Thomas³³ ID | Marjana Westergren³⁴ ID |

Sven Winter¹⁰,³⁵ ID | Linda Laikre²⁵ ID | Alexander Kopatz³⁶ ID |

**Correspondence:** Alexander Kopatz (alexander.kopatz@nina.no)

**Received:** 9 May 2024

| Revised: 2 October 2024

| Accepted: 5 October 2024

**Funding:** This work was supported by European Cooperation in Science and Technology (CA 18134, CA 23121); Horizon 2020 Framework Programme (862221) (FORGENIUS); Scientific Grant Agency VEGA (1/0328/22); IdEx Bordeaux University, Investments for the Future program / GPR Bordeaux; Swiss National Foundation, Project (IZCOZO_198147); Norges Forskningsråd (160022/F40); Javna Agencija za Raziskovalno Dejavnost RS, (P1-0386, P4-0107); Romanian Ministry of Research, Innovation and Digitalization funds, PN23090304 (12N/01.01.2023); HEFCW Higher Education Investment and Recover (HEIR) Fund for Research; Biodiversa+ (ANR-23-EBIP-0003-06); Austrian Science Fund, Project I5081-B; Agence Nationale de la Recherche, (ANR-10-LABEX-0025); Ministerio de Ciencia, Innovación y Universidades (PID2020-118028GB-I00, PRE2022-105110); Svenska Forskningsrådet Formas (2019-05503, 2020-01290); People's Trust for Endangered Species; Swedish Environmental Protection Agency (SEPA).

**Keywords:** biodiversity monitoring | bridging science-to-application gap | effective number of breeders | genetic diversity | genetic indicators | Kunming-Montreal global biodiversity framework | Ne | species conservation and management

## ABSTRACT

Effective population size (Ne) is one of the most important parameters in evolutionary biology, as it is linked to the long-term survival capability of species. Therefore, Ne greatly interests conservation geneticists, but it is also very relevant to policymakers, managers, and conservation practitioners. Molecular methods to estimate Ne rely on various assumptions, including no immigration, panmixia, random sampling, absence of spatial genetic structure, and/or mutation-drift equilibrium. Species are, however, often characterized by fragmented populations under changing environmental conditions and anthropogenic pressure. Therefore, the estimation methods' assumptions are seldom addressed and rarely met, possibly leading to biased and inaccurate Ne estimates. To address the challenges associated with estimating Ne for conservation purposes, the COST Action 18134, Genomic Biodiversity Knowledge for Resilient Ecosystems (G-BIKE), organized an international workshop that met in August 2022 in Braşov, Romania. The overarching goal was to operationalize the current knowledge of Ne estimation methods for conservation practitioners and decision-makers. We set out to identify datasets to evaluate the sensitivity of Ne estimation methods to violations of underlying assumptions and to develop data analysis strategies that addressed pressing issues in biodiversity monitoring and conservation. Referring to a comprehensive body of scientific work on Ne, this meeting report is not intended to be exhaustive but rather to present approaches, workshop findings, and a collection of papers that serve as fruits of those efforts. We aimed to provide insights and opportunities to help bridge the gap between scientific research and conservation practice.

<footnote>For affiliations refer to page 13.</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2024 The Author(s). Evolutionary Applications published by John Wiley & Sons Ltd.</footnote>
# 1 | Applicable Methods for Estimating Effective Population Size Are Needed

Effective population size (*Ne*), defined as the size of an ideal population that experiences the same amount of genetic drift and increase of inbreeding as the real population (Wright 1931), is one of the most important parameters for assessing the long-term viability of species and is, therefore, an important measure of conservation biology. The effective size of a population is essentially an evolutionary analogue to the census size (*Nc*), and it is a quantity that correlates to the loss or maintenance of genetic diversity and inbreeding within a population (Waples 2024a; Waples 2022). Higher *Ne* results in more maintenance of genetic diversity or lower levels of inbreeding and a faster response to natural selection and, thereby, adaptation to environmental changes. Hence, populations with higher *Ne* are expected to have higher survival probability. In 2022, due to the general acknowledgement of its importance for biodiversity conservation, *Ne* became the basis for a headline indicator for the monitoring and reporting of genetic diversity under the monitoring framework of the UN's Convention on Biological Diversity (CBD) Kunming-Montreal Global Biodiversity Framework (GBF) (i.e., headline indicator A.4; CBD 2022; Hoban, da Silva, et al. 2024; Hoban, da Silva, et al. 2023). Consequently, effective population size is now embraced by governmental bodies and policy stakeholders, including national focal points for the CBD. In addition, effective population size has been included in relevant Essential Biodiversity Variables (EBV) for Genetic Composition (Hoban et al. 2022). Thus, practical and easy-to-use tools are needed to allow a diverse group of users to monitor and report progress on effective population size (Mastretta-Yanes et al. 2024). However, choosing which method to apply and how to interpret the results is not straightforward. Given the different types of effective population sizes (Box 1), all referred to as *Ne*, the plethora of methods to calculate them, and the increasing number of different data sources available, we believe that there is a lack of scientifically evaluated and harmonized guidance for global, national, and regional reporting of *Ne*.

Depending on how a particular study is designed, from sampling to the analysis method used, estimates of *Ne* from the same population can vary by orders of magnitude, not least

because different types of *Ne* (Box 1) have different meanings in space (Ryman, Laikre, and Hössjer 2019; Waples 2024a) and time (Nadachowska-Brzyska, Konczal, and Babik 2022; Tenesa et al. 2007) (see Figure 1). Different population properties affect *Ne* estimates, even when these converge to the same value under random sampling and certain ideal theoretical conditions, i.e., the population is isolated, of constant size, panmictic, has non-overlapping generations, and is in mutation-drift equilibrium (Ryman, Laikre, and Hössjer 2019; Waples 2024a). However, the lack of differentiation among different types of effective population size, often indistinguishably termed *Ne*, leads to confusion (e.g., Fady and Bozzano 2021; Hoban, Paz-Vinas, et al. 2021).

The definition and, consequently, the value of *Ne* will also depend on the spatial extent of the target population and the sampling scheme. Populations can be isolated or connected by gene flow, forming metapopulations. When populations are not isolated, the administrative boundaries often do not coincide with the biological population, and it can be difficult to determine their size. Sampling schemes can involve several interconnected populations, a single population regularly sampled (connected or not with others), or a portion of a larger continuous population (Box 2).

On the temporal scale, *Ne* can refer to historical *Ne* or contemporary *Ne*. Historical *Ne* is a geometric mean of *Ne* per generation over many generations. As such, it explains the current genetic make-up of a population and can be difficult to link with historical events, such as past management or anthropogenic environmental change. Contemporary *Ne* indicates the *Ne* of the current generation (or a few previous generations) and reflects the drift to be expected in the near future. This is of relevance for the ongoing monitoring of populations.

In real-world conservation management, where formerly large populations have often become fragmented into small subpopulations, almost none of the underlying assumptions (i.e., isolation, panmixia, constant population size, etc.) are met (Ryman, Laikre, and Hössjer 2019). Thus, estimates of *Ne* obtained under realistic circumstances may be more or less inaccurate. Moreover, for practical conservation, *Ne* estimates may not be what nature managers, policymakers, and researchers believe they represent at the spatial and temporal scales. At the spatial scale, depending on how sampling was conducted relative to the actual population range and the method used, one could be estimating the *Ne* of a part of the population (subpopulation) or of the entire metapopulation (see Figure 1A and Ryman, Laikre, and Hössjer 2023; Waples 2010). When estimating *Ne* for conservation, it is essential to first determine the spatial scale of the ancestral and/or current metapopulation, as overlooking the importance of the spatial scale leads to dubious results (Clarke et al. 2024). As management units, sometimes determined by political boundaries, rarely correspond completely to biological populations, *Ne* estimates may not accurately reflect the status of the assessed populations and species, potentially misleading conservation planning, decisions, and actions. At the temporal scale, and depending on the estimation method, *Ne* estimates might reflect the historical *Ne* across several, up to hundreds of generations (i.e., coalescent *Ne*), or, when using methods for calculating contemporary *Ne*, the *Ne* estimate obtained might represent the *Ne* in the last two to three generations (Nadachowska-Brzyska, Konczal, and Babik 2022).

**BOX 1** | What is *Ne*?

Effective population size (*Ne*) is defined as the size of an ideal population that experiences the same amount of a given genetic property as the real population. In its purest sense, it assumes that a population is isolated and is at mutation-drift equilibrium. There are many different types of *Ne* (inbreeding, variance, additive variance, eigenvalue, coalescence, metapopulation *Ne*), which are identical when the population is closed and at mutation-drift equilibrium. We refer to Ryman, Laikre, and Hössjer (2019) for a comprehensive overview of how these differ when these conditions are not met. In the context of conservation genetics, the genetic properties ideally used to define *Ne* are additive variance *Ne*, allele frequency variance *Ne*, or inbreeding *Ne*, but other properties such as coalescence or linkage disequilibrium *Ne* can also be used. See our glossary (Box 2) for the definitions of these properties.
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Schematic representation of a metapopulation evolving through time as an example, intended to highlight the possible ambiguity of *Ne* estimations. The X-axis indicates populations' spatial distribution (sites) along one spatial dimension, for simplicity. As we go up and forward in time (Y-axis), subpopulations disappear, and others are formed, but the metapopulation as a whole is maintained. (A) Sampling a single subpopulation (IIb) in the present (*t*0) and applying different *Ne* estimation methods may result in vastly different *Ne* estimates representing different aspects of the effective size, which are all commonly called "*Ne*". Disambiguation of these different meanings is essential in conservation. Depending on the approach, one can estimate from the same sample local contemporary *Nex* (e.g., using linkage disequilibrium, kinship, or a temporal method), contemporary *NeMeta* (when based on heterozygosity (*He*) decay across time; this requires two samples across time; pink), coalescent *Ne* (when based on its current *He*, and assuming mutation-drift equilibrium; chartreuse), *NeMeta* at different times in the past (blue, seagreen), but never past *Nex*. (B) Methods that estimate recent *Ne* trajectories (0–200 generations ago) will initially reflect local *Ne*, but will increasingly reflect metapopulation *Ne*, and samples taken in different subpopulations but with origins in the same metapopulation will eventually converge on the same *NeMeta*, which is the sum of the past *Nex* (here at *t*-2). The risk is that this is interpreted as a population decline, whereas it represents a confounding effect of spatial scale (Novo et al. 2023). (C) A landscape-level schematic depiction of the processes occurring in (A) and (B).</figcaption></figure>

Despite the pronounced scientific knowledge-to-application gap, genetic diversity concepts are increasingly being integrated into mainstream conservation, management, and biodiversity policy (Bertola et al. 2024; Hoban, Bruford, et al. 2021), where *Ne* remains a crucial summary statistic to evaluate the long-term survival capacity of natural populations (Hoban, Paz-Vinas, et al. 2021). Our article describes a workshop focused on refining methods for testing *Ne* using real-world datasets. Attendees collaborated to address challenges such as data availability, missing data, and testing barriers across various taxa. The key outcome was to align conservation theory with practical

challenges by estimating population sizes (*Nc*, *Ne*), focusing on species of conservation concern, and including diverse life histories and taxonomic groups to maximize conservation impacts.

## 1.1 | Complexity and Reality of *Ne* Estimates

Over the past decade, the accessibility of DNA-based and genetic monitoring has increased due to declining sequencing costs, wider availability of genomic data across many species, capacity-building endeavors, and investments from international (e.g.,
**Additive variance**—Total effect on a trait stemming from one or more gene loci. **Census size (Nc)**—Number of individuals in a population that are reproductively mature. **Coalescence**—In population genetics, a model of how alleles sampled from a population may have originated from a common ancestor. **Demographic bottleneck**—an event that drastically reduces the census size of a population. **Effective population size (Ne)**—Size of an ideal population that experiences the same rate of genetic drift as the observed population. **Genetic drift**—Random sampling of allelic variants that may lead to changes in the frequency of existing alleles from one generation to the next due to chance. **Isolation by distance (IBD)**—The decrease in the genetic similarity among individuals or populations as the geographic distance between them increases. **Life-history traits**—A set of coevolved traits that affect an individual's survival and reproductive potential. **Linkage disequilibrium (LD)**—The nonrandom association of alleles at different loci. **Metapopulation**—In population genetics, a group of spatially separated populations of the same species that are connected by gene flow. **Mutation-drift equilibrium**—The balance between new mutations introducing genetic diversity and random genetic drift removing (fixing) variants in a population. **Panmixia**—Random mating of individuals within a population that results in equal parental contributions to the next generation. Under Hardy-Weinberg assumptions, random mating occurs when allele frequencies accurately predict genotype frequencies.

EU), national, and private initiatives in conservation genomics (e.g., Earth Biogenome Project, EBP; International Barcode of Life, iBOL; or European Reference Genome Atlas, ERGA) (Theissinger et al. 2023). It has become practical and affordable to genotype individuals for tens of thousands of markers and estimate *Ne* with confidence intervals. However, numerous theoretical, technical, and methodological issues when estimating *Ne* need to be considered (Cox, Neyrinck, and Mergeay 2024; Gargiulo, Decroocq, et al. 2024; Mergeay et al. 2024; Pérez-Sorribes et al. 2024; all in this Special Issue *Effective population size in conservation and biodiversity monitoring*) depending on the specific genetic markers, analysis methods, and sampling schemes.

Conservation genetic studies aimed at estimating allele frequencies have typically sampled 30–50 individuals, allowing the calculation of useful summary statistics (Allendorf 2017). When multiple sites are sampled, *F*-statistics might be used to infer the genetic structure among subpopulations. *Ne* can be estimated from such samples using single-sample estimator approaches (Jones and Wang 2010; Waples and Do 2010). These methods, however, can be sensitive to violations of the model assumptions, potentially leading to seemingly precise *Ne* estimates (i.e., with narrow confidence intervals) that may, however, be highly biased (Nunney 2016; Ryman, Laikre, and Hössjer 2023; Waples 2010).

Existing methods for *Ne* estimation vary widely in what they intend to estimate (*Ne* trajectories vs. point estimates; historical vs. contemporary *Ne*), the genetic signal they capture, and the

data they require. Table 1 provides a brief description of the most commonly used methods, along with their main caveats. Most methods will assume neutrality and an isolated, random mating population with discrete generations. Population structure, in particular, biases *Ne* estimates obtained by many methods (Chikhi et al. 2010).

For a more complete description of available methods and detailed accounts of their associated temporal aspects, see Nadachowska-Brzyska, Konczal, and Babik (2022).

Here, we use the terminology of Ryman, Laikre, and Hössjer (2019) to designate specific aspects of *Ne*. We distinguish between the *Ne* of a subpopulation *x* in isolation (*Nex*) and the realized *Ne* of subpopulation *x* when the joint effects of drift and gene flow are taken into account (*NeRx*), metapopulation *Ne* (*NeMeta*), and coalescent *Ne* (*NeCo*), based on frequently used methods for *Ne* estimation. Although important in their own way, we do not explore the differences among variance *Ne*, inbreeding *Ne*, gene diversity *Ne*, the effective number of breeders *Nb*, or additive variance *Ne* (Ryman, Laikre, and Hössjer 2019; Waples 2005). Notably, the common case of isolation by distance (IBD) in continuous populations can be considered as the ‘neighbourhood’ effective size (=*Nex* of the neighbourhood) if the samples originate from a single neighbourhood (Neel et al. 2013; Cox, Neyrinck, and Mergeay 2024). *NeCo* can be seen as the long-term *Ne* that reflects the gene diversity under the assumption of mutation-drift equilibrium.

To illustrate the ambiguity of these different types of *Ne*, a simplified example is provided in Figure 1A, where we consider a population consisting of two isolated subpopulations (I and IIb), with each subpopulation experiencing random mating. Suppose that subpopulation IIb is sampled at time *t*₀; we could estimate *Nex* from linkage disequilibrium (LD) (Waples and Do 2008) or sibship frequency (Jones and Wang 2010). With two samples from subpopulation IIb across a certain time span, we could also calculate (the variance) *Nex* using a temporal method, or at least the harmonic mean across the sampled time span. Under some circumstances, we could estimate *NeRx* using a temporal method that calculates the net genetic drift over that time span considering gene flow (Ryman, Laikre, and Hössjer 2019, but see Nunney 2016). Although at migration-drift equilibrium, *NeRx* is similar across subpopulations and of the same magnitude as *NeMeta*, *Ne* values obtained from the same subpopulation can easily be an order of magnitude different, yet they are all called “the effective size” (see Figure 1A–C).

It is possible to estimate the change in *Ne* over time for a specific sample using methods that trace back the historical trajectory of *Ne* over recent time (10s to 100s of generations) or ancient times (10³ to 10⁵ generations ago) (Nadachowska-Brzyska, Konczal, and Babik 2022). When making inferences over time, we also need to acknowledge the limitations of changes in the spatial scale of reconstructions. While we sample an individual at time *t*, we also sample half of its parents' and a quarter of each of its grandparents' genes. We are also sampling across a wider geographic area as the spatial origins of ancestors of each individual widen the spatial scale due to gene flow across ancestral subpopulations. Therefore, when we sample a subpopulation and get an estimate of its *Ne* across time, it becomes increasingly difficult
<table><caption>TABLE 1 | Some methods commonly used to estimate effective population size Ne.</caption><thead><tr><th>Estimation</th><th>Time scale</th><th>Type of method</th><th>Methods</th><th>Data required</th><th>Caveats</th><th>References</th></tr></thead><tbody><tr><td>Effective population size (Ne) trajectories</td><td>Historical</td><td>Coalescent-based</td><td>Skyline plots</td><td>DNA sequences</td><td>Ne reflects historical Ne of the total population. Sensitive to past population structure, which is often unknown.</td><td>Drummond et al. 2005; Ho and Shapiro 2011</td></tr><tr><td></td><td>Historical</td><td>Sequentially Markovian Coalescent (SMC)</td><td>PSMC, MSMC</td><td>Single (PSMC) or multiple (MSMC) genomes sequenced to high coverage</td><td>Population structure can create spurious signals of population size change. Sampling regime can be important.</td><td>Li and Durbin 2011; Schiffels and Durbin 2014; Schiffels and Wang 2020</td></tr><tr><td></td><td>Historical</td><td>Site frequency spectrum (SFS)</td><td>Stairway plot</td><td>Folded/unfolded SFS from hundreds of samples</td><td>May confound changes in gene flow and demographic changes.</td><td>Liu and Fu 2015, 2020</td></tr><tr><td></td><td>Recent</td><td>Linkage Disequilibrium (LD)</td><td>GONE, LinkNe, SNeP</td><td>Phased or unphased genome-wide genotype data</td><td>Will initially estimate local Ne and increasingly estimate metapopulation Ne, unless the population has always been closed.</td><td>Barbato et al. 2015; Hollenbeck, Portnoy, and Gold 2016; Santiago et al. 2020, 2024</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 1 | (Continued)</caption><thead><tr><th>Estimation</th><th>Time scale</th><th>Type of method</th><th>Methods</th><th>Data required</th><th>Caveats</th><th>References</th></tr></thead><tbody><tr><td rowspan="2">Effective population size (Ne)</td><td>Long-term</td><td>Site frequency spectrum (SFS)</td><td>dadi, fastsimcoal2</td><td>Folded/unfolded SFS from tens to hundreds of samples</td><td>May confound changes in gene flow and demographic changes.</td><td>Gutenkunst et al. 2009; Excoffier et al. 2013; Excoffier et al. 2021</td></tr><tr><td>Contemporary</td><td>Linkage Disequilibrium (LD)</td><td>LDNe, NeEstimator V2</td><td>Genotype data from a single time-point sample</td><td>Assumes independent markers, discrete generations, closed populations, and random mating. Very sensitive to spatial genetic structure.</td><td>Do et al. 2014; R. S. Waples and Do 2008</td></tr><tr><td>Contemporary</td><td>Heterozygosity Excess</td><td>NeEstimator V2</td><td>Genotype data from a single time-point sample</td><td></td><td>Assumes the only source of Hardy-Weinberg deviations is local inbreeding. Rarely accurate when Ne is not very small. To be avoided.</td><td>Balloux 2004; Do et al. 2014</td></tr><tr><td>Contemporary</td><td>Temporal</td><td>NeEstimator V2; NB (R package), decay of heterozygosity over time</td><td>Genotype data from two or more samples separated by a known number of generations</td><td></td><td>Avoids confusing spatial and temporal genetic structure when sampling in metapopulations. May confound local and metapopulation Ne when migration rate is high.</td><td>Do et al. 2014; Hui, Brenas, and Burt 2021; Hui and Burt 2015; R. S. Waples 1989</td></tr><tr><td>Contemporary</td><td>Sibship assignment</td><td>COLONY 2</td><td>Frequencies of full- and half-sib dyads inferred from genotype data of single cohorts</td><td></td><td>Assumes discrete generations. Not sensitive to spatial genetic structure, but sensitive to the assumption of random sampling. Provides Nb of the sampled area.</td><td>Jones and Wang 2010; Wang 2009</td></tr><tr><td>Contemporary</td><td>Approximate Bayesian Computation (ABC)</td><td>OneSAMP</td><td>Summary statistics obtained from genotype data</td><td></td><td>Computationally intensive when the number of loci is large. Estimates Nex.</td><td>Hong et al. 2024; Tallmon et al. 2008</td></tr></tbody></table>
to interpret the *Ne* value if the population was not isolated across
its entire history (Pérez-Sorribes et al. 2024).

To illustrate that there is not a single measure of *Ne* that provides
the definitive effective size of a population, we can consider the
human population history. From a modern sample of human ge-
nomes, the genome-wide nucleotide diversity reflects that our
early ancestors underwent a prolonged bottleneck of about 1000
(effective) individuals (Hu et al. 2023). The long-term coalescent
*NeCo*, which we can consider as the harmonic mean of the *Ne* of
each generation, is still very low in the human population (tens
of thousands) because of that bottleneck. However, if we were to
calculate the contemporary *NeMeta* of the global human popu-
lation based on the variance in reproductive success, we would
find *NeMeta* to be around 3.8 billion. Both estimates of *Ne* are
correct and useful, but they represent different concepts.

To visualize the challenges in estimating Ne in metapopu-
lations, we can consider dynamic metapopulations (spatial
variation) across time (temporal variation) to understand that
some subpopulations could disappear as time progresses.
Figure 1A,C provide schematic representations illustrating the
dynamics of metapopulations and the challenges in estimating
Ne. Depending on the methodology and sampling design, from
a sample taken at t0, one could estimate the contemporary Ne
of the metapopulation (NeMeta) or the contemporary local Ne
of a single subpopulation (Nex). But sampling population could
also be used to estimate the ancestral (t-2) NeMeta. However,
there are also countless ways model assumptions can be violated
that can under- or overestimate the particular Ne of interest,
especially when Ne's different spatial and temporal types are
confused.

When a *Ne* exceeds 500–1000 individuals, populations can gen-
erally maintain sufficient adaptive genetic variation (Frankham,
Bradshaw, and Brook 2014). The 2022 Kunming-Montreal
Global Biodiversity Framework (GBF) has included specific
goals and targets on safeguarding genetic diversity in the wild
(Goals A and C; Targets 4 and 13) and specifically recognized
that an effective size larger than 500 is required to maintain
evolutionary potential (headline indicator A.4; CBD 2022). This
criterion will likely seep through other biodiversity policy and
management instruments (e.g., biodiversity strategies and ac-
tion plans; Hoban, Hvilsom, et al. 2024), hence being of further
significance in future conservation planning. This raises the
question of both method and *Ne* type: what *Ne* should we focus
on for present-day conservation questions, and how can we best
estimate it?

## 1.2 | Operationalizing *Ne* Estimation for End Users: Bridging Science and Conservation Practice for Better Management of Genetic Diversity Within Species

Reflecting the extensive body of work dedicated to developing
methods and tools for estimating *Ne* using simulations and em-
pirical data (e.g., Frankham, Bradshaw, and Brook 2014; Gilbert
and Whitlock 2015; Marandel et al. 2020; Nadachowska-Brzyska,
Konczal, and Babik 2022; Neel et al. 2013; Nunney 1999, 2016;
Palstra and Ruzzante 2011; Ryman, Laikre, and Hössjer 2019;

Tallmon, Luikart, and Beaumont 2004; Waples and Do 2010),
to name a few, the members of Working Group 2 of the EU
Cost Action G-BiKE; (https://g-bikegenetics.eu/en) organized
a workshop to focus on the evaluation and implementation of
*Ne* in biodiversity monitoring for better species management. A
genetic diversity indicator that can use census population size
(*Nc*) as a proxy for *Ne* (Hoban et al. 2020; Laikre et al. 2020)
was recently adopted by CBD parties as a headline indicator
A.4 for the monitoring framework of the Kunming-Montreal
GBF. Hoban et al. (2020) defined indicator 1 as “The number of
populations within a species with an effective population size
(*Ne*) above 500 compared to the number below 500” (headline
indicator A.4 of the GBF, CBD 2022). When no direct estimate
of *Ne* is available, typically due to a lack of genetic data, it is
suggested to use *Nc* as a proxy, using as a rule of thumb an av-
eraged *Ne/Nc* ratio of 0.10. Although this may be a conservative
estimate (Clarke et al. 2024), the *Ne* > 500 threshold has been
criticized for being overly liberal in some cases, rather requiring
*Ne* > 1000 (Frankham, Bradshaw, and Brook 2014), especially in
species with a low fecundity and therefore a high *Ne/Nc* ratio
(Pérez-Pereira et al. 2022). The proxy-based methodology, using
largely *Nc* or proxies of *Nc* to assess the CBD indicators, was
developed elsewhere (Hoban, da Silva, et al. 2023; Mastretta-
Yanes et al. 2024; Hoban, da Silva, et al. 2024; Hoban, Hvilsom,
et al. 2024).

In our hybrid meeting in Brašov, 26 experts from diverse
European and international origins convened to discuss the
pragmatic challenges of conservation implementing theoretical
frameworks (Figure 2), and this was followed by numerous vir-
tual meetings that included additional experts. Presentations at
the workshop from attendees on the projects they were involved
in included overviews of possible issues, available data, missing
data, prospects, and potential barriers and solutions to *Ne* indi-
cator estimation in their chosen taxa: Iberian lynx (*Lynx pardi-
nus*), Wolverine (*Gulo gulo*), Grey wolf (*Canis lupus*), Alpine ibex
(*Capra ibex*), European beech (*Fagus sylvatica*), maritime pine
(*Pinus pinaster*), otters (*Lutra lutra*), and Wels catfish (*Silurus
glanis*). We note that the *Ne* being referred to here is the short
term/recent *Ne*, which determines the maintenance of genetic
diversity, not the long-term or coalescent *Ne*.

The central goal of the workshop was to conduct the ground-
work to standardize protocols for the application of *Ne* estima-
tors, with a focus on (1) determining the most robust methods
across various scenarios and (2) elucidating the process of deriv-
ing a consensus *Ne* estimate for species of conservation signifi-
cance, particularly as it was under the umbrella of EU Cost in
the European context. The workshop evaluated widely distrib-
uted animal and plant species for which both census population
size estimates and independent calibration of *Ne* estimates were
available, laying the foundation for our analyses and evaluation.
During the workshop, several challenges emerged in under-
standing *Ne* estimates. These included how to delineate popula-
tions across the continent and nations and establish the spatial
scale at which *Ne* is to be estimated, how to best select methods
based on the interplay between sampling design constraints,
life-history traits (social structure, dispersal capacity, overlap-
ping generations, etc.), and methodological assumptions, and
how we could gain better knowledge on these examples through
forward simulations.
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Participants of the international workshop, funded by the COST Action 18134, Genomic Biodiversity Knowledge for Resilient Ecosystems (G-BiKE), August 2022 in Braşov, Romania. Participants who joined virtually are not shown.</figcaption></figure>

Addressing the challenge of defining populations involves balancing management considerations with biological and environmental factors that account for admixture, demographic and spatial expansion, but also ongoing fragmentation and range shifts of natural populations. The challenge of selecting the best tools and estimates involves consideration of temporal design (e.g., single vs. multiple events), sampling designs, and selecting appropriate approaches to estimate specific types of *Ne* while being aware of the assumptions and biases when assumptions are not met and of the type of *Ne* that is estimated with each approach.

For the effective size larger than the *Ne*500 criterion, the true *Ne* of interest is the additive variance *Ne*, which defines the rate of loss of additive genetic variance, or evolutionary potential (Ryman, Laikre, and Hössjer 2019). This is mostly a theoretical concept, but it is best approximated by the contemporary meta-population (*NeMeta*) or the realized inbreeding effective size (*NeRx*) of subpopulations within a metapopulation (Ryman, Laikre, and Hössjer 2019). In isolated populations, *Nex* and *NeRx* are evidently identical. In theory, some temporal methods (estimating *Ne* from the net change in gene diversity across a certain time interval) could estimate *NeRx*. In practice, this often depends on additional assumptions, and estimates can be hugely biased (Nunney 2016). Also, temporal methods that are based on measuring the variance in allele frequency across time will tend to yield a value close to *Nex* (Ryman, Laikre, and Hössjer 2019). In practice, we are often limited to methods that estimate *Nex*, whereas the metric of interest for the *Ne*500 threshold value is rather *NeMeta*. As long as the absolute migration rate is low, the influence of gene flow on contemporary *Nex* is small. To estimate *Nex*, two methods are frequently used, and their sensitivities to

assumptions and their performance (precision and accuracy in relation to sample size and the number of markers) have been tested extensively in silico (Do et al. 2014; Neel et al. 2013; Wang 2016; Waples 2021; Waples, Antao, and Luikart 2014). The sibship frequency method (Jones and Wang 2010) is not dependent on random mating and is rather insensitive to spatial genetic structure (Wang 2016). It requires random sampling and a good representation across the entire population distribution. However, when the true *Ne* is very large, it becomes imprecise unless the sample size is > 10% of the true *Ne*. The accuracy of the estimates obtained with the linkage-disequilibrium method (Waples and Do 2008) is strongly dependent on spatial genetic structure and on the sampling strategy adopted; for instance, the method can provide unbiased estimates when local sampling is carried out in a subpopulation model unless the migration rate is high (Cox, Neyrinck, and Mergeay 2024; Neel et al. 2013). Mergeay et al. (2024) provide examples of this sensitivity compared to the sibship frequency method.

As long as metapopulations consist of well-connected subpopulations (with one migrant per generation as a bare minimum for connectivity), we can, in theory, approximate *NeMeta* by taking the sum of the *Nex* of all subpopulations (Cox, Neyrinck, and Mergeay 2024; Mergeay et al. 2024; Ryman, Laikre, and Hössjer 2019). Note that this approach is intended for an island model with symmetrical gene flow. In other cases (e.g., asymmetrical gene flow, very uneven subpopulation sizes, linear metapopulations, populations with extensive two-dimensional isolation by distance, frequent extinction-recolonization dynamics), more targeted models may be needed to estimate *NeMeta* (Maruyama and Kimura 1980; Whitlock and Barton 1997; Nunney 1999). When gene flow falls below one migrant per
generation, the correlation of allele frequencies among sub-
populations becomes very weak, gene diversity within subpop-
ulations rapidly decreases relative to the metapopulation, and
inbreeding within subpopulations becomes increasingly im-
portant (Wright 1951). Consequently, it is of little use to estimate
NeMeta in a conservation context when gene flow drops below
one migrant per generation.

Addressing taxon-specific issues involves identifying mating
systems, spatial structuring of populations, geographic distri-
bution within countries, and availability of genetic data. We
also tried to understand the relative influences of drift, inbreed-
ing, and selection, which constitutes another major challenge.
As such, Ne estimation tools used in conservation need to be
forward compatible: we must ensure that they are easily inte-
grated, among other things, with simulation approaches, species
distribution modeling and climate change models (Haller and
Messer 2019).

Tools such as GONE (Santiago et al. 2020) seem particularly relevant to reconstructing *Ne* changes that happened since the large-scale influence of humans on biomes, ecosystems, and populations, as they provide a view on *Ne* in the recent past before we started monitoring biodiversity declines but after we started having a clear impact. Such tools complement methods that estimate the coalescent *Ne* or reconstruct long-term *Ne* trajectories (Excoffier et al. 2013; Gutenkunst et al. 2009; Li and Durbin 2011; Liu and Fu 2015; Schiffels and Durbin 2014) and provide helpful insight into pre-anthropogenic reference values of *Ne* or target values for ecosystem and population restoration in the long run. Even though GONE has been extensively tested

in silico (Novo et al. 2023; Santiago et al. 2020) and even with
experimental populations (Novo et al. 2023), empirical testing
with real and often messy data, using legacy datasets to explore
the benefits and limitations of the method, remains rare and
needed (Gargiulo, Decroocq, et al. 2024).

Two complementary approaches were proposed during the workshop (Figure 3): one focusing on constructing hypothetical datasets using simulations to test a range of alternative scenarios on *Ne* estimation where biases might exist (Figure 3, scenario A). The second approach was concerned with the manipulation of existing empirical datasets to mimic some of the likely biases that may occur and assess the effects of biases on the estimates (Figure 3, scenario B). The aim of these approaches was also to evaluate the performance of different software and how spatial and/or temporal scales affect *Ne* estimates.

In terms of parameter manipulation discussed during the work-
shop, our list is not exhaustive and could be modified based on
the characteristics of the dataset. It is augmented by Hoban,
Bertorelle, and Gaggiotti (2012), where additional information
can also be found, including details on simulation applications,
evaluation of simulator capabilities, and guidance for their use.
Moreover, Hoban (2014) analyzed several case studies illustrat-
ing the use of simulations, elucidating their specific advantages
and necessity, and exploring alternative or complementary (non-
simulation) approaches.

The suggestions raised during the workshop for parameter
manipulation included *sample characterization* such as (i)
*sample sizes* that might be simply manipulated, for instance,

<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | A schematic overview to evaluate various methods used to estimate the *Ne* by incorporating different techniques and underlying assumptions: The two approaches involved in parameter manipulation during the workshop were constructing hypothetical datasets using simulations to test a range of alternative scenarios on *Ne* estimation (scenario A) and manipulation of existing empirical datasets to mimic some of the likely biases that may occur (scenario B). Genetic markers such as short tandem repeats (STRs or microsatellites) or single nucleotide polymorphisms (SNPs) are used independently or in combination. *Ne* estimation involves different methods with divergent assumptions, including LD-based approaches and kinship analysis. Similar to the first scenario (A), these methods may use STR, SNP, or a blend of both. Finally, *Ne* estimation strategies may involve consistent methodologies with variations in including or excluding spatial and/or temporal samples. These variations allow for an evaluation of how sample selection influences the accuracy of *Ne* estimation. These analyses may use STR, SNP, or a combination of genetic markers.</figcaption></figure>
by subsampling a reduced number of individuals: the size
of the sample should be realistic as a function of *Nc*, and (ii)
*sample distribution*, for example, different relevant sampling
designs could be considered, depending on the population
configuration and structure, including within meta, continu-
ous, and isolated populations. An important aspect raised was
how samples should be distributed based on what is known
about dispersal and neighborhood size for the species (e.g.,
Cox, Neyrinck, and Mergeay 2024). Additional specific ele-
ments discussed were the *single temporal* sample estimates
that can be compared with *multisample temporal* estimates
where temporal sampling is available and, for the latter, differ-
ent *times between sampling events*, for example, as a function
of generation time, can be considered to compare *Ne* estimates
across different timespans. Parameter manipulation requires
consideration of *relatedness*; thus, keeping all relatives in the
data versus pruning relatives can be done for estimations
based on LD to ensure families do not dominate the dataset
and *migration* (*Nm*, gene flow) is testing > 1 or < 1 (by moving
genotypes from one population to another).

When DNA or genetic data is unavailable, as in many countries
and regions (Pearman et al. 2024), proxy-based indicator values
of *Ne* are extremely important to evaluate and track multiple
species affordably (Mastretta-Yanes et al. 2024). Such proxy-
based indicators might, for instance, identify populations in
urgent need of genetic monitoring and management (e.g., small
and/or isolated populations), for which genetic data can be pro-
duced for a full assessment of genetic composition and change,
such as small and/or isolated populations (Hoban, Paz-Vinas,
et al. 2024). A small population (in terms of census size) might
be indicative of low effective population sizes, hence compro-
mising the maintenance of genetic diversity over time by these
populations. Once such populations are identified using afford-
able proxy-based indicators, full genetic assessments needing
DNA data production could preferentially target such popula-
tions to evaluate migration rates, inbreeding, the occurrence
of genetic bottlenecks, etc. In this Special Issue of Evolutionary
Applications, Mergeay et al. (2024) found a good correspondence
between direct *Ne* estimates and *Nc* values for wolf populations
in Europe. It is important, however, to study more in-depth the
relationship between *Ne* and proxies across species to identify
situations where proxies serve their purpose but also where we
need actual genetic data (Hoban, da Silva, et al. 2024). This ap-
proach can help us focus on species and populations where ge-
netic and genomic resources must be developed.

## 1.3 | Legacy Datasets to Test Method Suitability, Sampling Designs and Model Assumptions

We searched for so-called legacy datasets: public or own geno-
typic data archives with sufficient metadata, large sample sizes,
and, where possible, genotypic information from different ge-
netic marker types. Especially large and spatially explicit sam-
pling designs would allow us to test the importance of spatial
sampling design, sample size, etc., which pertains to model as-
sumptions of particular *Ne* estimation methods. A legacy dataset
is a large, well-annotated archived dataset that allows the calcu-
lation of specific properties of real populations, which can also
be verified with independent data. In the context of population

genetics, and especially of *Ne* estimations, these would be large
genotypic or genomic datasets of real species and populations
with well-known properties associated with metadata of census
size, age of individuals, possibly individual-based spatial and
ecological information or pedigree data, and data on reproduc-
tion and other life history traits (Pierson et al. 2018). Such data-
sets can be used to test the sensitivity of *Ne* estimation methods
to underlying model assumptions, detect biases, and, by com-
parison, find what methodology and spatial sampling design
best fit the known or expected *Ne*. It comes closest to simulated
data, with the advantage of no dependence on the simulation
assumptions. Analyses of such datasets can support the devel-
opment of guidelines for sampling and analyzing species and
populations with similar properties. A disadvantage of legacy
datasets is that, unlike simulated data, the entire population is
unlikely to be fully sampled, whereas simulated data can pro-
vide a complete sample.

In practice, only some legacy datasets fulfill all relevant crite-
ria to carry out sensitivity analyses and test model assumptions.
However, there are hundreds, if not thousands, of datasets that
actually meet many or most of these criteria (Leigh et al. 2021),
and which allow us to test aspects of model assumptions in *Ne*
estimation. To develop standards for monitoring *Ne*, we sought
to explore using legacy datasets to test the sensitivity of molecu-
lar *Ne* estimation methods to underlying model assumptions and
optimize sampling designs for future projects. This Special Issue
(*Effective population size in conservation and biodiversity moni-
toring*) presents papers that explore and analyze legacy datasets
to better understand the sensitivity of *Ne* estimation meth-
ods to model assumptions (Cox, Neyrinck, and Mergeay 2024;
Gargiulo, Decroocq, et al. 2024; Mergeay et al. 2024; Pérez-
Sorribes et al. 2024).

## 2 | Working Group Discussions and Output

### 2.1 | Challenges to *Ne* Estimation Differ Among Higher Taxa

Specific working groups discussed factors that affect *Ne* estima-
tions in species of animals (reptiles, amphibians, fish, and mam-
mals) and plants, and the available datasets were evaluated. The
discussions also helped identify the common factors influencing
*Ne* estimates across taxa. The potential influential factors on the
estimation of *Ne* are reflected and summarized in a multi-layer
schematic that represents aspects of the available data, species
life histories, and population characteristics (Figure 4). Finally,
the discussions helped working groups to identify datasets that
have served as the basis for research that is reported in this
Special Issue and elsewhere.

### 2.2 | Population Structure: Continuous Distribution, Isolation-By-Distance, and Recent Expansions

Accurate identification of populations and population structure
may impact *Ne* estimation and arise in multiple higher taxa.
Recent population recovery has resulted in the re-establishment
of connectivity between previously isolated, genetically
<figure><img src="image_7.png" /><figcaption>**FIGURE 4** | A general representation of the complexity of estimating effective population size (Ne). The figure presented is a conceptual model illustrating the complex framework used to assess genetic indicators in the context of conservation genetics. It details the interactions between the different levels of analysis and the factors that influence the assessment of genetic diversity in various species. At the highest level, 'spatial genetic clustering of populations' is highlighted, distinguishing between isolated populations, metapopulations, and continuous populations. This classification is key to understanding gene flow and genetic structure, which are fundamental to conservation strategies. At the middle level, the "life history level" includes aspects such as demographic history, generation time, reproductive strategies, differences in reproductive success, and hybridization. These elements influence the effective population size and the Ne/Nc ratio, which are essential parameters in the study of population genetics; this life-history data primarily provides insights into local Ne. Genetic markers and time-series data are essential for comprehending the structure and dynamics of populations at a spatial scale, yet they have the potential to introduce biases that could diminish the effectiveness of the analysis. The framework underscores the necessity of synthesizing data from genetic markers with an in-depth understanding of species' life histories. Additionally, it highlights the imperative to secure the availability of extensive genetic data, which is critical to informing effective conservation strategies.</figcaption></figure>

differentiated units; what appears to be a spatially contiguous population may have significant spatial genetic structuring and violate the assumption of a single panmictic population. Some carnivores and larger mammals in Europe have experienced recent population recoveries from small isolated populations yet may still exhibit substantial spatial genetic structuring (Thomas et al. 2022). Challenges emerged from complexity in defining populations for accurate Ne estimation, especially for mammal species exhibiting hybridization (Adavoudi and Pilot 2021) and population structure and continuous species distributions (Adavoudi and Pilot 2021; Iacolina et al. 2021; Randi 2007). Identifying genetic boundaries among populations is important for determining appropriate sampling strategies to avoid pooling populations. In contrast, widely distributed species of plants may experience isolation by distance (IBD) and absence of panmixia, which may be difficult to compensate for with any sampling strategy. Similarly, Ne estimation for fish and other riverine species populations is likely complicated by spatial solid patterns of genetic variation, isolation by distance, and potentially metapopulation dynamics. The influence of population traits on Ne, particularly differences in demographic stochasticity and reproductive variance, requires further investigation (May et al. 2023; Wright, Schofield, and Mathews 2021).

Different characteristics affecting Ne estimation were identified for fish, depending on whether the species is a cartilaginous or bony fish species and the species' main habitat (marine, rivers, lakes, and ponds). Difficulties in Ne estimation arising from the typically large Nc and Ne and associated stochasticity observed for marine fishes have been discussed previously (e.g., Marandel et al. 2019; Montes et al. 2016). However, cartilaginous fish like rays and sharks, while mainly marine, seem not to display Ne values as high as those of bony marine fishes (Hoban et al. 2020), and the potential effects of the low reproductive output and high longevity of cartilaginous fishes compared to bony marine fishes were discussed. Ne variation within and across taxonomic groups has recently been addressed (Hoban, da Silva, et al. 2024). A talk during the workshop further addressed technical limitations related to estimating Ne and Nc in a large-bodied fish species (*Silurus glanis*) that inhabits large river systems and has undergone a demographic expansion (Paz-Vinas et al. 2024). Finally, the determination of population extent is further complicated by hybridization between species. For instance, for amphibians, the groups identified the potential importance of hybridization following secondary contact (e.g., for salamanders, Bruni et al. 2023; Patton et al. 2020). Hybridization could potentially affect Ne estimation in many plant species.
In contrast, the group discussions recognized population char-
acteristics that may facilitate *Ne* estimation when population
definition or extent is relatively easy to define. The strong spa-
tial structure, genetic isolation, and clear population boundar-
ies of many reptile species and pond-breeding amphibians were
highlighted as factors that might facilitate *Ne* estimation, as
these characteristics of populations may approximate the many
assumptions made by most *Ne* estimation methods. In fish spe-
cies inhabiting isolated lakes and ponds, conformity to some
assumptions of the Wright-Fisher model (i.e., no immigration,
constant population size) might be more frequently met than in
complex riverscapes, limiting potential biases in *Ne* estimation
(Neel et al. 2013; Waples 2024b).

## 2.3 | Effects of Life History Variation on *Ne* Estimation

Compared to potential impacts that affect species across sev-
eral taxa, some population characteristics appear fairly re-
stricted to one taxon. For example, the effects of the existence
of plant seed banks on *Ne* estimation were identified as requir-
ing study. Seed banks result in overlapping generations even
in annual species, and models need development to examine
the potential effects on *Ne* estimation. Seed banks can com-
pensate for fluctuations in population sizes typical of some
plant species by increasing *Ne* and delaying the loss of genetic
diversity (Nunney 2002). Also, smolts of Pacific salmon may
also reproduce at varying ages (Waples 2006). Additional
limitations on *Ne* estimation are associated with life-history
traits, including mating system and reproductive strategies,
which influence how *Ne* varies for *Nc* and the magnitude of
the *Ne/Nc* ratio (Gargiulo, Budde, and Heuertz, 2024). Some
subsequent research has attempted to use existing literature
and data to predict the direction of the bias associated with
these limitations (e.g., Gargiulo et al. 2023; Neel et al. 2013;
Waples 2016; Waples et al. 2013). Such predictions are chal-
lenging, as the combination of different life-history traits
would affect *Ne* and the *Ne/Nc* ratios possibly in contrasting
directions and magnitudes.

The working groups concurred that overlapping generations,
demographic fluctuations, and additional factors in long-lived
species certainly complicate *Ne* estimation. For example, in a
study focusing on mammals, Pérez-Sorribes et al. (2024) lever-
aged two open-access genomic datasets from wolf populations
in Minnesota and Scandinavia. These data sets represented
populations with differing histories and provided known cen-
sus population size *Nc* over the past 40–120 years. High-density
SNP genotypes or whole genome sequencing (WGS) data were
available for testing how well GONE (Santiago et al. 2020) re-
constructed real demographic changes over time, given their
complex histories and certain known violations of underlying
assumptions. The authors found good concordance between
estimated *Ne* and trends in census size data, but the recon-
struction of *Ne* highlighted the difficulty of interpreting results
in spatially structured populations that had undergone demo-
graphic fluctuations.

Additional life history characteristics were identified as factors
potentially influencing *Ne* estimation broadly across taxa. The

propensity of some reptile lineages to have morphologically
cryptic species, as in some lizards (Pinho et al. 2022), and/
or overlapping generations in long-lived species was noted.
Analytical challenges are presented by the huge genome sizes
of some amphibians (Liedtke et al. 2018) and the strong vari-
ation in *Ne/Nc* ratio reported for some amphibians (Hoban
et al. 2020). Polyploidy and genome size could also present ana-
lytical challenges in plants. Integrating *Ne* estimates with other
data types such as census population size, demography and pop-
ulation structure, and ecological data is extremely important as
it allows the calibration of molecular *Ne* estimation methods and
tests the sensitivity to violations of model assumptions (Mergeay
et al. 2024).

## 2.4 | Aspects of Datasets

The group discussed factors linked to sampling strategies
known to bias *Ne* estimation, opportunities generated by new *Ne*
software for use with plant data, and that very few plant species
have readily available genomic resources, such as reference ge-
nomes at the chromosome level. Census sizes (*Nc*) are also mostly
unavailable and difficult to determine in plants. This makes
some recently developed software, such as GONE (Santiago
et al. 2020), unsuitable. In contrast, resources are extensive
for some charismatic mammal species (Mergeay et al. 2024;
Pérez-Sorribes et al. 2024). Similarly, Gargiulo, Decroocq, et al.
(2024) explored the limitations of plant genomic datasets when
estimating recent historical *Ne* using GONE. In particular, ge-
nomic datasets from non-model species are usually derived from
reduced-representation methods, and linkage maps and refer-
ence genomes at the chromosome level are seldom available,
all factors that present constraints to using GONE. The authors
extracted genomic data from four plant species and showed how
the accuracy and precision of *Ne* estimates changed with the ex-
tent of missing data, the number of SNPs and individuals sam-
pled, and the lack of information about the location of SNPs on
chromosomes. The latter factor, in particular, had not been pre-
viously explored with empirical data and produced a significant
upward bias in the *Ne* estimation. The authors also evaluated
the influence of population structure and gene pool admixture
for one of the datasets, pointing out that this is influenced by the
demographic history of each gene pool (e.g., recent bottlenecks).
Furthermore, they evaluated the consistency of the *Ne* estimates
obtained with GONE for the most recent generations and the
contemporary *Ne* estimates (Santiago et al. 2024) and based on
NeEstimator (Do et al. 2014). They showed a clear agreement
between the estimates obtained with the latter two programs.
Finally, they proposed a set of recommendations when estimat-
ing *Ne* in plants using GONE.

## 2.5 | Immediate Response to Identified Issues

Through the momentum of this workshop, G-BIKE COST
Action funded targeted Short Term Scientific Missions (STSMs)
of approximately a month to target specific questions in depth,
thereby testing the robustness of molecular methods and help-
ing produce guidelines for particular taxa and situations. The
STSM on plants explored some of these biological and techni-
cal limitations. Results from these scientific missions have
been reported (Cox, Neyrinck, and Mergeay 2024; Gargiulo, Decroocq, et al. 2024; and Pérez-Sorribes et al. 2024).

## 2.6 | Outstanding Questions to Be Addressed

Building on decades of theoretical and empirical work on Ne, we aimed to prepare the groundwork for testing the sensitivity of assumptions and gaining a deeper understanding of the reliability of Ne estimates for the latest Global Biodiversity Framework. We have presented and discussed the challenges to arrive at the optimal decision. It is crucial that scientists meticulously review all existing methods and tools for Ne, evaluate their current application in various species groups, and ascertain their appropriateness for future management. This comprehensive evaluation ensures that our decision-making process is grounded in the most reliable evidence available, enabling us to make informed choices.

We lack a comprehensive synthesis of practical field applications and technological advancements in conservation genetics and genomics that would serve as the foundation for developing operational guidelines tailored to end users in conservation. Clearly, there is a need for standardized protocols, tools, and resources that can be readily implemented by practitioners. Further, ongoing collaboration between researchers, conservation practitioners, policymakers, and stakeholders is essential to ensure that guidelines are relevant, practical, and effectively disseminated. Fortunately, several studies on this challenge have been published in the past (Heuertz et al. 2023; Hoban, Bruford, et al. 2021; Holderegger et al. 2019; Kershaw et al. 2022; Lundmark et al. 2017; Taft et al. 2020). Lastly, there is a need for capacity building and training for practitioners to use the proposed tools and for scientists to be trained in the practicalities of management and constraints of real-world situations. Fostering collaboration and knowledge exchange between practitioners and scientists could enable the advancement of refined field protocols for sampling and protocols for data analysis. Therefore, also research investment is crucial for advancing sampling protocol optimisation, and refining analytical techniques. It further includes advancements in DNA sequencing technologies, bioinformatics software, and field sampling equipment. Rigorous validation and testing of protocols in real-world conservation scenarios are necessary to ensure their effectiveness and reliability. It involves conducting pilot studies and field trials to evaluate the performance of new protocols across different species and environmental conditions. Such investments and collaboration will enhance training programs and educational resources to equip conservation practitioners and the next generation of scientists with the knowledge and skills. Integrating refined sampling and analysis protocols into broader conservation planning frameworks ensures that genetic data are effectively utilized in decision-making processes.

## 3 | Conclusions

Despite making foundational progress in exploring current knowledge on methods of Ne estimation, some open questions need further investigation. A primary concern is the lack of a comprehensive synthesis of field applications and technological

advancements, which would be useful as a foundation for guidelines explicitly tailored to end-users interested in Ne estimation and application of Essential Biodiversity Variables (EBVs) for genetic composition (Hoban et al. 2022). Furthermore, there is an urgent need to standardize protocols, tools, and resources by using genomics data that practitioners can readily implement, thereby ensuring consistency, reliability, and comparability in their conservation strategies and population monitoring. Strengthening collaboration among various stakeholders to share expertise, data, and resources is necessary for resolving assumptions and developing strategies and solutions for data analyses. This can consolidate effective population size Ne as a headline indicator for the Global Biodiversity Framework and additionally contribute to practitioners' acceptance of Ne as an EBV (Hoban et al. 2022).

Investment in genetic and genomic approaches as well as tools is pivotal to enhancing the efficiency and accuracy of sampling and analysis. Rigorous validation and testing of new protocols in real-world scenarios are necessary to ascertain their effectiveness and reliability across different species and environmental conditions. The provision of training programs and educational resources is essential to equip the new generation of conservation practitioners and scientists with the necessary skills and knowledge. Nevertheless, we have laid some groundwork and advocated for increased efforts to develop practical and operational guidelines for end-users in the field of conservation genetics and genomics. Further efforts, projects, and initiatives are necessary to better address the current application and reliability of Ne estimates in various species groups and ascertain their appropriateness to provide practical and operational guidelines for conservation genetics and genomics end-users.

### Affiliations

¹Department of Wildlife, National Institute for Research and Development in Forestry ‘Marin Dracea’, Brașov, Romania | ²Department of Silviculture, Faculty of Silviculture and Forest Engineering, Transilvania University of Brașov, Brașov, Romania | ³Research Institute for Nature and Forest, Geraardsbergen, Belgium | ⁴Applied Population Genetics and Conservation Genomics, Department of Biology, KU Leuven, Leuven, Belgium | ⁵Department of Forest Production and Products, University of Ibadan, Ibadan, Nigeria | ⁶Department of Biology, Istiklal Yerleskesi, Budur Mehmet Akif Ersoy University, Science and Art Faculty, Burdur, Türkiye | ⁷Dokuz Eylül University, Buca Education Faculty, Mathematics and Science Education, Biology Education, Izmir, Türkiye | ⁸Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland | ⁹Gran Paradiso National Park, Alpine Wildlife Research Center, Noasca, Italy | ¹⁰Research Institute of Wildlife Ecology, University of Veterinary Medicine Vienna, Vienna, Austria | ¹¹University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, Koper, Slovenia | ¹²Faculty of Environmental Protection, Velenje, Slovenia | ¹³Department of Animal Science, University of Zagreb, Faculty of Agriculture, Zagreb, Croatia | ¹⁴Institute of Animal Sciences, Hungarian University of Agriculture and Life Sciences (MATE), Kaposvár, Hungary | ¹⁵Royal Botanic Gardens, Richmond, UK | ¹⁶Department of Ecology and Evolution, Estación Biológica de Doñana, Seville, Spain | ¹⁷INRAE, Univ. Bordeaux, BIOGECO, Cestas, France | ¹⁸WSL Swiss Federal Research Institute, Birmensdorf, Switzerland | ¹⁹The Center for Tree Science, The Morton Arboretum, Lisle, Illinois, USA | ²⁰The Committee on Evolutionary Biology, The University of Chicago,
Chicago, Illinois, USA | ²¹Royal Zoological Society of Scotland, Edinburgh, UK | ²²Institute of Biodiversity and Ecosystem Research at Bulgarian Academy of Sciences, Sofia, Bulgaria | ²³Technical University in Zvolen, Zvolen, Slovakia | ²⁴Czech University of Life Sciences Prague, Faculty of Forestry and Wood Sciences, Department of Forest Ecology, Suchdol, Praha, Czech Republic | ²⁵Department of Zoology, Stockholm University, Stockholm, Sweden | ²⁶Behavioral Ecology Research Group, Leipzig University, Leipzig, Germany | ²⁷Max-Planck Institute for Evolutionary Anthropology, Department of Human Behaviour, Ecology and Culture Deutscher Platz 6, Leipzig, Germany | ²⁸Universite Claude Bernard Lyon 1, Villeurbanne, France | ²⁹Department of Plant Biology and Ecology, Faculty of Sciences and Technology, University of the Basque Country UPV/EHU, Leioa, Spain | ³⁰IKERBASQUE Basque Foundation for Science, Bilbao, Spain | ³¹BC3 Basque Center for Climate Change, Leioa, Spain | ³²Israel Oceanographic and Limnological Research, National Institute of Oceanography, Haifa, Israel | ³³School of Biosciences, Cardiff University, Cardiff, UK | ³⁴Slovenian Forestry Institute, Ljubljana, Slovenia | ³⁵Senckenberg Biodiversity and Climate Research Centre, Frankfurt Am Main, Frankfurt, Germany | ³⁶Norwegian Institute for Nature Research, Trondheim, Norway

## Acknowledgements

This workshop was the brainchild of our beloved colleague and friend Michael W. Bruford (1963–2023), a true leader of the conservation genetics community, who is greatly missed since passing away in 2023. He pioneered conservation genetics, emphasized genetic diversity in policy, and fostered collaboration with warmth and compassion. This manuscript is dedicated to Mike, who will always be remembered. For those wanting to read more about Mike’s influential career at the forefront of evolutionary biology, conservation, molecular ecology, and agricultural biodiversity, along with his lasting impact, the obituaries are available (Goossens and Orozco-Ter Wengel 2023; Hoban, Segelbacher, et al. 2023; Orozco-terWengel et al. 2023). This publication is based on work from COST Action Genomic Biodiversity Knowledge for Resilient Ecosystems (G-BiKE), CA 18134, supported by COST, www.cost.eu, as well as by the newly funded COST Action Genetic Nature Observation and Action (GENOA) CA 23121, www cost.eu. AF was supported by the Romanian Ministry of Research, Innovation, and Digitalization funds PN23090304 (12N/01.01.2023). IB was supported by the Swiss National Foundation (SNF project IZCOZO_198147). EB was supported by the Slovenian Research Agency programme groups P1-0386. SCG-M and AT were funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 862221 (FORGENIUS). This research has been co-funded by the Agence Nationale de la Recherche Investissement d’Avenir grant Center for the study of Biodiversity in Amazonia (ANR-10-LABEX-0025) and the Biodiversa+ project GINAMO (ANR-23-EBIP-0003-06). The study also received financial support from the French government in the framework of the IdEx Bordeaux University ‘Investments for the Future’ program/ GPR Bordeaux Plant Sciences (France). LL and VK were supported by Formas (grant 2020-01290) and the Swedish Research Council (grant 2019-05503). Views and opinions expressed, are however, those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. VK was supported by the Swedish Environmental Protection Agency (SEPA). PK was supported by Scientific Grant Agency VEGA (1/0328/22). PBP was supported by a grant from the Ministerio de Ciencia e Innovación, Spain (PID2020-118028GB-100). LPS was supported by a FPI grant from the Ministerio de Ciencia e Innovación, Spain (PRE2022-105110). NET was supported by funding from People’s Trust for Endangered Species and HEFCW Higher Education Investment and Recover (HEIR) Fund for Research. MW was supported by the Slovenian Research Agency, programme group P4-0107 Forest Biology, Ecology, and Technology.

SW was supported by the Austrian Science Fund (FWF) project I5081-B. AK was supported by the NINA basic funding, financed by The Research Council of Norway, project no. 160022/F40. Organism silhouettes were obtained from PhyloPic (www.phylopic.org). We would like to thank Robin Waples and two anonymous reviewers for their valuable comments and feedback on an earlier version of the manuscript.

## Conflicts of Interest

The authors declare no conflicts of interest. Joachim Mergeay, Roberta Gargiulo, and Isa-Rita M. Russo are editorial board members of Evolutionary Applications and co-authors of this article. To minimize bias, they were excluded from all editorial decision-making related to this article.

## Data Availability Statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the study at hand. Code Availability: No code is available to this article as no new data were analyzed or new code was created in this study.

## References

Adavoudi, R., and M. Pilot. 2021. "Consequences of Hybridization in Mammals: A Systematic Review." *Genes* 13, no. 50: 1–26. https://doi.org/10.3390/genes13010050.

Allendorf, F. W. 2017. "Genetics and the Conservation of Natural Populations: Allozymes to Genomes." *Molecular Ecology* 26: 420–430. https://doi.org/10.1111/mec.13948.

Balloux, F. 2004. "Heterozygote Excess in Small Populations and the Heterozygote-Excess Effective Population Size." *Evolution; International Journal of Organic Evolution* 58: 1891–1900. https://doi.org/10.1111/j.0014-3820.2004.tb00477.x.

Barbato, M., P. Orozco-terWengel, M. Tapio, and M. W. Bruford. 2015. "SNeP: A Tool to Estimate Trends in Recent Effective Population Size Trajectories Using Genome-Wide SNP Data." *Frontiers in Genetics* 6, no. 109: 1–6. https://doi.org/10.3389/fgene.2015.00109.

Bertola, L. D., L. Quinn, K. Hanghoj, et al. 2024. "Giraffe Lineages Are Shaped by Major Ancient Admixture Events." *Current Biology: CB* 34, no. 7: 1576–1586.e5. https://doi.org/10.1016/j.cub.2024.02.051.

Bruni, G., A. Chiocchio, G. Nascetti, and R. Cimmaruta. 2023. "Different Patterns of Introgression in a Three Species Hybrid Zone Among European Cave Salamanders." *Ecology and Evolution* 13, no. 8: e10437. https://doi.org/10.1002/ece3.10437.

CBD. 2022. "Kunming-Montreal Global Biodiversity Framework." https://www.cbd.int/doc/decisions/cop-15/cop-15-dec-04-en.pdf.

Chikhi, L., V. C. Sousa, P. Luisi, B. Goossens, and M. A. Beaumont. 2010. "The Confounding Effects of Population Structure, Genetic Diversity and the Sampling Scheme on the Detection and Quantification of Population Size Changes." *Genetics* 186, no. 3: 983–995. https://doi.org/10.1534/genetics.110.118661.

Clarke, S. H., E. R. Lawrence, J.-M. Matte, et al. 2024. "Global Assessment of Effective Population Sizes: Consistent Taxonomic Differences in Meeting the 50/500 Rule." *Molecular Ecology* 33: e17353. https://doi.org/10.1111/mec.17353.

Cox, K., S. Neyrinck, and J. Mergeay. 2024. "Dealing With Assumptions and Sampling Bias in the Estimation of Effective Population Size: A Case Study in an Amphibian Population." *Evolutionary Applications* 17, no. 9: e70015. https://doi.org/10.1111/eva.70015.

Do, C., R. S. Waples, D. Peel, G. M. Macbeth, B. J. Tillett, and J. R. Ovenden. 2014. "NeEstimator v2: Re-Implementation of Software for the Estimation of Contemporary Effective Population Size (Ne) From
Genetic Data.” *Molecular Ecology Resources* 14, no. 1: 209–214. https://doi.org/10.1111/1755-0998.12157.

Drummond, A. J., A. Rambaut, B. Shapiro, and O. G. Pybus. 2005. "Bayesian Coalescent Inference of Past Population Dynamics From Molecular Sequences." *Molecular Biology and Evolution* 22, no. 5: 1185–1192. https://doi.org/10.1093/molbev/msi103.

Excoffier, L., I. Dupanloup, E. Huerta-Sánchez, V. C. Sousa, and M. Foll. 2013. "Robust Demographic Inference From Genomic and SNP Data." *PLoS Genetics* 9: e1003905. https://doi.org/10.1371/journal.pgen.1003905.

Excoffier, L., N. Marchi, D. A. Marques, R. Matthey-Doret, A. Gouy, and V. C. Sousa. 2021. "fastsimcoal2: Demographic Inference Under Complex Evolutionary Scenarios." *Bioinformatics* 37: 4882–4885. https://doi.org/10.1093/bioinformatics/btab468.

Fady, B., and M. Bozzano. 2021. "Effective Population Size Does Not Make a Practical Indicator of Genetic Diversity in Forest Trees." *Biological Conservation* 253: 108904. https://doi.org/10.1016/j.biocon.2020.108904.

Frankham, R., C. J. A. Bradshaw, and B. W. Brook. 2014. "Genetics in Conservation Management: Revised Recommendations for the 50/500 Rules, Red List Criteria and Population Viability Analyses." *Biological Conservation* 170: 56–63. https://doi.org/10.1016/j.biocon.2013.12.036.

Gargiulo, R., K. Budde, and M. Heuertz. 2024. "Mind the Lag: Understanding Delayed Genetic Erosion." *EcoEvoRxiv*: 1–15. https://doi.org/10.32942/x26s4x.

Gargiulo, R., V. Decroocq, S. C. González-Martínez, et al. 2024. "Estimation of Contemporary Effective Population Size in Plant Populations: Limitations of Genomic Datasets." *Evolutionary Applications* 17, no. 5: 1–16. https://doi.org/10.1111/eva.13691.

Gargiulo, R., R. S. Waples, A. K. Grow, et al. 2023. "Effective Population Size in a Partially Clonal Plant Is Not Predicted by the Number of Genetic Individuals." *Evolutionary Applications* 16: 750–766. https://doi.org/10.1111/eva.13535.

Gilbert, K. J., and M. C. Whitlock. 2015. "Evaluating Methods for Estimating Local Effective Population Size With and Without Migration." *Evolution; International Journal of Organic Evolution* 69: 2154–2166. https://doi.org/10.1111/evo.12713.

Goossens, B., and P. Orozco-Ter Wengel. 2023. "Michael (Mike) William Bruford (6th June 1963–13th April 2023)." *Conservation Genetics* 24: 933–934. https://doi.org/10.1007/s10592-023-01583-6.

Gutenkunst, R. N., R. D. Hernandez, S. H. Williamson, and C. D. Bustamante. 2009. "Inferring the Joint Demographic History of Multiple Populations From Multidimensional SNP Frequency Data." *PLoS Genetics* 5: e1000695. https://doi.org/10.1371/journal.pgen.1000695.

Haller, B. C., and P. W. Messer. 2019. "SLIM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model." *Molecular Biology and Evolution* 36: 632–637. https://doi.org/10.1093/molbev/msy228.

Heuertz, M., S. B. Carvalho, J. Galindo, et al. 2023. "The Application Gap: Genomics for Biodiversity and Ecosystem Service Management." *Biological Conservation* 278: 109883. https://doi.org/10.1016/j.biocon.2022.109883.

Ho, S. Y. W., and B. Shapiro. 2011. "Skyline-Plot Methods for Estimating Demographic History From Nucleotide Sequences." *Molecular Ecology Resources* 11: 423–434. https://doi.org/10.1111/j.1755-0998.2011.02988.x.

Hoban, S. 2014. "An Overview of the Utility of Population Simulation Software in Molecular Ecology." *Molecular Ecology* 23: 2383–2401. https://doi.org/10.1111/mec.12741.

Hoban, S., F. I. Archer, L. D. Bertola, et al. 2022. "Global Genetic Diversity Status and Trends: Towards a Suite of Essential Biodiversity Variables (EBVs) for Genetic Composition." *Biological Reviews of the

Cambridge Philosophical Society 97: 1511–1538. https://doi.org/10.1111/brv.12852.

Hoban, S., G. Bertorelle, and O. E. Gaggiotti. 2012. "Computer Simulations: Tools for Population and Evolutionary Genetics." *Nature Reviews. Genetics* 13: 110–122. https://doi.org/10.1038/nrg3130.

Hoban, S., M. Bruford, J. D'Urban Jackson, et al. 2020. "Genetic Diversity Targets and Indicators in the CBD Post-2020 Global Biodiversity Framework Must Be Improved." *Biological Conservation* 248: 108654. https://doi.org/10.1016/j.biocon.2020.108654.

Hoban, S., M. W. Bruford, W. C. Funk, et al. 2021. "Global Commitments to Conserving and Monitoring Genetic Diversity Are Now Necessary and Feasible." *Bioscience* 71: 964–976. https://doi.org/10.1093/biosci/biab054.

Hoban, S., J. M. da Silva, A. Hughes, et al. 2024. "Too Simple, Too Complex, or Just Right? Advantages, Challenges, and Guidance for Indicators of Genetic Diversity." *Bioscience* 74: 269–280. https://doi.org/10.1093/biosci/biae006.

Hoban, S., J. M. da Silva, A. Mastretta-Yanes, et al. 2023. "Monitoring Status and Trends in Genetic Diversity for the Convention on Biological Diversity: An Ongoing Assessment of Genetic Indicators in Nine Countries." *Conservation Letters* 16, no. 3: 1–12. https://doi.org/10.1111/conl.12953.

Hoban, S., C. Hvilsom, A. Abdeldjalil, et al. 2024. "How can Biodiversity Strategy and Action Plans Incorporate Genetic Diversity Concerns, Plans, Policies, Capacity, and Commitments?".

Hoban, S., I. Paz-Vinas, S. Aitken, et al. 2021. "Effective Population Size Remains a Suitable Pragmatic Indicator of Genetic Diversity for all Species, Including Forest Trees." *Biological Conservation* 253: 108906. https://doi.org/10.1016/j.biocon.2020.108906.

Hoban, S., I. Paz-Vinas, R. E. Shaw, et al. 2024. "DNA-Based Studies and Genetic Diversity Indicator Assessments Are Complementary Approaches to Conserving Evolutionary Potential." *Conservation Genetics* 25: 1147–1153. https://doi.org/10.1007/s10592-024-01632-8.

Hoban, S., G. Segelbacher, C. Vernesi, and I. R. Russo. 2023. "Michael W. Bruford (1963–2023)." *Nature Ecology & Evolution* 7: 1349–1350. https://doi.org/10.1038/s41559-023-02110-z.

Holderegger, R., N. Balkenhol, J. Bolliger, et al. 2019. "Conservation Genetics: Linking Science With Practice." *Molecular Ecology* 28: 3848–3856. https://doi.org/10.1111/mec.15202.

Hollenbeck, C. M., D. S. Portnoy, and J. R. Gold. 2016. "A Method for Detecting Recent Changes in Contemporary Effective Population Size From Linkage Disequilibrium at Linked and Unlinked Loci." *Heredity* 117: 207–216. https://doi.org/10.1038/hdy.2016.30.

Hong, A., R. G. Cheek, S. N. De Silva, et al. 2024. "ONeSAMP 3.0: Estimation of Effective Population Size via SNP Data from One Population." *G3 (Bethesda, Md.)* 14: jkae153. https://doi.org/10.1093/g3journal/jkae153.

Hu, W., Z. Hao, P. Du, et al. 2023. "Genomic Inference of a Severe Human Bottleneck During the Early to Middle Pleistocene Transition." *Science* 381: 979–984. https://doi.org/10.1126/science.abq7487.

Hui, T.-Y. J., J. H. Brenas, and A. Burt. 2021. "Contemporary Ne Estimation Using Temporally Spaced Data With Linked Loci." *Molecular Ecology Resources* 21: 2221–2230. https://doi.org/10.1111/1755-0998.13412.

Hui, T.-Y. J., and A. Burt. 2015. "Estimating Effective Population Size From Temporally Spaced Samples With a Novel, Efficient Maximum-Likelihood Algorithm." *Genetics* 200: 285–293. https://doi.org/10.1534/genetics.115.174904.

Iacolina, L., E. Buzan, T. Safner, et al. 2021. "A Mother's Story, Mitogenome Relationships in the Genus Rupicapra." *Animals* 11: 1065. https://doi.org/10.3390/ani11041065.
Jones, O. R., and J. Wang. 2010. "COLONY: A Program for Parentage and Sibship Inference From Multilocus Genotype Data." *Molecular Ecology Resources* 10, no. 3: 551–555. https://doi.org/10.1111/j.1755-0998.2009.02787.x.

Kershaw, F., M. W. Bruford, W. C. Funk, et al. 2022. "The Coalition for Conservation Genetics: Working Across Organizations to Build Capacity and Achieve Change in Policy and Practice." *Conservation Science and Practice* 4: 1–14. https://doi.org/10.1111/csp2.12635.

Laikre, L., S. Hoban, M. W. Bruford, et al. 2020. "Post-2020 Goals Overlook Genetic Diversity." *Science* 367: 1083–1085. https://doi.org/10.1126/science.abb2748.

Leigh, D. M., C. B. van Rees, K. L. Millette, et al. 2021. "Opportunities and Challenges of Macrogenetic Studies." *Nature Reviews. Genetics* 22, no. 12: 791–807. https://doi.org/10.1038/s41576-021-00394-0.

Li, H., and R. Durbin. 2011. "Inference of Human Population History From Individual Whole-Genome Sequences." *Nature* 475: 493–496. https://doi.org/10.1038/nature10231.

Liedtke, H. C., D. J. Gower, M. Wilkinson, and I. Gomez-Mestre. 2018. "Macroevolutionary Shift in the Size of Amphibian Genomes and the Role of Life History and Climate." *Nature Ecology & Evolution* 2: 1792–1799. https://doi.org/10.1038/s41559-018-0674-4.

Liu, X., and Y.-X. Fu. 2015. "Exploring Population Size Changes Using SNP Frequency Spectra." *Nature Genetics* 47: 555–559. https://doi.org/10.1038/ng.3254.

Liu, X., and Y.-X. Fu. 2020. "Stairway Plot 2: Demographic History Inference With Folded SNP Frequency Spectra." *Genome Biology* 21, no. 1: 280. https://doi.org/10.1186/s13059-020-02196-9.

Lundmark, C., K. Andersson, A. Sandström, and L. Laikre. 2017. "Effectiveness of Short-Term Knowledge Communication on Baltic Sea Marine Genetic Biodiversity to Public Managers." *Regional Environmental Change* 17, no. 3: 841–849. https://doi.org/10.1007/s10113-016-1077-1.

Marandel, F., G. Charrier, J.-B. Lamy, S. Le Cam, P. Lorance, and V. M. Trenkel. 2020. "Estimating Effective Population Size Using RADseq: Effects of SNP Selection and Sample Size." *Ecology and Evolution* 10, no. 7: 1929–1937. https://doi.org/10.1002/ece3.6016.

Marandel, F., P. Lorance, O. Berthelé, V. M. Trenkel, R. S. Waples, and J.-B. Lamy. 2019. "Estimating Effective Population Size of Large Marine Populations, Is It Feasible?" *Fish and Fisheries* 20, no. 1: 189–198. https://doi.org/10.1111/faf.12338.

Maruyama, T., and M. Kimura. 1980. "Genetic Variability and Effective Population Size When Local Extinction and Recolonization of Subpopulations Are Frequent." *Proceedings of the National Academy of Sciences* 77: 6710–6714.

Mastretta-Yanes, A., J. M. da Silva, C. E. Grueber, et al. 2024. "Multinational Evaluation of Genetic Diversity Indicators for the Kunming-Montreal Global Biodiversity Framework." *Ecology Letters* 27, no. 7: e14461. https://doi.org/10.1111/ele.14461.

May, S. A., J. J. Hard, M. J. Ford, K. A. Naish, and E. J. Ward. 2023. "Assortative Mating for Reproductive Timing Affects Population Recruitment and Resilience in a Quantitative Genetic Model." *Evolutionary Applications* 16, no. 3: 657–672. https://doi.org/10.1111/eva.13524.

Mergeay, J., S. Smet, S. Collet, et al. 2024. "Estimating the Effective Size of European Wolf Populations." *Evolutionary Applications* 17, no. 12: e70021. https://doi.org/10.1111/eva.70021.

Montes, I., M. Iriondo, C. Manzano, et al. 2016. "No Loss of Genetic Diversity in the Exploited and Recently Collapsed Population of Bay of Biscay Anchovy (*Engraulis encrasicolus*, L.)." *Marine Biology* 163, no. 5: 1–10. https://doi.org/10.1007/s00227-016-2866-2.

Nadachowska-Brzyska, K., M. Konczal, and W. Babik. 2022. "Navigating the Temporal Continuum of Effective Population Size."

*Methods in Ecology and Evolution* / *British Ecological Society* 13, no. 1: 22–41. https://doi.org/10.1111/2041-210x.13740.

Neel, M. C., K. McKelvey, N. Ryman, et al. 2013. "Estimation of Effective Population Size in Continuously Distributed Populations: There Goes the Neighborhood." *Heredity* 111, no. 3: 189–199. https://doi.org/10.1038/hdy.2013.37.

Novo, I., N. Pérez-Pereira, E. Santiago, H. Quesada, and A. Caballero. 2023. "An Empirical Test of the Estimation of Historical Effective Population Size Using *Drosophila melanogaster*." *Molecular Ecology Resources* 23, no. 7: 1632–1640. https://doi.org/10.1111/1755-0998.13837.

Nunney, L. 1999. "The Effective Size of a Hierarchically Structured Population." *Evolution; International Journal of Organic Evolution* 53, no. 1: 1. https://doi.org/10.2307/2640915.

Nunney, L. 2002. "The Effective Size of Annual Plant Populations: the Interaction of a Seed Bank With Fluctuating Population Size in Maintaining Genetic Variation." *American Naturalist* 160, no. 2: 195–204. https://doi.org/10.1086/341017.

Nunney, L. 2016. "The Effect of Neighborhood Size on Effective Population Size in Theory and in Practice." *Heredity* 117, no. 4: 224–232. https://doi.org/10.1038/hdy.2016.76.

Orozco-terWengel, P., E. Coissac, O. Hanotte, F. Pompanon, P. Sunnucks, and P. Taberlet. 2023. "Mike Bruford (1963–2023)." *Molecular Ecology* 3: 5448–5449. https://doi.org/10.1111/mec.17133.

Palstra, F. P., and D. E. Ruzzante. 2011. "Demographic and Genetic Factors Shaping Contemporary Metapopulation Effective Size and Its Empirical Estimation in Salmonid Fish." *Heredity* 107, no. 5: 444–455. https://doi.org/10.1038/hdy.2011.31.

Patton, A. H., M. J. Margres, B. Epstein, J. Eastman, L. J. Harmon, and A. Storfer. 2020. "Hybridizing Salamanders Experience Accelerated Diversification." *Scientific Reports* 10, no. 1: 6566. https://doi.org/10.1038/s41598-020-63378-w.

Paz-Vinas, I., G. Loot, S. Boulêtreau, et al. 2024. "Genetic-Based Inference of Densities, Effective and Census Sizes of Expanding Riverine Meta-Populations of an Invasive Large-Bodied Freshwater Fish (*Silurus glanis* L.)." *bioRxiv* (p. 2024.04.05.588309). https://doi.org/10.1101/2024.04.05.588309.

Pearman, P. B., O. Broennimann, T. Aavik, et al. 2024. "Monitoring of species' Genetic Diversity in Europe Varies Greatly and Overlooks Potential Climate Change Impacts." *Nature Ecology & Evolution* 8, no. 2: 267–281. https://doi.org/10.1038/s41559-023-02260-0.

Pérez-Pereira, N., J. Wang, H. Quesada, and A. Caballero. 2022. "Prediction of the Minimum Effective Size of a Population Viable in the Long Term." *Biodiversity and Conservation* 31: 2763–2780. https://doi.org/10.1007/s10531-022-02456-z.

Pérez-Sorribes, L., P. Villar-Yanez, L. Smeds, and J. Mergeay. 2024. "Comparing genetic Ne reconstructions over time with long-time wolf monitoring data in two populations." *Evolutionary Applications* 12: e70022. https://doi.org/10.1111/eva.70022.

Pierson, J. C., T. A. Graves, S. C. Banks, K. C. Kendall, and D. B. Lindenmayer. 2018. "Relationship Between Effective and Demographic Population Size in Continuously Distributed Populations." *Evolutionary Applications* 11, no. 7: 1162–1175. https://doi.org/10.1111/eva.12636.

Pinho, C., A. Kaliontzopoulou, C. A. Ferreira, and J. Gama. 2022. "Identification of Morphologically Cryptic Species With Computer Vision Models: Wall Lizards (Squamata: Lacertidae: Podarcis) as a Case Study." *Zoological Journal of the Linnean Society* 198, no. 1: 184–201. https://doi.org/10.1093/zoolinnean/zla087.

Randi, E. 2007. "Phylogeography of South European Mammals." In *Phylogeography of Southern European Refugia: Evolutionary Perspectives on the Origins and Conservation of European Biodiversity*, edited by S. Weiss and N. Ferrand, 101–126. Dordrecht, Netherlands: Springer. https://doi.org/10.1007/1-4020-4904-8_3.
Ryman, N., L. Laikre, and O. Hössjer. 2019. "Do Estimates of Contemporary Effective Population Size Tell Us What We Want to Know?" *Molecular Ecology* 28, no. 8: 1904–1918. https://doi.org/10.1111/mec.15027.

Ryman, N., L. Laikre, and O. Hössjer. 2023. "Variance Effective Population Size Is Affected by Census Size in Sub-Structured Populations." *Molecular Ecology Resources* 23, no. 6: 1334–1347. https://doi.org/10.1111/1755-0998.13804.

Santiago, E., A. Caballero, C. Köpke, and I. Novo. 2024. "Estimation of the Contemporary Effective Population Size From SNP Data While Accounting for Mating Structure." *Molecular Ecology Resources* 24, no. 1: e13890. https://doi.org/10.1111/1755-0998.13890.

Santiago, E., I. Novo, A. F. Pardiñas, M. Saura, J. Wang, and A. Caballero. 2020. "Recent Demographic History Inferred by High-Resolution Analysis of Linkage Disequilibrium." *Molecular Biology and Evolution* 37, no. 12: 3642–3653. https://doi.org/10.1093/molbev/msa169.

Schiffels, S., and R. Durbin. 2014. "Inferring Human Population Size and Separation History From Multiple Genome Sequences." *Nature Genetics* 46, no. 8: 919–925. https://doi.org/10.1038/ng.3015.

Schiffels, S., and K. Wang. 2020. "MSMC and MSMC2: The Multiple Sequentially Markovian Coalescent." *Methods in Molecular Biology* 2090: 147–166. https://doi.org/10.1007/978-1-0716-0199-0_7.

Taft, H. R., D. N. McCoskey, J. M. Miller, et al. 2020. "Research-Management Partnerships: An Opportunity to Integrate Genetics in Conservation Actions." *Conservation Science and Practice* 2, no. 9: 1–8. https://doi.org/10.1111/csp.2.218.

Tallmon, D. A., A. Koyuk, G. Luikart, and M. A. Beaumont. 2008. "COMPUTER PROGRAMS: Onesamp: A Program to Estimate Effective Population Size Using Approximate Bayesian Computation." *Molecular Ecology Resources* 8, no. 2: 299–301. https://doi.org/10.1111/j.1471-8286.2007.01997.x.

Tallmon, D. A., G. Luikart, and M. A. Beaumont. 2004. "Comparative Evaluation of a New Effective Population Size Estimator Based on Approximate Bayesian Computation." *Genetics* 167, no. 2: 977–988. https://doi.org/10.1534/genetics.103.026146.

Tenesa, A., P. Navarro, B. J. Hayes, et al. 2007. "Recent Human Effective Population Size Estimated From Linkage Disequilibrium." *Genome Research* 17, no. 4: 520–526. https://doi.org/10.1101/gr.6023607.

Theissinger, K., C. Fernandes, G. Formenti, et al. 2023. "How Genomics Can Help Biodiversity Conservation." *Trends in Genetics: TIG* 39, no. 7: 545–559. https://doi.org/10.1016/j.tig.2023.01.005.

Thomas, N. E., F. Hailer, M. W. Bruford, and E. A. Chadwick. 2022. "Country-Wide Genetic Monitoring Over 21 Years Reveals Lag in Genetic Recovery Despite Spatial Connectivity in an Expanding Carnivore (Eurasian Otter, *Lutra lutra*) Population." *Evolutionary Applications* 15, no. 12: 2125–2141. https://doi.org/10.1111/eva.13505.

Wang, J. 2009. "A New Method for Estimating Effective Population Sizes From a Single Sample of Multilocus Genotypes." *Molecular Ecology* 18, no. 10: 2148–2164. https://doi.org/10.1111/j.1365-294X.2009.04175.x.

Wang, J. 2016. "A Comparison of Single-Sample Estimators of Effective Population Sizes From Genetic Marker Data." *Molecular Ecology* 25, no. 19: 4692–4711. https://doi.org/10.1111/mec.13725.

Waples, R. 2024a. "The idiot's Guide to Effective Population Size." *Authorea Inc:* 1–51. https://doi.org/10.22541/au.172536694.46670112/v1.

Waples, R. S. 1989. "A Generalized Approach for Estimating Effective Population Size From Temporal Changes in Allele Frequency." *Genetics* 121, no. 2: 379–391. https://doi.org/10.1093/genetics/121.2.379.

Waples, R. S. 2005. "Genetic Estimates of Contemporary Effective Population Size: To What Time Periods Do the Estimates Apply?" *Molecular Ecology* 14, no. 11: 3335–3352. https://doi.org/10.1111/j.1365-294X.2005.02673.x.

Waples, R. S. 2006. "Seed Banks, Salmon, and Sleeping Genes: Effective Population Size in Semelparous, Age-Structured Species With Fluctuating Abundance." *American Naturalist* 167, no. 1: 118–135.

Waples, R. S. 2010. "Spatial-Temporal Stratifications in Natural Populations and How They Affect Understanding and Estimation of Effective Population Size." *Molecular Ecology Resources* 10, no. 5: 785–796. https://doi.org/10.1111/j.1755-0998.2010.02876.x.

Waples, R. S. 2016. "Life-History Traits and Effective Population Size in Species With Overlapping Generations Revisited: the Importance of Adult Mortality." *Heredity* 117, no. 4: 241–250. https://doi.org/10.1038/hdy.2016.29.

Waples, R. S. 2021. "Relative Precision of the Sibship and LD Methods for Estimating Effective Population Size With Genomics-Scale Datasets." *Journal of Heredity* 112, no. 6: 535–539. https://doi.org/10.1093/jhered/esab042.

Waples, R. S. 2022. "What Is Ne, Anyway?" *Journal of Heredity* 113, no. 4: 371–379. https://academic.oup.com/jhered/article-abstract/113/4/371/6582684.

Waples, R. S. 2024b. "Practical Application of the Linkage Disequilibrium Method for Estimating Contemporary Effective Population Size: A Review." *Molecular Ecology Resources* 24, no. 1: e13879. https://doi.org/10.1111/1755-0998.13879.

Waples, R. S., T. Antao, and G. Luikart. 2014. "Effects of Overlapping Generations on Linkage Disequilibrium Estimates of Effective Population Size." *Genetics* 197, no. 2: 769–780. https://doi.org/10.1534/genetics.114.164822.

Waples, R. S., and C. Do. 2008. "Ldne: A Program for Estimating Effective Population Size From Data on Linkage Disequilibrium." *Molecular Ecology Resources* 8, no. 4: 753–756. https://doi.org/10.1111/j.1755-0998.2007.02061.x.

Waples, R. S., and C. Do. 2010. "Linkage Disequilibrium Estimates of Contemporary N e Using Highly Variable Genetic Markers: A Largely Untapped Resource for Applied Conservation and Evolution." *Evolutionary Applications* 3, no. 3: 244–262. https://doi.org/10.1111/j.1752-4571.2009.00104.x.

Waples, R. S., G. Luikart, J. R. Faulkner, and D. A. Tallmon. 2013. "Simple Life-History Traits Explain Key Effective Population Size Ratios Across Diverse Taxa." *Proceedings. Biological Sciences/The Royal Society* 280, no. 1768: 20131339. https://doi.org/10.1098/rspb.2013.1339.

Whitlock, M. C., and N. H. Barton. 1997. "The Effective Size of a Subdivided Population." *Genetics* 146: 427–441.

Wright, P. G. R., H. Schofield, and F. Mathews. 2021. "Can Effective Population Size Estimates be Used to Monitor Population Trends of Woodland Bats? A Case Study of *Myotis bechsteinii*." *Ecology and Evolution* 11, no. 5: 2015–2023. https://doi.org/10.1002/ece3.7143.

Wright, S. 1931. "Evolution in Mendelian Populations." *Genetics* 16, no. 2: 97–159. https://doi.org/10.1093/genetics/16.2.97.

Wright, S. 1951. "The Genetical Structure of Populations." *Annals of Eugenics* 15, no. 4: 323–354. https://doi.org/10.1111/j.1469-1809.1949.tb02451.x.