L’écologie du Desman des Pyrénées (Galemys pyrenaicus), mammifère semi-aquatique endémique de la péninsule ibérique et des Pyrénées, demeure encore très peu connue. Les objectifs de cette thèse, dans le cadre d’un Plan National d’Actions, ont été d’identifier les variables environnementales agissant sur la répartition de l’espèce à différentes échelles spatiales, en considérant sa détectabilité imparfaite (i.e. fausses absences et fausses présences). Une probabilité de détection élevée, mais spatialement hétérogène à l’échelle des Pyrénées françaises, a été mise en évidence. La distribution du Desman des Pyrénées s’est également révélée spatialement structurée et majoritairement influencée par des facteurs propres aux milieux aquatiques, mais en forte régression depuis les années 80. Ces résultats ont permis de proposer des mesures de conservation pour cette espèce menacée. / The ecology of the Pyrenean desman (Galemys pyrenaicus), a small semi-aquatic mammal endemic to the Iberian Peninsula and the Pyrenees, remains still largely unknown. The aim of this PhD thesis conducted within the framework of a National Action Plan was to identify the environmental variables influencing the Desman distribution at various spatial scales, by accounting for its imperfect detection (i.e. false absences and false presences). A high, but spatially heterogeneous at the French Pyrenees extent, probability of detection was highlighted. The distribution of the Pyrenean Desman was also emphasized to be spatially structured and mainly influenced by aquatic factors, but severely contracting for the last 25 years. These results enabled to suggest conservation measures for this endangered species.

L’Afrique centrale atlantique (ACA) englobe l’ensemble du domaine bas-guinéen, les îles du Golfe de Guinée et une partie de l’archipel afromontagnard. Plusieurs centres d’endémisme ont été identifiés en son sein et sont généralement considérés comme liés à la présence de refuges forestiers durant les périodes glaciaires. Cependant, l’origine de cet endémisme, sa localisation et les méthodes permettant d’identifier ces centres restent controversées. La localisation de ces zones d’endémisme et des plantes rares qu’elles abritent, est pourtant un prérequis indispensable pour la mise en place de politiques cohérentes de conservation et demeure une priorité pour les organisations privées, institutionnelles ou gouvernementales actives dans la gestion et le maintien durable de la biodiversité.<p><p>Cette étude phytogéographique porte sur la famille des Orchidaceae et est basée sur l’analyse de la distribution des taxons endémiques de l’ACA. Elle s’appuie sur un jeu de données original résultant d’un effort d’échantillonnage important au Cameroun et d’un travail d’identification et de localisation de spécimens dans les principaux herbaria européens abritant des collections d’ACA. Durant cette étude, (i) nous avons tout d’abord identifié ces taxons endémiques et documenté leur distribution au travers de plusieurs contributions taxonomiques et floristiques, (ii) nous nous sommes ensuite intéressé aux nouvelles méthodes permettant d’analyser ces données d’herbier de plantes rares et donc pauvrement documentées, testant aussi l’intérêt des Orchidaceae comme marqueurs chorologiques, et finalement, appliquant ces méthodes à notre jeu de données, (iii) nous avons délimité des centres d’endémisme et identifié les territoires phytogéographiques des Orchidaceae en ACA.<p><p>(i) Une révision taxonomique des genres Chamaeangis Schltr. et Stolzia Schltr. a été réalisée respectivement. Sept nouveaux taxons ont été décrits: Angraecum atlanticum Stévart & Droissart, Chamaeangis spiralis Stévart & Droissart, Chamaeangis lecomtei (Finet) Schltr. var. tenuicalcar Stévart & Droissart, Polystachya engogensis Stévart & Droissart, Polystachya reticulata Stévart & Droissart, Stolzia repens (Rolfe) Summerh var. cleistogama Stévart, Droissart & Simo et Stolzia grandiflora P.J.Cribb subsp. lejolyana Stévart, Droissart & Simo. Plusieurs notes taxonomiques, phytogéographiques et écologiques supplémentaires ont également été redigées. Au total, nous avons identifié 203 taxons d’Orchidaceae endémiques d’ACA parmi lesquels 193 sont pris en compte pour l’étude des patrons d’endémisme.<p><p>(ii) Au Cameroun, les patrons de distribution des Orchidaceae et des Rubiaceae endémiques d’ACA ont été étudiés conjointement. Des méthodes de rééchantillonnage des données (raréfaction) ont été appliquées pour calculer des indices de diversité et de similarité. Elles ont permis de corriger les biais liés à la variation de l’effort d’échantillonnage. Un gradient de continentalité a été observé, les parties côtières étant les plus riches en taxons endémiques d’ACA. Contrairement à la région du Mont Cameroun et aux massifs de Kupe/Bakossi qui ont connu une attention particulière des politiques et des scientifiques, la partie côtière du sud Cameroun, presque aussi riche, reste mal inventoriée pour plusieurs familles végétales.<p><p>Cette analyse à l’échelle du Cameroun a également permis de comparer les patrons d’endémisme des Orchidaceae et des Rubiaceae. Les différences observées seraient principalement dues à la présence d’Orchidaceae terrestres dans les végétations basses et les prairies montagnardes de la dorsale camerounaise alors que les Rubiaceae sont généralement peu représentées dans ces habitats. Au sein des habitats forestiers, la concordance entre les patrons d’endémisme des Orchidaceae et des Rubiaceae remet en question l’utilisation des capacités de dispersion des espèces comme critère pour choisir les familles permettant l’identification des refuges forestiers et semble ainsi confirmer la pertinence de l’utilisation des Orchidaceae comme marqueur chorologique.<p><p>La distribution potentielle a été utilisée pour étudier en détail l’écologie, la distribution et le statut de conservation de Diceratostele gabonensis Summerh. une Orchidaceae endémique de la région guinéo-congolaise uniquement connue d’un faible nombre d’échantillons. Cette méthodologie semble appropriée pour compléter nos connaissances sur la distribution des espèces rares et guider les futurs inventaires en Afrique tropicale.<p><p>(iii) En ACA, les Orchidaceae permettent d’identifier plusieurs centres d’endémisme qui coïncident généralement avec ceux identifiés précédemment pour d’autres familles végétales. Ces constats supportent aussi l’utilisation des Orchidaceae comme marqueur chorologique. La délimitation des aires d’endémisme des Orchidaceae a ainsi permis de proposer une nouvelle carte phytogéographique de l’ACA. Les éléments phytogéographiques propres à chacune des dix phytochories décrites ont été identifiés et leurs affinités floristiques discutées. Les résultats phytogéographiques obtenus (a) soutiennent l’existence d’une barrière phytogéographique matérialisée par la rivière Sanaga entre les deux principaux centres et aires d’endémisme de l’ACA, (b) étendent l’archipel afromontagnard situé principalement au Cameroun au plateau de Jos (Nigeria) et (c) montrent l’importance de la chaîne montagneuse morcelée Ngovayang-Mayombe pour la distribution de l’endémisme en ACA. Cette chaîne de montagne, qui s’étend le long des côtes de l’océan du sud Cameroun au Congo-Brazzaville et qui correspond à plusieurs refuges forestiers identifiés par de nombreux auteurs, est ici considérée comme une seule aire d’endémisme morcelée./<p>Atlantic central Africa (ACA) covers the Lower Guinean Domain, the four islands of the Gulf of Guinea and a part of the afromontane archipelago. Different centres of endemism have been identified into this area and are usually considered as related to glacial forest refuges. However, the origin of this endemism, the localization of the centres and the methods employed to identify these centres are subject to debate. Yet, the localization of these centres of endemism and the identification of the rare plants they harbor is an essential prerequisite to setting up rational conservation policies, and remains a priority for private, institutional and governmental organizations which are dealing with the sustainable management of biodiversity.<p><p>This phytogeographical study focuses on Orchidaceae and analyses the distribution of the taxa endemic to ACA. We use an original dataset resulting from an important sampling efforts and the identification of specimens coming from all the principal herbaria where collections from ACA are housed. During this study, (i) we first identified the taxa endemic to ACA and documented their distribution through several taxonomic and floristic contributions, (ii) we used and developed new methods allowing to correct for sampling bias associated with the use of rare and poorly documented taxa, testing at the same time the use of Orchidaceae as chorological markers, and finally, applying these methods to our dataset, (iii) we delimited the centres of endemism and identified the phytogeographical territories of Orchidaceae in ACA.<p><p>(i) A taxonomic revision of Chamaeangis Schltr. and Stolzia Schltr. respectively was carried out. Seven new taxa were described: Angraecum atlanticum Stévart & Droissart, Chamaeangis spiralis Stévart & Droissart, Chamaeangis lecomtei (Finet) Schltr. var. tenuicalcar Stévart & Droissart, Polystachya engogensis Stévart & Droissart, Polystachya reticulata Stévart & Droissart, Stolzia repens (Rolfe) Summerh var. cleistogama Stévart, Droissart & Simo and Stolzia grandiflora P.J.Cribb subsp. lejolyana Stévart, Droissart & Simo. Several additional taxonomic, phytogeographical and ecological notes were also published. We finally identified 203 Orchidaceae taxa endemic to ACA, among which 193 were used to study the patterns of endemism.<p><p>(ii) In Cameroon, the distribution patterns of both Orchidaceae and Rubiaceae endemic to ACA were studied. Subsampling methods (rarefaction) were applied to calculate diversity and similarity indices and to correct potential bias associated with heterogeneous sampling intensity. A gradient of continentality was confirmed in Cameroon, the coastal part being the richest in taxa endemic to ACA. The Cameroon Mountain and the Kupe/Bakossi mountain massifs have received a great consideration of politics and scientists. On the contrary, the Southern coastal part of Cameroon, though almost as rich as the Northern part, remains poorly known for several plant families.<p>This analysis also allowed us to compare patterns of endemism of Orchidaceae and Rubiaceae. The differences observed could be mainly due to the terrestrial habit of some Orchidaceae, which are only found in the grasslands of the highest part of the Cameroonian volcanic line where endemic Rubiaceae are rare. Within forest habitats, the concordance between the patterns of endemism of Orchidaceae and Rubiaceae question the widespread use of dispersal ability as a selection criterion for the families used to identify forest refuges. This also confirms the relevance of Orchidaceae as chorological marker.<p><p>Species distribution modelling was used of an in depth study of the ecology, the distribution and the conservation status of Diceratostele gabonensis Summerh. an Orchidaceae endemic to the Guineo-Congolian regional centre of endemism which is only known from very few collections. This method is proved to be appropriate to complete our knowledge on the distribution of rare plant species and to guide the future inventories in tropical Africa.<p><p>(iii) In ACA, an analysis of the distribution of endemic Orchidaceae confirmed the presence and location of several centres of endemism previously identified on the basis of other plant families. This result again supports the use of Orchidaceae as a chorological marker. The chorological study of the endemic Orchidaceae allowed us to propose a new phytogeographical map for ACA. Phytogeographical elements for each of the ten phytochoria described were identified and their floristic affinities were also discussed. Our results (a) support the existence of a phytogeographical barrier, materialized by the Sanaga River, between the two main centres and area of endemism of the ACA, (b) extend the limits of the afromontane archipelago to the Jos Plateau in Nigeria and (c) show the importance of the Ngovayang-Mayombe line to explain the distribution of endemism in ACA. This mountainous line, stretching along the ocean coast from Southern Cameroon to Congo-Brazzaville, corresponds to several forest refuges identified by many authors, and is here considered as an unique but discontinuous area of endemism.<p><p><p> / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Abstract Not Available.

La compréhension des interactions liant la répartition des espèces, la biodiversité, les habitats marins et le changement climatique est nécessaire voire fondamentale pour la mise en oeuvre d’une gestion efficace de la conservation, par exemple la mise en place d’aires marines protégées. Dans cette étude, nous avons travaillé sur l’évolution de richesse de 89 espèces de poissons notées «rares» ou «exotiques» (observées en dehors de leur aire de répartition connue) lié au changement climatique. Nous avons modélisé et prédit leur distribution saisonnière par le modèle SIG en fonction de leurs niches écologiques (déterminée dans cette étude). En superposant tous les modèles en fonction du temps, cette approche permet d’identifier des zones d’occupation préférentielle de forte biodiversité (hotspots). La méthode offre une alternative pour mesurer la richesse d’espèces de façon saisonnière dans des zones peu connues, et de suivre leur mouvement au cours de temps, puis avoir information de base sur l’efficacité de positionnement des aires marines protégés liées à ces zones hotspots. La zone d’étude s’est située dans trois grands écosystèmes marins : le courant des Canaries, le plateau sud de l’Atlantique Européen et les mers celtiques. La région centrale est une zone de transition (entre les eaux tropicales et tempérés) connue pour sa sensibilité aux effets du changement climatique. De 1982 à 2012, la SST augmente constamment au fil du temps, avec des tendances et des magnitudes qui varient selon l’écosystème. Une augmentation du nombre d'espèces dans un écosystème dans une période a été généralement suivie par une tendance à la baisse ou à la hausse dans des écosystèmes adjacents. Les niches écologiques des espèces étudiées ont été estimées par l’extraction des valeurs environnementales à l’échelle mondiale au point d'occurrence au moment de l'observation. Les résultats de niches sont cohérents avec ceux obtenus à partir d’études observationnelles ou expérimentales. La flexibilité du modèle SIG nous a permis de suivre l'évolution saisonnière de distribution des espèces au fil du temps. En général, les espèces montrent une tendance à élargir leur distribution vers le nord, montrant l'effet du réchauffement de l'océan sur la distribution des poissons marins. L’approche de modèle peut être utilisée pour modéliser la distribution des espèces moins connues, ou dans des zones où les données d’occurrences sont peu nombreuses, ainsi que pour prédire le modèle de distribution future. L'analyse spatiale de la superficie des AMPs (Aires Marines Protégées) par pays appartenant à la zone d'étude, montre que le Royaume-Uni puis la France possèdent le plus grand nombre d'AMP ainsi que les superficies totales protégées les plus importantes. La fréquence à laquelle les AMPs (Aires Marines Protégées) sont touchées par les zones de hotspots est fortement influencée par les variations de l’environnement, les zones favorables évoluant alors au fil des saisons. Ainsi, il est important de prendre en compte les variations saisonnières pour la création des AMPs afin de préserver les capacités adaptative des espèces soumises au changement global. / Understanding connectivities among species distributions, biodiversity, marine habitats and climate change is necessary for the design of an effective conservation management, such as in the implementation of marine protected area (MPA). In this study, we observed the richness of 89 "rare" or "exotic" fish species (observed outside their known distribution range) related to climate change. We modeled and predicted their seasonal distributions according to the species ecological niches (determined in this study) using the GIS model. Superposing the models of all species using GIS, we determined the preferential zones or zones of high biodiversity (hotspots) over time. The GIS approach offers an alternative to measure seasonal species richness in poor-data areas. This approach allowed also species track movement over time. This information could be then used to measure the effectiveness of MPA positioning related to the hotspot areas. Our study area covers a wide latitudinal range of the Eastern Atlantic waters, from the warm tropical/subtropical waters to the temperate waters. This area is located in three large marine ecosystems: the Canary current, the South European Atlantic Shelf and the Celtic Seas. The transitional zone in the central region has well known for its sensitivity to the detection of climate change. From 1982 to 2012, the SST in all of studied ecosystems has increased consistently over time, with magnitude and trend varied among ecosystems. The change of number of species in each decadal period differed among ecosystems. Increasing number of species in an ecosystem was generally followed by decreasing trend in adjacent ecosystems. Species ecological niches were obtained by extracting the environmental values in the location of species occurrence at the time of observation. The environmental data and the occurrence records used were at global scale, and the methods yields coherent results with the results obtained from observational studies. The flexibility of GIS Model used in this study allowed us to follow the evolution of species seasonal distribution over time. Generally, most of the studied species showed a northbound trend in their distribution. These northbound tendencies were more evident in the middle region, confirming the effect of global warming in shifting marine species distribution. This approach provides an alternative of measuring seasonal richness of poor-known species and/or modeling in poor-data areas. The results present a complete picture of predictive number of species in an area over time. MPAs superficial analysis by country (countries lying in the study area) showed that UK has the highest number of MPA and the largest protected areas, following by France and Mauritania. Frequencies of the MPAs touched by the hotspot were strongly influenced by seasonal variations. Thus, considering seasonal variations in a conservation effort could preserve species adaptive variation under environmental changes. Overall, our works provide several alternative methods for species distribution studies and for studies poor-known species in data-poor area. The results provide evidences of ocean warming effect in shifting marine fish distribution.

Abstract Eutrophication and global warming are increasingly causing deterioration of aquatic ecosystems, and boreal freshwaters are especially vulnerable to these changes. Anthropogenic pressures and landscape characteristics influencing the functioning and structure of ecosystems vary with spatial scale (grain size i.e. study unit and extent i.e. study area). This emphasises that the understanding of spatial scale is a vital element when studying species distribution patterns. Moreover, spatial scale is often neglected in ecological assessments, in which the degree of ecological integrity of an ecosystem is assessed using selected biological groups. One of these groups is aquatic macrophytes. The aims of this thesis were (i) to study the distribution and richness of aquatic macrophytes in the boreal region in Finland at multiple scales and (ii) to evaluate the performance of ecological assessment metrics selected for Finnish lake macrophytes. The spatial extent at which aquatic macrophytes were studied had an important influence on the patterns found. Climatic factors associated with latitudinal and altitudinal gradient determined macrophytes at broad extent, although the patterns changed at finer regional extent. Moreover, this strong effect of climate could lead to the widening of distribution ranges of helophytes in boreal catchments during the 21st century due to the climate change. Many of these species have already widened their range limits during the previous century and increasing temperatures may create new niches for vegetation to colonize. Lake macrophyte richness, turnover and quality metrics showed a clear relationship with nutrient concentration in waters at landscape and regional extent. Helophytes and metrics were positively or inversely negatively related to nutrients, whereas species turnover and other life-form groups had a unimodal or non-significant response to nutrient availability. In addition, land use (agricultural and urban areas and forestry ditch drainage) influenced macrophytes directly through shore morphology changes and indirectly through water quality. Macrophytes were also explained at various scales by area and depth, which were related to habitat heterogeneity, and aquatic plants responded to water ionic and electrical characteristics (pH, alkalinity and conductivity). Ecological quality metrics of macrophytes appeared to be scale dependent, since land use adjacent to the lake shoreline had a higher influence on the metrics compared to land use of the whole catchment. However, the scale-related pattern in the effect of land use was not congruent between metrics, as the Trophic Index showed poorer performance compared to the proportion of type-specific species and Percent Model Affinity. This was presumably due to lack of helophytes in the species pool used and to reference values which were defined across lake types in the Trophic Index. Uneven performance of the metrics derived from different biological groups suggests that an approach integrating multiple lines of evidence on ecological status appears most feasible for assessment of the overall lake status. / Tiivistelmä Vesistöjen rehevöityminen ja ilmastonmuutos heikentävät vesiekosysteemien laatua, ja boreaaliset sisävedet ovat erityisen alttiita näiden uhkatekijöiden aiheuttamille muutoksille. Ihmistoiminnan aiheuttamien muutoksien ja luontaisten maisematekijöiden merkitys vesiekosysteemien toimintaan ja rakenteeseen vaihtelee mittakaavan (tutkimusyksikön ja -alueen) mukaan. Kuitenkin spatiaalisen mittakaavan merkitys on usein unohdettu ekologisissa arvioinneissa, joissa selvitetään ekosysteemin luonnontilaisuutta eri biologisilla lajiryhmillä. Vesikasvit ovat yksi usein käytetty biologinen ryhmä järvien ekologissa arvioinneissa. Tämän tutkimuksen tarkoitus on (i) tutkia vesikasvien levinneisyyttä ja runsautta Suomessa useissa mittakaavoissa, ja (ii) arvioida ekologisten luokittelumuuttujien toimivuutta järvien vesikasveilla eri mittakaavoissa. Mittakaava, jossa vesikasveja tutkittiin, vaikutti merkittävästi saatuihin tuloksiin. Leveysasteeseen ja korkeuteen liittyvä gradientti määritti vesikasvien levinneisyyttä alueellisessa mittakaavassa. Lisäksi ilmaston voimakas vaikutus vesikasveihin voi johtaa niiden levinneisyysrajojen laajenemiseen, koska ilmastonmuutos saattaa luoda edullisemmat kasvuolosuhteet kasvillisuudelle tällä vuosisadalla. Monet vesikasvilajit ovat jo levinneet pohjoisemmaksi 1900-luvulla, ja lämpötilojen nousu voi lisätä ekolokeroita vesikasvien levittäytymiselle. Vesikasvien runsaus, lajimäärä ja luokittelumuuttujat olivat selkeästi yhteydessä vesien ravinteisuuteen maisemallisessa ja alueellisessa mittakaavassa. Ilmaversoisilla vesikasveilla ja luokittelumuuttujilla oli positiivinen tai käänteisesti negatiivinen suhde ravinteisiin, kun taas lajimäärä ja muut vesikasvien kasvumuodot olivat unimodaalisessa tai merkityksettömässä yhteydessä ravinteisuuteen. Lisäksi maankäyttö, erityisesti maatalous, kaupunkiasutus ja metsäojitus, vaikutti vesikasveihin suoraan rantavyöhykkeen morfologisin muutoksin tai epäsuorasti ravinteisuuden kautta. Vesikasvien levinneisyyttä ja runsautta selitti myös pinta-ala ja syvyys, jotka liittyivät elinympäristön heterogeenisyyteen, sekä veden fysikaalis-kemialliset ominaisuudet, kuten pH, alkaliniteetti ja sähkönjohtokyky. Ekologiset luokittelumuuttujat olivat riippuvaisia mittakaavasta, koska rantavyöhykkeen läheisellä maankäytöllä oli suurempi merkitys muuttujille kuin koko valuma-alueen maankäytöllä. Kuitenkin mittakaavan merkitys vaihteli eri muuttujien välillä, kun referenssi-indeksi osoitti heikompaa vastetta maankäyttöön eri mittakaavoissa kuin tyyppilajien suhteellinen osuus ja prosenttinen mallin samankaltaisuus. Tämä luultavasti johtui siitä, että referenssi-indeksissä ilmaversoiset vesikasvit puuttuivat tutkittavista lajeista ja referenssiarvot olivat yhteiset riippumatta järvityypistä. Eri biologisiin ryhmiin perustuva luokittelujärjestelmä ilmensi hyvin vaihtelevasti ekologista laatua, minkä vuoksi eri muuttujia yhdistävä menetelmä, joka arvioi vesimuodostuman kokonaistilaa, on toteuttamiskelpoisin lähestymistapa boreaalisissa järvissä.

Orientador: Clarisse Palma da Silva / Resumo: Áreas úmidas são ambientes na interface terrestre e aquática, onde sazonalmente a disponibilidade de água pode estar em excesso ou em escassez. A história geológica da bacia amazônica está intimamente relacionada com a presença de áreas úmidas em grandes extensões espaciais e temporais e em variadas tipologias. Dentre as tipologias de áreas úmidas presentes na Amazônia as áreas alagáveis ao longo das planícies de inundação dos grandes rios são possivelmente as que possuem maior extensão territorial. Esta tese aborda o papel das áreas úmidas para a diversidade de árvores na Amazônia. As florestas que crescem em áreas úmidas possuem menor diversidade de espécies arbóreas em relação às florestas em ambientes terrestres (i.e., florestas de terra-firme); possivelmente devido às limitações ecológicas e fisiológicas relacionadas a saturação hídrica do solo e as inundações periódicas. Entretanto, nas áreas úmidas da Amazônia já foram registradas 3,515 espécies de árvores (Capítulo 2), uma quantidade comparável à da diversidade na Floresta Atlântica. Em relação às florestas de terra-firme da Amazônia, as espécies de árvores que ocorrem em áreas úmidas tendem a apresentar maiores áreas de distribuição e amplitudes de tolerâncias de nicho ao longo da região Neotropical (Capítulo 3). A composição florística e a distância filogenética entre espécies arbóreas nas florestas de várzea da Amazônia central mudam amplamente entre localidades (Capítulo 4). O gradiente ambiental contido entre as ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Wetlands are in the interface of terrestrial and aquatic environments, where seasonally water availability may be in excess or scarcity. Geological history of Amazon basin is closely linked with a huge temporal and spatial extents of wetlands. Nowadays, floodplains (i.e., Vázea and Igapó) are the wetlands with greatest coverage in Amazon. The present thesis is focused on the role of wetlands to tree species diversity in Amazon. Wetland forests have lower tree species diversity than upland forests (i.e., Terra-Firme); most likely due to ecological and physiological limitations. Notwithstanding, in Amazonian wetland forests 3,515 tree species already were recorded, (Chapter 2), which is comparable to tree species diversity in the Atlantic Forest. Wetland tree species show greater ranges sizes and niche breadth compared to tree species do not occur in wetlands (Chapter 3). Floristic compositional turnover and phylogenetic distances between floodplain forests in Central Amazon is high (Chapter 4). The most influential driver of floristic compositional turnover was the geographic distances between localities, whereas phylogenetic distances is driven mainly by the environmental gradients between forests. Furthermore, in general, the most abundant species are those that shows greater co-occurrence associations (Chapter 5). Co-occurrence structure is influenced by biotic interactions like facilitation and competition among species, but also by niche similarities indicated in the evol... (Complete abstract click electronic access below) / Doutor

Die Hauptursachen für die derzeitige weltweite Krise der biologischen Vielfalt sind Lebensraumzerstörung und Übernutzung. Wir wissen jedoch nicht, wie sich diese beiden Faktoren einzeln und zusammen auf die verschiedenen Aspekte biologischer Vielfalt auswirken und wie sie sich im Laufe der Zeit verändern. Da beide Bedrohungen weit verbreitet sind, verhindern dies die Entwicklung wirksamer Schutzstrategien. Das übergeordnete Ziel dieser Arbeit war räumliche und zeitliche Veränderungsmuster der Auswirkungen von Lebensraumzerstörung und Übernutzung auf die biologische Vielfalt zu verstehen. Ich habe diese Bedrohungsgeographien mit hoher räumlicher Auflösung und über drei Jahrzehnte hinweg für verschiedene Aspekte biologischer Vielfalt untersucht: Arten, Lebensgemeinschaften und taxonomische, phylogenetische und funktionale Facetten biologischer Vielfalt. Ich konzentrierte mich auf den 1,1 Millionen km² großen Gran Chaco, den größten tropischen Trockenwald der Welt und einen globalen Entwaldungs-Hotspot. Meine Ergebnisse zeigen, dass sich im Laufe von 30 Jahren die räumlichen Auswirkungen der einzelnen Bedrohungen auf größere Gebiete ausdehnten als nur auf die abgeholzte Fläche. Dies führte zu einem Verlust an hochwertigen und sicheren Gebieten für den Jaguar, die gesamte Großsäugergemeinschaft und alle Facetten der Säugetiervielfalt. Beide Bedrohungen trugen wesentlich zum Rückgang biologischer Vielfalt bei, ihre relative Bedeutung variierte jedoch je nach Art und Facette der biologischen Vielfalt. Zudem haben die Gebiete, in denen beide Bedrohungen zusammenwirken, im Laufe der Zeit zugenommen, was den Verlust der biologischen Vielfalt wahrscheinlich noch verschlimmert hat. Diese Arbeit unterstreicht, wie wichtig es ist, die Auswirkungen mehrerer Bedrohungen im Laufe der Zeit gemeinsam zu bewerten, um den menschlichen Einfluss auf die biologische Vielfalt besser verstehen zu können und wirksame Schutzstrategien zu finden. / The main drivers of the current global biodiversity crisis are habitat destruction and overexploitation. Yet, we lack understanding of their individual and combined spatial impact on different aspects of biodiversity, and how they change over time. Because both threats are common, these knowledge gaps preclude building more effective conservation strategies. The overarching goal of this thesis was to understand how the impacts of habitat destruction and overexploitation on biodiversity change in space and over time. I assessed these geographies of threat at high spatial resolutions and over three decades for different biodiversity aspects: species, communities, and the taxonomic, phylogenetic, and functional facets of biodiversity. I focused on the 1.1 million km² Gran Chaco, the largest tropical dry forest globally, and a global deforestation hotspot. Results reveal that over 30 years, the spatial impacts of each threat expanded over larger areas than the area deforested. This resulted in widespread losses of high-quality and safe areas for the jaguar, the entire larger mammal community and for all facets of the mammalian diversity. Such declines suggest a generalized biotic impoverishment that includes the loss of species, evolutionary history, and ecological functions across much of the Chaco. Both threats contributed substantially to biodiversity declines, and their relative importance varied among species and biodiversity facets. Moreover, the areas where both threats synergize increased over time, likely exacerbating biodiversity losses. For each biodiversity aspect, I identified priority areas of safe and high-quality habitats, and hotspots of high threat impacts, which could guide more effective complementary proactive and reactive conservation strategies. This thesis highlights the importance of jointly assessing the impact of multiple threats over time to better understand the impact of humans on biodiversity and to identify effective ways to mitigate them. / Los principales factores de la actual crisis de la biodiversidad global son la destrucción del hábitat y la sobreexplotación. Sin embargo, desconocemos su impacto espacial, tanto individual como combinado, sobre los diferentes aspectos de la biodiversidad, y cómo cambian en el tiempo. Como ambas amenazas son comunes, estos vacíos de conocimiento impiden elaborar estrategias de conservación más eficaces. El objetivo general de esta tesis fue comprender cómo los impactos de la destrucción del hábitat y la sobreexplotación en la biodiversidad cambian en el espacio y en el tiempo. Evalué estas geografías de las amenazas a altas resoluciones espaciales y a lo largo de tres décadas para diferentes aspectos de la biodiversidad: especies, comunidades y las facetas taxonómica, filogenética y funcional de la biodiversidad. Me centré en el Gran Chaco, de 1,1 millones de km², el mayor bosque seco tropical del mundo y un foco global de deforestación. Los resultados revelan que, a lo largo de 30 años, los impactos espaciales de cada una de las amenazas se extendieron por areas mayores que la superficie deforestada. Esto dio lugar a pérdidas extendidas de áreas seguras y de alta calidad para el jaguar, la comunidad de mamíferos grandes y para todas las facetas de la diversidad de mamíferos. Estos declives sugieren un empobrecimiento biótico generalizado que incluye la pérdida de especies, historia evolutiva y funciones ecológicas en gran parte del Chaco. Ambas amenazas contribuyeron sustancialmente al declive de la biodiversidad, y su importancia relativa varió entre especies y facetas de la biodiversidad. Además, las áreas en las que ambas amenazas sinergizan aumentaron en el tiempo, probablemente exacerbando las pérdidas de biodiversidad. Para cada aspecto de la biodiversidad, identifiqué áreas prioritarias de hábitats seguros y de alta calidad, y focos de alto impacto de las amenazas, que podrían orientar estrategias de conservación complementarias más eficaces, tanto proactivas como reactivas. Esta tesis destaca la importancia de evaluar conjuntamente el impacto de múltiples amenazas a lo largo del tiempo para comprender mejor el impacto de los humanos en la biodiversidad e identificar vías eficaces para mitigarlas.

The most common tool to predict future changes in species range are species distribution models. These models do, however, often underestimate potential future habitat, as they do not account for phenotypic plasticity and local adaptation, although being the most important processes in the response of tree populations to rapid climate change. Here, we quantify the difference in the predictions of future range for Norway spruce, by (i) deriving a classic, occurrence-based species distribution model (OccurrenceSDM), and (ii) analysing the variation in juvenile tree height and translating this to species occurrence (TraitSDM). Making use of 32 site locations of the most comprehensive European trial series that includes 1,100 provenances of Norway spruce originating from its natural and further beyond from its largely extended, artificial distribution, we fit a universal response function to quantify growth as a function of site and provenance climate. Both the OccurrenceSDM and TraitSDM show a substantial retreat towards the northern latitudes and higher elevations (−55 and −43%, respectively, by the 2080s). However, thanks to the species’ particularly high phenotypic plasticity in juvenile height growth, the decline is delayed. The TraitSDM identifies increasing summer heat paired with decreasing water availability as the main climatic variable that restricts growth, while a prolonged frost-free period enables a longer period of active growth and therefore increasing growth potential within the restricted, remaining area. Clear signals of local adaptation to climatic clines spanning the entire range are barely detectable, as they are disguised by a latitudinal cline. This cline strongly reflects population differentiation for the Baltic domain, but fails to capture the high phenotypic variation associated to the geographic heterogeneity in the Central European mountain ranges paired with the species history of postglacial migration. Still the model is used to provide recommendations of optimal provenance choice for future climate conditions. In essence, assisted migration may not decrease the predicted range decline of Norway spruce, but may help to capitalize on potential opportunities for increased growth associated with warmer climates.

Urgent scientifically-informed action is needed to stabilise the Earth System amidst anthropogenic climate change. Particularly, the notable transgression of the ‘biosphere integrity’ Planetary Boundary needs to be addressed. Modern Earth System Models struggle to accurately represent regional to local-scale climate features and biodiversity aspects. Recent developments allow to tackle these issues using Artificial Intelligence. This dissertation focuses on two main aspects: (i) deriving high spatio-temporal resolution climate data from coarser models; and (ii) integrating high-temporal-resolution climate data into Species Distribution Models. Three specific objectives were defined: Obj1 Improving Perfect Prognosis – Statistical Downscaling methods through modern Deep Learning algorithms. Obj2 Downscaling a high-resolution multivariate climate ensemble. Obj3 Employ the resulting dataset to improve Species Distribution Models’ projections. The objectives are connected to the three articles that support this cumulative dissertation. Its scope is limited to the Free State of Saxony, Germany, where local high-resolution climate data and high-quality observations of endangered vascular plant species were employed. From a broader perspective, these efforts should contribute to the overarching goal of bridging the gap between the scales of species distribution and climate models while establishing open-source, reproducible, and scalable containerised frameworks. Recent Deep Learning algorithms were leveraged to accomplish (i). The proposed frameworks enhance previous performance of Perfect Prognosis – Statistical Downscaling approaches, while ensuring repeatability. The key near-surface variables considered are precipitation, water vapour pressure, radiation, wind speed, and, maximum, mean and minimum temperature. The assumptions that support the Perfect Prognosis approach were thoroughly examined, confirming the robustness of the methods. The downscaled ensemble exhibits a novel output resolution of daily 1 km, which can serve as input for multiple climate impact studies, especially for local-scale decision-making and in topographically complex regions. Considerable methodological implementations were proposed and thoroughly analysed to achieve (ii). Despite notable limitations, Species Distribution Models are frequently used in climate change conservation planning. Thus, recent developments in climate data resolution could improve their usefulness and reliability, which have been previously constraint to coarse temporal aggregates in the projection domain. The presented framework provides fine-grained species suitability projections and satisfactory spatio-temporal transferability, albeit worrying trends. These improved projections are a step forward towards tailored conservation efforts. Limitations of Machine Learning methods and Species Distribution Models are addressed. Substantial avenues for future improvements are thoroughly discussed. As results suggest further reduction of suitable habitats, yet another call for swift action towards low-carbon societies is made. This requires maximising climate change mitigation and adaptation measures, along with a swift transition from short-term profit-driven policies to long-term sustainable development, but primarily, a collective shift in consciousness from anthropocentric positions to ecocentric policies and societies.:Contents Declaration of conformity........................................................ I Abstract....................................................................... III Zusammenfassung.................................................................. V Resumen........................................................................ VII Acknowledgments................................................................. IX List of Figures................................................................. XV List of Tables................................................................. XIX Symbols and Acronyms........................................................... XXI I Prelude & Foundations 1 1 Introduction................................................................... 3 1.1 Motivation – Human Impact on Earth....................................... 3 1.2 Earth System Modelling and Downscaling................................... 5 1.3 Biosphere’s Response to Recent Changes................................... 8 1.4 Species Distribution Models.............................................. 9 1.5 Objectives.............................................................. 10 1.6 Scope................................................................... 10 1.7 Outline................................................................. 10 2 Methodological Basis.......................................................... 13 2.1 Introduction to Artificial Intelligence Methods......................... 13 2.1.1 Artificial Intelligence........................................... 13 2.1.2 Machine Learning.................................................. 14 2.1.3 Deep Learning..................................................... 14 2.2 Downscaling Techniques.................................................. 15 2.2.1 Dynamical Downscaling............................................. 15 2.2.2 Statistical Downscaling........................................... 15 2.2.2.1 Model Output Statistics................................... 16 2.2.2.2 Perfect Prognosis......................................... 16 2.3 Species Distribution Models: Temporal Aspects........................... 17 2.4 Computational Framework................................................. 18 2.4.1 High-Performance Computing........................................ 18 2.4.2 Containers........................................................ 18 2.5 Remarks on Reproducibility.............................................. 19 II Articles’ Synthesis 21 3 Data.......................................................................... 23 3.1 Study Area.............................................................. 23 3.2 ReKIS................................................................... 24 3.3 ERA5.................................................................... 24 3.4 CORDEX.................................................................. 24 3.5 Species Occurrences..................................................... 25 3.6 WorldClim............................................................... 26 4 Methodological Implementations................................................ 27 4.1 Advancing Statistical Downscaling....................................... 27 4.1.1 Transfer Function Calibration.................................... 27 4.1.2 Evaluation....................................................... 29 4.1.3 Repeatability.................................................... 29 4.2 Downscaling a Multivariate Ensemble..................................... 30 4.2.1 Transfer Function Adaptations.................................... 30 4.2.2 Validation....................................................... 30 4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31 4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32 4.3.1 Climate Data..................................................... 32 4.3.1.1 Predictor Sets.......................................... 32 4.3.1.2 Temporal Approaches..................................... 33 4.3.2 SDM Implementation............................................... 33 4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33 4.3.4 Meta-analysis.................................................... 34 4.3.5 Pseudo-Reality Assessment........................................ 34 4.3.6 Spatio-Temporal Transferability.................................. 34 5 Results & Discussions......................................................... 35 5.1 Advancing Statistical Downscaling....................................... 35 5.1.1 Performance Improvement.......................................... 35 5.1.2 Repeatability.................................................... 36 5.1.3 Transfer Function Suitability.................................... 38 5.2 Downscaling a Multivariate Ensemble..................................... 39 5.2.1 Transfer Function performance.................................... 39 5.2.2 Bias-Correction.................................................. 40 5.2.3 Pseudo-Reality................................................... 42 5.2.4 Projections...................................................... 43 5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45 5.3.1 Predictor Set Evaluation for H2k................................. 45 5.3.2 Temporal Approach Comparison..................................... 46 5.3.3 Spatio-Temporal Transferability.................................. 47 5.3.4 Suitability Projections.......................................... 47 III Insights 51 6 Summary....................................................................... 53 6.1 Article A1.............................................................. 53 6.2 Article A2.............................................................. 54 6.3 Article A3.............................................................. 56 7 Conclusions and Outlook....................................................... 59 References 65 Articles 81 A1 Repeatable high-resolution statistical downscaling through deep learning..... 83 A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain............................................................. 103 A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu- tion Model..................................................................... 127 / Um das Erdsystem angesichts des anthropogenen Klimawandels zu stabilisieren, sind Maßnahmen auf Basis wissenschaftlicher Erkenntnisse dringend erforderlich. Insbesondere muss die drastisch Überschreitung der planetaren Grenze ‘Integrität der Biosphäre’ angegangen werden. Bisher haben aber Modelle des Erdsystems Schwierigkeiten, regionale bis lokale Klimamerkmale und Aspekte der Biodiversität genau abzubilden. Aktuelle Entwicklungen ermöglichen es, diese Herausforderungen mithilfe von Künstlicher Intelligenz anzugehen. Diese Dissertation konzentriert sich auf zwei Hauptaspekte: (i) die Ableitung von Klimadaten mit hoher räumlicher und zeitlicher Auflösung aus groberen Modellen und (ii) die Integration von Klimadaten mit hoher zeitlicher Auflösung in Modelle zur Artverbreitung. Es wurden drei konkrete Ziele definiert: Ziel1 Verbesserung von Perfect Prognosis – Statistische Downscaling-Methoden durch moderne Deep Learning-Algorithmen Ziel2 Downscaling eines hochauflösenden multivariaten Klimaensembles Ziel3 Verwendung des resultierenden Datensatzes zur Verbesserung von Prognosen in Modellen zur Artverbreitung Diese Ziele werden in drei wissenschaftlichen Artikeln beantwortet, auf die diese kumulative Dissertation sich stützt. Der Anwendungsbereich erstreckt sich auf den Freistaat Sachsen, Deutschland, wo lokale hochauflösende Klimadaten und hochwertige Beobachtungen gefährdeter Gefäßpflanzenarten verwendet wurden. In einer breiteren Perspektive tragen diese Bemühungen dazu bei, die Kluft zwischen regionalen sowie zeitlichen Skalen der Artverbreitung und Klimamodellen zu überbrücken und gleichzeitig Open-Source-, reproduzierbare und skalierbare containerisierte Frameworks zu etablieren. Aktuelle Deep Learning-Algorithmen wurden eingesetzt, um Hauptaspekt (i) zu erreichen. Die vorgeschlagenen Frameworks verbessern die bisherige Leistung von Perfect Prognosis – Statistische Downscaling-Ansätzen und gewährleisten gleichzeitig die Wiederholbarkeit. Die wichtigsten bodennahen Variablen, die berücksichtigt werden, sind Niederschlag, Wasserdampfdruck, Strahlung, Windgeschwindigkeit sowie Maximal-, Durchschnitts- und Minimaltemperatur. Die Annahmen, die den Perfect Prognosis-Ansatz unterstützen, wurden analysiert und bestätigen die Robustheit der Methoden. Das downscaled Ensemble weist eine neuartige Auflösung von 1 km auf Tagesbasis auf, welches als Grundlage für mehrere Studien zu den Auswirkungen des Klimawandels dienen kann, insbesondere für Entscheidungsfindung auf lokaler Ebene und in topografisch komplexen Regionen. Es wurden umfassende methodische Implementierungen vorgeschlagen und analysiert, um Hauptaspekt (ii) zu erreichen. Trotz großer Einschränkungen werden Modelle zur Artverbreitung häufig in der Klimaschutzplanung eingesetzt. Daher könnten aktuelle Entwicklungen in der Klimadatenauflösung deren Nützlichkeit und Zuverlässigkeit verbessern, die bisher auf grobe zeitliche Aggregatformen im Projektionsbereich beschränkt waren. Das vorgestellte Framework bietet feingliedrige Prognosen zur Eignung von Arten und zufriedenstellende räumlich-zeitliche Übertragbarkeit, trotz besorgniserregender Trends. Diese verbesserten Prognosen sind ein Schritt in Richtung maßgeschneiderter Naturschutzmaßnahmen. Einschränkungen von Machine Learning-Methoden und Modellen zur Artverbreitung werden untersucht. Substanzielle Möglichkeiten zur zukünftigen Verbesserung werden ausführlich erörtert. Da die Ergebnisse darauf hinweisen, dass geeignete Lebensräume weiter abnehmen, wird erneut zum schnellen Handeln in Richtung kohlenstoffarmer Gesellschaften aufgerufen. Dies erfordert die Maximierung von Maßnahmen zur Bekämpfung des Klimawandels und zur Anpassung, zusammen mit einem raschen Übergang von kurzfristig Profitorientierten Politiken zu langfristiger nachhaltiger Entwicklung, aber vor allem zu einem kollektiven Bewusstseinswandel von anthropozentrischen Positionen zu ökozentrischen Politiken und Gesellschaften.:Contents Declaration of conformity........................................................ I Abstract....................................................................... III Zusammenfassung.................................................................. V Resumen........................................................................ VII Acknowledgments................................................................. IX List of Figures................................................................. XV List of Tables................................................................. XIX Symbols and Acronyms........................................................... XXI I Prelude & Foundations 1 1 Introduction................................................................... 3 1.1 Motivation – Human Impact on Earth....................................... 3 1.2 Earth System Modelling and Downscaling................................... 5 1.3 Biosphere’s Response to Recent Changes................................... 8 1.4 Species Distribution Models.............................................. 9 1.5 Objectives.............................................................. 10 1.6 Scope................................................................... 10 1.7 Outline................................................................. 10 2 Methodological Basis.......................................................... 13 2.1 Introduction to Artificial Intelligence Methods......................... 13 2.1.1 Artificial Intelligence........................................... 13 2.1.2 Machine Learning.................................................. 14 2.1.3 Deep Learning..................................................... 14 2.2 Downscaling Techniques.................................................. 15 2.2.1 Dynamical Downscaling............................................. 15 2.2.2 Statistical Downscaling........................................... 15 2.2.2.1 Model Output Statistics................................... 16 2.2.2.2 Perfect Prognosis......................................... 16 2.3 Species Distribution Models: Temporal Aspects........................... 17 2.4 Computational Framework................................................. 18 2.4.1 High-Performance Computing........................................ 18 2.4.2 Containers........................................................ 18 2.5 Remarks on Reproducibility.............................................. 19 II Articles’ Synthesis 21 3 Data.......................................................................... 23 3.1 Study Area.............................................................. 23 3.2 ReKIS................................................................... 24 3.3 ERA5.................................................................... 24 3.4 CORDEX.................................................................. 24 3.5 Species Occurrences..................................................... 25 3.6 WorldClim............................................................... 26 4 Methodological Implementations................................................ 27 4.1 Advancing Statistical Downscaling....................................... 27 4.1.1 Transfer Function Calibration.................................... 27 4.1.2 Evaluation....................................................... 29 4.1.3 Repeatability.................................................... 29 4.2 Downscaling a Multivariate Ensemble..................................... 30 4.2.1 Transfer Function Adaptations.................................... 30 4.2.2 Validation....................................................... 30 4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31 4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32 4.3.1 Climate Data..................................................... 32 4.3.1.1 Predictor Sets.......................................... 32 4.3.1.2 Temporal Approaches..................................... 33 4.3.2 SDM Implementation............................................... 33 4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33 4.3.4 Meta-analysis.................................................... 34 4.3.5 Pseudo-Reality Assessment........................................ 34 4.3.6 Spatio-Temporal Transferability.................................. 34 5 Results & Discussions......................................................... 35 5.1 Advancing Statistical Downscaling....................................... 35 5.1.1 Performance Improvement.......................................... 35 5.1.2 Repeatability.................................................... 36 5.1.3 Transfer Function Suitability.................................... 38 5.2 Downscaling a Multivariate Ensemble..................................... 39 5.2.1 Transfer Function performance.................................... 39 5.2.2 Bias-Correction.................................................. 40 5.2.3 Pseudo-Reality................................................... 42 5.2.4 Projections...................................................... 43 5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45 5.3.1 Predictor Set Evaluation for H2k................................. 45 5.3.2 Temporal Approach Comparison..................................... 46 5.3.3 Spatio-Temporal Transferability.................................. 47 5.3.4 Suitability Projections.......................................... 47 III Insights 51 6 Summary....................................................................... 53 6.1 Article A1.............................................................. 53 6.2 Article A2.............................................................. 54 6.3 Article A3.............................................................. 56 7 Conclusions and Outlook....................................................... 59 References 65 Articles 81 A1 Repeatable high-resolution statistical downscaling through deep learning..... 83 A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain............................................................. 103 A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu- tion Model..................................................................... 127 / Acción urgente científicamente informada es necesaria para estabilizar el sistema terrestre en medio del cambio climático antropogénico. En particular, la notable transgresión del límite planetario de ’integridad de la biosfera’ debe abordarse. Los modernos modelos del sistema terrestre tienen dificultades para representar con precisión las características climáticas a escala regional y local, así como los aspectos de la biodiversidad. Desarrollos recientes permiten abordar estos problemas mediante la inteligencia artificial. Esta disertación se enfoca en dos aspectos principales: (i) derivar datos climáticos de alta resolución espacio-temporal a partir de modelos más gruesos; y (ii) integrar datos climáticos de alta resolución temporal en modelos de distribución de especies. Se definieron tres objetivos específicos: Obj1 Mejorar los métodos de pronóstico perfecto – reducción de escala estadística mediante algoritmos modernos de aprendizaje profundo. Obj2 Generar un conjunto climático multivariado de alta resolución. Obj3 Emplear el conjunto de datos resultante para mejorar las proyecciones de los modelos de distribución de especies. Los objetivos están vinculados a los tres artículos que respaldan esta disertación acumulativa. Su alcance se limita al Estado Libre de Sajonia, Alemania, donde se emplearon datos climáticos locales de alta resolución y observaciones de alta calidad de especies de plantas vasculares en peligro de extinción. Desde una perspectiva más amplia, estos esfuerzos deberían contribuir a la meta general de cerrar la brecha entre las escalas de la distribución de especies y los modelos climáticos, mientras que se establecen marcos de trabajo contenedorizados de código abierto, reproducibles y escalables. Algoritmos recientes de aprendizaje profundo fueron aprovechados para lograr (i). Los marcos de trabajo propuestos mejoran el rendimiento previo de los métodos de pronóstico perfecto – reducción de escala estadística, al tiempo que garantizan la repetibilidad. Las variables clave de la superficie cercana consideradas son precipitación, presión de vapor de agua, radiación, velocidad del viento, así como la temperatura máxima, media y mínima. Se examinaron meticulosamente las suposiciones que respaldan el método de pronóstico perfecto, confirmando la robustez de las propuestas. El conjunto reducido de escala exhibe una novedosa resolución diaria de 1 km, el cual puede servir como insumo para múltiples estudios de impacto climático, especialmente para la toma de decisiones a nivel local y en regiones topográficamente complejas. Se propusieron y analizaron minuciosamente considerables implementaciones metodológicas para lograr (ii). A pesar de sus notables limitaciones, los modelos de distribución de especies son utilizados con frecuencia en la planificación de la conservación debido al cambio climático. Por lo tanto, los desarrollos recientes en la resolución de datos climáticos podrían mejorar su utilidad y confiabilidad, ya que antes se limitaban a agregados temporales gruesos en el caso de las proyecciones. El marco de trabajo presentado proporciona proyecciones de idoneidad de especies detalladas y una transferibilidad espacio-temporal satisfactoria, aunque con tendencias preocupantes. Estas proyecciones mejoradas son un paso adelante en los esfuerzos de conservación a la medida. Se abordan las limitaciones de los métodos de aprendizaje automático y de los modelos de distribución de especies. Se discuten a fondo posibilidades sustanciales para futuras mejoras. Dado que los resultados sugieren una mayor reducción de hábitats adecuados, se hace otro llamado a la acción rápida hacia sociedades bajas en carbono. Esto requiere maximizar las medidas de mitigación y adaptación al cambio climático, junto con una transición rápida de políticas orientadas a beneficios a corto plazo hacia un desarrollo sostenible a largo plazo, pero principalmente, un cambio colectivo de conciencia, desde posiciones antropocéntricas hacia políticas y sociedades ecocéntricas.:Contents Declaration of conformity........................................................ I Abstract....................................................................... III Zusammenfassung.................................................................. V Resumen........................................................................ VII Acknowledgments................................................................. IX List of Figures................................................................. XV List of Tables................................................................. XIX Symbols and Acronyms........................................................... XXI I Prelude & Foundations 1 1 Introduction................................................................... 3 1.1 Motivation – Human Impact on Earth....................................... 3 1.2 Earth System Modelling and Downscaling................................... 5 1.3 Biosphere’s Response to Recent Changes................................... 8 1.4 Species Distribution Models.............................................. 9 1.5 Objectives.............................................................. 10 1.6 Scope................................................................... 10 1.7 Outline................................................................. 10 2 Methodological Basis.......................................................... 13 2.1 Introduction to Artificial Intelligence Methods......................... 13 2.1.1 Artificial Intelligence........................................... 13 2.1.2 Machine Learning.................................................. 14 2.1.3 Deep Learning..................................................... 14 2.2 Downscaling Techniques.................................................. 15 2.2.1 Dynamical Downscaling............................................. 15 2.2.2 Statistical Downscaling........................................... 15 2.2.2.1 Model Output Statistics................................... 16 2.2.2.2 Perfect Prognosis......................................... 16 2.3 Species Distribution Models: Temporal Aspects........................... 17 2.4 Computational Framework................................................. 18 2.4.1 High-Performance Computing........................................ 18 2.4.2 Containers........................................................ 18 2.5 Remarks on Reproducibility.............................................. 19 II Articles’ Synthesis 21 3 Data.......................................................................... 23 3.1 Study Area.............................................................. 23 3.2 ReKIS................................................................... 24 3.3 ERA5.................................................................... 24 3.4 CORDEX.................................................................. 24 3.5 Species Occurrences..................................................... 25 3.6 WorldClim............................................................... 26 4 Methodological Implementations................................................ 27 4.1 Advancing Statistical Downscaling....................................... 27 4.1.1 Transfer Function Calibration.................................... 27 4.1.2 Evaluation....................................................... 29 4.1.3 Repeatability.................................................... 29 4.2 Downscaling a Multivariate Ensemble..................................... 30 4.2.1 Transfer Function Adaptations.................................... 30 4.2.2 Validation....................................................... 30 4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31 4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32 4.3.1 Climate Data..................................................... 32 4.3.1.1 Predictor Sets.......................................... 32 4.3.1.2 Temporal Approaches..................................... 33 4.3.2 SDM Implementation............................................... 33 4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33 4.3.4 Meta-analysis.................................................... 34 4.3.5 Pseudo-Reality Assessment........................................ 34 4.3.6 Spatio-Temporal Transferability.................................. 34 5 Results & Discussions......................................................... 35 5.1 Advancing Statistical Downscaling....................................... 35 5.1.1 Performance Improvement.......................................... 35 5.1.2 Repeatability.................................................... 36 5.1.3 Transfer Function Suitability.................................... 38 5.2 Downscaling a Multivariate Ensemble..................................... 39 5.2.1 Transfer Function performance.................................... 39 5.2.2 Bias-Correction.................................................. 40 5.2.3 Pseudo-Reality................................................... 42 5.2.4 Projections...................................................... 43 5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45 5.3.1 Predictor Set Evaluation for H2k................................. 45 5.3.2 Temporal Approach Comparison..................................... 46 5.3.3 Spatio-Temporal Transferability.................................. 47 5.3.4 Suitability Projections.......................................... 47 III Insights 51 6 Summary....................................................................... 53 6.1 Article A1.............................................................. 53 6.2 Article A2.............................................................. 54 6.3 Article A3.............................................................. 56 7 Conclusions and Outlook....................................................... 59 References 65 Articles 81 A1 Repeatable high-resolution statistical downscaling through deep learning..... 83 A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain............................................................. 103 A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu- tion Model..................................................................... 127

Agri-environment schemes (AESs), ecological focus areas (EFAs), and organic farming are the main tools of the common agricultural policy (CAP) to counteract the dramatic decline of farmland biodiversity in Europe. However, their effectiveness is repeatedly doubted because it seems to vary when measured at the field-versus-landscape level and to depend on the regional environmental and land-use context. Understanding the heterogeneity of their effectiveness is thus crucial to developing management recommendations that maximize their efficacy. Using ensemble species distribution models and spatially explicit field-level information on crops grown, farming practice (organic/conventional), and applied AES/EFA from the Integrated Administration and Control System, we investigated the contributions of five groups of measures (buffer areas, cover crops, extensive grassland management, fallow land, and organic farming) to habitat suitability for 15 farmland bird species in the Mulde River Basin, Germany. We used a multiscale approach to identify the scale of effect of the selected measures. Using simulated land-use scenarios, we further examined how breeding habitat suitability would change if the measures were completely removed and if their adoption by farmers increased to meet conservation-informed targets. Buffer areas, fallow land, and extensive grassland were beneficial measures for most species, but cover crops and organic farming had contrasting effects across species. While different measures acted at different spatial scales, our results highlight the importance of land-use management at the landscape level—at which most measures had the strongest effect. We found that the current level of adoption of the measures delivers only modest gains in breeding habitat suitability. However, habitat suitability improved for the majority of species when the implementation of the measures was increased, suggesting that they could be effective conservation tools if higher adoption levels were reached. The heterogeneity of responses across species and spatial scales indicated that a mix of different measures, applied widely across the agricultural landscape, would likely maximize the benefits for biodiversity. This can only be achieved if the measures in the future CAP will be cooperatively designed in a regionally targeted way to improve their attractiveness for farmers and widen their uptake.