The delay between disturbance events and genetic responses within populations is a common but surprisingly overlooked phenomenon in ecology and evolutionary and conservation genetics. If not accounted for when interpreting genetic data, this time lag problem can lead to erroneous conservation assessments. We (i) identify life-history traits related to longevity and reproductive strategies as the main determinants of time lags, (ii) evaluate potential confounding factors affecting genetic parameters during time lags, and (iii) propose approaches that allow controlling for time lags. Considering the current unprecedented rate of loss of genetic diversity and adaptive potential, we expect our novel interpretive and methodological framework for time lags to stimulate further research and discussion on the most appropriate approaches to analyse genetic diversity for conservation.

Enabling food security requires access to a broad range of genetic resources to facilitate crop breeding. This need is increased in a climate change scenario, which will require the production of novel crops adapted to new conditions. However, many major crops have reduced genetic diversity due to the genetic bottlenecks that they have experienced during their domestication and subsequent breeding. Crop wild relatives (CWRs) remain underexploited in plant breeding programmes, mostly because of the lack of knowledge of their cross-compatibility with crops. In this study, we use a combination of phylogenetic distance metrics, cytogenetic compatibility data (e.g. chromosome number and ploidy) and information about breeding systems to predict interspecific cross-compatibility between crop and wild species and hence identify crop wild phylorelatives (CWPs) (i.e. CWRs that can breed with the crop). We illustrate this concept using cultivated asparagus as a model by integrating previous cross-compatibility knowledge and CWR classifications into a phylogenetic framework reconstructed using available sequence data. Our approach aims to reinforce the use of the gene pool classification system of CWRs of Harlan and De Wet, since CWPs are estimated to belong to the secondary gene pool and non-CWPs to the tertiary gene pool. Identifying CWPs unlocks novel uses of genetic resources, although such data are available for less than half of the known CWRs (43.4% with sequence data and 32.5% with known ploidy). The need to conserve plants that provide or enhance provisioning ecosystem services, including CWRs, is clear if we are to rise to the global challenge of ensuring food security for all. However, basic knowledge about their conservation status is still lacking, with only c. 20% of CWRs assigned an IUCN red list assessment, 23% of which are Data Deficient (DD). Using the CWP classification presented here to define CWRs will contribute towards helping to prioritize CWRs for IUCN assessments and, where prioritised, conservation.

Effective population size (N_e) is a pivotal evolutionary parameter with crucial implications in conservation practice and policy. Genetic methods to estimate N_e have been preferred over demographic methods because they rely on genetic data rather than time‐consuming ecological monitoring. Methods based on linkage disequilibrium (LD), in particular, have become popular in conservation as they require a single sampling and provide estimates that refer to recent generations. A software program based on the LD method, GONE, looks particularly promising to estimate contemporary and recent‐historical N_e (up to 200 generations in the past). Genomic datasets from non‐model species, especially plants, may present some constraints to the use of GONE, as linkage maps and reference genomes are seldom available, and SNP genotyping is usually based on reduced‐representation methods. In this study, we use empirical datasets from four plant species to explore the limitations of plant genomic datasets when estimating N_e using the algorithm implemented in GONE, in addition to exploring some typical biological limitations that may affect N_e estimation using the LD method, such as the occurrence of population structure. We show how accuracy and precision of N_e estimates potentially change with the following factors: occurrence of missing data, limited number of SNPs/individuals sampled, and lack of information about the location of SNPs on chromosomes, with the latter producing a significant bias, previously unexplored with empirical data. We finally compare the N_e estimates obtained with GONE for the last generations with the contemporary N_e estimates obtained with the programs currentNe and NeEstimator.

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 Brașov, 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.

Ecologically dominant species are primary determinants of ecosystem function, especially in grassy ecosystems, but the history and biology of grassy ecosystems in Madagascar are poorly understood compared to those of Africa. Loudetia simplex is a C4 perennial grass that is adapted to fire and common to dominant across Africa. It is also widespread across central Madagascar in what are often thought to be human-derived grasslands, leading us to question how recently L. simplex arrived and how it spread across Madagascar. To address this, we collected population genetic data for 11 nuclear and 11 plastid microsatellite loci, newly developed for this study, for > 200 accessions from 78 populations of L. simplex, primarily from Madagascar and South Africa. Malagasy and African populations are genetically differentiated and harbour distinct plastid lineages. We demonstrate distinct geographically clustered diploid, tetraploid and hexaploid groups. The Malagasy hexaploid populations cluster into northern and southern types. In South Africa, diploid populations in the Drakensberg are distinct from tetraploid populations in north-eastern South Africa. Different genetic clusters are associated with significantly different precipitation and temperature. We conclude that L. simplex is native to both Madagascar and South Africa, probably with a single colonization event from Africa to Madagascar followed by pre-human diversification of L. simplex populations in Madagascar.