Biology subjects

Hooten, M.

Publications and source records attributed to Hooten, M..

7 recordsLinked to original sources

bioRxiv2026

Primary productivity declines when species composition and climate are mismatched

Climate change drives shifts in species composition, but turnover in many communities lags behind the current pace of change. Anticipating the impact of the resulting community-climate disequilibria on ecosystem functioning is critical. Present-day communities may already be out of equilibrium with climate, providing an opportunity to estimate the effects of disequilibrium before they become more widespread. We analyzed plant community composition and function data from [~]60,000 rangeland monitoring sites across the western US to measure how community-climate disequilibrium contributes to spatial and temporal variation in net primary productivity (NPP) - a key ecosystem function. We found that communities were already substantially out of equilibrium with climate and accounting for this disequilibrium helped explain patterns of NPP. Communities farthest from equilibrium were less productive than those that were closely matched with climate. Our findings suggest that future increases in community-climate disequilibrium may further impair ecosystem functioning.

ecology↗

bioRxiv2026

Co-limitation by stable, dynamic and directional habitat features shapes climate vulnerability in an alpine specialist

Alpine ecosystems are among the most climate sensitive on Earth, yet logistical challenges and detection biases often impede robust assessment of alpine dependent species. We investigated habitat associations and density patterns of the Sierra Nevada subspecies of the Gray-crowned Rosy-Finch (Leucosticte tephrocotis dawsoni), an alpine obligate and regional endemic, over five breeding seasons from 2018 to 2022 using hierarchical distance sampling and mark-recapture distance sampling to explicitly account for imperfect detection and spatial heterogeneity. Density estimates tracked annual snowpack variation, ranging from 4.77 individuals/km{superscript 2} in a low snow year to 12.08 individuals/km{superscript 2} in a high snow year. Abundance was highest near persistent snow patches that provide foraging habitat and near cliffs that provide nesting substrate, and declined sharply above approximately 10% woody cover, with densities approaching zero beyond approximately 25%, indicating a steep ecological threshold. In contrast, the proportion of surveyed blocks with detections remained relatively stable across years. Together, these patterns indicate a three timescale co-limitation framework in which breeding habitat is shaped by static features (cliffs), dynamic annual drivers (snowpack), and longer-term directional change (woody encroachment). By linking population density to climate sensitive habitat features, this study provides a high-resolution abundance-based baseline for long term monitoring and offers a framework for evaluating climate vulnerability in alpine and other resource-limited systems. Open Research StatementData necessary to replicate the analyses and results presented in this manuscript will be archived in the Dryad Digital Repository upon acceptance, with no embargo on the material. The R code associated with this manuscript is not novel. All analyses use publicly available packages and functions without modification, including mrds (v2.3.0), unmarked (v1.2.5), tidyverse (v2.0.0), MuMIn (v1.47.5), and standard ggplot2 visualization tools. All code is properly cited within the manuscript and publicly available through CRAN. Complete analysis scripts will be archived in Dryad alongside the data upon acceptance to facilitate full reproducibility of results.

ecology↗

bioRxiv2024

Framework for Martini-based Coarse-grained Model of Enzymes: Model Development and Experimental Validation

Recent experiments have shown that enzyme activity can preserved in harsh environments by complexing enzyme with polymer into a Protein-Polymer Hybrid (PPH). In a successful PPH, heteropolymer strands bind to the enzyme surface and restrain the folded protein without adversely affecting the binding and active sites. It is believed that hybridization is driven by non-covalent interactions at the enzyme surface including hydrophobicity and electrostatics. Molecular modeling of these interactions is not practical at the all-atom scale due to the long timescales and large particle counts needed to characterize binding. Protein structure at the scale of amino acid residues is parsimoniously represented by a coarse-grained model in which one particle represents several atoms, significantly reducing the cost of simulation. In this study we present two coarse-grained enzyme models - lipase and dehalogenase - prepared using a top-down modeling strategy. We simulate each enzyme in aqueous solution and calculate statistics of protein surface features and shape descriptors. The values from the coarse-grained data are compared with the same calculations performed on all-atom reference systems, revealing key similarities of surface chemistry at the two scales. Structural measures are calculated from the all-atom reference systems and compared with estimates from small-angle X-ray scattering (SAXS) experiments, with good agreement between the two. The described procedures of modeling and analysis comprise a framework for the development of coarse-grained models of protein surfaces with validation to experiment.

biophysics↗

bioRxiv2024

A mechanistic statistical approach to infer invasion characteristics of human-dispersed species with complex life cycle

The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine patterns of range expansion. This is partly because current analytical models fail to integrate the invaders life-history dynamics with heterogeneity in human-mediated dispersal patterns. And partly because classical statistical methods often fail to provide reliable estimates of model parameters due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel (Dreissena polymorpha) expansion in North America through the commercial shipping network. Our analysis underscores the importance of correcting spatial biases and leveraging priors to answer questions, such as where and when the zebra mussels were introduced and what life-history characteristics make these mollusks successful invaders.

ecology↗

bioRxiv2023

Bridging the gap between movement data and connectivity analysis using the time-explicit Step Selection Function (tSSF)

BackgroundUnderstanding how to connect habitat remnants to facilitate the movement of species is a critical task in an increasingly fragmented world impacted by human activities. The identification of dispersal routes and corridors through connectivity analysis requires measures of landscape resistance but there has been no consensus on how to calculate resistance from habitat characteristics, potentially leading to very different connectivity outcomes. MethodsWe propose a new model called the time-explicit step selection function (tSSF) that can be directly used for connectivity analysis in the context of the spatial absorbing Markov chain (SAMC) framework without requiring arbitrary transformations. The tSSF model combines a time model with a standard selection function and can provide complementary information regarding how animals use landscapes by separately assessing the drivers of time to traverse the landscape and the drivers of habitat selection. These models are illustrated using GPS-tracking data from giant anteaters (Myrmecophaga tridactyla) in the Pantanal wetlands of Brazil. ResultsThe time model revealed that the fastest movements tended to occur between 8 pm and 5 am, suggesting a crepuscular/nocturnal behavior. Giant anteaters moved faster over wetlands while moving much slower over forests and savannas, in comparison to grasslands. We found that wetlands were consistently avoided whereas forest and savannas tended to be selected. Importantly, this model revealed that selection for forest increased with temperature, suggesting that forests may act as important thermal shelters when temperatures are high. Finally, the tSSF results can be used to simulate movement and connectivity within a fragmented landscape, revealing that giant anteaters will often not use the shortest-distance path to the destination patch (because that would require traversing a wetland, an avoided habitat) and that approximately 90% of the individuals will have reached the destination patch after 49 days. ConclusionsThe approach proposed here can be used to gain a better understanding of how landscape features are perceived by individuals through the decomposition of movement patterns into a time and a habitat selection component. This approach can also help bridge the gap between movement-based models and connectivity analysis, enabling the generation of time-explicit results.

animal behavior and cognition↗

bioRxiv2018

Accounting for location uncertainty in azimuthal telemetry data improves ecological inference

Characterizing animal space use is critical to understand ecological relationships. Despite many decades of using radio-telemetry to track animals and make spatial inference, there are few statistical options to handle these unique data and no synthetic framework for modeling animal location uncertainty and accounting for it in ecological models. We describe a novel azimuthal telemetry model (ATM) to account for azimuthal uncertainty with covariates and propagate location uncertainty into ecological models. We evaluate the ATM with commonly used estimators in several study design scenarios using simulation. We also provide illustra-tive empirical examples, demonstrating the impact of ignoring location uncertainty within home range and resource selection analyses. We found the ATM to have good performance and the only model that has appropriate measures of coverage. Ignoring animal location un-certainty when estimating resource selection or home ranges can have pernicious effects on ecological inference. We demonstrate that home range estimates can be overly confident and conservative when ignoring location uncertainty and resource selection coefficients can lead to incorrect inference and over confidence in the magnitude of selection. Our findings and model development have important implications for interpreting historical analyses using this type of data and the future design of radio-telemetry studies.

ecology↗

bioRxiv2017

Reflected stochastic differential equation models for constrained animal movement

Movement for many animal species is constrained in space by barriers such as rivers, shore-lines, or impassable cliffs. We develop an approach for modeling animal movement constrained in space by considering a class of constrained stochastic processes, reflected stochastic differential equations. Our approach generalizes existing methods for modeling unconstrained animal movement. We present methods for simulation and inference based on augmenting the constrained movement path with a latent unconstrained path and illustrate this augmentation with a simulation example and an analysis of telemetry data from a Steller sea lion (Eumatopias jubatus) in southeast Alaska.

animal behavior and cognition↗