DOI: 10.5061/dryad.ns1rn8q14
title: Wildlife density estimation by distance sampling: A novel technique with movement compensation

description: Estimates of population density are fundamental to wildlife conservation
 and management. Distance sampling from line transects is a widely used
 sample count method and is most often analysed using Distance software.
 However, this method has limited capabilities with mobile populations
 (e.g., birds), which tend to encounter an observer more often than
 immobile ones. This paper presents a novel distance sampling method based
 on a different set of models and assumptions, named WildlifeDensity after
 its associated software. It is based on mechanistic modelling of visual
 detections of individuals or groups according to radial distance from the
 observer or perpendicular distance from the transect line. It also
 compensates for population–observer relative movement to avoid the
 detection overestimates associated with highly mobile populations. The
 models are introduced in detail and then tested in three ways: 1)
 WildlifeDensity is applied to several ‘benchmark’ populations of known
 density and no-to-low mobility, 2) the movement compensation model is
 tested on two highly mobile songbird populations, and 3) a fairly
 difficult case is analysed: a low-density, highly mobile bird population
 in a forest habitat. The results show that 1) using either radial or
 perpendicular distance data from surveys of immobile populations,
 WildlifeDensity provides similar estimates (and errors) to Distance, with
 radial WildlifeDensity analysis appearing to be slightly better for
 surveys of low-mobility populations (kangaroos), 2) the movement
 compensation model effectively removes the correlation between observer
 speed and detection numbers, and 3) WildlifeDensity provides acceptable
 estimates where conventional Distance analysis overestimates density due
 to high movement. In summary, WildlifeDensity extends the capabilities of
 distance sampling by 1) compensating for movement, 2) not requiring
 complete detectability on the transect line and 3) supporting the use of
 radial distances, which simplifies fieldwork and increases measurement
 accuracy.
description: Data were collected by line transect distance sampling of
 wildlife populations. Data were processed by analysis
 in the computer programs <em>WildlifeDensity</em> and
 <em>Distance</em>.
description: # Wildlife density estimation by distance sampling: A novel technique with
 movement compensation Dataset DOI:
 [10.5061/dryad.ns1rn8q14](10.5061/dryad.ns1rn8q14) ## Description of the
 data and file structure ### Files and variables This folder contains the
 data for the article: Wildlife Density Estimation by Distance Sampling: A
 Novel Technique with Movement Compensation \[Access this dataset on Dryad:
 [https://doi.org/10.5061/dryad.ns1rn8q14](https://doi.org/10.5061/dryad.ns1rn8q14)] Author details: David G. Morgan1, John R. Gibbens1, Ed. T. Conway(dec)., Graham Hepworth2, James Clough1 1School of Biosciences, 2School of Mathematics and Statistics, University of Melbourne, Parkville, Australia Correspondence: David G. Morgan. Email: [d.morgan@unimelb.edu.au](mailto:d.morgan@unimelb.edu.au) The data are organised with reference to the associated figures, tables and/or sections in the article, as described below. Files with the extensions .xls and .xlsx are for use in the Microsoft Excel spreadsheet program. --- Figs.1 a & b Folder: Fig1 Description: Detection data, Grey fantail, Otway Ranges National Park, Australia. All-year data collected under temperate weather conditions and pooled Fig.1a: Description: Histogram of grey fantail detection nos. vs radial distance intervals (m). File: Morgan_2024_Fig1a.xlsx. Fig.1b: Description: Histogram of grey fantail detection nos. vs perpendicular distance intervals (m). File: Morgan_2024_Fig1b.xlsx. --- Fig.5 Folder: Fig5 Description: Comparison graph of three variables with two y-axes for probability and a shared x-axis (horizontal distance). Surveyed population: Helmeted honeyeater (Lichenostomus melanops cassidix) in open riparian woodland, Yellingbo Nature Conservation Reserve, central Victoria, Australia. Sampling: Pooled data collected from repeated samples along fixed line transects, under relatively uniform temperate weather conditions, September 1995 – March 1996. Sampling protocol: Similar to those outlined for songbirds and other terrestrial vertebrates in the paper. Observer: Stephen Headey Files: 1. WildlifeDensity data file [Morgan_2024_Fig5.WDdata]. 2. WildlifeDensity output graph of observed and estimated numbers in each distance class [Morgan_2024_Fig5.graphData]. 3. WildlifeDensity ".results" file. This was generated with selection of the option "Tabulate final estimated function values for each distance class" under the "Options" tab in WildlifeDensity. The three probabilities graphed in Fig 5 can be found in the lower portion of the .results file [Morgan_2024_Fig5.results]. 4. Tabulated detection probabilities. [Morgan_2024_Fig5.xlsx]. This data was used to create Fig.5. It contains the "Distance" (x-values), and columns for each of the three y-variables: g(d) (probability of detection if present), Q(d) (probability of an undetected animal being present) and Pd (probability of being present and detected). --- Fig.8 Folder: Fig8 Description: Graph of population-observer speed ratio k vs movement compensation factor J File: Excel spreadsheet Morgan_2023_Fig8.xls. Description: This file contains values for the two variables in the graph: k and J --- Section 3.1 - Test 1: Known-density populations Comparison of density estimates by distance sampling and known densities. Five surveys. Results in Table 2. Folder: Test1 Survey 1 Description: Field data from results of a perpendicular distance line transect survey of static objects set out in a sagebrush meadow in Logan, Utah in ref [23]. Investigator: J. L. Laake Files: Detection data within WildlifeDensity file [Run05074.WDdata]. WD output results [Run05074.results]. WD output graph [Run05074.graphdata]. Detection data within Distance file & folder [Laake.dst/.dat. All ".dat" folders contain a database file for use in DISTANCE: "DistData.mdb"]. Survey 2 Description: Field data from results of a perpendicular distance aerial line transect survey of feral pig carcasses following a cull in Arnhem Land woodland in ref [24]. One side of the transect was observed, so a multiplier of 2 is used. Investigator: J. Hone Files: Detection data within WildlifeDensity file [Run05093y.WDdata]. WD output results [Run05093y.results]. WD output graph [Run05093y.graphdata]. Detection data within Distance file & folder [Hone 1988.dst/.dat. All ".dat" folders contain a database file for use in DISTANCE: "DistData.mdb"]. Table 2 Survey 3 Description: Field data from a radial and a perpendicular distance line transect survey of randomly dispersed numbered tags attached at eye level to tree trunks in open sclerophyll forest. Location: Near Clonbinane, central Victoria, Australia. Surveyed by a single observer walking a single line transect twice, in opposing directions. Student project by J. Wischusen, Zoology Department, University of Melbourne. Notes: The survey was replicated five times (Surveys 3a-e) with different observers. Two files are given for each WD item, being for radial and perpendicular analyses, respectively, where a letter "y" in the file name indicates a perpendicular analysis. Distance analyses have a .dst file with associated .dat folder containing a database file for use in DISTANCE: "DistData.mdb". Survey 3a Observer: ‘Person A’ Files: Detection data within WildlifeDensity files [Run14041.WDdata and Run14041y.WDdata]. WD output results [Run14041.results and Run14041y.results]. WD output graphs [Run14041.graphdata and Run14041y.graphdata]. Detection data within Distance file & folder [Morgan_2024_Table2_Survey3a.dst/.dat] Raw perpendicular data: Table2_Survey3a.txt. Survey 3b Observer: ‘Person B’ Files: Detection data within WildlifeDensity files [Run14042.WDdata and Run14042y.WDdata]. WD output results [Run14042.results and Run14042y.results]. WD output graphs [Run14042.graphdata and Run14042y.graphdata]. Detection data within Distance file & folder [Morgan_2024_Table2_Survey3b.dst/.dat]\ Raw perpendicular data: Table2_Survey3b.txt. Survey 3c Observer: ‘Person C’ Files: Detection data within WildlifeDensity files [Run14043.WDdata and Run14043y.WDdata]. WD output results [Run14043.results and Run14043y.results]. WD output graphs [Run14043.graphdata and Run14043y.graphdata]. Detection data within Distance file & folder [Morgan_2024_Table2_Survey3c.dst/.dat] Raw perpendicular data: Table2_Survey3c.txt. Survey 3d Observer: ‘Person D’ Files: Detection data within WildlifeDensity files [Run14044.WDdata and Run14044y.WDdata]. WD output results [Run14044.results and Run14044y.results]. WD output graphs [Run14044.graphdata and Run14044y.graphdata]. Detection data within Distance file & folder [Morgan_2024_Table2_Survey3d.dst/.dat] Raw perpendicular data: Table2_Survey3d.txt. Survey 3e Observer: ‘Person E’ Files: Detection data within WildlifeDensity files [Run14046.WDdata and Run14046y.WDdata]. WD output results [Run14046.results and Run14046y.results]. WD output graphs [Run14046.graphdata and Run14046y.graphdata]. Detection data within Distance file & folder [Morgan_2024_Table2_Survey3e.dst/.dat] Raw perpendicular data: Table2_Survey3e.txt. Table 2 Surveys 4 & 5 Surveys 4 and 5a-e were of known density kangaroo populations. Their relatively slow overall movement speed of ±5 m/min classes them as ‘relatively immobile’. The data were used with the permission of the investigator, Dr C. J. Southwell, a senior research scientist at the Australian Antarctic Division, Hobart, Tasmania, Australia. Survey 4: Collected during daylight in a fenced area of open, arid low shrubland near the Darling River in western New South Wales, Australia. Population density pre-determined by drive count. Files: Detection data within WildlifeDensity files (radial and perpendicular analyses, respectively): [Morgan_2024_Table2_Survey4_radial.WDdata, Morgan_2024_Table2_Survey4_perp.WDdata]. WD output results [Same file names with .results extension]. WD output graphs [Same file names with .graphData extension]. Detection data within Distance file & folder [Morgan_2024_Table2_Survey4.dst, Morgan_2024_Table2_Survey4.dat] Raw perpendicular data: Morgan_2024_Table2_Survey4.txt. Survey 5: Conducted in a small number of enclosed, staffed natural areas within the Tidbinbilla Nature Reserve, Canberra, ACT, Australia. Population numbers low and known by staff. Timing as follows: Survey 5a: Daylight Survey 5b: Daylight, soon after Survey 5a Survey 5c: At night by spotlight, soon after Survey 5b. Survey 5d: Daylight, soon after Survey 5c. Survey 5e: At night by spotlight, soon after Survey 5d. Files: as per Survey 4. For each of the 5 surveys there is 1) a WildlifeDensity radial analysis, 2) a WildlifeDensity perpendicular analysis, 3) a Distance analysis and 4) raw perpendicular data. File names include "Survey5a" - "Survey5e". --- Section 3.2 - Test 2: Movement compensation Folder: Test2 Description: Graphs of detection frequency vs observer speed for the grey fantail and red wattlebird. Data obtained by past students of David Morgan. Used with permission. Not available for publication. Fig.9a: Description: Graph of observer speed (m/min) vs a) detection frequency (grey fantail) and b) detection frequency adjusted by the movement compensation factor J. File: Morgan_2024_Fig9a.xlsx. Description: The file contains the raw survey data ("Transect duration" and "No. sighted") plus the calculations and totals used to create the graphs. As the sample size was large, the data are binned into intervals of the x-variable. The graphed data are shown in the columns headed "Observer speed class centre (m/min)" (x-axis values), "Detection frequency (actual)" (y-axis values - dots) and "Adjusted detection frequency (div by J)" (y-axis values - stars). There are also columns for calculating the latter, being the “Animal-to-observer relative speed k”, the movement compensation factor “J”. The trendlines shown on the figure are logarithmic, generated automatically in Aabel NG2 graphing software. Fig.9b: Description: Graph of observer speed (m/min) vs a) detection frequency (red wattlebird) and b) detection frequency adjusted by the movement compensation factor J. File: Morgan_2024_Fig9b.xlsx. Description: The file contains the raw survey data ("Transect duration" and "Detection frequency (actual)") plus the calculations and totals used to create the graphs. The graphed data are shown in the columns headed "Observer speed (m/min)" (x-axis values) and "Detection frequency (actual)" (y-axis values - dots) and "Adjusted detection frequency (div by J)" (y-axis values - stars). There are also columns for calculating the latter, being the “Animal-to-observer relative speed k” and the movement compensation factor “J”. The trendlines shown on the figure are logarithmic, generated automatically in Aabel NG2 graphing software Section 3.3 - TEST 3: A difficult population (low visibility, low density, high relative movement) Folder: Test3 Fig.10a: Description: Clustered column graph comparing monthly density estimates of grey fantails by 4 methods (48 data points with errors). File: Morgan_2024_Fig10_data.xlsx Data for the graph were obtained from the following analyses: Folder: Test3_WildlifeDensity_Monthly_Analyses Method 1: 12 WildlifeDensity radial runs Files: 36 WD files - a .wddata, .results and .graphData file for each data point. Method 2: 12 WildlifeDensity perpendicular runs Files: 36 WD files - a .wddata, .results and .graphData file for each data point. Folder: Test3_Distance_BreedingSeason_Analyses Method 3: 12 Distance runs Files: .dst/.dat file for each data point. Method 4: Nesting analysis File: Morgan_2024_Test3_Nesting_Estimate.txt Fig.10b: Description: Clustered column graph comparing breeding season density estimates of grey fantails by 4 methods (16 data points with errors). File: Morgan_2024_Fig10_data.xlsx Data for the graph were obtained from the following analyses: Folder: Test3_WildlifeDensity_BreedingSeason_Analyses. Contains the following: Method 1: 4 WildlifeDensity radial runs Files: 12 WD files - a .wddata, .results and .graphData file for each data point. Method 2: 4 WildlifeDensity perpendicular runs Files: 12 WD files - a .wddata, .results and .graphData file for each data point. Folder: Test3_Distance_BreedingSeason_Analyses Method 3: 4 Distance runs Files: 8 files (.dat/.dst for each data point. All ".dat" folders contain a database file for use in DISTANCE: "DistData.mdb") Method 4: Nesting analysis File: Morgan_2024_Test3_Nesting_Estimate.txt --- SOFTWARE The paper has an associated software program (WildlifeDensity), which is used to analyse distance sampling data. It has been uploaded to Dryad/Zenodo with this submission. The code is deposited in the software repository GitHub. [https://github.com/eclipser42/wide2/tree/WD-2_5](https://github.com/eclipser42/wide2/tree/WD-2_5) It is also available at Zenodo. DOI: [https://doi.org/10.5281/zenodo.11402294](https://doi.org/10.5281/zenodo.11402294) Also used for comparison was the existing software Distance (version 7.5). Available from [https://distancesampling.org/Distance/distance75download.html](https://distancesampling.org/Distance/distance75download.html) WILDLIFEDENSITY README INFORMATION (IMPORTANT) WildlifeDensity only works on the MacOS operating system (Apple Mac computers). As the current version (2.5) has not yet been notarised by Apple, a security warning may be shown when attempting to open WildlifeDensity. Use the following procedure to use WildlifeDensity. 1. Download and Open the WiDe.WD-2_5.dmg file. 2. Copy WildlifeDensity.app from the disk image to a your Applications folder (or any other convenient folder). 3. Open WildlifeDensity.app by clicking on the icon. 4. A security message will be shown: ‘“WildlifeDensity.app” can’t be opened because Apple cannot check it for malicious software.’ Click “OK”. 5. Go to System Settings &gt; Privacy & Security. 6. Scroll down to the message ‘“WildlifeDensity.app” was blocked from use because it is not from an identified developer.’ Click ‘Open Anyway’, and enter your computer password if prompted. 7. See message: ‘“WildlifeDensity.app” can’t be opened because Apple cannot check it for malicious software.’ Click “Open”. 8. Proceed to use WildlifeDensity.