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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, 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 bodennahe, 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 r

[Aim]: Theoretical, experimental and observational studies have shown that biodiversity–ecosystem functioning (BEF) relationships are influenced by functional community structure through two mutually non-exclusive mechanisms: (1) the dominance effect (which relates to the traits of the dominant species); and (2) the niche partitioning effect [which relates to functional diversity (FD)]. Although both mechanisms have been studied in plant communities and experiments at small spatial extents, it remains unclear whether evidence from small-extent case studies translates into a generalizable macroecological pattern. Here, we evaluate dominance and niche partitioning effects simultaneously in grassland systems world-wide. [Location]: Two thousand nine hundred and forty-one grassland plots globally. [Time period]: 2000–2014. Major taxa studied. Vascular plants. [Methods]: We obtained plot-based data on functional community structure from the global vegetation plot database “sPlot”, which combines species composition with plant trait data from the “TRY” database. We used data on the community-weighted mean (CWM) and FD for 18 ecologically relevant plant traits. As an indicator of primary productivity, we extracted the satellite-derived normalized difference vegetation index (NDVI) from MODIS. Using generalized additive models and deviation partitioning, we estimated the contributions of trait CWM and FD to the variation in annual maximum NDVI, while controlling for climatic variables and spatial structure. [Results]: Grassland communities dominated by relatively tall species with acquisitive traits had higher NDVI values, suggesting the prevalence of dominance effects for BEF relationships. We found no support for niche partitioning for the functional traits analysed, because NDVI remained unaffected by FD. Most of the predictive power of traits was shared by climatic predictors and spatial coordinates. This highlights the importance of community assembly processes for BEF

Aim: Theoretical, experimental and observational studies have shown that biodiversity–ecosystem functioning (BEF) relationships are influenced by functional community structure through two mutually non-exclusive mechanisms: (1) the dominance effect (which relates to the traits of the dominant species); and (2) the niche partitioning effect [which relates to functional diversity (FD)]. Although both mechanisms have been studied in plant communities and experiments at small spatial extents, it remains unclear whether evidence from small-extent case studies translates into a generalizable macroecological pattern. Here, we evaluate dominance and niche partitioning effects simultaneously in grassland systems world-wide. Location: Two thousand nine hundred and forty-one grassland plots globally. Time period: 2000–2014. Major taxa studied: Vascular plants. Methods: We obtained plot-based data on functional community structure from the global vegetation plot database “sPlot”, which combines species composition with plant trait data from the “TRY” database. We used data on the community-weighted mean (CWM) and FD for 18 ecologically relevant plant traits. As an indicator of primary productivity, we extracted the satellite-derived normalized difference vegetation index (NDVI) from MODIS. Using generalized additive models and deviation partitioning, we estimated the contributions of trait CWM and FD to the variation in annual maximum NDVI, while controlling for climatic variables and spatial structure. Results: Grassland communities dominated by relatively tall species with acquisitive traits had higher NDVI values, suggesting the prevalence of dominance effects for BEF relationships. We found no support for niche partitioning for the functional traits analysed, because NDVI remained unaffected by FD. Most of the predictive power of traits was shared by climatic predictors and spatial coordinates. This highlights the importance of community assembly processes for BEF relations

Aim Oceanic islands possess unique floras with high proportions of endemic species. Island floras are expected to be severely affected by changing climatic conditions as species on islands have limited distribution ranges and small population sizes and face the constraints of insularity to track their climatic niches. We aimed to assess how ongoing climate change affects the range sizes of oceanic island plants, identifying species of particular conservation concern. Location Canary Islands, Spain. Methods We combined species occurrence data from single-island endemic, archipelago endemic and nonendemic native plant species of the Canary Islands with data on current and future climatic conditions. Bayesian Additive Regression Trees were used to assess the effect of climate change on species distributions; 71% (n = 502 species) of the native Canary Island species had models deemed good enough. To further assess how climate change affects plant functional strategies, we collected data on woodiness and succulence. Results Single-island endemic species were projected to lose a greater proportion of their climatically suitable area (x ̃ = −0.36) than archipelago endemics (x ̃ = −0.28) or nonendemic native species (x ̃ = −0.26), especially on Lanzarote and Fuerteventura, which are expected to experience less annual precipitation in the future. Moreover, herbaceous single-island endemics were projected to gain less and lose more climatically suitable area than insular woody single-island endemics. By contrast, we found that succulent single-island endemics and nonendemic natives gain more and lose less climatically suitable area. Main Conclusions While all native species are of conservation importance, we emphasise single-island endemic species not characterised by functional strategies associated with water use efficiency. Our results are particularly critical for other oceanic island floras that are not constituted by such a vast diversity of insular woody species as the

Aim: Understanding how grain size affects our ability to characterize species responses to ongoing climate change is of crucial importance in the context of an increasing awareness for the substantial difference that exists between coarse spatial resolution macroclimatic data sets and the microclimate actually experienced by organisms. Climate change impacts on biodiversity are expected to peak in mountain areas, wherein the differences between macro and microclimates are precisely the largest. Based on a newly generated fine-scale environmental data for the Canary Islands, we assessed whether data at 100 m resolution is able to provide more accurate predictions than available data at 1 km resolution. We also analysed how future climate suitability predictions of island endemic bryophytes differ depending on the grain size of grids. Methods: We compared the accuracy and spatial predictions using ensemble of small models for 14 Macaronesian endemic bryophyte species. We used two climate data sets: CHELSA v1.2 (~1 km) and CanaryClim v1.0 (100 m), a downscaled version of the latter utilizing data from local weather stations. CanaryClim also encompasses future climate data from five individual model intercomparison projects for three warming shared socio-economic pathways. Results: Species distribution models generated from CHELSA and CanaryClim exhibited a similar accuracy, but CanaryClim predicted buffered warming trends in mid-elevation ridges. CanaryClim consistently returned higher proportions of newly suitable pixels (8%–28%) than CHELSA models (0%–3%). Consequently, the proportion of species predicted to occupy pixels of uncertain suitability was higher with CHELSA (3–8 species) than with CanaryClim (0–2 species). Main conclusions: The resolution of climate data impacted the predictions rather than the performance of species distribution models. Our results highlight the crucial role that fine-resolution climate data sets can play in predicting the potential dist

The documentation of biodiversity distribution through species range identification is crucial for macroecology, biogeography, conservation, and restoration. However, for plants, species range maps remain scarce and often inaccurate. We present a novel approach to map species ranges at a global scale, integrating polygon mapping and species distribution modelling (SDM). We develop a polygon mapping algorithm by considering distances and nestedness of occurrences. We further apply an SDM approach considering multiple modelling algorithms, complexity levels, and pseudo-absence selections to map the species at a high spatial resolution and intersect it with the generated polygons. We use this approach to construct range maps for all 1957 species of Fagales and Pinales with data compilated from multiple sources. We construct high-resolution global species richness maps of these important plant clades, and document diversity hotspots for both clades in southern and south-western China, Central America, and Borneo. We validate the approach with two representative genera, Quercus and Pinus, using previously published coarser range maps, and find good agreement. By efficiently producing high-resolution range maps, our mapping approach offers a new tool in the field of macroecology for studying global species distribution patterns and supporting ongoing conservation efforts.

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades-including a maturation of relevant theory and key concepts-but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an "Overview" talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades—including a maturation of relevant theory and key concepts—but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an “Overview” talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades-including a maturation of relevant theory and key concepts-but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an "Overview" talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.

Aim: This work explores whether the commonly observed positive range size-niche breadth relationship exists for Fagus, one of the most dominant and widespread broad-leaved deciduous tree genera in temperate forests of the Northern Hemisphere. Additionally, we ask whether the 10 extant Fagus species' niche breadths and climatic tolerances are under phylogenetic control. Location: Northern Hemisphere temperate forests. Taxon: Fagus L. Methods: Combining the global vegetation database sPlot with Chinese vegetation data, we extracted 107,758 releves containing Fagus species. We estimated biotic and climatic niche breadths per species using plot-based co-occurrence data and a resource-based approach, respectively. We examined the relationships of these estimates with range size and tested for their phylogenetic signal, prior to which a Random Forest (RF) analysis was applied to test which climatic properties are most conserved across the Fagus species. Results: Neither biotic niche breadth nor climatic niche breadth was correlated with range size, and the two niche breadths were incongruent as well. Notably, the widespread North American F. grandifolia had a distinctly smaller biotic niche breadth than the Chinese Fagus species (F. engleriana, F. hayatae, F. longipetiolata and F. lucida) with restricted distributions in isolated mountains. The RF analysis revealed that cold tolerance did not differ among the 10 species, and thus may represent an ancestral, fixed trait. In addition, neither biotic nor climatic niche breadths are under phylogenetic control. Main Conclusions: We interpret the lack of a general positive range size-niche breadth relationship within the genus Fagus as a result of the widespread distribution, high among-region variation in available niche space, landscape heterogeneity and Quaternary history. The results hold when estimating niche sizes either by fine-scale co-occurrence data or coarse-scale climate data, suggesting a mechanistic link between f

Montane cloud forests are areas of high endemism, and are one of the more vulnerable terrestrial ecosystems to climate change. Thus, understanding how they both contribute to the generation of biodiversity, and will respond to ongoing climate change, are important and related challenges. The widely accepted model for montane cloud forest dynamics involves upslope forcing of their range limits with global climate warming. However, limited climate data provides some support for an alternative model, where range limits are forced downslope with climate warming. Testing between these two models is challenging, due to the inherent limitations of climate and pollen records. We overcome this with an alternative source of historical information, testing between competing model predictions using genomic data and demographic analyses for a species of beetle tightly associated to an oceanic island cloud forest. Results unequivocally support the alternative model: populations that were isolated at higher elevation peaks during the Last Glacial Maximum are now in contact and hybridizing at lower elevations. Our results suggest that genomic data are a rich source of information to further understand how montane cloud forest biodiversity originates, and how it is likely to be impacted by ongoing climate change.