Biology subjects

Fuller, A. K.

Publications and source records attributed to Fuller, A. K..

5 recordsLinked to original sources

bioRxiv2026

A Rare T-Cell Factor 4 Lineage-negative Epithelial Stem Cell Supports Wound Repair and APC-deletion-induced Colon Tumorigenesis

To maintain barrier homeostasis, the colonic and intestinal epithelial lining is continually renewed by rapidly proliferating epithelial crypt base columnar (CBC) stem cells that reside at the base of crypts. Using mouse lineage tracing, immunohistochemistry, and single-cell sequencing, we have identified a rare, non-CBC, T-cell factor 4 lineage-negative (Tcf4 Lin-) stem cell population that gives rise to secretory and absorptive precursors. Following endoscopic biopsy-induced injury, Tcf4 Lin- stem cells are recruited to the wound bed and to the site of expanding crypts and function in barrier restoration and wound repair. We show that in a Tcf4-haploinsufficient background, the Tcf4 Lin-, but not the Tcf4 Lin+, cell population represents the cell of origin for colon tumors driven by deletion of Apc. Our results provide a foundation for understanding Apc-allele-specific differences during colon tumorigenesis and identify a new stem-cell population that may prove valuable in the treatment of diseases caused by intestinal barrier homeostasis defects.

cell biology↗

bioRxiv2020

Spatial Proximity Moderates Genotype Uncertainty in Genetic Tagging Studies

Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower quality samples cannot be used, and they do not allow for errors in individual identification. We introduce the Genotype Spatial Partial Identity Model (SPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers (Pekania pennanti) in which 37% of samples were originally discarded because of uncertainty in individual identity. The Genotype SPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected 3 errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63% and 42%, respectively, using 3 PCRs per genetic sample. Further, the simulations showed that the Genotype SPIM model parameters are identifiable with only one PCR per sample, and that accuracy and precision are relatively insensitive to the number of PCRs for high quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high quality samples, but when using the Genotype SPIM, genotyping protocols could be more efficient by devoting more resources to low quality samples. SignificanceWe present a new statistical framework for the estimation of animal demographic parameters, such as abundance, density, and growth rate, from noninvasive genetic samples (e.g., hair, scat). By integrating a genetic classification model with a spatial population model, we show that accounting for spatial proximity of samples reduces genotype uncertainty and improves parameter estimation. Our method produces a fundamentally different approach to genetic capture-recapture by sharing information between the normally disjunct steps of assigning individual identities to genetic samples and modeling population processes. Further, it leads to more efficient protocols for processing genetic samples, which can lower project costs and expand opportunities for applying noninvasive genetics to conservation and management problems.

ecology↗

bioRxiv2018

Adaptive Sampling for Spatial Capture-Recapture: An efficient sampling scheme for rare or patchily distributed species

Rare species present challenges to data collection, particularly when the species is spatially clustered over large areas, such that the encounter frequency of the organism is low. Sampling where the organism is absent consumes resources, and offers relatively low-quality information which are often difficult to model using standard statistical methods. In adaptive sampling, a probabilistic sampling method is employed first, and additional effort is allocated in the vicinity of sites where some measured index variable - assumed to be proportional to local population size - exceeds an a priori threshold. We applied this principle to the spatial capture-recapture (SCR) analytical framework in a Bayesian hierarchical model incorporating capture-recapture (CR) and index information from unsampled sites to estimate density. We assessed the adaptively sampled SCR model (AS-SCR) by simulating CR data and compared performance with a standard SCR baseline (F-SCR), adaptive SCR discarding index information (AS-SCR-), and standard SCR applied at a simple random sample of sites. Under AS-SCR, we observed minimal bias and comparable variance with respect to parameter estimates provided by the standard F-SCR model and sampling implementation, but with substantially reduced effort and significant cost saving potential. This represents the first application of adaptive sampling to SCR.

ecology↗

bioRxiv2018

Citizen science data enhance spatio-temporal extent and resolution of animal population studies

Informed management and conservation decisions for animal populations often require data at sufficient geographic, temporal, and demographic resolutions for precise and unbiased estimates of parameters including population size and demographic rates. Recently developed integrated population models estimate such parameters by unifying population presence-absence and demographic data, and we demonstrate how citizen science offers a cost-efficient mechanism to collect such data. We describe the early results of iSeeMammals, a citizen science project that collects opportunistic data on the black bear population in New York State by enlisting volunteers to collect data through observations, hikes, and trail cameras. In 10 months, iSeeMammals increased the spatio-temporal extent of data collection by approximately fourfold and reduced cost by 83% compared to systematic sampling. In combination with other datasets in integrated population model frameworks, large, spatiotemporally extensive datasets from citizen science projects like iSeeMammals can help improve inferences about population-level structure and dynamics.

ecology↗

bioRxiv2017

Unifying Population and Landscape Ecology with Spatial Capture-recapture

Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio-temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population structure. Spatial capture-recapture (SCR) represents an individual-based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g., camera trapping, non-invasive DNA sampling). We describe ways in which SCR methods stand to revolutionize the study of animal population ecology.

ecology↗