Although forest succession has traditionally been approached as a deterministic process, successional trajectories of vegetation change vary widely, even among nearby stands with similar environmental conditions and disturbance histories. Here, we provide the first attempt, to our knowledge, to quantify predictability and uncertainty during succession based on the most extensive long-term datasets ever assembled for Neotropical forests. We develop a novel approach that integrates deterministic and stochastic components into different candidate models describing the dynamical interactions among three widely used and interrelated forest attributes—stem density, basal area, and species density. Within each of the seven study sites, successional trajectories were highly idiosyncratic, even when controlling for prior land use, environment, and initial conditions in these attributes. Plot factors were far more important than stand age in explaining successional trajectories. For each site, the best-fit model was able to capture the complete set of time series in certain attributes only when both the deterministic and stochastic components were set to similar magnitudes. Surprisingly, predictability of stem density, basal area, and species density did not show consistent trends across attributes, study sites, or land use history, and was independent of plot size and time series length. The model developed here represents the best approach, to date, for characterizing autogenic successional dynamics and demonstrates the low predictability of successional trajectories. These high levels of uncertainty suggest that the impacts of allogenic factors on rates of change during tropical forest succession are far more pervasive than previously thought, challenging the way ecologists view and investigate forest regeneration.

Hundreds of dams have been proposed throughout the Amazon basin, one of the world’s largest untapped hydropower frontiers. While hydropower is a potentially clean source of renewable energy, some projects produce high greenhouse gas (GHG) emissions per unit electricity generated (carbon intensity). Here we show how carbon intensities of proposed Amazon upland dams (median = 39 kg CO2eq MWh−1, 100-year horizon) are often comparable with solar and wind energy, whereas some lowland dams (median = 133 kg CO2eq MWh−1) may exceed carbon intensities of fossil-fuel power plants. Based on 158 existing and 351 proposed dams, we present a multi-objective optimization framework showing that low-carbon expansion of Amazon hydropower relies on strategic planning, which is generally linked to placing dams in higher elevations and smaller streams. Ultimately, basin-scale dam planning that considers GHG emissions along with social and ecological externalities will be decisive for sustainable energy development where new hydropower is contemplated.

Abstract. A number of large hydropower dams are currently under development or in an advanced stage of planning in the Magdalena River basin, Colombia, spelling uncertainty for the Mompós Depression wetlands, one of the largest wetland systems in South America at 3400 km 2 . Annual large-scale inundation of floodplains and their associated wetlands regulates water, nutrient, and sediment cycles, which in turn sustain a wealth of ecological processes and ecosystem services, including critical food supplies. In this study, we implemented an integrated approach focused on key attributes of ecologically functional floodplains: (1) hydrologic connectivity between the river and the floodplain, and between upstream and downstream sections; (2) hydrologic variability patterns and their links to local and regional processes; and (3) the spatial scale required to sustain floodplain-associated processes and benefits, like migratory fish biodiversity. The implemented framework provides an explicit quantification of the nonlinear or direct response relationship of those considerations with hydropower development. The proposed framework was used to develop a comparative analysis of the potential effects of the hydropower expansion necessary to meet projected 2050 electricity requirements. As part of this study, we developed an enhancement of the Water Evaluation and Planning system (WEAP) that allows resolution of the floodplains water balance at a medium scale (∼ 1000 to 10 000 km 2 ) and evaluation of the potential impacts of upstream water management practices. In the case of the Mompós Depression wetlands, our results indicate that the potential additional impacts of new hydropower infrastructure with respect to baseline conditions can range up to one order of magnitude between scenarios that are comparable in terms of energy capacity. Fragmentation of connectivity corridors between lowland floodplains and upstream spawning habitats and reduction of sediment loads show the greatest impacts, with potential reductions of up to 97.6 and 80 %, respectively, from pre-dam conditions. In some development scenarios, the amount of water regulated and withheld by upstream infrastructure is of similar magnitude to existing fluxes involved in the episodic inundation of the floodplain during dry years and, thus, can also induce substantial changes in floodplain seasonal dynamics of average-to-dry years in some areas of the Mompós Depression.

Proposed hydropower dams at more than 350 sites throughout the Amazon require strategic evaluation of trade-offs between the numerous ecosystem services provided by Earth’s largest and most biodiverse river basin. These services are spatially variable, hence collective impacts of newly built dams depend strongly on their configuration. We use multiobjective optimization to identify portfolios of sites that simultaneously minimize impacts on river flow, river connectivity, sediment transport, fish diversity, and greenhouse gas emissions while achieving energy production goals. We find that uncoordinated, dam-by-dam hydropower expansion has resulted in forgone ecosystem service benefits. Minimizing further damage from hydropower development requires considering diverse environmental impacts across the entire basin, as well as cooperation among Amazonian nations. Our findings offer a transferable model for the evaluation of hydropower expansion in transboundary basins.

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Este libro nos pone al día en tres grandes bloques, uno inicial donde se aborda el estado del conocimiento tanto desde la perspectiva biótica como física de la cuenca, la geología, hidrología, sedimentología y su relación con los ecosistemas y la biodiversidad acuática; los peces, su diversidad taxonómica y genética, ecología e historias de vida y los servicios ecosistémicos que brindan como recursos pesqueros. En la segunda parte se consideran los conflictos ambientales en la cuenca, desde temas como la contaminación y la modificación del hábitat; los peces introducidos (exóticos y trasplantados y la “presión” de una demanda cada vez mayor de la pesca. En la sección tercera, tras una interpretación concienzuda de los capítulos anteriores y el conocimiento de los investigadores, se hace un análisis detallado de todas las amenazas, así como las estrategias de conservación y manejo del recurso íctico. Todo ello, sienta las bases para una serie de recomendaciones para la toma de decisiones que son recogidas al final de la obra.

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This study explores the ways to introduce physical interpretability into the process of optimizing operating rules for multireservoir systems with multiple objectives. Prior studies applied the concept of direct policy search (DPS), in which the release policy is expressed as a set of parameterized functions (e.g., neural networks) that are optimized by simulating the performance of different parameter value combi-nations over a testing period. The problem with this approach is that the operators generally avoid adopting such artificial black-box functions for the direct real-time control of their systems, preferring simpler tools with a clear connection to the system's physics. This study addresses this mismatch by replacing the black-box functions in DPS with physically based parameterized operating rules, for example by directly using target levels in dams as decision variables. This leads to results that are physically interpretable and may be more acceptable to operators. The methodology proposed in this work is applied to a network of five reservoirs and four power plants in the Nechí catchment in Colombia, with four interests involved: average energy generation, firm energy generation, flood hazard, and flow regime alteration. The release policy is expressed depending on only 12 parameters, which significantly reduces the computational complexity compared to existing approaches of multiobjective DPS. The resulting four-dimensional Pareto-approximate set offers a variety of operational strategies from which operators may choose one that corresponds best to their preferences. For demonstration purposes, one particular optimized policy is selected and its parameter values are analyzed to illustrate how the physically based operating rules can be directly interpreted by the operators.