Marine Ecology Progress Series 507:81

This supplement accompanies the article

A biomechanical and optimality-based derivation of prey-size dependencies in planktonic prey selection and ingestion rates

Kai W. Wirtz*

Helmholtz-Zentrum Geesthacht, Institute for Coastal Research, 21501 Geesthacht, Germany *Corresponding author: [email protected] Marine Ecology Progress Series 507: 81–94 (2014)

SUPPLEMENT

∑ S-I. Prey assemblages and size distributions 1/4), total grazing rate G = i Gi becomes largely independent from species resolution; the overall food Prey-size distributions often follow a log-normal availability in a broader size class enables a consis- function (Armstrong 2003, Quintana et al. 2008, tent application of functional grazing responses

Schartau et al. 2010). More complicated distribution (Gi(x), see below). Gi is down-scaled to the individual forms can be represented by a superposition of N species i’ using the selection kernel and Eq. (4): 2 2 −−ss’(opt ii’ ) −− ’(opt ) log-normal functions. Let the coefficients pi, obeying GGPi’’= i ⋅ i e/e Pi with s’ = s/(1 + ∑ 2 ∑ i pi = 1, denote the relative contribution of each sub- 2s σ,i ), and subsequent renormalization by i’Gi’ to distribution which again is characterized by specific ensure mass conservation of the algorithm.  2 values for mean (log) size i and variance σ,i . Eq. (S1) can also be used to represent multimodal distribution functions that can arise from intensive 2 2 pi −−()/2i σ,i PP = tot e (S1) grazing within a certain sub-range (Hahn & Hofle ∑ 2 iN=…1, 2πσ,i 1999, Havlicek & Carpenter 2001, Schartau et al. This representation also emulates a multi-species 2010). In correspondence to grazing holes in the food 2 ≥ (model) assemblage so that σ,i describes the size spectrum, multi-modality of P (N 2) gains impor- variance of a single population. The superposition tance when assessing a model with size-selective notion enables collecting individual species i’ of a grazing. Multi-modal or complicated prey size-distri- large assemblage into groups i. Within multi-species butions are to be studied in subsequent works, which food-web models, different partitioning of total food can be realized via complementing the formulas ∑ biomass among many prey species leads to different derived here by the summation i =1,…N. outcomes and, thus, to an inconsistent dependency of Still, for many experiments with restricted food total grazing N on the partitioning resolution repertoire, a single elementary distribution ( = 1) ∑ i’ Gi' N N (Tilman 1982, Visser & Fiksen 2013). A species will suffice. Even in situ samples for distinct func- assembling algorithm based on Eq. (S1) avoids this tional groups (e.g. phytoplankton, zooplankton) are anomaly. Using the group index i the algorithm often fitted by one log-normal function if a reduced aggregates the species resolution by calculating accuracy can be tolerated (Quintana et al. 2008). group-based grazing rates Gi that also allow mean- Therefore, the number of peaks in the food particle ingful interpretation of feeding half-saturation and distribution is in this study shrunk to the case N =1. affinity (see also Appendix A-IV in the main article).

Still, the group-based rates Gi have to be linked to the species-specific grazing rate Gi’ on prey i’. Each S-II. Functional grazing response species contributes to 2 adjacent groups i and i + 1 and optimal affinity where the group index i derives from the condition  ≤  ≤   2 i i’ i +1. The group characteristics pi, i, and σ,i The mechanistic grazing rate G (Eq. A11 in the are recursively calculated from the contributing spe- main article) derived in Appendix A-IV in the main cies, weighted according to the respective distance article and Wirtz (2012) contains as major argument   ⋅ | i – i’|. The procedure is repeated until the mean the ratio between external (Az P) and internal (I max )  group sizes i converge, which is generally the case food processing rates. At very large x, i.e. at high after a few iterations. If the initial spacing or bin encounter capture rates, we have G = I max ⋅ x ⋅ 00  Δ n n+1 width, Δ = ii+1 − , is set sufficiently large (e.g. (–x )/(–x ) = I max ; at very small x, grazing is affinity ⋅ ⋅ controlled (G = I max x = Az P). For the more typical 2 average feeding characteristics (I max and P) cal- intermediate values of x, the grazing function G(n,x) culated here already facilitate incorporation of covers a wide range of situations, from those with size-selective grazing into ecosystem models. The high intermittency in feeding sub-stages (low n) to same holds for multi-species prey representations as those with high synchrony (large n); corresponding shown in Appendix A-II in the main article. to these situations, G(n,x) in Eq. (A11) changes from a Holling-type-II (n = 1), or an Ivlev (n = 2), to a rec- tangular Holling-type-I (n →∞) functional represen- S-III. Optimality in feeding selectivity tation. Az in Eq. (A11) represents the food affinity that in turn is directly related to variable feeding In this work, optimal foraging theory is also used activity of the consumer. Within the size-based to estimate the degree of selectivity that maximizes approach proposed here, it is hardly conceivable grazing. As shown in Fig. 6 in the main article, G how this activity can be distinguished with respect to is a uni-modal function of s because higher selec- single prey size classes. Activity can either be absent tivity increases the efficacy of ingestion (I max ) but or present, no matter what the specific characteristics lowers the availability of food (P). The mathemati- of the next encountered particle are. Activity is a pre- cal condition for grazing optimization in terms of requisite of selection (ƒsel) and cannot be formulated selectivity, as a function of prey size. A predator may reject a ∂G ∂G ∂I max ∂G ∂P certain prey size class (described by ƒsel), or can = + = 0 (S3) decide to become inactive under generally unfavor- ∂s ∂I max ∂s ∂P ∂s able feeding conditions, a situation that may reflect contains the 2 partial derivatives that follow from Eq. the prey size distribution as a whole, but the pursue (A11) state A , as such, cannot be written in meaningful z ∂G G ∂G x ∂G ∂G x terms of . As a consequence, G also has to be formu- = − and = (S4) I I x I P x P lated independently from specific prey size classes, ∂ max max∂ max ∂ ∂ but using bulk properties of the prey field. The selectivity effect on maximal ingestion rate 2 Behavioral down-regulation of clearance activity ∂∂IsIssmax/3/(46)=+ max can be calculated from ∂ ∂ Az has been proposed by Wirtz (2013) to follow opti- Eq. (5), and the effect on food availability P/ s 2 2 mal foraging theory. Optimal clearance activity in = –P σ /(1 + 2sσ ) from Eq. (4). Although exact solu- x (Az) fulfills tions of Eq. (S3) need to be found numerically, model implementation may be facilitated by approximate, ∂G = Ixmax ⋅ ’ (S2) closed expressions, similar to the solution for A . At ∂x z low food ( 5 ), we can rewrite the grazing term n P Pcrit Pcrit ∂G 1(+ nx−− n 1) x  ⋅ ⋅ withx ’= and = I max G I max x n/(n + 1), the last factor coming from a lin- n+1 2 P ∂x ()1− x earisation of the terms in Eq. (A11) (e.g. xn  1 – n ⋅

Pcrit defines the food concentration required to com- logx). At low to intermediate values of s, we can also 2 2 pensate activity costs and risks. These consumer simplify (1 + 2sσ )/(4s + 6) to (1 + 3σ )/12. Then, by losses comprise energy expenditures for filtering using the activity optimization Eqs. (S2) & (S3), we activity and swimming (Lehman 1976), or the related obtain an estimate for the optimal selectivity, risk of being detected by a predator (Visser et al. 2 13+ σ n P 2009). Eq. (S2) can be solved using the numerical (S5) s = 2 ⋅ ⋅ –y 1/12+ 4σ n + 1 Pcrit approximation: Az = I max /Pe with y = 10x’/(n + 1) – 4/(n +2). where the additional correction in the denominator 2 Using such estimates of Az, we could further inte- (+1/12) stabilizes the expression at low σ . At highly  grate over the functional response dependent on abundant food levels (P 5Pcrit), where G I max , Imax() and P times the feeding kernel ƒsel. In theory, optimal selectivity according to Eq. (S3) is in the ∞  ∂ ∂ –1 the average grazing rate GA(,ƒ(),z sel P I max ())d order of I max /( G/ x·x) which can be very large ∫−∞ is a more precise quantity than the grazing function dependent on the functional response at high P, here computed at the average characteristics I max and P. If quantified by the synchrony parameter n. At large n a second-order moment approximation is used, how- and P, selectivity optimization Eq. (S3) at P  Pcrit  ever, this integral again becomes a function of I max may result in s 10 up to 100. The latter case trans- and P and a term containing the prey size variance lates to a size feeding range of 1–2 µm for a consumer 2 σ (e.g. Bolker & Pacala 1997, Wirtz 2000). Hence the specialized on 10 µm items.

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