10.5061/DRYAD.95X69P8JX
Prati, Sebastian
0000-0001-9878-3848
University of Tromsø - The Arctic University of Norway
Henriksen, Eirik
University of Tromsø - The Arctic University of Norway
Smalås, Aslak
University of Tromsø - The Arctic University of Norway
Knudsen, Rune
University of Tromsø - The Arctic University of Norway
Klemetsen, Anders
University of Tromsø - The Arctic University of Norway
Sánchez-Hernández, Javier
King Juan Carlos University
Per-Arne, Amundsen
University of Tromsø - The Arctic University of Norway
The effect of inter-and intraspecific competition on individual and
population niche widths – a four-decade study on two interacting salmonids
Dryad
dataset
2021
Trophic ecology
competitive interactions
long-term studies
niche theory
FOS: Biological sciences
Salmo trutta
Salvelinus alpinus
2021-06-22T00:00:00Z
2021-06-22T00:00:00Z
en
414187 bytes
6
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Competition is assumed to shape niche widths, affecting species survival
and coexistence. Expectedly, high interspecific competition will reduce
population niche widths, whereas high intraspecific competition will do
the opposite. Here we test in situ how intra- and interspecific
competition affects trophic resource use and the individual and population
niche widths of two lacustrine fish species, Arctic charr and brown trout,
covering a 40 year study period with highly contrasting competitive
impacts prior to and following a large-scale fish culling experiment.
Initially, an overcrowded Arctic charr population dominated the study
system, with brown trout being nearly absent. The culling experiment
reduced the littoral Arctic charr density by 80%, whereupon brown trout
gradually increased its density in the system. Thus, over the study
period, the Arctic charr population went from high to low intraspecific
competition, followed by increasing interspecific competition with brown
trout. As hypothesized, the relaxed intraspecific competition following
the experimental culling reduced individual diet specialization and
compressed population niche width of Arctic charr. During the initial
increase of the brown trout population, there was a large dietary overlap
between the two species. Over the subsequent intensified interspecific
competition from the population build-up of brown trout, their trophic
niche overlap chiefly declined due to a dietary shift of Arctic charr
towards enhanced zooplankton consumption. Contrary to theoretical
expectations, the individual and population niche widths of Arctic charr
increased with intensified interspecific competition. In contrast, the
diet and niche width of brown trout remained stable over time, confirming
its competitive superiority. The large-scale culling experiment and
associated long-term research revealed pronounced temporal dynamics in
trophic niche and resource use of the inferior competitor, substantiating
that intra- and interspecific competition have large and contrasting
impacts on individual and population niches.
Fish sampling and processing Charr and trout were sampled annually from
the littoral habitat (<15 m depth) in August from 1980 to 2019
using single-meshed gillnets of various mesh sizes prior to 1989 and
thereafter multi-meshed gillnets with panels of eight different mesh sizes
ranging from 10 to 45 mm, knot to knot (Table 1). The nets fished in the
lake overnight for approximately 12 hours. Fork length and other
parameters not used in the current study (weight, sex, and gonad
maturation) of all fish were recorded in the field and stomach samples
were collected. Catch per unit effort (CPUE), defined as the number of
fish caught per 100 m2 gillnets per night, was estimated as a proxy for
the littoral abundance of charr and trout. In the lab, stomachs were
opened, and the fullness degree was determined on a scale from 0 to 100%
(Amundsen and Sánchez‐Hernández 2019). Prey items were identified at the
lowest taxonomical level, and their relative contribution to total stomach
fullness (expressed in percentage) was calculated according to Amundsen
(1995). Prey taxa were then grouped into twelve categories: (I) small
cladoceran zooplankton (Bosmina spp.), (II) large cladoceran zooplankton
(Daphnia spp. and Holopendium gibberum), (III) predatory cladoceran
zooplankton (Bythotrephes longimanus, and Polyphemus pediculus), (IV)
copepod zooplankton (cyclopoid and calanoid copepods), (V) amphipods
(Gammarus lacustris), (VI) mollusks (Radix peregra, Planorbis sp., Valvata
sp., and Pisidium sp.), (VII) pleuston (terrestrial and hatching aquatic
insects), (VIII) Chironomidae pupae, (IX) Chironomidae larvae, (X)
Trichoptera larvae (house-living and free-living), (XI) other benthos
(Ephemeroptera nymphs, Plecoptera nymphs, Megaloptera larvae, Tipulidae
larvae, Coleoptera, and the chydorid cladoceran Eurycercus lamellatus.),
and (XII) fish (three-spined stickleback, charr, and unidentified fish
remains). These prey categories were used for a simplified visualization
of temporal dietary changes, whereas un-pooled prey data were used for the
subsequent dietary analyses. For the dietary analyses, stomachs with a
fullness degree below 10% or containing only unidentified prey were
removed from the dataset. Each individual stomach content was then
standardized to estimate prey abundance as the mean contribution of each
prey category to the diet. The fish were divided into the three size
classes (<150 mm, 150-299 mm, and >300 mm) to study
ontogenetic dietary shifts over the 40 year study period and if these
shifts might be influenced by an increase in individual dietary
specialization as explained earlier, where individuals switch to
alternative resources to mitigate the effects of competition (Araújo et
al. 2011). By pooling data in five-year sampling periods, the 150 and 299
mm size class provided large enough sample sizes for temporal comparisons.
Since the number of samples in this size class ranged from 86 to 200
individuals for charr and 60 to 129 for trout, 86 charr and 60 trout
stomachs were randomly selected from each sampling period to avoid sample
size bias in subsequent analyses. No significant size differences among
sampling periods (ANOVA, all P>0.05) were detected within each size
group. A total of 1424 charr and 621 trout were included in the analyses.
Statistical analysis Descriptive and inferential analyses were performed
with the open-source software Rstudio (version 1.1.423, Rstudio Inc.),
based in R (version 3.5.1, R Core Team). We used a permutational
multivariate analysis of variance (PERMANOVA) to assess dietary
composition differences between sampling periods and host species
(Anderson 2005). A Bray-Curtis based non-metric multidimensional scaling
(NMDS) was further used to graphically illustrate any dietary differences
between charr and trout among different sampling periods. To determine
which prey contributed the most to the observed differences, we opted for
Sum-of-LR, a multivariate method based on generalized linear model with
negative binomial errors (Warton et al. 2012, Wang et al. 2012). We chose
this method over the more widely used similarity percentage (SIMPER)
analysis as the latter can confound strong between-group effect with large
within-group variance, yielding misleading results (Warton et al. 2012).
We measured the total niche width (TNW) of populations applying the
Shannon index of diversity to the population’s distribution of resource
use (Roughgarden 1979). We then partitioned TNW into the within-individual
component of niche width (WIC), which is the average individual niche
width, and the between-individual component of niche width (BIC), which is
the variation between individuals’ niche positions, such that TNW = WIC +
BIC (Roughgarden 1972). To assess the impact of trout density on charr’s
niche, we correlated TNW, WIC, and BIC values with CPUE using Spearman’s
correlation coefficient with Bonferroni’s correction. To evaluate the
degree of individual diet specialization, we used multiple measures for a
more robust assessment of this multifaceted trait than can be accomplished
using a single metric. Individual diet specialization can be expressed as
the variation between an individual diet and the population diet or
between an individual and other individuals. We therefore calculated the
WIC/TNW ratio, which provides a measure of specialization by individuals
within a population, with specialization being high when WIC/TNW is low.
Additionally, the degree of individual diet specialization was assessed
with the level of diet variation (E; Araújo et al. 2008), the proportional
similarity index (PS_i; Bolnick et al. 2003), and the individual
specialization index (IS and V; Bolnick et al. 2002, 2007). We used
variance inflation factor (VIF) to detect multicollinearity (correlation
between predictors) among individual specialization indexes. A VIF value
< 3 indicate lack of collinearity (Zuur et al. 2010). Collinearity
was detected among indexes (all VIF values >3); hence, we opted to
use WIC/TNW values to represent individual specialization. Finally, we
tested relationships between sampling periods, WIC, BIC and WIC/TNW values
using a generalized least squares model (GLS) using the nlme package
(Pinheiro et al. 2021). To account for temporal autocorrelation, we used
the autoregressive term AR1. Model fit was evaluated with the
autocorrelation function ACF and partial autocorrelation function PACF and
the fit between residuals versus fitted values. Data from all sampling
periods were used to assess the first two hypotheses with the intent of
inferring if temporal changes in individual and population niche widths in
charr were likely due to decreased intra-specific competition or increased
inter-specific competition. To assess the impact of trout density on
charr’s individual specialization, we correlated WIC/TNW values with CPUE
using Spearman’s correlation coefficient with Bonferroni’s correction.
Calculation of TNW, WIC, BIC, WIC/TNW, E, PS_i and IS were performed with
the RInSp package (Zaccarelli et al. 2013). Interspecific diet overlap was
calculated with the Schoener’s overlap index α=1- 1/(2 )(Σ∣Pxj-Pyj∣ x 100
(Schoener 1970), where Pxj and Pyj are the relative abundance of diet item
j in the stomach of species x and y, respectively. The index ranges from 0
to 100% with values of 0 indicating absence of diet overlap and values of
100% indicating a complete dietary overlap. Additionally, for the 150-299
mm size classes of charr and trout, we calculated the pairwise diet
similarity (PSij) between each pair of heterospecific individuals i and j:
(PS)ij=∑k(min(Pik ),Pjk), where Pik and Pjk are the proportions of the
Kth prey type in individual i’s and j’s diet (Bolnick and Paull 2009). A
value of 0 indicates that the paired individuals do not share common prey,
while values close to 1 indicate that they consume the same prey in
identical proportions. (PS)ij was calculated with the RInSp package
(Zaccarelli et al. 2013) Effects of resource pulses We run additional
analyses in order to test whether the outcomes remain the same after
excluding a resource pulse, i.e. infrequent, large‐magnitude and
short‐duration events of increased resource availability (Yang et al.
2010). Temporally superabundant food sources might lead to a convergence
in the resource use of co-occurring predators, altering their immediate
trophic interactions (Lack 1946, Croxall et al. 1999, Selva et al. 2012).
More specifically, the superabundance of a single prey potentially may
temporally influence individual specialization and resource partitioning
(Meyer, 1989; Malmquist et al., 1992; Robinson and Wilson, 1998). In
subarctic lakes, hatching chironomid pupae cyclically occur in
superabundance during midsummer, constituting a resource that is typically
included in the fish diet when abundantly present (Adalsteinsson, 1979;
Amundsen and Klemetsen, 1988). A superabundance of Chironomidae hatching
and emergence were observed in the field within several of the sampled
years (1980, 1986, 1994, 2002, 2007, 2011, 2014, and 2018). This massive
hatching is mainly by a single species, Heterotrissocladius subpilosus,
and lasts for only 2-3 weeks in early summer. The species strongly
dominates the profundal benthos as larvae (Klemetsen et al. 1992). Given a
particularly strong presence of Chironomidae pupae in 1980, which was the
only observation available for the pre-culling period, we also addressed
our research hypotheses following the exclusion of this prey type. We
excluded Chironomidae pupae to reduce bias in interspecific competition
metrics among sampling periods as events of Chironomidae pupae
superabundance would have been more diluted among pooled periods compared
to a single event. Hence, we repeated the above procedures and analysis on
a subset of 464 charr and 294 trout excluding this prey from the diet.