Fish Population In Lake Annecy Biology Essay

Published:

1. Contribution of obligatory and voluntary fisheries statistics to the knowledge of whitefish population in Lake Annecy (France). Published in “Fisheries Research”

2. Evaluation of food web and fish dietary niches in oligotrophic Lake Annecy by combine use of gut content and stable isotope analysis. Submitted for publication in “Lake and Reservoir Management”

3. Preliminary trophic network analysis of subalpine Lake Annecy (France) using an Ecopath model. Published in Knowledge and Management of Aquatic Ecosystems.

4. Growth and catch trends of whitefish (Coregonus lavaretus) population in oligotrophic Lake Annecy, France . Under revision

5. Comparative effectiveness, growth and dispersal of stocked Arctic char (Salvelinus alpinus) from different origins in Lake Annecy. Submitted for publication in Knowledge and Management of Aquatic Ecosystems.

6. Application of Ecopath model to study the trophic network of an oligotrophic subarctic lake. (Short Communication). Under work.

Abbreviations

SILA Le Syndicate Mixte du Lac d'Annecy

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SIA Stable Isotope Analysis

GCA Gut Content Analysis

EBFM Ecosystem-based fishery management

ANOSIM Analysis of Similarities

SIMPER Similarity percentages

YoY Young of the Year

YCS Year Class Strength

ALP Annecy Lac Peche

CPUE Catch per unit Effort

VBGF Von Bertalanffy Growth Function

LGC Lake Geneva

LAW Lake Annecy wild

LGW Lake Geneva fish

DDAF Direction Départementale de l'Agriculture et de la Forêt de Haute-Savoie ONEMA Office national de l'eau et des milieux aquatiques

EwE Ecopath with Ecosim

P Production

Q Consumption

B Biomass

M Natural Mortality

PPR Primary Production Required

ACKNOWLEDGEMENTS

To Daniel Gerdeaux whose wisdom, talent enormous skills and guidance has made all this task possible and helped me to solve the technical problems and resolve different things.

To all the research and administrative staff at INRA Thonon, especially Valerie Hamelet for all her technical help and ??? for putting a smile on my face every morning

To my late father for all his inspiration at start of my career, my mother for her un-measurable love and encouragement, my brothers and their family for all their support over the years. Especially lot of strength has been obtained from my beloved wife Sidra, who came to my life during last year of my thesis and inspired me to finish the work well in time.

I would like to thank all my friends and colleagues who cheered me and always lent a supporting hand when required.

I also appreciate the help all the anglers and commercial fishermen of Lake Annecy, especially Bernad Curt for his help during samples and data collection.

I am also grateful to Direction Départementale de l'Agriculture et de la Forêt de Haute-Savoie (DDAF), Syndicat Mixte du Lac d'Annecy (SILA) and , Annecy Lac Pèche (ALP) and Office national de l'eau et des milieux aquatiques(ONEMA) for partial research fundings and help in data collection and samplings.

This thesis would have been impossible to conduct without financement of HEC, Pakistan through SFERE, France and INRA France for scholarship grants,other fundings all the academic and administrative help during my study.

I would also like to thank Ross Tallman, and Fisheries and Oceans Canada for providing me an opertunity to work at Freshwater Institute Winnipeg under Canada Interchange programme during the last year of my PhD studies.

Abstract

The thesis focuses on different aspects of ecology and management of commercially important fish species in Lake Annecy: an oligotrophic lake situated in Rhône-Alpes region of France. The main objectives of the study were to evaluate the growth of whitefish and Arctic char in Lake Annecy, study the trophic interactions in foodweb using stable isotope analysis (SIA) and Gut Content Analysis (GCA), and to propose a trophic model of the lake using Ecopath with Ecosim (EwE) software using data collected mostly during the last 10 years from commercial and recreational fisheries monitoring.

Lake Annecy is important for both recreational and commercial fisheries and is under continuous obligatory monitoring for fish catches since 1986. About 20 volunteer anglers also provided additional data about their fish catches since 1992. The fisheries data from different sources was compared for measured length distributions in the commercial fishery and the estimated length distributions from angler's catches filtered by a selectivity curve. Statistically the two distributions were found to be the same. The CPUE calculated from both obligatory and volunteer angler data are also well correlated. The results showed that volunteer anglers catch data could provide enough reliable information about the population size and structure and is reliable enough to be a cost-effective fisheries management technique.

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Temporal diet pattern was studied for five fish species in Lake Annecy using carbon and nitrogen stable isotopes and gut content analysis to investigate their diet and food web position during 10 years. Observations on stomach content data reveal variety of feeding habits with some diet overlap between whitefish, Arctic char and perch during the spring months. Food resources appeared to be partitioned among the Lake Annecy fish community during summer growth months possibly limiting competitive interactions. Nitrogen isotopes indicated very low inter-specific variability while δ13C showed variation among fish species. Stable isotope analysis and multiple-source MixSIR mixing were found to be useful tools that can improve the knowledge about food web dynamics when used in combination with gut content analysis. Data collected over more than 10 years from literature, survey report and information from commercial and recreational fisheries was used for trophic analysis of Lake Annecy food web with help of Ecopath with Ecosim (EwE) software to present a trophic model. Detritus was found to be almost 50% of the total system throughput and had an important role in the lake ecosystem. The values of different ecosystem attributes and ascendancy showed the sta­bility of the ecosystem. Mixed trophic analysis indicated that zoobenthos had a positive effect on most of the fish functional groups. The trophic transfer efficiency was highest in Lake Annecy at TL III Ecotrophic Efficiency of all commercially important fish groups were less than 0.5, showing sustainable fisheries. the percentage of Primary Production Required (PPR) to support fisheries in Lake Annecy was 14.98% of total primary production which is somewhat less than the global value of 23.6% for fresh water lakes

Whitefish is the most important commercial and recreational fish species in the lake. Growth of whitefish population of Lake Annecy was studied using fisheries data collected by recreational and commercial fisheries during last 20 years to evaluate any effect of change in environment and density. Scales of whitefish collected from recreational and commercial fish catch were used to determine age and to estimate back-calculated length. The results showed strong correlation between yearly mean of zooplankton biovolume and growth increment during 2nd year. Some relationships were found between whitefish growth and changing fish catches. However, whitefish growth was not found to dependent on any change in mean temperature, total phosphorous concentration and YCS of cohorts. Arctic char Salvelinus alpinus is being stocked in Lake Annecy with fish seed coming from different origins to improve the catches. The effectiveness, growth and dispersal after release of fingerlings of Arctic char stocked in 1997 and 2001 were studied. Juveniles produced from hatchery captive brood stock were found to be more effective in fish catch and also showed better dispersal and growth after stocking as compared to brood stock caught from wild. The growth of progeny of wild Arctic char brood stock was also not very much different from hatchery reared stock. Mean effectiveness of stocking was found to be 14 %. The portion of natural breeding was found to be almost half in the catches. The studies above provided some useful information about the current status of Lake Annecy fisheries and food web and could be helpful t and for future management of Lake Annecy from fisheries and ecosystem management point of view.

1- Introduction

Ecosystem based approach is getting importance for proper fisheries management of aquatic resources and fisheries managers are beginning to grab the potentials of ecosystem-based fishery management (EBFM) to improve the sustainability of fisheries resources. To work on ecosystem level, an understanding of ecosystem food web and trophic structure is very essential for fishery assessment and management. The single species approach is also still tractable in fisheries management. Data on fish growth are very important for assessing the status of fisheries of the particular species and how that species is responding to the exploitation (Walter and Martell 2004). Such data can be helpful for the fisheries managers to plan the exploitation of the species. Fish stocking is one of the important aspect of fisheries management and has been used as a management tool for centuries. Studying the success of stocked fish is one of the important tasks of fisheries management.

Lake Annecy is one of the most pure lake in Europe and very famous for water sports and angling. It is an oligotrophic lake with an average total phosphorus concentration around 4 µg.l-1. Despite being an oligotrophic lake, there are a many potential food resources for fishes that occupying both littoral and pelagic habitats, including benthic invertebrates, zooplankton, macrophytes and other fish. The total fish yield from Lake Annecy is 8-15 kg.ha-1.year-1 (Gerdeaux et al., 2002), which is considered as good for an oligotrophic lake (Downing et al., 1990). The fish fauna of Lake Annecy is dominated by whitefish but also many other commercially important fish species coexist in the lake including Arctic char, trout, perch, pike and roach. The species composition and trophic status of Lake Annecy food web has not changed much changed during last few 20 years. However there are some non linear fluctuations in the biomass of its different components both at upper and lower level especially in zooplankton (Gerdeaux et al., 2009). There are also some non linear fluctuations in fish catches especially of salmonids during last few years (Gerdeaux, 2008). Therefore, understanding of the food web structure in the Lake Annecy and growth of important fish species is very essential to support current and future fisheries management decisions.

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To reveal the answers to these questions, the main objectives were

* To evaluate the importance of commercial and recreational fisheries data in fisheries management

* To investigate the niche segregation between different fish species during different seasons by examination of diet and stable isotope analysis.

* To study the trophic interaction between fish species and their potential prey.

* To evaluate the growth of whitefish during the last twenty years to investigate any decline

* To evaluate the stocking and growth of Arctic char coming from different origins

1.1 The Site

Lake Annecyis a oligotrophic perialpine lake of glacial origin situated at an altitude of 445.6 meter in Haute-Savoiedepartment of Rhone Alps region in France (45o50'N, 6o40'E). Lake Annecy was formed about 18,000 years ago, as a result of melting of large Alpine glaciers. It is the second biggest lake situated entirely in France, after theLake Bourget. It is also described as one the most beautiful lakes in Europe with mountains encircling the entire lake and reflecting in clear sky water. Also considered as one of the cleanest lake in Europe because of strict environmental regulations. Lake Annecy was an oligotrophic lake upto 1940,s, but water quality of the lake started degrading from 1945 to 1960,s because of increase in the anthropogenic activities. Preventive measures were started in 1967 by establishment of treatment plant. Since then oligotrophication is taking place. It is also a very famous tourist destination for water sports and sports fisheries. The lake is fed by many small rivers from the surrounding mountains including Ire,Eaumorte,Laudon,BornetteandBiolon.

Outlet of lake is through Thiou River flows out from the northern end of the lake to joins the Fier River, and finally enters the Rhone River. It has a surface area of 27 km2, a shoreline length of 35 km, and a maximum depth of 64.7 m. The lake consists of two basins. The northern main basin accounts for about 78% of the whole lake area, while the southern sub-basin accounts for only 22% of the total lake area (Figure 1). The temperatures on the surface rarely exceed 23°C in the summer and are never below 4°C in the winter. Lake Annecy is a monomictic lake never covered by ice. Important physicochemical parameter of Lake Annecy is given in Table 1.

Fishing is a very important practice in Lake Annecy. Lake Annecy is a public water for fisheries management and it fisheries is being managed by Departmental Directorate of Agriculture and Forest (DDAF) on the behalf of the community. The density of fish was very poor at the beginning of 19th century as compared to other lakes in the region. At the end of the 19th century, Whitefish and Arctic char were introduced in the lake in 1890 and 1888 respectively (Le Roux 1908). Introduction of new species has resulted in a typical fisheries composition of subalpine lakes with development of whitefish and Arctic char. There are two types of fisheries in Lake Annecy, amateurs (anglers) and professionals (commercial fishermen). The number of professional fishermen was 38 in 1968 which was reduced drastically to 4 in 1998. At present, number of amateur fishermen is around 1100.

The fisheries resources of Lake Annecy are not very diverse, but few species are highly regarded by both recreational and commercial fishermen for sport and monetary value. The present fish community is mainly composed of salmonids lake trout (Salmo trutta), Arctic char (Salvelinus alpinus) and whitefish (Coregonus lavaretus) which represent more than 80% of the yield. Pike (Esox lucius) and perch (Perca fluviatilis) are also caught and produce 10 % of the yield. Burbot (Lota lota) and some cyprinids (roach (Rutilus rutilus), bream (Abramis brama), carp (Cyprinus carpio)) make up the last 10%. Lake Annecy is typically a coregonid lake (Gerdeaux et al. 2006). Like all other big European lakes, the fisheries in Lake Annecy also depend on whitefish. The whitefish population is maintained in lake without stocking whereas the Arctic char was constantly stocked because of the lack of appropriate spawning sites. There is lot of fluctuations in fisheries catches in Lake Annecy during last few decades especially in whitefish. During the 1980's, commercial fishermen were catching two thirds of the total catches of whitefish which were less than 15 tones but number of anglers increased and they asked for equal share in the fisheries resources. The fishing pressure increased a lot during 1990's and the total whitefish catches were often above 25 tones (9.3 kg. ha-1) with huge inter-annual fluctuations. After a increasing trend of total catches during 1990's, there is a decreasing trend with very low catches in 2006 and 2007. The fluctuations of catches are not totally synchronous between and amature and commercial fishermen. Arctic char, the 2nd most important fisheries in Lake Annecy is mostly shared by the anglers getting almost 88% of the catches. It also showed yearly fluctuations during last few decades.

Many studies have been made on physical, chemical and biological aspects of Lake Annecy and its different functional groups by different researchers, and institutions since 1960's. From 1966 to 1981 and since 1990, the Lake Annecy is monitored by SILA (Le Syndicate Mixte du Lac d'Annecy) for physical, chemical and biological parameters of Lake Annecy with help of Lakes Hydrobiology Station (INRA) Thonon. Since 1987, fishing at Lake Annecy is followed every year with help of commercial and recreational fishermen. A comprehensive report was published by Gerdeaux et al. (2000) on trophic functions of Lake Annecy as a part of 9th Contract Plan of Rhone-Alpes Region Environment Programme. That report includes studies on Macrobenthos, Zooplankton, Phytoplankton, Microorganisms and diet of some important fishes. Some work has been done on feeding habits of juvenile and adult fishes in Lake Annecy. Cretenoy and Gerdeaux (1997) studied the changes in diet of whitefish larvae from with growth. Gerdeaux et al. (2002) studied the diet and seasonal patterns of food composition of whitefish in Lake Annecy and compared it with the diet of the few other species of the fish community. Onneville et al (2007) studied ontogenetic changes in the diet of whitefish larvae and observed that density and availability of the potential prey could affect the dynamics of the ontogenetic changes in the diet. Domaizon et al. (2006) found that mixotrophic flagellates represent an important link in the flux of materials through planktonic food webs in Lake Annecy studies the taxonomic composition and their grazing rates of flagellate in Lake Annecy.

Some work has been done on the benthic fauna of Lake Annecy. Muthon and Dubois (2001) studied the Molluscs communities in littoral zone of lake Annecy and found Lake Annecy an oligohumic lake characterized by a decrease in organic matter content with depth. Mouthon (2002) compared the mollusks community in Lake Annecy with the one studies during 1930's. Verneaux and Verneaux (2002) studied the bathymetric pattern of the macrobenthic community of Lake Annecy and found it related to an optimal efficiency of organic matter assimilation within the food web and showing higher biogenic capacity. Borderelle (2005) studies the biological quality assessment of Lake Annecy, using Lake Biological Index and found that Lake Annecy showed much higher LBI value as compared to some other lakes in the region. Some work has been done on the seasonal biomass of fish community using hydroacoustics. Guillard et al (2006) studied YoY perch biomass by hydroacoustic and found an increase in YoY perch biomass during summer and possibly associated with collapse of theDaphniapopulation by late July, and copepods one month later. Masson et al (2001) also found that fish biomass in Lake Annecy was dominated by YoY perch in upperwarmer layers and salmonids in the colder and oxygenated deep layers. Fish biomass was distributed alonga strong increasing onshore to offshore gradient at night,whereas crustacean prey showed a decreasing gradient. Dubois et al. (1988) studied the aquatic fauna of lake Annecy and found submerged vegetation dominated by Charophyceae. Wajtenka et al (1988) and Caranhac and Gerdeaux (2000) presented a size-and age-based simulation model for evaluating management strategies of dual exploitation of whitefish in Lake Annecy.

Some researchers have used stable isotopes analysis to study the lake ecology. Gerdeaux and Perga (2006) studied the trophic status of Lake Annecy with help of SIA of whitefish scales and found only minor changes during the fifty years. Perga and Gerdeaux (2006) also used δ13C and δ15N values in crustacean taxa and found the crustacean food web of Lake Annecy being much complex. The crustacean taxa apparently obtained theircarbonfrom different primary sources and their relative trophic positions changed during the year. Dufour et al (2007) used carbon and oxygen stable isotopes to prevail that δ13C values in conjunction with δ18O values from whitefish otoliths in lake Annecy and discovered that it can be used as powerful proxies of fish environment, behavior, and evolution. Perga et al. (2010) reconstructed the changes in the planktonic food web of Lake Annecy over the past century using paleoecological techniques, carbon and nitrogen stable isotope signatures of organic matter from sediments, and time series data and found comparatively high export of phytoplankton-derived organic matter to the sediment as a result of strong top-down effects on the planktonic food chain. Gerdeaux et al. (1990) studied the result of stocking of Salmo trutta in Lake Annecy from 1964 to 1977 in Lake Annecy with help of professional fishermen and found that growth of fish depends on size of stocking. No comprehensive study has been made so far to study the trophic interactions in Lake Annecy with ecosystem approach.

2- Fisheries Management Tools & Techniques

Management of fisheries resources is very important to ensure that the stocks could be harvested at sustainable level. Fisheries management tools can be divided broadly into two types, Input controls and output controls. Input controls limit the amount of effort fishermen can put into their fishing activities and hence indirectly control the fish catches eg number of licenses, the length and mesh size of nets etc. Output controls limits the amount of fish taken out from water, for example yearly quota. Such controls are usually used for single species management and require the use of log books and controls. The recreational fisheries management is a bit different from commercial as it not only take into account the total harvest but also the sharing of resources between the fishers. There are also input controls for recreational fisheries eg methods or type of fishing gears or baits, and some output controls like bag limits, or monthly or annual quota. Setting minimum legal size is also a management policy tool which is used to make sure that fish should not be caught at least before the 1st breeding. But habitat management is also an important part of fisheries management because fisheries management can be effective only, when habitat will be managed properly.

2.1- Use of commercial and recreational catches data for fisheries management

Fisheries management requires high-quality observations and well-supported predictions about the status and dynamics of fish populations. Proper planning and management of fisheries resources depends upon the quality of data. Because fish is hidden under the water, collection of fisheries data is a costly and time consuming process and involves special gill netting, trap netting or some passive techniques such as electro-fishing and acoustic techniques creep survey commercial catch monitoring and neither can provide all the needed information and all the methods are different in cost effectiveness. Depending upon resources and objectives, different strategies can be adopted. There are two main types of fisheries data. Fisheries-independent data are collected through scientific surveys whileFisheries-dependent data are defined as fishery statistics collected directly from recreational and commercial fishing activities. Fishery dependent data from recreational fisheries are essential for assessing the mortality and other stresses that result from fishing. These data provide a direct measure of the effectiveness of management and regulation. The statistics obtained from recreational and commercial fisheries can also be used for fish stock assessments. Such data also provide information about fishing efforts, kinds and number of gears used, target species and age and size composition of fish and help to support fisheries management decisions. Such fisheries statistics are also helpful to provide long term trends in relative abundance of target species (Sztramko, et al., 1991; Kerr, 1998; Maclennan, 1996). Anglers diaries are usually used by the fisheries management to gather information on recreational fisheries. Cooke et al., (2000) demonstrated that angler's diaries can provide the majority of data for fisheries management for specialized fisheries in Ontario. Several fisheries management agencies especially in central Europe and North America regularly run such type to program to collect fisheries data involving anglers and commercial fishermen.

Angler diaries are considered as relatively low cost fisheries assessment tool to monitor fisheries. Many researchers have evaluated the effectiveness of angler diaries programmes and have found positive relationship between angler diaries, creel surveys and electrofishing (Green et al. 1986; Prentice et al. 1995). Several long term monitoring programmes have been developed and runned successfully especially in North America and are generally accepted as the most economic method of collecting angler provided data (Cook et al. ??? Pollock, 1994). However there can be many biases related to the information provided by the anglers' diaries. One of the biggest problem in anglers diaries is the involvement of as many anglers as possible and then to screen out the reliable data. Such biases can be removed by selecting batch of volunteer anglers who could be trained technically for fisheries data collection. Fisheries management has some special indicators to evaluate the fisheries including length frequency data, mean size or weight of individual and catch per unit effort (CPUE). If it takes more effort to catch the same amount of fish, it means that the fish stock is decreasing. Fisheries can be managed with bag limits, size restriction, seasonal closers, and stock enhancements.

2.2.1- Context of Study

Since 1992, a special group of about twenty volunteer anglers was created at Lake Annecy in collaboration with INRA and ALP to provide fisheries data. They were trained to measure and weigh each fish they catch and keep and also total length of each fish they catch & release. They also provided information on the duration, location of the fishing trip, number of hooks, the sex of the fish, and marking, if any on the stocked fish. It was the additional data to what obligatory provided by all the anglers and commercial fishermen. Length frequency data, size or weight at catch and CPUE are two very important tools for fisheries management often collected as a basis for various types of growth and age assessments and indices for fisheries management. Therefore, it is interesting to examine variability of these parameters derived from different sources. The specific objectives of the present studies are to evaluate the effectiveness of fisheries data provided by group of volunteers by comparing length frequency distribution, mean size at catch and CPUE and compare it with obligatory data provided by other anglers and commercial gillnet fisheries. If fisheries statistics obtained from volunteer anglers are comparable to those from obligatory angling and commercial fisheries data, the collection of data from volunteers can be a cost-effective data source for fisheries management

2.2.2- Studies done

Obligatory fisheries data provided by the anglers, data provided by voluntary fishermen and data obtained from monitoring of commercial catches between 1992-1997 was utilized for present study. Length frequency distribution obtained from volunteer anglers data was filtered with the gillnet selectivity curve (Caranhac and Gerdeaux, 2000) and compared with the actual length frequency distribution obtained from commercial fisheries. Both the distributions were found statistically same. Mean individual weight taken from anglers obligatory log books was compared with weight obtained from commercial catch. The fluctuations in mean weight were same in both type of fisheries, thought the range of fluctuations was large in anglers catch than in commercial catch. Mean number of whitefish per trip and the mean weight (kg) in a gillnet during one night were used to calculate CPUE from recreational and commercial fisheries respectively. There was no correlation between commercial and recreational fisheries CPUE. However, strong relationship was found between obligatory and voluntary CPUE from recreational fisheries.

3-Studies of Trophic Interactions in Lakes

3.1Partitioning of Feeding Resources in Fish Communities

Species usually share three types of resources: food, space and time. Fish usually prefer the habitat where feeding is more profitable energetically (Werner and Mittelbach, 1981) but inter-specific and intra-specific competitions have very strong influence on feeding behavior and feeding habitat. Competition for a resource such as food are expected to be more strong between closely related species in the same community eg salmonids, since closely related species show very little evolutionary difference. Species in a fish community reduce the potential of any competition among them by resource partitioning along spatial and temporal axes (Schoener, 1983; Piet and Guruge). However trophic separation has been pointed out as the most important mechanism of resource partitioning in fish assemblages (Ross, 1986). From a trophic point of view, the different species within a fish community can feed on same type of food but they can do so in varying amounts and from different places. Fish resource partitioning can be studies with help of dietary data (Linke et al. 2001). Analysis of interaction among coexisting species with a niche overlap index is useful to reveal the significance of different niche parameters in community and how these niche dimensions are used by fishes to reduce competition and allow species to coexist with partionining of resources. (Macpherson 1981). The diet resource partitioning among species can be checked with Schoener's Index (Schoener 1974) and Analysis of Similarities (ANOSIM) (Clarke et al 2005). Nowadays fish trophic interactions and foodweb structures are often studied jointly with help of Gut Content Analysis (GCA) and Stable Isotopes Analysis.

3.2.1 Use of Gut Content Analysis and Stable Isotope Analysis to study trophic interactions in Fishes

Gut content analysis (GCA) is a primary tool for quantifying the diet and can define diet changes on monthly and seasonal level to give better picture of trophic interactions. It is a useful tool to understand fish feeding ecology and is considered to be a standard practice for diagnosing fish trophic relationships. It has appeared to be the most reliable method in order to obtain seasonal variation in trophic interactions (Pasquaud et al., 2007). GCA over long periods of time can give insights into temporal trends in feeding patterns that may not be evident in other techniques. Accurate quantitative analysis of trophic interaction can be helpful to answer many important questions in ecology including dynamics of food webs, exploitation and management of resources and assessment of human activities impact on ecosystem (Gorokhova & Lehtiniemi 2007). However there are many disadvantages of GCA because it provides only snapshot of the diet over days or months depending upon type of food, and temperature. Also, it is some time very difficult to identify some soft and rapidly digesting items in prey. For animals that macerate their food in gut having feed in form of fluid, it is difficult to identify the items without molecular identification techniques at DNA level (Harper et al. 2005).

Stable Isotope Analysis are helpful to study food webs when stomach contents are difficult to identify or when there is uncertainty regarding the assimilation efficiency of various food items. Many ecologists have now realized the power of SIA, especially when used to complement more conventional techniques (Grey, 2006). Stable isotope ratio in proteins of consumers actually reflects stable isotope ratio in the diet proteins in a predictable manner (Hobson, 1999). δ15N generally shows a stepwise enrichment at each trophic level and as a result the δ15N values in the tissues of consumers tend to be between 2·5‰ to 4‰ greater than those of their diets (Bearhop et al., 2002; Vander Zanden and Rasmussen, 2001). Therefore δ15 N can provide estimates of trophic position of the animal. The ratio of δ13C also shows increases with trophic level, but to lesser extent than δ15N, in the order of 1‰ (DeNiro & Epstein, 1978; Peterson & Fry, 1987; Vander Zanden and Rasmussen, 2001). So δ13C ratio reflects diet composition and can be used to determine ultimate sources of dietary carbon (Post, 2002; Borderelle et al., 2009). This stepwise consistent change could be helpful to quantify relative trophic position, which can also be correlated with dietary changes in fish (Cabana & Rasmussen, 1994). δ15N and δ13C in muscle tissues reflects food consumed during the spring and summer growth period (Perga & Gerdeaux, 2005). Because of such qualities, Stable Isotope Analysis is a powerful tool to study diet composition.

It is a common practice by the food ecologists to present δ13C-δ15N bi-plots with species, individuals or populations plotted based on their mean stable isotope values. Relative position of species in this bi-plot could be used to gather the aspects of food web structure. However, it is very difficult to get the complete structure of food web by applying just one technique. A variety of methods has been developed by ecologists to study data from SIA. The data from SIA can be used to calculate trophic positions (Vander Zanden et al., 1997; Post, 2002; Layman et al., 2005; Rybczynski et al., 2008;) relative contribution of prey to consumers, (Vander Zanden & Vadeboncoeur, 2002; Phillips and Greg, 2003; Clarke et al., 2005), niche shifts (Post, 2003) intraspecific diet variability (Dufour et al., 1998; Bolnick et al., 2003; Bearhop et al., 2004; Matthews & Mazumder, 2004; Duponchelle et al., 2005) and communitywide trophic structure (Layman et al., 2007).

As stable isotope ratios in an organism's tissues are resulting from all trophic pathways ending in that individual, they can also be used as a mean to portray the trophic niche. There are advantages of natural variations in stable isotopes ratios as they reflect the underlying aspects of species trophic niche (Layman et al., 2007). Some studies have discovered that variation associated with the mean isotopic signature combined with GCA can be used to measure dietary niche width (Bearhop et al., 2004). Both GCA and SIA have some advantages and disadvantages that make them complementary for characterization of food web, trophic interactions and interpretation of data (Parkyn et al., 2001, Davenport & Bax, 2002; Jardine et al., 2003; Grey et al., 2002; Rybczynski et al., 2008; Perga & Gerdeaux, 2005; Duponchelle, 2005). Ecologist has recommended that while studying food webs, whenever possible; SIA should be combined with GCA (Whitledge and Rabeni, 1997; Beaudoin et al., 1999; Johannsson et al., 2001; Grey et al., 2002; Renones et al., 2002; Christensen and Moore 2009).

3.1.3 Context of Study

Lake Annecy, although an oligotrophic lake, has a number of potential food resources for the fish that occupy both littoral and pelagic habitats; including benthic invertebrates, zooplankton, macrophytes and prey fish. . The species composition and trophic status of Lake Annecy food web has not changed during last ten years. However there are non linear fluctuations in the biomass of its different components both especially in zooplankton (Gerdeaux et al., 2009) and in fish catches especially of whitefish and Arctic char during last few years (Gerdeaux, 2007). Changes in the biomass proportions of food web components can cause changes in trophic interaction and may results in changes in diet of fish community. Fisheries management in Lake Annecy is focused on salmonids. Whitefish population is maintained by natural breeding while Arctic char and trout are stocked annually which is a costly practice. Fisheries management at Lake Annecy want to identify that how the resources are partitioned within the ecosystem, what is the potential for competition among salmonids, and how much pike and perch have influence on lower trophic levels through predation. Previous studies have used the GCA (Gerdeaux et al. 2002) to establish patterns of food web structure in Lake Annecy but the conclusions from that study were not very useful for the management. We present the use of both SIA and GCA to improve the understanding of the food web and propose some conclusions for the fishery management. We used an extensive survey during 10 years on isotopic analysis of food web components and seasonal GCA during 2008 and compare it with earlier work (Gerdeaux et al. 2002) to examine the food web structure, dietary niches and overlaps among important fish species in Lake Annecy. We evaluate the results obtained by GCA by using SIA and MixSIR mixing model to understand how the two different methods are useful to understand the trophic ecology of Lake Annecy and improve the fishery management.

3.1.3 Studies done

Fish were sampled mostly from commercial fisheries. For GCA, an attempt was made to collect samples during all the months of fishing season from February to October. The volume of each type of prey was assessed in the gut contents (Hyslop, 1980). The diet overlap between different species was calculated on seasonal basis using the Schoener Index (Schoener, 1974). One-way pairwise analysis of similarity (ANOSIM), based on the Bray-Curtis similarity matrix, was used to test for interspecies differences at P <0.001 between fish diets. To illustrated diet overlap non-metric multi-dimensional scaling (MDS) based upon a Bray-Curtis similarity matrix was used. Similarity percentages (SIMPER) were used to identify the dietary categories which contributed most to any differences between the seasonal diets of the various species by combining the diet items into four categories. 465 adult fish samples, 29 juvenile fish samples, 77 samples of zooplankton, and 263 samples of benthos between 1998-2008 were used for SIA in present study. Samples for SIA were collected mostly during the autumn season so that they represent the diet during the summer growth months. Samples of YoY of perch, roach, benthic invertebrates that were eaten by fish and bulk zooplankton were also collected for SIA. Analyses were done using Europa Scientific ANCA-NT 20-20 with a NCA-NT Solid/Liquid preparation module. Results are reported as δ values, and ‰ deviations from standards (atmospheric nitrogen or Pee Dee Belemnite carbon), using

δ15 N or δ13C = [(RSAMPLE - RSTANDARD) / RSTANDARD] x 1000

The MixSIR mixing model (Moore & Semmens, 2008) uses a Bayesian approach used to calculate the contribution of different sources to the isotope values of predators using fractionation values of 2.3 ± 1.61 for δ15 N and 0.4 ± 1.20 for δ13C for aquatic organisms (McCutchan et al., 2003).

The R-statistic values in the pairwise comparisons in the ANOSIM and Schoener Index concluded that the diet of whitefish was statistically different from that of the Arctic char except in spring and from that of perch, except in the winter. The same was true for Arctic char and perch, with a higher Schoener index during winter and spring, and a lower one during summer the main feeding seasons. MDS ordination of the seasonal volumetric data for the dietary samples of four species also demonstrated the seasonal points of most of the species discrete from each other. Mean values of δ15N were slightly lower for pike and perch than for arctic char and whitefish. Inter-specifically whitefish and Arctic char δ15N values were statistically the same but δ13C values were largely different. Perch and pike δ15N and δ13C isotopic values were also different statistically but to a lesser extent. The δ15N- δ13C bi-plot also shows that the mean values of δ13C of different fish species were usually distinct during the ten years. Bulk zooplankton isotope signatures showed seasonal variations for all the years investigated. Considerable variations were found in chironomid isotope signatures for both the littoral and profundal zones even at the same depth. The mixing model identified zooplankton as the main prey item for both whitefish and Arctic char in 2008 and YoY perch was important diet component of both Arctic char and perch.

3.2.1- Importance of Lake Trophic Network Analysis

The fisheries management is moving away from single species management and towards an ecosystem based approach (Dame & Christian, 2006). For proper sustainable ecosystem management, knowledge of species interrelationships within an ecosystem is prerequisite. Therefore an understanding of trophic structure is very essential for ecological studies and fishery assessment and management. The measurement of energy and material flows between the various ecosystem components provides significant insight into the fundamental structure and function of the system (Ulanowicz, 1986). Trophic network analyses actually estimates the components within a food web using input/output, trophic and cycle analysis to evaluate the ecosystem properties. Direct and indirect trophic effects for each component in the network are quantified and the full dependency of one compartment relative to all other compartments can be determined. Trophic network analysis can also be used to quantify the health, integrity and maturity of ecosystems (Christensen & Pauly, 1998) and it also helps to evaluate the magnitude of stress imposed on an ecosystem (Mageau et al. 1998). Such complex interactions components of ecosystems can be integrated very well using ecological models. Research efforts using trophic network analyses in ecology have produced methodological, theoretical and empirical advances and development of software packages of different models for ecological trophic analysis. One of the important software that has been developed to perform ecological network analysis is Ecopath with Ecosim EwE (Christensen et al 2008). EwE is a mass balance model which provides an excellent means of studying the behavior of an aquatic ecosystem.

3.2.2- Ecosystem Based Fisheries Management

Ecosystem-based fishery management (EBFM) is a new direction for fishery management, with focus on ecosystem as a priority rather than the target species. The overall objective of EBFM is a sustainable and healthy ecosystem to support the fisheries. Single species fisheries management focuses usually on maximizing the catch of a single target species and often ignores habitat, predators, and prey of the target species and other ecosystem components and their interactions. On other hand EBFM approach takes into account all the interactions of target species with other components of ecosystem including predators, competitors, prey complex interactions between fishes and their habitat; and the effects of fishing on fish stocks. The traditional single-species approach of fisheries management is easy to use and can be helpful to provide some management decisions, but this approach may not be sufficient. On the other hand, the EBFM can be very complicated taking into account all the parameters, and results could be very unpredictable. Usually, the first approach in EBFM is to develop a conceptual model that can put the fisheries in the framework of ecosystem knowledge and theory. It is very difficult to gather all the data require for a comprehensive EBFM but a preliminary model can be helpful for effective single-species management of target species with the addition of precautionary measures for unknown or lesser known ecosystem components.

3.2.3 Ecopath with Ecosim Approach

The Ecopath approach was initiated in early 1980's by Polovina ( Polovina, 1984) and after series of development during 1990's took the shape of an integrated software package called as Ecopath with Ecosim, EwE (Christensen and Walters 2004). The Ecopath Model (EwE) uses mass balance principles to estimate flows and examine the ecosystem's dynamic. The description of state of an ecosystem helps to describe the changes in biomass and trophic interactions in time and space. Application of Ecopath model helps ecologists to better understand the significance of these relationships in the overall ecosystem context. Ecopath provides an estimate of biomasses, trophic flows, and mortality rates for some reference year or multi-year averaging window. Ecopath models describe the structure and characteristics of a system's food web and to provide a framework and synthesis for learning about whole communities and their respective parts. On the basis of availability of data and significance of the species the food web can be divided into functional groups of ecologically similar species, but may also includes individual species or life-stages of individual species which are connected by matter flux. Data on biomass, consumption, ecotrophic efficiency, quantitative diet composition of these groups and catches estimates are required as input for Ecopath model. Ecopath model contains tools to distinguishing the relative potency of the various forces shaping structure of the communities. There are many holistic indicators that integrate the ecosystem process and can be useful in quantification of ecosystem state and maturity and development. Such indicators can be used to evaluate not only the impact of human activities but also climate change on the ecosystem by studying the resulting changes in development and state of maturity of an ecosystem and to develop management policies for the future. Ecopath models, actually represents the food web of a given ecosystem at a given time but can be used to compare different time periods by constructing different models and comparing their attributes ( Heymans et al., 2004). Ecopath parameterization is based on an assumption of mass balance approach over a given time period. Ecopath model is based on two basic equations

The total production rate Pi for each group i can be

Pi = Yi +M2i × Bi + Ei + BAi +M0i × Bi

where Yi is the total rate of i, M2i is the instantaneous predation rate for group i, Ei the net migration rate (emigration − immigration), Bai is the biomass accumulation rate for i, while M0i is the ‘other mortality' rate for i. Pi is calculated as the product of Bi, the biomass of i and (P/B)i, the production/biomass ratio for i.

The 2nd master equation of Ecopath model is

Consumption = production + respiration +unassimilated food

Ecoranger, is a re-sampling routine in Ecopath software to incorporate the input probability distributions of the biomasses, consumption and production rates, ecotrophic efficiencies, catch rates, and diet compositions using Monty Carlo approach (Christensen and Walters 2004). The ecosystem stability can also be predicted, according to Odum's theory of ecosystem development. Many network analysis indices are also produced by Ecopath, which are useful to determine the ecosystem's structure, maturity, and stability under present and future scenarios. Using network analysis, the ecosystem network can be mapped into a linear food chain, and energy transfer efficiency can be predicted for various tropic levels. A simple future simulation of fisheries scenario can be performed by keeping fishing mortality rate either constant or increasing or decreasing it using Ecosim approach in EwE. Ecosim can provide a basis for exploring the ecosystem consequences of future management options using time series data.

3.2.3.1- Context of Study

Ecopath modeling though very popular to study marine and coastal ecosystems; lesser works have been reported for use of Ecopath in lakes ecosystem modeling as compared to marine or coastal areas. It has been used to study the freshwater ecosystems but mostly in tropical lakes and reservoirs. It has never been used before in for Arctic, Alpine or sub alpine lake . In France, there is only one report of use of Ecopath for lake ecosystem modeling (Reyes-Marchant et al. 1993) The main purpose of present study is an attempt for the first time, to make a steady state model of trophic interactions in Lake Annecy characterizing food web interactions between different functional groups and analyzing the energy flow utilizing present available data from last decade by using Ecopath with Ecosim (EwE) with special emphasis on fish community. The rationale behind this study is to present a tool to understand the interactions between various components and for evaluation and management of Lake Annecy ecosystem especially from fisheries prospective.

3.2.3.2 - Work done

The different functional groups used for Ecopath modeling of Lake Annecy include important fish species; common whitefish, Arctic char, lake trout, pike, perch, burbot, tench and roach. All other minor fishes were grouped as Cyprinids. Other important groups used for modeling included zoobenthos, zooplankton, phytoplankton and macrophytes. Fishing was divided into two fleets, recreational and commercial. An initial model was made using mean data from 1998 to 2005. Another model was prepared using fresh available data in 2008 especially recent work on fish diet and compared with previous model. Data obtained from commercial and recreational fisheries monitoring in Lake Annecy and mean temperature was used to estimate comparative biomass, P/B ratio and Q/B using Von Bertalanffy Growth Function (VBGF) parameters and various empirical relationships (Pauly 1980, Palomares and Pauly 1989,1998, Froese and Binohlan 2000). Diet matrixes were estimated using Gerdeaux et al. (2002), Janjua and Gerdeaux (unpublished) other published literature from the region. Biomass, P/B ratio and Q/B ratio of other functional groups was computed from other works on Lake Annecy, empirical equations (Brey 1999) and data from literature. Mass balance routine in Ecopath software was used to balance the model and Ecoranger routine in EwE was used to specify probability distributions for the input variables.

Ecological analysis integrated in EwE were used to examine different indicators which describe trophic flows, transfer efficiencies, maturity of the ecosystem, fisheries stability and analyze the properties of the lake's ecosystem. There was not much difference between the ecological parameters of 1998-2005 and 2008 model. Mixed Trophic Impact Analysis (MTI) was used to study the impact of direct and indirect interactions. The basic input/output parameters for 2008 model are given in Table ??. The EE of most of the commercial fish species in Lake Annecy was less than 0.5. Gross efficiency was lower for cyprinids and roach because of the low quality of their preferred prey, macrophytes. The system statistics of Lake Annecy's ecosystem in 1998-2005 and 2008 are given in Table??. and diagram presenting flows between different components is in figure??. Trophic structure analysis showed that most of the biomass and flows were confined to trophic levels I and II. The trophic transfer efficiency was higher at TL III. The average trophic transfer level of the Lake Annecy system was 9.5% -11%. In Lake Annecy, the total flow originated from detritus was almost 50%. The relative value of ascendancy was 29.7 to 30.2, which is low with relatively high overhead. Most of the fish production was confined to TL III as were the catches and more than 80% of the fish catch were aslo confined to trophic level 3. The highest primary production requirement for sustainable catch was estimated highest for pike followed by the whitefish. The MTI showed the positive impact of zoobenthos on most of the fishes, except whitefish. Phytoplankton, zooplankton and detritus also had a positive impact on most of the consumer fish groups. Both commercial and recreational fisheries show a mixed type of impact. Commercial fisheries show a negative impact on most of the commercially and recreationally important fish groups, except Arctic char for which it is a little positive. Zoobenthos were also found to be keystone species (0.066). The keystone functional groups have keystoneness proposed index close to or greater than zero. Recreational fisheries' impact was negative on whitefish, Arctic char and pike. Both types of fisheries show a positive impact on carps, which are not commercially important in Lake Annecy. The gross efficiency of the fishery was 0.0011 and average trophic level of fish catch was from 3.17 to 3.29. The percentage of Primary Production Required (PPR) to support fisheries in Lake Annecy was 11.15% (2008) to 14.98% (98-05) of total primary production.

Parameter

Value

Units

Sum of all consumption

787.438

t·km-2·year-1

Sum of all exports

635.535

t·km-1

Sum of all respiratory flows

314.462

t·km-1

Sum of all flows into detritus

973.015

t·km-1

Total system throughput

2710

t·km-1

Sum of all production

1216

t·km-1

Mean trophic level of the catch

3.17

Gross efficiency (catch/net p.p.)

0.001172

Calculated total net primary production

1020.159

t·km-2·year-1

Total primary production/total respiration

3.244

Net system production

705.697

t·km-2·year-1

Total primary production/total biomass

20.412

Total biomass/total throughput

0.018

Total biomass (excluding detritus)

49.979

t·km-2

Total catches

1.196

t·km-2

Connectance Index

0.258

t·km-2·year-1

System Omnivory Index

0.107

4- Studies on Fish Growth

4.1 Importance of fish age and growth in fisheries management

Age and growth are the terms usually used together in fisheries biology. However these terms have the different meanings. According to DeVries and Frie (1996):“Age refers to some quantitative description of the length of time that an organism has lived, whereas growth is the change in body or body part size between two points in time, and growth rate is a measure of change in some metric of fish size as a function of time.” Both age and length data is used to describe the growth of the fish. The study of growth means basically the determination of the body size as a function of age. Fish growth data is very important to evaluate the fisheries status of a species and how that species could respond to the exploitations. Such data can be used for developing fisheries management plans. Reliable estimates of fish age and growth are basics to understand the dynamics of fish population (Walters and Martell 2004). This data provides basic insight into the biology of concerned species and basic information for its stock management.

The rate at which a fish grows is direct function of the amount of energy consumed as feed and the efficiency with which it is used (Baker et al. 1993). Growth of the fish can be described by the equation.

Growth = f [(food intake) (fraction digested) - metabolism - food collection - reproduction)]

All the components in the growth equation are influenced by environmental factors in one way or another and which in turn, influence the growth rate of fish. Understanding of the effects of biotic and abiotic factors on fish growth is very important both from commercial and scientific point of view (Thomas and Eckmann 2007). Water temperature and lake trophic status are the most important abiotic factors while availability of food is the most important biotic factor. Food is not only important in term of biomass of prey but also the biomass of the predators competing for the same prey. Growth also depends on the quality or type of food available because it affects the energy available for the growth. Preys with fewer densities are usually difficult to find and result in lower growth efficiencies. Water temperature influences the fish growth directly by influencing the rate and efficiency of digestion and indirectly by the availability of the food.

The ability to perform age determinations based on the examination of hard anatomical parts is of fundamental importance in fisheries research. Precise and accurate age information is the key to obtaining quality estimates of growth and other vital rates such as natural mortality and longevity, and is essential for successful fisheries management.

Successive progression of modes in length frequency data can also be used to explicate the size and age composition of fish population. Such progression of modes are corresponds to age classes (Ricker 1975; Pauly and Morgan 1987). This approach can be used when it is not possible to find the age of individual fish and requires that the age classes should be distinct enough to be identifies in length frequency. However, fish age classes overlap when fish become older and its growth becomes asymptotic. Another method is the marking of fish with internal or external tags or marks. However this practice can be costly and time consuming .

The acquisition of fish age and growth data from the analysis of calcified structures such as scales or otolits is the most popular method and the core of fish stock assessment. Many different methods can be used for age determination in fish which involves counting the growth zones that are formed at intervals in hard structures of fish including scales, otoliths, vertebra and spines.

Scales of fishes are remarkable structures. Much information can be obtained about the growth history and longevity of individual fish by close examination of their scales or other bony structures. The back-calculation technique is useful for determining more precisely a fish's growth during each year of life prior to the sampling date. The results might reveal, for example, that a fish which is of average size for its age now, grew fast in certain earlier years and slow in other years.

4.2 Growth of Whitefish in Lake Annecy

Whitefish was 1st introduced in Lake Annecy in 1888 (Le Roux 1908). The last stocking of whitefish was done in 1978. Since then the population of whitefish in Lake Annecy is maintained naturally (Gerdeaux 1988). The main aim of whitefish management in Lake Annecy is to share the resources equally between the anglers and the commercial fishermen. In 1988, the number of fishermen were 5 and since 1998, they are 4 only. At present, there are more than 1000 anglers. Before 1974, minimum legal size of whitefish catch was 23 cm which was increased to 26 cm in 1960,s considering whitefish as salmonids. After the 1st research on whitefish fisheries by Wieniawski (1972), legal size was increased to 40 cm. Since then, Lake Annecy fisheries is under continuous monitoring and legal size for whitefish is 38 cm since 1994. However commercial fisherman usually catch fish with mean size of 42 cm using 54 mm gillnets. So usually commercial fishermen catch whitefish after its1st reproduction. From 1955 to 1970s, catches of whitefish weight steadily decreased from 40 tons to less than 5 tons. Catches remained below 5 tons from 1966 to 1982.In From 1982, after decrease in mesh size from 65 mm 52 mm, the catches started increasing and went upto 33 tons and 36 tons in 1990 and 1999 respectively. Despite of fluctuation in commercial and recreational shares and total whitefish catches whitefish yields are remained largely unchanged since the 1990s. Since 2000, the average whitefish catch per year is about 20 tons of which 38 % goes to recreational fisheries and 62% goes to commercial fisheries. The fishery is closed during the breeding season for whitefish, ie, the second weekend of October to last weekend of January.Reproduction takes place in December, and spawning in Lake Annecy occurs in shallow areas (Crétenoy, 1996).

4.2.1 Context of study

The common whitefish (Coregonus lavaretus) is the most important and popular fish species in central European lakes (Gerdeaux 2004) important both for commercial and recreational purpose. There was quite abrupt increase in whitefish growth in European lakes during 1960-1970 because of eutrophication (Nümann 1972). However, during 1980's protective measures were under taken to control the effects of anthropogenic activities in the lakes and that results in reoligotrophication and as a result decrease in growth of whitefish (Müller and Mbwenembo Bia 1998, Eckmann et al. 2007). Thus lake trophic status is one of the most important factors that can affect the whitefish growth by bringing in changes in primary and secondary production (Downing et al. 1990). Commercial fisheries can also cause evolutionary changes in the life history eg size at age or age at maturity by size selective harvesting. (Sinclair et al. 2002: Conover and Munch 2002). Though the Lake Annecy never became eutrophic during last century, still there was continuous decrease in phosphorus level and zooplankton biomass. Whitefish is predominantly zooplanktonivore and adult fish feed majorly on Daphnia (Gerdeaux et al 2002). Despite of fluctuation in zooplankton biomass in Lake Annecy, the whitefish yield was not changed much since 1990. Whitefish yield is shared almost equally between the anglers and four commercial fishermen and it is one of the main goals of fisheries management in Lake Annecy. However, during the last few years, despite of having almost constant CPUE, this ratio is getting disturbed, creating the question for the management about the population of whitefish. So it is important to study the growth pattern of whitefish in Lake Annecy to observe any change. The scope of present study is to document and evaluate the temporal growth trend of whitefish in Lake Annecy, to observe any effect of temperature, trophic status, food availability and stock strength on its growth and to evaluate any effect of change in growth on fluctuations in whitefish catches. To address these questions we evaluated the 30 years series of data

4.1.2 Studies done

Fisheries data was collected from recreational and commercial fishermen in Lake Annecy monitoring from 1984 to 2008 . Scales taken from dorsal area between the posterior part of the dorsal fin and the lateral line were used to determine age of fish from annuals checks. Growth was back-calculated using a binomial regression of total scale radius versus total length after Francis (1990) and Caranhac and Gerdeaux (2001). A total of 7749 fish were studies for the period 1987 to 2008 with total length at capture range from 380 to 570 mm (Table 1). Mean lengths and standard deviation at successive years of life were calculated for each generation. Samples were analyzed with regard to age and observed length frequency of cohort. Virtual age class strength factor of cohort in fish catch was calculated by fitting cumulative frequency distribution from gaussian frequency distributions to the observed length distribution. Limnological data was borrowed from the Lake Annecy annual water quality monitoring reports by the INRA including mean temperature, total phosphorus content during spring turnover and zooplankton biomass. Growth of fish was observed during five years for each whitefish cohort entering into the fisheries from 19?? to 2006 and was co-related with the changes in temperature, total phosphorous, food availability and fish density. The relationships between back calculated yearly annual length increment of whitefish and mean temperature, total phosphorus, annual catches and YCS of cohort in catches were examined using linear correlation coefficients among all pair wise groups. No significant relationship was found between fluctuations in growth, total phosphorus concentration in the lake and the temperature. The growth during the first year was found almost constant. However there were some fluctuations in annual growth increment during 2nd and 3rd year of growth. There was a clear influence