The moon’s influence on the activity of tropical forest mammals
Data files
Aug 23, 2024 version files 1.38 MB
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functions.R
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InputData1.RData
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InputData2.RData
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InputData3.RData
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README.md
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vignette_LunarDielAnalysis.html
Abstract
Changes in lunar illumination alter the balance of risks and opportunities for animals, influencing activity patterns and species interactions. We examine if and how terrestrial mammals respond to the lunar cycle in some of the darkest places: the floors of tropical forests. We analyzed long-term camera trapping data on 86 mammal species from 17 protected forests on three continents. Conservative categorization of activity during the night revealed pronounced avoidance of moonlight (lunar phobia) in 12 species, compared with pronounced attraction to moonlight (lunar philia) in only 3 species. However, half of all species in our study responded to lunar phases, either changing how nocturnal they were, altering their overall level of activity, or both. Avoidance of full moon was more common, exhibited by 30% of all species compared with 20% of species that exhibited attraction. Nocturnal species, especially rodents, were over-represented among species that avoided the full moon. Artiodactyla was more prominent among species attracted to the full moon. Our findings indicate that lunar phases influence animal behavior even beneath the forest canopy. Such impacts may be exacerbated in degraded and fragmented forests. Our study offers a baseline representing relatively intact and well-protected contexts together with an intuitive approach for detecting activity shifts in response to environmental change.
README: The moon’s influence on the activity of tropical forest mammals
https://doi.org/10.5061/dryad.kkwh70sbz
Description of the data and file structure
Data and code accompanying the article "The moon’s influence on the activity of tropical forest mammals" (Bischof et al. 2024, DOI: 10.1098/rspb.2024.0683). The data and code are also available at https://github.com/richbi/TropicalMoon.
The file vignette_LunarDielAnalysis.html provides step-by-step explanations and code for performing the analyses described in the article. The following additional files are provided with this submission and are required to run the code in the vignette:
functions.R
InputData1.RData
InputData2.RData
InputData3.RData
The data have been processed and are set up as inputs for 3 Bayesian multinomial models described in the article. Each InputData file (in native RData format) contains data objects (lists) required by the NIMBLE models run in the study. These objects are a data object (nimData), a constants object (nimConstants), an initial values object (nimInits), and a species information object (species.info).
The core elements in the nimData object are counts of observations (15-minute intervals with at least one detection of a given species at a unique camera trap). These are aggregated by protected area and species. For example, nimdata1$y.diel contains the counts of observations during daylight, twilight, and night (in separate columns). Similarly, nimdata1$y.lunar contains the counts of observations during a full moon, new moon, and transitional lunar phases.
The nimConstants objects contain values of constants (e.g., covariate values, dimensions, etc.), organized as list elements. These objects also contain information about offset (a measure of relative survey effort, see Methods description in the associated article for a description) during each diel and lunar period. For example, nimConstants1$offset.diel is a matrix of values giving the offset during day, night, and twilight (columns); nimConstants1$offset.lunar gives the offset during a full moon, new moon, and transitional lunar phases. In addition, the nimConstants objects contain vectors of the total number of observations of a species during the entire day (daylight and night, e.g., nimConstants1$N.diel) and only during the night (e.g., nimConstants1$N.lunar). For analysis 3, counts of observations (nimdata3$y.diel and nimdata3$y.lunar), counts of available time slots (nimConstants3$offset.diel and nimConstants3$offset.lunar), and the total number of observations (nimConstants3$N.diel and nimConstants3$N.lunar) are further segregated into the diel and lunar periods (consisting of multiple days) they are associated with (see Methods description in the associated article for more information).
The nimInits objects are empty: the models randomly sample initial values from the prior distributions.
The species.info objects include a data frame with taxonomic information for each species, aligned (rows) with the observation data in nimData objects associated with the corresponding analysis (1, 2, and 3). The model definitions for analyses 1, 2, and 3 are provided in the vignette (vignette_LunarDielAnalysis.html ) and can be used to better understand the configuration and use of the information in the InputData files.
Sharing/Access information
The original camera trapping data can be obtained from the following source: https://app.wildlifeinsights.org/explore
Code/Software
The file vignette_LunarDielAnalysis.html provides step-by-step explanations and code for performing the analyses described in the article.
Methods
We derived observations of mammal activity in protected tropical forests from camera trap data collected as part of the Tropical Ecology Assessment and Monitoring (TEAM) Network (1). Following a common protocol (2), cameras were deployed between 2008 and 2017 throughout 17 protected areas in Indomalaya, the Neotropics, and the Afrotropics. The number of years of deployment varied between protected areas (2 to 10 years; mean = 6.8 years), as did the number of locations sampled (60-90 camera trap locations; total: 1062). Spatial configuration and deployment were standardized, with cameras configured in either a 1x1km or 2x2km regular grid, at a height of approximately 30-50 centimeters off the ground. On average, cameras were active for 33.2 days (SD=7.5). However, cameras were rotated sequentially until all sites were sampled within the wider sampling season. As a result, multiple lunar cycles are recorded at each protected area within a sampling season. For additional information about camera trapping protocols and species identification, see Rovero and Ahumada (1). In this analysis, we included more than 2.1M photographs of 86 mammal species, i.e., species with ≥ 25 detection events (number of 15-minute intervals with at least one detection at a camera trap) during the night (total across all protected areas; Electronic Supplementary Materials Tables S1-S3). Observations associated with species identifications that were flagged as uncertain were excluded from the analysis. Due to concerns about identification, species in the genus Tragulus were considered jointly (Tragulus sp.). Observations were aggregated and prepared as input for Bayesian models as described in the README file.
References:
1. Rovero F, Ahumada J. The Tropical Ecology, Assessment and Monitoring (TEAM) Network: An early warning system for tropical rain forests. Sci Total Environ [Internet]. 2017;574:914–23. Available from: https://www.sciencedirect.com/science/article/pii/S0048969716320769
2. Jansen PA, Ahumada J, Fegraus E, O’Brien T. TEAM: a standardised camera trap survey to monitor terrestrial vertebrate communities in tropical forests. Camera Trapp Wildl Res Manag. 2014;263–70.