Biology subjects

Potts, J. R.

Publications and source records attributed to Potts, J. R..

10 recordsLinked to original sources

bioRxiv2025

Bone Mineralization and Metabolism are Altered in a Rat Model of Brachial Plexus Birth Injury

Brachial plexus birth injury (BPBI) is a common nerve injury incurred during a difficult childbirth when the brachial plexus nerve bundle is excessively stretched, resulting in functional arm impairment in 30-40% of those affected. Injury can present in two different locations, modeled in rats as postganglionic and preganglionic neurectomies. Osseous deformities are present following both injury types. However, the underlying factors behind these deformities are not fully understood. While past studies have explored muscle structure and altered mechanical joint loading as factors, bone metabolism, muscle composition, and muscle-bone crosstalk have not been fully explored. Using postganglionic and preganglionic BPBI rat models and a disuse model, bone metabolism, muscle composition, and muscle-bone crosstalk were explored. Dynamic histomorphometry and similar methods were used to characterize humeral growth and humeral growth plate activity to understand bone metabolism, muscle fibrosis was analyzed to assess muscle composition, and FGF-2 quantification was performed to assess muscle-bone crosstalk. Postganglionic injury portrayed more changes in the humeral diaphyseal region than preganglionic and displayed reduced bone metabolism on the endosteal surface while preganglionic displayed reduced bone metabolism on the periosteal surface. However, only preganglionic showed significantly lower growth plate activity. In regards to fibrosis, both injury types showed fibrosis in the biceps but only preganglionic showed fibrosis in the subscapularis. The limb disuse model did not show fibrosis. Additionally, preganglionic had an increased production of FGF-2 signaling more so in the subscapularis. Overall, deformities from postganglionic injury may be from bone formation and bone resorption while deformities from preganglionic injury are likely from an overall reduction in bone growth that is not solely from limb disuse. The fibrosis and FGF-2 signaling alterations seen are not likely to be the direct cause of osseous deformity and the drivers behind the alterations are likely different between postganglionic and preganglionic injuries.

bioengineering↗

bioRxiv2025

Predicting animal movement with deepSSF: a deep learning step selection framework

O_LIPredictions of animal movement are vital for understanding and managing wild populations. However, the fine-scale, complex decision-making of animals can pose challenges for the accurate prediction of trajectories. Step selection functions (SSFs), a common tool for inferring relationships between animal movement and the environment, are also increasingly used to simulate animal trajectories for prediction. Although admitting a lot of flexibility, the SSF framework is limited to its reliance on pre-defined functional forms for fitting to data, and SSFs that involve complex functional forms to model detailed processes can be prohibitively difficult to fit and interpret. C_LIO_LIHere, we present deepSSF, an approach to fit and predict animal movement data using deep learning. Whilst not specific to any particular model, we denote the deepSSF approach as building and training a neural network architecture that receives multiple environmental layers and scalar values as inputs, and outputs a single layer representing the next-step probability. To demonstrate a deepSSF model, we build a model in PyTorch that has distinct but interacting habitat selection and movement subnetworks, which allows for explicit representation of both processes and interpretable intermediate outputs. We apply our model to GPS data of introduced water buffalo (Bubalus bubalis) in the tropical savannas of Northern Australia. C_LIO_LIOur deepSSF model was able to learn features that are present in the habitat covariate layers, such as linear features (rivers, forest edges), and the composition of certain habitat areas, without having to specify them pre-emptively within the SSF framework. It was also able to capture the complex interactions between the habitat covariates as well as temporal dynamics across time of day and year. C_LIO_LIWe expect that the deepSSF approach will generate accurate and informative animal movement trajectories, which can be used for deepening our understanding of animal-environment systems and for the practical management of species. Considering the wide range of deep learning tools, the deepSSF approach could be extended to represent memory and social dynamic processes, with the potential for integrating non-spatial data sources such as accelerometers and physiological sensors. C_LI

ecology↗

bioRxiv2024

Variations in nonlocal interaction range lead to emergent chase-and-run in heterogeneous populations

In a chase-and-run dynamic, the interaction between two individuals is such that one moves towards the other (the chaser), while the other moves away (the runner). Examples can be found in both interacting cells and interacting animals. Here we investigate the behaviours that can emerge at a population level, for a heterogeneous group that contains subpopulations of chasers and runners. We show that a wide variety of patterns can form, from stationary patterns to oscillatory and population-level chase-and-run, where the latter describes a synchronised collective movement of the two populations. We investigate the conditions under which different behaviours arise, specifically focusing on the interaction ranges: the distances over which cells or organisms can sense one anothers presence. We find that when the interaction range of the chaser is sufficiently larger than that of the runner - or when the interaction range of the chase is sufficiently larger than that of the run - population-level chase-and-run emerges in a robust manner. We discuss the results in the context of phenomena observed in cellular and ecological systems, with particular attention to the dynamics observed experimentally within populations of neural crest and placode cells.

cell biology↗

bioRxiv2024

Simulating animal movement trajectories from temporally dynamic step selection functions

Understanding and predicting animal movement is fundamental to ecology and conservation management. Models that estimate and then predict animal movement and habitat selection parameters underpin diverse conservation applications, from mitigating invasive species spread to enhancing landscape connectivity. However, many predictive models overlook fine-scale temporal dynamics within their predictions, despite animals often displaying fine-scale behavioural variability that might significantly alter their movement, habitat selection and distribution over time. Incorporating fine-scale temporal dynamics, such as circadian rhythms, within predictive models might reduce the averaging out of such behaviours, thereby enhancing our ability to make predictions in both the short and long term. We tested whether the inclusion of fine-scale temporal dynamics improved both fine-scale (hourly) and long-term (seasonal) spatial predictions for a significant invasive species of Northern Australia, the water buffalo (Bubalus bubalis). Water buffalo require intensive management actions over vast, remote areas and display distinct circadian rhythms linked to habitat use. To inform management operations we generated hourly and dry season prediction maps by simulating trajectories from static and temporally dynamic step selection functions (SSFs) that were fitted to the GPS data of 13 water buffalo. We found that simulations generated from temporally dynamic models replicated the buffalos crepuscular movement patterns and dynamic habitat selection, resulting in more informative and accurate hourly predictions. Additionally, when the simulations were aggregated into long-term predictions, the dynamic models were more accurate and better able to highlight areas of concentrated habitat use that might indicate high-risk areas for environmental damage. Our findings emphasise the importance of incorporating fine-scale temporal dynamics in predictive models for species with clear dynamic behavioural patterns. By integrating temporally dynamic processes into animal movement trajectories, we demonstrate an approach that can enhance conservation management strategies and deepen our understanding of ecological and behavioural patterns across multiple timescales.

ecology↗

bioRxiv2022

How to scale up from animal movement decisions to spatio-temporal patterns: an approach via step selection

O_LIUncovering the mechanisms behind animal space use patterns is of vital importance for predictive ecology, thus conservation and management of ecosystems. Movement is a core driver of those patterns so understanding how movement mechanisms give rise to space use patterns has become an increasingly active area of research. C_LIO_LIThis study focuses on a particular strand of research in this area, based around step selection analysis (SSA). SSA is a popular way of inferring drivers of movement decisions, but, perhaps less well-appreciated, it also parametrises a model of animal movement. Of key interest is that this model can be propogated forwards in time to predict the space use patterns over broader spatial and temporal scales than those that pertain to the proximate movement decisions of animals. C_LIO_LIHere, we provide a guide for understanding and using the various existing techniques for scaling-up step selection models to predict broad scale space use patterns. We give practical guidance on when to use which technique, as well as specific examples together with code in R and Python. C_LIO_LIBy pulling together various disparate techniques into one place, and providing code and instructions in simple examples, we hope to highlight the importance of these techniques and make them accessible to a wider range of ecologists, ultimately helping expand the usefulness of step selection analysis. C_LI

ecology↗

bioRxiv2022

Beyond resource selection: emergent spatio-temporal distributions from animal movements and stigmergent interactions

A principal concern of ecological research is to unveil the causes behind observed spatio-temporal distributions of species. A key tactic is to correlate observed locations with environmental features, in the form of resource selection functions or other correlative species distribution models. In reality, however, the distribution of any population both affects and is affected by those surrounding it, creating a complex network of feedbacks causing emergent spatio-temporal features that may not correlate with any particular aspect of the underlying environment. Here, we study the way in which the movements of populations in response to one another can affect the spatio-temporal distributions of ecosystems. We construct a stochastic individual-based modelling (IBM) framework, based on stigmergent interactions (i.e. organisms leave marks which cause others to alter their movements) between and within populations. We show how to gain insight into this IBM via mathematical analysis of a partial differential equation (PDE) system given by a continuum limit. We show how the combination of stochastic simulations of the IBM and mathematical analysis of PDEs can be used to categorise emergent patterns into homogeneous vs. heterogeneous, stationary vs. perpetually-fluctuating, and aggregation vs. segregation. In doing so, we develop techniques for understanding spatial bifurcations in stochastic IBMs, grounded in mathematical analysis. Finally, we demonstrate through a simple example how the interplay between environmental features and between-population stigmergent interactions can give rise to predicted spatial distributions that are quite different to those predicted purely by accounting for environmental covariates.

ecology↗

bioRxiv2021

Solving the Sample Size Problem for Resource Selection Analysis

O_LISample size sufficiency is a critical consideration for conducting Resource-Selection Analyses (RSAs) from GPS-based animal telemetry. Cited thresholds for sufficiency include a number of captured animals M [≥] 30 and as many relocations per animal N as possible. These thresholds render many RSA-based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. C_LIO_LIWe provide the first comprehensive solution for RSA sample size by deriving closed-form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 2 biologically meaningful quantities: habitat selection strength and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores, and herbivores living in boreal, temperate, and tropical forests, montane woodlands, swamps, and arctic tundra). C_LIO_LIOur analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results contradict conventional wisdom by demonstrating that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with far fewer data than is commonly assumed. C_LIO_LIWe identify several critical steps in implementing these equations, including (i) a priori selection of expected model coefficients, and (ii) sampling intensity for background (absence/pseudo-absence) data within a given definition of availability. We show that random sampling of background data violates the underlying mathematics of RSA, leading to incorrect values for necessary M and N and potentially incorrect RSA model outputs. We argue that these equations should be a mandatory component for all future RSA studies. C_LI

ecology↗

bioRxiv2020

Periscope Proteins are variable length regulators of bacterial cell surface interactions

Changes at the cell surface enable bacteria to survive in dynamic environments, such as diverse niches of the human host. Here, we reveal "Periscope Proteins" as a widespread mechanism of bacterial surface alteration mediated through protein length variation. Tandem arrays of highly similar folded domains can form an elongated rod-like structure; thus variation in the number of domains determines how far an N-terminal host ligand binding domain projects from the cell surface. Supported by newly-available long-read genome sequencing data, we propose this new class could contain over 50 distinct proteins, including those implicated in host colonisation and biofilm formation by human pathogens. In large multi-domain proteins, sequence divergence between adjacent domains appears to reduce inter-domain misfolding. Periscope Proteins break this "rule", suggesting their length variability plays an important role in regulating bacterial interactions with host surfaces, other bacteria and the immune system.

microbiology↗

bioRxiv2020

Why did the animal turn? Time-varying step selection analysis for inference between observed turning points in high frequency data

O_LIStep selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as "selecting" each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data is increasingly gathered at very high frequencies, often [≥]1Hz, so it may be possible to exploit these data to perform more behaviourally-meaningful step selection analysis. C_LIO_LIHere, we present a technique to do this. We first use an existing algorithm to determine the turning-points in an animals movement path. We define a "step" to be a straight-line movement between successive turning-points. We then construct a generalised version of integrated SSA (iSSA), called time-varying iSSA (tiSSA), which deals with the fact that turning-points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free-ranging goats (Capra aegagrus hircus), comparing our results to those of regular iSSA with locations that are separated by a constant time-interval. C_LIO_LIUsing (regular) iSSA with constant time-steps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter. C_LIO_LIBy constructing a step selection technique that works between observed turning-points of animals, we enable step selection to be used on high-frequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turning-points can be viewed as decisions, our method places step selection analysis on a more behaviourally-meaningful footing compared to previous techniques. C_LI

ecology↗

bioRxiv2020

Parametrising diffusion-taxis equations from animal movement trajectories using step selection analysis

O_LIMathematical analysis of partial differential equations (PDEs) has led to many insights regarding the effect of organism movements on spatial population dynamics. However, their use has mainly been confined to the community of mathematical biologists, with less attention from statistical and empirical ecologists. We conjecture that this is principally due to the inherent difficulties in fitting PDEs to data. C_LIO_LITo help remedy this situation, in the context of movement ecology, we show how the popular technique of step selection analysis (SSA) can be used to parametrise a class of PDEs, called diffusion-taxis models, from an animals trajectory. We examine the accuracy of our technique on simulated data, then demonstrate the utility of diffusion-taxis models in two ways. First, we derive the steady-state utilisation distribution in a closed analytic form. Second, we give a simple recipe for deriving spatial pattern formation properties that emerge from inferred movement-and-interaction processes: specifically, do those processes lead to heterogeneous spatial distributions and if so, do these distributions oscillate in perpetuity or eventually stabilise? The second question is demonstrated by application to data on concurrently-tracked bank voles (Myodes glareolus). C_LIO_LIOur results show that SSA can accurately parametrise diffusion-taxis equations from location data, providing the frequency of the data is not too low. We show that the steady-state distribution of our diffusion-taxis model, where it exists, has an identical functional form to the utilisation distribution given by resource selection analysis (RSA), thus formally linking (fine scale) SSA with (broad scale) RSA. For the bank vole data, we show how our SSA-PDE approach can give predictions regarding the spatial aggregation and segregation of different individuals, which are difficult to predict purely by examining results of SSA. C_LIO_LIOur methods give a user-friendly way in to the world of PDEs, via a well-used statistical technique, which should lead to tighter links between the findings of mathematical ecology and observations from empirical ecology. By providing a non-speculative link between observed movement behaviours and space use patterns on larger spatio-temporal scales, our findings will also aid integration of movement ecology into understanding spatial species distributions. C_LI

ecology↗