Biology subjects

Nell, L. A.

Publications and source records attributed to Nell, L. A..

8 recordsLinked to original sources

bioRxiv2026

TPCdesign: A guide to designing informative thermal performance experiments

1. Thermal performance curves (TPCs) are widely used to quantify temperature dependence of biological traits, but estimating them requires experiments across multiple temperatures. Because these experiments can be costly, researchers must balance the number and placement of temperature treatments against replication within treatments. Evenly spaced temperatures are a common default, but may allocate effort inefficiently when thermal responses are nonlinear or strongly asymmetric. 2. We developed TPCdesign, an R package for planning temperature-trait experiments. TPCdesign uses a pseudo-Bayesian (robust) D-optimal experimental design to identify sets of temperatures expected to provide the most information about a TPC, while allowing users to incorporate prior knowledge of thermal limits and curve shape, uncertainty in that knowledge, available numbers of treatments, and practical restrictions on temperature spacing. 3. Using simulation-based worked examples, we show that optimized designs can recover TPCs more accurately than evenly spaced designs, particularly for asymmetric curves and when the number of available temperature treatments is modest. Optimization primarily improves resolution of regions where performance changes rapidly and can substantially improve estimation of upper thermal limits while retaining information across the rest of the curve. 4. TPCdesign provides a practical framework for allocating limited experimental resources according to the biological response researchers seek to estimate. Rather than prescribing a universal set of temperatures, it allows experimental designs to reflect existing biological knowledge, uncertainty, and logistical constraints, improving the information gained from costly thermal performance experiments.

ecology↗

bioRxiv2026

Natural enemies mediate the impact of plant microbiota on insect-borne virus transmission

Individual hosts can be protected from disease vectors through their microbiota, but how those benefits scale up to the host population level is rarely obvious. Disease spread may be inhibited through reduced vector visitation or amplified as vectors redirect their attention to unprotected hosts. The outcome--suppression or escalation of disease outbreaks--is likely to be influenced by natural enemies that redistribute or suppress vector populations. The challenge of determining how these processes interact across scales is exemplified by pea aphids, major virus vectors in pea crops that are commonly managed using parasitoid wasps. Recent evidence suggests that epiphytic bacteria in the genus Pseudomonas can repel or kill pea aphids, yet whether Pseudomonas-mediated protection from aphids complements or undermines parasitoid-based vector control remains unknown. We developed a multi-scale mathematical model to investigate why Pseudomonas complements versus undermines biocontrol of aphid-vectored virus outbreaks. Because Pseudomonas-induced mortality leaves aphids patchily distributed, the effect of Pseudomonas on virus outbreaks depends most strongly on how well parasitoids track aphid densities: When parasitoids efficiently locate aphids, Pseudomonas inhibits virus outbreaks by reducing aphid densities. However, when parasitoids are less efficient, they often search unproductively for aphids on Pseudomonas-inhabited plants with few aphids, slowing parasitoid population growth. Reduced parasitism intensifies aphid crowding on Pseudomonas-free plants and boosts the production of winged aphids that accelerate viral spread. Counterintuitively, the more effective Pseudomonas is at killing aphids, the more strongly it generates spatial variability and promotes virus spread. Our results show how community context dictates whether individual microbiota protection slows or accelerates viral outbreaks at the population scale.

ecology↗

bioRxiv2025

Regional species coexistence despite local priority effects: the overlooked role of dispersal-community feedback

Many cases of animal-mediated dispersal are non-random, with the animals altering their movement pattern in response to the local species composition of the organisms that the vectoring animals disperse. Yet, this dispersal-community feedback has received little attention in metacommunity ecology. We use a mathematical model to show that dispersal-community feedback can promote regional species coexistence. As a well-characterized system, our model focuses on nectar-inhabiting bacteria and yeast that are dispersed by pollinators and affected by priority effects within flowers once dispersed. Model analysis suggests that bacteria and yeast coexist regionally only when their occurrence in flowers influences the frequency of flower visits by pollinators. This microbe-pollinator feedback creates positive density dependence in each plant, causing competitive exclusion at the plant scale, but spatial partitioning across multiple plants, realizing coexistence at this scale. Our finding highlights dispersal-community feedback as an overlooked potential mechanism of species coexistence.

ecology↗

bioRxiv2023

Shared features underlying compact genomes and extreme habitat use in chironomid midges

Non-biting midges (family Chironomidae) are found throughout the world in a diverse array of aquatic and terrestrial habitats, can often tolerate harsh conditions such as hypoxia or desiccation, and have consistently compact genomes. Yet we know little about the shared molecular basis for these attributes and how they have evolved across the family. Here, we address these questions by first creating high-quality, annotated reference assemblies for Tanytarsus gracilentus (subfamily Chironominae, tribe Tanytarsini) and Parochlus steinenii (subfamily Podonominae). Using these and other publicly available assemblies, we created a time-calibrated phylogenomic tree for family Chironomidae with outgroups from order Diptera. We used this phylogeny to test for features associated with compact genomes, as well as examining patterns of gene family evolution and positive selection that may underlie chironomid habitat tolerances. Our results suggest that compact genomes evolved in the most recent common ancestor of Chironomidae and Ceratopogonidae, and that this occurred mainly through reductions in non-coding regions (introns, intergenic sequences, and repeat elements). Gene families that significantly expanded in Chironomidae included biological processes that may relate to tolerance of stressful environments, such as temperature homeostasis, inflammatory response, melanization defense response, and trehalose transport. We identified a number of genes with evidence for positive selection in Chironomidae, notably sulfonylurea receptor, peroxiredoxin-1, and protein kinase D. Our results help to understand the genomic basis for the small genomes and extreme habitat use in this widely distributed group. Significance StatementChironomid midges are known for having small genomes and being able to tolerate many forms of environmental stress, yet little is known of the shared features of their genomes that may underlie these traits. We found that reductions in non-coding regions coincide with small chironomid genomes, and we identified duplicated and/or selected genes that may equip chironomids to tolerate harsh conditions. These results describe the key genomic changes in chironomid midges that may explain their ability to inhabit a range of extreme habitats across the world.

genomics↗

bioRxiv2022

When should coevolution among competitors promote coexistence versus exclusion?

Coevolution of competitors can lead to niche partitioning promoting coexistence or to heightened conflicts promoting competitive exclusion. If both are possible, when should coevolution favor coexistence versus exclusion? We investigated this question with a general eco-evolutionary model in which species can reduce the interspecific competition they experience through evolutionary investments in two types of competitive traits: partitioning traits that promote coexistence and conflict traits that promote exclusion. We found that communities were generally mixed, consisting of species investing in both trait types or mixtures of species specializing in one type. For each species, its competitors abundances and investments determined its experienced competition, and stronger competition begot greater competitive trait investment. Species investing in conflict traits strengthened competition for other species both directly and indirectly, whereas partitioning traits only weakened competition via direct effects. Conflict traits were therefore the stronger driver of community-wide investments in all traits. However, species investing most in conflict traits experienced less competition, so they ultimately evolved least investment, making them most likely to be excluded by the next invader. Thus, coevolution may provide an open door for species that play nice and a revolving door of exclusion for those that do not.

ecology↗

bioRxiv2021

Quantifying community responses to environmental variation from replicate time series

Time-series data for ecological communities are increasingly available from long-term studies designed to track species responses to environmental change. However, classical multivariate methods for analyzing community composition have limited applicability for time series, as they do not account for temporal autocorrelation in community-member abundances. Furthermore, traditional approaches often obscure the connections between responses at the community level and those for individual taxa, limiting their capacity to infer mechanisms of community change. We show how linear mixed models that account for group-specific temporal autocorrelation and observation error can be used to infer both taxon- and community-level responses to environmental predictors from replicated time-series data. Variation in taxon-specific responses to predictors is modeled using random effects, which can be used to characterize variation in community composition. Moreover, the degree of autocorrelation is estimated separately for each taxon, since this is likely to vary due to differences in their underlying population dynamics. We illustrate the utility of the approach by analyzing the response of a predatory arthropod community to spatiotemporal variation in allochthonous resources in a subarctic landscape. Our results show how mixed models with temporal autocorrelation provide a unified approach to characterizing taxon- and community-level responses to environmental variation through time.

ecology↗

bioRxiv2020

phyr: An R package for phylogenetic species-distribution modelling in ecological communities

O_LIModel-based approaches are increasingly popular in ecological studies. A good example of this trend is the use of joint species distribution models to ask questions about ecological communities. However, most current applications of model-based methods do not include phylogenies despite the well-known importance of phylogenetic relationships in shaping species distributions and community composition. In part, this is due to lack of accessible tools allowing ecologists to fit phylogenetic species distribution models easily. C_LIO_LITo fill this gap, the R package phyr (pronounced fire) implements a suite of metrics, comparative methods and mixed models that use phylogenies to understand and predict community composition and other ecological and evolutionary phenomena. The phyr workhorse functions are implemented in C++ making all calculations and model estimations fast. C_LIO_LIphyr can fit a variety of models such as phylogenetic joint-species distribution models, spatiotemporal-phylogenetic autocorrelation models, and phylogenetic trait-based bipartite network models. phyr also estimates phylogenetically independent trait correlations with measurement error to test for adaptive syndromes and performs fast calculations of common alpha and beta phylogenetic diversity metrics. All phyr methods are united under Brownian motion or Ornstein-Uhlenbeck models of evolution and phylogenetic terms are modelled as phylogenetic covariance matrices. C_LIO_LIThe functions and model formula syntax we propose in phyr serves as a simple and unified framework that ignites the use of phylogenies to address a variety of ecological questions. C_LI

ecology↗

bioRxiv2019

jackalope: a swift, versatile phylogenomic and high-throughput sequencing simulator

High-throughput sequencing (HTS) is central to the study of population genomics and has an increasingly important role in constructing phylogenies. Choices in research design for sequencing projects can include a wide range of factors, such as sequencing platform, depth of coverage, and bioinformatic tools. Simulating HTS data better informs these decisions. However, current standalone HTS simulators cannot generate genomic variants under even somewhat complex evolutionary scenarios, which greatly reduces their usefulness for fields such as population genomics and phylogenomics. Here I present the R package jackalope that simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. Genomic variants can be simulated using phylogenies, gene trees, coalescent-simulation output, population-genomic summary statistics, and Variant Call Format (VCF) files. jackalope can simulate single, paired-end, or mate-pair Illumina reads, as well as reads from Pacific Biosciences. These simulations include sequencing errors, mapping qualities, multiplexing, and optical/PCR duplicates. It can read reference genomes from FASTA files and can simulate new ones, and all outputs can be written to standard file formats. jackalope is available for Mac, Windows, and Linux systems.

genomics↗