Biology subjects

Fryxell, J. M.

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

5 recordsLinked to original sources

bioRxiv2026

Integrating animal experience into state-switching habitat selection models

Movement models often assume that animal decisions depend on perceived environmental conditions. However, optimal foraging and cognition theory predict that accumulated experiences should drive changes in animal motivation and decision-making. Here, we propose a mechanistic framework that models state-switching habitat selection as a history-dependent process, where internal states, such as fear or energy, emerge as latent variables accumulating through environmental exposure. Simulations showed that our model accurately recovers memory timescales and cumulative effects of environmental exposure on behavioural switching that would be undetected by existing state-switching habitat selection models. Applied to woodland caribou, it reveals that an individual integrates predation risk experienced over the previous 15 days, but food intake over only 2 days, when deciding to remain or leave an area. Our model advances perspectives on movement ecology by quantifying how accumulated experiences shape changes in motivation driving animal movement decisions.

ecology↗

bioRxiv2024

A test for microbiome-mediated rescue via host phenotypic plasticity in Daphnia

Phenotypic plasticity is a primary mechanism by which organismal phenotypes shift in response to the environment. Host-associated microbiomes often exhibit considerable shifts in response to environmental variation and these shifts could facilitate host phenotypic plasticity, adaptation, or rescue populations from extinction. However, it is unclear how much shifts in microbiome composition contribute to host phenotypic plasticity, limiting our knowledge of the underlying mechanisms of plasticity and, ultimately, the fate of populations inhabiting changing environments. In this study, we examined phenotypic responses and microbiome composition in 20 genetically distinct Daphnia magna clones exposed to non-toxic and toxic diets containing Microcystis, a cosmopolitan cyanobacteria and common stressor for Daphnia. Daphnia exhibited significant plasticity in survival, reproduction, and population growth rates in response to Microcystis exposure. However, the effects of Microcystis exposure on the Daphnia microbiome were limited, with the primary effect being differences in abundance observed across five bacterial families. Moreover, there was no significant correlation between the magnitude of microbiome shifts and host phenotypic plasticity. Our results suggest that microbiome composition played a negligible role in driving host phenotypic plasticity or microbiome-mediated rescue. One sentence summaryDaphnia exhibits considerable plasticity in individual and population-level responses to a cosmopolitan stressor, yet shifts in microbiome composition are not correlated with the magnitude of this plasticity.

ecology↗

bioRxiv2024

Transgenerational epigenetic inheritance increases trait variation but is not adaptive

Understanding processes that can produce adaptive phenotypic shifts in response to rapid environmental change is critical to reducing biodiversity loss. The ubiquity of environmentally induced epigenetic marks has led to speculation that epigenetic inheritance could potentially enhance population persistence in response to environmental change. Yet, the magnitude and fitness consequences of epigenetic marks carried beyond maternal inheritance are largely unknown. Here, we tested how transgenerational epigenetic inheritance (TEI) shapes the phenotypic response of Daphnia clones to the environmental stressor Microcystis. We split individuals from each of eight genotypes into exposure and control treatments (F0 generation) and tracked the fitness of their descendants to the F3 generation. We found transgenerational epigenetic exposure to Microcystis led to reduced rates of survival and individual growth and no consistent effect on offspring production. Increase in trait variance in the F3 relative to F0 generations suggests potential for heritable bet hedging driven by TEI, which could impact population dynamics. Our findings are counter to the working hypothesis that TEI is a generally adaptive mechanism likely to prevent extinction for populations inhabiting rapidly changing environments. One sentence summaryTransgenerational epigenetic inheritance in Daphnia exposed to Microcystis revealed negative fitness effects on survival and growth rates, challenging hypotheses of a general selective advantage.

evolutionary biology↗

bioRxiv2023

Intraspecific genetic variation is critical to robust toxicological predictions in Daphnia

Environmental risk assessment is a critical tool for protecting aquatic life and its effectiveness is predicated on predicting how natural populations respond to contaminants. Yet, routine toxicity testing typically examines only one genotype, which may render risk assessments inaccurate as populations are most often composed of genetically distinct individuals. To determine the importance of intraspecific variation in the translation of toxicity testing to populations, we quantified the magnitude of genetic variation within 20 Daphnia magna clones derived from one lake using whole genome sequencing and phenotypic assays. We repeated these assays across two exposure levels of microcystins, a cosmopolitan and lethal aquatic contaminant produced by harmful algal blooms. We found considerable intraspecific genetic variation in survival, growth, and reproduction, which was amplified by microcystins exposure. Finally, using simulations we demonstrate that the common practice of employing a single genotype to calculate toxicity tolerance failed to produce an estimate within the 95% confidence interval over half of the time. These results illuminate the importance of incorporating intraspecific genetic variation into toxicity testing to reliably predict how natural populations will respond to aquatic contaminants.

pharmacology and toxicology↗

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↗