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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Mar. Sci.</journal-id>
<journal-title>Frontiers in Marine Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Mar. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-7745</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fmars.2024.1358111</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Marine Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Impacts of locally managed periodic octopus fishery closures in Comoros and Madagascar: short-term benefits amidst long-term decline</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>McCabe</surname>
<given-names>Mary K.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
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<contrib contrib-type="author">
<name>
<surname>Mudge</surname>
<given-names>Laura</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
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<contrib contrib-type="author">
<name>
<surname>Randrianjafimanana</surname>
<given-names>Tahiry</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<contrib contrib-type="author">
<name>
<surname>Rasolofoarivony</surname>
<given-names>Nestin</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<contrib contrib-type="author">
<name>
<surname>Vessaz</surname>
<given-names>Fanny</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
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<contrib contrib-type="author">
<name>
<surname>Rakotonirainy</surname>
<given-names>Robertin</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Said</surname>
<given-names>Faissoil Ahmed</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
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<contrib contrib-type="author">
<name>
<surname>Rakotoarisoa</surname>
<given-names>Fran&#xe7;ois</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<contrib contrib-type="author">
<name>
<surname>Ratoniherison</surname>
<given-names>Tsiorisoa</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<contrib contrib-type="author">
<name>
<surname>Box</surname>
<given-names>Steve</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Cox</surname>
<given-names>Courtney</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
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<aff id="aff1">
<sup>1</sup>
<institution>Barefoot Ocean</institution>, <addr-line>Houston, TX</addr-line>, <country>United States</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Blue Ventures Conservation</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Dahari</institution>, <addr-line>Mutsamudu</addr-line>, <country>Comoros</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Carlo Pipitone, CNR - IAS Institute of Anthropic Impacts and Sustainability in Marine Environment, Italy</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Pablo Pita, University of A Coru&#xf1;a, Spain</p>
<p>Warwick Hugo Sauer, Rhodes University, South Africa</p>
<p>Edison D. Macusi, Davao Oriental State University (DOrSU), Philippines</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Mary K. McCabe, <email xlink:href="mailto:katie@barefootocean.org">katie@barefootocean.org</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>10</day>
<month>05</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>11</volume>
<elocation-id>1358111</elocation-id>
<history>
<date date-type="received">
<day>19</day>
<month>12</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>16</day>
<month>04</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 McCabe, Mudge, Randrianjafimanana, Rasolofoarivony, Vessaz, Rakotonirainy, Said, Rakotoarisoa, Ratoniherison, Box and Cox</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>McCabe, Mudge, Randrianjafimanana, Rasolofoarivony, Vessaz, Rakotonirainy, Said, Rakotoarisoa, Ratoniherison, Box and Cox</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Octopus fisheries face heightened demand driven by depleted fish stocks and escalating economic value, leading to overextraction of octopus populations and magnifying threats to coastal ecosystems and the livelihoods of fishers who depend on them. Implementation of management strategies such as periodic fishing closures can offer sanctuary for octopus, providing the necessary space and time for the growth and reproduction of this rapidly proliferating species. Despite evidence of positive management outcomes, the role of periodic closures in the long-term sustainability of octopus populations remains uncertain. Here, we examine trends in octopus landings over time and the effects of periodic closures along the Mozambique Channel from Comoros to Madagascar (12&#xb0;-25&#xb0;S). Long-term trends in octopus landings varied across three regions: generally stable over a 6-year period in Comoros, increasing over a 4-year period in the Barren Isles and declining over a 17-year period in Atsimo Andrefana, Madagascar. We observed a significant increase in mean catch per unit effort (CPUE, kilograms/fisher-day), and mean total catch (kilograms/day) following periodic closures across villages extracting octopus from closure areas. When evaluating each region independently, we observed this positive effect across Comoros and Atsimo Andrefana, Madagascar while trends in the Barren Isles showed no effect of octopus closures. We applied generalized linear mixed models to examine the influence of 10 covariates on mean daily total catch and CPUE within 7 days and 30 days post-closure. Baseline conditions (quantified by the pre-closure CPUE and daily total catch), closure duration and start month of closure had positive, statistically significant correlations with post-closure CPUE and daily total catch. Additionally, closure duration from 13 to 16 weeks and closure size of approximately 70 hectares were qualitatively associated with net gains in CPUE. Although periodic closures yield positive outcomes for octopus fisheries, the effects may be short-lived, as observed in Atsimo Andrefana, Madagascar, where catch declined over the long-term despite temporary improvements. Our findings underscore a need for a diverse portfolio of management strategies to restore and sustain populations over the long-term.</p>
</abstract>
<kwd-group>
<kwd>periodic closure</kwd>
<kwd>octopus</kwd>
<kwd>fishery</kwd>
<kwd>management</kwd>
<kwd>West Indian Ocean</kwd>
<kwd>Africa</kwd>
<kwd>Comoros</kwd>
<kwd>Madagascar</kwd>
</kwd-group>
<counts>
<fig-count count="7"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="80"/>
<page-count count="17"/>
<word-count count="8225"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Marine Fisheries, Aquaculture and Living Resources</meta-value>
</custom-meta>
</custom-meta-wrap>
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</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Octopus fisheries are an integral part of the diet and economy of coastal communities and are threatened by multiple stressors including climate change, overfishing, illegal fishing and habitat destruction (<xref ref-type="bibr" rid="B6">Barnes-Mauthe et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B51">Muttenzer and Andriamahefazafy, 2021</xref>; <xref ref-type="bibr" rid="B24">Cooke, 2022</xref>; <xref ref-type="bibr" rid="B29">Farquhar et&#xa0;al., 2022</xref>). Octopuses play an important ecological role as both predator and prey in the marine food web, thus contributing to the overall health and balance of the coastal ecosystem (<xref ref-type="bibr" rid="B85">Yarnall, 1969</xref>; <xref ref-type="bibr" rid="B31">Forsythe and Hanlon, 1997</xref>; <xref ref-type="bibr" rid="B35">Guard, 2009</xref>). The emergence of global markets has shifted many small-scale fisheries from primarily subsistence-based to trade-focused, consequently exposing marine resources to the peril of overexploitation (<xref ref-type="bibr" rid="B41">Iida, 2005</xref>; <xref ref-type="bibr" rid="B26">Cripps and Gardner, 2016</xref>). Moreover, declining traditional finfish catches have compelled fishers to shift their attention to invertebrates, often leading to cascading impacts on the marine food web (<xref ref-type="bibr" rid="B3">Anderson et&#xa0;al., 2011</xref>). Today, octopus fisheries are expanding worldwide to meet demand and fill market gaps left by overexploited fish species, such as tuna, grouper and snapper (<xref ref-type="bibr" rid="B64">Sauer et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B56">Ospina-Alvarez et&#xa0;al., 2022</xref>). From East Africa to Hawaii, small-scale octopus fisheries produce upwards of 88,000 tons of catch a year (<xref ref-type="bibr" rid="B83">Willer et&#xa0;al., 2023</xref>). <italic>Octopus cyanea</italic>, also known as the day or big blue octopus, is a steadily harvested resource from Indo-Pacific reefs (<xref ref-type="bibr" rid="B52">Norman, 1991</xref>; <xref ref-type="bibr" rid="B44">Loganimoce et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B83">Willer et&#xa0;al., 2023</xref>). Approximately 3,000 tons of octopus are exported annually from the West Indian Ocean (WIO), worth an estimated value of US$12.2 million per year (<xref ref-type="bibr" rid="B61">Rocliffe and Harris, 2016</xref>).</p>
<p>Small-scale fisheries in the coastal villages of southwest Madagascar are estimated to employ 87% of the adult population and generate approximately 82% of household income (<xref ref-type="bibr" rid="B6">Barnes-Mauthe et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B54">Oliver et&#xa0;al., 2015</xref>). Fishers from Madagascar are part of a larger market of buyers and exporters that supply octopus to foreign markets (<xref ref-type="bibr" rid="B40">Humber et&#xa0;al., 2006</xref>). The Barren Isles fishery is seasonal, with fishers migrating annually to the islands from April to November following local weather patterns (<xref ref-type="bibr" rid="B51">Muttenzer and Andriamahefazafy, 2021</xref>). The arid climate and soil profiles of western Madagascar do not support large agricultural production, one of many reasons why the cultural identity and economy of the Vezo (&#x2018;people of the sea&#x2019;) are naturally intertwined with the highly productive marine systems of the Mozambique Channel (<xref ref-type="bibr" rid="B5">Astuti, 1995</xref>). Throughout the Comoros Islands, local fish consumption can constitute as much as 50% of landings, with large pelagic fish making up approximately 75% of the country&#x2019;s overall fish consumption (<xref ref-type="bibr" rid="B13">Breuil and Grima, 2014</xref>). Comoros lacks the capacity for octopus storage and transportation to support larger export markets and hence remains a subsistence-based fishery with most fishers also maintaining other sources of income (<xref ref-type="bibr" rid="B1">Ahamada et&#xa0;al., 2008</xref>; <xref ref-type="bibr" rid="B37">Hauzer et&#xa0;al., 2013a</xref>; <xref ref-type="bibr" rid="B33">Freed and Granek, 2014</xref>).</p>
<p>As the demand and price of octopus have risen, the once predominantly female fishers that glean for octopus in the rocky intertidal at low tide have been joined by more male fishers that spear dive at deeper depths, impacting catch across all regions (<xref ref-type="bibr" rid="B36">Guard and Mgaya, 2002</xref>; <xref ref-type="bibr" rid="B27">Doherty et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B61">Rocliffe and Harris, 2016</xref>). Both male and female fishing associations exist in Comoros, where men historically fished for pelagic and reef fishes on small-scale fleets of pirogues and motorboats (<xref ref-type="bibr" rid="B27">Doherty et&#xa0;al., 2015</xref>). However, interviews with local fishers reported declines in catch abundance and mean fish sizes in recent decades (<xref ref-type="bibr" rid="B37">Hauzer et&#xa0;al., 2013a</xref>, <xref ref-type="bibr" rid="B38">b</xref>), suggesting more fishers may now be participating in the octopus fishery and &#x2018;fishing down&#x2019; marine food webs (<xref ref-type="bibr" rid="B61">Rocliffe and Harris, 2016</xref>; <xref ref-type="bibr" rid="B64">Sauer et&#xa0;al., 2021</xref>).</p>
<p>The small-scale octopus fisheries of Madagascar and Comoros are managed both nationally and locally. Seasonal octopus closures banning all octopus harvesting on the west coast of Madagascar (from December 15 to January 31) and on the east coast of Madagascar (from June 1 to July 15) were established by the national government in 2005. A minimum size limit of 350g was also established by the national government for <italic>O. cyanea</italic> (<xref ref-type="bibr" rid="B61">Rocliffe and Harris, 2016</xref>). However, since the most common method of fishing (gleaning) is unselective, size limits are difficult to implement and enforce. As an additional measure to safeguard well-established traditional fishing grounds, local management groups and fishing associations implement community-level periodic closures of a predefined fishing ground that can vary in duration and size, typically occurring outside or in complement to the national closure (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;1</bold>
</xref>). These closures typically represent approximately one-fifth of a village&#x2019;s total fishing grounds. When implemented in conjunction with other management strategies like size limits, gear restrictions, species bans and limited access, periodic closures can expedite population recovery and maximize catch (<xref ref-type="bibr" rid="B42">Johannes, 1982</xref>; <xref ref-type="bibr" rid="B22">Cohen and Foale, 2013</xref>; <xref ref-type="bibr" rid="B16">Carvalho et&#xa0;al., 2019</xref>).</p>
<p>In Madagascar, the first locally-led periodic closure was implemented in the village of Ankereo, Atsimo Andrefana, in 2004. Initial success of this management initiative catalyzed the establishment of the first locally managed marine area (LMMA) in Madagascar and inspired neighboring villages to establish similar management structures with closure implementations. Today, independent civil society network initiatives such as MIHARI, a national Malagasy network of LMMAs, connects fishers across the country by supporting workshops, advocating for community rights and fisheries reform. The research results and best practices shared through these network forums have prompted numerous management measures across the country, including the initial Barren Isles octopus closures in 2018. Subsequently, the Vezo Miray Nosy Barren Association was founded as an officially recognized legal entity entrusted with the responsibility of overseeing the LMMA in this area. The management associations in southwest Madagascar and Barren Isles are legally created and empowered to set and enforce local rules known as Dina and manage local resources in their designated areas. In the Comoros archipelago, periodic closures were first established in 2016 supported by the Moheli National Park and governmental (WorldBank) projects. While LMMAs have not been created in Comoros as of yet, locally managed male and female fishing associations exist in some villages and implement periodic closures in collaboration with local authorities.</p>
<p>Considering octopus life history traits when designing closure parameters such as duration, season, habitat and size can increase the probability of octopus reaching reproductive age, enabling fishers to capture larger octopus with comparable effort (<xref ref-type="bibr" rid="B40">Humber et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B28">Emery et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B34">Goetze et&#xa0;al., 2018</xref>). This, in turn, can lead to increased income for fishers (<xref ref-type="bibr" rid="B54">Oliver et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B69">Silas et&#xa0;al., 2022</xref>). Periodic closures are argued to be most advantageous to this fast-growing invertebrate during reproduction and brooding seasons to protect larval cohorts (<xref ref-type="bibr" rid="B22">Cohen and Foale, 2013</xref>; <xref ref-type="bibr" rid="B28">Emery et&#xa0;al., 2016</xref>), as well as the post-settlement stage, when growth rates are maximized (<xref ref-type="bibr" rid="B11">Benbow et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B54">Oliver et&#xa0;al., 2015</xref>). Females are known to brood in the deep rocky subtidal (~100m) (<xref ref-type="bibr" rid="B78">Van Heukelem, 1976</xref>; <xref ref-type="bibr" rid="B39">Herwig et&#xa0;al., 2012</xref>). <italic>O. cyanea</italic> paralarvae can spend approximately 30 to 60 days in the water column traveling more than 100 km before settling in a benthic coral environment (<xref ref-type="bibr" rid="B81">Wells and Wells, 1970</xref>; <xref ref-type="bibr" rid="B79">Van Heukelem, 1973</xref>; <xref ref-type="bibr" rid="B12">Bower et&#xa0;al., 1999</xref>; <xref ref-type="bibr" rid="B17">Casu et&#xa0;al., 2002</xref>; <xref ref-type="bibr" rid="B50">Murphy et&#xa0;al., 2002</xref>). Local environmental conditions such as wind speed, sea surface temperature (SST) and chlorophyll-a can affect octopus landings; often on a time lag due to their influence on food availability at the paralarval stage (<xref ref-type="bibr" rid="B20">Chande et&#xa0;al., 2021</xref>). Growth and maturation can vary greatly based on latitude and local environmental factors (<xref ref-type="bibr" rid="B36">Guard and Mgaya, 2002</xref>; <xref ref-type="bibr" rid="B39">Herwig et&#xa0;al., 2012</xref>; <xref ref-type="bibr" rid="B59">Raberinary and Benbow, 2012</xref>; <xref ref-type="bibr" rid="B70">Silas et&#xa0;al., 2021</xref>). Based on the life-history study of <italic>O. cyanea</italic>, researchers in Tanzania have recommended (April-July) periodic closures to protect growth periods; or the use of rotational closures for these cyclic spawners (<xref ref-type="bibr" rid="B36">Guard and Mgaya, 2002</xref>; <xref ref-type="bibr" rid="B20">Chande et&#xa0;al., 2021</xref>). Past studies from Madagascar recommended closures from June to August and September to November to protect settlers from recruitment peaks during maximum growth periods (<xref ref-type="bibr" rid="B59">Raberinary and Benbow, 2012</xref>; <xref ref-type="bibr" rid="B54">Oliver et&#xa0;al., 2015</xref>). The high plasticity of octopus life history traits and opportunistic feeding regimes are ideal for managing adaptive octopod populations via closures amid modern fishery and climate stressors (<xref ref-type="bibr" rid="B15">Caballero-Alfonso et&#xa0;al., 2010</xref>; <xref ref-type="bibr" rid="B58">Quetglas et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B63">Rodhouse, 2013</xref>; <xref ref-type="bibr" rid="B62">Rodhouse et&#xa0;al., 2014</xref>). Studies suggest that octopus fisheries could be benefiting from a changing ocean environment; octopuses were found to grow quicker and mature earlier at increased temperatures (<xref ref-type="bibr" rid="B32">Forsythe and Hanlon, 1988</xref>; <xref ref-type="bibr" rid="B18">Caverivi&#xe8;re et&#xa0;al., 1999</xref>; <xref ref-type="bibr" rid="B68">Semmens et&#xa0;al., 2004</xref>; <xref ref-type="bibr" rid="B4">Andr&#xe9; et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B77">Uriarte et&#xa0;al., 2012</xref>; <xref ref-type="bibr" rid="B14">Caamal-Monsreal et&#xa0;al., 2016</xref>). These plastic and condensed biological stages make octopus fisheries adaptable to future climate scenarios and suitable for dynamic closure schedules.</p>
<p>While there is evidence suggesting that periodic closures of octopus fisheries have effectively enhanced fisher catch and income, further research is required to explore optimal closure design parameters and formulate long-term management strategies (<xref ref-type="bibr" rid="B10">Benbow and Harris, 2011</xref>; <xref ref-type="bibr" rid="B54">Oliver et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B69">Silas et&#xa0;al., 2022</xref>). Here, we examine long-term trends in octopus catch (&gt; 4 years) and short-term effects of periodic octopus closures (7 and 30 days) across Madagascar and Comoros. Our analyses establish baselines for octopus catch, identify major drivers of positive closure outcomes and provide management recommendations for designing effective closures. Additionally, we examine the ongoing use of closures in the Atsimo Andrefana region of Madagascar.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Study system</title>
<p>Octopus landings and periodic closure data were analyzed from three regions in Southeast Africa situated along the Mozambique Channel (<xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1A, B</bold>
</xref>). The selection of these three regions was informed by the continued efforts of local communities to implement periodic closures and the availability of reliable octopus landings data. The first region is located in Comoros (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1C</bold>
</xref>) on the southwest side of the island Anjouan (Nzwani). The other two regions are situated on the west side of Madagascar: the Barren Isles in the Melaky region (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1D</bold>
</xref>), which is locally managed by the Vezo Miray Nosy Barren (VMNB) Association and the southwest region located within the Atsimo Andrefana region (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1E</bold>
</xref>), which includes, Manjaboake, Sambele, Soariake, Teariake and Velondriake LMMAs. The latitudinal range of our tropical study region extends from 12&#xb0;S to 25&#xb0;S.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Map of Africa <bold>(A)</bold> with an inset of the sampling region <bold>(B)</bold> focusing on octopus fishery closures on the island of Anjouan (Nzwani), Comoros Islands <bold>(C)</bold>, the Barren Isles, Madagascar <bold>(D)</bold> and the Atsimo Andrefana province, Madagascar <bold>(E)</bold>. Blue polygons represent the closure areas. Black circles are the villages where octopus landings have been sampled. Villages that were included in the closure analyses are colored blue.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1358111-g001.tif"/>
</fig>
<p>Circulation patterns through the Mozambique Channel originate from the WIO. The westward flow of the South Equatorial Current (SEC) is deflected on the northern tip of Madagascar imparting a series of clockwise and anticlockwise eddies and gyres around the Comoros archipelago (<xref ref-type="bibr" rid="B67">Schott et&#xa0;al., 2009</xref>). The main current ultimately splits with the East African Coastal Current (EACC) carrying water north past Tanzania and a complex system of mesoscale (100-300 km) eddies dominating meridional circulation south through the Mozambique Channel (<xref ref-type="bibr" rid="B23">Collins et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B72">Ternon et&#xa0;al., 2014</xref>). The eddies force deep water toward the surface, promoting localized upwelling and a high level of marine productivity and biodiversity (<xref ref-type="bibr" rid="B72">Ternon et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B21">Charles et&#xa0;al., 2020</xref>). Comoros is located in the Northern Mozambique Channel where coral reefs are reported to be the most structurally complex and diverse in the WIO (<xref ref-type="bibr" rid="B53">Obura et&#xa0;al., 2018</xref>). The Barren Isles lie at the shallower, narrow boundary adjacent to the Davie Ridge, which separates the two basins of North and South Mozambique Channel (<xref ref-type="bibr" rid="B21">Charles et&#xa0;al., 2020</xref>). The Atsimo Andrefana region is geographically influenced by the Southwest Madagascar Coastal Current (SMACC), which flows south opposite to the dominant winds, impacting local upwelling and larval transport patterns (<xref ref-type="bibr" rid="B60">Ramanantsoa et&#xa0;al., 2018</xref>). Complex connectivity and dispersal patterns exist throughout the Channel, evident by the differentiation of genetic lineages in some, but not all marine species across known barriers (<xref ref-type="bibr" rid="B53">Obura et&#xa0;al., 2018</xref>). Genetic studies of <italic>O. cyanea</italic> found a high level of connectivity among WIO populations, with low but significant differentiation between North and South Madagascar due to potential restricted gene flow (<xref ref-type="bibr" rid="B80">Van Nieuwenhove et&#xa0;al., 2019</xref>). Understanding circulation, dispersal and connectivity patterns can aid in the identification of important marine areas in the region.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Periodic closures</title>
<p>Local community groups determined the location, size, temporal period (season) and duration of periodic closures with the support of partners dedicated to supporting small-scale fishery management including Blue Ventures, Dahari, Reef Doctor, Wildlife Conservation Society (WCS), World Wildlife Fund (WWF), Institut Halieutique et des Sciences Marines (IHSM) and government authorities. Participatory mapping exercises allowed community groups to define spatial boundaries of each closure in Google Earth. Areas were typically closed from 6 to 19 weeks and ranged in size from 7 to 245 ha. A closure is determined by the spatial area restricted from fishing, and a closure event is characterized by both the designated spatial area and the dates during which fishing is prohibited. The initiation month of each closure&#x2019;s temporal period was determined using a combination of traditional knowledge and reproductive studies from each region. All octopus fishing is prohibited in closure areas during closure events. Closure surveillance is organized by local communities across all regions. This process typically includes 2-3 individuals patrolling the area several times a week during both the day and night. Surveillance efforts were assumed to be consistent across closure events. Incorporating quantitative measures of surveillance and enforcement into future studies is recommended to strengthen our understanding of their impact on periodic closures.</p>
<p>To evaluate the effectiveness of periodic closures, we analyzed landings data by village rather than focusing solely on octopus harvested from closure areas. Fishers commonly harvest from various locations during a single trip, making it unreliable to assign individual octopus to specific fishing sites. Moreover, this approach allowed us to capture trends of all landed octopus, not just those harvested from closure areas. This is important for understanding changes during closure periods as fishers shift fishing activities to open areas. A total of 449 closure events occurred across the three regions between 2004 and 2022. Changes in octopus landings were analyzed for both a period of 7-days and 30-days before and after a closure. These time intervals were chosen to examine the closure effect after the first tidal series (~7days) and into the second or third set of spring low tides (~30days). For inclusion in the analysis, a closure event required a minimum of three days of observations both before and after the closure, with at least five fishers and ten octopuses documented (<xref ref-type="bibr" rid="B54">Oliver et&#xa0;al., 2015</xref>). For the 30-day period, 93 out of the 449 closure effects had sufficient data for analysis; and for the 7-day period, 64 out of 449 closure events had sufficient data for analysis (see <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;1</bold>
</xref> for full closure details).</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Octopus landings</title>
<p>Octopus catch data was collected at landing sites across 45 villages. In each village, enumerators collected octopus landings data at access points between the village and the reef or at local village markets. Enumerators included both women and men from the local community. Involving community members in this process builds local capacity for gathering and understanding fisheries data while ensuring consistency, as fishers trust the data collector. NGOs provided enumerator trainings on fisher survey content and data collection methodology. Data were collected using standard survey forms recorded on paper or through one of two digital systems: KoboToolbox (<ext-link ext-link-type="uri" xlink:href="https://www.kobotoolbox.org/">https://www.kobotoolbox.org/</ext-link>) in Madagascar and Abalobi (<ext-link ext-link-type="uri" xlink:href="https://abalobi.org/">https://abalobi.org/</ext-link>) in Comoros. To ensure data quality throughout the year, ongoing training and support were provided, accompanied by data verification processes. Data collectors aimed to record octopus landings four to five days per week throughout the year, documenting information such as date, village, fisher&#x2019;s name, gender, species, octopus sex, total catch weight, individual weight, fishing site, fishing method and gear type. Given that enumerators are community members it was not possible to collect data every day. Data was recorded on the same days each week as much as possible to reduce bias. Daily catch per unit effort (kg/fisher-day), daily total octopus catch (kg/day) and the number of fishers (n/day) were estimated for each village associated with a closure. Refer to <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref> for a summary of octopus data collected across all villages and time periods.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Closure events and octopus data summaries across regions.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left"/>
<th valign="top" align="left">Comoros*</th>
<th valign="top" align="left">Barren Isles**</th>
<th valign="top" align="left">Atsimo Andrefana</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Number of villages</td>
<td valign="top" align="left">5</td>
<td valign="top" align="left">7</td>
<td valign="top" align="left">33</td>
</tr>
<tr>
<td valign="top" align="left">Number of trips</td>
<td valign="top" align="left">11,132</td>
<td valign="top" align="left">2,576</td>
<td valign="top" align="left">335,318</td>
</tr>
<tr>
<td valign="top" align="left">Total closure events</td>
<td valign="top" align="left">17</td>
<td valign="top" align="left">36</td>
<td valign="top" align="left">396</td>
</tr>
<tr>
<td valign="top" align="left">Closure start months</td>
<td valign="top" align="left">January, May, November</td>
<td valign="top" align="left">June</td>
<td valign="top" align="left">May, June, July, September, November, December</td>
</tr>
<tr>
<td valign="top" align="left">Closure duration range</td>
<td valign="top" align="left">32 - 142 days</td>
<td valign="top" align="left">52 - 89 days</td>
<td valign="top" align="left">32 - 216 days</td>
</tr>
<tr>
<td valign="top" align="left">Dates of<break/>data collection</td>
<td valign="top" align="left">4/27/2017 -<break/>10/29/2022</td>
<td valign="top" align="left">6/3/2018 -<break/>08/31/2022</td>
<td valign="top" align="left">11/9/2004 - 12/15/2021</td>
</tr>
<tr>
<td valign="top" align="left">Total days recorded</td>
<td valign="top" align="left">714 days</td>
<td valign="top" align="left">238 days</td>
<td valign="top" align="left">4212 days</td>
</tr>
<tr>
<td valign="top" align="left">Total kg recorded</td>
<td valign="top" align="left">20,629.4 kg</td>
<td valign="top" align="left">16,802.2 kg</td>
<td valign="top" align="left">844,419.3 kg</td>
</tr>
<tr>
<td valign="top" align="left">Mean CPUE by fisher</td>
<td valign="top" align="left">1.62 kg/fisher-day</td>
<td valign="top" align="left">5.23 kg/fisher-day</td>
<td valign="top" align="left">2.29 kg/fisher-day</td>
</tr>
<tr>
<td valign="top" align="left">Mean CPUE by trip</td>
<td valign="top" align="left">1.62 kg/trip-day</td>
<td valign="top" align="left">5.23 kg/trip-day</td>
<td valign="top" align="left">2.53 kg/trip-day</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>*Includes only closures from the island of Anjouan (Nzwani). **Data was only collected in the 30 days pre/post closures at three locations in Barren Isles: Nosy Marify, Nosy Maroantaly and Nosy Lava.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Environmental variables</title>
<p>Habitat type, sea surface temperature (SST) and larval connectivity have the potential to influence variability in octopus catch (<xref ref-type="bibr" rid="B66">Scheel et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B80">Van Nieuwenhove et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B20">Chande et&#xa0;al., 2021</xref>). To account for these environmental factors across our study regions and villages, we obtained data on habitat coverage, mean SST and larval import at each closure location. Although wind speed may also have an impact on octopus catch, we did not include it in our analysis because of insufficient satellite data coverage throughout our study period. <italic>O. cyanea</italic> live and forage within rocky substrate and coral reefs, while also maintaining dens in naturally occurring holes in the hard substratum (<xref ref-type="bibr" rid="B31">Forsythe and Hanlon, 1997</xref>). Benthic habitat maps, sourced from the <xref ref-type="bibr" rid="B2">Allen Coral Atlas (2022)</xref>, were intersected with closure area shapefiles in QGIS to determine the proportion of the closure covered by suitable octopus habitat. Coverage (%) of each habitat class was calculated by dividing the intersected or overlapping habitat type area by the closure&#x2019;s total area. The classes of coral/algae, rubble and rock were combined (summed percent cover) to create a &#x201c;coral complex&#x201d; class that is of ecological importance to our targeted coastal octopus populations.</p>
<p>The mean temperature over the four months preceding the opening date of each closure was determined using satellite-derived SST. The temperature during this time period is representative of conditions that can potentially influence growth during the larval and juvenile stage and size of octopus when harvested (<xref ref-type="bibr" rid="B70">Silas et&#xa0;al., 2021</xref>). Data was extracted from NOAA ERDDAP satellite database using the &#x2018;rerddap&#x2019; and &#x2018;rerddapXtracto&#x2019; packages in R (<xref ref-type="bibr" rid="B47">Mendelssohn, 2022</xref>; <xref ref-type="bibr" rid="B19">Chamberlain, 2023</xref>). The date range, latitude, longitude and a radius of 0.2-degree units, was used to define the parameters for satellite data extracted at each closure location. Average SST (&#x2018;erdMH1sstd8dayR20190SQ&#x2019;) was obtained from AquaMODIS 8-day composite data (<ext-link ext-link-type="uri" xlink:href="https://coastwatch.pfeg.noaa.gov/erddap/">https://coastwatch.pfeg.noaa.gov/erddap/</ext-link>).</p>
<p>Ecological connectivity, estimated as larval import, was derived from a species-specific biophysical model simulating probable dispersal and settlement patterns of four key ecological and commercial fish species (<italic>Epinephelus malabaricus, Lethrinus lentjan, Caesio teres</italic> and <italic>Scarus ghobban</italic>) across coral reef habitats in Mozambique, Comoros and the western coast of Madagascar (Treml and Critchell, 2020<xref ref-type="fn" rid="fn1">
<sup>1</sup>
</xref>). The model was developed to inform marine reserve network design supported by an international NGO, Rare, using aspatially explicit biophysical modelling framework (<xref ref-type="bibr" rid="B73">Treml et&#xa0;al., 2008</xref>, <xref ref-type="bibr" rid="B74">Treml et&#xa0;al., 2012</xref>, <xref ref-type="bibr" rid="B75">Treml et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B43">Krueck et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B48">Muenzel et&#xa0;al., 2023</xref>). Twenty years of hydrodynamic data, the product of an ocean circulation model developed in the Delft3D modelling system, were used to force the biophysical model. The native model output had an unstructured variable resolution ranging from 1 km nearshore and around 15 km near the open ocean boundaries. The biophysical model incorporated species-specific biological parameters including relative adult density, reproductive output, spawning phenology, periods of pre-competency, competency, dispersal duration and larval mortality. The life history parameters determine when and what proportion of larvae settle in habitat cells over time. Larval import was defined as the product of the number of nodes that a focal node is connected to and the average weight to these nodes adjusted by the tuning parameter (alpha) of 0.5 which gives equal importance to both the number and weight of connections (<xref ref-type="bibr" rid="B55">Opsahl et&#xa0;al., 2010</xref>). Across the modeled species, larval competency ranged from 10 to 24 days and pelagic larval duration (PLD) ranged from 31 to 43 days. Spawning windows included January to April, March to August, and year-round. The larval mortality rate during the dispersal period was assumed to be 5% per day for all species (<xref ref-type="bibr" rid="B82">White et&#xa0;al., 2014</xref>). Octopus have a typical pelagic larval duration of 30 to 60 days, competency window of approximately 15 days and likely spawn throughout the year. As biophysical models for octopus were not conducted within our study area, we used the mean larval import value across the four modeled fish species to represent general ecological connectivity at coral reef patches across Madagascar and Comoros. See <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;2</bold>
</xref> for a full list of environmental and contextual variables.</p>
<sec id="s2_4_1">
<label>2.4.1</label>
<title>Trend analysis in octopus catch</title>
<p>To understand the trends in octopus catch over the entire duration of data collection, we evaluated trends in the average monthly CPUE (kg/fisher-day) and total catch (kg/day) for each region. Data was aggregated by month across each village to account for potential inaccuracies and gaps in daily data collection. As not all villages had comprehensive time series data (with some data collection focused only around closure periods), a regional trend analysis provided a more accurate depiction of the general context regarding the variation in octopus catch over time. We used generalized additive models (GAMs) to test the relationships between mean monthly CPUE (kg/fisher-day) and total catch (kg/day) over time. GAMs were preferred to generalized linear models as we expected non-linear relationships between variables. The best models were chosen based on the lowest Akaike&#x2019;s information criterion (AIC) and all models used a Gamma distribution and log function. Smoothing parameters were estimated using REML. GAMs were run using the &#x2018;mgcv&#x2019; package (<xref ref-type="bibr" rid="B84">Wood, 2011</xref>) in R. The model fits were validated by examining the residual plots.</p>
</sec>
<sec id="s2_4_2">
<label>2.4.2</label>
<title>Impact of closures on octopus catch</title>
<p>Paired Wilcoxon tests were used to determine significant differences in our response variables (i.e. CPUE or total catch) before and after a closure, within both 7-day and 30-day time intervals. These differences were evaluated across all regions and within each individual region. To identify effective size and duration of closures, we performed a breakpoint regression analysis between each of these variables and the absolute change in mean CPUE before and after a closure. We used the &#x2018;segmented&#x2019; package in R (<xref ref-type="bibr" rid="B49">Muggeo, 2017</xref>) to identify significant change points which may represent a minimum duration or size of closure that provides a net positive change in CPUE.</p>
</sec>
<sec id="s2_4_3">
<label>2.4.3</label>
<title>Drivers of closure success</title>
<p>A mixed model statistical approach was used to evaluate the relationship between response and predictor variables associated with each closure. Using the lme4 package in R (<xref ref-type="bibr" rid="B8">Bates et&#xa0;al., 2015</xref>), we developed generalized linear mixed effect models to test closure effects on four response variables: mean village total catch and CPUE within 7- and 30-days post-closure. The fixed effects of the final models included the pre-closure catch/CPUE, closure size (ha), duration (days), period (starting month of the closure, 1-12), nth closure for that village, number of villages associated with the closure, proximity of the closure to the village (km), larval connectivity, mean SST, coral complex (%) cover and microalgal (%) cover. Three closure events within the Barren Isles region at the village Nosy Lava were removed from the mixed model analysis due to a lack of habitat coverage data. Village and starting year of the closure were considered as random effects, accounting for potential spatial or temporal variation. Raw data and model residuals were analyzed for normality, heteroscedasticity, outliers and collinearity. Mean fishers per closure was excluded from the mixed model analysis due to its use when calculating CPUE. The benthic categories of sand and seagrass (%) coverage were removed as predictor variables due to high collinearity with coral complex cover (VIF &gt;5). The fixed-effects variables are on scales that differ in orders of magnitude, so to allow for direct comparison of regression coefficients, all fixed-effect variables were centered and scaled prior to modeling, using the gscale function in R (<xref ref-type="bibr" rid="B45">Long, 2022</xref>). Distribution fitting and comparison of candidate models was completed to guide model specification and ultimately determine a final global model structure for each response variable. This included checking model assumptions (e.g. multicollinearity, homogeneity of variance, etc.), comparing model distribution fits and evaluating random-effects structure using the &#x2018;performance&#x2019; R package (<xref ref-type="bibr" rid="B46">L&#xfc;decke et&#xa0;al., 2021</xref>).</p>
<p>Using the &#x2018;MuMIn&#x2019; package in R (<xref ref-type="bibr" rid="B7">Barto&#x144;, 2023</xref>), we conducted model selection on each global model, running all possible combinations of co-variables fitted by maximum likelihood, to identify the top models that best explain the response variables. Models with &#x394;AICc &lt; 2 were retained for model averaging, as they have strong evidence for containing the best fit model. Full averaged coefficient estimates were used to determine significant relationships between predictor and response variables. <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;3</bold>
</xref> reports a summary of top models, including which predictors were retained and the model AIC value, and top results were then model averaged to determine estimates and importance. These results helped determine which predictor variables are associated with positive catch outcomes to inform management recommendations.</p>
</sec>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Varying trends of octopus catch across the regions</title>
<p>Trends in monthly octopus CPUE and total catch varied across regions (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>). Mean monthly CPUE and total catch in Comoros indicate a steady extraction of octopus neither significantly increasing or decreasing between 2017 and 2022 <italic>(F = 3.471, p = 0.067; F = 1.118, p = 0.406)</italic>. Since the monitoring of octopus landings began in 2018 across the Barren Isles, mean monthly CPUE has increased over time <italic>(F =6.929, p = 0.0052)</italic> and mean monthly total catch exhibits an increasing trend although not statistically significant <italic>(F = 1.201, p = 0.317)</italic>. From 2004 to 2011, we observed an increase in mean monthly CPUE in Atsimo Andrefana followed by a steady decrease through 2021 <italic>(F = 14.05, p = &lt;0.001)</italic>. Similarly, we observed fluctuations in mean monthly total catch from 2004 to 2021, with catch declining over the past 6 years <italic>(F = 3.05, p =0.005)</italic>.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Temporal trends of mean <bold>(A)</bold> CPUE and <bold>(B)</bold> daily total catch across regions. CPUE and total catch was averaged by month among all villages in a region. Solid lines represent the gam-smoothed temporal trends of each region and confidence intervals are represented by the surrounding transparent color. Note different scales on the x-axis across regions.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1358111-g002.tif"/>
</fig>
<p>After examining 93 closures across 26 villages, we observed significant increases in median total octopus catch and CPUE within both 7 and 30 days following periodic closures (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>, <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Median octopus CPUE (&#xb1; standard error) increased from 2.0 &#xb1; 0.12 kg/fisher-day 30 days before a closure to 2.5 &#xb1; 0.12 kg/fisher-day after a closure (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3A</bold>
</xref>, <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Median CPUE also increased from 2.1 &#xb1; 0.16 kg/fisher-day to 2.8 &#xb1; 0.17 in the 7-day time interval (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3B</bold>
</xref>). Median daily total catch weight recorded within 30 days of a closure increased by 95% from 19.5 &#xb1; 1.62 kg/day to 38.0 &#xb1; 3.37 kg/day across villages (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3C</bold>
</xref>, <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). The mean number of fishers increased from 10.3 &#xb1; 6.3 fisher/day before the closure to 16.4 &#xb1; 10.0 fishers/day after the closure. Median daily total catch in the 7 days prior to a closure was 24.4 &#xb1; 2.13 kg/day (11.5 &#xb1; 6.9 fishers/day) and increased to 62.4 &#xb1; 8.32 kg/day after the closure with approximately 23.6 fishers/day (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3D</bold>
</xref>). Median CPUE (30-days) post-closure increased significantly in the Atsimo Andrefana and Comoros regions (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>), while CPUE did not differ before and after closures in the Barren Isles (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>). Median daily total catch increased significantly after a closure in all regions (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4B</bold>
</xref>, <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Comparison of <bold>(A, B)</bold> mean CPUE (kg/fisher-day) and <bold>(C, D)</bold> mean daily total catch (kg/day) per village in the 30 and 7 days before and after a closure. Each box or interquartile range (IQR) extends from the 25<sup>th</sup> to the 75<sup>th</sup> percentile; vertical lines (&#x2018;whiskers&#x2019;) represent &#xb1;1.5*IQR; bold black lines represent median values; points represent individual village means per closure. Blue diamonds represent the mean values pre/post closure across all regions. Stars represent a significant difference before and after closures using a paired Wilcoxon test, with significant codes: 0; ***, 0.001; **, 0.01; *, 0.05; 0.1; 1.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1358111-g003.tif"/>
</fig>
<table-wrap-group id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Comparison of median values (M) and standard error (se) before and after a periodic closure using a paired Wilcoxon test (<italic>W</italic>, p-values).</p>
</caption>
<table-wrap>
<table frame="hsides">
<thead>
<tr>
<th valign="top" colspan="6" align="left">A. Before/after comparison of all regions</th>
</tr>
<tr>
<th valign="top" align="left"/>
<th valign="middle" align="left">N</th>
<th valign="middle" align="left">Before<break/>(M &#xb1; se)</th>
<th valign="middle" align="left">After<break/>(M &#xb1; se)</th>
<th valign="middle" align="left">
<italic>W</italic>-value</th>
<th valign="middle" align="left">p-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">CPUE - 30 days</td>
<td valign="middle" align="left">93</td>
<td valign="middle" align="left">2.0 &#xb1; 0.12</td>
<td valign="middle" align="left">2.5 &#xb1; 0.12</td>
<td valign="middle" align="left">903</td>
<td valign="middle" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">CPUE - 7 days</td>
<td valign="middle" align="left">64</td>
<td valign="middle" align="left">2.1 &#xb1; 0.16</td>
<td valign="middle" align="left">2.8 &#xb1; 0.17</td>
<td valign="middle" align="left">430</td>
<td valign="middle" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Total daily catch - 30 days</td>
<td valign="middle" align="left">93</td>
<td valign="middle" align="left">19.5 &#xb1; 1.62</td>
<td valign="middle" align="left">38.0 &#xb1; 3.37</td>
<td valign="middle" align="left">192</td>
<td valign="middle" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Total daily catch - 7 days</td>
<td valign="middle" align="left">64</td>
<td valign="middle" align="left">24.4 &#xb1; 2.13</td>
<td valign="middle" align="left">62.4 &#xb1; 8.32</td>
<td valign="middle" align="left">93</td>
<td valign="middle" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap>
<table frame="hsides">
<thead>
<tr>
<th valign="top" colspan="6" align="left">B. 30-day before/after comparison within each region</th>
</tr>
<tr>
<th valign="top" align="left"/>
<th valign="middle" align="left">N</th>
<th valign="middle" align="left">Before<break/>(M &#xb1; se)</th>
<th valign="middle" align="left">After<break/>(M &#xb1; se)</th>
<th valign="middle" align="left">
<italic>W</italic>-value</th>
<th valign="middle" align="left">p-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Comoros CPUE</td>
<td valign="middle" align="left">12</td>
<td valign="middle" align="left">1.4 &#xb1; 0.11</td>
<td valign="middle" align="left">2.3 &#xb1; 0.15</td>
<td valign="middle" align="left">1</td>
<td valign="middle" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Barren Isles CPUE</td>
<td valign="middle" align="left">9</td>
<td valign="middle" align="left">5.0 &#xb1; 0.47</td>
<td valign="middle" align="left">4.4 &#xb1; 0.62</td>
<td valign="middle" align="left">23</td>
<td valign="middle" align="left">1.0</td>
</tr>
<tr>
<td valign="top" align="left">Atsimo Andrefana CPUE</td>
<td valign="middle" align="left">72</td>
<td valign="middle" align="left">2.1 &#xb1; 0.09</td>
<td valign="middle" align="left">2.5 &#xb1; 0.11</td>
<td valign="middle" align="left">525</td>
<td valign="middle" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Comoros total daily catch</td>
<td valign="middle" align="left">12</td>
<td valign="middle" align="left">8.5 &#xb1; 2.79</td>
<td valign="middle" align="left">32.7 &#xb1; 7.81</td>
<td valign="middle" align="left">3</td>
<td valign="middle" align="left">
<bold>0.002**</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Barren Isles total daily catch</td>
<td valign="middle" align="left">9</td>
<td valign="middle" align="left">16.7 &#xb1; 2.50</td>
<td valign="middle" align="left">32.2 &#xb1; 11.7</td>
<td valign="middle" align="left">0</td>
<td valign="middle" align="left">
<bold>0.004**</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Atsimo Andrefana total daily catch</td>
<td valign="middle" align="left">72</td>
<td valign="middle" align="left">22.5 &#xb1; 1.88</td>
<td valign="middle" align="left">42.1 &#xb1; 3.85</td>
<td valign="middle" align="left">140</td>
<td valign="middle" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Here we include the number of closure events (N), median CPUE and total daily catch across all villages and regions for the 30- and 7-day periods (A) and a 30-day before/after comparison by each region (B). Bold p-values represent a significant p-value with significant codes: 0; ***, 0.001; **, 0.01; *, 0.05; 0.1; 1.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</table-wrap-group>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Comparison of mean <bold>(A)</bold> CPUE (kg/fisher-day) and <bold>(B)</bold> mean daily total catch by each region in the 30 days before and after a periodic closure. Each box or interquartile range (IQR) extends from the 25<sup>th</sup> to the 75<sup>th</sup> percentile; vertical lines (&#x2018;whiskers&#x2019;) represent &#xb1;1.5*IQR; bold black lines represent median values; points represent individual village means per closure. Blue diamonds represent the mean values pre/post closure across all regions. Stars represent a significant difference before and after closures using a paired Wilcoxon test, with significant codes: 0; ***, 0.001; **, 0.01; *, 0.05; 0.1; 1.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1358111-g004.tif"/>
</fig>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Baseline catch, closure duration and seasonality are important predictors of success</title>
<p>Pre-closure catch values (i.e., baseline catch), closure duration and month of closure were positively correlated with post-closure CPUE and total catch (<xref ref-type="fig" rid="f5">
<bold>Figures&#xa0;5</bold>
</xref>, <xref ref-type="fig" rid="f6">
<bold>6A&#x2013;G</bold>
</xref>, <xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). We observed statistically significant positive relationships between baseline catch and all response variables, duration and all response variables except post-closure daily total catch within 30 days of opening, and closure month and post-closure CPUE within 7 days of opening. None of the predictor variables were negatively associated with closure outcomes. On average, CPUE increased by 0.63 kg/fisher-day in villages with fishers harvesting from closures with baseline CPUE less than 2.4 kg/fisher-day. Conversely, villages linked to closures with higher baseline, or CPUE between 2.5 and 7 kg/fisher-day, observed an increase of 0.22 kg/fisher-day (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure&#xa0;1</bold>
</xref>).</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Influence of predictor variables on the post-closure value of catch/CPUE within 7 and 30 days after closure opening. Points represent full averaged coefficient estimates and horizontal lines represent confidence intervals. Positive response estimates are to the right of the &#x2018;0&#x2019; and negative responses are to the left. Estimates that are significantly different from zero are colored green if positive and red if negative.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1358111-g005.tif"/>
</fig>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Relationship between response variables (post-closure total catch and CPUE) and covariates. For plots <bold>(A&#x2013;G)</bold>, black points represent the mean post-closure values for a closure event. Blue lines depict the mean ( &#xb1; 95% confidence interval in gray shading) of the predicted linear response variable as a function of a given covariate. Plot G shows the comparison of mean post-CPUE values across different start months.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1358111-g006.tif"/>
</fig>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Model averaged estimates, standard error, z-value and p-values for each predictor variable.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Approach and fixed effects</th>
<th valign="top" align="left">Estimate</th>
<th valign="top" align="left">Standard error</th>
<th valign="top" align="left">z-value</th>
<th valign="top" align="left">p-value</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="top" colspan="5" align="left">Post-closure catch &#x2013; 30 days</th>
</tr>
<tr>
<td valign="top" align="left">Duration</td>
<td valign="bottom" align="left">0.13</td>
<td valign="bottom" align="left">0.12</td>
<td valign="bottom" align="left">1.11</td>
<td valign="bottom" align="left">0.267</td>
</tr>
<tr>
<td valign="top" align="left">Month or period</td>
<td valign="bottom" align="left">0.07</td>
<td valign="bottom" align="left">0.07</td>
<td valign="bottom" align="left">0.96</td>
<td valign="bottom" align="left">0.339</td>
</tr>
<tr>
<td valign="top" align="left">Pre-closure value</td>
<td valign="bottom" align="left">0.57</td>
<td valign="bottom" align="left">0.11</td>
<td valign="bottom" align="left">5.35</td>
<td valign="bottom" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Surface area</td>
<td valign="bottom" align="left">0.11</td>
<td valign="bottom" align="left">0.09</td>
<td valign="bottom" align="left">1.22</td>
<td valign="bottom" align="left">0.221</td>
</tr>
<tr>
<td valign="top" align="left">Microalgae</td>
<td valign="bottom" align="left">-0.01</td>
<td valign="bottom" align="left">0.03</td>
<td valign="bottom" align="left">0.43</td>
<td valign="bottom" align="left">0.670</td>
</tr>
<tr>
<td valign="top" align="left">N-th closure</td>
<td valign="bottom" align="left">0.04</td>
<td valign="bottom" align="left">0.09</td>
<td valign="bottom" align="left">0.44</td>
<td valign="bottom" align="left">0.660</td>
</tr>
<tr>
<td valign="top" align="left">Coral complex</td>
<td valign="bottom" align="left">0.02</td>
<td valign="bottom" align="left">0.05</td>
<td valign="bottom" align="left">0.33</td>
<td valign="bottom" align="left">0.742</td>
</tr>
<tr>
<td valign="top" align="left">No. of villages</td>
<td valign="bottom" align="left">-0.01</td>
<td valign="bottom" align="left">0.03</td>
<td valign="bottom" align="left">0.25</td>
<td valign="bottom" align="left">0.805</td>
</tr>
<tr>
<td valign="top" align="left">Proximity</td>
<td valign="bottom" align="left">-0.01</td>
<td valign="bottom" align="left">0.02</td>
<td valign="bottom" align="left">0.16</td>
<td valign="bottom" align="left">0.876</td>
</tr>
<tr>
<th valign="top" colspan="5" align="left">Post-closure catch &#x2013; 7 days</th>
</tr>
<tr>
<td valign="top" align="left">Duration</td>
<td valign="bottom" align="left">0.01</td>
<td valign="bottom" align="left">0.002</td>
<td valign="bottom" align="left">3.41</td>
<td valign="bottom" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Month or period</td>
<td valign="bottom" align="left">0.0002</td>
<td valign="bottom" align="left">0.0007</td>
<td valign="bottom" align="left">0.33</td>
<td valign="bottom" align="left">0.744</td>
</tr>
<tr>
<td valign="top" align="left">Pre-closure value</td>
<td valign="bottom" align="left">0.005</td>
<td valign="bottom" align="left">0.001</td>
<td valign="bottom" align="left">3.52</td>
<td valign="bottom" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Surface area</td>
<td valign="bottom" align="left">0.001</td>
<td valign="bottom" align="left">0.001</td>
<td valign="bottom" align="left">0.58</td>
<td valign="bottom" align="left">0.560</td>
</tr>
<tr>
<td valign="top" align="left">Microalgae</td>
<td valign="bottom" align="left">-0.003</td>
<td valign="bottom" align="left">0.001</td>
<td valign="bottom" align="left">1.91</td>
<td valign="bottom" align="left">0.056</td>
</tr>
<tr>
<td valign="top" align="left">N-th closure</td>
<td valign="bottom" align="left">0.002</td>
<td valign="bottom" align="left">0.002</td>
<td valign="bottom" align="left">0.99</td>
<td valign="bottom" align="left">0.320</td>
</tr>
<tr>
<td valign="top" align="left">Proximity</td>
<td valign="bottom" align="left">-0.0001</td>
<td valign="bottom" align="left">0.0006</td>
<td valign="bottom" align="left">0.19</td>
<td valign="bottom" align="left">0.819</td>
</tr>
<tr>
<td valign="top" align="left">Connectivity</td>
<td valign="bottom" align="left">0.0001</td>
<td valign="bottom" align="left">0.0006</td>
<td valign="bottom" align="left">0.23</td>
<td valign="bottom" align="left">0.819</td>
</tr>
<tr>
<th valign="top" colspan="5" align="left">Post-closure CPUE &#x2013; 30 days</th>
</tr>
<tr>
<td valign="top" align="left">Duration</td>
<td valign="bottom" align="left">0.26</td>
<td valign="bottom" align="left">0.08</td>
<td valign="bottom" align="left">3.08</td>
<td valign="bottom" align="left">
<bold>0.002**</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Month or period</td>
<td valign="bottom" align="left">0.08</td>
<td valign="bottom" align="left">0.08</td>
<td valign="bottom" align="left">0.96</td>
<td valign="bottom" align="left">0.338</td>
</tr>
<tr>
<td valign="top" align="left">Pre-closure value</td>
<td valign="bottom" align="left">0.60</td>
<td valign="bottom" align="left">0.09</td>
<td valign="bottom" align="left">6.47</td>
<td valign="bottom" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Surface area</td>
<td valign="bottom" align="left">0.01</td>
<td valign="bottom" align="left">0.03</td>
<td valign="bottom" align="left">0.19</td>
<td valign="bottom" align="left">0.849</td>
</tr>
<tr>
<td valign="top" align="left">Connectivity</td>
<td valign="bottom" align="left">0.02</td>
<td valign="bottom" align="left">0.05</td>
<td valign="bottom" align="left">0.35</td>
<td valign="bottom" align="left">0.726</td>
</tr>
<tr>
<td valign="top" align="left">Microalgae</td>
<td valign="bottom" align="left">-0.01</td>
<td valign="bottom" align="left">0.03</td>
<td valign="bottom" align="left">0.26</td>
<td valign="bottom" align="left">0.796</td>
</tr>
<tr>
<th valign="top" colspan="5" align="left">Post-closure CPUE &#x2013; 7 days</th>
</tr>
<tr>
<td valign="top" align="left">Duration</td>
<td valign="bottom" align="left">0.39</td>
<td valign="bottom" align="left">0.13</td>
<td valign="bottom" align="left">3.00</td>
<td valign="bottom" align="left">
<bold>0.0027**</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Month or period</td>
<td valign="bottom" align="left">0.25</td>
<td valign="bottom" align="left">0.10</td>
<td valign="bottom" align="left">2.51</td>
<td valign="bottom" align="left">
<bold>0.012*</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Pre-closure value</td>
<td valign="bottom" align="left">0.44</td>
<td valign="bottom" align="left">0.13</td>
<td valign="bottom" align="left">3.41</td>
<td valign="bottom" align="left">
<bold>&lt;0.001***</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">N-th closure</td>
<td valign="bottom" align="left">-0.03</td>
<td valign="bottom" align="left">0.09</td>
<td valign="bottom" align="left">0.32</td>
<td valign="bottom" align="left">0.747</td>
</tr>
<tr>
<td valign="top" align="left">Coral complex</td>
<td valign="bottom" align="left">0.02</td>
<td valign="bottom" align="left">0.06</td>
<td valign="bottom" align="left">0.30</td>
<td valign="bottom" align="left">0.764</td>
</tr>
<tr>
<td valign="top" align="left">Proximity</td>
<td valign="bottom" align="left">-0.01</td>
<td valign="bottom" align="left">0.04</td>
<td valign="bottom" align="left">0.25</td>
<td valign="bottom" align="left">0.805</td>
</tr>
<tr>
<td valign="top" align="left">Mean SST</td>
<td valign="bottom" align="left">-0.01</td>
<td valign="bottom" align="left">0.06</td>
<td valign="bottom" align="left">0.24</td>
<td valign="bottom" align="left">0.812</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>All predictor variables were centered and scaled prior to modeling. Terms were dropped from models using a chi-square test. Bold p-values represent a significant p-value with significant codes: 0; ***, 0.001; **, 0.01; *, 0.05; 0.1; 1.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Seasonality or closure start month was only significantly correlated to CPUE within 7-days post-closure (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6H</bold>
</xref>). Closures beginning in June had an average of 3.0 kg/fisher-day post-closure and closures beginning in November had an average of 3.4 kg/fisher-day (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6H</bold>
</xref>); representing an increase of +13.1% and +101.2%, respectively, from baseline values. We are not able to make inferences about the correlation between seasonality and CPUE for other months, due to the small sample size as a result of either fewer closures or lack of available landings data. Of the 38 closures beginning in June, 5 were in Barren Isles and 33 were in Atsimo Andrefana, with varying results. In the Barren Isles, the average change in CPUE was not significantly different from zero (see <xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>). All closures in this region occurred in June, thus, we cannot make inferences about the influence of closure seasonality (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure&#xa0;2</bold>
</xref>). In Atsimo Andrefana, June closures increased by 18.4% CPUE (kg/fisher-day). No closures in Comoros began in June. Closures beginning in November occurred in both Comoros and Atsimo Andrefana, with an average increase of +115.6% kg/fisher-day and +97.1% kg/fisher-day, respectively (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure&#xa0;2</bold>
</xref>).</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Investigating optimal closure size and duration</title>
<p>While no statistically significant breakpoints were identified in a segmented regression (<italic>Davies test p = 0.83</italic>), visual inspection of the trend suggests a change in slope at approximately 87 &#xb1; 10.5 days or ~12 weeks (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7A</bold>
</xref>). From this point, we see the largest increase in &#x394;CPUE from +0.4 kg/fisher-day at ~87 days (~12 weeks) to +1.8 kg/fisher-day at 115 days (~16 weeks). All closures with a duration &gt;95 days (13 weeks) resulted in a net gain in CPUE (n=16). Closures with shorter durations (&lt;87 days) had more mixed results (48 closures with +&#x394;CPUE, 21 closures with -&#x394;CPUE).</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>The relationship between the absolute change in CPUE within 30 days of closure and closure duration <bold>(A)</bold> and size <bold>(B)</bold>. Black points represent the change in CPUE for each closure event. Black lines represent a breakpoint regression with change in slopes identified at the green dotted lines. Gray lines represent Loess-smoothed conditional means. The green shaded bar represents the management recommendations of duration and size based on results from the breakpoint regression.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1358111-g007.tif"/>
</fig>
<p>The relationship between closure size and &#x394;CPUE was less clear, and again no statistically significant breakpoints were identified in a segmented regression (<italic>Davies test p = 0.30</italic>). However, visual inspection of the data suggests that &#x394;CPUE increased with closure size up to approximately 70 hectares (+0.88 kg/fisher-day), which represents the closure size at which mean &#x394;CPUE peaks (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7B</bold>
</xref>). This increasing trend does not necessarily continue with increasing size; when examining the 52 closures greater than 70 ha in size, 39 experienced a positive net gain (+&#x394;) in CPUE, 11 did not (&#x394;CPUE &lt; 0) and 2 had &#x394;CPUE ~0.</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>We observed short-term positive effects following periodic octopus closures across our three study regions in Madagascar and Comoros, and positive long-term trends (increasing or maintained landings) across two of our study regions. Significant increases in median CPUE (octopus kg/fisher-day) and daily total catch (kg/day) were observed when examining catch within both 7- and 30-days post-closure. On average, fishers across the three study regions caught 0.5 kg more octopus 30 days after a closure compared to 30 days before a closure (from 2.0 &#xb1; 0.12 kg/fisher-day to 2.5 &#xb1; 0.12 kg/fisher-day) and 0.7 kg more octopus 7 days after a closure compared to 7 days before a closure (from 2.1 &#xb1; 0.16 kg/fisher-day to 2.8 &#xb1; 0.17 kg/fisher-day). Change in daily total catch per village diminished over time when comparing 7- and 30-days post-closure. Median daily total catch increased by 156% in the 7-day period and increased by 95% in the 30-day period. This can be explained by an observed decrease in the mean number of fishers recorded per day, from an average of 24 fishers/day to 16 fishers/day.</p>
<p>CPUE recorded from fishers across Comoros showed consistent catch levels from 2017 to 2022 (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>). In comparison to the two study regions in Madagascar, CPUE and daily total catch are notably lower across Comoros. Fishers in these villages rely heavily on the extraction of marine resources, however, absence of an export market and refrigeration facilities keeps the octopus fishery local and subsistence based (<xref ref-type="bibr" rid="B37">Hauzer et&#xa0;al., 2013a</xref>). The highest median CPUE was observed in the Barren Isles, where the catch has shown consistent growth since 2018, most likely driven by sales to export markets. The uncompromising weather patterns and geographical location of these islands prohibit human populations from permanently settling and restricts fishing activity to only seven months out of the year (April to November). Thus, a naturally occurring 4-month closure takes place here every year in addition to the austral summer closures, enhancing benefits to fishers and contributing to the high pre-closure values.</p>
<p>
<xref ref-type="bibr" rid="B54">Oliver et&#xa0;al. (2015)</xref> found that median CPUE increased by approximately 87% in the 30 days after a closure relative to 30 days before (from 2.37 ( &#xb1; 0.33) kg/fisher-day to 4.42 ( &#xb1; 0.51) kg/fisher-day) across 36 closures implemented between 2004 and 2011 in the Atsimo Andrefana region. We observed a lower pre-closure median CPUE (2.1 &#xb1; 0.09 kg/fisher-day) and an approximate increase in CPUE of 19% across 72 closures between the years of 2008 and 2019. Our data indicated that median CPUE across Atsimo Andrefana increased between 2004 and 2006, followed by a consistent decline beginning in 2011 (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>). Thus, while we did not observe a consistent decline in daily total village catch on average, individual fishers caught more between 2006 and 2011 compared to 2022. This is likely due to the 26% increase in the mean number of fishers recorded each day from the periods 2005-2010 to 2015-2020 (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure&#xa0;3</bold>
</xref>). Our findings are consistent with previous studies, finding that the mean harvestable sizes and weights of octopus landings in the region have declined (<xref ref-type="bibr" rid="B36">Guard and Mgaya, 2002</xref>; <xref ref-type="bibr" rid="B35">Guard, 2009</xref>; <xref ref-type="bibr" rid="B65">Sauer et&#xa0;al., 2011</xref>).</p>
<p>Though periodic closures are suitable for managing short-lived and rapidly growing taxa like octopus, the design of closures is crucial for their effectiveness. We identified three primary drivers of closure success, defined as increased octopus CPUE and total daily catch: 1) baseline conditions, 2) closure duration and 3) seasonality (closure start month). Relationships between each of these closure variables and catch outcomes suggest the potential for maximum benefits when closures are placed in areas with lower baseline catch, have a duration of at least 13 to 16 weeks and commence in June or November. While we observed positive closure effects across all baseline conditions, the villages with lower baseline CPUE (or lower fishing pressure) have the greatest returns (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6D</bold>
</xref>, <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure&#xa0;1</bold>
</xref>). Based on our analyses, we defined lower baseline catch as approximately 2 kg/fisher-day and 20 kg/day at the village level.</p>
<p>Our findings indicate that there is a predominantly positive change in CPUE across villages linked to closure durations of at least 13 weeks, with maximum benefits observed at approximately 16 weeks (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7A</bold>
</xref>). This aligns with findings of previous studies demonstrating benefits from closures with durations ranging from 10 days to 3 months (<xref ref-type="bibr" rid="B36">Guard and Mgaya, 2002</xref>; <xref ref-type="bibr" rid="B30">Fern&#xe1;ndez-Rueda and Garc&#xed;a-Fl&#xf3;rez, 2007</xref>; <xref ref-type="bibr" rid="B54">Oliver et&#xa0;al., 2015</xref>). Closure duration is closely associated with increased growth capacity in octopuses. An <italic>O. cyanea</italic> individual can gain 100 to 200 g over a period of approximately two weeks (<xref ref-type="bibr" rid="B79">Van Heukelem, 1973</xref>). This implies that conservatively, fishing protection through closures has the potential to contribute an estimated 600-1200 g per octopus in 12 weeks or 800-1600 g in 16 weeks. Longer closure durations may be required if pre-closure fishing pressure is particularly high resulting in low overall octopus biomass.</p>
<p>Managers can also maximize growth potential by aligning closures with reproductive cycles, thereby, providing protection during critical spawning and recruitment periods. In our assessment of post-closure CPUE within the 7 days following a closure opening, 49 out of 61 closures initiated in June (n=38) and November (n=11). In Atsimo Andrefana an increase in CPUE of approximately 97% was observed across villages associated with closures commencing in November; all occurring between the years of 2008 and 2009 with a duration of 12 or more weeks (n = 8). After 2010, most closures in Atsimo Andrefana occurred between the months of June and August in response to studies for a duration of approximately two months. <xref ref-type="bibr" rid="B59">Raberinary and Benbow (2012)</xref> also suggested closure periods between September and November. On average, we observed an increase in CPUE of only 18% across villages associated with closures that started in June after 2010. Based on our results, this gain could be maximized by extending the duration of June closures to at least 13 weeks and/or potentially implementing additional closures in November. While our data indicates a more pronounced change in CPUE linked to November closures as opposed to June closures; the imbalance in sample size makes it difficult to conclusively recommend closures start in one month versus another when positive short-term benefits in octopus catch were observed for both.</p>
<p>Although closure size was not identified as a primary factor influencing positive management outcomes in our models, our breakpoint analysis suggests an optimal closure size of 50 to 100 hectares. Within this range the majority of closures exhibit an increase in CPUE after the closure period (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7B</bold>
</xref>). As closure size increased from 100 hectares to approximately 200 hectares, we observed a more varied change in CPUE. Large closure areas may be difficult to enforce, minimizing positive closure effects. However, here in our study region compliance was often successful, with records indicating very few infractions. The impact of closure areas is contingent on the availability of suitable habitat, such as rocky coral reefs. Closures in our analysis that were of the suggested optimal size (50-100 ha) contained on average 68% suitable habitat. Across our study area, closures with at least 50 hectares of suitable habitat were associated with increases in CPUE. <italic>O. cyanea</italic> has relatively small home ranges compared to other marine resources (e.g., fish) and has been reported to have dens distributed approximately 30 m apart and forage on average 81 m each trip from the den (<xref ref-type="bibr" rid="B31">Forsythe and Hanlon, 1997</xref>). Our recommended closure size has the potential to provide protection for octopus as well as other species with similar home ranges. Larval connectivity was also not a major driver of closure success. Mean larval import values across four fish species was used to represent general connectivity and applied to octopus which may underestimate variation in octopus connectivity across our study area. A spatial network of periodic closures that protects key larval source and sink areas would facilitate population connectivity and recovery via the export from brooders and the import of settling larvae (<xref ref-type="bibr" rid="B43">Krueck et&#xa0;al., 2022</xref>). In the future, we suggest the development of larval dispersal models for octopus to improve our understanding of how connectivity influences octopus population dynamics and the outcomes of periodic closures.</p>
<p>Periodic closures have demonstrated success in delivering short-term benefits for fishers and fostering engagement in locally led management practices. However, this strategy alone is not likely to ensure the long-term sustainability of the fishery. Our findings underpin the importance of a comprehensive ecosystem-based management approach that optimizes closure duration, season, size and habitat coverage for octopus. In addition, we suggest that minimum size limits, gear restrictions and limited access be considered when areas are open to fishing to support reproduction potential and population growth. Management recommendations derived from the response of <italic>O. cyanea</italic> fisheries to periodic closures in Madagascar and Comoros can be used to inform comprehensive management approaches that incorporate diverse and complementary strategies for adaptive management, ensuring the long-term sustainability of this highly prized and adaptive invertebrate.</p>
</sec>
<sec id="s5" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="s6" sec-type="author-contributions">
<title>Author contributions</title>
<p>MM: Writing &#x2013; review &amp; editing, Writing &#x2013; original draft, Visualization, Validation, Supervision, Investigation, Formal analysis, Data curation, Conceptualization. LM: Writing &#x2013; review &amp; editing, Writing &#x2013; original draft, Visualization, Validation, Formal analysis, Data curation. TR: Writing &#x2013; review &amp; editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization. NR: Writing &#x2013; review &amp; editing, Validation, Supervision, Resources, Project administration, Investigation, Data curation. FV: Writing &#x2013; review &amp; editing, Validation, Supervision, Resources, Project administration, Data curation. RR: Writing &#x2013; review &amp; editing, Validation, Supervision, Project administration, Investigation, Data curation. FS: Writing &#x2013; review &amp; editing, Validation, Supervision, Resources, Project administration, Investigation, Data curation. FR: Writing &#x2013; review &amp; editing, Validation, Supervision, Resources, Project administration, Data curation. TR: Writing &#x2013; review &amp; editing, Validation, Supervision, Project administration, Investigation, Data curation. SB: Writing &#x2013; review &amp; editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization. CC: Writing &#x2013; review &amp; editing, Writing &#x2013; original draft, Visualization, Supervision, Resources, Project administration, Funding acquisition, Formal analysis, Conceptualization.</p>
</sec>
</body>
<back>
<sec id="s7" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research, authorship and publication of this article. Funding for data collection was provided by private donors to Blue Ventures Conservation and Dahari. Funding in the Comoros included support from the Oak Foundation #OCAY-18-750, the Tusk Trust and the UK Government through &#x201c;The Darwin Initiative&#x201d; (Grant #27-006). Special thanks to our partners at WWF via the following grants: 03/CONV/FY19/TNR, 54/CTR/FY19/TNR, 239/CTR/FY20, 174/CTR-SPAC/FY21/TNR.</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>The authors want to acknowledge our partners in Comoros and Madagascar for collecting and providing the data, which without this research would not be possible. We would also like to thank all the donors that have supported this vital work over the years.</p>
</ack>
<sec id="s8" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>TR, NR, RR, FR, TR and SB work for Blue Ventures, which facilitated the implementation of the community-based management at these sites in Madagascar. FV and FS work for Dahari, which facilitated the implementation of sites in Comoros.</p>
<p>The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.</p>
<p>The authors declare that this study received funding from private donors. The funders had the following involvement in the study: support and provision of funding for community-led data collection.</p>
</sec>
<sec id="s9" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s10" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fmars.2024.1358111/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fmars.2024.1358111/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="DataSheet_1.pdf" id="SM1" mimetype="application/pdf"/>
</sec>
<fn-group>
<fn id="fn1">
<label>1</label>
<p>Treml, E. A., and Critchell, K. (2020). Larval dispersal modeling for reserve design in Mozambique, Unpublished.</p>
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