Biology subjects

Zizka, A.

Publications and source records attributed to Zizka, A..

At least 19 recordsLinked to original sources

bioRxiv2026

biomes: An R package for reproducible occurrence-to-biome classification using 31 global biome schemes

Abstract 1. Biomes are continental-scale vegetation units widely used in ecological and evolutionary research, often together with large-scale species-distribution data. Many conceptually distinct biome schemes exist, differing in the underlying definition, delineation methodology and spatial resolution, yet most studies using biomes default to a few readily available schemes rather than choosing the one best suited to the research question. 2. We present biomes, an R package distributing spatially explicit raster layers of 31 published global biome schemes in a harmonised format at a 10 x 10 km resolution, together with metadata and a concise description. To facilitate their use, biomes provides a user-friendly classification workflow to classify occurrence records into specific biomes and a data-driven ranking algorithm to help researchers to choose the most suitable biome scheme for their data and research question in a transparent, reproducible way. 3. The biomes workflow can be applied to any dataset of georeferenced terrestrial occurrences. It (1) accepts either user-provided occurrence records or records downloaded by the package, (2) ranks the schemes according to their fit to the occurrence data, (3) assigns the records to specific biomes, and (4) summarises the resulting taxon-biome patterns. We demonstrate this workflow using 17,030 occurrence records of 185 species of Bombacoideae (Malvaceae). The analysis identified the vegetation-based biome scheme of Ramankutty and Foley (1999) as the best fit and recovered the forest and savanna biome signal known in the group from the literature, with tropical evergreen woodland being the biome with the largest share of records (46.1%) and highest number of species (162 of 185 species; 87.6%). 4. biomes provides user-friendly access to technically standardised and spatially explicit layers of 31 biome schemes, and thereby enables the transparent, data-driven choice of biome schemes tailored to a specific research data and questions, improving the reproducibility and the specificity of biome use across ecological and evolutionary research.

bioinformatics↗

bioRxiv2026

Chemodiversity is an independent synecological dimension of plant form and function

Plant trait spaces have advanced ecology by reducing the vast diversity of plant form and function to a few axes of trait variation. Yet, such frameworks remain dominated by autoecological traits related to resource acquisition and growth, while largely overlooking the traits mediating interactions with other organisms. Chemodiversity, the richness, evenness and chemical disparity of volatile organic compounds (VOCs) emitted by leaves and flowers to attract, deter, or otherwise affect biotic interaction partners, may represent such an overlooked synecological dimension of plant functional variation. By integrating the chemodiversity of floral (n = 859 species) and vegetative (n = 159 species) VOC profiles into the global spectrum of plant form and function, we show that chemodiversity is a) independent from the classical axis of variation in plant size and leaf economics but b) is linked to biotic interactions in organ-specific ways. Flower-visitor richness increases with floral scent chemodiversity, a pattern supported by an analysis of global interaction data and a meta-analysis of the specialization and generalization of flower-animal interactions. Vegetative VOC chemodiversity has context-dependent positive and negative effects on herbivore richness. VOC chemodiversity represents a distinct ecological strategy that defines interaction niches, thereby contributing to ecological differentiation and coexistence among otherwise functionally similar species.

ecology↗

bioRxiv2026

How many bromeliads are there?

- In many taxa the expected number of species is still uncertain, hampering conservation, especially in the diverse tropical regions of Earth. Here we use the plant family Bromeliaceae as a model to explore the history and future of species discovery from the Neotropics global biodiversity hotspot. - We use a newly curated complete list of described species together with species geographic distributions and information on taxonomic authors to explore patterns of past species description. Furthermore, we use logistic models to estimate the expected number of species in the family, the subfamilies, the largest genera and the relationship with geographic range. - In the past species discovery was dominated by researchers from Europe (mid-18th to mid-20th century), then researchers from the USA (mid to end of 20 century) and finally researchers from Latin America (21st century). The average number of species described per year increased from 0.6 between 1750 and 1800 to 33.8 between 2001 and 2025. Furthermore, description shifted from widespread species to species with smaller ranges, mostly from Brazil and the Andes from Bolivia to Mexico. We project the expected number of Bromeliaceae species at 6,658 to 7,498, leaving the current number of described species only at 55 to 49%. - Our results illustrate changes in the history of species description in the last centuries, confirm the progression from large range to smaller range species as the taxonomic treatment of the groups progressed, and illustrates Brazil, Mexico and the Andean region as hotspots for future species description.

plant biology↗

bioRxiv2025

Global convergence in wood evolution is driven by drought on continents and frost-free temperatures on islands

Phylogenetically derived woodiness (DW), the evolutionary reversion from herbaceousness to woodiness in angiosperms, is one of the most conspicuous characteristics of (sub)tropical island floras. Here, we show that DW across continents is more common than previously thought, especially in frost-free and open habitats with pronounced seasonal drought. Using a novel dataset on the evolution of woodiness in angiosperms, we discovered substantially more derived woody species (DWS) and independent evolutionary transitions on continents compared to islands (4,808 species and 513 transitions vs. 1,084 and 175, respectively). However, we identified more insular DWS hotspots (22) than the four continental DWS hotspots: the Andes, Southern Africa, the Old-World Dry Belt and Australia. A structural equation model controlling for total species richness suggests that aridity is the strongest predictor of the number of DWS on continents, while frost-free temperatures best predict DWS on islands. Precipitation seasonality and mean elevation emerge as additional significant predictors in both cases, with a further potential role for light-prone open habitats. In summary, the diverse global drivers behind the hundreds of independent woodiness shifts highlight the existence of various mechanisms that lead to increased wood formation in stems, confirming its adaptive value over evolutionary time.

evolutionary biology↗

bioRxiv2025

Legacy of the lost and pressure of the present: Malagasy plant seeds retain megafauna dispersal signatures but downsize under human footprint

Past megafaunal extinctions and ongoing declines of remaining large-bodied frugivores limit the dispersal of large-seeded plants, contributing to their (local) demise, thereby downsizing plant seeds across assemblages. Here, we test this hypothesis by integrating trait and distribution data for 2,852 endozoochorous plant species, 48 extant and 15 extinct frugivore species across 361 assemblages on Madagascar. Using structural equation models, we show that mega-seeded plants - with seeds that are too large to be swallowed by any extant Malagasy frugivore - have larger seeds in assemblages where past megafrugivores were larger, highlighting the legacy of past interactions on persisting mega-seeded plants. Furthermore, human footprint reduces seed sizes directly and indirectly by downsizing of extant frugivore in the assemblages. Such human-driven seed downsizing can lead to erosions of important ecosystem functions such as tropical forest carbon storage.

ecology↗

bioRxiv2023

The evolutionary age-range size relationship is modulated by insularity and dispersal in plants and animals

Earth is home to millions of plant and animal species, with more than 40 thousand species facing extinction worldwide (Diaz et al. 2019). Species range size is particularly important in this context because it influences extinction risk (Purvis et al. 2000, Gaston & Fuller 2009), but the causes underlying the wide natural variation in range size remain poorly known. Here, we investigate how evolutionary age is related to present-day range size for over 25,000 species of mammals, birds, reptiles, amphibians, reef fishes, and plants. We show that, on average, older species have significantly larger ranges, but the effect of age on range size is modulated by clade, geographical context and dispersal ability. Specifically, age does not affect range size for island species, because islands limit dispersal and hence range size, regardless of species age. Furthermore, species from clades with high dispersal capabilities obtain large ranges faster, thereby further neutralizing the relationship between age and range size. Our results can help supporting global conservation priorities, by showing that species that are young, occupy islands, and/or are dispersal limited often have small ranges and therefore increased extinction risk.

ecology↗

bioRxiv2023

Automated assessments based on species distribution models can support regional Red Listing but need to be applied with caution: A case study from central Germany

AimExpert-based regional Red Lists (RL) carry conservation legislation globally. Yet, they are often difficult to reproduce and their regular compilation by a dwindling number of expert assessors burdens many regional conservation authorities. Here we batch-estimate RL indicators and extinction risk for >1,100 plant species and test the potential of this automated approach to support the expert-based RL process. LocationState of Hesse, Central Germany MethodsFirst, we estimated current population status, short-term population trend, long-term population trend, and extinction risk by binning existing occurrence probabilities modelled at three time slices with cut-off values derived from RL methodology. Subsequently we compared the results with the latest version of the Hessian expert-based RL using summary statistics and selected example species. ResultsWe find the assessments of extinction risk to agree in c. 60% of the cases, mostly for species not threatened with extinction. Existing mismatch was by one category in most cases, but up to 6 categories in some cases (mean: 1.6 categories). Furthermore, agreement was highest for extreme categories and very abundant species. Main conclusionsAutomated assessments were simplistic for many rare and taxonomically challenging species, but we considered them more accurate than the expert assessments for species with intermediate population size and for species of anthropogenic habitats. Furthermore, the automated assessments are particularly informative for the estimation of long-term and short-term population trends, for which experts are often left to guesstimate based on little data.

ecology↗

bioRxiv2023

Modelling the probability of meeting IUCN Red List criteria to support reassessments.

Comparative extinction risk analysis - which predicts species extinction risk from correlation with traits or geographical characteristics - has gained research attention as a promising tool to support extinction risk assessment in the IUCN Red List of Threatened Species. However, its uptake has been very limited so far, possibly because these models only predict a species Red List category, without indicating which Red List criteria may be triggered by which such approaches cannot easily be used in Red List assessments. We overcome this implementation gap by developing models that predict the probability of species meeting individual Red List criteria. Using data on the worlds birds, we evaluated the predictive performance of our criterion-specific models and compared it with the typical criterion-blind modelling approach. We compiled data on biological traits (e.g., range size, clutch size) and external drivers (e.g., change in canopy cover) often associated with extinction risk. For each specific criterion, we modelled the relationship between extinction risk predictors and species Red List category under that criterion using ordinal regression models. We found criterion-specific models were better at predicting threatened species compared to a criterion-blind model (higher sensitivity), but less good at predicting not threatened species (lower specificity). As expected, different covariates were important for predicting threat status under different criteria, for example change in annual temperature was important to predict criteria related to population trends, while clutch size was important for criteria related to restricted area of occupancy or small population size. Our criteria-specific method can support Red List assessors by producing outputs that identify species likely to meet specific criteria, and which are the most important predictors: these species can be prioritised for re-evaluation. We expect this new approach to increase the uptake of extinction risk models in Red List assessments, bridging a long-standing research-implementation gap.

ecology↗

bioRxiv2023

Spiny but photogenic: amateur sightings complement herbarium specimens to reveal the bioregions of cacti

PremiseCacti are characteristic elements of the Neotropical flora and of major interest for biogeographic, evolutionary, and ecological studies. Here we test global biogeographic boundaries for Neotropical Cactaceae using specimen-based occurrences coupled with data from visual observations, including citizen science records, as a means to tackle the known collection biases in the family. MethodsSpecies richness and record density were assessed separately for preserved specimens and human observations and a bioregional scheme tailored to Cactaceae was produced using the interactive web application Infomap Bioregions based on data from 261,272 point records cleaned through automated and manual steps. Key ResultsWe find that areas in Mexico and southwestern USA, Eastern Brazil and along the Andean region have the greatest density of records and the highest species richness. Human observations complement information from preserved specimens substantially, especially along the Andes. We propose 24 cacti bioregions, among which the most species-rich are, in decreasing order: northern Mexico/southwestern USA, central Mexico, southern central Mexico, Central America, Mexican Pacific coast, central and southern Andes, northwestern Mexico/extreme southwestern USA, southwestern Bolivia, northeastern Brazil, Mexico/Baja California. ConclusionsThe bioregionalization proposed shows novel or modified biogeographical boundaries specific to cacti, and can thereby aid further evolutionary, biogeographic, and ecological studies by providing a validated framework for further analyses. This classification builds upon, and is distinctive from, other expert-derived regionalization schemes for other taxa. Our results showcase how observation data, including citizen-science records, can complement traditional specimen-based data for biogeographic research, particularly for taxa with specific specimen collection and preservation challenges and those that are threatened or internationally protected.

plant biology↗

bioRxiv2022

Towards understanding diversity, endemicity and global change vulnerability of soil fungi

Fungi play pivotal roles in ecosystem functioning, but little is known about their global patterns of diversity, endemicity, vulnerability to global change drivers and conservation priority areas. We applied the high-resolution PacBio sequencing technique to identify fungi based on a long DNA marker that revealed a high proportion of hitherto unknown fungal taxa. We used a Global Soil Mycobiome consortium dataset to test relative performance of various sequencing depth standardization methods (calculation of residuals, exclusion of singletons, traditional and SRS rarefaction, use of Shannon index of diversity) to find optimal protocols for statistical analyses. Altogether, we used six global surveys to infer these patterns for soil-inhabiting fungi and their functional groups. We found that residuals of log-transformed richness (including singletons) against log-transformed sequencing depth yields significantly better model estimates compared with most other standardization methods. With respect to global patterns, fungal functional groups differed in the patterns of diversity, endemicity and vulnerability to main global change predictors. Unlike -diversity, endemicity and global-change vulnerability of fungi and most functional groups were greatest in the tropics. Fungi are vulnerable mostly to drought, heat, and land cover change. Fungal conservation areas of highest priority include wetlands and moist tropical ecosystems.

ecology↗

bioRxiv2022

Plant longevity, drought and island isolation favoured rampant evolutionary transitions towards insular woodiness

Insular woodiness (IW)--the evolutionary transition from herbaceousness towards woodiness on islands--is one of the most iconic features of island floras. Since pioneering work by Darwin and Wallace, five IW drivers have been proposed: (i) favourable aseasonal climate and (ii) lack of large native herbivores promote plant longevity that (iii) results in prolonged flowering favouring outcrossing. Alternatively, (iv) competition for sunlight requires taller and stronger stems, and (v) drought favours woodiness to safeguard root-to-shoot water transport. However, information on the occurrence of IW is fragmented, hampering tests of these potential drivers. Here, we identify 1,097 insular woody species (IWS) on 375 islands, and infer at least 175 evolutionary transitions on 31 archipelagos, concentrated in six angiosperm families. Structural equation models reveal that the IWS richness on oceanic islands correlates with aseasonal favourable climate, followed by increased drought and island isolation (approximating competition). When continental islands are included, reduced herbivory pressure by large native mammals, increased drought and island isolation are most relevant. The repeated evolution of IW opens promising avenues to disentangle the variation in gene regulatory networks triggering wood formation, and emphasize individual archipelagos as laboratories of evolution, where similar environmental conditions replicate convergent evolution of similar traits.

evolutionary biology↗

bioRxiv2021

IUCNN - deep learning approaches to approximate species' extinction risk

AimThe global Red List (RL) from the International Union for the Conservation of Nature is the most comprehensive global quantification of extinction risk, and widely used in applied conservation as well as in biogeographic and ecological research. Yet, due to the time- consuming assessment process, the RL is biased taxonomically and geographically, which limits its application on large scales, in particular for understudied areas such as the tropics, or understudied taxa, such as most plants and invertebrates. Here we present IUCNN, an R- package implementing deep learning models to predict species RL status from publicly available geographic occurrence records (and other traits if available). InnovationWe implement a user-friendly workflow to train and validate neural network models, and subsequently use them to predict species RL status. IUCNN contains functions to address specific issues related to the RL framework, including a regression-based approach to account for the ordinal nature of RL categories and class imbalance in the training data, a Bayesian approach for improved uncertainty quantification, and a target accuracy threshold approach that limits predictions to only those species whose RL status can be predicted with high confidence. Most analyses can be run with few lines of code, without prior knowledge of neural network models. We demonstrate the use of IUCNN on an empirical dataset of [~]14,000 orchid species, for which IUCNN models can predict extinction risk within minutes, while outperforming comparable methods. Main conclusionsIUCNN harnesses innovative methodology to estimate the RL status of large numbers of species. By providing estimates of the number and identity of threatened species in custom geographic or taxonomic datasets, IUCNN enables large-scale analyses on the extinction risk of species so far not well represented on the official RL.

ecology↗

bioRxiv2020

Automated conservation assessment of the orchid family using deep learning

IUCN Red List assessments are essential for prioritizing conservation needs but are resource-intensive and therefore only available for a fraction of global species richness. Tropical plant species are particularly under-represented on the IUCN Red List. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. Here, we present automated conservation assessments for 13,910 species of the diverse and globally distributed Orchid family (Orchidaceae), based on a novel method using a deep neural network (IUC-NN), most of which (13,049) were previously unassessed by the IUCN Red List. We identified 4,342 (31.2 % of the evaluated orchid species) as Possibly Threatened with extinction (equivalent to the IUCN categories CR, EN, or VU) and point to Madagascar, East Africa, south-east Asia, and several oceanic islands as priority areas for orchid conservation. Furthermore, the Orchid family provides a model, to test the sensitivity of automated assessment methods to issues with data availability, data quality and geographic sampling bias. IUC-NN identified threat-ened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias compared to the IUCN Red List, and was robust against low data availability and geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in achieving goals of identifying the species that are at greatest risk of extinction.

ecology↗

bioRxiv2020

Multiple origins of insular woodiness on the Canary Islands are consistent with palaeoclimatic aridification

AimInsular woodiness, referring to the evolutionary transition from herbaceousness towards woodiness on islands, has arisen at least 38 times on the Canary Islands. Distribution patterns and physiological experiments have suggested a link between insular woodiness and increased drought stress resistance in current-day species, but we do not know in which palaeoclimatic conditions these insular woody lineages originated. Therefore, we estimated the timing of colonisation events and origin of woodiness of multiple Canary Island lineages and reviewed the palaeoclimate based on literature. LocationCanary Islands (Spain). Taxon37 lineages, including 24 insular woody and 13 non-insular woody (i.e. herbaceous, ancestrally woody, and derived woody). MethodsTo enable a simultaneous dating analysis for all 37 lineages, two chloroplast markers (matK and rbcL) for 135 Canary Island species and 103 closely related continental relatives were sequenced and aligned to an existing matK-rbcL dataset including ca 24,000 species that was calibrated with 42 fossils from outside the Canaries. After constraining the species to the family level, 200 RAxML runs were performed and dated with TreePL. ResultsWoodiness in 80-90% of the insular woody lineages originated within the last 7 Myr, coinciding with the onset of major aridification events nearby the Canaries (start of north African desertification, followed by Messinian salinity crisis); in ca 55-65% of the insular woody lineages studied, woodiness developed within the last 3.2 Myr during which Mediterranean seasonality (yearly summer droughts) became established on the Canaries, followed by dry Pleistocene glacial fluctuations. Main conclusionsAlthough details of the initial colonisation and settlement of many island plant lineages remain elusive, our results are consistent with palaeodrought as a potential driver for woodiness in most of the insular woody Canary Island lineages studied.

evolutionary biology↗

bioRxiv2020

The Leipzig Catalogue of Vascular Plants (LCVP) - An improved taxonomic reference list for all known vascular plants

The lack of comprehensive and standardized taxonomic reference information is an impediment for robust plant research, e.g. in systematics, biogeography or macroecology. Here we provide an updated and much improved reference list of 1,315,479 scientific plant taxa names for all described vascular plant taxa names globally. The Leipzig Catalogue of Vascular Plants (LCVP; version 1.0.2) contains 351.176 accepted species (plus 6.160 natural hybrids), within 13.422 genera, 561 families and 84 orders. The LCVP a) contains more information on the taxonomic status of global plant names than any other similar resource and b) significantly improves the reliability of our knowledge by e.g. resolving the taxonomic status of [~]184.000 taxa names compared to The Plant List, the up to date most commonly used plant name resource. We used [~]4500 publications, existing relevant databases and available studies on molecular phylogenetics to construct a robust reference backbone. For easy access and integration into automated data processing pipelines, we provide an R-package (lcvplants) with the LCVP.

plant biology↗

bioRxiv2020

No one-size-fits-all solution to clean GBIF

Species occurrence records provide the basis for many biodiversity studies. They derive from georeferenced specimens deposited in natural history collections and visual observations, such as those obtained through various mobile applications. Given the rapid increase in availability of such data, the control of quality and accuracy constitutes a particular concern. Automatic filtering is a scalable and reproducible means to identify potentially problematic records and tailor datasets from public databases such as the Global Biodiversity Information Facility (GBIF; www.gbif.org), for biodiversity analyses. However, it is unclear how much data may be lost by filtering, whether the same filters should be applied across all taxonomic groups, and what the effect of filtering is on common downstream analyses. Here, we evaluate the effect of 13 recently proposed filters on the inference of species richness patterns and automated conservation assessments for 18 Neotropical taxa, including terrestrial and marine animals, fungi, and plants downloaded from GBIF. We find that a total of 44.3% of the records are potentially problematic, with large variation across taxonomic groups (25 - 90%). A small fraction of records was identified as erroneous in the strict sense (4.2%), and a much larger proportion as unfit for most downstream analyses (41.7%). Filters of duplicated information, collection year, and basis of record, as well as coordinates in urban areas, or for terrestrial taxa in the sea or marine taxa on land, have the greatest effect. Automated filtering can help in identifying problematic records, but requires customization of which tests and thresholds should be applied to the taxonomic group and geographic area under focus. Our results stress the importance of thorough recording and exploration of the meta-data associated with species records for biodiversity research.

ecology↗

bioRxiv2020

Exploring the impact of political regimes on biodiversity

National governments are the main actors responsible for mapping and protecting their biodiversity, but countries differ in their capacity, willingness, and effectiveness to do so. We quantify the global biodiversity managed by different regime types and developed a tool to explore the links between level of democracy and other key socio-economic variables with the number of natural history specimens registered within country boundaries. Using this tool, distinct and previously unknown patterns emerge around the world, that urge for increased collaboration between the natural and social sciences to further explore these patterns and their underlying processes.

ecology↗

bioRxiv2020

sampbias, a method for quantifying geographic sampling biases in species distribution data

Geo-referenced species occurrences from public databases have become essential to biodiversity research and conservation. However, geographical biases are widely recognized as a factor limiting the usefulness of such data for understanding species diversity and distribution. In particular, differences in sampling intensity across a landscape due to differences in human accessibility are ubiquitous but may differ in strength among taxonomic groups and datasets. Although several factors have been described to influence human access (such as presence of roads, rivers, airports and cities), quantifying their specific and combined effects on recorded occurrence data remains challenging. Here we present sampbias, an algorithm and software for quantifying the effect of accessibility biases in species occurrence datasets. Sampbias uses a Bayesian approach to estimate how sampling rates vary as a function of proximity to one or multiple bias factors. The results are comparable among bias factors and datasets. We demonstrate the use of sampbias on a dataset of mammal occurrences from the island of Borneo, showing a high biasing effect of cities and a moderate effect of roads and airports. Sampbias is implemented as a well-documented, open-access and user-friendly R package that we hope will become a standard tool for anyone working with species occurrences in ecology, evolution, conservation and related fields.

ecology↗