flapper

Routines for the analysis of passive acoustic telemetry data, including the reconstruction of fine-scale movement paths and emergent patterns of space use. https://edwardlavender.github.io/flapper/

Warning The flapper package has been superseeded by patter, which is simpler, faster and better tested. patter will be made available late 2023. Please get in touch to support beta testing.

flapper is an R package which provides tools for passive acoustic telemetry data. The package has been particularly motivated by the collection of acoustic and archival data from a Critically Endangered elasmobranch, the flapper skate (Dipturus intermedius), off the west coast of Scotland where a static passive acoustic telemetry array was established to examine the movements of individuals within a Marine Protected Area. flapper has been designed to complement existing packages for the analysis of these data (e.g. VTrack, glatos and fishtrack3d and actel), with a particular focus on the provision of movement modelling methods for passive acoustic telemetry systems that permit the reconstruction of fine-scale movement paths and emergent patterns of space use. To this end, flapper contains functions in the following themes:

• Data processing tools, including data assembly (e.g., range-testing datasets), the evaluation of false detections and quality checks;
• Spatial tools, including common spatial operations for the manipulation of spatial data, such as polygon inversion;
• Distance calculations, including the calculation of distances between receivers, along 3-dimensional movement paths, and of the shortest paths over a surface;
• Detection statistics, including metrics of sampling effort, such as detection area; and individual detection metrics, such as detection days and co-occurrence;
• Modelling methods, including a straightforward implementation of the mean-position algorithm for the estimation of centres of activity and new algorithms designed for improved estimates of space use and the reconstruction of movement paths;
• Simulation tools, including tools for the simulation of passive acoustic telemetry arrays, movement paths, detections and the comparison of simulated and reconstructed patterns of space use under different conditions;

flapper: An R package of routines for the analysis of passive acoustic telemetry data, especially the reconstruction of fine-scale movement paths and emergent patterns of space use. Inserted sample depth and acoustic time series were collected as part of the Movement Ecology of Flapper Skate project by Marine Scotland Science and NatureScot. The insert of the flapper skate is also courtesy of this project. The bathymetry data are sourced from the Ireland, Northern Island and Scotland Hydrographic survey (Howe et al., 2014. Earth Environ. Sci. Trans. R. Soc. Edinburgh 105, 273–284.). Plots were produced using the prettyGraphics package.

For full package details, vignettes and illustrated examples, go to https://edwardlavender.github.io/flapper/.

Highlights

The main highlights of the package are the provision of routines for the rapid calculation of biologically meaningful distances in areas with complex barriers to movement (e.g., coastline) alongside algorithms (most of which are exclusive to flapper) for reconstructing movements and patterns of space use from discrete detections at receivers, especially:

• lcp_*(). These functions facilitate the calculation of shortest paths and their distances between and around points using efficient C++ algorithms from the cppRouting package. This makes it easy to use biologically meaningful distances (that account for the bathymetric surface over which a benthic animal must move, if applicable, and barriers to movement) in movement models.
• coa(). This function implements the arithmetic version of the mean-position algorithm to estimate centres of activity (COAs) from discrete detections at receivers, given a detection matrix and the locations of receivers.
• ac(). The function implements the acoustic-container (AC) algorithm. This is a new approach which utilises the information provided by acoustic detections in the form of acoustic containers to reconstruct the expected time spent in different parts of a study area over the period of observations. Key innovations of this approach include the natural incorporation of barriers to movement (such as coastline), detection probability and information provided by the gaps between detections.
• dc(). This function implements the ‘depth-contour’ (DC) algorithm. This relates one-dimensional depth time series to a two-dimensional bathymetry surface to determine the extent to which different parts of an area might have (or have not) been used, or effectively represent occupied depths, over time.
• acdc(). This function implements the ‘acoustic-container depth-contour’ (ACDC) algorithm. This integrates the locational information provided by acoustic detections and concurrent depth observations to refine expectations of the time spent in different parts of a study area over the period of observations.
• pf() is a particle filtering routine that refines time-specific maps of the possible locations of an animal (from ac(), dc() or acdc()) via a particle simulation and filtering process that permits the reconstruction of movement paths over landscape.
• sim_*(). These functions provide flexible, joined-up routines for the simulation of receiver arrays, movement paths and detections.

Installation

Warning The flapper package has been superseeded by patter, which is simpler, faster and better tested. patter will be made available late 2023. Please get in touch to support beta testing.

This package requires R version ≥ 4.0. You can check your current version with R.version.string. Subsequent installation steps (may) require the devtools and pkgbuild packages, which can be installed with install.packages(c("devtools", "pkgbuild")). On Windows, package building requires Rtools. You can check whether Rtools is installed with pkgbuild::has_rtools(). If Rtools is not installed, it is necessary to download and install the appropriate version of Rtools before proceeding by following the instructions here.

Four packages (prettyGraphics, Tools4ETS, fasterRaster and glatos) are required or suggested from GitHub repositories (since they are not currently available from CRAN). These can be installed during the installation process (see below), but it is safer to install them sequentially as follows:

devtools::install_github("edwardlavender/prettyGraphics") # required
devtools::install_github("edwardlavender/Tools4ETS")      # required
devtools::install_github("adamlilith/fasterRaster")       # suggested
devtools::install_github("ocean-tracking-network/glatos") # suggested

To install these packages with their vignettes, add dependencies = TRUE and build_vignettes = TRUE as arguments to the code above (see ?devtools::install_github or ?devtools::install_url for further information). Then, you can install the development version of flapper from GitHub as shown below:

devtools::install_github("edwardlavender/flapper", dependencies = TRUE, build_vignettes = TRUE)

The dependencies = TRUE argument will also install any suggested packages, which are required by some functions/examples and to build vignettes (which will be added to the package in due course). To access the vignettes, use vignette("flapper_intro", package = "flapper") for a general introduction to the package. Note that vignettes have not yet been added to the package.

Example datasets

A key feature of the flapper package is that most functions are designed to be implemented using standard object types (e.g., dataframes and matrices) rather than package-specific object classes. For simplicity, flapper makes some assumptions about variable names that follow a consistent and logical structure (e.g., individual IDs are given as individual_id and receiver IDs are given as receiver_id) but, notwithstanding this framework, this structure means that the functions in the package are accessible and straightforward to use.

Functions are illustrated using simulated data and the following sample data collected from flapper skate off the west coast of Scotland:

• dat_ids is a dataset containing the characteristics of a sample of tagged flapper skate;
• dat_moorings is a dataset containing some sample passive acoustic telemetry receiver locations and associated information;
• dat_acoustics is a dataset containing some sample detection time series;
• dat_archival is a dataset containing some sample depth time series;
• dat_sentinel is a dataset containing some sample transmission–detection time series assembled from sentinel tags;

These example datasets were collected by Marine Scotland Science and NatureScot as part of the Movement Ecology of Flapper Skate project and belong to these organisations. If you wish to use these data, please contact Marine Scotland Science and NatureScot for further information.

Data processing tools

A number of functions facilitate the acquisition, assembly, processing and checking of passive acoustic telemetry time series:

• Data acquisition.
  • query_*() functions query online databases:
    • query_open_topo() queries the Topo Data Application Programming Interface for elevation/bathymetry data;
• Data assembly.
  • assemble_sentinel_counts() assembles counts of transmissions/detections from sentinel tags for modelling purposes (i.e., to model detection probability);
  • make_matrix_*() functions create matrices of individual and receiver deployment time series and detection time series:
    • make_matrix_ids() matricises individual deployment time series;
    • make_matrix_receivers() matricises receiver deployment time series;
    • make_matrix_detections() matricises detection time series;
  • make_df_*() functions (i.e., make_df_detections()) reverse this process;
• Data processing.
  • process_receiver_id() adds unique receiver IDs to a dataframe (i.e., if the same receiver has been deployed more than once);
  • process_false_detections_sf() passes putative false detections through a spatial filter which incorporates information on receiver locations and animal swimming speeds to interrogate their plausibility;
  • process_quality_check() passes acoustic data through some basic quality checks prior to analysis;
  • process_surface() determines an ‘optimum’ raster aggregation method and error induced by this process;

Spatial tools

A number of functions facilitate spatial operations that support common tasks and modelling algorithms:

• buffer_and_crop() buffers a spatial object (e.g., receiver locations) and uses this buffered object to crop another (e.g., the local bathymetry);
• get_intersection() intersects spatial geometries;
• xy_from_click() gets location coordinates from mouse clicks;
• crop_from_click() crops a raster to an area defined by mouse clicks;
• cells_from_val() returns the cells (or a raster of the cells) of a raster that are equal to a specified value or lie within a specified range of values;
• invert_poly() inverts a polygon (e.g, to define the ‘sea’ from a polygon of the ‘land’);
• mask_io() masks values in a raster that lie inside or outside of a spatial mask (e.g., to mask the ‘land’ from the ‘sea’);
• sim_surface() populates a raster with simulated values;
• split_raster_equally() splits a raster into equal pieces (using code from the greenbrown package);
• update_extent()shrinks or inflates an extent object;
• segments_cross_barrier() determines if Euclidean transects cross a barrier;

Distance calculations

Some functions facilitate standard distance calculations using Euclidean distances:

• dist_btw_clicks() calculates distances and draws segments between sequential mouse clicks on a map;
• dist_btw_receivers() calculates the Euclidean distances between all combinations of receivers;
• dist_btw_points_3d() calculates the Euclidean distances between points in three-dimensional space;
• dist_over_surface() calculates the total Euclidean distance along a path over a three-dimensional surface;

Often, Euclidean distances may not be a suitable representation of distance. This is especially the case for coastal benthic/demersal species in bathymetrically complex environments, for which navigation between locations may require movement over hilly terrain and around coastline. For this reason, a number of functions facilitate the calculation of shortest paths/distances:

• lcp_costs() calculates the distances between connected cells in a raster, accounting for planar (x, y, diagonal) and vertical (z) distances;
• lcp_graph_surface() constructs connected graphs for least-cost paths analysis;
• lcp_from_point() calculates least-cost distances from a point on a raster to all of the other cells of a raster;
• lcp_over_surface() calculates shortest path(s) and/or the distances of the shortest path(s) over a surface between origin and destination coordinates;
• lcp_interp() interpolates paths between sequential locations using least-cost paths analysis;
• lcp_comp() compares Euclidean and shortest-distance metrics for an area;

Detection statistics

A number of functions facilitate the calculation of detection statistics, including those related to sampling effort and to detections of individuals:

• get_detection_pr() calculates detection probability given a model for detection probability with distance;
• get_detection_containers() defines detection containers (areas within the maximum detection range) around receivers;
• get_detection_containers_overlap() identifies receivers with overlapping detection containers in space and time;
• get_detection_containers_envir() extracts environmental conditions from within receiver detection ranges, accounting for detection probability;
• get_detection_area_sum() calculates the total area surveyed by receivers;
• get_detection_area_ts() defines a time series of the area surveyed by receivers;
• get_n_operational_ts()defines a time series of the number of operational units (e.g., individuals at liberty or active receivers)
• get_id_rec_overlap() calculates the overlap between the deployment periods of tagged individuals and receivers;
• get_detection_days() calculates the total number of days during which each individual was detected (termed ‘detection days’);
• get_detection_clumps() identifies detection ‘clumps’ and calculates their lengths;
• get_detection_overlaps() identifies ‘overlapping’ detections;
• get_residents() identifies ‘resident’ individuals;
• make_matrix_cooccurence() computes a detection history similarity matrix across individuals;

Movement metrics

Building on the analysis of detection time series, some functions (get_mvt_*()) provide movement metrics:

• get_mvt_mobility_*() functions estimate swimming speeds:
  • get_mvt_mobility_from_acoustics() estimates swimming speeds from acoustic detections;
  • get_mvt_mobility_from_archival() estimates swimming speeds from archival time series;
• get_mvt_resting() identifies ‘resting’ behaviour from archival time series;
• get_hr_*() functions get animal ‘home ranges’:
  • get_hr_prop() gets a custom range from a utilisation distribution (UD);
  • get_hr_core() gets the ‘core range’ from a UD;
  • get_hr_home() gets the ‘home range’ from a UD;
  • get_hr_full() gets the ‘full range’ from a UD;

Modelling algorithms

The main thrust of flapper is the implementation of algorithms designed to reconstruct fine-scale movement paths and emergent patterns of space use in passive acoustic telemetry systems.

The centres of activity (COA) algorithm

Centres of activity (COA) are one of the most widely used metrics for the reconstruction of patterns of space use from passive acoustic telemetry data. Several methods have been developed to calculate COAs, but the mean-position algorithm is the commonest. To generate estimates of space use, COAs are usually taken as point estimates from which UDs (typically kernel UDs or KUDs) are estimated. flapper facilitates the implementation of this approach with the following functions:

• coa_setup_delta_t() informs decisions as to an appropriate time interval over which to calculate COAs;
• make_matrix_detections() summarises detections over time intervals (see above);
• coa() implements the arithmetic version of the mean-position algorithm to calculate COAs;
• kud_habitat(), kud_around_coastline() and kud_around_coastline_fast() facilitate the estimation of home ranges (e.g., from estimated COAs) in areas of complex coastline;

The flapper family of algorithms

Alongside the COA algorithm, this package introduces the flapper of algorithms for the inferring patterns of space use.

The ‘flapper’ family of algorithms. The acoustic-container (AC) branch utilises acoustic data (and/or ancillary information) to reconstruct the set of possible locations for an individual through time. The particle filtering (PF) branch refines this set via the implementation of a particle simulation and filtering approach for the reconstruction of possible movement paths.

AC/DC branch algorithms

The depth-contour (DC) algorithm

The depth-contour (DC) algorithm is the simplest. Whereas the COA approach only makes use of detections, the DC approach only uses depth observations. Specifically, this algorithm uses observed depths (± some error) to define the subset of possible locations of each individual within a defined area: for pelagic species, tagged individuals must be in an area where the seabed depth is at least as deep as the observed depth; for benthic/demersal species, tagged individuals must be in an area where the seabed depth is close to the observed depth. This is implemented via dc(). The ‘quick’ depth-contour (DCQ) algorithm, implemented via dcq(), uses a modified version of this algorithm for quicker run times.

The acoustic-container* (AC*) algorithm(s)

The flapper family-equivalent of the COA algorithm is the acoustic-container (AC) algorithm. This approach represents the information from acoustic detections in the form of acoustic containers, which contract and expand in line with our uncertainty in an individual’s location when it is detected and in the gaps between detections. The acoustic-container depth-contour (ACDC) algorithm combines the AC and DC algorithms, using passive acoustic telemetry data to inform the area within which depth contours are most likely to be found. These algorithms are implemented with the ac*() family of functions:

• acs_setup_mobility() examines the assumption of a constant ‘mobility’ parameter;
• acs_setup_containers() defines the detection containers for the algorithm(s);
• acs_setup_detection_kernels() defines detection probability kernels for the algorithm(s);
• ac() and acdc() implement the algorithm(s), via the back-end functions .acs_pl() and .acs();

AC/DC post-processing and analysis

The AC-branch functions (ac(), dc() and acdc()) all return objects of class acdc_archive. These can be processed and analysed using several key functions:

• acdc_simplify() simplifies acdc_archive-class objects into acdc_record-class objects;
• acdc_access_*() functions provide short-cuts to different elements of acdc_record-class objects:
  • acdc_access_dat() accesses stored dataframes;
  • acdc_access_timesteps() accesses the total number of time steps;
  • acdc_access_maps() accesses stored maps;
• acdc_plot_trace() plots acoustic container dynamics;
• acdc_plot_record() plots the results of the algorithm(s);
• acdc_animate_record() creates html animations of algorithm(s);

Particle filtering branch algorithms

Each algorithm (AC, DC and ACDC) can be extended through incorporation of a movement model to reconstruct movement paths over a surface that are consistent with the observations (and model assumptions). The resultant algorithms are termed the ACPF, DCPF and ACDCPF algorithms. The approach is implemented via a particle simulation and filtering process provided by the pf*() family of functions:

• pf_setup_movement_pr provides a simple movement model that defines the probability of movement between locations given the distance between them;
• pf_setup_record() creates an ordered list of input files;
• pf_setup_optimisers() controls optimisation settings;
• pf() implements the particle filtering routine;
• pf_access_history_files() lists particle histories saved to file;
• pf_access_history() accesses particle histories;
• pf_access_particles_unique() accesses unique particle samples;
• pf_plot_history() plots particle histories;
• pf_animate_history() animates particle histories;
• pf_simplify() processes particle histories and assembles movement paths;
• pf_plot_map() maps the expected ‘proportion-of-use’ (POU) across an area based on sampled particles or reconstructed paths;
• pf_kud() smooths POU maps using kernel smoothing;
• pf_kud_1() and pf_kud_2() apply kernel smoothing to sampled particles or reconstructed paths;
• pf_loglik() calculates the log-probability of reconstructed paths, given the movement model;
• pf_plot_1d() plots the depth time series from observed and reconstructed paths;
• pf_plot_2d() maps the reconstructed paths in two-dimensions;
• pf_plot_3d() maps the reconstructed paths in three-dimensions;

Simulation tools

flapper provides joined-up routines for the simulation of acoustic arrays, movement paths and detections at receivers:

• sim_array() simulates alternative array designs;
• sim_path_*() functions simulate discrete-time movement paths, including:
  • sim_path_sa(), supported by sim_steps() and sim_angles(), simulates movement paths (possibly in restricted areas) from step lengths and turning angles;
  • sim_path_ou_1() simulates movement paths under Ornstein-Uhlenbeck processes;
• sim_detections() simulates detections at receivers arising from movement paths under a diversity of detection probability models;

To evaluate the performance of alternative algorithms for reconstructing patterns of space use under different array designs, movement models and detections models, eval_by_kud() compares patterns of space use reconstructed from simulated and estimated movement paths using KUDs.

Parallelisation routines

Parallelisation in flapper is facilitated by the cl_*() function family:

• cl_lapply() is a wrapper for pbapply::pblapply() that handles cluster checking set up and closure, using the following functions:
  • cl_check() checks a cluster;
  • cl_cores() identifies the number of cores;
  • cl_chunks() defines chunks for parallelisation;
  • cl_export() exports objects required by a cluster;
  • cl_stop() closes a cluster;

Resources

For an overview of the flapper algorithms, see: Lavender, E., Biber, S., Illian, J., James, M., Wright, P. J., Thorburn, J., & Smout, S. (2023). An integrative modelling framework for passive acoustic telemetry. Methods in Ecology and Evolution, 00, 1–13. https://doi.org/10.1111/2041-210X.14193

For further code examples, see:

• flapper_demo for a demo of the flapper algorithms;
• flapper_sim for simulation-based explorations of the algorithms;
• flapper_appl for example real-world applications;

For further information of the flapper package, see:

• ?flapper::flapper for an overview of package functions;
• ?flapper::ac for information on specific functions (e.g., ac(), which implements the acoustic-container algorithm);

Disclaimers and troubleshooting

flapper is a new, proof-of-concept R package. It was written to support the implementation of a novel, mathematical framework for movement modelling in passive acoustic telemetry systems in our study system in Scotland. The functions are extensively documented but the package is at an early stage of evolution. All routines are experimental. Researchers interested in using the package are encouraged to get in touch while the methods and package remain at an early stage of evolution (edward.lavender@eawag.ch).

Associated packages

• prettyGraphics facilitates the production of pretty, publication-quality and interactive visualisations, with a particular focus on time series. This makes it easy to create abacus plots, visualise time series (across factor levels, at different temporal scales and in relation to covariates), bathymetric landscapes and movement pathways in three-dimensions, and detection similarity matrices.
• Tools4ETS provides a set of general tools for ecological time series, including for the definition of time categories, matching time series (e.g., detection observations with temporally varying environmental covariates), flagging independent time series and simulations.
• fvcom.tbx provides tools for the integration of hydrodynamic model predictions (from the Finite Volume Coastal Ocean Model) with ecological datasets (e.g., detection time series). This facilitates the inclusion of hydrodynamic model predictions as covariates in movement models and the validation of hydrodynamic model predictions with movement datasets or data collected from static acoustic receivers. This package was particularly motivated by the West Scotland Coastal Ocean Modelling System (WeStCOMS).

Citation

To cite package flapper in publications, please use Lavender et al. (2023). For residency analyses, please also cite Lavender et al. (2021).

Lavender, E. et al. (2021). Movement patterns of a Critically Endangered elasmobranch (Dipturus intermedius) in a Marine Protected Area. Aquatic Conservation: Marine and Freshwater Ecosystems, 32, 348–365. https://doi.org/10.1002/aqc.3753

Lavender, E. et al. (2023). An integrative modelling framework for passive acoustic telemetry. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.14193

For the shortest-path routines, please also consider citing cppRouting:

Larmet V (2022). cppRouting: Algorithms for Routing and Solving the Traffic Assignment Problem. R package version 3.1. https://CRAN.R-project.org/package=cppRouting.

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