Interspecific variation in life‐history traits and physiological limits can be linked to the environmental conditions species experience, including climatic conditions. As alpine environments are particularly vulnerable under climate change, we focus on the montane‐alpine fly Drosophila nigrosparsa. Here, we characterized some of its life‐history traits and physiological limits and compared these with those of other drosophilids, namely Drosophila hydei, Drosophila melanogaster, and Drosophila obscura. We assayed oviposition rate, longevity, productivity, development time, larval competitiveness, starvation resistance, and heat and cold tolerance. Compared with the other species assayed, D. nigrosparsa is less fecund, relatively long‐living, starvation susceptible, cold adapted, and surprisingly well heat adapted. These life‐history characteristics provide insights into invertebrate adaptations to alpine conditions which may evolve under ongoing climate change.
Social structure influences animal societies on various levels (e.g., relatedness, behaviour). In ants, both the number of matings per queen and the number of queens per colony can vary strongly. While workers from both monogynous and polygynous colonies often fight fiercely, in supercolonies (an extreme form of polygyny comprising thousands of queens in spatially separated but interconnected nests), non-nestmates interact peacefully. Studies on social and behavioural polymorphism within ant species can help elucidate their influence on genetic diversity and behaviour and the factors triggering variation in social structure and behaviour. Here, we reveal a behavioural and social polymorphism comprising monogyny with and without internest aggression in Tetramorium alpestre sampled in Tyrol, Austria. The social polymorphism is based on genetic and behavioural evidence and contrasts with the supercolonial organisation known from another location in Austria (Carinthia), 150 km away. Microsatellite genotyping using eight polymorphic loci revealed monogyny-monandry and high intranest pairwise relatedness. Interestingly, various experimental one-on-one worker encounters revealed only occasional aggressive behaviour between monogynous colonies, and thus a behavioural polymorphism. Mantel tests revealed a significant negative correlation between spatial distance and relatedness, while worker behaviour was not correlated with relatedness or spatial distance. These results indicate that behaviour might be influenced by other factors – for example, the experience of workers, ecological, chemical, and/or genetic factors not characterised in this study. However, workers distinguished nestmates from non-nestmates also when aggression was lacking. We hypothesise an adaptive value of reduced aggression. We speculate that the non-aggressive and partly aggressive encounters observed represent different options in the social structure of T. alpestre, the non-aggressiveness possibly also promoting supercolony development. The social and behavioural polymorphisms observed offer opportunities to identify the factors triggering these changes and thus further explore the behavioural and social polymorphism of this ant species.
Animal behaviour often is characterised by standardised assays. In social insects such as ants, behaviour assays are for example used to characterise aggressive and peaceful behaviour. Such assays differ in the number of individuals, the duration and place of assays, and the scoring scales. Also the behaviour indices used to summarise the results differ. Here, we compared five behaviour indices (Aggression Index, Mean Maximum Aggression Index; and the newly introduced Mean Maximum Peace Index, Mean Behaviour Index aggressive, and Mean Behaviour Index peaceful) using a scoring scale that comprises peaceful and aggressive behaviour. The indices were applied on eight simulations and three observed data sets. The five indices were correlated but frequently differed in their means. Multiple indices were needed to capture the complete behaviour range. Furthermore, subtle differences in workers’ behaviour, that is, differences that go beyond the presence/absence of aggression, were only identified when considering multiple indices. We infer that the indices applied are differently suited for different analyses. Fine-scale analyses of behavioural variation profit from using more than one index. The particular choice of index or indices likely influences the interpretation of behaviour and should be carefully done in the light of study species and research question.
Small, isolated populations are constantly threatened by loss of genetic diversity due to drift. Such situations are found, for instance, in laboratory culturing. In guarding against diversity loss, monitoring of potential changes in population structure is paramount; this monitoring is most often achieved using microsatellite markers, which can be costly in terms of time and money when many loci are scored in large numbers of individuals. Here, we present a case study reducing the number of microsatellites to the minimum necessary to correctly detect the population structure of two Drosophila nigrosparsa populations. The number of loci was gradually reduced from 11 to 1, using the Allelic Richness (AR) and Private Allelic Richness (PAR) as criteria for locus removal. The effect of each reduction step was evaluated by the number of genetic clusters detectable from the data and by the allocation of individuals to the clusters; in the latter, excluding ambiguous individuals was tested to reduce the rate of incorrect assignments. We demonstrate that more than 95% of the individuals can still be correctly assigned when using eight loci and that the major population structure is still visible when using two highly polymorphic loci. The differences between sorting the loci by AR and PAR were negligible. The method presented here will most efficiently reduce genotyping costs when small sets of loci (“core sets”) for long-time use in large-scale population screenings are compiled.
Understanding how organisms adapt to extreme environments is fundamental and can provide insightful case studies for both evolutionary biology and climate-change biology. Here, we take advantage of the vast diversity of lifestyles in ants to identify genomic signatures of adaptation to extreme habitats such as high altitude. We hypothesized two parallel patterns would occur in a genome adapting to an extreme habitat: 1) strong positive selection on genes related to adaptation and 2) a relaxation of previous purifying selection. We tested this hypothesis by sequencing the high-elevation specialist Tetramorium alpestre and four other phylogenetically related species. In support of our hypothesis, we recorded a strong shift of selective forces in T. alpestre, in particular a stronger magnitude of diversifying and relaxed selection when compared with all other ants. We further disentangled candidate molecular adaptations in both gene expression and protein-coding sequence that were identified by our genome-wide analyses. In particular, we demonstrate that T. alpestre has 1) a higher level of expression for stv and other heat-shock proteins in chill-shock tests and 2) enzymatic enhancement of Hex-T1, a rate-limiting regulatory enzyme that controls the entry of glucose into the glycolytic pathway. Together, our analyses highlight the adaptive molecular changes that support colonization of high-altitude environments.
This article documents the addition of 234 microsatellite marker loci to the Molecular Ecology Resources Database. Loci were developed for the following species: Acipenser sinensis, Aleochara bilineata, Aleochara bipustulata, Barbus meridionalis, Colossoma macropomum, Delia radicum, Drosophila nigrosparsa, Fontainea picrosperma, Helianthemum cinereum, Liomys pictus, Megabalanus azoricus, Pelteobagrus vachelli, Pleuragramma antarcticum, Podarcis hispanica type 1A, Sardinella brasiliensis and Sclerotinia homoeocarpa. These loci were cross-tested on the following species: Acipenser dabryanus, Barbus balcanicus, Barbus barbus, Barbus cyclolepis, Drosophila hydei, Drosophila melanogaster, Drosophila obscura, Drosophila subobscura, Fontainea australis, Fontainea fugax, Fontainea oraria, Fontainea rostrata, Fontainea venosa, Podarcis bocagei, Podarcis carbonelli, Podarcis liolepis, Podarcis muralis and Podarcis vaucheri.
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.
21Adaptation to climate can drive variation in diversification rates and species richness. 22As yet, potential patterns of genomic signatures in organisms adapting to more 61 extreme habitats, such as high elevations, have not been identified and, more 62 generally, there is no theory that predicts the rates of genomic change for extreme 63 habitats. Here, we hypothesise two parallel patterns to occur in a genome adapting to 64 an extreme habitat. On the one hand, strong positive selection on genes related to 65 adaptation, such as genes involved in metabolic pathways, and on the other hand, a 66 3 relaxation of previous selecting forces, as the conditions of the previous niche are 67 lacking in the new niche. The latter should lead to a reduced number and/or a 68 different set of genes under purifying selection. Specifically, in the case of high-69 elevation habitats, some heat-shock proteins (HSPs), necessary for coping with 70 extreme heat, should be under relaxation, because heat resistance is not strongly 71 selected for in the average alpine species. A problem with the hypothesis of these two 72 parallel patterns is that there is no test available for the functional consequences of 73 the loss or gain of a specific gene. There are often many pleiotropic effects for genes 74 that make interpretations difficult, and HSPs, expressed upon exposure to stress or 75 during development and growth, are likely to fall into this category. Therefore, we 76 expect signatures of strong positive selection in metabolic genes, and an increase in 77 the relaxation rates in other genes, without being able to specify which these might 78 be. Identifying such genes will help to set up hypotheses to test in the future. A 79 comparative approach offers the strongest method for testing our hypothesis, by 80 comparing genomes of species closely related to each other, but divergent in terms of 81 adaptation to different environments. However, this approach can be limited by 82 insufficient niche divergence within a group or the number of genomes sequenced. 83
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