10.5061/DRYAD.37PVMCVFV
Droge, Egil
0000-0002-2642-3859
University of Oxford
Creel, Scott
Montana State University
Becker, Matthew
Zambian Open University
Loveridge, Andrew
University of Oxford
Sousa, Lara
University of Oxford
Macdonald, David
University of Oxford
Assessing the performance of index calibration survey methods to monitor
populations of wide-ranging low-density carnivores
Dryad
dataset
2020
IUCN criteria
population monitoring
spoor counts
statistical power
track counts
2020-03-11T00:00:00Z
2020-03-11T00:00:00Z
en
https://doi.org/10.1002/ece3.6065
976773 bytes
7
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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.
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).