Abstract
Decisions regarding the selection and implementation of management strategies that constrain fishing pressure can be among the most difficult choices that fisheries managers and stakeholders must make. These types of decisions often need to be confronted in a data-limited context, where few if any management measures are currently in place or fisheries are managed independent of adequate scientific advice. This situation can sometimes create a high risk of overfishing and potential loss of economic and social benefits. To address this situation, simple model-free indicator-based frameworks have the potential to be effective decision-making platforms for fisheries where quantitative estimates of biomass and fishing mortality based reference points are lacking. In this paper, a multi-indicator framework is developed that enables decision-makers to proceed with management decisions in data-limited situations. Model-free indicators are calculated using trends in observed data, rather than stock assessment derived estimates of biomass and fishing mortality. The framework developed is adaptive so that adjustments to catch or effort are recursive and can respond to changing environments, socioeconomic conditions, and fishing practices. Using stakeholder-defined objectives as a foundation, indicators and reference points of fishery performance are chosen that can be evaluated easily by undertaking analyses of available data. Indicators from multiple data streams are used so that uncertainty in one indicator can be hedged through careful interpretation and corroboration of information from alternative indicators. During the adaptive management cycle, managers and stakeholders evaluate each indicator against the associated reference points to determine performance measures, interpret the results using scientific and local knowledge, and adjust fishery management tactics accordingly using pre-defined harvest control rules. The framework facilitates the interpretation of situations in which performance measures suggest divergent stock abundance or productivity levels. A case study is presented on this framework's development for conch and lobster fisheries of Belize.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 28-37 |
| Number of pages | 10 |
| Journal | Marine Policy |
| Volume | 76 |
| DOIs | |
| State | Published - Feb 1 2017 |
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Keywords
- Adaptive management
- Data-limited fisheries
- Decision-support frameworks
- Model-free indicators
ASJC Scopus subject areas
- Aquatic Science
- Environmental Science(all)
- Economics and Econometrics
- Management, Monitoring, Policy and Law
- Law
Cite this
An indicator-based adaptive management framework and its development for data-limited fisheries in Belize. / McDonald, Gavin; Harford, Bill; Arrivillaga, Alejandro; Babcock, Elizabeth A; Carcamo, Ramon; Foley, James; Fujita, Rod; Gedamke, Todd; Gibson, Janet; Karr, Kendra; Robinson, Julie; Wilson, Jono.
In: Marine Policy, Vol. 76, 01.02.2017, p. 28-37.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An indicator-based adaptive management framework and its development for data-limited fisheries in Belize
AU - McDonald, Gavin
AU - Harford, Bill
AU - Arrivillaga, Alejandro
AU - Babcock, Elizabeth A
AU - Carcamo, Ramon
AU - Foley, James
AU - Fujita, Rod
AU - Gedamke, Todd
AU - Gibson, Janet
AU - Karr, Kendra
AU - Robinson, Julie
AU - Wilson, Jono
PY - 2017/2/1
Y1 - 2017/2/1
N2 - Decisions regarding the selection and implementation of management strategies that constrain fishing pressure can be among the most difficult choices that fisheries managers and stakeholders must make. These types of decisions often need to be confronted in a data-limited context, where few if any management measures are currently in place or fisheries are managed independent of adequate scientific advice. This situation can sometimes create a high risk of overfishing and potential loss of economic and social benefits. To address this situation, simple model-free indicator-based frameworks have the potential to be effective decision-making platforms for fisheries where quantitative estimates of biomass and fishing mortality based reference points are lacking. In this paper, a multi-indicator framework is developed that enables decision-makers to proceed with management decisions in data-limited situations. Model-free indicators are calculated using trends in observed data, rather than stock assessment derived estimates of biomass and fishing mortality. The framework developed is adaptive so that adjustments to catch or effort are recursive and can respond to changing environments, socioeconomic conditions, and fishing practices. Using stakeholder-defined objectives as a foundation, indicators and reference points of fishery performance are chosen that can be evaluated easily by undertaking analyses of available data. Indicators from multiple data streams are used so that uncertainty in one indicator can be hedged through careful interpretation and corroboration of information from alternative indicators. During the adaptive management cycle, managers and stakeholders evaluate each indicator against the associated reference points to determine performance measures, interpret the results using scientific and local knowledge, and adjust fishery management tactics accordingly using pre-defined harvest control rules. The framework facilitates the interpretation of situations in which performance measures suggest divergent stock abundance or productivity levels. A case study is presented on this framework's development for conch and lobster fisheries of Belize.
AB - Decisions regarding the selection and implementation of management strategies that constrain fishing pressure can be among the most difficult choices that fisheries managers and stakeholders must make. These types of decisions often need to be confronted in a data-limited context, where few if any management measures are currently in place or fisheries are managed independent of adequate scientific advice. This situation can sometimes create a high risk of overfishing and potential loss of economic and social benefits. To address this situation, simple model-free indicator-based frameworks have the potential to be effective decision-making platforms for fisheries where quantitative estimates of biomass and fishing mortality based reference points are lacking. In this paper, a multi-indicator framework is developed that enables decision-makers to proceed with management decisions in data-limited situations. Model-free indicators are calculated using trends in observed data, rather than stock assessment derived estimates of biomass and fishing mortality. The framework developed is adaptive so that adjustments to catch or effort are recursive and can respond to changing environments, socioeconomic conditions, and fishing practices. Using stakeholder-defined objectives as a foundation, indicators and reference points of fishery performance are chosen that can be evaluated easily by undertaking analyses of available data. Indicators from multiple data streams are used so that uncertainty in one indicator can be hedged through careful interpretation and corroboration of information from alternative indicators. During the adaptive management cycle, managers and stakeholders evaluate each indicator against the associated reference points to determine performance measures, interpret the results using scientific and local knowledge, and adjust fishery management tactics accordingly using pre-defined harvest control rules. The framework facilitates the interpretation of situations in which performance measures suggest divergent stock abundance or productivity levels. A case study is presented on this framework's development for conch and lobster fisheries of Belize.
KW - Adaptive management
KW - Data-limited fisheries
KW - Decision-support frameworks
KW - Model-free indicators
UR - http://www.scopus.com/inward/record.url?scp=84996773896&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996773896&partnerID=8YFLogxK
U2 - 10.1016/j.marpol.2016.11.027
DO - 10.1016/j.marpol.2016.11.027
M3 - Article
AN - SCOPUS:84996773896
VL - 76
SP - 28
EP - 37
JO - Marine Policy
JF - Marine Policy
SN - 0308-597X
ER -