Ecological Data Requirements to Support Ecosystem-based Fisheries Management. Examples from Pelagic Longline Tuna Fisheries

Transcription

1 Ecological Data Requirements to Support Ecosystem-based Fisheries Management Examples from Pelagic Longline Tuna Fisheries 2 nd Symposium on Fishery Dependent Information 3-6 March 2014 Eric Gilman, EricLGilman@gmail.com Hawaii Pacific University

2 EBFM as an extension to conventional management Data requirements for data-deficient to data-rich ERAs Data requirements to monitor & manage problematic bycatch State of RFMO s ecosystem-based bycatch management Data requirements to estimate cryptic fishing mortality Data requirements to estimate community-level effects of fishing Recommendations to support transitioning to meet data requirements for EBFM

3 Ecosystem Approach to Fisheries Management Tries to sustain both integrity of the whole ecosystem and its services. Tries to balance competing societal objectives and equitably distribute benefits. Extends (& doesn t replace) conventional single-stock, single-fishery approaches. Conventional Maximize single-stock sustainable yield through stockspecific target & limit reference points & harvest control rules Narrow scale, single stocks of principal market species & single fisheries MCS&E for stocks of market species EAF Extension Maximize multispecies sustainable yield through ecosystem pressure, state and response indicators, ecosystem thresholds, and control rules that consider effects of fishing on target and associated and dependent species, habitat, and broader collateral effects (e.g., altered food web, reduced diversity) Nested scales, all manifestations of biodiversity (populations, stocks, habitats, communities) within a defined ecosystem, and regional to local fisheries Ecosystem-level MCS&E - associated & dependent species, habitat, collateral effects

4 Data Requirements for ERAs ERAs of the effects of fishing for data-poor to -rich fisheries can have objectives of identifying relative risks within or across affected species groups, absolute risks to populations, effects on habitat, and ecosystem-level effects. Data requirements increase and uncertainty declines as move from qualitative to quantitative methods. Qualitative (e.g., Scale Intensity Consequence Analysis). Data requirements = e.g., expert opinion on scale & intensity of exposure and consequences on the unit (stock/population/habitat/ecosystem). Semi-quantitative (e.g., Productivity Susceptibility Analysis most common approach by tuna RFMOs). Data requirements = see example - Cortes et al Model-based quantitative (e.g., conventional stock assessments, population and ecosystem models). Pop model data requirements = e.g., index of abundance, estimates of anthropogenic and natural mortality. Hybrids: (E.g., Marshall Islands LL fishery ERA - a mix of level 1 a& 2 methods)

5 Data Requirements for Problematic Bycatch Fisheries that target relatively fecund species w/ r-selected life history characteristics, such as tunas, can cause large impacts on incidentally caught associated & dependent species w/ K-selected lifehistory strategies, including seabirds, sea turtles, marine mammals & elasmobranchs. Considerations for data requirements to understand & manage problematic bycatch: Sampling methods. Random & balanced sampling across ports, vessel categories, season, fishing grounds. Monitoring rate. Objectives of analyses, frequency of bycatch interactions, amount of fishing effort, and distribution of catch and bycatch determine requisite onboard observer coverage rate. Electronic monitoring can complement human observer programs. Data collection protocols by observers, port-samplers, for logbooks, in experiments. E.g., may need data on total retained and discarded catch, fishing effort, gear designs, fishing methods, environmental parameters, data to assess efficacy of control measures. E.g., Data from the Hawaii longline observer program and experiments have been used to document temporal trends in bycatch, factors that significantly explain seabird, turtle, shark and cetacean catch rates and mortality, and efficacy of mitigation measures.

6 Mean 25% (±16% SD of the population) 2013 IUCN study assessed RFMOs ecosystem-based bycatch governance. Findings related to data requirements: Half of information needed to assess bycatch measures efficacy is collected by regional observers. Over two-thirds of RFMO-managed fisheries lack regional observer coverage. Of 13 RFMOs, only CCAMLR monitors ecosystem indicators (top predator populations) to assess ecosystem effects of prey fisheries.

7 EBFM Data Requirements: Cryptic Fishing Mortality EBFM requires estimates of TOTAL fishing mortality, including from cryptic, not readily detectable, sources. In demersal trawling, (i) escapees and released organisms may be predated; (ii) fisherydegraded habitat can cause increased predation & competition for shelter; (iii) cumulative stress from repeatedly avoiding capture can eventually contribute to mortality; and (iv) ghost fishing mortality occurs in derelict gear. There are some methods to estimate cryptic removals - e.g., we estimated seabird precatch losses in the Hawaii longline fishery by comparing the number observed caught during setting to the number retrieved; & satellite tags are used to estimate post-release survival.

8 EBFM Data Requirements: Collateral Effects There are large data requirements to understand community-level, collateral effects. Example 1: pelagic longline selective removal of apex predators has resulted in a top-down trophic effect by releasing pressure and increasing abundance of smaller-sized mid-trophic level species, altering the ecosystem size structure. Example 2: because tunas and other subsurface predators bring baitfish to the surface, fishery-reduced abundance of tunas reduces the availability of prey to seabirds (Au and Pitman 1986; Ballance et 1997; Hebshi et 2008).

9 Meeting EBFM Ecological Data Requirements Even rudimentary fisheries governance systems with limited data can transition to EBFM. Some simple steps towards EBFM are inexpensive and feasible now, e.g., employing ERA methods for data-deficient fisheries, and small changes to data collection protocols. Single-sector EBFM needs to be part of cross-sectoral marine spatial planning and EBM. E.g., in addition to fishing mortality, some marine species are subject to a wide range of other anthropogenic mortality sources, where effective governance of all these sources is needed. Furthermore, successful mitigation of the main global drivers of change and loss in marine biodiversity that adversely affect the fishing industry but are nominally caused by fishing, including marine pollution, climate change, habitat degradation & spread of IAS, will increasingly require cross-sectoral planning & management. Harmonizing RFMO data collection protocols would enable sharing resources for training & monitoring, and pooling datasets. Market-based mechanisms like MSC certification and FIPs can complement RFMO and domestic efforts to gradually transition to meeting data requirements to implement EBFM.