Spatial genetic structure, i.e. the non-random spatial distribution of genotypes, arises in populations because of different processes including spatially limited dispersal and selection. Knowledge on the spatial genetic structure of plant populations is important to assess biological parameters such as gene dispersal distances and the potential for local adaptations, as well as for applications in conservation management and breeding. In their work, Niskanen and colleagues demonstrate a multifaceted approach to characterise the spatial genetic structure in two replicate sites of a continuously distributed Scots pine population in South-Eastern Finland. They mapped and assessed the ages of 469 naturally regenerated adults and genotyped them using a SNP array which resulted in 157 325 filtered polymorphic SNPs. Their dataset is remarkably powerful because of the large numbers of both individuals and SNPs genotyped. This made it possible to characterise precisely the decay of genetic relatedness between individuals with spatial distance despite the extensive dispersal capacity of Scots pine through pollen, and ensuing expectations of an almost panmictic population.

The authors’ data analysis was particularly thorough. They demonstrated that two metrics of pairwise relatedness, the genomic relationship matrix (GRM, Yang et al. 2011) and the kinship coefficient (Loiselle et al. 1995) were strongly correlated and produced very similar inference of family relationships: >99% of pairs of individuals were unrelated, and the remainder exhibited 2nd (e.g., half-siblings) to 4th degree relatedness. Pairwise relatedness decayed with spatial distance which resulted in extremely weak but statistically significant spatial genetic structure in both sites, quantified as Sp=0.0005 and Sp=0.0008. These estimates are at least an order of magnitude lower than estimates in the literature obtained in more fragmented populations of the same species or in other conifers. Estimates of the neighbourhood size, the effective number of potentially mating individuals belonging to a within-population neighbourhood (Wright 1946), were relatively large with Nb=1680-3210 despite relatively short gene dispersal distances, σg = 36.5–71.3m, which illustrates the high effective density of the population. 

The authors showed the implications of their findings for selection. The capacity for local adaptation depends on dispersal distances and the strength of the selection coefficient. In the study population, the authors inferred that local adaptation can only occur if environmental heterogeneity occurs over a distance larger than approximately one kilometre (or larger, if considering long-distance dispersal). Interestingly, in Scots pine, no local adaptation has been described on similar geographic scales, in contrast to some other European or Mediterranean conifers (Scotti et al. 2023).

The authors’ results are relevant for the management of conservation and breeding. They showed that related individuals occurred within sites only and that they shared a higher number of rare alleles than unrelated ones. Since rare alleles are enriched in new and recessive deleterious variants, selecting related individuals could have negative consequences in breeding programmes. The authors also showed, in their response to reviewers, that their powerful dataset was not suitable to obtain a robust estimate of effective population size, Ne, based on the linkage disequilibrium method (Do et al. 2014). This illustrated that the estimation of Ne used for genetic indicators supported in international conservation policy (Hoban et al. 2020, CBD 2022) remains challenging in large and continuous populations (see also Santo-del-Blanco et al. 2023, Gargiulo et al. 2024).

References

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

Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2014). NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne ) from genetic data. Molecular Ecology Resources 14: 209–214. https://doi.org/10.1111/1755-0998.12157

Gargiulo R, Decroocq V, González-Martínez SC, Paz-Vinas I, Aury JM, Kupin IL, Plomion C, Schmitt S, Scotti I, Heuertz M (2024) Estimation of contemporary effective population size in plant populations: limitations of genomic datasets. Evolutionary Applications, in press, https://doi.org/10.1101/2023.07.18.549323

Hoban S, Bruford M, D’Urban Jackson J, Lopes-Fernandes M, Heuertz M, Hohenlohe PA, Paz-Vinas I, 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

Loiselle BA, Sork VL, Nason J & Graham C (1995) Spatial genetic structure of a tropical understorey shrub, Psychotria officinalis (Rubiaceae). American Journal of Botany 82: 1420–1425. https://doi.org/10.1002/j.1537-2197.1995.tb12679.x

Santos-del-Blanco L, Olsson S, Budde KB, Grivet D, González-Martínez SC, Alía R, Robledo-Arnuncio JJ (2022). On the feasibility of estimating contemporary effective population size (Ne) for genetic conservation and monitoring of forest trees. Biological Conservation 273: 109704. https://doi.org/10.1016/j.biocon.2022.109704

Scotti I, Lalagüe H, Oddou-Muratorio S, Scotti-Saintagne C, Ruiz Daniels R, Grivet D, et al. (2023) Common microgeographical selection patterns revealed in four European conifers. Molecular Ecology 32: 393-411. https://doi.org/10.1111/mec.16750

Wright S (1946) Isolation by distance under diverse systems of mating. Genetics 31: 39–59. https://doi.org/10.1093/genetics/31.1.39

Yang J, Lee SH, Goddard ME & Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics 88: 76–82. https://www.cell.com/ajhg/pdf/S0002-9297(10)00598-7.pdf

Species are immersed within communities in which they interact mutualistically, as in pollination or seed dispersal, or nonreciprocally, such as in predation or parasitism, with other species and these interactions play a paramount role in shaping biodiversity (Bascompte and Jordano 2013). Researchers have become increasingly interested in the processes that shape these interactions and how these influence community structure and responses to disturbances. Species interactions are often described using bipartite interaction networks and one important question is how the evolutionary history of the species involved influences the network, including whether there is phylogenetic signal in interactions, in other words whether closely related species interact with other closely related species (Bascompte and Jordano 2013, Perez-Lamarque et al. 2022). To address this question different approaches, correlative and model-based, have been developed to test for phylogenetic signal in interactions, although comparative analyses of the performance of these different metrics are lacking. In their article Perez-Lamarque et al. (2022) set out to test the statistical performance of two widely-used methods, Mantel tests and Phylogenetic Bipartite Linear Models (PBLM; Ives and Godfray 2006) using simulations. Phylogenetic signal is measured as the degree to which distance to the nearest common ancestor predicts the observed similarity in trait values among species. In species interaction networks, the data are actually the between-species dissimilarity among interacting species (Perez-Lamarque et al. 2022), and typical approaches to test for phylogenetic signal cannot be used. However, the Mantel test provides a useful means of analyzing the correlation between two distance matrices, the between-species phylogenetic distance and the between-species dissimilarity in interactions. The PBLM approach, on the other hand, assumes that interactions between species are influenced by unobserved traits that evolve along the phylogenies following a given phenotypic evolution model and the parameters of this model are interpreted in terms of phylogenetic signal (Ives and Godfray 2006). Perez-Lamarque et al (2022) found that the model-based PBLM approach has a high type-I error rate, in other words it often detected phylogenetic signal when there was none. The simple Mantel test was found to present a low type-I error rate and moderate statistical power. However, it tended to overestimate the degree to which species interact with dissimilar partners. In addition to the aforementioned analyses, the authors also tested whether the simple Mantel test was able to detect phylogenetic signal in interactions among species within a given clade in the phylogeny, as phylogenetic signal in species interactions may be localized within specific clades. The article concludes with general guidelines for users wishing to test phylogenetic signal in their interaction networks and illustrates them with an example of an orchid-mycorrhizal fungus network from the oceanic island of La Réunion (Martos et al 2012). This broadly accessible article provides a valuable analysis of the performance of tests of phylogenetic signal in interaction networks enabling users to make informed choices of the analytical methods they wish to employ, and provide useful and detailed guidelines. Therefore, the work should be of broad interest to researchers studying species interactions.  

References

Bascompte J, Jordano P (2013) Mutualistic Networks. Princeton University Press. https://doi.org/10.1515/9781400848720

Ives AR, Godfray HCJ (2006) Phylogenetic Analysis of Trophic Associations. The American Naturalist, 168, E1–E14. https://doi.org/10.1086/505157

Martos F, Munoz F, Pailler T, Kottke I, Gonneau C, Selosse M-A (2012) The role of epiphytism in architecture and evolutionary constraint within mycorrhizal networks of tropical orchids. Molecular Ecology, 21, 5098–5109. https://doi.org/10.1111/j.1365-294X.2012.05692.x

Perez-Lamarque B, Maliet O, Pichon B, Selosse M-A, Martos F, Morlon H (2022) Do closely related species interact with similar partners? Testing for phylogenetic signal in bipartite interaction networks. bioRxiv, 2021.08.30.458192, ver. 6 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.08.30.458192

Ecology needs rules stipulating how species distributions and ecological communities should be assembled along environmental gradients, but few rules have yet emerged in the ecological literature. The search of ecogeographical rules governing the spatial variation of birds colours has recently known an upsurge of interest in the litterature [1]. Most studies have, however, looked at pigmentary colours and not structural colours (e.g. iridescence), although it is know that color perception by animals (both birds and their predators) can be strongly influenced by light diffraction causing iridescence patterns on feathers.
In the present study [2], the authors study ca. 190 ecological communities of hummingbirds as a function of their iridescent colors, in a large study zone spanning varied habitats across Ecuador. They show that colour composition of local hummingbirds communities are shaped by two main processes :
(i) phenotyping clustering of birds with similar dorsal colours, due to local selection of species with similar camouflages against predators (i.e. some sort of mimetic circles).
(ii) phenotypic overdispersion of birds with distinct facial and ventral colours, resulting from character displacement and limiting reproductive interference.
I found this second result particularly interesting because it adds to the mounting evidence that character displacement (also for songs or olfactory signaling) allow local coexistence between closely-related bird species once they have reached secondary sympatry. It is important to note that not all color patches though to be involved in sexual selection followed this overdispersion rule -- throat and crown color patches were not found overdispersed. This suggests that further investigation is needed to determine how color variation shape the structure of hummingbird communities, or bird communities in general.
Another notable quality of the present study is that it is making extensive use of museum specimens and thus shows that very innovative research can be performed with museum collections.

References

[1] Delhey, K. (2019). A review of Gloger’s rule, an ecogeographical rule of colour: definitions, interpretations and evidence. Biological Reviews, 94(4), 1294–1316. doi: 10.1111/brv.12503
[2] Gruson, H., Elias, M., Parra, J. L., Andraud, C., Berthier, S., Doutrelant, C., & Gomez, D. (2019). Distribution of iridescent colours in hummingbird communities results from the interplay between selection for camouflage and communication. BioRxiv, 586362, v5 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/586362

Interspecific matings are by definition rare events in nature, but when they occur they can be very important, and not only because they might condition gene flow between species. Even when such matings have no genetic consequence, for instance if they do not yield any fertile hybrid offspring, they can still have an impact on the population dynamics of the species involved [1]. Such atypical pairings between heterospecific partners are usually regarded as detrimental or undesired; as they interfere with the occurrence or success of intraspecific matings, they are expected to cause a decline in absolute fitness.
The story is not always so simple however, and it might all depend on the timing of events and on the identity of the partners. Using the herbivorous mite Tetranychus urticae as a model, Clemente et al. [2] experimentally arranged matings with two other Tetranychus species that commonly share the same host plants as T. urticae. They carefully controlled the history of events: heterospecific matings could occur just before, just after, 24h before, or 24h after, a conspecific mating. Interestingly, the oviposition rate (total fecundity) of females was increased when mating with a heterospecific individual. This suggests that heterospecic sperm can stimulate oogenesis just as conspecific sperm does. Such a positive effect was observed for matings involving T. ludeni females and T. urticae males, but a negative effect is found in the interaction with T. evansi. Sex-ratio (fertilization success in those species) could also be impacted but, unlike fertilization, this occurred when the mating events were distant in time. This is is at odds with what is observed in conspecific matings, where sperm displacement occurs only if mating events are temporally close. Overall, the effects of heterospecific mating were quite variable and it is challenging to predict a single, general, effect of interspecific matings. The net effect will likely be context-dependent, depending on the relative frequency of the difference mating sequences and on how fecundity and sex-ratio contribute to overall fitness, both aspect strongly influenced by the population dynamics and structure.

References

[1] Gröning J. & Hochkirch A. 2008. Reproductive interference between animal species. The Quarterly Review of Biology 83: 257-282. doi: 10.1086/590510

[2] Clemente SH, Santos I, Ponce AR, Rodrigues LR, Varela SAM & Magalhaes S. 2017 Despite reproductive interference, the net outcome of reproductive interactions among spider mite species is not necessarily costly. bioRxiv 113274, ver. 4 of the 30th of June 2017. doi: 10.1101/113274

Genetic markers are used for in modern population genetics/genomics to uncover the past neutral and selective history of population and species. Besides Single Nucleotide Polymorphisms (SNPs) obtained from whole genome data, microsatellites (or Short Tandem Repeats, SSR) have been common markers of choice in numerous population genetics studies of non-model species with large sample sizes [1]. Microsatellites can be used to uncover and draw inference of the past population demography (e.g. expansion, decline, bottlenecks…), population split, population structure and gene flow, but also life history traits and modes of reproduction (e.g. [2,3]). These markers are widely used in conservation genetics [4] or to study parasites or disease vectors [5]. Microsatellites do show higher mutation rate than SNPs increasing, on the one hand, the statistical power to infer recent events (for example crop domestication, [2,3]), while, on the other hand, decreasing their statistical power over longer time scales due to homoplasy [6].
To perform such analyses, however, an excellent and reliable quality of data is required. As emphasized in the article by De Meeûs et al. [7] three main issues do bias the observed heterozygosity at microsatellites: null alleles, short allele dominance (SAD) and stuttering. These originates from poor PCR amplification. As a result, an excess of homozygosity is observed at the microsatellite loci leading to overestimation of the variation statistics FIS and FST as well as increased linage disequilibrium (LD). For null alleles, several methods and software do help to reduce the bias, and in the present study, De Meeûs et al. [7] propose a way to tackle issues with SAD and stuttering.
The authors study a dataset consisting of 387 samples from 61 subsamples genotyped at nine loci of the species Ixodes scapularis, i.e. ticks transmitting the Lyme disease. Based on correlation methods and FST, FIS they can uncover null alleles and SAD. Stuttering is detected by evaluating the heterozygote deficit between alleles displaying a single repeat difference. Without correction, six loci are affected by one of these amplification problems generating a large deficit of heterozygotes (measured by significant FIS and FST) remaining so after correction for the false discovery rate (FDR). These results would be classically interpreted as a strong Wahlund effect and/or selection at several loci.
After correcting for null alleles, the authors apply two novel corrections: 1) a re-examination of the chromatograms reveals previously disregarded larger alleles thus decreasing SAD, and 2) pooling alleles close in size decreasing stuttering. The corrected dataset shows then a significant excess of heterozygotes as could be expected in a dioecious species with strong population structure. The FDR correction removes then the significant excess of homozygotes and LD between pairs of loci. FST on the cured dataset is used to demonstrate the strong population structure and small effective subpopulation sizes. This is confirmed by a clustering analysis using discriminant analysis of principal components (DAPC).
While based on a specific dataset of ticks from different populations sampled across the USA, the generality of the authors’ approach is presented in Figure 6 in which they provide a step by step flowchart to cure microsatellite datasets from null alleles, SAD and stuttering. Several criteria based on FIS, FST and LD between loci are used as decision keys in the flowchart. An excel file is also provided as help for the curation steps. This study and the proposed methodology are thus extremely useful for all population geneticists working on non-model species with large number of samples genotyped at microsatellite markers. The method not only allows more accurate estimates of heterozygosity but also prevents the thinning of datasets due to the removal of problematic loci. As a follow-up and extension of this work, an exhaustive simulation study could investigate the influence of these data quality issues on past demographic and population structure inference under a wide range of scenarios. This would allow to quantify the current biases in the literature and the robustness of the methodology devised by De Meeûs et al. [7].

References

[1] Jarne, P., and Lagoda, P. J. (1996). Microsatellites, from molecules to populations and back. Trends in ecology & evolution, 11(10), 424-429. doi: 10.1016/0169-5347(96)10049-5
[2] Cornille, A., Giraud, T., Bellard, C., Tellier, A., Le Cam, B., Smulders, M. J. M., Kleinschmit, J., Roldan-Ruiz, I. and Gladieux, P. (2013). Postglacial recolonization history of the E uropean crabapple (Malus sylvestris M ill.), a wild contributor to the domesticated apple. Molecular Ecology, 22(8), 2249-2263. doi: 10.1111/mec.12231
[3] Parat, F., Schwertfirm, G., Rudolph, U., Miedaner, T., Korzun, V., Bauer, E., Schön C.-C. and Tellier, A. (2016). Geography and end use drive the diversification of worldwide winter rye populations. Molecular ecology, 25(2), 500-514. doi: 10.1111/mec.13495
[4] Broquet, T., Ménard, N., & Petit, E. (2007). Noninvasive population genetics: a review of sample source, diet, fragment length and microsatellite motif effects on amplification success and genotyping error rates. Conservation Genetics, 8(1), 249-260. doi: 10.1007/s10592-006-9146-5
[5] Koffi, M., De Meeûs, T., Séré, M., Bucheton, B., Simo, G., Njiokou, F., Salim, B., Kaboré, J., MacLeod, A., Camara, M., Solano, P., Belem, A. M. G. and Jamonneau, V. (2015). Population genetics and reproductive strategies of African trypanosomes: revisiting available published data. PLoS neglected tropical diseases, 9(10), e0003985. doi: 10.1371/journal.pntd.0003985
[6] Estoup, A., Jarne, P., & Cornuet, J. M. (2002). Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Molecular ecology, 11(9), 1591-1604. doi: 10.1046/j.1365-294X.2002.01576.x
[7] De Meeûs, T., Chan, C. T., Ludwig, J. M., Tsao, J. I., Patel, J., Bhagatwala, J., and Beati, L. (2019). Deceptive combined effects of short allele dominance and stuttering: an example with Ixodes scapularis, the main vector of Lyme disease in the USA. bioRxiv, 622373, ver. 4 peer-reviewed and recommended by Peer Community In Evolutionary Biology. doi: 10.1101/622373