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Assessing and predicting shing impacts and temporal shifts in
the sheries of a Tropical Reservoir in India
M FEROZ KHAN ( ferosekhan23@gmail.com )
CIFRI: Central Inland Fisheries Research Institute https://orcid.org/0000-0002-4109-0845
Preetha Panikkar
Central Inland Fisheries Research Institute Bangalore Oce
Vijayakumar Leela Ramya
Central Inland Fisheries Research Institute Bangalore Oce
Salim Sibina Mol
Central Inland Fisheries Research Institute Bangalore Oce
Basanta Kumar Das
Central Inland Fisheries Research Institute
Uttam Kumar Sarkar
Central Inland Fisheries Research Institute
Muttanahalli Eregowda Vijayakumar
Central Inland Fisheries Research Institute Bangalore Oce
Research Article
Keywords: Ecopath with Ecosim, Maximum Sustainable Yield. L Index, Reservoir, and Invasive Species
Posted Date: March 24th, 2022
DOI: https://doi.org/10.21203/rs.3.rs-1420353/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
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Abstract
Food Web Modelling is used as a method for an interpretation of ecological processes and for studying the impact of shing on
biodiversity. An overview of ecological dynamics, as well as the signicance of interactions between functional groups was obtained by
modelling Ecopath with Ecosim in Karapuzha reservoir, in India. The modelling exercise revealed shing and predator pre-interaction
from the main drivers of this ecosystem. This is the rst time in the Indian reservoir system, Ecopath with Ecosim was used to predict
temporal shifts caused by shing impacts. The mean trophic level of the catch was 2.53, not varying much throughout the entire time
series and the Kempton Index (Biomass diversity) has declined slightly. The values of the L index were between the reference values of
the L index 25% and L index 50%, implying overshing, the favourable impact of gillnet shing on some indegenous sh was likely due
to the predation release of invasive species such as
C. gariepinus
since this sh was caught by gillnets. Such results suggest more
work to determine if gillnet shing in this reservoir will help mitigate the negative effects of invasive sh. Snakeheads, however, which
have high market demand, appear not to be investigating the ability of gillnet shing in this reservoir to contribute to mitigating the
negative effects of invasive species. The modelling of Karapuzha reservoir showed a decline in the predicted biomass of some groups
(Eels, Snakeheads, Minor Carps and Barbs) because of trophic response and relationships, and failed to give maximum sustainable
yield .The new tools are designed to support policy decisions and incorporated into frameworks to integrate the effects of the stressors
mentioned above to promote policy decision-making, particularly for ecosystem-based management (EBM), which tests the
environment. The Ecosim routine shows that the presence of major carps does not affect other functional environmental organisations
in this state and has predicted those groups and are not detrimental. This study will assist Indian Major Carp Stock Managers in
enhancing sh productivity. New tools like this support policy-making integration with the impact of aforementioned stressors in
reservoirs. This is the rst time the Ecopath with Ecosim reservoir system has been predicted to temporarily shift due to shing effects.
This is the rst time. This study helps manage resource conservation and management of informed and ecosystem-based sheries
choices. Our study analysed the social and economic elements of Karapuzha Fisheries which might link all these subsystems within a
socio-ecological scenario. The modelling of the Karapuzha Reservoir provided an overall understanding of eco-dynamics and the role of
interactions among the sh groups particularly since it eliminates the detrimental effects, in particular it eliminates the detrimental
effects imposed by the invasive sh (
C. gariepinus O.mossambicus)
. Gillnet shing has a positive impact on several indigenous sh.
1. Introduction
Articial water bodies such as reservoirs are unique as they are composed of aquatic systems with lentic and lotic characteristics.
While freshwater sheries' productivity and overall economic values are small compared to marine, inland sheries have a key social
role to play since these are the income and protein source for rural people in rural locations (Bartley et al. 2015; Smith et al. 2005).
Fishing in reservoirs is small-scale and spatially dispersed, helping many subsistence shermen whose catches are sold and consumed
locally. The bulk of domestic shing is carried out in developing countries such as India where shing is essential to nutritional security
and poverty eradication (Cooke et al. 2016). Several sh species are harvested from tropical freshwater sheries, typically using similar
shing gear (Smith et al., 2005). In the case of sheries management in reservoirs, conventional input or output restrictions are applied.
(Agostinho et al. 2016, Arlinghaus et al. 2016). This ecosystem is affected by the deleterious impact of invasive species, through
trophic In this ecosystem, interactions and shing are important factors. Furthermore, if these shing activities are not adequately
supervised, they might have an inuence on the people as well as the structure and function of the ecosystem (Colletal. 2016). However,
reservoir ecosystem management should be done in combination with environmental protection (through dam control), taking into
consideration the detrimental impact on the recruitment of sh species. (Oliveira et al. 2015; Arlinghaus et al. 2016). In contrast to
agriculture, non-native invaders, temperature changes, and ecosystem alterations are some of the other repercussions of dams
(Agostinho et al. 2016; Walsh et al. 2016). Food Web Modelling can be used for interpreting environmental processes and studying the
effect of shing on biodiversity for the ecological aspect Ecological models are highly useful in examining such shery scenarios
because multiple degrees of effort and tropical scenarios exist. This methodology provides expertise to help guide and promote
management and policy decisions related to sheries' ecological aspects. Ecopath with Ecosim is a powerful and far-reaching tool for
analysing foodweb structure and how ecosystems impact policy decisions. It focuses particularly on how shing impacts the
ecosystem and nutrient regime. (Christensen and Walters 2004). Hundreds of models and their policies have been dramatically released
together over the last few years (Christensen and Walters 2004). Ecosim Simulations Allow the Ability to forecast intricate dynamics,
which allows the making of informed decision sheries reservoirs. A key principle of Ecosystem-based Fish Management (EBFM) is the
capacity for impacting the whole ecosystem through shing and the greater compromise interconnectedness of ecosystems (Link
2010). Changes to the ecosystem generally occur through interactions between sheries or predators. Fisheries Targeting Prey will also
inuence declining predator population growth and reproductive success at a sustainable pace (Walters and Martell 2004; Walters et al.
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2005; Pikitchetal. 2012). EBFM has become a regular goal and environmental managers are provided with an environmental analysis
reacting to different policies (Valette-Silver and Scavia 2003). Ecosystem models are increasingly being employed as tools for
environmental prediction when they are simulated. Ecosystem Simulation Ecosystem (EwE) software simulates dynamism, trophic
interactions, shing and environmental factors (Christensen and Walters 2004). Ecosystem impacts of small-scale shing on the
structure and function of Karapuzha’s ecosystem, located in a tribal-dominated Wayanad district of Kerala, India, are modelled using
Ecopath with Ecosim Software. Reservoir Plays an Integral Part in Giving Protein Source and Livelihood to Several Tribal Fishmen. This
study aims to (1) develop the Karapuzha Reservoir Food Web Ecopath Model and calibrate it by using the time series of catch and
shing efforts,(2) Evaluate the effect of shing on the structure and function of reservoir ecosystems using ecological indicators; (3)
simulate sheries effort and predict maximum sustainable yield and (4) Address ecosystem-based management in sheries.
2. Materials And Methods
2.1 Study Area
Karapuzha reservoir (Lat.11°37’NLong. 76° 10’ 30’’ E) situated at Wayanad District of Kerala State is a major reservoir suited shing and
for farming. (Fig.1). It has a canal system to irrigate an area of 5221ha acres of land in Vythiri and Sultanbathery Taluks in Wayanad
District. The conuence of Mananthavady and Panamaram Rivers forms the Kabini River. Other tributaries, such as Bhavanipuzha,
Karapuzha, and Narasipuzha, originate in the Western Ghats and run through the state of Kerala. This reservoir was built in 1979 with
the goal of irrigating roughly 9000 hectares. This reservoir was established on about 9000 hectares of land in 1979. The total water
spread area of this reservoir is 855 ha (8.55 sq. km) at Full Reservoir Level (FRL).
2.2 Modelling approach
2.3Ecopath withEcosim(EwE)
Ecopath is a tool designed to build, parameterize and analyse trophic models of aquatic and terrestrial ecosystems (Christensen et al.
2005). It relies on the basic constraints of mass balance to decide the trophic uxes among functional groups in accordance with its
method Polovina (1984) and was developed by Christensen and Pauly (1992). The programme was updated to include a time-dynamic
model (Ecosim) and a spatial dynamic model (Ecospace) for direct application in shery management. This tool has a signicant
advantage in terms of the applicability of a wide variety of hypotheses, such as thermodynamics, information theory, trophic level
description, and network analysis, all of which are important in ecosystem science. It was used to examine a variety of elements of the
resulting food web networks (Villanueva et al., 2008). Often, mass-balanced models allow for the comparison of different ecosystems
and ecosystems at various times. (Neiraetal. 2004; Panikkar and Khan, 2008; Shannon et al. 2004). Ecosim is a complicated simulation
on a system scale, with important parameters inherited from the Ecopath fundamental model. This study examines the transfer of
biomass between functional groups as a function of their abundance at certain harvest rates, taking into consideration trophic
relationships and foraging behaviour (Pauly et al. 2000). The Ecopath food web model of the Karapuzha Reservoir employs biomass
estimates of 14 functional groups, as well as their prey and predators, to depict how the food web linkages within the reservoir system
are mass-balanced. This food web model was created from the eld data using a similar model criteria and use of Christensen and
Walters methods (2004).) Earlier, Ecopath used steady-state assumptions, but is now focused on parameters of equilibrium over a span
of one year. With the ecopath parameterization, two master equations are used. The rst is to illustrate how to break the production into
components for each component. (Christensen and Walters 2004)
Where Bi is its biomass, Bj is the biomass of predator j,(Q/B)j is the consumption/biomass ratio of predator DCji is the fraction of prey i
in the diet of predator jYi
is the catch of functional group i, Ei is the migration rate ( emigration–immigration), and BAi is the biomass accumulation rate for
group i Other mortality is 1 - EEi, where EEi is ecotrophic eciency of group i, that is, the fraction of yearly productivity P/ Bi expended in
the ecosystem by predators and/or exported from the ecosystem by shing.
(1)
(2)
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Masterequation 2 shows the energy surplus of each group i, where its consumption rate Q/BI equals the production rate P/ Bi, the non-
assimilated food UNi and respiration RI: The Fig. 3 shows Biomass observed (points) and predicted (line) by EwE model for the main
species of Karazpuha. Biomass in tonnes /km2(Fig. 2)depicts the time series of observed total catch (tonnes/km2/yr), mean trophic
level of catch (TLc), L-index for Karapuzha and biomass diversity (Kempton Index).Table 1 List The species and/or functional groups
included in the model. Based on The model is mass-balanced on the fundamental assumption that total input equals total output
across all groups in the system (Banerjee et al. 2017). Biomass dynamics are described in various coupled differential equations in
Ecosim. Since it is well documented, it will not be claried here. ( Christensen et al. 2005). The Static Ecopath Model presupposes the
energy balance between functional groups (Alvarado,et al. 2019)and the selected year was 2008 during which information on local sh
has begun to be obtained, as the basis for the time dynamic ecosim model).We have been able to calibrate this temporal dynamic
model with the time series(2008–2011).We simulated different shery scenarios with 30 years from 2011(Fig. 3).The following
sections describe the theory and methods used in our modelling approach.
Table 1
Input and output parameters of the Karapuzha reservoir model
Group name Trophic
level Biomass
(t/km²) Production
/ biomass
(/year)
Consumption
/ biomass
(/year)
Ecotrophic
Eciency Production /
consumption
(/year)
FlowToDet
(t/km²/year) Omnivory
index
Aquatic Birds 3.79 0.765 0.33 0.84 0 0.39 0.381 0.147
Clarias
gariepinus
3.38 1.493 3.5 10.8 0.144 0.32 7.7 0.097
Eels 3.42 1.395 4.35 9.5 0.232 0.46 7.308 0.069
Snakeheads 3.63 2.734 1.77 4.9 0.362 0.36 5.769 0.026
Major Carps 2.79 3.58 3.06 6.26 0.901 0.49 5.565 0.169
Minor Carps 2.57 3.939 2.55 15.6 0.827 0.16 14.03 0.245
Barbs 2.38 3.288 5.37 50.5 0.418 0.11 43.49 0.236
Minnows 2.75 3.975 2.01 31.5 0.988 0.06 25.14 0.188
O.mossambicus
2.32 2.61 4.65 30.3 0.729 0.15 19.11 0.218
Crustaceans 2 3.7 3.53 25 0.639 0.14 23.22
Zoobenthos 2 11.88 14.45 43 0.665 0.34 159.8
Zooplankton 2 14.25 23.5 60 0.361 0.39 385
Macrophytes 1 9.562 34 0.726 89.14
Phytoplankton 1 29.7 46.76 0.638 502.1
Detritus 1 13 0.431 0.364
2.4 Data
The information gathered during eld studies conducted was used. The sh lengths were converted into biomass using the isometric
growth equation: W = a Lb, with wet weight, l being the standard length, an intercept, and b being the slope when a log transformation
determines the weight-length connection. (Alva-Basurto and González, 2014).The values are collected from the eld study and some
from Fishbase (Froese and Pauly 2006). Biomass (B; tons/km2) values were derived from single-species stock evaluations, estimated
through dividing catch with shing mortality. The consumption (Q) was estimated using an empirically constructed equation that
included morphometric data, ambient water temperature, and nutrition data (Pauly 1989, Palomares and Pauly 1998). Based on our
research into food and feeding patterns, a sh diet matrix was created for each species. The Unknown Biomass Non-sh groups were
derived from comparable ecosystem research. By Adding Natural Mortality Fishing Mortality, the P/B ratio, the total instantaneous
mortality was obtained (Paul 1980). P/B and Q/B Values are for the sh groups derived from Fishbase (Froese and Pauly 2010). There
are rather poor catches of various species in Karapuzha. The Nellarachal Fish Cooperative society provided the catch data. Figures for
shing encompass a 7-8-year period of exploitation. This study employed the Karapuzha Reservoir Ecopath model, which covers a
period of time. Fishfunctional, one detritus, and two primary producers were among the 14 functional groupings in 2008.
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(phytoplankton and macrophytes). While the Ecotrophic Eciency (EE) values were below one, we considered the Karapuzha Reservoir
to be balanced (Christensen et al. 2008). We have made use of evaluation of input parameters, biomass, and vital rates with a pre-
balancing tool (Link 2010). The food web was displayed with the following key input variables: biomass (B), production/biomass ratio
(P/B), consumption/biomass ratio (Q/B), diet composition, and shing. In the food web model, the following essential input parameters
were displayed: biomass(B), the production/biomass ratio (P/ B),the consumption/biomass ratio(Q/ B).
2.5 Ecosim module
This module provides a system-level dynamic simulation capacity that is focused on the Ecopath template with core beginning
parameters. Observing time and harvest rates as they change with biomass. Ecosim uses a system of differential equations that
expresses biomass ow between groups (Walters et al. 1997; Christensen and Walters 2004; Christensen et al. 2008; Heymans et al.,
2016). The root of ecosim is from the basic ecopath equation which is where dBi / dt refers to the biomass transition from the
functional group(Bi)overtime t, gi is the net gross eciency (production/consumption ratio), Qji,
(3)
consumption job, Qiji The consumption by, Ii is the immigration of group i, MO i, Fi and ei are the non-predation natural mortality, shing
mortality, and emigration of group i ,respectively. The consumption rates, Qij, are estimated based on the foraging arena theory when
biomasses are divided into vulnerable and non-vulnerable states. The transfer eciency, vij, between these two states determines
predator-prey interactions (bottom-up, mixed or top-down ( Christensen etal.,2008; Ahrenset et al., 2012)
(4)
While Aij is an active predator rate in search of, feeding on prey I, Bi is a biomass of Prey Pj is a biomass for predators and Vij is a
vulnerability of prey in predators. Flow control type i.e., vij = 2, vij = 1 and vij = 2,which represent mixed ow control, a bottom-up, and
top-down control, respectively ( Walters and Martell 2004; Christensen and Pauly 2004; Ahrens et al.2012). The parameters most
relevant for the calibration of Ecosim models are the exchange rates of vulnerability (vij) from prey to predator (Chagaris et al
2015).The rate of predation Once the predator concentrations in its Ecopath base numbers change, mortality remains roughly stable at
lower values of vij(2). We conducted a study to alter the vij is to reduce the total squared deviation (SS).)between the predicted and
observed biomass (Pranovi and Link 2009).In a follow-up diagnostic, we assessed how communities reacted to exceptionally high
shing and shing mortalities. Furthermore, the shing mortality rates in Ecosim were compared to the value estimate in the single-
species assessment model at maximum sustainable yield. (the time series the data captured between 1988 and 2011 was used as
effort. The Ecosim module compared the data observed with the predicted data for the evaluation of the t of the model. Ecosim's
model shows the number of squared deviations from the predicted log caught ( Christensen et al. 2008; Heymans et al. 2016). With a
compatible Plug-in, the step by step mounting process was used .The shing effort was described by the number of days (sum of
shing days of all shers per year). To evaluate the t of the model, the predicted and observed catch data was compared using the
Ecosim module. The model used by Ecosim is the number of squared deviations from log catches from predicted ones ( Christensen et
al. 2008; Heymans et al.,2016) The step-by-step tting process was used with an integrated plug-in (Scott et al. 2016).In this method,
the observations are automatically searched to best t over a set of hypotheses to evaluate shing effects (via effort timeseries/shing
mortality), shifts in prey predator dynamics vulnerability settings) and primary production variation ( PP anomaly: represents changes
in primary production that can be either time-series or time observations). The three variables (following Mackinson et al. 2009a) was
presented in each hypothesis tested. This method consisted of 7 steps in general (Table 1). This method uses Akaike Information
Criteria (AIC
AIC = nlog (minSS/ n) + 2k.
(5)
to check statistical hypotheses(Akaike,1974)associated with changes in predatory dynamics (also called vulnerabilities: min SS is the
minimum square sum based on the relationship between anticipated and observed data sets; k is the number of data sets. parameters.
Primary Production Changes (P anomalies, given P spline points for time series smoothing); shing impacts and permutations of the
factors mentioned above (Table 1). AIC is a model choice method which penalises the application of too many variables for selecting
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the "correct" version (one that provides the lowest AIC) which takes into consideration the best t. The second order, Akaike Information
Criterion (AICc), was calculatedas follows and was used in the present study:
AICc = AIC + 2k(k-1)/(n-k-1)(6)
for the dataset's limited sample size in our situation, the tting process has been performed ve times. The indicators in the Karapuzha
Reservoir are calculated using the "best"tted models when the time-dynamic tting process is done. The protocol begins using
ananalysis of reference templates (shing effects, predator-prey-interactions, PP- anomalies), then measuring different combinations of
these three variables (shing effects, predator-prey interactions, and PP- anomaly). Using a weighted sum of squared differences (SS)
and Akaike Information Criterion (AIC), the difference between model.The estimates and time-series observations that best t are
determined (Wagenmakers et al. 2004).During the Fitting Process, we estimated 11 parameters (vulnerability).Once the model with the
lowest AIC was established, we determined three metrics for the assessment of the ecological effect of shing: trophic level of catch
(TLc), biomass diversity Kempton index, Q), and loss of production caused by shing (L index). For the mean trophic level of shing
(TLc) reected a shing strategy which accounts for trophic levels of the species in the catch (Christensen1996; Paulyetal.,1998). This
indicator is the average TL from the species in the catches:
(7)
where is the catch of functional group i in year k, and TLi is the trophic level of the functional group i. The index of Kempton (Q) is a
biodiversity measure (Kempton 1976) Christensen et al. 2008, describe the slope of the cumulative abundance group curve. The indices
calculate mortality impacts in ecological models, whether from shing or climate change. Biomass diversity, considering only
organisms with trophic levels above 3 ( Ainsworth and Pitcher 2006; Christensen et al. 2008).According to Libralato et al., 2008,“By
calculating the the theoretical loss in secondary production was measured”. For quantifying the secondary production loss because of
shing operations, this index contains both ecological properties ( PP and transfer eciency) and shing features (trophic level of
catch and PP required)w here P1 shows the production of autotrophic and detritus( P1 = PP / FD, PP is the measured net PP and FD is
detrital ows) TE is an average transfer eciency for trophic levels, PR and TL, respectively, are primary production and trophic levels
for functional group i. For Ecosystem-based condence intervals, L index 50 and Lindex75%were used ( Libralato et al. 2008). The
dynamic module Using the Karapuzha reservoir model, Ecosim was utilised for a set of sheries simulations to examine probable
changes in ecological attributes as a result of increasing or reducing shing activity.. The various scenarios were simulated by
changing, at the same time, the shing effort of gillnets and by increasing shing effort by 25,50,75,and100%from baseline adopted in
2008 as a reduced shing effort value of 10% and 20%.From 2011to2041(30 years)simulation were carried out. We also made two
simulations with shing mortality that yielded maximum sustainable yield MSY) projections from the EwE programme. After the model
calibration, Ecosim Estimated the FMSY and MSY for each of the primary target species. For each functional group, we have calculated
two kinds of FMSY and MSY: stationary and full compensation with Ewe. In the stationary method, we obtained the MSY and FMSY
gures for each group shed using the Ecosim model of equilibrium for a range of shing mortality values, thus retaining steady other
groups of biomass (Fig. 6) This method assumes that the availability of food and the consequences of predation are both predictable,
and that the availability of food and the consequences of predation are both predictable. Fish mortality of other groups in the The base
values of ecopaths are constant. Differences are allowed with the full compensation method. In the biomasses of all groups and only in
response to changes induced by shing, We determined the FMSY equilibrium and full compensation for each species ( Walters et al.
2005).In two further simulations, we used the expected full compensation for FMSY and stationary FMSY. We analysed patterns of
biomass and catch in reference ( MSY and biomass thresholdand target) for the of these FMSY scenarios on the key targeted
organisms overshing of resources showing shing mortality above the FMSY (Stationary) reference points were discovered. We came
up with FMS equilibrium and full compensation for each species Walters etal.,2005).For each species, we followed the process of
Forrest et al. (2015), The biomass limit/target reference points for the classication of stocks in terms of their biomass levels were
determined to be 30% and 50% of the biomass in the rst year (0.3B19880.5B1988), respectively. The First Year Of The Model. Forrest
et al.2015 found the lowest biomass proportions in the rst year (20% and 40%). We Raised These Values: We already had pre-built
sheries on the Karapuzha Reservoir before 2008, to represent a higher level of overshing.
3. Results
3.1 Time series from the model tting:
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When baseline and trophic interactions were combined, we obtained signicant results in our model tting. (Step2 in Table 2 & Table 3).
The second major improvement resulted from the addition of trophic linkages to uctuations in PP anomaly alone (step 4 in Table 3)
from the baseline (AIC decreased by 8% more). Bottom-up control interactions and low vulnerabilities drive the Karapuzha reservoir
ecosystem, according to Ecosim results. (2). The model with the lowest AICc (23.8) explained nearly 17% of the variance in the catch
time series when compared to the baseline model, indicating that shing and predator-prey interactions (represented by (represented by
2 vulnerability parameters; ). Following the tting procedure, the functional groups with modied vulnerability levels (v) were
Clarias
gariepinus
= 8;EelsV = 7; For
O.mossambicus, C.gariepinus
, and Major carps, the model exaggerated catches. Data on minnows was
also not a good t.
Table 2
Steps of model ts following Mackinson, which include trophic interactions, shery and environmental driver(Primary productivity)
No. Steps Description
1 Baseline Trophic interactions with default vulnerabilities (Prey-predator). Drivers do not include
shery or environmental data
2 Baseline and Trophic interactions Different vulnerabilities are used to assess trophic interactions. Drivers do not include
shery or environmental data
3 Baseline and Environment PP anomaly is used as a driver. No shery data used
4 Baseline, Trophic interactions and
environment No shery data used
5 Fishery Driver is Fishing effort. Trophic interactions set at Default and environmental data not
used
6 Trophic interaction and shery Environmental data not used
7 Trophic interactions, environment
and shery Jointly all components included as drivers in the model
Table 3
Results of the temporally dynamic tting procedure of the Ecopath model following the procedure
suggested by Mackinson50 (Table 1). Vs is the number of vulnerabilities included in each iteration,
sPP the number of primary production spline points (for smoothing of the time series) k is the
number of parameters and %IF is the improved t compared to the baseline AICc. Vs and sPP are
shown only for those models with the lowest Akaike Information Criterion (AICc). The “best” models
(shown in bold and italics) are the ones yielding the lowest AICc and the one used to calculate
model-based indicators
Steps Vs sPP SS (min) K AICc %IF
1. Baseline 0 0 80.92 0 28.7
2.Baseline and Trophic interactions 2 0 52.05 2 23.8 -17.0
3.Baseline and Environment 0 2 80.86 2 33.5 16.7
4. Baseline Trophic interactions and Environment 2 2 52.50 4 30.9 7.7
5. Fishery 0 0 277.5 0 55.8 94.4
6. Trophic interactions and Fishery 2 0 114.3 2 41.1 43.2
7. Trophic interactions, Environment and Fishery 2 2 4 5 48.2 67.9
3.3 Fishing impact indicators with Ecosim
The total catch changed throughout time, with some maxima in 2006, 2008, 2011, 2013, and 2016. (Fig.2). Although the catch grows
as the shing effort increases to a certain point (Fig.4), a 10% reduction in the current shing effort achieves the maximum catch in
this reservoir. The mean trophic level of the catch in 2008 was 2.53, varying from 2.51 to 2.55 until 2008, there was a slight rising
tendency over the whole time series ( Fig .2). Biomass diversity (Kempton Index) has increased. (Fig.2). Biomass diversity (Kempton
Index) has declined since2015, but with a slight drop. The L index values were between the reference levels of L index 25% and
Lindex50%, suggesting overshing ( Librel et al. 2008).
3.4 Simulation of shing scenarios and maximum sustainable yield
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Increased shing effort scenarios resulted in bigger catches and lower L index values with simulations. (Fig. 5).In Scenes With
Improved Fishing Efforts, the Mean Tropical Level (TLC) of the catch was smaller than the baseline. In all situations, biomass diversity
increased compared to the baseline scenario. Under all simulated scenarios, the change in total catches did not vary at a signicant
rate. Figure 4) No signicant rate of change was shown in the L index except for lower shing effort scenarios. In the stationary mode,
the estimated maximum sustainable yield (MSY) from the full compensation mode was greater than the estimated MSY, in
Crustaceans, Eels,
O.mossambicus
, Snakeheads, and
Clarias gariepinus
, whereas MSY in stationary mode is used for other functional
groups. showed higher values. For a decade in the Ecosim equilibrium analysis, catches were over MSY in the case of
C.gariepinus
(Fig.
6). Barbs are overshed, and a 20% reduction in shing effort isn't enough to save them. Crustaceans produced a yield below MSY in
full compensatory mode. (Fig. 6) With increased shing efforts, the ndings of the simulation revealed a small rise in catch for Eels,
Minnows, and Crustaceans. These ndings could have a big impact. show that the biomass levels for these species are now too low to
support higher shing pressure. The shing mortality of
O.mossambicus
,
C.gariepinus
, and snakeheads were high at the start of the
time series. This increased mortality, combined with the negative consequences of climate change and ecosystem changes, resulted in
low biomass levels. Despite the fact that these considerations are sucient to prevent these species from being overshed, shermen
have not been cautioned because both
O.mossambicus
and
C.gariepinus
are invasive species. Between 2016 and 2045, the simulated
biomass of
C.gariepinus
, Eels and Minor carp were higher under decreased effort. The biomass increased with increased shing efforts
ins cenarios(Fig. 7),and for other functional groups, the biomass increased with increased shing efforts. Biomass generally exhibits
considerable changes over simulated years and scenarios. As shing efforts double,
C. gariepinus
becomes overexploited. This sh will
be intensively shed beyond 2020. Over shing efforts beyond 2035 will lead to a sharp decline and this sh may disappear from this
reservoir by 2040. Major carps are overexploited, and frequent stockings are needed to keep the shery alive. Minor carps are
overshed, and a 10% reduction in shing effort will aid in their recovery. Eels are underexploited and have a lot of room for expansion
in this reservoir, but snakeheads are overexploited. Barbs and minnows are also conrm projected biomass within their limit (0.3B
2006) and target (0.55B 2008) (Fig. 7). The stock of crustaceans is very high, and increased shing efforts will yield more catch
throughout the simulated period, except in 2023 and 2035. The stock status was for
C. gariepinus
, which had estimated biomass below
both the biomass limit and the target for 15 years beginning 2020. After 2020, the estimated biomass for this stock will go below the
biomass target, but only in the scenario is in full compensation mode until 2035, and the decrease is complete by 2040. In the case of
Barbs, the biomass will be revived after 2030. Beginning in 2020 and lasting until 2028, the Biomassis will be less than 0.30 percent.
However, the biomass is is is below 0.3 percent and in full compensation mode throughout the simulation time. However, under
scenarios with decreased shing efforts for eels, we observed higher biomass levels. In the simulations that used FMSY values, the
predicted biomass was lower than the limit for
C. gariepin
us, snakeheads, major carps, Minor carps, and Barbs (Fig.7
). Puntius ticto
was the most popular of the barbs dominating, followed by
P. dubois, P. sopho
re, and
P. sarana
, resulting in the formation of a shery in
the reservoir. These shes feed on Zoobenthos, zooplankton, phytoplankton, and benthic algae are all examples of zoobenthos
(Panikkar and Khan 2008). Crustaceans remained at or above biomass targets under FMSY full compensation and stationary
conditions, respectively, throughout the. simulation period, except for a few years. Estimates of declining mortality that resulted in
maximum sustainable yield (FMSY and MSY) were less for the stationary mode than for the full compensation mode with the
exception of snakeheads, minor carps and minnows,. The estimated biomass reveals that the only stocks that were below MSY were
Snakeheads. major carps, minor carps, and
O.mossambicus
were caught between 2012 and 2016. Eels were caught below the MSY
level in 2009, 2012, 2014, and 2019. This is not due to overshing, as the results show that other variables are at play. Apart from the
effects of shing, it impacted the population dynamics. There was considerable ooding in the state of Kerala in 2018, which may have
impacted the Eels.
4. Discussion
4.1 Ecopath model of Karapuzha Reservoir
The modelling exercise showed that in comparison to other reservoirs, the Karapuzha Reservoir Foodweb is maintained by a detritus-
based chain with a high recycling rate.. The favourable impact of gillnet shing on individual species was most likely attributable to the
predation release of invasive species, such as
C. gariepinus
, because they were captured in gillnets. Such ndings imply that more
research is needed to study the possibility of gillnet shing in this reserve to contribute to minimising the harmful effects of invasive
species. Gillnets are multi-species gear that capture both invasive and indigenous species.. Thus, increased gillnet shing efforts may
be investigated to see if they are successful management action based on their impact on species composition, interactions between
both species, and capture rates., and catch rates using future simulations. This could aid in reducing the negative effects of invasive
species on shing activities in this reserve (Philippsen et al. 2019). Barbs and tilapia had the highest shing mortality rates in the
Page 9/17
Ecopath models, as predicted. (2006),. However, there was no organised shing during this period, and the sh are commercially
important in this part of Kerala. Fishing mortalities in the Karapuzha Reservoir were low for species that are currently mostly targeted
(SnakeHeads, Major Carps, Minor Carps, and Eels), as shers only began to pursue such species. Following that, major carps were
stocked, and a Tribal shing society was formed, and tribal shermen were given coracles and nets as part of a government scheme to
help them with these shing tools, resulting in increased shing efforts. During the impoundment, the reservoir has trophic bursts and
sh biomass increases, so shing mortality was low in this reservoir. Unlike in Italy's Baipur Reservoir in Brazil, where shing is
abundant during the initial heterotrophic phase, the phase is characterised by strong organic matter intake and a growth in terms of
species richness and abundance (Agostinho et al. 2016). These circumstances give rise to new possibilities.. These conditions lead to
new lentic sheries being exploited and local shing opportunities being improved (Petrere 1996).There is an alternate management
measure in this period to minimise large sh deaths by the amount of new shermen entering sheries right after reservoir construction.
4.2 Ecosim model of Karapuzha Reservoir
The primary factors illustrating capture patterns in the Karapuzha Reserve were shing impact and predator-prey interactions. There
has been a mix of ow control (v = 2) in the model of this reservoir with the lower AICc and AIC bottom-up and top-down controls. The
shift from migratory and large species to stationary and medium-sized species in sh assemblage is envisaged after impoundment
(Agostinho et al. 2016). Hence we would also predict a decrease in the mean trophic level of catch, yet we are. As a result, a decline in
mean trophic level of catch is yet to be predicted; (Philippsen et al. 2018). A large piscivorous (
C. gariepinus
and snakeheads
)
were
among the most commonly captured species from the beginning of the time period, along with
O.mossambicus.
.It feeds largely on
detritus, dipterans, zooplankton and zoobenthos and is among the most prominent in the Region of the south Indian reservoirs (Khan
and Panikkar 2009). They will be replaced The is a slight decline in TLc by Major carps after socking this reservoir is of great ecological
importance. The three Indian Majors Catla Catala, Labeo Rohita and
Cirrhinus mrigala
are signicant but these do not breed Species
Indian Reservoirs Production for enhancement of sh production This Fish are stocked in Indian reservoirs.. Although the L-index has
not changed substantially over the years, this indicator suggests a signicant risk (between 25 and 50%) of overexploitation resulting in
ecological consequences (Libralato et al. 2008) .There was not much Change In Biomass Diversity (Kempton's index).
4.3 Simulation of shing scenarios and highest sustainable yield
For most functional groups, FMSY and MSY achieved complete compensation at greater levels than the MSY reference point
(Mackinson et al. 2009), and the model provides increasing surplus output in response to increasing levels of shing mortality.
However, these FMSY estimates were larger in stationary mode compared to full compensation for several taxa, such as snakeheads
and carp, likely due to their underestimated sensitivity. (v = 1;low predators inuence).The full compensation method contains indirect,
compensating reactions to changes in the number of target species ( Walters et al. 2005). ecosystem's ability to (Walter et al. 2005).
The capacity of the ecosystem to take into account predation loss demands surplus production to reach MSY (Mackinson et al. 2009).
The impact of indirect compensatory reactions, which would provide identical FMSY estimations for both the stationary and full
compensation modes, might thus be minimal. Ecosim may overstate FMSY if the vulnerability of the groups is calculated at v = 2 and
the level of biomass in the Ecopath base scenario is signicantly lower than the unshed biomass (Christensenetal.2008). The
projected biomass indicates that between 2012 and 2016, major carps, minor carps, and were the only stocks under MSY. There is
signicant scientic evidence, as well as shermen's indigenous knowledge, that dam activities have a negative impact on sheries.
(Hoeinghaus et al. 2009; Oliveira et al. 2015). While there are no environmental anomalies in our ecological models, there is solid
scientic evidence and shermen's indegenous knowledge on the negative effect of dam operations on shing yield. (Agostinho 2016;
Philippsenetal. 2016).
In terms of food resources, the ecological overlap between invasive and native cichlid species is quite minimal, owing to differences in
dispersal patterns across the day of the several species under consideration. This may be compared to ndings made in Parakrama,
another tropical reservoir in Sri Lanka, Panikkar and Khan2008). Many state governments in India have banned the culture of
C.gariepinus
. Amid the This sh entered the reservoir via sh culture ponds due to rain and oods. Although this sh is unwanted by
many it is more expensive than tilapia since there is a market in some locations.. Snakeheads, on the other hand, which have a high
market demand, do not appear to be in a position to sustain greater shing attempts. Considering, the predicted biomass simulation
exercise for the Karapuzha reservoir, the notion of FMSY estimation, which is compensatory owing to tropical response and
relationships, the simulation exercise for groups (Eels, Snakeheads Minor Carps and Barbs) did not generate maximum sustainable
production after applying for both stationary and full compensation FMSY. Also, higher shing pressure on the non-native
C.gariepinus
would help manage its harmful effects on indigenous species. Karapuzha showed a general decrease in the mean trophic level of the
catch.. The ndings show excessive shing mortality and food web erosion have been observed as a result of shing. With rapidly
Page 10/17
increasing anthropogenic pressure on inland water, there is a substantial risk of the system beyond the "point-of-no-return," severely
limiting possible ecosystem-service possibilities for future generations, endangering biodiversity and possibly the economy. Climate
variability and changes in shing pressure should improve modelling approaches, as indicated in this projection. They work in tandem
with changes in the global climate (Mora,C.et al. 2013). The assessment of their cumulative temporal impact was not reported, which is
dicult because human stress uctuates over time (Halpern,B.et al. 2015). The impact of these stressors is rapidly increasing, and it is
important to assess interactions between humans, the environment and organisms, and how these dynamics affect the sustainability
of the products and services offered.
Ecosystem modelling techniques can assist in the analysis and identication of possibly relevant choices for sustainable human
activities and healthy aquatic environmental protection. Given the fact that climatic variability, as well as changes associated with
shing demands, should enhance methods such as the one presented here are necessary to forecast the effect in the above-mentioned
pressures. Despite its shortcomings, this model may be able to describe temporal trends in sheries across the Karapuzha reservoir.
5. Conclusion
An overview of the complexity of ecosystems and the signicance of interactions between the sh groups was obtained by modelling
the Karapuzha reservoir ecosystem. In particular, since it eliminates the detrimental effects imposed by invasive sh (
C. gariepinus
O.mossambicus
). Gillnet shing has a positive effect on several indigenous species. Thus, further research is needed to examine how
invasive species may be controlled with gillnets.. The decrease in their number in reservoir Karapuzha is a key step forward for invasive
species reduction. We recognise that for invasive species this is a signicant step towards the eradication of their population in
Karapuzha reservoir. as well as invasive species. The decrease in their number in reservoir Karapuzha is a key step forward for invasive
species However, our ndings are important, including comprehensive evaluation, placing it from an ecosystem perspective by analysis
including functional groups and sheries and the impact of intensive shing. Our research analysed the biological characteristics of
Karapuzha sheries and further investigations on the social and economic components of Karpuzha sheries that may connect all of
these subsystems within the context of a socio-ecological system for EBFM. However, there should be numerous further stages to
develop shortly giving improved information supporting regional conservation policies and strategies First, space-time analysis can
detect ecological trends which can help spatial management directly(for example, prioritization and promotion of contacts between
scientists and policy-makers, environmental managers, conservationists, politicians and the general public. Second, inclusion is the
driving force of human disturbances (e.g., aquaculture, invasive species, climate change, acidication, pollution).This modelling activity
is necessary to boost its dependability as simultaneous cumulative impacts affect ecosystems. New scientic tools were created to
render this forecasting more effective as habitats have overlapping collective threats. These tools can promote policy decision-making
by integrating the effects of the stressors mentioned above in common frameworks (particularly concerning the approach to
ecosystem-based management (EBM), which tests the environment.
Declarations
Ethics approval and consent to participate:
The submitted manuscript is not submitted in any other journal.
This manuscript doesn’t involve the use of any live animal or human data or tissue. Fish samples were directly obtained from the
commercial catches were used.
Consent to Participate: Not applicable
Consent for publication: The approval for submitting manuscript received from ICAR- Central Inland Fisheries Research Institute.
Availability of data and materials: Data will be available based on request and supporting les are also submitted.
Competing interests: All authors of this manuscript have no conicts of interest about the submitted manuscript.
Funding: The research was conducted with the fund support of ICAR- Central Inland Fisheries Research Institute.
Authors contribution
Page 11/17
MFK – Drafted the manuscript and analysed data, PP – Collected sh catch data , VLR – Analysed phytoplankton, SSM- Analysed
diets, UKS – Analysed Zooplankton BKD - Conducted experimental shing, MEV- Collected sh samples from landing centres
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Figures
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Figure 1
Karapuzha Reservoir, in Wayanad District of Kerala, India
Figure 2
Time series of observed total catch (tonnes/km2/yr), mean trophic level of catch (TLc), L-index for Karapuzha reservoir model and
biomass diversity (Kempton index).
Page 14/17
Figure 3
Biomass observed (points) and predicted (line) by EwE model for the main species of Karapuzha reservoir shery. Biomass in
tonnes/km2.
Page 15/17
Figure 4
Estimated catches (tonnes/km2) for the main target species in Karapuzha reservoir under different shing effort scenarios for 27 years
(2019-2045). Base: Baseline (shing effort value from 2006), Fmsy_full and Fmsy_statare the estimated Fmsy from full compensation
and stationary modes, respectively.
Page 16/17
Figure 5
Biomass estimates (tonnes/km2) from Ecosim simulations for the main target species in Karapuzha reservoir sheries. Base: Baseline
(shing effort value from 2006), Fmsy_full and Fmsy_stat are the estimated Fmsy from full compensation and stationary modes,
respectively.
Page 17/17
Figure 6
Fishing mortality (year-1) estimated by the foodweb model of Karapuzha reservoir under different scenarios of shing effort for the 27
years simulations (2019-2045). Base: Baseline (shing effort value from 2006), Fmsy_full and Fmsy_stat are the estimated Fmsy from
full compensation and stationary modes, respectively.
Figure 7
This image is not available with this version.
