Biology subjects

Ward, E. J.

Publications and source records attributed to Ward, E. J..

10 recordsLinked to original sources

bioRxiv2026

Thermal niche warming is more consistent than range shifts in marine species under climate change

Marine species are widely expected to shift poleward or into deeper waters in response to rising ocean temperatures. However, our knowledge is primarily based on studies examining range shifts along single dimensions at a time (e.g., latitude or depth). Failing to address how movements along multiple dimensions interact, including associated changes in thermal exposure, may result in misleading conclusions and predictions of species distribution and community composition under global warming. To address this knowledge gap, we here develop and apply a multidimensional framework that jointly evaluates climate-driven redistribution of marine fish across latitude, longitude, depth and realized thermal niches, based on long-term scientific bottom-trawl surveys throughout the North Atlantic and Northeast Pacific. Our results show that net redistributions are generally small and highly region-specific, while the realized thermal niches of species have warmed substantially over the past three decades. These findings demonstrate that spatial redistribution is generally failing to keep pace with rising temperatures and challenge the prevailing assumption that marine species will move to escape warming. This has direct implications for biodiversity indicators that rely on distributional shifts as evidence of climate impacts, as well as climate-informed management and conservation of marine ecosystems, fisheries, and biodiversity at large.

ecology↗

bioRxiv2025

Approximating spatial processes with too many knots degrades the quality of probabilistic predictions

Spatial and spatiotemporal models are increasingly critical for understanding species distributions, tracking population change, and informing conservation decisions. As biological processes are influenced by increasing external pressures, including human disturbance or environmental change, accurate model predictions become essential for adaptive management. However, the reliability of spatial predictions depends on often-overlooked modelling choices, including the spatial resolution used to approximate underlying processes. Using long term monitoring data from a large-scale groundfish survey in the California Current ecosystem, we investigated how spatial model complexity affects the quality of ecological predictions and derived indices used for management. We fit spatial and spatiotemporal models of ocean temperature and fish biomass density for 27 commercially important species using varying levels of spatial resolution. We evaluated both in-sample and out-of-sample prediction, and effects on area-weighted biomass indices. Counter to common assumptions, increasing spatial approximation resolution did not universally improve predictions. Our case studies demonstrate that for many datasets, out-of-sample prediction quality peaked at intermediate spatial resolutions and declined at the finest scales. Through simulation testing, we found this pattern was strongest when spatial patterning had a small range and high spatial variance, and observation error was low. For most species, spatial resolution had a minimal effect on biomass trend estimates used in management, but for several commercially important rockfish species, resolution choices substantially affected both the scale and uncertainty of population indices. Our findings demonstrate that spatial model specification can substantially affect ecological inference, with direct implications for management and conservation planning. We provide practical guidance for ecologists on selecting appropriate spatial complexity through cross-validation. When out-of-sample prediction is a focus, appropriate approximation complexity should improve both parameter estimation accuracy and derived quantities.

ecology↗

bioRxiv2025

Spatiotemporal Models Reveal Dynamic Growth Patterns in U.S. West Coast Groundfish

Variability in somatic growth of marine fish affects their reproductive potential and survival, and therefore, the productivity of a population. Understanding how growth might vary among species with similar traits can improve predictions of population status and responses to environmental change. We used geostatistical models to characterize spatiotemporal variability in growth rate and body condition, two interrelated traits associated with somatic growth, of nine commercially important U.S. West Coast groundfish species with a range of life-history traits and depth distributions. Our models uncover spatiotemporal variability in growth rate and body condition in all nine groundfish species with limited trends shared among species with similar traits, suggesting a greater influence from niche partitioning acting on local scales. Such interspecific differences in growth rate and body condition responses also occurred at regional scales, with some species exhibiting positive responses while others declined. These findings reveal the dynamic nature of somatic growth among groundfish species and provide insight into potential mechanisms of its variability that could be considered within climate-enhanced assessments of population status for marine fish.

ecology↗

bioRxiv2025

Advancing spatially explicit fisheries management with age-structured species distribution models

Many species distribution models (SDMs) incorporate external information, such as environmental or habitat features, yet the majority overlook age-specific information. Including age information may be particularly valuable for species exhibiting age-related spatial patterns driven by ontogenetic shifts, recruitment dynamics, and selective fishing pressures. Here, we develop an age-structured SDM framework using data from the west coast of the USA and concentrate on two species with distinct life histories: North Pacific hake (Merluccius productus) and sablefish (Anoplopoma fimbria). We validate our approach by forecasting age classes across survey locations in future years; these results highlight that forecast models have predictive skill (sablefish more so than hake) and that the predictive ability is highest for older age classes. Using predictions of age-1 sablefish as an example, we demonstrate how our models may be used to forecast future bycatch risk in space, helping increase the efficiency of fisheries. Our framework is applicable to a wide range of survey types and platforms around the world and supports the development of spatially explicit management strategies and optimal allocation of fishing effort.

ecology↗

bioRxiv2024

Metabolic interactions underpinning high methane fluxes across terrestrial freshwater wetlands

Current estimates of wetland contributions to the global methane budget carry high uncertainty, particularly in accurately predicting emissions from high methane-emitting wetlands. Microorganisms mediate methane cycling, yet knowledge of their conservation across wetlands remains scarce. To address this, we integrated 1,118 16S rRNA amplicon datasets (116 new), 305 metagenomes (20 new) that yielded 4,745 medium and high-quality metagenome assembled genomes (MAGs; 617 new), 133 metatranscriptomes, and annual methane flux data across 9 wetlands to create the Multi-Omics for Understanding Climate Change (MUCC) v2.0.0 database. This new resource was leveraged to link microbiome compositional profiles to encoded functions and emissions, with specific focus on methane-cycling populations and the microbial carbon decomposition networks that fuel them. We identified eight methane-cycling genera that were conserved across wetlands, and deciphered wetland specific metabolic interactions across marshes, revealing low methanogen-methanotroph connectivity in high-emitting wetlands. Methanoregula emerged as a hub methanogen across networks and was a strong predictor of methane flux, demonstrating the potential broad relevance of methylotrophic methanogenesis in these ecosystems. Collectively, our findings illuminate trends between microbial decomposition networks and methane flux and provide an extensive publicly available database to advance future wetland research.

microbiology↗

bioRxiv2022

sdmTMB: an R package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields

Geostatistical spatial or spatiotemporal data are common across scientific fields. However, appropriate models to analyse these data, such as generalised linear mixed effects models (GLMMs) with Gaussian Markov random fields (GMRFs), are computationally intensive and challenging for many users to implement. Here, we introduce the R package sdmTMB, which extends the flexible interface familiar to users of lme4, glmmTMB, and mgcv to include spatial and spatiotemporal latent GMRFs using an SPDE-(stochastic partial differential equation) based approach. SPDE matrices are constructed with fmesher and estimation is conducted via maximum marginal likelihood with TMB or via Bayesian inference with tmbstan and rstan. We describe the model and explore case studies that illustrate sdmTMBs flexibility in implementing penalised smoothers, non-stationary processes (time-varying and spatially varying coefficients), hurdle models, cross-validation and anisotropy (directionally dependent spatial correlation). Finally, we compare the functionality, speed, and interfaces of related software, demonstrating that sdmTMB can be an order of magnitude faster than R-INLA. We hope sdmTMB will help open this useful class of models to a wider field of geostatistical analysts.

ecology↗

bioRxiv2022

Placental inflammation leads to abnormal embryonic heart development

Placental and embryonic heart development occurs in parallel, and these organs have been proposed to exert reciprocal regulation during gestation. Poor placentation has been associated with congenital heart disease (CHD), an important cause of infant mortality. However, the mechanisms by which altered placental development can lead to CHD remain unresolved. In the current study we show that neutrophil-driven placental inflammation leads to inadequate placental development and loss of barrier function. Consequently, placental inflammatory monocytes of maternal origin become capable to migrate to the embryonic heart and alter the normal composition of resident cardiac macrophages and cardiac tissue structure. This cardiac impairment continues into postnatal life, hindering normal tissue architecture and function. Finally, we demonstrate that tempering placental inflammation can rescue this fetal cardiac defect and is sufficient to promote normal cardiac function in postnatal life. Taken together, our observations provide a mechanistic paradigm whereby neutrophil-driven inflammation in pregnancy can preclude normal embryonic heart development as a direct consequence of poor placental development.

immunology↗

bioRxiv2020

Pseudoreplication in genomics-scale datasets

In genomics-scale datasets, loci are closely packed within chromosomes and hence provide correlated information. Averaging across loci as if they were independent creates pseudoreplication, which reduces the effective degrees of freedom (df) compared to the nominal degrees of freedom, df. This issue has been known for some time, but consequences have not been systematically quantified across the entire genome. Here we measured pseudoreplication (quantified by the ratio df/df) for a common metric of genetic differentiation (FST) and a common measure of linkage disequilibrium between pairs of loci (r2). Based on data simulated using models (SLiM and msprime) that allow efficient forward-in-time and coalescent simulations while precisely controlling population pedigrees, we estimated df and df/df by measuring the rate of decline in the variance of mean FST and mean r2 as more loci were used. For both indices, df increases with Ne and genome size, as expected. However, even for large Ne and large genomes, df for mean r2 plateaus after a few thousand loci, and a variance components analysis indicates that the limiting factor is uncertainty associated with sampling individuals rather than genes. Pseudoreplication is less extreme for FST, but df/df [≤]0.01 can occur in datasets using tens of thousands of loci. Commonly-used block-jackknife methods consistently overestimated var(FST), producing very conservative confidence intervals. Predicting df based on our modeling results as a function of Ne, L, S, and genome size provides a robust way to quantify precision associated with genomics-scale datasets.

genetics↗

bioRxiv2019

Exploring Perceptual Illusions in Deep Neural Networks

Perceptual illusions--discrepancies between what exists externally and what we actually see--reveal a great deal about how the perceptual system functions. Rather than failures of perception, illusions expose automatic computations and biases in visual processing that help make better decisions from visual information to achieve our perceptual goals. Recognizing objects is one such perceptual goal that is shared between humans and certain Deep Convolutional Neural Networks, which can reach human-level performance. Do neural networks trained exclusively for object recognition \"perceive\" visual illusions, simply as a result of solving this one perceptual problem? Here, I showed four classic illusions to humans and a pre-trained neural network to see if the network exhibits similar perceptual biases. I found that deep neural networks trained exclusively for object recognition exhibit the Muller-Lyer illusion, but not other illusions. This result shows that some perceptual computations that are similar to humans may come \"for free\" in a system with perceptual goals similar to humans.

neuroscience↗

bioRxiv2018

Not-so-working memory: Drift in fMRI pattern representations during maintenance predicts errors in a visual working memory task

Working memory (WM) is critical to many aspects of cognition, but it frequently fails. Much WM research has focused on capacity limits, but even for single, simple features, the fidelity of individual representations is limited. Why is this? We used fMRI and a pattern-based index of \"representational drift\" to investigate how ongoing changes in brain activity patterns throughout the WM maintenance period predicted performance, using a delayed-match-to-sample task for a single item with a single critical feature: orientation. In trials where the target and probe stimuli matched, participants incorrectly reported more non-matches when their activity patterns drifted away from the target. In trials where the target and probe did not match, participants incorrectly reported more matches when their activity patterns drifted towards the probe. Our results suggest that WM errors are not simply due to unstructured noise, but also drift within representation space that can be indexed by neuroimaging.

neuroscience↗