Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro

The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results

Characterization of K-Complexes and Slow Wave Activity in a Neural Mass Model

NREM sleep is characterized by two hallmarks, namely K-complexes (KCs) during sleep stage N2 and cortical slow oscillations (SOs) during sleep stage N3. While the underlying dynamics on the neuronal level is well known and can be easily measured, the resulting behavior on the macroscopic population level remains unclear. On the basis of an extended neural mass model of the cortex, we suggest a new interpretation of the mechanisms responsible for the generation of KCs and SOs. As the cortex transitions from wake to deep sleep, in our model it approaches an oscillatory regime via a Hopf bifurcation. Importantly, there is a canard phenomenon arising from a homoclinic bifurcation, whose orbit determines the shape of large amplitude SOs. A KC corresponds to a single excursion along the homoclinic orbit, while SOs are noise-driven oscillations around a stable focus. The model generates both time series and spectra that strikingly resemble real electroencephalogram data and points out possible differences between the different stages of natural sleep

Baryon content in a sample of 91 galaxy clusters selected by the South Pole Telescope at 0.2 <z < 1.25

We estimate total mass (M500), intracluster medium (ICM) mass (MICM), and stellar mass (M) in a Sunyaev–Zel’dovich effect (SZE) selected sample of 91 galaxy clusters with masses M500 2.5 × 1014 M and redshift 0.2 < z < 1.25 from the 2500 deg2 South Pole Telescope SPT-SZ survey. The total masses M500 are estimated from the SZE observable, the ICM masses MICM are obtained from the analysis of Chandra X-ray observations, and the stellar masses M are derived by fitting spectral energy distribution templates to Dark Energy Survey griz optical photometry and WISE or Spitzer near-infrared photometry. We study trends in the stellar mass, the ICM mass, the total baryonic mass, and the cold baryonic fraction with cluster halo mass and redshift. We find significant departures from self-similarity in the mass scaling for all quantities, while the redshift trends are all statistically consistent with zero, indicating that the baryon content of clusters at fixed mass has changed remarkably little over the past ≈9 Gyr. We compare our results to the mean baryon fraction (and the stellar mass fraction) in the field, finding that these values lie above (below) those in cluster virial regions in all but the most massive clusters at low redshift. Using a simple model of the matter assembly of clusters from infalling groups with lower masses and from infalling material from the low-density environment or field surrounding the parent haloes, we show that the measured mass trends without strong redshift trends in the stellar mass scaling relation could be explained by a mass and redshift dependent fractional contribution from field material. Similar analyses of the ICM and baryon mass scaling relations provide evidence for the so-called ‘missing baryons’ outside cluster virial regions

species traits

The functional traits of the species used in this study. Traits covered: Leaf area, Specific leaf area, Leaf dry matter content, Canopy height, Stem specific density, Leaf C, Leaf N, Leaf P. Units are given in the second sheet of the table. For methods, please refer to the original publication. This is the original version as used in the publication. The latest version is always in the TRY database (https://www.try-db.org)

plot elevation

Elevation of the plots used in this study. Units are given in the second sheet of the table. For precipitation, mean temperature, and mean humidity, please refer to Mwangomo, E., Otte, I., Detsch, F., Nauss, T., Hemp, A. & Appelhans, T. (2014) Monthly and annual climate data averaged from 2011 to 2013 for 79 research plots on the southern slopes of Mt. Kilimanjaro - V 1.0. pp. DOI: 10.5281/zenodo.11695

Data from: Trait patterns of epiphytes compared to other plant life forms along a tropical elevation gradient

Compared to other plant life forms, epiphytes remain understudied. Understanding the responses of epiphytes to changing environmental conditions is necessary to predict changes in ecosystem functioning especially in subtropical and tropical regions. We investigated the functional traits of epiphytes along a large elevation gradient on Mount Kilimanjaro, Tanzania. We measured traits of co-occurring trees and terrestrial non-tree life forms, and compared changes in community-weighted means of traits (CWM) and trait spread, the range of observed trait values. We chose traits linked to growth and persistence: leaf area, specific leaf area, leaf dry matter content, stem specific density, plant height, leaf carbon, leaf nitrogen, and leaf phosphorus. For most traits, differences in community-weighted means between life forms exceeded differences within life forms along the elevation gradient. Many CWM showed linear changes with elevation, but no response and unimodal patterns were also frequent. This was best explained by temperature, or a combination of temperature with precipitation or humidity, indicating effects of these factors on the distribution of epiphytic and non-epiphytic species. Trait spread did not change with elevation in nearly half of the traits, but hump-shaped patterns were also common, probably a result of weaker environmental filtering in the gradient center. The magnitude of trait spread, i.e. the variability between species of the same life form within communities, was highest for terrestrial non-trees. Excluding ferns from the analyses lead to marked differences in trait patterns for epiphytes, as ferns made up 59 % of the epiphytic species, while playing a minor role in the other groups. The observed differences can be explained by a dichotomy in epiphytic life strategies, with tough-leaved xero-tolerant species on one side and succulent soft-leaved species on the other. However, the influence of phylogeny was lower than expected from the taxonomic composition of the three life form groups. Our results emphasize that environmental constraints act upon functional traits of epiphytes, trees and terrestrial non-trees. The differences in trait expressions, arguably adaptations of the different life forms, need to be taken into account in conservation contexts as well as when modeling the effects of global change on ecosystems

iKNOW: A platform for knowledge graph construction for biodiversity

Nowadays, more and more biodiversity datasets containing observational and experimental data are collected and produced by different projects. In order to answer the fundamental questions of biodiversity research, these data need to be integrated for joint analyses. However, to date, too often, these data remain isolated in silos.Both in academia and industry, Knowledge Graphs (KGs) are widely regarded as a promising approach to overcome issues of data silos and lack of common understanding of data (Fensel and Şimşek 2020). KGs are graph-structured knowledge bases that store factual information in the form of structured relationships between entities, like “tree_species has_trait average_SLA” or “nutans is_observed_in SCH_Location" (Hogan et al. 2021). In our context, entities could be, e.g., abstract concepts like a kingdom, a species, or a trait, or a concrete specimen of a species. Example relationships could be "co-occurs" or, "possesses-trait". KGs for biodiversity have been proposed by Page 2019 and have also been the topic at prior TDWG conferences *1 (Page 2021). However, to date, uptake of this concept in the community has been rather slow (Sachs et al. 2019).We argue that this is at least partially due to the high effort and expertise required in developing and managing such KGs. Therefore, in our ongoing project, iKNOW (Babalou et al. 2021), we aim to provide a toolbox for reproducible KG creation. While iKNOW is still in an early stage, we aim to make this platform open-source and freely available to the biodiversity community. Thus, it can significantly contribute to making biodiversity data widely available, easily discoverable, and integratable.For now, we focus on tabular datasets resulting from biodiversity observation or sampling events or experiments. Given such a dataset, iKNOW will support its transformation into (subject, predicate, object) triples in the RDF standard (Resource Description Framework). Every uploaded dataset will be considered as a subgraph of the main KG in iKNOW. If required, data can be cleaned. After that, the entities and relationships among them should be extracted. For that, a user will be able select one of the existing semi-automatic tools available on our platform (e.g., JenTab (Abdelmageed and Schindler 2020)). The entities in this step can be linked to respective global identifiers in Wikidata, GBIF, the Global Biodiversity Information Facility, or any other user-selected knowledge resource. In the next step, (subject, predicate, object) triples based on the extracted information from the previous steps will be created. After these processes, the generated sub-KG can be used directly. However, one can take further steps such as: Triple Augmentation (generate new triples and extra relations to ease KG completion), Schema Refinement (refine the schema, e.g., via logical reasoning for the KG completion and correctness), Quality Checking (check the quality of the generated sub-KG), and Query Building (create customized SPARQL queries for the generated KG).iKNOW will include a wide range of functionalities for creating, accessing, querying, visualizing, updating, reproducing, and tracking the provenance of KGs. The reproducibility of such a creation is essential to strengthening the establishment of open science practices in the biodiversity domain. Thus, all information regarding the user-selected tools with parameters and settings, along with the initial dataset and intermediate results, will be saved in every step of our platform. With the help of this, users can redo the previous steps. Moreover, this enables us to track the provenance of the created KG.The iKNOW project is a joint effort by computer scientists and domain experts from the German Centre for Integrative Biodiversity Research (iDiv). As a showcase, we aim to create a KG of plant-related data sources at iDiv. These include, among others: TRY (the plant trait database) (Kattge and DÍaz 2011), sPlot (the database about global patterns of taxonomic, functional, and phylogenetic diversity) (Bruelheide and Dengler 2019), and PhenObs (the dataset of the global network of botanical gardens monitoring the impacts of climate change on the phenology of herbaceous plant species) (Nordt and Hensen 2021), LCVP, the Leipzig Catalogue of Vascular Plants, (Freiberg and Winter 2020), and many others.The resulting KG will serve as a discovery tool for biodiversity data and provide a robust infrastructure for managing biodiversity knowledge. From the biodiversity research perspective, iKNOW will contribute to creating a dataset following the Linked Open Data principles by interlinking to cross-domain and specific-domain KGs. From the computer science perspective, iKNOW will contribute to developing tools for dynamic, low-effort creation of reproducible knowledge graphs

Plant traits mediate the effects of climate on phytophagous beetle diversity on Mt. Kilimanjaro

Patterns of insect diversity along elevational gradients are well described in ecology. However, it remains little tested how variation in the quantity, quality, and diversity of food resources influence these patterns. Here we analyzed the direct and indirect effects of climate, food quantity (estimated by net primary productivity), quality (variation in the specific leaf area index, leaf nitrogen to phosphorus and leaf carbon to nitrogen ratio), and food diversity (diversity of leaf traits) on the species richness of phytophagous beetles along the broad elevation and land use gradients of Mt. Kilimanjaro, Tanzania. We sampled beetles at 65 study sites located in both natural and anthropogenic habitats, ranging from 866 to 4,550 m asl. We used path analysis to unravel the direct and indirect effects of predictor variables on species richness. In total, 3,154 phytophagous beetles representing 19 families and 304 morphospecies were collected. We found that the species richness of phytophagous beetles was bimodally distributed along the elevation gradient with peaks at the lowest (˜866 m asl) and upper mid-elevations (˜3,200 m asl) and sharply declined at higher elevations. Path analysis revealed temperature- and climate-driven changes in primary productivity and leaf trait diversity to be the best predictors of changes in the species richness of phytophagous beetles. Species richness increased with increases in mean annual temperature, primary productivity, and with increases in the diversity of leaf traits of local ecosystems. Our study demonstrates that, apart from temperature, the quantity and diversity of food resources play a major role in shaping diversity gradients of phytophagous insects. Drivers of global change, leading to a change of leaf traits and causing reductions in plant diversity and productivity, may consequently reduce the diversity of herbivore assemblages

Relationships between abiotic environment, plant functional traits, and animal body size at Mount Kilimanjaro, Tanzania

The effect-response framework states that plant functional traits link the abiotic environment to ecosystem functioning. One ecosystem property is the body size of the animals living in the system, which is assumed to depend on temperature or resource availability, among others. For primary consumers, resource availability may directly be related to plant traits, while for secondary consumers the relationship is indirect. We used plant traits to describe resource availability along an elevational gradient on Mount Kilimanjaro, Tanzania. Using structural equation models, we determined the response of plant traits to changes in precipitation, temperature and disturbance with and assessed whether abiotic conditions or community-weighted means of plant traits are stronger predictors of the mean size of bees, moths, frugivorous birds, and insectivorous birds. Traits indicating tissue density and nutrient content strongly responded to variations in precipitation, temperature and disturbance. They had direct effects on pollination and fruit traits. However, the average body sizes of the animal groups considered could only be explained by temperature and habitat structure, not by plant traits. Our results demonstrate a strong link between traits and the abiotic environment, but suggest that temperature is the most relevant predictor of mean animal body size. Community-weighted means of plant traits and body sizes appear unsuitable to capture the complexity of plant-animal interactions