Patterns of single nucleotide polymorphism (snp) variation: further insights into the complex history of the iberian honeybee

The Iberian Peninsula harbours the greatest honeybee genetic diversity and complexity in Europe. The challenge of deciphering the mechanisms underlying such complexity has led to numerous morphological and molecular marker-based surveys of the Iberian honeybee. Yet, in spite of the numerous studies, the evolutionary processes underlying patterns of Iberian honey bee genetic diversity remain poorly understood. Early phylogeographical studies of morphology and allozymes revealed the existence of a gradient extending from Africa to northern Europe, with Iberian honeybees showing intermediate phenotypes. This pattern raised the hypothesis of an African origin and a mechanism of primary intergradation for the Iberian honeybee (and the black honeybee) origin. Maternal patterns tell a different history. Mitochondrial DNA (mtDNA) surveys have revealed the co-occurrence of highly divergent lineages forming a south-north cline, a pattern that is more compatible with a recent secondary contact hypothesis. Adding to the complexity, microsatellite variation supports neither hypothesis as microsatellites showed virtually no differentiation and no traces of African genes in Iberian honeybees. In an attempt to resolve this debate we recently performed a fine resolution (both geographic and genomic) survey of the Iberian honeybee using SNPs, genotyped using automated high throughput technologies of Illumina, and mtDNA sequencing data of the tRNAleu-cox2 highly polymorphic region. The dataset was analyzed using a battery of methods implemented by the programs ARLEQUIN, STRUCTURE, ADEGENET, TESS, BAYESCAN, among others. The results were interesting: contrasting with microsatellites, SNPs were able to recover the clinal signal in the Iberian Peninsula producing a spatial pattern that was concordant with mtDNA. However, the differentiation levels within Iberian populations and between Iberian and northern African populations do not support a recent secondary contact. Herein, the results of the SNP surveys will be presented and new hypotheses will be discussed.Fundação para a Ciência e Tecnologi

Padrões geográficos de diversidade genética da abelha melífera em Portugal (continente e ilhas)

A Europa alberga três linhagens evolutivas da abelha melífera (Apis mellifera L.), nomeadamente: linhagem M (oeste e norte europeia), linhagem C (sudeste Europeia) e linhagem A (Africana). A linhagem C inclui mais de 6 subespécies destacando-se, pela sua importância na apicultura mundial, a A. m. ligustica (abelha italiana) e A. m. carnica (abelha carniola). Por sua vez, a linhagem M agrupa apenas a A. m. mellifera (abelha preta) e A. im. iberiensis (abelha ibérica). Enquanto a distribuição natural da abelha preta inclui uma boa parte das regiões da Europa Ocidental e Central até à Península Escandinava, Alemanha e Polónia, a abelha ibérica está confinada à Península Ibérica. Porém, é nesta região da Europa que a abelha melífera exibe maior diversidade genética, resultante da coexistência de abelhas de origem Europeia (M) e Africana (A). Num contexto de profundas alterações ambientais (e.g. alterações climáticas, poluição, pesticidas, novos patogénios e parasitas), a preservação deste valioso património é crucial pois a sobrevivência a longo prazo de qualquer organismo depende da sua diversidade genética

Comparing two in-house developed SNP assays for inferring population structure in the honey bee (Apis mellifera L.)

The honey bee, Apis mellifera L., is under pressure globally due to several factors, one of them is the large-scale introduction of foreign queens and/or colonies which act as vectors of pathogens, and also threaten the genetic integrity of native populations. Different molecular tools have been developed to monitor the genetic integrity of the populations. SNPs (Single Nucleotide Polymorphism) have been preferred because are easily transferred between laboratories, have a low genotyping error, provide high-quality data, and are suitable for automation. Here, we compared the genotyping results obtained with two medium-density-SNP assays previously developed. One of assays was designed from 88 whole genomes of Apis mellifera iberiensis and 44 C-lineage individuals (the main ancestry of commercial bees) using fixed SNPs (FST=1) distributed in the 16 honey bee chromosomes. The other assay was designed from variation in immune genes using a discovery panel of 123 whole genomes, representing seven subspecies (A. m. iberiensis, A. m. mellifera, A. m. intermissa, A. m. sahariensis, A. m. ligustica, A. m. carnica, A. m. siciliana and three lineages (A, M and C). All the samples are from the native range of each subspecies and they were taken from inside the hives, placed in absolute ethanol and stored at -20ºC until DNA extraction. The tools were compared using 473 samples from the Azores, which harbour a genetically complex honey bee population. The samples were genotyped using the iPLEX MassARRAY® MALDI-TOF system. The membership proportions of each individual (Qvalue) were calculated using ADMIXTURE considering two genetic groups (K=2), with 10,000 iterations in 20 independent runs. Our results show that both assays provide similar Q-values, with a Pearson’s correlation of 0.89. Only 9.5% of the samples have an absolute Q-value difference > 0.10. The choice of the best SNP assay depends on the subspecies and the aim of the project. While the immune assays can be applied in different subspecies the other assay was specifically designed for A. m. iberiensis. Furthermore, if there is disease data available, the immune assay caninfo:eu-repo/semantics/publishedVersio

Forest landscape ecology and global change: what are the next steps?

In this chapter, we summarize current trends and challenges and future research directions in forest landscape ecology and in management related to global change. We discuss the available knowledge in forest landscape ecology and the possibilities of using this knowledge to support management under changing conditions. We also discuss the forest sector’s preparedness to deal with changes in management and how forest landscape ecology can guide this management. Forest landscape ecology has gathered substantial knowledge on patterns, processes, tools, and methods that can support forest and landscape management during changing scenarios. We recognize that existing knowledge is incomplete and that a substantial portion of our knowledge is uncertain, that variability in landscape conditions and various forms of error compound the problem, that we still lack considerable knowledge in some fields, and that there are likely to be knowledge gaps we are not aware of. We nonetheless face the challenge of responding to change based on the available knowledge

Geometric contrast feature for automatic visual counting of honey bee brood capped cells

Assessment of honey bee colony strength by measuring adults or brood is often required for ecological studies. The brood has typically been estimated through a subjective mode (Lieberfelder method), although it can also be objectively determined by counting (manually or automatically) the brood cells (capped or uncapped) from digital images. The manual counting of capped cells is highly prone to errors and a time-consuming and tedious task. An automatic way to accomplish that task allows reducing those drawbacks. The main challenge for developing an automatic method is, however, the presence of intraclass color variation; it is not possible to make a reliable detection based just on the pixel color presented by the capped cells. While several researchers are using the Hough transform to solve that problem, at certain light, noise, and surface conditions the automatic detection fails. After carefully observing capped cell regions of several combs, we identified a set of geometrical relations that could be used to build a consistent contrast feature. That feature is the key to detect the capped cells with a high accuracy in our work. A functional optimizer is performing a searching on the image looking for the locations that maximize the contrast on that feature. Our experimental results are showing a good detection rate (over 96%), despite the wide intraclass color variation. This research is funded through the 2013-2014 BiodivERsA/FACCE-JPI Joint call for research proposals, with the national funders FCT (Portugal), CNRS (France), and MEC (Spain).info:eu-repo/semantics/publishedVersio

Forest landscapes and global change: Challenges for research and management

Climate change, urban sprawl, abandonment of agriculture, intensifi cation of forestry and agriculture, changes in energy generation and use, expansion of infrastructure networks, habitat destruction and degradation, and other drivers and pressures of change are occurring at increasing rates globally. They affect ecological patterns and processes in forest landscapes and modify ecosystem services derived from those ecosystems. Consequently, the landscapes that are rapidly changing in response to these pressures present many new challenges to scientists and managers. Although it is not uncommon to encounter the terms “global change” and “landscape” together in the ecological literature, there has been no adequate global analysis of drivers of change in forest landscapes and their ecological consequences. Providing such an analysis is the goal of this volume: an exploration of the state of knowledge of global changes in forested landscapes, with an emphasis on their causes and effects, and the challenges faced by researchers and land managers who must cope with these changes. This book was based on the IUFRO Landscape Ecology Working Group International Conference that took place in Bragança, Portugal, in September 2010 under the theme “Forest Landscapes and Global Change: New Frontiers in Management, Conservation and Restoration”. The event brought together more than 300 landscape ecologists from almost 50 countries and 5 continents, who came to expand their knowledge and awareness of global changes in forest landscapes. We hope that the syntheses in this book, prepared by a diverse group of scientists who participated in the conference, will enhance the global understanding of a range of topics relevant to change in forest landscapes and stimulate new research to answer the questions raised by these authors. First, we introduce the broad topic of forest landscape ecology and global change. This is followed by chapters that identify and describe major agents of landscape change: climate (Iverson et al.), wildfi re (Rego and Silva), and human activities (Farinaci et al.). The next chapters address implications of change for ecosystem services (Marta-Pedroso et al.), carbon fl uxes (Chen et al.), and biodiversity conservation (Saura et al.). A subsequent chapter describes methodologies for detecting and monitoring landscape changes (Gómez-Sanz et al.) and is followed by a chapter that highlights the many challenges facing forest landscape managers amidst global change (Coulson et al.). Finally, we present a summary and a synthesis of the main points presented in the book (Azevedo et al.). Each chapter was inspired by the research experience of the authors, augmented by a review and synthesis of the global scientifi c literature on relevant topics, as well as critical input from multiple peer reviewers. The intended audience for this book includes graduate students, educators, and researchers in landscape ecology, conservation biology, and forestry, as well as land-use planners and managers. We trust that the wide range of topics, addressed from a global perspective by a geographically diverse group of contributing authors from Europe, North America, and South America, will make this volume attractive to a broad readership.We gratefully acknowledge the following peer reviewers who helped improve the content of this book: Berta Martín, Bill Hargrove, Bob Keane, Colin Beier, Don McKenzie, Eric Gustafson, Franz Gatzweiler, Geoff Henebry, Kurt Riitters, Maria Esther Núñez, Michael Ter-Mikaelian, Tom Nudds, and Yolanda Wiersma. As well, we thank Geoff Hart for assistance with editing and Janet Slobodien and Zachary Romano for assistance with publishing. We also thank FCT (the Foundation for Science and Technology, Portugal), CIMO (the Mountain Research Centre, Portugal), and IPB (the Polytechnic Institute of Bragança, Portugal) for their support during the preparation of this volume.info:eu-repo/semantics/publishedVersio

Geometric contrast feature for automatic visual counting of honey bee brood capped cells

Assessment of honey bee colony strength by measuring adults or brood is often required for ecological studies. The brood has typically been estimated through a subjective mode (Lieberfelder method), although it can also be objectively determined by counting (manually or automatically) the brood cells (capped or uncapped) from digital images. The manual counting of capped cells is highly prone to errors and a time-consuming and tedious task. An automatic way to accomplish that task allows reducing those drawbacks. The main challenge for developing an automatic method is, however, the presence of intraclass color variation; it is not possible to make a reliable detection based just on the pixel color presented by the capped cells. While several researchers are using the Hough transform to solve that problem, at certain light, noise, and surface conditions the automatic detection fails. After carefully observing capped cell regions of several combs, we identified a set of geometrical relations that could be used to build a consistent contrast feature. That feature is the key to detect the capped cells with a high accuracy in our work. A functional optimizer is performing a searching on the image looking for the locations that maximize the contrast on that feature. Our experimental results are showing a good detection rate (over 96%), despite the wide intraclass color variation. This research is funded through the 2013-2014 BiodivERsA/FACCE-JPI Joint call for research proposals, with the national funders FCT (Portugal), CNRS (France), and MEC (Spain).info:eu-repo/semantics/publishedVersio

Modelos de previsão de ataque de escolitídeos em povoamentos de Pinus pinaster Aiton

El presente trabajo pretende contribuir al desarrollo de la protección integrada de Pinus pinaster Aiton en Portugal a través de la elaboración de modelos de previsión del ataque de escolítidos que permiten proceder a la estimación cualitativa del riesgo. Estos modelos han sido llamados modelos de susceptibilidad, conocidos en la bibliografía anglo-sajona como hazard rating systems. El estudio se ha realizado en masas puras de P. pinaster en el Perímetro Forestal de Meia Via (Amarante; Portugal) sometidos al ataque de escolítidos. En estas masas se han evaluado variables dendrométricas y de la estación. Del conjunto de variables independientes estudiadas, la edad, el diámetro, la densidad, y el espesor de la corteza han sido las que han mostrado una fuerte correlación con la mortalidad causada por escolítidos (variable dependiente). El estudio de la relación entre estas variables ha indicado que la función logística es la que mejor se ajusta al conjunto de los datos. Se han obtenido de esta manera modelos de regresión simples que permiten prever la mortalidad causada por poblaciones endémicas, siendo mejores los que incluyen el diámetro y la edad de la masa de P. pinaster

DeepWings©: automatic wing geometric morphometrics classification of honey bee (Apis mellifera) subspecies using deep learning for detecting landmarks

Honey bee classification by wing geometric morphometrics entails the first step of manual annotation of 19 landmarks in the forewing vein junctions. This is a time-consuming and error- prone endeavor, with implications for classification accuracy. Herein, we developed a software called DeepWings © that overcomes this constraint in wing geometric morphometrics classification by automatically detecting the 19 landmarks on digital images of the right forewing. We used a database containing 7634 forewing images, including 1864 analyzed by F. Ruttner in the original delineation of 26 honey bee subspecies, to tune a convolutional neural network as a wing detector, a deep learning U-Net as a landmarks segmenter, and a support vector machine as a subspecies classifier. The implemented MobileNet wing detector was able to achieve a mAP of 0.975 and the landmarks segmenter was able to detect the 19 landmarks with 91.8% accuracy, with an average positional precision of 0.943 resemblance to manually annotated landmarks. The subspecies classifier, in turn, presented an average accuracy of 86.6% for 26 subspecies and 95.8% for a subset of five important subspecies. The final implementation of the system showed good speed performance, requiring only 14 s to process 10 images. DeepWings © is very user-friendly and is the first fully automated software, offered as a free Web service, for honey bee classification from wing geometric morphometrics. DeepWings© can be used for honey bee breeding, conservation, and even scientific purposes as it provides the coordinates of the landmarks in excel format, facilitating the work of research teams using classical identification approaches and alternative analytical tools.Financial support was provided through the program COMPETE 2020—POCI (Programa Operacional para a Competividade e Internacionalização) and by Portuguese funds through FCT (Fundação para a Ciência e a Tecnologia) in the framework of the project BeeHappy (POCI-01- 0145-FEDER-029871). FCT provided financial support by national funds (FCT/MCTES) to CIMO (UIDB/00690/2020).info:eu-repo/semantics/publishedVersio

The atlantic side of the iberian peninsula: a hot-spot of novel maternal honey bee diversity

The Iberian Peninsula harbors one the highest mitocondrial DNA (mtDNA) diversity ever reported for honey bee subspecies. This finding is explained not only by the co-occurrence of two divergent evolutionary lineages, western European (lineage M) and African (lineage A), but also by the higher variability of African and western European haplotypes. Indeed, over 36 haplotypes of western European and African ancestry, which form complex networks, have been reported for this area of the honey bee natural range. While studies on the diversity patterns of central and Mediterranean Iberian populations are abundant, the genetic composition of populations inhabiting the Atlantic side was until recently virtually unknown. Using the popular DraI test (PCR amplification and restriction of the intergenic tRNAleu-coxII region) we performed a fine scale genetic survey of the honey bee populations from Portugal. Adding to the 24 previously described African haplotypes, of which 17 are found in the Iberian Peninsula, 13 unreported haplotypes of African ancestry were found in our survey, which represent an addition of 54% of new variation. The fragment sizes ranged from approximately 800 to 1200 bp and the restriction length of the new haplotypes were very distinct from those reported in the literature. To further confirm the novelty of these haplotypes, we sequenced the aforementioned mtDNA region. Herein we present a phylogenetic analysis of these novel haplotypes