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.

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, 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., 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 (Arapaima sp.) 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 Arapaima sp. 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-PO₄ concentration was determined by light absorbance of ortho-molybdate blue at 882 nm and N-NO₃ 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 was initially mapped in the field and then independently estimated in ArcGIS (version 10.2) using 5-m resolution RapidEye^© 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 index (SI) was measured as SI = (P/200)*(πA)^0.5, 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^© images.

2.5 | Phytoplankton biomass estimates

We used Chl-a 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 Chl-a 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-a, total phosphorus and nitrogen. For Chl-a, 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-a (μg l^–1) was determined spectrophotometrically, following sonication and overnight extraction in 90% acetone (10 ml) at 4°C. Chl-a extracts were subsequently centrifuged, decanted and read in a spectrophotometer in a 1-cm glass cuvette at 750, 664, 647, 630 nm. Chl-a 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.

There are some missing value which was completed with NA. For any further questions please write to jvpiedade@gmailcom.