DOI: 10.5061/dryad.95x69p8jx
title: The effect of inter-and intraspecific competition on individual and population niche widths – a four-decade study on two interacting salmonids

description: 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.
description: <strong>Fish sampling and
 processing</strong><br> Charr and trout were sampled annually
 from the littoral habitat (&lt;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 (&lt;150 mm, 150-299 mm, and &gt;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&gt;0.05) were detected within each size
 group. A total of 1424 charr and 621 trout were included in the
 analyses. <strong>Statistical
 analysis</strong> 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 &lt;
 3 indicate lack of collinearity (Zuur et al. 2010). Collinearity was
 detected among indexes (all VIF values &gt;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) <strong>Effects of resource
 pulses</strong> 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.