Biology subjects

Haest, B.

Publications and source records attributed to Haest, B..

4 recordsLinked to original sources

bioRxiv2026

A hidden biomass flow - 10,000 tons of insects fly over Switzerland

Flying insects are the most diverse and abundant group of animals using the airspace to travel and spread across regions. Through movements that can span multiple life stages and generations, aerial insect migrations shape the ecosystems where they emerge but also connect ecosystems across sometimes vast distances. Yet even basic data on the abundance of aerial insects is still scarce. Their small sizes and often high-altitude movements make it challenging to describe flyways or measure this transport of biomass within or between regions. We used a remote-sensing approach with vertical looking radars to quantify insect migration across Switzerland. Switzerland harbours complex terrain, with the Alps, Jura mountains, and pre-Alpine lowlands featuring starkly contrasting topography. At three locations within these three main habitats, we recorded a total of 6.6 million medium- to large-sized individual insects during the 8-month study period. Extrapolating this to the whole of Switzerland results in an estimated number of 21 billion insects. Adding the presumed additional proportion of small insects, this translates to a rough estimate of 10,000 tons of insect biomass across Switzerland per year. Our results show that insect migration remains substantial even in topographically complex landscapes and seems strikingly synchronized over a relatively wide area. Mountain passes in particular act as key conduits for insects traversing the Alps, with many crossings occurring at high altitudes and with air temperatures below 10 {degrees}C. Nevertheless, our study is only a first step unraveling insect movements across space and time and their large-scale synchronicity. Significance StatementInsect abundance is of great importance for most terrestrial food chains, including human agriculture. While ground-based studies have elucidated many relationships within trophic systems, very little is known about the effects of their large-scale movements through the airspace. We show how insect movements, dispersal and migration, are spatially and temporarily correlated across a wide area of complex terrain and provide a rough estimate of the insect biomass involved. Temperature is a crucial factor in the extent of activity; however, our results show that low temperatures (<10{degrees}C) do not prevent massive insect migrations when crossing mountain ranges. Our results demonstrate the large, coordinated flow of insect biomass across the landscape, which needs to be studied in much more detail to better understand the spread of pests and beneficial insects, their impact on local communities, and the transmission of diseases.

ecology↗

bioRxiv2024

Derivation of aerial insect concentration with a 94 GHz FMCW cloud radar

Aerial insects are vital for nature and society. Though methods to observe flying insects have consistently improved in the last decades, insects remain difficult to monitor systematically and consistently over large spatial and temporal scales. Remote sensing with radars has proved to be one of the more effective tools for observation. However, as radars are most sensitive to targets that are of similar size to the radar wavelength, the detectable sub-group of aerial insects of a certain size range depends on the employed radar. Here, we present a novel method based on spectral data of zenith-pointing Doppler cloud radars to estimate insect concentration in a vertical profile. Multiple meteorological state-of-the-art algorithms are combined to extract insect signals from the radar data and quantify their abundance from 150 m to 3000 m above the ground. For evaluation, this method is applied to triple frequency data from X-band ({lambda} = 3.2 cm), Ka-band ({lambda} = 0.85 cm) and W-band ({lambda} = 0.32 cm) Doppler cloud radars from a three months summertime observation period in Germany. Observations of a case study of two elevated aerial insect layers are presented. Furthermore, differences in diel cycles of aerial insect concentration (aic) obtained from the three radar instruments are compared. With its superior sensitivity to very small insects like aphids, W-band shows the highest aerial insect concentrations during day-time in the lower altitudes. Generally, the higher wavelength radars show higher aerial insect concentrations in higher altitudes (Ka-band) and during the night (X-band).

ecology↗

bioRxiv2023

The unexpected consequences of predictor error in ecological model selection

1O_LIThe ability to select statistical models based on how well they fit an empirical dataset is a central tenet of modern bioscience. How well this works, though, depends on how goodness-of-fit is measured. Likelihood and its derivatives (e.g. AIC) are popular and powerful tools when measuring goodness-of-fit, though inherently make assumptions about the data. One such assumption is absence of error on the x-axis (i.e. no error in the predictor). This, however, is often not correct and deviations from this assumption are often hard (or impossible) to measure. C_LIO_LIHere, we show that, when predictor error is present, goodness-of-fit as perceived using likelihood will increase with decreases in sample size, effect size, predictor error and predictor variance. This results in predictors with increased effect size, predictor variance or predictor error being punished. As a consequence, we suggest that larger effect sizes are biased against in likelihood-based model comparison. Of note: (i) this problem is exacerbated in datasets with larger samples sizes and a broader range of predictor values - typically considered desirable biological data collection; and (ii) the magnitude of this effect is non-trivial given that proxy error (caused by using correlates of a predictor rather than the predictor itself) can lead to unexpectedly high amounts of error. C_LIO_LIWe investigate the effects of our findings in an empirical dataset of wood anemone (Anemone nemorosa) first flowering date regressed against temperature. Our results show that the proxy error caused by using air temperature rather than ground temperature results in a {triangleup}AIC of around 3. We also demonstrate potential consequences for model selection procedures with autocorrelation (e.g. sliding window approaches). Via simulation we show that in the presence of predictor error AIC will favour autocorrelated, lower effect size predictors (such as those found on the edges of predictive windows), rather than the a priori specified true window. C_LIO_LIOur results suggest significant and far-reaching implications for biological inference with model selection for much of todays ecology using observational data under non-experimental conditions. We assert that no obvious, globally-applicable solution to this problem exists; and propose that quantifying predictor error is key in accurate ecological model selection going forward. C_LI

ecology↗

bioRxiv2021

Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches

The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here, we present a spatial proteogenomic atlas of the healthy human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and bile-duct macrophages. We also uncover the respective spatially-resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that bile-duct macrophages are induced by local lipid exposure, while Kupffer cells crucially depend on their crosstalk with hepatic stellate cells via the evolutionarily-conserved ALK1-BMP9/10 axis.

immunology↗