# Evaluation of the use of drones to monitor a diverse crocodylian assemblage in West Africa

Clément Aubert, Gilles Le Moguédec, Cindy Assio, Rumsais Blatrix, Michel N’Dédé Ahizi, Georges Codjo Hedegbetan, Nathalie Gnanki Kpera, Vincent Lapeyre, Damien Martin, Pierrick Labbé, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Clément Aubert, Gilles Le Moguédec, Cindy Assio, Rumsais Blatrix, Michel N’Dédé Ahizi, et al. Evaluation of the use of drones to monitor a diverse crocodylian assemblage in West Africa. Wildlife Research, 2022, 49 (1), pp.11-23. 10.1071/WR20170. hal-03265779

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Submitted on 6 Oct 2022

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<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Evaluation of the use of drone-station monitor in diverse croco-
dylian assemblage in West Africa

ClementAubert ID A,B,C,K, GillesLeMogue´decD, CindyAssioA,
Rumsa¨isBlatrixE, MichelN´de´de

'Ahizi C, F, Georges Codjo Hedegbetan G, Nathalie Gnanki Kpera H, Vincent Lapeyre I,
Damien Martin G, Pierrick Labbe B, and Matthew H. Shirley A,C

Abstract

**Context.** West African crocodylian populations are declining and in need of conservation action. Surveys and other monitoring methods are critical component of crocodile conservation programs; however, surveys are often hindered by logistical, financial and detectability constraints. Increasingly used in wildlife monitoring programs, drone-scanenhance monitoring and conservation efficacy.

**Aims.** This study aimed to determine standard drone-crocodylians survey protocol and evaluate the drones' ability to survey the diverse crocodylian assemblage of West Africa.

**Methods.** We surveyed crocodile populations in Benin, Co'ted'Ivoire, and Niger in 2017 and 2018, by using the DJI Phantom 4 Pro drone and via traditional diurnal and nocturnal spotlight surveys. We used a series of test flights to first evaluate the impact of drones on crocodylian behaviour and determine standard flight parameter set for optimised detectability. We then, consecutively, implemented the three survey methods at 23 sites to compare the efficacy of drones against traditional crocodylians survey methods.

**Key results.** *Crocodylus suchus* can be closely approached (– 10m altitude) and consumer-graded drones donotelicit flight responses in West African largemammals and birds at altitudes of – 40–60m. Altitude and other flight parameters did not affect detectability, because high-resolution photos allowed accurate counting. Observer experience, field conditions (e.g. wind, sun reflection), and site characteristics (e.g. vegetation, homogeneity) all significantly affected detectability. Drone-based crocodylian surveys should be implemented from 40 m altitude in the first third of the day. Comparing survey methods, drones performed better than did traditional diurnal surveys but worse than standard nocturnal spotlight counts. The latter not only detected more individuals, but also a greater size-class diversity. However, drone-surveys provide advantages over traditional methods, including precise size estimation, less disturbance, and the ability to cover greater and more remote areas. Drone survey photos allow for repeatable and quantifiable habitat assessments, detection of encroachment and other illegal activities, and leave a permanent record.

<footnote>AInstituteofEnvironment,FloridaInternationalUniversity,BiscayneBayCampus,AC1210,3000N.E.151stStreet,NorthMiami,FL33181,USA.</footnote>

<footnote>BInstitutdesSciencesdel'EvolutiondeMontpellier(UMR5554,CNRS-UM-IRD-EPHE),Universite'deMontpellier,Montpellier,34095Cedex5,France.</footnote>

<footnote>CProjectMecistops,615WatersideWay,Sarasota,FL34242,USA.</footnote>

<footnote>DAMAP,Université Montpellier,INRAE,CIRAD,CNRS,IRD,Montpellier,France.</footnote>

<footnote>ECEFE,UniversityofMontpellier,CNRSUniversityPaulVale
'ryMontpellier3,EPHE,IRD,1919routededeMende,34293Montpellier,France.</footnote>

<footnote>F Laboratoire d'Environnement et de Biologie Aquatique, Université d'Angui, Abrogoua, 02BP801Abidjan02.</footnote>

<footnote>GCentreRe´gionaldeRechercheetd'EducationpourunDe´veloppementInte´gre'(CREDI-
ONG),BP471Abomey-Calavi,Re´publiqueduBe´nin.</footnote>

<footnote>HNationalInstituteofAgriculturalResearchofBenin(INRAB),BP1915Abomey-
Calavi,Re´publiqueduBe´nin.</footnote>

<footnote>I ConservationProgram,ZoologicalSocietyofLondon,Regent'sPark,London,EnglandNW14RY.</footnote>

<footnote>JInstitut Universitaire de France, 1 Rue Descartes, 75231 Cedex 05, Paris.</footnote>

<footnote>KCorresponding author. Emails:clement.aubert34@hotmail.fr; clement.aubert@umontpellier.fr</footnote>
**Conclusions.** Overall, drones offer a valuable and cost-effective alternative for surveying crocodylian populations with compelling secondary benefits, although they may not be suitable in all cases and for all species.

**Implications.** We propose a standardised and optimised protocol for drone-based crocodylians to survey that could be used for sustainable conservation programmes of crocodylians in West Africa and globally.

Keywords: *Crocodylus*, *Mecistops*, *suchus*, *elephant*, *UAV*, *Pendjari*.

## Introduction

Drones are an increasingly useful and used tool in conservat ionscience and natural resources management, and they are already revolutionising research into wildlife and habitats (Evanset al. 2016). Droneshaveseveraladvantagesovertraditionalmeth odsof observation. They can collect very high-resolution images (McEvoyet al. 2016), are cheaper and safer than helicopters and small bush planes (Ogden 2013; Zahawiet al. 2015), and they can successfully perform autonomous flights over varying dis-tances (Floreano and Wood 2015; Venturaet al. 2016). Even though they are advanced technology, commercially available, consumer drones are relatively easy to pilot and require limited training. Importantly, for conservation, unmanned aerial vehicles (UAVs) have a small ecological footprint that does a gasoline-powered aircraft and their quiet engines have less stress on the natural wildlife (V asetal. 2015). Finally, drones can closely approach any object and their remotely piloted aircraft for long-distance flight allows researchers to access dangerous or remote areas and approach challenging species safely (Gademeret al. 2009). As a result, drones are now being used for habitat monitoring (Koh and Wich 2012), 3D mapping (Liseinet al. 2014), animal population censuses (Hodgsonet al. 2018), and even in anti-poaching (Mukwazvure and Magadza 2014).

As charismatics species with a high potential ecological, economic, and sociocultural importance (Somaweera et al. 2020), crocodylians are globally embraced as important targets. Species for conservation and management programmes. They have also been shown to be ideal indicators of critical habitat and ecosystem restoration initiatives (Droulers 2004; Maz zottietal. 2009). Unfortunately, crocodylians also represent one of the most threatened vertebrate orders, with 25% of recognised species listed as Critically Endangered. Global crocodylians declines have been attributed to many of the same factors as most species globally, including habitat loss (Myerset al. 2000), conflicts with artisanal fisheries (Brashareset al. 2004), bush-meat trafficking (Shirleyet al. 2009); Covey and McGraw 2014), hydrocarbon pollution (Dallmeier et al. 2006), and illicit trade in skins (Thorbjarnarson 1999).

Crocodylians and their unique natural histories pose many challenges for researchers and program

managers seeking to determine program efficacy via established monitoring protocols. They are cryptic, mostly nocturnal, mostly aquatic, and, in places with more than one species, often exhibit partitioningsuch that the best survey method for one species is not always the best for others (Shirley and Eaton 2012). Crocodylian surveys are typically implemented as nocturnal spotlights or from boat on foot (Shirley and Eaton 2012). Although these methods are proven effective (Ferreira and Pienaar 2011; Shirley et al. 2012), they are, nonetheless, limited by habitat
structural heterogeneity and inaccessibility of
many wetland habitats, they are time-
consuming, and often requires significant human
resources. Further, the close approach required
to identify and demographically categorise
detected individuals
may have unknown consequences for the animals.

Drones may provide an opportunity to overcome some of these constraints, with costs lower or equal to those of traditional methods. Droneshaverecently been used to investigate aspects of crocodylian populations in the USA (Martin et al. 2012; Elsey and Troscha 2016), Asia (Evans et al. 2016; Thapa et al. 2018), Australia (Harvey and Hill 2003; Bevan et al. 2018), Argentina (Scarpa and Pin 2019) and South Africa (Ezat et al. 2018). Most of these studies focused on mapping and counting crocodylians, whereastwo compared drone studies to traditional daytime on-ground surveys (Ezat et al. 2018; Thapa et al. 2018). On ly Bevan et al. (2018) have evaluated optimal drone survey parameters (such as height and speed) for one of more crocodilians. West Africa presents a unique setting in which to test the efficacy of drones as tools for crocodylian populations. Here, three endemics species are al locally unique and have different conservation tatuses, and yet often occur sympatrically. The most abundant of these species, the West African crocodile (Crocodylussuchus), is distribut ed throughout West Africa and occupies habitats ran ging from coastal forested lagoons and large woody deo drivers all the way into northern savanna and Sahel habitats (Brito et al. 2011; Cunningham et al. 2016).

Crocodylussuchus is a cavity-nestling species that often basks during the day and is currently being evaluated for inclusion on the IUCN Red List. The West African war crocodile (Osteolaemussp. nov. a ff. tetraspis) is a small, forest-dwelling species that can also be found in forested ha bitats, adjacent coastally located lagoons, and in riparian habi tats in northern savannas (Wait ku wait 1989; Eaton 2 010). Osteolae- mussp. nov. aff. tetraspis is a round- nestling species that is rarely seen during the day (Wa it ku wait 1989) and is currently being evaluated for in clusion on the IUCN Red List. Finally, the West Afr icanslender- snout ed crocodile (Mecistopscataphractus) is a diurnally-sized, forested wetland- dwelling species predominantly found in the forest e dsouthern wetland habitats and the wooded wetland dhabitat of the north (Wait ku wait 1989; Shirley 2 010). Its a round- nestling species that is sometimes found as fallen trees in submerged rocks during the day (Shirley et al. 2018). Mecistopscataphractus is listed as critically endangered on the IUCN Red List (Shirley 2014).

In the present study, we assessed the efficacy of drones on crocodiles as survey tools for this diverse crocodylia n species assembly in West Africa. We compared

drones to traditional daytime and night-
time counting methods, and investigated how flight
parameters affect detectability
and disturbance. We, thus, alsopropose standardised and
<figure><img src="image_3.png" /><figcaption>**Fig.1.** (a) Distribution of study areas in West Africa. (b) WNational Park (WNP), Niger. (c) Comoe´ National Park (CNP), Co^ted´ Ivoire. (d) Pendjari National Park (PNP), Benin. (e) Azagny National Park (ANP), Co^ted´ Ivoire. Studysitesareponds and riversections(redstars); the map is based on the Landscapes of West Africa atlas (CILSS2016).</figcaption></figure>

optimised protocol for drone-based crocodylian
surveys, and discuss how drones can help establish
evidence-based directives for sustainable conservation
programs of crocodylians in West Africa, and globally.

is known to contain all three West African crocodylia
nspecies (Waitkuwait 1989). Wesurveyed as small por-
tion of the Iringou River and as small pool 'Mareaux Bu-
ffles.'

Materialsandmethods

Studyareas

We implemented this work in four different study
sites in three different countries in West Africa, as
follows (Fig.1, Supple-mentarymaterialTableS1):

1. Pendjari National Park (PNP), Benin. We surveyed PNP from 18 March to 12 April 2017. PNP is located in north-western Benin (10830°-11830°N, 0850°-2800°E) and comprises 273123 ha of Sudano-Guineansavanna, including a diversity of wetland habitats ranging from the meandering Pendjari River to a series of natural and artificially main-tained dams. It has a marked dry season (generally from November to April) and a single rainy season (generally from June to October, Rouxel 2010). This park is known to contain only *C. suchus*, which is abundant (Chirio 2009). In PNP, we surveyed crocodiles in a diversity of natural and artificial pools (Table S1).

2. Comoe
'NationalPark(CNP),Co^ted'Ivoire.WesurveyedCN
P from 28 July to 1 August 2017. CNP is located in
north-easternCo^ted'Ivoire(885°-986°N,381°-
484°W)andcom-prises1 149 150haof Sudano-
Guineansavanna.ItisthelargestprotectedareainWestAf-
ricaandwasgazettedasa
UNESCO World Heritage Site in 1983
(UNESCO2003).

It contains a diversity of habitats, including tropical grass
slands and wooded savannas (Seydouet al. 2017).
This park
For logistical reasons unrelated to the methodology, we were unable to implement the diurnal surveys and all drone survey replicates on the Comoe´ and Iringourivers.

3. AzagnyNationalPark(ANP),Co^ted'Ivoire.Wesurveyed ANP from 26 June to 30 June 2017. ANP is located on the coast of Cote d'Ivoire (5814°-5831°N, 4876°-5801°W) and comprises 19400ha of subequatorial wetland (Djaha et al. 2008).

It was classified as a Ramsar site in 1996 (Ramsar 2018). Its climate comprises a long rainy season (generally late April to mid-July), followed by a prolonged dry season (generally from December to April; Avenard 1971). ANP habitats are composed of large swaths of Raphia hookeris wampland, mangroves (Rhizophora racemosa and Avicennia africana), andaman madecan all linking the Ebrie 'Lagoonto the Bandama River (Ake' Assi 1984). This park is known to contain all three West African crocodylian species (Shirley and Yaokokore-Beibro 2008). We surveyed 5 km of the Azagny canal divided into five 1 km contiguous sections.

4. W National Park (WNP), Niger. We surveyed WNP from 12 February to 17 April 2018. WNP is located in south-western Niger (12835°-11854°N, 2804°-2850°E) and comprises 330 000 ha of Sahelian and Sudano-Guinean savannas vegetation. It is an id park, receiving an average of 6 40mm of rain generally from May to September/October (Ipavec et al. 2007). Niger's Park is part of the trinational WAP complex, which is the largest transboundary protected area in West Africa, comprising over 1033

900 ha, and is classified as a World Heritage site (Inoussa et al. 2017). Its wetland habitats include the Niger River, the Tapoa River, and series of natural and artificially maintained dams. This park is known to contain only *C. suchus*, which is abundant (Shirley and Eaton 2008; Chirio 2009). We surveyed

2.5 km of the Tapoa River divided into five 500 m contiguous sections.
Testingflightplansanddatacollectiontominimisedisturban
ceandmaximisedetectability

We collected all described drone data using a
Phantom 4
Pro(DJI,China)operatedfromaSamsungGalaxyTab6(Sa
msung,South Korea) using a DJI GO 4 tablet-based app.
The Phantom
4Prohasamaximumflighttimeof 30min,amaximumspee
d 7/70kmh⁻¹, andapilot-
controlledrangeof5km.Itcomesequippedwitha20MPca
merawithahighdefinition4K/60fps

video capacity. We programmed all flight plans using
Pix4Dcapture software (Pix4D, Switzerland). We
ultimately assem-bledandortho-
rectifiedallimagesusingAgisoftPhotoscanProver.

1.2.5.2594, which isnow Agisoft Metashape
(Agisoft, Russia), and imported them into QGIS ver.
2.8.6 (QGis Devel-opmentTeam,USA)foranalysis.

Prior to implementing any drone-based crocodile
surveys, wewanted first to test the drone for disturbance
effects on thecrocodiles that might affect drone-based
survey results (e.g.fleeing, submersion, or other
evasive manoeuvres). We
alsowantedtominimiseextremedisturbancetootherspeci
espen-tially encountered during surveys, and thus
defined the mini-
mumflightheightwithoutdisturbanceforeachspeciesgro
upasthelastflightaltitudebeforethealtitudeduringwhicht
heyfled.To test for disturbance, we flew the drone for
28 min over BaliPond (PNP), starting at 80 m and
descending 5 m every 2
min(thetimeittooktoflyaslow,steadylaparoundthepond)
toanaltitudeof5m.Weadditionallyapproachedspecificr
ocodileswhile they were basking, starting from 10 m,
and

descendingslowlyto1m,todeterminethealtitudeatwhich
eywouldflee.Five observers equipped with binoculars
observed an equalportion of the pond and its shores,
monitoring the behaviour ofthe crocodiles, both on
land and in the water, and other
speciespresent(suchaselephants,warthogsandbirds).

Also, before implementing any drone-based
crocodile surveys, we wanted to test flight, ambient
light, and photographicparameters to optimise
detectability in the resulting images.Higher-altitude
flights increase surface-area coverage relative tobattery
power by decreasing necessary flight duration. But,when
flight altitude increases, photo resolution decreases
(e.g.from 0.62 cm²per pixel at 20 m to 1.22 cm²per
pixel at 40 maltitude); however, the number of photos
to be processed alsodecreases. To establish flight and
photographic parameters thatoptimise detectability, we
flew four test flight sessions over BaliPond (PNP)
consecutively on the same day, with 20 min inter-vals
between each session. Each session included four
flights,eachatadifferentaltitude(20,25,30and40m),corr
esponding

todifferentphotoresolutions(0.62,0.72,0.95and1.22cm²perpix
el),resultinginfourmapspersessioncoveringthe1hapondarea.W
eimportedthemapsintoaGIS,wherefiveexperienced,independe
ntobserverscountedthenumberofindividualcroco-dylians they
detected in each of the 16 maps by placing
ageoreferenceddotoneachdetectedcrocodile.Welimitedobserv
ersto10minpermapandtheywereblindtothecorresponding
flight parameters. We additionally asked all
theobserverstorankeachmap(from1to4;1low,2average,
3 good,4verygood)onthebasisoftheirperceptionoftheimageq
ualityandapparenteaseofsearchingforcrocodiles.Toverify
observer reliability, a sixth observer performed
anaposteriorirecountonallmapswithouttimelimittoestimate

the number of individuals that went undetected and to
estimatethe frequency of false detections. All analyses
considered timeof day of flight, flight altitude, map
rank, and observer identity
asfactorsinfluencingcrocodilecounts.

Comparingdronestotraditionalcrocodilesurveymethods

We compared the effectiveness of drone surveys to
two tradi-tional crocodile survey protocols, namely,
diurnal counts andnight spotlight counts. We
implemented each of the three surveytypes successively,
following the protocols below, on the sameday, starting
with a drone survey, at 23 sites (Fig.1, Supple-
mentary material Table S1). We conducted diurnal
and noctur-nal surveys in the same area at each site as
the area covered bythe corresponding drone flight. At
each site, we collected thefollowing additional data:
cloud cover, ¼ aquatic ¼ vegetation den-
sity,vegetationcover,visualestimation,andwindspeed.
Wescored each of the first three covariates on a
quantitative scalefrom0to4(00%,11–25%,226–
50%,351–75%and
4¼76–100%), and visually assessed wind speed, scoring
it on aqualitativescalefrom0to 4.

(1) Dronesurveys

Following the results of our optimal flight
evaluations (seeResults), we flew drone surveys at an
altitude of 40 m and at aspeed of 5 m s⁻¹with
908camera orientation, autonomouslyfollowingapre-
programmedflightplanfromtake-offtoland-
ing. For each site, we repeated the same flight plan three
times inthe same day (if the logistics allowed), namely,
once
between0900hoursand1100hours,oncebetween1300hou
rsand1500hours,andoncebetween1700hoursand1900ho
urs.Weprogrammed the drone to take photos at regular
intervals thatensured a minimum 60% overlap
between two
consecutiveimagestooptimisephotocollationandavoids
hadowsonmaps(Koh and Wich2012). We made maps
from each survey
asdescribedaboveandvisuallysearchedmapstoidentifyto
species (using head shape visible in photographs) and
quantifythenumberofcrocodilesdetected(Fig.2).
## (2) Diurnalsurveys

We counted crocodiles immediately following the dronecount, searching for crocodiles with the aid of binoculars. Wetraversedthestudyploteitheronfootorbyusing a3.5mzodiacwith a 15 hp outboard motor travelling at a constant speed of6-8 km h⁻¹. Because of logistical issues, we could not alwaysreplicate the diurnal survey protocols three times (once perdrone count). For each detected crocodile, we identified it tospecies and took a GPS point of its location. Where crocodilescould not be approached for classification, we noted the sightingas eyesonly(EO).

## (3) Nocturnalspotlightsurveys

Wecountedcrocodilesonetimeeachnightfollo wingstandardeyeshinespotlightprotocol(e.g.Shir leyetal.2009),startingandfinishing each survey between 2000 hours and 0200 hours. Weused a Streamlight Waypoint 550 lm spotlight and a 1-W LEDheadlamptodetectcrocodiles.Wetraversedth estudyploteitheronfootorbyusinga3.5mzodiacwi tha15hpoutboardmotortravellingataconstantspe edof6-8kmh⁻¹Foreachdetected
<figure><img src="image_4.png" /><figcaption>**Fig.2.Crocodile** counts and mapping from drone photos.</figcaption></figure>

(a) Themainmapistheaggregationof120orthorectifiedphotos. Therepointsarefordetectedcrocodile s, whichweredetected(b,e) onshore and (c,d) inthewater. Flightparameters:altitude40m,speed5 ms⁻¹, overlap 60%. TapoaRiver, W NationalPark, Niger.

crocodile, weidentifiedittospeciesandtookaGPSpointofi tslocation. Wherecrocodilescouldnotbeapproachedforcl assifi-cation,wenotedthesightingaseyesonly(EO).

Statisticalanalysis

We used Poisson regressions, a particular case of
generalisedlinear model (McCullagh2018), to model
crocodile count
data with the logarithm of the link function. When relevant,
we used the quasi-Poisson distribution instead of the
simple Poisson
to take into account overdispersion in the data. We used like
liko odoratio tests (LRT) to study the effect of covariates and i
nteractions among covariates. We performed all analyses in
R version 3.5.2 (R Core Team 2018).

To assess flight parameters and the analysis of the
observereffect,wemodelledcrocodilecountdataagainstst
meofdayofflight,flightaltitude,maprankandobserveride
nty.Weseparately modelled the number of false or
missed detectionsagainstthesamecovariates.

Tocomparethethreesurveymethods,wemodelledcroc
odilecount data against site identity (10, 8, and 5 sites
for Benin, Coted'Ivoire and Niger respectively) and
count method (3
droneinvestigationsduringtheday,1diurnalsurvey,and1
nocturnalsurvey). For drone surveys only, we
modelled crocodile
countsagainsttimeofday,windstrength,cloudcoverand,a
dditionally,asiteeffect.We,ultimately,didnotinclude the
vegetationindexin the model as a covariate because
there is no variation of
thisvariablewithinasite.Weanalyseddataindependentlyf
reachcountry andforallcountriescombined.

Results

Testingflightplansanddatacollectiontominimisedisturb
anceandmaximisedetectability

At Bali Pond (PNP), we found that *Crocodylus
suchus* was the least disturbed by the
droneofallspecies present, with the flee
altitude ranging from 1 to 10 m (Fig. 3). In contrast, all mammalswerethemostsensitivetothedrones, withfleealtitu desrangingfrom 60 m for *Loxodonta africanana* 20 m for *Hippopotamusamphibius*and *Papioanubis*, whereasbird fleealtitudesrangedfrom 10 to 15 m (Fig. 3). Flight responses of other speciespresentaroundthepondindicatednobehaviouralcha ngeinthecrocodiles. On the basis of these results, we determined thatcrocodylianspeciesinWestAfricawereunlikelytobeper turbedbydronesurveystothepointoffleeingexceptbelow11 mand,therefore, crocodile drone surveys should be flown at altitudesabove thisminimum.

Inouranalysisoftestflightparametersthatoptimisedetect

ability, everycovariate, exceptaltitude, hadasignificantimp actoncounts(Fig. 4a, Table1). Wefoundthatthefiveindepen dentobserverscounted, onaverage, 18.21crocodilespermap, ranging from 4 to 39, where the best observer counted, onaverage, 28.38 and the worst observer 9.44. The independent, unconstrained observer counted an average of 34.94 crocodilesper map (24–47), and found, on average, 1.23 more crocodilesthandidthebestobserverand3.7morethanthewor stobserver(Table1). The variation between observers was significant, bothincluding and excluding (result not shown) the independent, unconstrainedobserverimplementingtheexha ustivecount(Table1).

In terms of false detections or undetected individuals, alti-tude, map quality, and time of day had no significant relation-ship, but observer identity and time of day had a significantimpact (Table1). On average, the five observers missed 16.79individualspermap, rangingfromsevenforthebestobs erverto

23.19 for the worst, and the inter-observer differences werehighlysignificant($F_{4,74}$$23.98$, $P1.8x10^{-12}$; Fig.4b, Table1). Observers made an average of 0.63 false detectionsper map, ranging from 0.44 to 1.06, although inter-

observerdifferenceswerenotsignificant($F_{4,74}$$141.0$, $P$$140.41$ ; Fig.4c,
<figure><img src="image_5.png" /><figcaption>**Fig.3.Drone**flightaltitude(m)atwhichspeciesobservedatBaliPond,PendjariNationalPark,Benin,fledthedrone.</figcaption></figure>

Table1). Generally, the best observers were either those
with themost experience in the field and/or with
technology. There was asignificant difference in the
number of crocodiles
observedacrossthefoursessions(i.e.timeofday; *F*<sub>3,71</sub>4.86,
*P*<sub>0.004</sub>),withmorecrocodilesbeingdetectedduringthefir
session. As altitude decreased (e.g. map resolution
increased),theobserversdidnotdetectmorecrocodiles(*F*<sub>1
.703.06,P*<sub>0.085</sub>); Fig.4a, Table1), nordidtheydetectmorecr
ocodilesonmapstheyjudgedtobeofhigherquality(*F*<sub>1,69</sub>4.
19,*P*<sub>0.044</sub>; Table1). On the basis of these results, we
flew allsubsequent drone surveys at 40 m altitude (see
above), anddelayed flights when elephants and
buffalos were present toavoid disturbance.

Comparingdronestotraditionalcrocodilesurveymethods

We detected very few crocodylians in Cote d'Ivoire,
namely,
Oatallsitesbyallmethods, exceptatMareauxBuffsleswhere
wedetected 0 by drone, 0 by day count, and 3 by night
count. We ultimately excluded Cote d'Ivoire from
further analysis. InBenin, we detected 49 crocodiles
by drone, 30 by day
survey, and71bynightsurvey, wheremostofthesedetect
ionswereexclusively in the Bali pond in PNP (Site
1; Supplementarymaterial Fig. S1). In Niger, we
detected 156 crocodiles by
drone,32bydaysurvey,and311bynightsurvey(Fig.S1).
Weulti-
matelyanalyseddatafromBeninandNigerseparatelytore
ducethechanceforbiasowingtothedifferenceinscaleofnu
mberofcrocodiles detected. We found that night
surveys detected sig-nificantly more crocodiles than
either of the other two
surveymethodsinbothcountries,andthatdronesdetecteds
ignificantlymorecrocodilesthandidstandarddaysurveys
(Fig.5a, Table2:Niger F<sub>2,18</sub>¼38.70, P<sub>1/4</sub>3.56×10<sup>-6</sup>;
Benin: F<sub>2,28</sub>¼59.39, P<sub>1/4</sub>5.7×10<sup>-5</sup>; Fig.5b).

Environmental factors on the detection efficiency of drone

We observed no effect of the site on the number of
crocodiles detected in Niger ($F_{4,10} \frac{40.648}{40.65}$; Table3),
whereas in Benin the site effect was significant ($F_{4,10} \frac{4890.21}{4}$,

We ultimately analysed data from Benin and Niger separately to
ducethechanceforbiasowingtothedifferenceinscaleofnu
mberofcrocodiles detected. We found that night
surveys detected significantly more crocodiles than
either of the other two
surveymethodsinbothcountries,andthatdrones detected
ignificantly more crocodiles than did standard daysurveys
(Fig.5a, Table2: Niger $F_{2,18} \frac{438.70}{43.56} \times 10^{-6}$;
Benin: $F_{2,28} \frac{459.39}{45.7} \times 10^{-5}$; Fig.5b).
P2.610<sup>-7</sup>; Table3). Time of day was not significant inNiger (F<sub>2,8</sub>0.46,P0.655; Table3), but it was significant inBenin (F<sub>2,8</sub>97.74,P9.810<sup>-5</sup>; Table3). Wind intensity had no significant effect in Niger (F<sub>3,5</sub>1.022,P0.457; Table3) or Benin F<sub>3,5</sub>4.91,P0.060; Table3). Ultimately, we did not model cloud cover because there was not enough variation across days, sites or times.

## Discussion

We sought to assess the efficacy of drones as crocodile survey tools for a diverse crocodylian species assemblage in West Africa. In so doing, we tested several flight parameters that allowed us to also establish a standardised and optimised protocol for drone-based crocodylian surveys. We found that drones were more effective in crocodiles survey tool(s) than were traditional day surveys for crocodylians in West Africa. However, as with traditional day surveys, drone surveys were less effective than were traditional nights surveys both because crocodiles are generally more available and detectable at night and also because typical consumer drones do not currently have nocturnal filming capacity. Further, we found that drone flight para-meters that optimise flight efficiency and area coverage are more important considerations for flight planning than are characteristics we pre-suppose will affect subsequent detectability or disturbance. Here, we discuss each of these in turn.

## Developing standard flight protocols for use of drones in crocodylians surveys

We assessed the effect of altitude, map rank, time of day, observer bias and disturbance on crocodiles using drone stop-pose as standard protocol for such future studies. We found that image resolution, which enforces the standard camera (4K resolution) on the DJI Phantom 4 Pro, washes out, so that we found no effect on crocodile counts up to 40 m altitude. Forty metres or altitude is an optimal flight height to achieve time- and power-efficient coverage of sites; however, future studies should include testing protocols for crocodiles detectability at higher

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<figure><img src="image_6.png" /><figcaption>Table 1. Results of generalised linear model assessing the influence of offflight, photo, and observer characteristics on the number of crocodiles counted by drones</figcaption></figure>

<table><thead><tr><th>Variable</th><th>d.f.</th><th>num.</th><th>d.f.denom.</th><th>F-value</th><th>P-</th></tr></thead><tbody><tr><td colspan="6"><strong>valueNumber of crocodiles counted</strong></td></tr><tr><td>Observeridentity</td><td>4</td><td>74</td><td>22.44</td><td>6.73E-12</td><td></td></tr><tr><td>Timeof flight</td><td>3</td><td>71</td><td>4.86</td><td>0.003978</td><td></td></tr><tr><td>Flightaltitude</td><td>1</td><td>70</td><td>3.06</td><td>0.084852</td><td></td></tr><tr><td>Map rank</td><td>1</td><td>69</td><td>4.19</td><td>0.044535</td><td></td></tr><tr><td colspan="6"><strong>Number of false detections</strong></td></tr><tr><td>Observeridentity</td><td>4</td><td>74</td><td>1.0044</td><td>0.4113</td><td></td></tr><tr><td>Timeof flight</td><td>3</td><td>71</td><td>1.4943</td><td>0.2238</td><td></td></tr><tr><td>Flightaltitude</td><td>1</td><td>70</td><td>2.496</td><td>0.1187</td><td></td></tr><tr><td>Map rank</td><td>1</td><td>69</td><td>1.5352</td><td>0.2195</td><td></td></tr><tr><td colspan="6"><strong>Number of missed detections</strong></td></tr><tr><td>Observeridentity</td><td>4</td><td>74</td><td>23.9791</td><td>1.855E-12</td><td></td></tr><tr><td>Timeof flight</td><td>3</td><td>71</td><td>12.8099</td><td>9.548E-07</td><td></td></tr><tr><td>Flightaltitude</td><td>1</td><td>70</td><td>0.9668</td><td>0.3289</td><td></td></tr><tr><td>Map rank</td><td>1</td><td>69</td><td>0.6862</td><td>0.4103</td><td></td></tr></tbody></table>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 4.** Observer effects on crocodiled detection.</figcaption></figure>

(a) Distribution of crocodile counts for each observer and flight altitude. Each observer (A–E, each with a different colour) had 10 mintocounts for crocodiles on the reconstituted maps (Bali pond, Pendjari National Park, Benin), and the independent, unconstrained observer (IUO) had no time limit.

(b) Number of missed detections per observer and (c) number of false detections per observer. The boxplots represent the aggregated counts for each height (median, 25% and 75% quartiles, whiskers representing 5% and 95% quartiles).

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

Drone Day Benin Night

<caption>**Fig. 5.** Number of crocodiles detected for each survey protocol in (a) Niger and (b) Benin. The boxplots represent the median, 25% and 75% quartiles, whiskers representing 5% and 95% quartiles, and dots the outliers.</caption>
## Table2.Resultsofgeneralisedlinearmodelcomparing crocodyliansurveymethods

Site isthesiteidentity (asafixedeffect).Protocol referstosurveymethod(dronevsdiurnalcountvsnocturnal spotlightcount).Windandcloudcoverare both categoricalcovariates

<table><thead><tr><th>Variable</th><th>d.f. num.</th><th>d.f. denom.</th><th>F-value</th><th>P-</th></tr></thead><tbody><tr><td>valueBenin</td><td></td><td></td><td></td><td></td></tr><tr><td>Site</td><td>9</td><td>30</td><td>92.7694</td><td>2.119E-15</td></tr><tr><td>Protocol</td><td>2</td><td>28</td><td>15.7479</td><td>5.690E-05</td></tr><tr><td>Wind</td><td>3</td><td>25</td><td>5.5811</td><td>0.005296</td></tr><tr><td>Cloudcover</td><td>3</td><td>22</td><td>14.2627</td><td>2.222E-05</td></tr><tr><td>Niger</td><td></td><td></td><td></td><td></td></tr><tr><td>Site</td><td>4</td><td>20</td><td>2.3649</td><td>0.10693</td></tr><tr><td>Protocol</td><td>2</td><td>18</td><td>38.6973</td><td>3.365E-06</td></tr><tr><td>Wind</td><td>3</td><td>15</td><td>1.2516</td><td>0.33146</td></tr><tr><td>Cloudcover</td><td>2</td><td>13</td><td>3.7614</td><td>0.05142</td></tr></tbody></table>

## Table3.Resultsofgeneralisedlinearmodelassessingth eimpactofenvironmental variables on drone surveys

Siteisthesiteidentity(asafixedeffect).TimeofdayandW indarebothcategorical covariates

<table><thead><tr><th>Variable value</th><th>d.f.num.</th><th>d.f. denom.</th><th>F-value</th><th>P-</th></tr></thead><tbody><tr><td>Benin</td><td></td><td></td><td></td><td></td></tr><tr><td>Site</td><td>4</td><td>10</td><td>890.208</td><td>2.574E-07</td></tr><tr><td>Time of day</td><td>2</td><td>8</td><td>97.739</td><td>9.823E-05</td></tr><tr><td>Wind</td><td>3</td><td>5</td><td>4.911</td><td>0.05955</td></tr><tr><td>Niger</td><td>4</td><td>10</td><td>0.6480</td><td>0.6524</td></tr><tr><td>Site</td><td></td><td></td><td></td><td></td></tr><tr><td>Time of day</td><td>2</td><td>8</td><td>0.4610</td><td>0.6550</td></tr><tr><td>Wind</td><td>3</td><td>5</td><td>1.0223</td><td>0.4569</td></tr></tbody></table>

altitudeoffurtheroptimisecoverage,time,andbatteryeffi ciency,especiallyashigher-resolutioncamerasbecomeavailable.Theabilitytozoomi ntoimagesbecauseofthehigh4Kresolutionrenderedfalse detectionsrare,regardlessoftheobserver.Asaresult,obse rverbiasfarexceededtechnicalflightbiasasthemostinflue ntialfactoraffectingdrone-basedcroco-dile surveys, as it has been shown for standard spotlight counts(Nicholsetal.2000;Shirleyetal.2012).Countingcrocodilesonmapimagesatedious,time-consumingtaskthatrequiresintenseconcentration;limite dto10minpermap,ittook3hforobserverstocountcrocodil

complicated. In the middle of the day, as the sun orientationapproaches 908from the water surface, reflections back to thecamera effectively whiteout patches of habitat (Fig.6c). Theseobservations are similar to those in other drone studies foraquatic-wildlifemonitoring(Kiszkaetal.2016;Linchant etal.2018).Meteorologicalconditionssuchaswindarealr eadyknowntobeunfavourableforobservingcrocodiles(S hirleyet al.2012). Sun reflection and wind-generated waves

alsodegradethequalityoftheaerialphotosanddisruptthea ssemblyof tiled photos (Fig.6b). Further, windy conditions make smalldrone flights challenging. For these reasons, we recommendflights in themorning (from0900 hoursto 1100hours).

Despite their small size, drones have been shown to bedisturbing to wildlife (McEvoyet al.2016), especially whenanimals are approached too closely (Bennitt et al.2019) or atsensitivenestingandbreedingsites(Dulavaetal.2015;P omeroyetal.2015;Weissensteineretal.2015).Weassesse dthealtitudeatwhichspeciescommonlyfoundinourstudy sitesfledduetothepresenceofthedrone.Interestingly, Cro codylussuchusfledthedroneattheclosestapproachaltitud eofanyspeciesatourstudysite, and even showed signs of being more tolerant than othercrocodylianspecies(e.g. Crocodylusporosus; Beva netal.2018).Indeed,withthesamedronemodelandsimilar methods,Bevanetal. (2018)observedthatC porosusrespondedtodronesat30m

withlateralheadmovementsandsubmergedorretreatedto esonthe161-hatestmaps,andittooktheindependent,unconstrainedobserver5 htodoanexhaustivesearch.Investmentin/ engagementwiththestudyandindividualexperiencearecriticalto achievinggoodresults.Ofsecondaryimportance, somemeteorologicalconditionscanhinderreconstructionorqual ityofmapimages(Fig.6a).Althoughtherewasnosignificanteffec toftimeofdayonthenumberofcrocodilesdetectedbythedroneint hepresentstudy,therewasanoticeabledecreaseinthequalityofae rialimagesintheevening.Tocounterbalancethelackofflight,thec ameraautomaticallyincreasesthesensorsensitivity(ISO),thusde gradingthequalityoftheimage.Thismaynot,ultimately,affectth edetectabilityofcrocodilesbyskilledobservers,althoughsiteswi thmoredebriswillcertainlybemore
deeper water at 10 m altitude. Although, due to
our single testflight at different altitudes, we do
not exclude the possibility
of habituation of the crocodilesto the drone at higher
altitudes, and cautions should be used for future proje-
cts at altitudes lower than 40 m. In comparison,
mammals fled at much higher
altitudes; however, the results for these other species
may not be presen-
tative, given that single flight and the down numbers of
individuals present, ranging from 1 to 10 or so,
depending on the
species, compared with 100% of the crocodiles.

Were porotheret the first indications for African bu-
ffalo (Syncerus caffer), which fled at 50m. Our resul-
ts are congruent with those of Bennett et al. (2019),
who also found that elephants (Loxodonta
africana) were perturbed at 60 m. Several
studies have shown that elephants avoid bees
(Vollrath and Douglas-Hamilton 2002; Ngama et
al. 2016; King et al. 2017) and
thenoise emitted by the drone, and it could be confus-
ed for as warm of bees. Other species were much
more tolerant of the drone, including hippos
which fled at 20 m approach altitude, as
was found in other studies (Linchant et al. 2018; Inm
an et al. 2019). Finally, bird of many species fled the dr-
one at an altitude of only 10–
15 m, being congruent with what has been observed
about other sites for other bird species (Vas et al. 2015; Mc
Evoy et al. 2016; Brisson-Curadeau et al. 2017;
Rush et al. 2018). The increased altitudes
precipitating a flight response in large
mammals will not likely result in decreased
detection of these species,
given their normou size. And, even though drones
have the possibility of disturbing these species,
they are likely to be less disrup-
tive than humans in close approach on foot or in an aut
omobile, and observation from a drone is less danger
ous for the observers (Mulero-Pa´zma
´nyet al. 2017). Importantly, fleeing is not the only way
havioural evidence of disturbance by drones on wild
life, but rather the last-resort behaviour. For
crocodylians, we did not observe other behaviours
potentially indicative of
disturbance (such as repositioning, leg and head mov
ements and
<figure><img src="image_14.png" /><figcaption>**Fig. 6.** Image-quality disturbance owing to wind and sunlight. (a) This reconstructed image of the Canard pond (PNP, Benin) shows how (b) the small waves generated by wind (yellow square in a) and (c) sunlight reflection (green square in a) can affect the quality of the image, and thus potentially affect the crocodile count. (d) Part of this image is also truncated (red square in a) because it affects the homogeneity prevents photo assembly into map reconstruction.</figcaption></figure>

submersion), but did not look for behaviours such as wing extension (Weimerskirch et al. 2018), vocalisations (Wilson et al. 2020) and head movements (Bennitt et al. 2019) in the other species present at our study sites because of our focus on crocodiles. Nonetheless, we find it interesting to present these results, given the very few data available for wildlife—drone interactions in West Africa.

### Efficacy of and uses for drones to survey crocodiles in West Africa and elsewhere

We aimed to compare drone counts with traditional diurnal and nocturnal crocodile surveys. We found that nocturnal spotlight counts detected significantly more crocodiles (87%) than either of the two other methods, although we detected 231% more crocodiles by drone than during traditional diurnal surveys, being congruent with previous studies counting crocodiles with drones in Africa (Ezat et al. 2018). The drone was incapable of detecting individuals smaller than 100 cm in total length in most conditions; however, this is also a standard problem of diurnal crocodylian surveys (Shirley and Eaton 2012). Unfortunately, the drone we used was not mounted with technology enabling

nocturnal drone surveys for more direct comparison with nocturnal spotlight surveys. As consumer drones with camera technologies permitting night filming become unavailable, we recommend testing nocturnal drone surveys as a potentially promising avenue for future research.
Despite detecting fewer individuals during the daytime thando traditional nocturnal surveys, drone surveys are likely tobringseveraladvantagesincrocodyliansurveyscompared withstandardspotlightortraditionaldiurnalsurveys.Forone ,because of the high-resolution map images (1.22 cm²per pixelat 40 m), drones allow for unbiased measurement of the detectedindividual’s size on the basis of eitherin situscaling or use ofstandard head length to total length ratios (e.g. Fukudaet al.2013).Althoughthisiscertainlypossiblethroughcloseap proach of individuals during nocturnal surveys, even expertobservers are shown to be error prone (Choquenot and Webb1987),andthecloseapproachnecessarycanbestressful fortheanimals.Thesephotoscouldalsobeusedtoidentifyindi vidualspresent at study sites, resighting either artificial tags or naturalcrocodylian markings (Swanepoel1996; Bouwman and Cronje2016; Boucheretal.2017;Coetzeeet al.2018).

Dronesprovidenotonlyarigorousandnon-invasivewaytocharacterise observed individuals, but also leave a permanentrecordofobservations,anadvantagethatcannotb eunderstated(Kelaheretal.2020).Maprecordscanbeusedfo rlaterverifica-tion of number, size, species and position, including habitatoccupied,ofalldetectedcrocodiles.Thereduceddistu rbancetoanimals compared with foot and boat surveys will also likelyresult in less double counting of individuals as they flee andsubmergeonlytoresurfaceelsewhere.Thishaslongbeenr ecognisedasanadvantageinaerialsurveysforcrocodiles,but
with the added advantage of objective counts from photos or duc-ing aerial survey observer bias and limitations (Nicholset al. 2000). Drones share other advantages and disadvantages with manned aerial surveys. Both can be used to cover more of the territory faster and for less cost than for boats, surveys, butbot fail to detect diverse ecoworlds (Bayliss et al. 1986). However, drones are cheaper than manned craft, and with simpler logistics, small ecological footprints, and introduce less disturbance.

Because of the elevated point of view, drones can cover a meseveral habitat-related visibility issue of crocodiles. The presence of plants and the complexity of the habitat strongly affect ground visibility and are a principal source of bias in estimating crocodylian populations (Shirley et al. 2012). In our study, we detected more crocodilians using a drone, which is a new aditional day survey despite the shrubby vegetation cover on the banks and the presence of aquatic plants on the Tapoa River. Drones effectively allow observers to see typically only non-observable space, in this case, beyond the first layer of vegetation, and more generally, including otherwise inaccessible or distant habitats (Vaser et al. 2015). However, when habitats are too homogeneous, such as the centre of the water without the shore line or other features (e.g. aquatic vegetation, rocks, tree trunks) in the field of view, map reconstruction becomes nearly impossible because of the low number of reference points between photos (Fig. 6d). The constant improvement of image compilation and orthorectification soft-wares should mitigate some of theveninalization in the coming years. Additionally, the presence of vegetation above study sites makes aerial photos with a camera oriented at 90° more relevant.

The latter is a major problem for forested waterways where crocodilians and the water's edge will always be undetectable from above. In West Africa, this means that drone surveys may never be a relevant method for counting Mecistops cataphractus and Osteolaemus spp., which both prefer forested habitats and nest under closed canopy forest cover (Waitkuwait 1989; Shirley et al. 2018). Indeed, our drone surveys did not detect either of theses species at site where they are known to be present. However, in Coted'Ivoire, this is also likely to do as much with their rarity as with detectability issues, because our nocturnal surveys also failed to detect them (Shirley et al. 2009, 2018). Drones are increasingly used to survey forest vertebrates, although more generally primates, birds, and other species that live and or nest in the canopy (Weissensteiner et al. 2015; Wichetal. 2015; Bonnin et al. 2018).

Finally, drones are increasingly inexpensive (less than US$1500), easy to master, and often facilitate field

logistics, and reduce costs compared with other crocodile survey methods. Drone surveys do not require a boat, fuel, driver, and multiple observers with strong field experience, unlike standard nocturnal and diurnal surveys (Shirley and Eaton 2012). However, the limited battery life of the drone can limit the extent of the study area, access to electricity to recharge batteries, limit the echoice of study sites, and increasingly strict national laws and protected areas regulations concerning drone usage may prevent some users from employing drones.

Beyonddrones: some additional considerations and observations from the present study

These surveys represent some of the first published information on crocodylian populations in Coted'Ivoire, Benin and Niger.
We surveyed sites that contained up to three different croco-dylian species, including the Critically Endangered *Mecistopscataphractus*; however, we detected only *Crocodylus suchus*. This species is the most widespread and found in the greatest diversity of habitats (Kofron 1992; Teller 'aet al. 2008; Britoetal. 2011; Luisellietal. 2012). In contrast, *Os teolaemusspp.* and *Mecistops cataphractus*, although ranging from coastal swamp forest to gallery forest, wetland habitats in Guineansavanna, are dependent on forested habitats (Waitkuwait 1989; Shirleyet al. 2018). The lack of observations during nocturnal spotlight surveys at two sites where they were known to be present is of concern. At these sites, we observed use of sustainable and illegal fishing and palm cultivation (ANP) and counted more than 60 gold panning grafts on the Comoe River (CNP).

Our study also underscored certain conservation issues for *Crocodylus suchus*. Night surveys on the Pendjari River and in the surrounding ponds showed an extremely low crocodyliandensity, with often no individuals being observed, whereas this species was seemingly abundant only a decade ago (Pooley 1982; Chirio 2009). In contrast, we observed many signs of poaching and fishing, including such as fishing nets, traps and smoking platforms, along the river that form part of the border between the Pendjari National Park (Benin) and Arly National Park (Burkina Faso). While poaching and fishing camps were mostly on the Burkina side, poachers extracted wildlife from within the boundaries of both parks. Fishing and poaching activities had already precipitated the near extinction of croco-diles in the Niger River bordering the WAP complex before 2010 (Shirley and Eaton 2008). By comparison, the Bali ponds situated closer to the centre of the PNP and with more tourist presence, had many crocodiles. Similarly, crocodiles are extremely abundant in the Tapoa River (Park W, Niger), near the main ranger station and tourist axes (Shirley and Eaton 2008). African Park stookover management of Pendjari in 2018 and W (Benin) in 2020, which will hopefully result in increasing protection for the crocodiles and all wildlife in this critical conservation area. Drones may even provide valuable tool for remote detection of these illegal activities.

## Conclusions

Protecting crocodylians and their habitats is an urgent conservation need, especially in West Africa where they are not typically present on the conservation agenda. Drones provide an inexpensive and effective tool for assessing and monitoring crocodylian populations in some

ecological contexts. They offer advantages of reduced impacts on wildlife, limiting risks for observers, easy logistics, potentially larger survey-area coverage, and data security. Further work is merited across these regions, including their full potential, both for crocodylians and wildlife and protected areas.

## Conflict of interest

The authors declare no conflict of interest.

## Acknowledgements

Wethank Christine Kouman, Lae'titia Mathon, Olivier Thaler, Christina Gomez, Diane Hirth Murdock, Audrey Ipavec, Kirk Gastrich, Marie-He 'le'ne Filippi, Alain Botton, Justine Boutry, and Gwe 'nae'lduclos from WIPSEA
for their support. We thank the Zoological Society of London (ZSL), NatureConserv'Action,HorstOebelfromtheDeutsche Gesellschaftfu¨rInter-nationale Zusammenarbeit (GIZ), and Juan Lapuente from the Station deRechercheenEcologiedelaComoe

'(SREC)forhelpingwiththelogistics.Wethankthestaffof CentreNationaldeGestiondesRe

'servesdeFaune(CENAGREF),l'OfficeIvoiriendesParc etRe

'serves(OIPR),theIvorianMinisteredesEauxetForets,an
dtheEauxetFore^tsduNigerforauthorisingthis research.
In Benin, this research was authorised under permit
number110/17/CENAGREF/DG/DT/DAF/ADfromCE
NAGREF.InCoted'Ivoire,this research was authorised un
derpermit number189/MESRS/DGRIfromthe Direction
Generale de la Recherche et de l'Innovation
(Ministere

del'EnseignementSuperieuretdelaRechercheScientifiq
ue).TheprojectwasfundedbytheFondationBIOTOPE,IUC
N/SSCCrocodileSpecialistGroup

StudentResearchAssistanceScheme,FondsdeSolidarite
'etdeDe'vel-oppurementdesInitiativesE

'tudiantesdel'Universite

'deMontpellier,Ondulia,anOkpal crowfunding campaign, and ProjetMecistops.

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**Supplementary Material**

# Evaluation of the use of drones to monitor a diverse crocodylian assemblage in West Africa

Clément Aubert<sup>A,B,C,K</sup>, Gilles Le Moguédec<sup>D</sup>, Cindy Assio<sup>A</sup>, Rumsaïs Blatrix<sup>E</sup>, Michel N'dédé Ahizi<sup>C,F</sup>, Georges Cogo Hébert<sup>G</sup>, Nathalie Gnanki Kpera<sup>H</sup>, Vincent Lapeyre<sup>I</sup>, Damien Martin<sup>G</sup>, Pierrick Labbé<sup>B,J</sup> and Matthew H. Shirley<sup>A,C</sup>

<sup>A</sup>Institute of Environment, Florida International University, Biscayne Bay Campus, AC1210, 3000 N.E. 151st Street, North Miami, FL 33181, USA.

<sup>B</sup>Institut des Sciences de l'Evolution de Montpellier (UMR 5554, CNRS-UM-IRD-EPHE), Université de Montpellier, Montpellier, 34095 Cedex 5, France.

<sup>C</sup>Project Mecistops, 615 Waterside Way, Sarasota, FL 34242, USA.

<sup>D</sup>AMAP, Université Montpellier, INRAE, CIRAD, CNRS, IRD, Montpellier, France.

<sup>E</sup>CEFE, University of Montpellier, CNRS University Paul Valéry Montpellier 3, EPHE, IRD, 191 route de Mende, 34293 Montpellier, France.

<sup>F</sup>Laboratoire d'Environnement et de Biologie Aquatique, Université de Nangui, Abrogoua, BP 801, Abidjan 02.

<sup>G</sup>Centre Régional de Recherche et d'Éducation pour un Développement Intégré (CREDI-ONG), BP 471, Abomey-Calavi, Républiquedu Bénin.

<sup>H</sup>National Institute of Agricultural Research of Benin (INRAB), BP 1915 Abomey-Calavi, Républiquedu Bénin.

<sup>I</sup>Conservation Program, Zoological Society of London, Regent's Park, London, England NW14RY.

<sup>J</sup>Institut Universitaire de France, 1 Rue Descartes, 75231 Cedex 05, Paris

<sup>K</sup>Corresponding author. Email: clement.aubert34@hotmail.fr; clement.aubert@umontpellier.fr
<figure><img src="image_15.png" /><figcaption>**Figure S1.** Number of crocodiles detected at each site by the three different survey methods. We counted crocodiles at 23 sites (Table S1) across Benin (light grey), Niger (black), and Cote d'Ivoire (dark grey) using three protocols: drone (triangles) and ground/van counting by day (rounds) or by night (squares).</figcaption></figure>
<table><caption>TableS1Characteristicsofandcrocodiledetectionsateachstudysite</caption><thead><tr><th rowspan="3">Country</th><th rowspan="3">NationalParc</th><th rowspan="3">SiteName</th><th rowspan="3">Site Number</th><th rowspan="3">Coveredareabydrone</th><th colspan="5">No.ofCrocodilesDetected</th></tr><tr><th colspan="3">Drone(session)</th><th rowspan="2">Day Survey</th><th rowspan="2">Night Survey</th></tr><tr><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><td>Benin</td><td>Pendjari</td><td>MareBali</td><td>1</td><td>13,175m²</td><td>36</td><td>16</td><td>48</td><td>28</td><td>46</td></tr><tr><td>Benin</td><td>Pendjari</td><td>MareBaobab</td><td>2</td><td>4,603m²</td><td>0</td><td>-</td><td>-</td><td>0</td><td>8</td></tr><tr><td>Benin</td><td>Pendjari</td><td>MareTiabiga</td><td>3</td><td>21,620m²</td><td>1</td><td>0</td><td>0</td><td>1</td><td>3</td></tr><tr><td>Benin</td><td>Pendjari</td><td>MareKoudjedougou1</td><td>4</td><td>24,142m²</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Benin</td><td>Pendjari</td><td>MareKoudjedougou2</td><td>5</td><td>21,950m²</td><td>0</td><td>-</td><td>-</td><td>0</td><td>1</td></tr><tr><td>Benin</td><td>Pendjari</td><td>MareDiwouni</td><td>6</td><td>38,608m²</td><td>0</td><td>0</td><td>0</td><td>0</td><td>3</td></tr><tr><td>Benin</td><td>Pendjari</td><td>MareFogou</td><td>7</td><td>26,354m²</td><td>0</td><td>-</td><td>-</td><td>0</td><td>0</td></tr><tr><td>Benin</td><td>Pendjari</td><td>MareSacree</td><td>8</td><td>10,583m²</td><td>0</td><td>-</td><td>-</td><td>0</td><td>4</td></tr><tr><td>Benin</td><td>Pendjari</td><td>MareCanard</td><td>9</td><td>21,750m²</td><td>0</td><td>0</td><td>0</td><td>1</td><td>3</td></tr><tr><td>Benin</td><td>Pendjari</td><td>MareYangouali</td><td>10</td><td></td><td>0</td><td>-</td><td>-</td><td>0</td><td>3</td></tr><tr><td>Niger</td><td>W</td><td>Tapoa1</td><td>11</td><td>500m</td><td>18</td><td>24</td><td>39</td><td>7</td><td>94</td></tr><tr><td>Niger</td><td>W</td><td>Tapoa2</td><td>12</td><td>500m</td><td>33</td><td>29</td><td>14</td><td>5</td><td>71</td></tr><tr><td>Niger</td><td>W</td><td>Tapoa3</td><td>13</td><td>500m</td><td>28</td><td>5</td><td>11</td><td>2</td><td>58</td></tr><tr><td>Niger</td><td>W</td><td>Tapoa4</td><td>14</td><td>500m</td><td>31</td><td>15</td><td>9</td><td>3</td><td>67</td></tr><tr><td>Niger</td><td>W</td><td>Tapoa5</td><td>15</td><td>500m</td><td>15</td><td>21</td><td>25</td><td>15</td><td>21</td></tr><tr><td>Coted'Ivoire</td><td>Azagny</td><td>Canal1</td><td>16</td><td>1,000m</td><td>0</td><td>-</td><td>-</td><td>0</td><td>0</td></tr><tr><td>Coted'Ivoire</td><td>Azagny</td><td>Canal4</td><td>17</td><td>1,000m</td><td>0</td><td>-</td><td>-</td><td>0</td><td>0</td></tr><tr><td>Coted'Ivoire</td><td>Azagny</td><td>Canal5</td><td>18</td><td>1,000m</td><td>0</td><td>-</td><td>-</td><td>0</td><td>0</td></tr><tr><td>Coted'Ivoire</td><td>Azagny</td><td>Canal6</td><td>19</td><td>1,000m</td><td>0</td><td>-</td><td>-</td><td>0</td><td>0</td></tr><tr><td>Coted'Ivoire</td><td>Azagny</td><td>Canal7</td><td>20</td><td>1,000m</td><td>0</td><td>-</td><td>-</td><td>0</td><td>0</td></tr><tr><td>Coted'Ivoire</td><td>Comoé</td><td>Pontdroit</td><td>21</td><td>250m</td><td>0</td><td>-</td><td>-</td><td>-</td><td>0</td></tr><tr><td>Coted'Ivoire</td><td>Comoé</td><td>Pontgauche</td><td>22</td><td>250m</td><td>0</td><td>-</td><td>-</td><td>-</td><td>0</td></tr><tr><td>Coted'Ivoire</td><td>Comoé</td><td>Mareaubuffle</td><td>23</td><td>5800m²</td><td>0</td><td>-</td><td>-</td><td>0</td><td>3</td></tr></tbody></table>