Tools for designing a joint monitoring programme

Transcription

1 Tools for designing a joint monitoring programme Anne Sell, Francisco Marco Rius, Julia Wischnewski, Gert Van Hoey Thünen Institute of Sea Fisheries, Germany Institute of Agriculture and Fishery research, Belgium

2 Objectives Joint monitoring why? The Marine Strategy Framework Directive calls for measures to achieve Good Environmental Status in European marine ecosystems Indicator based monitoring shall assess ecosystem status Due to the federal structure of the EU, each country will report for their waters But: Ecological processes are not restricted by national boundaries, and ecosystem understanding requires an holistic approach. => A Joint Monitoring Programme in EU waters would: Include complementary sampling schemes of the countries involved => provide full coverage and reduce duplication Assure standardization of methods Allow monitoring the indicators of ecosystem status at the relevant spatial scales across the region of the European sea Increase efficiency by finding the best options for coupled monitoring of multiple indicators

3 Case Study: Sharks & Rays Elasmobranchs (sharks & Rays): Species of interest as possible indicators of ecosystem state. Different degree to which they are targeted by fisheries. Several species with conservation plans. => Case study includes a combination of 8 species. Opportunities: Existing international surveys; longterm data series. Challenge: Data deficiency due to low abundances. Need to judge population status based on rare occurence. Little access to additional data beyond the international fisheries surveys. Existing fisheries surveys have independent main objectives.

4 Current sampling programme Sampling design of the IBTS survey A systematic grid of ICES rectangles, sampled twice annually In the following, we propose alternative sampling designs, which proved more efficient for our case studies. To be kept in mind: In reality, the IBTS has mutliple additional objectives, which are not covered in our analysis!

5 Tools [I] Stratification of the survey area Statistical approach stratification Goal: Most efficient sampling for an indicator Achieving the best representation of the entire population/indicator with a given amount of effort. The sampling effort needed for same accuracy may be reduced through stratification. Process of stratification: Deviding the survey area into suitable (spatial) subunits, which are sampled and evaluated separately. The variability between the strata is greater than the variability within the strata

6 Tools [I] Stratification based on response variable Case Study: Sharks & Rays Stratification for a single indicator regression tree approach Station latitude and longitude as discriminating factor 2 3 > 51.8 N N 1.2 W > 1.2 W 6.4 E > 6.4 E

7 Optimization of sampling design for multiple indicators Stratification based on ecosystem characteristics Stratification based on a combination of parameters that remain rather constant over time => Ecosystem based stratification We used the strata defined in the EU FP7 project VECTORS for the ecosystem model Atlantis (Hufnagl et al., unpublished data)

8 Tools [II] Allocation of stations Methods for allocation of samples Allocation of stations between strata Different options exist for allocating stations to the defined strata. In JMP, we applied the Neyman allocation procedure, because it best most effectively maximized survey precision (reduction of variance). It takes more variables into account than just the size of the strata (as in figure =>)

9 Optimization of sampling design for multiple indicators

10 Optimization of sampling design for multiple indicators Henk Heessen Raja clavata Thornback ray

11 Optimization of sampling design for multiple indicators Niels Daan Scyliorhinus canicula Lesser spotted dogfish

12 Tools [III] Breakpoint analysis for appropriate sample size

13 Tools [III] Breakpoint analysis for appropriate sample size

14 Raja clavata Thornback ray Henk Heessen 800 stations per 3 year period (~ 270/ yr) 1200 stations per 3 yrs (~ 400/ yr) 1800 stations per 3 yrs (~ 600/ yr) Conditions in this Case Study: Zero inflated and overdispersed catch data => negative binomial distribution model => temporal trend non linear

15 Scyliorhinus canicula Lesser spotted dogfish Niels Daan 800 stations per 3 year period (~ 270/ yr) 1200 stations per 3 yrs (~ 400/ yr) 1800 stations per 3 yrs (~ 600/ yr) Conditions in this Case Study: Zero inflated and overdispersed catch data => negative binomial distribution model => temporal trend non linear

16 Joint monitoring program for three case studies, based on ecosystem based stratification ( Atlantis ), and allocation of stations to the individual strata according to Neyman approach. Stations in map: samples recommended per case study for one common sampling event. Total station number is reduced where possible through combined sampling for 2 or 3 case studies.