Biology subjects

Yearsley, J. M.

Publications and source records attributed to Yearsley, J. M..

5 recordsLinked to original sources

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

Resource competition drives an invasion-replacement event among shrew species on an island

O_LIInvasive mammals are responsible for the majority of native species extinctions on islands. While most of these extinction events will be due to novel interactions between species (e.g. exotic predators and naive prey), it is more unusual to find incidences where a newly invasive species causes the decline/extinction of a native species on an island when they normally coexist elsewhere in their overlapping mainland ranges. C_LIO_LIWe investigated if resource competition between two insectivorous small mammals was playing a significant role in the rapid replacement of the native pygmy shrew (Sorex minutus) in the presence of the recently invading greater white-toothed shrew (Crocidura russula) on the island of Ireland. C_LIO_LIWe used DNA metabarcoding of gut contents from >300 individuals of both species to determine each species diet and measured the size of individuals (weight and length) during different stages of the invasion in Ireland (before, during and after the species come into contact with one another) and on a French island where both species have long coexisted (acting as a natural control site). Dietary composition, niche width and overlap and size were compared in these different stages. C_LIO_LIThe size of the invasive C. russula and composition of its diet changes between when it first invades an area and after it becomes established. Individuals are larger and they consume larger invertebrates at the invasion front, before switching towards the smaller prey taxa that are more essential for the survival of the native species after establishment. As a result, the level of interspecific dietary overlap increases from between 11-14% when they first come into contact with each other to between 39-46% after the invasion. C_LIO_LIHere we show that an invasive species can quickly alter its dietary niche in a new environment, leading to negative impacts that were not previously predicted based on the coexistence of these species in other parts of their mainland ranges. As well as causing the replacement of a native small mammal, the invasive shrew may be rapidly exhausting local resources of larger invertebrate species. These subsequent changes in terrestrial invertebrate communities could have severe impacts further downstream on ecosystem functioning and services. C_LI

ecology↗

bioRxiv2022

Phenotypic Variation from Waterlogging in Multiple Perennial Ryegrass Varieties under Climate Change Conditions

Identifying how various components of climate change will influence ecosystems and vegetation subsistence will be fundamental to mitigate negative effects. Climate change-induced waterlogging is understudied in comparison to temperature and CO2. Grasslands are especially vulnerable through the connection with global food security, with perennial ryegrass dominating many flood-prone pasturelands in North-western Europe. We investigated the effect of long-term waterlogging on phenotypic responses of perennial ryegrass using four varieties grown in atmospherically controlled growth chambers (ambient vs 2050, +2{degrees}C and eCO2) during two months of peak growth. Using image analysis and PCA methodologies, we assess how multiple predictors (phenotypic, environmental, genetic and temporal) influence overall plant performance and productivity. Long-term waterlogging was found to reduce leaf-colour intensity, with younger plants having purple hues indicative of anthocyanins. Plant performance and yield was lower in waterlogged plants, with tetraploid varieties coping better than diploid ones. The climate change treatment was found to reduce colour intensities further. Flooding was found to reduce plant productivity via reductions in colour pigments and root proliferation. These effects will have negative consequences for global food security from facing extreme weather events and flooding. Our approach can be adapted as plant health diagnostics tools via remote sensing and drone-technology.

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↗