About

Noa Pinter-Wollman is a professor in the Department of Ecology and Evolutionary Biology at UCLA. Her research focuses on understanding how complex biological systems emerge from interactions among multiple agents working together towards collective goals. She examines the role of group composition and interaction networks in shaping collective phenotypes, utilizing a combination of field and laboratory studies, computer simulations, theoretical work, image analysis, and social network analysis. Her work aims to elucidate how local signals and intricate interaction networks influence the success of groups, such as animal colonies and social networks, contributing to the broader understanding of collective behavior and evolution.

Selected publications

• Relationship between spatial and social phenotypes in an avian scavenger

  2026-02-04

  articleOpen accessSenior author

  Animals may interact incidentally, by sharing space, or intentionally, by seeking out interactions. Understanding which elements of social interactions can be explained by spatial behavior and which cannot may uncover drivers of individual fitness and population functioning. In an avian scavenger, we tested how space use covaried with social position while flying, feeding, and roosting. We also identified the deviation of observed social centrality from chance and examined how this non-incidental social centrality covaried with space use. In flight, space use was positively associated only with observed social centrality, suggesting that interactions while flying emerge primarily from co-movement. In contrast, space use covaried with non-incidental social centrality while roosting, suggesting a stronger importance of social preferences when interacting at roost sites. Our work demonstrates that the role of animal movements in shaping social interactions differs across social situations. Such an understanding of the spatial-social interface is essential for predicting population responses to environmental changes and conserving threatened species.

  PublisherOA PDFDOI

• Social network analysis

  Elsevier eBooks · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Social organization and physical environment shape the microbiome of harvester ants

  Animal Microbiome · 2025-03-19 · 1 citations

  articleOpen accessSenior author

  All animals harbor microbiomes, which are obtained from the surrounding environment and are impacted by host behavior and life stage. To determine how two non-mutually exclusive drivers - physical environment and social organization - affect an organism's microbiome, we examined the bacterial communities within and around nests of harvester ants (Veromessor andrei). We collected soil and nest content samples from five different ant nests. We used 16S rRNA gene sequencing and calculated alpha and beta diversity to compare bacterial diversity and community composition across samples. To test the hypotheses that physical environment and/or social organization impact ant colonies' community of microbes we compared our samples across (i) sample types (ants, brood, seeds and reproductives (winged alates), and soil), (ii) soil inside and outside the nest, and (iii) soil from different chamber types. Interestingly, we found that both the environment and social organization impact the bacterial communities of the microbiome of V. andrei colonies. Soil from the five nests differed from one another in a way that mapped onto their geographical distance. Furthermore, soil from inside the nests resembled the surrounding soil, supporting the physical environment hypothesis. However, the bacterial communities associated with the contents within the nest chambers, i.e., ants, brood, seeds, and reproductives, differed from one another and from the surrounding soil, supporting the social organization hypotheses. This study highlights the importance of considering environmental and social factors in understanding microbiome dynamics.

  PublisherOA PDFDOI

• Uncovering multiple influences on space use by deer mice using large ecological networks

  Oecologia · 2025-06-03

  articleOpen access

  Space use by animals is affected by multiple factors; previous researchers have examined the effects of influences, such as sex, body condition, and population density on home range area. However, evaluating the simultaneous influences of multiple factors on animal space use has been relatively intractable due to sample size limitations. We capitalize on National Ecological Observatory Network (NEON) data to ask what factors determine space use by deer mice (genus Peromyscus). We examined data from 10 years of repeated captures of individually-identified mice at 36 sites across North America. We confirmed previous findings that males have larger home ranges than females and that home range area decreases with increasing animal density. In addition, our large sample size (N = 2,420 individuals) enabled us to examine the interacting influences of these, and other, phenotypic and extrinsic factors using a robust statistical framework. We found that the relationship between body condition and home range area differs between male and female mice, and that habitat type, latitude, and animal density all interact to influence space use. We conclude that data from large ecological networks can be used to examine important behavioral questions that have long eluded investigators.

  PublisherOA PDFDOI

• Nest-founding paper wasps aggregate in “social leks” and decrease aggression during partner selection

  Current Zoology · 2025-07-19 · 2 citations

  articleOpen access

  Abstract Many animals live in cooperative social groups. The success of social animals is thought to be influenced by both an animal's own characteristics and by the characteristics of its social partners. As a result, some social animals may devote substantial time and energy to assessing potential partners. Here, we study the process of social group formation in Polistes fuscatus paper wasps. Prior to founding nests, wasps engage in extended social partner sampling where they synchronously “shop” for co-foundresses. We quantify wasp behavior during partner sampling using day censuses and recording aggregations. We found that wasps preferentially aggregated at specific locations, without resources or nests, consistent with the formation of a “social lek.” In aggregations, wasps engaged in a range of aggressive interactions. At the beginning of the shopping period, wasps engaged in more intensely aggressive behavior and they observe conspecifics fight. Later in the shopping season, wasps engaged in less aggressive behavior. Overall, P. fuscatus devote substantial time and energy to a period of partner selection prior to nest foundation that is suggestive of lekking.

  PublisherOA PDFDOI

• Social interactions in an avian scavenger are driven by social preference and movement, depending on the social situation

  2025-05-05

  preprintOpen accessSenior author

  Animals may interact incidentally, by sharing space, or intentionally (by seeking out interactions). Untangling social and non-social interaction drivers is essential for understanding the spatial-social interface, which influences individual fitness and population functioning. In an avian scavenger, we tested how space use covaried with social position while flying, feeding, and roosting. We identified the deviation of observed sociality from chance and examined how this intentional sociality covaried with space use. Space use was positively associated only with observed social position in flight, suggesting these interactions emerge primarily from spatial overlap. In contrast, space use covaried with intentional roost sociality, indicating greater importance of social preferences at roosts. This work demonstrates how distinguishing intentional and incidental encounters reveals environmental and social drivers of behavior, and how those differ across social contexts. Such mechanistic understanding of the spatial-social interface is essential for predicting population responses to environmental changes and conserving threatened species.

  PublisherOA PDFDOI

• Is cooperation relevant to ant invasiveness? Insights from cooperative food transport

  Biological Invasions · 2025-04-01 · 2 citations

  articleSenior author
  PublisherDOI

• Density-dependent network structuring within and across wild animal systems

  Nature Ecology & Evolution · 2025-09-04 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

• Using accelerometer‐based behavioural classification to enhance scavenger conservation

  Journal of Applied Ecology · 2025-10-19

  articleOpen access

  Abstract Human activities are endangering animal species globally, and implementing effective conservation strategies requires understanding animal behaviour and ecology. Advancements in GPS tracking technology, accelerometry and machine learning algorithms are allowing the in situ study of animal movement and behaviour remotely. However, the challenge of building supervised machine learning algorithms and collecting the large datasets required to train them is hampering the widespread use of these tools. Additionally, the reliability of these models in classifying unobserved behaviours is rarely validated, resulting in possible classification errors. We built a supervised accelerometer‐based behavioural classification model for griffon vultures ( Gyps fulvus ). Similarly to most other avian scavenger populations worldwide, griffons are critically endangered in Israel and neighbouring countries, mostly due to feeding on poisoned carcasses. Thus, identifying this scavenger's feeding behaviour and foraging areas is crucial for their conservation. We trained a Random Forest model on acceleration data of 14 captive and 17 free‐roaming griffons. We classified 5783 behavioural observations into 6 classes: feeding, lying, standing, other ground behaviours, flapping and soaring flight. The model performed well (0.96 accuracy, 0.89 precision and 0.82 recall) and, importantly, feeding behaviours were accurately classified (0.87 precision, 0.92 recall). We calculated an observation‐specific confidence score and demonstrated its effectiveness in identifying true‐ and false‐positive classifications, in both captive and free‐roaming individuals. Finally, we used our model to reliably identify feeding hotspots, where vultures can be at higher risk of poisoning. Synthesis and applications. We provide a tool to help identify vulture feeding hotspots, supporting carcass management efforts to prevent poisoning. Integrated with near real‐time tracking, our model can support global efforts to combat scavenger poisoning. The training dataset, model and codes are provided in a user‐friendly platform, along with a conceptual framework, to encourage use by ecologists and conservation practitioners.

  PublisherOA PDFDOI

• Using accelerometer-based behavioral classification to enhance scavenger conservation

  2025-07-08 · 2 citations

  preprintOpen access

  Human activities are endangering animal species globally and implementing effective conservation strategies requires understanding animal behavior and ecology. Technological advancements in GPS tracking technology, accelerometry, and machine learning algorithms are now making it possible to study animal movement and behavior remotely. However, due to the challenge of building supervised machine learning algorithms and collecting the large datasets required to train them, the use of these algorithms is still not common practice. Additionally, after building the algorithms, their reliability in classifying unobserved behaviors is rarely validated, resulting in possible classification errors. Here, we built a supervised accelerometer-based behavioral classification model for griffon vultures (Gyps fulvus). This scavenger is critically endangered in Israel and neighboring countries, mostly due to mass poisonings at carcass feeding events. In fact, poisoning is one of the main threats to scavenger populations worldwide. Thus, identifying this scavenger’s feeding behavior and foraging areas is crucial for their conservation. We trained a random forest model on acceleration data of 14 captive and 17 free-roaming griffons. We collected 5783 behavioral observations grouped into 6 distinct classes: feeding, lying, standing, other ground behaviors, flapping and soaring flight. The classification model performed well (0.96 accuracy, 0.89 precision and 0.82 recall) and, importantly, feeding behaviors were accurately classified (0.87 precision, 0.92 recall). Importantly, we calculated an observation-specific confidence score and demonstrated its effectiveness (for all but one of the behavioral classes) in identifying true- and false-positive classifications, in both captive and free-roaming individuals. Further, our classification model enables us to identify vulture feeding hotspots, potentially aiding the implementation of conservation actions related to carcass management. Finally, our training dataset and model are provided in a user-friendly platform and accompanied by a conceptual framework, to encourage use by ecologists and conservation practitioners overcoming the data-analysis challenges involved in this powerful approach.

  PublisherOA PDFDOI

Labs

• Pinter-Wollman LabPI