LANDSCAPE STRUCTURE AND GENETIC VARIATION IN NATURAL ZOOPLANKTON POPULATIONS:
CLADOCERANS IN A SET OF INTERCONNECTED PONDS
ERIK MICHELS
Promotoren: Prof. Dr. L. De Meester Prof. Dr. F. Ollevier Laboratorium voor Aquatische Ecologie Katholieke Universiteit Leuven
Leden van de examencommissie:
Prof. Dr. T. Backeljau Prof. Dr. L. Brendonck Prof. Dr. K. Schwenk Prof. Dr. F. Volckaert
Het onderzoek voorgesteld in dit proefschrift werd uitgevoerd aan het Laboratorium voor Aquatische Ecologie (K.U.Leuven). Het onderzoek werd gesteund door een beurs verleend door het Instituut voor de bevordering van het Wetenschappelijk-Technologisch onderzoek in de industrie (I.W.T.).
KATHOLIEKE UNIVERSITEIT LEUVEN FACULTEIT WETENSCHAPPEN DEPARTEMENT BIOLOGIE Laboratorium voor Aquatische Ecologie
Landscape structure and genetic variation in natural zooplankton populations: cladocerans in a set of interconnected ponds
Landschapsstructuur en genetische variatie in natuurlijke zoöplanktonpopulaties: cladoceren in een aantal onderling verbonden vijvers
Promotoren: Prof. Dr. L. De Meester Proefschrift voorgedragen tot Prof. Dr. F. Ollevier behalen van de graad van Doctor in de Wetenschappen door
Erik Michels
2001
Contents
Dankwoord
Chapter I General introduction and outline of the thesis: The use of a 1 landscape approach in population genetics
Chapter II Zooplankton on the move: first results on the quantification of 39 dispersal of zooplankton in a set of interconnected ponds
Chapter III Geographic and genetic distance among zooplankton populations 55 in a set of interconnected ponds: a plea for using GIS modelling of the effective geographic distance
Chapter IV Microgeographic genetic structure of local Daphnia ambigua 75 populations connected by high dispersal rates
Chapter V Genetic differentiation for ecologically relevant traits in a patchy 111 metapopulation of the cyclical parthenogen Daphnia ambigua
Chapter VI Genetic differentiation in Polyphemus pediculus populations 137
General discussion 157
Summary 165
Samenvatting 171
Publications 177
Dankwoord
Net als ik dacht dat ik er bijna was, besefte ik dat een dankwoord schrijven eigenlijk niet zo gemakkelijk is! Niet omdat ik het moeilijk heb om jullie te bedanken, wel omdat het gevaar groot is om in clichés te vervallen. Toch wil ik beginnen met iedereen te bedanken die in de voorbije jaren, ieder op zijn manier, er toe bijgedragen heeft dat dit boekje eindelijk voor u ligt. De voorbije jaren op het labo waren niet alleen wetenschappelijk zeer boeiend, maar gingen ook gepaard met de nodige toffe (en dikwijls memorable) momenten. Ik wil in de eerste plaats mijn promotoren bedanken die dit onderzoek in goede banen hebben geleid. Prof. Ollevier wil ik bedanken voor de gelegenheid die hij mij bood om in “het labo” steeds in de beste omstandigheden onderzoek te kunnen verrichten. Aan Luc De Meester heb ik ontzettend veel te danken. Luc’s wetenschappelijk inspiratie en enthousiasme werkten reeds tijdens het maken van mijn licentiaatsthesis aanstekelijk. Zonder “de hand van De Meester”, die ooit op een mooie zondagnamiddag in augustus de grote lijnen voor dit onderzoek op papier zette, zou dit doctoraat er dan ook nooit gekomen zijn. Luc zijn talloze commentaren en suggesties waren soms moeilijk leesbaar, maar brachten (eens ontcijferd) vaak een nieuw licht op dit onderzoek en werkten meestal zeer stimulerend. Ik ben Luc eveneens zeer dankbaar omdat hij mij de voorbije jaren ook de gelegenheid gaf om de wijde (wetenschappelijke) wereld te verkennen. Ik wil ook Luc Brendonck bedanken voor zijn niet aflatende interesse in dit onderzoek en zijn kritische maar constructieve commentaren op dit werk. Luc was reeds van bij het begin van dit onderzoek als “streng maar rechtvaardig IWT commissielid” bij dit onderzoek betrokken, maar ook nadien kon ik steeds bij hem terecht voor advies of gewoon een babbel. Bovendien apprecieer ik het ten zeerstse dat hij, na een gekneusde rib en enkele voltreffers, het nog steeds aandurft met mij te squashen. Hierbij wil ik ook de overige leden van de examencommissie: Prof. Filip Volckaert, Prof. Klaus Schwenk en Prof. Thierry Backeljau bedanken voor het grondig lezen en kritisch beoordelen van dit werk. Dit onderzoek zou onmogelijk geweest zijn zonder de gewaardeerde medewerking van de vzw Natuurreservaten (ondertussen Natuurpunt) en Willy Peumans, de conservator van De Maten. Ik besef maar al te goed wat een luxe het is geweest om te kunnen samenwerken met collega’s en studenten die ook goede vrienden geworden zijn. Dit doctoraat is voor een groot deel het resultaat van de wel bijzonder prettige samenwerking met Karl Cottenie die ik dan ook zeer dankbaar ben. Karl was mijn “partner in crime” tijdens het vele veldwerk in De Maten dat hierdoor bij momenten een avontuurlijk karakter kreeg en genoeg anekdotes opleverde voor menig straf verhaal. De bijdrage van Karl aan dit onderzoek beperkt zich echter niet tot het veldwerk of zijn statistisch advies (KISS* altijd en overal). Zijn inbreng in de uitwerking van de ideeën in dit werk zijn op minst aanzienlijk te noemen.
Joost en Hanne, samen met Karl collega’s van het eerste uur, zorgden van in het begin mee voor de toffe werksfeer en zaten nooit verlegen om een wedstrijdje (s’ nachts op de fiets) of een weddenschap (ergens te velde). Ik wil zeker ook Lies (hoezo stress?) Neys en Koen (FT) De Gelas bedanken voor de zeer constructieve en plezante samenwerking tijdens hun thesis en ook erna. Lies wil ik speciaal bedanken voor haar hulp bij het maken van de figuur op de voorpagina van dit boekje. Katrien Uyttebroek zal ik mij altijd herinneren omdat ze zich tijdens haar thesis niet liet ontmoedigen door de moeilijkheden bij de kweek van D. ambigua. Verder wil ik Elke, Conny, Martine, An W., Ria, Tine, Els, Ellen, Steven, Frank, Joachim, Ronald, Eddy, Stef, Wouter, Jimmy, Joost R. Jochen, Gerald, Gregory, Jeroen en alle andere collega’s nog bedanken voor voor de toffe momenten aan de koffietafel en voor al die keren dat ze mij geholpen hebben bij allerhande kleine en minder kleine “doctoraatsperikelen”. Ik wil zeker ook niet nalaten al de vrijwilligers te bedanken die ooit hebben geholpen tijdens de jaarlijkse wonderbaarlijke visvangst in De Maten. Nicole Podoor en Wouter Rommens bedank ik voor hun hulp bij het analyseren van waterstalen uit De Maten en voor het bezorgen van gegevens over de waterplantenvegetatie in De Maten. Daarnaast zijn er natuurlijk nog al mijn vrienden die ik wil bedanken voor het bezorgen van de nodige (in)spanning en avontuur ter tijd en stond. Mijn (schoon)ouders, broer en zussen, bedank ik voor alles wat ze al die jaren voor mij gedaan hebben… Maar als er iemand is die ik niet genoeg kan bedanken is het Anne-Sophie. Zij heeft haar kersverse echtgenoot gedurende deze laatste drukke maanden vaak moeten missen en heeft alle “ups & downs” van dichtbij meegemaakt. Ik ben haar dan ook zeer dankbaar dat ze mij er steeds opnieuw van kon overtuigen dat het ook deze keer wel in orde zou komen.
* Keep It Super Simple
Introduction and outline
Chapter I
General introduction and outline of the thesis: The use of a landscape approach in population genetics
Abstract
This chapter presents an outline of the present work. In a short introduction, we point to the maintenance of genetic variation as a central aspect of conservation and evolutionary biology and underline the relevance of a landscape based metapopulation approach in population genetic research in zooplankton. Next, we briefly overview processes contributing to the genetic differentiation among zooplankton populations. In this respect we discuss patterns of local genetic differentiation as revealed by (quasi-)neutral markers and the study of ecological relevant traits and we focus at the difference between permanent and intermittent populations of cyclical parthenogens. In addition, we point at the extensive genetic polymorphism for predator induced defence mechanisms. As metapopulation biology provides a useful theoretical framework to study genetic differentiation in a set of zooplankton populations linked by dispersal, we introduce the basic concepts of metapopulation biology and highlight important recent developments in this field, i.e. the synthesis of metapopulation studies and landscape ecology. From this overview, we argue that a landscape based approach may offer an attractive framework for population genetic studies. At the end of this chapter we provide some historical and ecological background information to the study area, the pond complex of
1 Chapter I the nature reserve De Maten and present the species characteristics of the two model species, Daphnia ambigua (Scourfield, 1947) and Polyphemus pediculus (Müller, 1785).
Introduction
Genetic variation is a conditio sine qua non for evolution, since it enables natural populations to adapt genetically to the constantly changing environmental conditions. During the last three decades, the maintenance of genetic diversity in natural populations has been a central issue in evolutionairy biology and a major concern in conservation biology (Soulé & Simberloff, 1986; Lande, 1988; Loeschke et al., 1994; Frankham, 1995; Avise & Hamrick, 1996; Spellerberg, 1996; Meffe & Caroll, 1997). Since the early studies by Lewontin in 1974, genetic studies using allozyme markers and more recently polymerase chain reaction enhanced DNA techniques have revealed a substantial genetic diversity in most natural populations. Several hypotheses have been proposed to explain the maintenance of genetic diversity in populations. Spatial and/or temporal heterogeneity of the habitat may contribute to the maintenance of genetic polymorphism via genotype-environment interactions (Hedrick et al., 1976; Hedrick, 1986; Via & Lande, 1987; Van Tienderen, 1991). In addition, frequency-dependent selection and heterosis have also been proposed as mechanisms to maintain genetic diversity (Hori, 1993; Ridley, 1996). Finally, a substantial part of genetic variation for proteins and DNA markers in natural populations may be explained by neutral evolution (Kimura & Otha, 1971; Maruyama & Kimura, 1980; Lynch, 1994). Even though each of these hypotheses may contribute to the maintenance of genetic polymorphism in natural populations, their relative importance in explaining the observed genetic variation in natural populations is still a matter of debate (Cook, 1991; Ridley, 1993). Slatkin (1977) was among the first to introduce the ecological concept of a metapopulation into population genetics. Since then, a growing number of both theoretical and empirical studies have examined how metapopulation structure could influence genetic variation in natural populations (e.g. Wade & McCauley, 1988, McCauley, 1991; Hastings & Harrison, 1996; Hedrick, 1996; Planes et al., 1996; Hedrick & Gilpin, 1997; Hanski & Gilpin, 1997; Pannell & Charlesworth, 1999). Recently, the idea that the incorporation of metapopulation structure may often be incomplete unless the models are framed in a context of the underlying landscape mosaic starts to get growing support. As such, we witness the integration of metapopulation biology and landscape ecology.
2 Introduction and outline
Questions concerning the maintenance of genetic polymorphism and the genetic consequences of a metapopulation structure have also been addressed in ecological research in zooplankton (e.g. Hebert & Crease, 1983; Korpelainen, 1984, 1986; Weider & Hebert, 1987; Innes, 1991; Brendonck et al., 2000). Pajunen (1986) and Bengtsson (1989, 1991) were among the first to propose a metapopulation concept for Daphnia species co-occurring in pools on rock outcrops on the shores of Sweden. Several authors studied the genetic structure of Daphnia populations inhabiting small pools on rocky shores in Canada (Weider & Hebert, 1987a; 1987b; Wilson & Hebert, 1992, 1993). The latter studies, however, dealt with obligately parthenogenetic Daphnia species. This is important, because the mode of reproduction (in casu cyclical parthenogenesis versus obligate parthenogenesis) has important consequences on the genetic structure of Daphnia populations and dynamics of genetic differentiation among these populations (Hebert, 1987; De Meester, 1996b). In this chapter, we focus at the genetic structure of zooplankton populations and discuss patterns and processes determining genetic diversity within and genetic differentiation among natural populations of cyclically parthenogenetic cladocerans as revealed by the study of neutral markers as well as ecological relevant traits. We introduce the basic concepts of metapopulation biology and landscape ecology and point at the recent development of the synthesis of metapopulation studies and landscape ecology. With this overview we want to illustrate the landscape based approach that was used in this study, can offer an attractive framework for population genetic studies. In this study, populations of two cladoceran species in the pond complex in the nature reserve De Maten are used as a model system we discuss the history of the study site, we give an overview of the ecological data that were gathered within the framework of the present and other ongoing studies in De Maten and introduce the model species.
The genetic structure of zooplankton populations
General population genetic theory predicts that high dispersal rates among populations, if translated into high levels of gene flow, promotes the mixing of alleles and prevents local populations from differentiating genetically from each other. Limited dispersal and low levels of gene flow among populations, on the other hand, may promote genetic differentiation among populations as a result of drift and/or natural selection (Slatkin, 1985). The island-like nature of lentic freshwater habitats set boundaries for dispersal and gene flow between
3 Chapter I populations and therefore promotes for genetic differentiation among populations (De Meester, 1996b). As most Cladocera, Daphnia reproduces by cyclical parthenogenesis (Hebert, 1987, Fig. 1). Females reproduce by amictic parthenogenesis as long as environmental conditions are favourable. Parthenogenetic reproduction may be maintained during many generations. The parthenogenetically produced female offspring are genetically identical to their mothers, this results in a population composed of a number of clonal lineages (Carvalho, 1994). In response to environmental cues associated with the onset of unfavourable conditions (e.g. changes in photoperiod; low food concentration, high population density and the presence of predators; see Stross, 1987; Hobaek & Larsson, 1990; Kleiven et al., 1992; Pijanowska & Stolpe, 1996), males are produced by amictic parthenogenesis and the females produce (haploid) sexual eggs that need to be fertilised. Once the sexual eggs are fertilised, they are deposited in a protected envelope called ephippium (Hebert, 1987; Zaffagninni, 1987).
Sexual Parthenogenesis reproduction
Figure 1: Cyclical parthenogenetic reproduction mode in Daphnia. Females produce by amictic parthenogenesis as long as growth conditions remain favourable. In response to environmental cues associated with the onset of unfavourable conditions, males and sexual eggs are produced. Sexual eggs are resting eggs that can survive harsh conditions and hatch when conditions become favourable again (modified from Hughes, 1989).
4 Introduction and outline
The sexual eggs are resting eggs that are very resistant do harsh conditions such as mechanical disturbance, drying and freezing and exposure to digestive enzymes. They hatch when conditions turn favourable again. By virtue of their capacity to survive harsh conditions, resting eggs enable passive dispersal of animals from one habitat to another via wind (Jenkins and Underwood, 1998; in Branchiopoda: Brendonck & Riddoch, 1999), water (Havel et al., 2000) or via organisms that move actively to other habitats (e.g. waterfowl: Maguire, 1960; Proctor, 1964; Proctor & Malone, 1965; Mellors, 1975; Charalimbides et al., 2000). The production of resting eggs thus facilitates the rapid colonisation of new habitats (Hebert & Moran, 1980; Pajunen, 1986; Jenkins, 1995; Jenkins & Buikema, 1998). It is clear that the cyclical reproduction mode of Daphnia and the production of resting eggs have important consequences for the genetic structure and population dynamics in Daphnia populations. In cyclical parthenogenetic zooplankton, genetic polymorphism may be enhanced by the continuous input of new genotypes through hatching from the resting egg bank. The effect of this input of new genotypes will be greater to the extent that resting eggs survive for many years, such that allelic variants that have been selected for for many years ago can be reintroduced in the active population (Hairston & De Stasio, 1988). The population genetic structure of natural zooplankton populations has been studied extensively for more than three decades (see reviews by Hebert, 1987; Carvalho, 1994; De Meester, 1996b). Since the pioneering studies of Paul Hebert and coworkers, a large number of studies have reported on the genetic variation within and genetic differentiation among Daphnia populations. From the start (Hebert, 1974a, 1974b) a distinction has been made between populations that are either permanently present in the habitat and intermittent populations that go through a regular phase of sexual reproduction and survive harsh conditions (drying out in summer, or freezing in winter) as resting eggs (De Meester, 1996b). Populations living in intermittent habitats are characterised by high genetic diversity and relatively stable genotype frequencies that are in close agreement with Hardy-Weinberg expectations. This has been explained by the regular bouts of sexual reproduction. In case deviations from Hardy-Weinberg expectations occur, these are often in the direction of heterozygote deficiencies, which may be due to simultaneous hatching of genotypes from different generations (cfr. resting egg bank). The genetic structure of intermittent populations is thus largely determined by the recurrent phase of sexual reproduction, and is in essence comparable with the genetic structure of sexual organisms. In contrast, permanent Daphnia
5 Chapter I populations are mostly characterised by low genotypic diversity, rapid and pronounced shifts in genotype frequencies during the course of the growing season, and strong deviations from Hardy-Weinberg expectations. The deviations from Hardy-Weinberg expectations are mostly in the direction of heterozygote excess. The genetic structure of permanent populations is mainly determined by the extended periods of clonal selection and genetic erosion during the phase of parthenogenetic reproduction. Most studies using (quasi-)neutral markers have indicated that isolation-by–distance patterns among unconnected permanent zooplankton populations are weak, largely due to strong genetic differences among nearby populations (Hebert, 1974; Boileau & Hebert 1988, Innes, 1991; Boileau et al., 1992; Vanoverbeke & De Meester, 1997). In this respect, De Meester (1996b) pointed at an apparent paradox in Daphnia, with observations of extensive local genetic differentiation between neighbouring Daphnia populations on the one hand, and a high dispersal potential and a rapid colonisation of new habitats on the other hand. This apparent paradox of high dispersal capacity and low gene flow can be explained by a combination of stochastic and selection driven processes that have been shown to be particularly effective in several aquatic organisms (see review De Meester et al., in press). The parthenogenetic reproduction mode in Daphnia allows for rapid growth rates in populations after a historical colonisation event from a few founding propagules. This rapid population growth restricts the capacity for growth for secondary colonizers and thus strongly buffers against changes in allele frequencies due to gene flow (Boileau et al., 1992). The persistence of founding effects is further enhanced by the presence of large resting egg banks (Hairston, 1996; Brendonck et al., 1998). In addition, rapid adaptation of resident populations to local environmental conditions further reduces gene flow among populations (De Meester, 1993; 1996b), because subsequent colonizing genotypes must cope with a high number of individuals of locally adapted genotypes. De Meester et al. (in press) integrate the above mentioned processes into one hypothesis called the monopolisation hypothesis which elaborates on the persistent founder effect hypothesis that was originally presented by Boileau et al. (1992). Comparing the patterns of genetic differentiation among zooplankton populations both at neutral markers and at ecologically relevant traits creates the opportunity to gain insight in the relative importance of random genetic drift or gene flow versus natural selection as evolutionairy forces driving the process of genetic differentiation among zooplankton populations (De Meester, 1996b). The study of genetic differentiation for ecologically relevant traits may indeed reflect a pattern of local adaptation to ecological conditions (in
6 Introduction and outline zooplankton: Parejko & Dodson, 1991; Leibold & Tessier, 1991; Parejko & Dodson, 1991; Spitze, 1993; De Meester, 1996a; Boersma et al, 1998; Declerck et al., 2001). Spitze (1993) directly compared among populational genetic differentiation in (quasi-)neutral traits with differentiation in quantitative traits that are likely to be the subject of natural selection (also see Morgan et al. 2001). Depending on the trait considered, genetic differentiation between populations was higher (body size) or lower (relative fitness) than for allozymes, reflecting disruptive and stabilizing selection, respectively. Cousyn et al. (2001) used the same approach to show that genetic changes in phototactic behaviour of a particular Daphnia populations through time (cfr. resting eggs isolated from different depths in the resting egg bank) was due to natural selection. The occurrence of local adaptation for ecologically relevant traits depends on the strength of natural selection, the amount of initial genetic variation on which natural selection can act, the speed at which local populations can respond to selection, and the rate of the homogenizing effect of gene flow (Endler, 1986; Hartl & Clark, 1989; De Meester, 1996b).
Predator-induced defences
Daphnia exhibits phenotypic plasticity for a wide array of predator-induced defence mechanisms. Differences in life-history characteristics, morphology and behaviour can have important consequences for the vulnerability of zooplankton to visual predators (see Tollrian & Harvell, 1999). In the presence of predator-specific kairomones, a phenotypic shift to trait values less vulnerable for predation by the specific predator is observed (see overviews in Larsson & Dodson, 1993 and Tollrian & Harvell, 1999). In addition, several studies have shown the existence of genetic polymorphism for phenotypic plasticity responses to predator- specific kairomones in relation to life-history characteristics (Weider & Pijanowska, 1993; Reede & Ringelberg, 1995) as well as phototactic behaviour in Daphnia (De Meester, 1993, 1996a). However, not all inducible responses that have been reported in Daphnia are activated in all genotypes. Inducible defence mechanisms seem indeed to be uncoupled (De Meester & Pijanowska, 1997; Boersma et al., 1998). More specifically, Boersma et al. (1998) showed that the receptor-effector pathway in Daphnia is genotype-dependent. Hence, a given chemical signal could result in different response combinations in different Daphnia genotypes. Yet, co-adaptation between life-history characteristics and behavioural traits has been observed in Daphnia (De Meester, 1994; Reede & Ringelberg, 1995; De Meester et al.,
7 Chapter I
1995). De Meester et al. (1995) showed that different response combinations can result in identical fitness values which may offer an explanation for the extensive genetic polymorphism for inducible defence responses that seems to be maintained in natural Daphnia populations.
Metapopulation biology
The metapopulation concept already exists for more than 30 years in ecological literature, but the problem that really reawakened the metapopulation idea was the incidence of rapid fragmentation of once continuous habitats by the spread of human activities. Ensuing, metapopulation theory has played an important role in population genetics and conservation biology (Shafer, 1990; Harrison, 1994; McCullough, 1996; Harrison & Bruna, 1999). The idea that colonisation and extinctions can determine local diversity through dispersal was first incorporated in ecology through the theory of island biogeography (McArthur & Wilson, 1967). McArthur & Wilson’s (1967) island theory stated that the number of species in insular habitats is set by an equilibrium between distance-dependent colonisation and area dependent exinction. Moreover, the island theory predicts that the smaller and more isolated a habitat is, the fewer species it will support. McArthur & Wilson’s island theory changed ecological thinking by identifying the spatial configuration of habitats as an important influence on populations and communities. Metapopulation theory resembles island biogeography in focussing on patchy habitats, extinctions and colonisations, but differs in assuming a network of small patches with no persistent mainland habitat, and focussing on the dynamics of only one species instead of that of communities. The classic single-species metapopulation model, hereafter referred to as the “classic metapopulation model”, was originally introduced by Levins (1969) in a publication on the demographic and genetic consequences of environmental heterogeneity for biological control (see Box 1). Levins (1969) presented a model of a population of populations in which local extinctions are balanced by immigration from other populations. As long as the rate of recolonisation exceeds the rate of extinction, the metapopulation can persist even though no given subpopulation in a patch may survive continuously over time. Levins (1969, 1970) stated that colonisation and extinction of the local populations in the metapopulation are as births and deaths of individuals in a local population. A fundamental assumption of the classic metapopulation model is that space is discrete and that there is a clear distinction between habitat patches that are suitable for the focal species and the rest of the environment often
8 Introduction and outline called the matrix. In this context, a patch can thus be described as a discrete area that possesses all the resources necessary to maintain a local population for some time. Further assumptions of the classic metapopulation model are that each subpopulation has an equal contribution to the population size and that the probability of dispersal between sites is approximately equal, irrespective of the distance among patches. Each patch is equally prone to extinction and persistence occurs only at a regional scale. The concept of an ideal metapopulation according to Levins includes three other simplifying assumptions: (1) habitat patches have equal areas and isolation, (2) local populations in the metapopulation have entirely independent (uncorrelated) dynamics, and (3) the exchange rate of individuals is so low that migration of individuals has no real effect on the local dynamics in the existing populations: local dynamics occur on a fast time scale in comparison with metapopulation dynamics. The simplest models of metapopulation dynamics make predictions about the effects of habitat fragmentation, habitat size and isolation on the persistence of metapopulations (Hanski, 1998). However, as pointed out by Harrison (1991, 1994), Hastings & Harrison (1994) and others, the simplified assumptions of these first simple metapopulation models needed some rethinking as the classic extinction-centered definition was too simplistic and did not apply very well to the complexity of most populations in nature. Little by little, new models have incorporated many additional aspects of population and community dynamics (Gilpin & Hanski, 1991). Since the beginning of the nineties, a less strict operational definition of a metapopulation has been used (see Hanksi & Simberloff, 1997). Contrary to the classic definition of a metapopulation, the two key premises of the new metapopulation concept are that (1) populations are structured in assemblages of local breeding populations and (2) migration among local populations has some effect on local dynamics, including the reestablishment following extinction (Hanski & Simberloff, 1997). These premises contrast with those of standard models of demography, population growth, population genetics and community interactions that all assume a panmictic population structure with all individuals equally likely to interact with any others (Hanski & Simberloff, 1997). Metapopulation studies therefore have shed a new light on such phenomena as patterns of distribution and population turnover dynamics in fragmented landscapes (Hanski, 1997; Harrison & Taylor, 1997; Thomas & Hanski, 1997), landscape ecology (Wiens, 1996, 1997) and community structure (Holt, 1997). Perhaps the most important aspect of this “new approach to metapopulations” is that the extinction–colonisation dynamics are not essential anymore, whereas the interaction among locally breeding populations has become the key feature.
9 Chapter I
Harrison & Hastings (1996) and Harrison & Taylor (1997) proposed a number of alternative metapopulation models based on the nature and direction of the interactions among local populations within the metapopulation (see Box 2). (a) Local populations may be unequal in size or longevity, creating mainland-island metapopulation dynamics. (b) Local populations may be so strongly interconnected by dispersal that local extinctions are very unlikely to occur and there is a single patchy metapopulation. (c) In contrast, populations may be so weakly connected by dispersal that that local extinction is not balanced by recolonisation, and the non-equilibrium metapopulation is on its way to total extinction. (e) Finally, in the intermediate case, all previous types of metapopulations are combined.
Holyoak & Ray (1999), however, argue that classifying metapopulations according to approximate types entails the risk of loosing subtle structural differences that could have dramatic dynamic consequences. Moreover, the imposition of different “types” of metapopulations (see Harrison & Hastings, 1996; Harrison & Taylor, 1997) may discourage investigation of several mechanisms that may affect dynamics in all systems irrespective this categorisation. In order to facilitate the discovery of general behaviour in realistic metapopulations, Holyoak & Ray (1999) suggest a simple framework to guide research. They propose a n-dimensional hypervolume for metapopulations whose axes describe aspects of metapopulation structure (e.g. population connectivity; patch size diversity; variation in patch quality, and environmental correlation). The position of a metapopulation within the space defined by theses structural axes determines its potential to exhibit certain dynamics.
Genetic consequences of a metapopulation structure
It is clear that, besides important population-dynamic consequences, the exchange of individuals among populations within a metapopulation may also affect the distribution of genetic variation among local populations, as genetic variants that go extinct in local habitat patches may be reintroduced by dispersal from neighbouring patches (Hanski & Gilpin, 1997). Extinction and recolonisation dynamics in a metapopulation can either increase or decrease genetic differentiation among local populations and may lead to a loss of genetic variation stored in a regional set of populations (McCauley, 1991). Slatkin (1977) provided the first theoretical study of the effects of recurrent extinction and recolonisation on genetic diversity. Slatkins model started from the following assumptions: (1) extinct populations in a metapopulation are replaced by new populations, which are founded by k individuals; (2)
10 Introduction and outline population size of recolonised patches grow immediatly to size N; (3) surviving populations of N individuals exchange migrants at migration rate m. Slatkin (1977) discriminated between two contrasting types of founding groups when extinct patches are colonised: (1) the “propagule pool” model, in which all (k) colonists originate from a single patch in the metapopulation, and (2) the “migrant pool” model, in which (k) colonists are drawn randomly from the whole metapopulation. The two models of group formation have very different outcome for the population genetic structure of the metapopulation. In the “propagule pool” models, provided that k
11 Chapter I other hand provides the ideal conditions for population divergence, provided that populations do not become extinct too fast. In neither case, will extinction and recolonisation be a major force shaping patterns of genetic variation. Changing ecological circumstance, leading to changing rates of migration, however, may offer the most interesting opportunities for metapopulation processes to shaping patterns of genetic variation (Harisson & Hastings, 1996).
BOX 1 : The classic metapopulation model
An extract of the original publication by Levins (1969)
The purpose of this report is to show that the pattern of environmental variation in space and time can be utilised in the control of pests and to indicate the information which is needed for the selection of the most promising predator… Let N be the number of local populations at a given time; let T be the total number of sites that can support local populations; M is the migration rate (probability that migrants from any given population reach another site) and E is the extinction probability for a local population. Then the new populations are being established at a rate which depends on the migration rate times the probability that that the site is reached is vacant, or
mN(1- N/T) and populations are being eliminated at the rate EN. Thus the change of N with time is given by
dN/dt = mN(1 – N/T) – EN (1)
N will reach an equilibrium when the right side of the equation is 0. This gives the equilibrium level of
N = T(1- E/m) (2)
Equilibrium occurs at N for which the Migration and extinction rates are equal. When N is large, a given change in E (from E1 to E2) produces a small change in N. But near the flat part of the migration curve the same change from E3 to E4 has a greater effect. If extinction rate exceeds migration as in E5, the population disappears.
12 Introduction and outline
BOX 2: Different types of metapopulations According to Harrison & Taylor (1997)
Different types of metapopulations. Filled circles: occupied habitat patches; empty circles: vacant habitat patches; dotted lines: boundaries of local populations; arrows: dispersal. (a) classic metapopulation model; (b) mainland-island model; (c) patchy population; (d) non-equilibrium metapopulation; (e) intermediate case combining features of (a), (b), (c) and (d).
Metapopulation dynamics and landscape ecology
Landscape ecology has provided new ways to explore aspects of spatial heterogeneity and to discover how spatial pattern controls ecological processes. Landscape ecology is a relatively recent discipline, dealing with the effects of spatial pattern on ecological processes and the way fluxes (e.g. fluxes of organisms) are controlled within heterogeneous matrices (Pickett & Cadenaso, 1995). It is a broadly interdisciplinary field, bringing together workers from a wide array of areas (e.g. ecologists, landscape architects, land-use planners, geographers,
13 Chapter I conservation biologists, spatial statisticians, and ecosystem modellers) (Foreman & Godron, 1986; Wiens, 1997, 1999). Many theoretical models dealing with spatial heterogeneity are limited to simple, spatial implicit models in which the locations of the patches are not specified. Moreover, spatial pattering is often reduced into patches and an ecologically neutral matrix (Kareiva, 1990; Wiens, 1997). As such, patches are assumed to be internally homogeneous and all dynamics are thought to occur within patches. Until a decade ago, classic metapopulation theory was elaborating on this patch-matrix approach in which the environmental matrix was viewed as an ecologically neutral matrix inhibiting interactions (e.g. colonisation, dispersal, gene flow) among populations that inhabit habitat patches. Dispersal among habitats is a key feature of metapopulations. Dispersal among patches is, however, not a simple function of the organism itself, but also depends on the landscape through which it migrates. To emphasise the interaction between species characteristics and landscape structure in determining movement of organisms among habitat patches, Merriam (1984) introduced the concept of landscape connectivity. Landscape ecologists use the notion of landscape connectivity to quantify the extent of relatedness among similar habitats for a specific organism. As such, connectivity among habitats reflects the hindrance to dispersal of individuals of a given species caused by habitat fragmentation (Merriam, 1984; Schippers et al., 1996; Schumacher, 1996; With et al., 1997; Tischendorf & Fahrig, 2000). Landscape connectivity refers to the degree to which the landscape facilitates or impedes movement among patches (Hansson, 1991). In metapopulations, connectivity is not only a property of the landscape but also an attribute of each patch, indicating how accessible the patch is for individuals from other patches. Connectivity is not only dependent on Euclidean distance among habitat patches. Besides internal habitat characteristics, other important aspects are the presence of connecting elements among habitat patches (corridors), corridor quality and the properties of environmental matrix in which habitat patches are embedded (Hanski, 1998). Wiens (1996, 1997) and Hanski (1998) made a plea for a landscape-based view on metapopulations, incorporating various aspects of landscape ecology in (spatially explicit) metapopulation models. The term “spatially explicit” refers to the fact that models keep track of the exact postion of patches (Kareiva & Wennergren, 1995). Important information that should be integrated in metapopulation models includes variation in patch quality, variation in the quality of the surrounding environment, boundary effects and the influence of interjacent landscapes on patch connectivity and dispersal success among isolated habitat patches (see Box 3). Integration of the ideas of landscape ecology in metapopulation models offers the
14 Introduction and outline opportunity to go beyond the simple patch-matrix approach and adopt a more realistic spatially textured view of heterogeneity. In landscape ecology, the environmental matrix itself is spatially structured, and the spatial relations among patches play an active role in determining dynamics within patches of interest. Patches are viewed as components in a landscape mosaic, and events happening within patches in a landscape may be contingent on the composition and dynamics of other elements of the landscape mosaic (Wiens, 1995). Kareiva & Wennergren (1995) reviewed literature on spatially explicit ecological models in search of general principles, and concluded that the insights generated by spatially explicit models can be used to solve practical problems in, for instance, conservation management. It is, however, important to realise that maps integrating information on fragmentation and habitat structure alone do not lend much insight without hard data on how species disperse and interact with other species. A decade ago, Hanski & Gilpin (1991) predicted that the fusion between metapopulation studies and landscape ecology should make an interesting scientific synthesis. Today, the ideas of landscape ecology are getting generally recognised across metapopulation biology, population genetics and community ecology (see Hanski, 1998; Hanski & Ovaskainen, 2000; Vos et al., 2001).
15 Chapter I
BOX 3: The relevance of Landscape ecology to metapopulation dynamics
A: Metapopulations in theory. The solid patches are occupied and are linked by intermittent migration, wheareas the hatched patch is suitable habitat that is presently unoccupied. The background matrix has no effect on interpatch movements, although the distance between patches and their arrangement may. B: Metapopulation in reality. The patches are the same, but the “matrix” is a landscape mosaic of various patches and corridors. Movement pathways among suitable patches, and the probability that migrating individuals will reach the patches, are affected by the explicit spatial configuration of the landscape (from Wiens, 1997).
16 Introduction and outline
An introduction to our model system and model organisms
This study focuses on landscape ecological aspects of the pond complex of De Maten and the population genetics of two Cladocera model species: Daphnia ambigua and Polyphemus pediculus inhabiting this pond complex.
The study area: De Maten
In this section, we will give a brief overview of the history of the study site and provide additional information on the (landscape) ecological characterisation. The pond complex in De Maten provides an ideal setting to test the role of landscape structure in the process of genetic differentiation among zooplankton populations on a small geographic scale. It contains 34 ponds of different size that are situated closely to each other. Moreover, since the ponds are directly connected to each other via a system of rivulets and overflows, dispersal pathways among them are well defined. This enables direct quantification of dispersal rates. Finally, even though the ponds are strongly connected, they differ widely in their ecological characteristics. The pond complex is part of the nature reserve De Maten (50°57’ N, 5°27’ E) situated on the territory of Genk and Diepenbeek (province of Limburg, Belgium; see Fig 2). The area is classified as protected nature reserve since 1976 and nowadays covers a surface of nearly 310 ha, from which 217 ha are managed by Natuurpunt (formerly Natuurreservaten vzw). De Maten is part of the larger pond district of Midden Limburg that ranges from Diepenbeek over Genk, and Hasselt to Lummen. With respect of geology, the area is situated on the Diepenbeek Pediment, a slope in the south-west end of the Campine tableland on the partitioning of the high and low Campine. The tableland itself consists of a two meter sand shield on top of a gravel layer. The geological origin of the Campine tableland dates back from 350.000 years ago, when gravel was deposited on tertiary sand and silt layers. Two hundred thousand years ago, parts of the gravel and sand were transported from the tableland into the valley between the Campine tableland and the Demer river. The long sand dunes in De Maten in between the patches of wet moorland were created through wind action. The pond complex is a typical example of an ancient chain of fishing ponds. The ponds were created around the Middle Ages through peat digging and the construction of dykes. Fish farming continued until 1990 (Daniels, 1998; W. Peumans, pers. comm). The landscape in De Maten is composed of various habitat types that surround the network of shallow ponds boarded with reed banks (see Fig 3) and maintains a high species diversity. The
17 Chapter I landscape in De Maten is composed of several patches of dry sand dunes partially covered with Calluna vegetation, the wet parts of the area are covered with Erica, Molinia and hayfields, small wood lots and moorland (Dewyspelaere, 1994; Daniels, 1998). In order to preserve this typical landscape for future generations, the area is continuously managed (e.g. grazing by Galloway cattle; cutting of sods, cutting of woodlots, drainage of individual ponds and repair of dykes; see Fig 4). The are is protected as Important Bird Area (I.B.A.). The pond complex is 3 km long and 1.5 km wide. The pond complex is approximately linear in structure with one main branch and two smaller ones (Fig. 5). The upstream pond in the Northeast corner of the nature reserve (pond 32) is situated at 55 m above sea level, whereas the downstream ponds in the Southern end of the area are situated at approximately 40 m above sea level. This results in an altitudinal difference between upstream and downstream ponds of 15 m, creating a unidirectional flux through the pond complex. The main sources of water are two rivulets, which provide almost half of the water in the system. The main rivulet, the Stiemerbeek, feeds pond 32 and all downstream ponds. The second rivulet, the Heiweyerbeek, feeds a subset of ponds located in the Northwest corner of the area and flows directly into pond 18. The ponds are also fed by ground water. At the end of the pond complex, the water is diverted back into the Stiemerbeek which flows into the Demer river. Ponds are connected to each other via two types of connecting elements. Permanent rivulets, drawn from the Stiemerbeek are ensuring the daily waterflow through the system. In addition, ponds may also be connected by direct overflows.
Genk
Figure 2: Situation of the study area: the nature reserve De Maten (Genk, Province of Limburg, Belgium). A detailed map of the study area is presented in Fig 5.
18 Introduction and outline
Figure 3: Picture showing ponds in De Maten in spring. Pond 13 (left) and pond 20 (right).
Figure 4: An example of nature management in nature reserve De Maten: cutting of sods (left) creating suitable habitat for Drosera (right).
19 Chapter I
An important aspect of this study was to test the impact of landscape structure (facilitating or impeding dispersal among habitats) versus ecological differences among habitats (creating different selective environments) on the genetic differentiation among cladoceran populations in the pond complex in De Maten. In this respect, accurate morphomertical and ecological data of the model system were indispensable. In 1996, at the start of this study, we composed a detailed map of the pond complex using the topographic maps of the National Geographic Institute (NGI scale 1/10000) supplemented with field observations on the localisation of connecting elements among ponds. A morphometrical characteristisation of different ponds (surface, perimeter, distance among ponds), was carried out using GIS software (IDRISI, Version 2.0; Eastman, 1997) and the digitised map. The results of this analysis are presented in Table 1. Ponds in the system vary widely in habitat size, ranging from 0.07 ha (pond 34) to 9.52 ha (pond 18). Morphometric data supplemented with results of field measurements of flow rate in connecting elements were used in Chapter II (also see Michels et al., 2001a) to estimate retention time in different ponds and to calculate the impact of passive zooplankton dispersal on standing stocks in source and target populations. The morphometrical characterisation of the pond complex and field data on flow rates were used to calculate the effective geographic distance among ponds in a GIS environment (see Chapter III; see also Michels et al., 2001b).
As no detailed information on the morphometrical and ecological characteristics of the pond complex were available at the start of this study in 1996, an important amount of work has been invested in the ecological characterisation of the system. This ecological characterisation involved a substantial amount of fieldwork and was done in collaboration with Karl Cottenie and with the help of many volunteers. From 1996 onwards, the ecological characteristics (biotic and abiotic) in all ponds were monitored yearly in the summer season. Additional measurements were done throughout the year in specific ponds in the framework of ongoing research. All ponds were monitored for water transparency (quantified as Secchi depth), pH, conductivity, concentration of total phosphorous, nitrate concentration, chlorophyll-a concentration and water temperature.
The study of genetic variation for ecologically relevant traits may reflect adaptation to local ecological conditions. Since fish predation is an important selective force in natural zooplankton populations (Gliwicz & Pijanowska, 1986; Kerfoot & Sih, 1997, we deemed it necessary to estimate fish predation pressure in the ponds in De Maten. Therefore, we monitored local fish predation pressure in all ponds using fyke nets at the end of September-
20 Introduction and outline beginning of October, during the four consecutive years of this study. During this survey, all 34 ponds in De Maten were sampled twice for two to three days. All fish were identified up to species level, counted per species and weight and the standard length was measured of a subsample of 40 specimens. Although fykes, no doubt yield a biased view, this methods did allow a rough estimate of local predation pressure by planktivorous fish in terms of Catch per Unit Effort (CUE; g/fyke per day) and gave insight in the local fish community structure. From 1998 onwards, fish community in local ponds was also surveyed by various methods, including electrical fishing by Steven Declerck and coworkers. The fish community in De Maten is dominated by brown bullhead (Ameiurus nebulosus) and pumpkinseed sunfish (Lepomis gibbosus), two invasive species that are typical for ponds in the area of Midden Limburg. In Table 2, a list of most common fish species that have been observed in De Maten is presented. Based on mark-recapture experiments in nine ponds in the centre of the pond complex, the average fish biomass was estimated to be around 150 kg/ha (Declerck, et al., unpubl. data).
Figure 5 : Topographic map of the study area the nature reserve De Maten, Genk ; Belgium. (National Geographical Institute 26/(5-6))
21 Chapter I
Figure 6, taken from Cottenie et al. (2001), shows the results of a Redundancy Analysis of the ecological variables measured in 1996 and ponds in De Maten, to illustrate ecological differences among ponds. The clustering was based on zooplankton data collected in 1996. The ponds in Figure 6 are grouped in clearwater (group 1: ponds 2, 13, 14, 15, 18, 21, 33 and 34) intermediate (group 2: ponds 16, 19, 24, 25, 26, 27, 28, 30, 31 and 32) and turbid water ponds (group 3: 3, 7, 8, 9, 10, 11, 12, 17, 22, 23 and 29), inspired by the theory of two alternative stable equilibria in shallow lakes (Scheffer et al., 1993; Scheffer, 1998). With this figure Cottenie et al., (2001) wanted to test whether the environmental factors related with zooplankton community structure. As can be seen on this graph, clearwater ponds are characterised by high Secchi depths and low fish densities, whereas turbid ponds are characterised by low Secchi depths, high fish densities and high chlorophyll a concentrations.
Thanks to parallel research by Karl Cottenie and Wouter Rommens, detailed information is available on the zooplankton and macro-invertebrate community structure as well as on the development, structure and species composition of the macrophyte vegetation in different ponds in De Maten. During the summer months, Wouter Rommens, recorded the density of the submersed macrophytes as well as the development of littoral zone. This resulted in a fairly complete dataset on connectivity, morphometric characteristcs, ecological characteristics, development of vegetation and community structure at three trophic levels (zooplankton, macro-invertebrates and fish).
Table 1: Habitat specific characteristics of ponds in De Maten: pond number, surface (ha) and perimeter (m). Perimeter and surface were calculated using GIS software (IDRISI; Version 2.0, Eastman, 1997) and digitised topographic maps (scale 1/10000) of the area. The situation of ponds is shown in Fig. 5.
Pond Surface (ha) Perimeter (m) Pond Surface (ha) Perimeter (m) 1 0.27 436.59 18 9.52 1521.53 2 1.34 614.15 19 5.18 1192.45 3 7.43 1370.70 20 1.44 944.16 4 0.41 405.27 21 0.33 481.45 5 0.48 374.25 22 0.51 387.33 6 1.06 504.60 23 1.33 607.18 7 0.29 339.32 24 1.25 553.95 8 2.63 922.61 25 0.70 416.96 9 0.63 402.30 26 0.25 281.43 10 1.58 594.70 27 1.42 695.87 11 0.49 356.14 28 1.91 920.80 12 3.04 940.97 29 1.69 729.79 13 2.91 1084.88 30 1.26 660.61 14 0.55 444.82 31 1.81 709.12 15 1.41 778.13 32 2.83 1405.53 16 0.40 318.72 33 0.25 249.48 17 0.43 441.34 34 0.07 144.69
22 Introduction and outline
+1.0 13
29
divMI FM 30 16 22 14
depth SM
EM 15 denMI 12 18 19 O2 pH 24 2 PF/BF denF secchi 21 11 25 33 TP area Fe 7 3 6 8 34 28 23 chla 27 cond N 9 17 26
10
31 32 -1.0 -1.0 +1.0
Figure 6: Results from redundancy analysis. Pattern of environmental (independent) variables measured in 1996 and ponds. The position of the pond is indicated by the number of the pond. Abbreviations: O2=oxygen concentration, cond=conductivity, Fe=Fe concentration, secchi=secchi-disk depth, N=N-NO3 concentration, TP=total phosphorous concentration, Chla=chlorophyll-a concentration, EM=emergent macrophyt cover, FM=floating macrophyte cover, SM=submersed macrophyte cover, denMI=total density of macroinvertebrates, PF/BF=ratio planktivorous versus benthivorous fish densities. ●=group 1 (clearwater); ■=group 2 (intermediate ponds); x=group 3 (turbid ponds) (Taken from Cottenie et al., 2001)
23 Chapter I
Table 2: List of the most common fish species observed in De Maten from 1996 until 2000.
Family Scientific name English name Dutch name
Anguillidae Anguilla anguilla Eel Paling Centrarchidae Lepomis gibbosus Pumpkinseed sunfish Zonnebaars Cyprinidae Abramis brama Bream Brasem Blicca bjoerkna White bream Kolblei Carassius gibelio Prussian carp Giebel Cyprinus carpio Common carp Karper Gobio gobio Gudgeon Riviergrondel Leucaspius delineatus Belica Vetje Leuciscus leuciscus Dace Serpeling Pseudorasbora parva Topmouth gudgeon Blauwbandgrondel Rutilus rutilus Roach Blankvoorn Scardinius erythrophthalmus Rudd Rietvoorn Tinca tinca Tench Zeelt Esocidae Esox lucius Pike Snoek Ictaluridae Ameiurus nebulosus Brown bullhead Bruine Amerikaanse dwergmeerval Gasterosteidae Gasterosteus aculeatus Three-spine stickleback Driedoornige stekelbaars Pungitius pungitius Nine-spine stickleback Tiendoornige stekelbaars Percidae Gymnocephalus cernuus Ruffe Pos Perca fluviatilis Perch Baars Sander lucioperca Pikeperch Snoekbaars Umbridae Umbra pygmaea Eastern mudminnow Amerikaanse hondsvis
Model organisms
D. ambigua Daphnia ambigua (Scourfield, 1947) is a small Daphnia species with a maximum body size of 1.3 mm (Fig. 7). It is the most common Daphnia species at our study site. D. ambigua is an invasive species in Europe and it is generally accepted that it has been imported from the American continent (Dumont, 1974; Flössner & Kraus, 1976; Zofková, 2000). In its native region, D. ambigua is common in lakes and ponds in the Southern part of North America, and it range extends through Central and South America (Brooks, 1957). The species was first recorded on the European continent in Kew Botanical Gardens and expanded its range on the European continent (Flössner & Kraus, 1976; Zofková, 2000. It has a relatively small head, the carapax valves are almost circular in lateral view, and the tail spine is relatively short (Fig. 7). In Belgian D. ambigua, the number of teeth on the ventral rim of the postabdomen varies from 7-11 (Dumont, 1974). The postabdominal claw has small pecten. Adults are often
24 Introduction and outline characterised by a distinctive spine-like helmet, induced in the presence of Chaoborus chemicals (Hebert & Grewe, 1985; Hanazato, 1990). In habitats without Chaoborus, the animals lack this helmet and may be confused with D. parvula. Both species can be distinguished by the spinulation of the postabdominal claw. In D. ambigua the pecten on the postabdominal claw are relatively small. In D. parvula the middle pecten is relatively solid and are about twice as long as the distal one (Brooks, 1957). During the present study, no individuals with helmets were observed, even though Chaoborus is present in some of the ponds in De Maten (EM, pers. obs.).
Polyphemus pediculus
Polyphemus pediculus (Müller, 1785), is another cyclical parthenogenetic cladoceran that occurs in De Maten. P. pediculus (Onychopoda, Polyphemidae) is a relatively small zooplankton species (adult females range from 0.5 - 1.5 mm; Green 1966) that is the only representative of the family of the Polyphemidae in freshwater habitats (Fig. 8). The species occurs in large lakes and small ponds throughout the Northern temperate zone. Unlike Daphnia, Polyphemus is a visually hunting carnivorous cladoceran that feeds on microzooplankton (e.g. rotifers and small cladocerans). Cannibalism has also been reported (Packard, 2001). In contrast to most Daphnia, P. pediculus is not a pelagial species but typically occurs among submersed macrophytes in the littoral zone (Hutchinson, 1967).
25 Chapter I
Outline of this thesis
In the present study, we use zooplankton in a set of interconnected ponds in the nature reserve De Maten (Genk, Belgium) as a model system to test the role of spatial structure (facilitating or impeding dispersal among habitats) versus ecological differences among habitats (creating different selective environments) on the genetic differentiation among natural zooplankton populations. Due to the presence of direct connections among ponds in De Maten, we expect strong interactions among the zooplankton populations inhabiting these ponds, which may result in very low levels of genetic differentiation for (quasi-)neutral markers among them. Although most of the ponds share the same water supply, there are profound ecological differences among ponds with respect to water transparency, development of submerged macrophytes and fish predation pressure. This situation, on the other hand, may lead to different selection pressure in different environments, which may promote genetic differentiation for ecologically relevant traits. We aim at an integrated view on genetic variation based on (quasi-)neutral markers (allozymes) as well as on ecologically relevant traits (body size characteristics and phototactic behaviour). In addition, we determine to which extent connected zooplankton populations in De Maten correspond with the concept of a metapopulation, in which local populations are separate entities linked by dispersal among them, or should be concerned as one single large population. Besides the role of other habitat specific factors, we want to test the hypothesis that larger habitats are charcterised by a higher genetic diversity than smaller ones. We focus on natural populations of two cladoceran models species, Daphnia ambigua (Scourfield, 1947) and Polyphemus pediculus (Müller, 1785) that differ in biology and abundance in our modelsystem, in order to determine the impact of species specific characteristics in the process of genetic differentiation among zooplankton populations. Interaction among spatially structured populations is a key premise of a metapopulation and is an important factor driving the process of genetic differentiation among populations. Direct quantifications of dispersal rates among natural populations are notoriously difficult to obtain and few studies have endevoured such quantifications to date. Cyclical parthenogenetic zooplankton inhabiting systems of interconnected ponds, however, create the unique opportunity to quantify dispersal rates of the active zooplankton component directly in the field. Chapter II reports on a field study in which the interaction among zooplankton populations in the study system was assessed. We measured water flow rates and quantified
26 Introduction and outline zooplankton dispersal rates via connecting elements among ponds. Zooplankton dispersal rates among populations were found to be substantial in most zooplankton groups. It is evident that in systems with well defined pathways for dispersal, Euclidean distance among habitats is not an appropriate measure of connectivity to assess the interaction among populations. In Chapter III we used a landscape based approach to model the effective geographic distance among interconnected ponds in a GIS environment. We used the existing network of connecting elements among ponds as a source field to which dispersal was restricted and incorporated field data on flow rates and zooplankton dispersal rates in the force field driving the passive dispersal of zooplankton via the network of overflows among ponds. We tested the effectiveness of our modelling approach in estimating true connectivity among habitats by validating the models using data on the genetic differentiation for (quasi-)neutral markers (allozymes) among D. ambigua populations in a subset of ponds in De Maten. As successful dispersal is reflected by gene flow, the pattern of genetic differentiation among populations should reflect effective geographical distance. Our GIS based model provided a better approximation of the true rates of genetic exchange among populations than mere Euclidean geographic distances. In Chapter IV, we investigated to what extent D. ambigua populations in De Maten are differentiated genetically from each other for neutral markers despite the high observed dispersal rates. Moreover, we wanted to know whether D. ambigua populations in different ponds in De Maten should be considered as a single panmictic population or a genetically structured metapopulation. We used the effective geographic distance as well as ecological distance among ponds to determine the relative importance of both factors in explaining the microgeographic genetic structure of D. ambigua populations in De Maten. Finally, we tested the hypothesis that larger habitats are characterised by higher genetic diversity and whether the pattern of genetic differentiation develops during the course of a growing season. In Chapter V, we investigated in laboratory experiments whether genetic differentiation among D. ambigua populations in De Maten linked by high dispersal rates can also be detected for ecologically relevant traits. Therefore, we quantified phototactic behaviour and a set of key life history traits in the presence and absence of fish chemicals for D. ambigua clones that were isolated from ponds that differed strongly in ecological characteristics (water transparency and fish predation). First, we wanted to find out whether the presence of fish chemical induces changes in the selected traits in D. ambigua. As the pattern of genetic differentiation for ecologically relevant traits may reflect adaptation to local ecological conditions, we investigated whether the observed genetic differences among
27 Chapter I populations are concordant with the hypothesis of local adaptation. Phototactic behaviour and body size characteristics have been shown to be valuable candidates to detect local adaptation in natural Daphnia populations (Boesma et al., 1998). In the final Chapter VI, we studied the pattern of genetic differentiation for neutral markers in Polyphemus pediculus, a cladoceran model species inhabiting the pond complex of De Maten, and compared the obtained pattern with that of D. ambigua. The dispersal of P. pediculus among populations is expected to be much lower than in D. ambigua because Polyphemus is a relatively strong swimmer and occurs in aggregates associated with littoral vegetation. Moreover, P. pediculus is a less generalist species, so it is expected that establishment success of dispersing individuals will be very limited compared to a generalist species such as D. ambigua, resulting in a different pattern in genetic differentiation.
28 Introduction and outline
Figure 7: Daphnia ambigua adult female (left), detail of female postabdomen (top right) and female abdomen (bottom right). Foto’s taken from Cladocera website: http://www.cladocera.uoguelph.ca; by C. Rowe & P.D.N. Hebert, 2000.
Figure 8: Polyphemus pediculus adult female (left); Release of brood by adult P. pediculus female (right). Foto’s W. by Van Egmont taken from website Microscopy-UK and Micscape Magazine http://www.microscopy-uk.org.uk/mag/.
29 Chapter I
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37 Chapter I
38 Quantification of dispersal in zooplankton
Chapter II
Zooplankton on the move: first results on the quantification of dispersal of zooplankton in a set of interconnected ponds
With K. Cottenie, L. Neys & L. De Meester Hydrobiologia (2001) 442: 117-126
Abstract
In systems of interconnected ponds or lakes, the dispersal of zooplankton may be mediated by the active population component, with rivulets and overflows functioning as dispersal pathways and the dispersal being unidirectional. Such systems offer the possibility to study the impact of high dispersal rates on local population dynamics and community structure, and provide opportunities to quantify dispersal in the field in a straightforward manner. In this study, dispersal of active zooplankton populations among interconnected ponds was quantified directly in the field by sampling the small waterways connecting ponds. The number of dispersing zooplankton sampled in connecting elements was on average high (almost 4000 ind.h-1). However, the contribution of dispersing individuals to total population size in the target ponds was limited (<1% / 24 h.). Only a weak diel pattern in dispersal rates was observed.
39 Chapter II
Introduction
Dispersal of individuals among habitats affects not only the dynamics, persistence and genetic structure of local populations, but also may contribute to changes in community structure (Slatkin, 1985, 1987; Jenkins, 1995; Palmer et al., 1996; Thomas et al., 1998; Bohonak, 1999a, 1999b; Dieckmann et al., 1999; Lukaszewski et al., 1999). Movement pathways among populations and the probability that dispersing individuals will reach a suitable habitat are affected by the explicit spatial configuration, the connectivity of different habitat types and the surrounding landscape (Foreman, 1995; Wiens, 1997; Hanski, 1998). As in many organisms, direct estimates of dispersal rates in zooplankton are very difficult to obtain, and few studies have endeavoured such quantifications, (Jenkins & Underwood, 1998; Bohonak & Whiteman, 1999; Brendonck & Riddoch, 1999). To date, studies of dispersal in zooplankton have focused primarly on the chance effects of occasional resting egg dispersal and their presumed vectors (Talling 1951; Maguire, 1960; Proctor, 1964; Proctor & Malone, 1965; Frey, 1982; Hanski & Ranta, 1983; Pajunen, 1986; Havel & Hebert, 1993; Boileau & Taylor, 1994; Jenkins & Buikema, 1998; Jenkins & Underwood, 1998; Brendonck & Riddoch, 1999; Bohonak & Whiteman, 1999). Because of the island-like nature of ponds and lakes, dispersal of the active population is usually not considered. In systems with interconnected ponds or lakes, and rivers downstream of reservoirs, however, dispersal may also be mediated by water currents that move the active population component (Ward, 1975; Sandlund, 1982; Akopian et al., 1999). In such systems, the dispersal of zooplankton may be a largely unidirectional and ongoing process, with rivulets and overflows functioning as dispersal pathways. In cladocerans, parthenogenetic females can carry several embryos, and dispersal of the active population is potentially very important over relatively short distances. Although resting eggs are most important for long-range dispersal in cladocerans, dispersal of parthenogenetic females is likely to be quantitatively more important in a situation of interconnected neighbouring ponds. Jenkins & Underwood (1998) observed that actual zooplankton dispersal by wind, rain or duck feces, appears to happen infrequently in and only for a few species. In a situation of interconnected ponds, the number of individuals of different species entrained by the water flow will be influenced by the physical properties of the connecting elements, habitat preferences and behavioural characteristics of the dispersing species. Animals may, to some extent, avoid being trapped in the overflow current through
40 Quantification of dispersal in zooplankton negative rheotaxis (Stavn, 1970). As the intakes of overflows are situated at the surface and in the littoral zone, one may also expect a diel rhythm in the number of animals being transported (Jann & Bürgi, 1988). Many zooplankton species engage in a diel vertical migration where more individuals are found at the surface during the night than during the day. In addition, negatively rheotactic behaviour can be stronger in light than in darkness (Stavn, 1970), and daily cycles in swarming behaviour (Jacobsen & Johnsen, 1988) may also influence patterns of dispersal through overflows. In the present study, we quantitatively sampled connecting elements and target ponds in a set of 34 interconnected ponds during an intensive field survey to (1) quantify dispersal of active zooplankton populations via connecting elements, and (2) evaluate the importance of dispersal of zooplankton among water bodies in terms of its impact on population densities in target populations. In addition, we attempted to (3) investigate the influence of diel rhythms on the dispersal behaviour.
Materials and Methods
Study site
The study site is a set of 34 neighbouring and interconnected shallow ponds in the nature reserve De Maten (50° 57´ N, 5° 27´ E; Genk, Province of Limburg, Belgium, Figure 1). The set of ponds were created in the middle ages and were used for fish farming until 1990 (Daniels, 1998). Geographically, the ponds are situated very close to each other (the whole system of interconnected ponds covers less than 200 ha; Daniels, 1998) and are highly connected. However, they differ widely both in biotic (e.g. predation pressure, development of littoral zone) and abiotic (e.g. water transparency) characteristics (Cottenie et al., 2001). The ponds in the complex are in an approximately linear setting, with a 15 m drop between the most upstream and the most downstream ponds, provoking a unidirectional waterflow through the system. The main sources of water are two rivulets (I1 and I2), of which I2 primarily feeds a subset of ponds located in the N-W corner of the area (Fig. 1). In addition, the ponds are fed by groundwater. At the lower end of the nature reserve, the water coming from the ponds is again diverted into the Stiemerbeek, the main rivulet. The presence of well- defined overflows and rivulets between ponds offers an opportunity to study dispersal directly in the field in a very straightforward and quantitative way. We assume that virtually all zooplankton dispersal will occur through the connections (i.e. that dispersal of resting eggs
41 Chapter II over land is quantitatively negligible in comparison with dispersal of the active population component; see also Jenkins & Underwood, 1998).
Genk
I3 19
21 18 I 1 2 20 34 # 7 13 # # I 3 2 11 15 17 1 10 # 29 30 # 8 27 9 # 31 # # # 32 # 24 6 12 22 # # 28 4 5 14 16 33 23 25 26
O2 O1 0 05 1 Km
.
Figure 1: Geographic position of the study site, the nature reserve De Maten (50° 57´ N, 5° 27´ E; Genk, Province of Limburg, Belgium). Map of the pond complex showing pond numbers. Inflow at I1 (Pond 32), I2 (Stiemerbeek) and I3 (Pond 18). Outflow at O1 (pond 23) and O2 (pond 7). The overall direction of water flow is from Pond 32 to Ponds 3 and 5. The total altitudinal difference between the ponds is 15 m.
Quantification of dispersal rates
The dispersal of zooplankton through the connecting elements between ponds was quantified in August 1998. The overflows in De Maten are small (average width is approximately 60 cm) and therefore easily manageable. The sampling device consisted of a plankton net (mesh size 64 µm; ∅ 20 cm) and a collector (50 ml), mounted on a PVC tube (length 1m; ∅ 20 cm) and an artificial dam (board of 60x80 cm; Fig. 2). By placing the device in an overflow, the entire water current was forced through the plankton net. Each overflow was sampled once for one hour. All measurements of zooplankton dispersal rates (quantified as the number of individuals/taxon.h-1) were done between 9 h and 18 h to prevent possible interference of a diel rhythm. The flow in the connecting elements was quantified by measuring the velocity of the water flow and the morphometry (cross section) of each element. We sampled all connecting elements that were functional during August 1998 (N=31). Some of the connecting elements could not be sampled because they were shut at the moment of sampling.
42 Quantification of dispersal in zooplankton
Simultaneously with the quantification of dispersal, zooplankton was sampled in the pelagic zone of target ponds with a Schindler-Patalas trap (4 x 12 l = 48 l; mesh size 64 µm; concentrated in 60 ml). Total numbers of individuals per taxon were counted in a 3 ml subsample. We distinguished between major zooplankton groups (copepods and cladocerans), and within the cladocerans, we distinguished among the Chydoridae and the genera Daphnia, Ceriodaphnia and Bosmina. We also distinguished between nauplii, Cyclopoida and Calanoida. The abundances of Calanoida were, however, very low and are therefore not included in this analysis. The same laboratory protocol was used for the counting and identification of both the dispersal and target pond samples. The impact of dispersal on the total target population size of major zooplankton groups was estimated as the total inflow by dispersal in the target population in 24 h (dispersal data presented in Table 1) divided by the estimated total population size per species (density of major zooplankton groups presented in Table 2). In four connecting elements (16-33; 17-15; 15-13 and 15-12, see legend Table 1 and Fig. 1) the dispersal of zooplankton via overflows was quantified at 4 hour intervals during a 24 h cycle in order to evaluate differences in dispersal rates between day and night. Sampling started at 13.30 h, August 3th, 1998. Differences in dispersal behaviour between day and night were analysed by planned comparisons between day (13.30h, 17.30h and 8.30h.) and night (21.30h, 1.30h and 4.30h) dispersal rates (% of individuals of each taxon caught during each sampling period), after a repeated measures ANOVA. All statistical analysises were prefomed using the statistical computing package STATISTICA (Statsoft, 1994).
43 Chapter II
Figure 2: Sampling device for the quantification of dispersal of zooplankton in overflows. Artificial dam (60 x 80 cm); PVC tube (Ø 20 cm); plankton net (64 µm; Ø 20 cm) and collector (50 ml).
Results
Quantification of dispersal rates
Table 1 summarizes the dispersal rates (ind.h-1) for all taxa and connecting elements, whereas Figure 2 shows the pattern for the total zooplankton, Daphnia and for Ceriodaphnia. Table 2 shows the densities (ind./l) of the different zooplankton groups in the ponds. The number of animals sampled in the overflows varies widely, ranging from 15 to more than 3x104 ind.h-1. On average, the dispersal rates are high, with >3.6x103 ind.h-1 (i.e. an estimated >8.6x104 ind. per 24h). Figure 3 shows the results of the 24 h cycle on dispersal behaviour in different groups. For each taxonomic group, the dispersal data at 4 h intervals are shown. For Ceriodaphnia, a marginally significant difference in dispersal behaviour between day and night could be detected (df: 1; MS effect: 2364.8; MS error: 278.9; f: 8.49; p = 0.062) and p- values were also lower than 0.1 for cyclopoid copepods. Table 3 shows the total pond volume and an estimation of the impact of dispersal on target populations for all taxa. For total zooplankton, the highest contribution to the standing stock in the target population was
44 Quantification of dispersal in zooplankton observed in pond 22, with an estimated 0.63 % impact in 24 h. For most ponds, the impact of dispersal was less than 0.05%, indicating that it would take 100 days to replace 5 % of the resident zooplankton population if immigration was the only mechanism at work. On average, the highest impact of dispersal on the target population was found for Chydoridae (0.48 %) and the lowest impact for copepods (Nauplii: 0.05%; Cyclopoida: 0.22%). The variation within taxa, however, was considerable. The correlation between dispersal rates in outflows with the zooplankton density in source ponds was significant for Cyclopoida (R = 0.57; p = 0.011), Ceriodaphnia (R = 0.74; p = 0.035) and Chydoridae (R = 0.67; p = 0.0085), but not for Nauplii (R = 0.22; p = 0.49), Daphnia (R = 0.57; p = 0.14) and Bosmina (R= 0.34; p = 0.76).
45 Chapter II
Table 1: Dispersal rates (# ind.h-1) of major zooplankton groups and the total dispersal rate, via connecting elements in pond complex De Maten. The number of an overflow is reflecting the number of the source pond followed by the number of the target pond. Pond numbers are shown in Fig. 1. R 11 = rivulet coming from pond 11. 2-5a section before outflow from pond 3; and 2-5b section downstream outflow pond 3 (see Fig 1). I1 and I3 are inflows and O1 and O2 are outflows (see Fig. 1). (-) No organisms observed in sample.
Dispersal rates of zooplankton via connecting elements (# ind.h-1)
Copepoda Cladocera Connecting Nauplii Cyclopoida Daphnia Ceriodaphnia Bosmina Chydoridae Total Element (# ind.h-1) 60 60 - - - - 120 I 1-32 32-17 - - - - - 15 15 30-29 450 1950 75 - 15 660 3150 29-27 1515 825 - - - 390 2730 28-26 - 8925 675 - - 2535 12135 32-20 - 38 - - - - 38 27-24 315 480 15 - 15 720 1545 26-25 45 5400 390 - - 9585 15420 25-22 14900 15850 30 3775 600 3425 38580 24-22 - 75 - - - 135 210 22-23 4740 3435 - 510 - 1175 9860 17-16 - 30 - - - 30 60 16-33 - 152 - - 38 950 1140 17-15 15 - - - - 75 90 15-12 2075 1275 - 450 - 525 4325 15-13 4525 875 - 350 - 600 6350 13-11 - 15 - - - - 15 12-11 555 765 105 105 135 - 1665 11-10 - 30 - - - 360 390 10-7 1290 2130 165 195 1500 495 5775 9-7 225 870 - 45 15 45 1200 R 11-8 - 345 - 75 345 1140 1905 R 11-7 30 30 15 15 45 1665 1800 7-2 375 255 - - 30 645 1305 8-2 240 105 30 30 225 195 825 - 15 - - 30 690 735 7- O1 2-5a 45 75 - - 45 30 195 2-5b 120 15 - - 30 195 360 75 75 630 30 - 675 1485 6-O1 105 195 - - - - 300 I 3-18 20-18 - 45 15 60 270 315 705 Average 1056.67 1430.16 69.19 181.94 107.68 879.68 3691.23 S.D. 2880.50 3274.64 173.45 680.00 289.84 1789.44 7480.91
46 Quantification of dispersal in zooplankton
Total zooplankton
300 735 1305 5775 195 15 6350 15 390 38 825 90 705 2730 1140 60 # 19 1545 05 360 1485 210 3150 120 1800 4325 1200 1665 9860 12135 15420 0 0.5 1 Km
. 38580
Daphnia
30 15
#
75 630 165 15 105 30 675 390 0 0.5 1 Km .
Ceriodaphnia
75 30 350 60
#
30 510 3775 15 105 450 45 195 0 0.5 1Km
.
Figure 3: Dispersal of the active zooplankton population via connecting elements. Dispersal rates (# ind.h-1) of the total zooplankton (top), Daphnia (middle) and Ceriodaphnia (bottom). The complete data set is summarized in Table 1.
47 Chapter II
Table 2: Density of zooplankton (ind./l) of major zooplankton groups in source and target populations. Samples were taken simultaneously with the quantification of dispersal in connecting elements. Pond numbers are shown in Fig 1. (-) no organisms observed in sample.
Densities of zooplankton in ponds (# ind./l)
Copepoda Cladocera Pond Nauplii Cyclopoida Daphnia Ceriodaphnia Bosmina Chydoridae Total (# ind./l) 2 5.94 0.63 - - 0.31 - 6.88 3 139.69 12.81 - 1.88 5.94 6.88 167.2 5 53.44 1.25 - - 1.25 - 55.94 6 5.63 1.88 55 33.44 10.31 - 106.26 7 15.31 5.94 - - 628.13 7.5 656.88 8 30.31 5.63 0.31 0.31 0.31 - 36.87 9 6.88 5.63 - - 4.06 - 16.57 10 68.75 32.81 1.25 2.5 78.44 0.63 184.38 12 55.94 110.63 32.81 8.13 94.06 1.88 303.45 13 71.25 14.69 0.94 1.88 1.56 - 90.32 14 0.31 - - - - 0.31 0.62 15 0.63 0.63 - 1.56 0.31 0.63 3.76 18 - 200 40.31 0.31 0.31 - 240.93 20 - - 18.75 - 68.13 0.94 87.82 22 48.44 5 - 0.94 1.25 0.94 56.57 23 110.94 5.94 - - 9.38 3.13 129.39 24 33.13 12.19 - - - 0.94 46.26 25 53.44 68.75 0.63 48.44 3.75 27.81 202.82 26 60 32.5 0.63 0.94 16.25 18.44 128.76 27 53.44 9.69 - - - 1.88 65.01 28 - 100.94 26.25 - - 0.94 128.13 29 11.88 4.38 - - - 1.88 18.14 30 6.25 1.25 - 0.31 - 0.31 8.12 32 3.44 14.06 0.94 0.31 32.81 - 51.56 34 7.81 0.94 - - 10.94 12.19 31.88
48 Quantification of dispersal in zooplankton
Table 3: Impact of zooplankton dispersal on target populations. Pond volume and estimated % inflow on estimated population size of major zooplankton groups and total zooplankton (ZP) in target populations. Impact of dispersal was estimated as the total by dispersal inflow per major zooplankton group in target population in 24h (dispersal data presented in Table 1) divided by the estimated total population size per species (density of major zooplankton groups presented in Table 2). Pond numbers are shown in Fig. 1. (-) No organisms observed in sample.
Impact of zooplankton dispersal on target populations (% inflow) Copepoda Cladocera Total Pond Volume Nauplii Cyclopoida Daphnia Ceriodaphnia Bosmina Chydoridae ZP (m3) 2 5961.73 0.042 0.23 - - 0.33 - 0.13 4 835.52 0.0089 0.20 - - 0.17 - 0.029 7 824.36 0.30 1.49 - - 0.0072 0.86 0.039 8 6538.81 - 0.023 - 0.089 0.41 - 0.019 10 2888.79 - 0.00076 - - - 0.48 0.0018 12 9544.69 0.0093 0.0029 - 0.014 - 0.070 0.0036 13 11425.31 0.0133 0.013 - 0.039 - - 0.018 15 4564.10 0.0125 - - - - 0.063 0.013 18 14949.16 - 0.00019 0.000059 0.031 0.14 - 0.00067 20 4213.81 ------0.00025 22 1654.91 0.45 0.48 - 5.82 0.70 5.49 0.63 23 4287.73 0.024 0.32 - - - 0.21 0.043 24 4847.61 0.0047 0.019 - - - 0.38 0.017 25 1917.67 0.0011 0.098 0.78 - - 0.43 0.095 26 724.36 - 0.91 3.55 - - 0.46 0.31 27 5380.83 0.013 0.038 - - - 0.093 0.019 29 5720.50 0.016 0.19 - - - 0.15 0.073 32 4080.81 0.010 0.0025 - - - - 0.0014 Average 0.050 0.22 0.24 0.33 0.098 0.48 0.077 S.D. 0.12 0.39 0.85 1.37 0.19 1.27 0.16
49 Chapter II
50 70 Chydoridae Bosmina 60 40 p = 0.43 50 p = 0.83
30 40 % % 20 30 20 10 10 0 0
13.30 h 17.30 h 21.30 h 1.30 h 4.30 h 8.30 h. 13.30 h 17.30 h 21.30 h 1.30 h 4.30 h 8.30 h.
70 60 60 Ceriodaphnia 50 Daphnia 50 p = 0.062 40 p = 0.38 40 30 30 % % 20 20
10 10
0 0
13.30 h 17.30 h 21.30 h 1.30 h 4.30 h 8.30 h 13.30 h 17.30 h 21.30 h 1.30 h 4.30 h 8.30 h.
50 Cyclopoida 40 p = 0.086 30
% 20
10
0
13.30 h 17.30 h 21.30 h 1.30 h 4.30 h 8.30 h.
Figure 4: Diel rhythm in dispersal (% of the total number of individuals of each taxon caught during a given sampling period; average ± S.D. of four connecting elements). p-levels are the result of planned comparisons between day (13.30h, 17.30h and 8.30h) and night (21.30h, 1.30h and 4.30h) dispersal rates following a repeated measurement ANOVA. Vertical scaling was adjusted per graph.
Discussion
From a landscape point of view, the pond complex in De Maten is a system characterised by limited but well defined pathways for passive dispersal. This creates the possibility to quantify passive dispersal of the active zooplankton population among ponds directly in the field. Using a simple sampling device, we were able to quantify dispersal of the active component of different zooplankton groups via overflows. Although our study is limited in
50 Quantification of dispersal in zooplankton detail by the fact that it provides only a record of the momentary dispersal during the sampling period (August 1998), it is one of the first studies to quantify dispersal among a set of zooplankton populations directly, and provides some insight concerning the pattern and strength of interactions among populations. There is only a weak diel pattern in dispersal rates, which was (marginally) significant for Ceriodaphnia only. Yet, Fig. 4 suggests a tendency for slightly higher numbers of individuals during the night than during the day for all taxonomic groups. As the intake of the overflows lies at the surface in the littoral zone, any of the following processes may contribute to this tendency for higher numbers in the overflows at night. (1) Diel vertical migration, with the animals coming near the surface at night; (2) diel horizontal migration and reduced swarming at night, with the animals being less closely associated with macrophytes at night; (3) reduced negative rheotaxis in the dark (Stavn, 1970). The average number of dispersing zooplankton organisms was high (>1000 ind.h-1), but varied strongly among overflows. Part of this variation seems to be accounted for by variation in population densities in the source populations. Especially for Cyclopoida, Ceriodaphnia and Chydoridae, the correlation between the number of animals in the overflows and the population densities in the source pond was positive and highly significant. This indicates that population densities in the source ponds can be used to roughly predict dispersal rates among populations. Although thousands of dispersing individuals were found in most overflows, the relative contribution of dispersing individuals to the total population size in target populations was limited for nearly all overflows and analysed zooplankton groups (<1 % /24h,) except for Ceriodaphnia and Chydoridae in pond 22 (<6 % /24h). The limited effect on population dynamics is reinforced by the fact that it is unlikely that all immigrants survive in the target populations, an aspect that we want to focus on in further studies. Local biotic interactions can play a major role in excluding invaders from target populations (Shurin, 2000). Akopian et al. (1999) investigated zooplankton populations at a reservoir-river interface to find out their fate when released into the river. The reservoir acts as a source of zooplankton in the river, but once released in the river, the zooplankton community structure clearly changed. One of the main loss factors was found to be selective fish predation on large zooplankton species that developed in the reservoir. The metacommunity concept, defined as a set of local communties in different locations, coupled by dispersal of one of more of their constituent members (Gilpin & Hanski,
51 Chapter II
1991) can be used as a theoretical framework to describe interactions among zooplankton communities in a mosaic landscape (Holt, 1997). It is expected that the substantial flow of organisms through the pond system has a homogenizing effect on the zooplankton community structure (e.g. preventing local extinctions). Assemblages of coexisting species are formed by immigration from a regional pool of colonists and by local interactions among species with the physical environment. A variety of evidence supports both local (Brooks & Dodson, 1965; Carter et al., 1986; Leibold, 1999; Lukaszewiski et al., 1999) and regional (Bengtsson, 1991; Jenkins & Buikema, 1998) hypotheses of zooplankton community structure. However, the relative contributions of local and regional (e.g. dispersal) processes warrants further studying (Shurin et al., 2000). Dispersal only limits the diversity of very young communities (Jenkins & Buikema, 1998). In a study of isolated (non-connected) ponds, Shurin et al. (2000) observed that species richness in zooplankton communities is under strong local control, with dispersal at fairly small regional scales being of minor importance in generationg differences among lakes. Dispersal may result in gene flow if dispersing individuals can establish in target populations and dispersal results in successful reproduction in the new habitat. Gene flow acts as a homogenizing force, resulting in the mixing of alleles and preventing local populations from genetic differentiation for neutral markers (Slatkin, 1985, 1987). Our data suggest that even in a highly connected system of ponds, dispersal rates are not sufficient to drastically influence population dynamics, at least not during the summer period. It is, however, possible that the local population dynamics may be more influenced by dispersal during spring, when the dispersal through overflows may result in a better synchronisation of the population dynamics in the different ponds.
Acknowledgements
We like to thank Andrew Bohonak, David Jenkins, Luc Brendonck, Maarten Boersma and an anonymous reviewer for their constructive comments and suggestions on an earlier version of this manuscript. We thank vzw Natuurpunt and especially the warden Willy Peumans for permission to carry out this study in De Maten and for their full cooperation. Lieve Symons and Stef Usé were very helpful in constructing the sampling devices. Nele Nuytten, Koen FT De Gelas, Wim WN Van Gils and Guy Knaepkens are acknowledged for practical help during the (night) sampling, Jo Vingerhoeds for his help with Fig. 2 and Konjev Desender for the map of Belgium in Fig 1. Erik Michels is a fellow of the Institute for the promotion of Scientific Technological Research (I.W.T). Karl Cottenie is a research assistent of the Fund for Scientific Research (F.W.O.).
52 Quantification of dispersal in zooplankton
References
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53 Chapter II
Jann, B. & H. Bürgi, 1988. The drift of zooplankton in a lake outlet (Glatt) in a day-night-rhythm depending from the water level. Schweizer Zeitschrift für Hydrologie 50: 87-95. Jenkins, D.G. 1995. Dispersal limited zooplankton distribution and community composition in new ponds. Hydrobiologia 313/314. 15-20. Jenkins, D.G. & A.L. Buikema, 1998. Do similar communities develop in similar sites ? A test with zooplankton structure and function. Ecological Monographs 68: 421-443. Jenkins, D.G. & M.O. Underwood, 1998. Zooplankton may not disperse readily in wind, rain, or waterfowl. Hydrobiologia 387/388: 15-21. Leibold, M.A. 1999. Biodiversity and nutrient enrichment in pond plankton communities. Evolutionary Ecology Research 1: 73-95. Lukaszewiski, Y., Arnott, S.E. & T.M. Frost, 1999. Regional versus local processes in determining zooplankton community composition of Little Rock Lake, Wisconsin, USA. Journal of Plankton Research 21: 991-1003. Maguire, B. (1960) The passive dispersal of small aquatic organisms and their colonization of isolated bodies of water. Ecological Monographs 33: 161-185. Pajunen, V.I., 1986. Distributional dynamics of Daphnia species in a rock-pool environment. Annales Zoologici Fennici. 23: 131-140. Palmer, M.A., Allan, D. & C.A. Butman, 1996. Dispersal as a regional process affecting the local dynamics of marine and stream benthic invertebrates. Trends in Ecology and Evolution 11: 322- 325. Proctor, V.W., 1964. Viability of crustacean eggs recoverd from ducks. Ecology 45: 656-658. Proctor, V.W. & C. Malone, 1965. Further evidence of the passive dispersal of small aquatic organisms via the intestinal tracts of birds. Ecology 46: 728-729. Sandlund, O.T., 1982. The drift of zooplankton and microzoobenthos in the river Strandaelva, western Norway. Hydrobiologia 94: 33-48. Shurin, J.B. 2000. Dispersal limitation, invasion resistance, and the structure of pond zooplankton communities. Ecology, 81: 3074-3086. Shurin, J.B., J.E. Havel, M.A Leibold & B. Pinel-Alloul. 2000. Local and regional zooplankton species richness: a scale-indepenent test for saturation. Ecology 81: 3062-3073. Slatkin, M., 1985. Gene flow in natural populations. Annual Review of Ecology and Systematics 16: 393-430. Slatkin, M., 1987. Gene flow and the geographic structure of natural populations. Science 237: 787- 792. Statsoft, 1994. STATISTICA for the windows operating system. Statsoft Inc. Tulsa. Stavn, R.H., 1970. The application of the dorsal light reaction for orientation in water currents by Daphnia magna Straus. Zeitschrift für Vergleichende Physiologie 70: 349-362. Talling, J.F., 1951. The element of chance in pond populations. The Naturalist 839: 157-170. Thomas, E.P., D.W. Blinn & P. Kleim, 1998. Do xeric landscapes increase genetic divergence in aquatic ecosystems ? Freshwater Biology 40: 587-593. Ward, J.V., 1975. Downstream fate of zooplankton from a hypolimnial release mountain reservoir. Internationale Verhaltungen Limnology 19: 1798-1804. Wiens, J.A., 1997. Metapopulation dynamics and landscape ecology. In Hanski, I. & M.E. Gilpin (Eds.). Metapopulation biology: ecology, genetics and evolution, pp. 43-62. Academic Press, San Diego.
54 Modelling effective geographic distance
Chapter III
Geographic and genetic distances among zooplankton populations in a set of interconnected ponds: a plea for using GIS modelling of the effective geographic distance
With K. Cottenie, L. Neys, K. De Gelas, P. Coppin & L. De Meester Molecular Ecology (2001) 10, 1929-1938
Abstract
In systems of interconnected ponds or lakes, the dispersal of zooplankton may be mediated by the active population component, with rivulets and overflows functioning as dispersal pathways. Using a landscape-based approach, we modelled the effective geographic distance among a set of interconnected ponds (De Maten, Genk, Belgium) in a GIS environment. The first model (Landscape Model; LM) corrects for the presence of direct connections among ponds and was based on the existing landscape structure (i.e. network of connecting elements among ponds, travelling distance and direction of the current). A second model (Flow Rate Model; FRM) also incorporated field data on flow rates in the connecting elements as driving force for the passive dispersal of the active zooplankton population component. Finally, the third model (Dispersal Rate Model; DRM) incorporated field data on zooplankton dispersal rates. An analysis of the pattern of genetic differentiation among Daphnia ambigua
55 Chapter III populations inhabiting ten ponds in the pond complex reveals that the effective geographic distance as modelled by the flow rate and the dispersal rate model provide a better approximation of the true rates of genetic exchange among populations than mere Euclidean geographic distances or the landscape model that takes solely the presence of physical connections into account.
Introduction
Metapopulation theory is commonly used as a framework to describe interactions among discontinuous populations in mosaic landscapes (Antonovics et al., 1997; Hanski & Gilpin, 1997). During the last decades, Levins’ (1969) classic metapopulation concept has been substantially broadened (see Hanski& Gilpin, 1991; Hanski & Gilpin, 1997). Dispersal is considered as a key feature in the dynamics of metapopulations. Dispersal involves the movement of organisms (or their propagules) from their current (source) habitat to other suitable target habitats (Hansson, 1991). Dispersal plays an important ecological and evolutionary role (Dieckman et al., 1999). It not only affects the dynamics and persistence of local populations, species extinction rates and the colonisation of vacant patches, but also contributes to the shaping of community structure (Jenkins, 1995) and genetic structure of populations (McCauley, 1991; Ollivieri et al., 1995). Dispersal is indeed a prerequisite of gene flow, and only results in true gene flow when followed by successful establishment in the new habitat. Gene flow acts as a homogenising force, resulting in the mixing of alleles and preventing local populations from genetic differentiation (Slatkin, 1985, 1987). Wiens (1996, 1997) and Hanski (1998) made a plea for a landscape-based view on metapopulations, incorporating various aspects of landscape ecology in metapopulation models. Important information includes variation in patch quality, variation in the quality of the surrounding environment, boundary effects and the influence of interjacent landscapes on patch connectivity and dispersal success among isolated habitat patches. In real landscapes, the dispersal pathways among habitat patches and the probability that dispersing individuals will reach suitable patches are not only affected by the spatial arrangement of the different habitat types, but also by the connectivity among these habitat types and the explicit spatial configuration of the surrounding landscape (Merriam et al., 1989; Knaapen et al., 1992; Wiens, 1997).
56 Modelling effective geographic distance
To quantify the hindrance to dispersal caused by habitat fragmentation, landscape ecologists use the notion of landscape connectivity (Merriam, 1984, Schippers et al., 1996; Schumacher, 1996; With et al., 1997). Landscape connectivity refers to the degree to which the landscape facilitates or impedes movement among patches (Hansson, 1991). In metapopulations, connectivity is not only a property of the landscape but also an attribute of each patch, indicating how accessible the patch is for individuals from other patches. The connectivity of a patch increases with decreasing distances, and increasing sizes of other existing populations, given a species-specific migration rate. The distance may be the Euclidean distance or a more complex measure taking into account the influence of landscape structure on dispersal (Hanski, 1998). In zooplankton, resting stages are considered to be major dispersing agents. They are passively transported by wind, water or organisms that can migrate actively to other habitats (Proctor, 1964; Proctor & Malone, 1965). In systems with interconnected water bodies, however, dispersal may also be mediated by water currents carrying along the active population component (Sandlund, 1982; Akopian et al., 1999; Michels et al., 2001; Chapter II). Although resting stages are no doubt important for long-range dispersal in zooplankton, dispersal of the active population component is likely to be quantitatively much more important for neighbouring ponds that are physically connected by rivulets and overflows than dispersal of resting stages (Jenkins & Underwood, 1998, Brendonck & Riddoch, 1999; Michels et al., 2001; Chapter II). In such systems, the Euclidean distance may not be the appropriate measure for inter-patch connectivity. The connectivity among zooplankton populations, i.e. the probability of successful dispersal of zooplankton organisms among populations, will be determined by the presence of a functional connection, the length of the dispersal pathway between source and target population (Schippers et al., 1996), the physical properties of the connecting elements (Michels et al., 2001; Chapter II), the habitat preferences and the behavioural characteristics of the dispersing organisms (Jann & Bürgi, 1988; Michels et al., 2001; Chapter II). In an effort to quantify the effective geographic distance, the connectivity among zooplankton populations in a set of interconnected ponds located in the nature reserve De Maten was modelled in a GIS environment. We consecutively used three approaches to model the effective geographic distance among ponds. The first model (Landscape Model; LM) corrects for the presence of direct connections among ponds and was based on the existing landscape structure (i.e. network of connecting elements among ponds, travelling distance and direction of the current). A second model (Flow Rate Model; FRM) incorporated, besides the
57 Chapter III landscape factors used in the first model, also field data on the flow rates in the connecting elements as driving force for the passive dispersal of the active zooplankton population component. The third model (Dispersal Rate Model; DRM) incorporated travelling distance and direction of the current as well as field data on the number of animals that travel through the overflows (dispersal rates). We tested the effectiveness of our modelling approach in estimating true connectivity among habitats by validating the models using data on genetic differentiation for presumably neutral markers (allozymes) among populations of the zooplankton species Daphnia ambigua inhabiting a number of ponds in the study area. As successful dispersal is reflected by gene flow, data on genetic differentiation among populations should reflect effective geographic distances, at least under the assumption that there are no differences in establishment success among ponds.
Material and methods
Study site: De Maten
The study site consists of a series of 34 neighbouring and interconnected shallow ponds situated in the nature reserve De Maten (50° 57´ N, 5° 27´ E; Genk, Belgium, see Fig. 1). The ponds are located close to each other (the whole set of ponds covers an area of less than 200 ha) and are all well connected to each other. Yet, the ponds differ widely both in biotic (e.g., fish predation pressure, development of littoral zone) and abiotic (e.g., water transparency) characteristics (Cottenie et al., 2001). The pond complex is approximately linear in structure, with one main branch and two smaller ones (Fig. 1). There is an altitude difference of 15 m between the upstream and downstream ponds, creating a unidirectional waterflow throughout the pond complex. The main sources of water are two rivulets. The main rivulet is the Stiemerbeek, feeding pond 32 and all downstream ponds. The second rivulet feeds a subset of ponds located in the N-W corner of the area and flows directly in pond 18 (see Fig. 1). The ponds are also fed by groundwater. At the lower end of the pond complex, the water coming from the ponds is channelled again into the Stiemerbeek. The presence of well-defined overflows between ponds enabled us to quantify dispersal directly in the field. Virtually all zooplankton dispersal will be effected through these connections, and the latter can be quantitatively sampled. In our analysis, we assume that the impact of dispersal through resting stages via wind or waterfowl is negligible in the face of high dispersal rates of the active
58 Modelling effective geographic distance population component through overflows. Dispersal through the overflows has indeed been found to be substantial (Michels et al., 2001; Chapter II).
19
18 9 21 1 11 20 7 34 13 2 15 17 3 8 10 29 30 27 31 24 32 12 6 22 28 14 16 33 25 23 26 4 5
0 0.5 11Km Km
Figure 1: Geographic position of the study site, the nature reserve De Maten (50° 57´ N, 5° 27´ E; Genk, Province of Limburg, Belgium). Map of the pond complex showing pond numbers. The geographic location of the clusters that are identified by the NMDS plots of the effective geographic distance matrices obtained by the FRM and DRM are indicated with dotted lines (see Fig. 2). Inflow at I1, I2 (Pond 32) and I3 (Pond 18). Outflow at O1 (Pond 23) and O2 (pond 7). The overall direction of water flow is from Pond 32 to Ponds 3 and 5. The total altitudinal difference between the ponds is 15 m. Pond numbers of the ponds from which Daphnia ambigua was sampled for genetic analysis (4, 9, 10, 12, 14, 19, 21, 23, 27 and 34) are underlined.
Effective geographic distance
We define the effective geographic distance as the effort a dispersing zooplankton organism has to exert to travel from the “source” pond to the “target” pond via the pathway of connecting elements (e.g. rivulets or overflows) between them. In a heterogeneous landscape, the effective geographic distance is function of the travelling distance and the permeability of the interjacent landscape elements a dispersing organism encounters on its route. The effective geographic distance was modelled in a GIS (Geographic Information System) environment using the IDRISI software package (Version 2.0; Eastman, 1997). Two conceptually different techniques were used: cost analysis, using the VARCOST function (LM) and dispersal analysis, using the DISPERSE function (FRM and DRM). In all three models,
59 Chapter III the direction of the driving force for each connecting element was defined according to its water flow orientation since the passive dispersal of zooplankton with the water flow is an anisotrope process. As a consequence all three GIS models resulted in asymmetric distance matrices. A grid overlay of 0.5 x 0.5 m was used.
Landscape Model (LM): The landscape model was the first step in the development of a landscape-based GIS model that was to provide a more realistic measure of the interactions and connectivity between neighbouring zooplankton populations, replacing simple geographic distance among populations as a reflection of connectivity. In the LM, effective geographic distances among interconnected ponds were modelled using the VARCOST function in IDRISI. VARCOST computes an anisotropic cost surface, based on an anisotropic friction surface, that has motive energy behind it. In the LM model, dispersal pathways among ponds were restricted to the network of connecting elements among them. No dispersal could be effectuated upstream or via the terrestrial matrix. All connecting elements were assigned the same permeability. In this way, the LM reflects differences in travelling distance via connecting elements among ponds.
Flow Rate Model (FRM): The force field used in the FRM reflected the actual force for the passive dispersal of zooplankton in the connecting elements. The driving force in a specific connection was modelled to be proportional to the actual water flow in the concerning element. Flow rates (m³/s) were estimated based on field data of water velocity and measurements of the cross section of the overflows. Water velocity (m/s) in all connecting elements was measured by timing of the interval between adding NaCl and the passage of a conductivity peak 10 meter downstream, averaged over four measurements. The cross section of each element was measured at two meter intervals along the connecting element. The applied method of measuring flow rates was chosen because the limited depth in most of the overflows made it impossible to use a “propeller-type” water velocity meter. For each connecting element, the direction of the force was defined according to its water flow orientation, since the passive dispersal of zooplankton with the water flow is an anisotrope process. The resulting effective geographic distance matrices in the FRM and DRM were calculated using the DISPERSE function in IDRISI. DISPERSE is a function similar to VARCOST, but it models phenomena that have no motive force behind it. Rather, it is subject to anisotropic forces that cause it to move. For the technical details on these functions, we refer to Eastman (1997). Compared to the LM, the FRM approach can be expected to offer a
60 Modelling effective geographic distance more realistic approximation of the connectivity among a set of interconnected ponds, as it takes flow rates into consideration.
Dispersal Rate Model (DRM): Finally, the force field of the DRM was based on the actual dispersal rates of the zooplankton in the connecting elements. The driving force in a specific connection was proportional to the dispersal rate in this connecting element. Using driving forces proportional to the actual dispersal data of the connecting elements is expected to offer the most realistic approximation of the true in situ connectivity. Dispersal of zooplankton (number of organisms of a given species per hour) between ponds was quantified once for a one-hour period, by placing plankton nets into all functional connecting elements (N=31; Michels et al., 2001; Chapter II). Using an artificial dam (60 x 80 cm), the overall water mass of the overflow was forced through a tube (length 1m; ∅ 20 cm) onto the plankton net (mesh size 64 µm; ∅ 20). All measurements of zooplankton dispersal rates were done between 9h and 18h in mid summer 1998 to prevent interaction between diel vertical and horizontal migration of zooplankton and individuals dispersing via the overflow. The intake of the overflows is situated in the littoral zone and at the water surface. For more details on the method for the quantification of zooplankton dispersal among populations and on the results for the different zooplankton species, we refer to Michels et al. (2001; Chapter II). The resulting (effective) geographic distance matrix from each model was visualised by Nonmetric Multidimensional Scaling (NMDS) in the STATISTICA software package (Version 5.1; StatSoft Inc., 1997). This method computes the Euclidean distances in a specific set of dimensions (we used a two-dimensional plot) corresponding to ranking of the original distances (Kruskall, 1964). A mantel test (999 permutations) in TFPGA (Miller, 1997) was used to test whether the distances in lower triangular matrices resulting from each model were correlated (Mantel, 1967).
Study organism: Daphnia
Daphnia (Cladocera: Anomopoda), are characterised by cyclical parthenogenetic reproduction. Under favourable conditions, females reproduce by amictic parthenogenesis, resulting in the formation of clones. Parthenogenic reproduction may be maintained during several generations, resulting in a population that consists of clonal lineages (Carvalho, 1994). Under unfavourable environmental conditions, such as low food concentration, high population density, changes in photoperiod and the presence of predators, the animals
61 Chapter III reproduce sexually, resulting in the formation of resting eggs (Hebert, 1987; Zaffagnini, 1987). The resting eggs are encapsulated in a protective envelope. As they are very resistant, they facilitate dispersal and the colonisation of new habitats (De Meester, 1996).
Daphnia ambigua SCOURFIELD, 1947 is the most abundant Daphnia species in De Maten. D. ambigua is common in the nearctic region and has invaded Europe in the second half of the twentieth century (Maier, 1996). Dumont (1974) first reported the species in Belgium. In November 1998, all 34 ponds of De Maten were screened for the presence of D. ambigua. In all populations where D. ambigua occurred at detectable densities, adult D. ambigua were sampled with a plankton net (mesh size 64 µm). To prevent interference from genotype-dependent vertical habitat selection (De Meester et al., 1994), the whole water column (approximately 1 m deep) was sampled. Sixty adult individuals were selected randomly from each sample and stored at -80°C prior to electrophoretic analysis to prevent denaturation of enzymes (Richardson et al., 1986). In addition, a few clones were maintained in the laboratory to be used as electrophoretic reference markers.
Genetic analysis
The genetic distance among D. ambigua populations was determined using allelic variation at allozyme loci, using cellulose acetate electrophoresis following the protocol of Hebert & Beaton (1994). In total, 12 enzyme loci were screened for polymorphism and reliable staining in Daphnia ambigua populations of De Maten: aspartate amino transferase (AAT E.C. 2.6.1.1.), amylase (AMY E.C. 3.2.1.1.), aldehyde oxidase (AO E.C. 1.2.3.1); arginine phosphokinase (APK E.C. 2.7.3.3); fumarate hydratase (FUM E.C. 4.1.2.2); isocitrate dehydrogenase (IDH E.C. 1.1.1.42); lactate dehydrogenase (LDH E.C. 1.1.1.27); malate dehydrogenase (MDH E.C. 1.1.1.37); malate esterase (ME E.C. 1.1.1.40); mannosephosphate isomerase (MPI E.C. 5.3.1.8); glucophospho isomerase (GPI E.C. 5.3.1.9); phosphoglucomutase (PGM E.C. 2.7.5.1). However, only GPI and PGM were found to be polymorphic. Contrary to other more abundant and better-studied Daphnia species such as D. magna and D. pulex, D. ambigua populations in general seem to be characterised by very low genetic polymorphism. This observation is in agreement with the results obtained by Platt & Spitze (2000), who screened nine loci in subtropical D. ambigua populations and observed only three polymorphic loci, and Zofková (2000; all examined populations in the Czech Republic proved to be monomorphic in this survey involving 6 loci). An average of 60 adult individuals of each population were assayed for electrophoretic variation at the two polymorphic loci: GPI (dimer, two alleles) and PGM (monomer, three alleles). The extent of
62 Modelling effective geographic distance
genetic differentiation between populations was determined using pairwise GST (Nei, 1973, 1977) calculated for each pair of populations separately in GENETIX (Belkhir et al., 1996). The genetic data were used to validate the models on effective geographic distance. We performed Mantel tests (999 permutations) in TFPGA (Miller, 1997) to check for significant correlations between measures of genetic differentiation between all possible pairs of populations (pairwise GST) and geographic distances or effective geographic distances between ponds as generated by the GIS models (only incorporating the smallest (effective) geographic distance between two ponds) (Mantel, 1967). A sequential Bonferroni test was applied for multiple testing (Rice, 1989).
Results
Effective geographic distance
Fig. 2 visualises the effective geographic distance among ponds in De Maten obtained by the three models in NMDS plots. For comparison, a visualisation by NMDS of the original geographic distance matrix is also presented. The arrows indicate the direction of the current and thus the asymmetry of the effective geographic distance matrix. The correlations between the geographic distance matrix and the LM (R = 0.82) and between the FRM and the DRM (R = 0.48) were both highly significant by a Mantel test (Table 1). The three main clusters in the effective geographic distance matrix resulting from the FRM and the DRM correspond with three distinct branches of the pond complex De Maten: the upstream ponds (ponds 22-32, see Fig. 1), the downstream ponds (2-17, see Fig. 1) and the ponds located N-W of the remainder of the complex (ponds 18-21, see Fig. 1). Starting at the most upstream pond (pond 32, see Fig. 1), the Stiemerbeek provides the water supply to the rest of the complex. There is no direct connection between the different clusters of ponds, only an indirect one, via the rivulet. As a result, the ponds belonging to different clusters are relatively isolated from each other. Within each of the branches, however, overflows between ponds provide direct ways for zooplankton dispersal from pond to pond. Ponds 1 and 34 lack any physical connection to other ponds and could therefore not be included in the effective geographic distance matrices.
63 Chapter III
19
33 21
16 14
23 24 22 27 23 1 33 17 22 26 30 25 2928 9 13 4 6 31 15 17 11 5 24 12 32 25 15 10 16 2 9 26 2 8 27 28 7 10 12 14 11 3 29 13 7 20 3 8 30 31 4 32 18 34 5 6
19 20 18 21 Geographic distance Flow rate model
31 32 30 33
16 14
29 24 4 9 23 5 22 3 8 27 2 17 28 28 30 29 31 4 7 26 6 5 6 10 25 12 7 25 27 2 15 11 24 20 11109 26 13 32 17 18 13 5 19 3 8
14 21 23 22 12
16 19 20 33 18 Landscape Model Dispersal Rate Model 21
Figure 2: Visualisation by Nonmetric Multi Dimensional Scaling (NMDS) of the geographic distance matrix (top left; stress value: 0.06) and the effective geographic distance matrices among ponds in De Maten obtained by three GIS models. Distances among ponds reflect the effective distance a dispersing zooplankton organism has to cover among populations in the pond complex. The landscape model (LM; bottom left; stress: 0.07) takes into account the existing network of connecting elements and direction of the current. The flow rate model (FRM; top right; stress value: 0.19) also takes into account the flow rates measured in the overflows, whereas the dispersal rate model (DRM; bottom right; stress value: 0.17) attributes permeability to overflows in relation to dispersal rates quantified in the field. Pond numbers are shown in Fig. 1. Topographic location of ponds in the clusters that are identified by the FRM and DRM is shown in Fig. 1. Ponds 1 and 34 are not included in the NMDS plots of the LM, FRM and DRM, as these ponds lack any physical connection to the remainder of the pond complex. The arrows indicate the direction of the current (geographic distance) or the asymmetry of the effective geographic distance matrices (LM, FRM and DRM).
64 Modelling effective geographic distance
Table 1: Results of a Mantel test, testing for the correlation among the different (effective) geographic distance matrices. Correlation coefficient are given below diagonal, p-levels above diagonal. Values with p-levels < 0.05 are indicated in bold.
Geographic distance Landscape model Flow rate model Dispersal rate model
Geographic distance - 0.001 0.54 0.28
Landscape model 0.82 - 0.59 0.38
Flow rate model -0.13 -0.18 - 0.001
Dispersal rate model 0.021 0.02 0.48 -
Validation of the models using genetic data
In Table 2 the allele frequencies at the GPI and PGM locus of D. ambigua populations sampled in De Maten in November 1998 and sample sizes are presented. In Table 3 the pairwise measures for genetic differentiation (GST) among the D. ambigua populations are presented. The correlation between the pairwise measures for genetic differentiation (GST) among the D. ambigua populations and the corresponding geographic and effective geographic distances was significant after sequential Bonferroni corrections for the FRM and DRM models only (Table 4). This is suggestive of a pattern of isolation-by-distance. No significant correlation was found between pairwise GST values among the D. ambigua populations and the effective geographic distance obtained by the LM model nor between pairwise GST values and the corresponding geographic distances. We explored the relationship of genetic differentiation among D. ambigua populations with effective geographic distance further by analysing allelic differentiation among D. ambigua populations that belong to specific clusters as described by FRM and DRM (see Table 3; Fig. 1). No significant genetic differentiation in allele frequencies was found among ponds that clustered together in terms of effective geographic distance as calculated by FRM and DRM (p>0.05; Exact test) except for population 4 that showed significant genetic differentiation from all other populations within this cluster. Allelic differentiation among the populations from different clusters obtained by FRM and DRM (see Fig. 2) were all highly significant (p< 0.01; Exact test). Even though one must be careful with this interpretation because of the higher statistical power of the analysis
65 Chapter III of among-cluster genetic differentiation, the pattern is in full agreement with the results of the Mantel test.
Table 2: Allele frequencies at the GPI (two alleles) and PGM locus (three alleles) of D. ambigua populations sampled in De Maten in November 1998. Sample sizes are given between brackets. Alleles at each locus were labeled according to their mobility: S: slow; M: medium; F: fast.
Population GPI PGM Population GPI PGM 4 S 0.59 S 0.26 19 S 0.54 S 0.38 F 0.41 M 0.45 F 0.46 M 0.34 (57) F 0.29 (62) F 0.28 9 S 0.49 S 0.26 21 S 0.62 S 0.37 F 0.51 M 0.6 F 0.38 M 0.38 (58) F 0.14 (51) F 0.25 10 S 0.5 S 0.21 23 S 0.59 S 0.12 F 0.5 M 0.63 F 0.41 M 0.53 (59) F 0.16 (49) F 0.35 12 S 0.54 S 0.18 27 S 0.51 S 0.13 F 0.54 M 0.68 F 0.49 M 0.52 (61) F 0.14 51) F 0.35 14 S 0.5 S 0.13 34 S 0.65 S 0.29 F 0.5 M 0.66 F 0.35 M 0.53 (48) F 0.21 (61) F 0.18
Table 3: Pairwise GST values (Nei, 1973, 1977) quantifying genetic differentiation among the D. ambigua populations in De Maten. Pond numbers are presented in Fig. 1.
Population 4 9 10 12 14 19 21 23 27 34 4 - 9 0.015 - 10 0.017 0.001 - 12 0.020 0.003 0.001 - 14 0.019 0.006 0.003 0.002 - 19 0.007 0.023 0.031 0.037 0.038 - 21 0.005 0.024 0.029 0.034 0.038 0.003 - 23 0.007 0.021 0.017 0.018 0.012 0.025 0.022 - 27 0.008 0.016 0.014 0.016 0.009 0.021 0.024 0.003 - 34 0.006 0.014 0.014 0.016 0.022 0.017 0.008 0.015 0.022 -
66 Modelling effective geographic distance
Table 4: Results of a Mantel test, testing for the correlation between genetic distances (pairwise GST; Nei 1973, 1977) among the D. ambigua populations inhabiting a subset of ponds in De Maten and the effective geographic distance matrices for the same ponds obtained by the Landscape model, Flow rate model and Dispersal rate model, and the correlation between the genetic distance among the D. ambigua populations and the geographic distance. * p<0.05; ** p<0.01. † significant at α = 0.05 after sequential Bonferroni correction for multiple testing (Rice, 1989)
Genetic distance
R p-level Geographic distance -0.088 0.32
Landscape model 0.13 0.23
Flow rate model 0.48 0.0028** †
Dispersal rate model 0.38 0.012* †
Discussion
The particular topography of the pond complex studied, with the presence of various types of direct connections among ponds, suggests that (1) Euclidean distances among the ponds are no valid measures of potential dispersal rates, (2) the dispersal of zooplankton may be strongly influenced by the physical properties of the connecting elements (also see Knaapen et al., 1992; Conrad et al., 1999), and (3) it is justified to assume that dispersal of the active population component among ponds in this system will be quantitatively much more important than the occasional dispersal of resting eggs via water, wind or waterfowl (see Jenkins & Underwood, 1998, Brendonck & Riddoch, 1999; Michels et al., 2001; Chapter II). We therefore used a landscape-based approach to model effective geographic distance among zooplankton populations inhabiting this set of interconnected habitats. We used two conceptually different types of models. The LM model reflects differences in length of the dispersal pathway in a more realistic way than merely Euclidean distances among ponds, but did not differentiate for varying degrees of hindrance for dispersal among connecting elements. The FRM and DRM did differentiate among overflows to correct for the varying degree to which populations are physically connected, by incorporating information on flow rates and observed dispersal rates, respectively. The distance matrix resulting from LM still correlated significantly with the matrix of Euclidean geographic distances (Fig. 2 and Table 2), but did not improve the correlation with the observed pattern of genetic differentiation among populations inhabiting the ponds studied. The distance matrices obtained by the FRM
67 Chapter III and DRM did correlate significantly with the matrix of pairwise measures of genetic differentiation among D. ambigua populations inhabiting the ponds. This indicates that in modelling the effective geographic distance among connected ponds, it does not suffice to take the structure of physical links among populations into account, but one has to take the varying hindrance to dispersal through these physical links into consideration. The FRM and DRM perform equally well in their congruence with the genetic data. Moreover, these two models yield similar distance matrices in the study site (Table 1; Fig. 2). Our findings suggest that the FRM and DRM are the biologically most realistic models of effective geographic distance, describing connectivity among the interconnected zooplankton populations in De Maten. The direct quantification of dispersal rates among populations, although in principle the preferred approach, is very time consuming, labour intensive and often exceedingly difficult (see Bossart & Prowell, 1998; Whitlock & McCauley, 1999). As a result, only a limited number of studies have endeavoured such a direct quantification of dispersal, and all of the obtained estimates are either strongly limited in time or space (Jenkins & Underwood, 1998; Bohonak & Whiteman, 1999; Brendonck & Riddoch, 1999; Michels et al., 2001; Chapter II). We consider our estimates of dispersal rates as providing an indication of the strength of the interactions among pairs of populations rather than providing detailed measures of true dispersal rates. Although our estimates are a much better approximation of true dispersal rates than is known for most natural populations (but see Bohonak, 1999), it is clear that dispersal estimates based on a survey of one hour only provides a record of the momentary dispersal during the sampling period rather than of true average dispersal rates. Although it is theoretically possible to sample all overflows among the 34 ponds semi-continuously for a full growing season, the limited survey carried out for the present study already involved a substantial investment in field work as well as counting efforts (see also Michels et al., 2001; Chapter II). Given that the two models (FRM and DRM) differ considerably in the amount of field work needed to provide estimates of the driving forces (e.g. measurements of flow rates versus the quantification of dispersal rates), the similarity in the resulting distance matrices suggests that quantifying flow rates may offer a fast and relatively straightforward method to assess interactions among zooplankton populations that inhabit interconnected habitats that are linked by dispersal. In the present study, testing for a correlation between effective geographic distance and data on genetic differentiation among D. ambigua populations offered a solid validation of the GIS-based effective geographic distance models. Our data provide observational evidence for a link between structural connections (connectedness; Baudry & Merriam, 1988) and the process of
68 Modelling effective geographic distance gene flow among spatially structured populations (functional connectivity). Genetic differentiation among populations as measured in the present study reflects successful dispersal, i.e. dispersal followed by successful establishment in the target population. The ecological conditions that dispersing individuals encounter in the target habitat can be different from the conditions in the source habitat (Kalisz et al., 1999). This is also the case in the study site of the present study, since some of the ponds differ strongly in their ecology (Cottenie et al., 2001). Yet, our observation of a significant correlation between a measure of gene flow and a measure of effective geographic distance, reflecting a pattern of isolation-by- distance, indicates that establishment success does not differ widely among pairs of ponds. This, combined with the observation of a significant genetic differentiation among pond clusters in this albeit strongly connected set of ponds (see Michels et al., 2001; Chapter II) actually suggests that establishment success of dispersing individuals may be quite low in all target ponds of this system. To evaluate the importance of geographic distances among populations and their relationship with genetic distances, both the biology of the organism studied and features of the terrain must be considered. Even though the low resolution of the genetic marker used in this study, our data indicate that populations inhabiting neighbouring ponds are very similar genetically, whereas more distant populations tend to be more dissimilar. The relationship between genetic and effective geographic distances observed by us can not be explained by the existence of an ecological gradient. Cottenie (unpubl. manuscr.) did not find any correlation between ecological and geographic distances in De Maten. Moreover, this hypothesis would require either (1) strong linkage at the level of the whole metapopulation (i.e. the same clones occurring in the different local populations), which is unlikely given that the different populations all have a resting egg bank of their own, or (2) sufficiently strong selection at the loci studied themselves. Although it is known that some allelic variants of allozymes are not selectively neutral (see Riddoch, 1993 for a review on GPI), we could not observe any indication of non-neutral behaviour of allozyme alleles yet in any of our population genetic studies on Daphnia (Vanoverbeke et al., unpubl. data). Our study illustrates how a landscape-based approach can offer a valid alternative in the analysis of the genetic structure of spatially structured populations when the classic approach (using Euclidean geographic distances) fails. In systems with well-defined pathways for dispersal, effective geographic distance as modelled in GIS provides a better explanation of true dispersal than Euclidean distance. We believe the effective geographical distance concept
69 Chapter III has a broad applicability and can easily be adapted to other systems, such as the genetic differentiation of aquatic organisms inhabiting river basins.
Acknowledgments
The authors thank L. Excoffier and two anonymous reviewers for constructive comments on an earlier version of this manuscript. K. Nackaerts and O. Honnay made valuable comments on the GIS modelling and landscape ecology. We are grateful to vzw Natuurpunt and especially Willy Peumans for permission to carry out this study in De Maten and for their full cooperation. This study was financially supported by project VLINA/96/1 of the Flemish Government and by project G.0358.01 of the Fund for Scientific Research, Flanders. E.M. and K.D.G. are fellows of the Flemish Institute for the promotion of Scientific-Technological Research in Industry (I.W.T). K.C. is a research assistent of the Fund for Scientific Research (F.W.O.).
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With, K., R.H. Gardner & M.G. Turner 1997. Landscape connectivity and population distributions in heterogeneous envionments. Oikos 78: 151-169.
Whitlock, M.C. & D.E. McCauley 1999. Indirect measures of gene flow and migration: Fst ≠ 1/(4Nm + 1). Heredity 82: 117-125.
Zaffagnini, F. 1987. Reproduction in Daphnia. In Peters, R.H. & R. de Bernardi (Eds.). Daphnia, pp. 245-284. Istituto Italiano di Idrobiologia, Pallanza.
Zofková, M. 2000. Phenotypic variability and genetic diversity of species Daphnia ambigua Scourfield and Daphnia parvula Fordyce. Unpublished masters thesis, Charles University, Prague.
73 Chapter III
74 Genetic variation at neutral markers in D. ambigua
Chapter IV
Microgeographic genetic structure of local Daphnia ambigua populations connected by high dispersal rates
Manuscript together with K. De Gelas, K. Cottenie & L. De Meester
Abstract Using allozyme variation at two polymorphic loci, the structure of genetic variation in a set of highly connected Daphnia ambigua populations was surveyed. The ponds in the study area (De Maten, Genk, Belgium) cover a very limited geographic scale (distances among ponds range from 8 to 2000 m) and zooplankton populations inhabiting these ponds are characterised by high dispersal rates. All D. ambigua populations were in Hardy-Weinberg equilibrium both at the beginning (April) as well as at the end (November) of the growing season, reflecting the fact that the populations are intermittent and go through a yearly phase of sexual reproduction. Yet, shifts in multilocus genotype (MLG) frequencies and a decrease in MLG diversity during the course of the growing season could be observed in all populations for which data on April and November are available. Despite the documented high
75 Chapter IV dispersal rates between the populations in this system, the D. ambigua populations inhabiting these ponds were found to be genetically differentiated from each other, both with respect to allele frequencies and MLG frequencies. The level of allelic differention was low (GST = 0.015 - 0.023), but highly significant. Genetic divergence tended to increase during the growing season. A significant correlation was observed between genetic distance (allele frequencies as well as MLG frequencies) and effective geographical distance was observed at the end of the growing season, but not at the beginning of the growing season. Spatial structure of the pond complex was found to be more important than ecological distance among habitats in the structuring of genetic variation of the studied D. ambigua populations. Based on these findings, we suggest that the interactions among D. ambigua populations inhabiting the pond complex in De Maten are in agreement with the concept of a patchy metapopulation structure.
Introduction The spatial structure of populations was already a key element in some early concepts and models of population ecology, genetics and adaptive evolution (see Hanski (1998) and references therein). During the past decades, the implications of spatial structure and population dynamics have become widely recognised in population biology (Slatkin, 1985, 1987; Kareiva, 1990; Hedrick & Gilpin, 1997; Hanski, 1998). Recently, it has become clear that populations occupy habitat patches that are embedded in a complex mosaic environmental matrix. Hence, the dispersal pathways among suitable patches within this matrix and the probability that dispersing individuals will reach these patches are affected by the explicit spatial configuration of the surrounding environmental matrix (Merriam et al., 1989; Wiens, 1997; Michels et al., 2001b; see Chapter III). General population genetic theory predicts that high dispersal rates among populations, if translated into high levels of gene flow, promotes the mixing of alleles and prevents local populations from differentiating genetically from each other. Limited dispersal and low levels of gene flow among populations, on the other hand, may promote genetic differentiation among populations as a result of drift and/or natural selection (Slatkin, 1985). The scale at which spatial patterns in genetic distribution develop, however, depends on the
76 Genetic variation at neutral markers in D. ambigua complex interaction between the extent of gene flow among populations, genetic drift and the strength of natural selection acting on adaptive quantitative traits (Hartl & Clark, 1989). Dispersal is a prerequisite of gene flow, but it will only result in effective gene flow when followed by successful reproduction in the new habitat (Slatkin, 1985). Spatial structuring of genetic variation may also result from ecological differences among populations creating different selective environments. In addition to the development of genetic differentiation due to a differential impact of natural selection, differences in local ecological conditions in source and target habitats may also reduce the establishment success of dispersing individuals in target populations and thus diminish the effect of homogenization through gene flow (Kalisz et al., 1999; Michels et al., 2001b; see Chapter III). More than a decade ago, Burton (1983) and Boileau et al. (1992) already stated that the relationship between population genetic structure, indirect estimates of gene flow and quantification of dispersal rates is ambiguous and resistant to generalization. Recently, the limitations of in situ quantification of dispersal and its extrapolations towards indirect measurements of gene flow among populations has been the subject of much debate (see Bossart & Prowell, 1998; Bohonak, 1999; Whitlock & McCauley, 1999). As in many organisms, direct estimates of dispersal rates in zooplankton are exceedingly difficult to obtain (see Bossart & Prowell, 1998; Bohonak, 1999), and only few studies have endeavoured such quantifications to date (Jenkins & Underwood, 1998; Bohonak & Whiteman, 1999; Brendonck & Riddoch, 1999; Michels et al., 2001a, see Chapter II). Bohonak (1999) reviewed the available literature searching for correlations between tendencies for dispersal and genetic differentiation in different groups of organisms. He concluded that there is indeed a relationship between dispersal capacity and genetic differentiation in a majority of animal species studied. Moreover, Bohonak (1998) himself was able to show that indirect estimates of gene flow corresponded well with field data of salamander mediated dispersal in an anostrocan. Brendonck et al. (2000) however, did not find a clearcut pattern between geographic distance and genetic distance in a study on anostrocan metapopulations and argued that more information is needed on the effect of geographic scale in shaping the genetic structure of freshwater zooplankton. In his review on patterns and processes of genetic differentiation among zooplankton populations, De Meester (1996b) pointed at an apparent paradox in Daphnia, with observations of extensive local genetic differentiation between neighbouring Daphnia
77 Chapter IV populations on the one hand, and a high dispersal potential and a rapid colonization of new habitats on the other hand. These observations suggest that effective gene flow in Daphnia is much lower than the observed dispersal rates, possible due to competition between migrant genotypes with resident genotypes (e.g. priority effect) and/or outbreeding depression associated with local adaptation (Templeton, 1986; De Meester, 1993). De Meester et al. (in press) discuss this paradox and its solution in more detail. Given the observations that neighbouring Daphnia populations in isolated ponds can often be highly genetically differentiated from each other (Innes, 1991; Vanoverbeke & De Meester; 1997; De Meester, 1996b) and show even strong tendencies for local adaptation (Declerck et al., 2001), one may wonder how strong the homogenizing force of gene flow may be while it still allows some genetic differentiation among local natural populations inhabiting ecologically different habitats. Moreover, in many zooplankton taxa, the existence of large egg banks further enhances founding effects and rapid adaptation of populations to local conditions. This strongly reduces gene flow among populations and further increases the persistence of founder effects (Meester et al., in press). Zooplankton in interconnected ponds provide a good model system to test the role of spatial structure (facilitating or impeding dispersal) versus differences in local ecological conditions (creating different selective environments) in the structuring of genetic variation among local populations. Given the obvious unsuitable nature of the terrestrial matrix for aquatic organisms, habitat boundaries of individual ponds and the dispersal pathways through connections between ponds are well defined. This facilitates direct quantification of dispersal rates by quantitative sampling of the connections among ponds (e.g. Michels et al., 2001a; see Chapter II). Moreover, ongoing dispersal of the active population component driven by water flow is likely to be quantitatively more important than the occasional dispersal of resting eggs via the feathers or digestive tract of waterfowl (Proctor, 1964; Proctor & Malone, 1965; Mellors, 1975, Charalimbides et al., 2000), wind (Jenkins & Underwood, 1998; Brendonck & Riddoch, 1999) or water flow (Havel et al., 2000; Michels et al., 2001a; see Chapter II). The population genetic structure of natural zooplankton populations has been studied extensively for more than three decades (see reviews by Hebert, 1987; Carvalho, 1994; De Meester 1996b). The majority of these studies, however, focussed on genetic differentiation among isolated populations (see De Meester, 1996b and
78 Genetic variation at neutral markers in D. ambigua references therein). Only little attention has been given to the microgeographic genetic structure among cyclic parthenogenetic zooplankton populations inhabiting systems of interconnected lakes or ponds (but see Weider, 1989; Jacobs, 1990). It is often observed that isolation-by–distance patterns among unconnected cyclically parthenogenetic zooplankton populations are weak, largely due to strong genetic differences among nearby populations (Hebert, 1974; Boileau & Hebert, 1988; Innes, 1991; Boileau et al., 1992; Vanoverbeke & De Meester, 1997). In this study, we analyse the genetic structure of D. ambigua populations inhabiting a pond complex (De Maten, Genk, Belgium) that consists of 34 connected ponds that are situated geographically very close to each other. Due to the presence of direct connections among the ponds, the zooplankton populations inhabiting them are characterised by high dispersal rates (see Michels et al., 2001a, 2001b; Chapters II & III). Despite the fact that nearly all ponds share the same water supply, the ponds in the system are ecologically very different in terms of water transparency and fish predation pressure (see Cottenie et al., 2001). Hence, the question arises on the relative importance of geographic structure and ecological differences in the structuring of genetic variation in this metapopulation. In this study, we used allozymes to (1) survey genetic variation within and among populations of Daphnia ambigua in this system. More specifically, we asked (2) whether D. ambigua populations in De Maten are differentiated genetically from each other despite the high observed dispersal rates; (3) whether genetic differentiation among ponds becomes more enhanced during the course of the growing season; (4) whether the genetic structure is mainly determined by the explicit spatial structure of the pond complex or by differences in environmental characteristics among ponds; (5) whether genetic diversity within populations is dependent on habitat size, and (6) whether the D. ambigua populations inhabiting the pond complex in De Maten can be considered to be part of a metapopulation.
79 Chapter IV
Materials and methods
Study site De Maten The study site is a set of 34 neighbouring and interconnected shallow ponds in the nature reserve De Maten (50° 57´ N, 5° 27´ E; Genk, Belgium, see Figure 1). The waterflow through the pond complex causes unidirectional ongoing dispersal through active reproducing parthenogenetic females, with rivulets and overflows functioning as effective dispersal pathways. Based on the effective geographical distances among ponds as calculated by Michels et al. (2001b; see Chapter III), the pond complex in De Maten is characterised by three major clusters: the upstream ponds (ponds 22-32), the downstream ponds (2-17) and the ponds located N-W of the remainder of the complex (ponds 18-21, Fig. 1). For more details concerning the ecological characteristics, a description of the landscape matrix, the spatial configuration and connectivity of the pond complex, we refer to Cottenie et al. (2001; unpubl. manuscr.) and Michels et al. (2001a, 2001b; see Chapters II & III).
19
18 9 21 1 11 20 7 34 13 2 15 17 3 8 10 29 30 27 31 24 32 12 6 22 28 14 16 33 25 23 26 4 5
0 0.5 11Km Km
Figure 1: Map of the pond complex in the nature reserve De Maten showing pond numbers. The overall direction of water flow is from Pond 32 to Ponds 3 and 5. Dotted lines indicate the position of the major clusters in terms of effective geographic distance: the upstream ponds (ponds 22-32), the downstream ponds (2-17) and the ponds located N-W of the remainder of the complex (ponds 18-21; also see Michels et al., 2001b ; Chapter III). Ponds from which D. ambigua were sampled in April: 2; 3; 6; 8; 9; 10; 13; 15; 18; 22; 23; 24; 27; 28 and 30. Ponds sampled in November: 4; 9; 10; 12; 14; 19; 21; 23; 27 and 34.
80 Genetic variation at neutral markers in D. ambigua
Model organism: Daphnia ambigua
Daphnia (Crustacea: Anomopoda), are characterised by cyclical parthenogenetic reproduction. During favourable conditions, females reproduce by amictic parthenogenesis, resulting in a population that consists of clonal lineages (Carvalho, 1994). During unfavourable environmental conditions, such as low food concentration, high population density, changes in photoperiod and the presence of predators, the animals reproduce sexually, resulting in the formation of resting eggs (Hebert, 1987; Zaffagnini, 1987). The resting eggs are encapsulated in a protective envelope, the ephippium. As they are very resistant to mechanical stress, digestive enzymes, freezing and drying (Proctor, 1964; Fryer, 1996), they facilitate long-range dispersal and the colonization of new habitats (De Meester, 1996b; De Meester et al., in press). Daphnia ambigua Scourfield, 1947, is the most abundant Daphnia species in De Maten. All 34 ponds were screened for the presence of D. ambigua in April and November 1998. In populations were D. ambigua densities were sufficiently dense, adult D. ambigua were sampled with a plankton net (mesh size 64 µm). In total, samples from 15 populations were analysed in April, and from 10 populations in November. Due to variable population densities, only four populations could be analysed both at the beginning and at the end of the growing season. To prevent bias through genotype-dependent vertical habitat selection (Weider 1984; De Meester et al., 1994), the whole water column (approximatley 1 m deep) was sampled. Sixty adult individuals were selected randomly from each sample and were stored at -80°C prior to electrophoresis to prevent denaturation of the enzymes (Richardson et al., 1986). In addition, a few clones were maintained in the laboratory to be used as electrophoretic reference markers.
Electrophoretic procedure
The genetic structure of the D. ambigua populations was analysed using cellulose acetate gel electrophoresis, carried out on Titan III cellulose acetate plates following the protocol of Hebert & Beaton (1994). We assayed approximatly 50 adult D. ambigua individuals of each population for electrophoretic variation at GPI (E.C. 5.3.1.9) and PGM (E.C. 2.7.5.1). To align banding patterns from different runs and populations, we used a D. ambigua laboratory clone as reference marker. Both loci
81 Chapter IV were assayed for each individual, yielding two-locus genotypes for each animal. We hereafter refer to distinct two-locus genotypes as multilocus genotypes (MLG), with the understanding that they represent at least one, but more likely, a group of clones. As many as ten other enzyme loci [aspartate amino transferase (AAT E.C. 2.6.1.1); amylase (AMY E.C. 3.2.1.1); aldehyde oxydase (AO E.C. 1.2.3.1); arginine phosphokinase (APK E.C. 2.7.3.3); fumarate hydratase (FUM E.C. 4.1.2.2); isocitrate dehydrogenase (IDH E.C. 1.1.1.42); lactate dehydrogenase (LDH E.C. 1.1.1.27); malate dehydrogenase (MDH E.C. 1.1.1.37); malate esterase (ME E.C. 1.1.1.40); mannosephosphate isomerase (MPI E.C. 5.3.1.8)] were screened for polymorphism and reliable staining in an initial set of approximately 100 individuals derived from 15 populations but were found to be monomorphic or stained weakly and were therefore not routinely run.
Data analysis
Within-population genetic diversity
Within-population allelic genetic diversity was quantified as expected heterozygosity. Exact tests for Hardy-Weinberg equilibrium were performed in TFPGA (Miller, 1997). Fixation index for deviation of Hardy-Weinberg equilibrium were calculated in GENETIX (Belkhir et al., 1996). The number of observed and the number of expected multilocus genotypes (MLG) was used as a first estimate of within population MLG diversity. We also calculated whether the number of MLG differs significantly (p<0.05) from a random sample based on allele frequencies using the software HWclon (J. Vanoverbeke, unpubl.). In addition, we also quantified MLG diversity as the reciprocal of the Simpson index (1/λ), representing the probability that two randomly drawn individuals have the same multilocus genotype (Lande, 1996). The Simpson index not only takes into account the number of different MLG but also their relative abundance. Given the low number of polymorphic allozyme loci in D. ambigua, the number of MLG probably strongly underestimates the real number of clones in the population. We tested whether downstream ponds within pond clusters (see Fig. 1) are characterised by higher MLG diversity and thus function as a MLG sink, by
82 Genetic variation at neutral markers in D. ambigua calculating the correlation between local MLG diversity and the total pond load of the corresponding pond. The total pond load corresponds with the total (cumulative) number of upstream ponds that are connected with a given pond. High pond loads indicate that a pond may function as a sink for many source ponds, whereas ponds characterised by low ponds load are more likely to act as sources (see Pulliam, 1988; Dias, 1996).
Genetic diversity versus habitat size
We tested the prediction that populations inhabiting larger habitats are characterised by higher genetic diversity by testing the correlation between pond surface area as an estimate of habitat size and average heterozygosity and MLG diversity, respectively. Cyclically parthenogenetic Daphnia populations inhabiting intermittent populations start the growing season from sexual eggs and they build up clonal populations during the course of the growing season. As such, we anticipate that allelic diversity at the start of the growing season may be considered a good measure of the amount of allelic diversity that can be maintained in the population throughout the years, including the potential buffering effect of the resting egg bank. At the end of the growing season, MLG diversity is informative on the amount of genetic diversity that can be maintained in the local habitat in the face of clonal selection. Finally, the difference in expected heterozygosity between the beginning of the growing season and the one predicted from allelic frequencies at the end of the growing season may indicate mechanisms that help maintain genetic diversity in the population, such as the occurrence of different periods of sexual mixis throughout the growing season or a buffering effect of the resting egg bank through overlapping generations combined with fluctuating selection.
Genetic differentiation among populations
F-statistics calculated according to Nei (1977, GST) were computed in GENETIX (Belkhir et al., 1996) to quantify the extent of allelic differentiation between populations. Significance of genetic differentiation was tested using a randomisation test on allelic frequencies in TFPGA (Miller, 1997). In the specific setting of cyclical parthenogenetic reproducing Daphnia populations inhabiting interconnected ponds among which dispersal of
83 Chapter IV parthenogenetic individuals is possible, it is relevant to compare the distribution of specific multi-locus genotypes among populations because, at least theoretically, a given MLG may represent the same clone in different populations. In unconnected populations among which sexually reproduced resting eggs are the only mode of dispersal, the same MLG in different ponds would always represent different clones in cyclically parthenogenetic species. We used RxC resampling tests (Miller, 1997) to test whether MLG frequencies differ significantly among populations.
Spatial structure versus environmental differences
To test for isolation-by-distance, we performed Mantel tests (Mantel, 1967) looking for an association (10000 permutations) between pairwise GST values between all possible pairs of populations and effective geographic distance between the corresponding pairs of ponds, using the freeware Mantel function (Reynolds J.H. & Bolker B., available from aus.stats.s) for use in S (Professional version, Insight, 2000). In addition, we also performed a Mantel test between pairwise genetic distance and ecological distances between ponds. The effective geographic distance between two populations was modelled in a G.I.S. environment using the IDRISI software package (Version 2.0; Eastman, 1997) and reflects the effort a dispersing zooplankton organism has to exert to travel from the “source” pond to the “target” pond via the pathway of connecting elements (e.g. rivulets or overflows) between them. The effective geographic distance is function of the travelling distance and proportional to the flow rate measured in the connecting elements a dispersing organism encounters on its route. The data on effective geographic distance among ponds were based on flow rates and were taken from Michels et al. (2001b; see Chapter III). Ecological distances among ponds were computed as Euclidean distance calculated with field data collected in 1998 on biotic and abiotic factors in ponds in De Maten (e.g. depth; secchi depth; pH; conductivity; O2-concentration; concentration of total phosphorus; nitrogen concentration; chlorophyll-a concentration; density of submerged macrophytes; diversity of marco-invertebrates; total fish density). More details on the measurement of the ecological variables in 1998 are given in Cottenie et al. (unpubl. manuscr.).
84 Genetic variation at neutral markers in D. ambigua
Results
Genetic diversity within populations
Table 1 shows allele frequencies of the two loci (PGM and GPI) and sample sizes for the April samples; allele frequencies for the samples taken in November are given in Michels et al. (2001b; see Chapter III). Table 2 shows values of observed and expected heterozygosity for each locus and averaged over both loci for both sample periods. After sequential Bonferroni correction, no significant departures from Hardy- Weinberg equilibrium were detected for the two analysed loci (Table 3). In Figure 2, the allele frequencies of different alleles at the two loci are plotted according to the position of the habitat along the pond complex for April and November to verify the occurrence of clines. No strong clinal patterns in allele frequencies could be detected along the whole pond complex. Within some of the clusters of ponds as determined by GIS modelling (see Michels et al. 2001b; Chapter III; also see Fig 1), there is a tendency for clinal variation (e.g. for GPI in April and PGM in November in pond cluster 2-15; downstream cluster). We tested, whether MLG diversity was correlated with the total pond load (Fig. 3). The correlation between total pond load and MLG diversity was not significant neither in April (R = -0.038; p = 0.89) nor in November (R = -0.30; p = 0.44), suggesting that ponds with high pond load are not richer in MLG diversity than source ponds.
85 Chapter IV
Table 1: Allele frequencies at the GPI and PGM locus of D. ambigua populations in De Maten for April 1998. Sample sizes are given between parentheses. Alleles at each locus were labelled according to their mobility: S: slow; M: medium; F: fast. The number of the population refers to the pond number as indicated in Figure 1. Allele frequencies at the two loci of populations sampled in November are given in Michels et al. (2001b; Chapter III).
APRIL
Population GPI PGM Population GPI PGM 2 S 0.51 S 0.25 18 S0.45 S 0.44 (61) F 0.49 M 0.48 (60) F 0.55 M 0.35 F 0.27 F 0.21
3 S 0.52 S 0.23 22 S 0.56 S 0.29 (32) F 0.48 M 0.48 (51) F 0.44 M 0.46 F 0.29 F 0.25
6 S 0.36 S 0.12 23 S 0.50 S 0.23 (61) F 0.64 M 0.52 (51) F 0.50 M 0.61 F 0.36 F 0.16
8 S 0.54 S 0.26 24 S 0.54 S 0.15 (59) F 0.46 M 0.54 (37) F 0.46 M 0.56 F 0.20 F 0.29
9 S 0.57 S 0.19 27 S 0.56 S 0.29 (50) F 0.43 M 0.51 (32) F 0.44 M 0.45 F 0.30 F 0.26 10 S 0.54 S 0.26 28 S 0.61 S 0.21 (58) F 0.46 M 0.56 (35) F 0.39 M 0.51 F 0.18 F 0.28
13 S 0.68 S 0.20 30 S 0.44 0.20 (61) F 0.32 M 0.56 (27) F 0.56 0.65 F 0.21 0.15 15 S 0.58 S 0.21 (44) F 0.42 M 0.50 F 0.29
86 Genetic variation at neutral markers in D. ambigua
Table 2: Observed (ho) and expected (he) heterozygosity at the two studied loci and averaged over both loci in the D. ambigua populations in De Maten sampled at the beginning (April) and at the end (November) of the growing season. The number of the population refers to the pond number as indicated in Figure 1.
APRIL PGI PGM Average over loci
ho he ho he ho(g) he(g) 2 055 050 073 063 064 057 3 0.52 0.50 0.79 0.63 0.65 0.57 6 0.39 0.46 0.67 0.59 0.53 0.53 8 0.67 0.50 0.70 0.60 0.68 0.55 9 0.42 0.49 0.70 0.61 0.56 0.55 10 0.51 0.50 0.58 0.59 0.54 0.54 13 0.50 0.43 0.72 0.57 0.61 0.50 15 0.56 0.49 0.63 0.62 0.59 0.55 18 0.61 0.49 0.71 0.64 0.66 0.57 22 0.44 0.49 0.64 0.64 0.54 0.57 23 0.52 0.50 0.59 0.55 0.56 0.52 24 0.58 0.50 0.64 0.58 0.61 0.54 27 0.45 0.49 0.61 0.64 0.53 0.57 28 0.50 0.48 0.72 0.62 0.61 0.55 30 0.37 0.49 0.56 0.52 0.46 0.51 Average 0.51 0.49 0.67 0.60 0.59 0.55 NOVEMBER PGI PGM Average over loci
ho he ho he ho(g) he(g) 4 045 048 055 065 050 049 9 0.55 0.50 0.68 0.55 0.62 0.53 10 0.56 0.50 0.52 0.53 0.54 0.52 12 0.45 0.50 0.47 0.49 0.46 0.48 14 0.45 0.50 0.51 0.50 0.48 0.49 19 0.51 0.50 0.72 0.66 0.61 0.58 21 0.60 0.47 0.67 0.66 0.63 0.57 23 0.49 0.48 0.70 0.58 0.59 0.53 27 0.57 0.50 0.64 0.59 0.61 0.55 34 0.39 0.45 0.77 0.61 0.59 0.53 Average 0.50 0.49 0.62 0.58 0.56 0.53
87 Chapter IV
Tabel 3: Fixation indices in the different D. ambigua populations in De Maten for both loci analysed in April and November 1998. Significance levels for deviations from Hardy-Weinberg equilibrium calculated using χ²-tests are marked with*. None of the deviations were significant at the table-wide level, after sequential Bonferroni correction (Rice, 1989). The number of the population refers to the pond number as indicated in Figure 1.
April GPI PGM November GPI PGM Population F F Population F F 2 -0.099 -0.234 4 0.069 0.157 3 -0.017 -0.226 9 -0.099 -0.221 6 0.167 -0.137 10 -0.089 0.035* 8 -0.336 * -0.163 12 0.092 0.034 9 0.153 -0.131 14 0.117 -0.004 10 -0.014 0.021 19 -0.016 -0.074 13 -0.147 -0.257* 21 -0.250 -0.014* 15 -0.135 0.002 23 -0.003 -0.192 18 -0.220 -0.105 27 -0.133 -0.074 22 0.117 0.010 34 0.149 -0.258* 23 -0.030 -0.070 24 -0.161 -0.082 27 0.093 0.074 28 -0.038 -0.160 30 0.268 -0.057 Average -0.027 -0.101 -0.016 -0.061
The MLG frequencies observed in the analysed D. ambigua populations are presented as pie diagrams in Figure 4. The number of multilocus genotypes (MLG) and MLG diversity detected on the basis of electrophoretic variation at two loci (PGM and GPI) in different ponds during both sampling periods are presented in Table 4. The average number of MLG was 13.7 for April and 11.4 for populations sampled in November, respectively. Given the low number of polymorphic loci, the maximum number of detectable MLG was limited to 18. Average MLG diversity in April was 8.8 and 8.4 in November. The highest MLG diversity was observed in population 22 in April (CD = 12.13) and in population 19 in November (CD = 11.56). Figure 5 shows that MLG diversity in November tends to be lower than in April in all four populations for which we have data on the two dates. This decrease in the number of MLG and MLG diversity is significant (paired t-test: df = 3; p= 0.005 and p= 0.018 respectively). This suggests a significant impact of clonal selection averaging to a loss of approximately 17% of MLG diversity during the course of a growing season.
88 Genetic variation at neutral markers in D. ambigua
Relation between genetic diversity and habitat size
We tested the prediction that populations inhabiting larger habitats are characterised by higher genetic diversity, using pond surface area as an estimate of habitat size and average heterozygosity and MLG diversity as measures for genetic diversity. We observed a significant positive correlation between the average heterozygosity and the logarithmically transformed surface area for populations sampled in April (R = 0.64; p = 0.011). This relation was not significant for populations sampled in November (R = -0.2; p = 0.57). For MLG diversity, no significant correlation was observed with pond surface area, neither in April (R = -0.34; p = 0.22) nor in November (R = 0.25; p = 0.4).
Genetic differentiation among populations
For both sampling periods, GST values averaged across all loci indicate relatively low, but highly significant genetic differentiation among the sampled D. ambigua populations (Table 5). Interestingly, populations sampled at the end of the growing season were, averaged over all loci, slightly more differentiated than populations sampled at the beginning of the growing season. (April: overall GST = 0.015; p
<0.0001; November: overall GST = 0.023; p <0.0001; an increase of 32 %). Both loci had negative values of FIS and contributed to heterozygote excess (Table 3). In
Appendix 1 the pairwise measures for genetic differentiation (GST) among the D. ambigua populations sampled in April are presented; values of pairwise GST among the D. ambigua sampled in November are given in Michels et al. (2001b; Chapter III). The analysis of genetic differentiation among D. ambigua populations based on MLG composition using a RxC resampling test also indicates a highly significant differentiation in MLG composition among D. ambigua populations in April as well as in November (both: p < 0.0001; S.E.< 0.0001).
The results of RxC resampling tests indicate that genotype frequencies were significantly different between the beginning and the end of growing season in two of the populations that were sampled both in April and November (population 9: p = 0.024 and S.E. = 0.0043; population 23: p = 0.022 and S.E. = 0.004).
89 Chapter IV
0.70 0.7 GPI April PGM April 0.65 0.6
0.60 0.5
0.55 0.4 0.50 0.3 0.45 Allele frequency Allele frequency Allele 0.2 0.40 S allele S allele 0.1 0.35 M allele F allele F allele
0.30 0.0 2 3 6 8 9 10 13 15 18 222324 2728 30 2368910131518222324272830 Population Population
0.70 0.8
GPI November PGM November S allele 0.65 0.7 M allele F allele 0.60 0.6
0.55 0.5
0.50 0.4
0.45 0.3 Allele frequency allele frequency allele
0.40 0.2
S Allele 0.35 0.1 F allele
0.30 0.0 4 9 10 12 14 19 21 23 27 34 4 9 10 12 14 19 21 23 27 34 Population Population
Figure 2: Allele frequencies of the different alleles at the GPI (two alleles) and PGM locus (three alleles) in D. ambigua sampled in ponds in De Maten in April (top) and November (bottom). Ponds were ranked according to their position along the pond complex and grouped (connecting lines) per cluster presented in Fig. 1. Alleles were named according to their electrophoretic mobility (S: slow; M: medium; F: fast).
90 Genetic variation at neutral markers in D. ambigua
15 April November 11 14 10 13 12 9 11 8 10 7 MLG diversity 9 MLG diversity 6 8 7 5
246810 246810 Total pond load Total pond load
Figure 3: Relation between total pond load and D. ambigua multilocus genotype (MLG) diversity observed in a specific pond sampled in April and November. Total pond load corresponds with cumulative number of upstream ponds that are connected with a specific pond.
91 Chapter IV
FFMF SSSM FFMM SSSF
FFSF SSMM
FFSM SSMF
FFSS SSFF
SFFF SFSS
SFMF SFSM SFMM SFSF
Figure 4: Pie diagrams of multilocus genotype (MLG) frequencies of D. ambigua populations sampled in different ponds in De Maten in April (top) and November (bottom). MLG correspond with electrophoretic mobility at the GPI (two alleles) and PGM (three alleles) locus. Alleles were named according to their electrophoretic mobility (S: slow; M: medium; F: fast). Ponds from which D. ambigua were sampled in April: 2; 3; 6; 8; 9; 10; 13; 15; 18; 22; 23; 24; 27; 28 and 30. Ponds sampled in November: 4; 9; 10; 12; 14; 19; 21; 23; 27 and 34. Pond numbers are shown in Fig. 1.
92 Genetic variation at neutral markers in D. ambigua
Table 4: The number of observed and the number of expected multilocus genotypes and MLG diversity in the analysed D. ambigua populations sampled in April (top) and November (bottom) in De Maten. Populations in which the number of clonal groups differs significantly (p< 0.05) from a random sample based on allele frequencies are indicated with * (calculated with HWclon; Joost Vanoverbeke unpubl.). The number of the population refers to the pond number as indicated in Figure 1.
April # observed # expected Observed MLG Expected MLG multilocus multilocus diversity diversity Populations genotypes genotypes 2 14 16 1 797 * 11 2 * 3 12 13.0 8.71 9.6 6 13 14.0 6.62 *8.9 * 8 14 15.1 7.26 *10.1 * 9 15 14.8 9.77 10.1 10 16 14.9 9.58 9.8 13 12 13.9 6.87 *8.7 * 15 15 14.4 9.99 10.1 18 12 15.7 8.39 * 11.0 * 22 16 15.2 12.1 10.8 23 13 13.2 8.57 8.7 24 12 12.7 7.03 8.8 27 16 14.0 11.7 10.2 28 13 13.4 9.82 9.6 30 12 10.8 8.01 7.5 Average 13.7 14.1 8.80 9.7
November # observed # expected Observed MLG Expected MLG multi locus multilocus diversity diversity Populations genotypes genotypes Populations * 9 11 * 13.6 * 6.2 8.8 * 10 13 12.8 7.4 8.2 12 10 10.6 6.9 6.6 14 13 12.6 8.1 7.9 19 17 16.6 11.6 * 11.9 21 14 15.2 8.6 11.0 * 23 11 11.9 7.1 8.2 27 13 14.2 8.2 * 9.3 34 13 14.7 7.7 9.7 * Average 11.4 13.8 8.4 9.3
93 Chapter IV
10 MLG diversity
9
April November
8 27 9 10 23 Population
Figure 5: Multilocus genotype diversity in D. ambigua populations that where sampled both in April and November. Ponds were ranked according to descending MLG diversity. Pond numbers are presented in Fig.1.
Table 5: F-statistics based on allele frequencies within D. ambigua population sampled in De Maten at the beginning (April) and at the end (November) of the growing season. Overall significance over loci was determined using Fischer’s exact test. *** p<0.0001; ** p <0.001
Locus FIS FIT FST April GPI -0.040 -0.023 0.016 ** PGM -0.100 -0.084 0.014*** All -0.073 -0.057 0.015***
November GPI -0.017 -0.013 0.004 PGM -0.061 -0.019 0.039*** All -0.041 -0.016 0.023***
94 Genetic variation at neutral markers in D. ambigua
Table 6: Results of Mantel tests, testing for a correlation between genetic distances ((A) pairwise GST; (B) Euclidean distance based on multilocus genotype (MLG) composition) among D. ambigua populations inhabiting a subset of ponds in De Maten and the ecological distances among ponds (Euclidean distance based on dataset on biotic and abiotic variables measured in ponds; see Cottenie et al., unpubl. manuscr.) or the effective geographical distances among ponds as modelled in Michels et al., (2001b; Chapter III). ‡ data extracted from Michels et al., 2001b. ** p < 0.01.*** p <0.0001 †significant at α=0.05 after sequential Bonferonni correction for multiple testing (Rice, 1989).
Genetic Distance
(A) Pairwise Gst (B) Euclidean distance (MLG) R p-level R p-level
APRIL Ecological distance 0.048 0.32 0.033 0.41 Effective geographical distance 0.10 0.078 0.097 0.15 NOVEMBER Ecological distance -0.020 0.55 -0.017 0.53 Effective geographical distance 0.48‡ 0.0028** † 0.61 0.0002***†
The impact of spatial structure of pond complex and ecological distance among ponds
A major focus of this study was to determine whether the explicit spatial structure of the pond complex or rather the ecological distances among ponds contributed to the genetic structuring of the D. ambigua populations in De Maten. The results of the Mantel tests, testing for a correlation among genetic distance and ecological distance as well as among genetic and effective geographic distances in both seasons (April and November) are given in Table 6. For populations sampled in April, no significant correlation was found between genetic distance and effective geographic distance nor between genetic and ecological distances between ponds. For population sampled in November, however, the correlation between genetic distance and effective geographic distance was highly significant (see also Michels et al., 2001b; Chapter III) whereas no significant correlation could be found between genetic and ecological distances between ponds.
95 Chapter IV
The correlation of the different distance matrices (effective geographic distance and ecological distance) with pairwise values of Euclidean distances among populations based on MLG frequencies yielded a similar pattern as was obtained for data on genetic distance using pairwise GST (Table 6). For populations sampled in April, the relation between Euclidean distance and effective geographic distance was not significant. At the end of the growing season (November), however, the correlation between Euclidean distance and effective geographic distance is significant. Despite the lack of a significant isolation-by-distance geographic pattern at the beginning of the growing season, we could find indications for a subtle genetic structure in the D. ambigua metapopulation in April. The allelic differentiation among populations that belong to different clusters in terms of effective geographic distance was highly significant (p <0.01; Exact test; clusters of ponds are shown in Fig. 1). In addition, no significant genetic differentiation in allele frequencies was found among ponds that belong to the same cluster (p >0.05; Exact test) except for population 6 that showed significant genetic differentiation from all other populations within its cluster. These results indicate a pattern related to major dispersal pathways, but independent of geographic distance per se.
Discussion
D. ambigua in Europe The D. ambigua populations in De Maten are characterised by an overall low level of polymorphism for allozyme loci, illustrated by our observations that only two out of 12 loci tested were polymorphic. This may, however, be a general characteristic of this species as our observations of low polymorphism for allozymes are in agreement with the results of the only two previous studies on the population genetic structure of this species. Platt & Spitze (2000) observed that only three allozyme loci (PGM, PEP2 and FUM) out of eight were polymorphic in subtropical populations of D. ambigua. Zofková (pers. comm) reported that all examined D. ambigua populations in the Czech Republic proved to be monomorphic in a survey involving six allozyme loci including PGM and GPI. To the extent that the level of polymorphism for allozyme loci observed by us is still lower than that reported by Platt & Spitze (2000), it may
96 Genetic variation at neutral markers in D. ambigua reflect the fact that D. ambigua is an exotic species in Europe. The complete absence of polymorphism for allozyme loci in Czech D. ambigua populations observed by Zofková (2000) supports the idea that the low genetic variability observed in European D. ambigua populations could indeed be due to the fact that this species has been introduced in Europe recently. Moreover, Zofková (2000) found an absolute genetic congruence of Czech D. ambigua populations with American populations based on a comparison of RFLP fragments of 16S rDNA. This evidence supports the hypothesis of Flössner & Kraus (1976) concerning the presumed American origin of D. ambigua (Zofková, 2000). To date, the exact mode of introduction of D. ambigua on the European continent remains unclear. Daphnia ambigua was originally described by Scourfield (1947) from a little pond in the Kew Botanical Gardens, London, U.K. and then repeatedly recorded from other lakes and ponds in London parks (Flössner & Kraus, 1976). Hence, the introduction of D. ambigua through human activities, such as the introduction associated with imported aquatic plants combined with a rapid spreading with transport of plants and fish, is a plausible explanation (Zofková, 2000). The species then suddenly appears in water bodies in different localities along the southern and western part of the European continent starting from the early seventies (first record in Belgium by Dumont, 1974; Germany: Flössner & Kraus, 1976; France: Amoros, 1980; Slovakia: Vranovský & Terek, 1996). This pattern suggests a secondary eastward expansion of the species within Europe. The fact that we observed a higher genetic polymorphism of D. ambigua in the Belgian populations than was observed for the Czech populations (Zofková, pers. comm.) is in line with this hypothesis. D. ambigua are well adapted to survive in habitats with strong fish predation, due to their relatively small body size (D. ambigua in De Maten were ± 1 mm; Chapter V). The competitive ability of D. ambigua (see Maier, 1996) and its potential to adapt to predators (e.g. see Hanazato, 1990; Hanazato & Ooi, 1992) may explain the success of the invasion of this species in Europe.
Genetic erosion during the growing season
The D. ambigua populations in De Maten are characterised by genotype frequencies in Hardy-Weinberg equilibrium both at the beginning and at the end of the growing season. We did observe, however, a significant decrease in MLG diversity during the
97 Chapter IV growing season for the limited set of populations that could be sampled both in April and November. Overall, our data suggest that D. ambigua populations in De Maten correspond with the characteristics of a typically intermittent Daphnia population, with genotype frequencies that are in close agreement with Hardy-Weinberg expectations and recruitment of new genotypes from the resting egg bank at the beginning of the growing season and clonal selection during the season (Lynch, 1984; Hebert, 1987; Carvalho & Crisp, 1987; Lynch & Spitze, 1994; De Meester, 1996b). These findings are in agreement with the findings on (subtropical) D. ambigua by Platt & Spitze (2000). They concluded that the studied D. ambigua population was likely to be temporary, or at least had a substantial periodic recruitment from sexual resting eggs. Two previous studies on D. ambigua reported that no sexual females were observed (Dumont, 1974; Zofková, 2000). Dumont (1974) suggested that this species might reproduce in Belgium by parthenogenesis for several years uninterruptedly. Our findings suggest, however, that this is unlikely for the D. ambigua populations in De Maten, given that genotype frequencies were all in Hardy- Weinberg equilibrium. Moreover, the populations seem to disappear in winter and reappear the following spring (Michels & De Gelas, pers. obs.).
Genetic diversity versus population size
It is generally recognised that ecological diversity increases with increasing habitat size. In addition, population size tends to be positively correlated with habitat size. Large habitats will have a larger stock of resting eggs than smaller habitats and hence a higher recruitment of MLG from the resting egg bank (see Carvalho & Wolf, 1989; Wolf & Carvalho, 1889; Jacobs, 1990). The recruitment from sexual eggs from the resting egg bank can have a profound impact on the genetic structure of cyclic parthenogenetic Daphnia populations (Korpelainen, 1986b; Lynch & Deng, 1994). The presence of an active egg bank containing viable D. ambigua ephippia in De Maten has been confirmed by hatching experiments using sediments originating from De Maten (Jochen Vandekerkhoven, pers. comm.). Hence, it can be expected that large ponds are characterised by a higher genetic (allelic and MLG) diversity compared to small habitats (see Vanoverbeke, unpubl. manuscr.). A significant positive relationship between heterozygosity and habitat size in De Maten was indeed found at the beginning of the growing season. At the end of the growing season,
98 Genetic variation at neutral markers in D. ambigua however, no significant positive relation was found between heterozygosity and habitat size. MLG diversity did not show a positive relationship with habitat size, neither in April nor in November.
Genetic differentiation among populations Using data on allele frequencies as well as data on the MLG composition, we observed fine-scale genetic differentiation among the D. ambigua populations inhabiting the interconnected ponds in De Maten. Genetic differentiation tends to increase towards the end of the growing season (April GST = 0.015 versus November
GST = 0.023). The observed GST values for genetic differentiation among interconnected D. ambigua population in De Maten are very low compared to values for genetic differentiation among non-connected Daphnia populations (0.13-0.68; see Innes, 1991). Weider (1989) also observed that genetic differentiation among Polyphemus pediculus populations in interconnected lakes was much lower than levels of genetic differentiation observed among isolated lakes. Given that the dispersal among interconnected water bodies may also be mediated by water currents carrying actively reproducing individuals (Akopian et al., 1999; Michels et al., 2001a; Chapter II), it is obvious that interconnected lakes provide greater opportunities for gene flow than isolated lakes. Therefore, our observation that genetic differentiation among D. ambigua populations in De Maten is highly significant is somewhat unexpected, especially in the face of (1) the presence of connecting elements among ponds that function as direct pathways for passive dispersal of parthenogenetic females and (2) the very small geographic scale of this survey (distance among ponds varied from only 8 to 2100 m, which is well below the spatial resolution in most population genetic studies in zooplankton), (3) the observation that dispersal rates among ponds were found to be substantial (up to 60 Daphnia ind./h; see Michels et al., 2001a; Chapter II) and (4) the low resolution of the genetic marker used. Gene flow among populations as measured in the present study reflects successful dispersal, i.e. dispersal followed by successful establishment in the target population (Slatkin, 1985). Some of the ponds in De Maten differ strongly in their ecology (Cottenie et al., 2001, unpubl. manuscr.). Hence, the ecological conditions that dispersing individuals encounter in the target habitat can be very different from the conditions in the source
99 Chapter IV habitat, potentially reducing establishment success (Michels et al., 2001b; Chapter III).
Temporal patterns Our results suggest that the geographic pattern of genetic differentiation (both for allele frequencies and for MLG composition) became largely established during the course of a growing season. At the beginning of the growing season, most populations are characterised by a large number of MLG hatching from the resting egg bank and are not yet dominated by a few dominant MLG (see Lynch 1984; Carvalho & Crisp, 1987). As differences in MLG composition build up during the growing season, the significant correlation between genetic distance among populations and the effective geographic distance observed at the end of the growing season indicates that there is a significant influence of passive dispersal with water flow on MLG composition. The shift in MLG frequencies during the growing season observed for populations 9 and 23 is suggestive for fitness differences among different clonal groups. These fitness differences are probably not associated with the studied loci itself, but are probably due to fitness differences in linked loci. This observation is in agreement with the findings of Korpelainen (1986a) who observed that shifts in MLG composition in D. magna populations during the growing season often have a unique character in different habitats. The pairwise GST value among ponds 9 and 23 for
November is higher than the value for April (pairwise GST April = 0.0096 versus pairwise GST November = 0.02), suggesting that natural selection and/or genetic drift, has resulted in genetic differentiation during the course of the growing season. The fact that such diverging patterns are observed in Daphnia populations inhabiting the pond complex of De Maten is remarkable given that the populations are strongly connected. This limited impact of clonal selection may be explained by the short period for clonal selection (Lynch & Spitze, 1994). In this respect, however, it is also important to note that our estimations of the number of MLG are very conservative, due to the limited number of polymorphic loci involved in our analysis.
Geographic versus ecological distance as structuring factors
The correlations between the genetic distance (pairwise GST and Euclidean distance among populations based on MLG frequencies) with the effective geographic distance was significant at the end of the growing season (November; also see Michels et al.,
100 Genetic variation at neutral markers in D. ambigua
2001b; Chapter III), whereas this correlation was not significant at the beginning of the growing season (April). These findings suggest that the spatial pattern of genetic differentiation that was observed for the populations sampled in November develops during the growing season, i.e. in a time span of approximately seven months. Given the profound differences in ecology among the ponds of De Maten, we tested whether differences in ecological characteristics among ponds influenced the pattern of genetic differentiation among the studied D. ambigua populations. The correlation between genetic distances among D. ambigua populations and ecological distances among their habitats was, however, not significant. Given the presumed neutrality of allozymes (but see Johanneson & Johanneson, 1989; Riddoch, 1993; Tatarenkov & Johanneson, 1994) this lack of a correlation of allelic variation with ecological characteristics is not unexpected. In order to test for local adaptation in D. ambigua inhabiting ecologically different ponds, studies on genetic differentiation for ecologically relevant traits are needed. Data from well-designed studies on ecologically relevant traits are less abundant than studies using neutral markers and often reveal a different pattern. Whereas the analysis of (quasi-)neutral markers emphasises the importance of long lasting founder effects and genetic drift, the pattern of local genetic differentiation for ecologically relevant traits may often reveal local adaptation associated with ecological differences among habitats (De Meester, 1996a, b; Lynch et al., 1999; Cousyn et al., 2001; Jaramillo-Correa et al., 2001; Morgan et al., 2001; Reede & Frankham, 2001). Summarising, our results suggest that the spatial structure of the pond complex rather than ecological differences among ponds determines the genetic structure with respect to neutral markers in the D. ambigua populations in De Maten.
D. ambigua in De Maten as a patchy metapopulation
Metapopulation biology is concerned with the dynamic consequences of dispersal among local populations and thus provides a useful framework to describe interactions among spatially structured populations (Gilpin & Hanski, 1997; Hanski, 1998; also see Chapter I). The dynamics of local populations and the structure of the pond complex in De Maten do not conform to the classical metapopulation concept as proposed by Levins (1970), given that the patches within this study-site are ecologically different (Cottenie et al., 2001; unpubl. manuscr.) and local populations
101 Chapter IV are intermittent (this study). The zooplankton inhabiting the pond complex in De Maten, however, meets the broader view of a metapopulation (sensu Hanski & Simberloff, 1997) since (1) they are clearly spatially structured into assemblages of local (breeding) populations, (2) dispersal among populations is subtantial (Michels et al., 2001a; Chapter II) and (3) dispersal among local populations affects population genetic structure (Michels et al., 2001b; Chapter III; this study). The observation of highly significant genetic differentiation among the three major pond clusters in terms of effective geographical distance both at the beginning and at the end of the growing season illustrates that the Daphnia population in De Maten should not be considered to be a single large (panmictic) population. Rather, our data suggest that the patchy metapopulation model as proposed by Harrison (1991) and Harrison & Taylor (1997) may be an appropriate model for the Daphnia populations in the pond complex of De Maten. In the patchy metapopulation model, recurrent exinction events of local patches are absent or unimportant due to the high degree of interaction among patches. The latter is clearly the case for the zooplankton in the pond complex in De Maten (Michels et al., 2001a; Chapter II). The three major pond clusters (see Michels et al., 2001b; Chapter III) can then be considered as a structuring force, as they are entities with their own specific genetic structure (both for allele and MLG frequencies).
Acknowledgements We thank Joost Vanoverbeke, Filip Volckaert and Luc Brendonck for discussions and constructive comments on earlier versions of this manuscript. We thank Magdalena Zofková for providing information on Czech D. ambigua populations. We thank vzw Natuurpunt and especially the warden Willy Peumans for permission to carry out this study in De Maten and for their full cooperation. EM and KDG acknowledge a scholarship provided by the Institute for the promotion of Scientific Technological Research (I.W.T). KC is a research assistent of the Fund for Scientific Research. This study was financially supported by project VLINA/96/1 of the Flemish Government and by project G.0358.01 of the Fund for Scientific Research, Flanders.
102 Genetic variation at neutral markers in D. ambigua
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108
Appendix
Appendix 1: Pairwise GST values (Nei, 1973, 1977) quantifying genetic differentiation among the D. ambigua populations in De Maten sampled in April. The number of the population refers to the pond number as indicated in Figure 1.
2 3 6 8 9 10 13 15 18 22 23 24 27 28 30 2 - 3 0.0024 - 6 0.027 0.015 - 8 0.0007 0.0029 0.025 - 9 0.0004 0.0019 0.021 0.0037 - 10 0.0014 0.004 0.025 0.0002 0.0051 - 13 0.012 0.017 0.055 0.01 0.0096 0.012 - 15 0.0032 0.0021 0.024 0.0033 0.0002 0.0049 0.0085 - 18 0.015 0.016 0.036 0.02 0.027 0.021 0.051 0.026 - 22 0.0009 0.0019 0.027 0.0028 0.0033 0.0042 0.014 0.0023 0.013 - 23 0.0056 0.0077 0.023 0.0026 0.0096 0.0015 0.017 0.01 0.028 0.01 - 24 0.0063 0.0032 0.017 0.0042 0.0013 0.0049 0.012 0.0023 0.033 0.0071 0.0067 - 27 0.0012 0.0019 0.027 0.0029 0.0033 0.0043 0.014 0.0023 0.013 0.00 0.014 0.0071 - 28 0.0041 0.004 0.031 0.0041 0.001 0.56 0.0051 0.0005 0.00 0.0035 0.011 0.0035 0.0034 - 30 0.013 0.013 0.02 0.0085 0.017 0.0066 0.029 0.019 0.034 0.019 0.002 0.012 0.019 0.021 -
Genetic differentiation for ecologically relevant traits in D. ambigua
Chapter V
Genetic differentiation for ecological relevant traits in a patchy metapopulation of the cyclical parthenogen Daphnia ambigua
Manuscript together with K. Uyttebroek & L. De Meester
Abstract In a cohort life table experiment, we tested whether genetic variation for a set of key life history traits was present among eleven Daphnia ambigua clones cultured in the presence and the absence of fish chemicals that were isolated from six interconnected ponds in the nature reserve De Maten that differed in water transparency. In addition, using a simple experimental set-up, we quantified the phototactic behaviour of these clones cultured in the presence and absence of fish chemicals. We detected significant genetic variation for phototactic behaviour and marginally significant genetic variation for size at maturity among D. ambigua clones isolated from ponds that differed in water transparency. The presence of fish chemicals did not alter the phototactic behaviour dramatically and did not affect key life history traits. We observed a significant positive relationship between average phototactic behaviour for each population and size at maturity, two clearly fitness related traits in Daphnia. Most of the genetic differences could be attributed to a clone isolated from pond 34, a clearwater pond that is not directly connected to the pond complex. Even though we experienced problems
111 Chapter V while establishing laboratory cultures from the field, and only a very limited set of clones were examined, the observed differences in phototactic behaviour and size at maturity of D. ambigua clones isolated from these six ecological contrasting ponds were in agreement with the expectation under the hypothesis of local adaptation. Local adaptation for phototactic behaviour in D. ambigua clones from De Maten was not expressed in terms of changes in behaviour upon exposure to fish chemicals, but rather at the mean value over environments.
Introduction
The presence of planktivorous fish is an important selective force in natural zooplankton populations (Gliwicz & Pijanowska, 1986; Lampert, 1987; Kerfoot & Sih, 1997). This is emphasized by the existence of a wide array of predator-induced defence mechanisms in zooplankton (see Tollrian & Harvell, 1999). Planktivorous fish are visually hunting predators that are highly efficient and positively size-selective (Gliwicz & Pijanowska, 1986; Lampert, 1987; Link, 1996). Differences in life-history characteristics in zooplankton have important consequences for the vulnerability of zooplankton to visual predators (see Tollrian & Harvell, 1999). Behavioural traits are also involved in strategies to minimise the effects of visual predation by planktivorous fish. Among those, phototactic behaviour, a key determinant of diel vertical migration (DVM) and depth selection behaviour (DSB), has been shown to be important in predator avoidance (De Meester et al., 1995; De Meester et al., 1999; Ringelberg, 1999). DSB in zooplankton refers to the fact that zooplankton exhibits behavioural mechanisms which results in a specific (daytime, nighttime) distribution relative to the vertical stratification of the water column. This stratification of the water column relates to changes in important selective factors such as temperature, light intensity, food quality, food quantity and predation pressure. DVM is a special case of DSB, in which the preferred depth changes in a diel pattern (De Meester et al., 1999). DVM in zooplankton is a widespread phenomenon that has been observed in nearly all classes of planktonic organisms in all kind of marine and limnetic biota (see reviews by Cushing, 1951; Haney, 1993). Although the adaptive significance of DVM has been the subject of many debates in the past, it is now generally recognised that the avoidance of predators is an ultimate key factor (Lampert, 1989; 1993; De Meester et al., 1999). During the most typical, so-called nocturnal DVM, the zooplankton travels down the water column at the break of dawn, and stays in the
112 Genetic differentiation for ecologically relevant traits in D. ambigua deeper strata of the water column during the day. Around sunset, the animals migrate back to the upper water layers to feed on algae (Cushing, 1951; Stich & Lampert, 1981; Ringelberg & Flik, 1994). Primary phototaxis is a highly heritable trait in Daphnia (De Meester, 1991) and is relevant to daytime depth distribution (De Meester, 1993). Daphnia clones may become negatively phototactic in the presence of fish kairomones and more positively phototactic in the presence of kairomones produced by invertebrate predators such as Chaoborus larvae (Ringelberg, 1991; De Meester, 1993; Larsson & Dodson, 1993; Nesbitt et al., 1996). Comparing the patterns of genetic differentiation among zooplankton populations both at neutral markers and at ecologically relevant traits, creates the opportunity to gain insight in the relative importance of random genetic drift or gene flow versus natural selection as evolutionary forces driving the process of genetic differentiation among zooplankton populations (De Meester, 1996b). Moreover, the study of genetic differentiation for ecologically relevant traits may also reflect local adaptation to ecological conditions (De Meester, 1996a, b; 1997). The occurrence of local adaptation for ecologically relevant traits depends on the strength of natural selection, the amount of initial genetic variation on which natural selection can act, the speed at which local populations can respond to selection, and the rate of the homogenizing effect of gene flow (Endler, 1986; Hartl & Clark, 1989; De Meester, 1996b). Observations of genetic differentiation for ecologically relevant traits among Daphnia populations and evidence for local adaptation in Daphnia populations with respect to predator-induced defences have been reported by a number of authors (see Leibold & Tessier, 1991; Parejko & Dodson, 1991; Spitze, 1993; De Meester, 1996a; Boersma et al, 1998). Phototactic behaviour as well as plasticity in this behaviour upon exposure to fish kairomones was found to differ strongly among Daphnia populations inhabiting ponds that are characterised by different fish predation pressure regimes in their natural habitat (De Meester, 1996a; Boersma et al., 1998). Moreover, the results of Boersma et al. (1998) indicate that, for a set of 12 traits studied, phototactic behaviour and size at maturity show the most striking evidence for local adaptation to predation pressure. Two recent studies on Daphnia suggest that local adaptation to local environmental conditions may occur at a small spatial scale (cfr. D. galeata populations whose habitats are separated <5 m; Declerck et al., 2001) and within a short time span (rapid evolutionary changes in phototactic behaviour within one population; Cousyn et al., 2001). In the face of these observations, we wanted to investigate whether genetic differentiation for ecologically relevant traits among Daphnia populations can also be observed in a system that is characterised by both profound ecological differences among local habitats, but also by high
113 Chapter V connectivity among populations, the latter being expected to homogenize populations genetically, at least when dispersal translates into gene flow. Such a situation is found in fish pond complexes in which a set of ponds are connected by overflows. The pond complex De Maten (see also Cottenie et al., 2001; Michels et al., 2001a, b; see previous Chapters), is a good example. It contains a network of 34 shallow ponds that are directly connected to each other by overflows and rivulets. Yet, the ponds in this system differ in their ecological characteristics (e.g. water transparency and fish densities; see Cottenie et al., 2001). In previous studies, we quantified zooplankton dispersal rates among populations (see Michels et al., 2001a; Chapter II) and we screened genetic variation within and genetic differentiation among interconnected D. ambigua populations using allozyme markers (see Chapter IV). Despite the observation of high dispersal rates among populations, we observed low but highly significant genetic differentiation among D. ambigua populations at the beginning
(April) and at the end (November) of the growing season (GST = 0.015 - 0.023; see Chapter IV). One possible explanation of this pattern is that ecological differences among ponds are strong enough to result in strong differentiating selection pressure, resulting in a lowering of effective gene flow. If so, we should see a tendency for local adaptation in these ponds, and a pattern of genetic differentiation for ecological relevant traits should at least be so pronounced as for neutral markers. As phototactic behaviour and body size characteristics have been shown to be valuable candidates to detect local adaptation in natural Daphnia populations (Boersma et al., 1998), we selected these traits to see whether D. ambigua genotypes with different phototactic behaviour and/or life-history characteristics may predominate in different ponds in the pond complex of De Maten. To date, little is known on phenotypic plasticity upon exposure to fish chemicals in phototactic behaviour and life history characteristics in natural D. ambigua (see Pace et al., 1984). In an effort to quantify phototactic behaviour and some key life-history charcteristics of D. ambigua clones, we carried out a cohort life-table experiment with clones isolated from ponds in De Maten that differed in water transparency. D. ambigua clones were cultured in presence and absence of fish chemicals. More specifically our aims were to investigate (1) whether a genetic variation for the selected traits can be observed among D. ambigua clones isolated from De Maten; (2) whether the presence of fish kairomones induces changes in phototactic behaviour and/or life history charcteristics in this species; (3) whether a correlation can be found between phototactic behaviour and life history characteristics of D.
114 Genetic differentiation for ecologically relevant traits in D. ambigua ambigua clones, (4) and whether there are differences among local populations that are concordant with a pattern of local adaptation.
Material and Methods
Study site: De Maten The study site consists of a series of 34 neighbouring and interconnected shallow ponds situated in the nature reserve De Maten (50° 57´ N, 5° 27´ E; Genk, Belgium, see Fig. 1). The ponds are located close to each other (the whole set of ponds covers an area of less than 200 ha) and are all well connected to each other (for details see Michels et al., 2001a, 2001b; Chapters II & III). Nearly all ponds in De Maten share the same water supply. Yet, the ponds differ widely both in fish predation pressure, development of submerged macrophytes and water transparency. Based on field data collected in 1999 on water transparency (Secchi depth readings taken in August), we categorized the ponds as clearwater ponds (1, 2, 3, 5, 6, 13, 14, 15, 16, 24, 25, 28, 30, 31, 33 and 34; Secchi depth >50 cm), intermediate ponds (4, 7, 8, 18, 19, 20, 23, 26, 27, 29; Secchi depth 31-50 cm) and turbid ponds (9, 10, 11, 12, 17, 21, 22 and 32; Secchi depth <30 cm; see Fig. 1). Clearwater ponds on average are characterised by low to moderate fish densities, intermediate ponds have moderate fish densities, whereas turbid ponds are characterised by high to very high fish densities (see Fig 2; Michels, Cottenie, Knaepkens & Declerck, unpubl. data).
Sampling and culture of Daphnia ambigua
Daphnia ambigua SCOURFIELD, 1947 is the most abundant Daphnia species in De Maten. D. ambigua populations were sampled in November 1999 with a plankton net (mesh size 64 µm) from all ponds in De Maten in which the D. ambigua populations where sufficiently dense. To prevent interference from genotype-dependent vertical habitat selection (De Meester et al., 1994), the whole water column (approximately 1 m) was sampled. Each sampled pond was categorized as clear, intermediate or turbid ponds according to Secchi depth data of 1999. For each selected population, approximately 25 adult parthenogenetic females were picked out randomly and were isolated individually in glass vessels of 250 ml containing filtered pond water (filtered twice over a 0.4 µm Sartorius filter). After four days, pond water was gradually replaced by dechlorinated tap water. The first week juvenile animals were daily fed
115 Chapter V approximately 1.105 Scenedesmus acutus cells.ml-1. Twice a week, 100 µl bacteria were added as a horse-manure extract during the first week (Peters, 1987). From the second week onwards, food concentration was gradually increased and restored daily to approximately 2.5 . 105 Scenedesmus acutus cells ml-1. To prevent that animals would be stuck in the surface film of the culture medium, a few cetyl-alcohol crystals (± 1.2 mg) were added to the cultures (Desmarais, 1996). Once a clone became established in the laboratory, these laboratory conditions proved satisfactory to obtain very healthy animals. Yet, mortality of clones in the first week after isolation from the field was massive. To exclude maternal effects, all clones were first cultured under the experimental conditions for two generations prior to experiments (Lynch & Ennis, 1983). Experimental animals were cultured under standardised conditions in one liter jars. Density was kept relatively low: 20-30 individuals l-1. Food concentration was daily restored to 2.5 – 3.105 Scenedesmus acutus cells ml-1. No bacteria were added to experimental cultures. There were two treatments: presence and absence of fish chemicals. Daily, one fourth of the culture medium was replaced by an equivalent volume of fresh medium (either dechlorinated tap water or filtered fish conditioned medium). Dechlorinated tap water was exposed to the air for 24h. Medium loaded with fish kairomones was prepared daily by allowing two Leuciscus idus (Teleostei, Cyprinidae) of approximate 8 cm length to swim for 24 h in a 60 l aquarium filled with dechlorinated tap water. Daily feeding of the fish with Daphnia was done in a separate aquarium to avoid contamination of the fish medium with alarm substances that may be released by the Daphnia. Each day, 12 l of the medium was filtered over 40 µm. The density of fish in the fish conditioned medium was relatively high, corresponding to about one fish in 30 l. Daily refreshment of the medium was necessary, as fish chemicals may loose their activity in 24 h due to bacterial degradation (Larsson & Dodson, 1993; Loose, 1993). For each clone-treatment combination, at least four replicate lines were set up.
Quantification of phototactic behaviour The experimental set-up to quantify phototactic behaviour is basically the same as described in Michels et al. (1999, 2000). The experimental cuvet consists of two glass columns that are placed concentrically into each other: an outer column, that can be connected to a flow through system (the latter was not used in the present experiments) and an inner column (10 cm height; 5 cm ∅) in which the test animals were placed (Figure 3). The top and bottom of the inner column was made of plankton gauze to prevent test animals to swim out and was
116 Genetic differentiation for ecologically relevant traits in D. ambigua
Clear ponds
Genk Intermediate ponds
19 Turbid ponds
21 18 1 20 34 # 7 13 # 3 2 11 15 17 # 10 # 29 30 # 8 27 9 # 31 # # # 32 # 24 6 12 22 # # 28 4 5 14 16 33 23 25 26
0 05 1 Km
. Figure 1: Geographic position of the study site, the nature reserve “De Maten” (50° 57´ N, 5° 27´ E; Genk, Province of Limburg, Belgium). Map of the pond complex showing pond numbers. Colours indicate water transparency based on Secchi depth data in 1999. Sampled populations: 34 (clearwater pond); 18 and 19 (intermediate ponds) and 12, 21 and 9 (turbid water ponds).
120
100
80
60
40 Secchi depth (cm) depth Secchi 20
0
-20 0 5000 10000 15000 20000 25000 30000 35000 Fish biomass (CUE)
Figure 2: Relationship between water transparency (Secchi depth in cm; average over 1996, 1997, 1998 and 1999) and average fish biomass (CUE = Catch per Unit Effort; g/fyke per day) over 1996, 1997, 1998 and 1999 in ponds in De Maten. Data points originating from ponds included in this study are presented as open dots.
117 Chapter V
externally divided into four compartments of 2.5 cm length each: an upper compartment U, two middle compartments M1 and M2 and a lower compartment L. During an experiment, the cuvet is placed in a light-tight PVC box (16 x 28 x 35 cm, provided with a peep-hole (∅ 1 cm) for observations, and illuminated from above with a fiber light (150 W halogen light source) positioned two cm above the water surface in the outer column. Preliminary experiments showed that the phototactic behaviour of D. ambigua was stabilised when a light diffusing white plastic screen between the end of the fiber light and the top of the experimental chamber was used (Michels & Uyttebroek, personal observation). All experiments were done in a temperature-buffered room (20° C ± 1°C) and in the early afternoon (start 13.00-15.00h) to minimise possible effects of an endogenous rhythm. Each experiment was carried out with ten adults in their second adult instar. Each experiment was carried out with animals from a different culture, to obviate common environment effects (Hurlbert, 1984). All animals were cultured at 20° C (± 1°) and in a long-day photoperiod (14 h light-10 h dark). The protocol for the quantification of phototactic behaviour is illustrated in Figure 4. Prior to an experiment, ten animals are adapted for three hours to clear light conditions in the absence of food. After this adaptation, test animals are transferred into the experimental cuvet that is placed in the PVC test box and given a dark adaptation of five minutes. Subsequently, the light is switched on for an observation period of 10 min. An experiment lasts 10 min. At one-minute intervals, the number of animals in each compartment is counted. The phototactic behaviour of the test population is characterised by the phototactic index PI = (U - L) / (U + M1 + M2 + L), averaged over the last five minutes of the experiment to minimise interference from the initial fright reaction (see Fig. 4). Values for the phototactic index can range from 1 for extremely positive behaviour to -1 for extremely negative phototactic behaviour (De Meester, 1991).
Measurement of life history characteristics
We experienced severe problems in establishing D. ambigua cultures under standardised laboratory conditions. This was observed in several previous attempts to establish lines in the laboratory too (E.M., unpubl. data). Mortality rates up to 90 % during the first stage of the establishment explain why only few clones per population where included in this study, resulting in an unbalanced sampling design and are the reason why aims number 4 could only partially studied.
118 Genetic differentiation for ecologically relevant traits in D. ambigua
D. ambigua were found to be very sensitive to experimental manipulations. For this reason we preferred to carry out a cohorte life-table experiment instead of culturing each animal individually. We also limited our observations to measurements on one adult instar. We carried out a cohort life table experiment using cohorts of 20 individuals inoculated as neonates less than 24 h old in one liter culture jars. On the day of the release of the brood in the brood chamber (= age at maturity in days), the total body length and length of the spina of five animals selected randomly per replicate line were measured according to Hanazato (1991; see also Fig. 6) to the nearest 0.1 mm using a Olympus dissecting microscope (type SZX12) with scaled ocular. Animals used for life-history measurements were not used for the quantification of phototactic behaviour.
Figure 3: Test cuvet for the quantification of phototactic behaviour in Daphnia. Test animals are placed in the central cylinder, which is externally divided into four compartments (U, M1, M2, and L) of 2.5 cm. Top (1) and bottom (2) of the central cylinder are closed with plankton gauze. During an experiment, the cuvet is placed in a light-tight PVC box and illuminated from above with a fiber light source with a plastic screen to obtain a more diffuse light source.
119 Chapter V
1.0 10 U − L 0.8 PI = ∑ = -0.4 0.6 i=6 U + M1 + M2 + L 0.4
0.2
0.0
-0.2
Phototactic index Phototactic -0.4
-0.6
-0.8
-1.0
Pre experimental adaptation 12345678910 Observation period 5’ dark 3 h light adaptation adaptation 0-5’ stabilisation 6-10’ Calculation PI
Figure 4: Schematic representation of experimental protocol for the quantification of phototactic behaviour of Daphnia and an example of the vertical distribution of test animals during the course of an experiment with D. ambigua. Prior to the experiment, animals are adapted for three hours to clear light conditions in the absence of food. After inoculation of the experimental animals in the test chamber, a dark adaptation of five minutes is given as a standarised way to start experiments. Subsequently, the light is switched on for an observation period of 10 minutes. The phototactic index (PI) is calculated as the value of the animals in the upper minus lower compartment divided by the total number of animals averaged over the last five minutes of the experiment.
Statistical analysis We are aware that the limited set of clones that could be surveyed does neither allow to determine genetic variation within populations nor an in-depth analysis of genetic differentiation between populations for the selected traits. Yet, our results provide a useful first insight in the pattern of genetic variation differentiation for ecologically relevant traits in the pond system of De Maten. Data on the phototactic behaviour and on life history characteristics of different clones cultured in the presence and absence of fish kairomones were analysed by two-way repeated measurements ANOVA, with population and treatment as fixed factors, and clone as repeated measurement, and using the average values of a trait for each clone in the absence and presence of fish kairomones as input data (Sokal and Rohlf, 1995). Planned comparisons were used to compare the average phototactic behaviour, size at maturity, spina length and age at maturity among ponds in the light of a priori expectations contrasting (1) clear and turbid
120 Genetic differentiation for ecologically relevant traits in D. ambigua pond or (2) clear and intermediate + turbid ponds. Homogeneity of variance was tested using Bartlett’s test. Due to the limited set of clones per population in this study, we could not perform heritability estimates for the selected traits in local D. ambigua populations. Assuming, however, that D. ambigua populations in De Maten make up a (patchy) metapopulation (see Chapter IV), we calculated approximate heritabilities for phototactic behaviour for the whole metapopulation. Although the clones studied do not represent a time random sample from this metapopulation, there was no artificial bias introduced by us - the unbalanced sampling design stems from high mortality rates, and it is unlikely that this would create differences among populations. The approximate clonal repeatability calculated by us at least give an indication on the genetic component of the observed genetic variance in the selected traits in the metapopulation. The clonal repeatability (heritability in the broad sense; hB²) was estimated for both treatments (presence and absence of fish kairomones). The heritability for a specific trait in a population can be defined as the ratio of additive genetic variance (VA) to the phenotypic variance (VP = VG + VE ): hB² = VA/ (VG + VE) (Falconer & Mackay, 1996). VE is equal to MS error, whereas VA is estimated as (MS effect - MS error) / N (with N= 4; number of experiments per clone). In addition to the basic clonal repeatability analysis, we also calculated approximate heritabilities for phototactic behaviour and size at maturity in the absence and presence of fish kairomones for populations that were artificially constructed by each time leaving out the value of one single population from the whole dataset. This method allows to estimate the contribution of a given pond to the genetic variation of the trait studied. All statistical analyses were performed using the statistical computing package STATISTICA (Statsoft, 1994).
Spatial structure versus ecological differences
The relative impact of spatial structuring of the pond complex (effective geographic distance; see Michels et al. 2001b; Chapter III) versus differences in environmental conditions among local habitats was tested in a Mantel test (720 permutations), using the freeware Mantel function (Reynolds J.H. & Bolker B., available from aus.stats.s) for use in S (Professional version, Insight, 2000). Euclidean distances among populations were calculated for different traits. We hereafter refer to the Euclidean distance among populations calculated for a given trait as for instance EUCLDISTphototaxisNF, referring to phototaxis and the non-fish treatment. Ecological distances among ponds (hereafter referred to as ENVIRONMENT) were
121 Chapter V computed as Euclidean distances among ponds using data from 1999 on biotic and abiotic factors in ponds of De Maten (i.e. Secchi depth, chlorophyll a concentration, macrophyte abundance, total fish density). The effect of the explicit spatial structure was determined by using the effective geographic distance among ponds (hereafter referred to as SPATIAL STRUCTURE). The data on effective geographic distance among ponds were based on flow rates and were taken from Michels et al. (2001b; see Chapter III). The effective geographic distance between two populations was modelled in a G.I.S. environment using the IDRISI software package (Version 2.0; Eastman, 1997) and reflects the effort a dispersing zooplankton organism has to exert to travel from the “source” pond to the “target” pond via the pathway of connecting elements.
Results Quantification of phototactic behaviour The phototactic behaviour (average PI ± 2 × S.E) of D. ambigua clones isolated from clear, intermediate and turbid ponds in De Maten cultured in the presence and absence of fish kairomones is plotted in Figure 5. Clones isolated from pond 34 are characterised by a moderately positive phototactic behaviour (average P.I. >0.0), whereas all other clones exhibit a negatively phototactic response (see also De Meester, 1991). The differences in phototactic behaviour among the two treatments where small in most clones. The phototactic behaviour of a D. ambigua clone cultured in the presence of fish kairomones was in most cases equal or slightly lower than the phototactic behaviour of the same clone cultured in the absence of fish kairomones, except in clone 34/10. Clone 12/17 is the only clone that exhibits a pronounced shift to more negatively phototactic response in the presence of fish chemicals. Two-way repeated measurement ANOVA demonstrated significant differences in phototactic behaviour among D. ambigua clones isolated from different ponds in De Maten and a significant effect of fish kairomones (Table 1). The results of a contrast analysis indicates that D. ambigua clones isolated from the clearwater pond (pond 34) exhibit a highly significant different phototactic behaviour than clones isolated from turbid ponds (ponds 12, 9 and 21; p = 0.0027), the difference between clearwater and turbid water ponds is also significant (p = 0.005) both in the presence as well as in the absence of fish kairomones. The difference between turbid and intermediate ponds is not significant.
122 Genetic differentiation for ecologically relevant traits in D. ambigua
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4 Phototactic behaviour Phototactic -0.6 Non fish -0.8 z Fish ∆ -1.0
34(23) 34(10) 18(14) 18(16) 19(21) 19(20) 9(17) 12(17) 12(13) 21(1) 21(28) Clones Clear Intermediate Turbid
Figure 5: Phototactic behaviour of eleven Daphnia ambigua clones isolated from several ponds in De Maten, cultured in the presence and absence of fish kairomones (average PI ± 2S.E.). Clones are labelled as the number of the pond from which they were isolated followed by a number established during isolation from the habitat. Ponds were ranked according to water transparency: clearwater pond: 34; intermediate ponds 18 and 19; turbid ponds 12, 9 and 21. For pond numbers see Fig. 1.
123 Chapter V
Table 1: Two-way repeated measurement ANOVA table: effects of population and treatment on the (1) phototactic behaviour; (2) Size at maturity; (3) Spina length and (4) Age at maturity of D. ambigua clones isolated from six different ponds in De Maten. * p< 0.05; n.s. p< 0.05.
Effect Df MS effect df Error MS Error F p-level Significance (1) Phototactic behaviour Population 5 0,35 6 0,061 5,79 0,027 * Treatment 1 0,031 6 0,0038 8,055 0,030 * Interaction 5 0,0077 6 0,0038 2,01 0,21 n.s (2) Size at maturity Population 5 0.022 2 0.0014 15.37 0,062 n.s. Treatment 1 0.0027 2 0.00022 12.33 0,072 n.s Interaction 5 0.0010 2 0.00022 4.59 0,19 n.s (3) Spina length Population 5 0.00056 2 0.00053 1,054 0,55 n.s. Treatment 1 0.000016 2 0.000032 0,49 0,56 n.s Interaction 5 0.000025 2 0.000032 0,76 0,65 n.s (4) Age at maturity Population 5 0,49 2 1,71 0,29 0,89 n.s. Treatment 1 0,71 2 0,38 1,88 0,30 n.s Interaction 5 0,25 2 0,38 0,65 0,70 n.s
Tabel 2: Approximated calculation of hB² of phototactic behaviour in D. ambigua in De Maten cultured in the presence (Fish) and absence (Non-fish) of fish kairomones calculated over all six analysed populations and as well as in six subpopulations created by leaving out data from one subpopulation. Significant values are indicated with *. Numbers refer to ponds presented in Fig 1.
Phototactic behaviour Non-fish Fish All 6 populations 0.53* 0.79* Without population 34 -0.059 0.35 Without population 18 0.63* 0.75* Without population 21 0.75* 0.83* Without population 19 0.53 0.77* Without population 12 0.47 0.80* Without population 9 0.57* 0.79*
124 Genetic differentiation for ecologically relevant traits in D. ambigua
Approximate heritability values for phototactic behaviour in D. ambigua clones isolated from De Maten and cultured in the presence and absence of fish kairomones are given in Table 2. The data suggest that both in the absence and presence of fish kairomones, a significant amount of phenotypic variation in phototactic behaviour has a genetic component. The values of the heritability estimates suggest that this genetic component is substantial. The data also suggest that these genetic differences largely disappear when population 34 is left out from the dataset.
Key life-history characteristics Figure 6 shows the age at maturity, size at maturity and spina length averaged over five animals for each of four replicate cultures of a total of 8 clones. The results of two-way repeated measurement ANOVA indicate marginally significant (p = 0.06) differences among size at maturity among ponds in De Maten, as well as a marginal significant effect (p = 0.07) of the presence of fish kairomones on size at maturity of D. ambigua clones (Table 1). Contrast analysis reveals that average size at maturity of clones from pond 34 was significantly different from the average size of clones from turbid ponds (p = 0.033), but only marginally significant from the size at maturity of clones isolated from intermediate and turbid ponds (p = 0.052). Spina length and age at maturity were not significantly different among D. ambigua clones isolated from different ponds in De Maten, and no significant effect of treatment was observed (Table 1). Phototactic behaviour and body size characteristics have been shown to be valuable candidates to detect local adaptation in natural Daphnia populations (Boersma et al., 1998). As in this study significant differences among populations were only observed for these two traits, we will limit further analysis to phototactic behaviour and size at maturity. The heritability estimate for size at maturity was only marginally significant.
Correlations among traits Our results indicate that differences in phototactic behaviour as well as differences in life history charcteristics among D. ambigua populations in De Maten may be associated with differences among clearwater ponds (pond 34) and turbid ponds (ponds 12, 9 and 21). We observed a significant positive linear relationship between the average phototactic behaviour for each population and size at maturity of D. ambigua clones originating from De Maten and cultured in the presence and absence of fish kairomones (Figure 7).
125 Chapter V
1.15 0.17
0.16 Non fish 1.10 Fish
0.15 1.05
0.14 1.00 0.13 0.95 0.12 0.90 0.11 Spina length (mm) Size at maturity (mm) maturity at Size 0.85 0.10 Non fish 0.80 Fish 0.09
0.75 0.08 34(10) 18(14) 18(16) 19(20) 9(17) 12(17) 12(13) 21(28) 34(10) 18(14) 18(16) 21(28) 19(20) 12(17) 12(13) 9(17) Clone Clone
Clear Intermediate Turbid Clear Intermediate Turbid
10
Non fish Fish 9
8
7 Age at maturity (dagen) Age at 6
5 34(10) 18(14) 18(16) 21(28) 19(20) 12(17) 12(13) 9(17) Clone
Clear Intermediate Turbid
Figure 6: Size at maturity (mm), spina length (mm) and age at maturity (days) of Daphnia ambigua clones isolated from several ponds in De Maten, cultured in the presence and absence of fish kairomones (average trait ± 2S.E.). Specification of measurements of Daphnia ambigua according to Hanazato (1991): TBL = total body length; S = spina length. Clones are labelled as the number of the pond from which they were isolated followed by a number established during isolation from the habitat (1-25; number in parentheses). Ponds were ranked according to water transparency: clearwater pond: 34; intermediate ponds 18 and 19; turbid ponds 12, 9 and 21. For pond numbers see Fig. 1.
Spatial structure versus environmental differences Table 3 shows the results of the Mantel tests, testing for a correlation among Euclidean distances based on the two most important ecological relevant traits (phototaxis and size at maturity) in both treatments and ecological distance among habitats (ENVIRONMENT), and effective geographic distance among ponds (SPATIAL STRUCTURE). Both the correlations between EUCLDISTphototaxis and (1) ENVIRONMENT and with (2) SPATIAL STRUCTURE are significant (only marginally significant for ENVIRONMENT). For EUCLDISTsize,
126 Genetic differentiation for ecologically relevant traits in D. ambigua correlations are only significant with SPATIAL STRUCTURE. The results of the analysis are strongly influenced by pond 34 (clearwater pond) that is characterised by a different ecology as well as by an extreme value for effective geographic distance (because it is not connected to any other ponds). The correlation between EUCLDISTspina with ENVIRONMENT was significant in the absence of fish chemicals and marginally significant in the presence of fish chemicals. In Figure 8 the positive relationship between average phototactic behaviour of clones in a given pond in the presence and absence of fish kairomones and water transparency in that pond (Secchi depth) is plotted. This relation was significant for both treatments (absence of kairomones: R = 0.80; p = 0.05; presence of kairomones: R = 0.86; p = 0.03). In Figure 9, the relationship between average phototactic behaviour of clones from a given pond in the presence and absence of fish kairomones and average fish biomass in the different ponds is plotted. This relation, showing a negative trend, is not significant in both treatments (absence of kairomones: R = 0.71; p = 0.11; presence of kairomones: R= 0.76; p = 0.07)
Table 3: Results of Mantel tests, testing for a correlation between Euclidean distance among ponds based on different ecological relevant traits for both treatments (e.g. EUCLDISTphototaxisNF = distance among ponds in De Maten based on the phototactic bahaviour of D. ambigua clones in the non-fish treatment) and the Euclidean distance based on biotic and abiotic variables measured in ponds (ENVIRONMENT; see Cottenie et al., unpubl. manuscr.) or the effective geographical distances among ponds (SPATIAL STRUCTURE) as modelled in Michels et al. (2001b; Chapter III). Significant values are given in bold. None of the correlations are significant at the table-wide level (Bonferroni correction; Rice, 1989)
SPATIAL STRUCTURE ENVIRONMENT R p-level R p-level
EUCLDISTphototaxisNF 0.66 0.018 0.60 0.082 EUCLDISTphototaxisF 0.53 0.035 0.64 0.040 EUCLDISTsizeNF 0.81 0.013 0.32 0.140 EUCLDISTsizeF 0.80 0.010 0.30 0.150
127 Chapter V
1.0 Non fish 1.0 Fish 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 -0.8 Phototactic behaviour Phototactic
Phototactic behaviour Phototactic -0.8 -1.0 -1.0 0.90 0.95 1.00 1.05 1.10 1.15 0.85 0.90 0.95 1.00 1.05 1.10 Size at maturity (mm) Size at maturity (mm)
Figure 7: Regression line with 95% confidence interval showing the relation between the average value per pond for phototactic behaviour and size at maturity of D. ambigua clones isolated from different ponds in De Maten, cultured in the absence (Non Fish; left panel) and presence (Fish; right panel) of fish kairomones. (Non- fish: p =0.01; Fish: p = 0.014)
1.0 1.0 Fish 0.8 Non fish 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 -0.8 -0.8 Phototactic behaviour -1.0 behaviour Phototactic -1.0 10 20 30 40 50 60 10 20 30 40 50 60 Secchi depth (cm) Secchi depth (cm)
Figure 8: Regression line with 95% confidence interval of the relation between the average value per pond of phototactic behaviour of D. ambigua clones isolated from different ponds in De Maten, cultured in the absence (Non Fish; left panel) and presence (Fish; right panel) of fish kairomones, and Secchi depth as measured in the corresponding ponds (Non-fish: p =0.054; Fish: p = 0.027)
128 Genetic differentiation for ecologically relevant traits in D. ambigua
1.0 1.0 0.8 Non fish Fish 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 -0.8 -0.8 Phototactic behaviour Phototactic behaviour -1.0 -1.0 2000 4000 6000 8000 10000 12000 2000 4000 6000 8000 10000 12000 Fish biomass (CUE) Fish biomass (CUE)
Figure 9: Regression line with 95% confidence interval of the relation between the average value per pond of phototactic behaviour of D. ambigua clones isolated from different ponds in De Maten, cultured in the absence (Non Fish; left panel) and presence (Fish; right panel) of fish kairomones and average fish biomass of the corresponding ponds (Non-fish: p =0.11; Fish: p = 0.076).
Discussion
We detected significant genetic variation for phototactic behaviour and marginally significant genetic variation for size at maturity among D. ambigua clones isolated from the pond complex De Maten. Our data suggest that these differences may be related to differences in water transparency and fish predation pressure in the ponds in this system. Despite the profound ecological differences among ponds, these observations are remarkable given the high dispersal rates and high connectivity among the majority of the habitats in this system as reported in previous work (see Michels et al., 2001a, 2001b; Chapters II & III). By far most of the genetic differences in ecological relevant traits were due to the clones isolated from pond 34, a clearwater pond that is not directly connected to the other ponds of the system. Yet, pond 34 lies very close to several other ponds of the system, the nearest distance to a pond, connected to the system, being less than five meter. During winter flooding, there is occasionally contact with the neighbouring pond causing an inflow of zooplankton and fish in pond 34 (Michels & Cottenie, pers. obs.).
129 Chapter V
Admittedly, this study suffered from practical constraints while establishing D. ambigua clones in the laboratory. This accounts for the limited set of clones and populations that was surveyed in this experiment. Despite several attempts to optimise protocols for the isolation of clones from the field, we were not able to solve this problem. We are fully aware that these constraints do not permit an in-depth analysis of genetic differentiation between D. ambigua populations for the selected traits, and that the interpretation of our results needs to be done with caution. In addition, when comparing these results with the findings of previous work on genetic variation for neutral markers in D. ambigua populations inhabiting this system (see Chapters III & IV), it is important to remark that both studies involve different growing seasons (1998 and 1999, respectively), and subtle biotic and abiotic changes may have occurred in the pond complex during the time span bridging the two studies. Cladocerans, Daphnia in particular, are important prey species for planktivorous fish, since they are often the larger zooplankton in limnetic biota and planktivorous fish are visually hunting, positively size selective predators (Gliwicz & Pijanowska, 1986; Lampert, 1987). Daphnia are able to detect both vertebrate and invertebrate predators by the presence of so-called kairomones. Predator kairomones are specific chemicals unintentionally exuded by the predators that are beneficial to the prey as they allow the prey to assess predation risk. To reduce their vulnerability to visual predators, Daphnia show predator induced shifts in several traits (see Tollrian & Harvell, 1999). Boersma et al. (1998) showed that among a set of 12 traits, phototactic behaviour (diel vertical migration) and size at maturity showed the strongest pattern of local adaptation in four Daphnia magna populations. In the present study too, these two traits (phototactic behaviour and size at maturity) were indeed the only two traits out of four in which we observed a tendency for genetic differentiation. This study is to our knowledge among the first that attempted to quantify phototactic behaviour in D. ambigua in presence of fish kairomones (but see Dodson, 1988 for a study on the response of D. ambigua on invertebrate kairomones). The limited size and transparency of D. ambigua hampers experimental observations. Compared to the typical “hop hop sink” behaviour exhibited by D. magna, D. ambigua exhibits a faster, less regular swimming behaviour in the experimental set up as used by De Meester (1991). Using a more diffuse light source in the experimental set up, however, seems to appease the swimming behaviour of D. ambigua. The variance of the phototactic index for D. ambigua in our experiments is comparable to the variance observed in similar experiments with D. magna (see De Meester, 1991; Michels et al., 1999, 2000; Dang Kieu et al., 2001). The presence of fish chemicals does not alter the phototactic behaviour of D. ambigua clones dramatically. This is in contrast
130 Genetic differentiation for ecologically relevant traits in D. ambigua to the observations in D. magna, but is not unexpected, since D. ambigua is a species that occurs typically in ponds characterised by strong fish predation, and as such may be constitutively defended (Maier, 1996). Due to its small size and its high transparency, the species is less sensitive to visual predation by planktivorous fish than larger Daphnia species (e.g. D. magna). In previous studies testing for the effect of fish kairomones on the phototactic behaviour of the much larger D. magna, large inter-clonal differences with respect to phenotypic plasticity of behavioural changes in the presence of fish kairomones were observed (De Meester, 1993; De Meester & Cousyn, 1997; Michels et al., 1999). Whereas some D. magna clones show a striking change in phototactic behaviour in the presence of fish kairomones, others did not alter their behaviour. Although only a limited number of clones could be included in our analysis, the differences in phototactic behaviour and size at maturity that we observed among the clones isolated from ecologically contrasting ponds are in agreement of expectations under the hypothesis of local adaptation. Clearwater ponds tend to be characterised by lower fish biomass and more submersed vegetation than turbid ponds, providing shelter for the zooplankton (see Burks et al., 2001). Thus the tendency of animals from clearwater ponds to exhibit a less negatively phototactic behaviour and that of animals from clear and intermediate ponds to mature at a bigger size than animals from turbid ponds is in line with expectations. This suggests that, in such highly connected system as the one studied here, there is a tendency for local adaptation. Interestingly, the most pronounced differences in behaviour among clones lies at a different place in the turbidity gradient (i.e. between clearwater and intermediate ponds) than that for life history traits (between intermediate and turbid ponds). In contrast to earlier findings on D. magna (De Meester, 1996a; Boersma et al., 1998; Cousyn et al., 2001), local adaptation for phototactic behaviour in D. ambigua clones in De Maten is not expressed in terms of the changes in behaviour upon exposure to fish kairomones, but rather at the mean value over environments. As already mentioned, we could only observe a very weak effect of fish kairomones in the present study. The present data reveal a relationship between the structure of variation for ecologically relevant traits and spatial structure as well as the pattern of environmental differences in the pond complex of De Maten. Comparing with the data on genetic variation for neutral markers (see Michels et al., 2001b; Chapters III & IV) we observed a tendency for a stronger positive relationship with environmental differences among ponds and a less clear relationship with spatial structure in the present dataset. This is in line with expectations, as environmental gradients may be more reflected in variation for ecological relevant traits (due
131 Chapter V to differential natural selection) than in variation for neutral markers, which is expected to largely reflect patterns of gene flow (dispersal pathways; see Michels et al., 2001b; Chapter III). From a landscape ecological and conservation genetical point of view, our results suggest that differences in connectivity among habitats in the pond complex of De Maten are an important factor structuring genetic variation in local zooplankton populations in this metapopulation. Our data suggest that in further conservation management of this system, and especially in the planning of the restoration of dykes and connecting elements among habitats, it might be advantageous to maintain a few more isolated habitats in the system, thus establishing a variety of connectivity among ponds in the system.
Acknowledgements We thank Koen De Gelas, Joost Vanoverbeke and Karl Cottenie for constructive discussions and comments on earlier versions of this manuscript. Karl Cottenie and Joost Raeymaekers are acknowledged for statistical consultancy. Frank Van De Meutter and Wouter Rommens for providing ecological data from De Maten. We thank vzw Natuurpunt and especially the warden Willy Peumans for permission to carry out this study in De Maten and for their full cooperation. Jo Vingerhoeds for his help with Fig. 2. EM acknowledges a scholarship provided by the Institute for the promotion of Scientific Technological Research (I.W.T).
References
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134 Genetic differentiation for ecologically relevant traits in D. ambigua
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135 Chapter V
136 Genetic differentiation in Polyphemus populations
Chapter VI
Genetic differentiation in Polyphemus pediculus populations
Manuscript together with E. Audenaert & L. De Meester
Abstract
Using allozyme variation at two loci, the genetic structure of the predatory littoral-zone Cladoceran, Polyphemus pediculus in five interconnected ponds in the nature reserve De Maten was examined. Contrary to the general abundance of another model organism in this study, D. ambigua, the distribution of P. pediculus in ponds in De Maten was found to be rather limited. P. pediculus was polymorphic at three out of eight allozyme loci. Most genotype frequencies were in Hardy-Weinberg equilibrium. In two populations, however, significant deviations from Hardy-Weinberg equilibrium were observed at the AO locus due to a heterozygote deficiency. P. pediculus populations were found to be genetically differentiated both with respect to allele frequencies and MLG frequencies. The level of genetic differentiation was moderate (GST = 0.089) but highly significantly different from zero, and was found to be more pronounced compared to levels of genetic differentiation in populations another model species, D. ambigua inhabiting the pond complex sampled at the beginning of the growing season. Contrary to D. ambigua sampled at the end of the growing season, no significant correlation was found between genetic distance and effective geographic distance as modelled in previous work (Michels et al., 2001b; Chapter III) indicating that the impact of dispersal on the genetic structuring of this species less is
137 Chapter VI important as for D. ambigua. The different patterns of genetic differentiation observed in P. pediculus compared to D. ambigua could be related to the different habitat preference, the limited abundance in the model system and the low dispersal rates of P. pediculus due to its strong swimming capacities and association with submersed macrophytes.
Introduction Studies on genetic differentiation among zooplankton populations have mainly focussed on Daphnia populations inhabiting unconnected water bodies (see De Meester, 1996 for a review). To date, only little attention has been given to the microgeographic genetic structure among cyclic parthenogenetic zooplankton populations inhabiting systems of interconnected lakes or ponds (but see Weider, 1989; Jacobs, 1990; Chapter IV). In the previous chapters, we have enlighten how landscape structure (in casu the presence of direct connections, the spatial arrangement and connectivity among habitat patches) can facilitate dispersal among zooplankton populations through passive dispersal of parthenogenetic females with water flow in a system of interconnected ponds. Based on genetic data based on neutral markers of the most common Daphnia species inhabiting our model system (D. ambigua), we concluded that landscape structure rather than ecological differences among ponds is important in determining genetic differentiation among local Daphnia populations in De Maten (see Chapters III & IV). Despite the documented high dispersal rates between the Daphnia populations in this system (see Michels et al. 2001a; Chapter II), the D. ambigua populations inhabiting specific ponds were found to be genetically differentiated from each other, both with respect to allele and MLG frequencies. The level of allelic differention was low (GST = 0.015 - 0.023) but highly significantly different from zero. Moreover, genetic divergence tended to increase during the growing season. We suggested that the Daphnia populations in the pond complex of De Maten make up a patchy metapopulation in which recurrent exinction events of local patches are absent or unimportant, due to the high degree of interaction among patches (see Harrison, 1991; Harrison & Taylor, 1997; see also Chapter IV). In the present study, we focus on Polyphemus pediculus (Müller, 1785), another cyclically parthenogenetic cladoceran that occurs in De Maten. P. pediculus differs from Daphnia with respect to habitat preferences, abundance in our model system and the production of resting stages. Polyphemus pediculus (Onychopoda, Polyphemidae) is a relatively small zooplankton species (adult females range from 0.5 - 1.5 mm; Green 1966) that is the only representative of the family of the Polyphemidae in freshwater habitats. The
138 Genetic differentiation in Polyphemus populations species occurs in large lakes and small ponds throughout the Northern temperate zone (Packard, 2001). Unlike Daphnia, Polyphemus is a visually predating carnivorous cladoceran that feeds on microzooplankton (e.g. rotifers and small cladocerans) but cannibalism has also been reported (Packard, 2001). In addition, it is not a pelagial species but typically occurs between submersed macrophytes in habitats that are characterised by relatively clear water (Hutchinson, 1967). In situ densities of Polyphemus can be very variable due to aggregation behaviour (Butorina 2001; Packard, 2001; EM, pers. obs.). The growing season of Polyphemus lasts from March-April until August-September (Butorina, 2000; EM, pers. obs.), which is considerable shorter than the growing season of most Daphnia species in the study region (March–November; EM, pers. obs.). The length of the growing season contributes strongly to the impact of clonal selection (Hebert, 1987; Vanoverbeke, unpubl. manuscr.). Polyphemus reproduces by cyclical parthenogenesis, amictic females produce parthenogenetic broods during favourable conditions. At the end of the growing season or when conditions become unfavourable, males appear in the population that fertilise sexual resting eggs (Green, 1966; Carvalho, 1994). Contrary to Daphnia, Polyphemus does not produce a resistant (ephippial) envelope around its resting eggs (Green, 1966; Butorina, 1998). Instead, Polyphemus’ resting eggs are covered with two external envelopes, the chitinous envelope being surrounded by a transparent mucous envelope. This envelope not only protects the eggs against harsh environmental conditions such as mechanical disturbance, freezing, drying, but the mucous also causes eggs to stick to the aquatic vegetation (Butorina, 1998). Polyphemus does not deposit its eggs in the open water, but on solid surfaces, firmly stuck to the object on which they were laid on (Green, 1966; Butorina, 1998). Hence, dispersal of Polyphemus resting eggs between habitats is expected to be limited since they tend to sink to the bottom or are attached to aquatic macrophytes (Weider, 1989). Moreover, it is expected that in Polyphemus dispersal of the active population component, generally considered to be the most important dispersal mode among interconnected water bodies (see Jenkins & Underwood, 1998; Michels et al, 2001a; Chapter III), will be less prominent than in other cladocerans because the animals occur in aggregates within littoral vegetation and are relatively strong swimmers (see Young & Taylor, 1990) that may be able to swim against currents near overflows through negative rheotaxis. Given that habitat quality for Polyphemus may vary considerably among ponds in De Maten (see Cottenie et al., 2001), it is expected that establishment success of dispersing individuals will be limited compared to more generalist species such as Daphnia ambigua.
139 Chapter VI
To date, only Weider (1989) studied the genetic structure of P. pediculus populations. He focussed on a set of large connected and unconnected lakes in Northern Germany. The genetic structure of these Polyphemus populations was found to be comparable to the genetic structure of large-lake Daphnia populations. Polyphemus populations inhabiting interconnected lakes were found to be less differentiated than populations inhabiting unconnected lakes (Weider, 1989). In the present study, we compare the patterns of genetic differentiation for neutral markers among populations of a typical littoral species, Polyphemus pediculus with that of Daphnia ambigua populations in the same set of interconnected ponds. P. pediculus is characterised by a more patchy distribution in our model system, and passive dispersal of the active population component is expected to be less important than in D. ambigua. More specifically, we want to (1) survey within population genetic diversity and among population genetic differentiation in P. pediculus populations in De Maten, (2) test the hypothesis that genetic diversity within populations is dependent on habitat size, (3) test whether the genetic structure of local populations is mainly determined by the explicit spatial structure of the pond complex or by differences in environmental characteristics among ponds, (4) compare genetic structure of P. pediculus with previous findings on the genetic structure of D. ambigua.
Materials and methods Study site and sampling
The study site is a set of 34 neighbouring and interconnected shallow ponds in the nature reserve De Maten (50° 57´ N, 5° 27´ E; Genk, Belgium, see Figure 1). For more details concerning the ecological characteristics of the ponds, a description of the landscape matrix, and the spatial configuration and connectivity of the pond complex, we refer to Cottenie et al. (2001; unpubl. manuscr.) and Michels et al. (2001a, 2001b; see Chapters II & III). Ponds in De Maten were screened for the presence of Polyphemus from April until August 1997. In habitats were Polyphemus populations were sufficiently dense, a sample was collected in or near the littoral zone (submerged macrophyte vegetation) with a plankton net (mesh size 64µm). The results of a survey for the presence of Polyphemus in different ponds in De Maten over five consecutive months (April-August) are presented in Figure 2 (also see Table 1). Polyphemus was characterised by a patchy distribution along the pond complex and was detected in 12 different, mostly clearwater ponds in De Maten over the period from April until August 1997. Although individuals of Polyphemus were detected in several ponds
140 Genetic differentiation in Polyphemus populations starting from April onwards (Fig. 2), samples from only five populations yielded sufficient individuals for electrophoretic analysis. All samples are from May, i.e. relatively short after the re-establishment of the populations from resting eggs. Four of the five ponds from which Polyphemus were collected (9; 15; 28; 30 and 31; Fig 2) were in 1997 (except pond 30) in the clearwater state at the time of sampling (average Secchi depth = 86 cm).
Clear ponds
Intermediate ponds
Turbid ponds 19
21 18 1 20 34 # 7 13 # 3 2 11 15 17 # 10 # 29 30 # 8 27 9 # 31 # # # 24 32 # 6 12 22 # # 28 4 5 14 16 33 23 25 26
0 05 1 Km
.
Figure 1: Map of the pond complex showing pond numbers. Colours indicate water transparency based on Secchi depth data of 1997. Ponds in which Polyphemus pediculus was present (0-10 ind.) are presented in Fig 2.
Electrophoretic procedure
The genetic structure of the P. pediculus populations in De Maten was analysed using cellulose acetate gel electrophoresis, carried out on Titan III cellulose acetate plates following the protocol of Hebert & Beaton (1994). In an initial survey, we screened eight enzyme loci that were also assayed in the study by Weider (1989): phospho gluco mutase (PGM E.C. 2.7.5.1); gluco phospho isomerase (GPI E.C. 5.3.1.9); AO aldehyde oxydase (AO E.C. 1.2.3.1); aspartate amino transferase (AAT E.C. 2.6.1.1); fumarate hydratase (FUM E.C. 4.1.2.2); malate dehydrogenase (MDH E.C. 1.1.1.37); mannose phosphate isomerase (MPI E.C. 5.3.1.8) and malate esterase (ME E.C. 1.1.1.40). Only two enzyme systems: PGM (monomer) and AO (dimer) stained reliably and were found to be polymorphic in P. pediculus
141 Chapter VI populations in De Maten, and thus were run routinely. Sample sizes in the individual populations varied between 30 and 50 individuals. Both loci were assayed for each individual, yielding two-locus genotypes for each animal. We hereafter refer to distinct two-locus genotypes as multilocus genotypes (MLG), with the understanding that they represent at least one, but more likely, a group of clones.
April July
34 8 # # # # 15 # #
# # # # # # # ## # # ## ## # ## 31 4 .
May August
3 13 21 8 27 34 30 # # # # 15 # #
# # # # # # # # # # ## # # ## ## 31 4 9 25 28 . 00.5 1Km
June
# # 15 # # # # # ## # ## 31 4 . 9 33
Clearwater pond
Intermediate -Turbid pond
Figure 2: Occurrence of Polyphemus pediculus in ponds in De Maten during period April-August 1997. Water transparency (based on Secchi depth data of 1997) of different ponds is indicated as grayscale (also see Fig. 1). Ponds in which Polyphemus were detected are outlined with a thick line. Ponds from which Polyphemus were sampled for genetic analysis : 31, 30, 28, 15 and 9 (also see Fig 1). Picture credits Polyphemus: Royal British Columbia Museum.
142 Genetic differentiation in Polyphemus populations
Table 1: Occurrence of Polyphemus pediculus in ponds in De Maten during the period April-August 1997. *: Ponds in which Polyphemus was detected but in insufficient number for genetic analysis (1-15 ind.). Sample: Polyphemus populations from which at least 30 individuals were assayed for electrophoresis. Pond numbers are presented in Fig 1. Water transparency (Clear; Intermediate and Turbid) in different ponds in De Maten in 1997 and the localisation of Polyphemus populations that were included in the analysis are presented in Fig 2.
Pond April May June July August 1 2 3 * 4 *** 5 6 7 8 ** 9 Sample * 10 11 12 13 ** 14 15 * Sample * 16 17 18 19 20 21 * 22 * 23 24 25 * 26 27 * 28 Sample 29 30 Sample 31 Sample ** 32 33 * 34 **
143 Chapter VI
Data analysis
Within-population genetic diversity
Within-population allelic genetic diversity was quantified as expected heterozygosity. Exact tests for Hardy-Weinberg equilibrium were performed in TFPGA (Miller, 1997). Fixation indices for deviation of Hardy-Weinberg equilibrium were calculated in GENETIX (Belkhir et al., 1996). We calculated whether the number of MLG differed significantly (p<0.05) from a random sample based on allele frequencies using the software HWclon (Joost Vanoverbeke, unpubl.). In addition, we also quantified MLG diversity as the reciprocal of the Simpson index (1/λ), representing the probability that two randomly drawn individuals have the same multilocus genotype (Lande, 1996). The Simpson index not only takes into account the number of different MLG, but also their relative abundance. Given the low number of polymorphic allozyme loci in P. pediculus, the number of MLG no doubt strongly underestimates the real number of clones in the population. We tested whether downstream ponds within clusters are characterised by higher MLG diversity and function as a MLG sink, by testing for a correlation between local MLG diversity and the total pond load of the corresponding pond. Total pond load corresponds with the total (cumulative) number of upstream ponds that are connected with a given pond. High pond loads indicate that a pond may function as a sink, whereas ponds characterised by low pond load are more likely to act as sources (see Pulliam, 1988; Dias, 1996).
Genetic differentiation among populations
F-statistics according to Nei (1977, GST) were used to determine genetic differentiation among populations. For both loci, Fis, Fit and FST values were computed. Pairwise GST values between all possible pairs of populations were computed in GENETIX (Belkhir et al., 1996) to quantify the extent of genetic differentiation between populations. Significance of genetic differentiation was tested using a randomisation test on allelic frequencies in TFPGA (Miller, 1997).
Spatial structure versus environmental differences
To test for isolation-by-distance, we performed Mantel tests (Mantel, 1967) looking for an association (10000 permutations) between pairwise GST values between all possible pairs of populations and effective geographic distance (SPATIAL STRUCTURE), using the freeware
144 Genetic differentiation in Polyphemus populations
Mantel function (Reynolds J.H. & Bolker B., available from aus.stats.s) for use in S for use in S plus (Professional version, Insight, 2000). In addition, we also performed a Mantel test between pairwise GST values and ecological distances between ponds (ENVIRONMENT). Form more details on the modelling of the effective geographic distance we refer to Michels et al., 2001b; Chapter III). Ecological distances among ponds (ENVIRONMENT) were computed as Euclidean distance among ponds calculated with field data collected in 1997 on biotic and abiotic factors in the ponds of De Maten (e.g. depth; Secchi depth; pH; conductivity; O2-concentration; concentration of total phosphorus; nitrogen concentration; chlorophyll-a concentration; density of submerged macrophytes; diversity of marco- invertebrates; total fish density). For more details on the measurement of the ecological variables in 1997, we refer to Cottenie et al. (unpubl. manuscr.).
Relation between genetic diversity and habitat size
We tested the prediction that populations inhabiting larger habitats are characterised by higher genetic diversity by calculating the correlation between pond surface area as an estimate of habitat size and average heterozygosity and MLG diversity, as measures for genetic diversity. In this specific setting of cyclical parthenogenetic reproducing Polyphemus populations inhabiting interconnected ponds among which dispersal of parthenogenetic individuals is possible, it is relevant to study the distribution of specific MLG among populations because, at least theoretically, a given MLG may represent the same clone in different populations. We used RxC resampling tests (Miller, 1997) to test whether MLG frequencies differ significantly among populations.
Results Genetic diversity
There were two alleles at the PGM locus and three alleles at the AO locus. In Table 2, allele frequencies of the two loci and sample sizes in different ponds are presented. Table 3 shows values of observed and expected heterozygosity for each locus and averaged over the two loci. In two populations (populations 30 and 28) allele frequencies at the AO locus deviated from Hardy-Weinberg expectations due to heterozygote deficiency (Table 4). Deviation in allele frequencies from Hardy-Weinberg expectations at the PGM locus in population 31, however, became unsignificant after Bonferroni correction.
145 Chapter VI
The number of multilocus genotypes (MLG) and MLG diversity detected on the basis of electrophoretic variation at the two analysed loci in different ponds are presented in Table 5. The average number of MLG detected on the basis of two loci was 8, whereas the average expected number of MLG was 8.68. For what concerns MLG diversity, the observed versus expected average value are 4.9 and 5.3, respectively. The maximum number of detectable MLG based on the alleles present in the populations was limited to 18. The correlation between total pond load and MLG diversity was not significant (R² = 0.13; p = 0.55) suggesting that ponds with high pond load are not richer in MLG diversity than source ponds (Figure 3).
Table 2: Allele frequencies at locus PGM (two alleles) and AO (three alleles) of Polyphemus pediculus populations in De Maten, sampled in May 1997. Sample sizes are given between brackets. Alleles at each locus were labelled according to their mobility: S: slow; M: medium; F: fast. The number of the population refers to the pond number as indicated in Figure 1.
Population PGM AO 30 S 0.71 S 0.29 (40) F 0.29 M 0.64 F 0.07
28 S 0.64 S 0.38 (51) F 0.36 M 0.60 F 0.02
15 S 0.81 S 0.69 (36) F 0.19 M 0.31 F 0.00
31 S 0.59 S 0.23 (44) F 0.41 M 0.73 F 0.04
9 S 0.83 S 0.17 (35) F 0.17 M 0.83 F 0.00
Table 3: Observed (ho) and expected (he) heterozygosity at the two studied loci and averaged over both loci in the Polyphemus pediculus populations in De Maten sampled in May 1997. The number of the population refers to the pond number as indicated in Figure 1.
PGM AO Average over loci
ho he ho he ho(g) he(g) 30 038 041 053 046 046 044 28 0.53 0.46 0.26 0.50 0.40 0.48 15 0.33 0.31 0.33 0.42 0.33 0.37 31 0.26 0.50 0.30 0.41 0.28 0.46 9 0.23 0.28 0.23 0.28 0.23 0.28
146 Genetic differentiation in Polyphemus populations
Tabel 4: Fixation indices (F; values <0: excess of heterozygotes; values >0 deficiency of heterozygotes) in the different Polyphemus pediculus populations in De Maten for both loci analysed in May 1997. Significance levels for deviations from Hardy-Weinberg equilibrium calculated using χ²-tests are marked with ***: p<0.01; +: p>0.05 at the table-wide level, after sequential Bonferroni correction (Rice, 1989). For localisation of the populations, see Fig. 1.
April PGM Significance AO Significance Population F F 30 -0.07 0.42 *** 28 -0.13 0.50 *** 15 -0.54 - 0.055 31 -0.30 *+ 0.30 9 0.18 0.21
Table 5: The number of observed and expected multilocus genotypes (MLG) and MLG diversity in the analysed Polyphemus pediculus populations sampled in May 1997 in De Maten. Significant deviations (p< 0.05) between the observed and expected values are indicated in bold (calculated with Hwclon; Joost Vanoverbeke unpubl.).
April # observed # expected Observed MLG Expected MLG Populations MLG MLG diversity diversity 30 10 10.9 6.1 6.6 28 8 10.3 6.1 6.5 15 7 6.7 4.8 4.3 31 8 9.9 4.6 5.9 9 7 5.6 3.1 3.2
20
18
16
14
12
MLG diversity MLG 10
8
6
4 12345678 Total pond load
Figure 3: Relation between total pond load and multilocus genotypes (MLG) diversity observed in the Polyphemus pediculus population sampled in May 1997. Total pond load corresponds with cumulative number of upstream ponds that are connected with a specific pond. R² = 0.13; p = 0.55.
147 Chapter VI
Relation between genetic diversity and habitat size Figure 4 shows the relation between genetic diversity and pond surface area for the populations studied. The relation between expected heterozygosity and habitat size was significant (R² = 0.82; p = 0.032). No significant relation was observed between MLG diversity and habitat size (R² = 0.45; p = 0.22).
10 0.6
8 0.5
6 0.4
4 0.3 MLG diversity MLG Heterozygosity
2 0.2
0 0.1 0123 0123 Habitat size (Ha) Habitat size (Ha)
Figure 4: relation between MLG diversity and habitat size in Polyphemus pediculus populations in De Maten. R² = 0.45; p = 0.22 (Left) and relation between expected heterozygosity over the two loci (PGM; AO) and habitatsize in Polyphemus pediculus populations in De Maten. R² = 0.83 p = 0.032 (Right).
Spatial genetic structure
The MLG frequencies observed in the analysed D. ambigua populations are presented as pie diagrams in Figure 5. The analysis of genetic differentiation among P. pediculus populations based on MLG composition using a RxC resampling test, indicates highly significant differences in MLG composition among P. pediculus populations (p < 0.0001; S.E.< 0.0001). Genetic differentiation among all sampled P. pediculus populations as measured by
GST values was 0.089 and was highly significantly different from zero (Table 6). In Table 7 the pairwise measures for genetic differentiation (pairwise GST) among the sampled P. pediculus populations are presented. Using an exact test, highly significant differentiation (p <0.0001) among Polyphemus populations belonging to different clusters in terms of effective geographic distance (upstream cluster: 31; 30; 28 versus downstream cluster: 15 and 9) was observed (see also Michels et al., 2001b; Chapter III). Polyphemus populations belonging to the upstream cluster were not
148 Genetic differentiation in Polyphemus populations genetically differentiated (p = 0.17). Genetic differentiation among the two populations belonging to the downstream cluster (populations 15 and 9; see Michels et al., 2001b; Chapter III), however, was highly significant (p= 0.005).
#
0 0.5 1Km
. FFFF SSSS FFMM SSSM
FFSS SSMM
SFFF SSFF
SFMF SFSS SFMM SFSM
Figure 5: Pie diagrams of multilocus genotype (MLG) frequencies of P. pediculus populations sampled in different ponds in De Maten in May 1997. MLG correspond with electrophoretic mobility at the PGM (two alleles) and AO locus (three alleles). Alleles were named according to their electrophoretic mobility (S: slow; M: medium; F: fast). Ponds from which P. pediculus were sampled in May: 9, 15; 28, 30 and 31 Pond numbers are presented in Fig. 1.
Table 6: F-statistics based on allele frequencies within Polyphemus pediculus populations sampled in De Maten in May 1997. Overall significance over loci was determined by Exact text for population differentiation.
Locus FIS FIT FST Significance
PGM -0.074 -0.033 0.038 *** AO 0.320 0.400 0.130 *** All 0.130 0.270 0.089 *** *** p< 0.0001
149 Chapter VI
Table 7: Pairwise GST values (Nei, 1973, 1977) quantifying genetic differentiation among Polyphemus pediculus populations in De Maten sampled in May 1997. The number of the population refers to the pond number as indicated in Fig. 1.
30 28 15 31 9 30 - 28 0.0068 - 15 0.083 0.027 - 31 0.012 0.066 0.041 - 9 0.027 0.053 0.170 0.041 -
Spatial structure versus ecological differences. In Table 8 the results of the Mantel test are presented, testing for a correlation among genetic differentiation (pairwise GST) and ecological distance (ENVIRONMENT) as well as effective geographic distance among ponds in the pond complex in De Maten (SPATIAL
STRUCTURE). Neither the relation between the pairwise GST among Polyphemus populations and SPATIAL STRUCTURE nor the relation between pairwise GST and ENVIRONMENT was significant. In addition, we also tested the relation between between pairwise GST and geographic distance among populations, but this relation also failed significance.
Table 8: Results of a Mantel test, testing for a correlation between pairwise GST among Polyphemus pediculus populations sampled in May 1997 in De Maten and the Euclidean distance based on dataset on biotic and abiotic variables measured in ponds (ENVIRONMENT)) or the effective geographical distances among ponds (SPATIAL STRUCTURE) as modelled in Michels et al. (2001b; Chapter III) and the correlation between pairwise GST among Polyphemus pediculus populations and geographic distance.
Genetic distance R p-level
SPATIAL STRUCTURE 0.089 0.52 ENVIRONMENT - 0.066 0.41 GEOGRAPHIC DISTANCE 0.10 0.34
150 Genetic differentiation in Polyphemus populations
Discussion
Patchy distribution of Polyphemus in De Maten P. pediculus is a typically littoral zooplankton species that has been described as widespread in ponds and lakes throughout the Northern temperate zone (Packard, 2001). This is, however, not the case in Belgium, and in the pond complex of De Maten too, the incidence of dense populations of P. pediculus was found to be very limited. After an intensive screening within the submersed vegetation of all ponds in De Maten, P. pediculus was detected in a total of 12 out of 35 ponds over a five-month period. In many ponds, P. pediculus was detected only on a single occasion. Moreover, in most cases only a few (<15) individuals could be collected. The maximum period that P. pediculus was detected uninterruptedly in a pond was three months. P. pediculus is a visual predator that feeds on small zooplankton, requires clearwater habitats and is typically associated with submersed macrophytes (Hutchinson, 1967). Contrary to Daphnia, P. pediculus is an active feeder (Packard, 2001) and the swimming speed of P. pediculus chasing a prey has been recorded to be 15.4 mm.s-1 (Young & Taylor, 1990). These characteristics are expected to reduce passive dispersal of the active population among ponds in De Maten since (1) individuals hiding within the vegetation are less prone to be entrapped in the water current carrying them along into overflows (see Michels et al., 2001a; Chapter II); (2) the rather strong swimming capacities of P. pediculus may enable this species to avoid being drawn into water currents through a negatively rheotaxis response; (3) due to its high habitat specificity, successful establishment in downstream target populations is expected be low due, compared to more abundant zooplankton species (e.g. Daphnia). Previous quantification of zooplankton dispersal rates (see Michels et al., 2001a; Chapter II) showed that dispersal rates of P. pediculus via connecting elements was very rare (< 20 ind. for a total of 31 connecting elements and 1 h observation time; Michels, Cottenie & Neys, unpubl. data). Given the discontinous distribution of P. pediculus in the pond complex of De Maten, it is justified to assume that a stepping stone dispersal pattern as observed for other zooplankton species may be less appropriate for P. pediculus since dispersal is limited and ponds that would act as stepping stones are likely to be unfavourable for P. pediculus.
Genetic diversity within Polyphemus pediculus populations
Three out of eight allozyme loci screened were found to be polymorphic in P. pediculus populations in De Maten. However, only PGM and AO were used to assess genetic diversity, as consistent scoring of the ME locus proved to be very difficult. Our observations are
151 Chapter VI comparable with the findings of Weider (1989) who observed only three (PGM, GPI, and AO) out of eight allozyme loci to be polymorphic in German P. pediculus populations. Average observed heterozygosity over the two loci in P. pediculus (= 0.34) in De Maten was considerable lower than average heterozygosity in Daphnia ambigua populations (0.59) in the same pond complex (see Chapter IV). In two populations (30 and 28) significant deviations from Hardy-Weinberg expectations were observed at the AO locus. These deviations were due to a heterozygote deficiency, which may be explained by selection, non-random mating, scoring artefacts or mixing of populations. The latter can be due to hatching from resting eggs from different generations or mixing of populations via overflows. It is justified to assume that the P. pediculus populations were sampled near the beginning of the growing season. The number of MLG and MLG diversity was relatively low compared to MLG diversity in Daphnia ambigua populations sampled in April 1998. In addition, the observed number of MLG (as well as MLG diversity) was already significantly lower in two populations (30 and 28) than could be expected from a random sample based on observed allele frequencies. This is suggestive for clonal selection in these populations. To date, however, potential fitness differences among P. pediculus clones remain untested since this species is very difficult to culture under laboratory conditions (Weider, 1989; Michels, pers. obs.). The observation of significant deviations from Hardy-Weinberg equilibrium contrasts with the observations of Weider (1989), who found that all 20 P. pediculus populations studied were in Hardy-Weinberg equilibrium. Moreover, this observation contrasts with the patterns generally observed in of intermittent cladoceran populations (Hebert, 1974, 1987; Hebert & Moran, 1980; Korpelainen, 1984).
Genetic diversity versus habitat size
Larger habitats are generally characterised by higher ecological diversity than smaller habitats. We observed a significant correlation between heterozygosity and habitat size in P. pediculus populations, whereas MLG diversity was not significantly correlated with habitat size. In P. pediculus, resting eggs are not prone to long range dispersal by waterfowl or wind action, since resting eggs lack an ephippial case and are attached to the substrate on which they are laid. Compared to D. ambigua sampled at the beginning of the growing season (1998; R= 0.064; p = 0.011) the correlation between genetic diversity and habitat size was much stronger in P. pediculus (see Chapter IV)
152 Genetic differentiation in Polyphemus populations
Genetic differentiation among P. pediculus populations
We observed a distinct spatial structure in the subset of P. pediculus populations inhabiting the pond complex in De Maten. The level of genetic differentiation among P. pediculus populations in De Maten was moderate (GST = 0.089) but highly significantly different from zero. The observed values for genetic differentiation are very low compared to Daphnia inhabiting unconnected habitats (0.13-0.68, see Innes, 1991), but are considerable higher than values for genetic differentiation among D. ambigua populations sampled at the beginning of the growing season in De Maten (GST = 0.015). Yet, contrary to the D. ambigua populations inhabiting this system (see Chapter IV), no significant correlation was found between pairwise GST and the geographic and effective geographic distance among P. pediculus populations, nor between pairwise GST and ecological distance among ponds. To evaluate the importance of geographical distances among populations and their relationship with genetic distance, the biology of the organism should be taken into account. P. pediculus differs considerably in biology from D. ambigua, abundance in the study system and dispersal capacities compared with D. ambigua. Gene flow among populations reflects successful dispersal, i.e. dispersal followed by successful establishment in target populations (Slaktin, 1985). The upstream populations (populations 31; 30 and 28) show only little genetic differentiation among them, whereas the highest differentiation was observed among populations 15 and 9 (pairwise GST = 0.17) and among populations 30 and 15 (pairwise GST = 0.083). In this respect, it is important to remark that the upstream ponds are nearly all directly connected with each other, or had only one pond (pond 29) in between them. The two downstream ponds, are separated by as many as three ponds among them. Two of these ponds (Ponds 11 and 10) are turbid and are characterised by low numbers of submersed marcophytes, and can thus be considered as unfavourable habitat for P. pediculus. The pattern of enhanced genetic differentiation among P. pediculus populations that are belonging to the same cluster in terms of effective geographic distance (see Michels et al., 2001b; Chapter III) but are separated by unfavourable habitats for this species, suggests that establishment success and dispersal is indeed dependent on the characteristics of the habitats that function as stepping stones. This again underlines the importance of landscape ecological information in understanding genetic structure. In the only other population genetic study on P. pediculus, Weider (1989) found that genetic differentiation among populations inhabiting unconnected lakes in Northern Germany was higher (FST = 0.33; based on four allozyme loci,) than genetic differentiation among
153 Chapter VI
populations inhabiting a series of connected lakes (Schwentine lakes; FST = 0.021). Weider (1989) hypothesised that active dispersal via channels may account for the reduced levels of genetic differentiation among connected lakes. Connected lakes where also characterised by relatively high genotypic diversity compared to unconnected lakes. Genetic differences among P. pediculus populations in De Maten was higher than that observed by Weider (1989) for populations inhabiting connected lakes, even though geographic distance among ponds in De Maten is much smaller than the distance among the Schwentine lakes (approximately 3 km). The whole of these findings points to the importance of ecological difference among ponds reducing gene flow (see Hansson, 1991) and illustrates the contribution of species specific factors in the genetic structuring of cladoceran populations.
Acknowledgements We thank Koen De Gelas, Karl Cottenie, Luc Brendonck and Filip Volckaert for constructive discussions and comments on earlier versions of this manuscript. Karl Cottenie performed Mantel tests in S plus. We thank vzw Natuurpunt and especially the warden Willy Peumans for permission to carry out this study in De Maten and for their full cooperation. EM acknowledges a scholarship provided by the Institute for the promotion of Scientific Technological Research (I.W.T).
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155 Chapter VI
Michels, E., K. Cottenie, L. Neys, K. De Gelas, P. Coppin & L. De Meester 2001b. Geographical and genetic distances among zooplankton populations in a set of interconnected ponds: a plea for using GIS modelling of the effective geographical distance. Molecular Ecology 10: 1929-1938.
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156 General discussion
General discussion
The population genetic structure of natural zooplankton populations has been studied extensively for more than three decades (see reviews by Hebert, 1987; Carvalho, 1994; De Meester, 1996b). It has often been observed that isolation-by-distance patterns among interconnected cyclically parthenogenetic Daphnia populations are weak, largely due to pronounced genetic differences (for neutral markers) among nearby populations (Korpelainen, 1984; Innes, 1991; Boileau et al., 1992; Vanoverbeke & De Meester 1997). In addition, several studies on genetic variation for ecologically relevant traits in Daphnia provided evidence for genetic adaptation to local environmental conditions (see Leibold & Tessier, 1991; Spitze, 1993; De Meester, 1996a; Declerck et al., 2001). On the whole, these results suggest that effective gene flow may often be much lower than observed dispersal rates. This discrepancy may be due to competition between migrant genotypes with resident genotypes and/or outbreeding depression associated with local adaptation (De Meester, 1996b; De Meester et al., in press; Okamura & Freeland, in press). Comparing the patterns of genetic variation among zooplankton populations both at neutral markers and at ecologically relevant traits creates the opportunity to gain insight in the relative importance of random genetic drift or gene flow versus natural selection as evolutionary forces driving the process of genetic differentiation among zooplankton populations.
Landscape structure and population genetics Wiens (1996, 1997) and Hanski (1998) made a plea for a landscape-based view on metapopulations, incorporating various aspects of landscape ecology in metapopulation models. We incorporated information on landscape structure in our analysis of genetic differentiation among a set of spatially structured zooplankton populations linked by dispersal. A closer look at the landscape structure of our model system (Chapters II & III) revealed that Euclidean distance among habitats is not an appropriate distance measure to model connectivity among zooplankton populations in a system in which dispersal pathways are essentially canalised to connecting elements among ponds. The results of Chapter II indeed illustrated that, from a landscape point of view, the pond complex of De Maten is a system with well-defined pathways for passive dispersal of zooplankton. GIS modelling of the effective geographic distance in which landscape structure, physical properties of the connecting elements and zooplankton dispersal rates were taken into account (Chapter III) was found to provide a better approximation of effective dispersal rates and relative rates of
157 General discussion gene flow than merely Euclidean distance among ponds. Our work thus illustrates that a landscape-based approach can offer a valid alternative for the analysis of the genetic structure in spatially structured populations when the classic approach fails. Slowly, the need to integrate a landscape-based approach in population genetic analyses is getting generally recognised. Independent from our study, Vos et al. (2001) used a similar approach in a population genetic study on the moor frog (Arva arvalis) to test the impact of the landscape mosaic on the connectivity among patches. Vos et al. (2001) showed that a distance measure that was corrected for relative amounts of habitat type with a known positive (e.g. hedgerows and ditches) or negative impact (e.g. dry open areas and roads) on dispersal of moor frogs resulted in a better explanation for the observed pattern of genetic differentiation among frog populations than mere geographic distance.
Dispersal and inter-populational genetic differentiation Dispersal among spatially structured populations is a key premise of a metapopulation (Hanski & Simberloff, 1997) and is an important factor contributing to the community structure (Bengtsson, 1991; Jenkins & Buikema, 1998) and genetic differentiation among populations (Slatkin, 1985, 1987; Thomas et al., 1998; Bohonak, 1999a; 1999b; Vos et al., 2001). General population genetic theory predicts that high dispersal rates among populations, if translated into gene flow, promotes the mixing of alleles and prevents populations from differentiating genetically from each other. Limited dispersal and low levels of gene flow, on the other hand, may promote genetic differentiation among populations as a result of drift and/or natural selection (Slatkin, 1985). Direct quantification of dispersal rates among natural populations are not only notoriously difficult to obtain (see Bossart & Prowell, 1998; Bohonak, 1999a; Whitlock & McCauley, 1999 for a discussion of the limitations of methods for the direct quantification of dispersal), but these studies are also very time-consuming (e.g. in zooplankton: see Jenkins & Underwood, 1998; Brendonck & Riddoch, 1999; Bohonak & Whiteman, 1999). In this work, we took advantage of the opportunity created the fact that virtually all dispersal of zooplankton among interconnected ponds occurs through the connecting elements that can readily be sampled to quantify dispersal rates directly in the field. In addition, we assessed the genetic structure at neutral markers of D. ambigua and P. pediculus populations in a subset of interconnected ponds in De Maten. The dispersal rates of the active population component among ponds in De Maten were on average very high (Chapter II) and were influenced by the particular topography of the pond complex, the physical properties of the connecting elements among ponds and ecological characteristics of
158 General discussion the investigated zooplankton groups. Despite the observations of high dispersal rates among Daphnia populations in De Maten (Chapter II), we observed genetic differentiation at neutral markers among D. ambigua populations inhabiting different ponds in this pond complex. The significant genetic differentiation in this albeit strongly connected set of ponds suggests that establishment success of dispersing individuals may be very low in local ponds.
Spatial versus ecological distance among habitats as structuring forces In this work, we studied cladocerans in a set of interconnected ponds as a model system to test the role of spatial structure (facilitating or impeding dispersal among habitats) versus ecological differences among habitats (creating different selective environments) on the genetic differentiation among natural zooplankton populations at (quasi-)neutral genetic markers (allozymes) and for ecologically relevant traits (phototactic behaviour and body size characteristics). Our results suggest that landscape structure rather than ecological differences among ponds contribute to the genetic differentiation for neutral markers among Daphnia populations (Chapter IV). Given the presumed neutrality of allozymes (but see Riddoch, 1993 for exceptions at the GPI locus), this lack of correlation of allelic variation with ecological differences is not unexpected. The study of ecological relevant traits in the same species, however, revealed a different pattern, with the structure of variation for the studied traits being associated with both spatial structure as well as with the pattern of environmental differences in the pond complex De Maten (Chapter V). Compared to the pattern observed for genetic variation at neutral markers, the pattern of genetic variation for ecological relevant traits thus revealed a tendency for a stronger relationship with environmental differences among ponds and a less clear relationship with spatial structure. This is in line with expectations, as environmental gradients may be more reflected in variation for ecological relevant traits (due to differential selection) than in variation for neutral markers, which is expected largely to reflect patterns of gene flow. Similar differences in patterns of genetic differentiation for neutral markers and quantitative traits have been found in other models systems (Jaramillo-Correa, 2001; Reede & Frankham, 2001). Recent studies on Daphnia have suggested that adaptation to local environmental conditions may occur at a small spatial scale (cfr. D. galeata populations whose habitats are separated <5m; Declerck et al., 2001) as well as within a short time span (rapid evolutionary changes in ecological relevant traits within one population; Hairston et al., 1999; Cousyn et al., 2001). In the face of these observations, we tested whether we could detect a tendency for local adaptation in the ponds of De Maten, given their high degree of connectivity. Most of
159 General discussion the genetic variation observed by us in ecologically relevant traits among D. ambigua clones in De Maten was due to clones isolated from a pond that is not directly connected to the remainder of the pond complex. Yet, the tendency of D. ambigua clones isolated from clearwater ponds to exhibit a less negatively phototactic behaviour and that of animals from clear and intermediate ponds to mature at bigger size than animals from turbid ponds is in agreement with expectations under the hypothesis of local adaptation. This suggests that even in such a highly connected system as the pond complex in De Maten, ecological differences among habitats may be strong enough to result in significant genetic differentiation among populations
Species-specific effects on genetic differentiation among local populations In an effort to get an insight in the importance of species-specific factors in the process of genetic differentiation among populations, we compared patterns of genetic variation for neutral markers in two cladoceran species, Daphnia ambigua (Scourfield, 1947) and Polyphemus pediculus (Müller, 1785). These species clearly differ in their biology and abundance in our model system. Compared to levels of genetic differentiation among D. ambigua populations sampled at the beginning of the growing season, genetic differentiation among P. pediculus populations was considerably higher. Moreover, no significant relation was observed between genetic distance and the effective geographic distance among habitats, nor between genetic distance and ecological distances among ponds. This suggests that the impact of dispersal on the genetic structure of P. pediculus is less important than in D. ambigua (see also Weider, 1989). Dispersal rates of P. pediculus in the pond complex of De Maten are indeed expected to be lower than among D. ambigua populations, due to its stronger swimming capacitiy and its association with submersed macrophytes.
Concluding remarks A decade ago, Hanski & Gilpin (1991) predicted that the fusion of metapopulation studies and landscape ecology should make an exiting scientific synthesis. The research presented in this thesis illustrates the importance of landscape structure and habitat as well as species-specific factors in the genetic differentiation among local cladoceran populations. We believe that this approach might have a broad applicability in future population genetic research, and we hope that the approach taken in this thesis will not remain restricted to cladocerans or to sets of interconnected ponds.
160 General discussion
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161 General discussion
Hanski, I. & M. Gilpin 1991. Metapopulation dynamics: brief history and conceptual domain. In Gilpin, M. & I. Hanski (Eds.). Metapopulation dynamics: empirical and theoretical investigations. pp. 3- 16. Academic Press, London.
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162 General discussion
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163 General discussion
164 Summary
Summary
In the present study, we use zooplankton in a set of interconnected ponds as a model system to test the role of spatial structure (facilitating or impeding dispersal among habitats) versus ecological differences among habitats (creating different selective environments) on the genetic differentiation among natural populations. We aim at an integrated view on genetic variation based on (quasi-)neutral markers (allozymes) as well as on ecologically relevant traits (body size characteristics and phototactic behaviour). In addition, we want to determine to which extent connected zooplankton populations in De Maten correspond to the concept of a metapopulation, in which local populations are separate entities linked by dispersal among them, or should be concerned as one single large population. In order to test the importance of species-specific factors in the process of genetic differentiation among populations, we focus on natural populations of two cladoceran models species, Daphnia ambigua (Scourfield, 1947) and Polyphemus pediculus (Müller, 1785) that are clearly differing in their biology and abundance in our model system. The pond complex in De Maten consists of a network of 34 shallow ponds of varying size that are directly connected to each other by rivulets and overflows. Due to the presence of direct connections among ponds, we expect strong interactions among the zooplankton populations inhabiting these ponds, which may result in very low levels of genetic differentiation for (quasi-)neutral markers among them. Although most of the ponds share the same water supply, profound ecological differences among ponds with respect to water transparency, development of submerged macrophytes and fish predation pressure are present. This situation, on the other hand, may lead to different selection pressures in different environments, which may promote genetic differentiation for ecologically relevant traits. The results from Chapter II revealed that, from a landscape point of view, the pond complex of De Maten is a system with well-defined pathways for passive dispersal of zooplankton. We were able to quantify dispersal of the active population component of different zooplankton groups directly via sampling source and target populations and connecting elements among ponds. Even though only a momentary record of dispersal rates in connecting elements was obtained, this study provided a first insight in the pattern and strength of interaction among zooplankton populations in De Maten. The results of this field study also indicated that the dispersal of the active population component among ponds in De Maten is strongly influenced by the particular topography of the pond complex, the physical properties of the connecting elements among ponds and habitat preferences of different
165 Summary zooplankton groups. The number of dispersing individuals sampled in connecting elements were on average very high (>1000 ind./h), but varied widely among overflows and among species. Some of this variation was accounted for by variation in population densities in the source populations. For three zooplankton groups (Cyclopoida, Ceriodaphnia and Chydoridae), a highly significant positive correlation was observed between the number of dispersing individuals in connecting elements and the population densities in the source pond. Even though this study was limited in its level of detail, our findings suggested that the relative contribution of dispersing individuals to the total population size in target populations is rather limited for nearly all ponds and all zooplankton groups. In Chapter III we showed how a landscape-based approach can offer a valid alternative for the analysis of the genetic structure in spatially structured populations when the classic approach fails. From the results obtained in Chapter II, it became apparent that Euclidean distance among habitats is not an appropriate distance measure to model connectivity among zooplankton populations in a system such as De Maten, where dispersal of zooplankton is mainly mediated by passive transport of the active population component and dispersal pathways are essentially restricted to connecting elements among ponds. In order to evaluate the relative contribution of the explicit geographic structure of the pond complex in the genetic structuring of local zooplankton populations in De Maten, we modelled in Chapter III the connectivity among zooplankton populations as the effective geographic distance using a landscape based approach in a GIS environment. The effective geographic distance was defined as the effort a dispersing zooplankter has to exert to disperse from a source to a target pond via a pathway of connecting elements between them. The first simple Landscape Model (LM) corrected for the presence of direct physical links and differences in pathway length among populations. In the Flow Rate Model (FRM) and Dispersal Rate Model (DRM), field data on flow rates or dispersal rates, obtained in Chapter II, were incorporated in order to differentiate for varying physical properties among connecting elements. However, as field data of flow rates and dispersal rates reflected only a momentary record of the interactions in the study system, they should be considered as providing an indication of the strength of the interaction rather than a detailed measure of true dispersal rates. Yet, using data on genetic differentiation for neutral markers among a number of D. ambigua populations sampled in De Maten to validate the three GIS based models, we found that the effective geographic distance as modelled in the FRM and DRM provided a better explanation of true dispersal rates than merely Euclidean distance among ponds or the distance matrix obtained by the LM. Based on
166 Summary the modelling of effective geographic distance three main clusters in the pond complex could be discriminated, corresponding with three distinct branches of the pond complex. In Chapter IV, we analysed the genetic structure for (quasi-)neutral markers (allozymes) of D. ambigua populations in De Maten at the beginning and at the end of the growing season. The observation of low polymorphism for allozyme loci in D. ambigua populations in De Maten corresponded with the results of previous studies on this species (Platt & Spitze, 2000; Zofková, 2000) and may be related to exotic status of D. ambigua in Europe. The genetic structure of D. ambigua populations in De Maten corresponded with the characteristics of a typical intermittent Daphnia population, with recruitment of new genotypes from the resting egg bank and clonal selection during the season (Hebert, 1987; Lynch & Spitze, 1994). Despite the observations of high dispersal rates among Daphnia populations in De Maten (Chapter II), D. ambigua populations were found to be genetically differentiated from each other for neutral markers both with respect of allele frequencies and multi locus genotype frequencies. Moreover, it was found that the geographic pattern of genetic differentiation among D. ambigua populations became established during the course of the growing season. Populations inhabiting neighbouring ponds were genetically very similar, whereas more distant ponds tended to be more differentiated. Differences in ecology among ponds, however, did not explain the observed pattern of genetic differentiation for neutral markers. The latter hypothesis was found to be very unlikely in D. ambigua populations in De Maten, as this would require strong linkage at the level of the whole metapopulation, which is very unlikely given that the populations all contain an individual resting egg bank, or strong selection at the allozyme loci themselves (e.g. see Riddoch, 1993), which is also considered as unlikely for Daphnia populations in De Maten. The results of Chapter IV thus suggest that landscape structure (facilitating dispersal among interconnected ponds) rather than ecological differences among ponds contributed in the genetic differentiation among D. ambigua populations in De Maten. Based on the strong interactions among populations, quantified in Chapter II together with the observation of highly significant differentiation for neutral markers among the three clusters in terms of effective geographic distance both at the beginning as at the end of the growing season, we suggested that D. ambigua populations in De Maten should be considered as a patchy metapopulation (see Harrison & Taylor, 1997) rather than a single large spatially structured population. In Chapter V, we measured genetic variation for these traits in a cohort life table experiment to see whether genetic variation for ecologically relevant traits was present among D. ambigua clones isolated from ponds in De Maten that differed in water transparency and
167 Summary fish predation pressure. In addition, we tested whether the observed differences were in agreement with expectations under the hypothesis of local adaptation. We experienced, however, severe problems while establishing laboratory cultures from field samples resulting in an unbalanced sampling design and a limited set of clones that was included in the study. Even though ponds in de Maten are so strongly connected, the observed differences in phototactic behaviour and size at maturity show a tendency for local adaptation. Clones isolated from the clear pond exhibited a less negatively phototactic behaviour than clones isolated from intermediate and turbid ponds. In addition, it was observed that animals from clear and intermediate water ponds mature at a bigger size than animals isolated from turbid water ponds. In contrast with earlier findings on D. magna (De Meester, 1996a; Boersma et al., 1998; Cousyn et al., 2001), the results of Chapter V indicate that local adaptation for phototactic behaviour is not expressed in terms of behaviour upon exposure to fish kairomones, but rather at the mean value over environments. Compared to the results of genetic variation in D. ambigua for neutral markers (Chapter IV), the results of Chapter V showed a tendency for a more positive relationship between genetic variation for phototactic behaviour and size at maturity with ecological differences among ponds and a less clear relationship with the spatial structure of the pond complex. Most of the genetic variation in ecologically relevant traits, however, was due to clones isolated from pond 34, a clear water pond that is not directly connected to the remainder of the pond complex. This is in line with expectations as we expected environmental gradients to be more reflected in variation for ecologically relevant traits due to differential selection than variation in neutral traits, which is expected to reflect patterns of gene flow. Finally, in Chapter VI, we compared the pattern of genetic differentiation for neutral markers among populations of Polyphemus pediculus with that obtained for D. ambigua populations in De Maten (Chapter IV). We observed a pronounced genetic structure for P. pediculus populations in a subset of ponds in De Maten with levels of genetic differentiation among P. pediculus populations in De Maten being moderately but highly significantly different from zero. Compared to levels of genetic differentiation in D. ambigua populations sampled at the beginning of the growing season, genetic differentiation among P. pediculus populations was considerable higher. Moreover, no significant relation was observed between genetic distance and the effective geographic distance among habitats, nor between genetic distance and ecological distances among ponds. These results indicate that, contrary to D. ambigua populations sampled in a different growing season, the impact of dispersal on the genetic structuring of this species less important as in P. pediculus. Dispersal rates of P.
168 Summary pediculus are indeed expected to be lower than among D. ambigua populations, due to its strong swimming capacities and its association with submersed macrophytes. The observation of highly significant genetic differences among two P. pediculus populations that belong to the same cluster in terms of effective geographic distance (Chapter III) suggest that interject turbid ponds may act as a strong barrier for successful dispersal among habitats due to the specific habitat requirements of this species and the limited dispersal. The research presented in this thesis illustrates the importance of landscape structure and habitat as well as species-specific factors in the genetic differentiation among local cladoceran populations. We believe that this approach might have a broad applicability in future population genetic research, and hope that the approach taken in this thesis will not remain restricted to cladocerans or to sets of interconnected ponds.
169 Summary
170 Samenvatting
Samenvatting
In het huidige proefschrift willen we nagaan in welke mate een uitgesproken ruimtelijke landschapsstructuur (die dispersie tussen populaties kan bevorderen of belemmeren), dan wel ecologische verschillen tussen habitats (die verschillende selectieve omgevingen kunnen creëren) bijdragen tot de genetische differentiatie tussen zoöplanktonpopulaties in het vijvercomplex van het natuurreservaat De Maten (Genk, België). Hierbij streven we naar een geïntegreerd beeld van genetische diversiteit zowel op basis van (quasi-)neutrale merkers (allozymes) als op basis van een aantal duidelijk fitness gerelateerde kenmerken (fototactisch gedrag en levensgeschiedeniskenmerken). Bovendien willen we onderzoeken in welke mate de onderling verbonden zoöplanktonpopulaties in het modelsysteem De Maten beantwoorden aan het concept van een metapopulatie, waarbij de verschillende populaties als min of meer onafhankelijke entiteiten worden beschouwd en onderling verbonden zijn door dispersie, of eerder beschouwd moeten worden als één grote populatie. Voor dit onderzoek spitsen we ons toe op populaties van Daphnia ambigua (Scourfield, 1947) en Polyphemus pediculus (Müller, 1785), twee modelsoorten die duidelijk verschillen in levenswijze en in voorkomen in het studiegebied. Hiermee willen we nagaan wat de rol is van soortspecifieke factoren in het tot stand komen van genetische differentiatie. De vijvers in De Maten dateren uit de Middeleeuwen en werden tot omstreeks 1990 gebruikt voor viskweek. Het vijvercomplex zelf bestaat uit een netwerk van 34 onderling verbonden vijvers van uiteenlopende grootte. Mede door de aanwezigheid van directe verbindingen tussen de vijvers, verwachten we dat er sterke interacties tussen de betrokken zoöplanktonpopulaties optreden. Op basis van de sterke interactie tussen populaties verwachten we dat genetische differentiatie voor neutrale merkers zal worden tegengewerkt. Anderzijds verschillen vijvers in De Maten, niettegenstaande de gemeenschappelijke watertoevoer, toch in belangrijke mate inzake waterhelderheid, de ontwikkeling van ondergedoken waterplanten en vispredatie. Dit kan mogelijk aanleiding geven tot verschillende selectieve krachten tussen vijvers waardoor genetische differentiatie voor ecologisch relevante kenmerken kan ontstaan. In Hoofdstuk II toonden we aan dat het vijvercomplex in De Maten vanuit landschapsecologisch standpunt kan beschouwd worden als een systeem met duidelijk afgelijnde verbindingen voor passieve dispersie van zoöplankton. Door middel van bemonsteringen in bron- en doelpopulaties en tussenliggende vijververbindingen, zijn we erin geslaagd de dispersie van de actieve zoöplanktonpopulatie rechtstreeks te kwantificeren en de
171 Samenvatting relatieve impact van dispersie op de totale zooplanktondensiteit in de doelpopulatie in te schatten. Hoewel, deze veldstudie slechts een momentopname opleverde van de dispersie tussen populaties, laten de resultaten ons toe een beeld te vormen van het algemeen patroon en de kracht van de interacties die optreden tussen onderling verbonden zoöplanktonpopulaties in De Maten. Het aantal disperserende individuen dat werd bemonsterd ter hoogte van de verbindingselementen was gemiddeld relatief hoog maar varieerde sterk tussen de verschillende verbindingselementen en tussen de verschillende zoöplanktongroepen. Voor drie zoöplankton groepen (Cyclopoida, Ceriodaphnia en Chydoridae) stelden we een hoog significant positief verband vast tussen het aantal disperserende individuen ter hoogte van de verbindingselementen en de populatiedensiteiten van de betrokken soorten in de bronpopulatie. Algemeen werd voor alle zoöplanktongroepen vastgesteld dat het relatieve aandeel van de door dispersie aangevoerde individuen ten opzichte van de totale populatiegrootte eerder gering was en dit in vrijwel alle vijvers. Onze resultaten tonen aan dat de waargenomen dispersie in sterke mate bepaald wordt door de expliciete ruimtelijke structuur van het vijvercomplex, de fysische eigenschappen van de verbindingselementen en de habitatvereisten van de verschillende zoöplanktongroepen. Een landschapsecologische benadering kan een bruikbaar alternatief vormen bij de analyse van de genetische structuur van ruimtelijk gestructureerde populaties (Hoofdstuk III). Op basis van de morfometrische karakterisatie van het studiegebied (Hoofdstuk II) kon worden verwacht dat de Euclidische afstand tussen vijvers geen goede maat is voor het inschatten van de connectiviteit tussen onderling verbonden zoöplanktonpopulaties in De Maten. In tegenstelling tot niet verbonden populaties, verloopt de dispersie tussen verbonden zoöplanktonpopulaties immers hoofdzakelijk door met de stroming meegevoerde parthenogenetische wijfjes in plaats van occasioneel passief transport van rusteieren door onder meer watervogels. Bovendien zijn de dispersiewegen tussen onderling verbonden populaties duidelijk afgelijnd en beperkt tot het netwerk van vijververbindingselementen. Om na te gaan in welke mate de expliciete ruimtelijke structuur van het vijvercomplex bijdraagt tot de genetische differentiatie tussen de zoöplanktonpopulaties werd de connectiviteit tussen zoöplanktonpopulaties gemodelleerd met behulp van een Geografisch Informatie Systeem (GIS). De effectieve geografische afstand werd gedefinieerd als de uiteindelijke afstand die een disperserend organisme moet afleggen doorheen het netwerk van verbindingselementen tussen bron- en doelpopulatie. In het Landschapsmodel werd niet enkel rekening gehouden met de aanwezigheid van rechtstreekse verbindingen tussen vijvers, maar ook met verschillende af te leggen afstanden tussen habitats via deze verbindingen. In het Debiet
172 Samenvatting
Model en het Dispersie Model, werden veldgegevens met betrekking tot kwantificatie van debieten en dispersie van zoöplankton (Hoofdstuk II) geïntegreerd om verschillen in fysische eigenschappen van vijververbindingen in te schatten. Tenslotte werden de verschillende modellen gevalideerd aan de hand van het patroon van genetische differentiatie voor neutrale merkers tussen een aantal D. ambigua populaties in de betrokken vijvers in De Maten. Hieruit bleek dat de effectieve geografische afstand verkregen uit het Debiet Model en het Dispersie Model een betere maat vormt om de interactie tussen onderling verbonden vijvers te modelleren dan effectieve geografische afstand op basis van het Landschapsmodel of louter Euclidische afstand. Op basis van verschillen in effectieve geografische afstand tussen habitats kan het vijvercomplex van De Maten worden onderverdeeld in drie clusters, die in grote lijnen overeen komen met de vertakkingen van het vijvercomplex. In Hoofdstuk IV hebben we aan de hand van allozyme electroforese de genetische structuur voor neutrale merkers van een aantal D. ambigua populaties in de Maten bepaald bij het begin en op het einde van het groeiseizoen. De genetische polymorfie voor allozyme loci in de door ons onderzochte D. ambigua populaties was relatief laag. Deze bevindingen waren in overeenstemming met de resultaten van voorgaande populatiegenetische studies bij deze soort (Platt & Spitze, 2000; Zofková, 2000). Een mogelijke verklaring hiervoor kan gevonden worden in het feit dat D. ambigua in onze streken een exoot is die uit de Verenigde Staten afkomstig is. De genetische structuur van de D. ambigua populaties in De Maten komt overeen met de genetische structuur van tijdelijke Daphnia populaties. Kenmerkend hiervoor is het ontluiken van nieuwe genotypes uit de rusteierenbank aan het begin van het groeiseizoen en optreden van clonale selectie doorheen het groeiseizoen (Hebert, 1987; Lynch & Spitze, 1994). Verrassend genoeg stelden we vast dat de D. ambigua populaties in De Maten toch een zekere mate van genetische differentiatie voor (quasi-)neutrale merkers vertoonden. De waarden voor genetische differentiatie waren vrij laag maar zeer sterk verschillend van nul. Bovendien stelden we vast dat de genetische differentiatie naar het einde van het groeiseizoen meer uitgesproken werd. Het patroon van genetische differentiatie tussen D. ambigua populaties in De Maten, suggereert “isolation-by-distance” i.e. onderling verbonden vijvers vertonen slechts een geringe genetische differentiatie, terwijl populaties uit verder uit elkaar gelegen vijvers een sterkere differentiatie vertonen. Dit is een aanwijzing dat de ruimtelijke structuur van het vijvercomplex in belangrijke mate bijdraagt tot het waargenomen patroon van genetische differentiatie. Bovendien suggereren deze vaststellingen dat ondanks de hoge waargenomen dispersie tussen populaties (Hoofdstuk II), die verondersteld wordt verschillen in allelfrequenties tussen populaties te homogeniseren, slechts
173 Samenvatting een gering aantal individuen zich na dispersie kan handhaven in de doelpopulatie. Er werd evenwel geen verband gevonden tussen de genetische differentiatie voor neutrale merkers in D. ambigua populaties en ecologische verschillen tussen de betrokken vijvers. Dit zou echter zeer onwaarschijnlijk zijn aangezien individuele populaties over een eigen rusteierenbank beschikken en deze hypothese een sterke koppeling veronderstelt tussen de verschillende patches van de metapopulatie. Een andere verklaring zou kunnen zijn dat er selectie optreedt op allozyme loci zelf (zie Riddoch, 1993), maar ook dit is zeer onwaarschijnlijk in De Maten. Op basis van de vaststellingen dat de ruimtelijk gestructureerde D. ambigua populaties onderling sterke interacties vertonen en populaties die tot verschillende vijverclusters (Hoofdstuk III) behoren genetisch gedifferentieerd waren, terwijl populaties die tot dezelfde cluster behoorden nagenoeg geen differentiatie vertoonden, werd tenslotte gesuggereerd dat de D. ambigua populaties in het vijvercomplex in De Maten kunnen beschouwd worden als een patchy metapopulatie (zie Harisson & Taylor, 1997). Fototactisch gedrag en lichaamskenmerken zijn zeer geschikt voor het aantonen van lokale adaptatie in natuurlijke Daphnia populaties (zie Boersma et al., 1998). In Hoofdstuk V hebben we in een cohorte levenstabel experiment genetische variatie voor deze kenmerken in aan- en afwezigheid van vischemicaliën bepaald. Hiertoe isoleerden we een aantal D. ambigua clones afkomstig uit vijvers in De Maten die duidelijk verschilden met betrekking tot waterhelderheid en visdensiteit. Aansluitend werd het fototactisch gedrag van de betrokken clones, die werden gekweekt in aan- en afwezigheid van vischemicaliën, bepaald. Met dit experiment wilden we nagaan of zelfs in een dergelijk sterk verbonden systeem een patroon van genetische differentiatie voor ecologisch relevante kenmerken kon gevonden worden dat in overeenstemming is met de hypothese van lokale adaptatie. Het opkweken van uit het veld geïsoleerde dieren leverde evenwel aanzienlijke praktische problemen op. Hierdoor kon slechts een beperkt aantal clones onderzocht worden. Niettegenstaande de sterke interacties tussen populaties, bleken de waargenomen verschillen in fototactisch gedrag een tendens tot lokale adaptatie te vertonen. De clones afkomstig uit een heldere vijver (vijver 34), die gekarakteriseerd was door een lage visdensiteit en een sterke ontwikkeling van ondergedoken waterplanten, bleken een minder uitgesproken negatief fototactisch gedrag te vertonen dan clones afkomstig uit meer troebele vijvers. In tegenstelling tot eerder onderzoek bij D. magna (De Meester, 1996; Boersma et al., 1998; Cousyn et al., 2001) was de tendens tot lokale adaptatie niet merkbaar in de gedragsverandering na blootstelling aan vischemicaliën, maar eerder door de gemiddelde waarde over de behandelingen. In vergelijking met de resultaten bekomen door de studie van (quasi-)neutrale merkers (Hoofdstuk IV) duiden de resultaten van
174 Samenvatting
Hoofdstuk V op een meer positieve relatie tussen de genetische variatie voor twee van de bestudeerde kenmerken (fototactisch gedrag en grootte bij maturiteit) en de ecologische verschillen tussen de betrokken vijvers, terwijl een minder duidelijke relatie werd vastgesteld tussen de genetische differentiatie en de ruimtelijke structuur van het vijvercomplex. Het grootste aandeel van de genetische variatie was afkomstig van clones geïsoleerd uit vijver 34, een kleine zeer heldere vijver die niet in verbinding staat met de rest van het vijvercomplex. Deze vaststelling is niet geheel onverwacht aangezien ecologische verschillen tussen vijvers eerder weerspiegeld zullen worden in genetische variatie voor ecologisch relevante kenmerken ten gevolge van differentiële selectie, dan in genetische variatie voor (quasi-) neutrale kenmerken die eerder patronen van genmigratie zullen weerspiegelen. Om het belang van soortspecifieke factoren in het tot stand komen van genetische differentiatie tussen natuurlijke zoöplanktonpopulaties na te gaan hebben we in het laatste hoofdstuk tenslotte het patroon van genetische differentiatie voor (quasi-)neutrale merkers in een aantal Polyphemus pediculus populaties vergeleken met de eerder bekomen patronen in D. ambigua (Hoofdstuk IV). De genetische differentiatie tussen vijf P. pediculus populaties in De Maten was eerder beperkt maar duidelijk meer uitgesproken dan de genetische differentiatie zoals vastgesteld in D. ambigua populaties die werden bemonsterd rond hetzelfde tijdstip maar in een ander groeiseizoen. In tegenstelling tot D. ambigua was er voor de onderzochte P. pediculus populaties geen significant verband met effectieve geografische afstand net zo min als met ecologische afstanden tussen vijvers. Deze resultaten suggereren dat de rol van rechtstreekse verbindingen en dispersie voor P. pediculus populaties in het tot stand komen van genetische differentiatie, minder belangrijk is dan voor D. ambigua populaties. We verwachten ook dat de dispersie tussen P. pediculus populaties veel geringer zal zijn dan de dispersie tussen D. ambigua populaties aangezien deze soort sterk geassocieerd voorkomt met ondergedoken waterplanten en beschouwd wordt als een relatief goede zwemmer. De significante differentiatie tussen P. pediculus populaties behorend tot éénzelfde vijver cluster, wijzen erop dat mede door de specifieke habitatvereisten van deze soort, troebele vijvers mogelijk een barrière vormen voor dispersie tussen verbonden habitats. Het hier voorgestelde onderzoek illustreert het belang van landschapsstructuur en soortspecifieke factoren in het tot stand komen van genetische differentiatie tussen lokale cladocerenpopulaties. Wij menen dat onze benadering een brede toepasbaarheid kan hebben in verder populatiegenetisch onderzoek en we hopen dat deze benadering niet beperkt blijft tot cladocerenpopulaties of onderling verbonden vijversystemen.
175 Samenvatting
References
Boersma, M., P. Spaak & L. De Meester 1998. Predator-mediated plasticity in morphology, life- history and behaviour of Daphnia: The uncoupling of responses. American Naturalist 152: 237- 248.
Cousyn, C., L. De Meester, J.K. Colbourne, L. Brendonck, D. Verschuren & F. Volckaert 2001. Rapid, local adaptation of zooplankton behavior to changes in predation pressure in the absence of neutral genetic changes. Proceedings of the National Academy of Science 98: 6256-6260.
De Meester, L. 1996. Evolutionary potential and local genetic differentiation in a phenotypically plastic trait of a cyclical parthenogen. Evolution 50: 1293-1298.
Harrison, S. & A.D. Taylor 1997. Empirical evidence for metapopulation dynamics. In Hanski, I.A. & M.E. Gilpin (Eds.). Metapopulation Biology, Ecology, Genetics and Evolution, pp. 27-42. Academic Press, San Diego.
Hebert, P.D.N. 1987. Genetics of Daphnia. In Peters, R.H. & R. de Bernardi (Eds.). Daphnia, pp. 439- 460. Istituto Italiano di Idrobiologia, Pallanza.
Lynch, M. & K. Spitze 1994. Evolutionary genetics of Daphnia. In Real, L.A. (Ed.). Ecological genetics, pp. 109-128. Princeton University Press, Princeton.
Platt, T. & K. Spitze 2000 Genetic variation in a subtropical population of Daphnia. Hydrobiologia 435: 191-196.
Riddoch, B.J. 1993. The adaptive significance of electrophoretic mobility in phosphoglucose isomerase (PGI). Biological Journal of the Linnean Society 50: 1-17.
Zofková, M. 2000. Phenotypic variability and genetic diversity of species Daphnia ambigua Scourfield and Daphnia parvula Fordyce. Unpublished masters thesis, Charles University, Prague.
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Publications
Michels, E. & L. De Meester 1998. The influence of food quality on the phototactic behaviour of Daphnia magna Straus. Hydrobiologia 379: 199-206.
Michels, E., M. Leynen, C. Cousyn, L. De Meester & F. Ollevier 1999. Phototactic behaviour of Daphnia as a tool in the continuous monitoring of water quality: experiments with a positively phototactic Daphnia magna clone. Water Research 33: 401-408.
Michels, E., S. Semsari, C. Bin & De Meester L. 2000. The effect of sublethal doses of cadmium on the phototactic behaviour of Daphnia magna. Ecotoxicology and Environmental Safety 47: 261-265.
Michels, E., K. Cottenie, L. Neys & L. De Meester 2001. Zooplankton on the move: first results on the quantification of dispersal of zooplankton in a set of interconnected ponds. Hydrobiologia 442: 117-126.
Michels, E., K.Cottenie, L. Neys, K. De Gelas, P. Coppin & L. De Meester 2001. Geographical and genetic distances among zooplankton populations in a set of interconnected ponds : a plea for using GIS modelling of the effective geographical distance. Molecular Ecology 10: 1929-1938.
Dang Kieu, N., E. Michels & L. De Meester 2001. Phototactic behavior of Daphnia and the continuous monitoring of water quality: interference of fish kairomones and food quality. Environmental Toxicology and Chemistry 20: 1098-1103.
Cottenie, K., N. Nuytten, E. Michels & L. De Meester 2001. Zooplankton community structure and environmental conditions in a set of interconnected ponds. Hydrobiologia 442: 339-350.
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