Jūras piekrastē ligzdojošo putnu ekoloģija

Advisor: Jānis PriednieksIzpētīti vairāki nozīmīgi Latvijas jūras piekrastē ligzdojošo putnu ekoloģijas aspekti: ligzdošanas blīvums, teritoriju izvēle, ligzdvietu izvēle un ligzdošanas bioloģija. Kā modeļsugas izmantoti galvenokārt bridējputni: upes tārtiņš, jūrasžagata un ķīvīte. Atrastas vairākas likumsakarības, kas ļauj labāk izprast piekrastes putnu ekoloģiju un veikt to aizsardzības pasākumus

Why socio-political borders and boundaries matter in conservation.

Acting to demarcate the spatial limits of decision-making processes, socio-political boundaries are an inevitable part of a human-dominated world. Rarely coincident with ecological boundaries, and thus having no ecological functional role by themselves, they nevertheless impose substantial costs on biodiversity and ecosystem conservation by fragmenting ownership, governance, and management. Where boundaries are in place, a lack of coordination on either side of a boundary affects the efficiency and efficacy of ecosystem management. We suggest four research pathways which will enhance our ability to address the adverse effects of socio-political borders on conservation: (i) scale-matching, (ii) quantification of the mutual economic benefits of conservation across boundaries, (iii) determining transboundary societal values, and (iv) acknowledging the importance of stakeholder behaviour and incentives

Nest covering in plovers: how modifying the visual environment influences egg camouflage

This is the author accepted manuscript. The final version is available from Wiley Open Access via the DOI in this record.The dataset related to this article is available at http://hdl.handle.net/10871/23232 .Camouflage is one of the most widespread antipredator defences, and its mechanistic basis has attracted considerable interest in recent years. The effectiveness of camouflage depends on the interaction between an animal's appearance and its background. Concealment can therefore be improved by changes to an animal's own appearance, by behaviorally selecting an optimal background, or by modifying the background to better match the animal's own appearance. Research to date has largely focussed on the first of these mechanisms, whereas there has been little work on the second and almost none on the third. Even though a number of animal species may potentially modify their environment to improve individual-specific camouflage, this has rarely if ever been quantitatively investigated, or its adaptive value tested. Kittlitz's plovers (Charadrius pecuarius) use material (stones and vegetation) to cover their nests when predators approach, providing concealment that is independent of the inflexible appearance of the adult or eggs, and that can be adjusted to suit the local surrounding background. We used digital imaging and predator vision modeling to investigate the camouflage properties of covered nests, and whether their camouflage affected their survival. The plovers' nest-covering materials were consistent with a trade-off between selecting materials that matched the color of the eggs, while resulting in poorer nest pattern and contrast matching to the nest surroundings. Alternatively, the systematic use of materials with high-contrast and small-pattern grain sizes could reflect a deliberate disruptive coloration strategy, whereby high-contrast material breaks up the telltale outline of the clutch. No camouflage variables predicted nest survival. Our study highlights the potential for camouflage to be enhanced by background modification. This provides a flexible system for modifying an animal's conspicuousness, to which the main limitation may be the available materials rather than the animal's appearance.J.T., J.W-A. and M.S. were funded by a Biotechnology and Biological Sciences Research Council (BBSRC) grant BB/J018309/1 to M.S., and a BBSRC David Phillips Research Fellowship (BB/G022887/1) to M.S., and C.N.S was funded by a Royal Society Dorothy Hodgkin Fellowship, a BBSRC David Phillips Fellowship (BB/J014109/1) to C.N.S. and the DST-NRF Centre of Excellence at the FitzPatrick Institute. In South Africa we thank CapeNature and the FitzPatrick Institute for their support, and Jan & Malani Kotze, Steve Lombard and other land-owners for kindly allowing access to their land. We thank Juan Amat and an anonymous referee for helpful comments

Poetry as a Creative Practice to Enhance Engagement and Learning in Conservation Science

Creativity is crucial to the capacity to do science well, to communicate it in compelling ways, and to enhance learning. Creativity can be both practiced and enhanced to strengthen conservation science professionals’ efforts to address global environmental challenges. We explore how poetry is one creative approach that can further conservation scientists’ engagement and learning. We draw on evidence from peer-reviewed literature to illustrate benefits of integrating science and poetry, and to ground our argument for the growth of a science-poetry community to help conservation scientists develop skills in creative practices as a component of professional development. We present examples from literature as well as two short poetry exercises for scientists to draw on when considering writing poetry, or deciding on forms of poetry to include, in their practice. Opportunity exists to grow science–poetry projects to further our understanding of what such initiatives can offer

Juras piekraste ligzdojoso putnu ekologija

Separate summary in Latvian, English, RussianAvailable from Latvian Academic Library / LAL - Latvian Academic LibrarySIGLELVLatvi

Goodness of fit tests for bivariate distributions

Diplomdarba mērķis ir analizēt, pielietot, salīdzināt un implementēt programmā R dažādus statistiskos testus divdimensionālu gadījumu lielumu sadalījumu pārbaudei. Hipotēžu pārbaude par sadalījumu veidu ir viena no svarīgākajām problēmām matemātiskajā statistikā. Tiek analizēti testi vienkāršas hipotēzes pārbaudei par divdimensionāla vienmērīgā sadalījuma pārbaudi: Kolmogorova-Smirnova tests, Hī-kvadrāta un datu virzīts gludais tests. Šiem testiem tiek veikta empīriskā jaudas analīze, pēc tam iegūtie rezultāti tiek pielāgoti praktiskai datu problēmai. Darbā tiek apskatīti arī testi saliktas hipotēzes pārbaudei par normālo sadalījumu: Šapiro-Vilksa tests, Lilifora un divi datu virzīti testi. Īpaša uzmanība tiek pievērsta dažādām versijām datu virzītiem Neimaņa gludajiem testiem, kas ieguvuši popularitāti pēc 1994. gada Ledvinas publikācijas, kur Neimaņa statistikai tiek pielietots Švarca selekcijas kritērijs.The aim of this diploma paper is to analyse, use in practise, compare and implement in language R different statistical tests for verifying distribution of bivariate random variables. Testing hypotheses about the distribution is one of the most important problems in mathematical statistics. Different tests for bivariate uniform distribution hypothesis testing have been analysed: Kolmogorov-Smirnov test, chi-square, and data-driven smooth test. These tests have been subjected to empirical power analysis; subsequently the given results are applied to practical data problem. The work also covers several tests for verifying composite hypothesis of bivariate normal distribution: Shapiro-Wilk, Lillefor's test and two data-driven tests. Particular attention is paid to the different versions of data-driven Neyman smooth tests, that have gained popularity after publications of 1994 by Ledwina; these publications showed Schwartz-Bayes selection criterion applied to the Neyman smooth test

Image Captioning with Convolutional and Recurrent Neural Networks

Automātiskā attēlu aprakstīšana ir fundamentāla mākslīgās inteliģences problēma, ku- ra apvieno kompjūter-redzes un naturālās valodas apstrādes algoritmus. Šajā darbā tiks apskatīta šī nozare, pielietots apbalvojumus izcīnījis modelis un pētītas šī modeļa variāci- jas. Attēlus aprakstošais modelis ir mākslīgais neironu tīkls, kurš sevī apvieno konvolūciju un rekurento neironu tīklu arhitektūras. Darbā vispirms abas arhitektūras ir apskatītas atsevišķi, kā arī teorija, uz kuru balstās modelis. Apskatītais modelis un tā variācijas tiek salīdzinātas, izmantojot klasiskos mašīnmācīšanās rādītājus un mašīntulkošanā izman- totas metrikas. Pēc dažiem rādītājiem oriģinālā modeļa uzlabojumi izrādījās lietderīgi. Modeļu apmācībā tika izmantots populārs mašīnmācīšanās rīks Tensorflow un program- mēšanas valoda Python.Automatic image captioning is fundamental artificial inteligence problem which is a fusion of computer vision and natural language processing. In this work image captinio- ning field will be explored. Award winning image captioning model revisited and explo- red its variations. Image captioning model is an artifical neural network which consists of conolutional neural network and a recurrent neural network. In this work both the- se branches of architecture are studied theoretically and practically. Variations of the award winning image captioning model was compared with classical machine learning and machine translation metrics. According to some metrics the variations of the original model turned out usefull. Training of the models was done in popular machine learning tool Tensorflow and Python programming language