Biology subjects

Sadykova, D.

Publications and source records attributed to Sadykova, D..

5 recordsLinked to original sources

bioRxiv2026

An Eco-Evolutionary Modelling Framework for Mosquito Host Specialisation

Host preference varies widely across mosquitoes, with many species feeding opportunistically on diverse vertebrate hosts, while others show strong fidelity to specific hosts. Anthropophilia, the behavioural preference for feeding on humans, is a defining characteristic of some mosquito species responsible for the transmission of major human diseases, including malaria, dengue, and yellow fever. The evolution of anthropophilia therefore has profound epidemiological implications because increased human biting elevates vectorial capacity and disease transmission potential. However, the ecological and evolutionary mechanisms driving this extreme specialisation have not yet been fully elucidated and remain difficult to unify across laboratory and field studies. Here we present an eco-evolutionary modelling framework that links genetically determined mosquito traits with spatially structured host environments. Our framework integrates innate olfactory sensitivity, blood meal-derived fitness benefits, and spatio-temporal host accessibility. Two complementary indices are introduced: a local specialisation index, capturing short-term ecological feeding strategies, and a co-evolutionary index, capturing long-term genetic coupling between host detection and resource utilisation. Our results demonstrate that host specialisation is not a default evolutionary outcome but an environmentally gated process, which is favoured in resource-poor or temporally varying habitats and strongly filtered by seasonality. The framework yields testable predictions regarding when specialisation emerges, persists, or collapses, with direct implications for predicting vector-borne disease risk in changing environments

evolutionary biology↗

bioRxiv2023

Weak interactions cause poor performance of common network inference models

O_LINetwork inference models have been widely applied in ecological, genetic and social studies to infer unknown interactions. However, little is known about how well the models perform and whether they produce reliable results when confronted with networks where weak interactions predominate and for different amounts of data. This is an important consideration as empirical interaction strengths are commonly skewed towards weaker interactions, which is especially relevant in ecological networks, and a number of studies suggest the importance of weak interactions for ensuring the dynamic stability of a system. C_LIO_LIHere we investigate four commonly used network methods (Bayesian Networks, Graphical Gaussian Models, L1-regularised regression with the least absolute shrinkage and selection operator, and Sparse Bayesian Regression) and employ network simulations with different interaction strengths to assess their accuracy and reliability. C_LIO_LIThe results show poor performance, in terms of the ability to discriminate between existing relationships and no relationships, in the presence of weak interactions, for all the selected network inference methods. C_LIO_LIOur findings suggest that though these models have some promise for network inference with networks that consist of medium or strong interactions and larger amounts of data, data with weak interactions does not provide enough information for the models to reliably identify interactions. Therefore, networks inferred from data of that type should be interpreted with caution. C_LI

ecology↗

bioRxiv2022

Multi-group biodiversity theory and its application to multi-species maximum sustainable yield problem

We introduce the group-based approach, use it to develop a multi-group biodiversity theory, and apply it find solutions to the multi-species maximum sustainable yield problem for a mixed species fishery. The group-based approach to community ecology is intermediate between classical species-centric and more recent trait-based (species-less) approaches. It describes ecological communities as composed of conspecific groups rather than species (as in classical models) or species-less individuals (as in trait-based models), and reconsiders community structure as results of inter-group resource competition. The approach respects species affiliation and recognises the importance of trait trade-offs at the conspecific group level. It offers an alternative to both classical and trait-based approaches and, remarkably, provides a complete analytical description of the community structure in the bench-mark case of zero-sum resource redistribution. HighlightsO_LIWe introduce a group-based approach to modelling of ecological communities and develop a multi-group biodiversity theory. C_LIO_LIA classification of intergroup interactions is established, based on the type of contest (which is determined by the relative role of qualitative vs. quantitative factors) and the accounting of resource redistribution. C_LIO_LIFor pure resource competition, we obtain the full analytic description of multi-group and multi-species community structure and its dynamics, including the processes of fission-fusion and invasion-extinction among groups. C_LIO_LIA principle of competitive coexistence is formulated, which explains the existence of conspecific groups as a mechanism for avoiding competitive exclusion. C_LIO_LIWe apply the theory to harvesting multi-species communities (e.g., for fisheries) and derive analytic expression for total catch and approximate solutions for multi-species maximum sustainable yield (MSY). C_LI

ecology↗

bioRxiv2022

Modelling the distribution of rare invertebrates by correcting class imbalance and spatial bias

AimSoil arthropods are important decomposers and nutrient cyclers, but are poorly represented on national and international conservation Red Lists. Opportunistic biological records for soil invertebrates are often sparse, and contain few observations of rare species but a relatively large number of non-detection observations (a problem known as class imbalance). Robinson et al. (2018) proposed a method for sub-sampling non-detection data using a spatial grid to improve class balance and spatial bias in bird data. For taxa that are less intensively sampled, datasets are smaller, which poses a challenge because under-sampling data removes information. We tested whether spatial under-sampling improved prediction performance of species distribution models for millipedes, for which large datasets are not available. We also tested whether using environmental predictor variables provided additional information beyond what is captured by spatial position for predicting species distributions. LocationIsland of Ireland. MethodsWe tested the spatial under-sampling method of Robinson et al. (2018) by using biological records to train species distribution models of rare millipedes. ResultsUsing spatially under-sampled training data improved species distribution model sensitivity (true positive rate) but decreased model specificity (true negative rate). The decrease in specificity was minimal for rarer species and was accompanied by substantial increases in sensitivity. For common species, specificity decreased more, and sensitivity increased less, making spatial under-sampling most useful for rare species. Geographic coordinates were as good as or better than environmental variables for predicting distributions of two out of six species. Main ConclusionsSpatial under-sampling improved prediction performance of species distribution models for rare soil arthropod species. Spatial under-sampling was most effective for rarer species. The good prediction performance of models using geographic coordinates is promising for modeling distributions of poorly studied species for which little is known about ecological or physiological determinants of occurrence.

ecology↗

bioRxiv2020

Data quantity is more important than its spatial bias for predictive species distribution modelling

Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of 1) spatial bias in training data, 2) sample size (the average number of observations per species), and 3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but only when the bias was relatively strong. Sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

ecology↗