Marine Protected Areas: Smart Investments in Ocean Health

Expanding ocean protection could return an increase in jobs, resources and services that far outweigh the costs, according to an analysis of new research commissioned by WWF on marine protected areas. The analysis comes months before governments make critical decisions that will direct the fate of the ocean for generations to come.The analysis shows that every dollar invested to create marine protected areas – commonly known as MPAs – is expected to be at least tripled in benefits returned through factors like employment, coastal protection, and fisheries.The new analysis is based on a WWF-commissioned study produced by Amsterdam's VU University, modelling MPA expansion at both the 10 per cent and 30 per cent target levels. The report found that increased protection of critical habitats could result in net benefits of between US$490 billion and US$920 billion accruing over the period 2015-2050. WWF recommends 30 per cent global coverage of MPAs by 2030 in order to secure the most complete benefits for people and the ocean

Data from: The potential for spatial distribution indices to signal thresholds in marine fish biomass

The frequently observed positive relationship between fish population abundance and spatial distribution suggests that changes in distribution can be indicative of trends in abundance. If contractions in spatial distribution precede declines in spawning stock biomass (SSB), spatial distribution reference points could complement the SSB reference points that are commonly used in marine conservation biology and fisheries management. When relevant spatial distribution information is integrated into fisheries management and recovery plans, risks and uncertainties associated with a plan based solely on the SSB criterion would be reduced. To assess the added value of spatial distribution data, we examine the relationship between SSB and four metrics of spatial distribution intended to reflect changes in population range, concentration, and density for 10 demersal populations (9 species) inhabiting the Scotian Shelf, Northwest Atlantic. Our primary purpose is to assess their potential to serve as indices of SSB, using fisheries independent survey data. We find that metrics of density offer the best correlate of spawner biomass. A decline in the frequency of encountering high density areas is associated with, and in a few cases preceded by, rapid declines in SSB in 6 of 10 populations. Density-based indices have considerable potential to serve both as an indicator of SSB and as spatially based reference points in fisheries management

Data from: The potential for spatial distribution indices to signal thresholds in marine fish biomass

The frequently observed positive relationship between fish population abundance and spatial distribution suggests that changes in distribution can be indicative of trends in abundance. If contractions in spatial distribution precede declines in spawning stock biomass (SSB), spatial distribution reference points could complement the SSB reference points that are commonly used in marine conservation biology and fisheries management. When relevant spatial distribution information is integrated into fisheries management and recovery plans, risks and uncertainties associated with a plan based solely on the SSB criterion would be reduced. To assess the added value of spatial distribution data, we examine the relationship between SSB and four metrics of spatial distribution intended to reflect changes in population range, concentration, and density for 10 demersal populations (9 species) inhabiting the Scotian Shelf, Northwest Atlantic. Our primary purpose is to assess their potential to serve as indices of SSB, using fisheries independent survey data. We find that metrics of density offer the best correlate of spawner biomass. A decline in the frequency of encountering high density areas is associated with, and in a few cases preceded by, rapid declines in SSB in 6 of 10 populations. Density-based indices have considerable potential to serve both as an indicator of SSB and as spatially based reference points in fisheries management

S1 Stock assessment model information

Stock, Spawning Stock Biomass model, Sourc

S2 Dataset SSB and source data

Year, Spawning Stock Biomass in tonnes, Spawning Stock Number tonnes (x1000), Area, Strata, Species and Data sourc

Exponent c values with 95% confidence interval for different categories of High Density Area (HDA) for six stocks on the Scotian Shelf from significant concave relationships according to <i>SSB</i> = <i>b</i> * <i>HDA</i>^<i>c</i>.

<p>Exponent c values with 95% confidence interval for different categories of High Density Area (HDA) for six stocks on the Scotian Shelf from significant concave relationships according to <i>SSB</i> = <i>b</i> * <i>HDA</i>^<i>c</i>.</p

Area Occupied (AO) and Spawning Stock Biomass (SSB) time series (left) and associated cross correlation functions (right).

<p>These show maximum cross correlation between AO and SSB at a lag h = -1 for Western Scotian Shelf (WSS) cod and Eastern Scotian Shelf (ESS) cod and h = -2 for winter flounder (See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120500#pone.0120500.t004" target="_blank">Table 4</a> for the models used to create the time series residuals).</p

High Density Area (HDA) 10-Spawning Stock Biomass (SSB) time series (left) and associated cross correlation functions (right).

<p>These show maximum cross correlation between HDA 10 and SSB at a lag h = -1 for Western Scotian Shelf (WSS) cod and Eastern Scotian Shelf (ESS) cod and h = -6 for American plaice (See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120500#pone.0120500.t004" target="_blank">Table 4</a> for the models used to create the time series residuals).</p

Significant convex relationships between Area Occupied (AO) and Spawning Stock Biomass (SSB) for Western Scotian Shelf (WSS) cod, Eastern Scotian Shelf (ESS) cod and between AO and Spawning Stock Number (SSN) for winter flounder 4X, according to <i>SSB</i> = b * <i>AO</i>^<i>c</i>.

<p>Significant convex relationships between Area Occupied (AO) and Spawning Stock Biomass (SSB) for Western Scotian Shelf (WSS) cod, Eastern Scotian Shelf (ESS) cod and between AO and Spawning Stock Number (SSN) for winter flounder 4X, according to <i>SSB</i> = b * <i>AO</i>^<i>c</i>.</p

Results of the time series analyses of concave (type I) and convex (type III) Spatial Distribution Metric (SDM)- Spawning Stock Biomass (SSB) relationships wherein SDM precedes changes in SSB, indicated by negative lag h.

<p>The time series residuals were obtained using OLS: ordinary least squares regression, GLS-AR1: generalized least squares regression with an auto-correlation or auto-regressive process of order 1, GLS-ARMA (p,q): generalized least squares regression with a p order auto-regressive and q order moving average process, Log: indicates that the SSB time series was log-transformed before applying regression analysis.</p><p>*Indicates the SDM is inverted. A negative <i>ϱ</i>_<i>SDM</i>,_S<i>SB</i> indicates a negative relationship between SDM and SSB.</p><p>Results of the time series analyses of concave (type I) and convex (type III) Spatial Distribution Metric (SDM)- Spawning Stock Biomass (SSB) relationships wherein SDM precedes changes in SSB, indicated by negative lag h.</p