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The interaction between the spatial distribution of resource patches and
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population density: consequences for intraspecific growth and
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morphology
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Running headline: Spatial resource distribution and population density
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B. Jacobson1*, J.W.A. Grant2 and P.R. Peres-Neto1†
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Département des Sciences Biologiques, Université du Québec à Montréal, C.P. 8888,
Succ. Centre-Ville, Montréal, Québec H3C3P8 Canada
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Department of Biology, Concordia University, 1455 de Maisonneuve Blvd. West,
Montréal, Québec, H3G1M8, Canada
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Corresponding author: jacobson.bailey@courrier.uqam.ca
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Canada Research Chair in Spatial Modelling and Biodiversity
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Summary
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1. How individuals within a population distribute themselves across resource patches
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of varying quality has been an important focus of ecological theory. The ideal free
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distribution predicts equal fitness amongst individuals in a 1:1 ratio with
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resources, whereas resource defence theory predicts different degrees of
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monopolization (fitness variance) as a function of temporal and spatial resource
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clumping and population density.
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2. One overlooked landscape characteristic is the spatial distribution of resource
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patches, altering the equitability of resource accessibility and thereby the effective
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number of competitors. While much work has investigated the influence of
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morphology on competitive ability for different resource types, less is known
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regarding the phenotypic characteristics conferring relative ability for a single
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resource type, particularly when exploitative competition predominates.
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3. Here we used young-of-the-year rainbow trout (Oncorhynchus mykiss) to test
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whether and how the spatial distribution of resource patches and population
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density interact to influence the level and variance of individual growth, as well as
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if functional morphology relates to competitive ability. Feeding trials were
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conducted within stream channels under three spatial distributions of nine
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resource patches (distributed, semi-clumped and clumped) at two density levels (9
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and 27 individuals).
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4. Average trial growth was greater in high-density treatments with no effect of
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resource distribution. Within-trial growth variance had opposite patterns across
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resource distributions. Here, variance decreased at low-population, but increased
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at high-population densities as patches became increasingly clumped as the result
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of changes in the levels of interference vs. exploitative competition. Within-trial
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growth was related to both pre- and post-trial morphology where competitive
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individuals were those with traits associated with swimming capacity and
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efficiency: larger heads/bodies/caudal fins and less angled pectoral fins.
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5. The different degrees of within-population growth variance at the same density
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level found here, as a function of spatial resource distribution, provide an
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explanation for the inconsistencies in within-site growth variance and population
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regulation often noted with regard to density-dependence in natural landscapes.
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Key-words: intraspecific competition, landscape structure, morphometrics, negative
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density dependence, phenotype-dependent growth, stream fish
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Introduction
How individuals within a population distribute themselves across resource patches
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of varying quality has been a central focus of ecological theory. The ideal free
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distribution (IFD) proposes that competitively equivalent individuals with perfect
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knowledge of and the cost-free ability to move throughout the landscape distribute
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themselves in a 1:1 ratio with resources such that fitness across patches is equal (Fretwell
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& Lucas 1970; Lomnicki 1988; Kennedy & Gray 1993). In reality, individual fitness
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within populations often varies as a function of unequal competitive abilities (Abrahams
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1986; Kennedy & Gray 1993); however, the presence of unequal competitors within a
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landscape does not necessarily result in variation in resource gain (Grand & Grant 1994).
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Tregenza, Hack & Thompson (1996), for instance, found that good competitors only had
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a fitness advantage at low densities because at high densities individuals switched from
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interference to exploitative (alternatively, contest to scramble) competition.
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The influence of environmental properties (sensu Grant 1993) on the ability for an
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individual to monopolize resources has been well investigated within the realm of
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behavioural ecology. At the scale of an individual resource patch, resource defence
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theory predicts a dome-shaped relationship between the economic defensibility of a
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resource patch and either the degree of temporal and spatial resource clumping or the
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number of potential competitors within the population (Grant 1993; Robb & Grant 1997;
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Grant, Gaboury & Levitt 2000; Ward, Webster & Hart 2006). Taken together, within-
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population fitness (growth) variation will not only depend on the amount of aggression
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(i.e. interference competition) and resource monopolization (Grant & Guha 1993; Weir &
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Grant 2004; Noël, Grant & Carrigan 2005), but also on the equality of resource
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accessibility (Boujard, Labbé & Aupérin 2002).
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One overlooked landscape characteristic (but see Silver et al. 2000) that has the
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potential to influence resource accessibility, and thus, relative fitness amongst individuals
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is the spatial distribution of resource patches. When patches are spread across the
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landscape, individuals may be able to settle into patches with few interactions between
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competitors, thus leading to more even resource access, partitioning and fitness (Noël,
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Grant & Carrigan 2005). Conversely, as patches become increasingly clumped in space,
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the effective number of competitors within the population will increase due to a decrease
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in foraging area (Fausch 1984; Noël, Grant & Carrigan 2005), in turn decreasing the
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equality of resource accessibility and potentially increasing fitness variation. In stream
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fishes, where much of the work on density-dependent growth and regulation (e.g.
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mortality, emigration) has been conducted, within-site growth variance has largely been
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attributed to variation in foraging site (patch) quality (Site Quality Hypothesis; SQH)
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(Newman 1993; Ward et al. 2007; Lobón-Cerviá 2010). Thus, the potential influence of
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the spatial distribution of resources on growth variation may provide an alternative
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explanation for the differential patterns of individual growth (Imre, Grant & Cunjak
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2010) and distribution found across natural populations.
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Just which individuals in the population are able to monopolize resources and
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have higher relative fitness is a function of their relative competitive ability. In stream
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fishes, competitive ability is often linked to body size, with larger individuals able to gain
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access to the best foraging patches (Fausch 1984; Ward, Webster & Hart 2006), though
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the causal order of this relationship has been questioned (Ward, Webster & Hart 2006).
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While the resource polymorphism literature has investigated the influence of morphology
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on competitive ability for different resources (Smith & Skúlason 1996; Bolnick 2004;
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Araújo et al. 2008; Edelaar, Siepielski & Clobert 2008), less is known about the
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phenotypic characteristics dictating relative competitive ability for a single resource type
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when size variation is inconsequential. This is a particularly relevant question within a
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single young age class where size may initially be somewhat invariant, but growth ability
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has large impacts for subsequent survival (Ward, Nislow & Folt 2009; Lobón-Cerviá
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2010). In such cases, competitive ability may be based on an individual’s ability to use its
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environment more efficiently. Within riffle habitat sections of streams, for example,
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where greater water velocity is related to increased resource delivery (Gotceitas & Godin
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1992), an individual’s ability to gain resources may be linked to its swimming capacity
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and ability to hold station against the current (Ward, Webster & Hart 2006; Leavy &
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Bonner 2009).
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In this study, we set out to test whether and how the spatial distribution of
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resource patches with equal quality (contrary to the SQH) and population density interact
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to influence the mean and variance of within-population growth. In addition, we tested
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whether an individual’s morphology relates to its ability to obtain resources (compete).
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To do so, we conducted feeding trials within flow-through stream channels using young-
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of-the-year rainbow trout (O. mykiss) under three different spatial distributions of nine
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resource patches: evenly distributed, semi-clumped and clumped (Fig. 1). Such trials
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were conducted at two density levels: low, one individual per patch and high, three
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individuals per patch. We expected (i) mean growth to be greater within high-density
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trials due to a decrease in energy expended on aggression (i.e. less interference
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competition), as per resource defence theory (Kim & Grant 2007); (ii) within-trial growth
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variance to increase with increased resource patch clumping due to changes in effective
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competitor number (Fausch 1984; Noël, Grant & Carrigan 2005) and resource
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accessibility; and (iii) individual growth to be most similar in low-density distributed
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resource trials due to a 1:1 competitor to resource patch ratio, as per the IFD (Noël, Grant
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& Carrigan 2005). Lastly, we expected (iv) competitive ability, irrespective of density
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and resource distribution, to be based on functional morphology and its relation to
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swimming ability (Ward, Webster & Hart 2006).
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Materials and Methods
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Species and experimental set-up
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We acquired 500 young-of-the-year rainbow trout (Oncorhynchus mykiss) (Fig.
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1a) 5cm in length from Pisciculture des Arpents Verts, Québec, Canada for use in our
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experiments. When not in use fish were housed within two circular 133L constant-flow
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tanks (flow at 50% depth=0.25m/s; water temperature=18-19.5°C) on a 12h light:12h
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dark cycle (Brown & Brown 1993) (lights on at 7am) and fed a maintenance ration of dry
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food pellets (Skretting extruded salmonid feed). Housing conditions were monitored, and
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daily feedings provided by animal care staff at Concordia University, Québec, Canada.
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Experimental trials were conducted within four flow-through experimental stream
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channels (Fig. 1b) under the same light regime. Each channel was lined to a depth of
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2.5cm with small natural-coloured aquarium gravel wherein nine terracotta “saucers”
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were embedded, flush with the bed of gravel, to act as resource patches. Flow in each
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channel was held constant across all trials (average flow at 50% depth=0.15-0.18m/s
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across channels). Water temperature within the system (c. 18°C; Li & Brocksen 1977)
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was controlled by the amount of de-chlorinated city water entering each channel and was
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continuously recorded every 15 min by a HOBO temperature/light data logger (Onset
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Computer Corporation, Bourne, MA, USA) placed in each channel. Loggers were
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checked in the morning before feeding and again at the end of the light cycle (average
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across channels ± SD: week 1 19.1± 0.6°C; week 2 18.7 ± 0.2°C; week 3 17.7 ± 0.8°C;
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week 4 18.2 ± 0.2°C; and week 5 17.7 ± 0.5°C) to ensure that temperatures remained
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within the preferred range for rainbow trout (Wood, Grant & Belanger 2012). To record
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feeding dynamics and inter-individual interactions throughout the light cycle, digital
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colour CCD bullet cameras were connected to a surveillance system (GeoVision, Inc.,
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Irvine, CA, USA) and mounted directly above each channel; with the exception of
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recording periods, channels were covered with large-weave netting to prevent fish from
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jumping.
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Experimental procedure
To determine the influence of spatial resource distribution and competitor density
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on growth, feeding trials were conducted under a 3x2 factorial design testing three
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different resource distributions [distributed (D), semi-clumped (S) and clumped (C), Fig.
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1b] at each of two densities (low n=9 and high n=27) with three replicates per treatment.
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Trials were conducted four at a time from 14 October to 22 November 2011, where each
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week the individuals and treatments tested were both randomly selected and assigned to
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each stream channel.
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Each trial lasted a total of 8 days. On day 1, individuals were selected from
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housing tanks, anesthetized using a 1 clove oil:10 ethanol mixture (active agent eugenol)
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(Anderson, McKinley & Colavecchia 1997; Keene et al. 1998) and each given a unique
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identifier by way of two subcutaneous VIE tags (visible implant elastomer; Northwest
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Marine Technologies, Shaw Island, WA, USA) in one or two of four fluorescent colours
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in one or two of several body locations. Individuals were then photographed on one
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lateral side for pre-trial morphology, weighed for initial mass and measured for initial
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body and caudal widths (Fig. 1a). After recovering from anesthesia, individuals were
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introduced into a stream channel and remained unfed for the day (Olsson, Svanbäck &
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Eklöv 2007). On days 2-7, resources were added once daily to stream channels, evenly
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divided across the nine patches, given at a level of 10% (Brown & Brown 1993) of the
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total initial mass of individuals within the trial per day in order to encourage growth.
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Feeding was recorded on days 2 and 7 (i.e. initial and final feedings) from the time food
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entered the system until the end of the light cycle. Note that while no mortality occurred
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as the result of handling or experimental treatment, there were some incidences of
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individuals jumping out of the channels during initial and final recording and thus final
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densities did not always match initially set levels (average density per treatment, low
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density: D=8.67, S=8, C=7.67, high density: D=25.67, S=24.67, C=26; see also outliers
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below). When this occurred during day 1 or 2, individuals were identified and the food
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level adjusted to remain at 10% of the remaining trial initial mass. Lastly, on day 8
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individuals were not fed (Noël, Grant & Carrigan 2005) to ensure the same level of
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gastric evacuation (Fausch 1984; Currens et al. 1989) and near the end of the light cycle
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were anaesthetized, identified, weighed for final mass and photographed on the same
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lateral side for post-trial morphology. Individuals were afterwards returned to a separate
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holding tank to ensure that no individual was used more than once.
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Data analyses
Three growth metrics were calculated for each individual and used as response
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variables within analyses: (i) individual growth Gind was calculated as the difference
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between log10 transformed final and initial mass (Noël, Grant & Carrigan 2005); (ii)
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frowth variance Gvar was calculated as the absolute value of the difference between Gind
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and the average Gind of the trial in which the individual participated. This metric, when
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used in an analysis of variance (ANOVA), results in a test for variance differences rather
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than means across treatments (Zar 2010); and lastly, (iii) relative growth Grel was
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calculated as the difference between Gind and the average Gind of the trial in which the
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individual participated and classified individuals based on their growth relative to their
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trial average (i.e. either less or greater growth than the average).
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To determine the influence of treatment type on trial growth, Gind (differences in
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average growth across treatments) and Gvar (differences in growth variance across
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treatments) were used as response variables in separate mixed-model ANOVAs with the
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fixed effects of density, spatial resource distribution and the density x spatial distribution
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interaction and the random effects of channel, week in which the trial was conducted and
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initial mass or initial mass variance (calculated as per Gvar). Given the differences in
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variances across treatments (a measure of interest of this study, i.e. Gvar), weights
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proportional to group variances were used (Pinheiro & Bates 2000).
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In order to determine the influence of treatment type on the spatial dynamics of
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resource patch use as well as the amount of inter-individual aggression (i.e. interference
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competition) all recordings of the final feeding (day 7) were analyzed, as it was assumed
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that recorded behaviour had at this point become established (Noël, Grant & Carrigan
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2005). Spatial feeding dynamics were characterized and recorded as the number of
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seconds within a minute that at least one individual was at least one-half of a body length
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within each patch, recorded every 5 min for the first 15 min of feeding during which time
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feeding activity was at its highest. As all replicates for any given treatment type had
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similar dynamics, information garnered from the final replicate only is presented.
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Aggressive interactions were characterized as chases, defined as a unidirectional
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swimming burst of one fish towards another lasting for at least one body length (Noël,
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Grant & Carrigan 2005; Kim & Grant 2007; Wood, Grant & Belanger 2012), and scored
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for the same 3 min as spatial feeding dynamics for each video. Due to issues of low video
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quality and the tightly packed fast movements that occurred during feeding at high
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densities, each video was scored twice and the total number of observed chases averaged
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for each replicate. This average was then divided by the number of fish within the trial to
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control for differences in density (Robb & Grant 1998; Kim & Grant 2007) and the
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number of chases per fish used as the response variable within a mixed-effect ANOVA
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with density, spatial distribution and the density x spatial distribution interaction entered
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as fixed effects and the channel and week in which the trial was conducted entered as
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random effects.
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To determine if there was an influence of an individual’s morphology on their
ability to gain resources (grow) within trials, geometric morphometrics were used.
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Twenty-two landmarks were placed on both pre- and post-trial photographs (Fig. 1a)
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using the program tpsDIG2 (Rohlf 2005a) and converted, separately, into partial warps
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(shape variables) using the programs Coordgen6 and PCAgen6 (Sheets 2004a, 2004b).
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Pre- and post-trial warps were then entered as separate fixed effects within two mixed-
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model ANOVAs using Gind and Grel as the response variables with channel and week
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entered as random effects. Note that three individuals, randomly distributed across
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treatments, were identified as outliers with respect to post-trial morphology and were
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removed from all analyses. To visualize which morphological features differed between
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individuals with greater or less relative growth, deformation grids were produced for pre-
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and post-trial morphologies of Grel classifications using the program tpsRegr (Rohlf
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2005b). Lastly, as competitive ability within fish is often linked with length/size (Ward,
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Webster & Hart 2006) and this effect is removed through standardization in geometric
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morphometrics (Zelditch et al. 2004), regressions were performed for each treatment to
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test for the presence of a relationship between Gind (growth) and each of log10 initial mass
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(Massi), initial standard length (Lengthi), initial body width (BodyWi) and initial caudal
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width (CaudalWi) (size). The significance of these correlations was determined with
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mixed-effect regression using Gind as the response and each variable, separately per
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treatment, as the fixed effect with random factors as above. All mixed-model ANOVAs
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were run using the nlme package (Pinheiro et al. 2012) in R version 2.13.0 (R
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Development Core Team, 2011 Vienna, Austria) and regressions were performed using
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Statistica 6.0 (StatSoft, Inc., Tulsa, OK, USA).
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Results
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The degree of individual growth (Gind) within trials was found to differ as a
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function of density, with average growth greater within high-density compared to low-
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density treatments irrespective of the spatial distribution of resource patches (Table 1 and
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Fig. 2a). Interestingly, the degree to which growth varied amongst individuals within
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trials (Gvar) differed significantly across all factors, spatial distribution, density and their
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interaction (Table 1); the variance decreased in low-density trials but increased in high-
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density trials as resources became more spatially clumped (Fig. 2b). Such greater growth
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variance in high-density spatially clumped treatments occurred due to the weight loss
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(negative growth) of 14 individuals throughout the trials; only two individuals lost weight
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in the distributed and one each in the semi-clumped and clumped treatments at low
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densities.
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Observation of videos recorded during the final feeding revealed different spatial
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dynamics (Fig. 3a) and amounts of aggressive interactions between individuals as a
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function of density and resource distribution (Table 1 and Fig. 3b). Overall, more
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aggressive interactions (interference competition) occurred between individuals within
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low-density compared to high-density trials, with the number of chases per fish
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decreasing as spatial resource clumping increased. As reflected by the spatial dynamics of
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patch use (location and shading within Fig. 3a), within low-density distributed trials,
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individuals tended to chase and exclude others from the resource arena wherein foraging
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attempts were made mainly at previously sampled patches. Conversely, within clumped
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trials, patches were relatively more evenly utilized. Likewise, the spatial dynamics of
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patch use for high-density trials also reflected more even resource partitioning with
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individuals divided amongst and continually circulating over all patches. However,
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within clumped trials, several individuals never appeared to attempt foraging. Across all
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treatments, when not foraging, individuals remained within the downstream portion of the
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stream channel.
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The degree to which individuals grew within a trial, irrespective of treatment, was
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found to be a function of their morphology; both pre- and post-trial morphology
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correlated with individual growth (Gind) and differentiated between individuals of
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opposite relative growth (Grel) (Table 1). Such effects could also be seen in deformation
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grids of pre- and post-trial morphology as a function of Grel (Fig. 4), where the primary
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differences between groups in pre-trial morphology were differences in pectoral fin
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angle, head/body and caudal fin size and head shape and body height with regards to
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post-trial morphology. Growth was, with few exceptions, unrelated to initial fish
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length/size, with regressions overall largely non-significant (Table 2).
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Discussion
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The main goal of our study was to test whether the spatial distribution of
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resources and population density interact in determining the growth and growth variance
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(i.e. fitness) amongst individuals within a landscape. We found that this interaction can
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generate different levels of fitness variation within a population through differences in the
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amount of interference competition, thus causing individuals with specific morphologies
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(phenotype) to be conferred more or less relative fitness dependent on treatment. Such
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results have important consequences for the demographics of populations and degree of
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regulation expected in natural systems of varying structure, potentially explaining several
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inconsistencies found in the literature regarding density-dependent growth patterns (see
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Implications).
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Inter-treatment growth patterns: variance and competition
Results from our study clearly show that the spatial distribution of resource
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patches themselves, irrespective of quality, can influence the degree and distribution of
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relative fitness (i.e. growth) amongst individuals within a population through changing
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the effective number of competitors for patches within the system. We expected the
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distribution to follow an IFD, with growth variance lowest in low-density distributed
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resource trials, but increasing with increased resource clumping (Noël, Grant & Carrigan
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2005). Instead, opposite patterns of growth variance were found across resource
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distributions between density treatments; variance was greatest when resources were
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clumped at high densities but when resources were distributed at low densities. The high
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variance in growth found in low-density distributed trials, as evidenced from feeding
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recordings, was a function of increased interference competition; individuals were
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observed attempting to defend patches and others attempting to forage at only those
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already sampled. As patches became increasingly clumped at low densities, the effective
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number of competitors increased. Consequently, interactions switched from interference
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to exploitative competition, which accounted for the decrease in growth variance found
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here, likely because defense was too costly with increasing competitor pressure (Grant
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1993; Grant & Guha 1993; Tregenza, Hack & Thompson 1996; Syarifuddin & Kramer
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1996; Kaspersson, Höjesjö & Pedersen 2010).
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Lower levels of interference competition were found within high-density trials,
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(Brown, Brown & Srivastava 1992; Jobling & Baardvik 1994), as individuals cycled in
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and out of resource patches (Robb & Grant 1997) and were able to spread themselves
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more evenly amongst patches. As aggression has important associated fitness costs, such
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as increasing energy expenditure, social stress and resource loss while defending (Li &
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Brocksen 1977; Kim & Grant 2007; Kaspersson, Höjesjö & Pedersen 2010), the reduced
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interference competition costs are likely responsible for the greater average growth,
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irrespective of resource distribution, found in high-density trials. That said, while the
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exploitative competition utilized at high densities is proposed to allow for a more even
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distribution of resources amongst individuals (Grant 1993; Jobling & Baardvik 1994;
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Boujard, Labbé & Aupérin 2002; Weir & Grant 2004), our results suggest that even
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partitioning does not occur past a certain threshold. Indeed, the decrease in the equality of
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resource accessibility at high densities, also found in other studies (Syarifuddin & Kramer
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1996; Boujard, Labbé & Aupérin 2002), caused individuals to cease feeding, leading to
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negative growth and increased growth variance, with increased spatial resource clumping
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(Li & Brocksen 1977; Grant & Kramer 1990).
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The influence and shaping of phenotype
While competitive ability is often suggested to be related to body size (Post,
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Parkinson & Johnston 1999; Ward, Webster & Hart 2006; Kaspersson, Höjesjö &
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Pedersen 2010), we found no significant relationship between an individual’s level of
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growth and their initial mass, length, body or caudal width. Instead, body shape was the
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important component in obtaining resources, as levels of individual and relative growth
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correlated with pre-trial morphology. Here individuals with greater than average trial
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growth had a morphology indicating greater swimming ability and turning stability:
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relatively deeper heads/bodies and larger caudal fins (Gatz 1979; Webb 1984; Ojanguren
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& Braña 2003; Peres-Neto & Magnan 2004). These individuals also had pectoral fins that
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were more flatly angled, potentially indicating more efficient maneuvering (Drucker &
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Lauder 2003) and reduced energy expenditure through holding position against current on
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the substrate (Fausch 1984). As swimming ability has been proposed to relate to
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competitive ability in both exploitative and interference contexts (Ward, Webster & Hart
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2006), such phenotypic characteristics likely accounted for the greater growth of these
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individuals. Post-trial morphology was even more highly correlated with trial growth,
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potentially reflecting both the relative effects of the amount of growth on body shape in
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and of itself (Currens et al. 1989; Borcherding & Magnhagen 2008), as well as potential
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investment in plasticity (i.e. morphological modulation) by individuals who had greater
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levels of growth (Olsson, Svanbäck & Eklöv 2007), acting to further increase their
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competitive ability. Indeed, individuals with greater relative growth were those with
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characteristics of benthic habitat use (more dorsally placed eyes and greater body height)
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(Gatz 1979; Mason et al. 2007) and exploitative ability (increased swimming ability and
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turning stability) (Webb 1984; Ojanguren & Braña 2003; Peres-Neto & Magnan 2004). In
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contrast, individuals with less relative growth were characterized by more slender bodies
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and longer heads, potentially indicative of under-nourishment (Currens et al. 1989;
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Olsson, Svanbäck & Eklöv 2007; Borcherding & Magnhagen 2008).
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Implications for population regulation in natural landscapes
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Within natural landscapes, as density increases, mean growth is often found to
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follow a negative power curve (Jenkins et al. 1999; Grant & Imre 2005), with growth
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variance increasing (as per the SQH) (Newman 1993; Keeley 2001; Lobón-Cerviá 2010).
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After density-dependent emigration and mortality, however, the resulting patterns are
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much more variable; exhibiting negative power curves, negative linear relationships of
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varying strength, to no relationship between average growth and density (Newman 1993;
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Jenkins et al. 1999; Post, Parkinson & Johnston 1999; Keeley 2001; Imre, Grant &
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Cunjak 2005), with little consistency in associated variance-density relationships (Imre,
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Grant & Cunjak 2010). As, here, we found different degrees of growth variance at the
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same population density as a function of the spatial distribution of resource patches, our
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results have the potential to shed light on such inconsistencies in natural systems. For
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example, landscapes with high densities and clumped resources, rather than distributed,
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may be characterized by high growth variance and greater degrees of density-dependent
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emigration and mortality. Hence the degree of spatial resource clumping may modify the
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strength of the negative power curve of growth versus density.
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While we found positive instead of negative density-dependent growth, such a
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finding is common within experimental systems where food is given proportional to
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density (Brown, Brown & Srivastava 1992). Within natural systems, site-resource levels
399
are assumed to be constant across densities (Kaspersson, Höjesjö & Pedersen 2010)
400
where low-density sites have a greater amount of resource relative to competitor number
401
than high density (Noël, Grant & Carrigan 2005; Ward et al. 2007). Should we have
402
provided resource levels more in line with natural systems, it is conceivable that we
18
403
would have also found negative density dependence with variance remaining higher at
404
high densities.
405
Our findings show the potential for the spatial structure of resource patches,
406
irrespective of other potentially varying characteristics (i.e. resource quality, patch size),
407
to influence population growth through changing the effective number of competitors
408
within the system, and therefore, the degree to which competitive ability (phenotype)
409
confers enhanced fitness. Future work should seek to understand how the spatial
410
distribution of patches varying in quality may interact to influence within-population
411
growth variance and the consequences of such variance for the regulation of natural
412
populations, potentially allowing for further insight as to the decisions, pressures and
413
structuring factors, which underlie patterns of individual distribution within landscapes.
414
415
Acknowledgements
416
We would like to thank the animal care staff at Concordia University who cared for
417
housed fish on a day-to-day basis and István Imre as well as an anonymous referee for
418
comments on the manuscript. This research was supported by an NSERC (Natural
419
Sciences and Engineering Research Council of Canada) grant to PPN.
420
421
Data Accessibility
422
Data available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.7nb71
423
(Jacobson, Grant & Peres-Neto 2015).
424
425
References
19
426
Abrahams, M.V. (1986) Patch choice under perceptual constraints: a cause for departures
427
from an ideal free distribution. Behavioral Ecology and Sociobiology, 19, 409-
428
415.
429
Anderson, W.G., McKinley, R.S. & Colavecchia, M. (1997) The use of clove oil as an
430
anesthetic for rainbow trout and its effects on swimming performance. North
431
American Journal of Fisheries Management, 17, 301-307.
432
Araújo, M.S., Guimarães, P.R.Jr., Svanbäck, R., Pinheiro, A., Guimarães, P., Dos Reis,
433
S.F. & Bolnick, D.I. (2008) Network analysis reveals contrasting effects of
434
intraspecific competition on individual vs. population diets. Ecology, 89, 1981-
435
1993.
436
Bolnick, D.I. (2004) Can intraspecific competition drive disruptive selection? An
437
experimental test in natural populations of sticklebacks. Evolution, 58, 608-618.
438
Borcherding, J. & Magnhagen, C. (2008) Food abundance affects both morphology and
439
440
behaviour of juvenile perch. Ecology of Freshwater Fish, 17, 207-218.
Boujard, T., Labbé, L. & Aupérin, B. (2002) Feeding behaviour, energy expenditure and
441
growth of rainbow trout in relation to sotcking density and food accessibility.
442
Aquaculture Research, 33, 1233-1242.
443
444
445
Brown, G.E. & Brown, J.A. (1993) Do kin always make better neighbours?: the effects of
territory quality. Behavioral Ecology and Sociobiology, 33, 225-231.
Brown, G.E., Brown, J.A. & Srivastava, R.K. (1992) The effect of stocking density on
446
the behaviour of Arctic charr (Salvelinus alpinus L.). Journal of Fish Biology, 41,
447
955-963.
448
Currens, K.P., Sharpe, C.S., Hjort, R., Schreck, C.B. & Li, H.W. (1989) Effects of
20
449
different feeding regimes on the morphometrics of chinook salmon
450
(Oncorhynchus tshawytscha) and rainbow trout (O. mykiss). Copeia, 3, 689-695.
451
Drucker, E.G. & Lauder, G.V. (2003) Function of pectoral fins in rainbow trout:
452
behavioral repertoire and hydrodynamic forces. The Journal of Experimental
453
Biology, 206, 813-826.
454
Edelaar, P., Siepielski, A.M. & Clobert, J. (2008) Matching habitat choice causes directed
455
gene flow: a neglected dimension in evolution and ecology. Evolution, 62, 2462-
456
2472.
457
458
459
460
461
462
Fausch, K.D. (1984) Profitable stream positions for salmonids: relating specific growth
rate to net energy gain. Canadian Journal of Zoology, 62, 441-451.
Fretwell, S.D. & Lucas, H.L.Jr. (1970) On territorial behavior and other factors
influencing habitat distribution in birds. Acta Biotheoretica, 19, 16-36.
Gatz, A.J.Jr. (1979) Ecological morphology of freshwater stream fishes. Tulane Studies
in Zoology and Botany, 21, 91-124.
463
Gotceitas, V. & Godin, J-G.J. (1992) Effects of location on food delivery and social
464
status on foraging-site selection by juvenile Atlantic salmon. Environmental
465
Biology of Fishes, 35, 291-300.
466
Grand, T.C. & Grant, J.W.A. (1994) Spatial predictability of resources and the ideal free
467
distribution in convict cichlids, Cichlasoma nigrofasciatum. Animal Behaviour,
468
48, 909-919.
469
470
471
Grant, J.W.A. (1993) Whether or not to defend? The influence of resource distribution.
Marine Behaviour and Physiology, 23, 137-153.
Grant, J.W.A. & Guha, R.T. (1993) Spatial clumping of food increases its
21
472
monopolization and defense by convict cichlids, Cichlasoma nigrofasciatum.
473
Behavioral Ecology, 4, 293-296.
474
475
476
Grant, J.W.A. & Imre, I. (2005) Patterns of density-dependent growth in juvenile streamdwelling salmonids. Journal of Fish Biology, 67, 100-110.
Grant, J.W.A. & Kramer, D.L. (1990) Territory size as a predictor of the upper limit to
477
population density of juvenile salmonids in streams. Canadian Journal of
478
Fisheries and Aquatic Sciences, 47, 1724-1737.
479
Grant, J.W.A., Gaboury, C.L. & Levitt, H.L. (2000) Competitor-to-resource ratio, a
480
general formulation of operational sex ratio, as a predictor of competitive
481
aggression in Japanese medaka (Pisces: Oryziidae). Behavioral Ecology, 11, 670-
482
675.
483
Imre, I., Grant, J.W.A. & Cunjak, R.A. (2005) Density-dependent growth of young-of-
484
the-year Atlantic salmon Salmo salar in Catamaran Brook, New Brunswick.
485
Journal of Animal Ecology, 74, 508-516.
486
Imre, I., Grant, J.W.A. & Cunjak, R.A. (2010) Density-dependent growth of young-of-
487
the-year Atlantic salmon (Salmo salar) revisited. Ecology of Freshwater Fish, 19,
488
1-6.
489
Jacobson, B., Grant, J.W.A. & Peres-Neto, P.R. (2015) Data from: The interaction
490
between the spatial distribution of resource patches and population density:
491
consequences for intra-specific growth and morphology. Dryad Digital
492
Repository http://dx.doi.org/10.5061/dryad.7nb71.
493
Jenkins, T.M.Jr., Diehl, S., Kratz, K.W. & Cooper, S.D. (1999) Effects of population
494
density on individual growth of brown trout in streams. Ecology, 80, 941-956.
22
495
Jobling, M. & Baardvik, B.M. (1994) The influence of environmental manipulations on
496
inter- and intra-individual variation in food acquisition and growth performance
497
of Arctic charr, Salvelinus alpinus. Jounal of Fish Biology, 44, 1069-1087.
498
Kaspersson, R., Höjesjö, J. & Pedersen, S. (2010) Effects of density on foraging success
499
and aggression in age-structured groups of brown trout. Animal Behaviour, 79,
500
709-715.
501
502
503
Keeley, E.R. (2001) Demographic responses to food and space competition by juvenile
steelhead trout. Ecology, 82, 1247-1259.
Keene, J.L., Noakes, D.L.G., Moccia, R.D. & Soto, C.G. (1998) The efficacy of clove oil
504
as an anaesthetic for rainbow trout, Oncorhynchus mykiss (Walbaum).
505
Aquaculture Research, 29, 89-101.
506
Kennedy, M. & Gray, R.D. (1993) Can ecological theory predict the distribution of
507
foraging animals? A critical analysis of experiments on the Ideal Free
508
Distribution. Oikos, 68, 158-166.
509
Kim, J-W. & Grant, J.W.A. (2007) Effects of patch shape and group size on the
510
effectiveness of defence by juvenile convict cichlids. Animal Behaviour, 73, 275-
511
280.
512
Leavy, T.R. & Bonner, T.H. (2009) Relationships among swimming ability, current
513
velocity association, and morphology for freshwater lotic fishes. North American
514
Journal of Fisheries Management, 29, 72-83.
515
Li, H.W. & Brocksen, R.W. (1977) Approaches to the analysis of energetic costs of
516
intraspecific competition for space by rainbow trout (Salmo gairdneri). Journal of
517
Fisheries Biology, 11, 329-341.
23
518
519
520
521
522
Lobón-Cerviá, J. (2010) Density dependence contrains mean growth rate while enhancing
individual size variation in stream salmonids. Oecologia, 164, 109-115.
Lomnicki, A. (1988) Population ecology of individuals. Princeton University Press, New
Jersey.
Mason, N.W.H., Lanoiselée, C., Mouillot, D., Irz, P. & Argillier, C. (2007) Functional
523
characters combined with null models reveal inconsistency in mechanisms of
524
species trunover in lacustrine fish communities. Oecologia, 153, 441-452.
525
526
527
Newman, R.M. (1993) A conceptural model for examining density dependence in the
growth of stream trout. Ecology of Freshwater Fish, 2, 121-131.
Noël, M.V., Grant, J.W.A. & Carrigan, J.G. (2005) Effects of competitor-to-resource
528
ratio on aggression and size variation within groups of convict cichlids. Animal
529
Behaviour, 69, 1157-1163.
530
Ojanguren, A.F. & Braña, F. (2003) Effects of size and morphology on swimming
531
performance in juvenile brown trout (Salmo trutta L.). Ecology of Freshwater
532
Fish, 12, 241-246.
533
534
535
Olsson, J., Svanbäck, R. & Eklöv, P. (2007) Effects of resource level and habitat type on
behavioral and morpholoigcal plasticity in Eurasian perch. Oecologia, 152, 48-56.
Peres-Neto, P.R. & Magnan, P. (2004) The influence of swimming demand on
536
phenotypic plasticity and morphological integration: a comparison of two
537
polymorphic charr species. Oecologia, 140, 36-45.
538
539
540
Pinheiro, J.C. & Bates, D.M. (2000) Mixed-effects models in S and S-PLUS. Springer,
Verlag New York, Inc.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & the R Development Core Team (2012)
24
541
nlme: Linear and nonlinear mixed effects models. R package version 3.1-105.
542
Post, J.R., Parkinson, E.A. & Johnston, N.T. (1999) Density-dependent processes in
543
structured fish populations: interaction strengths in whole-lake experiments.
544
Ecological Monographs, 69, 155-175.
545
Robb, S.E. & Grant, J.W.A. (1998) Interactions between the spatial and temporal
546
clumping of food affect the intensity of aggression in Japanese medaka. Animal
547
Behaviour, 56, 29-34.
548
Rohlf, F.J. (2005a) tpsDIG2. Program for digitizing landmarks and outlines on image
549
files, scanner or video. [windows], version 2. Department of Ecology and
550
Evolution, State University of New York, Stony Brook (available from
551
http://life.bio.sunysb.edu/morph/).
552
Rohlf, F.J. (2005b) tpsRegr. Program for analyzing landmark data by thin-plate splines
553
using multivariate multiple regression of shape. [windows], version 1.31.
554
Department of Ecology and Evolution, State University of New York, Stony
555
Brook (available from http://life.bio.sunysb.edu/morph/).
556
Sheets, H.D. (2004a) CoordGen6. IMP-Integrated Morphometrics Package
557
IMPBasics.exe, version 6a. Coordinate Generating Utility, also file translator
558
to/from X1Y1, TPS file formats. Department of Physics, Casinus College, Buffalo,
559
New York (available from: http://www3.canisius.edu/~sheets/morphsoft.html).
560
Sheets, H.D. (2004b) PCAGen6. IMP-Integrated Morphometrics Package
561
IMPBasics.exe, version 6a. Principal components analysis based on partial warps
562
scores and principal axis scores. Department of Physics, Casinus College,
25
563
Buffalo, New York (available from:
564
http://www3.canisius.edu/~sheets/morphsoft.html).
565
Silver, P., Cooper, J.K., Palmer, M.A. & Davis, E.J. (2000) The arrangement of resources
566
in patchy landscapes: effects on distribution, survival, and resource acquisition of
567
chironomids. Oecologia, 124, 216-224.
568
Smith, T.B. & Skúlason, S. (1996) Evolutionary significance of resource polymorphisms
569
in fishes, amphibians, and birds. Annual Review of Ecology and Systematics, 27,
570
111-133.
571
Syarifuddin, S. & Kramer, D.L. (1996) The effect of group size on space use and
572
aggression at a concentrated food source in blue gouramis, Trichogaster
573
trichopterus (Pisces: Belontiidae). Environmental Biology of Fishes, 46, 289-296.
574
Tregenza, T., Hack, M.A. & Thompson, D.J. (1996) Relative competitive success of
575
unequal competitors changes with overall density. Oikos, 77, 158-162.
576
Ward, D.M., Nislow, K.H. & Folt, C.L. (2009) Increased population density and
577
suppressed prey biomass: relative impacts on juvenile Atlantic salmon growth.
578
Transactions of the American Fisheries Society, 138, 135-143.
579
580
Ward, A.J.W., Webster, M.M & Hart, P.J.B. (2006) Intraspecific food competition in
fishes. Fish and Fisheries, 7, 231-261.
581
Ward, D.M., Nislow, K.H., Armstrong, J.D., Einum, S. & Folt, C.L. (2007) Is the shape
582
of the density-growth relationship for stream salmonids evidence for exploitative
583
rather than interference competition? Journal of Animal Ecology, 76, 135-138.
584
Webb, P.W. (1984) Body and fin form and strike tactics of four teleost predators
26
585
attacking fathead minnow (Pimephales promelas) prey. Canadian Journal of
586
Fisheries and Aquatic Sciences, 41, 157-165.
587
588
589
Weir, L.K. & Grant, J.W.A. (2004) The causes of resource monopolization: interaction
between resource dispersion and mode of competition. Ethology, 110, 63-74.
Wood, J.L.A., Grant, J.W.A. & Belanger, M.H. (2012) Population density and territory
590
size in juvenile rainbow trout, Oncorhynchus mykiss: implications for population
591
regulation. Canadian Journal of Fisheries and Aquatic Sciences, 69, 1121-1128.
592
593
594
Zar, J.H. (2010) Biostatistical analysis, 5th edn. Prentice-Hall, Upper Saddle River, New
Jersey, USA.
Zelditch, M.L., Swiderski, D.L., Sheets, H.D. & Fink, W.L. (2004) Geometric
595
morphometrics for biologists: a primer. Elsevier Academic Press, New York,
596
USA.
597
598
599
600
601
602
603
604
605
606
607
27
608
Figure legends
609
610
Figure 1. Shape analysis and spatial resource distributions. (a) Young-of-the-year
611
rainbow trout with 22 landmarks used within morphological analyses, arrows denote
612
locations of body and caudal width caliper measurements. (b) Photo and schematics of
613
spatial resource distributions tested: distributed (D), semi-clumped (S) and clumped (C).
614
Flow denotes the direction of water movement through channels, water depth was c.
615
18cm, resource patch depth 1.1cm. All measurements within figure are in centimetre.
616
617
Figure 2. Results from mixed-model ANOVAs of growth outcomes of trials. (a) Growth
618
(Gind) outcome for trials as a function of spatial and density treatments. (b) Variance in
619
growth (Gvar) outcome for trials as a function of spatial and density treatments. Open
620
circles denote high density, filled low-density trials, error bars are 95% confidence
621
intervals.
622
623
Figure 3. Spatial feeding dynamics and aggressive interactions observed within video
624
recordings. (a) Feeding dynamics of individuals throughout the first 15 min of feeding.
625
Shading is based on the number of seconds within 1 min that at least ½ body length of at
626
least one individual was within the resource patch. (b) Results from the mixed-model
627
ANOVA of the number of chases per fish within the first 15 min of feeding as a function
628
of spatial and density treatments. Open circles denote high density, filled low-density
629
trials, error bars are 95% confidence intervals.
28
630
Figure 4. Morphological differences between individuals with negative or positive (less
631
or greater) growth relative to their trial average (Grel) as a function of pre- or post-trial
632
morphology. In order to highlight differences variation is depicted at 10x the observed
633
range.
634
635
Figures
636
637
Figure 1.
(a)
(b)
28.6
18.5
18.5
57.25
28.6
7.6
37
18.5
S
28.6
74
flow
114.5
18.5
638
D
C
28.6
639
640
641
642
29
643
Figure 2.
644
(a)
0.14
645
646
0.12
Growth
647
648
0.10
649
650
0.08
651
0.06
Distributed
Distributed
Semi-clumped
Semi-clumped
Clumped
Clumped
Spatial distribution
(b)
652
653
0.10
654
Growth variance
0.08
655
656
0.06
657
0.04
658
659
0.02
660
0.00
Distributed
Distributed
Semi-clumped
Semi-clumped
Clumped
Clumped
661
Spatial distribution
662
663
664
665
30
666
Figure 3.
(a)
Low density
High density
0
1-15
16-30
31-45
46-60
5min
10min
15min
5min
10min
15min
Chases per fish
(b)
Distributed
Semi-clumped
Clumped
667
668
669
670
31
671
Figure 4.
Negative
relativeGrowth
growth
Negative Relative
Positive
Positive relative
Relative growth
Growth
Pre-trial
Pre-trial
Post-trial
Post-trial
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
32
688
Tables
689
690
Table 1. F-values from mixed-model ANOVAs regarding the influence of treatment type
691
and morphology on within-trial growth, as well as treatment type on the number of chases
692
per fish observed within feeding recordings. Numbers in brackets are degrees of freedom
Table 1
693
Factor
Treatment
Morphology
Space (2)
Density (1)
SxD (2)
Pre-trial (40)
Post-trial (40)
* p≤0.05
** p≤0.001
Gind
Response
Gvar
Chases
0.248
6.230*
0.952
9.524*
6.414*
9.959*
695
6.068*
44.841**
696
4.025
Gind
Grel
697
2.744**
5.584**
1.628*
3.230**
698
694
699
700
701
702
703
704
705
706
707
708
709
33
710
Table 2. Correlation coefficients (r) from regressions of the relationship between
711
individual growth (Gind) and initial size variables for each spatial resource distribution:
712
distributed (D), semi-clumped (S) and clumped (C). Numbers in brackets are the
713
combined number of individuals in the replicates of that treatment type
Table 2
Massi
Lengthi
BodyWi
CaudalWi
D (26)
Low density
S (24)
C (22)
D (77)
0.368
0.470*
0.323
0.309
0.105
0.169
0.188
0.098
0.746*
0.733
0.688
0.659
0.486*
0.565*
0.405
0.229
High density
S (73)
C (77)
0.299
0.391**
0.350*
0.262
-0.006*
-0.022*
-0.003
0.072
* p≤0.05
** p≤0.001
714
Treatment
Morphology
Pos
34