Dynamic Ocean Management Increases the Efficiency and Efficacy of Fisheries Management

In response to the inherent dynamic nature of the oceans and continuing difficulty in managing ecosystem impacts of fisheries, interest in the concept of dynamic ocean management, or real-time management of ocean resources, has accelerated in the last several years. However, scientists have yet to quantitatively assess the efficiency of dynamic management over static management. Of particular interest is how scale influences effectiveness, both in terms of how it reflects underlying ecological processes and how this relates to potential efficiency gains. Here, we address the empirical evidence gap and further the ecological theory underpinning dynamic management. We illustrate, through the simulation of closures across a range of spatiotemporal scales, that dynamic ocean management can address previously intractable problems at scales associated with coactive and social patterns (e.g., competition, predation, niche partitioning, parasitism, and social aggregations). Furthermore, it can significantly improve the efficiency of management: as the resolution of the closures used increases (i.e., as the closures become more targeted), the percentage of target catch forgone or displaced decreases, the reduction ratio (bycatch/catch) increases, and the total time-area required to achieve the desired bycatch reduction decreases. In the scenario examined, coarser scale management measures (annual time-area closures and monthly full-fishery closures) would displace up to four to five times the target catch and require 100-200 times more square kilometer-days of closure than dynamic measures (grid-based closures and move-on rules). To achieve similar reductions in juvenile bycatch, the fishery would forgo or displace between USD 15-52 million in landings using a static approach over a dynamic management approach

Dynamic Bayesian Networks as a Decision Support Tool for assessing Climate Change impacts on highly stressed groundwater systems

Bayesian Networks (BNs) are powerful tools for assessing and predicting consequences of water management scenarios and uncertain drivers like climate change, integrating available scientific knowledge with the interests of the multiple stakeholders. However, among their major limitations, the non-transient treatment of the cause-effect relationship stands out. A Decision Support System (DSS) based on Dynamic Bayesian Networks (DBNs) is proposed here aimed to palliate that limitation through time slicing technique. The DSS comprises several classes (Object-Oriented BN networks), especially designed for future 5 years length time steps (time slices), covering a total control period of 30 years (2070-2100). The DSS has been developed for assessing impacts generated by different Climate Change (CC) scenarios (generated from several Regional Climatic Models (RCMs) under two emission scenarios, A1B and A2) in an aquifer system (Serral-Salinas) affected by intensive groundwater use over the last 30 years. A calibrated continuous water balance model was used to generate hydrological CC scenarios, and then a groundwater flow model (MODFLOW) was employed in order to analyze the aquifer behavior under CC conditions. Results obtained from both models were used as input for the DSS, considering rainfall, aquifer recharge, variation of piezometric levels and temporal evolution of aquifer storage as the main hydrological components of the aquifer system. Results show the evolution of the aquifer storage for each future time step under different climate change conditions and under controlled water management interventions. This type of applications would allow establishing potential adaptation strategies for aquifer systems as the CC comes into effectThis study has been partially supported by the European Community 7th Framework Project GENESIS (226536) on groundwater systems and from the subprogram Juan de la Cierva (2010) of the Spanish Ministry of Science and Innovation as well as from the Plan Nacional I+D+i 2008-2011 of the Spanish Ministry of Science and Innovation (Subprojects CGL2009-13238-C02-01 and CGL2009-13238-C02-02). T. Finally, the authors want to thank the Segura River Basin Agency (Confederacion Hidrografica del Segura) for the data and information facilitated, and to all the stakeholders who have collaborated in this research.Molina, JL.; Pulido Velázquez, D.; García-Arostegui, J.; Pulido-Velazquez, M. (2013). Dynamic Bayesian Networks as a Decision Support Tool for assessing Climate Change impacts on highly stressed groundwater systems. Journal of Hydrology. 479:113-129. https://doi.org/10.1016/j.jhydrol.2012.11.038S11312947

A computational approach to managing coupled human–environmental systems: the POSEIDON model of ocean fisheries

Sustainable management of complex human–environment systems, and the essential services they provide, remains a major challenge, felt from local to global scales. These systems are typically highly dynamic and hard to predict, particularly in the context of rapid environmental change, where novel sets of conditions drive coupled socio-economic-environmental responses. Faced with these challenges, our tools for policy development, while informed by the past experience, must not be unduly constrained; they must allow equally for both the fine-tuning of successful existing approaches and the generation of novel ones in unbiased ways. We study ocean fisheries as an example class of complex human–environmental systems, and present a new model (POSEIDON) and computational approach to policy design. The model includes an adaptive agent-based representation of a fishing fleet, coupled to a simplified ocean ecology model. The agents (fishing boats) do not have programmed responses based on empirical data, but respond adaptively, as a group, to their environment (including policy constraints). This conceptual model captures qualitatively a wide range of empirically observed fleet behaviour, in response to a broad set of policies. Within this framework, we define policy objectives (of arbitrary complexity) and use Bayesian optimization over multiple model runs to find policy parameters that best meet the goals. The trade-offs inherent in this approach are explored explicitly. Taking this further, optimization is used to generate novel hybrid policies. We illustrate this approach using simulated examples, in which policy prescriptions generated by our computational methods are counterintuitive and thus unlikely to be identified by conventional frameworks

Examining Movement Dynamics of the Gulf Menhaden Fishery Using an Individual-based Model

This study investigates the movement and harvest dynamics of the Gulf Menhaden Brevoortia patronus fishery. The fishery-dependent data collected by NOAA (years 2006-2009 and 2011) describe vessel-specific information on catch locations (latitude and longitude) and magnitude of harvest in metric tons (mt). A series of probability distribution functions (PDFs) were fit to the frequency distributions of number of harvests per day (Poisson), between-harvest distances (gamma), and harvest magnitude (log-normal). These analyses were used to inform an individual-based model (IBM). The IBM was run under several different spatial restriction regimes, including (1) current regulations in Texas, Louisiana, Mississippi, and Alabama; (2) additional restrictions off the coast of Jackson County, MS; (3) an extension of current regulations to 2.6 km (two miles) from shore; and (4) closures of all Mississippi waters. This study describes fleet dynamics of one of the more important commercial fisheries in the region and illustrates how they can be simulated using a spatially-explicit IB

Towards Behavioural Models of Fleet Dynamics

Considerable attention has been applied to the development of models explaining how fish stocks change over space and time, from relatively simple stock-recruitment relationships to ecosystem models with a complex food web structure. However, in many case studies fishing effort is assumed to be exogenous and even in dynamic models pre-determined by external factors such as management. An increasing number of operational bio-economic models for the marine environment are including a fleet dynamics component, recognising that fishers respond to changing environmental and economic conditions. These models are based on different assumptions about behaviour. In this review, we examine models of fisher behaviour that are empirically or theoretically developed and tested. In particular, we focus on the range of different economic and social drivers and their relevance to different types of behaviour. We find that economic factors are a dominant driver included in many types of short term behavioural models, although we acknowledge and find that other social factors play a significant role in some types of behaviour. The link between management and economic impacts is reasonably well established, and most bioeconomic models are able to incorporate these feedback systems. Incorporating these other social-dependent behaviours into bioeconomic models, however, is likely to remain a challenge unless links between management and changes in the social drivers are developed.Keywords: Fish and Aquaculture Sectors Development, Fisheries Economics, Fishers Behavio

Extreme events, water quality and health: a participatory Bayesian risk assessment tool for managers of reservoirs

Extreme weather events pose major challenges for the delivery of safe drinking water, especially in a country like Australia. As a consequence, a participatory Bayesian Network modelling approach was used to develop a risk assessment tool for estimating, and ranking, water quality-related health risks associated with extreme weather events. The model was developed for a large dam supplying a water treatment plant in New South Wales, Australia. This methodological approach addresses challenges associated with fragmented data (for model parameterisation) and parameter uncertainty by eliciting and integrating quantitative and qualitative data (including expert opinions) into a single framework. Key-stakeholders were engaged in developing and then refining separate conceptual models around the three critical parameters of turbidity, water colour and Cryptosporidium sp. These three conceptual models were then combined into a single conceptual model, which then formed the basis for the Bayesian Network model. The final risk assessment tool was able to quantify the sensitivity of the water treatment plant's efficacy (ability to supply high quality potable water) in response to different extreme event scenarios. Overall, landslip-related events were the most concerning for water quality-related health risks, but an emergent outcome was how the scenarios were ranked quite differently depending on the group, and expertise of the stakeholders’ opinions used to run the model. Such tool can assist stakeholders for an effective long-term water resource management

Preferences and Values for the Gulf Coast Ocean Observing System

Integrated Ocean Observing Systems (IOOS) provide real time oceanic data and sea state forecasting information that is utilized by numerous public and private sectors engaging in maritime activities. The U.S. Gulf Coast constituent of this system (GCOOS) consists of 321 platforms, buoys, and sensors that provide measurements of wind speed, wave height, water quality, and other parameters. Government entities have proposed an expansion of this infrastructure by 40% at an estimated cost of $35 million for installation and$33 million annually for maintenance. As part of a larger project commissioned to estimate monetized benefits of this expansion, this study applied contingent valuation (CVM) methodology in a survey of avid IOOS users located in the Gulf and Atlantic regions of the United States (N=18,000; n=484). The objective was to estimate general preferences for IOOS data and specific values for the proposed GCOOS expansion. A probit model was used to examine factors associated with a respondent’s likelihood to support the expansion under a public referendum. Responses were solicited via six randomized treatments containing varying tax levels. A majority of respondents (74%) indicated support for the measure, with imputed willingness-to-pay (WTP) estimates ranging from $14.11 and$36.47 annually. Consistent with economic theory, the dollar amount of the tax was significant and negatively associated with referendum support. Proxies for avidity; however, proved either irrelevant or contrary to hypothesized effects. Vessel ownership, vessel size, distance traveled, and hours per trip were non-factors while the number of trips taken proved to be a significant, but negative predictor of referendum outcome. Alternatively, Gulf respondents engaged in fishing and fee-based services were more likely to support the measure indicating that proximity could be a more influential driver than avidity. Interpretation of these results is limited by the relatively small population queried. A broader depiction will emerge parallel versions of this survey are completed with larger populations. Taken together, these studies should prove valuable in characterizing preferences for IOOS data, assessing the economic merit of GCOOS expansion, and demonstrating the potential for non-market approaches in the valuation of publically-funded information systems

Understanding fleet behaviour to reduce uncertainty in tuna fisheries management

The behaviour of a fishing fleet is a critical, but all too often overlooked, uncertainty in the implementation of fisheries management. Unexpected responses by fishers to management controls, such as effort restrictions or spatial closures, can result in unintended and potentially undesirable outcomes. Whilst this uncertainty can be reduced by anticipating the behavioural response of a fishing fleet, it is first necessary to understand the characteristics and drivers of fleet behaviour. The aim of this thesis was to address gaps in knowledge of the behaviour of offshore tuna fleets, using the Indian Ocean tropical tuna purse seine fishery as a case study example. I used statistical modelling to examine the factors that influence the spatial behaviour of the purse seine fleet at broad spatiotemporal scales. This analysis revealed consistency in the use of seasonal fishing grounds by the fleet, as well as a forcing influence of biophysical ocean conditions on the allocation of effort. These findings, which suggested strong inertia in fleet spatial behaviour, have important implications for predicting the response of the fleet to certain natural events or management measures (e.g. spatial closures). To better understand the impact of spatial closures on purse seine fleet dynamics, I used the statistical model of fleet behaviour to isolate the policy effect of two recent closures on fleet behaviour. By comparing the observed behaviour of the fleet against a model-generated counterfactual scenario I revealed, in the case of one of the closures, a policy effect that was inconsistent between years, and that the absence of fishing effort in the closed area was explained primarily by biophysical ocean conditions. These findings demonstrate the importance of using a counterfactual approach to evaluate spatial closures in open ocean systems where fleet behaviour is influenced by highly variable biophysical conditions. Fish aggregating devices (FADs) have become a dominant fishing practice in tuna purse seine fisheries worldwide, and I examined the influence of the use of FADs on purse seine fleet dynamics in the Indian Ocean. I reviewed historical catch trends and spatiotemporal patterns of fleet behaviour and linked this to the use of FADs. I also reviewed the existing management of FAD-fishing and speculated at the influence of possible future management measures on the behaviour of the fleet. Finally, I used a scenario planning approach to think about how the main drivers of purse seine fleet behaviour might change in the future, and how this might affect fleet dynamics. This analysis served to highlight aspects of purse seine fleet behaviour that should be a priority consideration of tuna fishery managers and policy makers. This thesis showed fleet behaviour to be a dynamic aspect of tuna fisheries management, and stressed the importance of anticipating the response of fleets to management measures in order to avoid unintentional outcomes. The understanding of purse seine fleet behaviour developed throughout this thesis provides a good basis for building the anticipation of fleet behaviour into existing management tools and processes.Open Acces

Resilience, collapse and reorganization of a rangeland socio-ecological system in South Africa

Communal rangelands in semi-arid areas are complex socio-ecological systems (SES). Their complexity arises from non-linear feedbacks between the social- and the ecosystem. To understand the social system requires tackling institutional issues associated with common pool resource governance. Moreover, assessing ecosystem dynamics commands to acknowledge high climatic variability in semi-arid areas. This thesis quantifies the dynamics of a communal livestock production SES in a former homeland of South Africa using a SES modelling approach. Here, a social agent based model is combined with a biomass growth model of the rangeland. The coupling of both models is achieved by full integration on software (Java) level. Accordingly, the resulting model does account for ecological complexity. The latter constitutes a contribution to the methodological advancement of bio-economic modelling insofar as bio-economic models strongly simplify ecological processes. The SES model is specified based on primary data from a case study. On a conceptual level, the three main chapters in this thesis investigate aspects of SES resilience, collapse and reorganization. Specifically, chapter two assesses social welfare impacts from reorganizing resource use by the adjustment of stocking rates and alterations of spatio-temporal grazing patterns. Chapter 3 explores the effect of a local norm on SES dynamics with a focus on collapse vs. stability. Finally, chapter 4 quantifies the resilience on multiple scales of the SES towards droughts, a loss of social embededdness and a significant change in subsidization. We found that the adjustment of stocking rates yields higher social benefits compared to the (re)-introduction of rotational grazing in a system assumed to be void of institutional arrangements. In a second step, we identified the existence of a local norm indirectly impacting resource use by endogenous stocking rate adjustments. The existence of the informal institution significantly contributes to the long-term stability of the SES by reducing the chance for collapse. The emergence of norm-following behaviour is fostered by climatic variability. The SES was resilient towards droughts and a change in subsidization. It was however not resilient towards a loss in social embededdness. At another level, only the introduction of a basic income grant was able to stop a process of structural change eroding household resilience. The introduction of a basic income grant enabled poorer households to successfully compete with richer ones without jeopardizing the resilience of the coupled system