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Shabanov, I.

Publications and source records attributed to Shabanov, I..

3 recordsLinked to original sources

Population and community responses to the fast, slow, and seasonal components of environmental variation

Theory suggests that different components of environmental fluctuations, from daily and seasonal cycles to multidecadal trends, can have distinct and even opposing effects on species abundances and community dynamics, depending on their specific adaptations. But empirical research that deconstructs the influence of these different cycles on communities is lacking. Here, we used long-term biological monitoring data together with flow records of rivers across New Zealand to (i) investigate the role of fast, slow, and seasonal river-flow fluctuations in structuring macroinvertebrate communities; and (ii) to assess whether life-history and mobility traits mediate the response. Using joint species distribution models, we found striking differences in taxon and community responses to the different components of river flow variation. Responses to slow fluctuations were generally stronger and better predicted by traits, while responses to seasonal fluctuations were highly heterogeneous. Fast increases in flow, typical of flooding events, had pervasive negative effects on species abundances, but the severity of impact partly depended on mobility traits. Our results suggest that different ecological mechanisms underpin the response to distinct environmental fluctuations, highlighting the value of jointly considering multiple temporal scales of variation and species functional traits to understand and predict how communities reorganise under fluctuating environmental regimes.

ecology↗

Climate-Driven Forest Reassembly Follows Divergent Functional Pathways in Cold- and Warm-Adapted Communities

AimClimate change leads to the widespread rearrangement of forests predominantly towards warm-adapted lower-elevation species. However, understanding the context-specific community assembly mechanisms that underlie these changes requires a mechanistic understanding of plant responses. Here, we use functional traits and diversity indices to identify distinct responses of warm- and cold-adapted forest communities. MethodsOur analysis used seven traits, including leaf morphology, size, nutrient content, and foliar isotopes across 3,625 sites over 49 years. We derived functional strategies from community-weighted mean trait values, assessed assembly mechanisms using five multi-trait functional diversity indices, and estimated structural change and turnover rates using abundance-based dissimilarity metrics. ResultsForest composition shifted significantly towards warm-adapted, lowland species with increasing temperature (+0.12 {degrees}C/decade) and decreasing precipitation (0.87%/decade). Communities concurrently increased in functional and species richness, but became functionally more similar, indicating niche packing. Disturbance recruitment shifted warm-adapted forests towards acquisitive traits, and increases in anisohydric strategies, reflecting possible effects of CO2 fertilisation under drought stress. Cold-adapted forests, in contrast, shifted to conservative strategies and were subject to stronger abiotic filtering. Moreover, changes in nitrogen isotope ratios point towards the importance of ectomycorrhizal associations to persist under climate change. Ecotonal mixed forests showed both responses and experienced the highest turnover. Main conclusionsNew Zealand forests follow biome-specific trajectories, characterised by distinct assembly mechanisms and context-dependent ecological responses. While uniform trends obscure these responses, they can be disentangled using a functional diversity framework that incorporates isotopic traits, offering a practical means to detect and interpret community reassembly under climate change.

ecology↗

HARLEY: A Semi-Automated detection of foci in fluorescence images of yeast

Quantification of cellular structures in fluorescence microscopy data is a key means of understanding cellular function. Unfortunately, numerous cellular structures present unique challenges in their ability to be unbiasedly and accurately detected and quantified. In our studies on stress granules in yeast, users displayed a striking variation of up to 3.7-fold in foci calls and were only able to replicate their results with 62-78% correlation, when requantifying the same images. To facilitate consistent results we developed HARLEY (Human Augmented Recognition of LLPS Ensembles in Yeast), a customizable software for detection and quantification of stress granules in S.cerevisiae. After a brief model training on ~20 cells the detection of foci is fully automated and based on closed loops in intensity contours, constrained only by the a-priori known size of the features of interest. Since no shape is implied, this method is not limited to round features, as is often the case with other algorithms. Candidate features are annotated with a set of geometrical and intensity-based properties to train a kernel Support Vector Machine to recognize features of interest. The trained classifier is then used to create consistent results across datasets. HARLEY is aimed at users without technical expertise, allows for batch processing and is freely available, which should be of broad interest to users focused on analysis of microscopy data in yeast.

bioinformatics↗