DOI: 10.5061/dryad.15dv41nw0
title: Phytplanction Juruá River

description: 1. Tropical floodplains secure the protein supply of millions of people,
 but only sound management can ensure the long-term continuity of such
 ecosystem services. Overfishing is a widespread threat to multitrophic
 systems, but how it affects ecosystem functioning is poorly understood,
 particularly in tropical freshwater foodwebs. Models based on temperate
 lakes frequently assume that primary producers are mostly bottom-up
 controlled by nutrient and light limitations, with negligible effects of
 top-down forces. Yet this assumption remains untested in complex tropical
 freshwater systems experiencing marked spatiotemporal variation. 2. We use
 consolidated community-based fisheries management practices and spatial
 zoning to test the relative importance of bottom-up vs top-down drivers of
 phytoplankton biomass, controlling for the influence of local to landscape
 heterogeneity. Our study focuses on 58 large Amazonian floodplain lakes
 under different management regimes that resulted in a gradient of
 apex-predator abundance. These lakes, distributed along ~600 km of a major
 tributary of the Amazon River, varied widely in size, structure, landscape
 context, and hydrological seasonality. 3. Using generalized linear models,
 we show that community-based fisheries management, which controls the
 density of apex predators, is the strongest predictor of phytoplankton
 biomass during the dry season, when lakes become discrete landscape units.
 Water transparency also emerges as an important bottom-up factor, but
 phosphorus, nitrogen, and several lake and landscape metrics had minor or
 no effects on phytoplankton biomass.  During the wet-season food
 pulse, when lakes become connected to adjacent water bodies and homogenize
 the landscape, only lake depth explained phytoplankton biomass. 4.
 Synthesis and applications. Tropical freshwaters fisheries typically
 assume that fish biomass is controlled by bottom-up mechanisms, so that
 overexploitation of large predators would not affect overall ecosystem
 productivity. Our results, however, show that top-down forces are
 important drivers of primary productivity in tropical lakes, above and
 beyond the effects of bottom-up factors. This helps us understand the
 enormous success of community-based “fishing agreements” in the Amazon.
 Multiple stakeholders should embrace socio-ecological management practices
 that shape both bottom-up and top-down forces to ensure biodiversity
 protection, sustainable fisheries yields, and food security for local
 communities and regional economies.
description: Study area This study was carried out at 58
 large floodplain lakes (mean dry-season area = 114.6 ± 129.2 ha)
 distributed within and outside two contiguous sustainable-use protected
 areas, which encompass 919,882 ha of upland and seasonally-flooded forests
 along ~600 km of the Juruá River, a large meandering tributary of the
 Amazon River located in the central-western portion of the Amazon basin
 (Fig. 2). The Juruá watershed is one of the principal sources of inorganic
 and organic matter for the Amazon lowlands (McClain & Naiman,
 2008) and contributes high loads of suspended sediments associated with
 high turbidity and nutrient concentrations (Sioli, 1986).
 The Juruá floodplain is governed by a pronounced annual flood
 pulse that often exceeds 10 m in amplitude (Hawes & Peres, 2016).
 Floodplain lakes were sampled during both the high-water (March-April
 2014) and low-water periods (August-September 2014). There are two main
 lake types spread across the floodplain: oxbow lakes,<i>
 </i>formed through a complex process of meander cut-offs (Stølum,
 1996), and ria lakes, which were formerly deeply incised river valleys,
 usually near upland forests (Bertani et al.,<i> </i>2015).
 Average lake depth was 11.8 ± 6.4 m and 14.1 ± 6.5 m during the wet
 season, and 4.2 ± 3.4 m and 6.7 ± 3.5 m during the dry season for oxbow
 and ria lakes, respectively. Due to its high productivity, the Juruá River
 is one of the most important sources of fish traded across the Brazilian
 Amazon (Batista & Petrere Jr, 2003). This river also shows a
 pronounced level of socio-political organization, where local communities
 have been playing a central role in successful community-based
 arrangements that focus on aquatic resources (Campos-Silva &
 Peres, 2016; Campos-Silva et al., 2017; Campos-Silva et al.,
 2018).  Fisheries management and top-down
 control During the low-water season, Amazonian lakes
 become discrete landscape features, enabling no-take management based on
 the effective protection of floodplain lakes by excluding outside users.
 This management is organized around the concept of ‘Fishing Accords’ among
 all local communities from sustainable-use reserves, non-resident
 commercial fishers, and the Fishermen Cooperative of Carauari (the nearest
 urban center). For this study, we contrast two classes of lakes:
  Unprotected and Protected. Unprotected lakes include open-access lakes
 which are accessible to commercial fishing boats and subsistence-use lakes
 which are restricted to supplying local subsistence needs for the local
 community. In contrast, protected lakes exclude both commercial and
 subsistence fisheries but allow a sustainable harvest quota of arapaima in
 some managed lakes, once a year (see Campos-Silva & Peres, 2016).
 The effects of such fishing management on top predator levels has been
 already demonstrated (Campos-Silva & Peres, 2016). In some years,
 population size of giant arapaima (<i>Arapaima sp</i>.) was
 30-fold higher in protected lakes when compared with unprotected lakes.
 However, in order to test the assumption that protected lakes hold a
 higher density of apex predators than unprotected lakes, we conducted a
 survey on giant arapaima <i>Arapaima sp.</i> during the dry
 season of 2014.  Counts were based on standardized surveys, which were
 facilitated by the air-breathing of arapaima, whereby experienced
 fishermen can detect individuals as they break the water surface to breath
 (see details in Castello, 2004). 2.3 | Bottom-up
 control Nutrient levels were determined from water
 samples collected on all lakes in April 2014 (wet season) and September
 2014 (wet season). Total nitrogen (TN) and total phosphorus (TP) were
 determined using the simultaneous analysis method of Valderrama (1981) for
 both the high- and low-water seasons. Samples were first digested with a
 mixture of potassium peroxydisulphate, boric acid and sodium hydroxide in
 an autoclave. After digestion, P-PO<sub>4</sub> concentration
 was determined by light absorbance of ortho-molybdate blue at 882 nm and
 N-NO<sub>3</sub> was determined, following reduction with
 Cadmium and addition of sulfanilamide/ N-(1-naphthyl)-ethylenediamine
 dihydrochloride reagent, by light absorbance at 543 nm. Water transparency
 was estimated for each lake using the depth of the Secchi disk, that was
 measured at a deeper portion of each lake during the high- and low-water
 seasons. Finally, lake macrophyte cover<i> </i>was initially
 mapped in the field and then independently estimated in ArcGIS (version
 10.2) using 5-m resolution RapidEye<sup>©</sup> images from
 August to October 2013. 2.4 | Landscape and lake
 variables Since Amazonian lakes are highly variable
 and inserted in different landscape contexts, our analyses contained
 several additional explanatory variables designed to control for such
 heterogeneity. Lake metrics were lake area, lake shape, lake depth, and
 lake type. Lake area was measured as the area (ha) of the water mirror.
 Shape<i> </i>index (SI) was measured as SI =
 (P/200)*(πA)<sup>0.5</sup>, where P is the lake perimeter and
 A is the lake area. SI represents the deviation from a perfect circle, for
 which a circular lake shows a maximum SI = 1.0 (Patton, 1975).  Depth was
 defined as the maximum lake depth, measured in the field in each lake
 during both the high- and low-water seasons. Lake geomorphology was
 classified into two types: oxbow lakes and ria lakes. The landscape
 variable considered here were connectivity, which reflect if the lake is
 connected with the main river or not, and distance to river channel,
 estimated as the nearest Euclidian distance between the lake edge and the
 river channel. This variable is used as a proxy of time of isolation,
 since lakes near the river channel tend to be connected sooner during the
 high waters, exhibiting a shorter isolation period. All spatial metrics
 were extracted in ArcGIS 10.2 using classified
 RapidEye<sup>©</sup> images. 2.5 |
 Phytoplankton biomass estimates We used
 Chl-<i>a</i> concentration as a proxy of phytoplankton biomass
 and productivity, but we note that primary productivity is a combination
 of metabolic and anabolic processes that we did not measured in this
 study. For <i>Chl-a</i> estimates we sampled at the deepest
 point in each lake, during both the high- and low-water season. We first
 determined the extent of the euphotic zone, using the depth of a Secchi
 disk multiplied by a factor of 2.7 (Cole, 1994), and then integrated the
 euphotic zone using a 20-l bucket and a vertical Van Dorn sampler (3 l). A
 2-l water subsample was then transferred from the bucket to a polyethylene
 terephthalate (PET) bottle, which was stored on ice for subsequent
 determination of Chl-<i>a</i>, total phosphorus and nitrogen.
 For<b> </b>Chl-<i>a,</i><b> </b>water
 samples were filtered through a GF/F glass fiber filter, after a time
 period, which never exceeded 8 hours after sampling. The concentration of
 Chl-<i>a</i> (μg l<sup>–1</sup>) was determined
 spectrophotometrically, following sonication and overnight extraction in
 90% acetone (10 ml) at 4°C. Chl-<i>a</i> extracts were
 subsequently centrifuged, decanted and read in a spectrophotometer in a
 1-cm glass cuvette at 750, 664, 647, 630 nm. Chl-<i>a</i>
 concentration was then calculated with the tri-chromatic equations of
 Strickland and Parsons (1968). Filtration was conducted in a field lab in
 a diesel-powered boat under dim light. Filters were then frozen until
 further laboratory analysis (INPA, Manaus, Brazil) within a maximum of
 eight weeks.
description: There are some missing value which was completed with NA. For any
 further questions please write to jvpiedade@gmailcom.