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Despite its relevance, the economic contribution of small-scale fisheries to poverty alleviation is still poorly understood. This study investigates why some fishers perform economically better in fisheries than others under similar conditions and whether these variations in performance were due to individual adaptive strategies related to fishing technology and effort. A pairwise comparison between fishers’ income from the Brazilian equatorial region in 1994 and 2014 was performed while modeling individual changes related to the fishing activity (Generalized Linear Model, GLM) and the factors that would explain why fishers became richer or poorer over time (Proportional odds model). Fisher’s geographical region, the use of motorized boats and the adoption of hookah compressors explained income in 1994, whereas having larger boats and fishing with hook and line explained it in 2014. Fishers were slightly more likely to gain income if they changed their type of boat. Some fishers are trapped in poverty, and the changes they made were either not enough to leave this condition or made it worse. Escaping poverty traps in fisheries may require efforts beyond those available to the individuals, especially as stocks become increasingly overfished. Graphical Abstract
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Rev Fish Biol Fisheries
Adaptive factors andstrategies insmall‑scale fisheries
LudmilaM.A.Damasio ·
MariaGraziaPennino· SebastiánVillasante·
AdrianaRosaCarvalho· PriscilaF.M.Lopes
Received: 14 February 2022 / Accepted: 21 December 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
Abstract Despite its relevance, the economic con-
tribution of small-scale fisheries to poverty allevia-
tion is still poorly understood. This study investigates
why some fishers perform economically better in
fisheries than others under similar conditions and
whether these variations in performance were due
to individual adaptive strategies related to fish-
ing technology and effort. A pairwise comparison
between fishers’ income from the Brazilian equato-
rial region in 1994 and 2014 was performed while
modeling individual changes related to the fishing
activity (Generalized Linear Model, GLM) and the
factors that would explain why fishers became richer
or poorer over time (Proportional odds model). Fish-
er’s geographical region, the use of motorized boats
and the adoption of hookah compressors explained
income in 1994, whereas having larger boats and
fishing with hook and line explained it in 2014. Fish-
ers were slightly more likely to gain income if they
changed their type of boat. Some fishers are trapped
in poverty, and the changes they made were either
not enough to leave this condition or made it worse.
Escaping poverty traps in fisheries may require efforts
beyond those available to the individuals, especially
as stocks become increasingly overfished.
Supplementary Information The online version
contains supplementary material available at https:// doi.
org/ 10. 1007/ s11160- 022- 09750-7.
Graduate Program inEcology, Federal University ofRio
Grande doNorte, Campus Universitário Lagoa Nova,
Natal, RN59078-900, Brazil
L.M.A.Damasio· M.G.Pennino· A.R.Carvalho·
Fishing Ecology, Management andEconomics Group
(FEME), Department ofEcology, Universidade Federal
doRio Grande doNorte – UFRN, Natal, RN, Brazil
P. F. M. Lopes
BW Institute, Rua Sueli Brasil Flores Number 88,
Araruama,RiodeJaneiro78970-000, Brazil
Instituto Español de Oceanografía (IEO, CSIC), Centro
Oceanográfico de Vigo, Subida a Radio Faro, 50-52,
36390Vigo,Pontevedra, Spain
Statistical Modeling Ecology Group (SMEG), Valencia,
Department ofApplied Economics, Faculty ofBusiness
Administration andManagement, University ofSantiago
de Compostela, 15782SantiagodeCompostela, ACoruña,
EqualSea Lab-CRETUS, SantiagodeCompostela,
ACoruña, Spain
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Graphical Abstract
Keywords Brazil· Overcapacity· Poverty-traps·
Socioeconomics· Small-scale fisheries
Fish are one of the world’s single-most traded food
commodities (Asche and Smith 2010). In 2016,
60 million tons of fish and fisheries products were
exported throughout the world, valued at USD 143
billion. In addition to their global commercial impor-
tance, fisheries employ 40.3 million people world-
wide, while fish are one of the main proteins con-
sumed, especially among the poor.
Small-scale fisheries (SSF), specifically, are
responsible for generating 90% of all jobs in the
global fishing sector, and for harvesting almost half of
global fish harvests, generally using less technologi-
cally intensive methods (Smith and Basurto 2019).
The fact that this type of fishery occurs mainly in
developing countries, where income generating activ-
ities may be lower, further increases its vulnerability
to different drivers (e.g., overfishing, climate change
and habitat loss) (Béné etal. 2005; FAO 2017).
The importance of SSF does not necessarily ensure
their ecological sustainability or their proper fisheries
management (Steneck and Pauly 2019). Many coun-
tries, especially in the global south, lack basic fish-
ing information such as harvesting tracking data (e.g.,
quantities, species, ex-vessel prices, discards, and
fishing grounds used) and vessel data (e.g., fleet size,
capacity of fishing boats and operating gears), needed
to implement effective policies (FAO 2018). The lack
of SSF data spans from very basic information on fish-
ers, such as the number of artisanal fishers in a given
place, to more detailed economic information, such
as fisher incomes and how they vary over time (Sala
etal. 2018). Still, it is well known that SSF is often
associated with processes of poverty, marginalization,
vulnerability and exclusion (Garcia etal. 2018). The
explanations for this correlation are multifold.
Generally, small-scale fishing is on the fringes
between the formal and informal activity. Accessing
certain public policies, especially those related to labor
rights, tend to encounter barriers because legal regula-
tions and normative instructions often trigger bureau-
cratic difficulties in accessing such policies (Garcia
etal. 2018). Another explanation is the poor biological
conditions of the exploited natural resources, caused
by the open-access nature of many fisheries and, par-
ticularly, by the lack of proper management of SSF
(Béné 2003; Sala et al. 2018). In addition, individu-
als who support themselves with livelihoods based on
the extraction of natural resources have to deal with a
highly variable income caused both by the economy,
such as changes in demand or price, and by natural
fluctuations in resources (Sethi etal. 2012).
There is no doubt that understanding the factors
that influence fisher incomes, which have economic
consequences on households, communities, and
countries, is critical to both better managing fisher-
ies and informing effective policy aimed at eradicat-
ing hunger and poverty towards achieving UN SDGs.
Therefore, the objective of this study is to analyze the
adaptive strategies that fishers (can or cannot afford
to) adopt that could help explain why some fish-
ers get caught in poverty traps while others see their
economic condition improve over time. A poverty
trap represents a situation where people are unable
to mobilize the resources needed to overcome shocks
or chronic low-income situations and, consequently,
remain in poverty despite their best efforts (Cinner
etal. 2009; Barrett etal. 2011).
A large Brazilian coastal region, marked by high
levels of poverty and a high dependency on the
oceans, was chosen as the study area, in which fisher-
ies were used to understand changes in income lev-
els over time. Specifically, it was evaluated whether
fishers that changed their target species or switched to
species of different prices, invested in larger or more
powerful boats or changed their fishing effort made
them more or less poor (i.e., changed their fishing
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income, used here as a proxy of economic perfor-
mance). It was assumed that changing was a neces-
sary requirement to improve economic conditions to
deal with failing resources, but that not all changes
would have the same effect. For example, increasing
boat size could eventually worsen poverty as their
expenses (e.g., fuel) are higher (Damasio etal. 2016).
Study area
The northeast is one of the poorest regions of Brazil,
with almost half of its population living in poverty
(NIS 2020). The eight coastal municipalities cho-
sen for data collection (Caiçara do Norte, Galinhos,
Guamaré, Macau, Porto do Mangue, Caucaia, São
Gonçalo do Amarante, and Paracuru) are located in
the states of Rio Grande do Norte and Ceará (Fig.1).
These places were specifically chosen because they
represent important landing ports for artisanal fisher-
ies (MMA 2008).
Overall, on the Brazilian northeastern coast, arti-
sanal marine extractive fishing predominates, whose
production represents 48% of the total fish in the
region (IBAMA 2007). Artisanal fishing is based on
the family unit and relies mainly on the use of small
vessels sometimes not owned by the fisher. When the
fisher does not own the boat, part of the production is
used to pay the boat owner’s rent. In these cases, the
working relationship between the boat owner and the
crew is informal. In general, the largest percentage of
the fish revenue is destined for the boat owner, who
bears the fishing expenses (engine oil and ice, for
example) and the maintenance costs.
The fishing sector in this Brazilian region has
endured without any significant or major technologi-
cal advance, perhaps with the exception of the use
of GPSs, which is now commonly used. A large part
of the fishing fleet is still formed by wooden vessels
that provide unsafe working conditions. Fishing infra-
structure, such as fish processing units and ice facto-
ries, is either in poor operating conditions or does not
exist (FAO 2008; MMA 2008).
Fig. 1 Study area highlighting the eight municipalities selected for this study
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The database used in this study came from a partner-
ship between the authors’ research group and Petro-
bras (the Brazilian state-owned oil company). The
former developed a protocol for the latter to collect
data on fisheries in areas potentially under the impact
of oil platforms (i.e., more likely to be affected by a
spill or even by socioeconomic changes driven by the
presence of the platform, as the attraction of outside
workers). The data were obtained using semi-struc-
tured interviews applied between December 2014
and December 2015 and aimed to obtain past (1994,
assumed to be 20years ago) and present (at the time
of data collection) information. These years, specifi-
cally, are not marked by any environmental disaster,
economic crises or strong El Niño event, which could
have biased the results.
A period of 20years was established as a reason-
able recall period because it was assumed to be suf-
ficient to detect major environmental and social
changes without requiring people to assess very old
memories (as in Tesfamichael et al. 2014; Dama-
sio et al. 2015), which are more subjected to bias
and memory losses (Diamond et al. 2020). To aid
the recall process, fishers’ memories for 1994 were
elicited by an important event in the Brazilian cul-
ture: winning the world cup. When asked about their
catches, the question would end with a reference to
the year when Brazil beat Italy in the final game.
Using eliciting landmark events is a technique shown
to work in this type of interview (Matlin 2009).
Following the Brazilian research code of eth-
ics (Federal Resolution No 466/2012), all fishers
were informed about the research aims, benefits,
and possible risks. Permission was granted by ver-
bal consent before the interview began. Data sam-
pling was approved by the Federal University of
Rio Grande do Norte Ethics Committee (CAAE
68,531,917.6.0000.5537). A total of 394 fishers were
interviewed. They were on average 49.3 years old,
had very low levels of schooling (2.8years on aver-
age, i.e., incomplete elementary school) and had lived
in the same village for most of their lives (average of
The interviews collected: (1) socioeconomic data,
such as age, education, fishing experience, and num-
ber of economic activities carried out, and (2) fishing
data, such as species caught, quantity of each species
caught, average fishing time per trip and name of fish-
ing grounds, for both 1994 and 2014/2015, in addi-
tion to more general technological information on
fishing, such as types and sizes of fishing boats, type
of fishing gear and the amount of fuel used on each
fishing trip (for the detailed questions, see Supple-
mentary Information).
Additionally, the Sea Around Us Database (seaar- was used to extract the ex-vessel price
per species. The latest prices available (2010) in the
database were used for 2014, and it was assumed
that no significant changes (other than inflation)
occurred during the period 2010–2014. The average
fisher income for each year (1994 and 2014) was cal-
culated by using the ex-vessel price per species and
the average amount of fish per target species that fish-
ers claimed to have caught on an average fishing trip,
multiplied by the number of fishing trips per month,
on average, in each period. To compare fishing
income with the country’s minimum wage in those
same years, the minimum wage for both years (1994
and 2014) was converted to USD, using the annual
average conversion rate of each year.
Fishing incomes were also compared with the
value of a Brazilian basket of goods. Given that this
value is adjusted according to inflation, it can more
accurately show the variation in fishing incomes dur-
ing the period analyzed. Minimum wage, on the other
hand, can be either adjusted below or above inflation.
The species were classified into three catego-
ries (first, second and third) according to the system
established by fishers and middlemen, which divides
the categories by price (established based on market
demand and desirability of a fish). Fish in the 1st cat-
egory are the most expensive, followed by fish in the
2nd category, and less valuable fish in the 3rd cate-
gory. Given that the fish price database obtained from
the Sea Around Us is national and not local, the fish
species cited by fishers were sorted from the high-
est to the lowest prices and then divided into three
quartiles in order to deal with the lack of local price
details. Fish in the first quartile of the national data-
base were assumed to also be in the first quartile in
the studied region. The first quartile included the 25%
most expensive species and was, therefore, classified
as 1st category. The third quartile included the 25%
cheapest species and was classified as 3rd category
species. The second quartile and the third quartile
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were pulled together to accommodate the remaining
species, which represented the 2ndcategory fish.
Statistical analyses
The analytical strategy adopted involved four steps:
(i) verify if the poorest fishers in 1994 remain the
poorest in 2014, i.e., whether they were in a poverty
trap (Spearman’s rank correlation); (ii) investigate
which variables (i.e., state, residence time, education,
fishing experience, number of economic activities,
type and size of boat, gear, fishing hours and category
of fish caught) influence income in each year (Gener-
alized Linear Model); (iii) test whether there is a cor-
relation between income in 1994 and the number of
changes a fisher will eventually perform until 2014,
as eventual negative correlation could suggest that
the poorest fishers simply could not afford to change
(Pearson’s correlation); and (iv) analyze whether the
number of changes a fisher performs in his fishing
grounds, effort and gear or target species influences
the variation of income he has between 1994 and
2014 (Proportional Odds Model).
Analyzing whetherfishers are inapoverty trap
To investigate whether fishers with the lowest income
in 1994 continued to have the lowest income in 2014,
all fishers were ranked by income, from the lowest
to the highest income, for both years. The two ranks
were then compared using the Spearman’s rank cor-
relation, considering the order that fishers appeared to
measure the strength of association between the two
ordinal variables.
The Spearman’s rank-order correlation is the
nonparametric version of the Pearson product-
moment correlation (see below). Spearman’s cor-
relation coefficient (ρ) measures the strength and
direction of the association between two ranked
variables (here, income in 1994 × income in 2014).
This measure was deemed appropriate given that
the idea was simply to compare whether fishers
maintained their income position over time, in rela-
tion to other fishers. For example, a strong correla-
tion would suggest low economic mobility (up or
down) among fishers.
Analyzing thevariables thataffect fisher incomes
each year
A Generalized Linear Model (GLM) was performed
for each year (two models) to investigate the influ-
ence of 11 independent variables on fisher incomes,
namely: State (categorical—Rio Grande do Norte
or Ceará), time of residence in the village (number
of years), formal education (number of years), age
(years), fishing experience (number of years), eco-
nomic activities (number of different activities),
vessel type (categorical—sail boat, motor canoe and
motorboat) and vessel size (meters), gear (categori-
cal—compressor, gillnet, line and trap), average
time spent on each fishing trip (hours) and category
of fish caught (categorical—1, 2 or 3, where 1 rep-
resents the most expensive fish and 3 the cheapest
ones). These variables represent the socioeconomic
characteristics of fishers as well as attributes of the
fishing material used by them. To achieve the nor-
mality assumption for the residuals, the response
variables (fisher incomes in each year) were loga-
rithmic transformed and a Gaussian distribution
with an identity link was implemented in the GLMs.
To determine which explanatory variables would
fit into the model, all variables were first checked
for correlation (see more details in Figures SI1 and
SI2 in the Supplementary Information) and then
evaluated for collinearity using the Variance Infla-
tion Factor (VIF). Only variables with a VIF < 3
were included in the model (Supplementary Infor-
mation Figures SI3 and SI4). To establish the best
model, two different measures were computed: the
Akaike Information Criterion (AIC) and the devi-
ance explained (D2%). The best model was the one
with the lowest AIC (Akaike 1974) and the high-
est D2%. Only the final model is presented in the
results. The software R (Team 2018) was used here,
together with the ‘car’ (Fox etal. 2019), ‘broom’
(Robinson and Hayes 2019), ‘Hmisc’ (Harrell Jr
2019), ‘jtools’ (Long 2019), ggplot2 (Wickham
et al. 2019), ‘ggstance’ (Henry et al. 2019), and
‘corrplot’ (Wei and Simko 2017) packages.
Analyzing whetheradaptive strategies are associated
withincome in1994
To investigate whether the fishers who had the low-
est income in 1994 remained with the lowest income
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in 2014 because, for example, their original financial
condition prevented them from making changes, a
Pearson’s correlation was performed. The intention of
this correlation was to verify whether income in 1994
was associated to the number of fisheries-related
changes made by fishers in the following years.
Investigating whetherfishing‑related changes
contributed toincrease fisher incomes
To analyze whether the number of changes made by
the fishers influenced income variation, the percent-
age of income variation between 1994 and 2014
was first calculated for each fisher. As some fishers
showed a negative variation in income (the income
in 2014 was lower than in 1994), a regular GLM was
not fit, because the interpretation of the estimated
coefficients of each explanatory variable would be
unclear. Thus, here it was opted for the categorization
of changes in income into four classes (see below),
which then allowed the use of a proportional odds
model, run with the package ‘mass’ in R Program,
using the function ‘polr’: where P gives the probabil-
ity of a level (j; here four levels) of income (Y) in one
of the two years;
is the intercept and
is the slope
coefficient for each predictor x (EquationS1).
In this model, the dependent variable ‘variation
in income’ was treated as a response scale (levels): 1
(n = 134)—income in 1994 was at least 50.1% higher
than in 2014 (highest losses); 2 (n = 111)—income
in 1994 was up to 50% higher than income in 2014
(intermediate losses); 3 (n = 56)—income in 2014
was up to 50% higher than income in 1994 (inter-
mediate gains); and 4 (n = 88)—income in 2014 was
at least 50.1% higher than income in 1994 (highest
gains). The classification of fishers into four groups,
instead of simply into losers and winners, intended to
account for the fact that those that lose the most may
be at a higher risk of being trapped in poverty.
The independent variables initially used in the
full model belonged to two different types. The first
one controlled for socioeconomic aspects: State (cat-
egorical—Rio Grande do Norte or Ceará), time of
residence in the village (number of years), formal
education (number of years), age (years), and fish-
ing experience (number of years). The second group
of variables accounted for any changes that fishers
made to how they fish. In this case, the number of
changes was summed (one point per change). These
changes included: type (50.2% of the fishers changed
their boat type) and size of boat (50.8%), category of
fish caught (53.3%), species caught (29.7%, here the
most caught species cited by the fishers in both years
was considered), time spent fishing (32.2%), and
gear used (14.47%). These were all binary variables
(0 = no change, 1 = changed).
The direction of the change (increase or decrease)
was not considered, as there was not enough varia-
tion in the data (most changes were in a single direc-
tion, such as increasing the size of the boat). Finally,
two other variables that were used interchangeably
in the model attempted to capture the total number
of changes in two different ways: number of changes
(sum of all the changes made up to a maximum of
5) and categorical changes, in which people that did
not perform any change were classified as “did not
change”, those performed between 1 and 3 changes
were classified as “few changes” and those that per-
formed more than 3 changes were classified as “many
changes”. Variables were dropped out of the initial
models whenever their significance was < 0.1. To
test if the final results were not obtained by chance,
a training data set was used with 80% of the original
data. All results were compared between the final
model and the training model. Given that the results
were very similar, the final model was considered
In addition to identifying the most significant
model, a confusion matrix was also calculated to test
whether the final model was capable to accurately
predict the category of ‘variation in income’ of a
For the vast majority of interviewed fishers, their
overall income continued to come only from fish-
ing: 355 fishers out of 394 listed fishing as their only
source of income in 1994, whereas 328 listed fishing
as their only source of income in 2014.
In general, fishing incomes were 30% lower in
2014 than in 1994 and fishers lost income regardless
of the comparison made, whether between minimum
wages or between Brazilian basket of goods. Whereas
in 1994 fisher incomes were 4.3 times the value of
minimum wage and 5.24 times the value of a basket
of goods, in 2014 fisher incomes were only 1.2 times
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the value of minimum wage and 3.11 times the value
of a basket of goods. Thus, in the most conservative
estimate (basket of goods), fishers lost 41% of their
income over the course of 20years.
Yet, not all fishers lost income in the same propor-
tion and some even increased their fishing income.
Of all the fishers interviewed, 38% (149) reported an
increase in their fishing income between 1994 and
2014, compared to 62% (245) who experienced a
Fishers caught in a poverty trap
The fishers with the lowest incomes in 1994 in gen-
eral tended to also have the lowest incomes in 2014
(p = 0.00, rho = 0.6).
Fisher income in each period
Fisher incomes in 1994 were mainly explained by
the State where fishing took place, their individual
experience, type of boat, the gear used, time spent
fishing, category of species caught, and, secondar-
ily, size of boat (Supplementary Information, Table
SI1) (Fig.2a). Those who used the hookah compres-
sor and larger motorized boats tended to have better
incomes (as these are on the right side of the graph
and further away from zero), whereas living in the
State of Rio Grande do Norte and catching 2nd and
3rd category fish had the opposite effect (on the left
side of the graph). Although less relevant (effect size
close to zero), having more experience in fishing and
spending longer hours fishing also contributed to hav-
ing higher incomes in 1994 (Fig.2a).
In 2014, higher incomes were mainly explained
by ownership of a larger boat, using hook and line
to fish, and more time spent fishing (Supplementary
Information, Table SI2). Although these three vari-
ables are significant, boat size has an effect size close
to zero (Fig.2b).
Number of adaptive strategies adopted did not
correlate with past income
Only 60 fishers (15.2%) made no changes to the fish-
ing technology used (size and type of boat and fish-
ing gear used), fishing hours, species and species cat-
egory caught or fishing grounds used. With respect to
fishers who made or experienced at least one change,
these changes occurred mainly in the category of fish
caught (53.7%), followed by boat type (50.1%), boat
size (49.1%), and time spent fishing (32.6%). Fish-
ers tended to continue catching higher value species
while also increasing the catch of lower value species.
They also tended to adopt larger, motorized boats,
and increase the average time spent fishing on each
trip. These changes were observed in both the fisher’s
group that had increased income and the fishers who
lost income. The analysis showed that income in 1994
did not associate with the number of changes made
by each fisher (R = 0.12), suggesting that the adop-
tion or not of changes between 1994 and 2014 was
not related to how much money fishers were making
from fishing in 1994.
Fig. 2 Effect size of significant variables in the GLM model
from fisher incomes in 1994 (a) and 2014 (b). When the vari-
able is on the negative side, the effect of the variable on the
response variable is negative, whereas if it is on the positive
side it has the opposite effect. The magnitude of the coeffi-
cient represents how important the variable is, the higher the
coefficient, the greater the effect. As none of the variables
crosses the zero, they are all significant. Acronyms are: State
RN = fishers in the state of Rio Grande do Norte (in compari-
son with fishers from Ceará State); Experience: fishing experi-
ence; Boat Type – 3: motor boat; Fish category: classification
of fish according to value, with 2 and 3 being the lowest levels
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Adaptive strategies to increase fisher incomes
The proportional odds logistic regression showed that
what most influenced whether a fisher gained little or
much or lost little or much of his income over time
was his initial income in 1994, his experience and
whether or not he changed the type of boat he owned
Specifically, fishers who changed their type of
boat, who were less experienced and who had lower
revenues in 1994, in that order, were slightly more
likely to be found in larger categories of earnings
(lost less or actually gained something) in 2014.
Although significant, the model should be interpreted
with caution, given the low value of the estimates,
especially for revenues in 1994 and experience. In
addition, the confusion matrix showed a poor overall
performance of the model (61.44% of misclassifica-
tion): it accurately predicted fishers who suffered gen-
eral losses of more than 50% (9% of incorrect clas-
sification), showed relatively good prediction power
for those who lost up to 50% of their income (31%
of misclassification), and could not predict well who
gained income (70 and 72% of misclassification for
gains > 50% and up to 50%, respectively).
In general, fishers from the equatorial region of Brazil
have become poorer over time, and the poorest fishers
in 1994 continued to be the poorest in 2014. Fishers’
income has declined by 41% while fishing remains
the only source of income to the interviewed fish-
ers. Perhaps in an effort to deal with harsher condi-
tions, most fishers made fishing-related changes that
increase fishing effort, such as fishing in larger, yet
small-scale, or more powerful vessels. But some of
the changes may also have been an adaptation to deal
with stock declines, including switching their focus to
catching less expensive and desirable species (second
and third category species).
Some factors that positively influenced income in
1994, such as living in the State of Ceará and using a
hookah compressor for gear, may be related to lobster
fishing. Lobster was one of the main export products
in the 1990’s and the State of Ceará has the largest
abundance in Brazil of two lobster species (Panulirus
argus e P. laevicauda) due to favorable oceanographic
conditions in the region (Fonteles Filho 1994). Hook-
ahs, however, were forbidden in 1995, although there
is poor enforcement of their use (Dias Neto 2010).
The capture of 2nd and 3rd category fish in 1994
is negatively related to income precisely because
they are the fish with the lowest economic values. In
2014, fishers using lines had higher yields, probably
because species caught through line fishing are usu-
ally large bodied species or with high commercial
value (MMA 2006). Interestingly, different gears had
a positive relationship with income in the years stud-
ied (hookah in 1994 and line in 2014), yet the vast
majority of fishers did not change the gear they use
for fishing.
The pattern of vessel improvement seen in this
study, which is related to income in the two years ana-
lyzed, has also been documented globally, whereby
motor boats and overall fleet sizes have increased by
more than six-fold, yet global catches have declined
(Rousseau etal. 2019). Previous studies carried out in
a close-by region suggest that in small-scale fisheries,
larger boats (but they remain artisanal or small-scale)
do not necessarily result in higher catches and more
profit, although they can increase fishing expenses
(Damasio etal. 2016, 2020). Clearly, income losses
are expected if costs rise without being followed by
equivalent increases in catch or in catch value. When
associated with declining fish stocks, and therefore
catches, improvements to boats may only be mask-
ing economic losses (Pauly etal. 2002; Damasio etal.
2016), delaying a real solution to ecological and eco-
nomic matters, and further worsening the financial
situation of fishers.
An important aspect identified among the factors
that put some fishers in the poorest economic strata
was not to change the type of boat they use. This
effect, although small, may suggest that changing to a
different type of boat, for example, from sailboat to a
Table 1 Significant predictors, derived from proportional
odds model, for the changes in income fishers over a 20-year
period (1: > 50% losses; 2: < 50% losses; 3: < 50% gains;
4: > 50% gains)
Variable Estimate SE t value p-value
Changed his boat
0.56131 0.18734 2.99 0.00273
Revenue in 1994 − 0.00064 0.000132 − 4.88 0.00000
Experience − 0.01599 0.00857 − 1.86 0.06234
Rev Fish Biol Fisheries
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motorboat, possibly allowed fishers to explore differ-
ent species or parts of stocks not previously accessed.
This result is a phenomenon known as hyperstability,
as the characteristics of the fishers and exploited fish
help to maintain catches as stocks decline (Erisman
et al. 2011). Hyperstability seems to be especially
likely to occur in the tropics because many commer-
cially important reef species form predictable repro-
ductive aggregations that are well known or easily
identified by fishers, especially as the latter adapt
their strategies to explore different grounds (Ham-
ilton et al. 2016). An indication that hyperstability
has probably happened in this region is the recorded
overfishing of species exploited in the region, such
as snappers and groupers, despite some evidence of
increased catches (Silvano etal. 2017). Another indi-
cation that fishers may be trying to adapt to declining
stocks is the fact that the spatial distribution of local
fisheries has shifted from shallow to deeper waters
(Damasio et al. 2020; Freire et al. 2021), which
allows them to exploit previously unassessed parts of
the stock.
With the data collected for this study it was not
possible to clearly identify factors that, on their own,
promote an increase or a decrease in incomes over
time. This was especially true for the factors that
could have contributed to making people less poor (as
measured through fishing income), as the model used
here did not perform well to predict strategies that
would have resulted in economic gains, possibly due
to the small sample size of fishers who have become
richer over time. However, the model predicted,
with good accuracy, factors that kept people poor or
impoverished them in the long run.
Although the results presented here are not a defin-
itive answer to the factors that promote an increase or
a decrease in incomes in fisheries, the fact that fish-
ers with lower incomes in 1994 remained as such in
2014 characterizes a poverty trap (Azariadis and Sta-
churski 2005; Cinner etal. 2009; Barrett etal. 2011).
A poverty trap emerges whenever the opportunity to
increase income or wealth is limited for those who
have very little to invest, but it does not impact those
who can invest a little more (Banerjee and Duflo
2012; Villasante etal. 2022).
One aspect that could explain increased fishing-
related poverty is the depletion of fish stocks, given
that despite their accumulated experience (over
20years), which should have facilitated their access
to stocks (Chen and Chiu 2009; Heldt etal. 2021),
fishers are now gaining less and catching more of
the less desirable species. Yet, we do not claim that
overfishing alone explains some aspects of poverty in
fishing communities (e.g., low physical and financial
capital) (Béné 2003; Hirway 2010).
Poverty is a multidimensional variable that mainly
encompasses breakdowns in socio-institutional mech-
anisms, leading to social exclusion, failure to access
rights and political powerlessness (Sen 1981). A lim-
ited understanding of the mechanisms driving aspects
of poverty may also contribute to accentuating it. For
example, harmful subsidies that neglect fishing costs
and reinforce fisher’s beliefs that improving boats
will lead to increased earnings, associated with a gen-
eral lack of fishing data, prevent fishers and manag-
ers from grasping the intertwined dynamics of (over)
fishing and social conditions (Sumaila et al. 2021).
This leads to a vicious cycle whereby poorer fish-
ers are less likely to emerge from declining fishing,
which further degrades the resource and generates
less income (Cinner 2005; Cinner etal. 2009; Villas-
ante etal. 2022). If fishers are trapped, it is unreason-
able to expect they will escape fishing-related poverty
on their own. In this case, specific socio-institutional
mechanisms are necessary (Béné 2003).
A solution widely explored in literature to improve
financial capital in communities that depend on the
exploitation of natural resources focuses on income
diversification. But this measure does not address
the cause of fishing poverty. Even though it is known
that fishing activity is subject to several fluctuations
in addition to market variations (natural fluctuations
of species, bad weather, loss or destruction of equip-
ment, etc.) fishers are often found in an economic,
political and institutional marginalization. Such a
state leads to most fishers being denied access to eco-
nomic institutions or even not getting help in times of
crisis, a small crisis like a boat breaking down can be
doom for a marginalized fisher. Without such access,
some fishers remain unable to reach the minimum
level of investment that would allow them to generate
greater financial benefits and to lift themselves out of
the level of low productivity and income poverty in
which they are trapped (Pauly 2005; Béné and Friend
Like elsewhere in the world, small-scale fishers
in Brazil face major threats and challenges that can
push them to the edge of poverty and misery, such as
Rev Fish Biol Fisheries
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conflicts over access to resources and fishing grounds,
depletion of fish stocks, depletion of grounds due to
coastal development, concentration of market power
and climate change (Jentoft etal. 2011; FAO 2016).
Poverty alleviation strategies directed at small scale
fisheries have mostly included attempts to acceler-
ate economic growth, market-led economic pol-
icy reforms and technological and infrastructural
improvements (World Fish Center 2005). The latter
are the most tangible actions that fishers can take
to maximize their earnings, as observed here. How-
ever, these actions overlook the consequences on fish
stocks and temporarily mask stock depletion by com-
pensating declining catches with increasing access to
otherwise inaccessible resources (Pauly etal. 2002).
As complex problems require complex solutions,
reducing poverty in fisheries requires interventions
aimed at improving access to public services (trans-
port, education, water, electricity, health) and eco-
nomic institutions, while actions are taken to improve
fisheries productivity. It should also involve the col-
lective effort of a set of actors and institutions: state
governments, civil society organizations, university,
fishing and fish workers (Jentoft etal. 2018).
Understanding the factors that influence small-scale
fisher income is important from both the point of
view of fisheries management and eradication of hun-
ger and poverty. This is especially relevant in places
like the Brazilian equatorial region where few liveli-
hood alternatives and social security programs are
available, and where poverty, measured as fishing-
related income, seems to have worsened over time
among fishers. Moreover, this is a region that has
been severely impacted by recent environmental dis-
asters (e.g., an oil spill in 2019) and had to deal with
the effects of a global pandemic (Silva etal. 2022).
It was certainly not ready to bear both burdens con-
sidering that the pre-disaster situation had already
exposed the existence of poverty traps.
In Brazil specifically, the collection of basic socio-
economic data, such as number of fishers and income,
in addition to basic fishing data, could contribute to
designing tailor-made strategies to lift people out of
poverty. Turning a blind eye to the efforts of fishers to
cope with their difficulties at a time when fish stocks
continue to deteriorate and recent environmental
and economic/health disasters have hit them almost
simultaneously, will only further endanger the live-
lihoods of fishers and accentuate the pressure on the
Acknowledgements LMAD thanks the Coordenação de
Aperfeiçoamento de Pessoal de Nível Superior (CAPES,
Brazil; Finance Code 001) for a PhD scholarship. ARCA
(313334/2018-8) and PFML thanks CNPq for a productivity
Grant (301515/2019-0).
Funding This research did not receive any specific grant
from funding agencies in the public, commercial, or not-for-
profit sectors.
Data availability All data generated or analysed during this
study are included in this published article [and its supplemen-
tary information files].
Conflict of interest The authors declare that they have no
known competing financial interests or personal relationships
that could have appeared to influence the work reported in this
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Landing data are the most basic information used to manage fisheries, although they are often unavailable or incomplete. The objective of this work was to reconstruct the national database of marine commercial landings for the Brazilian industrial and artisanal fisheries, from 1950 to 2015. Total landings increased strongly from 1950 to mid-1980s and suffered sharp decline in the early 1990s, mainly associated to the collapse of sardine fisheries. After that, another period of increasing landings was observed, but at a much lower rate. Industrial landings always surpassed artisanal landings in Brazilian waters, except for the beginning of the time series, when many industrial fleets had not started yet, and in the early 2000s, when a change in the methodology for collecting landing statistics was implemented in the state of Pará Frontiers in Marine Science | 1 July 2021 | Volume 8 | Article 659110 Freire et al. Brazilian Marine Commercial Landings leading to an overestimation of artisanal landings. Artisanal fisheries have been declining since 2005, which is worrisome due to the social impact it may have on local income and food security. Regional differences were also observed, with industrial landings being always higher than artisanal landings in southeastern-southern Brazil, while the opposite was true for the northern-northeastern regions. Higher landings were observed in the southeastern-southern regions when both artisanal and industrial fleets were combined. Sardine and demersal fishes were the main resources landed by industrial fishers. Artisanal fishers caught more species than their industrial counterpart, featuring Xiphopenaeus kroyeri, Cynoscion acoupa, and Ucides cordatus. Although the fishing of Epinephelus itajara was banned in Brazil, it continues to be landed. Yet, catches of this species and others under some threat status are still not properly registered, including: Carcharhinus longimanus, Galeorhinus galeus, Sphyrna lewini, Sphyrna mokarran, Pristis pectinata, and Pseudobatos horkelii. Fishing resources not identified in previous landing reconstruction efforts, such as sea urchins and sea cucumbers, have now been reported. The database presented here should be continuously updated and improved. It is of paramount importance to resume the collection of landing statistics, including information on fishing effort, to assess the relative impact of fisheries and environmental factors on the main Brazilian fishing stocks.
Harmful fisheries subsidies authored by Sumaila U.R. et al.
Small-scale fishers in the developing world have been particularly affected by the COVID-19 pandemic given that they belong to one of the most socioeconomically vulnerable groups. In Brazil, one of the countries most affected by the pandemic, it was expected early on that the economy and wellbeing of fishers would be negatively impacted, yet fishers were expected to show some adaptive and coping mechanisms. To assess whether this was the case, 40 fishers, who are also leaders of fishing associations representing over 80 thousand fishers throughout the country, were interviewed. Results revealed that female leaders appraised the economic and health / wellbeing impacts to be harsher on fishers than men did. Moreover, fishers on the coast were found to be better able to adapt than those inland, although both had low levels of adaptive capacity. The nature of coping and adaptive mechanisms was also found to be different between locations. Whereas leaders from coastal associations stated that most of the adaptive responses occurred in the post-harvest sector (e.g., changes to the types of sales and changes to supply chain actors), leaders from inland communities stated that the changes that occurred related specifically to fishing (e.g., decrease in effort and changes in fishing grounds). These findings suggest that: 1) women may be better prepared to respond to COVID-19 because their appraisal may be more realistic than men, 2) the historic vulnerability of fishing communities may limit their adaptative capacity, and 3) coastal fishers have likely found ways to maintain part of their trade, contrary to inland fishers. Thus, to better help small-scale fisheries to cope with this particular pandemic or other large disruptive impacts, it would be recommended to invest in women in leadership roles while also guaranteeing that fishers have the minimal conditions to cope with and adapt to impacts. The latter can be done by assuring emergency cash transfers for the duration of the impact, as with the still ongoing pandemic, and investing in building fisher resilience for future shocks.
A fisher’s experience may provide greater efficiency and enhanced catches through time. Compared to new entrants to the fishery, experienced fishers often have developed tactics: the advantage of knowing ‘the best’ fishing grounds, having established efficient fishing practices, and developing relationships and trust within their fishing community. The influence of experience on catch rates, a key indicator of abundance, may be particularly strong in fisheries such as abalone, which rely on hand-collection and local knowledge for catches. We investigated diver records for greenlip (Haliotis laevigata) and blacklip (Haliotis rubra) abalone fisheries in South Australia to 1) examine annual catch rates (CPUE) and the number of diver entrants among licenses, 2) determine if CPUE of new diver entrants is lower than average fleet CPUE, and 3) establish relationships between CPUE and experience. We additionally simulated fleet turnover in the South Australian Western Zone Abalone Fishery by randomly replacing 25 %, 50 %, and 75 % of daily CPUE records with new diver catch rates and applied decision rules in the proposed abalone harvest strategy that allow for, and if needed account for, the impacts of new divers on catch rates. Catch rates varied among licenses and, on average, each license had less than one diver entrant per year (i.e., on average 5–20 % fleet turnover). During the first three years post-entry, annual license catch rates were generally 3–10 % lower than fleet catch rates, and license CPUE was positively correlated to experience in both greenlip and blacklip fisheries. However, depressed catch rates of entrants were unlikely to impact fleet trends given the small proportion of new entrants annually, and in cases where new divers impact CPUE, the proposed harvest strategy enables adjustment of recommended catches at a fine spatial scale prior to calculating the total allowable commercial catch. Of greater concern is ≥ 50 % fleet turnover, which, during simulations of the Western Zone Fishery, depressed fleet CPUE by ∼1 kg/hr or more; a reduction that is sufficient to result in lower recommended catches and, if such exceptional circumstances were to occur, may require modification of harvest strategy application. Assessing fisher experience as a driver of catch rate, builds a greater understanding of primary indicators for stock assessment and, together with appropriate decision-making tools, provides a more reliable management outcome.
How accurate is memory? Although people implicitly assume that their memories faithfully represent past events, the prevailing view in research is that memories are error prone and constructive. Yet little is known about the frequency of errors, particularly in memories for naturalistic experiences. Here, younger and older adults underwent complex real-world experiences that were nonetheless controlled and verifiable, freely recalling these experiences after days to years. As expected, memory quantity and the richness of episodic detail declined with increasing age and retention interval. Details that participants did recall, however, were highly accurate (93%–95%) across age and time. This level of accuracy far exceeded comparatively low estimations among memory scientists and other academics in a survey. These findings suggest that details freely recalled from one-time real-world experiences can retain high correspondence to the ground truth despite significant forgetting, with higher accuracy than expected given the emphasis on fallibility in the field of memory research.
Small-scale fisheries are an important, yet neglected, millenarian activity that has been undergoing significant changes that threaten its future. Understanding how this activity is spatially distributed and the factors that drive its use of the marine space over time can shed some light on how fishing efforts and their impacts have moved over different parts of coastal marine ecosystems. This study investigated changes to the spatial distribution of small-scale fisheries along the Brazilian equatorial region between 1994 and 2014 and the factors, from ecological to socioeconomic, that influenced this shift. Bayesian hierarchical spatial models were used together with environmental variables, and species and fisheries data to identify fisheries spatial variations. Fisheries spatial transitions were also assessed to determine whether they occurred as a result of significant changes to target species and their abundance, fisheries technology and efforts, and/or economic revenues. A relevant shift in the fisheries spatial distribution was detected and demonstrated that fishing has been mostly moving from shallow to deeper waters. Although the target species remained the same in 1994 and 2014, abundance of these species decreased significantly over time, which has consequently affected fisher revenues. It is possible that, when new and further areas were exploited, initial catches were either better or similar to previous catch levels in older and closer grounds, which may have masked earlier signs of overfishing. Even small changes, such as shifting fishing grounds to a few kilometers offshore, could be a proxy for negative socioeconomic and ecological changes in fishing communities that is brought about by resource decline in areas closer to the coast. An important step toward detecting signs of ecological collapse with social consequences is to identify additional fisheries changes not commonly reported in landing data, such as ground shifts.
Steneck and Pauly present an historical account of the growth of the fishing industry and an update on the status of fish populations today, using several case studies to highlight the complex and profound effects that fishing has on marine ecosystems.