1. The spatio-temporal dynamics of fishing behaviour and its determinants in poor fishing communities Nick Hill 1,2 EJ Milner-Gulland 1, Marcus Rowcliffe 2, Guy Cowlishaw 2 1 Imperial College London. 2 Institute of Zoology, Zoological Society of London

2. Outline East Africa and small-scale fisheries Introduction to study site Livelihoods Effects on fishing behaviour

3. East Africa East Africa coastal population 22 million – 5-6% increase per annum Dependence on marine resources. Degradation: serious concern for livelihoods Failure to reduce degradation and poverty – Why?

4. Failure of fisheries management Why the failure? Behaviour poorly understood. Data aggregated spatially Data aggregated across fleet Consequence: Unable to predict response to management Policies and management unsuitable What is needed? Understanding of behaviour and its drivers

5. Study site Vamizi and Rongui Islands

6. Study site Tourism supports conservation and community development The only local development organization

7. Study site The marine resources are in excellent condition.

8. Vamizi Island communities Two community types: –Locals –Transients Each have different livelihoods

9. Hypothesis Livelihoods Fishing behaviour The role of fishing in livelihoods... …spatio-temporal dynamics of fishing: where, when and how much...effects... Fishing behaviour is driven by livelihoods: A livelihood: the capabilities, assets (including social and material resources) and activities required for a means of living. Objectives of fishing

10. Livelihoods Potential roles of fishing in livelihoods:

11. Livelihoods Livelihoods of transient communities? Livelihoods of local communities

12. Effect on fishing behaviour Temporal behaviour: – Frequency of fishing – Length of fishing trips – Investment in gear – Seasonality of fishing

13. Effect on fishing behaviour Environment Travel time Habitat Resource distribution Fishing location Landing sites Spatial behaviour Method: weighted suitability modelling Comparison of weightings for fishermen with different livelihoods Verification with fishing tracks

14. Effect on catch rates p < 0.05 n=22 n=35 n=28

15. Effect on catch rates

16. Conclusion Are spatio-temporal dynamics of fishing behaviour driven by livelihoods? Linking individual fishing behaviour to livelihoods may help us predict the effects of interventions.

17. Thank you! Maluan / Cabo Delgado Biodiversity and Tourism Project, Mozambique