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\begin{document}

\title{What the F-POD? Comparing the F-POD and C-POD for monitoring of harbour
porpoise (Phocoena phocoena).}



\author[1]{Nicole Todd}%
\author[2]{Ailbhe Kavanagh}%
\author[3]{Emer Rogan}%
\author[1]{Mark Jessopp}%
\affil[1]{University College Cork}%
\affil[2]{Marine Institute}%
\affil[3]{University College}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Passive acoustic monitoring (PAM) is a cost-effective method for
monitoring cetacean populations compared to techniques such as aerial
and ship-based surveys. The C-POD (Cetacean POrpoise Detector) has
become an integral tool in monitoring programmes globally for over a
decade, providing standardised metrics of occurrence that can be
compared across time and space. However, the phasing out of C-PODs
following development of the new F-POD (Full waveform capture Pod) with
increased sensitivity, improved train detection, and reduced false
positive rates, represents an important methodological change in data
collection, particularly when being introduced into existing monitoring
programmes. Here, we compare the performance of the C-POD with that of
its successor, the F-POD, co-deployed in a field setting for 15 months,
to monitor harbour porpoise (Phocoena phocoena). While similar temporal
trends in detections were found for both devices, the C-POD detected
only 58\% of the detection positive minutes (DPM), recorded by the
F-POD. Differences in detection rates were not consistent through time
making it difficult to apply a correction factor or directly compare
results obtained from the two PODs. To test whether these differences in
detection rates would have an effect on analyses of temporal patterns
and environmental drivers of occurrence, generalised additive models
(GAMs) were applied. No differences were found in seasonal patterns or
the environmental correlates of porpoise occurrence (month, diel period,
temperature, environmental noise, and tide). However, the C-POD failed
to detect sufficient foraging buzzes to identify temporal patterns in
foraging behaviour that were clearly shown by the F-POD. Our results
suggest that the switch to F-PODs will have little effect on determining
broad-scale seasonal patterns of occurrence, but may improve our
understanding of fine-scale behaviours such as foraging. We highlight
how care must be taken interpreting F-POD results as indicative of
increased occurrence when used in time-series analysis.%
\end{abstract}%



\sloppy


\emph{What the F-POD? Comparing the F-POD and C-POD for monitoring of
harbour porpoise (Phocoena phococena).}

\textbf{Authors names:} Nicole
Todd~\emph{\textsuperscript{1,2*}},\textbf{~}Ailbhe
Kavanagh\emph{\textsuperscript{~3}}, Emer
Rogan\emph{\textsuperscript{~2}},
Mark~Jessopp\emph{\textsuperscript{~1,2}}

\emph{1 MaREI Centre, Environmental Research Institute, University
College Cork}

\emph{2 School of Biological, Earth \& Environmental Sciences (BEES),
University College Cork}

\emph{3 Marine Institute, Oranmore, Co. Galway, Ireland}

\textbf{Correspondence}

Nicole Todd, MaREI Centre for Marine and Renewable Energy, Environmental
Research Institute, Beaufort Building, University College Cork.
Haulbowline Rd, Ringaskiddy. Co. Cork, Ireland

Email: nicole.todd@ucc.ie

\emph{}

\emph{Abstract:}

Passive acoustic monitoring (PAM) is a cost-effective method for
monitoring cetacean populations compared to techniques such as aerial
and ship-based surveys. The C-POD (Cetacean POrpoise Detector) has
become an integral tool in monitoring programmes globally for over a
decade, providing standardised metrics of occurrence that can be
compared across time and space. However, the phasing out of C-PODs
following development of the new F-POD (Full waveform capture Pod) with
increased sensitivity, improved train detection, and reduced false
positive rates, represents an important methodological change in data
collection, particularly when being introduced into existing monitoring
programmes. Here, we compare the performance of the C-POD with that of
its successor, the F-POD, co-deployed in a field setting for 15 months,
to monitor harbour porpoise (\emph{Phocoena phocoena)} . While similar
temporal trends in detections were found for both devices, the C-POD
detected only 58\% of the detection positive minutes (DPM), recorded by
the F-POD. Differences in detection rates were not consistent through
time making it difficult to apply a correction factor or directly
compare results obtained from the two PODs. To test whether these
differences in detection rates would have an effect on analyses of
temporal patterns and environmental drivers of occurrence, generalised
additive models (GAMs) were applied. No differences were found in
seasonal patterns or the environmental correlates of porpoise occurrence
(month, diel period, temperature, environmental noise, and tide).
However, the C-POD failed to detect sufficient foraging buzzes to
identify temporal patterns in foraging behaviour that were clearly shown
by the F-POD. Our results suggest that the switch to F-PODs will have
little effect on determining broad-scale seasonal patterns of
occurrence, but may improve our understanding of fine-scale behaviours
such as foraging. We highlight how care must be taken interpreting F-POD
results as indicative of increased occurrence when used in time-series
analysis.

\emph{Key words:}

Long-term monitoring programmes, click detector, cetaceans, C-POD,
F-POD, echolocation \emph{}

\emph{Introduction:}

Passive acoustic monitoring (PAM) is a well-established method used to
monitor acoustically active species and the habitat they reside in
(Merchant \emph{et al.} , 2015). Over the years PAM technology has
greatly advanced the field of cetacean ecology, allowing a
cost-effective alternative to extensive visual surveys that is not
reliant on daylight hours, favourable weather conditions and
availability of observers. Passive acoustic monitoring of cetaceans has
resulted in high-resolution data providing us with insights of
population size and abundance (Marques \emph{et al.} , 2013;
Amundin\emph{et al.} , 2022), habitat use (Fleming \emph{et al.} , 2018;
Palmer\emph{et al.} , 2019), and behaviour (Pirotta \emph{et al.} ,
2014; Malinka \emph{et al.} , 2021; Todd et al., 2022) for many species.
Such technology is also fundamental for long-term monitoring,
particularly with the increase in coastal developments and potential
disturbance from construction, marine renewable devices, shipping, and
fisheries (e.g., Todd et al., 2020, 2022; Omeyer \emph{et al.} , 2020;
Ramesh \emph{et al.} , 2021; Fernandez-Betelu, Graham and Thompson,
2022).

While there are many useful applications of PAM, fixed autonomous
acoustic recording devices can increase deployment times and sampling
frequencies (Sousa-Lima \emph{et al.} , 2013). Data loggers or
echolocation click detectors, such as the C-POD (Cetacean POrpoise
Detector) (Chelonia Ltd., 2022) are a user-friendly, relatively
inexpensive device which can be deployed for continuous monitoring
periods of 3-6 months. C-PODs detect individual echolocation clicks
between 20-160 kHz and have been a popular tool used to study odontocete
ecology and behaviour worldwide (e.g. Carstensen, Henriksen and
Teilmann, 2006; Simon et al., 2010; Nyk\selectlanguage{ngerman}änen, 2016; Jaramillo-Legorreta
et al., 2017; Garagouni, 2019). Although no waveform data are stored by
the devices, summary data on each click are preserved allowing
post-deployment classification of detected sounds into sequences called
click trains. Further data analysis is then performed where click trains
are assigned to dolphin or porpoise origins based on frequency and
bandwidth. While it is often not possible to differentiate between
dolphin species (Robbins et al., 2015), based on in-field testing,
Roberts and Read (2015) reported that C-PODs perform well with a
relatively high accuracy in detecting cetacean echolocation. C-PODs have
been used for over a decade and now form the basis of valuable long-term
monitoring datasets. The F-PODs (Full waveform capture Pods) are the
successor of the C-PODs, and the manufacturer is recommending a
transition from C-PODs to F-PODs as availability and support for C-PODs
will be limited in the coming years. This may have important
implications for long-term monitoring programmes (and the associated
archival data from such) as C-PODs are replaced due to equipment loss
(often as a result of storms, theft etc.) or reach the end of their
operational lifetimes. The F-PODs have been designed to improve and
upgrade the data associated with C-PODs by recording more details of
selected clicks including position of loudest cycle, frequency range and
capture of full waveform (Chelonia Ltd., 2022). These new features
enhance train detection, providing increased sensitivity with lower
false positive rates compared to C-PODs (Chelonia Ltd., 2022).

One of the main advantages of PAM is its potential to be implemented in
long-term monitoring programmes to study the change in species
occurrence and behaviour over longer temporal scales. Many studies
currently using C-PODs need to ensure the longevity of their data for
monitoring purposes, particularly in the light of climate change and
habitat alterations through coastal developments. However, to date there
have been no studies reporting how the C-POD and its successor the F-POD
compare in detection capacity and ability to identify trends in
spatio-temporal drivers of detected cetaceans. In this study, we used
data from a co-deployed C-POD and F-POD to compare the performance of
the PODs in detecting habour porpoise (\emph{Phocoena phocoena)} across
various commonly used detection metrics. Additionally, Generalised
additive models (GAMs) were used to explore how both PODs identifed
spatiotemporal variation in harbour porpoise occurrence and foraging
activity in relation to environmental variables.

\emph{Methods:}

\emph{Data collection:}

Between April 2021 and July 2022 click detectors were deployed off
Sherkin Island in Roaringwater Bay (51°27'40.7''N, 9°26'24.7''W), a
Special Area of Conservation (SAC) for harbour porpoise. Porpoise occur
in the bay throughout the year, peaking in autumn months, with an
estimated population (in 2008) of 117-201 individuals (NPWS, 2014),
although density estimates have reportedly declined in recent years with
the 2020 estimate of 0.61 individuals per km\textsuperscript{2} (O'Brien
\& Berrow, 2020). The deployment site is a relatively sheltered site on
the southwest coast of Sherkin Island, approximately 18m water depth,
with a predominantly sandy seafloor. A C-POD and F-POD were co-deployed
on a mooring line anchored to the seabed, positioned 5m above the
seabed, side by side in a custom-built acetal plastic frame to optimise
simultaneous detections on both devices (Appendix, Figure S1). The
devices were retrieved and redeployed every 3-4 months to ensure
continuity of acoustic recordings.

\emph{Data analysis:}

C-POD data were processed using the Chelonia CPOD.exe software (V. 2044)
and inbuilt KERNO classifier to detect harbour porpoise click trains.
F-POD data were processed in a similar manner using the custom F-POD.exe
software (V 1.1) and KERNO-F classifier (Chelonia Ltd., 2022). Click
trains were classified as ``NBHF'' (narrowband high frequency) and all
train quality classes were exported for further examination. Train
quality filters are defined as ``Hi'' (high),'' Mod'' (moderate), and
``Lo'' (low). All detections were visually verified following guidelines
from the manufacturer (Chelonia Ltd., 2022). Data were exported as
different detection metrics; number of clicks `NClx', detection positive
days `DPD', detection positive hours `DPH' and detection positive
minutes `DPM'.

Detection metrics were summarised for each deployment across three
groupings of train quality filters, specifically HiModLo, HiMod, and Hi,
reflecting commonly used groupings in the literature (Sarnochinska et
al., 2016; Clausen et al., 2018). Kendall's rank (non-parametric)
correlation tests were carried out between the detections on the C-POD
and F-POD at the scale of each temporal detection metric and for each
train quality classification.

Both monthly and seasonal DPH were summarised for both the C-POD and the
F-POD and compared using a detection ratio, expressed as: CF =
Det\_C/Det\_F. This ratio was used to explore the comparability between
the PODs across time and by what margin the F-POD detects more
echolocation clicks than the C-POD.

Data on echolocation clicks were also exported and used to identify
buzzes, assumed to be foraging behaviour (Verfuß et al., 2009), based on
the duration of the inter-click interval (ICI). Gaussian mixture models
were used to categorise echolocation clicks based on their ICI
(Pirotta\emph{et al.} , 2014). Buzzes were defined as echolocation
clicks with an ICI of less than 10 ms (Carlström, 2005). Detections were
then summarised as foraging buzzes per hour (BPH) for further analysis.

\emph{Environmental data:}

Sunrise, sunset and civil twilight times were extracted from
(www.timeanddate.com/sun) for Sherkin Island, and were used to calculate
the diel cycle phases (morning, day, evening, and night) (Carlström,
2005; Todd \emph{et al.} , 2009). Tide data were extracted from tide
tables for Roaringwater Bay
(www.tides4fishing.com/ie/munster/roaringwater-bay). Time difference to
nearest high tide was calculated, as well as phases of the tidal cycle
(ebb/flow/high/low water). Tidal range was also calculated as an
indicator of spring and neap tides. Hourly water temperature data were
obtained from the PODs, taken as the average value recorded by both PODS
to avoid any recording bias between PODs. While the PODs cannot provide
a direct measure of environmental noise levels, the click detection
algorithms record unfiltered short click-like events within the 20kHz --
160kHz bandwidth (\emph{Nall} ), and is recorded for each sampling
minute. A positive correlation has been found between\emph{Nall} and
environmental noise levels using a full-bandwidth recorder and is used
as a proxy for general noise levels (Clausen et al., 2018; Nuuttila et
al., 2018). We included this in models to investigate the effect of
varying environmental noise levels on POD detection performance. For the
purpose of interpretation of the results, seasons were defined as Spring
(March to May), Summer (June to August), Autumn (September to November)
and Winter (December to February).

\emph{Statistical modelling:}

Statistical analyses were undertaken using R version 4.1.2 (R Core Team,
2022). Prior to statistical modelling, data exploration was conducted
following Zuur et al., (2010). Autocorrelation was observed in the data
using ACF plots with \emph{itasdug} package (van Rij et al., 2015).
Generalised Additive Models (GAMs) were conducted using the
function\emph{bam} within the \emph{mgcv} package which is optimised to
deal with large data sets. GAMs were fitted with autoregressive (AR(1))
correlation structure to account for observed autocorrelation, and a
negative binomial error distribution (\emph{theta} values obtained using
function \emph{gam} and \emph{nb} distribution), with logarithmic link
function, to deal with zero-inflation in the data (Wood, 2011;
Wood\emph{et al.,} 2015). The \emph{rho} values for the AR structure
(which control the degree of permitted autocorrelation (Wood, 2017))
were determined using the \emph{itsadug} package and ACF plots. The
parameter gamma was set to 1.2 to reduce potential overfitting of
splines.

The data were analysed for every hour and the response variables used
were the number of minutes with porpoise detections for each hour (0-60
Detection positive minutes, or DPM) and the number of foraging buzzes
(ICI \textless{}10ms) recorded per hour. Explanatory variables included
diel period as a factor and month, temperature, noise, difference to
high tide and tidal range as smooth terms. Circular smoothers were used
for month and difference to high tide. Thin-plate regression splines
with shrinkage were used for the remaining smooth terms which return the
simplest effective spline. Generalized-cross validation and manual knot
selection were used, with chosen values visually selected based on the
trade-off between the overall simplicity of the model and the
explanatory power of smooth graphs. To decide between the appropriate
tidal variable for analysis each were included in the full model and
models compared based on AIC score. Time difference to high tide
resulted in the model with the lowest AIC and was used for further
analysis.

The relatedness between the smooth terms in the model were measured
using the function \emph{concurvity,} in a similar manner to variance
inflation factors used for Generalised Liner Models (GLM). Relatedness
was measured on a scale of 0-1, with 0 indicating no difference and 1
indicating that terms are identifiable from each other. Concurvity was
not found, so all terms were retained for analysis. Stepwise model
selection was performed where non-significant interactions were dropped
from the model (starting with the least significant) and model
validation repeated. Models were compared using AIC to choose the best
and final model. Model performance was checked using gam.check based on
traditional QQ plot and residual plots (Wood, 2006). Model goodness of
fit was described by deviance explained, and area under the receiver
operator curve (AUC), package \emph{caret} (Kuhn, 2008). AUC was
calculated by predicting a binomial response variable from the fitted
model and compared to the observed presence/ absence of the variable.
This results in a value ranging from 0-1, with values closer to 1
indicating better model fit (Boyce \emph{et al.,} 2002). Graphical
outputs were produced using the \emph{mgcViz} package (Fasiolo \emph{et
al} ., 2018) and \emph{ggplot2} (Wickham, 2009).

\emph{Results:}

\emph{Comparability in the detection of harbour porpoise}

The C-POD and F-POD were co-deployed for a continuous period of 444 days
between April 2021 and July 2022. Harbour porpoise detections were
recorded on 94\% of days on the C-POD (419 days), and 98\% on the F-POD
(433 days).

Across all deployments, total C-POD detections were lower than those
recorded using the F-POD (Figure 1). The margin of difference was highly
dependent on the detection metric and the train quality classification
category examined, with smallest differences noted when combining all
(visually validated) quality categories. While there was a significant
correlation between the detections on the co-deployed PODs at all
temporal scales, PODs were in greatest agreement at the broadest scale
metric of detection positive days (filter HiMod r= 0.86,
p\textless{}0.05; HiModLo r=0.84, p\textless{}0.05, Appendix Table S2),
and least comparable at the scale of number of clicks per hour (Figure
1, Appendix Table S2). Although variable across time, the increased
capacity for click detection by the F-POD was evident, with the F-POD
detecting 10 times or more the number of clicks detected by the C-POD
(Figure 1, Appendix Table S1). Considering the ``Hi'' filter alone
showed limited comparability between the C-POD and the F-POD, with
significant but weak correlation (Appendix Table S2). Moreover, using
only the high-quality classification filter meant that on average 75\%
of the F-POD DPH were not detected by the C-POD within the same quality
grouping. Using the HiMod or HiModLo groupings increased the
comparability of PODs, but the F-POD still consistently recorded more
harbour porpoise detections overall. There was a small proportion of
detection positive hours recorded on the C-POD that were not matched by
the F-POD. However, these visually validated C-POD detections often
matched unclassified NBHF clicks (i.e. not defined to be click trains by
any of the quality classes) on the F-POD. Furthermore, most unmatched
C-POD detections were weaker trains of Low-quality, occurring during
periods of increased ambient noise, and not classified by the more
conservative F-POD algorithms.

Over total 15-month period the PODs were deployed, a detection ratio of
1.38 was calculated using detection positive hours. Seasonal variability
in this detection ratio occurred with PODs least comparable in the
spring-summer when detection rates where lowest (ratios: Spring: 1.52,
Summer: 1.48, Autumn: 1.07, Winter: 1.37, Figure 2). Despite the
detection differences, both PODS similarly identified temporal patterns
of occurrence at hourly scales (DPM and DPH) using the HiMod or HiModLo
train quality groupings. There was a decrease in detections from April
to July and consistently more detections throughout the winter months
(Figure 2).

Harbour porpoise foraging behaviour was detected by both of the
co-deployed PODs throughout the deployment period. Buzz positive hours
(BPH), i.e. hours where foraging buzzes were identified, made up a low
proportion of the total recording hours, particularly for the C-POD
which detected less than a third of the BPH detected by the F-POD.
Foraging buzzes were found to account for approximately 8\% of the total
clicks recorded by the C-POD, compared to 26\% for the F-POD. The number
of buzz positive hours per day was found to vary seasonally, reflecting
the temporal patterns shown in overall harbour porpoise detections
(Figure 2), with low counts of BPH from May till July, and peaks shown
throughout winter as well as for August (Figure 2).

\emph{Spatiotemporal drivers of harbour porpoise occurrence and foraging
activity}

GAMs were run to compare the temporal trends and environmental
predictors of harbour porpoise occurrence between C and F-PODs. The best
models for detection positive minutes per hour (DPM/h) from both PODs
retained all explanatory variables (all variables significant in both
the C-POD and the F-POD models). Effect sizes of variables retained
within the models were remarkably consistent between the C-POD and F-POD
models, with the exception of noise, where this showed a considerably
higher effect within the C-POD model (Table 1).

Similar temporal trends were highlighted by the C-POD and F-POD models
in terms of harbour porpoise DPM/h per month and throughout the diel
cycle (Figure 3). Both tidal range and time to high tide were
significant, reflecting similar trends from both devices with an
increase in detections during the ebb tide and during tidal ranges
associated with spring tides (Figure 3), however, both variables had
relatively small effect sizes within the model (Table 1). The C-POD
model highlighted a much greater effect of noise on harbour porpoise
detections, with a clear decrease in porpoise detections at higher noise
levels, occurring at a much lower noise threshold for the C-POD than the
F-POD (Figure 3). Harbour porpoise detections were found to decrease
with increasing water temperature for both devices (Figure 3).

In contrast to the similar temporal patterns shown via the occurrence,
strong differences occurred between models of foraging buzzes between
C-PODS and F-PODS using buzz clicks per hour (BPH) as the response
variable.

The C-POD foraging model did not retain any temporal variables despite
them remaining the most influential covariates within the F-POD foraging
model, likely due to a much-reduced sample size of identified feeding
buzzes by the C-POD. Neither time difference to high tide or tidal range
were found to influence harbour porpoise foraging behaviour in either
C-POD or F-POD models. The F-POD model suggested a decrease in foraging
buzzes between July and September, and an increase in foraging buzzes
detected during the day (Figure 4). Similar to the detection model,
foraging activity decreased with increasing water temperature, with
highest buzz detections around 10° C. In contrast to the C-POD model,
the F-POD foraging model found no significant effect of noise on
foraging activity (Table 2).

\emph{Discussion:}

To the best of our knowledge, this study is the first to evaluate the
performance of co-deployed C-POD and F-POD devices in a field setting
for monitoring harbour porpoise. Previous studies have evaluated the
performance of the C-POD with other types of full bandwidth recorders
(incl. Soundtrap, DMON), with the C-POD typically performing well with
an overall high degree of accuracy (Roberts and Read, 2015; Sarnocinska
et al., 2016; Jacobson et al., 2017). Our results suggest that at
appropriate temporal scales, C-PODs provide comparable results to the
newer F-PODs and with certain caveats, F-PODs would be suitable
replacements for C-PODs in existing monitoring programmes as C-PODs
reach the end of their serviceable lifetime.

Data from click detectors such as C-PODs, its predecessor T-PODs, and
now the F-PODs have been used to monitor small cetaceans, as well as
their responses to anthropogenic activities in numerous settings
including pile driving, seismic surveys, and fisheries deterrent devices
(e.g. Philpott et al., 2007; Thompson \emph{et al.} , 2013;
Omeyer\emph{et al.} , 2020; Todd et al., 2020). Our results show that
the F-POD consistently detects more echolocation clicks and foraging
buzzes than the C-POD across the temporal scales of minutes, hours and
days, as well as all train quality classification groupings. Lower
detection rates by the C-POD is to be expected due to advances in F-POD
electronics and software to capture more information on individual
echolocation clicks and enhance train detection (Chelonia Ltd, 2022).
This poses a potential issue for researchers engaged in long-term
monitoring, with questions about how comparable different POD types may
be, potentially affecting time-series as C-PODs are eventually replaced
by F-PODs. This study shows that both C-PODs and F-PODs detected similar
patterns of occurrence and echolocation activity. As indicated by Garrod
et al. (2018), detection metrics at a minimum of an hourly scale are
representative of relative occurrence, enabling temporal trends to be
determined. Detections at the broader scale of detection positive days
were found to match best between both PODs with little discrepancy
between them. Therefore, for direct comparison between C-POD and F-POD
data, detection positive days, and using combined classifications of Hi
Mod and Lo, is the only detection metric recommended. However, such a
metric would be insufficient for identifying fine-scale temporal
patterns of occurrence of behaviour in response to factors such as
diurnal changes in prey availability or tidal state, both known to
influence harbour porpoise occurrence and feeding behaviour (Schaffeld
et al., 2016; Zein et al., 2019)

Our results highlighted that F-PODs appear to be much more capable at
identifying harbour porpoise click trains with confidence than the C-POD
(i.e. classified as high quality by the KERNO classifier). However, when
considering the combined train quality classification groupings (i.e.
HiMod and HiModLo) the two PODs are substantially more comparable.
Researchers considering using a time series consisting of data from
C-PODs and F-PODs for analysis of temporal trends should consider using
the combined classifications, provided extensive visual validation is
followed, particularly for low quality trains to eliminate possible
false positive detections. The enhanced train detection specified by the
manufacturer has also been demonstrated in our results with F-POD
continually detecting more harbour porpoise detections than the C-POD by
a factor of 1.38 across the deployment period. Comparability between the
PODs was however found to be variable between seasons, with the highest
detection ratio in spring and summer making detection rates on the PODs
less comparable. Detection ratios such as explored here could be
investigated further within monitoring programmes looking to transition
to the use of F-PODs. Understanding how the devices compare in various
deployment sites can help for the interpretation of long-term data
beyond the lifespan of the C-POD and avoid misinterpretation of the data
(for example interpreting a false increase in occurrence due to
differing device sensitivities).

Investigating spatial and temporal patterns in species occurrence is
often the crux of ecological monitoring (e.g., Jones et al., 2014;
Williamson et al., 2017; Zein et al., 2019). Generalised additive models
were used to investigate whether detections from both types of POD have
the capacity to identify the same temporal drivers of porpoise
occurrence and foraging activity. The occurrence models for both PODs
highlighted the same temporal patterns, and same environmental
predictors with very similar effect sizes, suggesting that analyses
using data from both C-PODs and F-PODs will not be affected greatly by
POD type. However, it would be prudent to include POD type as a fixed
factor in any such analysis. Conversely, the models investigating
feeding buzzes did not provide analogous results. No temporal patterns
were found using the C-POD data, possibly due to the much lower
detection rates of feeding buzzes using C-PODs, with the F-POD detecting
three times as many foraging buzzes in comparison, and 10 times as many
echolocation clicks overall. The higher detection rates of feeding
buzzes by the F-POD enabled detection of temporal patterns including an
increase in foraging buzzes from autumn to winter, and an increase in
foraging activity during the day. The specific nature of the
relationships and their ecological context is outside of the scope of
the current study, but the contrasting ability of the PODs to detect
feeding buzzes is particularly relevant in the context of integrating
F-PODS into long-term datasets. The increased click detection capacity
of the F-POD now enables fine-scale analysis of foraging or social
behaviours (demonstrated by high click rates (Clausen \emph{et
al.,}2010)), that has perhaps been missed or underestimated using
C-PODs. Additionally, F-PODs were found to be less effected by
environmental noise levels within the 20-160KHz noise band, as indicated
by\emph{Nall} (Clausen et al., 2018). It is plausible that decreased
detections on the C-POD during periods of increasing environmental noise
is a consequence of detector performance, which has been overcome during
the development of the F-POD in conjunction with the increased click
detection ability.

Long-term datasets and consistency of monitoring methods throughout the
duration of monitoring programmes are important to enable long-term
trends to be identified, particularly in areas of high conservation
importance. Changing controllable factors such as monitoring equipment
can skew our understanding of these long-term trends and in turn make it
more difficult to interpret a change in habitat use, or behaviour of a
species, which can be detrimental in the event of a disturbance or
imminent threat. Static acoustic monitoring using PODs has been an
integral part of cetacean monitoring programmes exploring habitat use
and behaviour. Our results show than the C-POD and the F-POD are
consistently comparable at the broad scale of identifying porpoise
presence, and produce similar results when modelling environmental
correlates of occurrence. However, the C-POD failed to detect the more
nuanced patterns detected by the F-POD, particularly when investigating
foraging behaviour versus occurrence. On account of its greater
sensitivity and increased detection rates for harbour porpoise the F-POD
certainly can be a useful tool to integrate into acoustic monitoring
programmes. While the introduction of F-PODs into long-term time series
is unlikely to change our understanding of the environmental drivers of
occurrence, it is advisable that detections from one POD type should not
be directly compared with detections from another as this could give
erroneous results of increased occurrence due to differing detection
rates rather than a true increase in individuals. Furthermore, any
studies transitioning between PODs or combining C-POD and F-POD data
should consider including POD type as a factor when conducting
time-series. While the current study only investigated comparability in
POD performance for detecting acoustic activity of harbour porpoise, it
is likely that analogous differences would be seen for other cetacean
species recorded by PODs. This study has given insights that the F-POD
will be invaluable for future monitoring of harbour porpoise and other
cetacean species, however care and consideration must be taken to make
C-POD data adaptable for the integration into future studies.

\emph{Acknowledgements:}

This work was funded by the Irish Research council Government of Ireland
Postgraduate Scholarship Scheme (Project ID: GOIPG/2019/2173). We would
like to thank Nick Tregenza for the loan of the F-POD that supported
this project and the technical support provided by the Chelonia team. We
thank Damien Haberlin and Tom Walsh for the design and construction of
the frame for the co-deployment of the PODs. We are grateful to Micheal
Collins skipper of the Kestrel, and for various field assistants for
facilitating the fieldwork in Roaringwater Bay.

\emph{Conflict of interest:}

The authors declare no conflict of interest.

\emph{Author contributions:}

\textbf{Nicole R. E. Todd:} Conceptualization; data curation; formal
analysis; investigation; methodology; writing -- original draft; writing
-- review and editing. \textbf{Ailbhe S. Kavanagh} : Conceptualization;
supervision; writing -- review and editing. \textbf{Emer Rogan} :
Conceptualization; supervision; writing -- review and
editing.\textbf{Mark Jessopp} : Conceptualization; project
administration; supervision; writing -- review and editing.

\emph{Data accessibility statement:}

All processed data and R code files to reproduce the results given in
this paper will be uploaded to Dryad repository.

\emph{}

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\emph{}

\emph{Tables} :\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Retained terms} & \textbf{C-POD model} & \textbf{C-POD model} &
\textbf{F-POD model} & \textbf{F-POD model}\tabularnewline
\midrule
\endhead
& p-value & Effect size & p-value & Effect size\tabularnewline
Month & \textbf{\textless{}0.001} & 18.84 (++) &
\textbf{\textless{}0.001} & 19.89 (++)\tabularnewline
Temp & \textbf{0.002} & 6.07 (+) & \textbf{\textless{}0.001} & 3.61
(+)\tabularnewline
Nall- noise proxy & \textbf{\textless{}0.001} & 59.57 (+++) &
\textbf{\textless{}0.001} & 2.90 (+)\tabularnewline
Tidal range & \textbf{0.01} & 6.53 (+) & \textbf{\textless{}0.001} &
3.75 (+)\tabularnewline
Diff. to HT & \textbf{\textless{}0.001} & 2.91 (+) &
\textbf{\textless{}0.001} & 1.26 (+)\tabularnewline
Diel period (Relative to Day) & \emph{Evening:} 0.2048 \emph{Morning:}
0.1271 \emph{Night:} \textbf{\textless{}0.001} & \emph{Evening:} -1.27
(-) \emph{Morning:} -1.53 (-) \emph{Night:} -4.42 (-) & \emph{Evening:}
\textbf{\textless{}0.001} \emph{Morning:} \textbf{0.01} \emph{Night:}
\textbf{\textless{}0.001} & \emph{Evening:} -3.98 (-) \emph{Morning:}
-2.56 (-) \emph{Night:} -7.23 (-)\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Retained terms} & \textbf{C-POD model} & \textbf{C-POD model} &
\textbf{F-POD model} & \textbf{F-POD model}\tabularnewline
\midrule
\endhead
& p-value & Effect size & p-value & Effect size\tabularnewline
Month & & & \textbf{\textless{}0.001} & 14.00 (++)\tabularnewline
Temp & \textbf{\textless{}0.001} & 21.09 (++) &
\textbf{\textless{}0.001} & 5.11 (+)\tabularnewline
Nall- noise proxy & \textbf{\textless{}0.001} & 17.38 (++) & 0.62 & 0.27
(+)\tabularnewline
Tidal range & & & 0.08 & 3.06 (+)\tabularnewline
Diff. to HT & 0.49 & 0 & 0.49 & 0\tabularnewline
Diel period (Relative to Day) & & & \emph{Evening:}
\textbf{\textless{}0.001} \emph{Morning:} \textbf{\textless{}0.001}
\emph{Night:} \textbf{\textless{}0.001} & \emph{Evening:} -4.02 (-)
\emph{Morning:} -2.41 (-) \emph{Night:} -6.46 (-)\tabularnewline
\bottomrule
\end{longtable}

\emph{Figure captions:}

Figure 1: Total Number of clicks (NClx), Detection positive minutes
(DPM), Detection positive hours (DPH), and Detection positive days (DPD)
for C-POD and F-POD co-deployment (different PODs specified in the
legend) collated per season across the deployment period (Note total
deployment days per season: Spring: 128, Summer: 138, Autumn: 92,
Winter: 91). HML represents High, Moderate, and Low-quality train
classification categories.

Figure 2: A) Median detection positive hours (DPH) for C-POD and F-POD
co-deployment. B) Median buzz positive hours (BPH, i.e. cumulative
number of hours per day where at least one foraging buzz was detected.
Different PODs specified in the legend. Boxplot shows the lower
quartile, the median, the upper quartile with the whiskers extending to
the most extreme data points (1.5 times either side of the interquartile
range).

Figure 3: Significant relationships for the final C-POD (left) and F-POD
(right) harbour porpoise detection model based on GAM/BAM standard
errors. Shaded areas represent 95\% confidence intervals.

Figure 4: Significant relationships for the final C-POD (left) and F-POD
(right) harbour porpoise foraging model based on GAM/BAM standard
errors. Shaded areas representing 95\% confidence intervals\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/photo-Phocoena/photo-Phocoena}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig1/Fig1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig2/Fig2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3/Fig3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig4/Fig4}

\end{center}
\end{figure}

\selectlanguage{english}
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