Descriptive Statistics.

<p>Descriptive Statistics.</p

The number of respondents who indicated with which frequency they would go diving more often per year in TURFs that have greater diversity and abundance of fish and invertebrates if there was a clear policy TURF dive access policy.

<p>N = 135.</p

Results of multinomial logistic regression with willingness to pay as the dependent variable.

<p>Not willing to pay was used as the reference category. Pseudo R² = 0.118 (Cox and Snell), 0.134 (Nagelkerke). Model Chi Squared (<i>df</i> = 4) = 17.008, <i>p</i> = 0.02.</p

The species and taxonomic groups indicated by respondents in response to the questions: which fish and invertebrates do you most like to observe when you go diving on the coast of central Chile.

<p>The species and taxonomic groups indicated by respondents in response to the questions: which fish and invertebrates do you most like to observe when you go diving on the coast of central Chile.</p

Information used to calculate the two components of the predicted fishing effort: fishing capacity and fishing suitability.

<p>(a) Spatial representation of sub-criteria weights measured using the AHP methodology. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176862#pone.0176862.s004" target="_blank">S2 File</a> for additional information on the approach used to map sub-criteria weights. (b) Households’ dependence on marine resources. Dots represent households and colors indicate their district-level level of dependence. (c) Fishing capacity, calculated using the cumulated distance to households within a 2-km radius, weighted by their level of dependence on marine resources.</p

Artisanal Spearfishery in Temperate Nearshore Ecosystems of Chile: Exploring the Catch Composition, Revenue, and Management Needs

<p>We used extensive field data on catch and effort as well as fisher interviews to characterize the catch composition and revenue associated with the unregulated artisanal spearfishery in Chile (18–33°S). Sampling was performed on commercial spearfishing trips (snorkel and hookah diving gear) between spring 2010 and summer 2011. Two-way crossed ANOVA showed significant effects of region (latitude) and dive gear on fishery variables such as biomass CPUE (CPUE<i>_b</i>), numeric CPUE (CPUE<i>_n</i>), catch species richness, fishing depth, cost, and income. Catches included 22 fish species from 15 families. Among the 23 species, 17 were associated with temperate rocky reef habitats: 14 carnivorous species, 2 omnivorous species, and 1 herbivorous species. Our results indicated that smaller, less-valuable rocky reef fishes (e.g., Peruvian Morwong <i>Cheilodactylus variegatus</i>, Chilean Sandperch <i>Pinguipes chilensis</i>, and Peruvian Rock Seabass <i>Paralabrax humeralis</i>) supported higher CPUE<i>_b</i> and CPUE<i>_n</i> than large, high-value, emblematic rocky reef species (e.g., Vieja <i>Graus nigra</i>, Galapagos Sheephead Wrasse <i>Semicossyphus darwini</i>, and Acha <i>Medialuna ancietae</i>). The CPUE<i>_b</i> was significantly higher for hookah fishers than for snorkel fishers. Our results revealed that artisanal spearfishing activities provide important revenue for the fishers (2–3 times the minimum monthly wage in Chile), thereby incentivizing a rapid expansion of this unregulated fishery. Management options based on territorial user rights and catch and size restrictions are discussed in light of these findings.</p> <p>Received December 19, 2013; accepted March 29, 2016</p

Spatial variation in predicted fishing effort.

<p>(a) Island-wide analysis highlights a high level of fine-scale spatial variation in predicted fishing effort. Highly exposed areas include (b) Taotaha, (c) Papetoai, (d) Atiha and (e) Maharepa villages.</p

Map of Moorea Island, French Polynesia.

<p>Orange circles represent the location of households surveyed to quantify criteria and sub-criteria weights. Thick lines denote municipality boundaries and thin lines district boundaries. Key place names are indicated either in blue (reef passages) or in black (villages).</p

Averaged sub-criteria weights (+/- 95%CI) obtained from AHP exercises performed with local fishers to estimate their preference for fishing grounds.

<p>The three sub-criteria ranked higher are indicated in bold.</p

Conceptual flowchart for selecting the best approach to map fishing effort according to availability of three critical factors to be considered by practitioners, namely the complexity of the social-ecological context, the availability of human and financial resources and the degree of cooperation possible with local fishers (i.e., mutual trust level).

<p>Techniques commonly used in each type of approach are indicated in boxes. The accuracy of each approach for place-based management (i.e., reliability of the gathered information, level of accuracy/resolution achieved and add-on information gathered during data collection) is provided. Although providing the most accurate estimates of fishing effort, fisheries approaches are unlikely to work in most small-scale fisheries due to the inherent complexity of the social-ecological context and the recurrent lack of logistical resources. Depending on the degree of participants’ engagement in the participatory process, information gathered through participatory approaches can be either highly (e.g., using self-reporting diaries and map-based interviews) or moderately (e.g., collective mapping of seascapes values and weightings of spatially-explicit criteria through Multiple-Criteria Decision Analysis) relevant for place-based management. Socioeconomic approaches rely on the extrapolation of social surrogates such as total or coastal population density, fisher or vessel density and may therefore fail to represent fine-scale patterns of the fishing effort (i.e., low accuracy for place-based management). The approach we present here proposes to combine the ability of participatory approaches to map fishers’ spatial preference (i.e., fishing suitability) with the power of socioeconomic approaches to estimate the fishery’s ability to extract resources (i.e., fishing capacity) and create fine-scale information on the spatial distribution of the fishing effort.</p