Biology subjects

Jeliazkov, A.

Publications and source records attributed to Jeliazkov, A..

3 recordsLinked to original sources

bioRxiv2025

Towards a multi-metric assessment of river restoration ecological outcomes across projects and contexts

The assessment of the ecological outcomes of river restoration projects remains a complex undertaking due to the high variability of responses observed. This variability is dependent on a number of factors, including the location of the site under study, the indicators and taxa utilised in the analysis. In this study, a multi-metric and multi-contextual approach was applied to analyse 62 river restoration projects located in France, Germany and the Netherlands. This methodological approach facilitated the characterisation of the responses exhibited by macroinvertebrate communities in response to restoration initiatives, and the subsequent identification of the environmental contexts and the categories of restoration measures that exerted influence on these responses. Utilising log-response ratios based on both taxonomic and functional ecological metrics, a k-means clustering analysis was performed. This analysis yielded two distinct groups of sites: the first exhibiting predominantly positive responses to restoration, and the second demonstrating neutral or marginally negative responses. Positive responses have been shown to be associated with ambitious restoration projects, which are characterised by measures targeting regional-scale processes. These measures include the implementation of projects designed to improve river longitudinal continuity in degraded catchment areas, where the density of obstacles and the intensity of agricultural use are particularly high. Conversely, restoration initiatives targeting local-scale issues within more natural contexts or at high altitude exhibited minimal to no ecological response. This study emphasises the significance of an integrated approach that incorporates various facets (taxonomic and functional) of biodiversity, along with a meticulous examination of the environmental context and project construction, to enhance the evaluation of the ecological benefits of river restoration. The findings of this study may also be applicable in the context of fieldwork, providing practitioners with a framework to inform the design of restoration projects.

ecology↗

bioRxiv2020

Disentangling metacommunity processes using multiple metrics in space and time

Metacommunity ecology has focused on using observational and analytical approaches to disentangle the role of critical assembly processes, such as dispersal limitation and environmental filtering. Many methods have been proposed for this purpose, most notably multivariate analyses of species abundance and its association with variation in spatial and environmental conditions. These approaches tend to focus on few emergent properties of metacommunities and have largely ignored temporal community dynamics. By doing so, these are limited in their ability to differentiate metacommunity dynamics. Here, we develop a Virtual ecologist approach to evaluate critical metacommunity assembly processes based on a number of summary statistics of community structure across space and time. Specifically, we first simulate metacommunities emphasizing three main processes that underlie metacommunity dynamics (density-independent responses to abiotic conditions, density-dependent biotic interactions, and dispersal). We then calculate a number of commonly used summary statistics of community structure in space and time, and use random forests to evaluate their utility for understanding the strength of these three processes. We found that: (i) time series are necessary to disentangle metacommunity processes, (ii) each of the three studied processes is distinguished with different descriptors, (iii) each summary statistic is differently sensitive to temporal and spatial sampling effort. Some of the most useful statistics include the coefficient of variation of abundances through time and metrics that incorporate variation in the relative abundances (evenness) of species. Surprisingly, we found that when we only used a single snapshot of community variation in space, the most commonly used approaches based on variation partitioning were largely uninformative regarding assembly processes, particularly, variation in dispersal. We conclude that a combination of methods and summary statistics will be necessary to understand the processes that underlie metacommunity assembly through space and time.

ecology↗

bioRxiv2019

Partitioning environment and space in species-by-site matrices: a comparison of methods for community ecology and macroecology

Community ecologists and macroecologists have long sought to evaluate the importance of environmental conditions in determining species distributions, community composition, and diversity across sites. Different methods have been used to estimate species-environment relationships, but their differences to jointly fit and disentangle spatial autocorrelation and structure remain poorly studied. We compared how methods in four broad families of statistical models estimated the contribution of the environment and space to variation in species binary occurrence and abundance. These methods included distance-based regression, generalized linear models (GLM, and the special case of RDA), generalized additive models (GAM), and tree-based machine learning (ML): regression trees, boosted regression trees (BRT), and random forests. The spatial component of the model consisted of spatial distance (in distance-based regression), Morans Eigenvector Maps (MEM; in GLM and ML), smooth spatial splines (in GAM), or tree-based non-linear modelling of spatial coordinates (in ML). We simulated typical site-by-species data to assess the methods performance in (1) fitting environmental and spatial models, and (2) partitioning the variation explained by environmental and spatial predictors. We observed marked differences in performance mostly caused by imbalanced performance in estimating environmental and spatial effects. Such differences also manifested when analyzing eight different empirical datasets. GLM and BRT with MEMs were generally the most reliable methods for partitioning the variation explained by environmental and spatial effects across a wide range of simulated scenarios. The remaining methods tended to underfit simulated spatial structures, causing underestimation of spatial fractions of variation. Our results suggest that previously overlooked methods for performing variation partitioning, especially tree-based ML, offer flexible approaches to analyze site-by-species matrices. We provide general guidelines on the usefulness of different models under different ecological and sampling scenarios, for species distribution modelling, community ecology, and macroecology.

ecology↗