DOI: 10.5061/dryad.37pvmcvfv
title: Assessing the performance of index calibration survey methods to monitor populations of wide-ranging low-density carnivores

description: Apex carnivores are wide-ranging, low-density, hard to detect, and
 declining throughout most of their range, making population monitoring
 both critical and challenging. Rapid and inexpensive index calibration
 survey (ICS) methods have been developed to monitor large African
 carnivores. ICS methods assume constant detection probability and a
 predictable relationship between the index and the actual population of
 interest. The precision and utility of the resulting estimates from ICS
 methods have been questioned. We assessed the performance of one ICS
 method for large carnivores - track counts - with data from two long-term
 studies of African lion populations. We conducted Monte Carlo simulation
 of intersections between transects (road segments) and lion movement paths
 (from GPS collar data) at varying survey intensities. Then, using the
 track count method we estimated population size and its confidence limits.
 We found that estimates either overstate precision or are too imprecise to
 be meaningful. Overstated precision stemmed from discarding the variance
 from population estimates when developing the method, and from treating
 the conversion from tracks counts to population density as a
 back-transformation, rather than applying the equation for the variance of
 a linear function. To effectively assess the status of species, the IUCN
 has set guidelines, and these should be integrated in survey designs. We
 propose reporting the Half Relative Confidence Interval Width (HRCIW) as
 an easily calculable and interpretable measure of precision. We show that
 track counts do not adhere to IUCN criteria, and we argue that ICS methods
 for wide-ranging low-density species are unlikely to meet those criteria.
 Established, intensive methods lead to precise estimates, but some new
 approaches, like short, intensive, (spatial) capture-mark-recapture
 (CMR/SECR) studies, aided by camera trapping and/or genetic identification
 of individuals, hold promise. A handbook of best practices in monitoring
 populations of apex carnivores is strongly recommended.
description: The dataset covers data from two distinct areas: Kafue National
 Park in Zambia and Hwange National Park in Zimbabwe. The dataset consists
 of shapefiles of the road network, the minimum convex polygons of lion
 movements (see below) and movement trajectories of GPS collared lions in
 each area. The GPS collar data has been processed to present daily
 movements where each line in the shapefile represents the 24-hour movement
 (from 6AM to 6AM) of a lion from a pride of a known size. Only lines are
 included for which Day Of Year (DOY) there was movement data for all
 collared lion prides within each area. The road network shapefiles are
 clipped to the 100% minimum convex polygon of lion locations used. The
 shapefiles with 'I100' in their name created in QGIS 3.2 with
 v.split with segments of max 10,000m, these are only used for simulations
 where 100% of the road network is used.  The R code
 randomly generates a chosen number of sets of transects of given length
 (in meters) and given intensity (as a percentage of the length of the
 total road network) and then intersects the the randomly generate
 transects with the lion movements to generate track count data as the
 number of tracks of carnivores observed per 100 km driven as the length of
 transects is known, the number of crossings, and pride size is lion
 trajectories crossing transects is known.
description: Within the R code a user has to set the following
 parameters: The chosen site (either "Hwhange"
 or "Kafue"), The survey intensity, which is
 the percentage of road network included in transects, in the manuscript we
 use 20, 40, 60, 80 and 100. The length of transects (in
 meters), in the manuscript we use 10,000 (for the first half of transects
 per set) and 5,000 (for the second half of transects per set).
 The coordinate reference system (crs), both our study area were
 '32735' The spacing of points. This
 determines how the road network will be broken up. The number entered
 represents the number of meters between each point. We used 50, which
 means that for the whole road network (which are lines) a point is created
 every 50m. A smaller number would mean more precision, but also (much)
 more time needed to create transects. The combination
 of intensity, lengths of transects and spacing of points determines how
 many transects will be generated per transect set and how many points
 there are per transect. I advise to start with a low intensity and short
 transects to get an idea how fast (or slow) things go. During transect
 creations the code will print every 10th point of a transect to show how
 it's progressing. One could consider overriding the calculating of
 number of needed transects by adding setting 'transectNR'
 manually. The minimal distance, in meters. This
 determines the distance which is used as a treshold if crossing points
 should be kept or not. If points are less distance from each other than
 specified only one will be kept. The default value we used is 1,000
 meters. Road buffer distance, this places a small
 buffer around the roads to ensure that no crossings are missed because of
 accuracy issues in the location of roads or locations of lions. The
 default value is set to 25 meters. The first and last
 transect sets. These are used to keep track of the number of transect sets
 created. Creating random transect sets can take considerable time, thus
 generated transects are saved as shapefiles so they can be re-used later.
 I advise with only generating a few transect sets first to determine how
 fast things go on your machine. The user will also have
 to create a folder called 'results' within their working
 directory. This folder will be used to store the output (shapefiles and
 image files of transects, image files of examples of crossings, csv files
 with transect set, transect number and transect lengths (in meters) and
 csv files with the coordinates of an intersection between transect and
 lion trajectory, DOY, pride, pride size, date, transect set, transect
 number, length of transect, id and simulation number ('run').
 Separate shapefile for surveys with 100% intensity are used, these are the
 same road networks, but pre-processed in QGIS (see above).