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\begin{document}

\title{Balancing between predation risk and food by boreal breeding ducks}



\author[1]{Sari Holopainen}%
\author[1]{Elmo Miettinen}%
\author[1]{Veli-Matti Väänänen}%
\author[1]{Petri Nummi}%
\author[2]{Hannu Pöysä}%
\affil[1]{University of Helsinki}%
\affil[2]{University of Eastern Finland}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\selectlanguage{english}
\begin{abstract}
1. Wetlands belong to the globally most threatened habitats, and
organisms depending on them are of conservation concern. Wetland
destruction and quality loss may affect negatively also boreal breeding
ducks in which habitat selection often needs balancing between important
determinants of habitat suitability. In Finland duck population
trajectories are habitat-specific, while the reasons behind are not
known. 2. In this research, the balance of nest predation risk and
invertebrate food abundance in boreal breeding ducks was studied in
Finland at 45 lakes and ponds in 2017 and 2018. Nest predation
experiments were conducted with artificial nests followed by wildlife
cameras during seven days. Invertebrates were sampled from the study
water bodies using emergence and activity traps. Duck pairs and broods
were also surveyed from these and 18 additional water bodies. 3. The
wildlife camera results indicate that predation risk was higher in the
water bodies surrounded by agricultural land than forestland. Ponds
(seasonal, beaver and man-made) had lower nest predation risk and they
were also more invertebrate-rich habitats than permanent lakes. In
addition, artificial nests further away from water bodies had higher
survival than shoreline nests. Habitat use of duck pairs (prior to
nesting) was not associated with invertebrate food, but duck broods
preferred habitats rich in food. 4. High nest predation pressure in
shorelines of especially agricultural landscapes may contribute the
declining population trends of ducks in Finland. Controlling predators
would be an important conservation action to improve duck breeding
success. This research underlines the benefits of the availability of
different water body types for the breeding ducks. There is an urgent
need to pay attention to protecting seasonal ponds, while the lack of
flooded waters may be mitigated by favouring beavers or man-made ponds.%
\end{abstract}%



\sloppy


Balancing between predation risk and food by boreal breeding ducks

Sari Holopainen \textsuperscript{1}, Elmo Miettinen\textsuperscript{2},
Veli-Matti V\selectlanguage{ngerman}äänänen \textsuperscript{1}, Petri
Nummi\textsuperscript{1}and Hannu Pöysä\textsuperscript{3}

1. Department of Forest Sciences, P.O. Box 27, University of Helsinki,
FI-00014 Helsinki, Finland. e-mail: sari.holopainen@helsinki.fi. Phone :
+358-504489538. Orcid: 0000-0002-3271-4468

2. Organismal and Evolutionary Biology Research Programme, Faculty of
Biological and Environmental Sciences, P.O.Box 62, University of
Helsinki, FI-00014 Helsinki, Finland. Orcid: 0000-0002-5354-7531

3. University of Eastern Finland, Department of Environmental and
Biological Sciences, POB 111, FI-80101 Joensuu, Finland.

\textbf{Key words}

Alien predator, invertebrate, nest predation, waterbird, wetland,
wildlife camera



\textbf{Abstract}

\begin{enumerate}
\tightlist
\item
  Wetlands belong to the globally most threatened habitats, and
  organisms depending on them are of conservation concern. Wetland
  destruction and quality loss may affect negatively also boreal
  breeding ducks in which habitat selection often needs balancing
  between important determinants of habitat suitability. In Finland duck
  population trajectories are habitat-specific, while the reasons behind
  are not known.
\item
  In this research, the balance of nest predation risk and invertebrate
  food abundance in boreal breeding ducks was studied in Finland at 45
  lakes and ponds in 2017 and 2018. Nest predation experiments were
  conducted with artificial nests followed by wildlife cameras during
  seven days. Invertebrates were sampled from the study water bodies
  using emergence and activity traps. Duck pairs and broods were also
  surveyed from these and 18 additional water bodies.
\item
  The wildlife camera results indicate that predation risk was higher in
  the water bodies surrounded by agricultural land than forestland.
  Ponds (seasonal, beaver and man-made) had lower nest predation risk
  and they were also more invertebrate-rich habitats than permanent
  lakes. In addition, artificial nests further away from water bodies
  had higher survival than shoreline nests. Habitat use of duck pairs
  (prior to nesting) was not associated with invertebrate food, but duck
  broods preferred habitats rich in food.
\item
  High nest predation pressure in shorelines of especially agricultural
  landscapes may contribute the declining population trends of ducks in
  Finland. Controlling predators would be an important conservation
  action to improve duck breeding success. This research underlines the
  benefits of the availability of different water body types for the
  breeding ducks. There is an urgent need to pay attention to protecting
  seasonal ponds, while the lack of flooded waters may be mitigated by
  favouring beavers or man-made ponds.
\end{enumerate}

Cover letter

Dear Editors,

we submit the MS ``Balancing between predation risk and food by boreal
breeding ducks'' for publication in the special issue of Ecology and
Evolution ``Ecological Insights from Camera Trapping''.

We studied the effects of wetland quality for boreal breeding ducks from
the two important aspects: predation and food. We used wildlife cameras
for reliable nest predator identification around different breeding
habitats of ducks. Interestingly, we did find that duck food
availability is low and nest predation pressure is high at permanent
lakes, while ponds (seasonal, beaver, man-made) were shown to be food
rich habitats with low nest predation pressure. However, ponds are
commonly not protected and for example seasonal ponds are typically
drained for farmland or to improve forest grow. Our results emphasize
the benefits of different wetland types in the landscape for the
breeding ducks. Even important bird lakes might have high predation
pressure, which underlines the need of predator control, especially
concerning alien species. We think that our manuscript is timely and
offer much needed information about conservation of wetland ecosystems
from aspects that are so far poorly covered. Wildlife cameras turned out
really useful method for identifying different predator species, even
they took the eggs without leaving any clues behind.

Sincerely, on behalf of the authors,

Sari Holopainen

\textbf{INTRODUCTION}

Humans have altered wetland ecosystems in numerous ways across the
globe, especially through drainage for agricultural land (Davidson,
2014; Gibbs, 2000; Hu et al., 2017; Kingsford et al., 2016). In boreal
forest areas, peatlands and other riparian forest habitats have been
ditched to increase wood production (Kuusisto et al., 1998). In addition
to direct wetland destruction, climate warming (McMenamin et
al\emph{.,}2008) and over-exploitation of wetland creating beavers
(Halley et al. 2021) have had an indirect effect on the amount and
distribution of wetlands. Furthermore, wetland quality is threatened for
instance due to eutrophication and alien species introductions (Fox et
al., 2019; Ramsar Convention Secretariat 2010; Ma et al., 2010;
Guillemain et al.\emph{,}2013; Nummi et al., 2019a). All in all,
human-induced environmental wetland change has therefore impacted
aquatic animals at multiple levels of organization from individuals to
landscapes (Sievers et al., 2018).

Habitat characteristics are important determinants of breeding densities
and production of ducks on boreal wetlands (Holopainen et
al\emph{.,}2015). Many boreal lakes lack the habitat structure and
sufficient food resources to support breeding ducks, making them
unsuitable for brood rearing (Sjöberg et al., 2000). Indeed, duckling
mortality at lakes with limited food resources, in particular
invertebrates, is high (Gunnarsson et al., 2004; Nummi \& Hahtola,
2008). In spring, however, patterns of snowmelt create annual variation
in the nature and extent of shallow flooded lakeshores affecting
littoral ecosystem productivity (Larmola et al\emph{.,} 2004). Seasonal
floods and ponds that commonly dry during the summer offer important
food-rich habitats for ducks (Holopainen et al., 2014), despite their
highly variable occurrence. In addition, habitat engineering by beavers
(\emph{Castor} spp.) modifies oligotrophic, sharp-edged boreal lakes
into productive shallow wetlands with ambiguous shorelines. Both beaver
ponds and seasonal ponds typically have varying shorelines and possibly
no fish or low fish densities (Nummi \& Hahtola, 2008).

Habitat selection of breeding ducks is not straight-forward, but will
possibly lead to trade-off situations, both between and within different
stages of the breeding season. For example, experimental data by
Gunnarsson and Elmberg (2008) suggests a trade-off between wetland use
and nest survival in forested versus agricultural landscapes in the
mallard (\emph{Anas platyrhynchos} ); wild waterfowl, including mallard,
seemed to prefer agricultural landscape, while facing higher nest
predation risk there. While predation risk largely determines nest site
use and nesting success, food resources and habitat structure are the
key characteristics affecting habitat use by duck pairs and broods as
well as subsequent breeding success in boreal lakes (Holopainen et
al\emph{.,} 2015). At wetlands, complex habitat structure and luxuriant
vegetation are linked, as the abundance of emergent vegetation typically
increases from nutrient-poor oligotrophic to nutrient-rich eutrophic
lakes (Kauppinen \& Väisänen, 1993; Holopainen \& Lehikoinen, 2021).

Changes in important boreal environmental characteristics may already
have affected breeding ducks negatively. Finnish national duck pair
surveys show declining trends for several species, but those breeding in
eutrophic lakes have declined more than in oligotrophic lakes
(Lehikoinen et al., 2016; Pöysä et al., 2013). In addition to
detrimental effects of over-growth of eutrophic waters,
disproportionally increased predator pressure is one of the suspected
reasons for the differences in population trajectories between habitats
and also between species within habitats (see Pöysä et al., 2019; Pöysä
\& Linkola 2021), potentially impacting flyway-level trends in
population size and structure (e.g. Brides et al., 2017). Indeed,
artificial duck nest experiments with wildlife cameras in northern
Europe have shown that nest predation rate is high especially around
wetlands and in agricultural landscape, where also alien mammal species
now occur (Holopainen et al., 2020a). As a result, duck species nesting
on eutrophic lakes and especially those preferring shorelines as nesting
places have, in all likelihood, experienced a major increase in predator
diversity and abundance, which may have contributed to their declining
population trends (Holopainen et al., 2021; Pöysä \& Linkola, 2021).

In this article the complex habitat-based associations with ducks
breeding success will be analysed. We will assess whether and how
habitat selection and brood production by boreal breeding ducks result
from a trade-off between nest predation risk and food
availability.~Specifically, wildlife cameras were used at artificial
nests (mimicking dabbling duck nests) to measure nest predation risk at
both the local habitat (shoreline nests versus forest nests) and
landscape (proportion of agricultural land versus forest in the
landscape) scales. Next, habitat selection of both breeding pairs and
broods were assessed emphasising the role of landscape and food
availability (invertebrate abundance). In addition, brood production at
the landscape scale was measured. The hypothesis is that while eutrophic
wetlands in agricultural landscapes typically produce more
invertebrates, they will also have a higher nest predation rate and thus
lower brood production. Furthermore, contrary to permanent lakes, it is
expected that flooded ponds offer the most food-rich brood habitats but
also safe nesting places.

\textbf{METHODS}

\textbf{2.1 Study areas}

For this study, water bodies surrounded with different proportions of
forest versus agricultural land were selected: different landscapes to
cover the whole gradient from fully forested to mainly agricultural were
chosen. Landscape might affect not only the lake trophic status, but
also the nest predator community, as stated by Holopainen et al.
(2020a). Studies were conducted at lakes and ponds at two areas in
Finland, Evo and Maaninka (Figure 1). Both areas have permanent lakes,
which carry water through the summer. Lake shorelines may be affected by
spring floods, but otherwise the water level is rather constant. The
trophic level among these lakes vary from oligotrophic to eutrophic (see
Holopainen \& Lehikoinen, 2021). In addition to permanent lakes, in both
areas the study included other wetlands, which were shallow and had
temporally varying shorelines: seasonal ponds, beaver ponds and man-made
ponds (hereafter ponds). Hereafter all lakes and ponds are called water
bodies. The same mammalian predators and all common corvid species occur
in both study areas (Lindén et al., 1996; Valkama et al., 2011).

Evo in southern Finland (61°12'N, 25°07'E) represents a typical boreal
forest landscape in Finland. Human settlements are scarce, with few
agricultural fields (hereafter fields) inside the study area and larger
agricultural lands south from the studied water bodies. The lands within
1 km of the Evo water bodies consists on average 1 \% of fields (range
0--6\%).

In Evo duck surveys were undertaken at 45 water bodies (27 permanent
lakes, 10 beaver ponds and 8 seasonal ponds) within a c.
39-km\textsuperscript{2} area. Water body size varied between 0.04--37
ha. The median shoreline length for permanent lakes was 1.0 km and for
ponds 0.5 km. From the 45 study water bodies, six seasonal, six beaver
ponds and 12 permanent lakes were chosen for nest predation experiments
and invertebrate surveys (for more information, see Supplementary
Appendix S1).

Maaninka in eastern Finland (63\selectlanguage{english}@15'N, 27@\selectlanguage{english}30'E) is a mosaic of
agricultural land and forests with some internationally important
bird-lakes (Natura 2000 and IBA -lakes; Leivo et al\emph{.,} 2002). The
area represents typical agricultural landscape of Finnish lake district.
Duck surveys were made at 18 lakes and seasonal ponds across c. 47
km\textsuperscript{2}: 17 water bodies were used for the experiments,
among them the important bird-lakes. All seasonal ponds were included
that occurred during the study years and to which permission was granted
from the landowners. The lands within 1 km of the water bodies consists
on average 59\% of fields (range 24--75\%), the remainder mainly private
forest. Water body sizes were 1--149 ha. The median shoreline length for
permanent lakes was 3.0 km and for ponds 1.1 km. In total there were
seven permanent lakes, two man-made ponds and eight seasonal ponds for
nest experiments and invertebrate surveys.

To estimate the landscape structure (i.e. the field percentage) near the
study water bodies, QGIS 2.18.7 (QGIS Development Team, 2017) and
topographic vector map (National Land Survey of Finland, 03/2019) were
used. The 1 km radius zones around the water bodies were measured from
the shoreline. We used the 1km radius, as in the comparisons of nest
predation patterns between 500 m and 250 m radius zones, the 250 m zone
has shown to have habitat specific effects, while the effects disappear
with a larger zone (Uusihakala, 2021). Thus we believe that 1 km radius
zone describes the landscape scale foraging range of mammals. Waters
from the zones were excluded in order to count the field percentage of
surrounding land areas.

\textbf{2.2 Artificial nest survival experiment with wildlife cameras}

Artificial nest experiments were conducted in 2017 and 2018 to study
nest predation rates at the water bodies. Nests were placed in sites
where a dabbling duck hen could possibly lay a clutch, based on our own
experience (nest site selection of boreal ducks is poorly studied,
review by Holopainen et al., 2015; see also V\selectlanguage{ngerman}äänänen et al., 2016). Some
dabbling duck species nest at the shoreline, while others can place
nests in the forest far from water bodies, so the artificial nest sites
reflected this distribution. Each nest contained two farmed mallard eggs
and some down from shot mallard females, mimicking the situation in the
early stage of egg laying. Nests were constructed to resemble real ones:
natural nest material from the nest surroundings was collected to form
c. 20 cm wide nest cup and cover the eggs lightly. Eggs were not covered
with down since ducks do not typically cover them before starting
incubation. Nests were set under small trees or bushes, so they were
hardly detectable from above. In open areas nests were established
within tussocks.

Experimental nests were established in pairs around the water bodies:
shoreline nests were placed less than five metres from shorelines and
forest nests at least 70 metres from the shoreline nest (for more
information, see Supplementary Appendix S2). In 2017 there were 46 nests
at Evo and 42 at Maaninka, and in 2018 the numbers were 48 and 42,
respectively (in total 178 nests). Density of the experimental nests was
c. 1.2 nest/km\textsuperscript{2} at Evo and 0.9
nests/km\textsuperscript{2} at Maaninka. At Evo, every water bodies had
only one nest pair a year, while at Maaninka there were fewer, but
larger water bodies, and thus 2--3 nest pairs around the water bodies
were established if possible. The same nest sites were used in both
years to minimize the site effect.

Wildlife cameras (20 Uovision UV595-Full HD 12MP and four Niteforce
Professional Trail Camera 12 MP) were set at artificial nests to
identify predator nest visits and depredation time. Light-triggered
passive wildlife cameras were ca. 1--1.5 m from nests, attached on trees
or 1 m stakes. Cameras were active the whole seven day period responding
to movement and were adjusted to take three pictures in a row, followed
by a one-minute pause. The nest experiment was started during the pair
survey (see 2.3 Duck and invertebrate surveys), the time when ducks
initiate egg laying. One nest experiment round took seven days, and all
the nests of a round were established and deconstructed on the same day
between 9 am and 16 pm. Two rounds of nest experiments with different
set of water bodies were carried out on each study area (i.e. two
one-week experiments with 20--24 nests at the time). Nests were not
visited by researchers during the seven-day exposure period.

\textbf{2.3 Duck and invertebrate surveys}

The duck species studied here are ground nesting and distributed widely
in the boreal zone: mallard, common teal (\emph{Anas crecca;} hereafter
teal), Eurasian wigeon (\emph{Mareca penelope} ), northern pintail
(\emph{Anas acuta} ), northern shoveler (\emph{Spatula clypeata} ),
garganey (\emph{S. querquedula} ) and tufted duck (\emph{Aythya
fuligula} ). All bred in Maaninka, but only the first two species bred
at Evo. Duck pair and brood surveys were made in 2017 and 2018 with
standard waterbird round count methods (Koskimies \& Väisänen, 1991). In
the round count the wetlands were surveyed by moving around the lake by
a boat, stand up paddling board or by foot near the shoreline so that
all the settled birds were detected with a high probability. Pair
surveys were conducted in April and May right after the ice melt, when
duck pairs occupy their breeding wetlands and are preparing for nesting.
Ice melting sets an exact time frame for the duck surveys (Pöysä, 1996,
2019), making it possible to calibrate the phenology between different
areas and years. Pairs and lone males were considered as pairs. Also
groups of 2--4 males were used to estimate the number of pairs (i.e.,
2--4 pairs). If the number of females surpassed the number of males at a
wetland, the number of females was used instead. Brood surveys were
conducted twice a year in the early June and July. The species, number
and age of ducklings were recorded for each brood (Pirkola \& Högmander,
1974). When studying brood habitat use, all brood observations were used
for the analyses to determine the diverging habitat use of different age
classes (i.e. some broods might occur twice in the analyses). When
analysing brood production, every brood was identified based on their
age and count, thus counting each brood only once.

Invertebrate trapping was conducted in the water bodies in June 2017 and
2018 during the first brood survey. All details of the trapping
procedure were identical between the 46 studied water bodies.
Free-swimming aquatic invertebrates were trapped with the activity trap
described in Elmberg et al. (1992). Emerging insects were captured with
emergence traps similar to those described by Danell and Sjöberg (1977).
Three traps of both types were set per water bodies in different
vegetation types for 48 hours. All the water bodies of each area were
trapped during one week. The two invertebrate measures were combined to
give a water body-level food abundance index (Holopainen et
al\emph{.,}2014) as an index of habitat quality (for more information
about local invertebrate catch and species-specific duck-invertebrate
associations, see Nummi et al., 2013; Nummi \& Väänänen, 2001).

\textbf{2.4 Statistical methods}

\emph{Daily survival analysis}

The daily survival of forest versus shoreline nests was compared for
predation risk, based on 175 nests (88 forest and 87 shoreline nests
surrounding in total of 46 water bodies) that survived the entire study
period or with the known depredation time (68 nests depredated).
Depredated nests where memory cards of the cameras failed were excluded
because of unknown predation date (\emph{N} = 2 in 2017). Furthermore,
the daily survival of shoreline nests around permanent lakes and ponds
was compared, based on 87 nests (41 with exact depredation time).

The GLMM framework was used to calculate daily nest survival probability
by using modified logistic regression, which incorporates the number of
exposure days (seven, each beginning at 12 pm) into the link function
(Shaffer, 2004). The logistic exposure method is a modification of
logistic regression and maximizes the use of nest survival data by
treating each measurement day as a discrete trial. Daily nest fate was
analysed as a binary response variable (1 = survived, 0 = depredated).
In the forest-shoreline comparison explanatory variables were ``DATE''
(1,\ldots{}7) and ``HABITAT'' (factorial: shoreline, forest). As nests
were established in pairs around the water bodies, one in the shoreline
and one further away from the shoreline, water body (``WETLAND\_ID'')
was used as a random factor. For larger lakes with more than one nest
pair, an individual ID was given for every pair of nests. When comparing
water body types, the explanatory variables were ``DATE'' and ``TYPE''
(factorial: lake, pond). ``WETLAND\_ID'' was used again as a random
factor, but this time it only meant shoreline nests. Field percentage
(``FIELD'') around water bodies was used as an explanatory variable in
both analyses. Year effect was found to be negligible during the data
exploration and was thus discarded from the analysis.

\emph{Pair and brood numbers}

Mann-Whitney \emph{U} -test for independent samples was used to compare
the overall pair and brood densities of all the studied duck species
between Maaninka and Evo. Furthermore, as mallard and teal are
generalist species and common at both study areas, hence providing
sufficient data, their pair and brood densities and brood production
between the two areas were compared separately. \emph{G} -test for
goodness-of-fit was used to compare species-specific proportions of
brooded and non-brooded pairs in 2017 and 2018 at Evo with those at
Maaninka.

Pair and brood data were zero-inflated, and when exploring the non-zero
part, there was still overdispersion. Thus zero-inflated negative
binomial models were used to explain variation in the number of pairs
and broods using glmmTMB (Brooks et al\emph{.,} 2017). All the analyses
were done in R 3.4.0 (R Core Team, 2017) and the data exploration was
done by following the protocol by Zuur et al. (2010). Water body size
was controlled for by including shoreline length (``SHORE'') as an
explanatory variable in all the models. Field percent (``FIELD'') was
used to indicate the type of the surrounding landscape around each
wetland (1 km buffer around the wetland) in every model. The amount of
food (``FOOD'', continuous) and wetland type (``TYPE'', two levels: lake
or pond) were used as wetland-level explanatory variables. In addition,
pair number (``PAIRS'') was included in the models explaining brood
numbers. However, as data exploration revealed that pair number and
shoreline length were strongly correlated (Pearson correlation r
\textgreater{} 0.6), shoreline length was discarded and the pair number
was kept, because the latter should more directly determine possible
brood production. Due to the nested structure of the data, wetland ID
was entered as random factor. Year effect was excluded because it failed
to improve model fit. All possible model combinations were fitted. We
chose between the best fitting models by using model specific AIC-values
and weights (\selectlanguage{greek}Δ\selectlanguage{english}AIC\textless{}2, where \selectlanguage{greek}Δ \selectlanguage{english}= AICi -- AICmin) (Burnham \&
Anderson, 2002). Due to model selection uncertainty, we calculated the
model-averaged slopes (\selectlanguage{greek}β-\selectlanguage{english}values) of the variables weighted by the Akaike
weights and their unconditional standard errors and 95\% unconditional
confidence intervals; all models in the candidate set were used (see
Burnham \& Anderson, 2002).

\emph{Invertebrate food abundance}

Linear mixed-effects modelling (nlme package, Pinheiro et al, 2018) was
used to study whether wetland type (``TYPE'', two levels: lake or pond)
affects the invertebrate food abundance index, incorporating shoreline
(``SHORE'') and field percentage (``FIELD'') as explanatory variables.

\textbf{RESULTS}

\textbf{3.1 Nest survival}

Of the artificial nests, 44\% (\emph{N} =39) were depredated in 2017 and
39\% (\emph{N} = 35) in 2018. The two year average nest predation rate
at Evo was 24\% and at Maaninka 61\%. Wildlife cameras revealed that
most common nest predator species were Eurasian magpie (\emph{Pica
pica,} 33\% of the depredated nests), raccoon dog (\emph{Nyctereutes
procyonoides} , 16\%), hooded crow (\emph{Corvus corone cornix} , 16\%;
Figure 2) and Eurasian jay (\emph{Garrulus glandarius} , 11\%). To a
lesser extent, nests were destroyed by pine martens (\emph{Martes
martes,} 7\%) and common ravens (\emph{Corvus corax,} 7\%), while one
nest per species were predated by the American mink (\emph{Neovison
vison} ), western marsh harrier (\emph{Circus aeruginosus} ), common
crane (\emph{Crus crus} ), European badger (\emph{Meles meles} ) and
domestic dog (\emph{Canis lupus familiaris} ). Two nest predators
remained unknown due to camera failure. An average depredation time for
the artificial nests was 2.9 days. Only four nests (2 in 2017 and 2 in
2018) were depredated in less than five hours after establishment
(minimum 1.5 hours), suggesting that the predators did not follow
researchers to the nests.

Daily nest survival was significantly higher in forest compared to
shoreline (Table 1). Nest survival also tended to have slight, but
significant negative relationship with field percentage around the water
bodies. As expected, shoreline nest daily survival was significantly
higher around ponds than around lakes (Table 2). Again, nest survival
tended to have slight, but significant negative relationship with field
percentage around the water bodies.

\textbf{3.2 Pair and brood numbers, and invertebrate food abundance}

Overall brood, but especially pair densities were higher in Maaninka
than in Evo (for pairs, \emph{N = 117, U} = 378, \emph{P} \textless{}
0.001; for broods, \emph{N} = 117, \emph{U} = 863, \emph{P} \textless{}
0.001; Table 3). The same pattern was also observed if only teal
densities were considered (pairs \emph{N = 117, U} = 495,
\emph{P}\textless{} 0.001; broods, \emph{N} = 117, \emph{U} = 986,
\emph{P}\textless{} 0.001, Table 3). Mallard pair, but not brood density
differed between the areas (pairs \emph{N = 117, U} = 884,
\emph{P}\textless{} 0.001; broods, \emph{N} = 117, \emph{U} = 1267,
\emph{P} = 0.242, Table 3). However, both mallard and teal \emph{per
capita} brood production was significantly higher in Evo than in
Maaninka (mallard\emph{G} = 20.7, df = 3, \emph{P} \textless{} 0.001;
teal \emph{G} = 52.2, df = 3, \emph{P} \textless{} 0.001; Table 3).

Three best models explained numbers of pairs within \selectlanguage{greek}Δ\selectlanguage{english}AIC \textless{}2
(Table 4). The base model (SHORE + FIELD; these variables were included
in all model) had the lowest AIC-value. The null model (intercept only)
had the poorest fit. Pair numbers increased with shoreline length and
field percentage, and ponds had fewer pairs than lakes. Food index
appeared not to contribute (Table 5).

Two well-fitting brood models were within \selectlanguage{greek}Δ\selectlanguage{english}AIC \textless{} 2 and both
included ``FOOD'' (Table 4). The null model (intercept only) had again
the poorest fit. Brood numbers increased with food abundance (Table 5),
but less so with number of pairs. Field percentage had only weakly
positive, but significant effect on brood numbers.

Water body type had a significant effect for the invertebrate food
index. Ponds had higher index than permanent lakes (Table 6), indicating
that ponds are more food rich habitats than lakes. Shoreline length or
field percentage around the water body did not explain the invertebrate
index.

\textbf{DISCUSSION}

Our results indicate that duck densities were higher in the agricultural
landscape. However, ducks appear to face a trade-off, because the
agricultural landscape also had a higher nest predation rate than the
barren forest landscape as revealed by experimental nests equipped with
wildlife cameras. Brood production per pair reflected this trade-off:
production was higher in the barren forest landscape than in the
agricultural landscape, which suggests the occurrence of a high nest
predation rate and/or high brood mortality in the agricultural
landscape. The results imply that the predator detection at artificial
duck nests with wildlife cameras actually reflects their visitation to
natural nests. However, our results only concern the early egg-laying
period, while predation later in the nest period, during incubation,
likely incurs a higher cost to the female.

\emph{4.1 Artificial nest survival}

Interestingly, corresponding trade-off between food abundance and nest
predation risk was not evident at the habitat-level, because ponds rich
in food also have the lowest nest predation rates. The wildlife camera
data show that the nests on the shores of seasonal, beaver or man-made
ponds had higher survival than nests on the shoreline of permanent
lakes. Because we tried to keep the nest cover constant between the
experimental nests, this difference in survival rates probably arises
from more heterogeneous shoreline habitats of ponds and/or the
availability of other abundant food resources ponds offer for predators.
It has been suggested that nest survival is a combination of large-scale
environmental factors and local nest-site characteristics. Landscape
productivity can affect general predator and prey abundance, but at the
nest-site level vegetation and nest location might affect nest
detectability and predator behaviour (Ringelman et al., 2018). For
example, predator foraging in the landscape may be concentrated at
habitat edges (Andr\selectlanguage{ngerman}én, 1995), such as the interface between terrestrial
and aquatic habitats. The occurrence of the edge effect may depend on
the predator community and predator behaviour (Pasitschniak-Arts et al.,
1998), and for instance whether productive wetlands attract and support
high number of predators (Stephens et al., 2005).

In the Evo area, mammalian predators have been found to occur more often
around beaver ponds than permanent lakes (Nummi et al., 2019b). Still,
higher predator occurrence around the beaver ponds was not reflected in
the nest predation results, indicating that indeed some habitat-related
factors there are working in favour of higher nest survival. It is
possible that variability in the shoreline creates circumstances under
which predators probably are not able to form long-term search images,
i.e. circumstances resembling those considered by Nams (1997) for prey
aggregated in space or time. We suggest that because predators may use
spatial memory to improve searching ef\selectlanguage{english}ficiency (Phillips et al., 2004),
their search around permanent lakes is more regular and effective than
around temporally unpredictable ponds. This underlines the potential
importance of seasonally flooded ponds for breeding ducks, especially in
agricultural areas.

Overall, nest predation risk was lower in forest compared to shoreline
nests, indicating occurrence of the edge effect between terrestrial and
aquatic ecotones. The pattern was not depending on the fields in the
landscape but emerged in both forested and agricultural landscapes.
Several currently threatened duck species in Finland are typically
nesting near the shoreline (e.g. common pochard \emph{Aythya ferina} ,
tufted duck), and may suffer from stronger nest predation rates than
more flexible nesters (e.g. mallard and teal; e.g. Poysa et al., 2019).
Nesting in forest may be safer, but on the other hand, newly hatched
ducklings will have to move a long way into water (Poysa \& Paasivaara,
2006).

Increased predator abundance and diversity are typical especially for
fragmented landscapes (Andren 1995; Pasitschniak-Arts \& Messier, 1995).
The pattern is particularly pronounced in agricultural landscapes, where
there are already high numbers of predators, such as corvids (Andren
1992; Roos 2002; Holopainen et al., 2020a). The results support these
observations: nest predation risk was higher in the agricultural
landscape, where high rates of corvid predation and richer predator
communities were observed with similarly executed wildlife
camera-artificial nest experiments (Holopainen et al. 2020a, 2020b).
Wildlife camera studies conducted both with artificial nests (Holopainen
et al. 2020b) and natural nests (Bell \& Conover, 2023) have proven that
after the initial depredation event, disturbed nests are often visited
by multiple secondary predators. Multiple mammalian visits lead not only
to an increased egg depredation rate, but also higher mortality risk for
the incubating female. Indeed, hens often abandon (partially) depredated
nests and even if incubation is continued, hatching success rate is low
(Ackerman et al., 2003; Bell \& Conover, 2023).

In Europe, the overall predator populations have increased during last
decades threatening bird populations (Roos et al., 2018). In addition to
native species, invasive alien predators such as raccoon dog have
dispersed widely (Bonesi \& Palazon, 2007; Kauhala \& Kowalczyk, 2011).
Raccoon dog nest predation can be destructive on islands (Dahl \& Ahlen,
2018), but its role as mainland duck nest predator has remained unclear
(Kauhala, 2004; Kauhala \& Auniola, 2001; Nummi et al., 2019a;
Sidorovich et al., 2008). Our nest predation results were based on
detailed predation information derived from camera trapping. Corvids and
raccoon dog were responsible for most of the nest depredation occurred
at experimental nests mimicking the situation in the early stage of egg
laying (Holopainen at al., 2020a). Without cameras, predator
identification is uncertain as it relies on the remains of eggshells or
other cues on the nest site (Lariviere, 1999).

We recognise that artificial nests give an uncertain reflection of
actual nest predation, and thus the intention in this study was not to
evaluate actual predation rates but only to study the habitat-specific
relative predation risk. Many important differences exist between real
and artificial nests that decrease the correspondence and are thus
recommended to consider whenever conducting artificial nest experiments
(Butler \& Rotella, 1998; Whelan et al., 1994; Wilson \& Brittingham,
1998; Part \& Wretenberg, 2002). Effort was put in to tackle the
uncertainties: real mallard eggs were used and the species observed in
the camera pictures are known predators of real duck nests (Opermanis et
al., 2001; Poysa et al., 1997), and therefore it is assumed that the
observed species do not differ from the actual nest predator assemblage.
As Anthony et al. (2006) showed with dusky Canada geese (\emph{Branta
canadensis occidentalis} ) artificial nests can be used to identify the
potential nest predator species and that the predator species ratios can
correspond those of the real nests. Our artificial nest density was low
ensuring that observations were independent. The lacking hen problem was
avoided by focusing only on the early egg-laying stage when females are
not on their nests, so the set-up resembles the actual situation.

Compared to natural nests, the use of cameras may affect nest survival,
typically by decreasing the predation rate (Richardson, et al, 2009);
this is a potential shortcoming that could not be avoided. It is also
acknowledged that this study design potentially emphasises the role of
visual predators, such as corvids, as nests were not necessarily hidden
as efficiently as a dabbling duck hen's nest would be. High corvid
predation rates may also be expected to occur at the early real nests,
as the duck nest predation rate in North America during the early part
of the breeding season was observed to positively relate to American
crow (\emph{Corvus brachyrhynchos} ) activity (Johnson et al., 1989).

The correspondence of the artificial nests with actual nest success
cannot be assessed, but brood production rates in the study areas have
been measured, which can be used as a rough estimate of predation
pressure. While there are still uncertainties in this method, we
emphasise that the problems underlined by the earlier studies have been
considered and the differences between real and artificial nests were
accordingly minimized; thus, we suggest that our data are suitable for
detecting trends in predation rates in relation to habitat (Wilson \&
Brittingham, 1998).

\emph{Invertebrate food abundance}

As expected, ponds (seasonal, beaver and man-made) were more
invertebrate-rich habitats than permanent lakes, while contrary to the
hypothesis, the percentage of field land around the wetlands did not
influence invertebrate index. Selecting a pond instead of a lake as a
breeding habitat would thus simultaneously minimize nest predation risk
and maximize food availability in any landscape.

Habitat use of duck pairs was not associated with invertebrate food,
whereas duck broods preferred habitats richer in food. The number of
broods at the water bodies was only weakly dependent on the number of
pairs, which can be a reflection of differing habitat requirements of
pairs and broods (Holopainen et al\emph{.,} 2015) or high nest predation
and brood mortality. Sjoberg et al. (2000) showed for mallards that all
lakes used by pairs are not suitable for broods, the difference in lake
use between pairs and broods being due to food limitation at the brood
stage (Gunnarsson et al\emph{.} , 2004). In boreal lakes food limitation
can be intensified due to food competition between ducks and fish (Nummi
et al., 2016). Income breeders, like teal, seem to avoid brood-stage
food limitation by congregating in beaver ponds and seasonal ponds where
invertebrate production is high and the habitat structure favourable for
brood foraging (Nummi \& Hahtola, 2008; Nummi \& Holopainen 2014). In
Evo it is known that teal brood production is following the flood
dynamics created by the beaver and spring floods (Holopainen et al.,
2014).

Interestingly, the results did not show that duck pairs or broods used
ponds more than permanent lakes. This contradicts the earlier long-term
results from the Evo area (Nummi \& Holopainen, 2014). It is possible
that ducks visit food-rich ponds for foraging in very short periods,
reducing the ability to detect them there (Nummi et al., 2019c).
Waterbird species may also differ in their ability to respond to
environmental factors, such as habitat variability (Wiens, 1976; Nummi
\& Poysa, 1997). For example, lapwings (\emph{Vanellus vanellus} ) are
known to nest in higher densities around flooded footdrains, and chicks
forage on the wet mud around these wet features supporting invertebrate
rich habitats (Eglington et al\emph{.,} 2008, 2010).

\emph{Conservation implications}

Successful management of ducks would demand understanding of the
relationship between habitat availability and predators (Drever et al.,
2004). This study emphasizes the benefits of the availability of
different water body types for breeding ducks. It showed that flooded
and/or seasonal ponds might be especially good habitats. It seems that
two important limiting factors of the breeding season -- nest survival
and amount of invertebrate food -- are higher there than on permanent
lakes.

Kubelka et al. (2018) showed that shorebirds have experienced a
worldwide increase in nest predation over the past decades and that the
pattern is especially pronounced in the high northern latitudes. 12 of
the 19 duck species living in Finland are already classified as
threatened to some degree by the Finnish red list (Lehikoinen et al.,
2019), underlining the urgent need for conservation actions. These
results indicate that while duck pair and brood densities are higher in
an agricultural landscape, brood production seems to be higher in
forested landscapes with lower nest predation rates. Thus, duck species
nesting at eutrophic lakes in agricultural areas and preferring
especially shorelines as nesting places, may suffer from high nest
predation rates, which may contribute to the declining population trends
(Lehikoinen et al\emph{.,} 2016; Poysa \& Linkola, 2021). It is
suggested that the nest predation pressure around these lakes has
increased due to the appearance of alien predators (Holopainen et al.,
2021; Poysa \& Linkola, 2021) and the disappearance of protective
umbrella species; for instance, loss of black-headed
gull\emph{Chroicocephalus ridibundus} colonies removes a local
protective ``umbrella'' of mobbing gulls, in a way that is thought to
expose the nests of associated waterbird to greater predation threat
(Poysa et al., 2019). Controlling predators, especially alien species,
would thus be an important conservation action to improve duck breeding
success (Dahl \& Ahlen, 2018; Garrettson \& Rohwer, 2001; Jaatinen et
al., 2022).

Seasonal pond ecosystems in the boreal biome remain poorly studied, even
so that in Finland the habitat type does not have a conservation status
evaluation done due to lacking information (Lammi et al. 2018).
Nevertheless, seasonal pond habitats are commonly not protected. The
loss of seasonal ponds has been dramatic in boreal biome due to
drainage, destruction and water regulation (Colburn, 2004), also in
Finland (Kuusisto et al\emph{.,} 1998). Furthermore, it is predicted
that climate change will reduce the extent of snowmelt dependant spring
flooding in the future (Veijalainen et al., 2010). In addition to
wetland restoration and blocking up drains, the lack of flooded waters
may be mitigated by favouring beavers (Hood \& Bayley, 2008; Nummi \&
Holopainen, 2020) or man-made wetlands (Danell \& Sjoberg, 1982;
Eglington et al\emph{.,} 2008; \selectlanguage{polish}Č\selectlanguage{english}ehovsk\selectlanguage{ngerman}á et al., 2022).

\textbf{ACKNOWLEDGEMENTS}

We wish to thank all the landowners for the cooperation. The grants from
Maj and Tor Nessling Foundation and Haavikko-foundation for SH, Kuopion
Luonnonystävät for V-MV and Suomen Riistanhoito-Säätiö and Lammi
Biological Station for EM are highly appreciated. We also wish to thank
Anthony Fox for constructive comments and all the field assistants.

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\textbf{TABLE 1} Model estimate for the daily survival rate of
artificial nests on shoreline and forest. DATE = exposure day
(1\ldots{}7), HABITAT = Forest (categorical factor, shoreline
represented by intercept), FIELD = field percentage within 1 000 m
buffer zone around the wetland. Random effect standard deviation for
WETLAND\_ID = 0.69.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& Estimate & SE & z-value & P\tabularnewline
\midrule
\endhead
(Intercept) & 2.182 & 0.373 & 5.856 & \textless{} 0.001\tabularnewline
DATE & 0.512 & 0.077 & 6.671 & \textless{} 0.001\tabularnewline
HABITAT (Forest) & 0.708 & 0.266 & 2.661 & 0.008\tabularnewline
FIELD & -0.021 & 0.006 & -3.719 & \textless{} 0.001\tabularnewline
\bottomrule
\end{longtable}

\textbf{TABLE 2} Model estimate for the daily survival rate of
artificial nests on lake and pond shoreline. DATE = exposure day
(1\ldots{}7), HABITAT = Pond (categorical factor, lake represented by
intercept), FIELD = field percentage within 1 000 m buffer zone around
the wetland. Random effect standard deviation for WETLAND\_ID = 1.12.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& Estimate & SE & \emph{z}-value & \emph{P}\tabularnewline
\midrule
\endhead
(Intercept) & 1.983 & 0.603 & 3.286 & 0.001\tabularnewline
DATE & 0.473 & 0.125 & 3.783 & \textless{} 0.001\tabularnewline
TYPE (Pond) & 1.122 & 0.534 & 2.102 & 0.036\tabularnewline
FIELD & -0.029 & 0.009 & -3.089 & 0.002\tabularnewline
\bottomrule
\end{longtable}

\textbf{TABLE 3} The average and range of pair and brood densities (all
duck species, teal, mallard/shoreline km) and brood production of teal
and mallard in Evo and Maaninka combining the years 2017--2018.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& Pairs/ shoreline km Average, median (range) & Broods/ shoreline km
Average, median (range) & Broods/ pair\tabularnewline
\midrule
\endhead
\emph{All species} & & &\tabularnewline
Evo & 0.9, 0.0 (0 -- 9.1) & 0.3, 0.0 (0 -- 4.5) &\tabularnewline
Maaninka & 5.1, 3.8 (0 -- 23.1) & 1.1, 0.3 (0 -- 15.2) &\tabularnewline
\emph{Teal} & & &\tabularnewline
Evo & 0.4, 0.0 (0 -- 9.1) & 0.2, 0.0 (0 -- 4.5) & 0.35\tabularnewline
Maaninka & 2.1, 1.4 (0 -- 10.9) & 0.5, 0.0 (0 -- 4.5) &
0.13\tabularnewline
\emph{Mallard} & & &\tabularnewline
Evo & 0.5, 0.0 (0 -- 6.8) & 0.1, 0.0 (0 -- 2.3) & 0.26\tabularnewline
Maaninka & 1.5, 0.6 (0 -- 7.1) & 0.2, 0.0 (0 -- 1.7) &
0.11\tabularnewline
\bottomrule
\end{longtable}

\textbf{TABLE 4} Models explaining the number of pairs and broods. Only
best models with \selectlanguage{greek}Δ\selectlanguage{english}AIC\textless{}2, where \selectlanguage{greek}Δ
\selectlanguage{english}\emph{=}AIC\emph{\textsubscript{i}} -- AIC\emph{\textsubscript{min,}}
are shown. TYPE = lake or pond, SHORE = wetland shoreline (km), FOOD =
invertebrate food index, FIELD = field percentage within 1 000 m buffer
zone around the wetland.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Model & df & AIC & \selectlanguage{greek}Δ\selectlanguage{english}AIC & \emph{w}\tabularnewline
\midrule
\endhead
\emph{Pairs} & & & &\tabularnewline
SHORE + FIELD & 6 & 322.886 & 0.000 & 0.424\tabularnewline
TYPE + SHORE + FIELD & 7 & 323.645 & 0.759 & 0.290\tabularnewline
FOOD + SHORE + FIELD & 7 & 324.850 & 1.964 & 0.159\tabularnewline
Intercept only & 1 & 348.806 & 25.920 & 0.000\tabularnewline
\emph{Broods} & & & &\tabularnewline
FOOD + PAIRS + FIELD & 7 & 178.883 & 0.000 & 0.656\tabularnewline
FOOD + TYPE + PAIRS + FIELD & 8 & 180.331 & 1.448 & 0.318\tabularnewline
Intercept only & 1 & 192.818 & 13.935 & 0.000\tabularnewline
\bottomrule
\end{longtable}

\textbf{TABLE 5} Model averaged parameter estimates, their standard
errors and unconditional 95 \% confidence intervals from models
explaining habitat use of pairs and broods. Estimates are based on all
models in the candidate model set using Akaike weights as weighting
factors. CI = confidence interval, TYPE = pond (compared to lake), SHORE
= wetland shoreline (km), FOOD = invertebrate food index, FIELD = field
percentage within 1 000 m buffer zone around the wetland.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& & SE & 95\% CI & 95\% CI\tabularnewline
\midrule
\endhead
& Estimate & & Lower & Upper\tabularnewline
\emph{Pairs} & & & &\tabularnewline
FOOD & 0.013 & 0.007 & -0.001 & 0.028\tabularnewline
TYPE (pond) & -0.490 & 0.038 & -0.579 & -0.429\tabularnewline
SHORE & 0.297 & 0.022 & 0.247 & 0.336\tabularnewline
FIELD & 0.028 & 0.000 & 0.026 & 0.029\tabularnewline
\emph{Broods} & & & &\tabularnewline
FOOD & 0.144 & 0.006 & 0.132 & 0.158\tabularnewline
TYPE (pond) & -0.331 & 0.365 & -0.719 & 0.874\tabularnewline
PAIRS & 0.066 & 0.012 & 0.058 & 0.105\tabularnewline
FIELD & 0.006 & 0.001 & 0.005 & 0.011\tabularnewline
\bottomrule
\end{longtable}

,

\textbf{TABLE 6} Parameters of the model explaining invertebrate food
index. TYPE = pond (lake presented by intercept), SHORE = wetland
shoreline (km), FIELD = field percentage within 1 000 m buffer zone
around the wetland. Random effect standard deviation for WETLAND\_ID =
3.373.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& Estimate & SE & \emph{t}-value & \emph{p}-value\tabularnewline
\midrule
\endhead
Intercept & 0.671 & 1.424 & 0.471 & 0.640\tabularnewline
TYPE (pond) & 3.618 & 1.604 & 2.256 & 0.030\tabularnewline
SHORE & -0.284 & 0.512 & -0.554 & 0.582\tabularnewline
FIELD & 0.036 & 0.026 & 1.408 & 0.166\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

of Finland)

Fig 1. Location of Finland in Europe and the maps of the study areas.
(Sources: Esri, National Land Survey)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2. Wildlife cameras revealed that the most common nest predator
species were A) Eurasian magpie (photo by Niteforce Professional Trail
Camera 12 MP), B) hooded crow (photo by Uovision UV595-Full HD 12MP) and
C) raccoon dog (still from video by Uovision with +2 eyeglass lens). D)
To adjust the focus of wildlife cameras into less than 1 meter, we
attached +2 eyeglass lens (``backwards'') in front of the wildlife
camera lens. Tape was then camouflaged.

\textbf{FUNDING INFORMATION}

Maj and Tor Nessling Foundation and Haavikko-foundation funded the work
of SH and Suomen Riistanhoito-S\selectlanguage{ngerman}äätiö and Lammi Biological Station funded
the work of EM. Kuopion Luonnonystävät gave a grant for wildlife cameras
for V-MV.

\textbf{CONFLICT OF INTEREST STATEMENT}

We declare that the authors of this article have no conflicts of
interest.

\textbf{DATA AVAILABILITY STATEMENT}

We confirm that the entire database used in this article is available in
the Appendix and supplementary material of this manuscript.

\textbf{AUTHOR CONTRIBUTIONS}

Idea for this study was from SH, PN and V-MV. SH, EM, PN and V-MV
contributed to the field work. SH did statistical analyses. All authors
contributed to the writing process.\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/Graphical-abstractv2/Graphical-abstractv2}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`Fig 2.eps`  available at \url{https://authorea.com/users/628413/articles/648996-balancing-between-predation-risk-and-food-by-boreal-breeding-ducks}

\selectlanguage{english}
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