Impacts of ENSO on small-scale fisheries catch in Gulf of California, Mexico
by
Shumin Zheng
Adviser: Dr. Martin Smith
April 2014
Masters project submitted in partial fulfillment of the
requirements for the Master of Environmental Management degree in
the Nicholas School of the Environment of
Duke University
2014
Table of Contents
Abstract ........................................................................................................................................................ 2
I. Introduction ............................................................................................................................................ 3
II. Data and Method ................................................................................................................................. 8
Basic set-up ............................................................................................................................................ 8
Data preparation............................................................................................................................... 11
Model specification .......................................................................................................................... 19
III. Results and Discussion ................................................................................................................. 22
Results .................................................................................................................................................. 22
Way forward ....................................................................................................................................... 26
IV. References ........................................................................................................................................ 28
Impacts of ENSO on small-scale fisheries catch in
Gulf of California, Mexico
Abstract
Climatic shocks impact fisheries and the livelihoods of millions of people. Fishermen have
various strategies to respond to shocks such as switching target species or engaging in
alternative income activities. However, it is unclear how biodiversity in fisheries mitigates
impacts to fishermen’s revenues. To answer this question, we focused on small-scale
fisheries in the Gulf of California (GoC) in this study and examined how species diversity
in fisheries may mitigate the impact of El Nino South Oscillation (ENSO) events. We
hypothesized that species with different life histories would respond to ENSO differently
in terms of direction and magnitude and that this would result in different impacts on
biomass and associated catches. As result, we expect that fisheries targeting species whose
responses towards ENSO are more heterogeneous will have more stable total catch and
revenue. To test this hypothesis we used detailed fisheries catch and price data (2001- 2010)
from government fishing offices in the GoC. The results of this research help shed light on
the role of biodiversity conservation in supporting fisheries and human well-being.
Key words: ENSO; Small-scale fisheries; Gulf of California (GoC); Biodiversity;
Stability of ecosystem productivity; Income stability
2
I. Introduction
Fisheries are an important source of food and income for about 520 million people
globally, and fish and fish-related products are the most highly traded food items globally
(FAO 2009). Small-scale fisheries in particular are incredibly important: they employ
more than 90 percent of the world’s 35+ million capture fishermen, and contribute over
half of the world’s marine and inland fish catch, nearly all of which is used for direct
human consumption (FAO 2009). Extreme climate events have impacted fisheries and
millions of people who are employed by small-scale fisheries. Climate change,
overfishing and ecosystem degradation may exacerbate these impacts and cause loss to
fishermen's revenues. In response to this potential loss, fishermen may switch target
species or simply turn to other economic activities. However the role of species diversity
in mitigating the climate impacts to fishermen's revenues is not fully understood.
Previous ecological studies have focused on the role of biodiversity in maintaining the
stability of ecosystem productivity (e.g., Yachi and Loreau 1999, Tilman et al. 2006);
however, few studies have linked biodiversity to stability in ecosystem services or human
well-being (except see Worm et al. 2006, Sanchirico, Smith, and Lipton. 2008). To
further our understanding, we aim to understand the climate impacts on the catch of
particular species and how the combined responses of different species to climate change
may buffer climate impacts on fishermen’s total catch and revenues.
I aim to investigate this issue for small-scale fisheries in the Gulf of California (GoC),
3
Mexico. The GoC is known for its diversity of marine species and it produces 60% of the
fisheries landings for Mexico (OECD 2006). In some areas of the GoC, almost all
landings (90%) come from small-scale fisheries that target hundreds of species (Erisman
et al. 2011). The GoC is strongly affected by El Nino Southern Oscillation (ENSO)
events (Polis 1997). The impact of ENSO events varies across the Gulf because of
differences in oceanography (Lluch-Cota et al. 2010). In addition, detailed ecological
studies have shown that some target fish species respond differently to the impacts of
ENSO because of differences in their life histories (Aburto et al 2007, 2011). Therefore
this study will mainly focus on the impacts of ENSO on fishery catch, controlling for
other socio-economic variables.
Specifically, Aburto et al (2007, 2011) showed that yellow snapper and grouper react in
opposite directions to changes in sea water temperature and nutrient availability (which
are two main measurements of the influence of ENSO, and are also two main
components of the index MEI that I will discuss in detail below). Furthermore, they
showed that a past ENSO event could have an impact on current fisheries landings
through its influence on the abundance of species juveniles (which are often referred as
‘recruits’). Thus, based on the biological mechanisms learned from Aburto, I could
examine differences in the impact of ENSO on the landings of particular species. If the
qualitative predictions hold, it can be expected, in the face of changes in climate
conditions, fisheries targeting various species would generate a relatively more stable
catch by inducing negative correlations in catch that, blended together, reduce overall
4
variance. This mechanism is similar to the one modeled in Sanchirico, Smith, and Lipton
(2008), although that study did not specifically focus on ENSO.
As noted above, ENSO is not the sole determinant of fisheries catch; the effect of ENSO
on catch of a particular species is modulated by socioeconomic factors as well.
Nagavarapu, Reddy, Mack Crane et al (2013) explored some of these factors and found
the presence of a fishing cooperative, its absolute number of members and ratio to
independent fishermen, the market price and price which the fishing cooperative offered
to its members are all relevant to the total catch and fishermen’s revenues. According to
Nagavarapu, Reddy, Mack Crane et al, limited competition and small ranging species
create incentives for the fishing cooperative that are similar to those experienced by a
sole owner of a resource because these conditions create de facto property rights. This
institutional arrangement allows the cooperative to use price it offers to control fishing
activity in response to ENSO-induced change in the growth rate. Therefore, it would be
ideal to take these factors into account when I examine 1) how the effect of ENSO on
fishery catch is modulated by socioeconomic factors and 2) how combined responses of
different species and fishermen to ENSO may buffer the impact on fishermen’s revenues.
Furthering the understanding of how the fisheries catch and fishermen’s revenue are
influenced by climate shocks such as ENSO and how these shocks are mediated by
biodiversity would provide necessary information for better fishing policies and benefit
the local economy. As Cheung et al. (2010) suggest, climate change would have large
impacts on global food supply by influencing marine capture fisheries. Their projection
5
in global catch potential for marine species under climate change has shown a general
decrease in the tropical areas (the decrease can be as high as 40%) and the change is most
pronounced in the Pacific Ocean. Therefore in face of this potential biodiversity and
revenue losses, adaptive fishing policies are essential to minimize the impacts of climate
changes through fisheries. Furthermore, Carson et al. (2008) has showed through
simulation modeling that various existing fishing policies would tend to “crash” a fishery
due to their assumptions of time-invariant growth rate. They argue cyclical environmental
conditions such as seasonal and inter-annual variation in sea surface temperature (SST)
would induce a cyclical growth rate.
El Niño-Southern Oscillation is a globally important climatic phenomenon that is
characterized by anomalies in the ocean-atmosphere system in the Equatorial Pacific
(NOAA/PMEL/TAO). El Nino phases describe periods with warm water anomalies,
while La Nina phases describe periods with cold water anomalies. The warm, nutrient
poor water of El Nino phases also tend to result in lower primary productivity in the
ocean, which may be measured by Chlorophyll α concentrations. Figure 1.1 illustrates
the cyclical features of ENSO by depicting the SST and Chlorophyll α concentration
around one of the fishing offices in GoC (La Paz) from 2001 to 2010. The figure
indicates strong seasonality, so understanding how the fisheries catch and fishermen’s
revenue are influenced by climate shocks with such cyclical patterns would help develop
better fishing policies.
6
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Chl α
SST
35
30
25
20
15
10
5
0
Average Sea Surface
Temperature (C)
Chl α (mg m-3)
Chl α and SST time series in La Paz
Time Series
JAN.2001 - DEC.2010
Figure 1.1 Chlorophyll α and sea surface temperature time series in La Paz
In what follows, section II describes the data collected and the model specification; the results and discussions are presented in section
III, also discussed are limitations of this study and suggestions for future researches and IV provides the references.
7
II. Data and Method
Basic set-up
Given the best data availability, to estimate the climate impacts on the catch of a
particular species and how this impact is modified by other related socio-economic
variables, I would estimate a model similar to the following:
𝑦𝑖𝑗𝑡 = 𝛿𝑖𝑗 + 𝛼𝑡 + 𝛽1 𝐸𝑁𝑆𝑂𝑖𝑡 + 𝛽2 𝐸𝑁𝑆𝑂𝑖𝑡 ∗ 𝑇𝑦𝑝𝑒𝐴𝑑𝑢𝑙𝑡𝑗 + 𝛽3 𝐸𝑁𝑆𝑂𝑖𝑡−𝑙𝑎𝑔𝑗
∗ 𝑇𝑦𝑝𝑒𝑅𝑒𝑐𝑟𝑢𝑖𝑡𝑗 + 𝜷𝟒 𝐸𝑁𝑆𝑂𝑖𝑡 𝑿𝒊𝒕 + 𝜀𝑖𝑗𝑡
Where:
𝒚𝒊𝒋𝒕 is the total catch of a particular species of species 𝑗 at fishing office 𝑖 in time 𝑡 , time
could either be month or year for different model specifications as discussed in the later
part of this section
𝜹𝒊𝒋 and 𝜶𝒕 are fixed effects for species 𝑗 at fishing office 𝑖 and time 𝑡 (monthly fixed
effect or/and yearly fixed effects). The yearly and monthly fixed effects, though
imperfect proxies, are expected to capture the influence of changes in fishing effort.
Including these features helps to isolate the effects of interest, namely the effects of
ENSO variables on catches.
8
𝑬𝑵𝑺𝑶𝒊𝒕 is an ENSO index, a continuous variable representing some futures of ENSO,
which will be discussed in details in the later part of this section
𝑻𝒚𝒑𝒆𝑨𝒅𝒖𝒍𝒕𝒋 and 𝑻𝒚𝒑𝒆𝑹𝒆𝒄𝒓𝒖𝒊𝒕𝒋 are two dummies indicating the direction of the ENSO
effect on species as an adult and recruit (species juveniles) respectively, taking value of 1, 0 or 1: 1 indicating positive effect, -1 indicating negative effect and 0 meaning no
effect.
ENSO have both positive ENSO and negative ENSO events; a positive ENSO event is El
Nino-like condition that is characterized by high sea surface temperature and low nutrient
availability (high SST and low Chl α ). Therefore when the species increases during a
positive ENSO event (or decrease during a negative ENSO event), the effect is coded as
1, and when the species increases during a negative ENSO event (or decrease during a
positive ENSO event), the effect is coded as -1.
Note that when ENSO affects recruits, there will be a lag of this effect on fish catch that
is equal to the time from recruitment to when the species reaches commercial size. The
directionality of the ENSO effects and the time lag for recruits are ecologically
understood and readily available in the data source. Whether the data are consistent with
this ecological understanding is an empirical question.
𝑿𝒊𝒕 's are various socio-economic and institutional variables. Ideally they fall into three
categories: institutional factors (such as ratio of cooperative members to total permit
holders, number of cooperatives, occurrence of locally generated rules); economic
alternatives (availability of non-fishing occupations, wages in non-fishing occupations);
9
and socio-economic characteristics (average education of fishermen, market value of
different species, marine diesel prices).
Note that some of these control variables are time-invariant and others are time-variant;
some of these variables are location specific and others are not. Therefore when the FE
estimator is fishing office FE, the main effect of variables that are time-invariant
(availability of non-fishing occupations, wages in non-fishing occupations) are absorbed
by office FE and should not be included; when the FE estimator is monthly or yearly FE,
the main effects of variables that are not location specific (such as average market value
and marine diesel prices) are absorbed by time FE and should not be included.
The coefficients of interest are 𝛽2, 𝛽3, and 𝛽4 . 𝛽2 describes the response of catch through
the effect of current ENSO on adults and 𝛽3 describes the response of catch through the
effect of past ENSO on past recruits. 𝛽4 generally describes the influence of different
socio-economic settings on the catches’ responses to ENSO events
10
Data preparation
(1) Data source
This Master Project has been done in collaboration with The Nature Conservancy (TNC),
so the data analyzed in this study are mostly obtained from/via TNC and include: 1)
species-specific catch data from 47 fisheries offices in the GoC for the 2001-2009 period
(Aburto, Reddy, Leslie et al. 2013) ; 2) a recently constructed (by TNC) database on life
history attributes (e.g., effects of ENSO on recruits and adults, growth rates, size and age
at commercial size) (Reddy, Aburto, Leslie et al. 2013); 3) fishing office-specific data on
Sea Surface Temperature and Chlorophyll α concentration (a proxy for nutrient
availability) around GoC for 2001 to 2010 period (Cavanaugh, Reddy, Leslie et al. 2013).
I have also obtained a 4) fish price index time series for the 2001-2010 period from
Sigbjørn Tveteras, Asche F, Bellemare MF, Smith M.D, Guttormsen AG, et al. (2012)
and 5) the Multivariate ENSO Index (MEI) series (NOAA, 2014)
(2) Data cleaning and modification
In the above databases, data are not perfectly cleaned and organized. Several issues
remained for me to address:
a) There are a number of observations taking extreme values for almost all species in
some particular year and fishing office. These apparent outliers have been revealed by
11
descriptive statistics.
Take Serranidae recorded in fishing office La Paz in 2008, for example. We can see
(from Figure 2.1 and Table 2.1 that) that almost over 80% of the catch observations take
value that is less than 500 but the largest value goes way up to 1650.
Serranidae catch recorded in office in La Paz
Percent of num. of Obs.
40%
35%
30%
25%
20%
15%
10%
5%
0%
Total catch (kg)
Figure 2.1 Serranidae catch recorded in office La Paz 2008
Table 2.1 Simple discretion for catch variable
Variable
Num. of Obs. Mean
Sd
Min
Median
Max
Catch (kg)
309
376.4
5
180
1650
335.4
b) Originally I am interested in the 46 fishing offices around the GoC, however in
database 1, not all the offices are consistently recorded, i.e., most fishing offices do not
have recordings before 2000, only three offices— La Paz, Loreto and Santa Rosalia—
have recordings from 1990 to 1999. Therefore, I start my analysis focusing on these three
‘priority offices.’ Having a longer time series is important in order to observe multiple El
12
Nino events, which typically have re-occur every 3-5 years.
c) Fishermen are required by law (LGPAS, 2007) to register their catch to the local
CONAPESCA fishing office before transporting or selling the fish or other harvested
species. Therefore, the catch observations in the CONAPESCA database represent the
record of catch reported to a fishing office on a particular day from a particular fishing
group (the fishing group is not identified in the database). Fishing groups may vary in
their practices for reporting catch. For instance, the time between the fishing trip and the
record of the catch may vary. (Leslie et al, in prep) In order to have a consistent time
period for catch observations, we summed fish catch observations and averaged fish price
observations by species and fishing office over each month.
d) Also, the recording date has not been recorded in a consistent manner: in the database
there are nine variables related to recording time, they are not recorded using an uniform
format, some uses day/month/year/, month/day/year, some are just year or month etc, but
none of them can provide the most comprehensive information. I need to cross check
these time variables and synthesize them into one time variable
e) For variable 𝑬𝑵𝑺𝑶𝒊𝒕 , I originally used the Multivariate ENSO Index (MEI) as the
index for ENSO. As its name suggest, MEI integrates multiple meteorological
components and better reflects the nature of this coupled ocean-atmospheric phenomenon
which is why it is often viewed as the most comprehensive index. (Mazzarella et al 2013)
Aburto et al (2007) further points out the MEI is particularly useful for the GoC due to its
13
correlation with fishery catch of two species in the Pacific coast of the GoC. However, in
this research, I am looking at a particular region and I would like to be able to capture the
heterogeneity in the climate impact across the GoC area. Thus, more spatially explicit
indices would be preferable. Therefore, I merged fishing office-specific data on Sea
Surface Temperature (SST) and Chlorophyll α concentration (Chl α) with the main
database.
Note that I now have two spatially explicit indices to use. The question is whether I
should use one over another or include them simultaneously in one model. Table 2.2.1
shows the correlation between Chl α and SST for three of our priority fishing offices:
Table 2.2.1 Correlation between Chl α and SST for priority fishing offices
Fishing office
Correlation between Chl and SST
La Paz
-0.4548
Loreto
.-5724
Santa Rosalia
-0.4384
p-value
0.0000
0.0000
0.0000
It is clear Chl α has a significant negative relationship with SST across all three offices.
With relatively small sample sizes, which is the case in the empirical work below,
including both indices could cause multicollinearity problems and render the standard
error imprecise.
In light of this potential of collinearity, I would also like to check if there will be
significant correlation between Chl α/SST and Fisher Fish Price Index (FPI). Table 2.2.2
shows the results of the test:
14
Table 2.2.2 Correlation between ENSO indices and FPI
Fishing office
Correlation between Chl α and FPI
p-value
Correlation between SST and FPI
p-value
La Paz
-0.123
Loreto
-0.171
Santa Rosalia
0.0568
0.1807
0.0619
0.5376
0.0148
-0.0314
-0.0558
0.8729
0.7349
0.5465
The FPI does not seem to have collinearity problems with ENSO indices if they were to
be included in the model simultaneously.
f) In the dataset that provided info on the effects of ENSO on species, i.e. 𝑻𝒚𝒑𝒆𝑨𝒅𝒖𝒍𝒕𝒋
and 𝑻𝒚𝒑𝒆𝑹𝒆𝒄𝒓𝒖𝒊𝒕𝒋, the effects are coded as -1, 0 and 1 according to the overall ENSO
effect Ocean Oscillation Index (ONI, which is another integrated but not spatial-specific
index for ENSO). Since I have used SST and Chl α as indices for ENSO, the effects
variables are recoded as 𝒔𝒔𝒕𝑨𝒅𝒖𝒍𝒕, 𝒔𝒔𝒕𝑹𝒆𝒄𝒓𝒖𝒊𝒕, 𝒄𝒉𝒍𝑨𝒅𝒖𝒍𝒕 𝒂𝒏𝒅 𝒄𝒉𝒍𝑹𝒆𝒄𝒓𝒖𝒊𝒕.
(positive ENSO event has positive ONI value and is associated with high SST and low
Chl α, for example, if adults of species A would tend to increase in response to positive
ENSO, then its response towards SST is coded as 1 and its response towards Chl α is
coded as -1 ).
g) In the database on life history attributes (database 2), I have species or families whose
response towards ENSO are ecologically understood, I have to find matches of these
species/families in database 1, those who appear in both databases and have sufficient
15
observations are the ‘priority species’ (see table 2.3 next page for details) that I will focus
on in this research.
h) Due to data availability at the time of completing this Masters Project, I only use fish
price index for the socio-economic and institutional variables (𝑿𝒊𝒕 ′s). I was able to extract
education level and construct potential non-fishing wages out of the Mexican economic
census, but somehow the data is not available for fishing offices La Paz and Loreto.
Because I include fixed effects at the office level, future work on institutional variables
would be warranted if there were variation over time in the institutional variables at the
office level. Otherwise, the fixed effects should capture these features despite being
unable to decompose any of the underlying structural effects.
After the data cleaning process, I have been able to come up a combination of speciesfishing office-monthly panel over a 10-year span (2000-2010) (see Table 2.3 next page)
16
Table 2.3 Species info (Reddy, Aburto, Leslie et al. 2013)
Market
Name
Cabrilla
Scientific Name
ENSO Effect on
Adults
ENSO Effect
on Recruits
Lag
Time
Description (note the numbers do not exclude observations according
to boat type)
21311 Obs, with 3606 in LA PAZ, 3341 in LORETO and 5170 in
SANTA ROSALA
Only 13 observations
1901 Obs, with 154 in LA PAZ, 70 in LORETO and 409 in SANTA
ROSALA
20682 Obs, 4187 in CD. CONSTITUCIN, 2849 in PUNTA ABREOJOS,
2529 in SAN CARLOS, 5436 SANTA ROSALA
1442 Obs, but 971 in ENSENADA, 106 in LA PAZ, 0 in LORETO and
SANTA ROSALA
3965 Obs, 2121 in MAZATLN
1407 Obs, 1334 in ENSENADA
41571 Obs, 1862 in LA PAZ, 305 in LORETO, 884 in SANTA
ROSALA
4693 Obs, 868 in LA PAZ, 155 in LORETO, 12 in SANTA ROSALA
5127 Obs, 2298 in GUAYMAS, 1223 in HUATBAMPO, 1294 in
MAZATLN
11833 Obs, 2838 in GUAYMAS, 3117 in HUATABAMPO, 4800 in
MAZATLN
36823 Obs, 10335 in ENSENADA, 15052 in GUAYMAS, 2208 in
HUATABAMPO, 1394 in PTO.ADOLFO LPEZ MATEOS, 7636 in SAN
CARLOS
994 Obs, 80 in LA PAZ, 64 in LORETO, 281 in SANTA ROSALA
31475 Obs, 1301 in BAHA ASUNCIN, 1347 in BAHA KINO, 2254
CD.CONSTITUCIN, 4207 in LA PAZ, 2141 in LORETO, 1865 in
PEITA DE JALTEMBA, 1810 in PUNTA ABREOJOS, 2107 in SAN
BLAS, 2221 in SAN CARLOS, 5594 in SANTA ROSALA
Serranidae
Paralabrax maculatofasciatus
0
0
-1
-1
7
7
Mycteroperca rosacea
0
-1
7
Paralabrax nebulifer
0
-1
7
Atun
Thunnus spp.
Thunnus albacares
Thunnus orientalis
-1
-1
-1
0
0
0
NA
NA
NA
Sierra
Sardina
Scomberomorus sierra
Scomber japonicus
1
0
0
-1
NA
1
Cengraulis mysticetus
0
-1
1
Opisthonema sp.
0
-1
1
Sardinops caeruleus
Seriola lalandi
0
1
-1
0
1
NA
Caranx sp.
No observations
1
0
NA
Haliotis corrugata
0
-1
1
4622 Obs, 2317 in BAHA TORTUGAS, 1392 in PUNTA ABREOJOS
Haliotis fulgens
Panulirus sp.
0
0
-1
1
1
3
Panulirus inflatus
0
1
3
Panulirus interruptus
0
1
3
6869 Obs, 2967 in BAHA TORTUGAS, 1253 in PUNTA ABREOJOS
1676 Obs, 780 in MAZATLAN, 253 in PEITA DE JALTEMBA
3321 Obs, 802 in CD. CONSTITUCIN, 712 in LA PAZ, 881 in PUNTA
ABREOJOS
12290 Obs, 4748 in BAHA TORTUGAS, 1278 in ENSENADA, 1005 in
PUNTA ABREOJOS
Jurel
Dorad
Abulon
amarillo
Abulon
azul
Langosta
Langosta
Caribe
Langosta
17
The “priority species” mentioned above are shown in Table 2.3, however only 4 species
are finally used in the analysis after I matched them with the “priority office”, the reason
of exclusion is: 1) the particular species doesn't have enough observations for all three
offices, some species have observations that are less than 50. Although some species are
only fished in some particular months and this seasonality could be absorbed by month
fixed effects, they might not have much significance and for I excluded them from this
study 2) for some species, all the observations turned out to be recorded in only one or
two years.
In addition, the number of observations used in the analysis is different from the numbers
shown in the last column because more observations are excluded for not being
considered as “small-scale” fishery. (Each observation is identified by a unique ID, and
the ID for those use a small boat for the fishing starts with letter “B”)
18
Model specification
In the first set of models, I ran a set of simple panels focusing on one species at a time.
The catch data is pooled over fishing offices, and I have fishing office as my panel
variable and sequential month (from Jan 2001 to Dec 2010, 120 months in total) as time
variable. I used a fixed effects (fishing-office FE) estimator and simply regressed total
catch of species j on ENSO indicator (here I only used MEI, not SST or Chl α) and fish
price index (FPI). The first set of models also include dummies for month and year (time
FE), and clustered standard errors are specified. Note these models exclude the ENSO
response variables (𝒎𝒆𝒊𝑨𝒅𝒖𝒍𝒕 𝒂𝒏𝒅 𝒎𝒆𝒊𝑹𝒆𝒄𝒓𝒖𝒊𝒕); the purpose of these models is to
examine the direction of the overall ENSO effect empirically on aggregate catch for each
species without distinguishing between adults and recruits.
Then by the basic setup:
𝑦𝑖𝑗𝑡 = 𝛿𝑖𝑗 + 𝛼𝑡 + 𝛽1 𝑀𝐸𝐼𝑡 + 𝛽2 𝐹𝑃𝐼𝑡 + 𝜀𝑖𝑗𝑡
Where:
𝒚𝒊𝒋𝒕 is the total catch of a particular species of species 𝑗 at fishing office 𝑖 in sequential
month t
𝜹𝒊𝒋 is fishing-office FE and 𝜶𝒕 is time FE (both monthly and yearly)
In the second set of FE models, catch data is pooled over both fishing offices and
19
multiple species. Unlike fishing offices being used as panel variable, every unique
combination of species-office (for example, La Paz-Serranidae, Loreto-Serranidae,
Loreto-Caranx sp) is used as panel variable. Then I used a FE estimator (fishing officespecies FE) to estimate the effect of ENSO on the catch. The catch was regressed on
ENSO indicator (MEI, SST and Chl α) , fish price index (FPI), and also ENSO response
variables ( 𝒎𝒆𝒊𝑨𝒅𝒖𝒍𝒕, 𝒎𝒆𝒊𝑹𝒆𝒄𝒓𝒖𝒊𝒕, 𝒔𝒔𝒕𝑨𝒅𝒖𝒍𝒕, 𝒔𝒔𝒕𝑹𝒆𝒄𝒓𝒖𝒊𝒕,
𝒄𝒉𝒍𝑨𝒅𝒖𝒍𝒕 𝒂𝒏𝒅 𝒄𝒉𝒍𝑹𝒆𝒄𝒓𝒖𝒊𝒕). These models also include month and year dummies (time
FE) and the standard errors are clustered around the each unique combination of species
and fishing office.
𝑦𝑖𝑗𝑡 = 𝛿𝑖𝑗 + 𝛼𝑡 + 𝛽1 𝐸𝑁𝑆𝑂𝑖𝑡 + 𝛽2 𝐸𝑁𝑆𝑂𝑖𝑡 ∗ 𝑇𝑦𝑝𝑒𝐴𝑑𝑢𝑙𝑡𝑗 +
𝛽3 𝐸𝑁𝑆𝑂𝑖𝑡−𝑙𝑎𝑔𝑗 ∗ 𝑇𝑦𝑝𝑒𝑅𝑒𝑐𝑟𝑢𝑖𝑡𝑗 + 𝜷𝟒 𝐹𝑃𝐼𝒊𝒕 + 𝜀𝑖𝑗𝑡
Where:
𝒚𝒊𝒋𝒕 is the total catch of a particular species of species 𝑗 at fishing office 𝑖 in sequential
month 𝑡
𝜹𝒊𝒋 is fishing-office FE and 𝜶𝒕 is time FE (both monthly and yearly)
𝑬𝑵𝑺𝑶𝒊𝒕 is an index for ENSO, in the second set of models, it can be MEI, SST or Chl α.
To be specific, when it is MEI, it should be written as 𝑀𝐸𝐼𝑡 ; when they are SST or Chl α,
they should be written as 𝑆𝑆𝑇𝑖𝑡 𝑎𝑛𝑑 𝐶ℎ𝑙𝑖𝑡 . Note the difference in subscripts, because MEI
does not capture the heterogeneity of the climate impact across the GoC area as SST and
Chl α do.
20
𝑻𝒚𝒑𝒆𝑨𝒅𝒖𝒍𝒕𝒋 and 𝑻𝒚𝒑𝒆𝑹𝒆𝒄𝒓𝒖𝒊𝒕𝒋 are two dummies indicating the direction of the ENSO
effect on species as adults and recruits respectively, taking value of -1, 0 or 1. Note in
original dataset 2, they are coded as the overall effect of ENSO; here since I am using
SST and Chl α in addition to MEI, the recoding was done according the description at the
beginning of this section.
21
III. Results and Discussion
Results
Table 3.1 Factors Affecting Fishery Catch.
Species
Scomberomorus sierra Scomber japonicus
437.9547
-9.075947
Variable
MEI
Serranidae
-257.3738
0.003
0.809
0.549
0.401
Fish Price Index
-48.55677
-47.62049
-5.53297
142.4447
0.152
0.16
0.216
0.335
346
3
0.4527
317
3
0.2402
177
2
0.2121
330
3
0.2478
Obs
Num. of Groups
Within-R2
Caranx sp
-1206.26
Note: Observations at Fishing Office-Species-Month level. Fixed effects and clustering by fishing office.
Month and year dummies included.
Table 3.1 shows the results of the first set of models described in the first part of second
section. As it indicates, ENSO has a significant (on a 0.01 level) negative effect on the
catch of Serranidae in the priority offices, however the efffects are less pronounced on
the other three speciese. The first set of models are designed to examine the direction
overall effect of ENSO empirically, to see how well the results fit previous
understanding, we combine Table 2.2 with Table 3.1:
22
Table 3.2 Empirical check
Previous understanding of
Effects on Adults
Effects on Recruits
Coefficient on MEI
Serranidae
0
-1 (7)
-257.37
p = 0.003
Scomberomorus sierra
1
0
437.95
p = 0.809
Scomber japonicus
1
-1 (1)
-9.08
p = 0.549
Caranx sp
1
0
-1206.26
p = 0.401
Note the number in the parenthesis is the lag time in years.
The data for first Serranidae fit the previous understanding (zero effect on adults negative
effect on recruits yield a overall negative result); whereas those of the other species do
not. The reasons might be: 1) ENSO has opposite effect on adults and recruits and the lag
time for the effects on recruits further counteract the positive effect on adults 2) the
coefficients on MEI are not significantly different from zero.
23
Table 3.3 shows the results for the second set of models.
Table 3.3
variable
MEI
MEI x Adult Effect
MEI x Recruit Effect
MEI
-198.9227
383.8255
0.686
0.359
-1065.412
0.338
42.70969
0.926
Chl
Coefficients on ENSO Index
Chl
386.6413
0.123
Chl x Adult Effect
Chl x Recruit Effect
SST
-656.6947
0.193
-1747.47
0.043
-10.92716
0.968
SST
371.1217
0.229
SST x Adult Effect
SST x Recruit Effect
Fish Price Index
Obs
Num. of Groups
Within-R2
47.65326
0.378
1170
11
0.1061
41.05722
0.435
1148
11
0.1097
60.72134
0.439
1170
11
0.1066
64.8734
0.441
1120
11
0.1122
85.58656
0.387
1160
11
0.1101
628.393
0.077
-398.0875
0.186
27.41985
0.538
85.34616
0.418
1110
11
0.1183
Note: Observations at Fishing Office-Species-Month level. Fixed effects and clustering by office-species.
Month and year time dummies included.
There are two interesting findings in the second set of models, as reflected in table 3.3.
Examining table 3.3 horizontally, we can see that the coefficients on Chl α and SST have
more significance than those on MEI; this might due to the fact that Chl α and SST are
24
location specific thus capture the heterogeneity of the ENSO effect across the GoC area,
as discussed in the second section. Also this holds for not only for models with just
ENSO response variables and FPI, but also for models with interaction terms with adults
and recruits respectively.
Vertically from table 3.3, we can see that overall, MEI has a negative effect on the total
catch of the priority species in three priority offices through its impacts on adults,
although its effect through recruits is not significant (indicated by large p values). One of
the reasons that the coefficients on the ENSORecruit (SSTRecruit and ChlRecruit) are
less significant might be that the effect of past ENSO on recruits needs time to be
observed, identifying this time-lag effect requires more observations covering a longer
period of time. Take Serranidae for example, the time from its recruitment to when it
reaches commercial size is 7 years, with observations of a 10 year span, only 3 years of
ENSO effect (2001, 2002 and 2003) could be observed and identified (in 2008, 2009 and
2010 respectively), rendering the estimation less significant.
25
Way forward
This research contains certain limitations in regard to how data are selected from datasets
and how they are modified, to further the analysis and to improve the regression outputs,
future researchers could focus on the following aspect:
1) take more fishing offices into consideration, as can be seen from table 2.3, more
fishing offices means more observations and more manipulations on the data;
2) extract or construct more socio-economic covariates from economic and demographic
census data to obtain better proxies for various socioeconomic settings and better
examine how the effect of ENSO is modulated by those socioeconomic factors;
3) focus on revenue rather than catch of the small-scale fishery, and focus on the
variation or fluctuation of the revenue, examine the effect of ENSO on the stability of
revenue;
4) examine data that covers a longer period of time, especially when the lagging effect
can be latent as long as 7 years.
Specifically with the data available, the additional analyses to consider are:
1) Estimating the second set of models with all species at the three offices rather than
just focusing on the four priority species. In cases where we do not have
information on the expected response to ENSO (i.e. no info on 𝒔𝒔𝒕𝑹𝒆𝒄𝒓𝒖𝒊𝒕 or
26
𝒎𝒆𝒊𝑨𝒅𝒖𝒍𝒕), we could either drop observations, or create a dummy variable
indicating when we have information on the expected response and when we do
not. Also, we would like to estimate it with revenue as the outcome variable.
2) Estimating a model of monthly catch and revenue at the three fishing offices using
a revenue weighted measure of species richness, FPI, fishing office FE, month,
year FE. The idea is to examine how the combined response of different species
may buffer the ENSO impacts on fishermen’s revenues. Specifically we could
estimate a model similar to this:
𝐶𝑉(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑖𝑡 ) = 𝛿𝑖𝑗 + 𝛼𝑡 + 𝛽1 𝐶𝑉(𝐸𝑁𝑆𝑂𝑖𝑡 ) + 𝛽2 𝐶𝑉(𝐸𝑁𝑆𝑂𝑖𝑡 ) ∗ 𝐼𝑖𝑡 + 𝛽3 𝑋𝑡 + 𝜀𝑖𝑗𝑡
Where:
𝐶𝑉(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑖𝑡 ) is the coefficient of variation in revenues (𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑖 = ∑𝑁
𝑗=1 𝑝𝑖𝑗 𝑦𝑖𝑗 ) at
fishing office i, where species prices are 𝑝𝑖𝑗 and catch are 𝑦𝑖𝑗 .
𝐼𝑖𝑡 follows the same formulation as the Shannon index for diversity except that pj is the
proportion of the revenue in a given month belonging to species j. And, R is the total
number of species.
𝐼𝑖𝑡 = − ∑𝑅𝑗=1 𝑝𝑗 ln 𝑝𝑗
the rest are the same as specified as before.
27
IV. References
1.
Aburto-Oropeza, O., Paredes, G., Mascarenas-Osorio, I., & Sala, E. (2010). Climatic influence
on reef fish recruitment and fisheries. Marine Ecology Progress Series, 410, 283-287.
2.
Aburto-Oropeza, O., Sala, E., Paredes, G., Mendoza, A., & Ballesteros, E. (2007). Predictability
of reef fish recruitment in a highly variable nursery habitat.Ecology, 88(9), 2220-2228.
3.
Carson, R. T., Granger, C., Jackson, J., & Schlenker, W. (2009). Fisheries management under
cyclical population dynamics. Environmental and resource economics, 42(3), 379-410.
4.
Cavanaugh, Reddy, Leslie et al. 2013 In prep.
5.
Cheung, W. W., Lam, V. W., Sarmiento, J. L., Kearney, K., Watson, R. E. G., Zeller, D., &
Pauly, D. (2010). Large‐scale redistribution of maximum fisheries catch potential in the global
ocean under climate change. Global Change Biology, 16(1), 24-35.
6.
Erisman, B. E., Paredes, G. A., Plomozo-Lugo, T., Cota-Nieto, J. J., Hastings, P. A., & AburtoOropeza, O. (2011). Spatial structure of commercial marine fisheries in Northwest Mexico. ICES
Journal of Marine Science: Journal du Conseil, 68(3), 564-571.
7.
Leslie et al, In prep
8.
Ley General de Pesca y Acuacultura Sustenables (LGPAS). (2007). CESAY » LGPAS.
Retrieved , from http://cesay.org.mx/informacion/marco-juridico/lgpas/
9.
Lluch-Cota, S. E., Parés-Sierra, A., Magaña-Rueda, V. O., Arreguín-Sánchez, F., Bazzino, G.,
Herrera-Cervantes, H., & Lluch-Belda, D. (2010). Changing climate in the Gulf of
California. Progress in Oceanography, 87(1), 114-126.
10. Mazzarella, A., Giuliacci, A., & Scafetta, N. (2013). Quantifying the Multivariate ENSO Index
(MEI) coupling to CO2 concentration and to the length of day variations. Theoretical and applied
climatology, 111(3-4), 601-607.
11. Nagavarapu, S., Reddy, S. M., Aburto, O., Mack-Crane, A., Leslie, H. M., & Sievanen, L.
(2013). Property Rights for Fishing Cooperatives: How (and How Well) Do They Work?.
12. NOAA. "Multivariate ENSO Index." NOAA Research. Physical Sciences Division, n.d. Web. 07
Apr. 2014. <http://www.esrl.noaa.gov/psd/enso/mei/>.
13. NOAA/PMEL/TAO: NOAA/PMEL/TAO: The El Niño story. Retrieved April, 2014, from
http://www.pmel.noaa.gov/tao/elnino/el-nino-story.html
14. OECD (2006). Agricultural and Fisheries Policies in Mexico: Recent Achievements, Continuing
the Reform Agenda, OECD Publishing.
15. Polis, G. A., Hurd, S. D., Jackson, C. T., & Piñero, F. S. (1997). El Niño effects on the dynamics
and control of anisland ecosystem in the Gulf of California.Ecology, 78(6), 1884-1897.
16. Reddy, Aburto, Leslie et al. 2013 In prep.
17. Sanchirico, J. N., Smith, M. D., & Lipton, D. W. (2008). An empirical approach to ecosystembased fishery management. Ecological Economics, 64(3), 586-596
28
18. Tilman, D., Reich, P. B., & Knops, J. M. (2006). Biodiversity and ecosystem stability in a decadelong grassland experiment. Nature, 441(7093), 629-632.
19. Tveterås, S., Asche, F., Bellemare, M. F., Smith, M. D., Guttormsen, A. G., Lem, A., ... &
Vannuccini, S. (2012). Fish Is Food-The FAO’s Fish Price Index.PLoS One, 7(5), e36731.
20. UN FAO. (2009). The state of world fisheries and aquaculture 2008.
21. Worm, B., Barbier, E. B., Beaumont, N., Duffy, J. E., Folke, C., Halpern, B. S., ... & Watson, R.
(2006). Impacts of biodiversity loss on ocean ecosystem services. science, 314(5800), 787-790.
22. Yachi, S., & Loreau, M. (1999). Biodiversity and ecosystem productivity in a fluctuating
environment: the insurance hypothesis. Proceedings of the National Academy of Sciences, 96(4),
1463-1468.
29