Biology subjects

Tabak, M.

Publications and source records attributed to Tabak, M..

4 recordsLinked to original sources

bioRxiv2026

WildObserve: Camera-trap image review software that learns species labels during annotation

Motion-activated camera traps are an effective tool for monitoring animals, but a single study can produce millions of images, which must be analyzed before they yield data. The burden of analyzing them has led many teams to automate the work with computer vision. These models lose accuracy on images unlike those they were trained on, however, and a general model may not use the categories a project needs. Adapting a model to a project usually requires at least one of the following: many labeled images per species, programming expertise, a graphics processing unit (GPU), or uploading images to a remote server. I describe WildObserve, a desktop application that adapts to each project without any of these requirements. Before a reviewer sees an image, MegaDetector finds animals, people, and vehicles and marks images that are likely empty, and SpeciesNet suggests a species for each animal. WildObserve keeps SpeciesNet's feature vector for each animal, so as the reviewer confirms or corrects labels, a project model (a logistic regression on those features) retrains in about a second on an ordinary laptop, learning the project's own species list and names. Images stay on the user's computer, model outputs and human decisions are kept separate, and exports record review and model provenance. On the Caltech Camera Traps dataset, WildObserve processed 8.1 images/s on an Apple M1 Pro laptop with Core ML (1.5 images/s on its CPU), species suggestions included. At a 0.5 threshold, it found animals in 97.1% of animal images but also put a box on 48.0% of images labeled empty. On 1,467 animal crops from 14 categories, SpeciesNet alone reached 0.919 balanced accuracy. The project model reached 0.886 with three labels per category and 0.937 with 25, and 0.931 on camera locations absent from its training labels; built on features from DINOv2, a general-purpose image model, it reached only 0.784 with 50. On the Oregon Critters dataset, released after SpeciesNet was trained, SpeciesNet reached 0.751 and the project model passed it with only ten labels per category (0.783). WildObserve gives a project species suggestions in its own categories, from a model it trains simply by reviewing, without writing code, needing a GPU, or sending images elsewhere.

ecology↗

bioRxiv2025

Improving a removal model for evaluating density changes in a widespread invasive species

Invasive species pose a significant threat to native ecosystems. Effective management of invasive species requires accurate abundance estimates through time, particularly at large spatial scales. Traditional methods of abundance estimation from management data, such as camera trap studies and harvest data, face challenges due to variations in sampling protocols and effort standardization. We propose an extension to a previously calibrated removal model by integrating into an Integrated Population Model (IPM) and incorporating multiple removal techniques. The model utilizes management and land cover data, both of which are readily available in many invasive species systems. We evaluate the model with a simulation study, then apply it to management data on wild pigs (Sus scrofa). Simulations showed that the model performs well across a wide range of densities and population trajectories. Density estimates were the most biased when effort and density were low and sharpshooting or fixed wing aircraft were used to conduct removals. We discuss the advantages and limitations of using removal models for invasive species management, how the model performs with respect to previous removal modeling efforts, how performance might be improved through changes to data management practices, and the models potential to inform decision-making processes for invasive species management. Data availabilityThe data for this manuscript is available from Dryad (http://datadryad.org/share/gFz3ClanB-8yPB5lQ1x-dl1JqI07CP3SX92P-9UyfaU). The code for this manuscript will is available from Zenodo (10.5281/zenodo.15784366).

ecology↗

bioRxiv2021

Transient population dynamics drive the spread of invasive wild pigs in North America

Invasion of nonindigenous species is considered one of the most urgent problems affecting native ecosystems and agricultural systems. Mechanistic models that account for short-term population dynamics can improve prediction because they incorporate differing demographic processes that link the environmental conditions of a spatial location explicitly with the invasion process. Yet short-term population dynamics are rarely accounted for in spatial models of invasive species spread. Accounting for transient population dynamics, we predict the population growth rate and establishment probability of wild pigs following introduction into any location in North America. We compared predicted population growth rate with observed geographic rates of spread and found significant relationships between the annual rate of spread and population growth rates. We used geospatial data on the distribution of mast producing tree species (a principle forage resource of wild pigs) and agricultural crops that can replace mast in their diets to predict population dynamics using transient population simulations. We simulated populations under different initial population sizes (i.e. number of introduced individuals, often termed propagule size) and for different amounts of time following introduction. By varying the initial population size and simulation time, we were able to identify areas in North America with high probability for establishment of wild pigs if introduced. Our findings can be used to inform surveillance and removal efforts to reduce the potential for establishment and spread of wild pigs.

ecology↗

bioRxiv2017

Plasmids, Viruses, And Other Circular Elements In Rat Gut

Circular DNA such as plasmids and some viruses is the major source of genetic variation in bacteria and thus has the same important evolutionary function as sexual reproduction in eukaryotic species: It allows dissemination of advantageous traits through bacterial populations. Here, we present the largest collection of novel complete extrachromosomal genetic elements to date, and compare the diversity, distribution, and content of circular sequences from 12 rat cecum samples from the pristine Falkland Islands and Danish hospital sewers, two environments with contrasting anthropogenic impact. Using a validated pipeline, we find 1,869 complete, circular, non-redundant sequences, of which only 114 are previously described. While sequences of similar size from the two environments share general features, the size distribution of the elements between environments differs significantly, with hospital sewer samples hosting larger circular elements than Falkland Island samples, a possible consequence of the massive anthropogenic influence in the hospital sewer environment. Several antibiotic resistance genes have been identified with a notably larger diversity in hospital sewer samples than in Falkland Islands samples in concordance with expectations. Our findings suggest that even though sequences of similar length carry similar traits, the mobilome of rat gut bacteria are affected by human activities in that sewer rats have larger elements and more diverse large elements than pristine island rats. More than 1000 novel and not classified small sequences was identified and hint the existence of a biological unit not previously described on a community level.\n\nList of figures O_LISampling sites and Rat gut anatomy\nC_LIO_LIRarefaction curves of circular elements\nC_LIO_LIElements shared between environments\nC_LIO_LISize distribution plots of rep, mob, stab, capsid carrying elements.\nC_LIO_LIGraphical representation of representative elements\nC_LIO_LIReplication genes content, diversity\nC_LIO_LIRep_2 plasmid replication gene phylogeny\nC_LIO_LITable Resfams findings\nC_LI\n\nList of appendices O_LIbasic statistics table\nC_LIO_LIPhylogeny of plasmid replication genes\nC_LIO_LIList of known plasmids and circular sequences found in 1869 elements\nC_LIO_LIAutomatic identification of identical circular elements with different breaking points\nC_LIO_LITest of separation of plasmids and viruses based on predicted, annotated genes\nC_LIO_LINo false positive circular sequences from MG1655 genomic sequencing.\nC_LI

microbiology↗