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Social relationship dynamics mediate climate impacts on income inequality: evidence from the Mexican Humboldt squid fishery

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Small-scale fisheries are critically important for livelihoods around the world, particularly in tropical regions. However, climate variability and anthropogenic climate change may seriously impact small-scale fisheries by altering the abundance and distribution of target species. Social relationships between fishery users, such as fish traders, can determine how each individual responds and is affected by changes in fisheries. These informal cooperative and competitive relationships provide access, support, and incentives for fishing and affect the distribution of benefits. Yet, individuals' actions and impacts on individuals are often the primary focus of the economic analyses informing small-scale fisheries' formal management. This focus dismisses relevant social relationships. We argue that this leads to a disconnect between reality and its model representation used in formal management, which may reduce formal fisheries management's efficiency and efficacy and potentially trigger adverse consequences. Here, we examine this argument by comparing the predictions of a simple bioeconomic fishery model with those of a social-ecological model that incorporates the dynamics of cooperative relationships between fish traders. We illustrate model outcomes using an empirical case study in the Mexican Humboldt squid fishery. We find that (1) the social-ecological model with relationship dynamics substantially improves accuracy in predicting observed fishery variables to the simple bioeconomic model. (2) Income inequality outcomes are associated with changes in cooperative trade relationships. When environmental temperature is included in the model as a driver of species production dynamics, we find that climate-driven temperature variability drives a decline in catch that, in turn, reduce fishers' income. We observe an offset of this loss in income by including cooperative relationships between fish traders (oligopoly) in the model. These relationships break down following species distribution changes and result in an increase in prices fishers receive. Finally, (3) our social-ecological model simulations show that the current fishery development program, which seeks to increase fishers' income through an increase in domestic market demand, is supported by predictions from the simple bioeconomic model, may increase income inequality between fishers and traders. Our findings highlight the real and urgent need to re-think fisheries management models in the context of small-scale fisheries and climate change worldwide to encompass social relationship dynamics. Supplementary information: The online version contains supplementary material available at (10.1007/s10113-021-01747-5).
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https://doi.org/10.1007/s10113-021-01747-5
ORIGINAL ARTICLE
Social relationship dynamics mediate climate impacts on income
inequality: evidence from the Mexican Humboldt squid fishery
Laura G. Elsler1·Timothy Haight Frawley2·Gregory L. Britten3·Larry B. Crowder2·Timothy C. DuBois4·
Sonja Radosavljevic4·William F. Gilly2·Anne-Sophie Cr´
epin4,5 ·Maja Schl ¨
uter4
Received: 22 January 2020 / Accepted: 6 January 2021
©The Author(s) 2021
Abstract
Small-scale fisheries are critically important for livelihoods around the world, particularly in tropical regions. However,
climate variability and anthropogenic climate change may seriously impact small-scale fisheries by altering the abundance
and distribution of target species. Social relationships between fishery users, such as fish traders, can determine how
each individual responds and is affected by changes in fisheries. These informal cooperative and competitive relationships
provide access, support, and incentives for fishing and affect the distribution of benefits. Yet, individuals’ actions and
impacts on individuals are often the primary focus of the economic analyses informing small-scale fisheries’ formal
management. This focus dismisses relevant social relationships. We argue that this leads to a disconnect between reality
and its model representation used in formal management, which may reduce formal fisheries management’s efficiency and
efficacy and potentially trigger adverse consequences. Here, we examine this argument by comparing the predictions of a
simple bioeconomic fishery model with those of a social-ecological model that incorporates the dynamics of cooperative
relationships between fish traders. We illustrate model outcomes using an empirical case study in the Mexican Humboldt
squid fishery. We find that (1) the social-ecological model with relationship dynamics substantially improves accuracy in
predicting observed fishery variables to the simple bioeconomic model. (2) Income inequality outcomes are associated with
changes in cooperative trade relationships. When environmental temperature is included in the model as a driver of species
production dynamics, we find that climate-driven temperature variability drives a decline in catch that, in turn, reduce fishers’
income. We observe an offset of this loss in income by including cooperative relationships between fish traders (oligopoly)
in the model. These relationships break down following species distribution changes and result in an increase in prices
fishers receive. Finally, (3) our social-ecological model simulations show that the current fishery development program,
which seeks to increase fishers’ income through an increase in domestic market demand, is supported by predictions from the
simple bioeconomic model, may increase income inequality between fishers and traders. Our findings highlight the real and
urgent need to re-think fisheries management models in the context of small-scale fisheries and climate change worldwide
to encompass social relationship dynamics.
Keywords Social structures ·Environmental changes ·Social-ecological systems modeling ·Inequality ·
Humboldt squid fishery
Introduction
Climate change and variability trigger changes in the
abundance and distribution of target species, influencing
Communicated by Shannon Hagerman
Laura G. Elsler
laura.elsler@su.se
Extended author information available on the last page of the article.
fishers, fish traders, and their relationships (Cinner et al.
2012; Pinsky and Mantua 2014; Britten et al. 2016; Elsler
2020). Regional fisheries’ productivity may shift by up
to 50% (Cheung et al. 2010), and species distributions is
likely to change in response to climate change (Pinsky et al.
2013). Climate-driven redistribution of fisheries’ maximum
catch potential is expected to be most severe across tropical
regions (Cheung et al. 2010), where informal cooperative
and competitive relationships between and amongst fishers
and traders govern small-scale fisheries (Drury O’Neill
et al. 2018; Ferrol-Schulte et al. 2014). Changes in species’
/ Published online: 24 March 2021
Regional Environmental Change (2021) 21: 35
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
dynamics can affect fishers’ livelihoods (Le Bris et al.
2018), including cases where they alter social relationships
that govern benefit distribution (Adger 1999; Drury O’Neill
et al. 2018). Effective fisheries management must then
develop an understanding of social relationship responses
to climate change and their consequences for managing the
distribution of fisheries’ benefits (Adger 1999). One recent
strand of empirical fisheries research has thus focused on the
impacts of climate change on fishers (Pinsky and Mantua
2014; Rogers et al. 2019) and the propagation of those
impacts to other users such as traders (Fleming et al. 2014).
In small-scale fisheries, social relationships can mediate
outcomes for local economies and welfare. Relationships
between traders can help create economic buffers for
fishers; for instance, traders might buy fish that they did not
demand but can sell it to another trader (Gonzalez-Mon et al.
2019). Fishers’ higher flexibility to catch what is available
can increase their income. However, social relations may
also negatively affect welfare outcomes. Competition and
dependence in relationships can help determine how social
relationships affect welfare outcomes. Empirical evidence
shows that traders can act as gatekeepers to global markets
(Ferse et al. 2014; Elsler et al. 2019), provide fishing
permits, equipment, or loans (Frawley et al. 2019c;Basurto
et al. 2013). These interactions can create dependencies
and reduce fishers’ ability to negotiate competitive prices
(Ferrol-Schulte et al. 2014; Basurto et al. 2020). Also,
cooperation amongst traders can reduce market competition
and enables them to fix the prices at which they buy fish
below market value (Wamukota et al. 2014). Low prices
paid by traders, in turn, can lead to low fishers’ income.
Thus, social relationships can be important determinants of
welfare outcomes in fisheries.
Prominent empirical evidence highlights the need to
incorporate climate change impacts on social relationships
to achieve effective fisheries management (Adger 1999;
Daw et al. 2009). Nonetheless, their inclusion remains
rare in the predictive bioeconomic models widely used
for management support (Clark 1985; Nielsen et al. 2018;
Prellezo et al. 2012;Naufaletal.2019;Cabraletal.
2018; Fryxell et al. 2017). Recent advances in bioeconomic
models involve assessing equity in social outcomes (Hutton
et al. 2016;Plaganyietal.2013) and fisheries interactions
with actors in the seafood supply chain (Christensen et al.
2011; Kaplan and Leonard 2012). These models include
static representations of social relationships. In contrast,
social-ecological models are a new class of models that
explicitly represent the importance of social dynamics and
relationships in response to and as drivers of ecological
dynamics (Schl¨uter et al. 2012;Ladeetal.2015).
We argue that social relationships are relevant to man-
aging fisheries effectively. Analogous to social relation-
ships, such relevance has been demonstrated for ecological
relationships. Ecosystem-based management scholars have
shown that omitting the effect of environmental changes
on ecological relationships, such as those between preda-
tor and prey, can lead to misguided assessments of fisheries
(McLeod and Leslie 2009). Omission of relevant social rela-
tionships may lead to similar biases. Climate change can
affect cooperative and competitive relationships between
fishery users (Lindkvist et al. 2017). Yet, understanding and
predicting the impacts of relationship dynamics on fishery
outcomes remain a major scientific gap. Social-ecological
models, particularly agent-based models, have highlighted
an important role for social relationship dynamics, such as
cooperation and competition between fishers, cooperatives,
and fish traders (BenDor et al. 2009; Lindkvist et al. 2017).
Therefore, social-ecological modeling approaches may be
useful to bridge the gap between existing bioeconomic mod-
els and the reality in which social relationship dynamics
shape small-scale fishery outcomes.
This paper investigates how fishery users’ relationships
can mediate the impacts of climate change (henceforth, inc-
luding climate variability changes) on the distribution of
fishery benefits. We chose the Humboldt squid fishery be-
cause of its significant and well-documented response to re-
cent changes in environmental and climate trajectories
(Frawley et al. 2019a) and can serve to motivate a reappra-
isal of small-scale fisheries management models worldwide.
We compare our model to a simple bioeconomic model
currently used for fisheries management. Then, we test
both models’ respective ability to predict observed fishery
variables. Finally, we use the models to analyze income and
income inequality in the squid fishery for different fisheries
development programs and climate change trajectories.
Thus, our analysis integrates social relationship dynamics
in predictive social-ecological models to support effective
small-scale fisheries management through these steps.
Social-ecological dynamics in the Mexican
Humboldt squid fishery
The Guaymas basin is the geographic center of the
Gulf of California. It represents a biologically productive
oceanographic transition zone where the ecosystem is
sensitive to changes in water temperature (Lluch-Belda
et al. 2003). Climate change in the Gulf of California is
strongly influenced by El Ni˜
no and La Ni˜
na oscillations
and an increasing temperature trend (Petatan-Ramirez et al.
2019) due to anthropogenic forcing. These atmospheric
and oceanographic drivers affecting mesoscale circulation
patterns, upwelling, and primary productivity are often
assessed using remotely sensed proxies like sea surface
temperature variability (SST) (Lluch-Cota et al. 2010;
Robinson et al. 2016; Frawley et al. 2019a).
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The Humboldt squid is a migratory, opportunistic
predator which forages in highly productive regions like the
Guaymas basin (Hoving et al. 2013). It has a very flexible
life history strategy and can change its phenotypic life span
and body size to accommodate variable environmental and
oceanographic conditions (Hoving et al. 2013). Though
the core fishing grounds of the Mexican commercial squid
fishery are offshore of the ports of Guaymas and Santa
Rosal`
ıa (Fig. 1), episodic landings have also been recorded
in the Northern Gulf and along the Pacific coast. Historical
catch fluctuations on either side of the Baja California
peninsula and the recent collapse of the fishery within
the Gulf have been reported to correlate with anomalous
climatic and oceanographic conditions (Hoving et al. 2013;
Frawley et al. 2019b).
Responding to changes in squid catch volumes and
migration patterns requires flexible and dynamic interac-
tions between fishers and traders. The squid fishery in Santa
Rosal`
ıa and Guaymas started commercially in the 1980s,
primarily to supply Asian markets (Cruz-Gonz´
alez et al.
2011). Our analysis focuses on small-scale fishers, who
landed the majority of squid after the 1990s (Cruz-Gonz´
alez
et al. 2011). This jig-based fishery allows opportunistic
fishing across Northwest Mexico. For instance, high catch
volumes in alternate locations such as Bah`
ıa Magdalena
and Guerrero Negro on the Pacific coast attract local fish-
ers and traders specialized in other species in addition to
the squid traders and fishers from Santa Rosal`
ıa and Guay-
mas (Schneller et al. 2015; Frawley et al. 2019c). Fishery
Fig. 1 The map illustrates three years with different climatic
conditions of Humboldt squid catches and fishers’ prices registered by
fishery reporting office. The markers’ size represents catch volumes
(tons); colors indicate ranges of fishers’ pricesin Mexican pesos
(MXN) per ton. Santa Rosal`
ıa and Guaymas constitute the core fishing
centers
permits are required to land and commercialize catches
in Mexico, squid included (Basurto et al. 2013). They
are bound to municipalities and often owned by traders
(Basurto et al. 2013). Consequently, fishers without permits
have to sell to traders to access the market and processing
plants.
The squid fishery is a crucial source of income
and regional economic development (SAGARPA 2017).
However, the benefits from squid catches are distributed
in a highly unequal way, which has been identified as a
key challenge in the fishery (Cruz-Gonz´
alez et al. 2011;
Zavala et al. 2005). During peak landings, the squid fishery
was ranked fourth by volume in Mexico and was the
target of numerous development programs (SAGARPA
2017; Moncaleano-Rubio 2015). Prices that fishers receive
are markedly below export price levels, despite limited
processing costs. On average, fishers receive 14% of the
export prices in Santa Rosal`
ıa, 15% in Guaymas, and 23%
in the remaining fishery reporting offices (SI Fig. S2).
The fishers’ prices have stagnated or even decreased in
some areas. In Guaymas, real prices decreased by 54%
between 1995 and 2004 (Cruz-Gonz´
alez et al. 2011). The
vast majority of fishers consider the prices paid for squid
as fixed by traders (82% Guaymas, 94% Santa Rosal`
ıa
(Cruz-Gonz´
alez et al. 2011)). Intermediary traders, who
supply the domestic market, for example, capture the
greatest margin of the final product prices (80.8% (Luna
Raya 2008)). Experts reported in interviews that they had
observed price-setting agreements between squid traders
before the beginning of a fishing season. For instance, “. . .
the plants and traders have a talk together and decide we
will pay 5 MXN, and then everybody knows it will be 5
MXN ...” (SI Table S1). Traders’ bargaining power is high
because they are well-organized amongst each other. The
long-term cooperation between traders in the squid fishery,
which enables them to fix the prices at which they buy fish
below market value, is central in this paper. The cooperation
between (mainly Asian) processors and traders has been
referred to as an oligopoly (Cruz-Gonz´
alez et al. 2011).
Fishers’ prices are affected by their ability to bargain,
development programs from the fishery authorities, and
climate change. Fishers are less able to organize, in
part, due to the ephemeral character of the fishery
and their dependence on fishing permits that traders
provide (Frawley et al. 2019a). Several official programs
address the need for “inclusive development,” for example,
development programs implemented under the Humboldt
Squid Management Plan (Zavala et al. 2005). One primary
development program is about increasing Mexican demand
for squid. It recognizes the oligopolistic market structure as
a problem and seeks to develop a domestic market for squid
products to raise squid fishers’ income (Zavala et al. 2005;
Cruz-Gonz´
alez et al. 2011).
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Climate change triggers changes in fishers’ prices, catch
volumes, and landing locations (Fig. 1,SIFig.S3). During
years with negative SST anomalies and high primary
productivity, high catch volumes and low fishers’ prices
typically prevail. Humboldt Squid is then primarily caught
in Guaymas and Santa Rosal`
ıa (Fig. 1, 2008). In the
transition from negative to positive SST anomalies, catch
volumes decline, and fishers’ prices are often higher in
Santa Rosal`
ıa and Guaymas but the highest in the remaining
fishery reporting offices, where a large proportion of total
catches may occur (Fig. 1, 2012). During years with positive
SST anomalies, fishers’ prices are intermediate to high, but
catch volumes are low, and the fishing fleet largely inactive
(Fig. 2, 2015). Overall, the observed mean of SST anomalies
increased over the investigated period, while catch volumes
subsequently decreased and collapsed by 2015 (SI Fig. S3).
Three models of the Mexican Humboldt
squid fishery
Our analysis aims to investigate small-scale fishery users’
relationships in response to climate change. We focus in
particular on the impacts of climate-driven adaptation of
target species on trade relationships. For this purpose,
we developed a suite of three dynamic models of the
squid fishery. To arrive at generalizable insights, we
combined previous knowledge from other case studies,
theory, and contextualize within the Humboldt squid fishery
(Magliocca et al. 2018). We derived our model assumptions,
illustrated as a causal loop diagram in Fig. 3, from
a combination of literature review, co-authors’ expert
knowledge, and in-depth expert interviews. We identified
experts through publications, analysis of policy narratives,
and recommendations from interviewees, i.e., snowball
sampling (Goodman 1961). The squid fishery experts
represented a diversity of knowledge fields ranging from
commercialization and trade, fishing fleet and practice,
climate change, and Humboldt squid (n=8).We
Fig. 2 The time series of SST anomalies and catch volume. Low levels
of SST are associated with La Ni˜
na and high levels with El Ni˜
no events
followed a semi-structured protocol but allowed for flexible
responses through semi-directed and open-ended questions
(Huntington 1998).
We then synthesized the causal loop diagram into three
systems of difference equations using variables, functional
forms, and relationships from theory and empirical data.
The data sets included socio-economic, oceanographic, cli-
matic, and fishing data (SI Table S3). We separated the data
for conceptualization and parameterization from those for
validation (SI Table S3). The resulting system of difference
equations represents hypothetical dynamics of all variables,
and in particular, their relationships identified in the cau-
sal loop diagram (3). Key variables include observed catch
volume, location, and fishers’ prices. We provide all func-
tional forms, parameter values, and detailed justifications in
the supplementary information (SI S1 and Table S4).
Our baseline model is a bioeconomic model (BEM) of
a type often used in real strategic fisheries management
(Nielsen et al. 2018). We adopted a functional form from pre-
viously published models for each of the bioeconomic var-
iables (Gordon 1954; Burgess et al. 2017;Mansaletal.
2014). From these models, we can provide insights into har-
vesting strategies, the squid population, and market dynam-
ics. Climate change is frequently represented in fishery mana-
gement models (Nielsen et al. 2018; Quaas et al. 2016). Our
environmental driver model (EDM) incorporates climate
change impacts (proxied by SST anomalies) on the squid
population to the BEM. This paper’s novelty is the social-
ecological model (SEM), which includes social relationship
dynamics in response to changes in the fishery, particu-
larly changes in trader cooperation driven by squid response
to climate change. For each of the EDM and SEM vari-
ables, we derived a discrete functional form building on a
mechanistic, empirical, or theoretical understanding of their
underlying processes. For instance, we assumed and imple-
mented an exponential break down of trader cooperation
following competitors’ new entry from insights based on the
theory of cooperation (Casari and Tagliapietra 2018). We
coded the models in Python and performed simulations to
test development programs’ effects on equitable outcomes.
We simulated two development program scenarios, repre-
senting current on-going policies identified using narrative
policy analysis (Roe 1994) and described in “Do develop-
ment programs support equitable benefit distribution in the
Humboldt Squid fishery?” section.
BEM—Bioeconomic model The BEM is based on a discrete
Gordon-Schaefer model with a constant growth rate and
constant carrying capacity (Gordon 1954). We adapted and
calibrated these parameters to match the empirical evidence
of the Humboldt squid fishery. We model effort as a function
of profit, where increasing profitability leads to higher effort
(SI S1 Eq. 2). The market prices are calculated as an
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Fig. 3 Causal loop diagram and conceptual representation of the three
models of the Mexican Humboldt squid fishery in this paper. The
models build on one another, adding complexity in each step. They
represent social, ecological, and market dynamics. The bioeconomic
model (BEM) represents species population, catch, effort, price, and
their interactions (blue). The environmental driver model (EDM) adds
changes in SST anomalies on catches (green). The social-ecological
model (SEM) also includes changes in SST anomalies on the propor-
tion of Pacific squid landings, trader cooperation, and differentiates
between fishers’ prices and market prices (orange)
isoelastic function, implying that prices respond instantly
to changes in traded volumes (SI S1 Eq. 3). We assume
the market to be in competitive equilibrium, which means
that the fishers’ prices are equal to the market prices after
subtraction of processing costs (SI S1 Eq. 4). We calculate
income for squid fishers and traders only based on the
squid fishery’s income, while we acknowledge income from
different sources (Frawley et al. 2019a).
EDM—Environmental driver model The EDM builds on
the BEM and incorporates the effect of changes in SST
anomalies on catches. We use SST anomalies with a
trend to represent climate change and a periodic fit to
represent climatic variability associated with El Ni˜
no and
La Ni˜
na cycles (SI S1 Eq. 4,Fig.S4). Climate change
affects mesoscale water circulation, upwelling, and primary
productivity in the Gulf of California (Lluch-Cota et al.
2010; Robinson et al. 2016). SST anomalies can indicate
these impacts of climate change. We model the effect of SST
anomalies on squid catches as changes in catchability. We
use two alternative setups to simulate the model. Either we
assume that catchability decreases linearly with decreasing
size of individual squid (SI S1 Eq. 6)orincreasingSST
anomalies (SI S1 Eq. 5). These variables are related, but
the squid’s size is a better predictor of catch (SI Fig. S3).
First, we assume changes in catchability due to changes
in squid size measured in mantle length. Temperate squid
phenotypes are long-lived (12–24 months), have large
mantle length (60–120 cm), make extended horizontal
migrations, and are caught in large volumes. A smaller
size (20–40 cm), short-lived (6 months) tropical phenotype
has been associated with the limited primary productivity
and warm water intrusion characteristic of El-Ni˜
no events
(Robinson et al. 2016; Hoving et al. 2013; Frawley et al.
2019b). Jig-based fishing methods are less efficient to target
this tropical phenotype. Second, based on measurements
from acoustic data, we assume changes in catchability
rather than in biomass; specifically, acoustic measurements
indicate that squid biomass did not decrease proportionally
to catch volumes in 2011 (Hoving et al. 2013) or 2013
(unpublished data with K. Benoit-Bird, MBARI) suggesting
changes in catchability rather than squid abundance. At
present, changes in catch volumes are explained by the
described phenotypic changes (Hoving et al. 2013). Further
discussion of these interactions is provided in SI S1 and S3.
SEM—Social-ecological model The SEM includes the
effects of changes in SST anomalies on the proportion
of Pacific squid landings, trader cooperation, and fishers’
price, in addition to the variables and interactions in the
BEM and EDM. The proportion of Pacific squid landings
indicates whether most squid is caught in alternate fishing
grounds or the Gulf of California’s core fishing grounds.
Based on empirical observations, we assume that the pro-
portion of Pacific landed squid has an inverse exponential
relationship to SST anomalies (SI S1 Eq. 7). Temperate
squid often migrates seasonally across the Guaymas basin
(Hoving et al. 2013). However, migration associated with
landings in locations outside the Guaymas basin and the
Gulf of California is less frequent and less understood.
These landings have correlated with climatic variation and
low primary productivity (Robinson et al. 2016). Still, it
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remains unclear whether these landed squid are individu-
als from the population found in the Gulf of California or
elsewhere.
Fishers’ prices increase if trader cooperation decreases.
We assume fishers’ prices to be proportional to market
prices and the level of trader cooperation while limited by
an empirically estimated maximum and minimum price (SI
S1 Eq. 10). Data from Santa Rosal`
ıa and Guaymas suggest
that high trader cooperation is associated with low fishers’
prices (Cruz-Gonz´
alez et al. 2011; Mascarenas 2017).
Empirical studies have shown that fishers’ prices increase
when squid aggregations are found and caught outside of
Santa Rosal`
ıa and Guaymas (SI Fig. S2). Hence, we assume
trader cooperation to be inversely exponentially related
to the proportion of Pacific landed squid. Traders enter
regions where two mechanisms reduce their bargaining
power: first, not all traders can buy and sell catches in
alternate locations because of the geographical limitation of
fishing permits (Basurto et al. 2013). Second, squid traders
must negotiate the fishers’ prices with the local traders
and fishing cooperatives because traders in the alternate
locations begin to buy and sell squid (Schneller et al. 2015).
In 2010, for example, “... the first wave of new fishers to
arrive in Puerto San Carlos had to sell to the permanent
canneries, and the sardine canneries even sent their own
boats to fish squid with hand lines, the big sardine boats
could fit like 20 people.” (SI Table S1). Our function thus
represents a deterioration in cooperation through an increase
in group size (Casari and Tagliapietra 2018). The group size
increases through the inflow of new traders.
Results
How well do the models reproduce the catch
and price dynamics of the Mexican Humboldt
squid fishery?
The SEM yields the best quantitative fit to independent
catch and fishers’ prices data from 2001 to 2016 in the
Humboldt squid fishery (Fig. 4a and b). To test the models’
performance against observed data, we perform Monte
Carlo simulations of the BEM, EDM, and SEM models
within the possible parameter ranges. We find that SEM
performs best in describing the variance observed in catches
and prices (fishers’ prices SEM r2= 75.6%; catch SEM
r2= 60.4%), although these numbers differ marginally
with those predicted by the EDM (fishers’ prices EDM
r2= 74.7%; catch EDM r2= 59.3%). In contrast, the
BEM predicts both variables poorly in the Monte Carlo
simulations (fishers’ prices BEM r2= 15.2%; catch BEM r2
= 3.7%). The SEM and EDM capture qualitative dynamics
of catches equally well. Both models also capture the
general downward trend and several years of peak catches.
Indeed, catch simulations are essentially the same in the
second half of the simulated period. Despite large observed
price differences, catch values are comparatively close
with or without integrating trader cooperation. The form
of the effort function causes this proximity. We used an
optimization approach to test whether results were sensitive
to the effort function (SI S2). Under the assumption of
optimizing effort to match the data, the BEM could closely
represent catch but not price dynamics (SI S2).
The SEM provides better qualitative predictions of
the price level and its increasing trend than the EDM,
which overestimates the average prices paid to fishers
approximately fourfold. This overestimation results from
the competitive equilibrium assumption of this model.
Unless the market prices are low, the fishers’ prices do
not reach the observed level. This result suggests that
the Mexican squid fishery market is not in a competitive
equilibrium. Also, the increasing price trend starting in 2010
in the EDM is significantly lagged: the predicted prices only
begin to increase during the final 3 years. Price flexibility
is the only model parameter affecting prices relative to
catch volumes. Thus, changes in catch volumes have to
be substantial before the market prices, and consequently,
fishers’ prices increase. Lag time for the SEM prediction
of increasing fishers’ prices is less prevalent because price
flexibility and trader cooperation affect price changes.
We conclude that trader cooperation and price flexibility
jointly affect fishers’ prices and thus drive price increases.
However, the SEM predicts fishers’ prices less well after
2014. First, the price drop in 2014 responds to the decline in
the proportion of Pacific squid landings as measured in that
year, not reflected in the price data. After 2015, predicted
prices of the EDM and SEM increase steeply. The validity
of price predictions for all models is limited when catch
volumes are close to zero. We further investigate changes in
SST anomaly and fishery development programs using the
SEM due to its higher prediction accuracy.
How does climate change affect fishers’ income and
price inequality?
Climate change reduces inequality in prices between fishers
and traders (Fig. 4c and SI Table S2). First, when mean SST
anomalies increase, the prices fishers and traders receive
converge. Second, a higher amplitude in SST anomalies
leads to earlier convergence between the prices. The
main reason for this convergence is changing SST
anomalies that affect the proportion of Pacific-landed squid.
Higher proportions of Pacific squid landings reduce trader
cooperation, which, in turn, increases the prices that fishers
receive. If low price inequality is a target, increases in mean
SST anomalies are thus desirable.
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Fig. 4 Predicted and measured catch volumes aand fishers’ prices bfor 2001–2016. Predictions of the SEM (yellow), EDM (green), BEM (blue),
and measured observations (red). Parameter values and functions are outlined in SI S1, SI Table S2 and S4. The simulations use time series inputs
of mantle length (as a proxy for SST anomalies) and proportion of Pacific squid landings (grey, right hand axis). The data represents observations
aggregated per year. The models were calibrated via a Monte Carlo process over the range of possible parameter values (SI Table S2). Thick
curves represent the mean, and the shaded bands represent the 95% confidence intervals. ceThe effect of trend and amplitude of SST anomalies
on the mean price gap and fishers’ income for SEM simulations. cThe mean price gap calculated as the ratio between fishers’ prices and traders’
prices (i.e., market prices). The areas in red represent large price differences. dMean fishers’ income. Areas in blue denote high fishers’ income.
e,fFishers and traders’ income 1990–2025 in two alternate fishery development programs with investments starting in 2005: demand develop-
ment (E) and ) cooperation development (E). Simulations (e,f) use the simulated proportion of Pacific squid landings and SST anomalies (grey,
right hand axis). eA program to increase domestic demand with the SEM (yellow) and BEM (blue). fA limitation of trader cooperation (SI S1
Eq. 8) using SEM (yellow)
Page 7 of 12 35Reg Environ Change (2021) 21: 35
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Increased climate change and variability have contrary
effects on mean fishers’ income (Fig. 4d). Elevated SST
anomalies are associated with low catch volumes (SI
Fig. S5) as well as high fishers’ prices. Translating this
result into the squid fishery’s current situation, a long-
term trend of increasing SST anomalies could lead to a
permanent fishing collapse. There are several mechanisms
that, if implemented into the model, may reverse the
collapse in catches. We have not included such mechanisms
(i.e., the development of alternative fishing gears or a
shift in the Pacific Decadal Oscillation) as their influence
remains uncertain. Judging from the empirically supported
SEM, a critical range for fishers’ income in the squid fishery
occurs at low mean SST anomalies and high amplitude. In
this climatic range, squid landings from the Pacific trigger
convergence in fishers’ prices and market prices while catch
volumes are still substantial and generate high income for
fishers.
Do development programs support equitable
benefit distribution in the Humboldt Squid fishery?
Inaccurate models can provide misleading guidance for
development programs. In the squid fishery, increasing
domestic demand may exacerbate already severe income
inequalities (Fig. 4e). The BEM and SEM illustrate opposite
effects in fishers’ and traders’ income following the
Humboldt Squid Management Plan’s domestic demand
development programs (Zavala et al. 2005; Luna Raya
2008). The SEM predicts that traders’ income increases
with demand, while the fishers’ income remains low, only
increasing during years when a high proportion of Pacific
squid is landed. Traders directly receive the market price,
which increases with demand, but high levels of trader
cooperation result in low fishers’ prices. Therefore, fishers
are unlikely to benefit from the increase in prices that a
higher demand would generate because of the high levels
of trader cooperation. In contrast, BEM is unable to predict
the changes in traders’ income resulting from increasing
demand. In the BEM, the results and thus the guidance
for the development program are directly opposite from
those in the SEM. Hence, the importance of accurate model
conceptualizations to provide correct guidance for fishery
development programs.
Furthermore, our SEM simulations show that limiting
trader cooperation is more effective than increasing demand
to reduce income inequality and increase fishers’ income
(Fig. 4f). Indeed, the margin traders can take from the
market prices is lower, so fishers receive higher prices.
Providing fishery permits to other traders and fishers or
increasing fishers’ ability to self-organize could be ways to
increase fishers’ bargaining power and thereby realize such
a program (Zavala et al. 2005). Additionally, if the reduction
in squid landings driven by climate occurs earlier than
predicted here (further discussion SI S3), limiting trader
cooperation in combination with increasing demand might
reduce short-term losses in fishers’ income from the lower
catch volumes.
Discussion and conclusions
Social relationship dynamics can stabilize livelihood out-
comes in fisheries—ignoring these dynamics in deci-
sion support for management could, therefore, exacerbate
inequalities in the Mexican Humboldt squid fishery. Under-
standing the future of fishery livelihoods impacted by cli-
mate change requires knowing their effect on fishery users’
individual behavior and their cooperative and competitive
relationships. Previous work had found that climate change
drives fishers’ income (Cinner et al. 2012). Additionally, our
results highlight that the effects of climate change on coop-
erative trade relationships are essential to determine changes
in fishers’ income. Using empirical data and a dynamic
SEM, we show that climate change and variability have bal-
ancing effects in the Mexican Humboldt squid fishery. If the
mean SST anomalies increase, fishers benefit from higher
prices due to reduced trader cooperation while catch vol-
umes significantly decrease. Conclusions about the benefits
and losses for fishers and traders due to climate change are
contingent on the complex trade-off between the effect of
climate change at the individual and collective levels.
Development programs that omit relationships between
fishery users can have negative consequences. Addressing
the impacts of such relationships can help develop
carefully crafted policies. Our SEM simulations show that
increasing demand would not increase fishers’ income in
the Humboldt squid fishery. This result contrasts with
what the BEM suggested, based exclusively on supply and
demand mechanisms, which determine prices. However,
the BEM fails to explain price formation in the Humboldt
squid fishery because it ignores traders’ price setting
behavior. In other social-ecological systems, models that
omit social relationships have misguided managers in the
past (Degnbol and McCay 2007). Although many of squid
fishery characteristics are idiosyncratic, cooperative and
competitive relationships between and amongst fishers
and traders and their influence on fishers’ livelihoods are
pervasive in small-scale fisheries (Ferrol-Schulte et al.
2014; Drury O’Neill et al. 2018). These challenge the
efficiency and efficacy of development programs that omit
such relationships in their planning.
Recognizing the effects of relationship dynamics on
fishery livelihoods can support carefully crafted fishery
development programs. Our SEM simulations of the Hum-
boldt squid fishery illustrate that increasing demand without
35 Page 8 of 12 Reg Environ Change (2021) 21: 35
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
mitigating the market power impacts of cooperative trade
relationships can exacerbate existing income inequalities.
Increasing demand does not achieve inclusive development
by increasing economic benefits for fishers required by the
fisheries management plan (Humboldt Squid Management
Plan (Zavala et al. 2005)). In contrast, an alternative, simu-
lated program to reduce trader cooperation reduces traders’
bargaining power and has a direct and positive effect on
fishers’ income in line with inclusive development. Provid-
ing licenses to new traders and fishers to sell and buy squid
could reduce the problem. In reality, the powerful traders
(and processors) who benefit from the current market con-
figuration are likely to pose major obstacles to providing
new licenses (Luna Raya 2008). Therefore, we expect that
appropriate timing for intervention is important (Walker
et al. 2020). During an El Ni˜
no period, we would expect this
intervention to be more feasible because there is already a
group of new entrant traders and fishers who also buy and
sell squid and reduce current traders power in the fishery.
However, if this turns out to be politically unfeasible, an
alternative could provide special trading rights to the current
traders, contingent on carefully negotiated minimum price
levels for fishers. An equal benefit distribution in the fish-
ery would improve fishers’ livelihoods and affect fishers’
interaction with the target species. These must be considered
beforehand.
We call for explicit inclusion of cooperative relationship
dynamics in management models of small-scale fisheries. It
is impossible to predict income and income inequality in the
squid fishery without explicitly accounting for relationships
between fishery users. Our simulations show that accurately
predicting fishers’ prices relies on integrating dynamic trade
relationships driven by adaptations of the squid population
to climate change. The influence of trade relationships
on prices and price transmission has been documented
frequently in small-scale fisheries (Drury O’Neill et al.
2018; Wamukota et al. 2014; Elsler 2020). While many
different mechanisms could explain this influence, our
general insight is that social relationship dynamics are
necessary to predict price dynamics. In our case study,
catch levels were similarly well explained by the simpler
EDM. However, we expect that predicting catch and
population dynamics will depend on the influence of social
relationships on fishing practices in many other small-scale
fisheries (Ferrol-Schulte et al. 2014).
Conceptualizing and implementing cooperative and com-
petitive relationships in small-scale fisheries’ predictive
models provide a social-ecological management perspec-
tive, but this does not come without challenge. We foresee
two main challenges for modeling other social-ecological
systems. First, in our case, the proportion of landed
squid was the main driver of trade relationships. Instead,
these relationships depend mostly on internal mechanisms
between fishery users, such as individual and recipro-
cal motivations. It becomes necessary to understand these
mechanisms either through empirical studies or by infer-
ring from theory. To this end, game theory and industrial
organization have identified candidate mechanisms such as
utility-based decisions and group size dynamics (Axelrod
et al. 1995; Casari and Tagliapietra 2018). The second chal-
lenge is methodological. The iterative and interdisciplinary
approach needed to conceptualize genuinely integrated
social-ecological models is time consuming. It requires the
active participation of experts of parts of the system and
navigating their willingness to collaborate.
Social-ecological modeling and its interdisciplinary
approaches can be a useful vehicle to bridge the gap
between current bioeconomic models and the reality in
which social relationship dynamics shape fishery outcomes.
Social-ecological models, like the one presented here,
blend bioeconomic approaches with ecological models and
knowledge from social science (Schl¨uter et al. 2012). The
development and adoption of these tools are rapidly growing
(Schl¨uter et al. 2012). These models enable us to challenge
the predictive capacity of models that omit the role of
social relationship dynamics in response to climate change.
Prediction accuracy is one important element for models
to be useful in guiding for formal fisheries management.
Our analysis provides the first, critical step towards the
inclusion of social relationship dynamics by advancing
social-ecological systems models in a formal fisheries
management context. Future work could further develop
this approach by incorporating relevant data on fishery
users’ relationships and systematically adapt the analysis to
various fishery contexts to support development programs
sensitive to informal cooperative and competitive dynamics.
Supplementary information The online version contains supplemen-
tary material available at (10.1007/s10113-021-01747-5)
Acknowledgements First and foremost, we are grateful to our
interviewees and the fishery participants for their time and for sharing
their knowledge about the squid fishery. We extend our thanks in the
field to the MAREA team that was instrumental in introducing L.G.E.
to Mexican small-scale fisheries. We thank dataMares and Universidad
Nacional Aut´
onoma de M´
exico (UNAM) for sharing fishery data. We
also thank our colleagues for technical help, comments, and discussion
of the manuscript: F. Diekert, J. Norberg, R. Blasiak, J. Gars, I. Fetzer,
K. Arroyo-Ramirez, U. Markaida, and M. Oostdijk.
Funding Open access funding provided by Stockholm University.
The research leading to these results received funding from the
European Research Council (ERC) under the European Union’s
Horizon 2020 research and innovation program (L.G.E. and M.S.
grant agreement No. 682472—MUSES). G.L.B. was supported by the
Simons Collaboration on Computational Biogeochemical Modeling
of Marine Ecosystems CBIOMES (Grant ID: 549931) and the
Simons Foundational Postdoctoral Fellowship in Marine Microbial
Ecology. W.F.G. was supported by National Science Foundation grants
(IOS-1557754, OCE-1338973 RAPID, IOS-142093 EAGER, OCE
Page 9 of 12 35Reg Environ Change (2021) 21: 35
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
0850839, and OCE 0526640), National Geographic Society (7578-
04 and 9366-13), Lindblad Expeditions (LXII-15), and the David
and Lucile Packard Foundation (2005-2800 and 32708). T.C.D. was
supported by the ERC under the European Union’s Horizon 2020
research and innovation program (grant agreement 743080—ERA).
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
if changes were made. The images or other third party material in
this article are included in the article’s Creative Commons licence,
unless indicated otherwise in a credit line to the material. If material
is not included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this licence, visit http://
creativecommonshorg/licenses/by/4.0/.
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... During anomalously warm years, squid catch volume declines but value increases as the resource becomes scarcer (Elsler et al., 2021). For most fishers, this means catching proportionally less squid and diversifying to other species. ...
... Importantly, social processes can also influence how fishers respond to resource scarcity. For instance, in areas of high cooperation between fish buyers, fishers may receive less value for their squid catch as buyers collude to fix prices (Elsler et al., 2021). Depending on existing cooperative or competitive relationships with buyers, fishers may respond differently to squid shortages by ei-ther focusing on scarce but valuable squid or diversifying effort into other fisheries. ...
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