300 K. Metcalfe et al.
nesting aggregations of leatherback Dermochelys coriacea, olive ridley Lepidochelys olivacea and green Chelonia mydas marine turtles (Witt et al., 2009;Metcalfe et al., 2015;Patrício et al., 2017, and references therein). Foraging grounds have, however, remained largely understudied despite records of their occurrence from Banc d’Arguin in Mauritania, West Africa, to Mussulo Bay in Angola, Southern Africa (Carr & Carr, 1991;Fretey, 2001; Cardona et al., 2009). As a result, conservation strategies have largely been directed at identify- ing threats, and establishing new or expanding existing pro- tected areas to protect key nesting beaches and inter-nesting habitats (Witt et al., 2008;Maxwell et al., 2011; Pikesley et al., 2018). Foraging grounds thus remain largely unprotected throughout the region, undermining ongoing conservation efforts for species such as the green turtle, whichmoves onto- genetically, with juveniles actively recruiting to neritic devel- opment habitats following several years of passive pelagic migration and then migrating to an adult foraging habitat that may also be shared with juveniles (Bjorndal, 2017). Given their proximity to the coast, neritic developmental
and foraging habitats are often exposed to a diverse range of pressures, including from fisheries, direct take, and habitat degradation linked to coastal development andmarine pollu- tion, with impacts on green turtles exacerbated by their slow growth, late onset of sexual maturity and low survivorship (Hirth, 1997). Therefore, understanding how green turtles use foraging habitats is essential to support more coherent marine spatial planning and conservation efforts, particularly as ontogenetic strategies may vary between genetic stocks or regional management units (Hamann et al., 2010). This is where satellite tracking can play a role, revealing the location and extent of important foraging habitats such as seagrass beds and macroalgal dominated reefs (Scott et al., 2012; Hays et al., 2018). Tracking animal movements can also high- light variability in life history patterns among disparate pop- ulations (Bolten, 2003;Godley etal., 2008)suchas the links between discrete foraging grounds and the degree of fidelity shown to these areas (Stokes et al., 2015). To address the absence of knowledge on foraging grounds
and inform conservation strategies along the Atlantic coast of Africa, we analyse historical satellite tracking data to pro- vide the first description of the spatial ecology and move- ment of green turtles tagged with PTTs at a neritic for- aging ground in Loango Bay, Republic of the Congo. Access to these data are timely given the Republic of the Congo recently announced its intention to create a marine conservation zone in Loango Bay to protect marine turtles and sharks (Our Ocean, 2016).
Study area
The Republic of the Congo is situated on the Atlantic coast of Central Africa, with Loango Bay, the focus of this study, 20 km north of the port city and economic capital, Pointe
Noire (Fig. 1). Loango Bay covers an area of c. 100 km2, bounded by Pointe Indienne to the south, and the Kouilou river mouth to the north, and is characterized by shallow sheltered waters (,10 m deep) that comprise a mosaic of habitats, including macroalgal dominated rocky reefs, and silt laden and sandy bottom habitats (Giresse et al., 1980; Malounguila-Nganga et al., 2017), which support a wide range of marine species (Girard et al., 2014).
Methods
Satellite tracking data collection and processing Telonics Inc. (Mesa, Arizona, USA) satellite PTT models TGM-4310-2 (153 g in air; n = 2), TGM-4410-2 (262 gin air; n = 3), and TAM-4510 (435 g in air; n = 5) were attached to 10 green turtles (individualsA–J) incidentally captured by artisanal fishers operating in Loango Bay and subsequently released at Pointe Indienne (Table 1, Fig. 1). PTTs were de- ployed during 2012–2014, with two deployments each in February, April and August, one deployment each in May, September, November and December, attached in accor- dance with established protocols (Godley et al., 2002). Data transmitted by PTTs were collected using the Argos
satellite system and downloaded with the Satellite Tracking and Analysis Tool (Coyne & Godley, 2005). For each PTT we used extremely parsimonious filters, per Varo‐Cruz et al. (2016), removing all positions with location class Z and 0, and retaining positions with classes A, B, 1, 2 and 3, and applied a user-defined speed threshold (.5 km/h) and azimuth filter (,20°), to remove implausible Argos loca- tions (Freitas et al., 2008;Witt et al., 2010) using the argos- filter package in R 3.5.0 (R Core Team, 2018). For each PTT, data were then resolved to single daily best quality locations (per Witt et al., 2010). If more than one location was de- termined with equal quality within a 24-h period, the first location was retained. This data reduction technique was adopted to minimize the spatial and temporal autocorrela- tion that inherently exists within animal movement tracking data sets (De Solla et al., 1999).
Spatial analyses
For each PTT we calculated daily and maximum dis- placement distance (km) from release location, and applied three established techniques typically used to describe core areas of occupancy and habitat use for a wide range of marine vertebrates (manta rays: Graham et al., 2012; basking sharks: Doherty et al., 2017; marine turtles: Winton et al., 2018): (1) 95% minimum convex polygons (MCPs), (2)a polygon sampling grid, and (3) kernel density estimation. Ninety-five percent MCPs were calculated using all filtered locations (excluding 5% of the most extreme locations from
Oryx, 2020, 54(3), 299–306 © 2020 Fauna & Flora International doi:10.1017/S0030605319000309
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