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\begin{document}

\title{Motion detection or time lapse? A comparison of camera trap triggers in
the monitoring of elusive ground dwelling birds}



\author[1]{Milaja Nykänen}%
\author[1]{Hannu Pöysä}%
\author[2]{Juho Matala}%
\author[1]{Mervi Kunnasranta}%
\affil[1]{University of Eastern Finland Faculty of Science and Forestry}%
\affil[2]{Natural Resources Institute Finland Joensuu}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
Wildlife cameras have emerged over the recent years as an effective
research tool for collecting various types of data on wild animals, and
they are used increasingly also in avian studies. However, choosing the
best method to collect data depends on the aim of the research and the
characteristics of the target species and its habitat. Here, we compared
the performance of game cameras taking images by passive infrared motion
sensitive (PIR) and time-lapse triggering in gathering occurrence and
relative abundance data of the taiga bean goose (Anser fabalis fabalis)
in peatlands across Finland in 2020-2021. We found the time lapse
trigger mode to be more efficient in collecting data on goose presence
than PIR triggering. However, there was no significant difference in the
goose numbers between the two trigger modes. We also found the capture
probability and relative abundance to vary between years, but this could
be attributed to random inter-annual variation. In general, we find time
lapse to be a more suitable method to study elusive ground dwelling
birds like the taiga bean goose due to fewer required visits to camera
sites compared to motion triggered cameras that may produce a lot of
empty images due to false triggering. This reduces the disturbance to
the geese and other wildlife during the sensitive breeding period.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Camera trapping has become a powerful research tool for collecting data
on wildlife because it can be carried out at a relatively low cost
compared to some other survey or monitoring methods that would require
extended human presence in the study area. Moreover, its non-invasive
nature in data collection enables the monitoring of elusive species,
also in remote locations (Burton et al. 2015, Caravaggi et al. 2017).
This approach has traditionally been used to gather information on
various aspects of large terrestrial mammals such as occurrence
(Salvatori et al. 2023), abundance (Taylor et al. 2022, Santini et al.
2022) and behaviour patterns (Li et al. 2020, Gracanin and Mikac 2022),
but nowadays it is used increasingly also for birds (Caravaggi et al.
2017). It is especially useful in the study of ground dwelling birds,
where game cameras have been recently used to describe, for example,
activity patterns (Nyk\selectlanguage{ngerman}änen et al. 2021), foraging (Sperry et al. 2021),
habitat use (Firth et al. 2020, Puffer et al. 2021), abundance (Kanka et
al. 2023) and predation pressure (Laux et al. 2022).

Despite the great potential of game cameras as an effective research
tool in a range of applications (Wearn and Glover-Kapfer 2019), they may
also have several limitations associated to them, such as variability in
camera performance or challenges in sampling design (Rovero et al. 2013,
Jacobs and Ausband 2018, Palencia et al. 2022, Santini et al. 2022). One
key aspect in camera performance is the trigger mode: in \emph{motion
sensitive} triggering a passive infrared (PIR) sensor triggers the
camera to capture an image, whereas in \emph{time lapse} triggering the
camera is programmed to take images at a predefined time interval.
Problems may occur, if the camera produces false triggers leading to
vast amounts of blank or empty images and therefore drains batteries and
fills memory card space. On the other hand, detections of target species
can be missed, if the camera is not triggered appropriately. This all
causes extra work for researchers and may bias the results of studies.

Here, we compare the performance of motion sensitive and time lapse
camera settings in gathering occurrence and relative abundance data of
the bean goose (\emph{Anser fabalis} ) in its breeding areas in Finland.
The bean goose is breeding sporadically in remote and inaccessible
habitats in the arctic and boreal zones from Fennoscandia to Western and
Eastern Siberia (Scott and Rose 1996, Kear 2005). The Western Palearctic
population of the species, consisting mainly of individuals belonging to
the subspecies taiga bean goose (\emph{A. f. fabalis} ), has declined in
recent decades (Fox et al. 2010, CAFF 2018), the conservation status of
the subspecies being considered Vulnerable in Finland (Lehikoinen et al.
2019). While efforts have been put into increasing the accuracy of
methods used to estimate taiga been goose numbers in the nonbreeding
season (Piironen et al., 2023), monitoring its numbers in the breeding
season remains a challenging obstacle to efficient conservation of the
subspecies. During the breeding season taiga bean goose is highly
elusive, and any survey method involving disturbance caused by the
presence of human observers will further reduce the detectability of the
species (Pirkola and Kalinainen 1984).

Hence, the main objective of this study was to gain a better
understanding of game camera trap performance for reaching more reliable
results with the most cost-effective data collection procedure for
ground dwelling avian studies, bean goose serving as a research species.
A specific aim of the study was to find out which trigger type, motion
sensor or time lapse, captures greater goose numbers or is associated
with higher daily capture probability, the latter being critical in
providing occurrence data.

\textbf{Materials and Methods}

The study regions covered altogether 19 known bean goose peatlands
across Finland in 2020 and 2021 (Fig. 1). The peatlands were located in
the provinces of North Karelia (N = 5), Northern Ostrobothnia (N = 8)
and Lapland (N = 6). Fifty-six and fifty-three camera traps were
deployed in 2020 and 2021, respectively, for the duration of the
breeding season from the beginning of May to August, covering the season
when the geese were nesting, caring for the offspring and moulting.
Study locations were divided into 6.25 ha grids and one or two grids
were used for camera trap monitoring, depending on size of the peatland
(see Nykänen et al., 2021). In each study location, 2--4 game cameras
were placed by experts specialized in bean goose ecology on peatlands
with ponds/water bodies. Motion sensitive cameras were set to capture
two still images with a 10 s delay between triggers, and time lapse
cameras were programmed to take two still images every 15 minutes.
Cameras were attached on top of each other (time lapse above the motion
sensor camera) to a tree or wooden pole at a height of 1 m above the
ground. Cameras were typically visited once or twice during the study
period to replace the memory cards and batteries, if needed. After each
field season, adult taiga bean geese and goslings were detected and
counted manually from the images (Fig. 2).

No permits concerning animal welfare/ethics for bean goose camera
trapping were required as birds were not intentionally approached, and
remote camera traps are considered to be a non-invasive study method.
Camera traps were set with landowners' permission on private land and on
state-owned areas with Metsähallitus permit (permit number MH1145/2018).

Generalized linear mixed modelling (GLMM) approach was used to
investigate whether there was a difference in total taiga bean goose
numbers (adults and goslings added together) captured by cameras
triggered by motion sensor vs cameras set to time lapse. The GLMM was
run in R (R Core Team 2023) using the package glmmTMB (Brooks et al.
2017) with the factors `trigger type' (motion sensor and time lapse) and
`year' (2020 and 2021) as fixed factors and with the factor `site' (19
individual peatland ponds) included as a random intercept in the model.
The varying effort, resulting from the different number of cameras
deployed on the study sites and the amount of time that the cameras were
recording over the study period, was accounted for by including an
offset-term in the model that was calculated by totalling the number of
time periods of recording per trigger type for each day. Due to the data
including a large number of zero values (some days and time-periods had
zero goose counts), we ran different candidate models with a Tweedie,
Poisson and two types of negative Binomial distributions (NB1 and NB2
parameterizations; Hardin and Hilbe 2018, Bolker 2022) with and without
accounting for the zero-inflation.

In addition, we ran a set of logistic models (see Table 1) to
investigate whether the camera trigger type affects the daily capture
probability (presence or absence in photos) of taiga bean geese. Here,
we define capture probability as ``the probability that an animal is
captured in a photo given it is present in the camera's viewshed'' as
per Moeller et al. (2023). Since it was not possible to include the
offset-term in this type of model, we added the camera effort as a
continuous covariate in the models. Other covariates were included as
fixed (`trigger type' and `year') or random (`site') factors the same
way as in the count models.

We then compared model fits using Akaike's Information Criterion (AIC)
values to determine the best fitting models. The goodness of fit of the
models were assessed by creating scaled quantile residual plots via
simulation using the R-package DHARMa (Hartig 2022).

\textbf{Results}

Altogether, the motion sensor triggered cameras captured 24,596 and
45,387 images and the time lapse cameras 134,979 and 142,506 images in
2020 and 2021, respectively.

The best fitting count model had a negative Binomial distribution (NB1
parameterization) and it accounted for zero-inflation (zero-inflation p
\textless{}0.001, Table 1). It included the fixed factors `trigger type'
and `year' without their interaction and the random intercept of `site'
(random effect variance (\selectlanguage{greek}σ\selectlanguage{english}\textsuperscript{2}) of 0.9062 and standard
deviation (SD) of 0.952). Taiga bean goose count was significantly
higher in 2021 compared to 2020 (p \textless{}0.001, Fig. 3), however,
the camera trigger type had no significant effect on the number of geese
(p = 0.129).

As the AIC values were within \textless{}2 of each other, the best
fitting logistic model on goose capture probability was selected based
on parsimony and it included the fixed terms `trigger type', `year' and
`effort' without interactions and the random intercept of `site' (random
effect \selectlanguage{greek}σ\selectlanguage{english}\textsuperscript{2} = 0.6215 and SD = 0.7883, Table 1). The
capture probability increased significantly with increasing camera
effort (p \textless{}0.001) and was significantly higher in the year
2021 (p \textless{}0.001) and marginally significantly higher (p =
0.049) with game cameras set to time lapse (Fig. 4).

\textbf{Discussion}

Time lapse cameras may allow the collation of more standardized data
than motion triggered cameras and potentially reduce the number of empty
images produced by false triggering. Our study shows that the overall
daily capture probability of taiga bean geese was higher with cameras
set to time lapse compared to motion sensitive cameras. However, there
was no significant difference in the daily number of geese captured with
the two trigger types. This difference in the results of the two models,
albeit quite subtle, could at least partly be explained by the motion
triggered cameras failing to capture the more distant animals, whereas
the time lapse method captures both near-by and distant animals. This
makes positioning of the camera traps on time lapse setting less
critical as animals can be detected even if they do not use their exact
assumed route or habitat. Indeed, in order for the animal to be captured
with the motion trigger mode, the PIR sensor must detect motion in the
trigger area while the animal is within the camera's viewable area
(Moeller et al. 2023).

Our study on taiga bean geese in their breeding areas shows that time
lapse cameras are more useful than motion triggered cameras in
collecting data on the habitat use of animals because of the higher
capture probability of the former. In addition, required memory card
space and battery needs are predictable. This is an important finding,
because the use of time lapse cameras reduces the need to visit the
camera sites for changing batteries and memory cards, hence reducing the
disturbance during the sensitive breeding period of the geese. An
important advantage also is that time lapse cameras will produce more
reliable data of sites that are used by taiga bean geese in the breeding
areas, an information crucially important for protection of the species.
This information is difficult to obtain using human observers as the
species is highly elusive and occupies very remote breeding areas.

Choosing the triggering mode depends on the research objectives and
characteristics of the target species and its habitat. As our study
shows, time-lapse may be more suitable, for example, in studies
involving habitat use and the designation of protected areas where it is
important to distinguish between used and unused sites. Motion trigger
mode, on the other hand, may be more useful in studies on movement or
behaviour, or in cases where the density of the animals is low, as it is
possible that the time lapse method may fail to detect the target
species altogether (Moeller et al. 2023). Moreover, motion triggered
cameras have been shown to capture a higher proportion of animals than
cameras operating on time lapse, such as in a study monitoring wildlife
underpass usage (Pomezanski and Bennett 2018).

Camera trapping has gained popularity in wildlife studies over the
recent years due to its efficiency to collect opportunistic image data
without the need to dedicate numerous hours of researchers' time present
on the study site. At the same time, large volumes of images collected
with this method continue to be one of the challenges in the subsequent
data management and analysis. For example, a single camera operating on
time lapse with a 15-minute interval, a setting used in this study,
outputs 96 photos in a 24-hour period, and accumulates a dataset
containing more than a thousand images over a study period longer than
ten weeks. On the other hand, motion sensitive triggering may produce
thousands of ``empty'' images in a short time period that still need to
be checked for the presence of the target species. In total, our
two-year study period produced nearly 350,000 images, which all were
gone through manually, forming the most labour intensive and costly part
of the study. In order to reduce the time allocated to this painstaking
process, machine learning techniques (automatic identification
algorithms) together with citizen science approach (e.g., Wei et al.
2020, Hilton et al. 2022, Bjerge et al. 2023) have the potential to
become some of the most important innovations in the cost-effective
identification and counting of animals from large camera trap datasets
in future studies.

We found a difference in the number of geese and in their capture
probability (i.e., presence) between the two study years. Unfortunately,
because standardized monitoring data of the annual numbers of the taiga
bean goose in the breeding areas are not available, it is not possible
to say if the between-year difference reflects a real difference of
breeding numbers or just random variation. Nevertheless, the findings of
this study and those of Nyk\selectlanguage{ngerman}änen et al. (2021), together covering data
from four successive breeding seasons (2018\selectlanguage{english}-2021), suggest that wildlife
cameras are a feasible method for monitoring taiga bean goose during the
breeding season. First, they can reveal between-year differences both in
the numbers and occurrence of the species. Second, wildlife cameras set
at fixed sites are a cost-effective method to gather highly comparable
long-term data from remote breeding areas that are difficult to access
and cover using other survey methods. And finally, non-expert citizens
could be engaged in the monitoring based on wildlife cameras, making it
possible to cover a large number of potentially important breeding sites
(peatlands).

\textbf{Data availability}

Data and the R-script used to generate the analyses can be found in the
online supporting information.

\textbf{References}

Bjerge, K., Alison, J., Dyrmann, M., Frigaard, C.E., Mann, H.M.R., Hoye,
T.T., 2023. Accurate detection and identification of insects from camera
trap images with deep learning. PLOS Sustainability and Transformation
2, e0000051. https://doi.org/10.1371/journal.pstr.0000051

Bolker, B., 2022. Getting started with the glmmTMB package. 8pp.
\textless{}https://cran.r-project.org/web/packages/glmmTMB/vignettes/glmmTMB.pdf\textgreater{}

Brooks, M.E., Kristensen, K., Benthem, K.J. van, Magnusson, A., Berg,
C.W., Nielsen, A., Skaug, H.J., Machler, M., Bolker, B.M., 2017. glmmTMB
Balances Speed and Flexibility Among Packages for Zero-inflated
Generalized Linear Mixed Modeling. The R Journal 9, 378--400.

Burton, A.C., Neilson, E., Moreira, D., Ladle, A., Steenweg, R., Fisher,
J.T., Bayne, E., Boutin, S., 2015. REVIEW: Wildlife camera trapping: a
review and recommendations for linking surveys to ecological processes.
J Appl Ecol 52, 675--685. https://doi.org/10.1111/1365-2664.12432

CAFF, 2018. A Global Audit of the Status and Trends of Arctic and
Northern Hemisphere Goose Populations. Conservation of Arctic Flora and
Fauna International Secretariat, Akureyri, Iceland.

Caravaggi, A., Banks, P.B., Burton, A.C., Finlay, C.M.V., Haswell, P.M.,
Hayward, M.W., Rowcliffe, M.J., Wood, M.D., 2017. A review of camera
trapping for conservation behaviour research. Remote Sens Ecol Conserv
3, 109--122. https://doi.org/10.1002/rse2.48

Firth, A.G., Baker, B.H., Gibbs, M.-L., Brooks, J.P., Smith, R., Iglay,
R.B., Davis, J.B., 2020. Using cameras to index waterfowl abundance in
winter-flooded rice fields. MethodsX 7, 101036.
https://doi.org/10.1016/j.mex.2020.101036

Fox, A.D., Ebbinge, B.S., Mitchell, C., Heinicke, T., Aarvak, T.,
Colhoun, K., Clausen, P., Dereliev, S., Farago, S., Koffijberg, K.,
Kruckenberg, H., Loonen, M.J.J.E., Madsen, J., Mooij, J., Musil, P.,
Nilsson, L., Pihl, S., Van der Jeugd, H., 2010. Current estimates of
goose population sizes in western Europe, a gap analysis and an
assessment of trends. Ornis Svecica 20, 115--127.
https://doi.org/10.34080/os.v20.19922

Gracanin, A., Mikac, K.M., 2022. Camera traps reveal overlap and
seasonal variation in the diel activity of arboreal and semi-arboreal
mammals. Mamm Biol 102, 341--355.
https://doi.org/10.1007/s42991-021-00218-y

Hardin, J.W., Hilbe, J.M., 2018. Generalized linear models and
extensions, Fourth edition. ed. Stata Press, College Station, Texas. 598
pp.

Hartig, F., 2022. DHARMa: Residual Diagnostics for Hierarchical
(Multi-Level / Mixed) Regression Models.
\textless{}https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html\textgreater{}

Hilton, M.L., Goessling, J.M., Knezevich, L.M., Downer, J.M., 2022.
Utility of machine learning for segmenting camera trap time-lapse
recordings. Wildlife Society Bulletin 46, e1342.
https://doi.org/10.1002/wsb.1342

Jacobs, C.E., Ausband, D.E., 2018. An evaluation of camera trap
performance -- What are we missing and does deployment height matter?
Remote Sensing in Ecology and Conservation 4, 352--360.
https://doi.org/10.1002/rse2.81

Kahle, D., Wickham, H., 2013. ggmap: Spatial Visualization with ggplot2.
The R Journal 5, 144--161.

Kanka, P., Sukmasuang, R., Duengkae, P., Siripattaranukul, K., 2023.
Abundance and physical factors affecting the appearance of selected
terrestrial birds in Khao Yai National Park using camera trapping.
Biodiversitas 24. https://doi.org/10.13057/biodiv/d240127

Kear, J. (Ed.), 2005. Ducks, geese, and swans: Bird families of the
world. Oxford University Press, Oxford; New York. 930pp.

Laux, A., Waltert, M., Gottschalk, E., 2022. Camera trap data suggest
uneven predation risk across vegetation types in a mixed farmland
landscape. Ecology and Evolution 12. https://doi.org/10.1002/ece3.9027

Lehikoinen, A., Jukarainen, A., Mikkola-Roos, M., Below, A., Lehtiniemi,
T., Pessa, J., Rajasarkka, A., Rintala, J., Rusanen, P., Sirkia, P.,
Tiainen, J., Valkama, J., 2019. Birds, in: Hyvarinen, E., Juslen, A.,
Kemppainen, E., Uddstrom, A., Liukko, U.-M. (Eds.), The 2019 Red List of
Finnish Species. Ministry of the Environment \& Finnish Environment
Institute, Helsinki, Finland, pp. 560--570.

Li, J., Xue, Y., Zhang, Y., Dong, W., Shan, G., Sun, R., Hacker, C., Wu,
B., Li, D., 2020. Spatial and temporal activity patterns of Golden takin
(\emph{Budorcas taxicolor bedfordi} ) recorded by camera trapping. PeerJ
8, e10353. https://doi.org/10.7717/peerj.10353

Moeller, A.K., Waller, S.J., DeCesare, N.J., Chitwood, M.C., Lukacs,
P.M., 2023. Best practices to account for capture probability and
viewable area in camera-based abundance estimation. Remote Sensing in
Ecology and Conservation 9, 152--164. https://doi.org/10.1002/rse2.300

Nykanen, M., Poysa, H., Hakkarainen, S., Rajala, T., Matala, J.,
Kunnasranta, M., 2021. Seasonal and diel activity patterns of the
endangered taiga bean goose (\emph{Anser fabalis fabalis} ) during the
breeding season, monitored with camera traps. PLOS ONE 16, e0254254.
https://doi.org/10.1371/journal.pone.0254254

Palencia, P., Vicente, J., Soriguer, R.C., Acevedo, P., 2022. Towards a
best-practices guide for camera trapping: assessing differences among
camera trap models and settings under field conditions. Journal of
Zoology 316, 197--208. https://doi.org/10.1111/jzo.12945

Piironen, A., Fox, A.D., Kampe-Persson, H., Skyllberg, U., Therkildsen,
O.R., Laaksonen, T., 2023. When and where to count? Implications of
migratory connectivity and nonbreeding distribution to population
censuses in a migratory bird population. Population Ecology 65,
121--132. https://doi.org/10.1002/1438-390X.12143

Pirkola, M.K., Kalinainen, P., 1984. The status, habitats and
productivity of breeding populations of Bean Goose, \emph{Anser fabalis
fabalis} , in Finland. Swedish Wildlife Research 13, 9--48.

Pomezanski, D., Bennett, L., 2018. Developing recommendations for
monitoring wildlife underpass usage using trail cameras. Environ Monit
Assess 190, 413. https://doi.org/10.1007/s10661-018-6794-0

Puffer, S.R., Tennant, L.A., Lovich, J.E., Agha, M., Smith, A.L.,
Delaney, D.K., Arundel, T.R., Fleckenstein, L.J., Briggs, J., Walde,
A.D., Ennen, J.R., 2021. Birds not in flight: using camera traps to
observe ground use of birds at a wind-energy facility. Wildlife Research
49, 283--294. https://doi.org/10.1071/WR21071

R Core Team, 2023. R: A Language and Environment for Statistical
Computing. R Foundation for Statistical Computing, Vienna, Austria.

Rovero, F., Zimmermann, F., Berzi, D., Meek, P., 2013. ``Which camera
trap type and how many do I need?'' A review of camera features and
study designs for a range of wildlife research applications. Hystrix,
the Italian Journal of Mammalogy 24.
https://doi.org/10.4404/hystrix-24.2-8789

Salvatori, M., Oberosler, V., Rinaldi, M., Franceschini, A., Truschi,
S., Pedrini, P., Rovero, F., 2023. Crowded mountains: Long-term effects
of human outdoor recreation on a community of wild mammals monitored
with systematic camera trapping. Ambio.
https://doi.org/10.1007/s13280-022-01825-w

Santini, G., Abolaffio, M., Ossi, F., Franzetti, B., Cagnacci, F.,
Focardi, S., 2022. Population assessment without individual
identification using camera-traps: A comparison of four methods. Basic
and Applied Ecology 61, 68--81.
https://doi.org/10.1016/j.baae.2022.03.007

Scott, D.A., Rose, P.M., 1996. Atlas of the Anatidae populations in
Africa and western Eurasia. Wetlands International, Wageningen, The
Netherlands.

Sperry, J.H., O'Hearn, D., Drake, D.R., Hruska, A.M., Case, S.B.,
Vizentin-Bugoni, J., Arnett, C., Chambers, T., Tarwater, C.E., 2021.
Fruit and seed traits of native and invasive plant species in Hawai`i:
implications for seed dispersal by non-native birds. Biol Invasions 23,
1819--1835. https://doi.org/10.1007/s10530-021-02473-z

Taylor, J.C., Bates, S.B., Whiting, J.C., McMillan, B.R., Larsen, R.T.,
2022. Using camera traps to estimate ungulate abundance: a comparison of
mark--resight methods. Remote Sensing in Ecology and Conservation 8,
32--44. https://doi.org/10.1002/rse2.226

Wearn, O.R., Glover-Kapfer, P., 2019. Snap happy: camera traps are an
effective sampling tool when compared with alternative methods. R. Soc.
open sci. 6, 181748. https://doi.org/10.1098/rsos.181748

Wei, W., Luo, G., Ran, J., Li, J., 2020. Zilong: A tool to identify
empty images in camera-trap data. Ecological Informatics 55, 101021.
https://doi.org/10.1016/j.ecoinf.2019.101021

Wickham, H., 2016. ggplot2: Elegant Graphics for Data Analysis.
Springer-Verlag New York.

\textbf{Figure legends}

Fig. 1. Study areas. Location of the study areas (in provinces of
Lapland (1), Northern Ostrobothnia (2), and North Karelia (3)), the
peatlands where camera traps were deployed in 2020 and 2021. The maps
were drawn using R-packages ggplot2 (Wickham, 2016) and ggmap (Kahle and
Wickham, 2013) with map tiles by Stamen Design, under CC BY 3.0 (data by
OpenStreetMap, under ODbL). The inset map was drawn using Natural Earth
map data.

Fig. 2. Taiga bean geese captured with a PIR sensor triggered camera in
Pirttilammit, North Karelia, in 2021.

Fig. 3. Conditional effects of a) year and b) trigger type on the number
of taiga bean geese captured with game camera traps on peatland ponds in
Finland between 2020 and 2021. The difference was significant between
years (p \textless{}0.001) but not significant between trigger types (p
= 0.129).

Fig. 4. Conditional effects of a) year, b) trigger type, and c) camera
effort on the capture probability of taiga bean geese photographed with
game camera traps. The difference was significant between years (p
\textless{}0.001) and marginally significant between trigger types (p =
0.049). The amount of camera effort had a significant positive effect on
capture probability (p \textless{}0.001).

\textbf{Tables}

Table 1. Summary of candidate models ran to investigate the effect of
trigger type on the number and presence of taiga bean geese captured on
game camera traps. NB stands for negative Binomial with two different
parameterizations (Bolker, 2022; Hardin and Hilbe, 2018).\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Model formula} & \textbf{Distribution and type} & \selectlanguage{greek}\textbf{ΔAIC}\selectlanguage{english}
& \textbf{df}\tabularnewline
Goose count \textasciitilde{} Year + Trigger type + offset(log(effort))
+ (1\textbar{}Site)\textsuperscript{+} & NB1 + zero-inflation & 0 &
6\tabularnewline
Goose count \textasciitilde{} Year * Trigger type + offset(log(effort))
+ (1\textbar{}Site)\textsuperscript{+} & NB1 + zero-inflation & 0.9 &
7\tabularnewline
Goose count \textasciitilde{} Trigger type + offset(log(effort)) +
(1\textbar{}Site)\textsuperscript{+} & NB1 + zero-inflation & 16.5 &
5\tabularnewline
Goose count \textasciitilde{} Year + Trigger type + offset(log(effort))
+ (1\textbar{}Site)\textsuperscript{+} & Tweedie & 25.2 &
6\tabularnewline
Goose count \textasciitilde{} Year + Trigger type + offset(log(effort))
+ (1\textbar{}Site)\textsuperscript{+} & Tweedie + zero-inflation & 27.2
& 7\tabularnewline
Goose count \textasciitilde{} Year + Trigger type + offset(log(effort))
+ (1\textbar{}Site)\textsuperscript{+} & NB1 & 117 & 5\tabularnewline
Goose count \textasciitilde{} Year + Trigger type + offset(log(effort))
+ (1\textbar{}Site)\textsuperscript{+} & NB2 + zero-inflation & 147.4 &
6\tabularnewline
Goose count \textasciitilde{} Year + Trigger type + offset(log(effort))
& NB2 + zero-inflation & 231.4 & 5\tabularnewline
Goose count \textasciitilde{} Year + Trigger type + offset(log(effort))
+ (1\textbar{}Site)\textsuperscript{+} & NB2 & 268.2 & 5\tabularnewline
Goose count \textasciitilde{} Year + Trigger type + offset(log(effort))
+ (1\textbar{}Site)\textsuperscript{+} & Poisson + zero-inflation &
3879.7 & 5\tabularnewline
Goose count \textasciitilde{} Year + Trigger type + offset(log(effort))
+ (1\textbar{}Site)\textsuperscript{+} & Poisson & 22034.3 &
4\tabularnewline
\textbf{Model formula} & \textbf{Distribution and type} & \selectlanguage{greek}\textbf{ΔAIC}\selectlanguage{english}
& \textbf{df}\tabularnewline
Goose presence \textasciitilde{} Year + Trigger type + effort +
(1\textbar{}Site)\textsuperscript{+} & Binomial (logit-link) & 0 &
5\tabularnewline
Goose presence \textasciitilde{} Year + Trigger type * effort +
(1\textbar{}Site)\textsuperscript{+} & Binomial (logit-link) & 0.9 &
6\tabularnewline
Goose presence \textasciitilde{} Year * Trigger type * effort +
(1\textbar{}Site)\textsuperscript{+} & Binomial (logit-link) & 1.9 &
9\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{+} random intercept of `site'\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig/Fig}

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

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

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

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

