Biology subjects

Okamoto, K. W.

Publications and source records attributed to Okamoto, K. W..

3 recordsLinked to original sources

bioRxiv2026

Analyzing how habitat degradation drives extinction dynamics using physiologically-structured population models

Elucidating how habitat degradation facilitates extinction is critical for effective conservation efforts. Here, we propose integrating physiologically-structured population models into stochastic population viability analyses to assess how differing consequences of habitat degradation interact to drive extinction dynamics in a focal population. Using the isolated spectacled caiman Caiman crocodilus population/ecomorph from the Apaporis River as a case study, we find that threatening the resource base, which individuals increasingly rely upon, to outgrow vulnerable size ranges and mature accelerates extinction. We also found that when habitat degradation impacts both the primary adult and juvenile resource bases, this can have marked synergistic effects on threatening population viability. By contrast, destroying nesting sites has only a small effect on accelerating the impact of deteriorating prey availability. Through integrating community-level feedback between habitat degradation/change and population dynamics/structure, our approach provides a comparative framework for assessing the relative importance of distinct mechanisms through which habitat degradation ultimately drives extinction risk.

ecology↗

bioRxiv2026

PhagePickr: A bacteria-centric computational tool for designing evolution-proof phage cocktails

As antibiotic resistance poses a major threat to global health, phage therapy offers an alternative to antibiotic treatments in the face of multidrug-resistant bacteria. However, host resistance to phages is also well-documented. Current computational tools for phage cocktail design do not explicitly address the evolution of phage resistance, let alone through the profiling of bacterial receptors whose variability drives much of phage resistance. We introduce PhagePickr, a computational pipeline for the automated design of phage cocktails that minimize host resistance. Unlike other tools, PhagePickr selects phages based on bacterial surface receptor similarity and prioritizes phage diversity to prevent cross-resistance. The tool uses NCBI datasets, a Nearest Neighbors algorithm, and Multiple Sequence Alignment to identify phenotypically similar hosts and ensure phylogenetic diversity in the final cocktail. We evaluated the utility of PhagePickr on ESKAPE pathogens and two understudied bacteria species. The cocktails included candidate phages predicted to target diverse receptors, comprising both lytic phages with confirmed therapeutic potential and novel candidates from similar species. We demonstrate the tools utility in generating cocktails and its capacity to scale as current databases are updated. PhagePickr provides a novel bacteria-centric framework for designing resistance-proof cocktails by exploring shared phenotypes.

microbiology↗

bioRxiv2021

A critical assessment of the detailed Aedes aegypti simulation model Skeeter Buster 2 using field experiments of indoor insecticidal control in Iquitos, Peru

The importance of mosquitoes in human pathogen transmission has motivated major research efforts into mosquito biology in pursuit of more effective vector control measures. Aedes aegypti is a particular concern in tropical urban areas, where it is the primary vector of numerous flaviviruses, including the yellow fever, Zika, and dengue viruses. With an anthropophilic habit, Ae. aegypti prefers houses, human blood meals, and ovipositioning in water-filled containers. We hypothesized that this relatively simple ecological niche should allow us to predict the impacts of insecticidal control measures on mosquito populations. To do this, we use Skeeter Buster 2 (SB2), a stochastic, spatially explicit, mechanistic model of Ae. aegypti population biology. SB2 builds on Skeeter Buster, which reproduced equilibrium dynamics of Ae. aegypti in Iquitos, Peru. Our goal was to predict the response of mosquito populations to perturbations by indoor insecticidal spraying and widespread destructive insect surveys. To evaluate SB2, we conducted two field experiments in Iquitos, Peru: a smaller pilot study in 2013 (S-2013) followed by a larger experiment in 2014 (L-2014). Here, we compare model predictions with (pre-viously reported) empirical results from these experiments. In both simulated and empirical populations, repeated spraying yielded substantial yet temporary reductions in adult densities. The proportional effects of spraying were broadly comparable between simulated and empirical results, but we found noteworthy differences. In particular, SB2 consistently over-estimated the proportion of nulliparous females and the proportion of containers holding immature mosquitoes. We also observed less temporal variation in simulated surveys of adult abundance relative to corresponding empirical observations. Our results indicate the presence of ecological heterogeneities or sampling processes not effectively represented by SB2. Although additional empirical research could further improve the accuracy and precision of SB2, our results under-score the importance of non-linear dynamics in the response of Ae. aegypti populations to perturbations, and suggest general limits to the fine-grained predictability of its population dynamics over space and time.

ecology↗