skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Thursday, October 10 until 2:00 AM ET on Friday, October 11 due to maintenance. We apologize for the inconvenience.


This content will become publicly available on December 1, 2024

Title: Extensive behavioral data contained within existing ecological datasets
Long-term ecological datasets contain vast behavioral data, enabling the quantification of among individual behavioral variation at unprecedented spatiotemporal scales. We detail how behaviors can be extracted and describe how such data can be used to test new hypotheses, inform population and community ecology, and address pressing conservation needs.  more » « less
Award ID(s):
2110031
NSF-PAR ID:
10505546
Author(s) / Creator(s):
;
Publisher / Repository:
CellPress
Date Published:
Journal Name:
Trends in Ecology & Evolution
Volume:
38
Issue:
12
ISSN:
0169-5347
Page Range / eLocation ID:
1129 to 1133
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Anthropogenic environmental change is altering the behavior of animals in ecosystems around the world. Although behavior typically occurs on much faster timescales than demography, it can nevertheless influence demographic processes. Here, we use detailed data on behavior and empirical estimates of demography from a coral reef ecosystem to develop a coupled behavioral–demographic ecosystem model. Analysis of the model reveals that behavior and demography feed back on one another to determine how the ecosystem responds to anthropogenic forcing. In particular, an empirically observed feedback between the density and foraging behavior of herbivorous fish leads to alternative stable ecosystem states of coral population persistence or collapse (and complete algal dominance). This feedback makes the ecosystem more prone to coral collapse under fishing pressure but also more prone to recovery as fishing is reduced. Moreover, because of the behavioral feedback, the response of the ecosystem to changes in fishing pressure depends not only on the magnitude of changes in fishing but also on the pace at which changes are imposed. For example, quickly increasing fishing to a given level can collapse an ecosystem that would persist under more gradual change. Our results reveal conditions under which the pace and not just the magnitude of external forcing can dictate the response of ecosystems to environmental change. More generally, our multiscale behavioral–demographic framework demonstrates how high-resolution behavioral data can be incorporated into ecological models to better understand how ecosystems will respond to perturbations.

     
    more » « less
  2. Normative messaging interventions have proven to be a cost-effective strategy for promoting pro-environmental behaviors. The effectiveness of normative messages is partially determined by how personally relevant the comparison groups are as well as the lag of feedback. Using readily available energy use data has created opportunities to generate highly personalized reference groups based on households’ behavioral patterns. Unfortunately, it is not well understood how data granularity (e.g., minute, hour) affects the performance of behavioral reference group categorization. This is important because different levels of data granularity can produce conflicting results in terms of group similarity and vary in computational time. Therefore, this research aims to evaluate the performance of clustering methods across different levels of temporal granularity of energy use data. A clustering analysis is conducted using one-year of energy use data from 3,000 households in Holland, Michigan. The clustering results show that behavioral reference groups become the most similar when representing households’ energy use behaviors at a six-hour interval. Computationally, less granular data (i.e., six and twelve hours) takes less time than highly granular data which increases exponentially with more households. Considering the enormous scale that normative messaging interventions need to be applied at, using less granular data (six-hour intervals) will permit interveners to maximize the effectiveness of highly personalized normative feedback messages while minimizing computation burdens. 
    more » « less
  3. Abstract

    Visual systems have evolved to discriminate between different wavelengths of light. The ability to perceive color, or specific light wavelengths, is important as color conveys crucial information about both biotic and abiotic features in the environment. Indeed, different wavelengths of light can drive distinct patterns of activity in the vertebrate brain, yet what remains incompletely understood is whether distinct wavelengths can invoke etiologically relevant behavioral changes. To address how specific wavelengths in the visible spectrum modulate behavioral performance, we use larval zebrafish and a stereotypic light-search behavior. Prior work has shown that the cessation of light triggers a transitional light-search behavior, which we use to interrogate wavelength-dependent behavioral modulation. Using 8 narrow spectrum light sources in the visible range, we demonstrate that all wavelengths induce motor parameters consistent with search behavior, yet the magnitude of search behavior is spectrum sensitive and the underlying motor parameters are modulated in distinct patterns across short, medium, and long wavelengths. However, our data also establishes that not all motor features of search are impacted by wavelength. To define how wavelength modulates search performance, we performed additional assays with alternative wavelengths, dual wavelengths, and variable intensity. Last, we also tested blind larvae to resolve which components of wavelength dependent behavioral changes potentially include signaling from non-retinal photoreception. These findings have important implications as organisms can be exposed to varying wavelengths in laboratory and natural settings and therefore impose unique behavioral outputs.

     
    more » « less
  4. Theunissen, Frédéric E. (Ed.)
    Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone. 
    more » « less
  5. One goal of cognitive science is to build theories of mental function that predict individual behavior. In this project we focus on predicting, for individual participants, which specific items in a list will be remembered at some point in the future. If you want to know if an individual will remember something, one commonsense approach is to give them a quiz or test such that a correct answer likely indicates later memory for an item. In this project we attempt to predict later memory without ex- plicit assessments by jointly modeling both neural and behavioral data in a computational cognitive model which captures the dynamics of memory acquisition and decay. In this paper, we lay out a novel hierarchical Bayesian approach for combining neural and behavioral data and present results showing how fMRI signals recorded during the study phase of a memory task can improve our ability to predict (in held-out data) which items will be remembered or forgotten 72 hours later. 
    more » « less