10.5061/DRYAD.15DV41NZ5
Ahizi, Michel N'Dede
0000-0002-2211-8777
Université Nangui Abrogoua
Kouman, Christine Yaoua
Université Nangui Abrogoua
Ouattara, Allassane
Université Nangui Abrogoua
Kouame, N’Dri Pascal
Office Ivoirien des Parcs et Réserves
Dede, Azani
Office Ivoirien des Parcs et Réserves
Fairet, Emilie
Wildlife Conservation Society
Shirley, Matthew H.
Florida International University
Detectability and impact of repetitive surveys on threatened West African
crocodylians: Data M1
Dryad
dataset
2021
FOS: Biological sciences
Save Our Species Fund*
San Diego Zoo Institute for Conservation Research
https://ror.org/04fqe9p30
Christmas CrocFest*
St. Augustine Alligator Farm*
Oklahoma City Zoo*
Minnesota Zoo*
Future for Nature Foundation*
AZA Crocodilian Advisory Group*
2022-11-04T00:00:00Z
2022-11-04T00:00:00Z
en
https://doi.org/10.5281/zenodo.5645911
31616 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
West African crocodylians are among the most threatened and least studied
crocodylian species globally. Assessing population status and establishing
a basis for population monitoring is the highest priority action for this
region. Monitoring of crocodiles is influenced by many factors that affect
detectability, including environmental variables and individual or
population-level wariness. We investigated how these factors affect
detectability and counts of the Critically Endangered Mecistops
cataphractus and the newly recognized Crocodylus suchus. We implemented
195 repetitive surveys at 38 sites across Côte d’Ivoire between 2014 and
2019. We used an occupancy-based approach and a count-based GLMM analysis
to determine the effect of environmental and anthropogenic variables on
detection, and modeled crocodile wariness over repetitive surveys. Despite
their rarity and level of threat, detection probability of both species
was relatively high (0.75 for M. cataphractus and 0.81 for C. suchus), but
a minimum of two surveys was required to infer absence of either species
with 90% confidence. We found that detection of M. cataphractus was
significantly negatively influenced by fishing net encounter rate, while
high temperature for the previous 48h of the day of the survey increased
C. suchus detection. Precipitation and aquatic vegetation had significant
negative and positive influence, respectively, on M. cataphractus counts
and showed the opposite effect for C. suchus counts. We also found that
fishing encounter rate had a significant negative effect on C. suchus
counts. Interestingly, survey repetition did not generally affect wariness
for either species, though there was some indication that at least C.
suchus was more wary by the fourth replicate. These results are
informative for designing future survey and monitoring protocols for these
threatened crocodylians in West Africa, and for other endangered
crocodylians globally.
Crocodiles surveys_From 2014 to 2019, we conducted surveys of M.
cataphractus and C. suchus in different habitat types throughout Côte
d’Ivoire. We sampled 38 sites across the major ecoregions of the country,
which are representative of those of West Africa: Guinean forest (50% of
the country), the Sudano-Guinean zone (19% of the country), and the
Sudanian region (31% of the country). We surveyed crocodiles predominantly
during the dry season to increase detection rates (Fukuda et al., 2013).
We conducted standard nocturnal spotlight surveys (Chabreck 1966) from an
inflatable, outboard-powered boat with 15 hp engine at a cruising speed of
about 5.0 – 6.0 km/h, by inflatable kayak, and/or on foot. A single
observer conducted all surveys, who located crocodiles by their eyeshine
using either a 78 lumen LED headlamp (80% of observations) or a 550 lumen
LED spotlight (20% of observations), depending on the habitat , and
approached individuals as close as possible to visually determine species
and demographic class (i.e., by total length, TL). We classified
crocodiles that submerged before species and total length could be
determined as eyes only (EO). We tracked all survey routes and took
waypoints for each crocodile sighting using a handheld GPS. we surveyed
each site on three (from 2014 to middle 2016) or five (end of 2016 to
2019) consecutive nights. We surveyed a minimum survey distance of 10 km
at each site on each occasion. Environmental variables_We examined the
influence of 10 environmental and anthropogenic variables that were
previously shown to have significant influence on crocodile detection
probability and are relevant to our study species and habitat. We
measured six of these variables in the field before or during each survey:
moon phase (0 – 4), wave height and wind speed (0 – 3), precipitation the
day prior to the survey (0, 1), the amount of vegetation present along the
shoreline and fishing net encounter rate. We used a binary index of low
or high vegetation where low vegetation denotes a visible shoreline with
little to no overhanging vegetation and high aquatic vegetation denotes a
shoreline completely covered by overhanging or aquatic vegetation (Gardner
et al., 2016). We counted the number of fishing nets seen on the survey
as an index of the subsistence fishing threat (Shirley et al., 2009). We
assessed mean night air temperature and mean daily precipitation both on
the day of the survey and for the previous 48 h from remote sensed data
accessed through MODIS (Wan et al., 2015) and CHIRPS (Funk et al., 2015),
respectively. Prior to further analysis, we standardized all continuous
covariates and tested for multi-collinearity among independent variables
using the VIF function in the R package car (John et al., 2020). We found
evidence of collinearity for wind speed with wave height, and mean night
air temperature the day of the survey with mean night temperature for the
previous 48h. Wave height is often correlated with wind speed (Woodward
& Marion, 1978) and generally not significant in the small river
systems where we surveyed, so we removed wave height from subsequent
models. Likewise, we retained mean night temperature for the previous 48h
over mean night air temperature the day of the survey because of its more
significant individual effect in subsequent models (Couturier et al.,
2013). We conducted all subsequent analyses for each species
independently. Influence of environmental and anthropogenic variables on
detection probability.¾We assessed the influence of environmental and
anthropogenic variables on crocodile detection probability using both an
occupancy framework and with linear mixed models. For both model types we
included all data from all surveys in all years, though treated missing
data for repetitions four and five in years 2014 to mid 2016 differently.
We categorized missing values as NA in occupancy models, but used
imputation methods to infer missing values in GLMM analyses (see below;
Nakagawa & Freckleton, 2008). Within the occupancy framework, we
used a single season occupancy model to estimate detection probabilities
(p) (MacKenzie et al., 2006). To do this, we created a detection history
(0 = non detection, 1 = detection) for each site across all the survey
repetitions. For this analysis, we hypothesized that the populations were
closed during the survey period, no heterogeneity in detection occurred,
and the detection process was independent at each site (MacKenzie et al.,
2002). We used the method of “plausible combination” (Bromaghin et al.,
2013) for model selection and covariate evaluation, which is increasingly
recognized as a robust multi-stage strategy to assess the fit of single
season occupancy models (Morin et al., 2020). To derive detection
probability, and better understand the influence of covariates on
detection, we paired the most general sub-model for occupancy (ψ) with all
candidate sub-models for detection probability (p) using the dredge
function in the package MuMin (Barton, 2020) and returned the best model
(i.e., ∆AIC threshold of 0) (Morin et al., 2020). Ultimately, we assessed
all combinations of the best detection covariates. Because our focus was
exclusively on detection probability, we held occupancy constant in the
final analysis (i.e., (ψ.)p[covariate]) (Kroll et al., 2008; Moreira et
al., 2016; Phumanee et al., 2020), a standard practice when focused on one
component, or the other, in occupancy-based analyses (Cook et al., 2011;
Jeffress et al., 2011; Wagner et al., 2019). We ranked models using
Akaike’s Information Criterion corrected for small sample size (AICc) and
considered all models with ∆AICc ≤ 2 to be competitive models (Burnham
& Anderson, 2002). We considered a covariate significant if the
95% CI did not include zero (Bauder et al., 2017). We conducted all
analyses using the packages unmarked (Fiske et al., 2017) and AlCcmodavg
(Mazerolle, 2015) in R v4.0.2 (R Development core team, 2020). For GLMM
analysis, as our surveys varied from three to five replicates, we replaced
missing values (8.57% of the total dataset) in all sites with less than
five replicates using a multiple imputation procedure (Nakagawa &
Freckleton, 2008). Specifically, we used multiple imputation to fill in
18 (of 210; 8.6%) missing values for each of crocodile encounter rate,
moon phase, wind speed, amount of aquatic vegetation, fishing net
encounter rate, and precipitation the day prior to the survey. We
generated and combined 100 imputed datasets (Graham et al., 2007) using
the R package mice (Buuren & Groothuis-Oudshoorn, 2011). After
imputation, we determined whether the MICE algorithm has converged by
plotting parameters against the iteration number and found no definite
trends, indicating good convergence in the dataset including imputed
values (Buuren & Groothuis-Oudshoorn, 2011). We modeled crocodile
counts using the lmer function in the lme4 package (Bates et al., 2015).
We tested the same eight covariates included in the occupancy analysis as
fixed effects with site as a random effect. We fit 256 combinations for
each species, including the null and global models, without interaction
terms and ranked models by AICc. We considered all models with ∆AICc ≤ 2
to be competitive models (Burnham & Anderson, 2002). We obtained
model-averaged coefficients using the Model.avg function in MuMin. For
each coefficient, we report associate 95% confidence intervals (CIs) and
the coefficient estimate with shrinkage (also called “zero method”). We
considered a covariate significant if the 95% CI did not include zero
(Bauder et al., 2017). Effect of repetitive surveys on crocodile
wariness.—We used proportion of “EO” and zero detections across repeated
surveys as indexes to examine the effect of repetitive surveys on
crocodile wariness. We considered that a progressive reduction in the
number of individuals seen or formally identified (i.e., increase in the
number of “EO”) during surveys to be a reflection of an increase in
wariness. For each site where the presence of each crocodile species was
confirmed, we determined the proportion of observations that were EO
(EO/number of all observations) for each survey, where 0.0 represented no
wariness and 1.0 (represented by either 100% EO observations or zero
detections) represented complete wariness. For sites where the two
studied species were sympatric, we partitioned the EO observations by the
ratio of the number of individuals actually attributable to either
species. Because our surveys varied from three to five replicates, we used
multiple imputation as described above to estimate missing values for the
following covariates: index of wariness, aquatic vegetation, fishing net
encounter rate, and crocodile abundance for both species, resulting in
10.9% and 7.14% values derived from MI for M. cataphractus and C. suchus,
respectively. We assessed crocodile wariness as a function of the survey
replicate, the most important covariates for each species identified in
both occupancy and count-based GLMM analyses above, and crocodile
encounter rate as an index of abundance. We included encounter rate
because we hypothesized that population abundance may represent unmeasured
disturbance effects on individual wariness (i.e., for rare species, higher
abundance sites likely have less impacted or threatened population
histories, which may capture unmeasured/unmeasurable histories of
disturbance or harassment of individuals). The M. cataphractus model
included survey repetition, abundance, fishing net encounter rate, moon
phase, precipitation the day of the survey and for the previous 48h, and
aquatic vegetation as fixed factors. The model for C. suchus included
survey repetition, abundance, and temperature for the previous 48h of the
day of the survey and aquatic vegetation as fixed factors. Both species
models included site as a random effect. We used generalized linear mixed
effects modelling, fit by restricted maximum likelihood, using the package
lme4 (Bates et al., 2015) and Satterthawaite’s approximation from package
lmerTest (Kuznetsova et al., 2017) to assess each factor’s significance in
R v4.0.2 (R Development core team, 2020).
There are misssing values on the dataset and I included the code used for
imputed them.