Biology subjects

Vos, R. A.

Publications and source records attributed to Vos, R. A..

11 recordsLinked to original sources

bioRxiv2022

A TOMATO GENOME FROM THE ITALIAN RENAISSANCE PROVIDES INSIGHTS INTO COLUMBIAN AND PRE-COLUMBIAN EXCHANGE LINKS AND DOMESTICATION

AO_SCPLOWBSTRACTC_SCPLOWThe history of the tomatos interactions with humans spans both a pre-Columbian period of millennia of cultivation and domestication in South and Central America as well as the plants rise to becoming one of the worlds major cash crops following the arrival of Europeans in the Neotropics and the subsequent Columbian Exchange. In the process, the influence of successive regimes of artificial selection gave rise to an entangled species complex comprised of a wild ancestor and numerous cultivars exhibiting a great diversity of fruit phenotypes (often sorted in cherry and big types). Here, we provide a snapshot into these dynamics by presenting the ancient nuclear and plastid genomes of one of the oldest tomatoes still in existence: the almost half a millennium old En Tibi specimen from the Italian Renaissance. By placing the genome skimming data obtained from this specimen within the phylogenomic context of wild relatives and Neotropical tomato cultivars and land races, we are able to show that the most likely geographic origins of the specimens immediate ancestors, i.e. whence the exchange of this particular lineage originated, are around the Gulf of Mexico. We also show that this specimen was less inbred than present-day tomatoes, shedding light on the genomic signatures of historical domestication processes. Probing deeper into the ancestry that shaped extant genetic diversity within the complex, we reconstruct multiple ancestral populations with diffuse but distinct signatures in cherry and big tomatoes. The geographic structure of these ancestries within the nearest wild relative of tomatoes and the differential extent to which these ancestries are represented in domesticates points to early cultivation and human-assisted dispersal of tomatoes originating from the northern extreme of the natural range of the species. Our findings illustrate the inferences that can be drawn from ancient DNA extracted from herbarium specimens and historic plant collections. At the same time, we stress that our findings draw on multiple disciplines including ethnobotanical and historical research and on the (agri)cultural contributions of a variety of world cultures.

plant biology↗

bioRxiv2021

Refining Bulk Segregant Analyses: Ontology-Mediated Discovery of Flowering Time Genes in Brassica oleracea

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSBulk segregant analysis (BSA) can help identify quantitative trait loci (QTLs), but this may result in substantial bycatch of functionally irrelevant genes. Here we develop a Gene Ontology-mediated approach to zoom in on specific markers implicated in flowering time from among QTLs identified by BSA of the giant woody Jersey kale phenotyped in four bulks of flowering onset. ResultsOur BSA yielded tens of thousands of candidate genes. We reduced this by two orders of magnitude by focusing on genes annotated with terms contained within relevant subgraphs of the Gene Ontology. A further enrichment test led to the pathway for circadian rhythm in plants. The genes that enriched this pathway are attested from previous research as regulating flowering time. Some of these genes were also identified as having functionally significant variation compared to Arabidopsis ConclusionsWe validated and confirmed our ontology-mediated results through a more targeted, homology-based approach. However, our ontology-mediated approach produced additional genes of putative importance, showing that the approach aids in exploration and discovery. We view our method as potentially applicable to the study of other complex traits and therefore make our workflows available as open-source code and a reusable Docker container.

bioinformatics↗

bioRxiv2019

Modeling Abiotic Niches of Crops and Wild Ancestors Using Deep Learning: A Generalized Approach

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSUnderstanding what interactions and environmental factors shape the geographic distribution of species is one of the fundamental questions in ecology and evolution. Insofar as the focus is on agriculturally important species, insight into this is also of applied importance. Species Distribution Modeling (SDM) comprises a spectrum of approaches for establishing correlative models of species (co-)occurrences and geospatial patterns of abiotic environmental variables.\n\nMethodsHere, we contribute to this field by presenting a generalized approach for SDM that utilizes deep learning, which offers some improvements over current methods, and by presenting a case study on the habitat suitability of staple crops and their wild ancestors. The approach we present is implemented in a reusable software toolkit, which we apply to an extensive data set of geo-referenced occurrence records for 52 species and 59 GIS layers. We compare the habitat suitability projections for selected, major crop species with the actual extent of their current cultivation.\n\nResultsOur results show that the approach yields especially plausible projections for species with large numbers of occurrences (>500). For the analysis of such data sets, the toolkit provides a convenient interface for using deep neural networks in SDM, a relatively novel application of deep learning. The toolkit, the data, and the results are available as open source / open access packages.\n\nConclusionsSpecies Distribution Modeling with deep learning is a promising avenue for method development. The niche projections that can be produced are plausible, and the general approach provides great flexibility for incorporating additional data such as species interactions.

bioinformatics↗

bioRxiv2019

A Web Service for Assessing Insect Abundances for Meadow Birds by Image Analysis

AO_SCPLOWBSTRACTC_SCPLOWMeadow birds are a group of species native to the Netherlands characterized by breeding in meadows that has been in decline over the last several decades, despite widespread conservation efforts. Agricultural intensification is thought to be one of the main causes of this decline, but no yearly data exists on the surrounding ecology of these birds. Recent efforts have tried to assess the food supply of meadow birds by setting sticky traps and counting the number of insects caught on them. However, this approach cannot be applied on a large scale since counting the insects is very labour intensive and unappealing to the volunteers that contribute to this research. To get a better assessment of the food supply at a larger scale, we present a system to automate counting of insects on sticky traps. The system is intended to process uploaded images and metadata using computer vision techniques to determine the number of insects found in photographs taken from the sticky traps.

bioinformatics↗

bioRxiv2019

Availability of Insects as Feed for Meadow Bird Chicks Assessed Across Years by Batched Image Analysis of Sticky Traps

As of 1990, there are 27 bird species that have been assigned as meadow birds by the Dutch equivalent of the Farmland Bird Indicator (FBI). These birds have one common characteristic that classifies them as meadow birds: they prefer to breed in meadows. Since 1960, the overall number of meadow birds has been declining rapidly and recently only five species have shown increases. However, not only meadow birds have been declining, this same rate of decline is also seen in many vertebrate, insect, and plant species throughout Europe. Increasing agriculture and urbanisation are considered to be the main causes of these alarming declines and agri-environment schemes show insufficient effect. Not only decreased reproduction rate of meadow birds, but also decreased survival rate of meadow bird chicks may play an important role in the dropping meadow bird numbers. Most of the meadow birds eat insects and it is therefore hypothesized that their food supply is too low. The Louis Bolk Insitute and ANV Water, Land & Dijken have been setting sticky traps in several meadows and counting the number of trapped insects on each sticky trap to assess if the food supply of meadow birds is sufficient. However, counting the insects is very time consuming, unappealing, and error prone. Therefore, a system that uses image analysis to automatically count the insects was improved and deployed as a web application and command line application. This system analyses photographs of sticky traps and counts the insects found on the sticky traps that were set in May 2018. These results were compared to the number of counted insects on the sticky traps that were set in May 2017, tested if the difference was significant and if there was a correlation between the usage of certain management packages. The accuracy of the automated system was also tested by determining if automatically counted results were not significantly different from hand counted results. The results showed that the accuracy of the system was improved but was still unable to provide very reliable results, most likely due to the usage of low-quality photographs from 2017. The number of counted insects from the sticky traps that were set in 2017 was significantly lower as compared to 2018 and no actual correlation could be found between the number of insects and management packages. It is possible for insect populations to have grown this much, however, the difference in insect numbers could have been caused by the difference in temperature when placing the sticky traps, or the sticky traps being less sticky. It is also very likely that the number of insects on the traps in 2017 is lower due to the poor quality of the photographs, so fewer insects could be detected. If the insect populations have grown as significantly as is indicated from the results then it can be stated that the food supply of meadow birds is more sufficient as compared to 2017 and it would be probable that an increase in meadow birds has occurred or will occur in the near future. Further research should be conducted using high quality standardized photographs and carried out for multiple years to gain plentiful reliable data.

bioinformatics↗

bioRxiv2019

Differentiation between wild and domesticated Ungulates based on ecological niches

The domestication of flora and fauna is one of the most significant transitions in humankinds history. It changed human societies drastically with alterations in biodiversity, atmospheric composition and land use. Humans have domesticated relatively few large animals and all of them belong to the Ungulates, though they are only 15 species of the {+/-}150 that the entire group comprises. This can partially be explained by behavioral and life history pre-adaptations, e.g. social group structure, mating behavior, parent-young interaction, feeding behavior, and response to humans. The other dimension of proposed pre-adapatations concerns the biomes from which domesticated Ungulates originate. Here we test whether environmental preferences i.e. niches and related niche traits, differentiate between wild and domesticated Ungulates. We used three methods to determine the niche dimensions for each species and calculate overlap in niche space between them. Two methods are based on MaxEnt ecological niche models and one method uses raw occurrence data. Our results show that there is no weighted combination of environmental traits that clusters all domesticated Ungulates to the exclusion of all wild ones. On the contrary, domesticated Ungulates are overdispersed in niche space, indicating that the major pre-adaptations for domestication are not directly related to the abiotic niche. However, phylogenetic generalized linear modelling of selected niche dimensions does predict domestication significantly. We conclude that further research of other traits is needed.

evolutionary biology↗

bioRxiv2018

Taxonomic Classification of Ants (Formicidae) from Images using Deep Learning

The well-documented, species-rich, and diverse group of ants (Formicidae) are important ecological bioindicators for species richness, ecosystem health, and biodiversity, but ant species identification is complex and requires specific knowledge. In the past few years, insect identification from images has seen increasing interest and success, with processing speed improving and costs lowering. Here we propose deep learning (in the form of a convolutional neural network (CNN)) to classify ants at species level using AntWeb images. We used an Inception-ResNet-V2-based CNN to classify ant images, and three shot types with 10,204 images for 97 species, in addition to a multi-view approach, for training and testing the CNN while also testing a worker-only set and an AntWeb protocol-deviant test set. Top 1 accuracy reached 62% - 81%, top 3 accuracy 80% - 92%, and genus accuracy 79% - 95% on species classification for different shot type approaches. The head shot type outperformed other shot type approaches. Genus accuracy was broadly similar to top 3 accuracy. Removing reproductives from the test data improved accuracy only slightly. Accuracy on AntWeb protocol-deviant data was very low. In addition, we make recommendations for future work concerning image threshold, distribution, and quality, multi-view approaches, metadata, and on protocols; potentially leading to higher accuracy with less computational effort.

bioinformatics↗

bioRxiv2018

Image analysis for taxonomic identification of Javanese butterflies

Taxonomic experts classify millions of specimens, but this is very time-consuming and therefore expensive. Image analysis is a way to automate identification and was previously done at Naturalis Biodiversity Center for slipper orchids (Cypripedioideae) by the program OrchID. This program operated by extracting a pre-defined number of features from images, and these features were used to train artificial neural networks (ANN) to classify out-of-sample images. This program was extended to work for a collection of Javanese butterflies, donated to Naturalis by the Van Groenendael-Krijger Foundation. Originally, for the orchids, an image was divided into a pre-defined number of horizontal and vertical bins and the mean blue-green-red values of each bin were calculated (BGR method) to obtain image features. In the extended implementation, characteristic image features were extracted using the SURF algorithm implemented in OpenCV and clustered with the BagOfWords method (SURF-BOW method). In addition, a combination of BGR- and SURF-BOW was implemented to extract both types of features in a single dataset (BGR-SURF method). A selection of the butterfly and orchid images was made to create datasets with at least 5 and at most 50 specimens per species. The SURF-BOW and BGR-SURF methods were applied to both selected datasets, and the original BGR method was applied to the selected butterfly dataset. PCA plots were made to inspect visually how well the applied methods discriminated among the species. For the butterflies, both genus and species appeared to cluster together in the plots of the SURF-BOW method. However, no obvious clustering was noticeable for the orchid plots. The performance of the ANNs was validated by a stratified k-fold cross validation. For the butterflies, the BGR-SURF method scored best with an accuracy of 77%, against 71% for the SURF-BOW method and 66% for the BGR method, all for chained genus and species prediction with k = 10. The new methods could not improve the accuracy of the orchid classification with k = 10, which was 75% on genus, 52% on genus and section and 48% on genus, section and species in the original framework and now less than 25% for all. The validation results also showed that at least about 15 specimens per species were necessary for a good prediction with the SURF-BOW method. The BGR-SURF method was found to be the best of these methods for butterflies, but the original BGR method was best for the slipper orchids. In the future these methods may be tested with other datasets, for example with mosquitoes. In addition, other classifiers may be tested for better performance, like support vector machines.

bioinformatics↗

bioRxiv2018

TreeBASEdmp: A Toolkit for Phyloinformatic Research

Over the last 20 years, TreeBASE has acquired a substantial body of phylogenetic data, including more than 20,000 published phylogenies. Given latency issues and limited options when it comes to querying the database remotely, a simplified and consolidated version of the database, here called TreeBASEdmp, is made available for download, allowing biologists to design custom analyses of the data on their local computers. The database is indexed to support searching for phylogenetic topologies using nested sets and closure tables. Here we propose a new approach to find broadly-defined phylogenetic patterns, a method we call Generic Topological Querying, which allows the user to find hypotheses of relationship without being constrained to use particular sets of specific taxa. Additionally, we normalize as many leaf nodes as possible to an equivalent species rank identifier to assist in supertree synthesis. Our example script rapidly assembles sets of trees and generates a matrix representation of them for subsequent supertree generation.

evolutionary biology↗

bioRxiv2017

Evolutionary dynamics of mycorrhizal symbiosis in land plant diversification

Mycorrhizal symbiosis between soil fungi and land plants is one of the most widespread and ecologically important mutualisms on earth. It has long been hypothesized that the Glomeromycotina, the mycorrhizal symbionts of the majority of plants, facilitated colonization of land by plants in the Ordovician. This view was recently challenged by the discovery of mycorrhizal associations with Mucoromycotina in several early diverging lineages of land plants. Utilizing a large, species-level database of plants mycorrhizal associations and a Bayesian approach to state transition dynamics we here show that the recruitment of Mucoromycotina is the best supported transition from a non-mycorrhizal state. We further found that transitions between different combinations of either or both of Mucoromycotina and Glomeromycotina occur at high rates and found similar promiscuity among combinations that include either or both of Glomeromycotina and Ascomycota with a nearly fixed association with Basidiomycota. Our results demonstrate that under the most likely scenario symbiosis with Mucoromycotina enabled the establishment of early land plants.

evolutionary biology↗

bioRxiv2017

The Bio::Phylo libraries for phylogenetic data analysis, version 2.0

MotivationPhylogenetic analysis is a broad and expanding field that requires versatile programming toolkits to manage the various data types, file formats, and needs for scalability, simulation, visualization, and data exploration.\n\nResultsWe present version 2.0 of the Bio::Phylo libraries for phylogenetic data analysis. This new release represents a rewrite of the architecture, allowing for extensions that improve speed and persistence, as well as increased functionality in terms of analysis, data reading and writing, and visualization.\n\nAvailabilityThe package is released as open source software under the same terms as Perl itself and available from the comprehensive Perl archive network as well as directly from the source code repository.\n\nContactrutger.vos@naturalis.nl\n\nSupplementary informationSupplementary data are available as doi:10.5281/zenodo.1039210

bioinformatics↗