DOI: 10.5061/dryad.x95x69prm
title: Data from: Early detection of human impacts using acoustic monitoring: an example with forest elephants

description: The impacts of human activities and climate change on animal populations
 often take considerable time before they are reflected in typical measures
 of population health such as population size, demography, and landscape
 use. Earlier detection of such impacts could enhance the effectiveness of
 conservation strategies, particularly for species with slow population
 growth. Passive acoustic monitoring is increasingly used to estimate
 occupancy and population size, but this tool can also monitor subtle
 shifts in behavior that might be early indicators of changing
 impacts. Here we use data from an acoustic grid, monitoring 1250
 km2 of forest in the northern Republic of Congo, to study how forest
 elephants (Loxodonta cyclotis) assess the risk of poaching across a
 landscape that includes a national park as well as active and inactive
 logging concessions. By quantifying emerging patterns of behavior at the
 population level, arising from individual-based decisions, we gain an
 understanding of how elephants perceive their landscape along an axis of
 human disturbance. Forest elephants in relatively undisturbed
 forests are active nearly equally day and night. However, they become more
 nocturnal when exposed to a perceived risk such as poaching. We assessed
 elephant perception of risk by monitoring changes in the likelihood of
 nocturnal activity relative to differing levels of human activity. We show
 that logging is perceived to be a risk on short-time and small spatial
 scales but with little effect on animal density. However, risk avoidance
 persisted in areas with relatively easy access to poachers and in more
 open habitats where poaching has historically been concentrated. Increased
 nocturnal activity is a common response in many animals to human intrusion
 on the landscape. Provided a species is acoustically active, passive
 acoustic monitoring can measure changes in human impact at the early
 stages of such change, informing management priorities.
description: Core data are 24-hour continuous sound files acquired on 50
 digital sound recorders deployed in the northern Republic of Congo. Sound
 files are processed with a detection algorithm running in MatLab, which
 tags putative elephant rumble vocalizations. Output files from the
 detection process are reviewed to remove false positive detections. Data
 tables were compiled from the edited detector output files, which include
 the location, date, and time of each verified vocalization. Ecological
 metadata (season, habitat, study stratum, etc.) were then added to these
 files to construct analysis dataset tables.
description: # Data from: Early detection of human impacts using acoustic monitoring:
 an example with forest elephants This README file was generated on
 2024-07-09 by Peter H. Wrege ## GENERAL INFORMATION **Author Information**
 * Principal Investigator Contact Information * name: Peter H. Wrege *
 Institution: Cornell University * Email:
 [p.wrege@cornell.edu](mailto:p.wrege@cornell.edu) **Date of data
 collection:** 15Jan2017-27April2021 **Geographic location of data
 collection:** Nouabale-Ndoki National Park, Republic of Congo ##
 SHARING/ACCESS INFORMATION 1. **Licenses/restrictions placed on the
 data:** CC0 1.0 Universal (CC0 1.0) Public Domain 2. **Publications that
 cite or use the data:** Peter H. Wrege, Frelcia Bien-Dorvillon Bambi,
 Phial Jackel Ferdy Malonga, Onesi Jared Samba, Terry Brncic (2024). Early
 detection of human impacts using acoustic monitoring: an example with
 forest elephants. PLOS ONE 3. **Data is also available at:** The Registry
 of Open Data on Amazon Web Services “Sounds of Central African
 Landscapes”. This registry holds daily (~24hr) sound files recorded during
 the study, from each of the 50 monitoring sites. These sound files are
 'soundscapes', recording all ambient sounds, from which forest
 elephant 'rumble' vocalizations were tagged using detection
 algorithms or by hand-browsing. 4. **Recommended citation for this
 dataset:** Wrege et al. (2024). Data from: Early detection of human
 impacts using acoustic monitoring: an example with forest elephants. Dryad
 Digital Repository.
 [https://doi:10.5061/dryad.x95x69prm](https://doi:10.5061/dryad.x95x69prm)
 ## DATA & FILE OVERVIEW 1\. File List: * (A) allEleObs_dep1_10.txt *
 (B) dielPctWeek_dep1_10.txt * (C) dielPctWeek_kabo_dep1_10.txt * (D)
 gunShots_dep1_10.txt 2\. Relationship between files: File (A) is a
 comprehensive listing of all acoustic signals (aggregated per hour),
 accepted as a 'rumble' vocalization. Files (B) & (C) are
 aggregations, by week, of file (A), with additional environmental and
 location variables added for statistical analysis. File (D) is independent
 of other files. 3\. Data tables are provided as tab-delimited text files.
 ### DATA-SPECIFIC INFORMATION COMMON TO MOST FILES 1\. Variable List: *
 site (txt) - one of 50 recorder locations * year (num) - year * month
 (num) - month * day (num) - day * week (num) - week of the year * fiscalYr
 (num) - study year, beginning 15Dec2017 * stratum (txt) - one of three
 study strata. values: * PNNN = national park * KaboE = active logging *
 Bonio = inactive logging * season (txt) - &lt;60mm rain/month = dry *
 satHab3 (txt) - based on satellite image analysis: mixed forest (Fmixed),
 gilbertiodendron forest (Fmono), open * totDay (num) - number of calls
 recorded 0600-17:59 * totNite (num) - number of calls recorded 00:00-05:59
 plus 18:00-23:59 * totCalls (num) - total calls in 24hr recording day *
 contDensity (num) - total calls in 24hr recording day (for use as
 continuous predictor) ### DATA-SPECIFIC INFORMATION FOR:
 allEleObs\_dep1\_10.txt 1\. Number of variables: 10 2\. Number of
 cases/rows: 980691 3\. Unique Variable List: * diel (txt) -
 'day' = 06:00-17:59 hrs, 'nite' = 00:00-05:59,
 18:00-11:59 hrs (i.e., other than day)\ * count (num) - number of
 individual rumbles in the given hour * useCode (txt) - code to restrict
 the use of observation in some types of date or time-sensitive data
 aggregates due to imprecise date information for a given sound file.
 Values: * n = do not use * all = all analyses * addZeroHr = some daily
 files that started 1hr later on a given date than desired. We examined the
 potential for bias because of this missing hour but decided no correction
 was necessary because the frequency of rumble calls was so sparse. *
 weekOK = sound files with uncertainty about the exact date, because of
 temporary malfunction of recorders, but accurate enough for aggregation on
 the week level (most analyses). 4\. Missing data codes: na = hour of call
 unknown (clock error), but date OK ### DATA-SPECIFIC INFORMATION FOR:
 dielPctWeek\_dep1\_10.txt 1\. Number of Variables: 14 2\. Number of
 cases/rows: 3422 3\. Unique Variable List: * contMonth (num) - the
 sequential month of the study, beginning with 12 = December 2017 (for
 plotting) * numInStrat (num) - number of recording sites within a stratum
 4\. Missing data codes: none ### DATA-SPECIFIC INFORMATION FOR:
 dielPctWeek\_kabo\_dep1\_10.txt 1\. Number of Variables: 13 2\. Number of
 cases/rows: 610 3\. Unique Variable List: * exposHist5 (txt) - code for
 each recording site indicating the number of years since active logging
 within that sector of the logging concession. Values: * active = logging
 activity during that fiscal year * done1-done6 = logging ended 1-6 years
 previous * preExp = logging began in the following fiscal year 4\. Missing
 data codes: none ### DATA-SPECIFIC INFORMATION FOR: gunShots\_dep1\_10.txt
 1\. Number of Variables: 8 2\. Number of cases/rows: 86 3\. Unique
 Variable List: * eventID (num) - unique identifier for each gunshot event,
 including all gunshots within 30 min of one another * sumShots (num) -
 number of individual discharges in the event 4\. Missing data codes: none