Biology subjects

Wolfson, D.

Publications and source records attributed to Wolfson, D..

5 recordsLinked to original sources

bioRxiv2024

High variability of migration strategies in a re-established Trumpeter Swan population

BackgroundThe Interior Population (IP) of trumpeter swans (Cygnus buccinator), formerly extirpated by market hunting, was re-established in eastern North America by releasing individuals from both migratory and non-migratory populations. Their current annual movement patterns are largely unknown. Our goal was to describe their seasonal movements and quantify the proportion of the IP that is migratory, the extent and phenology of seasonal movements, and associations between movement patterns and breeding status and breeding location. MethodsWe deployed 113 GPS-GSM transmitters on IP trumpeter swans in six U.S. states and one Canadian province across the current IP breeding range. Using data from 252 swan-years, we estimated dates of migration events by segmenting the annual cycle using piecewise regression models fit to each yearly time-series of displacement from the breeding site. We fit a latent-state model to characterize population-level associations between breeding latitude and maximum extent of migration, and linear mixed models to quantify associations between individual characteristics (e.g., breeding status, sex) and migration phenology. ResultsAt the individual level, 59% of swans moved to distant non-breeding-period areas (long-distance migration, defined as moving >100 km from the breeding site), 16% exhibited regional migration (25-100 km from breeding site), 19% exhibited non-migratory but local movements (<25 km from breeding site), and 6% exhibited multiple migration strategies. Swans breeding at more-northern latitudes departed their territories earlier in autumn and returned later in the spring than those breeding at more southern latitudes. Breeding swans departed later in the autumn than non-breeders, but breeding status did not have a strong association with arrival in the spring. ConclusionIP trumpeter swans are partial migrants, with a continuum of strategies each year, from local movements to long-distance migration. Much of the variability in movement patterns was related to factors tied to natural history demands (e.g., breeding status) and response to environmental conditions (e.g., through associations with breeding latitude).

ecology↗

bioRxiv2023

Identifying signals of memory from observations of animal movements in Plato's cave

Incorporating memory (i.e., some notion of familiarity or experience with the landscape) into models of animal movement is a rising challenge in the field of movement ecology. The recent proliferation of new methods offers new opportunities to understand how memory influences movement. However, there are no clear guidelines for practitioners wishing to parameterize the effects of memory on moving animals. We review approaches for incorporating memory into Step-Selection Analyses (SSAs), a frequently used movement modeling framework. Memory-informed SSAs can be constructed by including spatial-temporal covariates (or maps) that define some aspect of familiarity (e.g., whether, how often, or how long ago the animal visited different spatial locations) derived from long-term telemetry data. We demonstrate how various familiarity covariates can be included in SSAs using a series of coded examples in which we fit models to wildlife tracking data from a wide range of taxa. We discuss how these different approaches can be used to address questions related to whether and how animals use information from past experiences to inform their future movements. We also highlight challenges and decisions that the user must make when applying these methods to their tracking data. By reviewing different approaches and providing code templates for their implementation, we hope to inspire practitioners to investigate further the importance of memory in animal movements using wildlife tracking data.

ecology↗

bioRxiv2023

Modeling individual variability in habitat selection and movement using integrated step-selection analyses

1. Integrated step-selection analyses (ISSAs) are frequently used to study habitat selection using animal movement data. Methods for incorporating random effects in ISSAs have been developed, making it possible to quantify variability among animals in their space-use patterns. Although it is possible to model variability in both habitat selection and movement parameters, applications to date have focused on the former despite the widely acknowledged and important role that movement plays in determining ecological processes from the individual to ecosystem level. One potential explanation for this omission is the absence of readily-available software or examples demonstrating methods for estimating movement parameters in ISSAs with random effects. 2. We demonstrated methods for characterizing among-individual variability in both movement and habitat-selection parameters using a simulated data set and by fitting two models to an acoustic telemetry data set containing locations of 35 red snapper (Lutjanus campechanus). Movement kernels were assumed to depend on either the type of benthic reef habitat in which the fish was located (model 1) or the distance between the fishs current location and nearest edge habitat (model 2). In both models, we also quantified habitat selection for different benthic habitat classes and distance to edge habitat, and we allowed for individual variability in movement and habitat-selection parameters using random effects. 3. The simulation example highlights the benefits of a mixed effects specification, namely we can increase precision when estimating individual-specific movement parameters by borrowing information across like individuals. In our applied example, we found substantial among-individual variability in both habitat selection and movement parameters. Nonetheless, most red snapper selected for hardbottom habitat and for locations nearer to edge habitat. They also moved less when in hardbottom habitat. Turn angles were frequently near {+/-}{pi}, but were more dispersed when fish were far away from edge habitat. 4. We provide code templates and functions for quantifying variability in movement and habitat-selection parameters when implementing ISSAs with random effects. In doing so, we hope to encourage ecologists conducting ISSAs to take full advantage of their ability to model among-individual variability in both habitat-selection and movement patterns.

ecology↗

bioRxiv2022

Inhibition of Tgfβ signaling enables durable ventricular pacing by TBX18 gene transfer

Implantable cardiac pacemaker devices are generally effective for patients with symptomatic bradyarrhythmia. However, device-dependent cardiac pacing is far from ideal and often inadequate, particularly for pediatric patients who need to go through invasive revision of the indwelling hardware. Biological pacemakers have been proposed as device-free alternatives to the current treatment, but sustained, unwavering biological pacing beyond days after the biologic delivery has not been demonstrated. We have previously demonstrated that re-expression of an embryonic transcription factor, TBX18, could reprogram ventricular cardiomyocytes into induced pacemaker myocytes (iPMs). Here, we report that exogenous expression of TBX18 per se leads to severe fibrosis in situ, impairing the iPMs ability to pace together. Acute fibrosis is accompanied with proliferation and activation of cardiac fibroblasts via Tgf{beta}-Smad2/3 pathway. Small molecule inhibition of Tgf{beta} signaling mitigated the interstitial remodeling, independent from TBX18-induced iPM reprogramming at the single-cell level. Direct and focal gene transfer of TBX18 into the left ventricular myocardium created ventricular pacing in a rat model of chronic atrioventricular block, but such activity began to wane in a week. In contrast, a combination therapy consisting of TBX18 gene transfer and Tgf{beta} inhibition enabled sustained biological pacing beyond the four-week study period. Our data demonstrate that inhibition of Tgf{beta} signaling suffices to achieve durable cardiac pacing by TBX18-induced biological pacemakers.

cell biology↗

bioRxiv2021

Using Piecewise Regression to Identify Biological Phenomena in Biotelemetry Datasets

Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animals location, movement characteristics, external environmental conditions, and internal physiological status. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval. We provide five example applications highlighting a variety of taxonomic species, data streams, timescales, and biological phenomena. These examples include a short-term behavioral response (flee and return) by a trumpeter swan (Cygnus buccinator) immediately following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose (Alces alces); a physiological response (spike in heart-rate) in a black bear (Ursus americanus) to a stressful stimulus (presence of a drone); a mortality event of a trumpeter swan signaled by changes in collar temperature and Overall Dynamic Body Acceleration; and an unsupervised method for identifying the onset, return, duration, and staging use of sandhill crane (Antigone canadensis) migration. We implement analyses using the mcp package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criterion and cross-validation measures. This approach uses simple modeling approaches that are accessible to a wide audience and is a straightforward means of assessing a variety of biologically relevant changes in animal behavior.

ecology↗