Population dynamics of benthic shellfisheries in the north of Chile: the role of denso-dependent processes
and oceanographic forcing
Marcelo M. Rivadeneira1, 2
, Raúl Ulloa3
, Adolfo Vargas4,5
& Cristian Hudson5
1Centro de Estudios Avanzados en Zonas Áridas (CEAZA) & Facultad de Ciencias del Mar, Universidad
Católica del Norte, P.O. Box 117, Coquimbo, Chile
2Center for Advanced Studies in Ecology and Biodiversity (CASEB), Departamento de Ecología Pontificia
Universidad Católica de Chile, Alameda 340, Santiago, Chile
3Centro Regional de Investigación Pesquera, Instituto Nacional de Pesca, Dirección General de
Investigación Pesquera Pacifico Norte. Miguel Alemán sur Nº605, C.P. 85400, Guaymas, Sonora, México
4Centro de Investigación y Desarrollo de Profesionales Marinos Pacífico Ltda. Vivar 1218, Iquique, Chile
5Programa Magíster en Ciencias Aplicadas mención Biología Pesquera, Departamento Ciencias del Mar,
Universidad Arturo Prat, P.O. Box 121, Iquique, Chile
The need of establish sustainable fishing policies clashes against the incapability of many models to
integrate mechanistically, yet in a simple way, the role played by biological (i.e. endogenous) and physical
(i.e. exogenous, oceanographic) forcing in the regulation of population dynamics. In this work we evaluate
the importance of endogenous and exogenous factors in the regulation of population dynamic in 10 benthic
species commercially exploited by the fishing artisanal fleet along northern coast of Chile. The information
was gathered from annual time series of CPUE (captures per unit of effort) between 1998 and 2005. The
population growth rate (i.e. interannual changes in the CPUE) was modeled as an autoregressive lineal
process, evaluating the importance of first-order lags (1 year) of CPUE, and oceanographic factors (southern
oscillation index, sea surface temperature, and upwelling index). In all species, the best models included the
effect of both endogenous and exogenous factors, explaining a high percentage of the variance of the
population growth rate (in average 88 %). These results support the idea that the studied populations,
despite being under an exploitation regime, show a strong regulation seemingly produced by the combined
effect of intraspecific interactions and physical forcing molding the abundance during the early stages of the
species. Our results highlight the importance of these kinds of models as a powerful tool to evaluate the
processes underlying the regulation of natural populations.
Key words: artisanal fisheries, Pacific South America, autoregressive model, El Niño, coastal upwelling
La necesidad de establecer medidas de manejo pesquero sustentable se ve entorpecida por la incapacidad
de muchos modelos de incorporar mecanísticamente, pero aún de manera simple, el rol desempeñado por
forzantes biológicos (i.e. endógenos) y físicos (i.e. exógenos u oceanográficos) en la regulación de las
dinámicas poblacionales. En este trabajo evaluamos la existencia de regulación poblacional en 10 especies
bentónicas explotadas comercialmente por la flota pesquera artesanal en el norte grande de Chile. La
información fue obtenida a partir de series de tiempo anuales de capturas por unidad de esfuerzo (CPUE)
entre 1998 y 2005. Las tasas de crecimiento poblacional (i.e. cambios interanuales en la CPUE) fueron
modeladas como un proceso autoregresivo lineal, evaluándose la importancia de retrasos de primer orden
(1 año) en las capturas por unidad de esfuerzo y de factores oceanográficos (índice de oscilación del sur,
temperatura superficial del mar, e índice de surgencia costera). En todas las especies se verificó que los
mejores modelos incorporaron efectos denso-dependientes endógenos y exógenos, los cuales explicaron un
alto porcentaje de la varianza (en promedio 88%) de la tasa de crecimiento poblacional. Estos resultados
apoyan la idea de que las poblaciones estudiadas, a pesar de estar sometidas a un régimen de explotación,
exhiben una fuerte regulación, la que aparentemente estaría dada por interacciones intra-específicas y
forzamientos físicos que moldearían ciclos de vida tempranos de las especies. Los resultados aquí
obtenidos subrayan la utilidad de este tipo de modelos como valiosa herramienta para evaluar los procesos
subyacentes a regulación de las poblaciones naturales.
Palabras clave: pesquería artesanal, Pacífico de Sudamérica, modelo autoregresivo, biológicEl Niño,
The collapse of many fisheries along the global ocean has emphasized the urgent need to unveil the latest
processes and mechanisms that govern the dynamics of temporary space populations. In this sense, the
explicit incorporation of environment in the evaluation of fisheries is to be considered as relevant end to
achieve this objective (Wiff & Quiñones 2004). There are numerous examples that demonstrate the strong
dependence of the pelagic fisheries of the oceanographic conditions prevailing (Mantua et al. 1997, Yáñez et
al. 2001, Mantua & Hare 2002). However, the direct application and success of the conclusions reached
toward management measures is still in its infancy (Yañez et al. 2001). The need to implement better
models that make it possible to understand the dynamic fishing has led to the implementation of numerous
analytical perspective that explicitly or implicitly considered the role of the environment (e.g. , models based
on individuals, models of energy acquisition, analysis age/size structured, neural networks). However, its
application and implementation requires the generation of new and complex biological information, which is
not feasible in most cases. The scenario is even more complex in the case of benthic species of Chile, where
the bulk of the recent research has focused on descriptions bio-fishing (Defeo & Castile 1998, Castilla &
Defeo 2001, Leiva & Castile 2001).
The management recommendations have been based, in the last few years, in the implementation of marine
protected areas or areas of co-management fisheries (Fernandez & Castile 2005), however, those
recommendations underpinning success in a limited knowledge of the dynamic spatio-temporal system.
Even more, the very few models of management applied to benthic species in northern Chile that consider
the environment in an explicit manner (The Field 2002, Ortiz & Wolff 2002a, b, c), are of purely local area
and are methodologically difficult to implement (Why?). In a temporal context, fluctuations in the fisheries
have been associated with variations oceanographic, operants to macro-spatial scales and low-frequency
(Mantua et al. 1997, Chavez et al. 1999, Yáñez et al. 2001, Mantua & Hare 2002, Yáñez et al. 2002). In
most cases however, the fishing environmental analysis has been carried out mostly with a
phenomenological philosophy, for example leaving in evidence the importance of the sea surface
temperature (Freon & Yanez 1995) or of the turbulence (Yañez et al. 2001) In the interannual variations in
the catch of pelagic resources. However, this perspective does not account for the processes and/or
regulatory mechanisms that subyacerian to this correlation. Prospects more refined models based on
deterministic or stochastic majority have considered the role of endogenous factors (density-dependence),
but not of the environmental conditions in the population regulation (wiff & Quiñones 2004). The explicit
incorporation of endogenous and exogenous factors (environmental conditions) as determinants of
population dynamics has been developed widely in the environmental field in the last few decades (Lima et
al. 2001, Lima et al. 2002, Stenseth et al. 2002, Stenseth et al. 2003). Basically it states that the temporal
variations (e.g. , inter-annual) of the stocks would emerge as part of an internal dynamic, in which the built-in
bias toward the populations to expand (i.e. exponential growth) would be offset by density-dependent
processes (e.g. , intraspecific competition, competition inter-specific, predation).
In this framework, the role of environmental forcings (e.g. , the Child, transport offshore) would be made a
manifest through the dense structure-dependence on the population, and could be modeled explicitly, in
linear terms or non-linear (Stenseth et al. 2002). The action of both mechanisms and its time lag would be
causing the populations to vary in the time (short and/or medium-term), although in the long term, the
abundance of the population is stationary (do permanently stationary or determined by the load capacity of
the environment, which may also have different levels? ). Then, the existence of natural cycles in the
abundance of populations, evident also in benthic species, that could and should be considered explicitly in
the fisheries management plans. In the present study raises the explicit use of this approach of analysis is to
allow glimpse possible processes and mechanisms that govern temporary fluctuations in benthic fisheries in
the north of Chile.
In particular, it evaluates the adjustment autoregressive models that consider endogenous and exogenous
effects on the population dynamics of 10 species. This step would be key in the generation of effective
management measures for these resources.
Material y métodos
Obtaining time series of CPUE: information was obtained from a database of the follow-up project of
benthic fisheries executed by the Fisheries Development Institute of Chile (FIFG), where are documented
catches (kg) for various benthic resources in a total of 231 sites (backgrounds) distributed from Africa to the
south of Antofagasta (ca.18 ° -25 °S), covering an area of ca. 800 Km of coastline, between the years 1998-
2005. In order to obtain a synoptic view, chose to ignore trends space as well as temporal trends intra-
annual, by expressing the total annual catch as for each resource. As a measure of fishing effort is used the
number of trips made to that origin for that resource and year. Of the total of 30 species recorded only
analysis was performed with 10 of them, since they correspond to the 98% of this information (Table 1).
Obtaining of oceanographic information: synoptic oceanographic information was obtained in the form of
annual averages for the entire region considering the period 1998-2005. Three oceanographic variables
were analyzed: sea surface temperature (SST), index of Southern Oscillation (SOI), and index of upwelling
(UPW). It generated an average annual regional TSM using the information obtained for three main ports
(Arica, Iquique, Antofagasta) using in-situ measurements (CENDOHC-SHOA). The average annual value of
SOI and UPW (transport offshore Ekman) was obtained from time series available at NOAA, and were
recovered in your web site (http://www.pfeg.noaa.gov:16080/products/PFEL/) finally these values were
obtained by averaging the values of 3 stations that are available for each fishing site in the region of study.
Analysis of population dynamics: We evaluated the population dynamics at the regional level of each of the
species, by modeling the time series of population growth rates (Lima 2001, Stenseth et al. 2002, Belgrano
et al. 2004B):
Where: it is the intrinsic rate of population growth in the t year, y, and corresponds to the CPUE in the t and
the previous year, respectively.
If this is a feature that gives an account of the relationship between y and , and the delays, then:
Where: It is a linear relationship of dependency between and the delays of abundance.
Due to the short length of the time series (8 years) were evaluated only dynamic of first order (delay for 1
year). In this way, in his simpler version (linear) population dynamics could be modeled on the basis of:
y are parameter to find the estimate of y
Where: ENV is from any of the oceanographic variables evaluated (TSM, SOI, and UPW).
The choice of the best models was carried out using the Akaike Information Criterion (AIC), where all the
models with a ∆AIC<2 and AIC weighted>0.2 , were considered equally probable. All analysis were carried
out using routines written in the program R (Team 2008).
The populations studied showed different temporal dynamics (Fig 2). The cholga mussel, crab and the hairy
octopus fluctuations showed relatively small populations during the period of analysis. Species such as the
locate and the choro showed sharp increases in the CPUE, whereas the piure, culengue showed drops and
clam population. Other species exhibited fluctuations with more marking, such as the scarlet macaw black,
whose abundance ranged over 2 orders of magnitude in the study. Of the total of 11 models evaluated for
each species (110 in total), only 1-2 were selected in each case (Table 2). The selected models in general
accounted for much of the variance of the population growth (R² average =0.88 [0.46 , 0.99 ], Table 2), so
that the population dynamics of these species was generally well captured by such models (Fig 2). All the
models selected incorporated at the same time effects density-dependent and at least one variable
oceanographic. More than half of the selected models (8 of 15) incorporated delays of first order in the CPUE
and the three variables studied oceanographic. The sign and intensity of the coefficients auto-regressive
associated was variable between species. While the ratio of density-dependence was always negative, the
coefficients associated with the TSM, SOI, and UPW were positive or negative.
The results confirm the existence of a marked population regulation in the 10 species of benthic
analysis. This regulation would emerge from the joint action of density-dependent processes and
oceanographic forcing through the feedback of population structure. Traditionally it has been
argued that the oceanographic forcing would have a leading role in fluctuations in abundance of
fisheries (Yañez et al. 2001, Yáñez et al. 2002, Lehodey et al. 2006, Friedemann & Wolff 2008).
However, oceanographic modeling of radiative forcing to the interior structure of the density-
dependent is somewhat recent (Belgrano et al. 2004A, 2004b, Pedraza-Garc ia & Cubillos 2008).
The results give evidence that there are endogenous and exogenous mechanisms that regulate the
overall dynamics of the populations studied. On the one hand, the existence of an endogenous
regulation density-dependent is evident by the negative sign of the coefficients of density-
dependence, α, of all the autoregressive models (Table 2), suggesting that feedback processes of
population of first order would be key in the population dynamics.
. In this sense, a large number of empirical studies validated the importance of larval settlement and
recruitment as a determinant of the structure of populations and marine communities on the coast of
Chile (Lakes et al. 2005, Navarrete et al. 2005). However, it cannot be ruled out a priori in the
existence of delays of higher order ( Pedraza-Garc ia & Cubillos 2008), which would reflect inter-
specific processes (e.g. competition and predation) whose relevance as mechanisms of population
regulation is suggested by empirical evidence (Castile 1999, Gaymer et al. 2004, Vásquez et al.
2006, Gaymer & Himmelman 2008, Navarrete & Manzur 2008). Time Series of greater extension
(> 10 years) would make possible the implementation of these models. On the other hand, these
processes of feedback also would be forced by exogenous factors (i.e. , oceanography) that would
work together by regulating populations, as has been observed in species of marine phytoplankton
(Belgrano et al. 2004A, 2004b).
Experimental studies show that the structure and dynamics of populations and marine communities
in the southeastern coast of the Pacific is heavily governed by stressors of low frequency as El
Niño(Chavez et al. 1999, Thiel et al. 2007) As well as by the larval dispersal patterns generated by
the cells of coastal upwelling (Lakes et al. 2005, Navarrete et al. 2005, Lakes et al. 2008). However
the population effects of these factors track structure density-dependent only have been evident in
the present study. The underlying mechanisms are much less well known, but it is possible that
factors such as SOI indirectly affect recruitment rates of the species, track changes in the intensity
of the transport offshore (Connolly & Roughgarden 1999), or changes in vital rates induced by the
temperature of the sea (Urban 1994, Wolff et al. 2007). However, the direction and intensity of the
coefficients associated with each variable oceanographic was highly variable between species,
which is not strange as there is empirical evidence that indicates that the responses of marine
populations to ENSO events would be species-specific (Díaz & System 1993). The foregoing
suggests that ecological features and/or the history of life could also play a predominant role in
population dynamics. Time series analysis more extensive and the explicit inclusion of the space
(e.g. , analyzing the spatial synchrony of population dynamics) in species with different life
strategies could shed more light on the mechanisms that govern the different population responses
to oceanographic forcing.
The present study demonstrated the feasibility of applying simple autoregressive models for
understanding the role of biological interactions and oceanographic stressors in the regulation of
marine populations. Similar models or more complex (i.e. , non-linear, second-order delays,
explicitly including interactions predator-prey, Pedraza-Garc ia & Cubillos 2008) could be applied
to other stocks for which there are estimates of relative abundance of species. The results obtained
by these simple models could serve as a basis for the development of more sophisticated models
with a view to a sustainable management of marine ecosystems.
Table 1. Main characteristics of the species in this study. Status of holdings according to SubPesca
(PE: full exploitation, THE: free access, LAC: free access to administrative restrictions).
Table 1. Main characteristics of the species this study. The exploitation status according SubPesca
Chile (PE: fully exploited, THE: open access, LAC: open access with administrative restrictions).
Fig 2. Population Dynamics of 10 marine species commercially extracted by the artisanal fishing
fleet between 1998-2005. The expected values were estimated using the parameters of the
autoregressive model with the best fit for each species (see Table 2).
Fig 2. Population dynamics of 10 marine species exploited by artisanal shellfisheries between
1998-2005. The expected values were estimated using the parameters yielded by the best
autoregressive model for each species (see Table 2).
Belgrano A, M Lima & NC Stenseth. 2004a. Non-linear dynamics in marine-phytoplankton population
systems. Marine Ecology Progress Series 273: 281-289.
Belgrano A, M Lima, NC Stenseth & O Lindahl. 2004b. Responses of phytoplankton communities to
climatic variability. En: Stenseth NC, G Ottersen, JW Hurrell & A Belgrano (eds). Marine ecosystems and
climate variation: The North Atlantic: A comparative perspective, pp. 109-114. Oxford University Press,
Castilla JC. 1999. Coastal marine communities: trends and perspectives from human-exclusion
experiments. Trends in Ecology & Evolution 14: 280-283.
Castilla JC & O Defeo. 2001. Latin American benthic shellfisheries: emphasis on co-management and
experimental practices. Reviews in Fish Biology and Fisheries 11: 1-30.
Connolly SR & J Roughgarden. 1999. Increased recruitment of northeast Pacific barnacles during the 1997
El Nino. Limnology and Oceanography 44: 466-469.
Chávez FP, PG Strutton, CE Friederich, RA Feely, GC Feldman, DC Foley & MJ McPhaden. 1999.
Biological and chemical response of the equatorial Pacific Ocean to the 1997-98 El Nino. Science 286: 2126-
Defeo O & JC Castilla. 1998. Harvesting and economic patterns in the artisanal Octopus mimus
(Cephalopoda) fishery in a northern Chile cove. Fisheries Research 38: 121-130.
Díaz A & L Ortlieb. 1993. El fenómeno" El Niño" y los moluscos de la Costa peruana. Bulletin de l'Institut
Français d'Études Andines 22: 159-177.
Fernández M & JC Castilla. 2005. Marine conservation in Chile: Historical perspective, lessons, and
challenges. Conservation Biology 19: 1752-1762.
Freon P & RE Yáñez. 1995. Influencia del medio ambiente en evaluación de stock: una aproximación con
modelos globales de producción. Investigaciones Marinas 23: 25-47.
Friedemann K & M Wolff. 2008. Environmental variability and fisheries: what can models do?. Reviews in
Fish Biology and Fisheries 18: 273-299.
Gaymer CF, C Dutil & JH Himmelman. 2004. Prey selection and predatory impact of four major sea stars
on a soft bottom subtidal community. Journal of Experimental Marine Biology and Ecology 313: 353-374.
Gaymer CF & JH Himmelman. 2008. A keystone predatory sea star in the intertidal zone is controlled by a
higher-order predatory sea star in the subtidal zone. Marine Ecology Progress Series 370: 143-153.
Lagos NA, JC Castilla & BR Broitman. 2008. Spatial environmental correlates of intertidal recruitment: A
test using barnacles in northern Chile. Ecological Monographs 78: 245-261.
Lagos NA, SA Navarrete, F Veliz, A Masuero & JC Castilla. 2005. Meso-scale spatial variation in
settlement and recruitment of intertidal barnacles along the coast of central Chile. Marine Ecology Progress
Series 290: 165-178.
Lehodey P, J Alheit, M Barange, T Baumgartner, G Beaugrand, K Drinkwater, JM Fromentin, SR Hare,
G Ottersen, RI Perry, C Roy, CD van der Lingen & F Werner. 2006. Climate variability, fish, and fisheries.
Journal of Climate 19: 5009-5030.
Leiva GE & JC Castilla. 2001. A review of the world marine gastropod fishery: evolution of catches,
management and the Chilean experience. Reviews in Fish Biology and Fisheries 11: 283-300.
Lima M. 2001. The dynamics of natural populations: feedback structures in fluctuating environments. Revista
Chilena de Historia Natural 74: 317-329.
Lima M, NC Stenseth & FM Jaksic. 2002. Food web structure and climate effects on the dynamics of small
mammals and owls in semi-arid Chile. Ecology Letters 5: 273-284.
Lima M, NC Stenseth, NG Yoccoz & FM Jaksic. 2001. Demography and population dynamics of the
mouse opossum (Thylamys elegans) in semi-arid Chile: seasonality, feedback structure and climate.
Proceedings of the Royal Society of London Series B, Biological Sciences 268: 2053-2064.
Mantua NJ & SR Hare. 2002. The Pacific decadal oscillation. Journal of Oceanography 58: 35-44.
Mantua NJ, SR Hare, Y Zhang, JM Wallace & RC Francis. 1997. A Pacific interdecadal climate oscillation
with impacts on salmon production. Bulletin of the American Meteorological Society 78: 1069-1079.
Navarrete SA & T Manzur. 2008. Individual -and population- level responses of a keystone predator to
geographic variation in prey. Ecology 89: 2005-2018.
Navarrete SA, EA Wieters, BR Broitman & JC Castilla. 2005. Scales of benthic-pelagic and the intensity
of species interactions: From recruitment limitation to top-down control. Proceedings of the National
Academy of Sciences of the United States of America 102: 18046-18051.
Ortiz M & M Wolff. 2002a. Application of loop analysis to benthic systems in northern Chile for the
elaboration of sustainable management strategies. Marine Ecology Progress Series 242: 15-27.
Ortiz M & M Wolff. 2002b. Dynamical simulation of mass-balance trophic models for benthic communities of
north-central Chile: assessment of resilience time under alternative management scenarios. Ecological
Modelling 148: 277-291.
Ortiz M & M Wolff. 2002c. Trophic models of four benthic communities in Tongoy Bay (Chile): comparative
analysis and preliminary assessment of management strategies. Journal of Experimental Marine Biology and
Ecology 268: 205-235.
Pedraza-Garcia M & LA Cubillos. 2008. Population dynamics of two small pelagic fish in the central-south
area off Chile: delayed density-dependence and biological interaction. Environmental Biology of Fishes 82:
Stenseth NC, A Mysterud, G Ottersen, JW Hurrell, KS Chan & M Lima. 2002. Ecological effects of
climate fluctuations. Science 297: 1292-1296.
Stenseth NC, G Ottersen, JW Hurrell, A Mysterud, M Lima, KS Chan, NG Yoccoz & B Adlandsvik.
2003. Studying climate effects on ecology through the use of climate indices: the North Atlantic Oscillation, El
Nino Southern Oscillation and beyond. Proceedings of the Royal Society of London Series B, Biological
Sciences 270: 2087-2096.
Team RDC. 2008. R: A language and environment for statistical computing. R Foundation for Statistical
Computing, Vienna, Austria. [en línea] <http://www.r-project.org/ >
Urban HJ. 1994. Upper Temperature Tolerance of 10 Bivalve Species Off Peru and Chile Related to El-
Nino. Marine Ecology Progress Series 107: 139-145.
Vásquez JA, JMA Vega & AH Buschmann. 2006. Long term variability in the structure of kelp communities
in northern Chile and the 1997-98 ENSO. Journal of Applied Phycology 18: 505-519.
Wiff R & R Quiñones. 2004. Parametrización ambiental en modelos biológico-pesqueros: una revisión.
Gayana 68: 76-92.
Wolff M, M Taylor, J Mendo & C Yamashiro. 2007. A catch forecast model for the Peruvian scallop
(Argopecten purpuratus) based on estimators of spawning stock and settlement rate. Ecological Modelling
Yáñez E, MA Barbieri, C Silva, J Castillo, K Nieto, G Böhm, F Gómez & J Córdova. 2002. Climate
variability and pelagic fisheries in the south-eastern Pacific. Investigaciones Marinas 30: 175-177.
Yáñez E, MA Barbieri, C Silva, K Nieto & F Espindola. 2001. Climate variability and pelagic fisheries in
northern Chile. Progress in Oceanography 49: 581-596.
Tabla 1. Principales características de las especies de este estudio. Estatus de explotación de acuerdo a SubPesca (PE: plena explotación, LA: libre acceso, LAC:
libre acceso con restricciones administrativas).
Table 1. Main characteristics of the species this study. The exploitation status according SubPesca Chile (PE: fully exploited, LA: open access, LAC: open access
with administrative restrictions).
N° de procedencias por año
Taxa Nombre científico Nombre
Promedio Promedio Mínimo Máximo
Gastropoda Thais chocolata (Duclos, 1832) Caracol
LAC 390 73 52 90
Echinoidea Loxechinus albus (Molina, 1782) Erizo PE 228 77 28 126
Bivalvia Aulacomya atra (Molina, 1782) Cholga PE 208 63 45 71
Tunicata Pyura chilensis Molina, 1782 Piure LA 199 65 31 83
Bivalvia Gari solida (Gray, 1828) Culengue PE 76 25 5 47
Bivalvia Protothaca thaca (Molina, 1782) Almeja PE 70 52 40 69
Bivalvia Mytilus chilensis (Hupé 1854) Choro PE 62 16 11 23
Decapoda Cancer setosus Fabricius, 1798 Jaiba peluda LAC 54 58 14 74
Gastropoda Fissurella latimarginata Lesson,
Lapa negra PE 51 25 7 51
Cephalopoda Octopus mimus Gould, 1852 Pulpo PE 45 130 84 154
Tabla 2. Evaluación de los 11 Modelos auto-regresivos de primer orden de análisis de las dinámicas poblacionales de cada especie (retraso de 1 año en la señal).
Table 2. Summary if the autoregressive models used to analyze the population dynamics of each species. 11 first-order autoregressive models (1 year lag) were
Coeficientes de regresión
Especie Modelo AIC ∆AIC AICp R² P
Almeja -13,77 0,00 0,519 0,969 0,009 -4,183 -1,194 0,058 0,525
-13,61 0,16 0,479 0,976 0,047 -3,409 -1,241 0,057 0,495 -0,001
-4,83 0,00 0,316 0,686 0,099 2,944 -0,489 -0,028
-3,91 0,92 0,200 0,731 0,217 4,109 -0,443 -0,031 -0,082
Cholga -38,97 0,00 1,000 0,997 0,007 3,309 -1,466 0,038 0,245 0,006
Choro -3,88 0,00 0,868 0,953 0,091 15,036 -1,110 -0,132 -0,588 -0,007
Culengue 10,09 0,00 0,522 0,824 0,321 -14,928 -2,099 0,182 1,316 0,026
Erizo -5,36 0,00 0,379 0,458 0,554 14,486 -0,460 -0,675 -0,005
-4,66 0,69 0,268 0,451 0,302 14,425 -0,631 -0,627
Jaiba peluda -12,34 0,00 0,605 0,976 0,047 4,428 -1,130 -0,017 0,004
-10,35 1,99 0,223 0,953 0,093 4,563 -1,137 -0,018 -0,006 0,004
Lapa negra 3,65 0,00 0,988 0,979 0,043 28,828 -2,315 -0,077 -1,249 0,052
Piure -25,92 0,00 0,998 0,994 0,011 -2,567 -0,829 0,057 0,388 0,004
Pulpo -14,05 0,00 0,577 0,968 0,062 0,463 -1,776 0,010 0,362 -0,005
-13,24 0,81 0,385 0,953 0,017 1,542 -1,565 0,256 -0,005
Fig. 1. Mapa del área de estudio en el norte grande de Chile, mostrando la localización
geográfica de las 231 áreas de pesca en el área de estudio (cuadrados claros).
Fig. 1. Map of the study area at the northern Chile region, showing the geographic location of
the 231 fishing localities used in this study (empty squares).
Fig. 2. Dinámica poblacional de 10 especies marinas extraídas comercialmente por la flota
pesquera artesanal entre 1998-2005. Los valores esperados se estimaron usando los
parámetros del modelo autoregresivo con el mejor ajuste para cada especie (ver Tabla 2).
Fig. 2. Population dynamics of 10 marine species exploited by artisanal shellfisheries between
1998-2005. The expected values were estimated using the parameters yielded by the best
autoregressive model for each species (see Table 2).
A particular slide catching your eye?
Clipping is a handy way to collect important slides you want to go back to later.