Box, S.

Canty, S.

Chollett, I.

Mumby, P.J.

English

Aquatic Commons

 Proceedings of the 65th Gulf and Caribbean Fisheries Institute                  November 5 – 9, 2012   Santa Marta, Colombia 
Modelling Fishers’ Activity as a Response to Weather and Economy:  
First Step Towards Building Adaptive Capacity under a Changing Climate  
 
Modelando las Actividades de los Pescadores como Respuesta al Clima y a la Economía:  
Un Primer paso para Incrementar la Capacidad de Adaptación bajo Cambio Climático 
 
Modéliser l'Activité des Pêcheurs en Réponse aux Conditions Météorologiques et Économique: 
Une Première Étape pour Renforcer les Capacités d'Adaptation au Changement Climatique 
 
ILIANA CHOLLETT1,2, STEVE CANTY3, STEPHEN BOX3,4,  and PETER J. MUMBY2,1 
1University of Exeter, Exeter, United Kingdom, 2University of Queensland, Brisbane, Australia. 
3Centre for Marine Ecology, Tegucigalpa, Honduras, 4Smithsonian Institution, Fort Pierce Florida 34945 USA. 
 
EXTENDED ABSTRACT 
 
Background  
The artisanal fishing sector provides about 90% of all fishing employment worldwide and nearly 25% of the world 
catches. Artisanal fishing is, however, an economically risky activity, due to the high livelihood and income dependency on 
local resources and their unpredictability. This makes the activity highly vulnerable to changes. Changes in weather and 
economy have the potential of modifying fishing activity through changes in target abundance and (most overlooked) 
fishermen behaviour. Changes in costs of fishing and expected catch value can shape where and how often fishermen will 
fish. On the other hand, weather aspects such as rainfall, strong winds, or storms can restrict the number of fishing days, and 
exposure to waves define fishing accessibility with areas that are too rough being unusable for fishing. 
 
Objectives  
Here we use novel methods to assess the effects of both weather and economical aspects on artisanal fishing activity. 
The uniqueness of this research include the spatially-explicit quantification of wave exposure (which restricts fishing 
access) and the use of a novel modelling approach (MaxtEnt, a presence-only model) due to the occurrence of unreliable 
zeros in our dataset, a common problem in fisheries data. We apply the model in Honduras, where weather seemed to be 
particularly relevant in shaping fishing activity.  
 
Methods 
We used daily fishing records for Utila, Bay Islands, Honduras, to assess how changes in weather affect fishermen 
access to fishing grounds. Daily records for the period August 2010 – September 2011 were recorded at the Utila Cays 
(16.0639°N, 86.9667°W). The 1,186 records collected include data on the date, departure time, coordinate of the fishing 
ground visited, and total landings. Daily in situ rainfall and wind speed data for Roatan airport (16.31°N, 86.53°W) were 
also obtained for the period 2010 – 2011. 
Wave exposure maps describe the sea condition and the degree of wave action on an open shore, and therefore are a 
proxy for site accessibility, with rough, high wave exposure areas being inaccessible for fishing. Daily wave exposure maps 
(at 50 m spatial resolution) for the period 2010 – 2011 were produced following the methods described in Chollett and 
Mumby (2012).  
Because the fisheries around Utila are unsaturated and many times suitable weather conditions are present but fishing 
grounds are still not visited, we could not apply traditional modelling methods to predict fishing activity. Therefore, we 
borrowed an approach used in ecological and conservation applications called MaxEnt, which allows predicting fishing 
activity from environmental predictors using presence-only data. As explanatory variables, we included weather (rainfall, 
wind speed, exposure to waves), economic (distance to port), and cultural (day of the week) variables. Additionally, fishing 
around Utila occurs during morning and evening hours, and we added that covariate as well (categorical: morning or 
afternoon fishing). We explored the initial dataset in order to remove highly correlated (and therefore redundant) variables 
before the analyses. Model refinement was done using the jackknife feature in MaxEnt to assess performance of each 
variable in terms of AUC (Area Under the Curve of Receiver-Operator Characteristic) gain. 
 
 
 
 
Page 60  65th Gulf and Caribbean Fisheries Institute  
 
RESULTS AND DISCUSSION  
The final model including five variables explains well 
the fishing activity patterns (AUC = 0.83). MaxEnt results 
indicate the distance to port (contributing 56% of the 
variability) is the most influential variable in the model, 
followed by time of fishing (30.7%). Weather and cultural 
variables have less importance in modulating the response. 
Fishing activity is associated to intermediate distances to 
port (5 – 40 km), which indicate avoidance of fishing 
grounds close to port (probably related to overexploitation 
of fish resources) and far away from it (likely related to 
lack of profitability of the trip if fuel expenses increase). 
Fishing activity is also associated to lower rainfall and 
wave exposure. The relative importance of these variables 
is, however, very small, 10.5 and 1.8%, respectively. 
These results could be considered an early warning 
signal of overfishing in the Bay Islands, which were 
considered as fisheries in their early stage with moderate 
fishing pressure. In this region, it seems unlikely for 
climate change to affect fishing activity. Projected 
increases in fuel costs, however, might be problematic. 
This model, coupled with economic and downscaled 
climate change predictions for the area, will be used to 
assess likely changes in fishing intensity under a changing 
climate and economy. The predictions would help, in turn, 
to build adaptive capacity to climate change in this 
vulnerable socio-economical system.  
 
KEY WORDS: Artisanal fisheries, weather, optimal 
foraging theory, climate change, adaptation 
 
LITERATURE CITED 
 
Chollett, I. and P. Mumby. 2012. Predicting the distribution of Monastrea 
reefs using wave exposure. Coral Reefs 31(2):493-503.  


Modelling Fishers' Activity as a Response to Weather and Economy: First Step Towards Building Adaptive Capacity under a Changing Climate

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Modelling Fishers' Activity as a Response to Weather and Economy: First Step Towards Building Adaptive Capacity under a Changing Climate

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