Alpine biome shifts are a model system to understand the factors driving and constraining major ecological transitions. Alpine environments (i.e., above the upper climatic treeline) are particularly exacting e.g., short growing seasons, high UV light radiation, and extreme temperature fluctuations daily and annually. Thus, evolving a trait syndrome that enables becoming alpine involves a multitude of trait complexes, integrating plant architecture (e.g., prostrate growth to leverage boundary layer effects), physiology (e.g., freezing resistance; UV protection), and reproductive traits (e.g., few-flowered inflorescences), jointly relating to these environmental challenges. In this talk, I ask the question if there is some systematic order in which ecological traits underpinning alpine biome shifts were acquired over macroevolutionary timescales. I approach this question in two ways: by analyzing the environmental niches in multiple dimensions of the high- and low-elevation plant species of the European Alps, and by focusing on phylogenetic sequences of acquisition in multiple clades of plants that diversified across mountain systems. Results suggest that trait evolution underpinning phylogenetic biome shifts constitutes a staggered acquisition of the components of complex syndromes, each component of which may exhibit rather strikingly different evolutionary patterns across clades.

Dispersal is a key process known to influence both speciation and extinction, however, the exact relationships appear to be scale- and context-dependent. Dispersal on ecological scales inhibits speciation due to increased gene flow, whereas dispersal across major barriers may increase speciation due to new opportunities for ecological specialisation and radiations. Similarly, low dispersal abilities have been linked to increased extinction rates, but in meta-populations, high levels of dispersal have also been linked to higher extinction risk due to synchronized population responses. Few studies have explicitly tested these hypotheses at macroecological and macroevolutionary scales across multiple taxa.

Using traits as a proxy for past dispersal ability in state-dependent speciation and extinction models we are linking dispersal to speciation and extinction in reptiles. Ultimately, species richness is the result of the interplay between speciation and extinction. Better understanding the links between dispersal and speciation/extinction will thus improve our knowledge of the origin of biodiversity.

What drives variation in resource use among species, trophic groups, and environments? Here, we report on a distributed experiment across Denmark, conducted mostly by children, to address this question in ants. Over two years, children conducted two-hour baiting experiments at 508 sites to investigate how ants foraged for a suite of resources. The distributed experiment, called ‘Myrejagten’ (the Ant Hunt), observed 15,329 workers from 26 species across a variety of environmental and temporal conditions, including building proportion, vegetation density, solar radiation, topographic wetness, coastal distance and Julian date. The experiment revealed that relative resource use varies among trophic levels with primary consumers showing an eight times higher preference for oil and predators a twice as high preference for protein and that environment has a significant effect on resource use. Taken together, our results highlight that engaging children can lead to discoveries at the forefront of ecology, such as the drivers of resource use at geographical scales, among species and trophic levels. Furthermore, we present some preliminary advances on an automated camera trap that can be deployed for continuous monitoring of ant resource use.

Opportunistic plant observations collected with plant ID apps such as Flora Incognita provide a rapidly growing source of spatiotemporal plant observation data. Here, we used such data to explore the question whether they can be used to detect changes in plant species phenologies. Examining 20 mostly herbaceous plant species in two consecutive years across Europe, we observed significant shifts in their flowering phenology, being more pronounced for springflowering species (6-17 days) compared to summer -flowering species(1-6 days). Moreover, we show that these data are suitable to model large-scale relationships such as "Hopkins' bioclimatic law" which quantifies the phenological delay with increasing elevation, latitude, and longitude. Here, we observe spatial shifts, ranging from -5 to 50 days per 1000m elevation, latitudinal shifts ranging from -1 to 4 days per degree northwards, and longitudinal shifts ranging from -1 to 1 day per degree eastwards, depending on the species. Our findings show that the increasing volume of purely opportunistic plant observation data already provides reliable phenological information, and therewith can be used to support global, high-resolution phenology monitoring in the face of ongoing climate change.

Ecologists have long sought universal principles that govern life's distribution on Earth. One foundational concept is the species-energy relationship, which suggests that areas with more available energy should support more species. However, linking biodiversity to energy metrics like temperature, water, and productivity often yields mixed results due to context dependence. These inconsistencies may stem from overlooking the impacts of spatial and historical processes and the influence of Earth's physical dynamics on climatic conditions, which significantly affect biodiversity patterns. Our study addresses these issues by analyzing the diversity-energy relationship within the framework of environmental space, as defined by Hutchinson's duality. Contrary to geographical analyses, in our approach, species sharing the same climate condition are grouped together even if they are not located in the same geographical area, effectively minimizing the influence of geographical processes on our analysis. Our findings reveal consistent patterns that starkly contrast with the extensive variability and context-specificity reported in previous studies. We confirm the long-standing theoretical predictions of species-energy relationships, highlighting that evaluations of these theories or hypotheses should also exclude spatial processes if they are not accounted for in the original assumptions.

To understand how species will be able to cope with changing climatic conditions, the integration of thermal physiology and biogeography bears great potential. However, it remains poorly understood whether relationships of thermal traits with the environment observed between species scale down to the intraspecific and scale up to the assemblage level with similar magnitude and direction. Here, we present results from thermal tolerance measurements in over 15,000 individuals representing 116 arthropod species along elevational gradients in Southern Asia. We quantified the associations between thermal traits and their determinants at different taxonomic aggregation levels and for two different mountain ranges.

Our findings show a consistent decrease in all thermal traits investigated with increasing elevation and an increase with the increase of temperature, especially at the assemblage level. Nevertheless, the distributional patterns of thermal traits exhibited greater variation and even contrast along the two elevational transects as well as at lower taxonomic levels. This implies that factors beyond elevation, including vegetation composition, microclimate, or landscape features, exert significant influence on the organisms’ thermal characteristics. Our study highlights this complexity of the interplay of thermal physiology and environment across different habitats and across biological scales.

We introduce mobile environmental sensing as a novel approach for mapping climate, soil, and disturbance factors across scales. Bio-indication allows to infer environmental conditions from plant occurrences based on their ecological niches. Leveraging automated plant identification apps such as Flora Incognita and citizen science millions of opportunistic plant occurrence data become available which will further increase in the future. In combination with recent advancements in pan European bio-indicator value systems this enables us to map environmental factors over large extents.

Currently, mobile sensing achieves resolutions of 0.25° across Europe and up to 100m in urban areas due to a higher citizen participation. Cross-consistency checks against traditional environmental data sources and expert vegetation mappings support a high reliability for temperature, soil pH, and salt variations based on mobile sensing. Importantly, we can also map environmental factors for which traditional data-sets are very uncertain or lacking and therefore allow new insights, such as disturbance severity and frequency, grazing pressure, mowing frequency, soil disturbance, light, soil moisture, and nutrients.

Three applications will illustrate the potential of mobile sensing: mapping of effective bio-environmental regions, high-resolution analysis of urban environments, and a community engagement to better understand ecosystem-atmosphere carbon and water fluxes measured by FLUXNET.

In the field of species distribution modeling, there is often a trade-off between interpretability and performance. This partly explains why the study of species distributions is often based on relatively simple models (GLM, MaxEnt, etc.). The advantage of these models is that they capture easily interpretable relationships between species distribution and environmental factors. In this presentation we describe deep learning-based species distribution models (Deep-SDMs) and their main features. Deep-SDMs have emerged recently and often lead to superior performance for predictions. As for JSDM (Joint Species Distribution Models), Deep-SDMs account for species co-occurrences, and have the ability to model and predict species communities. Deep-SDMs can bring several advantages to the field of SDM and macroecology more generally. In particular, their ability to efficiently predict species communities on big datasets by learning a common latent space and the ability to easily integrate complex data (aerial or satellite imagery, environmental neighborhood information). We show for exemple that Deep-SDMs can capture information about the spatial structure of the environment or the landscape to enable better performance than other state-of-the-art models.

Big data approaches, like large-scale camera trapping studies, are becoming more relevant for studying human-wildlife interactions. In addition, open-source object detection models are rapidly improving and have great potential to enhance the image processing of camera trap data. The resulting large-scale and long-term database enables to understand and predict spatiotemporal patterns of human activities in natural areas as well as their interactions with wildlife.

Building on own results, this talk first highlights the performance of open-source object detection model in visitor and wildlife monitoring. Since the accuracy of the detection model is very high, this approach is suitable for biogeographic analysis of spatiotemporal patterns of humans and wildlife. Besides the great acceleration in processing speed, the approach is suitable for long-term monitoring and allows reproducibility in scientific studies while complying with privacy regulations.

The talk will further show how such long-term monitoring can be used to predict visitor flows along a trail network using multiple spatiotemporal predictors. Building on this, the data allow the assessment of human-wildlife interactions and are thus able to enhanced the adaptation of management measures to reduce social and ecological conflicts.

The IUCN Red List of threatened species (RL) is the most authoritative global quantification of extinction risk, and widely used in ecological research and applied conservation. Yet, due to the time-consuming assessment process, the RL is taxonomically and geographically biased, in particular towards the global North and charismatic taxa. One promising approach to speed up RL assessments and overcome these biases is the use of AI to predict extinction risk based on the combination of information from digitized collection specimens and citizen science data with remote sensing information on the environment. Here, I present IUCNN, an approach using deep learning models to predict species RL status from publicly available geographic occurrence records (and other data, such as traits if available).

The global homogenisation of floral communities is a characteristic of the Anthropocene. Oceanic islands are especially prone to homogenisation due to their high invasibility and anthropogenically-caused extinctions and extirpations of unique native, and often endemic, species. In existing studies, the time horizon in which homogenisation is analysed rarely exceeds 500 years and pre-human times are often considered as a single point in time. Thereby, the temporal contextualisation of this process is disregarded. We analyse the similarity of island floras across the globe during the past 5000 years using fossil pollen data from sedimentary sequences. We track changes from a global, cross-ocean to a regional scale. While globally a clear harmonization trend can be detected, magnitudes of change in floral similarity differ at regional scales. This study underlines the usefulness of palynological data to support the analysis and contextualisation of biodiversity changes in the Anthropocene.

Global changes in biodiversity and their drivers are a major focus of scientific research. Recent studies have shown that taxonomic, phylogenetic and functional diversity can be combined to disentangle the drivers of community assembly. Due to their nature, islands are ideal biological model systems to study the effects of filters on diversity patterns. Although bird community assembly on islands are a popular subject of study, studies have been limited to single archipelagos or a few islands.

We used a comprehensive global dataset of 2719 islands and analysed the diversity of 4624 terrestrial breeding bird species. We calculated functional diversity as well as phylogenetic diversity and corrected both for species richness using null models. To analyse diversity patterns we used multivariate models.

We found a correlation between functional and phylogenetic diversity across biogeographic realms. However, functional diversity values were more clustered than expected based on species richness, while phylogenetic diversity was generally lower than expected. Correspondingly, dispersal and biotic and abiotic filters show strong effects that vary considerably between biogeographical realms.

Our results show strong regional effects on community assembly and highlight the importance of the interplay between different filters which a combined analysis of phylogenetic and functional diversity can disentangle.

How does marine biodiversity flourish on islands? Our understanding of island biodiversity is predominantly based on terrestrial examples, while processes governing marine biodiversity might differ completely. Here, we tested the effects of island size, isolation and age on fish taxonomic, functional and phylogenetic diversity in a unique island-like marine system - Indonesian tropical marine lakes. Marine lakes are bodies of seawater surrounded by land connected at different levels to the sea. Because of their high degree of natural replication combined with small and manageable sizes, they offer an unprecedented opportunity to investigate the processes shaping biodiversity. Additionally, we investigated whether fish communities in marine lakes are randomly assembled (a random subset from the regional species pool) or whether environmental or dispersal filters are at work (composed of species with similar traits such as high dispersal ability or environmental tolerance). This study is the first attempt to describe marine lake fish communities and to investigate how biodiversity arises and thrives in these isolated ecosystems, relevant in a world where nature is becoming increasingly fragmented.

Edaphic islands commonly host high numbers of habitat-specialized or endemic plant species. In the case of quartz islands in South Africa, many of these species are additionally characterized as dwarf succulent shrubs with unusual growth forms. The functional traits of these plants are poorly represented in trait databases and the distinctive succulent growth forms necessitate innovative approaches to trait measurement in addition to conventional protocols. In this study, we investigate whether and how the plant functional and biogeographic traits correspond to the specific soil abiotic and spatial characteristics of the edaphic islands, in comparison to the surrounding matrix. We use quantitative data on various traits of the most abundant perennial plant species (n = 195) found on a quartz island archipelago in the Knersvlakte nature reserve. We explore how a functional approach can enhance the comparison of different types of edaphic islands, enabling us to gain further insights into their unique or shared characteristics. Furthermore, we discuss where deviating from common standardized protocols for trait measurement in succulent plant species is unavoidable and which additional traits provide important information about the strategies of the studied edaphic specialists.

Acceleration of human activities over the past century might have caused a corresponding acceleration in the decline of abundance of species, but this has not been empirically assessed. Further, the temporal dynamics of abundance arises from a complex interaction between recruitment and loss of individuals, yet this interplay remains unexplored across large spatial scales. We address these gaps by examining temporal changes, acceleration, deceleration, and vital processes (i.e. recruitment and loss) across much of the North American avifauna from 1987 to 2021. We confirm the continent-wide decline of bird abundance, and pinpoint the regional hotspots of acceleration of this decline in the Mid-Atlantic region, Midwest, and California, matching broad spatial patterns of human activities. We further reveal that the increasing rate of loss is the primary process of acceleration of abundance decline in California and the Midwest, whereas a decrease in recruitment rate dominates in the Mid-Atlantic. Finally, we highlight a worrisome trend: 96% of bird species and 100% of families with increasing abundances are concurrently experiencing a decline in recruitment rate. Thus, we need conservation policies even for species that appear to be thriving. Simply preventing loss may not be enough, as we also need policies that enhance recruitment.

Human activities have altered the species composition of assemblages through introductions and extinctions, but it remains unclear how those changes can affect the different facets of biodiversity. Here we assessed the impact of changes in species composition on taxonomic, functional, and phylogenetic diversity across 281 bird assemblages worldwide. To provide a more nuanced understanding of functional diversity, we distinguished morphological from life-history traits. We showed that shifts in species composition could trigger a global decline in avian biodiversity due to the high number of potential extinctions. Moreover, these extinctions were not random but unique in terms of function and phylogeny at the regional level. Our findings demonstrated that non-native species cannot compensate for these losses, as they are both morphologically and phylogenetically close to the native fauna. In the context of the ongoing biodiversity crisis, such alterations in the functional and phylogenetic structure of bird assemblages could heighten ecosystem vulnerability.

Stored seeds of wild plant species serve as repositories of plant genetic diversity, and are valuable resources for evolutionary research, species conservation, and ecosystem restoration. However, long-term storage inevitably leads to the deterioration and loss of viability of the seed. While seed longevity is known to be species-specific, the significance of intraspecific variability in seed longevity, and how it varies across geographic ranges, remains unexplored. Here, we tested how seed longevity varies between and within species, and how it correlates with the climate in the source region. We focused on 42 common grassland species and 182 accessions from 23 regions across Europe. To simulate seed aging in storage, we exposed the seeds to artificial ageing conditions (60% relative humidity, temperature 45°C). The seed longevity strongly varied between species more than twenty-fold, increased with seed protein content, yet it did not depend on phylogeny. Within species, the seed longevity varied more than ten-fold between the accessions. It increased with the initial seed viability and mean annual temperature in the region of origin. Our results suggest that seed from warmer regions, especially those with high initial viability, survive longer in conservation seed banks.

In the face of increasing biodiversity threats, understanding the predictors of plant extinction risk is critical for conservation. Global assessments indicate over one-third of terrestrial plant species are rare, with regional extinctions outpacing documented global extinctions. Integrating national and regional Red Lists of vascular plants with plant trait data and phylogenies, we analyze patterns of regional plant extinctions globally, to discern the role of evolutionary and functional uniqueness in species' vulnerability. Specifically, we investigate whether phylogenetic marginality—species' evolutionary distinctiveness—correlates with their risk of regional and global extinction of vascular plants. Additionally, we examine if species that have gone extinct exhibit greater functional trait marginality, meaning they occupy more unique positions in the functional trait space compared to the broader species pool. This approach may prompt early conservation actions across countries, even before species are globally recognized as threatened, addressing the growing importance of extinction risk estimation for enhancing conservation initiatives amid escalating anthropogenic impact.

Endangered forest species often depend on slowly evolving structures of old-growth forests such as large-diameter trees and deadwood, or cautious or absent forest management. Small-scale private forests are particularly important in this regard, offering great structural diversity and varied landscapes shaped by the individual owners, and serve as refuges for forest biodiversity.

The Parcel Index of Conservation Attributes (PICA) is presented as a tool to quantify the value of individual small-scale private forest parcels in terms of crucial old-growth structures that are becoming rare in current forests. PICA values are defined by broadleaf tree volume, broadleaf deadwood, management intensity, biotope values (according to German federal law), and number of large-diameter trees.

The PICA components have been demonstrated to correlate with easily obtainable topographical data such as land use, plot geometry, and forest cover continuity. To aid nature conservation efforts in understanding the spatial distribution of valuable structures, we utilized machine learning algorithms to predict PICA values. We parameterized and evaluated different models using data collected from a field survey of 129 small-scale private forest parcels in the Lower Saxon Hills region. The models were then used to create a map of potential old-growth biodiversity hotspots.

Humans pose threats to numerous native species, while they also have introduced many species outside their native ranges, where some have become naturalized or even invasive. Most of the naturalized species are common at home, but some of them may actually be threatened. Naturalization of threatened species could be considered an accidental kind of ex-situ conservation, but with the potential to become invasive pose a conservation conflict. It remains, however, unknown how many threatened-but-naturalized species there are, what features they have, and in which regions they are native and naturalized. To address this, we combined databases on the global threat status and naturalization success of the world’s seed plants. We found that 231 threatened species have become naturalized elsewhere; most of them are trees or shrubs with economic uses such as provisioning of materials and landscaping. Europe received more threatened-but-naturalized species than expected, whereas Africa, which harbours the largest number, was under-represented. Australasia and Northern America were over-represented as donors of threatened species. Our study shows that some threatened plant species have managed to become naturalized outside their native range, although the number is relatively low. Future studies should test the potential conservation value of these naturalized populations.

Human induced climate change poses a threat to global biodiversity. Broad scale effects of climate change are often assessed on the basis of long-term changes in climatic conditions. However, the effect of increasing frequency and intensity of extreme weather events (EWE) due to climate change on biodiversity remains unclear. We introduce a general framework to investigate the effects of EWE on species. As a case study we train classical presence/absence models for 132 German bird species of conservation interest (species requiring assessment under the EU Bird Protection Areas guidelines) with monthly specific weather and remote sensing data over the time period of 1999 to 2022. The species-specific models predict the suitability through time from 1999 to 2022 for each month across Germany. With this approach, the suitability over all non-extreme months can be compared to the suitability in months with climatic extremes, to generate a measure of the impact of an extreme event on the distribution of a species. With this measure it is possible to identify geographic areas, species communities, taxonomic- and functional groups that may be vulnerable towards specific EWE.

New advances and technologies are leading to an unprecedented high resolution of community data, perhaps making it possible for the first time to unravel the mechanisms of metacommunity assembly. Environmental filtering, species interactions, ecological drift, and dispersal determine community composition in local communities, but disentangling their relative importance has proven elusive, likely due to inappropriate tools. Here, we show that joint species distribution models (JSDM) and variance partitioning can provide a solution. First, JSDM reveals the "internal structure" of communities and species that can be correlated in a second step against environmental and spatial distinctiveness, thereby revealing the importance of metacommunity assembly mechanisms. We demonstrate that this approach can detect environmental filtering and dispersal limitation in a pond metacommunity. We conclude that JSDMs are a powerful tool for metacommunity analysis, especially for large community data.

The Global Inventory of Floras and Traits (GIFT) is a global database of regional plant checklists that has proven successful in documenting biogeographical patterns of plants. Since the release of the first version of GIFT, the database kept on expanding. GIFT version 3.0 contains 5169 plant checklists referring to 3400 regions worldwide. These checklists include a total of 371,148 land plant species, mostly vascular plants, of which 354,848 have accepted species names, and species-level data for 109 functional traits.

This presentation will first introduce the GIFT database and present its structure with examples showing how to retrieve distribution data for specific taxonomic groups, functional traits at the species level, phylogenetic diversity, and environmental data at the regional level. Second, a comparison of data between the GIFT database and the GBIF repository will be presented.

Species distribution modeling (SDM) often performs poorly when evaluated against spatially independent test data. One contributing factor to this issue is the neglect of spatial autocorrelation during model training and validation, resulting in inflated performance metrics and the development of overly complex models. Among the SDM softwares used Maxent is one of the most widely utilized methods, largely attributable to its user-friendly graphical-user-interface (GUI). It has been shown that parameter tuning leads to better Maxent models in terms of complexity and performance. However, in nearly all published applications, Maxent is used with the default settings. The lack of model tuning and the ignoring of spatial autocorrelation may be related to the fact that the Maxent GUI does not include such functionalities.

We implemented tuning and validation functionalities that account for spatial autocorrelation in a software extension for Maxent: spatialMaxent (https://doi.org/10.1002/ece3.10635). We compared our results to models based on Maxent's default settings on a dataset with over 200 species. spatialMaxent outperformed the Maxent models in terms of model complexity and performance on spatially independent test data. All tuning functionalities in spatialMaxent are accessible via the user-friendly GUI, ensuring easy access for researchers and conservation practitioners alike.

Understanding the mechanisms shaping species distributions is crucial for deciphering biodiversity patterns. While correlative models like ecological niche models (ENM) and species distribution models (SDM) have been valuable in predicting potential distributions across various scales, process-explicit models offer a more nuanced understanding of distribution causes, abundance predictions, and dispersal dynamics. These models vary in algorithmic nature and input data requirements. I will discuss the distinctions between correlative and process-explicit models, illustrating with a case study on the invasive ambrosia beetle Xyleborus glabratus, a vector of Raffaelea lauricola causing laurel wilt disease in the southeastern USA. Facing challenges due to lack of demographic data, we developed a process-explicit model in the lab. Our simulations align spatio-temporally with known invasion dynamics and provide superior dispersal estimates compared to correlative approaches. Predictions highlight Mexico's favorable conditions for beetle establishment, particularly in the central west. These findings have implications for agricultural, forestry, and economic decision-making, emphasizing the value of multidisciplinary approaches. I will discuss process-explicit model advantages and limitations, encouraging further exploration in diverse research contexts.

The extraordinary species richness and endemism of the Indo-Australian Archipelago exists in one of the most geologically dynamic regions of the planet. Application of the biogeographic history and developed processes-based approaches has stimulated an increasing biogeographic work for aboveground animals and plants in this region. But it is basically unknown how the degree of biogeographic isolation may lead to differences in belowground biodiversity. Here, we used geographic distance and beta diversity partitioning to analyze the influence of biogeographic isolation on soil oribatid mite assemblages in eleven regions in the Indo-Australian Archipelago. On average, oribatid mite richness in the eleven regions showed pronounced endemism. The soil oribatid mites’ zoogeographic distribution pattern changed gradually from the west and east sides to the central regions, consisted with the combination of Weber’s line, Lydekker’s line and Holt’s line. That dissimilarity pattern, in which species turnover was the most critical driving process, was closely correlated with geographic distance, underlining the importance of biogeographic isolation in soil animal biogeography. Our results, for the first time, elucidate the processes-based soil biodiversity patterns in the Indo-Australian Archipelago and emphasize how long-lasting vicariance structure divergent diversity in this biodiversity hotspot region.

Environmental filtering processes are revealed by trait variation in communities, with the community-weighted mean (CWM) being a common metric to indicate optimal adaptive strategy of taxa and directionality of filtering processes. Proximity to CWM indicates of higher fitness, and deviations from this optimal value result in changes in relative abundances of coexisting species. We investigated patterns of intraspecific trait variation in four coexisting carrion beetle (Silphidae) species across elevational gradients in temperate forest ecosystems with distinct natural vegetation zones ranging from 950 m to 1700 m a.s.l.. For seven of the 12 traits, most of the variation was attributed to intraspecific variation. Niche breadth was positively correlated with relative species abundance for most traits. In addition, CWMs of traits associated with long-distance dispersal decreased with elevation, while those associated with microhabitat use showed opposite trends. Soil temperature influenced tibia length after controlling for species identity effects. Nicrophorus quadripunctatus and N. tenuipes supported the CWM-optimality hypothesis for body width and thorax width, while N. maculifrons and N. vespilloides showed an opposite pattern for body width and thorax length. Our study suggests that some functional traits are highly variable, which is likely to help carrion beetles adapt across elevations and vegetation types.