Evaluating the impact of scoring parameters on the structure of intra-specific genetic variation using RawGeno, an R package for automating AFLP scoring

<p>Abstract</p> <p>Background</p> <p>Since the transfer and application of modern sequencing technologies to the analysis of amplified fragment-length polymorphisms (AFLP), evolutionary biologists have included an increasing number of samples and markers in their studies. Although justified in this context, the use of automated scoring procedures may result in technical biases that weaken the power and reliability of further analyses.</p> <p>Results</p> <p>Using a new scoring algorithm, RawGeno, we show that scoring errors – in particular "bin oversplitting" (i.e. when variant sizes of the same AFLP marker are not considered as homologous) and "technical homoplasy" (i.e. when two AFLP markers that differ slightly in size are mistakenly considered as being homologous) – induce a loss of discriminatory power, decrease the robustness of results and, in extreme cases, introduce erroneous information in genetic structure analyses. In the present study, we evaluate several descriptive statistics that can be used to optimize the scoring of the AFLP analysis, and we describe a new statistic, the information content per bin (I_bin) that represents a valuable estimator during the optimization process. This statistic can be computed at any stage of the AFLP analysis without requiring the inclusion of replicated samples. Finally, we show that downstream analyses are not equally sensitive to scoring errors. Indeed, although a reasonable amount of flexibility is allowed during the optimization of the scoring procedure without causing considerable changes in the detection of genetic structure patterns, notable discrepancies are observed when estimating genetic diversities from differently scored datasets.</p> <p>Conclusion</p> <p>Our algorithm appears to perform as well as a commercial program in automating AFLP scoring, at least in the context of population genetics or phylogeographic studies. To our knowledge, RawGeno is the only freely available public-domain software for fully automated AFLP scoring, from electropherogram files to user-defined working binary matrices. RawGeno was implemented in an R CRAN package (with an user-friendly GUI) and can be found at <url>http://sourceforge.net/projects/rawgeno</url>.</p

Can root-associated fungi mediate the impact of abiotic conditions on the growth of a High Arctic herb?

Arctic plants are affected by many stressors. Root-associated fungi are thought to influence plant performance in stressful environmental conditions. However, the relationships are not well-known; do the number of fungal partners, their ecological functions and community composition mediate the impact of environmental conditions and/or influence host plant performance? To address these questions, we used a common arctic plant as a model system: Bistorta vivipara. Whole plants (including root system, n = 214) were collected from nine locations in Spitsbergen. Morphometric features were measured as a proxy for plant performance and combined with metabarcoding datasets of their root-associated fungi (amplicon sequence variants, ASVs), edaphic and meteorological variables. Seven biological hypotheses regarding fungal influence on plant measures were tested using structural equation modelling. The best-fitting model revealed that local temperature affected plants both directly (negatively aboveground and positively below-ground) and indirectly - mediated by fungal richness and the ratio of symbio- and saprotrophic ASVs. The influence of temperature on host plants is therefore complex and should be examined further. Fungal community composition did not impact plant measurements and plant reproductive investment was not influenced by any fungal parameters. The lack of impact of fungal community composition on plant performance suggests that the functional importance of fungi is more essential for the plant than their identity

Breeding den selection by Arctic foxes (Vulpes lagopus) in southern Yamal Peninsula, Russia

Selecting the right location for a den during the breeding season is a type of habitat selection in the Arctic fox (Vulpes lagopus) that is likely to affect its reproductive success. A den’s suitability likely depends on its ability to provide shelter, as well as its proximity to prey resources. Depending on the different relative risks that Arctic foxes may face across their broad circumpolar range, Arctic foxes may place different emphases on selection for shelter and prey resources in different ecosystems. Understanding the different requirements for reproduction under different ecological conditions is highly relevant to conservation efforts in areas where Arctic foxes are threatened by rapid environmental changes. Here, we investigated the relative selection for shelter and prey resources in southern Yamal Peninsula (Russia) using data from 45 dens collected over a 13-year period. Arctic foxes preferred to breed in dens with more den entrances; an indicator of shelter quality. Arctic foxes also preferred dens surrounded by more prey resources (quantified by the amount of river valley habitat), but this result was less conclusive. These results complement the findings reported from other study areas, illustrating that Arctic foxes in ecosystems with diverse predator communities may put emphasis on selection for shelter quality. In less productive ecosystems, Arctic foxes may rather put emphasis on selection for prey resources. As tundra ecosystems become more productive and generalist predators move north, the reproductive requirements and habitat selection of Arctic foxes may change accordingly, depending on the species’ ability to adapt

Interspecific and interploidal gene flow in Central European Arabidopsis (Brassicaceae)

Background Effects of polyploidisation on gene flow between natural populations are little known. Central European diploid and tetraploid populations of Arabidopsis arenosa and A. lyrata are here used to study interspecific and interploidal gene flow, using a combination of nuclear and plastid markers. Results Ploidal levels were confirmed by flow cytometry. Network analyses clearly separated diploids according to species. Tetraploids and diploids were highly intermingled within species, and some tetraploids intermingled with the other species, as well. Isolation with migration analyses suggested interspecific introgression from tetraploid A. arenosa to tetraploid A. lyrata and vice versa, and some interploidal gene flow, which was unidirectional from diploid to tetraploid in A. arenosa and bidirectional in A. lyrata. Conclusions Interspecific genetic isolation at diploid level combined with introgression at tetraploid level indicates that polyploidy may buffer against negative consequences of interspecific hybridisation. The role of introgression in polyploid systems may, however, differ between plant species, and even within the small genus Arabidopsis, we find very different evolutionary fates when it comes to introgression

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts

Open Access Journal (SHERPA RoMEO Green) DOI: 10.1007/s13280-016-0770-0Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region

Predator co-occurrence in alpine and Arctic tundra in relation to fluctuating prey

1. Large carnivores influence ecosystem dynamics in multiple ways, for example, by suppressing meso-carnivores and providing carrions for smaller scavengers. Loss of large carnivores is suggested to cause meso-carnivore increase and expansion. Moreover, competition between meso-carnivores may be modified by the presence of larger carnivores. In tundra ecosystems, the smallest meso-carnivore, the Arctic fox, has experienced regional declines, whereas its larger and competitively superior congener, the red fox, has increased, potentially due to changes in the abundance of apex predators. 2. We explored if variation in the occurrence of wolverine and golden eagle impacted the occurrence and co-occurrence of the Arctic fox and red fox in relation to varying abundances of small rodents within the Scandinavian tundra. 3. We applied multi-species occupancy models to an extensive wildlife camera dataset from 2011–2020 covering 98 sites. Daily detection/non-detection of each species per camera trap site and study period (late winter; March–May) was stacked across years, and species occupancy was related to small rodent abundance while accounting for time of the year and status of simulated carcass. 4. The Arctic fox was more likely to co-occur with the red fox when the wolverine was present and less likely to co-occur with the red fox when golden eagles were present and the wolverine was absent. Red foxes increased in occupancy when co-occurring with the larger predators. The Arctic fox responded more strongly to small rodent abundance than the red fox and co-occurred more often with the other species at carcasses when rodent abundance was low. 5. Our findings suggest that the interspecific interactions within this tundra predator guild appear to be surprisingly intricate, driven by facets of fear of predation, interspecific mediation and facilitation, and food resource dynamics. These dynamics of intraguild interactions may dictate where and when conservation actions targeted towards the Arctic fox should be implemented

Vicariance, dispersal, and hybridization in a naturally fragmented system: the afro-alpine endemics Carex monostachya and C. runssoroensis (Cyperaceae)

Published version available at https://doi.org/10.1007/s00035-015-0162-2. The naturally fragmented habitat on the tallest African mountains provides a good model system to study vicariance, dispersal, and hybridization. Many mountains are separated by lowland that likely was unsuitable for high-alpine plants even during cold climatic periods. We explore the relative importance of these processes using two endemic sister species: the widespread Ethiopian/eastern East African Carex monostachya and the mainly western East African C. runssoroensis. These bog-forming sedges co-occur in some mountains and are hypothesized to hybridize. The two species were distinctly differentiated for genome-wide Amplified Fragment Length Polymorphisms (AFLPs), also in one mountain where they co-occur. However, the plants from another mountain showed strong signals of admixture. The results suggest initial divergence into one western and one northern/eastern lineage, followed by long-distance dispersal resulting in secondary contact zones. Also within species genetic diversity was clearly structured with distinct genetic groups on some, but not all mountains. Differentiation levels varied considerably and did not always correspond to the extent of lowland habitat between mountains. The narrow Rift Valley in the otherwise nearly contiguous highlands in Ethiopia appears to present a much stronger barrier to dispersal than the extensive lowlands separating Ethiopia from East Africa. This may be a general pattern since it has been documented also for other afro-alpine specie

Genetic consequences of climate change for northern plants

Climate change will lead to loss of range for many species, and thus to loss of genetic diversity crucial for their long-term persistence. We analysed range-wide genetic diversity (amplified fragment length polymorphisms) in 9581 samples from 1200 populations of 27 northern plant species, to assess genetic consequences of range reduction and potential association with species traits. We used species distribution modelling (SDM, eight techniques, two global circulation models and two emission scenarios) to predict loss of range and genetic diversity by 2080. Loss of genetic diversity varied considerably among species, and this variation could be explained by dispersal adaptation (up to 57%) and by genetic differentiation among populations (FST; up to 61%). Herbs lacking adaptations for long-distance dispersal were estimated to lose genetic diversity at higher rate than dwarf shrubs adapted to long-distance dispersal. The expected range reduction in these 27 northern species was larger than reported for temperate plants, and all were predicted to lose genetic diversity according to at least one scenario. SDM combined with FST estimates and/or with species trait information thus allows the prediction of species' vulnerability to climate change, aiding rational prioritization of conservation efforts

Using machine learning to provide automatic image annotation for wildlife camera traps in the Arctic

Source at https://hdl.handle.net/10037/26504.The arctic tundra is considered the terrestrial biome expected to be most impacted by climate change, with temperatures projected to increase as much as 10 °C by the turn of the century. The Climate-ecological Observatory for Arctic Tundra (COAT) project monitors the climate and ecosystems using several sensor types. We report on results from projects that automate image annotations from two of the camera traps used by COAT: an artificial tunnel under the snow for capturing information about small mammals, and an open-air camera trap using bait that captures information of a range of larger sized birds and mammals. These traps currently produce over two million pictures per year. We have developed and trained several Convolutional Neural Network (CNN) models to automate annotation of images from these camera traps. Results show that we get a high accuracy: 97.84% for tunnel traps, and 94.1% for bait traps. This exceeds previous state of the art in animal identification on camera trap images, and is at a level where we can already relieve experts from manual annotation of images

Towards long-term records of rain-on-snow events across the Arctic from satellite data

Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snowpack properties are changing, and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. Specifically, satellite microwave observations have been shown to provide insight into known events. Only Ku-band radar (scatterometer) has been applied so far across the entire Arctic. Data availability at this frequency is limited, however. The utility of other frequencies from passive and active systems needs to be explored to develop a concept for long-term monitoring. The latter are of specific interest as they can be potentially provided at higher spatial resolution. Radar records have been shown to capture the associated snow structure change based on time-series analyses. This approach is also applicable when data gaps exist and has capabilities to evaluate the impact severity of events. Active as well as passive microwave sensors can also detect wet snow at the timing of an ROS event if an acquisition is available. The wet snow retrieval methodology is, however, rather mature compared to the identification of snow structure change since ambiguous scattering behaviour needs consideration. C-band radar is of special interest due to good data availability including a range of nominal spatial resolutions (10 m–12.5 km). Scatterometer and SAR (synthetic aperture radar) data have therefore been investigated. The temperature dependence of C-band backscatter at VV (V – vertical) polarization observable down to −40 ◦C is identified as a major issue for ROS retrieval but can be addressed by a combination with a passive microwave wet snow indicator (demonstrated for Metop ASCAT – Advanced Scatterometer – and SMOS – Soil Moisture and Ocean Salinity). Results were compared to in situ observations (snowpit records, caribou migration data) and Ku-band products. Ice crusts were found in the snowpack after detected events (overall accuracy 82 %). The more crusts (events) there are, the higher the winter season backscatter increase at C-band will be. ROS events captured on the Yamal and Seward peninsulas have had severe impacts on reindeer and caribou, respectively, due to ice crust formation. SAR specifically from Sentinel-1 is promising regarding ice layer identification at better spatial details for all available polarizations. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record, but the consideration of performance differences due to spatial and temporal cover, as well as microwave frequency, is crucial. Retrieval is most robust in the tundra biome, where results are comparable between sensors. Records can be used to identify extremes and to apply the results for impact studies at regional scale