Individual Differences Determine the Strength of Ecological Interactions

Individual differences determine the strength of ecological interactions

Jason I. Grifﬁthsa,b,1 , Dylan Z. Childsa , Ronald D. Bassarc, Tim Coulsond , David N. Reznicke, and Mark Reesa

aDepartment of Animal and Plant Sciences, University of Shefﬁeld, Shefﬁeld S10 2TN, United Kingdom; bDepartment of Mathematics, University of Utah, Salt Lake City, UT 84112; cBiology Department, Williams College, Williamstown, MA 01267; dDepartment of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom; and eDepartment of Biology, University of California, Riverside, CA 92521

Edited by Nils Chr. Stenseth, University of Oslo, Oslo, Norway, and approved June 3, 2020 (received for review January 12, 2020)

Biotic interactions are central to both ecological and evolutionary Demonstrating asymmetric competition in natural systems is dynamics. In the vast majority of empirical studies, the strength difficult, as the effect of large individuals on small ones has to of intraspecific interactions is estimated by using simple mea- be measured, and vice versa. Very few studies do this, and in sures of population size. Biologists have long known that these those that do, the results are often difficult to interpret. For are crude metrics, with experiments and theory suggesting that example, ref. 12 found that removal of large or small individuals interactions between individuals should depend on traits, such had no effect on growth of intermediate-sized fish. In contrast, as body size. Despite this, it has been difficult to estimate the adding large fish resulted in slower growth of intermediate-sized impact of traits on competitive ability from ecological field data, ones, whereas adding small fish had no effect, suggesting that and this explains why the strength of biotic interactions has asymmetric competition only occurs at artificially high densities. empirically been treated in a simplistic manner. Using long-term Experimental manipulations usually treat body size as discrete observational data from four different populations, we show that rather than continuous, where interactions occur over the com- large Trinidadian guppies impose a significantly larger compet- plete size range. These difficulties argue for the development itive pressure on conspecifics than individuals that are smaller; of a new approach that can estimate size-specific competitive in other words, competition is asymmetric. When we incorporate effects across the complete size spectrum at natural population this asymmetry into integral projection models, the predicted size densities. structure is much closer to what we see in the field compared with As a consequence of these difficulties, little is known about models where competition is independent of body size. This dif- where, in nature, the asymmetry of competition lies on the con- ference in size structure translates into a twofold difference in tinuum between the extreme competitive dominance of either reproductive output. This demonstrates how the nature of ecolog- large or small individuals and competitive equivalence (13). This ical interactions drives the size structure, which, in turn, will have is problematic, as a large body of theory has demonstrated important implications for both the ecological and evolutionary the importance of asymmetric competition for life-history evo- dynamics. lution (14–16) and population and community dynamics (13, 17–19). We provide a quantification of the degree of size- asymmetric competition in wild animal populations. Using size structure | asymmetric competition | Trinidadian guppies detailed individual-based life history data from four long-term field-study populations of the Trinidadian guppy (Poecilia reticulata), we quantify how the strength of competition influencing nteractions between the individuals in a population, such as Icompetition, cooperation, and cannibalism, have been shown to play a central role in governing both ecological and evolution- Significance ary dynamics. Over the past 150 years, the strength and impacts of these biotic interactions have often been assessed by deter- Demonstrating asymmetric competition in natural systems is mining how an individual’s performance relates to the density difficult, as the effect of large individuals on small ones has of competitors (1–3). This density dependence is a fundamen- to be measured, and vice versa. Numerous experiments have tal principle in ecology and determines the struggle for existence quantified one side of the interaction, typically the effect of that drives evolution (4). However, simple metrics, such as popu- large individuals on small ones. Here, we demonstrate, using lation density, entirely ignore differences between individuals in a long-term study of guppies, that an individual’s performance traits such as body size, weight, and condition, although numer- depends on its relative size, with large individuals being com- ous experimental studies and theory clearly indicate that the petitively dominant. Accurate prediction of both the mean strength of interaction between individuals should depend on and variance in body size was possible by using models incor- their traits (5–8). porating asymmetric competition, whereas in models where Individual traits often influence physiology and behavior. Body individuals are competitively equivalent, the predictions were size, in particular, frequently influences feeding rates and the poor. ability to preempt access to territories, mates, or resources (9,

10). This size-dependent ability to acquire resources leads to the Author contributions: J.I.G., D.Z.C., R.D.B., T.C., D.N.R., and M.R. designed research; J.I.G., expectation that competition will be size-asymmetric (11), with D.Z.C., D.N.R., and M.R. performed research; J.I.G., D.Z.C., and M.R. analyzed data; and interference competition leading to a competitive advantage for J.I.G., D.Z.C., R.D.B., T.C., D.N.R., and M.R. wrote the paper.y larger individuals. In contrast, for resource competition, compet- The authors declare no competing interest.y itive ability depends on the scaling of energy and maintenance This article is a PNAS Direct Submission.y costs, which, in ﬁsh, can lead to smaller individuals being dom- This open access article is distributed under Creative Commons Attribution-NonCommercial- inant (5). This means that competitive ability should depend NoDerivatives License 4.0 (CC BY-NC-ND).y not only on an individual’s absolute size, but also on the rel- Data deposition: Demographic data have been deposited in the publicly accessible Dryad ative size of its competitors. In contrast, the simplistic metrics Digital Repository (https://doi.org/10.5061/dryad.76hdr7stj).y underpinning our classical understanding of intraspeciﬁc biotic 1 To whom correspondence may be addressed. Email: [email protected] interactions assume that all individuals acquire resources at an This article contains supporting information online at https://www.pnas.org/lookup/suppl/ equal rate. This implies that competition is symmetric, and so doi:10.1073/pnas.2000635117/-/DCSupplemental.y differently sized individuals are competitively equivalent. First published July 6, 2020.

17068–17073 | PNAS | July 21, 2020 | vol. 117 | no. 29 www.pnas.org/cgi/doi/10.1073/pnas.2000635117 Downloaded by guest on September 28, 2021 Downloaded by guest on September 28, 2021 h rqec twihidvdastasto ewe ece ntetime the in a al. reaches indicates et coloration Griffiths between Dark transition reach. individuals same the which in at remaining frequency observations. individuals of represent The matrix locations (B) transition the color). this to probability. of algorithm (bar transition elements clustering high reach diagonal a specific The applying occasions. a by sampling identified into between subpopulations, categorized spatial were are Reaches observations height). Individual (bar streams the along distances 1. Fig. Individual- processes. these intensity for the account and to competition. included rates of were demographic intensity competition mean the of in altering vari- (24), deviations drives over climate availability Month-specific Trinidadian growth resource seasonal in and the Similarly, ability survival (23). in space flooding and variation especially time density-independent perturbations, inhabit Individuals stochastic drive (22). and events, interactions environment, biotic S1d). variable sex, of section and a impacts weight Appendix, individual’s the (SI an of on stream depend independent each to known for are datasets rates Demographic life-history mod- the additive to nonlinear hierarchical, els fitting by survival and growth monthly individuals which within patches compete. habitat 1B distinct (Fig. spatially occasions represent sampling reaches cer- of between degree reach high different a a with certainty stream colored classification the classified 1A, of (average were reach tainty (Fig. specific observations a Individual algorithm within occurring ). S1c stream’s clustering as section Each a Appendix , (SI using sub- 1A). spatial regions) by discrete (Fig. six consistently (reaches), and individuals three streams populations between of into the partitioned numbers was of population higher sections much the certain restructure harbored topologies, sometimes events stream two flooding detailed remaining Although streams canopies. The control neighboring intact streams are thinning. [CA]) with Two Caigual canopy and (21). [LL] Lalaja via enhanced (Lower streams streams experimentally attained four received productivity, have the [TY]) primary Taylor among and and [UL] Lalaja along (Upper Habi- varies (A20170006). Animal Protocol quality Use Institutional was Animal tat Riverside approved location Committee California, and Use of and weight Care University body a Appendix , individual (SI under of Trinidad conducted of Monitoring Range (20). Northern S1a) the tribu- section in predator-free streams independent montane four of in taries located are populations study The Methods then in dynamics We size-structured the individual. project populations. focal to interac- these ability the competitive our alters trait-dependent of tions quantifying size com- of how the structure examine size to the relative on depends petitors rates survival and growth A

Frequency of individuals influencing competition size-asymmetric of degree the determined We 1000 2000 3000 4000 1000 2000 3000 4000 0 0 h rqec fidvda bevtosa different at observations individual of frequency The (A) subplot). each of (panels streams Trinidadian four the of reaches discrete of Identification 010150 100 50 0 CA UL Distance down river (m) 010150 100 50 0 > 5) niiul aeymvdto moved rarely Individuals 95%). TY LL ,idctn htthe that indicating ), Reach 6 5 4 3 2 1 ln cain yicuignral itiue admefc terms, sam- random-effect between distributed and normally and individuals including among by vary occasions (the to pling slopes allowed quadratic-size were and intercepts sex- The linear-size including by and incorporated intercepts were sexes specific the more between (a survival in presentation and its growth given simplify is to model description the complete of summary following the in and transition, respectively. function, the over survival of probability where in structure: differences same the interindividual shared models describe survival and to growth added resulting The quality. also were effects specific fidvdaswt oywih qa ota ftefclindividual focal the of that to equal weight body (X with individuals ( of sizes different of als (t (i occasion individual sampling by or vary could Terms respectively. scales, transformed h oa mato optto na niiulsgot rsria was survival or superior. growth individual’s competitively an are on competition individuals of neg- smaller impact while total that individuals, The large indicate of values dominance population ative competitive total the on signify of based values values dependence density Nonzero classical size. to the reverts by model determined parameter is the individuals of sized value differently (X between individual asymmetry form tive focal functional the the individuals, of using two by scale) (X weight (natural competitor’s between the weight to interaction relative body the of on strength depended the was where competitors of density equivalent The (L). reach a as: calculated of (γ length effect the fixed a by scaled B g(E

Reach location (t+1) i 1 2 3 4 5 6 1 2 3 ,dntdby denoted ), omdlaymti optto,tedniyo optn individu- competing of density the competition, asymmetric model To u [Y (2) 123456 ] E ) respectively. , [Y = 123 ] β sete h xetdsz ttenx apigocso rthe or occasion sampling next the at size expected the either is sex (0) [i ] CA + UL N β (X sex (1) i , X ~ X [i ~ X PNAS ,a endi q .W rpthe drop We 1. Eq. in deﬁned as ), ] utb rnltdit n“qiaetdensity” “equivalent an into translated be must ) Reach location(t) and When φ. .Teipc fti qiaetdniywsthen was density equivalent this of impact The ). Z φ irt N ,atm-ayn admefc em(u term random-effect time-varying a ), (X + niaesz-smerccmeiin Positive competition. size-asymmetric indicate Z | i β , ee obd egto h rgnladlog- and original the on weight body to refer α(X ~ X sex (2) uy2,2020 21, July 1 2 3 4 5 1 2 3 4 ) IAppendix SI [i φ = i , ] Z = X X j 12345 irt 2 =1 j j 1234 n ) .Ti ri eednewscaptured was dependence trait This ). ,cmeiini ymti,adthe and symmetric, is competition 0, = + α(X (X u i (1) j /X i , + X | .Dfeecsin Differences S1d). section , TY LL i g j ) u ), φ o.117 vol. rt (2) steiett rlgtlink logit or identity the is h ereo competi- of degree The . + (γ r + | L r r u and o 29 no. rt (3) ) probability Transition N t ,rah(r reach ), (X subscripts β α(X 0.00 0.25 0.50 0.75 1.00 (3) | irt terms). ,and ), , 17069 i ~ X , rt X u [1] [2] ), (1) j i ), ), )

ECOLOGY Fig. 2. Observed and predicted growth (A) and survival (B) rates for females and males in the four stream populations. Demographic rates are plotted as a function of individual body weight (log-transformed). Observations are shown as points, and model expectations are indicated by lines. A growth rate of zero is indicated by a horizontal dashed line to identify the weight on the x axis at which no further increases are expected.

calculated by summing the interaction strengths, α(Xi, Xj), of all individuals sistent between stream populations. The growth rate of both in the reach. Consequently, under size-asymmetric competition (φ 6 =0), the sexes decreased nonlinearly with body weight. Males and females total interaction strength will vary depending on the focal individual’s size had similar growth rates at small sizes, but the growth rate of and the size distribution of its competitors. In the growth analysis, we ana- males decreased more rapidly. This caused males to reach a max- lyzed body weight (log-transformed) rather than length, as individuals at imum weight of around half the maximum for females. Survival their asymptotic length can lose or gain weight in response to competition and the environment. Individual-recapture and size-measurement rates are probability also varied with both sex and body weight. Female very high in this study system (over 90%). On occasions when individuals survival increased with weight initially; however, survival prob- were not recaptured, body size was imputed based on weight at prior and ability declined again in the largest individuals. Small males subsequent times (SI Appendix, section S1b). had a comparable survival to females; however, survival rates decreased with weight. As a result, males had a lower survival Results and Discussion probability than females. Observed patterns of growth and survival rates in both males Growth rates were inﬂuenced by size-asymmetric competitive and females were accurately described by the ﬁtted demographic interactions, with larger individuals being competitively domi- models, including size-asymmetric competition (Fig. 2). The sex nant and disproportionately suppressing the growth of smaller and size dependence of both demographic rates were highly con- individuals (φGrowth > 0; Fig. 3A). The degree of size-asymmetric

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Fig. 3. The estimated degree of size asymmetric competition inﬂuencing growth (A) and survival (B). The posterior densities for the asymmetry of competition parameters (φGrowth and φSurvival) are shown for each of the four streams. Distributions not overlapping zero indicate that there is signiﬁcant size asymmetry between competitors. Positive values indicate a competitive dominance of large individuals, and negative values imply a superiority of smaller individuals.

17070 | www.pnas.org/cgi/doi/10.1073/pnas.2000635117 Griffiths et al. Downloaded by guest on September 28, 2021 Downloaded by guest on September 28, 2021 optto nuniggot (φ growth influencing competition sex-specific and dotted) in experienced per- (black competitors 95th of stream. mean each density to signify overall equivalent 5th dashed) These the the (colored average panel. and show individuals. each area) by lines experienced in shaded competition Horizontal relationship the (dark regions of this region) quantiles shaded shaded in (light 75th centiles the variation to by The 25th it shown line. the competitors solid is of the time density by over equivalent shown the is and experiences weight body individual’s an experiences individual an density ( effective the measures density Equivalent (Left female 4. Fig. rftse al. et Griffiths 2A Fig. constrained as being size, asymptotic individuals their to large conse- close a of are they not response because is more the asymmetry was of competition competitive quence of This intensity time. the effect over and variable competitive ones, gup- large large small the disproportionately the from from a Conversely, competition 4). experienced of (Fig. pies effect competitors smallest no con- their despite almost individuals that, experienced largest implying the frequently environment, asymmetry, the in substantial fluctuations a siderable is This 1. P Equivalent density of competitors experienced α(X 1000 1500 1000 1500 1000 1500 1000 1500 500 500 500 500 0 0 0 0 i , h siae qiaetdniyo opttr xeine by experienced competitors of density equivalent estimated The X j ) naeae cosyas h xetdrltosi between relationship expected the years, across average, On )). n ae(Right male and ) . . . 0.8 0.6 0.4 0.2 Female niiul ntefu tempopulations. stream four the in individuals ) Body weight (g) .501 .50.20 0.15 0.10 0.05 Growth a ewe .5and 0.55 between was ) Male

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TY LL UL CA aso ifrn ie 2) ept hs ehv demonstrated have we this, Despite (29). These sizes (28). different individ- between of differentiation competition uals of niche degree the ontogenetic reduce will of behaviors degree a and symmetric S1e). with section asymmetric over Appendix, compared (SI the was competition we output change when reproductive larger structure predicted times the two size that found in mesocosm the we Using shifts data, population. the so of isometri- output weight, reproductive of scale total body probability not higher with does a output populations, cally have reproductive natural females and the larger reproducing, in mesocosms, values. in higher reproduction survival but toward quantify their distributions cannot much increases body-weight We lose the which not shifts conditions, do and these individuals under large asymmetry that weight bio- The means a weight. competition lose body would of of individuals proportion smallest unrealistic the models logically When but density-dependent symmetric. all simple that be high, predicted cap- to was poorly assumed density was was population process competition growth when smaller the tured growth much and that being survival revealed separate often components the weights of body- Examination body projected observed. of predicted the than variance with when symmetric, and inaccurate, contrast, be highly mean In were to distributions events. assumed environmen- weight flooding of was as impacts competition such random largely perturbations, This streams and 5A). tal along frequent (Fig. quality habitat streams the in the and variation spatial across the females despite and was made males competition. be both could symmetric for distributions simple body-weight of with prediction Accurate model the outperformed distributions. weights, body-weight body observed predicted with compared of were distributions which two compe- obtained size-asymmetric to we or constrained tition, symmetric was either containing competition demographic models simplified which (φ of in symmetric be set fitted, a (SI were First, next models the S1e). to section occasion sampling Appendix, body- one link project from to and distributions survival used and weight were growth individual (26) of (IPMs) processes the through models distributions projection population-size Integral project time. to ability our alter toward growth from away (25). redirected survival is are buffering sensitivity rates resources heightened growth if This that competition. expected suggests to com- survival, sensitive more not of are but impacts growth, size-asymmetric on of petition size occurrence levels population with The peak coupled structure. remained higher rates survival so considerably and (21), reached streams, densities high-productivity popula- in competitor as contrast, In streams, lower. the these are mask densities in tion or interactions overwhelm asymmetric may of influence 24), impacts (23, greatly perturbations, events environmental not flooding frequent did especially The competitors survival. of individual (φ composition size symmetric on the interactions approximately competitive were of survival effects the streams, (φ productivity probability dominant individuals survival competitively smaller the Larger of reduced 3). pro- Fig. disproportionately increased TY; individuals experimentally and with (UL streams ductivity the in competition (10). a weight follow with should preemption assump- resource the that to tion leading dynamic ratio, This with area–volume with surface scales month. individual’s consumption consistent a an resource in where is weight theories, competition budget body energy their asymmetric of of 40% to degree up and lose rates often growth variable can highly have individuals large that shows upe loehbtsz-eedn irhbttue(27) use microhabitat size-dependent exhibit also Guppies greatly competition size-asymmetric incorporating IPM The interactions competitive asymmetric how explored we Finally, size-asymmetric by influenced also was probability Survival 0 = PNAS .B osrcigIM sn demographic using IPMs constructing By ). Survival | uy2,2020 21, July > .I h w lower- two the In 3B). Fig. 0; | o.117 vol. Survival 2/3 | ' oe scaling power o 29 no. .Ta is, That 0). | 17071

ECOLOGY Asymmetric Symmetric 0.75

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1e−05 Predicted variance in body weight Predicted variance 1e−05 1e−04 1e−03 1e−02 1e−01 1e−05 1e−04 1e−03 1e−02 1e−01 Observed variance in body weight

Fig. 5. Relationships between the observed and predicted mean and variance in body-weight distributions after each sampling transition. Predictions are made by using an IPM with either size-asymmetric competition or symmetric competition. Predictions for females and males are made separately by using sex-speciﬁc IPMs. Points are colored according to the stream. The dashed diagonal line indicates the 1:1 correspondence between observations and predictions.

that guppy demography is strongly influenced by size-asymmetric how phenotypic variability drives both population dynamics and competition. Competition is likely to be even more asymmet- evolution. Biotic interactions undoubtedly play a central role in ric in species exhibiting size-based competitive hierarchies, such ecological and evolutionary dynamics. Our findings underline the as rutting ungulates, marine mammals, territorial birds, and a importance of determining the trait dependence of these inter- wide array of fish species. Size-asymmetric competition is also actions and the need to move away from crude metrics of density expected to influence reproductive demography, affecting both dependence. The classical approach, involving simple metrics the number and quality of offspring produced. In terms of selec- of density, is insufficient to capture the strength of interactions tion when the genetic influences on an individual’s phenotype between individuals, and, hence, a reevaluation of the role of arise from both its genotype and the genotypes of its competi- biotic interactions in both ecological and evolutionary processes tors, there are indirect genetic effects (30), which have important may now be required. implications for trait evolution (31, 32). As an individual’s body weight determines competitive ability and this, in turn, is influ- Data Availability. Demographic data have been deposited in the enced by many other traits, this suggests that indirect genetic publicly accessible Dryad Digital Repository (20). effects may be widespread. From an evolutionary perspective, these results imply that fitness will be frequency-dependent, ACKNOWLEDGMENTS. We thank Yuridia Reynoso and the numerous field resulting in potentially complex evolutionary dynamics (14–16). and laboratory technicians that helped with the long-term study. This research was funded by UK Natural Environment Research Council Grant The size-structured models of competition developed here can NE/K014048/1. The field research was funded by NSF Research Grants be used to assess the role of asymmetric competition on all com- EF0623632 and 9419823 and NSF Division of Environmental Biology Grant ponents of a life history. This will allow an understanding of 1556884.

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ECOLOGY