universidade federal do rio grande do norte centro de biociências
TRANSCRIPT
UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE
CENTRO DE BIOCIÊNCIAS
PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA
ANGÉLICA NAGATA DE SOUSA BORGES
‘MODELO DE AVALIAÇÃO DE RISCO’: SÃO O CRESCIMENTO E A
ESTEQUIOMETRIA DOS GIRINOS AFETADOS PELO EFEITO INTERATIVO
ENTRE A PRESENÇA DO PREDADOR E A DENSIDADE DE COESPECÍFICOS?
Natal
2013
ANGÉLICA NAGATA DE SOUSA BORGES
‘Modelo de avaliação de risco’: São o crescimento e a estequiometria dos girinos
afetados pelo efeito interativo entre a presença do predador e da densidade de
coespecíficos?
Dissertação apresentada ao Programa de
Pós-Graduação em Ecologia da
Universidade Federal do Rio Grande do
Norte – UFRN – como requisito para
obtenção do título de Mestre em Ecologia
Área de concentração: Ecologia de
Ecossistemas
Orientador (a): Prof. Drª. Luciana Silva
Carneiro
Natal
2013
Autorizo a reprodução e divulgação total ou parcial deste trabalho, por qualquer meio
convencional ou eletrônico, para fins de estudo e pesquisa, desde que citada à fonte.
UFRN / Biblioteca Central Zila Mamede
Catalogação da Publicação na Fonte
Borges, Angélica Nagata de Sousa.
“Modelo de avaliação de risco”: são o crescimento e a
estequiometria dos girinos afetados pelo efeito interativo entre a
presença do predador e da densidade de coespecíficos? / Angélica
Nagata de Sousa Borges. – Natal, RN, 2013.
36 f. : il.
Orientadora: Profª. Dra. Luciana Silva Carneiro.
Dissertação (Mestrado) – Universidade Federal do Rio Grande do Norte. Centro de Biociências. Programa de Pós-Graduação em
Ecologia.
1. Risco de predação (Ecologia) - Dissertação. 2. Ecologia do
estresse - Dissertação. 3. Efeitos não letais - Dissertação. 4.
Estequiometria ecológica - Dissertação. I. Carneiro, Luciana Silva. II.
Universidade Federal do Rio Grande do Norte. IV. Título.
RN/UF/BCZM CDU 591.5
Nome: BORGES, Angélica Nagata de Sousa.
Título: ‘Modelo de avaliação de risco’: São o crescimento e a estequiometria dos
girinos afetados pelo efeito interativo entre a presença do predador e da densidade
de coespecíficos?
Dissertação apresentada ao Programa de
Pós-Graduação em Ecologia da
Universidade Federal do Rio Grande do
Norte como requisito para obtenção do
título de Mestre em Ecologia
Aprovado (a) em 16 de julho de 2013
Banca Examinadora:
___________________________________________________________
Profª. Drª. Luciana Silva Carneiro (Orientadora)
Departamento de Botânica, Ecologia e Zoologia/UFRN
___________________________________________________________
Prof. Dr. José Luiz de Attayde
Departamento de Botânica, Ecologia e Zoologia/UFRN
___________________________________________________________
Dr. Rafael Dettogni Guariento
Ecology Brasil – Ecology and Environment do Brasil
Ao Rafael, com amor.
Nada disso seria possível sem você.
Obrigada por todo cuidado, incentivo e paciência.
AGRADECIMENTOS
É com muita alegria que expresso aqui a minha gratidão a todos que
participaram e contribuíram para a realização dessa pesquisa.
Primeiramente gostaria de agradecer à professora Drª Luciana Silva Carneiro,
pela orientação e paciência. Muito obrigada!
Gostaria de agradecer ainda:
Ao professor Dr. Adriano Caliman, pela participação ativa em todas as etapas
desta pesquisa, desde a elaboração do experimento até todas as sugestões de análises e
revisão do texto;
Ao Dr. Rafael Guariento, pela participação na elaboração do experimento e por
todas as suas correções;
À professora Drª Miriam Plaza Pinto, pelas valiosas sugestões e comentários;
Ao professor Dr. José Luiz de Attayde, que desde já agradeço pela avaliação
desse trabalho e todas as correções;
A todo pessoal da Escola Agrícola de Jundiaí (EAJ- UFRN), que cedeu o espaço
e os mesocosmos para a realização do experimento;
Ao professor Dr. Alex Poeta Casali da Universidade Federal da Paraíba (UFPB),
por gentilmente ceder os girinos utilizados no experimento;
A todos do Laboratório de Limnologia da Universidade Federal do Rio Grande
do Norte (UFRN), pela disponibilidade sempre que precisei preparar as amostras ou
tentei realizar as análises com o equipamento de vocês!
Ao Laboratório de Limnologia da Universidade Federal do Rio de Janeiro
(UFRJ) por permitir a realização das análises de nutrientes em suas dependências;
A todos do Laboratório de Ecologia Aquática (LEA) pela colaboração;
À Júnia Kizzy, minha companheira de todas as horas com quem sempre pude
contar. MUITO OBRIGADA! Você não tem noção da importância da sua amizade pra
mim!
Ao Jaqueiuto Silva, por toda ajuda na eletrizante captura das baratas d’água.
Além de todas as dicas sobre os girinos. Você foi demais!
À Camila Cabral, por todo apoio, desabafar com você permitiu que a minha
saúde mental permanecesse intacta (ou quase) durante essa empreitada;
À Letícia Quesado e à Laura Fernández, queridas amigas, pela disponibilidade
em revisar todo o texto desse trabalho;
Ao Guilherme Mazzochini, pela ajuda nas análises estatísticas;
A todos os professores do Programa de Pós-Graduação em Ecologia (PPGEco)
que contribuíram enormemente para minha formação;
Aos amigos da PPGEco;
À Universidade Federal do Rio Grande do Norte por toda estrutura;
Ao Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ)
pela concessão da bolsa de mestrado e todo auxílio financeiro necessário para realização
desta pesquisa;
Por fim, meu agradecimento especial à minha família (Mamãe, papai, irmãos e
sobrinha), por todo amor e incentivo – AMO VOCÊS! – e ao Rafael, meu amado
namorido, que além de todo amor e confiança, foi decisivo em todas as questões
práticas desta pesquisa.
OBRIGADA!
“– Mamãe, a senhora não acha a vida uma coisa extraordinária? – começou.
Sua mãe ficou tão espantada com a pergunta que não lhe ocorreu qualquer resposta...
– Sim – respondeu – Às vezes.
– Às vezes? Quero dizer... Você não acha surpreendente o simples fato de o mundo
existir?
– Sofia, do que você está falando?
– Estou perguntando uma coisa. Ou será que você acha o mundo uma coisa totalmente
normal?
– Sim. O mundo é uma coisa absolutamente normal. Na maioria das vezes.
Sofia entendeu que o filósofo tinha razão. Os adultos achavam o mundo uma coisa
evidente. Dormiam para sempre o sono encantado do cotidiano.
– Você apenas se habituou tanto com o mundo que ele não surpreende mais você. –
disse.
– Desculpe, mas não estou entendendo nada.
– Estou dizendo que você se acostumou demais com o mundo. Em outras palavras, você
está totalmente tapada.”
Jostein Gaarder em O Mundo de Sofia - Romance da história da filosofia.
RESUMO
BORGES, A. N. S. ‘Modelo de avaliação de risco’: São o crescimento e a
estequiometria dos girinos afetados pelo efeito interativo entre a presença do predador e
da densidade de coespecíficos? 2013. 36f. Dissertação (Mestrado) - Pós-Graduação
em Ecologia, Universidade Federal do Rio Grande do Norte, Natal, 2013.
Muitos organismos alteram o seu fenótipo para reduzir o risco de predação. No entanto,
tais modificações estão associadas a trade-offs, que podem ter efeitos negativos sobre o
crescimento e a reprodução destes organismos. Compreender como as presas avaliam o
risco de predação é fundamental para avaliar o valor adaptativo das mudanças
fenotípicas induzidas pelo predador e suas consequências ecológicas. Neste estudo nós
realizamos um experimento em mesocosmo para testar: i) se o crescimento e a
estequiometria dos girinos da espécie Lithobates catesbeianus é alterado em resposta a
presença de baratas d’água predadoras (Belostoma spp.); ii) se estas respostas dependem
da densidade de girinos no ambiente. Aqui nós mostramos que os girinos não têm o seu
crescimento nem a sua estequiometria afetada pela presença do predador, esteja os
girinos em baixas ou em altas densidades. Nossos resultados indicam que os girinos
expostos ao risco de predação regularam sua fisiologia a fim de preservar a homeostase
estequiométrica do seu corpo e excretas. Além disso, aponta a necessidade de
experimentos que elucidem em que condições o crescimento e a estequiometria de
girinos são modificados em resposta ao risco de predação.
Palavras- chave: risco de predação, ecologia do estresse, efeitos não letais.
ABSTRACT
BORGES, A. N. S. ‘Modelo de avaliação de risco’: São o crescimento e a
estequiometria dos girinos afetados pelo efeito interativo entre a presença do predador e
da densidade de coespecíficos? 2013. 36f. Dissertação (Mestrado) - Pós-Graduação
em Ecologia, Universidade Federal do Rio Grande do Norte, Natal, 2013.
Many prey organisms change their phenotype to reduce the predation risk. However,
such changes are associated with trade-offs, and can have negative effects on prey
growth or reproduction. Understand how preys assess the predation risk is essential to
evaluate the adaptive value of predator-induced phenotypic and its ecological
consequences. In this study, we performed a mesocosm experiment to test: i) if growth
and stoichiometry of Lithobates catesbeianus tadpoles is altered in response to giant
water bug presence (Belostoma spp.); ii) if these responses depend on tadpoles’ density
in environment. Here, we show that tadpoles’ growth and stoichiometry are not changed
by predator presence, neither in low nor in high densities. Our results suggest that
tadpoles exposed to predation risk regulate their physiology to preserve the elemental
stoichiometric homeostase of their body and excretion. Further, point out to need for
future studies that elucidate under what conditions growth and stoichiometry are
changed in response to predation risk.
Keywords: predation risk, ecology of stress, non-lethal effects.
SUMMARY
1. INTRODUCTION ............................................................................................................ 11
2. METHODS ..................................................................................................................... 14
2.1. EXPERIMENTAL DESIGN ......................................................................................... 14
2.2. CONTROL OF EXPERIMENTAL ABIOTIC CONDITIONS .............................................. 17
2.3. QUANTIFICATION OF EXCRETION AND BODY NUTRIENT STOICHIOMETRY .............. 17
2.4. STATISTICAL ANALYSIS ............................................................................................ 18
3. RESULTS ....................................................................................................................... 19
4. DISCUSSION .................................................................................................................. 24
REFERENCES ....................................................................................................................... 28
APPENDIX A......................................................................................................................... 33
APPENDIX B ......................................................................................................................... 36
11
1. INTRODUCTION
Many prey organisms modify their phenotype to increase their chances of
survival in predation risk situations (Lima 1998; Lima & Dill 1990). This includes
changes in behavior (McIntyre, Baldwin, & Flecker 2004; Peacor 2006; Peacor &
Werner 2000; Richardson 2001), morphology (Gómez & Kehr 2011; Kehr & Gómez
2009; McIntyre et al. 2004; Relyea 2001a), physiology (Archard et al. 2012; Barry &
Syal 2012; Hawlena & Schmitz 2010a; McPeek, Grace, & Richardson 2001) and
development (Skelly & Werner 1990; Steiner 2007b; Vonesh & Warkentin 2006).
However, such modifications can negatively affect other prey fitness components, such
as growth and reproduction, as a result of trade-off between reduce predation risk and
obtain food from environment (Relyea 2002b; Werner & Anholt 1993). Thus, it is
essential that prey be able to assess predation risk accurately to dose their phenotypic
response (Peacor 2003; Van Buskirk & Arioli 2002).
Recognize the predator presence in the environment is the first step to a prey
organism assess and react to predation risk (Hettyey et al. 2012). There are numerous
sensory pathways by which a prey can detect predator presence, such as vision, hearing
and chemical cues (Hettyey et al. 2012; Saidapur et al. 2009). The predator presence
detection may be either via direct contact or indirect recognition of cues released during
predator successful or unsuccessful attack on other prey individuals (Peacor 2003).
There are evidences that many species use this latter mechanism to assess, in part or in a
whole, the intensity of predation risk (Dicke & Grostal 2001; Peacor 2003)
Aquatic organisms, in particular, use primarily chemical cues to assess
predation risk (Brönmark & Hansson 2000; Kats & Dill 1998; Schoeppner & Relyea
2005) . Such cues are released by both predators, in feces or by-products exudates of
digestion, and conspecific preys, as a result of tissue damage (Brönmark & Hansson
2000; Ferland-Raymond et al. 2010; Fraker et al. 2009; Kats & Dill 1998; Schoeppner
& Relyea 2005). Chemical cues are easily dispersed in water and provide information
about predator’s specie, density and/or proximity of predators in the environment, and
predator diet composition (McCoy et al. 2012; Schoeppner & Relyea 2005, 2009; Van
Buskirk & Arioli 2002). Furthermore, the persistence of chemical cues related to past
predation events allow prey to get information about the likelihood of predator presence
and density (Kats & Dill 1998)
12
The level of chemical cues in the environment is proportional to prey density,
once the number of prey injured or killed by predator increases with prey availability
(Holling 1959; Van Buskirk et al. 2011). Therefore, a prey organism also must be able
to distinguish differences in conspecific density to assess and react accurately to
predation risk (Peacor 2003). Peacor (2003)proposed the ‘risk assessment’ model in
which argues that real predation risk in the environment is the ratio of intensity of risk
cue to conspecific density. This predation risk assessment estimated through conspecific
density prevents that prey overestimate the predation risk at high prey density or
underestimate it at low prey density (Peacor 2003), leading prey to invest in properly
anti-predator defense (Van Buskirk et al. 2011). The investment in maladaptive anti-
predator defenses has consequences to individual mortality, whether due to costs in
fitness or inefficiency in avoiding predator (Van Buskirk et al. 2011; Werner & Peacor
2003)
The main prediction of Peacor’s model is that variations in the ratio of risk cue-
to- prey density induce phenotypic defenses in prey organisms, regardless of whether
this variation is caused by changes in the intensity of risk cue or conspecific density.
However, it is difficult to prove that prey responses to variations in conspecific density
are related to ‘risk assessment’ mechanism because prey density are also related to
competitive interactions and anti-predator behaviors, as ‘dilution effect’ and ‘many eyes
effect’. Relyea (2002a) showed that competition for resources induces phenotypic
responses similar to those induced by predation risk. Other confounding factors are
‘many eyes effect’ and ‘dilution effect’. ‘Many eyes effect’ is a group behavior related
to detection of predator, in which, the higher the prey density the greater the possibility
of at least one prey individual detect the presence of predator and warn other preys
individuals (Pulliam 1973), while ‘dilution effect’ assumes that the chance of prey
individual be captured by predator is lower at higher prey density than at lower prey
densities because other prey individuals may be captured (Roberts 1996). Both anti-
predator behaviors predict the reduction in prey individual vigilance behavior in
response to increases in prey density, however there are no definitive prediction neither
about prey phenotype when variation in prey density do not affect the per capita risk of
predation nor about phenotypic changes other than vigilance behavior (Pulliam 1973;
Roberts 1996)
13
The responses to predation risk have effects that might cascade down on food
webs, affecting ecosystem properties and functions such as productivity, nutrient
cycling, food chain length, trophic biomass, and species diversity (Dickman et al. 2008;
Hawlena & Schmitz 2010a; Schmitz 2008b). Risk assessment’ model can have direct
and indirect implications for nutrient cycling as organisms at predation risk can alter
their resource use and nutrients excretion and consequently their nutritional budget
(Hawlena & Schmitz 2010a; b). The nutrient demand of an organism is affected by
intensity of its physiological processes, which in turn depends on its level of stress
(Hawlena & Schmitz 2010b; Steiner & Van Buskirk 2009). At a specific level of
predation risk, the increase in prey metabolism is the physiological trait more evident.
In the short term (i. e. minutes or hours), such metabolic increase ensures that prey can
be energetically able to avoid or fight its predator (Hawlena & Schmitz 2010b; Steiner
& Van Buskirk 2009). On the other hand, if such metabolic increase is maintained for
the long term (i. e. days or weeks), the prey is forced to relocate energy from growth or
storage to meet the metabolism energy demand. This mechanism may inhibit prey
biomass production, nitrogen excretion and, in extreme cases, promote breakdown of
body protein into glucose (i. e. gluconeogenesis) (Hawlena & Schmitz 2010a; b). In
addition, foraging behavior adjustments of the prey related to resource choice can
prevent deleterious effects of the increase in energy demand induced by the predator
(Hawlena & Schmitz 2010a; b). Therefore, alterations in prey nutrient excretion in
response to predation risk can affect the fast nutrient cycling, while alterations in prey
resource choice affect the slow nutrient cycling (Vanni 2002).
Ecological stoichiometry (ES) is a conceptual framework that analyzes
constrains and consequences of mass balance of multiple chemical elements in
consumer-resource interaction (Sterner & Elser 2002). ES has provided mechanisms to
understand how imbalances between organism and food affect its physiology,
population dynamics and ecosystem level processes. Consequently, ES also permits to
directly trace prey individual stoichiometry plasticity in response to predation risk,
altering prey’s body and excretion stoichiometry, which can reverberate in ecosystem
level processes (Hawlena & Schmitz 2010b; Leroux, Hawlena, & Schmitz 2012;
Schmitz 2008a; Sterner & Elser 2002). For example, Hawlena & Schmitz have
developed a series of experiments to investigate the role played by spider predation risk
on grasshoppers (prey) nutritional balance and their ecological consequences (Hawlena
14
& Schmitz 2010a; Hawlena et al. 2012; Schmitz 2008a). The authors showed that
Melanoplus femurrubrum grasshoppers facing spider predation risk has a greater
demand for carbon (C) than control grasshoppers, which leads to changes in
grasshoppers diet (Hawlena & Schmitz 2010a). The authors also showed that such
grasshoppers’ diet shift affects the nutrients that enter the detrital pool, which in turn
affects the litter decomposition (Hawlena et al. 2012). However, there are still too few
studies that investigate the prey stoichiometric response to predation risk and its
ecological consequences. In addition, the existing surveys are restricted to terrestrial
(Hawlena & Schmitz 2010a; Hawlena et al. 2012) and to pelagic model systems.
In this study we asked if prey at constant predation risk modifies its growth and
stoichiometry in response to differences in conspecific densities. Our hypotheses were
that predation risk: i) will negatively affect prey growth (biomass), and this effect will
be greater at low (vs. high) prey density; ii) will negatively affect prey body nitrogen
(N) and phosphorous (P) content, and this effect will be greater at low (vs. high) prey
density; iii) will positively affect prey excretion N and P content, and this effect will be
greater at low (vs. high) prey density; iv) via alterations in prey excretion, will
indirectly cascade to positively affect the periphyton nutrient (N: P) stoichiometry.
2. METHODS
2.1. EXPERIMENTAL DESIGN
We exposed small bullfrog tadpoles (Lithobates catesbeianus Shaw, 1802) to
chemical cues – predation risk – from the giant water bugs Belostoma spp. to test our
hypotheses. Relyea (2001b) reported water bug’s ability to capture, handle and consume
Lithobates catesbeianus tadpoles that co-occur in natural ponds in Michigan, USA. This
predator- prey pair was chosen as our study system because amphibian larvae exhibit a
great variety of plastic phenotypic responses to predators and both species are quite
amenable to experimental manipulation (Relyea 2001a). Experimental tadpoles
(individual length ~ 2, 5 cm) came from a frog farm located in Pium, Rio Grande do
Norte state (RN), while giant water bugs (individual length ~ 3 cm) were collected in
temporary pools in Santa Maria – RN.
The experiment was conducted outdoor at Agricultural School of Jundiai (EAJ-
UFRN), Macaíba, RN, Brazil. Experimental units were 250-L fiberglass tanks,
15
truncated cone shaped (0, 74-m diameter at base; 0, 98-m diameter of the aperture; 0,
53-m height). Tanks were filled with water from Jundiai Reservoir one month before
adding tadpoles and applying treatments. Mean total nitrogen and phosphorus of the
Jundiai Reservoir during experimental period were 82.77 µM and 6.98 µM,
respectively. All mesocosms were covered with mosquito nets to minimize
allochthonous input and to prevent oviposition and immigration by aquatic insects,
predators and competitors (Fig. 1).
Fig. 1. Experimental tanks field setup.
The experiment consisted of a 2 x 3 full factorial design with two levels of
predation risk (risk / no risk) and three levels of prey conspecific densities, 12, 24 and
36 ind/m3 (or 3, 6 and 9 individuals per mesocosm, respectively). Tadpoles’ densities
were chosen to ensure no intraspecific competition (Relyea 2002a). All treatments were
replicated three times (replica A, B and C) in complete block design for a total of 18
experimental units (see Fig. 2). The experiment lasted for 19 days from June 17th to July
5th, 2011.
16
Fig. 2. Experimental design
The feeding of tadpoles was based on industrial fish food to ensure a constant
per capita food level. Daily, we added industrial fish food (10% of tadpole mass) to all
mesocosms, according to Van Buskirk et al.(Van Buskirk et al. 2011). The periphyton
that grew up at the walls of the mesocosms also was food source available for the
tadpoles. Tadpole’s mortality was inspected and registered. However, dead tadpoles
were not replaced to ensure that the time of exposure to risk cues was the same between
tadpoles.
Predation risk was manipulated by caging two giant water bugs in individual
plastic floating cages inside mesocosms. Cages (~10-cm diameter and ~17-cm long)
were made of transparent PET bottles with ends enclosed by mosquito net and were
attached to a small piece of polystyrene foam to raise the top of the cage 3 cm from the
water surface, allowing the water bugs to breathe. Water bugs density per mesocosm
was based on previous studies, which found some effects tied to predation risk. Caged
water bugs were fed one conspecific tadpole every other day to maintain chemical cues
in the mesocosms. Tadpoles used to feed water bugs were housed in separate
17
mesocosms. We inspected the mesocosms daily and replaced any dead water bug, so
water bug density was constant throughout the experiment (Fig. 3).
Fig. 3. Illustration of plastic floating cages used in the present experiment to avoid predation of tadpoles
by water bugs.
At the end of the experiment, we conducted excretion experiments with the
tadpoles (see below) and afterwards tadpoles were frozen to posterior body nutrient (N
and P) analysis. Periphyton sample were collected by scrapping three random areas of
the mesocosms wall with a plastic card (i.e. stratified by depth). The scraped periphyton
were then rinsed into vials and filled up to 50 milliliters slurry with mineral water.
Samples were kept under frozen storage until the nutrient analysis.
2.2. CONTROL OF EXPERIMENTAL ABIOTIC CONDITIONS
Water samples were taken from all experimental tanks to quantify initial nutrient
concentrations at the beginning of experiment. Nitrogen (N) and phosphorous (P)
concentrations in water column were analyzed using the salicylate hypochlorite method
(Golterman, Clymo, & Ohnstad 1978) and ammonium-molybdate method (Strickland &
Parsons 1972), respectively. We used a two- way ANOVA with the Tukey as a post hoc
test (P < 0, 05) to compare N, P concentrations and N: P ratio in water column among
treatments.
2.3. QUANTIFICATION OF EXCRETION AND BODY NUTRIENT STOICHIOMETRY
We used methods described in Schaus et al. (1997) to quantify tadpole’s
excretion rates. On July 5th
, after 19 experimental days, all tadpoles of each mesocosm
treatment were captured and immediately placed in plastic containers filled with 150
milliliters of mineral water. Mineral water samples were collected to quantify initial
18
nutrient concentrations prior to tadpole addition. Tadpoles were incubated for 85 – 95
minutes (Whiles et al. 2009). At the end of incubations, animals were removed and kept
refrigerated for a couple of hours. Excretion and initial samples were filtered through
Whatman GF/C filters to remove faeces and other particles, stored in acid-washed vials
and frozen until nutrient analysis. Filtrate samples were analyzed for ammonia (NH3)
and orthophosphate (PO4-3
) using the salicylate hypochlorite method (Golterman et al.
1978) and ammonium-molybdate method (Strickland & Parsons 1972), respectively.
Mass-specific nutrient excretion rates, rex, were calculated as:
Where [N final] is nutrient, NH3 or PO4-3
, final concentration, [N initial] is nutrient, NH3 or
PO4-3
, initial concentration, V is container volume in liters, T is incubation time in hours
and B is tadpoles total dry biomass in grams.
To quantify tadpoles body nutrient stoichiometry, tadpoles were dried at 60ºC
for a minimum 48 hours, weighed (to the nearest 0.01g), grounded to a fine powder
with a mortar and pestle. Powder samples were digested with hydrochloric acid (HCl)
and potassium persulphate (K2S2O8) to convert particulate N and P to nitrate (NO3-) and
PO4-3
, respectively (Golterman et al. 1978). NO3-
was measured by the salicylate
hypochlorite method (Golterman et al. 1978), whereas PO4-3
was measured by the
ammonium-molybdate method (Strickland & Parsons 1972). N and P tissue content
were determined based on dry weight of samples used in analysis and was expressed in
N or P (µg) per total dry weight (mg) to tissue. We used the same analysis to assess N
and P content in periphyton biomass. Periphyton N and P content was expressed in N or
P (µg) per 50 milliliters aliquot to periphyton.
2.4. STATISTICAL ANALYSIS
We used an analysis of covariance (ANCOVA) to evaluate the individual and
interactive effects of predation risk (fixed factor) and tadpole density (covariate) on:
‐ Tadpoles’ mortality;
‐ Tadpoles’ growth;
‐ Tadpoles’ body content and stoichiometry – % N, %P and N: P ratio;
19
‐ Tadpoles’ excretion rate and stoichiometry – NH3, PO4-3
and NH3: PO4-3
ratio;
‐ Periphyton stoichiometry – N: P ratio.
According to our objectives, a significant interaction term between predation
risk and tadpole density confirm that density of preys mediate the effects of predation
risk on tadpole phenotype. Tadpole mortality unbalanced our experimental design and
therefore we use ANCOVA instead of 2-way ANOVA to analyze our data.
We tested the assumptions of normality and homocedasticity of the ANCOVA
to all dependent variables using Shapiro-Wilk test and Bartllet test (p > 0, 05),
respectively. Tadpoles’ growth, NH3 and PO4-3
excretion rate, and NH3: PO4-3
excretion
ratio data not satisfied these assumptions and were square root transformed. Body
nutrient stoichiometry values of replica A and B of the treatment risk with 12 ind/m3 are
missing because biological material was not enough to nutrient analysis.
ANCOVAs were performed in the R statistical programming environment
version 2·3·1 (R Development Core Team 2006). We used the “lm” (linear model)
function to carry out the ANCOVA in R. Raw data and R scripts see appendix A. All
graphics were performed using GraphPad Prism version 5.01 for Windows (GraphPad
Software, San Diego California USA).
3. RESULTS
Neither mortality, growth, body and excretion nutrient contents and ratios of
tadpoles, nor periphyton stoichiometry were significantly affected by predation risk and
conspecific density and by their interactions (Table 1; Fig. 4-8). Conspecific density had
only a tendency to on average increase N: P tadpole excretion ratio (Fig. 7E). Initial
nutrient condition in water column (N, P concentration and N: P ratio) was the same
among treatments (see appendix B), so that experimental abiotic conditions did not
affect the findings.
20
Table 1: Summary of the analyses of covariance (ANCOVA) testing the individual and interactive effects
of predation risk (categorical factor), density (covariate) and their interaction on tadpole mortality,
biomass, body and excretion N and P content and ratios, and periphyton N: P ratio.
Source of variation F P
Tadpole mortality
Density (D) 1,980 0,181
Risk (R) 0. 455 0. 511
D×R 0,160 0,694
Tadpole growth (biomass)
Density (D) 0,020 0,889
Risk (R) 0,192 0,667
D×R 0,074 0,789
Tadpole body N content
Density 0,240 0,632
Risk 2,027 0,180
D×R 2,574 0,134
Tadpole body P content
Density 0,169 0,687
Risk 0,540 0,476
D×R 0,592 0,456
Tadpole body N:P ratio
Density 0,343 0,568
Risk 2,381 0,148
D×R 2,471 0,141
Tadpole NH3 excretion rate
Density 0,439 0,518
Risk 0,161 0,693
D×R 0,001 0,976
Tadpole PO4 excretion rate
Density 0,293 0,596
Risk 0,102 0,753
D×R 0,504 0,489
Tadpole N:P excretion ratio
Density 3,458 0,084
Risk 0,924 0,352
D×R 1,270 0,278
Periphyton N:P ratio
Density 1,604 0,225
Risk 0,178 0,678
D×R 0,229 0,639
21
Fig. 4. Tadpoles relative mortality regressed against tadpoles’ density in the presence and absence of risk
predation cues (A). Solid and dashed trendlines depict the linear regressions for risk and no risk
treatment, respectively. P- values were obtained from raw data. P- values from ANCOVA are given for
the main effect of density (D), risk (R), and density x risk interaction (D x R). Panel (B) shows predation
risk main effect (mean ±SD).
Fig. 5. Tadpoles biomass regressed against conspecific density in the presence - filled symbols - and
absence - open symbols – of risk predation cues (A). Different symbols denote initial densities in each
treatment. Diamonds = 12 ind./m3; Circles = 24 ind./m
3; Square = 36 ind./m
3. Solid and dashed trendlines
depict the linear regressions for risk and no risk treatment, respectively. P- values were obtained from
transformed data. P- values from ANCOVA are given for the main effect of density (D), risk (R), and
density x risk interaction (D x R). Panel (B) shows predation risk main effect (mean ±SD).
22
Fig. 6. Body nutrient stoichiometry regressed against conspecific density in the presence and absence of
risk predation cues. (A) Body N content, (C) body P content and (E) body N: P. Solid and dashed
trendlines depict the linear regressions for risk and no risk treatment, respectively. P- values were
obtained from raw data. P- values from ANCOVA are given for the main effect of density (D), risk (R),
and density x risk interaction (D x R). Panels (B), (D) and (F) show predation risk main effect (mean
±SD).
23
Fig. 7. Excretion nutrient stoichiometry regressed against conspecific density in the presence and absence
of risk predation cues. (A) Mass- specific NH3 excretion rate, (C) mass- specific PO4-3
excretion rate and
(E) N: P excretion ratio. Solid and dashed trendlines depict the linear regressions for risk and no risk
treatment, respectively. P- values were obtained from transformed data. P- values from ANCOVA are
given for the main effect of density (D), risk (R), and density x risk interaction (D x R). Panels (B), (D)
and (F) show predation risk main effect (mean ±SD).
24
Fig. 8. Periphyton N: P ratio regressed against tadpoles density in the presence and absence of risk
predation cues (A). Solid and dashed trendlines depict the linear regressions for risk and no risk
treatment, respectively. P- values were obtained from raw data. P- values from ANCOVA are given for
the main effect of density (D), risk (R), and density x risk interaction (D x R). Panel (B) shows predation
risk main effect (mean ±SD).
4. DISCUSSION
We found no interactive or individual effects of predation risk and conspecific
density on tadpole growth, body and excretion nutrient content and stoichiometry. These
results show that giant water bug presence did not induce changes in Lithobates
catesbeianus tadpoles’ traits neither in low nor in high tadpoles densities, contradicting
our first, second and third hypotheses. Further, no changes in tadpole nutrient excretion
in response to predation risk invalidate our forth hypothesis that predation risk, via
alterations in prey excretion, would indirectly cascade to positively affect the periphyton
stoichiometry.
The results of this study do not allow us to evaluate the prediction of ‘risk
assessment’ model. We cannot say for certain that tadpoles perceived the giant water
bug presence because we did not observed significant differences between risk and no
risk treatments. Some confusion arises because there are evidences that L. catesbeianus
tadpoles exposed to giant water bug presence display morphological and behavioral
defenses in response to differences in conspecific density (Guariento 2012). Thus, if
tadpoles perceived the predator presence, as suggested by morphological and behavioral
25
changes observed, and yet they do not change their growth and stoichiometry, our
results can be related to elemental homeostatic regulation of vertebrate organisms. On
the other hand, if tadpoles do not perceived the predator presence there are no reason to
growth and stoichiometric changes.
Vertebrates have their elemental composition affected mainly by differential
phosphorous allocation to bony structures during ontogeny (Elser 2006; Elser et al.
1996; Frost & Elser 2008; Hendrixson, Sterner, & Kay 2007; Vanni et al. 2002).
Tadpoles at predation risk are able to accelerate their growth to decrease their stay in a
risky environment (Skelly & Werner 1990). In our experiment we observed no
significant differences in tadpole’s growth and nutrient content between risk and no risk
treatment, which suggest that L. catesbeianus tadpoles did not accelerate their growth as
an anti-predator strategy. Further, tadpoles’ stoichiometric body analyses were restricted
to nitrogen and phosphorous content and ratio due to shortage of biological material.
Thus, was not possible assess whether the risk cue from giant water bug induced
changes in carbon content as a result of energy limitation expected from predation
risk/foraging trade-off (Elser et al. 1996; Rinke, Hülsmann, & Mooij 2008; Steiner
2007a). Therefore, there is the possibility that prey changes its body C content, and
consequently C: N and C: P ratio.
However, it is important to highlight that tadpoles can react to the presence of
predator in many ways, such as changes in their behavior, morphology or development
(Werner & Anholt 1993). Several studies have reported giant water bug - induced
defenses in tadpoles. For example, McIntryre, Baldwin & Flecker (2004) reported that
Rana palmipes tadpoles became less active, developed deeper tail fin and muscle, and
displayed darker pigmentation in the presence of Belostoma water bug. Yet, Kehr &
Gómez (2009) observed that Rhinella schneideri tadpoles developed longer tail length
and shorter guts when exposed to caged Belostoma elegans predator. Among anti-
predator strategies, behavioral changes are the most documented in anuran species.
Guariento (2012) reported that L. catesbeianus tadpoles displayed
morphological and behavioral responses to giant water bug predation risk. The author
showed that these tadpoles displayed modification in tail morphology when exposed to
predation risk. Furthermore, Guariento (2012) observed that tadpoles at predation risk
tended to remain at the bottom of the mesocosms and only emerged in surface when
prey densities were high. These morphological and behavioral findings support ‘risk
26
assessment’ model. ‘Risk assessment’ model also was previously supported by Van
Buskirk et al. (2011) and McCoy (2007). Van Buskirk et al. (2011) showed that Rana
temporaria tadpoles behaviorally respond to per capita predation risk imposed by
Aeshna cyanea dragonfly larvae. McCoy (2007), in turn, showed that Hyla chrysoscelis
tadpoles display morphological changes in response to per capita predation risk
imposed by Lethocerus americanus giant water bug.
The spatial distribution of tadpoles in mesocosms observed by Guariento
(2012) may have favored to the absence of growth and nutrient stoichiometry responses
of L. catesbeianus tadpoles to predation risk. Fraker & Luttbeg (2012) suggest that prey
can manage its fear and predation risk by adjusting its space use. We limited the
predator space use, and consequently, the risk cue by caging giant water bugs in plastic
floating cages. Thus, it is possible that habitats shifts have been an effective tadpole’
defense mechanism against giant water bugs.
The fact that tadpoles can display behavioral responses to predation risk
(Guariento 2012) but did not alter their growth or stoichiometry also can be related to
fitness costs associated with these defenses. Previous studies states that costs to induce
and to reverse behavioral defenses, as reducing activity or increasing refuge use, are
low, which ensures a rapid response to threat (Relyea 2003b; Wcislo 1989). In contrast,
physiological adjustments to risk, as altering nutrient body budgets, are more costly
(Hawlena & Schmitz 2010b). Thus, low cost defenses should be adopted at first in
attempt to mitigate the risk of predation, reducing the need to engage in costly
physiological strategies/changes. This hypothesis is supported by the results found in
tadpoles’ mortality analysis. Tadpole mortality was not affected by predation risk and
prey density and by their interaction, suggesting that anti-predator defenses adopted by
L. catesbeianus tadpoles were effective to avoid mortality.
On the other side, ‘risk assessment’ model assumes that prey investment in
anti-predator defenses depends on both level of risk cue and prey density (Peacor 2003).
Van Buskirk et al. (2011) showed that prey anti-predator responses to differences in the
level of risk cue are more evident than to differences in prey density. This suggests that
the level of risk cue play a most important role in predation risk assessment. No
individual effect of predation risk on tadpole growth and stoichiometry indicates that
type (i.e., predator species) or concentration (i.e., predator number) of risk cue used in
our experiment was not appropriate to induce expected changes in tadpoles’ traits.
27
Previous studies showed that prey respond differently to different predator species
(Bernard 2006; Relyea 2001a; b), and adjust their response to number of both predator
and injured preys (Van Buskirk & Arioli 2002).There are evidences that anti-predator
response of tadpoles to giant water bugs is weaker than anti-predator response of
tadpoles to other predators (Gómez & Kehr 2011; Jara & Perotti 2009; Relyea 2001a; b,
2003a), but the reason for this differential prey response is not clear (Hettyey et al.
2011; Relyea 2001b).
Additionally, it is possible that the tadpoles have acclimated to predation risk
(Barry & Syal 2012; Steiner & Van Buskirk 2009). Steiner & Van Buskirk (2009)
suggest that the tadpoles acclimation to predation risk after long exposure to risk cues is
a way to minimize deleterious effects of predator - induced defenses on prey fitness.
They found that Rana temporaria tadpoles increase their metabolism at short-term
exposure to risk (i.e., hours). However, such change is not maintained at long-term (i.e.,
days, weeks). Our experiment lasted 19 days in order to simulate chronic risk of
predation, in which changes in prey nutritional budget are expected (Hawlena &
Schmitz 2010b). This experimental time is similar to the time used in other experiments
that investigate tadpole physiological responses to long-term exposure to risk (Barry &
Syal 2012; Steiner 2007a; Steiner & Van Buskirk 2009).
In summary, the results presented here show that L. catesbeianus tadpoles do
not change their growth and nutrient stoichiometry in response to predation risk based
on differences in conspecific density. Our findings are consistent with theory of
elemental homeostatic regulation of vertebrate organisms. However, additional
experiments using different risk cue type, concentration and duration are required to
elucidate whether and how physiological stoichiometric changes of prey are sensitive to
predation risk. We highlight the need for more studies focused upon understanding how
differential anti-predator is triggered.
28
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33
APPENDIX A
Table 2. Experiment raw data. The header was changed to perform ANCOVA in R.
Risk Replica
Tadpole
density
(ind/m3)
Tadpole
biomass
(g/ind.)
Tadpole Body Tadpole Excretion Periphyton %Tadpole
mortality %N %P N: P NH3
(mM g-1
h-1
)
PO4
(mM g-1
h-1
) NH3: PO4 N: P
presence A 4 0,010 0,292 0,042 7 6,663 0,333
presence B 8 0,003 0,178 0,089 2 15,294 0,667
presence C 8 0,035 14,457 1,455 9,935 0,551 0,086 6,385 11,313 0,667
presence A 24 0,06 10,64 1,292 8,234 0,119 0,124 0,962 15,065 1
presence B 16 0,005 11,982 1,557 7,696 1,864 0,295 6,308 5,328 0,667
presence C 16 0,006 18,734 1,465 12,79 0,414 0,113 3,667 6,892 0,667
presence A 28 0,003 11,156 3,201 3,485 2,174 0,239 9,901 3,602 0,778
presence B 28 0,013 11,05 1,597 6,919 0,718 0,057 12,636 6,415 0,778
presence C 32 0,025 9,57 1,832 5,222 0,398 0,069 5,781 9,850 0,889
absence A 4 0,015 15,413 1,65 9,34 0,059 0,059 1 11,137 0,333
absence B 12 0,04 14,303 1,949 7,34 0,153 0,054 2,857 4,506 1
absence C 12 0,01 11,626 1,185 9,81 0,706 0,314 2,250 5,343 1
absence A 24 0,039 14,937 1,935 7,719 0,055 0,002 29 5,975 1
absence B 20 0,013 10,191 2,277 4,476 0,014 0,035 0,4 3,253 0,833
absence C 16 0,013 11,845 1,764 6,715 1,158 0,188 6,15 5,702 0,667
absence A 20 0,007 13,637 1,438 9,481 0,604 0,066 9,2 7,127 0,556
absence B 32 0,029 12,971 2,102 6,171 0,413 0,142 2,903 3,637 0,889
absence C 28 0,006 12,943 1,254 10, 323 1,157 0,06 19,2 4,716 0,778
34
Scripts to carry out the ANCOVA in R:
1) Tadpoles’ mortality:
Data<-read.table("Exp.txt",h=T)
attach(Data)
Mortality<-lm(mort~risk*dens)
shapiro.test(residuals(Mortality))
bartlett.test(mort~Risco*dens)
drop1(Mortality,~.,test="F")
2) Tadpoles’ Growth:
biomass_ind_sqrt<-sqrt(biomass_ind)
BioInd<-lm(biomass_ind_sqrt~risk*dens)
plot(BioInd)
shapiro.test(residuals(BioInd))
bartlett.test(biomass_ind_sqrt~risk*dens)
drop1(BioInd,~.,test="F")
3) Tadpoles’ body stoichiometry:
a) N content
Nitrogen<-lm(N~risk*dens)
plot(Nitrogenio)
shapiro.test(residuals(Nitrogen))
bartlett.test(N~risk*dens)
drop1(Nitrogen,~.,test="F")
b) P content
Phosphorous<-lm(P~risk*dens)
plot(Phosphorous)
shapiro.test(residuals(Phosphorous))
bartlett.test(P~risk*dens)
drop1(Phosphorous,~.,test="F")
c) N: P ratio
RatioNP<-lm(NP~risk*dens)
shapiro.test(residuals(RatioNP))
bartlett.test(NP~Ratio*dens)
35
drop1(RatioNP,~.,test="F")
4) Tadpoles’ excretion stoichiometry:
a) NH3
N_exc_sqrt<-sqrt(N_exc)
NH3_2<-lm(N_exc_sqrt~risk*dens)
shapiro.test(residuals(NH3_2)
bartlett.test(N_exc_sqrt~risk*dens)
drop1(NH3_2,~.,test="F")
b) PO4
P_exc_sqrt<-sqrt(P_exc)
PO4_1<-lm(P_exc_sqrt~risk*dens)
shapiro.test(residuals(PO4_1
bartlett.test(P_exc_sqrt~risk*dens)
drop1(PO4_1,~.,test="F")
c) NH3: PO4
NP_exc_sqrt<-sqrt(NP_exc)
NPratio_1<-lm(NP_exc_sqrt~risk*dens)
shapiro.test(residuals(NPratio_1))
bartlett.test(NP_exc_sqrt~ risk*dens)
drop1(NPratio_1,~.,test="F")
5) Periphyton stoichiometry:
Periphyton<-lm(NP_per~risk*dens)
shapiro.test(residuals(Periphyton))
bartlett.test(NP_per~risk*dens)
plot(Periphyton)
drop1(Periphyton,~.,test="F")
36
APPENDIX B
Table 3. Comparison of the N, P concentrations and N: P ratio (mean ± SD) in water column among
treatments at the beginning of experiment. (*) denote treatments that are significantly different among
each other (2- way ANOVA, Tukey post hoc test; P < 0, 05).
Tadpole initial density
(ind./ m3)
Risk No Risk
a) [N]
12 82.656 (±1.926) 82.910 (±1.979)
24 84.675 (±1.514)* 77.785 (±4.3)*
36 86.623 (±0.172) 81.996 (±3. 876)
b) [P]
12 7.40 (±0.646) 5.3 (±14.690)
24 7.33 (±0. 441) 7 (±0.513)
36 7.47 (±0.191) 7.33 (±1,705)
c) N: P molar
12 11.191 (± 0.645) 20. 532
(±14.690)
24 11.563 (±0.44) 11.144
(±0.513)
36 11.603 (±0.191) 11.320
(±1.704)