Biology subjects

Winner, K.

Publications and source records attributed to Winner, K..

5 recordsLinked to original sources

bioRxiv2026

The Dark Ecology Dataset: Measurements of Aerial Biomass in US Weather Radar from 1995 to 2025

The US NEXRAD radar network has monitored the aerosphere over the US and its territories continuously since the 1990s and archived nearly 300 million radar volume scans. These data contain a wealth of information about the movements of birds, bats, and insects. Historically, this biological information was difficult to access due to the amount of data and challenges in analyzing it. In the last 15 years, fueled by computational and methodological advances, large-scale aeroecology research has blossomed. However, comprehensive analyses of the NEXRAD archive remain very costly. We collected measurements from every volume scan in the NEXRAD archive--nearly 300 million data files total--to assemble a dataset of aerial biological activity over the US from 1995 to 2025. The core data are vertical profiles, which summarize biological activity at different heights above the radar station for each volume scan. We also provide time series data products that aggregate vertical profiles to point measurements at radar stations across time. These data products can support a range of aeroecology analyses at significantly reduced effort.

ecology↗

bioRxiv2024

Scaling ecological niches from individuals to populations and beyond

The niche is a key concept that unifies ecology and evolutionary biology. However, empirical and theoretical treatments of the niche are mostly performed at the species level, neglecting individuals as important units of ecological and evolutionary processes. So far, a formal mathematical link between individual-level niches and higher organismal-level niches has been lacking, hampering the unification of ecological theories and more accurate forecasts of biodiversity change. To fill in this gap, we propose a bottom-up approach to derive population and higher organismal-level niches from individual niches. We demonstrate the power of our framework by showing that 1) the statistical properties of higher organismal-level niches (e.g. niche breadth, skewness etc.) can be partitioned into individual contributions; 2) the species-level niche shifts can be estimated by tracing the responses of individuals. Our method paves the way for a unifying niche theory and enables mechanistic assessments of organism-environment relationships across organismal scales.

ecology↗

bioRxiv2024

The Performance and Potential of Deep Learning for Predicting Species Distributions

Species distribution models (SDMs) address the whereabouts of species and are central to ecology. Deep learning (DL) is poised to further elevate the already significant role of SDMs in ecology and conservation, but the potential and limitations of this transformation are still largely unassessed. We evaluate DL SDMs for 2,299 terrestrial vertebrate and invertebrate species at continental scale and 1km resolution in a like-for-like comparison with latest implementation of classic SDMs. We compare two DL methods (a multi-layer perceptron (MLP) on point covariates and a convolutional neural network (CNN) on geospatial patches) against existing SDMs (Maxent and Random Forest). On average, DL models match, but do not surpass, the performance of existing methods. DL performance is substantially weaker for species with narrow geographic ranges, fewer data points, and those assessed as threatened and hence often of greatest conservation concern. Furthermore, information leakage across dataset splits substantially inflates performance metrics, especially of CNNs. We find current DL SDMs to not provide significant gains, instead requiring careful experimental design to avoid biases. However, future advances in DL-supported use of ancillary ecological information have the potential to make DL a viable instrument in the larger SDM toolbox. Realising this opportunity will require a close collaboration between ecology and machine learning disciplines.

ecology↗

bioRxiv2020

A unifying framework for quantifying and comparing n-dimensional hypervolumes

O_LIThe quantification of Hutchisons n-dimensional hypervolume has enabled substantial progress in community ecology, species niche analysis and beyond. While non-parametric methods for quantifying and comparing hypervolumes are popular, they do not support a partitioning of the different components and drivers of hypervolume variation. Here, we propose as alternative the use of multivariate normal distributions for the assessment and comparison of niche hypervolumes and introduce this as the multivariate-normal hypervolume (MVNH) framework. C_LIO_LIThe framework provides parametric measures of the size and dissimilarity of niche hypervolumes, each of which can be partitioned into biologically interpretable components. Specifically, We use 1) the determinant of the covariance matrix (i.e. the generalized variance) of a MVNH as a measure of total niche size, which can be partitioned into the components of univariate niche variances and a correlation component; and 2) the Bhattacharyya distance between two MVNHs as a measure of niche dissimilarity, which can be partitioned into the components of Mahalanobis distance between hypervolume centroids and the determinant ratio which measures hypervolume size difference. C_LIO_LIWe use empirical examples of community- and species-level analysis to demonstrate the new insights provided by these metrics. We show that the newly proposed framework enables us to quantify the relative contributions of different hypervolume components and to identify the drivers of functional diversity and environmental niche variation. C_LIO_LIOur approach overcomes several operational and computational limitations of non-parametric methods and provides a framework that offers both unification and granularity in the assessment of niche volumes and differences, which has wide implications for understanding niche evolution, niche shifts and expansion during biotic invasions etc. C_LI

ecology↗

bioRxiv2020

Global and national trends in documenting and monitoring species distributions

Conserving and managing biodiversity in the face of ongoing global change requires sufficient evidence to assess status and trends of species distributions. Here we analyze national trajectories in closing spatiotemporal knowledge gaps for terrestrial vertebrates (1950-2019) based on novel indicators of data coverage and sampling effectiveness. Despite a rapid rise in data coverage, particularly in the last two decades, strong geographic and taxonomic biases persist. For some taxa and regions, a tremendous growth in records failed to directly translate into newfound knowledge due to a sharp decline in sampling effectiveness. But nations coverage is stronger for species they hold greater stewardship for. As countries under the post-2020 Global Biodiversity Framework renew their commitments to an improved, rigorous biodiversity knowledge base, our findings highlight opportunities for international collaboration to close critical information gaps.

ecology↗